From a56332e097412520b9ea7d6b48d47294dd9d2330 Mon Sep 17 00:00:00 2001 From: chandrasekaranpradeep Date: Mon, 17 Feb 2025 23:09:33 +0000 Subject: [PATCH 1/8] Generate models ops tests using model analysis --- forge/test/models_ops/test_abs.py | 141 + forge/test/models_ops/test_add.py | 18561 +++++ forge/test/models_ops/test_advindex.py | 230 + forge/test/models_ops/test_argmax.py | 86 + forge/test/models_ops/test_avgpool1d.py | 96 + forge/test/models_ops/test_avgpool2d.py | 1472 + forge/test/models_ops/test_avgpool3d.py | 114 + forge/test/models_ops/test_broadcast.py | 172 + forge/test/models_ops/test_cast.py | 502 + forge/test/models_ops/test_clip.py | 1110 + forge/test/models_ops/test_concatenate.py | 12958 ++++ forge/test/models_ops/test_conv2d.py | 56618 ++++++++++++++++ forge/test/models_ops/test_conv2dtranspose.py | 541 + forge/test/models_ops/test_cosine.py | 230 + forge/test/models_ops/test_cumsum.py | 114 + forge/test/models_ops/test_embedding.py | 1690 + forge/test/models_ops/test_equal.py | 86 + forge/test/models_ops/test_exp.py | 343 + forge/test/models_ops/test_gelu.py | 957 + forge/test/models_ops/test_greater.py | 612 + forge/test/models_ops/test_identity.py | 1786 + forge/test/models_ops/test_index.py | 5686 ++ forge/test/models_ops/test_layernorm.py | 1668 + forge/test/models_ops/test_leakyrelu.py | 190 + forge/test/models_ops/test_less.py | 534 + forge/test/models_ops/test_matmul.py | 5686 ++ forge/test/models_ops/test_max.py | 103 + forge/test/models_ops/test_maxpool2d.py | 1874 + forge/test/models_ops/test_multiply.py | 18957 ++++++ forge/test/models_ops/test_notequal.py | 85 + forge/test/models_ops/test_pad.py | 390 + forge/test/models_ops/test_reciprocal.py | 2075 + forge/test/models_ops/test_reduceavg.py | 1169 + forge/test/models_ops/test_relu.py | 3920 ++ forge/test/models_ops/test_remainder.py | 86 + .../test/models_ops/test_repeatinterleave.py | 943 + forge/test/models_ops/test_reshape.py | 22668 +++++++ forge/test/models_ops/test_resize2d.py | 2089 + forge/test/models_ops/test_sigmoid.py | 2049 + forge/test/models_ops/test_sine.py | 230 + forge/test/models_ops/test_softmax.py | 990 + forge/test/models_ops/test_sqrt.py | 1979 + forge/test/models_ops/test_squeeze.py | 1178 + forge/test/models_ops/test_stack.py | 113 + forge/test/models_ops/test_subtract.py | 853 + forge/test/models_ops/test_tanh.py | 87 + forge/test/models_ops/test_transpose.py | 9540 +++ forge/test/models_ops/test_unsqueeze.py | 6764 ++ forge/test/models_ops/test_where.py | 856 + 49 files changed, 191181 insertions(+) create mode 100644 forge/test/models_ops/test_abs.py create mode 100644 forge/test/models_ops/test_add.py create mode 100644 forge/test/models_ops/test_advindex.py create mode 100644 forge/test/models_ops/test_argmax.py create mode 100644 forge/test/models_ops/test_avgpool1d.py create mode 100644 forge/test/models_ops/test_avgpool2d.py create mode 100644 forge/test/models_ops/test_avgpool3d.py create mode 100644 forge/test/models_ops/test_broadcast.py create mode 100644 forge/test/models_ops/test_cast.py create mode 100644 forge/test/models_ops/test_clip.py create mode 100644 forge/test/models_ops/test_concatenate.py create mode 100644 forge/test/models_ops/test_conv2d.py create mode 100644 forge/test/models_ops/test_conv2dtranspose.py create mode 100644 forge/test/models_ops/test_cosine.py create mode 100644 forge/test/models_ops/test_cumsum.py create mode 100644 forge/test/models_ops/test_embedding.py create mode 100644 forge/test/models_ops/test_equal.py create mode 100644 forge/test/models_ops/test_exp.py create mode 100644 forge/test/models_ops/test_gelu.py create mode 100644 forge/test/models_ops/test_greater.py create mode 100644 forge/test/models_ops/test_identity.py create mode 100644 forge/test/models_ops/test_index.py create mode 100644 forge/test/models_ops/test_layernorm.py create mode 100644 forge/test/models_ops/test_leakyrelu.py create mode 100644 forge/test/models_ops/test_less.py create mode 100644 forge/test/models_ops/test_matmul.py create mode 100644 forge/test/models_ops/test_max.py create mode 100644 forge/test/models_ops/test_maxpool2d.py create mode 100644 forge/test/models_ops/test_multiply.py create mode 100644 forge/test/models_ops/test_notequal.py create mode 100644 forge/test/models_ops/test_pad.py create mode 100644 forge/test/models_ops/test_reciprocal.py create mode 100644 forge/test/models_ops/test_reduceavg.py create mode 100644 forge/test/models_ops/test_relu.py create mode 100644 forge/test/models_ops/test_remainder.py create mode 100644 forge/test/models_ops/test_repeatinterleave.py create mode 100644 forge/test/models_ops/test_reshape.py create mode 100644 forge/test/models_ops/test_resize2d.py create mode 100644 forge/test/models_ops/test_sigmoid.py create mode 100644 forge/test/models_ops/test_sine.py create mode 100644 forge/test/models_ops/test_softmax.py create mode 100644 forge/test/models_ops/test_sqrt.py create mode 100644 forge/test/models_ops/test_squeeze.py create mode 100644 forge/test/models_ops/test_stack.py create mode 100644 forge/test/models_ops/test_subtract.py create mode 100644 forge/test/models_ops/test_tanh.py create mode 100644 forge/test/models_ops/test_transpose.py create mode 100644 forge/test/models_ops/test_unsqueeze.py create mode 100644 forge/test/models_ops/test_where.py diff --git a/forge/test/models_ops/test_abs.py b/forge/test/models_ops/test_abs.py new file mode 100644 index 000000000..1814e5b97 --- /dev/null +++ b/forge/test/models_ops/test_abs.py @@ -0,0 +1,141 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Abs0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, abs_input_0): + abs_output_1 = forge.op.Abs("", abs_input_0) + return abs_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Abs0, + [((2, 1, 1, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Abs0, + [((2, 1, 7, 7), torch.float32)], + {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99}, + ), + ( + Abs0, + [((1, 1, 256, 256), torch.float32)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Abs0, + [((1, 12, 128, 128), torch.float32)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Abs0, + [((1, 12, 384, 384), torch.float32)], + {"model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], "pcc": 0.99}, + ), + ( + Abs0, + [((1, 1, 32, 32), torch.float32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Abs") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_add.py b/forge/test/models_ops/test_add.py new file mode 100644 index 000000000..1d9202afe --- /dev/null +++ b/forge/test/models_ops/test_add.py @@ -0,0 +1,18561 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Add0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, add_input_0, add_input_1): + add_output_1 = forge.op.Add("", add_input_0, add_input_1) + return add_output_1 + + +class Add1(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add1_const_1", shape=(1,), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add1_const_1")) + return add_output_1 + + +class Add2(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add2.weight_1", forge.Parameter(*(2048,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add2.weight_1")) + return add_output_1 + + +class Add3(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add3.weight_1", forge.Parameter(*(1536,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add3.weight_1")) + return add_output_1 + + +class Add4(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add4.weight_1", forge.Parameter(*(1024,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add4.weight_1")) + return add_output_1 + + +class Add5(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add5.weight_1", + forge.Parameter(*(1500, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add5.weight_1")) + return add_output_1 + + +class Add6(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add6.weight_1", forge.Parameter(*(4096,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add6.weight_1")) + return add_output_1 + + +class Add7(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add7.weight_1", forge.Parameter(*(1280,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add7.weight_1")) + return add_output_1 + + +class Add8(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add8.weight_1", + forge.Parameter(*(1500, 1280), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add8.weight_1")) + return add_output_1 + + +class Add9(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add9.weight_1", forge.Parameter(*(5120,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add9.weight_1")) + return add_output_1 + + +class Add10(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add10.weight_1", forge.Parameter(*(384,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add10.weight_1")) + return add_output_1 + + +class Add11(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add11.weight_1", + forge.Parameter(*(1500, 384), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add11.weight_1")) + return add_output_1 + + +class Add12(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add12.weight_1", forge.Parameter(*(512,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add12.weight_1")) + return add_output_1 + + +class Add13(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add13.weight_1", + forge.Parameter(*(1500, 512), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add13.weight_1")) + return add_output_1 + + +class Add14(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add14.weight_1", forge.Parameter(*(768,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add14.weight_1")) + return add_output_1 + + +class Add15(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add15.weight_1", + forge.Parameter(*(1500, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add15.weight_1")) + return add_output_1 + + +class Add16(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add16.weight_1", forge.Parameter(*(3072,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add16.weight_1")) + return add_output_1 + + +class Add17(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add17_const_1", shape=(1, 1, 2, 1), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add17_const_1")) + return add_output_1 + + +class Add18(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add18_const_1", shape=(2, 1, 7, 7), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add18_const_1")) + return add_output_1 + + +class Add19(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add19_const_1", shape=(1, 1, 39, 39), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add19_const_1")) + return add_output_1 + + +class Add20(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add20.weight_1", forge.Parameter(*(3129,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add20.weight_1")) + return add_output_1 + + +class Add21(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add21.weight_1", forge.Parameter(*(16384,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add21.weight_1")) + return add_output_1 + + +class Add22(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add22.weight_1", forge.Parameter(*(2,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add22.weight_1")) + return add_output_1 + + +class Add23(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add23.weight_1", forge.Parameter(*(128,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add23.weight_1")) + return add_output_1 + + +class Add24(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add24.weight_1", forge.Parameter(*(30000,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add24.weight_1")) + return add_output_1 + + +class Add25(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add25.weight_1", forge.Parameter(*(8192,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add25.weight_1")) + return add_output_1 + + +class Add26(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add26_const_1", shape=(1, 1, 256, 256), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add26_const_1")) + return add_output_1 + + +class Add27(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add27_const_1", shape=(1, 1, 1, 128), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add27_const_1")) + return add_output_1 + + +class Add28(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add28_const_1", shape=(1, 1, 1, 384), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add28_const_1")) + return add_output_1 + + +class Add29(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add29.weight_1", forge.Parameter(*(9,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add29.weight_1")) + return add_output_1 + + +class Add30(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add30.weight_1", forge.Parameter(*(30522,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add30.weight_1")) + return add_output_1 + + +class Add31(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add31.weight_1", forge.Parameter(*(51200,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add31.weight_1")) + return add_output_1 + + +class Add32(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add32.weight_1", forge.Parameter(*(119547,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add32.weight_1")) + return add_output_1 + + +class Add33(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add33.weight_1", forge.Parameter(*(28996,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add33.weight_1")) + return add_output_1 + + +class Add34(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add34.weight_1", forge.Parameter(*(1,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add34.weight_1")) + return add_output_1 + + +class Add35(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add35_const_1", shape=(1, 1, 6, 6), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add35_const_1")) + return add_output_1 + + +class Add36(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add36_const_1", shape=(1, 1, 10, 10), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add36_const_1")) + return add_output_1 + + +class Add37(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add37.weight_1", forge.Parameter(*(12288,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add37.weight_1")) + return add_output_1 + + +class Add38(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add38_const_1", shape=(1, 1, 334, 334), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add38_const_1")) + return add_output_1 + + +class Add39(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add39_const_1", shape=(1, 1, 7, 7), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add39_const_1")) + return add_output_1 + + +class Add40(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add40.weight_1", forge.Parameter(*(2560,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add40.weight_1")) + return add_output_1 + + +class Add41(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add41.weight_1", forge.Parameter(*(10240,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add41.weight_1")) + return add_output_1 + + +class Add42(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add42_const_1", shape=(1, 1, 32, 32), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add42_const_1")) + return add_output_1 + + +class Add43(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add43_const_1", shape=(1, 1, 4, 4), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add43_const_1")) + return add_output_1 + + +class Add44(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add44_const_0", shape=(1, 1, 256, 256), dtype=torch.float32) + + def forward(self, add_input_1): + add_output_1 = forge.op.Add("", self.get_constant("add44_const_0"), add_input_1) + return add_output_1 + + +class Add45(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add45_const_1", shape=(1, 1, 128, 128), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add45_const_1")) + return add_output_1 + + +class Add46(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add46_const_1", shape=(1,), dtype=torch.int64) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add46_const_1")) + return add_output_1 + + +class Add47(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add47_const_1", shape=(1, 1, 12, 12), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add47_const_1")) + return add_output_1 + + +class Add48(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add48_const_1", shape=(1, 1, 11, 11), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add48_const_1")) + return add_output_1 + + +class Add49(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add49_const_1", shape=(1, 1, 13, 13), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add49_const_1")) + return add_output_1 + + +class Add50(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add50_const_1", shape=(1, 1, 5, 5), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add50_const_1")) + return add_output_1 + + +class Add51(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add51_const_1", shape=(1, 1, 29, 29), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add51_const_1")) + return add_output_1 + + +class Add52(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add52.weight_1", forge.Parameter(*(3584,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add52.weight_1")) + return add_output_1 + + +class Add53(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add53_const_1", shape=(1, 1, 35, 35), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add53_const_1")) + return add_output_1 + + +class Add54(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add54.weight_1", forge.Parameter(*(256,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add54.weight_1")) + return add_output_1 + + +class Add55(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add55.weight_1", forge.Parameter(*(896,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add55.weight_1")) + return add_output_1 + + +class Add56(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add56.weight_1", forge.Parameter(*(250002,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add56.weight_1")) + return add_output_1 + + +class Add57(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add57.weight_1", forge.Parameter(*(3,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add57.weight_1")) + return add_output_1 + + +class Add58(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add58_const_1", shape=(1, 1, 1, 61), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add58_const_1")) + return add_output_1 + + +class Add59(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add59_const_1", shape=(1, 16, 1, 61), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add59_const_1")) + return add_output_1 + + +class Add60(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add60_const_1", shape=(1, 8, 1, 61), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add60_const_1")) + return add_output_1 + + +class Add61(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add61_const_1", shape=(1, 6, 1, 61), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add61_const_1")) + return add_output_1 + + +class Add62(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add62_const_1", shape=(1, 12, 1, 61), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add62_const_1")) + return add_output_1 + + +class Add63(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add63.weight_1", forge.Parameter(*(48,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add63.weight_1")) + return add_output_1 + + +class Add64(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add64.weight_1", forge.Parameter(*(8,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add64.weight_1")) + return add_output_1 + + +class Add65(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add65.weight_1", forge.Parameter(*(96,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add65.weight_1")) + return add_output_1 + + +class Add66(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add66.weight_1", forge.Parameter(*(1000,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add66.weight_1")) + return add_output_1 + + +class Add67(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add67.weight_1", forge.Parameter(*(64,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add67.weight_1")) + return add_output_1 + + +class Add68(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add68.weight_1", forge.Parameter(*(12,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add68.weight_1")) + return add_output_1 + + +class Add69(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add69.weight_1", forge.Parameter(*(784,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add69.weight_1")) + return add_output_1 + + +class Add70(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add70.weight_1", + forge.Parameter(*(1, 197, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add70.weight_1")) + return add_output_1 + + +class Add71(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add71.weight_1", + forge.Parameter(*(1, 197, 192), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add71.weight_1")) + return add_output_1 + + +class Add72(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add72.weight_1", forge.Parameter(*(192,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add72.weight_1")) + return add_output_1 + + +class Add73(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add73.weight_1", + forge.Parameter(*(1, 197, 384), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add73.weight_1")) + return add_output_1 + + +class Add74(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add74.weight_1", forge.Parameter(*(144,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add74.weight_1")) + return add_output_1 + + +class Add75(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add75.weight_1", forge.Parameter(*(240,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add75.weight_1")) + return add_output_1 + + +class Add76(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add76.weight_1", forge.Parameter(*(288,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add76.weight_1")) + return add_output_1 + + +class Add77(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add77.weight_1", forge.Parameter(*(336,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add77.weight_1")) + return add_output_1 + + +class Add78(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add78.weight_1", forge.Parameter(*(432,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add78.weight_1")) + return add_output_1 + + +class Add79(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add79.weight_1", forge.Parameter(*(480,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add79.weight_1")) + return add_output_1 + + +class Add80(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add80.weight_1", forge.Parameter(*(528,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add80.weight_1")) + return add_output_1 + + +class Add81(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add81.weight_1", forge.Parameter(*(576,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add81.weight_1")) + return add_output_1 + + +class Add82(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add82.weight_1", forge.Parameter(*(624,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add82.weight_1")) + return add_output_1 + + +class Add83(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add83.weight_1", forge.Parameter(*(672,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add83.weight_1")) + return add_output_1 + + +class Add84(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add84.weight_1", forge.Parameter(*(720,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add84.weight_1")) + return add_output_1 + + +class Add85(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add85.weight_1", forge.Parameter(*(816,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add85.weight_1")) + return add_output_1 + + +class Add86(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add86.weight_1", forge.Parameter(*(864,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add86.weight_1")) + return add_output_1 + + +class Add87(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add87.weight_1", forge.Parameter(*(912,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add87.weight_1")) + return add_output_1 + + +class Add88(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add88.weight_1", forge.Parameter(*(960,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add88.weight_1")) + return add_output_1 + + +class Add89(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add89.weight_1", forge.Parameter(*(1008,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add89.weight_1")) + return add_output_1 + + +class Add90(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add90.weight_1", forge.Parameter(*(1056,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add90.weight_1")) + return add_output_1 + + +class Add91(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add91.weight_1", forge.Parameter(*(1104,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add91.weight_1")) + return add_output_1 + + +class Add92(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add92.weight_1", forge.Parameter(*(1152,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add92.weight_1")) + return add_output_1 + + +class Add93(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add93.weight_1", forge.Parameter(*(1200,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add93.weight_1")) + return add_output_1 + + +class Add94(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add94.weight_1", forge.Parameter(*(1248,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add94.weight_1")) + return add_output_1 + + +class Add95(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add95.weight_1", forge.Parameter(*(1296,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add95.weight_1")) + return add_output_1 + + +class Add96(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add96.weight_1", forge.Parameter(*(1344,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add96.weight_1")) + return add_output_1 + + +class Add97(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add97.weight_1", forge.Parameter(*(1392,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add97.weight_1")) + return add_output_1 + + +class Add98(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add98.weight_1", forge.Parameter(*(1440,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add98.weight_1")) + return add_output_1 + + +class Add99(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add99.weight_1", forge.Parameter(*(1488,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add99.weight_1")) + return add_output_1 + + +class Add100(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add100.weight_1", forge.Parameter(*(1584,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add100.weight_1")) + return add_output_1 + + +class Add101(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add101.weight_1", forge.Parameter(*(1632,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add101.weight_1")) + return add_output_1 + + +class Add102(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add102.weight_1", forge.Parameter(*(1680,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add102.weight_1")) + return add_output_1 + + +class Add103(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add103.weight_1", forge.Parameter(*(1728,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add103.weight_1")) + return add_output_1 + + +class Add104(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add104.weight_1", forge.Parameter(*(1776,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add104.weight_1")) + return add_output_1 + + +class Add105(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add105.weight_1", forge.Parameter(*(1824,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add105.weight_1")) + return add_output_1 + + +class Add106(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add106.weight_1", forge.Parameter(*(1872,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add106.weight_1")) + return add_output_1 + + +class Add107(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add107.weight_1", forge.Parameter(*(1920,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add107.weight_1")) + return add_output_1 + + +class Add108(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add108.weight_1", forge.Parameter(*(1968,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add108.weight_1")) + return add_output_1 + + +class Add109(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add109.weight_1", forge.Parameter(*(2016,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add109.weight_1")) + return add_output_1 + + +class Add110(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add110.weight_1", forge.Parameter(*(2064,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add110.weight_1")) + return add_output_1 + + +class Add111(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add111.weight_1", forge.Parameter(*(2112,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add111.weight_1")) + return add_output_1 + + +class Add112(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add112.weight_1", forge.Parameter(*(2160,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add112.weight_1")) + return add_output_1 + + +class Add113(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add113.weight_1", forge.Parameter(*(2208,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add113.weight_1")) + return add_output_1 + + +class Add114(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add114.weight_1", forge.Parameter(*(160,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add114.weight_1")) + return add_output_1 + + +class Add115(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add115.weight_1", forge.Parameter(*(224,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add115.weight_1")) + return add_output_1 + + +class Add116(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add116.weight_1", forge.Parameter(*(320,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add116.weight_1")) + return add_output_1 + + +class Add117(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add117.weight_1", forge.Parameter(*(352,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add117.weight_1")) + return add_output_1 + + +class Add118(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add118.weight_1", forge.Parameter(*(416,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add118.weight_1")) + return add_output_1 + + +class Add119(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add119.weight_1", forge.Parameter(*(448,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add119.weight_1")) + return add_output_1 + + +class Add120(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add120.weight_1", forge.Parameter(*(544,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add120.weight_1")) + return add_output_1 + + +class Add121(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add121.weight_1", forge.Parameter(*(608,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add121.weight_1")) + return add_output_1 + + +class Add122(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add122.weight_1", forge.Parameter(*(640,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add122.weight_1")) + return add_output_1 + + +class Add123(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add123.weight_1", forge.Parameter(*(704,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add123.weight_1")) + return add_output_1 + + +class Add124(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add124.weight_1", forge.Parameter(*(736,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add124.weight_1")) + return add_output_1 + + +class Add125(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add125.weight_1", forge.Parameter(*(800,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add125.weight_1")) + return add_output_1 + + +class Add126(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add126.weight_1", forge.Parameter(*(832,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add126.weight_1")) + return add_output_1 + + +class Add127(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add127.weight_1", forge.Parameter(*(928,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add127.weight_1")) + return add_output_1 + + +class Add128(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add128.weight_1", forge.Parameter(*(992,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add128.weight_1")) + return add_output_1 + + +class Add129(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add129.weight_1", forge.Parameter(*(1088,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add129.weight_1")) + return add_output_1 + + +class Add130(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add130.weight_1", forge.Parameter(*(1120,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add130.weight_1")) + return add_output_1 + + +class Add131(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add131.weight_1", forge.Parameter(*(1184,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add131.weight_1")) + return add_output_1 + + +class Add132(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add132.weight_1", forge.Parameter(*(1216,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add132.weight_1")) + return add_output_1 + + +class Add133(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add133.weight_1", forge.Parameter(*(1312,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add133.weight_1")) + return add_output_1 + + +class Add134(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add134.weight_1", forge.Parameter(*(1376,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add134.weight_1")) + return add_output_1 + + +class Add135(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add135.weight_1", forge.Parameter(*(1408,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add135.weight_1")) + return add_output_1 + + +class Add136(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add136.weight_1", forge.Parameter(*(1472,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add136.weight_1")) + return add_output_1 + + +class Add137(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add137.weight_1", forge.Parameter(*(1504,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add137.weight_1")) + return add_output_1 + + +class Add138(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add138.weight_1", forge.Parameter(*(1568,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add138.weight_1")) + return add_output_1 + + +class Add139(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add139.weight_1", forge.Parameter(*(1600,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add139.weight_1")) + return add_output_1 + + +class Add140(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add140.weight_1", forge.Parameter(*(1664,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add140.weight_1")) + return add_output_1 + + +class Add141(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add141.weight_1", forge.Parameter(*(1696,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add141.weight_1")) + return add_output_1 + + +class Add142(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add142.weight_1", forge.Parameter(*(1760,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add142.weight_1")) + return add_output_1 + + +class Add143(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add143.weight_1", forge.Parameter(*(1792,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add143.weight_1")) + return add_output_1 + + +class Add144(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add144.weight_1", forge.Parameter(*(1856,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add144.weight_1")) + return add_output_1 + + +class Add145(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add145.weight_1", forge.Parameter(*(1888,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add145.weight_1")) + return add_output_1 + + +class Add146(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add146.weight_1", forge.Parameter(*(16,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add146.weight_1")) + return add_output_1 + + +class Add147(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add147.weight_1", forge.Parameter(*(32,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add147.weight_1")) + return add_output_1 + + +class Add148(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add148.weight_1", forge.Parameter(*(24,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add148.weight_1")) + return add_output_1 + + +class Add149(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add149.weight_1", forge.Parameter(*(56,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add149.weight_1")) + return add_output_1 + + +class Add150(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add150.weight_1", forge.Parameter(*(112,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add150.weight_1")) + return add_output_1 + + +class Add151(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add151.weight_1", forge.Parameter(*(272,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add151.weight_1")) + return add_output_1 + + +class Add152(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add152.weight_1", forge.Parameter(*(2688,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add152.weight_1")) + return add_output_1 + + +class Add153(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add153.weight_1", forge.Parameter(*(40,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add153.weight_1")) + return add_output_1 + + +class Add154(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add154.weight_1", forge.Parameter(*(80,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add154.weight_1")) + return add_output_1 + + +class Add155(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add155.weight_1", forge.Parameter(*(36,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add155.weight_1")) + return add_output_1 + + +class Add156(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add156.weight_1", forge.Parameter(*(72,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add156.weight_1")) + return add_output_1 + + +class Add157(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add157.weight_1", forge.Parameter(*(20,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add157.weight_1")) + return add_output_1 + + +class Add158(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add158.weight_1", forge.Parameter(*(60,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add158.weight_1")) + return add_output_1 + + +class Add159(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add159.weight_1", forge.Parameter(*(120,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add159.weight_1")) + return add_output_1 + + +class Add160(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add160.weight_1", forge.Parameter(*(100,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add160.weight_1")) + return add_output_1 + + +class Add161(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add161.weight_1", forge.Parameter(*(92,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add161.weight_1")) + return add_output_1 + + +class Add162(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add162.weight_1", forge.Parameter(*(208,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add162.weight_1")) + return add_output_1 + + +class Add163(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add163.weight_1", forge.Parameter(*(18,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add163.weight_1")) + return add_output_1 + + +class Add164(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add164.weight_1", forge.Parameter(*(44,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add164.weight_1")) + return add_output_1 + + +class Add165(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add165.weight_1", forge.Parameter(*(88,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add165.weight_1")) + return add_output_1 + + +class Add166(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add166.weight_1", forge.Parameter(*(176,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add166.weight_1")) + return add_output_1 + + +class Add167(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add167.weight_1", forge.Parameter(*(30,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add167.weight_1")) + return add_output_1 + + +class Add168(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add168.weight_1", forge.Parameter(*(49,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add168.weight_1")) + return add_output_1 + + +class Add169(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add169.weight_1", forge.Parameter(*(196,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add169.weight_1")) + return add_output_1 + + +class Add170(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add170.weight_1", forge.Parameter(*(11221,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add170.weight_1")) + return add_output_1 + + +class Add171(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add171.weight_1", forge.Parameter(*(21843,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add171.weight_1")) + return add_output_1 + + +class Add172(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add172.weight_1", forge.Parameter(*(1001,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add172.weight_1")) + return add_output_1 + + +class Add173(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add173.weight_1", forge.Parameter(*(200,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add173.weight_1")) + return add_output_1 + + +class Add174(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add174.weight_1", forge.Parameter(*(184,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add174.weight_1")) + return add_output_1 + + +class Add175(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add175.weight_1", forge.Parameter(*(322,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add175.weight_1")) + return add_output_1 + + +class Add176(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add176_const_1", shape=(1, 1, 1, 3025), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add176_const_1")) + return add_output_1 + + +class Add177(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add177.weight_1", forge.Parameter(*(261,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add177.weight_1")) + return add_output_1 + + +class Add178(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add178_const_1", shape=(1, 1, 1, 50176), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add178_const_1")) + return add_output_1 + + +class Add179(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add179_const_1", shape=(4096,), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add179_const_1")) + return add_output_1 + + +class Add180(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add180_const_1", shape=(2,), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add180_const_1")) + return add_output_1 + + +class Add181(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add181_const_1", shape=(64, 1, 49, 49), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add181_const_1")) + return add_output_1 + + +class Add182(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add182_const_1", shape=(16, 1, 49, 49), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add182_const_1")) + return add_output_1 + + +class Add183(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add183_const_1", shape=(4, 1, 49, 49), dtype=torch.float32) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_constant("add183_const_1")) + return add_output_1 + + +class Add184(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add184.weight_1", + forge.Parameter(*(1, 197, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add184.weight_1")) + return add_output_1 + + +class Add185(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "add185.weight_1", forge.Parameter(*(728,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, add_input_0): + add_output_1 = forge.op.Add("", add_input_0, self.get_parameter("add185.weight_1")) + return add_output_1 + + +class Add186(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("add186_const_0", shape=(5880, 2), dtype=torch.float32) + + def forward(self, add_input_1): + add_output_1 = forge.op.Add("", self.get_constant("add186_const_0"), add_input_1) + return add_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Add0, + [((2, 1, 2048), torch.float32), ((2, 1, 2048), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Add0, + [((2, 1, 2048), torch.float32), ((1, 2048), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Add1, + [((2, 13, 1), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 12, 13, 13), torch.float32), ((2, 1, 1, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((2, 12, 13, 13), torch.float32), ((2, 12, 13, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((2, 13, 768), torch.float32), ((2, 13, 768), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Add2, + [((2, 13, 2048), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Add0, + [((2, 1, 1, 13), torch.float32), ((2, 1, 1, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((2, 32, 1, 13), torch.float32), ((2, 1, 1, 13), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Add0, + [((2, 1, 1536), torch.float32), ((2, 1, 1536), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Add0, + [((2, 1, 1536), torch.float32), ((1, 1536), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Add3, + [((2, 13, 1536), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Add0, + [((2, 24, 1, 13), torch.float32), ((2, 1, 1, 13), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Add0, + [((2, 1, 1024), torch.float32), ((2, 1, 1024), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Add0, + [((2, 1, 1024), torch.float32), ((1, 1024), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Add4, + [((2, 13, 1024), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Add0, + [((2, 16, 1, 13), torch.float32), ((2, 1, 1, 13), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 1, 1024), torch.float32), ((1, 1, 1024), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + "pt_perceiverio_deepmind_vision_perceiver_conv_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_fourier_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Add4, + [((1, 1, 1024), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + 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60, 60), torch.float32), ((32, 1, 1), torch.float32)], + {"model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_480x480"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 32, 60, 60), torch.float32), ((1, 32, 60, 60), torch.float32)], + {"model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_480x480"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 64, 30, 30), torch.float32), ((64, 1, 1), torch.float32)], + {"model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_480x480"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 64, 30, 30), torch.float32), ((1, 64, 30, 30), torch.float32)], + {"model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_480x480"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 128, 15, 15), torch.float32), ((128, 1, 1), torch.float32)], + {"model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_480x480"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 128, 15, 15), torch.float32), ((1, 128, 15, 15), torch.float32)], + {"model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_480x480"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 256, 10, 10), torch.float32), ((1, 256, 10, 10), torch.float32)], + {"model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_320x320"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 48, 320, 320), torch.float32), ((48, 1, 1), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", "pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 96, 160, 160), torch.float32), ((96, 1, 1), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", "pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 48, 160, 160), torch.float32), ((1, 48, 160, 160), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", "pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 96, 80, 80), torch.float32), ((1, 96, 80, 80), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", "pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 384, 40, 40), torch.float32), ((384, 1, 1), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", "pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 192, 40, 40), torch.float32), ((1, 192, 40, 40), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", "pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 768, 20, 20), torch.float32), ((768, 1, 1), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", "pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 384, 20, 20), torch.float32), ((1, 384, 20, 20), torch.float32)], + {"model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640"], "pcc": 0.99}, + ), + ( + Add186, + [((1, 5880, 2), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 5880, 2), torch.float32), ((1, 5880, 2), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 64, 112, 160), torch.float32), ((1, 64, 112, 160), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 128, 56, 80), torch.float32), ((1, 128, 56, 80), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 256, 28, 40), torch.float32), ((1, 256, 28, 40), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 512, 14, 20), torch.float32), ((1, 512, 14, 20), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 128, 28, 40), torch.float32), ((1, 128, 28, 40), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 64, 56, 80), torch.float32), ((1, 64, 56, 80), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 256, 14, 20), torch.float32), ((1, 256, 14, 20), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 4, 80, 80), torch.float32), ((4, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 1, 80, 80), torch.float32), ((1, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 4, 40, 40), torch.float32), ((4, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 1, 40, 40), torch.float32), ((1, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 80, 40, 40), torch.float32), ((80, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 4, 20, 20), torch.float32), ((4, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 1, 20, 20), torch.float32), ((1, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 80, 20, 20), torch.float32), ((80, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Add0, + [((1, 24, 208, 208), torch.float32), ((24, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 48, 104, 104), torch.float32), ((48, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 24, 104, 104), torch.float32), ((24, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 24, 104, 104), torch.float32), ((1, 24, 104, 104), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 96, 52, 52), torch.float32), ((96, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 48, 52, 52), torch.float32), ((48, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 48, 52, 52), torch.float32), ((1, 48, 52, 52), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 192, 26, 26), torch.float32), ((192, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 96, 26, 26), torch.float32), ((96, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 96, 26, 26), torch.float32), ((1, 96, 26, 26), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 192, 13, 13), torch.float32), ((192, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 4, 52, 52), torch.float32), ((4, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 1, 52, 52), torch.float32), ((1, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 80, 52, 52), torch.float32), ((80, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 4, 26, 26), torch.float32), ((4, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 1, 26, 26), torch.float32), ((1, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 80, 26, 26), torch.float32), ((80, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 96, 13, 13), torch.float32), ((96, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 4, 13, 13), torch.float32), ((4, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 1, 13, 13), torch.float32), ((1, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 80, 13, 13), torch.float32), ((80, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 64, 320, 320), torch.float32), ((1, 64, 320, 320), torch.float32)], + {"model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 128, 160, 160), torch.float32), ((1, 128, 160, 160), torch.float32)], + {"model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 256, 80, 80), torch.float32), ((1, 256, 80, 80), torch.float32)], + {"model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 512, 40, 40), torch.float32), ((1, 512, 40, 40), torch.float32)], + {"model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 1024, 20, 20), torch.float32), ((1, 1024, 20, 20), torch.float32)], + {"model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 16, 208, 208), torch.float32), ((16, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 16, 104, 104), torch.float32), ((16, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 32, 104, 104), torch.float32), ((32, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 16, 104, 104), torch.float32), ((1, 16, 104, 104), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 32, 52, 52), torch.float32), ((32, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 64, 52, 52), torch.float32), ((64, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 32, 52, 52), torch.float32), ((1, 32, 52, 52), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 64, 26, 26), torch.float32), ((64, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 128, 26, 26), torch.float32), ((128, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 64, 26, 26), torch.float32), ((1, 64, 26, 26), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 128, 13, 13), torch.float32), ((128, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Add0, + [((1, 64, 13, 13), torch.float32), ((64, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Add") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_advindex.py b/forge/test/models_ops/test_advindex.py new file mode 100644 index 000000000..28f5a7a98 --- /dev/null +++ b/forge/test/models_ops/test_advindex.py @@ -0,0 +1,230 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Advindex0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, advindex_input_0, advindex_input_1): + advindex_output_1 = forge.op.AdvIndex("", advindex_input_0, advindex_input_1) + return advindex_output_1 + + +class Advindex1(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("advindex1_const_1", shape=(1,), dtype=torch.int64) + + def forward(self, advindex_input_0): + advindex_output_1 = forge.op.AdvIndex("", advindex_input_0, self.get_constant("advindex1_const_1")) + return advindex_output_1 + + +class Advindex2(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "advindex2.weight_0", + forge.Parameter(*(169, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, advindex_input_1): + advindex_output_1 = forge.op.AdvIndex("", self.get_parameter("advindex2.weight_0"), advindex_input_1) + return advindex_output_1 + + +class Advindex3(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "advindex3.weight_0", + forge.Parameter(*(169, 6), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, advindex_input_1): + advindex_output_1 = forge.op.AdvIndex("", self.get_parameter("advindex3.weight_0"), advindex_input_1) + return advindex_output_1 + + +class Advindex4(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "advindex4.weight_0", + forge.Parameter(*(169, 12), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, advindex_input_1): + advindex_output_1 = forge.op.AdvIndex("", self.get_parameter("advindex4.weight_0"), advindex_input_1) + return advindex_output_1 + + +class Advindex5(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "advindex5.weight_0", + forge.Parameter(*(169, 24), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, advindex_input_1): + advindex_output_1 = forge.op.AdvIndex("", self.get_parameter("advindex5.weight_0"), advindex_input_1) + return advindex_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Advindex0, + [((448, 1024), torch.float32), ((1, 1), torch.int64)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Advindex0, + [((448, 1280), torch.float32), ((1, 1), torch.int64)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Advindex0, + [((448, 384), torch.float32), ((1, 1), torch.int64)], + {"model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Advindex0, + [((448, 512), torch.float32), ((1, 1), torch.int64)], + {"model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Advindex0, + [((448, 768), torch.float32), ((1, 1), torch.int64)], + {"model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Advindex0, + [((448, 1280), torch.float32), ((1, 2), torch.int64)], + {"model_name": ["pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf"], "pcc": 0.99}, + ), + ( + Advindex1, + [((1, 2), torch.float32)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + "pt_phi2_microsoft_phi_2_seq_cls_hf", + "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf", + "pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Advindex0, + [((32, 2), torch.float32), ((1,), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Advindex1, + [((1, 1, 1024, 72), torch.float32)], + { + "model_name": [ + "pt_nbeats_seasionality_basis_clm_hf", + "pt_nbeats_trend_basis_clm_hf", + "pt_nbeats_generic_basis_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Advindex2, + [((2401,), torch.int64)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Advindex3, + [((2401,), torch.int64)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Advindex4, + [((2401,), torch.int64)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Advindex5, + [((2401,), torch.int64)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "AdvIndex") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_argmax.py b/forge/test/models_ops/test_argmax.py new file mode 100644 index 000000000..c8059d5bc --- /dev/null +++ b/forge/test/models_ops/test_argmax.py @@ -0,0 +1,86 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Argmax0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, argmax_input_0): + argmax_output_1 = forge.op.Argmax("", argmax_input_0, dim=-1) + return argmax_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Argmax0, + [((1, 32), torch.int32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Argmax") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_avgpool1d.py b/forge/test/models_ops/test_avgpool1d.py new file mode 100644 index 000000000..2d3bf57c7 --- /dev/null +++ b/forge/test/models_ops/test_avgpool1d.py @@ -0,0 +1,96 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Avgpool1D0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool1d_input_0): + avgpool1d_output_1 = forge.op.AvgPool1d( + "", + avgpool1d_input_0, + kernel_size=[49], + stride=[49], + padding=[0, 0], + ceil_mode=False, + count_include_pad=True, + ) + return avgpool1d_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Avgpool1D0, + [((1, 768, 49), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[49]", + "stride": "[49]", + "padding": "[0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + }, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "AvgPool1d") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_avgpool2d.py b/forge/test/models_ops/test_avgpool2d.py new file mode 100644 index 000000000..2c870ac63 --- /dev/null +++ b/forge/test/models_ops/test_avgpool2d.py @@ -0,0 +1,1472 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Avgpool2D0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[1, 1], + stride=[1, 1], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[2, 2], + stride=[2, 2], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[56, 56], + stride=[56, 56], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D3(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[7, 7], + stride=[7, 7], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D4(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[14, 14], + stride=[14, 14], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D5(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[7, 7], + stride=[1, 1], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D6(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[10, 10], + stride=[10, 10], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D7(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[112, 112], + stride=[112, 112], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D8(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[28, 28], + stride=[28, 28], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D9(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[3, 3], + stride=[1, 1], + padding=[1, 1, 1, 1], + ceil_mode=False, + count_include_pad=False, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D10(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[8, 8], + stride=[8, 8], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D11(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[8, 8], + stride=[1, 1], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D12(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[6, 6], + stride=[6, 6], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D13(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[3, 3], + stride=[3, 3], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +class Avgpool2D14(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool2d_input_0): + avgpool2d_output_1 = forge.op.AvgPool2d( + "", + avgpool2d_input_0, + kernel_size=[5, 5], + stride=[5, 5], + padding=[0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool2d_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Avgpool2D0, + [((1, 256, 6, 6), torch.float32)], + { + "model_name": ["pt_alexnet_alexnet_img_cls_torchhub", "pt_rcnn_base_obj_det_torchvision_rect_0"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[1, 1]", + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D1, + [((1, 192, 56, 56), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[2, 2]", + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D2, + [((1, 192, 56, 56), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[56, 56]", + "stride": "[56, 56]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D1, + [((1, 384, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[2, 2]", + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D1, + [((1, 1056, 14, 14), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[2, 2]", + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 2208, 7, 7), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D1, + [((1, 128, 56, 56), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[2, 2]", + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D2, + [((1, 128, 56, 56), torch.float32)], + { + "model_name": ["pt_regnet_facebook_regnet_y_040_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[56, 56]", + "stride": "[56, 56]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D1, + [((1, 256, 28, 28), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[2, 2]", + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D1, + [((1, 896, 14, 14), torch.float32)], + { + "model_name": ["pt_densenet_densenet201_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[2, 2]", + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 1920, 7, 7), torch.float32)], + { + "model_name": ["pt_densenet_densenet201_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D1, + [((1, 640, 14, 14), torch.float32)], + { + "model_name": ["pt_densenet_densenet169_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[2, 2]", + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 1664, 7, 7), torch.float32)], + { + "model_name": ["pt_densenet_densenet169_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D1, + [((1, 512, 14, 14), torch.float32)], + { + "model_name": ["pt_densenet_densenet121_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[2, 2]", + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D4, + [((1, 512, 14, 14), torch.float32)], + { + "model_name": ["pt_regnet_facebook_regnet_y_040_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[14, 14]", + "stride": "[14, 14]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 1024, 7, 7), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet121_img_cls_torchvision", + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla102x_visual_bb_torchvision", + "pt_dla_dla102x2_visual_bb_torchvision", + "pt_dla_dla60x_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + "pt_googlenet_base_img_cls_torchvision", + "pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf", + "pt_mobilenet_v1_basic_img_cls_torchvision", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D5, + [((1, 1024, 7, 7), torch.float32)], + { + "model_name": ["pt_vovnet_vovnet39_obj_det_osmr", "pt_vovnet_vovnet57_obj_det_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 512, 7, 7), torch.float32)], + { + "model_name": ["pt_dla_dla34_visual_bb_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D0, + [((1, 512, 7, 7), torch.float32)], + { + "model_name": ["pt_vgg_19_obj_det_hf", "pt_vgg_vgg19_bn_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[1, 1]", + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D5, + [((1, 512, 7, 7), torch.float32)], + { + "model_name": ["pt_vovnet_vovnet27s_obj_det_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 256, 7, 7), torch.float32)], + { + "model_name": [ + "pt_dla_dla46x_c_visual_bb_torchvision", + "pt_dla_dla60x_c_visual_bb_torchvision", + "pt_dla_dla46_c_visual_bb_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D6, + [((1, 1792, 10, 10), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[10, 10]", + "stride": "[10, 10]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D7, + [((1, 32, 112, 112), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[112, 112]", + "stride": "[112, 112]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D2, + [((1, 96, 56, 56), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[56, 56]", + "stride": "[56, 56]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D2, + [((1, 144, 56, 56), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[56, 56]", + "stride": "[56, 56]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D8, + [((1, 144, 28, 28), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[28, 28]", + "stride": "[28, 28]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D8, + [((1, 240, 28, 28), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[28, 28]", + "stride": "[28, 28]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D4, + [((1, 240, 14, 14), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[14, 14]", + "stride": "[14, 14]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D4, + [((1, 480, 14, 14), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[14, 14]", + "stride": "[14, 14]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D4, + [((1, 672, 14, 14), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[14, 14]", + "stride": "[14, 14]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 672, 7, 7), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 1152, 7, 7), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 1280, 7, 7), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D7, + [((1, 48, 112, 112), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[112, 112]", + "stride": "[112, 112]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D7, + [((1, 24, 112, 112), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[112, 112]", + "stride": "[112, 112]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D8, + [((1, 192, 28, 28), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + "pt_regnet_facebook_regnet_y_040_img_cls_hf", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[28, 28]", + "stride": "[28, 28]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D8, + [((1, 336, 28, 28), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[28, 28]", + "stride": "[28, 28]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D4, + [((1, 336, 14, 14), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[14, 14]", + "stride": "[14, 14]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D4, + [((1, 960, 14, 14), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[14, 14]", + "stride": "[14, 14]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 960, 7, 7), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 1632, 7, 7), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 2688, 7, 7), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 1792, 7, 7), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 2048, 7, 7), torch.float32)], + { + "model_name": [ + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w44_pose_estimation_timm", + "pt_hrnet_hrnet_w32_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w64_pose_estimation_timm", + "pt_hrnet_hrnet_w40_pose_estimation_timm", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "ResNetForImageClassification", + "ResNet", + "pt_resnet_50_img_cls_timm", + "pt_resnext_resnext50_32x4d_img_cls_torchhub", + "pt_resnext_resnext101_32x8d_img_cls_torchhub", + "pt_resnext_resnext101_32x8d_wsl_img_cls_torchhub", + "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D5, + [((1, 2048, 7, 7), torch.float32)], + { + "model_name": [ + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w64_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w44_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w40_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w32_pose_estimation_osmr", + "pt_resnext_resnext26_32x4d_img_cls_osmr", + "pt_resnext_resnext101_64x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_osmr", + "pt_resnext_resnext14_32x4d_img_cls_osmr", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D9, + [((1, 384, 35, 35), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[3, 3]", + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "ceil_mode": "False", + "count_include_pad": "False", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D9, + [((1, 1024, 17, 17), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[3, 3]", + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "ceil_mode": "False", + "count_include_pad": "False", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D9, + [((1, 1536, 8, 8), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[3, 3]", + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "ceil_mode": "False", + "count_include_pad": "False", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D10, + [((1, 1536, 8, 8), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[8, 8]", + "stride": "[8, 8]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D11, + [((1, 1536, 8, 8), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[8, 8]", + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D12, + [((1, 768, 6, 6), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[6, 6]", + "stride": "[6, 6]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D13, + [((1, 1280, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[3, 3]", + "stride": "[3, 3]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D14, + [((1, 1280, 5, 5), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[5, 5]", + "stride": "[5, 5]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D8, + [((1, 320, 28, 28), torch.float32)], + { + "model_name": ["pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[28, 28]", + "stride": "[28, 28]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D2, + [((1, 16, 56, 56), torch.float32)], + { + "model_name": ["pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[56, 56]", + "stride": "[56, 56]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D4, + [((1, 96, 14, 14), torch.float32)], + { + "model_name": ["pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[14, 14]", + "stride": "[14, 14]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D4, + [((1, 120, 14, 14), torch.float32)], + { + "model_name": ["pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[14, 14]", + "stride": "[14, 14]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D4, + [((1, 144, 14, 14), torch.float32)], + { + "model_name": ["pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[14, 14]", + "stride": "[14, 14]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 288, 7, 7), torch.float32)], + { + "model_name": ["pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 576, 7, 7), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D8, + [((1, 72, 28, 28), torch.float32)], + { + "model_name": ["pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[28, 28]", + "stride": "[28, 28]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D8, + [((1, 120, 28, 28), torch.float32)], + { + "model_name": ["pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[28, 28]", + "stride": "[28, 28]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D3, + [((1, 1088, 7, 7), torch.float32)], + { + "model_name": ["pt_regnet_facebook_regnet_y_040_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[7, 7]", + "stride": "[7, 7]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D0, + [((1, 4096, 1, 1), torch.float32)], + { + "model_name": ["pt_vgg_vgg19_bn_obj_det_timm"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[1, 1]", + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool2D6, + [((1, 2048, 10, 10), torch.float32)], + { + "model_name": [ + "pt_xception_xception_img_cls_timm", + "pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "[10, 10]", + "stride": "[10, 10]", + "padding": "[0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "AvgPool2d") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_avgpool3d.py b/forge/test/models_ops/test_avgpool3d.py new file mode 100644 index 000000000..ee3fbc30d --- /dev/null +++ b/forge/test/models_ops/test_avgpool3d.py @@ -0,0 +1,114 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Avgpool3D0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, avgpool3d_input_0): + avgpool3d_output_1 = forge.op.AvgPool3d( + "", + avgpool3d_input_0, + kernel_size=[5, 1, 1], + stride=[1, 1, 1], + padding=[0, 0, 0, 0, 0, 0], + ceil_mode=False, + count_include_pad=True, + channel_last=0, + ) + return avgpool3d_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Avgpool3D0, + [((1, 1, 100, 54, 54), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[5, 1, 1]", + "stride": "[1, 1, 1]", + "padding": "[0, 0, 0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), + ( + Avgpool3D0, + [((1, 1, 260, 27, 27), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[5, 1, 1]", + "stride": "[1, 1, 1]", + "padding": "[0, 0, 0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "AvgPool3d") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_broadcast.py b/forge/test/models_ops/test_broadcast.py new file mode 100644 index 000000000..a3b881522 --- /dev/null +++ b/forge/test/models_ops/test_broadcast.py @@ -0,0 +1,172 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Broadcast0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, broadcast_input_0): + broadcast_output_1 = forge.op.Broadcast("", broadcast_input_0, dim=-3, shape=12) + return broadcast_output_1 + + +class Broadcast1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, broadcast_input_0): + broadcast_output_1 = forge.op.Broadcast("", broadcast_input_0, dim=-4, shape=1) + return broadcast_output_1 + + +class Broadcast2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, broadcast_input_0): + broadcast_output_1 = forge.op.Broadcast("", broadcast_input_0, dim=-2, shape=128) + return broadcast_output_1 + + +class Broadcast3(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, broadcast_input_0): + broadcast_output_1 = forge.op.Broadcast("", broadcast_input_0, dim=-2, shape=384) + return broadcast_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Broadcast0, + [((1, 1, 1, 128), torch.bool)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-3", "shape": "12"}, + }, + ), + ( + Broadcast1, + [((1, 1, 1, 128), torch.bool)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-4", "shape": "1"}, + }, + ), + ( + Broadcast2, + [((1, 12, 1, 128), torch.bool)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "shape": "128"}, + }, + ), + ( + Broadcast0, + [((1, 1, 1, 384), torch.bool)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-3", "shape": "12"}, + }, + ), + ( + Broadcast1, + [((1, 1, 1, 384), torch.bool)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-4", "shape": "1"}, + }, + ), + ( + Broadcast3, + [((1, 12, 1, 384), torch.bool)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "shape": "384"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Broadcast") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_cast.py b/forge/test/models_ops/test_cast.py new file mode 100644 index 000000000..ac7c97865 --- /dev/null +++ b/forge/test/models_ops/test_cast.py @@ -0,0 +1,502 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Cast0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, cast_input_0): + cast_output_1 = forge.op.Cast("", cast_input_0, dtype=torch.float32) + return cast_output_1 + + +class Cast1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, cast_input_0): + cast_output_1 = forge.op.Cast("", cast_input_0, dtype=torch.bool) + return cast_output_1 + + +class Cast2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, cast_input_0): + cast_output_1 = forge.op.Cast("", cast_input_0, dtype=torch.int32) + return cast_output_1 + + +class Cast3(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, cast_input_0): + cast_output_1 = forge.op.Cast("", cast_input_0, dtype=torch.int64) + return cast_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Cast0, + [((2, 1, 1, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast0, + [((2, 13, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast0, + [((2, 1, 1, 13), torch.bool)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast0, + [((2, 1, 7, 7), torch.int64)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast0, + [((2, 1, 7, 7), torch.bool)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast0, + [((1, 1, 1, 128), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_roberta_xlm_roberta_base_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast0, + [((1, 1, 256, 256), torch.int64)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast0, + [((1, 1, 256, 256), torch.bool)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast1, + [((1, 1, 256, 256), torch.bool)], + { + "model_name": [ + "pt_gpt2_gpt2_text_gen_hf", + "pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf", + "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + ( + Cast2, + [((1, 1, 256, 256), torch.bool)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.int32"}, + }, + ), + ( + Cast1, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + ( + Cast2, + [((1, 128), torch.bool)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.int32"}, + }, + ), + ( + Cast1, + [((1, 128), torch.int32)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + ( + Cast3, + [((1, 128), torch.int32)], + { + "model_name": [ + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.int64"}, + }, + ), + ( + Cast0, + [((1, 12, 128, 128), torch.bool)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast1, + [((1, 384), torch.int64)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + ( + Cast2, + [((1, 384), torch.bool)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dtype": "torch.int32"}, + }, + ), + ( + Cast1, + [((1, 384), torch.int32)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + ( + Cast0, + [((1, 12, 384, 384), torch.bool)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast1, + [((1, 1, 32, 32), torch.bool)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + ( + Cast0, + [((1, 1, 32, 32), torch.bool)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast1, + [((1, 1, 256, 256), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + ( + Cast1, + [((1, 1, 256, 256), torch.int32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + ( + Cast1, + [((1, 1, 7, 7), torch.bool)], + { + "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + ( + Cast0, + [((1, 1, 1, 7), torch.int64)], + { + "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast0, + [((1, 1, 32, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + ( + Cast2, + [((1, 32), torch.bool)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.int32"}, + }, + ), + ( + Cast3, + [((1,), torch.int32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.int64"}, + }, + ), + ( + Cast1, + [((1, 16, 320, 1024), torch.bool)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + ( + Cast1, + [((1, 16, 192, 640), torch.bool)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Cast") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_clip.py b/forge/test/models_ops/test_clip.py new file mode 100644 index 000000000..5138ef3c5 --- /dev/null +++ b/forge/test/models_ops/test_clip.py @@ -0,0 +1,1110 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Clip0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, clip_input_0): + clip_output_1 = forge.op.Clip("", clip_input_0, min=0.0, max=1.0) + return clip_output_1 + + +class Clip1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, clip_input_0): + clip_output_1 = forge.op.Clip("", clip_input_0, min=0.0, max=6.0) + return clip_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Clip0, + [((2, 1, 1, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "1.0"}, + }, + ), + ( + Clip0, + [((2, 1, 7, 7), torch.float32)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "1.0"}, + }, + ), + ( + Clip0, + [((1, 1, 256, 256), torch.float32)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "1.0"}, + }, + ), + ( + Clip0, + [((1, 12, 128, 128), torch.float32)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "1.0"}, + }, + ), + ( + Clip0, + [((1, 12, 384, 384), torch.float32)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "1.0"}, + }, + ), + ( + Clip0, + [((1, 1, 32, 32), torch.float32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "1.0"}, + }, + ), + ( + Clip1, + [((1, 72, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 120, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 480, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 672, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 960, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 24, 96, 96), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 48, 96, 96), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 48, 48, 48), torch.float32)], + { + "model_name": [ + "pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 96, 48, 48), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 96, 24, 24), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 192, 24, 24), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 192, 12, 12), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 384, 12, 12), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 384, 6, 6), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 768, 6, 6), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 32, 112, 112), torch.float32)], + { + "model_name": [ + "pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 64, 112, 112), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 64, 56, 56), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 128, 56, 56), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 128, 28, 28), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 256, 28, 28), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 256, 14, 14), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 512, 14, 14), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 512, 7, 7), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 1024, 7, 7), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 16, 48, 48), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 48, 24, 24), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 48, 12, 12), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 96, 12, 12), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 96, 6, 6), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 144, 6, 6), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 192, 6, 6), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 192, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 336, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 1280, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 96, 112, 112), torch.float32)], + { + "model_name": [ + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 96, 56, 56), torch.float32)], + { + "model_name": [ + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 144, 56, 56), torch.float32)], + { + "model_name": [ + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 144, 28, 28), torch.float32)], + { + "model_name": [ + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 192, 28, 28), torch.float32)], + { + "model_name": [ + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 192, 14, 14), torch.float32)], + { + "model_name": [ + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 384, 14, 14), torch.float32)], + { + "model_name": [ + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 576, 14, 14), torch.float32)], + { + "model_name": [ + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 576, 7, 7), torch.float32)], + { + "model_name": [ + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 960, 7, 7), torch.float32)], + { + "model_name": [ + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 1280, 7, 7), torch.float32)], + { + "model_name": [ + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 24, 80, 80), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 96, 80, 80), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 96, 40, 40), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 144, 40, 40), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 144, 20, 20), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 144, 10, 10), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 288, 10, 10), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 432, 10, 10), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 432, 5, 5), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 720, 5, 5), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 1280, 5, 5), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 384, 28, 28), torch.float32)], + { + "model_name": ["pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 576, 28, 28), torch.float32)], + { + "model_name": ["pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 960, 28, 28), torch.float32)], + { + "model_name": ["pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 16, 112, 112), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 16, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 96, 28, 28), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 96, 14, 14), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 96, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 240, 14, 14), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 240, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 120, 14, 14), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 144, 14, 14), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 144, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 288, 14, 14), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 288, 7, 7), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 288, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 576, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 1024), torch.float32)], + { + "model_name": ["pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 1024, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 240, 28, 28), torch.float32)], + { + "model_name": [ + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 200, 14, 14), torch.float32)], + { + "model_name": [ + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 184, 14, 14), torch.float32)], + { + "model_name": [ + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 480, 14, 14), torch.float32)], + { + "model_name": [ + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 672, 14, 14), torch.float32)], + { + "model_name": [ + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 672, 7, 7), torch.float32)], + { + "model_name": [ + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 1280, 1, 1), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 1280), torch.float32)], + { + "model_name": ["pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub"], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 256, 1, 1), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 512, 1, 1), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), + ( + Clip1, + [((1, 768, 1, 1), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"min": "0.0", "max": "6.0"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Clip") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_concatenate.py b/forge/test/models_ops/test_concatenate.py new file mode 100644 index 000000000..4b8927502 --- /dev/null +++ b/forge/test/models_ops/test_concatenate.py @@ -0,0 +1,12958 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Concatenate0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, concatenate_input_0, concatenate_input_1): + concatenate_output_1 = forge.op.Concatenate("", concatenate_input_0, concatenate_input_1, axis=-1) + return concatenate_output_1 + + +class Concatenate1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, concatenate_input_0, concatenate_input_1, concatenate_input_2, concatenate_input_3): + concatenate_output_1 = forge.op.Concatenate( + "", concatenate_input_0, concatenate_input_1, concatenate_input_2, concatenate_input_3, axis=-2 + ) + return concatenate_output_1 + + +class Concatenate2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, concatenate_input_0, concatenate_input_1): + concatenate_output_1 = forge.op.Concatenate("", concatenate_input_0, concatenate_input_1, axis=-2) + return concatenate_output_1 + + +class Concatenate3(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, concatenate_input_0): + concatenate_output_1 = forge.op.Concatenate("", concatenate_input_0, axis=-3) + return concatenate_output_1 + + +class Concatenate4(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, concatenate_input_0, concatenate_input_1): + concatenate_output_1 = forge.op.Concatenate("", concatenate_input_0, concatenate_input_1, axis=-3) + return concatenate_output_1 + + +class Concatenate5(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, concatenate_input_0, concatenate_input_1, concatenate_input_2): + concatenate_output_1 = forge.op.Concatenate( + "", concatenate_input_0, concatenate_input_1, concatenate_input_2, axis=-3 + ) + return concatenate_output_1 + + +class Concatenate6(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, concatenate_input_0, concatenate_input_1, concatenate_input_2, concatenate_input_3): + concatenate_output_1 = forge.op.Concatenate( + "", concatenate_input_0, concatenate_input_1, concatenate_input_2, concatenate_input_3, axis=-3 + ) + return concatenate_output_1 + + +class Concatenate7(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, concatenate_input_0, concatenate_input_1, concatenate_input_2, concatenate_input_3, concatenate_input_4 + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate8(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate9(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate10(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate11(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate12(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate13(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate14(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate15(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate16(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate17(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate18(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate19(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate20(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate21(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate22(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate23(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate24(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate25(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate26(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate27(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate28(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate29(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate30(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate31(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate32(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate33(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate34(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate35(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate36(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate37(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate38(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate39(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate40(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate41(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate42(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate43(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate44(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate45(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate46(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + concatenate_input_43, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + concatenate_input_43, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate47(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + concatenate_input_43, + concatenate_input_44, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + concatenate_input_43, + concatenate_input_44, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate48(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + concatenate_input_43, + concatenate_input_44, + concatenate_input_45, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + concatenate_input_43, + concatenate_input_44, + concatenate_input_45, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate49(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + concatenate_input_43, + concatenate_input_44, + concatenate_input_45, + concatenate_input_46, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + concatenate_input_43, + concatenate_input_44, + concatenate_input_45, + concatenate_input_46, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate50(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + concatenate_input_43, + concatenate_input_44, + concatenate_input_45, + concatenate_input_46, + concatenate_input_47, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + concatenate_input_43, + concatenate_input_44, + concatenate_input_45, + concatenate_input_46, + concatenate_input_47, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate51(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + concatenate_input_43, + concatenate_input_44, + concatenate_input_45, + concatenate_input_46, + concatenate_input_47, + concatenate_input_48, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + concatenate_input_7, + concatenate_input_8, + concatenate_input_9, + concatenate_input_10, + concatenate_input_11, + concatenate_input_12, + concatenate_input_13, + concatenate_input_14, + concatenate_input_15, + concatenate_input_16, + concatenate_input_17, + concatenate_input_18, + concatenate_input_19, + concatenate_input_20, + concatenate_input_21, + concatenate_input_22, + concatenate_input_23, + concatenate_input_24, + concatenate_input_25, + concatenate_input_26, + concatenate_input_27, + concatenate_input_28, + concatenate_input_29, + concatenate_input_30, + concatenate_input_31, + concatenate_input_32, + concatenate_input_33, + concatenate_input_34, + concatenate_input_35, + concatenate_input_36, + concatenate_input_37, + concatenate_input_38, + concatenate_input_39, + concatenate_input_40, + concatenate_input_41, + concatenate_input_42, + concatenate_input_43, + concatenate_input_44, + concatenate_input_45, + concatenate_input_46, + concatenate_input_47, + concatenate_input_48, + axis=-3, + ) + return concatenate_output_1 + + +class Concatenate52(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, concatenate_input_0, concatenate_input_1, concatenate_input_2): + concatenate_output_1 = forge.op.Concatenate( + "", concatenate_input_0, concatenate_input_1, concatenate_input_2, axis=-4 + ) + return concatenate_output_1 + + +class Concatenate53(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + concatenate_input_6, + axis=-4, + ) + return concatenate_output_1 + + +class Concatenate54(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("concatenate54_const_1", shape=(1, 3025, 258), dtype=torch.float32) + + def forward(self, concatenate_input_0): + concatenate_output_1 = forge.op.Concatenate( + "", concatenate_input_0, self.get_constant("concatenate54_const_1"), axis=-1 + ) + return concatenate_output_1 + + +class Concatenate55(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("concatenate55_const_1", shape=(1, 50176, 258), dtype=torch.float32) + + def forward(self, concatenate_input_0): + concatenate_output_1 = forge.op.Concatenate( + "", concatenate_input_0, self.get_constant("concatenate55_const_1"), axis=-1 + ) + return concatenate_output_1 + + +class Concatenate56(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward( + self, + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + ): + concatenate_output_1 = forge.op.Concatenate( + "", + concatenate_input_0, + concatenate_input_1, + concatenate_input_2, + concatenate_input_3, + concatenate_input_4, + concatenate_input_5, + axis=-1, + ) + return concatenate_output_1 + + +class Concatenate57(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, concatenate_input_0, concatenate_input_1, concatenate_input_2, concatenate_input_3): + concatenate_output_1 = forge.op.Concatenate( + "", concatenate_input_0, concatenate_input_1, concatenate_input_2, concatenate_input_3, axis=-1 + ) + return concatenate_output_1 + + +class Concatenate58(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, concatenate_input_0, concatenate_input_1, concatenate_input_2): + concatenate_output_1 = forge.op.Concatenate( + "", concatenate_input_0, concatenate_input_1, concatenate_input_2, axis=-2 + ) + return concatenate_output_1 + + +class Concatenate59(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, concatenate_input_0, concatenate_input_1, concatenate_input_2): + concatenate_output_1 = forge.op.Concatenate( + "", concatenate_input_0, concatenate_input_1, concatenate_input_2, axis=-1 + ) + return concatenate_output_1 + + +class Concatenate60(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("concatenate60_const_1", shape=(1, 5880, 1), dtype=torch.float32) + + def forward(self, concatenate_input_0, concatenate_input_2): + concatenate_output_1 = forge.op.Concatenate( + "", concatenate_input_0, self.get_constant("concatenate60_const_1"), concatenate_input_2, axis=-1 + ) + return concatenate_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Concatenate0, + [((1, 39, 64), torch.float32), ((1, 39, 64), torch.float32)], + { + "model_name": [ + "pt_deepseek_deepseek_math_7b_instruct_qa_hf", + "DeepSeekWrapper_decoder", + "pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 32, 39, 64), torch.float32), ((1, 32, 39, 64), torch.float32)], + { + "model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf", "DeepSeekWrapper_decoder"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate1, + [ + ((256, 1024), torch.float32), + ((256, 1024), torch.float32), + ((256, 1024), torch.float32), + ((256, 1024), torch.float32), + ], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-2"}, + }, + ), + ( + Concatenate0, + [((1, 256, 16, 32), torch.float32), ((1, 256, 16, 32), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 6, 32), torch.float32), ((1, 6, 32), torch.float32)], + { + "model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf", "pt_qwen1_5_qwen_qwen1_5_0_5b_clm_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 71, 6, 32), torch.float32), ((1, 71, 6, 32), torch.float32)], + {"model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 1, 6, 32), torch.float32), ((1, 1, 6, 32), torch.float32)], + {"model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 10, 128), torch.float32), ((1, 10, 128), torch.float32)], + { + "model_name": [ + "pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_1b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 12, 10, 128), torch.float32), ((1, 12, 10, 128), torch.float32)], + { + "model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 4, 10, 128), torch.float32), ((1, 4, 10, 128), torch.float32)], + { + "model_name": [ + "pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_1b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 8, 10, 128), torch.float32), ((1, 8, 10, 128), torch.float32)], + {"model_name": ["pt_falcon3_tiiuae_falcon3_1b_base_clm_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 334, 16), torch.float32), ((1, 334, 16), torch.float32)], + {"model_name": ["pt_fuyu_adept_fuyu_8b_qa_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 64, 334, 16), torch.float32), ((1, 64, 334, 16), torch.float32)], + {"model_name": ["pt_fuyu_adept_fuyu_8b_qa_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 64, 334, 32), torch.float32), ((1, 64, 334, 32), torch.float32)], + {"model_name": ["pt_fuyu_adept_fuyu_8b_qa_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 7, 128), torch.float32), ((1, 7, 128), torch.float32)], + {"model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 8, 7, 128), torch.float32), ((1, 8, 7, 128), torch.float32)], + {"model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 1, 7, 128), torch.float32), ((1, 1, 7, 128), torch.float32)], + {"model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 4, 32), torch.float32), ((1, 4, 32), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 32, 4, 32), torch.float32), ((1, 32, 4, 32), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 8, 4, 32), torch.float32), ((1, 8, 4, 32), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 256, 32), torch.float32), ((1, 256, 32), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 32, 256, 32), torch.float32), ((1, 32, 256, 32), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 8, 256, 32), torch.float32), ((1, 8, 256, 32), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 4, 64), torch.float32), ((1, 4, 64), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 32, 4, 64), torch.float32), ((1, 32, 4, 64), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 8, 4, 64), torch.float32), ((1, 8, 4, 64), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 128, 64), torch.float32), ((1, 128, 64), torch.float32)], + {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 32, 128, 64), torch.float32), ((1, 32, 128, 64), torch.float32)], + {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 8, 128, 64), torch.float32), ((1, 8, 128, 64), torch.float32)], + {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 12, 16), torch.float32), ((1, 12, 16), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_pytdml_token_cls_hf", "pt_phi2_microsoft_phi_2_token_cls_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 32, 12, 16), torch.float32), ((1, 32, 12, 16), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_pytdml_token_cls_hf", "pt_phi2_microsoft_phi_2_token_cls_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 32, 12, 32), torch.float32), ((1, 32, 12, 48), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_pytdml_token_cls_hf", "pt_phi2_microsoft_phi_2_token_cls_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 256, 16), torch.float32), ((1, 256, 16), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_clm_hf", "pt_phi2_microsoft_phi_2_pytdml_clm_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 32, 256, 16), torch.float32), ((1, 32, 256, 16), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_clm_hf", "pt_phi2_microsoft_phi_2_pytdml_clm_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 32, 256, 32), torch.float32), ((1, 32, 256, 48), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_clm_hf", "pt_phi2_microsoft_phi_2_pytdml_clm_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 11, 16), torch.float32), ((1, 11, 16), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_seq_cls_hf", "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 32, 11, 16), torch.float32), ((1, 32, 11, 16), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_seq_cls_hf", "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 32, 11, 32), torch.float32), ((1, 32, 11, 48), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_seq_cls_hf", "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 256, 48), torch.float32), ((1, 256, 48), torch.float32)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_clm_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 32, 256, 48), torch.float32), ((1, 32, 256, 48), torch.float32)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_clm_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 13, 48), torch.float32), ((1, 13, 48), torch.float32)], + { + "model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_token_cls_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 32, 13, 48), torch.float32), ((1, 32, 13, 48), torch.float32)], + { + "model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_token_cls_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 5, 48), torch.float32), ((1, 5, 48), torch.float32)], + { + "model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 32, 5, 48), torch.float32), ((1, 32, 5, 48), torch.float32)], + { + "model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 16, 6, 32), torch.float32), ((1, 16, 6, 32), torch.float32)], + {"model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_clm_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 29, 32), torch.float32), ((1, 29, 32), torch.float32)], + { + "model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_chat_clm_hf", "pt_qwen_v2_qwen_qwen2_5_0_5b_clm_hf"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 16, 29, 32), torch.float32), ((1, 16, 29, 32), torch.float32)], + {"model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_chat_clm_hf"], "pcc": 0.99, "op_params": {"axis": "-1"}}, + ), + ( + Concatenate0, + [((1, 35, 64), torch.float32), ((1, 35, 64), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 28, 35, 64), torch.float32), ((1, 28, 35, 64), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 4, 35, 64), torch.float32), ((1, 4, 35, 64), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate0, + [((1, 12, 35, 64), torch.float32), ((1, 12, 35, 64), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": 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"op_params": {"axis": "-3"}, + }, + ), + ( + Concatenate3, + [((1, 128, 28, 28), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + ], + "pcc": 0.99, + "op_params": {"axis": "-3"}, + }, + ), + ( + Concatenate4, + [((1, 128, 28, 28), torch.float32), ((1, 32, 28, 28), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + ], + "pcc": 0.99, + "op_params": {"axis": "-3"}, + }, + ), + ( + Concatenate5, + [((1, 128, 28, 28), torch.float32), ((1, 32, 28, 28), torch.float32), ((1, 32, 28, 28), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + ], + "pcc": 0.99, + "op_params": {"axis": "-3"}, + 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14, 20), torch.float32), + ((1, 128, 14, 20), torch.float32), + ], + {"model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate5, + [((1, 64, 28, 40), torch.float32), ((1, 64, 28, 40), torch.float32), ((1, 64, 28, 40), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate5, + [((1, 32, 56, 80), torch.float32), ((1, 32, 56, 80), torch.float32), ((1, 32, 56, 80), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 32, 28, 40), torch.float32), ((1, 32, 28, 40), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 64, 14, 20), torch.float32), ((1, 64, 14, 20), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate6, + [ + ((1, 384, 14, 20), torch.float32), + ((1, 384, 14, 20), torch.float32), + ((1, 384, 14, 20), torch.float32), + ((1, 384, 14, 20), torch.float32), + ], + {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate5, + [((1, 192, 28, 40), torch.float32), ((1, 192, 28, 40), torch.float32), ((1, 192, 28, 40), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate5, + [((1, 96, 56, 80), torch.float32), ((1, 96, 56, 80), torch.float32), ((1, 96, 56, 80), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 96, 28, 40), torch.float32), ((1, 96, 28, 40), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 192, 14, 20), torch.float32), ((1, 192, 14, 20), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate6, + [ + ((1, 3, 320, 320), torch.float32), + ((1, 3, 320, 320), torch.float32), + ((1, 3, 320, 320), torch.float32), + ((1, 3, 320, 320), torch.float32), + ], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"axis": "-3"}, + }, + ), + ( + Concatenate5, + [((1, 4, 80, 80), torch.float32), ((1, 1, 80, 80), torch.float32), ((1, 80, 80, 80), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"axis": "-3"}, + }, + ), + ( + Concatenate5, + [((1, 4, 40, 40), torch.float32), ((1, 1, 40, 40), torch.float32), ((1, 80, 40, 40), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"axis": "-3"}, + }, + ), + ( + Concatenate5, + [((1, 4, 20, 20), torch.float32), ((1, 1, 20, 20), torch.float32), ((1, 80, 20, 20), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"axis": "-3"}, + }, + ), + ( + Concatenate59, + [((1, 85, 6400), torch.float32), ((1, 85, 1600), torch.float32), ((1, 85, 400), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate6, + [ + ((1, 3, 208, 208), torch.float32), + ((1, 3, 208, 208), torch.float32), + ((1, 3, 208, 208), torch.float32), + ((1, 3, 208, 208), torch.float32), + ], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"axis": "-3"}, + }, + ), + ( + Concatenate4, + [((1, 24, 104, 104), torch.float32), ((1, 24, 104, 104), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 48, 52, 52), torch.float32), ((1, 48, 52, 52), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 96, 26, 26), torch.float32), ((1, 96, 26, 26), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate6, + [ + ((1, 192, 13, 13), torch.float32), + ((1, 192, 13, 13), torch.float32), + ((1, 192, 13, 13), torch.float32), + ((1, 192, 13, 13), torch.float32), + ], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 192, 13, 13), torch.float32), ((1, 192, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 192, 26, 26), torch.float32), ((1, 192, 26, 26), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 96, 52, 52), torch.float32), ((1, 96, 52, 52), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate5, + [((1, 4, 52, 52), torch.float32), ((1, 1, 52, 52), torch.float32), ((1, 80, 52, 52), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"axis": "-3"}, + }, + ), + ( + Concatenate5, + [((1, 4, 26, 26), torch.float32), ((1, 1, 26, 26), torch.float32), ((1, 80, 26, 26), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"axis": "-3"}, + }, + ), + ( + Concatenate5, + [((1, 4, 13, 13), torch.float32), ((1, 1, 13, 13), torch.float32), ((1, 80, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"axis": "-3"}, + }, + ), + ( + Concatenate59, + [((1, 85, 2704), torch.float32), ((1, 85, 676), torch.float32), ((1, 85, 169), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), + ( + Concatenate4, + [((1, 256, 40, 40), torch.float32), ((1, 512, 40, 40), torch.float32)], + {"model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 128, 80, 80), torch.float32), ((1, 256, 80, 80), torch.float32)], + {"model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 16, 104, 104), torch.float32), ((1, 16, 104, 104), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 32, 52, 52), torch.float32), ((1, 32, 52, 52), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 64, 26, 26), torch.float32), ((1, 64, 26, 26), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate6, + [ + ((1, 128, 13, 13), torch.float32), + ((1, 128, 13, 13), torch.float32), + ((1, 128, 13, 13), torch.float32), + ((1, 128, 13, 13), torch.float32), + ], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 128, 13, 13), torch.float32), ((1, 128, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 128, 26, 26), torch.float32), ((1, 128, 26, 26), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), + ( + Concatenate4, + [((1, 64, 52, 52), torch.float32), ((1, 64, 52, 52), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99, "op_params": {"axis": "-3"}}, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Concatenate") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_conv2d.py b/forge/test/models_ops/test_conv2d.py new file mode 100644 index 000000000..6738c103d --- /dev/null +++ b/forge/test/models_ops/test_conv2d.py @@ -0,0 +1,56618 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Conv2D0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[0, 0, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 1], + padding=[0, 0, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=4, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D3(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D4(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d4.weight_1", + forge.Parameter(*(64, 3, 11, 11), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d4.weight_1"), + stride=[4, 4], + padding=[2, 2, 2, 2], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D5(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d5.weight_1", + forge.Parameter(*(192, 64, 5, 5), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d5.weight_1"), + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D6(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d6.weight_1", + forge.Parameter(*(384, 192, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d6.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D7(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d7.weight_1", + forge.Parameter(*(256, 384, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d7.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D8(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d8.weight_1", + forge.Parameter(*(256, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d8.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D9(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d9.weight_1", + forge.Parameter(*(96, 3, 11, 11), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d9.weight_1"), + stride=[4, 4], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D10(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d10.weight_1", + forge.Parameter(*(256, 96, 5, 5), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d10.weight_1"), + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D11(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d11.weight_1", + forge.Parameter(*(384, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d11.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D12(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d12.weight_1", + forge.Parameter(*(384, 384, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d12.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D13(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d13.weight_1", + forge.Parameter(*(16, 1, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d13.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D14(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d14.weight_1", + forge.Parameter(*(4, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d14.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D15(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d15.weight_1", + forge.Parameter(*(768, 3, 16, 16), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d15.weight_1"), + stride=[16, 16], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D16(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d16.weight_1", + forge.Parameter(*(192, 3, 16, 16), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d16.weight_1"), + stride=[16, 16], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D17(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d17.weight_1", + forge.Parameter(*(384, 3, 16, 16), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d17.weight_1"), + stride=[16, 16], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D18(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d18.weight_1", + forge.Parameter(*(96, 3, 7, 7), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d18.weight_1"), + stride=[2, 2], + padding=[3, 3, 3, 3], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D19(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d19.weight_1", + forge.Parameter(*(192, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d19.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D20(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d20.weight_1", + forge.Parameter(*(48, 192, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d20.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D21(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d21.weight_1", + forge.Parameter(*(192, 144, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d21.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D22(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d22.weight_1", + forge.Parameter(*(192, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d22.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D23(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d23.weight_1", + forge.Parameter(*(192, 240, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d23.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D24(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d24.weight_1", + forge.Parameter(*(192, 288, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d24.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D25(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d25.weight_1", + forge.Parameter(*(192, 336, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d25.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D26(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d26.weight_1", + forge.Parameter(*(192, 384, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d26.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D27(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d27.weight_1", + forge.Parameter(*(192, 432, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d27.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D28(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d28.weight_1", + forge.Parameter(*(192, 480, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d28.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D29(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d29.weight_1", + forge.Parameter(*(192, 528, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d29.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D30(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d30.weight_1", + forge.Parameter(*(192, 576, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d30.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D31(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d31.weight_1", + forge.Parameter(*(192, 624, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d31.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D32(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d32.weight_1", + forge.Parameter(*(192, 672, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d32.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D33(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d33.weight_1", + forge.Parameter(*(192, 720, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d33.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D34(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d34.weight_1", + forge.Parameter(*(384, 768, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d34.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D35(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d35.weight_1", + forge.Parameter(*(192, 768, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d35.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D36(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d36.weight_1", + forge.Parameter(*(192, 816, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d36.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D37(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d37.weight_1", + forge.Parameter(*(192, 864, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d37.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D38(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d38.weight_1", + forge.Parameter(*(192, 912, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d38.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D39(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d39.weight_1", + forge.Parameter(*(192, 960, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d39.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D40(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d40.weight_1", + forge.Parameter(*(192, 1008, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d40.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D41(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d41.weight_1", + forge.Parameter(*(192, 1056, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d41.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D42(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d42.weight_1", + forge.Parameter(*(192, 1104, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d42.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D43(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d43.weight_1", + forge.Parameter(*(192, 1152, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d43.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D44(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d44.weight_1", + forge.Parameter(*(192, 1200, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d44.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D45(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d45.weight_1", + forge.Parameter(*(192, 1248, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d45.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D46(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d46.weight_1", + forge.Parameter(*(192, 1296, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d46.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D47(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d47.weight_1", + forge.Parameter(*(192, 1344, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d47.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D48(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d48.weight_1", + forge.Parameter(*(192, 1392, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d48.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D49(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d49.weight_1", + forge.Parameter(*(192, 1440, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d49.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D50(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d50.weight_1", + forge.Parameter(*(192, 1488, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d50.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D51(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d51.weight_1", + forge.Parameter(*(192, 1536, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d51.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D52(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d52.weight_1", + forge.Parameter(*(192, 1584, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d52.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D53(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d53.weight_1", + forge.Parameter(*(192, 1632, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d53.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D54(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d54.weight_1", + forge.Parameter(*(192, 1680, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d54.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D55(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d55.weight_1", + forge.Parameter(*(192, 1728, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d55.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D56(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d56.weight_1", + forge.Parameter(*(192, 1776, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d56.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D57(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d57.weight_1", + forge.Parameter(*(192, 1824, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d57.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D58(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d58.weight_1", + forge.Parameter(*(192, 1872, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d58.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D59(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d59.weight_1", + forge.Parameter(*(192, 1920, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d59.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D60(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d60.weight_1", + forge.Parameter(*(192, 1968, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d60.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D61(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d61.weight_1", + forge.Parameter(*(192, 2016, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d61.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D62(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d62.weight_1", + forge.Parameter(*(192, 2064, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d62.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D63(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d63.weight_1", + forge.Parameter(*(1056, 2112, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d63.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D64(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d64.weight_1", + forge.Parameter(*(192, 2112, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d64.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D65(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d65.weight_1", + forge.Parameter(*(192, 2160, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d65.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D66(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d66.weight_1", + forge.Parameter(*(64, 3, 7, 7), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d66.weight_1"), + stride=[2, 2], + padding=[3, 3, 3, 3], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D67(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d67.weight_1", + forge.Parameter(*(128, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d67.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D68(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d68.weight_1", + forge.Parameter(*(32, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d68.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D69(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d69.weight_1", + forge.Parameter(*(32, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d69.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D70(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d70.weight_1", + forge.Parameter(*(128, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d70.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D71(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d71.weight_1", + forge.Parameter(*(128, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d71.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D72(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d72.weight_1", + forge.Parameter(*(128, 160, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d72.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D73(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d73.weight_1", + forge.Parameter(*(128, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d73.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D74(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d74.weight_1", + forge.Parameter(*(128, 224, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d74.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D75(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d75.weight_1", + forge.Parameter(*(128, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d75.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D76(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d76.weight_1", + forge.Parameter(*(128, 288, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d76.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D77(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d77.weight_1", + forge.Parameter(*(128, 320, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d77.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D78(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d78.weight_1", + forge.Parameter(*(128, 352, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d78.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D79(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d79.weight_1", + forge.Parameter(*(128, 384, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d79.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D80(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d80.weight_1", + forge.Parameter(*(128, 416, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d80.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D81(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d81.weight_1", + forge.Parameter(*(128, 448, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d81.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D82(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d82.weight_1", + forge.Parameter(*(128, 480, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d82.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D83(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d83.weight_1", + forge.Parameter(*(256, 512, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d83.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D84(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d84.weight_1", + forge.Parameter(*(128, 512, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d84.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D85(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d85.weight_1", + forge.Parameter(*(128, 544, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d85.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D86(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d86.weight_1", + forge.Parameter(*(128, 576, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d86.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D87(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d87.weight_1", + forge.Parameter(*(128, 608, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d87.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D88(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d88.weight_1", + forge.Parameter(*(128, 640, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d88.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D89(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d89.weight_1", + forge.Parameter(*(128, 672, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d89.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D90(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d90.weight_1", + forge.Parameter(*(128, 704, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d90.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D91(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d91.weight_1", + forge.Parameter(*(128, 736, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d91.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D92(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d92.weight_1", + forge.Parameter(*(128, 768, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d92.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D93(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d93.weight_1", + forge.Parameter(*(128, 800, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d93.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D94(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d94.weight_1", + forge.Parameter(*(128, 832, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d94.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D95(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d95.weight_1", + forge.Parameter(*(128, 864, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d95.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D96(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d96.weight_1", + forge.Parameter(*(128, 896, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d96.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D97(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d97.weight_1", + forge.Parameter(*(128, 928, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d97.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D98(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d98.weight_1", + forge.Parameter(*(128, 960, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d98.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D99(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d99.weight_1", + forge.Parameter(*(128, 992, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d99.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D100(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d100.weight_1", + forge.Parameter(*(128, 1024, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d100.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D101(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d101.weight_1", + forge.Parameter(*(128, 1056, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d101.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D102(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d102.weight_1", + forge.Parameter(*(128, 1088, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d102.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D103(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d103.weight_1", + forge.Parameter(*(128, 1120, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d103.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D104(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d104.weight_1", + forge.Parameter(*(128, 1152, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d104.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D105(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d105.weight_1", + forge.Parameter(*(128, 1184, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d105.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D106(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d106.weight_1", + forge.Parameter(*(128, 1216, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d106.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D107(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d107.weight_1", + forge.Parameter(*(128, 1248, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d107.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D108(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d108.weight_1", + forge.Parameter(*(128, 1280, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d108.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D109(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d109.weight_1", + forge.Parameter(*(128, 1312, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d109.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D110(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d110.weight_1", + forge.Parameter(*(128, 1344, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d110.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D111(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d111.weight_1", + forge.Parameter(*(128, 1376, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d111.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D112(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d112.weight_1", + forge.Parameter(*(128, 1408, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d112.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D113(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d113.weight_1", + forge.Parameter(*(128, 1440, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d113.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D114(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d114.weight_1", + forge.Parameter(*(128, 1472, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d114.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D115(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d115.weight_1", + forge.Parameter(*(128, 1504, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d115.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D116(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d116.weight_1", + forge.Parameter(*(128, 1536, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d116.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D117(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d117.weight_1", + forge.Parameter(*(128, 1568, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d117.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D118(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d118.weight_1", + forge.Parameter(*(128, 1600, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d118.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D119(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d119.weight_1", + forge.Parameter(*(128, 1632, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d119.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D120(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d120.weight_1", + forge.Parameter(*(128, 1664, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d120.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D121(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d121.weight_1", + forge.Parameter(*(128, 1696, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d121.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D122(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d122.weight_1", + forge.Parameter(*(128, 1728, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d122.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D123(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d123.weight_1", + forge.Parameter(*(128, 1760, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d123.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D124(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d124.weight_1", + forge.Parameter(*(896, 1792, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d124.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D125(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d125.weight_1", + forge.Parameter(*(128, 1792, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d125.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D126(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d126.weight_1", + forge.Parameter(*(128, 1824, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d126.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D127(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d127.weight_1", + forge.Parameter(*(128, 1856, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d127.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D128(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d128.weight_1", + forge.Parameter(*(128, 1888, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d128.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D129(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d129.weight_1", + forge.Parameter(*(640, 1280, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d129.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D130(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d130.weight_1", + forge.Parameter(*(512, 1024, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d130.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D131(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d131.weight_1", + forge.Parameter(*(16, 3, 7, 7), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d131.weight_1"), + stride=[1, 1], + padding=[3, 3, 3, 3], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D132(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d132.weight_1", + forge.Parameter(*(16, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d132.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D133(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d133.weight_1", + forge.Parameter(*(32, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d133.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D134(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d134.weight_1", + forge.Parameter(*(64, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d134.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D135(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d135.weight_1", + forge.Parameter(*(64, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d135.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D136(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d136.weight_1", + forge.Parameter(*(64, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d136.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D137(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d137.weight_1", + forge.Parameter(*(64, 32, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d137.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D138(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d138.weight_1", + forge.Parameter(*(64, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d138.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D139(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d139.weight_1", + forge.Parameter(*(128, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d139.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D140(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d140.weight_1", + forge.Parameter(*(128, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d140.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D141(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d141.weight_1", + forge.Parameter(*(128, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d141.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D142(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d142.weight_1", + forge.Parameter(*(256, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d142.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D143(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d143.weight_1", + forge.Parameter(*(256, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d143.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D144(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d144.weight_1", + forge.Parameter(*(256, 896, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d144.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D145(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d145.weight_1", + forge.Parameter(*(512, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d145.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D146(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d146.weight_1", + forge.Parameter(*(512, 512, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d146.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D147(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d147.weight_1", + forge.Parameter(*(512, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d147.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D148(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d148.weight_1", + forge.Parameter(*(512, 1280, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d148.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D149(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d149.weight_1", + forge.Parameter(*(1000, 512, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d149.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D150(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d150.weight_1", + forge.Parameter(*(128, 32, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d150.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D151(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d151.weight_1", + forge.Parameter(*(256, 768, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d151.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D152(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d152.weight_1", + forge.Parameter(*(256, 1152, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d152.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D153(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d153.weight_1", + forge.Parameter(*(256, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d153.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D154(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d154.weight_1", + forge.Parameter(*(256, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d154.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D155(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d155.weight_1", + forge.Parameter(*(512, 1536, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d155.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D156(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d156.weight_1", + forge.Parameter(*(512, 2048, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d156.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D157(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d157.weight_1", + forge.Parameter(*(512, 2816, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d157.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D158(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d158.weight_1", + forge.Parameter(*(512, 512, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d158.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D159(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d159.weight_1", + forge.Parameter(*(512, 512, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d159.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D160(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d160.weight_1", + forge.Parameter(*(1024, 512, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d160.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D161(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d161.weight_1", + forge.Parameter(*(1024, 2560, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d161.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D162(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d162.weight_1", + forge.Parameter(*(1000, 1024, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d162.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D163(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d163.weight_1", + forge.Parameter(*(128, 32, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d163.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D164(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d164.weight_1", + forge.Parameter(*(128, 4, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d164.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D165(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d165.weight_1", + forge.Parameter(*(128, 4, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d165.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D166(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d166.weight_1", + forge.Parameter(*(256, 8, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d166.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D167(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d167.weight_1", + forge.Parameter(*(256, 8, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d167.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D168(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d168.weight_1", + forge.Parameter(*(512, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d168.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D169(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d169.weight_1", + forge.Parameter(*(512, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d169.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D170(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d170.weight_1", + forge.Parameter(*(1024, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d170.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D171(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d171.weight_1", + forge.Parameter(*(1024, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d171.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D172(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d172.weight_1", + forge.Parameter(*(1024, 1024, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d172.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D173(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d173.weight_1", + forge.Parameter(*(64, 2, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d173.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D174(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d174.weight_1", + forge.Parameter(*(64, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d174.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D175(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d175.weight_1", + forge.Parameter(*(64, 2, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d175.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D176(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d176.weight_1", + forge.Parameter(*(64, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d176.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D177(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d177.weight_1", + forge.Parameter(*(256, 640, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d177.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D178(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d178.weight_1", + forge.Parameter(*(1000, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d178.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D179(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d179.weight_1", + forge.Parameter(*(32, 32, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d179.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D180(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d180.weight_1", + forge.Parameter(*(32, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d180.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D181(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d181.weight_1", + forge.Parameter(*(32, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d181.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D182(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d182.weight_1", + forge.Parameter(*(32, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d182.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D183(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d183.weight_1", + forge.Parameter(*(256, 32, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d183.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D184(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d184.weight_1", + forge.Parameter(*(256, 4, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d184.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=64, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D185(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d185.weight_1", + forge.Parameter(*(256, 4, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d185.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=64, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D186(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d186.weight_1", + forge.Parameter(*(512, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d186.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D187(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d187.weight_1", + forge.Parameter(*(512, 8, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d187.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=64, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D188(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d188.weight_1", + forge.Parameter(*(512, 8, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d188.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=64, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D189(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d189.weight_1", + forge.Parameter(*(1024, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d189.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D190(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d190.weight_1", + forge.Parameter(*(1024, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d190.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=64, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D191(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d191.weight_1", + forge.Parameter(*(1024, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d191.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=64, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D192(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d192.weight_1", + forge.Parameter(*(2048, 512, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d192.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D193(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d193.weight_1", + forge.Parameter(*(2048, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d193.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=64, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D194(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d194.weight_1", + forge.Parameter(*(1024, 2048, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d194.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D195(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d195.weight_1", + forge.Parameter(*(2048, 1024, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d195.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D196(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d196.weight_1", + forge.Parameter(*(2048, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d196.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=64, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D197(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d197.weight_1", + forge.Parameter(*(512, 2304, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d197.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D198(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d198.weight_1", + forge.Parameter(*(512, 2560, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d198.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D199(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d199.weight_1", + forge.Parameter(*(512, 3328, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d199.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D200(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d200.weight_1", + forge.Parameter(*(48, 3, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d200.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D201(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=48, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D202(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d202.weight_1", + forge.Parameter(*(12, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d202.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D203(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d203.weight_1", + forge.Parameter(*(48, 12, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d203.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D204(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d204.weight_1", + forge.Parameter(*(24, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d204.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D205(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=24, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D206(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d206.weight_1", + forge.Parameter(*(6, 24, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d206.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D207(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d207.weight_1", + forge.Parameter(*(24, 6, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d207.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D208(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d208.weight_1", + forge.Parameter(*(24, 24, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d208.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D209(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d209.weight_1", + forge.Parameter(*(144, 24, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d209.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D210(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=144, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D211(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d211.weight_1", + forge.Parameter(*(6, 144, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d211.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D212(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d212.weight_1", + forge.Parameter(*(144, 6, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d212.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D213(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d213.weight_1", + forge.Parameter(*(32, 144, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d213.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D214(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d214.weight_1", + forge.Parameter(*(192, 32, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d214.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D215(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=192, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D216(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d216.weight_1", + forge.Parameter(*(8, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d216.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D217(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d217.weight_1", + forge.Parameter(*(192, 8, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d217.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D218(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d218.weight_1", + forge.Parameter(*(32, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d218.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D219(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=192, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D220(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d220.weight_1", + forge.Parameter(*(56, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d220.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D221(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d221.weight_1", + forge.Parameter(*(336, 56, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d221.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D222(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=336, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D223(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d223.weight_1", + forge.Parameter(*(14, 336, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d223.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D224(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d224.weight_1", + forge.Parameter(*(336, 14, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d224.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D225(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d225.weight_1", + forge.Parameter(*(56, 336, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d225.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D226(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=336, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D227(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d227.weight_1", + forge.Parameter(*(112, 336, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d227.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D228(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d228.weight_1", + forge.Parameter(*(672, 112, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d228.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D229(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=672, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D230(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d230.weight_1", + forge.Parameter(*(28, 672, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d230.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D231(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d231.weight_1", + forge.Parameter(*(672, 28, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d231.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D232(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d232.weight_1", + forge.Parameter(*(112, 672, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d232.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D233(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=672, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D234(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d234.weight_1", + forge.Parameter(*(160, 672, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d234.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D235(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d235.weight_1", + forge.Parameter(*(960, 160, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d235.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D236(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=960, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D237(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=960, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D238(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d238.weight_1", + forge.Parameter(*(40, 960, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d238.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D239(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d239.weight_1", + forge.Parameter(*(960, 40, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d239.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D240(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d240.weight_1", + forge.Parameter(*(160, 960, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d240.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D241(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d241.weight_1", + forge.Parameter(*(272, 960, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d241.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D242(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d242.weight_1", + forge.Parameter(*(1632, 272, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d242.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D243(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=1632, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D244(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d244.weight_1", + forge.Parameter(*(68, 1632, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d244.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D245(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d245.weight_1", + forge.Parameter(*(1632, 68, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d245.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D246(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d246.weight_1", + forge.Parameter(*(272, 1632, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d246.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D247(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1632, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D248(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d248.weight_1", + forge.Parameter(*(448, 1632, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d248.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D249(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d249.weight_1", + forge.Parameter(*(2688, 448, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d249.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D250(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=2688, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D251(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d251.weight_1", + forge.Parameter(*(112, 2688, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d251.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D252(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d252.weight_1", + forge.Parameter(*(2688, 112, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d252.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D253(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d253.weight_1", + forge.Parameter(*(448, 2688, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d253.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D254(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d254.weight_1", + forge.Parameter(*(1792, 448, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d254.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D255(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d255.weight_1", + forge.Parameter(*(32, 3, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d255.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D256(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d256.weight_1", + forge.Parameter(*(32, 3, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d256.weight_1"), + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D257(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D258(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d258.weight_1", + forge.Parameter(*(8, 32, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d258.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D259(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d259.weight_1", + forge.Parameter(*(32, 8, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d259.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D260(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d260.weight_1", + forge.Parameter(*(16, 32, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d260.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D261(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d261.weight_1", + forge.Parameter(*(96, 16, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d261.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D262(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=96, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D263(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=96, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D264(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d264.weight_1", + forge.Parameter(*(4, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d264.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D265(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d265.weight_1", + forge.Parameter(*(96, 4, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d265.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D266(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d266.weight_1", + forge.Parameter(*(24, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d266.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D267(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=144, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D268(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=144, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D269(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d269.weight_1", + forge.Parameter(*(24, 144, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d269.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D270(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=144, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D271(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d271.weight_1", + forge.Parameter(*(40, 144, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d271.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D272(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d272.weight_1", + forge.Parameter(*(240, 40, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d272.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D273(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=240, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D274(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d274.weight_1", + forge.Parameter(*(10, 240, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d274.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D275(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d275.weight_1", + forge.Parameter(*(240, 10, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d275.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D276(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d276.weight_1", + forge.Parameter(*(40, 240, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d276.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D277(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=240, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D278(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d278.weight_1", + forge.Parameter(*(80, 240, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d278.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D279(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d279.weight_1", + forge.Parameter(*(480, 80, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d279.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D280(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=480, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D281(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d281.weight_1", + forge.Parameter(*(20, 480, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d281.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D282(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d282.weight_1", + forge.Parameter(*(480, 20, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d282.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D283(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d283.weight_1", + forge.Parameter(*(80, 480, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d283.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D284(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=480, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D285(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d285.weight_1", + forge.Parameter(*(112, 480, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d285.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D286(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=672, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D287(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d287.weight_1", + forge.Parameter(*(1152, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d287.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D288(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=1152, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D289(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d289.weight_1", + forge.Parameter(*(48, 1152, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d289.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D290(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d290.weight_1", + forge.Parameter(*(1152, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d290.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D291(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1152, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D292(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d292.weight_1", + forge.Parameter(*(320, 1152, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d292.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D293(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d293.weight_1", + forge.Parameter(*(1280, 320, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d293.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D294(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=48, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D295(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d295.weight_1", + forge.Parameter(*(256, 2048, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d295.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D296(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d296.weight_1", + forge.Parameter(*(16, 3, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d296.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D297(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d297.weight_1", + forge.Parameter(*(8, 16, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d297.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D298(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=8, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D299(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d299.weight_1", + forge.Parameter(*(24, 16, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d299.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D300(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=12, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D301(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=16, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D302(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=16, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D303(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d303.weight_1", + forge.Parameter(*(36, 24, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d303.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D304(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=36, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D305(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d305.weight_1", + forge.Parameter(*(12, 72, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d305.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D306(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=72, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D307(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d307.weight_1", + forge.Parameter(*(20, 72, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d307.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D308(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d308.weight_1", + forge.Parameter(*(72, 20, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d308.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D309(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=20, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D310(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=24, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D311(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d311.weight_1", + forge.Parameter(*(40, 24, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d311.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D312(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d312.weight_1", + forge.Parameter(*(60, 40, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d312.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D313(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=60, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D314(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d314.weight_1", + forge.Parameter(*(32, 120, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d314.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D315(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d315.weight_1", + forge.Parameter(*(120, 32, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d315.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D316(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d316.weight_1", + forge.Parameter(*(20, 120, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d316.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D317(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d317.weight_1", + forge.Parameter(*(120, 40, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d317.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D318(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=120, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D319(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=40, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D320(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=40, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D321(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d321.weight_1", + forge.Parameter(*(80, 40, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d321.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D322(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d322.weight_1", + forge.Parameter(*(100, 80, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d322.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D323(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=100, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D324(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d324.weight_1", + forge.Parameter(*(40, 200, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d324.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D325(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d325.weight_1", + forge.Parameter(*(92, 80, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d325.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D326(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=92, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D327(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d327.weight_1", + forge.Parameter(*(40, 184, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d327.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D328(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d328.weight_1", + forge.Parameter(*(240, 80, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d328.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D329(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=240, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D330(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d330.weight_1", + forge.Parameter(*(120, 480, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d330.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D331(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d331.weight_1", + forge.Parameter(*(480, 120, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d331.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D332(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d332.weight_1", + forge.Parameter(*(56, 480, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d332.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D333(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=56, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D334(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=80, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D335(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d335.weight_1", + forge.Parameter(*(112, 80, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d335.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D336(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d336.weight_1", + forge.Parameter(*(336, 112, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d336.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D337(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=336, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D338(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d338.weight_1", + forge.Parameter(*(168, 672, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d338.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D339(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d339.weight_1", + forge.Parameter(*(672, 168, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d339.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D340(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d340.weight_1", + forge.Parameter(*(56, 672, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d340.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D341(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d341.weight_1", + forge.Parameter(*(80, 672, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d341.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D342(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=112, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D343(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d343.weight_1", + forge.Parameter(*(160, 112, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d343.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D344(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d344.weight_1", + forge.Parameter(*(480, 160, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d344.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D345(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d345.weight_1", + forge.Parameter(*(80, 960, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d345.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D346(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d346.weight_1", + forge.Parameter(*(240, 960, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d346.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D347(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d347.weight_1", + forge.Parameter(*(960, 240, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d347.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D348(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d348.weight_1", + forge.Parameter(*(1280, 960, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d348.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D349(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d349.weight_1", + forge.Parameter(*(192, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d349.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D350(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d350.weight_1", + forge.Parameter(*(128, 96, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d350.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D351(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d351.weight_1", + forge.Parameter(*(32, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d351.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D352(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d352.weight_1", + forge.Parameter(*(192, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d352.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D353(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d353.weight_1", + forge.Parameter(*(96, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d353.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D354(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d354.weight_1", + forge.Parameter(*(208, 96, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d354.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D355(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d355.weight_1", + forge.Parameter(*(48, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d355.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D356(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d356.weight_1", + forge.Parameter(*(64, 480, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d356.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D357(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d357.weight_1", + forge.Parameter(*(224, 112, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d357.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D358(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d358.weight_1", + forge.Parameter(*(64, 24, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d358.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D359(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d359.weight_1", + forge.Parameter(*(64, 512, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d359.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D360(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d360.weight_1", + forge.Parameter(*(256, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d360.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D361(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d361.weight_1", + forge.Parameter(*(288, 144, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d361.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D362(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d362.weight_1", + forge.Parameter(*(64, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d362.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D363(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d363.weight_1", + forge.Parameter(*(320, 160, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d363.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D364(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d364.weight_1", + forge.Parameter(*(320, 160, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d364.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D365(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d365.weight_1", + forge.Parameter(*(128, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d365.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D366(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d366.weight_1", + forge.Parameter(*(128, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d366.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D367(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d367.weight_1", + forge.Parameter(*(128, 528, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d367.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D368(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d368.weight_1", + forge.Parameter(*(128, 48, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d368.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D369(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d369.weight_1", + forge.Parameter(*(64, 3, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d369.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D370(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d370.weight_1", + forge.Parameter(*(64, 3, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d370.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D371(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d371.weight_1", + forge.Parameter(*(256, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d371.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D372(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d372.weight_1", + forge.Parameter(*(18, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d372.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D373(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d373.weight_1", + forge.Parameter(*(18, 18, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d373.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D374(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d374.weight_1", + forge.Parameter(*(18, 18, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d374.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D375(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d375.weight_1", + forge.Parameter(*(36, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d375.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D376(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d376.weight_1", + forge.Parameter(*(36, 36, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d376.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D377(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d377.weight_1", + forge.Parameter(*(36, 36, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d377.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D378(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d378.weight_1", + forge.Parameter(*(18, 36, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d378.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D379(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d379.weight_1", + forge.Parameter(*(36, 18, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d379.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D380(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d380.weight_1", + forge.Parameter(*(72, 36, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d380.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D381(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d381.weight_1", + forge.Parameter(*(72, 72, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d381.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D382(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d382.weight_1", + forge.Parameter(*(18, 72, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d382.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D383(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d383.weight_1", + forge.Parameter(*(36, 72, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d383.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D384(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d384.weight_1", + forge.Parameter(*(72, 18, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d384.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D385(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d385.weight_1", + forge.Parameter(*(144, 72, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d385.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D386(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d386.weight_1", + forge.Parameter(*(144, 144, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d386.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D387(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D388(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d388.weight_1", + forge.Parameter(*(144, 18, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d388.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D389(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d389.weight_1", + forge.Parameter(*(144, 36, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d389.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D390(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d390.weight_1", + forge.Parameter(*(256, 144, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d390.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D391(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d391.weight_1", + forge.Parameter(*(1024, 144, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d391.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D392(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d392.weight_1", + forge.Parameter(*(128, 72, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d392.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D393(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d393.weight_1", + forge.Parameter(*(512, 72, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d393.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D394(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d394.weight_1", + forge.Parameter(*(64, 36, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d394.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D395(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d395.weight_1", + forge.Parameter(*(256, 36, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d395.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D396(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d396.weight_1", + forge.Parameter(*(32, 18, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d396.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D397(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d397.weight_1", + forge.Parameter(*(128, 18, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d397.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D398(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d398.weight_1", + forge.Parameter(*(512, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d398.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D399(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d399.weight_1", + forge.Parameter(*(1024, 512, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d399.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D400(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d400.weight_1", + forge.Parameter(*(44, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d400.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D401(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d401.weight_1", + forge.Parameter(*(44, 44, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d401.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D402(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d402.weight_1", + forge.Parameter(*(44, 44, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d402.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D403(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d403.weight_1", + forge.Parameter(*(88, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d403.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D404(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d404.weight_1", + forge.Parameter(*(88, 88, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d404.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D405(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d405.weight_1", + forge.Parameter(*(88, 88, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d405.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D406(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d406.weight_1", + forge.Parameter(*(44, 88, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d406.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D407(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d407.weight_1", + forge.Parameter(*(88, 44, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d407.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D408(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d408.weight_1", + forge.Parameter(*(176, 88, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d408.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D409(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d409.weight_1", + forge.Parameter(*(176, 176, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d409.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D410(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d410.weight_1", + forge.Parameter(*(44, 176, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d410.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D411(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d411.weight_1", + forge.Parameter(*(88, 176, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d411.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D412(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d412.weight_1", + forge.Parameter(*(176, 44, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d412.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D413(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d413.weight_1", + forge.Parameter(*(352, 176, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d413.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D414(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d414.weight_1", + forge.Parameter(*(352, 352, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d414.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D415(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d415.weight_1", + forge.Parameter(*(352, 44, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d415.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D416(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d416.weight_1", + forge.Parameter(*(352, 88, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d416.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D417(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d417.weight_1", + forge.Parameter(*(256, 352, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d417.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D418(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d418.weight_1", + forge.Parameter(*(1024, 352, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d418.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D419(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d419.weight_1", + forge.Parameter(*(128, 176, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d419.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D420(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d420.weight_1", + forge.Parameter(*(512, 176, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d420.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D421(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d421.weight_1", + forge.Parameter(*(64, 88, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d421.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D422(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d422.weight_1", + forge.Parameter(*(256, 88, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d422.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D423(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d423.weight_1", + forge.Parameter(*(32, 44, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d423.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D424(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d424.weight_1", + forge.Parameter(*(128, 44, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d424.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D425(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d425.weight_1", + forge.Parameter(*(30, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d425.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D426(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d426.weight_1", + forge.Parameter(*(30, 30, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d426.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D427(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d427.weight_1", + forge.Parameter(*(30, 30, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d427.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D428(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d428.weight_1", + forge.Parameter(*(60, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d428.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D429(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d429.weight_1", + forge.Parameter(*(60, 60, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d429.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D430(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d430.weight_1", + forge.Parameter(*(60, 60, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d430.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D431(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d431.weight_1", + forge.Parameter(*(30, 60, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d431.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D432(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d432.weight_1", + forge.Parameter(*(60, 30, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d432.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D433(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d433.weight_1", + forge.Parameter(*(120, 60, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d433.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D434(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d434.weight_1", + forge.Parameter(*(120, 120, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d434.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D435(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d435.weight_1", + forge.Parameter(*(30, 120, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d435.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D436(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d436.weight_1", + forge.Parameter(*(60, 120, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d436.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D437(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d437.weight_1", + forge.Parameter(*(120, 30, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d437.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D438(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d438.weight_1", + forge.Parameter(*(240, 120, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d438.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D439(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d439.weight_1", + forge.Parameter(*(240, 240, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d439.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D440(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d440.weight_1", + forge.Parameter(*(240, 30, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d440.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D441(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d441.weight_1", + forge.Parameter(*(240, 60, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d441.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D442(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d442.weight_1", + forge.Parameter(*(256, 240, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d442.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D443(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d443.weight_1", + forge.Parameter(*(1024, 240, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d443.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D444(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d444.weight_1", + forge.Parameter(*(128, 120, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d444.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D445(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d445.weight_1", + forge.Parameter(*(512, 120, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d445.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D446(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d446.weight_1", + forge.Parameter(*(64, 60, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d446.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D447(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d447.weight_1", + forge.Parameter(*(256, 60, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d447.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D448(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d448.weight_1", + forge.Parameter(*(32, 30, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d448.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D449(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d449.weight_1", + forge.Parameter(*(128, 30, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d449.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D450(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d450.weight_1", + forge.Parameter(*(32, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d450.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D451(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d451.weight_1", + forge.Parameter(*(64, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d451.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D452(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d452.weight_1", + forge.Parameter(*(64, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d452.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D453(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d453.weight_1", + forge.Parameter(*(32, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d453.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D454(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d454.weight_1", + forge.Parameter(*(256, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d454.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D455(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d455.weight_1", + forge.Parameter(*(256, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d455.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D456(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d456.weight_1", + forge.Parameter(*(16, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d456.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D457(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d457.weight_1", + forge.Parameter(*(16, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d457.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D458(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d458.weight_1", + forge.Parameter(*(16, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d458.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D459(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d459.weight_1", + forge.Parameter(*(128, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d459.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D460(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d460.weight_1", + forge.Parameter(*(64, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d460.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D461(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d461.weight_1", + forge.Parameter(*(1024, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d461.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D462(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d462.weight_1", + forge.Parameter(*(512, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d462.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D463(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d463.weight_1", + forge.Parameter(*(32, 16, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d463.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D464(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d464.weight_1", + forge.Parameter(*(128, 16, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d464.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D465(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d465.weight_1", + forge.Parameter(*(128, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d465.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D466(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d466.weight_1", + forge.Parameter(*(512, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d466.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D467(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d467.weight_1", + forge.Parameter(*(512, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d467.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D468(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d468.weight_1", + forge.Parameter(*(48, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d468.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D469(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d469.weight_1", + forge.Parameter(*(48, 48, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d469.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D470(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d470.weight_1", + forge.Parameter(*(48, 48, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d470.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D471(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d471.weight_1", + forge.Parameter(*(96, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d471.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D472(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d472.weight_1", + forge.Parameter(*(96, 96, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d472.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D473(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d473.weight_1", + forge.Parameter(*(96, 96, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d473.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D474(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d474.weight_1", + forge.Parameter(*(48, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d474.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D475(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d475.weight_1", + forge.Parameter(*(96, 48, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d475.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D476(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d476.weight_1", + forge.Parameter(*(192, 96, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d476.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D477(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d477.weight_1", + forge.Parameter(*(192, 192, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d477.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D478(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d478.weight_1", + forge.Parameter(*(48, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d478.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D479(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d479.weight_1", + forge.Parameter(*(96, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d479.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D480(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d480.weight_1", + forge.Parameter(*(192, 48, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d480.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D481(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d481.weight_1", + forge.Parameter(*(384, 192, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d481.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D482(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d482.weight_1", + forge.Parameter(*(384, 48, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d482.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D483(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d483.weight_1", + forge.Parameter(*(384, 96, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d483.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D484(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d484.weight_1", + forge.Parameter(*(256, 384, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d484.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D485(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d485.weight_1", + forge.Parameter(*(1024, 384, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d485.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D486(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d486.weight_1", + forge.Parameter(*(512, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d486.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D487(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d487.weight_1", + forge.Parameter(*(64, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d487.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D488(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d488.weight_1", + forge.Parameter(*(256, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d488.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D489(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d489.weight_1", + forge.Parameter(*(32, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d489.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D490(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d490.weight_1", + forge.Parameter(*(128, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d490.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D491(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d491.weight_1", + forge.Parameter(*(40, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d491.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D492(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d492.weight_1", + forge.Parameter(*(40, 40, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d492.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D493(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d493.weight_1", + forge.Parameter(*(40, 40, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d493.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D494(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d494.weight_1", + forge.Parameter(*(80, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d494.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D495(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d495.weight_1", + forge.Parameter(*(80, 80, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d495.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D496(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d496.weight_1", + forge.Parameter(*(80, 80, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d496.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D497(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d497.weight_1", + forge.Parameter(*(40, 80, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d497.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D498(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d498.weight_1", + forge.Parameter(*(80, 40, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d498.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D499(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d499.weight_1", + forge.Parameter(*(160, 80, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d499.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D500(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d500.weight_1", + forge.Parameter(*(160, 160, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d500.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D501(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d501.weight_1", + forge.Parameter(*(40, 160, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d501.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D502(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d502.weight_1", + forge.Parameter(*(80, 160, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d502.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D503(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d503.weight_1", + forge.Parameter(*(160, 40, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d503.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D504(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d504.weight_1", + forge.Parameter(*(320, 320, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d504.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D505(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d505.weight_1", + forge.Parameter(*(320, 40, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d505.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D506(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d506.weight_1", + forge.Parameter(*(320, 80, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d506.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D507(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d507.weight_1", + forge.Parameter(*(256, 320, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d507.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D508(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d508.weight_1", + forge.Parameter(*(1024, 320, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d508.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D509(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d509.weight_1", + forge.Parameter(*(512, 160, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d509.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D510(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d510.weight_1", + forge.Parameter(*(64, 80, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d510.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D511(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d511.weight_1", + forge.Parameter(*(256, 80, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d511.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D512(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d512.weight_1", + forge.Parameter(*(32, 40, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d512.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D513(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d513.weight_1", + forge.Parameter(*(128, 40, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d513.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D514(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d514.weight_1", + forge.Parameter(*(32, 3, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d514.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D515(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d515.weight_1", + forge.Parameter(*(32, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d515.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D516(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d516.weight_1", + forge.Parameter(*(96, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d516.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D517(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d517.weight_1", + forge.Parameter(*(64, 160, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d517.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D518(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d518.weight_1", + forge.Parameter(*(96, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d518.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D519(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d519.weight_1", + forge.Parameter(*(64, 64, 1, 7), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d519.weight_1"), + stride=[1, 1], + padding=[3, 3, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D520(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d520.weight_1", + forge.Parameter(*(64, 64, 7, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d520.weight_1"), + stride=[1, 1], + padding=[0, 0, 3, 3], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D521(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d521.weight_1", + forge.Parameter(*(192, 192, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d521.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D522(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d522.weight_1", + forge.Parameter(*(96, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d522.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D523(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d523.weight_1", + forge.Parameter(*(96, 384, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d523.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D524(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d524.weight_1", + forge.Parameter(*(384, 384, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d524.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D525(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d525.weight_1", + forge.Parameter(*(224, 192, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d525.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D526(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d526.weight_1", + forge.Parameter(*(256, 224, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d526.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D527(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d527.weight_1", + forge.Parameter(*(224, 192, 1, 7), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d527.weight_1"), + stride=[1, 1], + padding=[3, 3, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D528(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d528.weight_1", + forge.Parameter(*(256, 224, 7, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d528.weight_1"), + stride=[1, 1], + padding=[0, 0, 3, 3], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D529(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d529.weight_1", + forge.Parameter(*(192, 192, 7, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d529.weight_1"), + stride=[1, 1], + padding=[0, 0, 3, 3], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D530(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d530.weight_1", + forge.Parameter(*(224, 224, 7, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d530.weight_1"), + stride=[1, 1], + padding=[0, 0, 3, 3], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D531(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d531.weight_1", + forge.Parameter(*(256, 224, 1, 7), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d531.weight_1"), + stride=[1, 1], + padding=[3, 3, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D532(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d532.weight_1", + forge.Parameter(*(192, 1024, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d532.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D533(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d533.weight_1", + forge.Parameter(*(256, 1024, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d533.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D534(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d534.weight_1", + forge.Parameter(*(256, 256, 1, 7), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d534.weight_1"), + stride=[1, 1], + padding=[3, 3, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D535(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d535.weight_1", + forge.Parameter(*(320, 256, 7, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d535.weight_1"), + stride=[1, 1], + padding=[0, 0, 3, 3], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D536(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d536.weight_1", + forge.Parameter(*(320, 320, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d536.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D537(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d537.weight_1", + forge.Parameter(*(256, 384, 1, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d537.weight_1"), + stride=[1, 1], + padding=[1, 1, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D538(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d538.weight_1", + forge.Parameter(*(256, 384, 3, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d538.weight_1"), + stride=[1, 1], + padding=[0, 0, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D539(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d539.weight_1", + forge.Parameter(*(448, 384, 3, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d539.weight_1"), + stride=[1, 1], + padding=[0, 0, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D540(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d540.weight_1", + forge.Parameter(*(512, 448, 1, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d540.weight_1"), + stride=[1, 1], + padding=[1, 1, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D541(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d541.weight_1", + forge.Parameter(*(256, 512, 1, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d541.weight_1"), + stride=[1, 1], + padding=[1, 1, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D542(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d542.weight_1", + forge.Parameter(*(256, 512, 3, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d542.weight_1"), + stride=[1, 1], + padding=[0, 0, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D543(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d543.weight_1", + forge.Parameter(*(256, 1536, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d543.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D544(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d544.weight_1", + forge.Parameter(*(768, 3, 32, 32), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d544.weight_1"), + stride=[32, 32], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D545(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d545.weight_1", + forge.Parameter(*(512, 3, 16, 16), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d545.weight_1"), + stride=[16, 16], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D546(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d546.weight_1", + forge.Parameter(*(512, 3, 32, 32), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d546.weight_1"), + stride=[32, 32], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D547(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d547.weight_1", + forge.Parameter(*(1024, 3, 32, 32), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d547.weight_1"), + stride=[32, 32], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D548(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d548.weight_1", + forge.Parameter(*(1024, 3, 16, 16), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d548.weight_1"), + stride=[16, 16], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D549(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d549.weight_1", + forge.Parameter(*(24, 3, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d549.weight_1"), + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D550(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d550.weight_1", + forge.Parameter(*(48, 24, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d550.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D551(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=48, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D552(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d552.weight_1", + forge.Parameter(*(96, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d552.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D553(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=96, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D554(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d554.weight_1", + forge.Parameter(*(96, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d554.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D555(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=192, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D556(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d556.weight_1", + forge.Parameter(*(384, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d556.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D557(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=384, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D558(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=384, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D559(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d559.weight_1", + forge.Parameter(*(384, 384, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d559.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D560(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d560.weight_1", + forge.Parameter(*(768, 384, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d560.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D561(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=768, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D562(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d562.weight_1", + forge.Parameter(*(768, 768, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d562.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D563(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=64, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D564(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=64, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D565(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=64, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D566(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=128, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D567(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=128, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D568(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=128, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D569(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=256, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D570(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=256, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D571(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=256, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D572(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=512, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D573(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=512, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D574(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=512, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D575(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1024, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D576(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d576.weight_1", + forge.Parameter(*(16, 3, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d576.weight_1"), + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D577(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d577.weight_1", + forge.Parameter(*(48, 8, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d577.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D578(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d578.weight_1", + forge.Parameter(*(8, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d578.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D579(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d579.weight_1", + forge.Parameter(*(16, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d579.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D580(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d580.weight_1", + forge.Parameter(*(16, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d580.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D581(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d581.weight_1", + forge.Parameter(*(1280, 112, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d581.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D582(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=192, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D583(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d583.weight_1", + forge.Parameter(*(64, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d583.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D584(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d584.weight_1", + forge.Parameter(*(384, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d584.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D585(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d585.weight_1", + forge.Parameter(*(64, 384, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d585.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D586(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d586.weight_1", + forge.Parameter(*(576, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d586.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D587(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=576, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D588(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=576, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D589(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=576, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D590(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d590.weight_1", + forge.Parameter(*(96, 576, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d590.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D591(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d591.weight_1", + forge.Parameter(*(160, 576, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d591.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D592(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=960, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D593(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d593.weight_1", + forge.Parameter(*(320, 960, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d593.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D594(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d594.weight_1", + forge.Parameter(*(16, 24, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d594.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D595(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d595.weight_1", + forge.Parameter(*(48, 144, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d595.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D596(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d596.weight_1", + forge.Parameter(*(288, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d596.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D597(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=288, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D598(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d598.weight_1", + forge.Parameter(*(48, 288, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d598.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D599(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d599.weight_1", + forge.Parameter(*(72, 288, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d599.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D600(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d600.weight_1", + forge.Parameter(*(432, 72, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d600.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D601(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=432, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D602(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[0, 1, 0, 1], + dilation=1, + groups=432, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D603(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d603.weight_1", + forge.Parameter(*(72, 432, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d603.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D604(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d604.weight_1", + forge.Parameter(*(120, 432, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d604.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D605(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d605.weight_1", + forge.Parameter(*(720, 120, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d605.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D606(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=720, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D607(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d607.weight_1", + forge.Parameter(*(120, 720, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d607.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D608(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d608.weight_1", + forge.Parameter(*(240, 720, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d608.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D609(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d609.weight_1", + forge.Parameter(*(1280, 240, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d609.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D610(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=2, + groups=384, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D611(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=2, + groups=576, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D612(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[4, 4, 4, 4], + dilation=4, + groups=960, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D613(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d613.weight_1", + forge.Parameter(*(21, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d613.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D614(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d614.weight_1", + forge.Parameter(*(16, 8, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d614.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D615(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d615.weight_1", + forge.Parameter(*(16, 16, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d615.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D616(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d616.weight_1", + forge.Parameter(*(72, 16, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d616.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D617(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=72, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D618(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=72, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D619(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d619.weight_1", + forge.Parameter(*(24, 72, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d619.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D620(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d620.weight_1", + forge.Parameter(*(88, 24, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d620.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D621(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=88, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D622(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d622.weight_1", + forge.Parameter(*(24, 88, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d622.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D623(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d623.weight_1", + forge.Parameter(*(96, 24, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d623.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D624(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=96, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D625(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d625.weight_1", + forge.Parameter(*(40, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d625.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D626(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d626.weight_1", + forge.Parameter(*(64, 240, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d626.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D627(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d627.weight_1", + forge.Parameter(*(240, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d627.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D628(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=120, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D629(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d629.weight_1", + forge.Parameter(*(48, 120, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d629.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D630(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d630.weight_1", + forge.Parameter(*(144, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d630.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D631(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=144, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D632(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d632.weight_1", + forge.Parameter(*(144, 40, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d632.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D633(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=288, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D634(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d634.weight_1", + forge.Parameter(*(288, 72, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d634.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D635(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d635.weight_1", + forge.Parameter(*(96, 288, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d635.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D636(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=576, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D637(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d637.weight_1", + forge.Parameter(*(144, 576, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d637.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D638(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d638.weight_1", + forge.Parameter(*(576, 144, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d638.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D639(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d639.weight_1", + forge.Parameter(*(1024, 576, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d639.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D640(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d640.weight_1", + forge.Parameter(*(64, 16, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d640.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D641(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d641.weight_1", + forge.Parameter(*(24, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d641.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D642(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d642.weight_1", + forge.Parameter(*(72, 24, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d642.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D643(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d643.weight_1", + forge.Parameter(*(40, 72, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d643.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D644(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d644.weight_1", + forge.Parameter(*(40, 120, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d644.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D645(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d645.weight_1", + forge.Parameter(*(200, 80, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d645.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D646(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=200, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D647(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d647.weight_1", + forge.Parameter(*(80, 200, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d647.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D648(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d648.weight_1", + forge.Parameter(*(184, 80, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d648.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D649(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=184, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D650(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d650.weight_1", + forge.Parameter(*(80, 184, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d650.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D651(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d651.weight_1", + forge.Parameter(*(128, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d651.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D652(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d652.weight_1", + forge.Parameter(*(256, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d652.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D653(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d653.weight_1", + forge.Parameter(*(512, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d653.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D654(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d654.weight_1", + forge.Parameter(*(256, 512, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d654.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D655(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d655.weight_1", + forge.Parameter(*(128, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d655.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D656(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d656.weight_1", + forge.Parameter(*(64, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d656.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D657(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d657.weight_1", + forge.Parameter(*(32, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d657.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D658(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d658.weight_1", + forge.Parameter(*(32, 96, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d658.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D659(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d659.weight_1", + forge.Parameter(*(16, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d659.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D660(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d660.weight_1", + forge.Parameter(*(16, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d660.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D661(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d661.weight_1", + forge.Parameter(*(1, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d661.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D662(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d662.weight_1", + forge.Parameter(*(64, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d662.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D663(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d663.weight_1", + forge.Parameter(*(128, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d663.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D664(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d664.weight_1", + forge.Parameter(*(256, 512, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d664.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D665(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D666(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d666.weight_1", + forge.Parameter(*(3, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d666.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D667(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d667.weight_1", + forge.Parameter(*(2, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d667.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D668(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d668.weight_1", + forge.Parameter(*(24, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d668.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D669(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d669.weight_1", + forge.Parameter(*(256, 3, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d669.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D670(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d670_const_1", shape=(64, 3, 11, 11), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d670_const_1"), + stride=[4, 4], + padding=[2, 2, 2, 2], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D671(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d671_const_1", shape=(192, 64, 5, 5), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d671_const_1"), + stride=[1, 1], + padding=[2, 2, 2, 2], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D672(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d672_const_1", shape=(384, 192, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d672_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D673(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d673_const_1", shape=(256, 384, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d673_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D674(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d674_const_1", shape=(256, 256, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d674_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D675(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d675.weight_1", + forge.Parameter(*(128, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d675.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=2, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D676(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d676.weight_1", + forge.Parameter(*(8, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d676.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D677(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d677.weight_1", + forge.Parameter(*(128, 8, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d677.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D678(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d678.weight_1", + forge.Parameter(*(128, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d678.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=2, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D679(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d679.weight_1", + forge.Parameter(*(192, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d679.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D680(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d680.weight_1", + forge.Parameter(*(192, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d680.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D681(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d681.weight_1", + forge.Parameter(*(192, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d681.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=3, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D682(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d682.weight_1", + forge.Parameter(*(192, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d682.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=3, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D683(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d683.weight_1", + forge.Parameter(*(192, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d683.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D684(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d684.weight_1", + forge.Parameter(*(512, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d684.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D685(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d685.weight_1", + forge.Parameter(*(512, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d685.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=8, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D686(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d686.weight_1", + forge.Parameter(*(48, 512, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d686.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D687(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d687.weight_1", + forge.Parameter(*(512, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d687.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D688(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d688.weight_1", + forge.Parameter(*(512, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d688.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=8, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D689(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d689.weight_1", + forge.Parameter(*(1088, 512, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d689.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D690(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d690.weight_1", + forge.Parameter(*(1088, 512, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d690.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D691(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d691.weight_1", + forge.Parameter(*(1088, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d691.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=17, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D692(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d692.weight_1", + forge.Parameter(*(1088, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d692.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D693(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d693.weight_1", + forge.Parameter(*(1088, 1088, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d693.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D694(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d694.weight_1", + forge.Parameter(*(1088, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d694.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=17, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D695(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d695.weight_1", + forge.Parameter(*(272, 1088, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d695.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D696(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d696.weight_1", + forge.Parameter(*(1088, 272, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d696.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D697(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d697.weight_1", + forge.Parameter(*(1024, 512, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d697.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D698(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d698.weight_1", + forge.Parameter(*(2048, 1024, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d698.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D699(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d699.weight_1", + forge.Parameter(*(2048, 2048, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d699.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D700(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d700.weight_1", + forge.Parameter(*(2048, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d700.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D701(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d701.weight_1", + forge.Parameter(*(2048, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d701.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D702(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d702.weight_1", + forge.Parameter(*(720, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d702.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D703(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d703.weight_1", + forge.Parameter(*(256, 2048, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d703.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D704(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d704.weight_1", + forge.Parameter(*(36, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d704.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D705(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d705.weight_1", + forge.Parameter(*(256, 512, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d705.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D706(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d706.weight_1", + forge.Parameter(*(32, 3, 7, 7), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d706.weight_1"), + stride=[4, 4], + padding=[3, 3, 3, 3], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D707(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d707.weight_1", + forge.Parameter(*(32, 32, 8, 8), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d707.weight_1"), + stride=[8, 8], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D708(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d708.weight_1", + forge.Parameter(*(64, 64, 4, 4), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d708.weight_1"), + stride=[4, 4], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D709(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d709.weight_1", + forge.Parameter(*(160, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d709.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D710(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d710.weight_1", + forge.Parameter(*(160, 160, 2, 2), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d710.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D711(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=640, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D712(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d712.weight_1", + forge.Parameter(*(256, 160, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d712.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D713(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d713.weight_1", + forge.Parameter(*(64, 3, 7, 7), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d713.weight_1"), + stride=[4, 4], + padding=[3, 3, 3, 3], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D714(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d714.weight_1", + forge.Parameter(*(64, 64, 8, 8), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d714.weight_1"), + stride=[8, 8], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D715(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d715.weight_1", + forge.Parameter(*(128, 128, 4, 4), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d715.weight_1"), + stride=[4, 4], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D716(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d716.weight_1", + forge.Parameter(*(320, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d716.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D717(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d717.weight_1", + forge.Parameter(*(320, 320, 2, 2), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d717.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D718(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1280, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D719(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d719.weight_1", + forge.Parameter(*(512, 320, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d719.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D720(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=2048, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D721(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d721.weight_1", + forge.Parameter(*(768, 3072, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d721.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D722(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d722.weight_1", + forge.Parameter(*(150, 768, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d722.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D723(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d723.weight_1", + forge.Parameter(*(150, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d723.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D724(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d724.weight_1", + forge.Parameter(*(16, 1024, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d724.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D725(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d725.weight_1", + forge.Parameter(*(24, 512, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d725.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D726(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d726.weight_1", + forge.Parameter(*(24, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d726.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D727(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d727.weight_1", + forge.Parameter(*(256, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d727.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D728(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d728.weight_1", + forge.Parameter(*(16, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d728.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D729(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d729.weight_1", + forge.Parameter(*(324, 1024, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d729.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D730(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d730.weight_1", + forge.Parameter(*(486, 512, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d730.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D731(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d731.weight_1", + forge.Parameter(*(486, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d731.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D732(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d732.weight_1", + forge.Parameter(*(324, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d732.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D733(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d733.weight_1", + forge.Parameter(*(96, 3, 4, 4), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d733.weight_1"), + stride=[4, 4], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D734(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d734.weight_1", + forge.Parameter(*(32, 3, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d734.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D735(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d735.weight_1", + forge.Parameter(*(128, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d735.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D736(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d736.weight_1", + forge.Parameter(*(32, 64, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d736.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D737(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d737.weight_1", + forge.Parameter(*(1, 32, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d737.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D738(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d738.weight_1", + forge.Parameter(*(256, 1024, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d738.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D739(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d739.weight_1", + forge.Parameter(*(128, 512, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d739.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D740(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d740.weight_1", + forge.Parameter(*(19, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d740.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D741(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d741.weight_1", + forge.Parameter(*(256, 3072, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d741.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D742(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d742.weight_1", + forge.Parameter(*(128, 768, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d742.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D743(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d743.weight_1", + forge.Parameter(*(64, 384, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d743.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D744(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d744.weight_1", + forge.Parameter(*(16, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d744.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D745(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d745.weight_1", + forge.Parameter(*(1, 16, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d745.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D746(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d746.weight_1", + forge.Parameter(*(4096, 512, 7, 7), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d746.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D747(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d747.weight_1", + forge.Parameter(*(4096, 4096, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d747.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D748(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d748.weight_1", + forge.Parameter(*(160, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d748.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D749(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d749.weight_1", + forge.Parameter(*(512, 1056, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d749.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D750(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d750.weight_1", + forge.Parameter(*(192, 512, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d750.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D751(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d751.weight_1", + forge.Parameter(*(768, 1472, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d751.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D752(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d752.weight_1", + forge.Parameter(*(192, 768, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d752.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D753(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d753.weight_1", + forge.Parameter(*(768, 1728, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d753.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D754(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d754.weight_1", + forge.Parameter(*(224, 768, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d754.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D755(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d755.weight_1", + forge.Parameter(*(224, 224, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d755.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D756(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d756.weight_1", + forge.Parameter(*(1024, 1888, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d756.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D757(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d757.weight_1", + forge.Parameter(*(224, 1024, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d757.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D758(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d758.weight_1", + forge.Parameter(*(1024, 2144, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d758.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D759(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d759.weight_1", + forge.Parameter(*(80, 128, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d759.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D760(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d760.weight_1", + forge.Parameter(*(256, 528, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d760.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D761(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d761.weight_1", + forge.Parameter(*(96, 256, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d761.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D762(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d762.weight_1", + forge.Parameter(*(384, 736, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d762.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D763(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d763.weight_1", + forge.Parameter(*(112, 384, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d763.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D764(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d764.weight_1", + forge.Parameter(*(112, 112, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d764.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D765(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d765.weight_1", + forge.Parameter(*(512, 944, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d765.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D766(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d766.weight_1", + forge.Parameter(*(256, 448, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d766.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D767(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d767.weight_1", + forge.Parameter(*(160, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d767.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D768(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=160, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D769(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d769.weight_1", + forge.Parameter(*(160, 160, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d769.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D770(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d770.weight_1", + forge.Parameter(*(512, 736, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d770.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D771(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d771.weight_1", + forge.Parameter(*(192, 512, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d771.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D772(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d772.weight_1", + forge.Parameter(*(768, 1088, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d772.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D773(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d773.weight_1", + forge.Parameter(*(224, 768, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d773.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D774(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=224, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D775(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d775.weight_1", + forge.Parameter(*(224, 224, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d775.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D776(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d776.weight_1", + forge.Parameter(*(1024, 1440, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d776.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D777(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d777.weight_1", + forge.Parameter(*(160, 512, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d777.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D778(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d778.weight_1", + forge.Parameter(*(512, 1312, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d778.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D779(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d779.weight_1", + forge.Parameter(*(1024, 1024, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d779.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D780(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d780.weight_1", + forge.Parameter(*(1024, 1024, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d780.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D781(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d781.weight_1", + forge.Parameter(*(64, 32, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d781.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D782(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d782.weight_1", + forge.Parameter(*(728, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d782.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D783(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d783.weight_1", + forge.Parameter(*(728, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d783.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D784(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=728, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D785(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d785.weight_1", + forge.Parameter(*(728, 728, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d785.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D786(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d786.weight_1", + forge.Parameter(*(1024, 728, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d786.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D787(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d787.weight_1", + forge.Parameter(*(1024, 728, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d787.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D788(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d788.weight_1", + forge.Parameter(*(1536, 1024, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d788.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D789(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1536, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D790(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d790.weight_1", + forge.Parameter(*(2048, 1536, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d790.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D791(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d791.weight_1", + forge.Parameter(*(256, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d791.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D792(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=728, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D793(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d793.weight_1", + forge.Parameter(*(728, 728, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d793.weight_1"), + stride=[2, 2], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D794(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1024, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D795(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d795.weight_1", + forge.Parameter(*(1536, 1536, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d795.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D796(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d796_const_1", shape=(32, 3, 6, 6), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d796_const_1"), + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D797(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d797_const_1", shape=(64, 32, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d797_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D798(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d798_const_1", shape=(32, 64, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d798_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D799(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d799_const_1", shape=(32, 32, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d799_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D800(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d800_const_1", shape=(32, 32, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d800_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D801(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d801_const_1", shape=(64, 64, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d801_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D802(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d802_const_1", shape=(128, 64, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d802_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D803(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d803_const_1", shape=(64, 128, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d803_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D804(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d804_const_1", shape=(64, 64, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d804_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D805(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d805_const_1", shape=(128, 128, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d805_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D806(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d806_const_1", shape=(256, 128, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d806_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D807(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d807_const_1", shape=(128, 256, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d807_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D808(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d808_const_1", shape=(128, 128, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d808_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D809(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d809_const_1", shape=(128, 128, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d809_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D810(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d810_const_1", shape=(256, 256, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d810_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D811(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d811_const_1", shape=(512, 256, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d811_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D812(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d812_const_1", shape=(256, 512, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d812_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D813(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d813_const_1", shape=(256, 256, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d813_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D814(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d814_const_1", shape=(512, 512, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d814_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D815(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d815_const_1", shape=(512, 1024, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d815_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D816(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d816_const_1", shape=(128, 512, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d816_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D817(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d817_const_1", shape=(64, 256, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d817_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D818(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d818_const_1", shape=(255, 128, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d818_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D819(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d819_const_1", shape=(255, 256, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d819_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D820(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d820_const_1", shape=(255, 512, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d820_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D821(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d821_const_1", shape=(80, 3, 6, 6), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d821_const_1"), + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D822(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d822_const_1", shape=(160, 80, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d822_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D823(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d823_const_1", shape=(80, 160, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d823_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D824(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d824_const_1", shape=(80, 80, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d824_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D825(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d825_const_1", shape=(80, 80, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d825_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D826(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d826_const_1", shape=(160, 160, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d826_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D827(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d827_const_1", shape=(320, 160, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d827_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D828(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d828_const_1", shape=(160, 320, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d828_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D829(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d829_const_1", shape=(160, 160, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d829_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D830(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d830_const_1", shape=(320, 320, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d830_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D831(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d831_const_1", shape=(640, 320, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d831_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D832(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d832_const_1", shape=(320, 640, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d832_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D833(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d833_const_1", shape=(320, 320, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d833_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D834(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d834_const_1", shape=(320, 320, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d834_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D835(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d835_const_1", shape=(640, 640, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d835_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D836(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d836_const_1", shape=(1280, 640, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d836_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D837(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d837_const_1", shape=(640, 1280, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d837_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D838(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d838_const_1", shape=(640, 640, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d838_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D839(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d839_const_1", shape=(640, 640, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d839_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D840(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d840_const_1", shape=(1280, 1280, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d840_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D841(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d841_const_1", shape=(1280, 2560, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d841_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D842(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d842_const_1", shape=(320, 1280, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d842_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D843(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d843_const_1", shape=(160, 640, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d843_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D844(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d844_const_1", shape=(255, 320, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d844_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D845(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d845_const_1", shape=(255, 640, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d845_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D846(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d846_const_1", shape=(255, 1280, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d846_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D847(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d847_const_1", shape=(64, 64, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d847_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D848(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d848_const_1", shape=(48, 3, 6, 6), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d848_const_1"), + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D849(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d849_const_1", shape=(96, 48, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d849_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D850(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d850_const_1", shape=(48, 96, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d850_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D851(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d851_const_1", shape=(48, 48, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d851_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D852(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d852_const_1", shape=(48, 48, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d852_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D853(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d853_const_1", shape=(96, 96, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d853_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D854(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d854_const_1", shape=(192, 96, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d854_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D855(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d855_const_1", shape=(96, 192, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d855_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D856(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d856_const_1", shape=(96, 96, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d856_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D857(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d857_const_1", shape=(192, 192, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d857_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D858(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d858_const_1", shape=(384, 192, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d858_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D859(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d859_const_1", shape=(192, 384, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d859_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D860(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d860_const_1", shape=(192, 192, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d860_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D861(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d861_const_1", shape=(384, 384, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d861_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D862(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d862_const_1", shape=(768, 384, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d862_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D863(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d863_const_1", shape=(384, 768, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d863_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D864(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d864_const_1", shape=(384, 384, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d864_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D865(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d865_const_1", shape=(768, 768, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d865_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D866(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d866_const_1", shape=(768, 1536, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d866_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D867(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d867_const_1", shape=(192, 768, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d867_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D868(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d868_const_1", shape=(96, 384, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d868_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D869(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d869_const_1", shape=(255, 192, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d869_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D870(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d870_const_1", shape=(192, 192, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d870_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D871(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d871_const_1", shape=(255, 384, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d871_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D872(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d872_const_1", shape=(384, 384, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d872_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D873(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d873_const_1", shape=(255, 768, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d873_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D874(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d874_const_1", shape=(16, 3, 6, 6), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d874_const_1"), + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D875(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d875_const_1", shape=(32, 16, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d875_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D876(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d876_const_1", shape=(16, 32, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d876_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D877(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d877_const_1", shape=(16, 16, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d877_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D878(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d878_const_1", shape=(16, 16, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d878_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D879(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d879_const_1", shape=(32, 128, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d879_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D880(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d880_const_1", shape=(255, 64, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d880_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D881(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d881_const_1", shape=(64, 3, 6, 6), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d881_const_1"), + stride=[2, 2], + padding=[2, 2, 2, 2], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D882(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d882_const_1", shape=(1024, 512, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d882_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D883(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d883_const_1", shape=(512, 512, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d883_const_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D884(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d884_const_1", shape=(512, 512, 3, 3), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d884_const_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D885(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d885_const_1", shape=(1024, 1024, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d885_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D886(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d886_const_1", shape=(1024, 2048, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d886_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D887(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d887_const_1", shape=(256, 1024, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d887_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D888(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d888_const_1", shape=(255, 1024, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d888_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D889(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d889_const_1", shape=(32, 3, 3, 3), dtype=torch.float32) + self.add_constant("conv2d889_const_2", shape=(32,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d889_const_1"), + self.get_constant("conv2d889_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D890(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d890_const_1", shape=(64, 32, 3, 3), dtype=torch.float32) + self.add_constant("conv2d890_const_2", shape=(64,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d890_const_1"), + self.get_constant("conv2d890_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D891(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d891_const_1", shape=(64, 64, 3, 3), dtype=torch.float32) + self.add_constant("conv2d891_const_2", shape=(64,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d891_const_1"), + self.get_constant("conv2d891_const_2"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D892(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d892_const_1", shape=(64, 64, 3, 3), dtype=torch.float32) + self.add_constant("conv2d892_const_2", shape=(64,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d892_const_1"), + self.get_constant("conv2d892_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D893(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d893_const_1", shape=(128, 64, 3, 3), dtype=torch.float32) + self.add_constant("conv2d893_const_2", shape=(128,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d893_const_1"), + self.get_constant("conv2d893_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D894(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d894_const_1", shape=(128, 128, 3, 3), dtype=torch.float32) + self.add_constant("conv2d894_const_2", shape=(128,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d894_const_1"), + self.get_constant("conv2d894_const_2"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D895(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d895_const_1", shape=(128, 128, 3, 3), dtype=torch.float32) + self.add_constant("conv2d895_const_2", shape=(128,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d895_const_1"), + self.get_constant("conv2d895_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D896(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d896_const_1", shape=(256, 128, 3, 3), dtype=torch.float32) + self.add_constant("conv2d896_const_2", shape=(256,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d896_const_1"), + self.get_constant("conv2d896_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D897(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d897_const_1", shape=(256, 256, 3, 3), dtype=torch.float32) + self.add_constant("conv2d897_const_2", shape=(256,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d897_const_1"), + self.get_constant("conv2d897_const_2"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D898(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d898_const_1", shape=(256, 256, 3, 3), dtype=torch.float32) + self.add_constant("conv2d898_const_2", shape=(256,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d898_const_1"), + self.get_constant("conv2d898_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D899(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d899_const_1", shape=(512, 256, 3, 3), dtype=torch.float32) + self.add_constant("conv2d899_const_2", shape=(512,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d899_const_1"), + self.get_constant("conv2d899_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D900(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d900_const_1", shape=(512, 512, 3, 3), dtype=torch.float32) + self.add_constant("conv2d900_const_2", shape=(512,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d900_const_1"), + self.get_constant("conv2d900_const_2"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D901(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d901_const_1", shape=(256, 512, 1, 1), dtype=torch.float32) + self.add_constant("conv2d901_const_2", shape=(256,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d901_const_1"), + self.get_constant("conv2d901_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D902(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d902_const_1", shape=(256, 256, 1, 1), dtype=torch.float32) + self.add_constant("conv2d902_const_2", shape=(256,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d902_const_1"), + self.get_constant("conv2d902_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D903(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d903_const_1", shape=(256, 1024, 1, 1), dtype=torch.float32) + self.add_constant("conv2d903_const_2", shape=(256,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d903_const_1"), + self.get_constant("conv2d903_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D904(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d904_const_1", shape=(512, 512, 1, 1), dtype=torch.float32) + self.add_constant("conv2d904_const_2", shape=(512,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d904_const_1"), + self.get_constant("conv2d904_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D905(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d905_const_1", shape=(128, 512, 1, 1), dtype=torch.float32) + self.add_constant("conv2d905_const_2", shape=(128,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d905_const_1"), + self.get_constant("conv2d905_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D906(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d906_const_1", shape=(128, 256, 1, 1), dtype=torch.float32) + self.add_constant("conv2d906_const_2", shape=(128,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d906_const_1"), + self.get_constant("conv2d906_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D907(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d907_const_1", shape=(128, 128, 1, 1), dtype=torch.float32) + self.add_constant("conv2d907_const_2", shape=(128,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d907_const_1"), + self.get_constant("conv2d907_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D908(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d908_const_1", shape=(128, 384, 1, 1), dtype=torch.float32) + self.add_constant("conv2d908_const_2", shape=(128,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d908_const_1"), + self.get_constant("conv2d908_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D909(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d909_const_1", shape=(64, 128, 1, 1), dtype=torch.float32) + self.add_constant("conv2d909_const_2", shape=(64,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d909_const_1"), + self.get_constant("conv2d909_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D910(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d910_const_1", shape=(64, 64, 1, 1), dtype=torch.float32) + self.add_constant("conv2d910_const_2", shape=(64,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d910_const_1"), + self.get_constant("conv2d910_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D911(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d911_const_1", shape=(64, 192, 1, 1), dtype=torch.float32) + self.add_constant("conv2d911_const_2", shape=(64,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d911_const_1"), + self.get_constant("conv2d911_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D912(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d912_const_1", shape=(4, 64, 1, 1), dtype=torch.float32) + self.add_constant("conv2d912_const_2", shape=(4,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d912_const_1"), + self.get_constant("conv2d912_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D913(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d913_const_1", shape=(4, 128, 1, 1), dtype=torch.float32) + self.add_constant("conv2d913_const_2", shape=(4,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d913_const_1"), + self.get_constant("conv2d913_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D914(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d914_const_1", shape=(4, 256, 1, 1), dtype=torch.float32) + self.add_constant("conv2d914_const_2", shape=(4,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d914_const_1"), + self.get_constant("conv2d914_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D915(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d915_const_1", shape=(80, 64, 1, 1), dtype=torch.float32) + self.add_constant("conv2d915_const_2", shape=(80,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d915_const_1"), + self.get_constant("conv2d915_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D916(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d916_const_1", shape=(80, 128, 1, 1), dtype=torch.float32) + self.add_constant("conv2d916_const_2", shape=(80,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d916_const_1"), + self.get_constant("conv2d916_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D917(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d917_const_1", shape=(80, 256, 1, 1), dtype=torch.float32) + self.add_constant("conv2d917_const_2", shape=(80,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d917_const_1"), + self.get_constant("conv2d917_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D918(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d918_const_1", shape=(64, 3, 3, 3), dtype=torch.float32) + self.add_constant("conv2d918_const_2", shape=(64,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d918_const_1"), + self.get_constant("conv2d918_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D919(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d919_const_1", shape=(1024, 512, 3, 3), dtype=torch.float32) + self.add_constant("conv2d919_const_2", shape=(1024,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d919_const_1"), + self.get_constant("conv2d919_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D920(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d920_const_1", shape=(512, 1024, 1, 1), dtype=torch.float32) + self.add_constant("conv2d920_const_2", shape=(512,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d920_const_1"), + self.get_constant("conv2d920_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D921(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d921_const_1", shape=(1024, 1024, 1, 1), dtype=torch.float32) + self.add_constant("conv2d921_const_2", shape=(1024,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d921_const_1"), + self.get_constant("conv2d921_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D922(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d922_const_1", shape=(1024, 2048, 1, 1), dtype=torch.float32) + self.add_constant("conv2d922_const_2", shape=(1024,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d922_const_1"), + self.get_constant("conv2d922_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D923(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d923_const_1", shape=(256, 768, 1, 1), dtype=torch.float32) + self.add_constant("conv2d923_const_2", shape=(256,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d923_const_1"), + self.get_constant("conv2d923_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D924(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d924_const_1", shape=(68, 128, 1, 1), dtype=torch.float32) + self.add_constant("conv2d924_const_2", shape=(68,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d924_const_1"), + self.get_constant("conv2d924_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D925(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d925_const_1", shape=(1, 17, 1, 1), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d925_const_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D926(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d926_const_1", shape=(68, 256, 1, 1), dtype=torch.float32) + self.add_constant("conv2d926_const_2", shape=(68,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d926_const_1"), + self.get_constant("conv2d926_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D927(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d927_const_1", shape=(68, 512, 1, 1), dtype=torch.float32) + self.add_constant("conv2d927_const_2", shape=(68,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d927_const_1"), + self.get_constant("conv2d927_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D928(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d928_const_1", shape=(80, 512, 1, 1), dtype=torch.float32) + self.add_constant("conv2d928_const_2", shape=(80,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d928_const_1"), + self.get_constant("conv2d928_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D929(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d929_const_1", shape=(16, 3, 3, 3), dtype=torch.float32) + self.add_constant("conv2d929_const_2", shape=(16,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d929_const_1"), + self.get_constant("conv2d929_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D930(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d930_const_1", shape=(32, 16, 3, 3), dtype=torch.float32) + self.add_constant("conv2d930_const_2", shape=(32,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d930_const_1"), + self.get_constant("conv2d930_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D931(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d931_const_1", shape=(32, 32, 3, 3), dtype=torch.float32) + self.add_constant("conv2d931_const_2", shape=(32,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d931_const_1"), + self.get_constant("conv2d931_const_2"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D932(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d932_const_1", shape=(32, 32, 3, 3), dtype=torch.float32) + self.add_constant("conv2d932_const_2", shape=(32,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d932_const_1"), + self.get_constant("conv2d932_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D933(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d933_const_1", shape=(64, 256, 1, 1), dtype=torch.float32) + self.add_constant("conv2d933_const_2", shape=(64,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d933_const_1"), + self.get_constant("conv2d933_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D934(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d934_const_1", shape=(32, 64, 1, 1), dtype=torch.float32) + self.add_constant("conv2d934_const_2", shape=(32,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d934_const_1"), + self.get_constant("conv2d934_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D935(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d935_const_1", shape=(32, 32, 1, 1), dtype=torch.float32) + self.add_constant("conv2d935_const_2", shape=(32,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d935_const_1"), + self.get_constant("conv2d935_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D936(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d936_const_1", shape=(32, 96, 1, 1), dtype=torch.float32) + self.add_constant("conv2d936_const_2", shape=(32,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d936_const_1"), + self.get_constant("conv2d936_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D937(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d937_const_1", shape=(4, 32, 1, 1), dtype=torch.float32) + self.add_constant("conv2d937_const_2", shape=(4,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d937_const_1"), + self.get_constant("conv2d937_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D938(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d938_const_1", shape=(80, 32, 1, 1), dtype=torch.float32) + self.add_constant("conv2d938_const_2", shape=(80,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d938_const_1"), + self.get_constant("conv2d938_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D939(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d939_const_1", shape=(48, 3, 3, 3), dtype=torch.float32) + self.add_constant("conv2d939_const_2", shape=(48,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d939_const_1"), + self.get_constant("conv2d939_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D940(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d940_const_1", shape=(96, 48, 3, 3), dtype=torch.float32) + self.add_constant("conv2d940_const_2", shape=(96,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d940_const_1"), + self.get_constant("conv2d940_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D941(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d941_const_1", shape=(64, 96, 1, 1), dtype=torch.float32) + self.add_constant("conv2d941_const_2", shape=(64,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d941_const_1"), + self.get_constant("conv2d941_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D942(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d942_const_1", shape=(96, 128, 1, 1), dtype=torch.float32) + self.add_constant("conv2d942_const_2", shape=(96,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d942_const_1"), + self.get_constant("conv2d942_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D943(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d943_const_1", shape=(192, 96, 3, 3), dtype=torch.float32) + self.add_constant("conv2d943_const_2", shape=(192,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d943_const_1"), + self.get_constant("conv2d943_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D944(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d944_const_1", shape=(128, 192, 1, 1), dtype=torch.float32) + self.add_constant("conv2d944_const_2", shape=(128,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d944_const_1"), + self.get_constant("conv2d944_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D945(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d945_const_1", shape=(192, 256, 1, 1), dtype=torch.float32) + self.add_constant("conv2d945_const_2", shape=(192,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d945_const_1"), + self.get_constant("conv2d945_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D946(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d946_const_1", shape=(384, 192, 3, 3), dtype=torch.float32) + self.add_constant("conv2d946_const_2", shape=(384,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d946_const_1"), + self.get_constant("conv2d946_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D947(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d947_const_1", shape=(256, 384, 1, 1), dtype=torch.float32) + self.add_constant("conv2d947_const_2", shape=(256,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d947_const_1"), + self.get_constant("conv2d947_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D948(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d948_const_1", shape=(384, 512, 1, 1), dtype=torch.float32) + self.add_constant("conv2d948_const_2", shape=(384,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d948_const_1"), + self.get_constant("conv2d948_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D949(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d949_const_1", shape=(768, 384, 3, 3), dtype=torch.float32) + self.add_constant("conv2d949_const_2", shape=(768,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d949_const_1"), + self.get_constant("conv2d949_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D950(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d950_const_1", shape=(512, 768, 1, 1), dtype=torch.float32) + self.add_constant("conv2d950_const_2", shape=(512,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d950_const_1"), + self.get_constant("conv2d950_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D951(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d951_const_1", shape=(768, 1024, 1, 1), dtype=torch.float32) + self.add_constant("conv2d951_const_2", shape=(768,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d951_const_1"), + self.get_constant("conv2d951_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D952(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d952_const_1", shape=(384, 768, 1, 1), dtype=torch.float32) + self.add_constant("conv2d952_const_2", shape=(384,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d952_const_1"), + self.get_constant("conv2d952_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D953(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d953_const_1", shape=(768, 1536, 1, 1), dtype=torch.float32) + self.add_constant("conv2d953_const_2", shape=(768,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d953_const_1"), + self.get_constant("conv2d953_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D954(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d954_const_1", shape=(192, 768, 1, 1), dtype=torch.float32) + self.add_constant("conv2d954_const_2", shape=(192,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d954_const_1"), + self.get_constant("conv2d954_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D955(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d955_const_1", shape=(192, 384, 1, 1), dtype=torch.float32) + self.add_constant("conv2d955_const_2", shape=(192,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d955_const_1"), + self.get_constant("conv2d955_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D956(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d956_const_1", shape=(192, 192, 1, 1), dtype=torch.float32) + self.add_constant("conv2d956_const_2", shape=(192,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d956_const_1"), + self.get_constant("conv2d956_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D957(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d957_const_1", shape=(192, 192, 3, 3), dtype=torch.float32) + self.add_constant("conv2d957_const_2", shape=(192,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d957_const_1"), + self.get_constant("conv2d957_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D958(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d958_const_1", shape=(192, 576, 1, 1), dtype=torch.float32) + self.add_constant("conv2d958_const_2", shape=(192,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d958_const_1"), + self.get_constant("conv2d958_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D959(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d959_const_1", shape=(96, 192, 1, 1), dtype=torch.float32) + self.add_constant("conv2d959_const_2", shape=(96,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d959_const_1"), + self.get_constant("conv2d959_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D960(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d960_const_1", shape=(96, 96, 1, 1), dtype=torch.float32) + self.add_constant("conv2d960_const_2", shape=(96,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d960_const_1"), + self.get_constant("conv2d960_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D961(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d961_const_1", shape=(96, 96, 3, 3), dtype=torch.float32) + self.add_constant("conv2d961_const_2", shape=(96,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d961_const_1"), + self.get_constant("conv2d961_const_2"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D962(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d962_const_1", shape=(96, 288, 1, 1), dtype=torch.float32) + self.add_constant("conv2d962_const_2", shape=(96,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d962_const_1"), + self.get_constant("conv2d962_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D963(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d963_const_1", shape=(96, 96, 3, 3), dtype=torch.float32) + self.add_constant("conv2d963_const_2", shape=(96,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d963_const_1"), + self.get_constant("conv2d963_const_2"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D964(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d964_const_1", shape=(68, 96, 1, 1), dtype=torch.float32) + self.add_constant("conv2d964_const_2", shape=(68,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d964_const_1"), + self.get_constant("conv2d964_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D965(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d965_const_1", shape=(192, 192, 3, 3), dtype=torch.float32) + self.add_constant("conv2d965_const_2", shape=(192,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d965_const_1"), + self.get_constant("conv2d965_const_2"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D966(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d966_const_1", shape=(68, 192, 1, 1), dtype=torch.float32) + self.add_constant("conv2d966_const_2", shape=(68,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d966_const_1"), + self.get_constant("conv2d966_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D967(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d967_const_1", shape=(384, 384, 1, 1), dtype=torch.float32) + self.add_constant("conv2d967_const_2", shape=(384,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d967_const_1"), + self.get_constant("conv2d967_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D968(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d968_const_1", shape=(384, 384, 3, 3), dtype=torch.float32) + self.add_constant("conv2d968_const_2", shape=(384,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d968_const_1"), + self.get_constant("conv2d968_const_2"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D969(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d969_const_1", shape=(68, 384, 1, 1), dtype=torch.float32) + self.add_constant("conv2d969_const_2", shape=(68,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d969_const_1"), + self.get_constant("conv2d969_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D970(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d970_const_1", shape=(80, 96, 1, 1), dtype=torch.float32) + self.add_constant("conv2d970_const_2", shape=(80,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d970_const_1"), + self.get_constant("conv2d970_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D971(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d971_const_1", shape=(80, 192, 1, 1), dtype=torch.float32) + self.add_constant("conv2d971_const_2", shape=(80,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d971_const_1"), + self.get_constant("conv2d971_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D972(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2d972_const_1", shape=(80, 384, 1, 1), dtype=torch.float32) + self.add_constant("conv2d972_const_2", shape=(80,), dtype=torch.float32) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_constant("conv2d972_const_1"), + self.get_constant("conv2d972_const_2"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D973(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d973.weight_1", + forge.Parameter(*(64, 12, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d973.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D974(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d974.weight_1", + forge.Parameter(*(4, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d974.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D975(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d975.weight_1", + forge.Parameter(*(1, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d975.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D976(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d976.weight_1", + forge.Parameter(*(80, 256, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d976.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D977(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d977.weight_1", + forge.Parameter(*(48, 12, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d977.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D978(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d978.weight_1", + forge.Parameter(*(48, 48, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d978.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D979(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d979.weight_1", + forge.Parameter(*(768, 384, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d979.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D980(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d980.weight_1", + forge.Parameter(*(768, 1536, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d980.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D981(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d981.weight_1", + forge.Parameter(*(192, 192, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d981.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D982(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d982.weight_1", + forge.Parameter(*(4, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d982.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D983(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d983.weight_1", + forge.Parameter(*(1, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d983.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D984(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d984.weight_1", + forge.Parameter(*(80, 192, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d984.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D985(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d985.weight_1", + forge.Parameter(*(384, 384, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d985.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D986(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d986.weight_1", + forge.Parameter(*(32, 12, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d986.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D987(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d987.weight_1", + forge.Parameter(*(4, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d987.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D988(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d988.weight_1", + forge.Parameter(*(1, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d988.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D989(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d989.weight_1", + forge.Parameter(*(80, 128, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d989.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D990(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d990.weight_1", + forge.Parameter(*(24, 12, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d990.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D991(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d991.weight_1", + forge.Parameter(*(48, 24, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d991.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D992(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d992.weight_1", + forge.Parameter(*(24, 24, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d992.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D993(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d993.weight_1", + forge.Parameter(*(1, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d993.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D994(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d994.weight_1", + forge.Parameter(*(80, 96, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d994.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D995(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d995.weight_1", + forge.Parameter(*(80, 12, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d995.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D996(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d996.weight_1", + forge.Parameter(*(80, 80, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d996.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D997(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d997.weight_1", + forge.Parameter(*(160, 320, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d997.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D998(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d998.weight_1", + forge.Parameter(*(320, 320, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d998.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D999(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d999.weight_1", + forge.Parameter(*(640, 320, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d999.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1000(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1000.weight_1", + forge.Parameter(*(320, 640, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1000.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1001(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1001.weight_1", + forge.Parameter(*(640, 640, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1001.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1002(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1002.weight_1", + forge.Parameter(*(1280, 640, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1002.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1003(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1003.weight_1", + forge.Parameter(*(1280, 2560, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1003.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1004(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1004.weight_1", + forge.Parameter(*(640, 640, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1004.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1005(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1005.weight_1", + forge.Parameter(*(1280, 1280, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1005.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1006(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1006.weight_1", + forge.Parameter(*(320, 1280, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1006.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1007(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1007.weight_1", + forge.Parameter(*(160, 640, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1007.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1008(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1008.weight_1", + forge.Parameter(*(320, 320, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1008.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1009(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1009.weight_1", + forge.Parameter(*(4, 320, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1009.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1010(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1010.weight_1", + forge.Parameter(*(1, 320, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1010.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1011(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1011.weight_1", + forge.Parameter(*(80, 320, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1011.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1012(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1012.weight_1", + forge.Parameter(*(640, 640, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1012.weight_1"), + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1013(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1013.weight_1", + forge.Parameter(*(1024, 512, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1013.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1014(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1014.weight_1", + forge.Parameter(*(16, 12, 3, 3), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1014.weight_1"), + stride=[1, 1], + padding=[1, 1, 1, 1], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1015(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, conv2d_input_0, conv2d_input_1): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + conv2d_input_1, + stride=[2, 2], + padding=[1, 1, 1, 1], + dilation=1, + groups=32, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1016(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1016.weight_1", + forge.Parameter(*(4, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1016.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1017(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1017.weight_1", + forge.Parameter(*(1, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1017.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +class Conv2D1018(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2d1018.weight_1", + forge.Parameter(*(80, 64, 1, 1), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2d_input_0): + conv2d_output_1 = forge.op.Conv2d( + "", + conv2d_input_0, + self.get_parameter("conv2d1018.weight_1"), + stride=[1, 1], + padding=[0, 0, 0, 0], + dilation=1, + groups=1, + channel_last=0, + ) + return conv2d_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Conv2D0, + [((1, 80, 3000, 1), torch.float32), ((1024, 80, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1, + [((1, 1024, 3000, 1), torch.float32), ((1024, 1024, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D0, + [((1, 80, 3000, 1), torch.float32), ((1280, 80, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1, + [((1, 1280, 3000, 1), torch.float32), ((1280, 1280, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D0, + [((1, 80, 3000, 1), torch.float32), ((384, 80, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1, + [((1, 384, 3000, 1), torch.float32), ((384, 384, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D0, + [((1, 80, 3000, 1), torch.float32), ((512, 80, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1, + [((1, 512, 3000, 1), torch.float32), ((512, 512, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D0, + [((1, 80, 3000, 1), torch.float32), ((768, 80, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1, + [((1, 768, 3000, 1), torch.float32), ((768, 768, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D2, + [((1, 768, 1, 128), torch.float32), ((768, 192, 1, 1), torch.float32)], + { + "model_name": ["pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "4", + "channel_last": "0", + }, + }, + ), + ( + Conv2D3, + [((1, 768, 128, 1), torch.float32), ((768, 768, 1, 1), torch.float32)], + { + "model_name": ["pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D2, + [((1, 768, 1, 128), torch.float32), ((3072, 192, 1, 1), torch.float32)], + { + "model_name": ["pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "4", + "channel_last": "0", + }, + }, + ), + ( + Conv2D2, + [((1, 3072, 1, 128), torch.float32), ((768, 768, 1, 1), torch.float32)], + { + "model_name": ["pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "4", + "channel_last": "0", + }, + }, + ), + ( + Conv2D4, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_alexnet_alexnet_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[4, 4]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D5, + [((1, 64, 27, 27), torch.float32)], + { + "model_name": ["pt_alexnet_alexnet_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D6, + [((1, 192, 13, 13), torch.float32)], + { + "model_name": ["pt_alexnet_alexnet_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D7, + [((1, 384, 13, 13), torch.float32)], + { + "model_name": ["pt_alexnet_alexnet_img_cls_torchhub", "pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D8, + [((1, 256, 13, 13), torch.float32)], + { + "model_name": ["pt_alexnet_alexnet_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D9, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[4, 4]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D10, + [((1, 96, 27, 27), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D11, + [((1, 256, 13, 13), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D12, + [((1, 384, 13, 13), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D13, + [((1, 1, 28, 28), torch.float32)], + { + "model_name": ["pt_autoencoder_conv_img_enc_github"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D14, + [((1, 16, 14, 14), torch.float32)], + { + "model_name": ["pt_autoencoder_conv_img_enc_github"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D15, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": [ + "pt_deit_facebook_deit_base_patch16_224_img_cls_hf", + "pt_deit_facebook_deit_base_distilled_patch16_224_img_cls_hf", + "pt_mlp_mixer_mixer_b16_224_miil_in21k_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_miil_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_in21k_img_cls_timm", + "pt_vit_google_vit_base_patch16_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[16, 16]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D16, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_deit_facebook_deit_tiny_patch16_224_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[16, 16]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D17, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_deit_facebook_deit_small_patch16_224_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[16, 16]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D18, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[3, 3, 3, 3]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D19, + [((1, 96, 56, 56), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D20, + [((1, 192, 56, 56), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D21, + [((1, 144, 56, 56), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D22, + [((1, 192, 56, 56), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D23, + [((1, 240, 56, 56), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D24, + [((1, 288, 56, 56), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D25, + [((1, 336, 56, 56), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D26, + [((1, 384, 56, 56), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D22, + [((1, 192, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision", "pt_regnet_facebook_regnet_y_040_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D20, + [((1, 192, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D23, + [((1, 240, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D24, + [((1, 288, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D25, + [((1, 336, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D26, + [((1, 384, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D27, + [((1, 432, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D28, + [((1, 480, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D29, + [((1, 528, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D30, + [((1, 576, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D31, + [((1, 624, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D32, + [((1, 672, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D33, + [((1, 720, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D34, + [((1, 768, 28, 28), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D26, + [((1, 384, 14, 14), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D20, + [((1, 192, 14, 14), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D27, + [((1, 432, 14, 14), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D28, + [((1, 480, 14, 14), torch.float32)], + { + "model_name": 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"channel_last": "0", + }, + }, + ), + ( + Conv2D1009, + [((1, 320, 20, 20), torch.float32)], + { + "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1010, + [((1, 320, 20, 20), torch.float32)], + { + "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1011, + [((1, 320, 20, 20), torch.float32)], + { + "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D734, + [((1, 3, 640, 640), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D134, + [((1, 32, 640, 640), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D182, + [((1, 64, 320, 320), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1013, + [((1, 512, 20, 20), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D156, + [((1, 2048, 20, 20), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D151, + [((1, 768, 40, 40), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D79, + [((1, 384, 80, 80), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D143, + [((1, 128, 80, 80), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1014, + [((1, 12, 208, 208), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D301, + [((1, 16, 208, 208), torch.float32), ((16, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "16", + "channel_last": "0", + }, + }, + ), + ( + Conv2D463, + [((1, 16, 104, 104), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D260, + [((1, 32, 104, 104), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D615, + [((1, 16, 104, 104), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D302, + [((1, 16, 104, 104), torch.float32), ((16, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "16", + "channel_last": "0", + }, + }, + ), + ( + Conv2D179, + [((1, 32, 104, 104), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1015, + [((1, 32, 104, 104), torch.float32), ((32, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "32", + "channel_last": "0", + }, + }, + ), + ( + Conv2D137, + [((1, 32, 52, 52), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D182, + [((1, 64, 52, 52), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D179, + [((1, 32, 52, 52), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D257, + [((1, 32, 52, 52), torch.float32), ((32, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "32", + "channel_last": "0", + }, + }, + ), + ( + Conv2D174, + [((1, 64, 52, 52), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D564, + [((1, 64, 52, 52), torch.float32), ((64, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "64", + "channel_last": "0", + }, + }, + ), + ( + Conv2D565, + [((1, 64, 52, 52), torch.float32), ((64, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "64", + "channel_last": "0", + }, + }, + ), + ( + Conv2D67, + [((1, 64, 26, 26), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D138, + [((1, 128, 26, 26), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D174, + [((1, 64, 26, 26), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D565, + [((1, 64, 26, 26), torch.float32), ((64, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "64", + "channel_last": "0", + }, + }, + ), + ( + Conv2D71, + [((1, 128, 26, 26), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D568, + [((1, 128, 26, 26), torch.float32), ((128, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "128", + "channel_last": "0", + }, + }, + ), + ( + Conv2D143, + [((1, 128, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D75, + [((1, 256, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D83, + [((1, 512, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D71, + [((1, 128, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D566, + [((1, 128, 13, 13), torch.float32), ((128, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "128", + "channel_last": "0", + }, + }, + ), + ( + Conv2D153, + [((1, 256, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D176, + [((1, 256, 26, 26), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D453, + [((1, 128, 52, 52), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1016, + [((1, 64, 52, 52), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1017, + [((1, 64, 52, 52), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1018, + [((1, 64, 52, 52), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1016, + [((1, 64, 26, 26), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1017, + [((1, 64, 26, 26), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1018, + [((1, 64, 26, 26), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D176, + [((1, 256, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D565, + [((1, 64, 13, 13), torch.float32), ((64, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "64", + "channel_last": "0", + }, + }, + ), + ( + Conv2D174, + [((1, 64, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1016, + [((1, 64, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1017, + [((1, 64, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + ( + Conv2D1018, + [((1, 64, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Conv2d") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_conv2dtranspose.py b/forge/test/models_ops/test_conv2dtranspose.py new file mode 100644 index 000000000..46e4d807d --- /dev/null +++ b/forge/test/models_ops/test_conv2dtranspose.py @@ -0,0 +1,541 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Conv2Dtranspose0(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2dtranspose0.weight_1", + forge.Parameter(*(4, 16, 2, 2), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_parameter("conv2dtranspose0.weight_1"), + stride=2, + padding=0, + dilation=1, + groups=1, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose1(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2dtranspose1.weight_1", + forge.Parameter(*(16, 1, 2, 2), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_parameter("conv2dtranspose1.weight_1"), + stride=2, + padding=0, + dilation=1, + groups=1, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose2(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2dtranspose2.weight_1", + forge.Parameter(*(64, 1, 4, 4), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_parameter("conv2dtranspose2.weight_1"), + stride=2, + padding=1, + dilation=1, + groups=64, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose3(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2dtranspose3.weight_1", + forge.Parameter(*(128, 1, 4, 4), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_parameter("conv2dtranspose3.weight_1"), + stride=2, + padding=1, + dilation=1, + groups=128, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose4(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2dtranspose4.weight_1", + forge.Parameter(*(256, 1, 4, 4), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_parameter("conv2dtranspose4.weight_1"), + stride=2, + padding=1, + dilation=1, + groups=256, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose5(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2dtranspose5.weight_1", + forge.Parameter(*(512, 256, 2, 2), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_parameter("conv2dtranspose5.weight_1"), + stride=2, + padding=0, + dilation=1, + groups=1, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose6(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2dtranspose6.weight_1", + forge.Parameter(*(256, 128, 2, 2), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_parameter("conv2dtranspose6.weight_1"), + stride=2, + padding=0, + dilation=1, + groups=1, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose7(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2dtranspose7.weight_1", + forge.Parameter(*(128, 64, 2, 2), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_parameter("conv2dtranspose7.weight_1"), + stride=2, + padding=0, + dilation=1, + groups=1, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose8(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "conv2dtranspose8.weight_1", + forge.Parameter(*(64, 32, 2, 2), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_parameter("conv2dtranspose8.weight_1"), + stride=2, + padding=0, + dilation=1, + groups=1, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose9(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2dtranspose9_const_1", shape=(128, 128, 2, 2), dtype=torch.float32) + self.add_constant("conv2dtranspose9_const_2", shape=(128,), dtype=torch.float32) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_constant("conv2dtranspose9_const_1"), + self.get_constant("conv2dtranspose9_const_2"), + stride=2, + padding=0, + dilation=1, + groups=1, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose10(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2dtranspose10_const_1", shape=(64, 64, 2, 2), dtype=torch.float32) + self.add_constant("conv2dtranspose10_const_2", shape=(64,), dtype=torch.float32) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_constant("conv2dtranspose10_const_1"), + self.get_constant("conv2dtranspose10_const_2"), + stride=2, + padding=0, + dilation=1, + groups=1, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose11(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2dtranspose11_const_1", shape=(256, 256, 2, 2), dtype=torch.float32) + self.add_constant("conv2dtranspose11_const_2", shape=(256,), dtype=torch.float32) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_constant("conv2dtranspose11_const_1"), + self.get_constant("conv2dtranspose11_const_2"), + stride=2, + padding=0, + dilation=1, + groups=1, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose12(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2dtranspose12_const_1", shape=(32, 32, 2, 2), dtype=torch.float32) + self.add_constant("conv2dtranspose12_const_2", shape=(32,), dtype=torch.float32) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_constant("conv2dtranspose12_const_1"), + self.get_constant("conv2dtranspose12_const_2"), + stride=2, + padding=0, + dilation=1, + groups=1, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose13(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2dtranspose13_const_1", shape=(192, 192, 2, 2), dtype=torch.float32) + self.add_constant("conv2dtranspose13_const_2", shape=(192,), dtype=torch.float32) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_constant("conv2dtranspose13_const_1"), + self.get_constant("conv2dtranspose13_const_2"), + stride=2, + padding=0, + dilation=1, + groups=1, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +class Conv2Dtranspose14(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("conv2dtranspose14_const_1", shape=(96, 96, 2, 2), dtype=torch.float32) + self.add_constant("conv2dtranspose14_const_2", shape=(96,), dtype=torch.float32) + + def forward(self, conv2dtranspose_input_0): + conv2dtranspose_output_1 = forge.op.Conv2dTranspose( + "", + conv2dtranspose_input_0, + self.get_constant("conv2dtranspose14_const_1"), + self.get_constant("conv2dtranspose14_const_2"), + stride=2, + padding=0, + dilation=1, + groups=1, + channel_last=0, + ) + return conv2dtranspose_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Conv2Dtranspose0, + [((1, 4, 7, 7), torch.float32)], + { + "model_name": ["pt_autoencoder_conv_img_enc_github"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose1, + [((1, 16, 14, 14), torch.float32)], + { + "model_name": ["pt_autoencoder_conv_img_enc_github"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose2, + [((1, 64, 28, 28), torch.float32)], + { + "model_name": ["pt_monodle_base_obj_det_torchvision"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "1", "dilation": "1", "groups": "64", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose3, + [((1, 128, 14, 14), torch.float32)], + { + "model_name": ["pt_monodle_base_obj_det_torchvision"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "1", "dilation": "1", "groups": "128", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose4, + [((1, 256, 7, 7), torch.float32)], + { + "model_name": ["pt_monodle_base_obj_det_torchvision"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "1", "dilation": "1", "groups": "256", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose5, + [((1, 512, 16, 16), torch.float32)], + { + "model_name": ["pt_unet_base_img_seg_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose6, + [((1, 256, 32, 32), torch.float32)], + { + "model_name": ["pt_unet_base_img_seg_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose7, + [((1, 128, 64, 64), torch.float32)], + { + "model_name": ["pt_unet_base_img_seg_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose8, + [((1, 64, 128, 128), torch.float32)], + { + "model_name": ["pt_unet_base_img_seg_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose9, + [((1, 128, 14, 20), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose10, + [((1, 64, 28, 40), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose11, + [((1, 256, 14, 20), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose9, + [((1, 128, 28, 40), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose10, + [((1, 64, 14, 20), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose12, + [((1, 32, 28, 40), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose13, + [((1, 192, 14, 20), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + ( + Conv2Dtranspose14, + [((1, 96, 28, 40), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Conv2dTranspose") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_cosine.py b/forge/test/models_ops/test_cosine.py new file mode 100644 index 000000000..2a74bb54c --- /dev/null +++ b/forge/test/models_ops/test_cosine.py @@ -0,0 +1,230 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Cosine0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, cosine_input_0): + cosine_output_1 = forge.op.Cosine("", cosine_input_0) + return cosine_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Cosine0, + [((1, 39, 128), torch.float32)], + { + "model_name": [ + "pt_deepseek_deepseek_math_7b_instruct_qa_hf", + "DeepSeekWrapper_decoder", + "pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Cosine0, + [((1, 6, 64), torch.float32)], + { + "model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf", "pt_qwen1_5_qwen_qwen1_5_0_5b_clm_hf"], + "pcc": 0.99, + }, + ), + ( + Cosine0, + [((1, 10, 256), torch.float32)], + { + "model_name": [ + "pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_1b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf", + ], + "pcc": 0.99, + }, + ), + (Cosine0, [((1, 334, 32), torch.float32)], {"model_name": ["pt_fuyu_adept_fuyu_8b_qa_hf"], "pcc": 0.99}), + (Cosine0, [((1, 7, 256), torch.float32)], {"model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], "pcc": 0.99}), + ( + Cosine0, + [((1, 4, 64), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Cosine0, + [((1, 256, 64), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Cosine0, + [((1, 4, 128), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Cosine0, + [((1, 128, 128), torch.float32)], + {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99}, + ), + ( + Cosine0, + [((1, 12, 32), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_pytdml_token_cls_hf", "pt_phi2_microsoft_phi_2_token_cls_hf"], + "pcc": 0.99, + }, + ), + ( + Cosine0, + [((1, 256, 32), torch.float32)], + {"model_name": ["pt_phi2_microsoft_phi_2_clm_hf", "pt_phi2_microsoft_phi_2_pytdml_clm_hf"], "pcc": 0.99}, + ), + ( + Cosine0, + [((1, 11, 32), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_seq_cls_hf", "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf"], + "pcc": 0.99, + }, + ), + ( + Cosine0, + [((1, 256, 96), torch.float32)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_clm_hf"], "pcc": 0.99}, + ), + ( + Cosine0, + [((1, 13, 96), torch.float32)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_token_cls_hf"], "pcc": 0.99}, + ), + ( + Cosine0, + [((1, 5, 96), torch.float32)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf"], "pcc": 0.99}, + ), + ( + Cosine0, + [((1, 29, 64), torch.float32)], + { + "model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_chat_clm_hf", "pt_qwen_v2_qwen_qwen2_5_0_5b_clm_hf"], + "pcc": 0.99, + }, + ), + ( + Cosine0, + [((1, 35, 128), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Cosine0, + [((1, 35, 64), torch.float32)], + {"model_name": ["pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf"], "pcc": 0.99}, + ), + ( + Cosine0, + [((1, 29, 128), torch.float32)], + { + "model_name": [ + "pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Cosine0, + [((1, 39, 64), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf"], "pcc": 0.99}, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Cosine") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_cumsum.py b/forge/test/models_ops/test_cumsum.py new file mode 100644 index 000000000..8f087200d --- /dev/null +++ b/forge/test/models_ops/test_cumsum.py @@ -0,0 +1,114 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Cumsum0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, cumsum_input_0): + cumsum_output_1 = forge.op.CumSum("", cumsum_input_0, dim=1) + return cumsum_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Cumsum0, + [((1, 256), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Cumsum0, + [((1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Cumsum0, + [((1, 128), torch.int32)], + { + "model_name": [ + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "CumSum") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_embedding.py b/forge/test/models_ops/test_embedding.py new file mode 100644 index 000000000..2b60e9dda --- /dev/null +++ b/forge/test/models_ops/test_embedding.py @@ -0,0 +1,1690 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Embedding0(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding0.weight_1", + forge.Parameter(*(2049, 2048), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding0.weight_1")) + return embedding_output_1 + + +class Embedding1(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding1.weight_1", + forge.Parameter(*(32128, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding1.weight_1")) + return embedding_output_1 + + +class Embedding2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, embedding_input_0, embedding_input_1): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, embedding_input_1) + return embedding_output_1 + + +class Embedding3(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding3.weight_1", + forge.Parameter(*(2049, 1536), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding3.weight_1")) + return embedding_output_1 + + +class Embedding4(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding4.weight_1", + forge.Parameter(*(2049, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding4.weight_1")) + return embedding_output_1 + + +class Embedding5(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding5.weight_1", + forge.Parameter(*(51865, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding5.weight_1")) + return embedding_output_1 + + +class Embedding6(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding6.weight_1", + forge.Parameter(*(51865, 1280), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding6.weight_1")) + return embedding_output_1 + + +class Embedding7(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding7.weight_1", + forge.Parameter(*(51865, 384), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding7.weight_1")) + return embedding_output_1 + + +class Embedding8(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding8.weight_1", + forge.Parameter(*(51865, 512), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding8.weight_1")) + return embedding_output_1 + + +class Embedding9(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding9.weight_1", + forge.Parameter(*(51865, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding9.weight_1")) + return embedding_output_1 + + +class Embedding10(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding10.weight_1", + forge.Parameter(*(51866, 1280), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding10.weight_1")) + return embedding_output_1 + + +class Embedding11(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding11.weight_1", + forge.Parameter(*(49408, 512), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding11.weight_1")) + return embedding_output_1 + + +class Embedding12(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding12.weight_1", + forge.Parameter(*(77, 512), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding12.weight_1")) + return embedding_output_1 + + +class Embedding13(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding13.weight_1", + forge.Parameter(*(102400, 4096), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding13.weight_1")) + return embedding_output_1 + + +class Embedding14(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding14.weight_1", + forge.Parameter(*(30000, 128), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding14.weight_1")) + return embedding_output_1 + + +class Embedding15(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding15.weight_1", + forge.Parameter(*(2, 128), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding15.weight_1")) + return embedding_output_1 + + +class Embedding16(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding16.weight_1", + forge.Parameter(*(512, 128), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding16.weight_1")) + return embedding_output_1 + + +class Embedding17(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding17.weight_1", + forge.Parameter(*(50265, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding17.weight_1")) + return embedding_output_1 + + +class Embedding18(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding18.weight_1", + forge.Parameter(*(30522, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding18.weight_1")) + return embedding_output_1 + + +class Embedding19(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding19.weight_1", + forge.Parameter(*(2, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding19.weight_1")) + return embedding_output_1 + + +class Embedding20(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding20.weight_1", + forge.Parameter(*(512, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding20.weight_1")) + return embedding_output_1 + + +class Embedding21(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding21.weight_1", + forge.Parameter(*(28996, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding21.weight_1")) + return embedding_output_1 + + +class Embedding22(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding22.weight_1", + forge.Parameter(*(2, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding22.weight_1")) + return embedding_output_1 + + +class Embedding23(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding23.weight_1", + forge.Parameter(*(512, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding23.weight_1")) + return embedding_output_1 + + +class Embedding24(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding24.weight_1", + forge.Parameter(*(51200, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding24.weight_1")) + return embedding_output_1 + + +class Embedding25(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding25.weight_1", + forge.Parameter(*(119547, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding25.weight_1")) + return embedding_output_1 + + +class Embedding26(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding26.weight_1", + forge.Parameter(*(28996, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding26.weight_1")) + return embedding_output_1 + + +class Embedding27(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding27.weight_1", + forge.Parameter(*(65024, 4544), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding27.weight_1")) + return embedding_output_1 + + +class Embedding28(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding28.weight_1", + forge.Parameter(*(131072, 3072), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding28.weight_1")) + return embedding_output_1 + + +class Embedding29(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding29.weight_1", + forge.Parameter(*(131072, 2048), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding29.weight_1")) + return embedding_output_1 + + +class Embedding30(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding30.weight_1", + forge.Parameter(*(256000, 2048), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding30.weight_1")) + return embedding_output_1 + + +class Embedding31(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding31.weight_1", + forge.Parameter(*(50257, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding31.weight_1")) + return embedding_output_1 + + +class Embedding32(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding32.weight_1", + forge.Parameter(*(50257, 2560), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding32.weight_1")) + return embedding_output_1 + + +class Embedding33(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding33.weight_1", + forge.Parameter(*(50257, 2048), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding33.weight_1")) + return embedding_output_1 + + +class Embedding34(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding34.weight_1", + forge.Parameter(*(128256, 2048), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding34.weight_1")) + return embedding_output_1 + + +class Embedding35(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding35.weight_1", + forge.Parameter(*(128256, 4096), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding35.weight_1")) + return embedding_output_1 + + +class Embedding36(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("embedding36_const_1", shape=(32000, 4096), dtype=torch.float32) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_constant("embedding36_const_1")) + return embedding_output_1 + + +class Embedding37(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding37.weight_1", + forge.Parameter(*(50272, 2048), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding37.weight_1")) + return embedding_output_1 + + +class Embedding38(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding38.weight_1", + forge.Parameter(*(2050, 2048), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding38.weight_1")) + return embedding_output_1 + + +class Embedding39(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding39.weight_1", + forge.Parameter(*(50272, 512), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding39.weight_1")) + return embedding_output_1 + + +class Embedding40(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding40.weight_1", + forge.Parameter(*(2050, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding40.weight_1")) + return embedding_output_1 + + +class Embedding41(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding41.weight_1", + forge.Parameter(*(50272, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding41.weight_1")) + return embedding_output_1 + + +class Embedding42(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding42.weight_1", + forge.Parameter(*(2050, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding42.weight_1")) + return embedding_output_1 + + +class Embedding43(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding43.weight_1", + forge.Parameter(*(51200, 2560), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding43.weight_1")) + return embedding_output_1 + + +class Embedding44(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding44.weight_1", + forge.Parameter(*(32064, 3072), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding44.weight_1")) + return embedding_output_1 + + +class Embedding45(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding45.weight_1", + forge.Parameter(*(151936, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding45.weight_1")) + return embedding_output_1 + + +class Embedding46(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding46.weight_1", + forge.Parameter(*(152064, 3584), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding46.weight_1")) + return embedding_output_1 + + +class Embedding47(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding47.weight_1", + forge.Parameter(*(151936, 1536), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding47.weight_1")) + return embedding_output_1 + + +class Embedding48(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding48.weight_1", + forge.Parameter(*(151936, 2048), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding48.weight_1")) + return embedding_output_1 + + +class Embedding49(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding49.weight_1", + forge.Parameter(*(151936, 896), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding49.weight_1")) + return embedding_output_1 + + +class Embedding50(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding50.weight_1", + forge.Parameter(*(250002, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding50.weight_1")) + return embedding_output_1 + + +class Embedding51(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding51.weight_1", + forge.Parameter(*(1, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding51.weight_1")) + return embedding_output_1 + + +class Embedding52(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding52.weight_1", + forge.Parameter(*(514, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding52.weight_1")) + return embedding_output_1 + + +class Embedding53(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding53.weight_1", + forge.Parameter(*(50265, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding53.weight_1")) + return embedding_output_1 + + +class Embedding54(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding54.weight_1", + forge.Parameter(*(30528, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding54.weight_1")) + return embedding_output_1 + + +class Embedding55(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding55.weight_1", + forge.Parameter(*(32128, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding55.weight_1")) + return embedding_output_1 + + +class Embedding56(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding56.weight_1", + forge.Parameter(*(32128, 512), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding56.weight_1")) + return embedding_output_1 + + +class Embedding57(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding57.weight_1", + forge.Parameter(*(256008, 2048), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding57.weight_1")) + return embedding_output_1 + + +class Embedding58(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "embedding58.weight_1", + forge.Parameter(*(256008, 1024), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, embedding_input_0): + embedding_output_1 = forge.op.Embedding("", embedding_input_0, self.get_parameter("embedding58.weight_1")) + return embedding_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Embedding0, + [((2, 1), torch.int64)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99, "max_int": 2048}, + ), + ( + Embedding1, + [((2, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "max_int": 32127, + }, + ), + ( + Embedding2, + [((13, 13), torch.int32), ((32, 12), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "max_int": 31, + }, + ), + ( + Embedding3, + [((2, 1), torch.int64)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99, "max_int": 2048}, + ), + ( + Embedding4, + [((2, 1), torch.int64)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99, "max_int": 2048}, + ), + ( + Embedding5, + [((1, 1), torch.int64)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99, "max_int": 51864}, + ), + ( + Embedding6, + [((1, 1), torch.int64)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99, "max_int": 51864}, + ), + ( + Embedding7, + [((1, 1), torch.int64)], + {"model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], "pcc": 0.99, "max_int": 51864}, + ), + ( + Embedding8, + [((1, 1), torch.int64)], + {"model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], "pcc": 0.99, "max_int": 51864}, + ), + ( + Embedding9, + [((1, 1), torch.int64)], + {"model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], "pcc": 0.99, "max_int": 51864}, + ), + ( + Embedding10, + [((1, 2), torch.int32)], + {"model_name": ["pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf"], "pcc": 0.99, "max_int": 51865}, + ), + ( + Embedding11, + [((2, 7), torch.int64)], + {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99, "max_int": 49407}, + ), + ( + Embedding12, + [((1, 7), torch.int64)], + {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99, "max_int": 76}, + ), + ( + Embedding13, + [((1, 39), torch.int64)], + {"model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf"], "pcc": 0.99, "max_int": 102399}, + ), + ( + Embedding13, + [((1, 39), torch.int32)], + {"model_name": ["DeepSeekWrapper_decoder"], "pcc": 0.99, "max_int": 102399}, + ), + ( + Embedding14, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + ], + "pcc": 0.99, + "max_int": 29999, + }, + ), + ( + Embedding15, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + ], + "pcc": 0.99, + "max_int": 1, + }, + ), + ( + Embedding16, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + ], + "pcc": 0.99, + "max_int": 511, + }, + ), + ( + Embedding17, + [((1, 256), torch.int64)], + {"model_name": ["pt_bart_facebook_bart_large_mnli_seq_cls_hf"], "pcc": 0.99, "max_int": 50264}, + ), + ( + Embedding2, + [((1, 256), torch.int64), ((1026, 1024), torch.float32)], + {"model_name": ["pt_bart_facebook_bart_large_mnli_seq_cls_hf"], "pcc": 0.99, "max_int": 1025}, + ), + ( + Embedding18, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + ], + "pcc": 0.99, + "max_int": 30521, + }, + ), + ( + Embedding19, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + ], + "pcc": 0.99, + "max_int": 1, + }, + ), + ( + Embedding20, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf", + ], + "pcc": 0.99, + "max_int": 511, + }, + ), + ( + Embedding21, + [((1, 384), torch.int64)], + { + "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], + "pcc": 0.99, + "max_int": 28995, + }, + ), + ( + Embedding22, + [((1, 384), torch.int64)], + { + "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], + "pcc": 0.99, + "max_int": 1, + }, + ), + ( + Embedding23, + [((1, 384), torch.int64)], + { + "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], + "pcc": 0.99, + "max_int": 511, + }, + ), + ( + Embedding21, + [((1, 128), torch.int64)], + { + "model_name": ["pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf"], + "pcc": 0.99, + "max_int": 28995, + }, + ), + ( + Embedding22, + [((1, 128), torch.int64)], + { + "model_name": ["pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf"], + "pcc": 0.99, + "max_int": 1, + }, + ), + ( + Embedding23, + [((1, 128), torch.int64)], + { + "model_name": ["pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf"], + "pcc": 0.99, + "max_int": 511, + }, + ), + ( + Embedding24, + [((1, 256), torch.int32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "max_int": 51199, + }, + ), + ( + Embedding25, + [((1, 128), torch.int64)], + { + 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{"model_name": ["pt_t5_google_flan_t5_small_text_gen_hf"], "pcc": 0.99, "max_int": 31}, + ), + ( + Embedding1, + [((1, 1), torch.int64)], + { + "model_name": ["pt_t5_t5_base_text_gen_hf", "pt_t5_google_flan_t5_base_text_gen_hf"], + "pcc": 0.99, + "max_int": 32127, + }, + ), + ( + Embedding2, + [((1, 1), torch.int32), ((32, 12), torch.float32)], + { + "model_name": ["pt_t5_t5_base_text_gen_hf", "pt_t5_google_flan_t5_base_text_gen_hf"], + "pcc": 0.99, + "max_int": 31, + }, + ), + ( + Embedding1, + [((1, 61), torch.int64)], + { + "model_name": ["pt_t5_t5_base_text_gen_hf", "pt_t5_google_flan_t5_base_text_gen_hf"], + "pcc": 0.99, + "max_int": 32127, + }, + ), + ( + Embedding2, + [((61, 61), torch.int32), ((32, 12), torch.float32)], + { + "model_name": ["pt_t5_t5_base_text_gen_hf", "pt_t5_google_flan_t5_base_text_gen_hf"], + "pcc": 0.99, + "max_int": 31, + }, + ), + ( + Embedding57, + [((1, 256), torch.int64)], + {"model_name": ["pt_xglm_facebook_xglm_1_7b_clm_hf"], "pcc": 0.99, "max_int": 256007}, + ), + ( + Embedding58, + [((1, 256), torch.int64)], + {"model_name": ["pt_xglm_facebook_xglm_564m_clm_hf"], "pcc": 0.99, "max_int": 256007}, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Embedding") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + max_int = metadata.pop("max_int") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_equal.py b/forge/test/models_ops/test_equal.py new file mode 100644 index 000000000..61f84b91e --- /dev/null +++ b/forge/test/models_ops/test_equal.py @@ -0,0 +1,86 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Equal0(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("equal0_const_1", shape=(1,), dtype=torch.int64) + + def forward(self, equal_input_0): + equal_output_1 = forge.op.Equal("", equal_input_0, self.get_constant("equal0_const_1")) + return equal_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Equal0, + [((1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Equal") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_exp.py b/forge/test/models_ops/test_exp.py new file mode 100644 index 000000000..4bc10a021 --- /dev/null +++ b/forge/test/models_ops/test_exp.py @@ -0,0 +1,343 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Exp0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, exp_input_0): + exp_output_1 = forge.op.Exp("", exp_input_0) + return exp_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Exp0, + [((1, 256, 10, 32), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 256, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 128, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 128, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 64, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 64, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 32, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 32, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 16, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 16, 320, 1024), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 256, 6, 20), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 256, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 128, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 128, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 64, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 64, 48, 160), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 32, 48, 160), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 32, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 16, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Exp0, + [((1, 16, 192, 640), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Exp") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_gelu.py b/forge/test/models_ops/test_gelu.py new file mode 100644 index 000000000..c389827d4 --- /dev/null +++ b/forge/test/models_ops/test_gelu.py @@ -0,0 +1,957 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Gelu0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, gelu_input_0): + gelu_output_1 = forge.op.Gelu("", gelu_input_0, approximate="none") + return gelu_output_1 + + +class Gelu1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, gelu_input_0): + gelu_output_1 = forge.op.Gelu("", gelu_input_0, approximate="tanh") + return gelu_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Gelu0, + [((2, 1, 8192), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((2, 1, 6144), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((2, 1, 4096), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 1024, 3000), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 1024, 1500), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 1500, 4096), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 1, 4096), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 1280, 3000), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 1280, 1500), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 1500, 5120), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 1, 5120), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 384, 3000), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 384, 1500), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 1500, 1536), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 1, 1536), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 512, 3000), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 512, 1500), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 1500, 2048), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 1, 2048), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu1, + [((1, 1, 2048), torch.float32)], + {"model_name": ["pt_t5_google_flan_t5_base_text_gen_hf"], "pcc": 0.99, "op_params": {"approximate": '"tanh"'}}, + ), + ( + Gelu0, + [((1, 768, 3000), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 768, 1500), torch.float32)], + { + "model_name": 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"pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 256, 2048), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 3136, 384), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 784, 768), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 196, 1536), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), + ( + Gelu0, + [((1, 197, 4096), torch.float32)], + { + "model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"approximate": '"none"'}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Gelu") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_greater.py b/forge/test/models_ops/test_greater.py new file mode 100644 index 000000000..ef40a6249 --- /dev/null +++ b/forge/test/models_ops/test_greater.py @@ -0,0 +1,612 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Greater0(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater0_const_1", shape=(1,), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater0_const_1")) + return greater_output_1 + + +class Greater1(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater1_const_1", shape=(1, 256, 10, 32), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater1_const_1")) + return greater_output_1 + + +class Greater2(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater2_const_1", shape=(1, 256, 20, 64), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater2_const_1")) + return greater_output_1 + + +class Greater3(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater3_const_1", shape=(1, 128, 20, 64), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater3_const_1")) + return greater_output_1 + + +class Greater4(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater4_const_1", shape=(1, 128, 40, 128), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater4_const_1")) + return greater_output_1 + + +class Greater5(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater5_const_1", shape=(1, 64, 40, 128), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater5_const_1")) + return greater_output_1 + + +class Greater6(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater6_const_1", shape=(1, 64, 80, 256), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater6_const_1")) + return greater_output_1 + + +class Greater7(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater7_const_1", shape=(1, 32, 80, 256), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater7_const_1")) + return greater_output_1 + + +class Greater8(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater8_const_1", shape=(1, 32, 160, 512), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater8_const_1")) + return greater_output_1 + + +class Greater9(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater9_const_1", shape=(1, 16, 160, 512), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater9_const_1")) + return greater_output_1 + + +class Greater10(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater10_const_1", shape=(1, 16, 320, 1024), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater10_const_1")) + return greater_output_1 + + +class Greater11(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater11_const_1", shape=(1, 256, 6, 20), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater11_const_1")) + return greater_output_1 + + +class Greater12(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater12_const_1", shape=(1, 256, 12, 40), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater12_const_1")) + return greater_output_1 + + +class Greater13(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater13_const_1", shape=(1, 128, 12, 40), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater13_const_1")) + return greater_output_1 + + +class Greater14(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater14_const_1", shape=(1, 128, 24, 80), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater14_const_1")) + return greater_output_1 + + +class Greater15(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater15_const_1", shape=(1, 64, 24, 80), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater15_const_1")) + return greater_output_1 + + +class Greater16(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater16_const_1", shape=(1, 64, 48, 160), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater16_const_1")) + return greater_output_1 + + +class Greater17(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater17_const_1", shape=(1, 32, 48, 160), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater17_const_1")) + return greater_output_1 + + +class Greater18(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater18_const_1", shape=(1, 32, 96, 320), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater18_const_1")) + return greater_output_1 + + +class Greater19(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater19_const_1", shape=(1, 16, 96, 320), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater19_const_1")) + return greater_output_1 + + +class Greater20(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("greater20_const_1", shape=(1, 16, 192, 640), dtype=torch.float32) + + def forward(self, greater_input_0): + greater_output_1 = forge.op.Greater("", greater_input_0, self.get_constant("greater20_const_1")) + return greater_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Greater0, + [((2, 1, 1, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Greater0, + [((2, 1, 7, 7), torch.float32)], + {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99}, + ), + ( + Greater0, + [((1, 1, 256, 256), torch.float32)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Greater0, + [((1, 12, 128, 128), torch.float32)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Greater0, + [((1, 12, 384, 384), torch.float32)], + {"model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], "pcc": 0.99}, + ), + ( + Greater0, + [((1, 1, 32, 32), torch.float32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + }, + ), + ( + Greater1, + [((1, 256, 10, 32), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater2, + [((1, 256, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater3, + [((1, 128, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater4, + [((1, 128, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater5, + [((1, 64, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater6, + [((1, 64, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater7, + [((1, 32, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater8, + [((1, 32, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater9, + [((1, 16, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater10, + [((1, 16, 320, 1024), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater11, + [((1, 256, 6, 20), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater12, + [((1, 256, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater13, + [((1, 128, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater14, + [((1, 128, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater15, + [((1, 64, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater16, + [((1, 64, 48, 160), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater17, + [((1, 32, 48, 160), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater18, + [((1, 32, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater19, + [((1, 16, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Greater20, + [((1, 16, 192, 640), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Greater") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_identity.py b/forge/test/models_ops/test_identity.py new file mode 100644 index 000000000..eb2d67156 --- /dev/null +++ b/forge/test/models_ops/test_identity.py @@ -0,0 +1,1786 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Identity0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, identity_input_0): + identity_output_1 = forge.op.Identity("", identity_input_0) + return identity_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Identity0, + [((2, 1, 2048), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((64, 1, 1), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((2, 13, 768), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((2, 12, 13, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((2, 13, 3072), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((64, 1, 13), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((2, 1, 8192), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((2, 1, 1536), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((48, 1, 1), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((48, 1, 13), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((2, 1, 6144), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((2, 1, 1024), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((32, 1, 1), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((32, 1, 13), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((2, 1, 4096), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1, 1024), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + "pt_t5_google_flan_t5_small_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 16, 1, 1), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 1500, 1024), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 16, 1500, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1500, 4096), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 16, 1, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1, 4096), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf", "pt_t5_t5_large_text_gen_hf"], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 1, 1280), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 20, 1, 1), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1500, 1280), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 20, 1500, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1500, 5120), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 20, 1, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1, 5120), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1, 384), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 6, 1, 1), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_tiny_speech_recognition_hf", + "pt_t5_google_flan_t5_small_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 1500, 384), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 6, 1500, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1500, 1536), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 6, 1, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1, 1536), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1, 512), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_base_speech_recognition_hf", + "pt_t5_t5_small_text_gen_hf", + "pt_t5_google_flan_t5_small_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 8, 1, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf", "pt_t5_t5_small_text_gen_hf"], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 1500, 512), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 8, 1500, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1500, 2048), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 8, 1, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1, 2048), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_base_speech_recognition_hf", + "pt_t5_t5_small_text_gen_hf", + "pt_t5_google_flan_t5_base_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 1, 768), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_small_speech_recognition_hf", + "pt_t5_t5_base_text_gen_hf", + "pt_t5_google_flan_t5_base_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 12, 1, 1), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_small_speech_recognition_hf", + "pt_t5_t5_base_text_gen_hf", + "pt_t5_google_flan_t5_base_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 1500, 768), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 12, 1500, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1500, 3072), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 12, 1, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 1, 3072), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf", "pt_t5_t5_base_text_gen_hf"], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 2, 1280), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 20, 2, 2), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 20, 2, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 2, 5120), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((16, 7, 7), torch.float32)], + {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 32, 39, 39), torch.float32)], + {"model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf", "DeepSeekWrapper_decoder"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 12, 204, 204), torch.float32)], + {"model_name": ["pt_vilt_dandelin_vilt_b32_mlm_mlm_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 204, 768), torch.float32)], + {"model_name": ["pt_vilt_dandelin_vilt_b32_mlm_mlm_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 12, 201, 201), torch.float32)], + {"model_name": ["pt_vilt_dandelin_vilt_b32_finetuned_vqa_qa_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 201, 768), torch.float32)], + {"model_name": ["pt_vilt_dandelin_vilt_b32_finetuned_vqa_qa_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 128, 128), torch.float32)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 64, 128, 128), torch.float32)], + { + "model_name": [ + 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"pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 4096, 128), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 4096, 512), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 1024, 320), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 1024, 1280), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 256, 512), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 768, 128, 128), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 256, 128, 128), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + }, + ), + ( + Identity0, + [((1, 3136, 96), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((64, 3, 49, 49), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((64, 49, 96), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((16, 6, 49, 49), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((16, 49, 192), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 784, 192), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((4, 12, 49, 49), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((4, 49, 384), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 196, 384), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 24, 49, 49), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + (Identity0, [((1, 4096, 1, 1), torch.float32)], {"model_name": ["pt_vgg_vgg19_bn_obj_det_timm"], "pcc": 0.99}), + ( + Identity0, + [((1, 197, 1024), torch.float32)], + {"model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Identity0, + [((1, 16, 197, 197), torch.float32)], + {"model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], "pcc": 0.99}, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Identity") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_index.py b/forge/test/models_ops/test_index.py new file mode 100644 index 000000000..a31c2d78b --- /dev/null +++ b/forge/test/models_ops/test_index.py @@ -0,0 +1,5686 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Index0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=1, stride=1) + return index_output_1 + + +class Index1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=1, stop=2, stride=1) + return index_output_1 + + +class Index2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=2, stop=3, stride=1) + return index_output_1 + + +class Index3(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=3, stop=4, stride=1) + return index_output_1 + + +class Index4(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=7, stride=1) + return index_output_1 + + +class Index5(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=64, stop=128, stride=1) + return index_output_1 + + +class Index6(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=64, stride=1) + return index_output_1 + + +class Index7(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=128, stride=1) + return index_output_1 + + +class Index8(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=384, stride=1) + return index_output_1 + + +class Index9(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=1, stride=1) + return index_output_1 + + +class Index10(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=1, stop=2, stride=1) + return index_output_1 + + +class Index11(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=256, stride=1) + return index_output_1 + + +class Index12(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=768, stop=1024, stride=1) + return index_output_1 + + +class Index13(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=1536, stop=1792, stride=1) + return index_output_1 + + +class Index14(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=2304, stop=2560, stride=1) + return index_output_1 + + +class Index15(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=512, stop=768, stride=1) + return index_output_1 + + +class Index16(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=1280, stop=1536, stride=1) + return index_output_1 + + +class Index17(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=2048, stop=2304, stride=1) + return index_output_1 + + +class Index18(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=2816, stop=3072, stride=1) + return index_output_1 + + +class Index19(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=256, stop=512, stride=1) + return index_output_1 + + +class Index20(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=1024, stop=1280, stride=1) + return index_output_1 + + +class Index21(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=1792, stop=2048, stride=1) + return index_output_1 + + +class Index22(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=2560, stop=2816, stride=1) + return index_output_1 + + +class Index23(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=32, stride=1) + return index_output_1 + + +class Index24(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=32, stop=64, stride=1) + return index_output_1 + + +class Index25(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=1, stop=32, stride=2) + return index_output_1 + + +class Index26(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=32, stride=2) + return index_output_1 + + +class Index27(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=-2, stride=1) + return index_output_1 + + +class Index28(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=-2, stop=-1, stride=1) + return index_output_1 + + +class Index29(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=72, stop=73, stride=1) + return index_output_1 + + +class Index30(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=128, stop=256, stride=1) + return index_output_1 + + +class Index31(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=16, stop=32, stride=1) + return index_output_1 + + +class Index32(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=16, stride=1) + return index_output_1 + + +class Index33(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=768, stride=1) + return index_output_1 + + +class Index34(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=768, stop=1536, stride=1) + return index_output_1 + + +class Index35(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=1536, stop=2304, stride=1) + return index_output_1 + + +class Index36(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=768, stride=1) + return index_output_1 + + +class Index37(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=768, stop=1536, stride=1) + return index_output_1 + + +class Index38(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=1536, stop=2304, stride=1) + return index_output_1 + + +class Index39(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=7, stride=1) + return index_output_1 + + +class Index40(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=256, stride=1) + return index_output_1 + + +class Index41(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=32, stride=1) + return index_output_1 + + +class Index42(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=31, stop=32, stride=1) + return index_output_1 + + +class Index43(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=32, stop=80, stride=1) + return index_output_1 + + +class Index44(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=10, stop=11, stride=1) + return index_output_1 + + +class Index45(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=3072, stride=1) + return index_output_1 + + +class Index46(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=3072, stop=6144, stride=1) + return index_output_1 + + +class Index47(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=6144, stop=9216, stride=1) + return index_output_1 + + +class Index48(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=48, stop=96, stride=1) + return index_output_1 + + +class Index49(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=48, stride=1) + return index_output_1 + + +class Index50(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=8192, stop=16384, stride=1) + return index_output_1 + + +class Index51(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=8192, stride=1) + return index_output_1 + + +class Index52(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=4, stop=5, stride=1) + return index_output_1 + + +class Index53(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=2, stop=258, stride=1) + return index_output_1 + + +class Index54(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=-1, stop=72, stride=1) + return index_output_1 + + +class Index55(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=12, stop=24, stride=1) + return index_output_1 + + +class Index56(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=12, stride=1) + return index_output_1 + + +class Index57(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=36, stop=48, stride=1) + return index_output_1 + + +class Index58(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=24, stop=36, stride=1) + return index_output_1 + + +class Index59(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=4, stride=1) + return index_output_1 + + +class Index60(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=4, stop=8, stride=1) + return index_output_1 + + +class Index61(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=-24, stop=96, stride=1) + return index_output_1 + + +class Index62(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=72, stride=1) + return index_output_1 + + +class Index63(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=1, stride=1) + return index_output_1 + + +class Index64(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=1, stop=2, stride=1) + return index_output_1 + + +class Index65(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=2, stop=3, stride=1) + return index_output_1 + + +class Index66(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=64, stride=1) + return index_output_1 + + +class Index67(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=64, stop=160, stride=1) + return index_output_1 + + +class Index68(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=160, stop=176, stride=1) + return index_output_1 + + +class Index69(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=88, stride=1) + return index_output_1 + + +class Index70(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=88, stop=132, stride=1) + return index_output_1 + + +class Index71(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=132, stop=176, stride=1) + return index_output_1 + + +class Index72(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=44, stride=1) + return index_output_1 + + +class Index73(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=44, stop=88, stride=1) + return index_output_1 + + +class Index74(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=88, stop=176, stride=1) + return index_output_1 + + +class Index75(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=128, stride=1) + return index_output_1 + + +class Index76(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=128, stop=256, stride=1) + return index_output_1 + + +class Index77(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=256, stop=288, stride=1) + return index_output_1 + + +class Index78(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=192, stride=1) + return index_output_1 + + +class Index79(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=192, stop=288, stride=1) + return index_output_1 + + +class Index80(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=288, stop=304, stride=1) + return index_output_1 + + +class Index81(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=160, stride=1) + return index_output_1 + + +class Index82(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=160, stop=272, stride=1) + return index_output_1 + + +class Index83(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=272, stop=296, stride=1) + return index_output_1 + + +class Index84(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=256, stop=280, stride=1) + return index_output_1 + + +class Index85(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=112, stride=1) + return index_output_1 + + +class Index86(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=112, stop=256, stride=1) + return index_output_1 + + +class Index87(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=256, stride=1) + return index_output_1 + + +class Index88(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=256, stop=416, stride=1) + return index_output_1 + + +class Index89(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=416, stop=448, stride=1) + return index_output_1 + + +class Index90(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=64, stop=192, stride=1) + return index_output_1 + + +class Index91(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=192, stop=448, stride=1) + return index_output_1 + + +class Index92(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=256, stop=384, stride=1) + return index_output_1 + + +class Index93(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=384, stop=448, stride=1) + return index_output_1 + + +class Index94(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=384, stride=1) + return index_output_1 + + +class Index95(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=384, stop=576, stride=1) + return index_output_1 + + +class Index96(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=576, stop=624, stride=1) + return index_output_1 + + +class Index97(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=18, stride=1) + return index_output_1 + + +class Index98(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=18, stop=54, stride=1) + return index_output_1 + + +class Index99(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=54, stop=126, stride=1) + return index_output_1 + + +class Index100(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=72, stride=1) + return index_output_1 + + +class Index101(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=72, stop=108, stride=1) + return index_output_1 + + +class Index102(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=108, stop=126, stride=1) + return index_output_1 + + +class Index103(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=36, stride=1) + return index_output_1 + + +class Index104(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=36, stop=54, stride=1) + return index_output_1 + + +class Index105(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=54, stop=72, stride=1) + return index_output_1 + + +class Index106(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=18, stop=36, stride=1) + return index_output_1 + + +class Index107(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=36, stop=72, stride=1) + return index_output_1 + + +class Index108(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=44, stop=132, stride=1) + return index_output_1 + + +class Index109(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=132, stop=308, stride=1) + return index_output_1 + + +class Index110(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=176, stride=1) + return index_output_1 + + +class Index111(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=176, stop=264, stride=1) + return index_output_1 + + +class Index112(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=264, stop=308, stride=1) + return index_output_1 + + +class Index113(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=30, stride=1) + return index_output_1 + + +class Index114(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=30, stop=90, stride=1) + return index_output_1 + + +class Index115(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=90, stop=210, stride=1) + return index_output_1 + + +class Index116(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=120, stride=1) + return index_output_1 + + +class Index117(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=120, stop=180, stride=1) + return index_output_1 + + +class Index118(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=180, stop=210, stride=1) + return index_output_1 + + +class Index119(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=60, stride=1) + return index_output_1 + + +class Index120(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=60, stop=90, stride=1) + return index_output_1 + + +class Index121(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=90, stop=120, stride=1) + return index_output_1 + + +class Index122(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=30, stop=60, stride=1) + return index_output_1 + + +class Index123(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=60, stop=120, stride=1) + return index_output_1 + + +class Index124(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=32, stride=1) + return index_output_1 + + +class Index125(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=32, stop=96, stride=1) + return index_output_1 + + +class Index126(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=96, stop=224, stride=1) + return index_output_1 + + +class Index127(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=128, stop=192, stride=1) + return index_output_1 + + +class Index128(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=192, stop=224, stride=1) + return index_output_1 + + +class Index129(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=64, stop=96, stride=1) + return index_output_1 + + +class Index130(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=96, stop=128, stride=1) + return index_output_1 + + +class Index131(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=32, stop=64, stride=1) + return index_output_1 + + +class Index132(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=64, stop=128, stride=1) + return index_output_1 + + +class Index133(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=16, stride=1) + return index_output_1 + + +class Index134(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=16, stop=32, stride=1) + return index_output_1 + + +class Index135(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=96, stop=112, stride=1) + return index_output_1 + + +class Index136(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=192, stop=256, stride=1) + return index_output_1 + + +class Index137(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=48, stride=1) + return index_output_1 + + +class Index138(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=48, stop=144, stride=1) + return index_output_1 + + +class Index139(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=144, stop=336, stride=1) + return index_output_1 + + +class Index140(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=288, stop=336, stride=1) + return index_output_1 + + +class Index141(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=96, stride=1) + return index_output_1 + + +class Index142(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=96, stop=144, stride=1) + return index_output_1 + + +class Index143(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=144, stop=192, stride=1) + return index_output_1 + + +class Index144(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=48, stop=96, stride=1) + return index_output_1 + + +class Index145(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=96, stop=192, stride=1) + return index_output_1 + + +class Index146(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=40, stride=1) + return index_output_1 + + +class Index147(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=40, stop=120, stride=1) + return index_output_1 + + +class Index148(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=120, stop=280, stride=1) + return index_output_1 + + +class Index149(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=160, stop=240, stride=1) + return index_output_1 + + +class Index150(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=240, stop=280, stride=1) + return index_output_1 + + +class Index151(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=80, stride=1) + return index_output_1 + + +class Index152(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=80, stop=120, stride=1) + return index_output_1 + + +class Index153(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=120, stop=160, stride=1) + return index_output_1 + + +class Index154(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=40, stop=80, stride=1) + return index_output_1 + + +class Index155(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=80, stop=160, stride=1) + return index_output_1 + + +class Index156(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=96, stop=160, stride=1) + return index_output_1 + + +class Index157(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=160, stop=224, stride=1) + return index_output_1 + + +class Index158(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=576, stop=768, stride=1) + return index_output_1 + + +class Index159(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=256, stop=640, stride=1) + return index_output_1 + + +class Index160(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=640, stop=1024, stride=1) + return index_output_1 + + +class Index161(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=256, stop=512, stride=1) + return index_output_1 + + +class Index162(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=512, stop=768, stride=1) + return index_output_1 + + +class Index163(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=768, stop=1024, stride=1) + return index_output_1 + + +class Index164(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=1024, stop=1280, stride=1) + return index_output_1 + + +class Index165(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=1280, stop=1536, stride=1) + return index_output_1 + + +class Index166(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=1536, stop=1792, stride=1) + return index_output_1 + + +class Index167(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=3, stop=56, stride=1) + return index_output_1 + + +class Index168(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=3, stride=1) + return index_output_1 + + +class Index169(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=3, stop=56, stride=1) + return index_output_1 + + +class Index170(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=3, stride=1) + return index_output_1 + + +class Index171(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=53, stop=56, stride=1) + return index_output_1 + + +class Index172(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=53, stride=1) + return index_output_1 + + +class Index173(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=53, stop=56, stride=1) + return index_output_1 + + +class Index174(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=53, stride=1) + return index_output_1 + + +class Index175(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=56, stride=2) + return index_output_1 + + +class Index176(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=1, stop=56, stride=2) + return index_output_1 + + +class Index177(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=56, stride=2) + return index_output_1 + + +class Index178(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=1, stop=56, stride=2) + return index_output_1 + + +class Index179(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=3, stop=28, stride=1) + return index_output_1 + + +class Index180(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=3, stop=28, stride=1) + return index_output_1 + + +class Index181(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=25, stop=28, stride=1) + return index_output_1 + + +class Index182(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=25, stride=1) + return index_output_1 + + +class Index183(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=25, stop=28, stride=1) + return index_output_1 + + +class Index184(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=25, stride=1) + return index_output_1 + + +class Index185(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=28, stride=2) + return index_output_1 + + +class Index186(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=1, stop=28, stride=2) + return index_output_1 + + +class Index187(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=28, stride=2) + return index_output_1 + + +class Index188(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=1, stop=28, stride=2) + return index_output_1 + + +class Index189(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=3, stop=14, stride=1) + return index_output_1 + + +class Index190(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=3, stop=14, stride=1) + return index_output_1 + + +class Index191(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=11, stop=14, stride=1) + return index_output_1 + + +class Index192(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=11, stride=1) + return index_output_1 + + +class Index193(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=11, stop=14, stride=1) + return index_output_1 + + +class Index194(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=11, stride=1) + return index_output_1 + + +class Index195(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=0, stop=14, stride=2) + return index_output_1 + + +class Index196(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-3, start=1, stop=14, stride=2) + return index_output_1 + + +class Index197(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=14, stride=2) + return index_output_1 + + +class Index198(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=1, stop=14, stride=2) + return index_output_1 + + +class Index199(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=2, stride=1) + return index_output_1 + + +class Index200(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=2, stop=4, stride=1) + return index_output_1 + + +class Index201(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=640, stride=2) + return index_output_1 + + +class Index202(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=1, stop=640, stride=2) + return index_output_1 + + +class Index203(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=640, stride=2) + return index_output_1 + + +class Index204(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=1, stop=640, stride=2) + return index_output_1 + + +class Index205(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=0, stop=416, stride=2) + return index_output_1 + + +class Index206(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-2, start=1, stop=416, stride=2) + return index_output_1 + + +class Index207(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=0, stop=416, stride=2) + return index_output_1 + + +class Index208(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, index_input_0): + index_output_1 = forge.op.Index("", index_input_0, dim=-1, start=1, stop=416, stride=2) + return index_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Index0, + [((2, 4, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, + }, + ), + ( + Index1, + [((2, 4, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "1", "stop": "2", "stride": "1"}, + }, + ), + ( + Index2, + [((2, 4, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "2", "stop": "3", "stride": "1"}, + }, + ), + ( + Index3, + [((2, 4, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "3", "stop": "4", "stride": "1"}, + }, + ), + ( + Index0, + [((2048, 2048), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, + }, + ), + ( + Index0, + [((2048, 1536), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, + }, + ), + ( + Index0, + [((2048, 1024), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, + }, + ), + ( + Index4, + [((1, 77), torch.int64)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "7", "stride": "1"}, + }, + ), + ( + Index5, + [((1, 32, 39, 128), torch.float32)], + { + "model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf", "DeepSeekWrapper_decoder"], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "64", "stop": "128", "stride": "1"}, + }, + ), + ( + Index6, + [((1, 32, 39, 128), torch.float32)], + { + "model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf", "DeepSeekWrapper_decoder"], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "64", "stride": "1"}, + }, + ), + ( + Index0, + [((1, 204, 768), torch.float32)], + { + "model_name": ["pt_vilt_dandelin_vilt_b32_mlm_mlm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, + }, + ), + ( + Index0, + [((1, 201, 768), torch.float32)], + { + "model_name": ["pt_vilt_dandelin_vilt_b32_finetuned_vqa_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, + }, + ), + ( + Index7, + [((1, 512), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "128", "stride": "1"}, + }, + ), + ( + Index8, + [((1, 512), torch.int64)], + { + "model_name": [ + "pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf", + "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "384", "stride": "1"}, + }, + ), + ( + Index0, + [((1, 128, 768), torch.float32)], + { + "model_name": [ + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + "pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, + }, + ), + ( + Index0, + [((2, 1024), torch.float32)], + { + "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, + }, + ), + ( + Index1, + [((2, 1024), torch.float32)], + { + "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "1", "stop": "2", "stride": "1"}, + }, + ), + ( + Index9, + [((2,), torch.float32)], + { + "model_name": [ + "pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf", + "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "1", "stride": "1"}, + }, + ), + ( + Index10, + [((2,), torch.float32)], + { + "model_name": [ + "pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf", + "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "1", "stop": "2", "stride": "1"}, + }, + ), + ( + Index11, + [((3072, 1024), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "256", "stride": "1"}, + }, + ), + ( + Index12, + [((3072, 1024), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "768", "stop": "1024", "stride": "1"}, + }, + ), + ( + Index13, + [((3072, 1024), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "1536", "stop": "1792", "stride": "1"}, + }, + ), + ( + Index14, + [((3072, 1024), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "2304", "stop": "2560", "stride": "1"}, + }, + ), + ( + Index15, + [((3072, 1024), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "512", "stop": "768", "stride": "1"}, + }, + ), + ( + Index16, + [((3072, 1024), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "1280", "stop": "1536", "stride": "1"}, + }, + ), + ( + Index17, + [((3072, 1024), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "2048", "stop": "2304", "stride": "1"}, + }, + ), + ( + Index18, + [((3072, 1024), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "2816", "stop": "3072", "stride": "1"}, + }, + ), + ( + Index19, + [((3072, 1024), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "256", "stop": "512", "stride": "1"}, + }, + ), + ( + Index20, + [((3072, 1024), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "1024", "stop": "1280", "stride": "1"}, + }, + ), + ( + Index21, + [((3072, 1024), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "1792", "stop": "2048", "stride": "1"}, + }, + ), + ( + Index22, + [((3072, 1024), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "2560", "stop": "2816", "stride": "1"}, + }, + ), + ( + Index23, + [((1, 256, 16, 64), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "32", "stride": "1"}, + }, + ), + ( + Index24, + [((1, 256, 16, 64), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "32", "stop": "64", "stride": "1"}, + }, + ), + ( + Index25, + [((1, 256, 16, 32), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "1", "stop": "32", "stride": "2"}, + }, + ), + ( + Index26, + [((1, 256, 16, 32), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "32", "stride": "2"}, + }, + ), + ( + Index0, + [((2, 768), torch.float32)], + { + "model_name": [ + "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, + }, + ), + ( + Index1, + [((2, 768), torch.float32)], + { + "model_name": [ + "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "1", "stop": "2", "stride": "1"}, + }, + ), + ( + Index27, + [((1, 6, 73, 64), torch.float32)], + { + "model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "-2", "stride": "1"}, + }, + ), + ( + Index28, + [((1, 6, 73, 64), torch.float32)], + { + 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"stop": "64", "stride": "1"}, + }, + ), + ( + Index23, + [((1, 1, 6, 64), torch.float32)], + { + "model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "32", "stride": "1"}, + }, + ), + ( + Index30, + [((1, 12, 10, 256), torch.float32)], + { + "model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "128", "stop": "256", "stride": "1"}, + }, + ), + ( + Index7, + [((1, 12, 10, 256), torch.float32)], + { + "model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "128", "stride": "1"}, + }, + ), + ( + Index30, + [((1, 4, 10, 256), torch.float32)], + { + "model_name": [ + "pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_1b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf", + 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torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "2", "stop": "4", "stride": "1"}, + }, + ), + ( + Index201, + [((1, 3, 640, 640), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "640", "stride": "2"}, + }, + ), + ( + Index202, + [((1, 3, 640, 640), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "1", "stop": "640", "stride": "2"}, + }, + ), + ( + Index203, + [((1, 3, 320, 640), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "640", "stride": "2"}, + }, + ), + ( + Index204, + [((1, 3, 320, 640), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "1", "stop": "640", "stride": "2"}, + }, + ), + ( + Index205, + [((1, 3, 416, 416), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "416", "stride": "2"}, + }, + ), + ( + Index206, + [((1, 3, 416, 416), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "1", "stop": "416", "stride": "2"}, + }, + ), + ( + Index207, + [((1, 3, 208, 416), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "416", "stride": "2"}, + }, + ), + ( + Index208, + [((1, 3, 208, 416), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "1", "stop": "416", "stride": "2"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Index") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_layernorm.py b/forge/test/models_ops/test_layernorm.py new file mode 100644 index 000000000..1e3af4161 --- /dev/null +++ b/forge/test/models_ops/test_layernorm.py @@ -0,0 +1,1668 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Layernorm0(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm0.weight_1", + forge.Parameter(*(2048,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm0.weight_2", + forge.Parameter(*(2048,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm0.weight_1"), + self.get_parameter("layernorm0.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm1(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm1.weight_1", + forge.Parameter(*(1536,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm1.weight_2", + forge.Parameter(*(1536,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm1.weight_1"), + self.get_parameter("layernorm1.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm2(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm2.weight_1", + forge.Parameter(*(1024,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm2.weight_2", + forge.Parameter(*(1024,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm2.weight_1"), + self.get_parameter("layernorm2.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm3(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm3.weight_1", + forge.Parameter(*(1280,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm3.weight_2", + forge.Parameter(*(1280,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm3.weight_1"), + self.get_parameter("layernorm3.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm4(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm4.weight_1", + forge.Parameter(*(384,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm4.weight_2", + forge.Parameter(*(384,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm4.weight_1"), + self.get_parameter("layernorm4.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm5(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm5.weight_1", + forge.Parameter(*(512,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm5.weight_2", + forge.Parameter(*(512,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm5.weight_1"), + self.get_parameter("layernorm5.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm6(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm6.weight_1", + forge.Parameter(*(768,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm6.weight_2", + forge.Parameter(*(768,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm6.weight_1"), + self.get_parameter("layernorm6.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm7(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm7.weight_1", + forge.Parameter(*(768,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm7.weight_2", + forge.Parameter(*(768,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm7.weight_1"), + self.get_parameter("layernorm7.weight_2"), + dim=-1, + epsilon=0.0, + ) + return layernorm_output_1 + + +class Layernorm8(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm8.weight_1", + forge.Parameter(*(128,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm8.weight_2", + forge.Parameter(*(128,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm8.weight_1"), + self.get_parameter("layernorm8.weight_2"), + dim=-1, + epsilon=0.0, + ) + return layernorm_output_1 + + +class Layernorm9(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm9.weight_1", + forge.Parameter(*(4096,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm9.weight_2", + forge.Parameter(*(4096,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm9.weight_1"), + self.get_parameter("layernorm9.weight_2"), + dim=-1, + epsilon=0.0, + ) + return layernorm_output_1 + + +class Layernorm10(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm10.weight_1", + forge.Parameter(*(1024,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm10.weight_2", + forge.Parameter(*(1024,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm10.weight_1"), + self.get_parameter("layernorm10.weight_2"), + dim=-1, + epsilon=0.0, + ) + return layernorm_output_1 + + +class Layernorm11(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm11.weight_1", + forge.Parameter(*(2048,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm11.weight_2", + forge.Parameter(*(2048,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm11.weight_1"), + self.get_parameter("layernorm11.weight_2"), + dim=-1, + epsilon=0.0, + ) + return layernorm_output_1 + + +class Layernorm12(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm12.weight_1", + forge.Parameter(*(4544,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm12.weight_2", + forge.Parameter(*(4544,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm12.weight_1"), + self.get_parameter("layernorm12.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm13(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm13.weight_1", + forge.Parameter(*(4096,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm13.weight_2", + forge.Parameter(*(4096,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm13.weight_1"), + self.get_parameter("layernorm13.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm14(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm14.weight_1", + forge.Parameter(*(64,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm14.weight_2", + forge.Parameter(*(64,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm14.weight_1"), + self.get_parameter("layernorm14.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm15(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm15.weight_1", + forge.Parameter(*(2560,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm15.weight_2", + forge.Parameter(*(2560,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm15.weight_1"), + self.get_parameter("layernorm15.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm16(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm16.weight_1", + forge.Parameter(*(192,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm16.weight_2", + forge.Parameter(*(192,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm16.weight_1"), + self.get_parameter("layernorm16.weight_2"), + dim=-1, + epsilon=0.0, + ) + return layernorm_output_1 + + +class Layernorm17(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm17.weight_1", + forge.Parameter(*(384,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm17.weight_2", + forge.Parameter(*(384,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm17.weight_1"), + self.get_parameter("layernorm17.weight_2"), + dim=-1, + epsilon=0.0, + ) + return layernorm_output_1 + + +class Layernorm18(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm18.weight_1", + forge.Parameter(*(768,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm18.weight_2", + forge.Parameter(*(768,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm18.weight_1"), + self.get_parameter("layernorm18.weight_2"), + dim=-1, + epsilon=1e-06, + ) + return layernorm_output_1 + + +class Layernorm19(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm19.weight_1", + forge.Parameter(*(512,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm19.weight_2", + forge.Parameter(*(512,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm19.weight_1"), + self.get_parameter("layernorm19.weight_2"), + dim=-1, + epsilon=1e-06, + ) + return layernorm_output_1 + + +class Layernorm20(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm20.weight_1", + forge.Parameter(*(1024,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm20.weight_2", + forge.Parameter(*(1024,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm20.weight_1"), + self.get_parameter("layernorm20.weight_2"), + dim=-1, + epsilon=1e-06, + ) + return layernorm_output_1 + + +class Layernorm21(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm21.weight_1", + forge.Parameter(*(322,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm21.weight_2", + forge.Parameter(*(322,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm21.weight_1"), + self.get_parameter("layernorm21.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm22(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm22.weight_1", + forge.Parameter(*(261,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm22.weight_2", + forge.Parameter(*(261,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm22.weight_1"), + self.get_parameter("layernorm22.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm23(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm23.weight_1", + forge.Parameter(*(32,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm23.weight_2", + forge.Parameter(*(32,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm23.weight_1"), + self.get_parameter("layernorm23.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm24(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm24.weight_1", + forge.Parameter(*(160,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm24.weight_2", + forge.Parameter(*(160,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm24.weight_1"), + self.get_parameter("layernorm24.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm25(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm25.weight_1", + forge.Parameter(*(256,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm25.weight_2", + forge.Parameter(*(256,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm25.weight_1"), + self.get_parameter("layernorm25.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm26(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm26.weight_1", + forge.Parameter(*(128,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm26.weight_2", + forge.Parameter(*(128,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm26.weight_1"), + self.get_parameter("layernorm26.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm27(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm27.weight_1", + forge.Parameter(*(320,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm27.weight_2", + forge.Parameter(*(320,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm27.weight_1"), + self.get_parameter("layernorm27.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm28(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm28.weight_1", + forge.Parameter(*(96,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm28.weight_2", + forge.Parameter(*(96,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm28.weight_1"), + self.get_parameter("layernorm28.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +class Layernorm29(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "layernorm29.weight_1", + forge.Parameter(*(192,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + self.add_parameter( + "layernorm29.weight_2", + forge.Parameter(*(192,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, layernorm_input_0): + layernorm_output_1 = forge.op.Layernorm( + "", + layernorm_input_0, + self.get_parameter("layernorm29.weight_1"), + self.get_parameter("layernorm29.weight_2"), + dim=-1, + epsilon=1e-05, + ) + return layernorm_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Layernorm0, + [((2, 1, 2048), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm1, + [((2, 1, 1536), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm2, + [((2, 1, 1024), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm2, + [((1, 1, 1024), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_perceiverio_deepmind_vision_perceiver_conv_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_fourier_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm2, + [((1, 1500, 1024), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm3, + [((1, 1, 1280), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm3, + [((1, 1500, 1280), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm4, + [((1, 1, 384), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm4, + [((1, 1500, 384), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm5, + [((1, 1, 512), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm5, + [((1, 1500, 512), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm6, + [((1, 1, 768), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm6, + [((1, 1500, 768), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm3, + [((1, 2, 1280), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm5, + [((2, 7, 512), torch.float32)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm7, + [((1, 204, 768), torch.float32)], + { + "model_name": ["pt_vilt_dandelin_vilt_b32_mlm_mlm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), + ( + Layernorm7, + [((1, 201, 768), torch.float32)], + { + "model_name": ["pt_vilt_dandelin_vilt_b32_finetuned_vqa_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), + ( + Layernorm1, + [((1, 1536), torch.float32)], + { + "model_name": ["pt_vilt_dandelin_vilt_b32_finetuned_vqa_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm8, + [((1, 128, 128), torch.float32)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), + ( + Layernorm9, + [((1, 128, 4096), torch.float32)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), + ( + Layernorm7, + [((1, 128, 768), torch.float32)], + { + "model_name": [ + "pt_albert_base_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), + ( + Layernorm6, + [((1, 128, 768), torch.float32)], + { + "model_name": [ + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm10, + [((1, 128, 1024), torch.float32)], + { + "model_name": [ + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), + ( + Layernorm11, + [((1, 128, 2048), torch.float32)], + { + "model_name": [ + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), + ( + Layernorm2, + [((1, 256, 1024), torch.float32)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm10, + [((1, 384, 1024), torch.float32)], + { + "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), + ( + Layernorm7, + [((1, 384, 768), torch.float32)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), + ( + Layernorm12, + [((1, 6, 4544), torch.float32)], + { + "model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm13, + [((1, 334, 4096), torch.float32)], + {"model_name": ["pt_fuyu_adept_fuyu_8b_qa_hf"], "pcc": 0.99, "op_params": {"dim": "-1", "epsilon": "1e-05"}}, + ), + ( + Layernorm14, + [((1, 334, 64, 64), torch.float32)], + {"model_name": ["pt_fuyu_adept_fuyu_8b_qa_hf"], "pcc": 0.99, "op_params": {"dim": "-1", "epsilon": "1e-05"}}, + ), + ( + Layernorm6, + [((1, 256, 768), torch.float32)], + { + "model_name": [ + "pt_gpt2_gpt2_text_gen_hf", + "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm15, + [((1, 256, 2560), torch.float32)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf", + "pt_phi2_microsoft_phi_2_clm_hf", + "pt_phi2_microsoft_phi_2_pytdml_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm0, + [((1, 256, 2048), torch.float32)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm6, + [((1, 32, 768), torch.float32)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm15, + [((1, 32, 2560), torch.float32)], + { + "model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm0, + [((1, 32, 2048), torch.float32)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm6, + [((1, 7, 768), torch.float32)], + { + "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm0, + [((256, 2048), torch.float32)], + { + "model_name": ["pt_opt_facebook_opt_1_3b_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm0, + [((32, 2048), torch.float32)], + { + "model_name": ["pt_opt_facebook_opt_1_3b_seq_cls_hf", "pt_opt_facebook_opt_1_3b_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm2, + [((1, 32, 1024), torch.float32)], + { + "model_name": ["pt_opt_facebook_opt_350m_qa_hf", "pt_opt_facebook_opt_350m_seq_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm6, + [((32, 768), torch.float32)], + { + "model_name": ["pt_opt_facebook_opt_125m_seq_cls_hf", "pt_opt_facebook_opt_125m_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm6, + [((256, 768), torch.float32)], + { + "model_name": ["pt_opt_facebook_opt_125m_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm15, + [((1, 12, 2560), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_pytdml_token_cls_hf", "pt_phi2_microsoft_phi_2_token_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm15, + [((1, 11, 2560), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_seq_cls_hf", "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm7, + [((1, 197, 768), torch.float32)], + { + "model_name": [ + "pt_deit_facebook_deit_base_patch16_224_img_cls_hf", + "pt_deit_facebook_deit_base_distilled_patch16_224_img_cls_hf", + "pt_vit_google_vit_base_patch16_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), + ( + Layernorm16, + [((1, 197, 192), torch.float32)], + { + "model_name": ["pt_deit_facebook_deit_tiny_patch16_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), + ( + Layernorm17, + [((1, 197, 384), torch.float32)], + { + "model_name": ["pt_deit_facebook_deit_small_patch16_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), + ( + Layernorm18, + [((1, 49, 768), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_b32_224_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-06"}, + }, + ), + ( + Layernorm6, + [((1, 49, 768), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm19, + [((1, 196, 512), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_s16_224_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-06"}, + }, + ), + ( + Layernorm19, + [((1, 49, 512), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_s32_224_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-06"}, + }, + ), + ( + Layernorm18, + [((1, 196, 768), torch.float32)], + { + "model_name": [ + "pt_mlp_mixer_mixer_b16_224_miil_in21k_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_miil_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_in21k_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-06"}, + }, + ), + ( + Layernorm6, + [((1, 196, 768), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm20, + [((1, 49, 1024), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_l32_224_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-06"}, + }, + ), + ( + Layernorm20, + [((1, 196, 1024), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_l16_224_in21k_img_cls_timm", "pt_mlp_mixer_mixer_l16_224_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-06"}, + }, + ), + ( + Layernorm2, + [((1, 512, 1024), torch.float32)], + { + "model_name": [ + "pt_perceiverio_deepmind_vision_perceiver_conv_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_fourier_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm21, + [((1, 3025, 322), torch.float32)], + { + "model_name": ["pt_perceiverio_deepmind_vision_perceiver_conv_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm22, + [((1, 50176, 261), torch.float32)], + { + "model_name": ["pt_perceiverio_deepmind_vision_perceiver_fourier_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm5, + [((1, 50176, 512), torch.float32)], + { + "model_name": ["pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm23, + [((1, 16384, 32), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm23, + [((1, 256, 32), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm14, + [((1, 4096, 64), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm14, + [((1, 256, 64), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm24, + [((1, 1024, 160), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm24, + [((1, 256, 160), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm25, + [((1, 256, 256), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm14, + [((1, 16384, 64), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm26, + [((1, 4096, 128), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm26, + [((1, 256, 128), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm27, + [((1, 1024, 320), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm27, + [((1, 256, 320), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm5, + [((1, 256, 512), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm28, + [((1, 3136, 96), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm4, + [((1, 784, 384), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm29, + [((1, 784, 192), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm4, + [((1, 196, 384), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm1, + [((1, 49, 1536), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + ( + Layernorm10, + [((1, 197, 1024), torch.float32)], + { + "model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "0.0"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Layernorm") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_leakyrelu.py b/forge/test/models_ops/test_leakyrelu.py new file mode 100644 index 000000000..01b5b7ec6 --- /dev/null +++ b/forge/test/models_ops/test_leakyrelu.py @@ -0,0 +1,190 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Leakyrelu0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, leakyrelu_input_0): + leakyrelu_output_1 = forge.op.LeakyRelu("", leakyrelu_input_0, alpha=0.10000000000000001) + return leakyrelu_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Leakyrelu0, + [((1, 32, 640, 640), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), + ( + Leakyrelu0, + [((1, 64, 320, 320), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), + ( + Leakyrelu0, + [((1, 32, 320, 320), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), + ( + Leakyrelu0, + [((1, 128, 160, 160), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), + ( + Leakyrelu0, + [((1, 64, 160, 160), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), + ( + Leakyrelu0, + [((1, 256, 80, 80), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), + ( + Leakyrelu0, + [((1, 128, 80, 80), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), + ( + Leakyrelu0, + [((1, 512, 40, 40), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), + ( + Leakyrelu0, + [((1, 256, 40, 40), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), + ( + Leakyrelu0, + [((1, 1024, 20, 20), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), + ( + Leakyrelu0, + [((1, 512, 20, 20), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), + ( + Leakyrelu0, + [((1, 256, 20, 20), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), + ( + Leakyrelu0, + [((1, 128, 40, 40), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"alpha": "0.10000000000000001"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "LeakyRelu") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_less.py b/forge/test/models_ops/test_less.py new file mode 100644 index 000000000..a57ac5a94 --- /dev/null +++ b/forge/test/models_ops/test_less.py @@ -0,0 +1,534 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Less0(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less0_const_1", shape=(1, 256, 10, 32), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less0_const_1")) + return less_output_1 + + +class Less1(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less1_const_1", shape=(1, 256, 20, 64), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less1_const_1")) + return less_output_1 + + +class Less2(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less2_const_1", shape=(1, 128, 20, 64), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less2_const_1")) + return less_output_1 + + +class Less3(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less3_const_1", shape=(1, 128, 40, 128), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less3_const_1")) + return less_output_1 + + +class Less4(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less4_const_1", shape=(1, 64, 40, 128), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less4_const_1")) + return less_output_1 + + +class Less5(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less5_const_1", shape=(1, 64, 80, 256), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less5_const_1")) + return less_output_1 + + +class Less6(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less6_const_1", shape=(1, 32, 80, 256), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less6_const_1")) + return less_output_1 + + +class Less7(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less7_const_1", shape=(1, 32, 160, 512), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less7_const_1")) + return less_output_1 + + +class Less8(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less8_const_1", shape=(1, 16, 160, 512), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less8_const_1")) + return less_output_1 + + +class Less9(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less9_const_1", shape=(1, 16, 320, 1024), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less9_const_1")) + return less_output_1 + + +class Less10(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less10_const_1", shape=(1, 256, 6, 20), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less10_const_1")) + return less_output_1 + + +class Less11(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less11_const_1", shape=(1, 256, 12, 40), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less11_const_1")) + return less_output_1 + + +class Less12(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less12_const_1", shape=(1, 128, 12, 40), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less12_const_1")) + return less_output_1 + + +class Less13(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less13_const_1", shape=(1, 128, 24, 80), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less13_const_1")) + return less_output_1 + + +class Less14(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less14_const_1", shape=(1, 64, 24, 80), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less14_const_1")) + return less_output_1 + + +class Less15(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less15_const_1", shape=(1, 64, 48, 160), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less15_const_1")) + return less_output_1 + + +class Less16(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less16_const_1", shape=(1, 32, 48, 160), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less16_const_1")) + return less_output_1 + + +class Less17(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less17_const_1", shape=(1, 32, 96, 320), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less17_const_1")) + return less_output_1 + + +class Less18(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less18_const_1", shape=(1, 16, 96, 320), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less18_const_1")) + return less_output_1 + + +class Less19(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("less19_const_1", shape=(1, 16, 192, 640), dtype=torch.float32) + + def forward(self, less_input_0): + less_output_1 = forge.op.Less("", less_input_0, self.get_constant("less19_const_1")) + return less_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Less0, + [((1, 256, 10, 32), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less1, + [((1, 256, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less2, + [((1, 128, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less3, + [((1, 128, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less4, + [((1, 64, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less5, + [((1, 64, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less6, + [((1, 32, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less7, + [((1, 32, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less8, + [((1, 16, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less9, + [((1, 16, 320, 1024), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less10, + [((1, 256, 6, 20), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less11, + [((1, 256, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less12, + [((1, 128, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less13, + [((1, 128, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less14, + [((1, 64, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less15, + [((1, 64, 48, 160), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less16, + [((1, 32, 48, 160), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less17, + [((1, 32, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less18, + [((1, 16, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Less19, + [((1, 16, 192, 640), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Less") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_matmul.py b/forge/test/models_ops/test_matmul.py new file mode 100644 index 000000000..b394108ba --- /dev/null +++ b/forge/test/models_ops/test_matmul.py @@ -0,0 +1,5686 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Matmul0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, matmul_input_0, matmul_input_1): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, matmul_input_1) + return matmul_output_1 + + +class Matmul1(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul1_const_1", shape=(1, 1, 39), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul1_const_1")) + return matmul_output_1 + + +class Matmul2(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul2_const_1", shape=(1, 1, 6), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul2_const_1")) + return matmul_output_1 + + +class Matmul3(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul3_const_1", shape=(1, 1, 10), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul3_const_1")) + return matmul_output_1 + + +class Matmul4(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul4_const_1", shape=(1, 1, 334), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul4_const_1")) + return matmul_output_1 + + +class Matmul5(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul5_const_1", shape=(1, 1, 7), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul5_const_1")) + return matmul_output_1 + + +class Matmul6(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "matmul6.weight_1", + forge.Parameter(*(768, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_parameter("matmul6.weight_1")) + return matmul_output_1 + + +class Matmul7(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "matmul7.weight_1", + forge.Parameter(*(768, 3072), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_parameter("matmul7.weight_1")) + return matmul_output_1 + + +class Matmul8(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "matmul8.weight_1", + forge.Parameter(*(3072, 768), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_parameter("matmul8.weight_1")) + return matmul_output_1 + + +class Matmul9(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul9_const_1", shape=(1, 1, 4), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul9_const_1")) + return matmul_output_1 + + +class Matmul10(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul10_const_1", shape=(1, 1, 256), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul10_const_1")) + return matmul_output_1 + + +class Matmul11(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul11_const_1", shape=(1, 1, 128), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul11_const_1")) + return matmul_output_1 + + +class Matmul12(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul12_const_1", shape=(1, 1, 12), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul12_const_1")) + return matmul_output_1 + + +class Matmul13(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul13_const_1", shape=(1, 1, 11), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul13_const_1")) + return matmul_output_1 + + +class Matmul14(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul14_const_1", shape=(1, 1, 13), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul14_const_1")) + return matmul_output_1 + + +class Matmul15(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul15_const_1", shape=(1, 1, 5), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul15_const_1")) + return matmul_output_1 + + +class Matmul16(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul16_const_1", shape=(1, 1, 29), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul16_const_1")) + return matmul_output_1 + + +class Matmul17(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul17_const_1", shape=(1, 1, 35), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul17_const_1")) + return matmul_output_1 + + +class Matmul18(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul18_const_1", shape=(12, 24), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul18_const_1")) + return matmul_output_1 + + +class Matmul19(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul19_const_1", shape=(12, 72), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul19_const_1")) + return matmul_output_1 + + +class Matmul20(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul20_const_1", shape=(4, 24), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul20_const_1")) + return matmul_output_1 + + +class Matmul21(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("matmul21_const_1", shape=(4, 72), dtype=torch.float32) + + def forward(self, matmul_input_0): + matmul_output_1 = forge.op.Matmul("", matmul_input_0, self.get_constant("matmul21_const_1")) + return matmul_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Matmul0, + [((2, 2048), torch.float32), ((2048, 2048), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((64, 1, 64), torch.float32), ((64, 64, 1), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((64, 1, 1), torch.float32), ((64, 1, 64), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((26, 768), torch.float32), ((768, 768), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((24, 13, 64), torch.float32), ((24, 64, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((24, 13, 13), torch.float32), ((24, 13, 64), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((26, 768), torch.float32), ((768, 3072), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((26, 3072), torch.float32), ((3072, 768), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((26, 768), torch.float32), ((768, 2048), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((26, 2048), torch.float32), ((2048, 2048), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((64, 1, 64), torch.float32), ((64, 64, 13), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((64, 1, 13), torch.float32), ((64, 13, 64), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((2, 2048), torch.float32), ((2048, 8192), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((2, 8192), torch.float32), ((8192, 2048), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((2, 1536), torch.float32), ((1536, 1536), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((48, 1, 64), torch.float32), ((48, 64, 1), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((48, 1, 1), torch.float32), ((48, 1, 64), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((26, 768), torch.float32), ((768, 1536), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((26, 1536), torch.float32), ((1536, 1536), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((48, 1, 64), torch.float32), ((48, 64, 13), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((48, 1, 13), torch.float32), ((48, 13, 64), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((2, 1536), torch.float32), ((1536, 6144), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((2, 6144), torch.float32), ((6144, 1536), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((2, 1536), torch.float32), ((1536, 2048), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((2, 1024), torch.float32), ((1024, 1024), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((32, 1, 64), torch.float32), ((32, 64, 1), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((32, 1, 1), torch.float32), ((32, 1, 64), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((26, 768), torch.float32), ((768, 1024), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((26, 1024), torch.float32), ((1024, 1024), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((32, 1, 64), torch.float32), ((32, 64, 13), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((32, 1, 13), torch.float32), ((32, 13, 64), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((2, 1024), torch.float32), ((1024, 4096), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((2, 4096), torch.float32), ((4096, 1024), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((2, 1024), torch.float32), ((1024, 2048), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 1024), torch.float32), ((1024, 1024), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((16, 1, 64), torch.float32), ((16, 64, 1), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((16, 1, 1), torch.float32), ((16, 1, 64), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((1, 1, 1024), torch.float32), ((1024, 1024), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_perceiverio_deepmind_vision_perceiver_conv_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_fourier_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((1500, 1024), torch.float32), ((1024, 1024), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((16, 1500, 64), torch.float32), ((16, 64, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((16, 1500, 1500), torch.float32), ((16, 1500, 64), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 1500, 1024), torch.float32), ((1024, 4096), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 1500, 4096), torch.float32), ((4096, 1024), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((16, 1, 64), torch.float32), ((16, 64, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((16, 1, 1500), torch.float32), ((16, 1500, 64), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 1, 1024), torch.float32), ((1024, 4096), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf", "pt_t5_t5_large_text_gen_hf"], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((1, 1, 4096), torch.float32), ((4096, 1024), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf", "pt_t5_t5_large_text_gen_hf"], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((1, 1, 1024), torch.float32), ((1024, 51865), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 1280), torch.float32), ((1280, 1280), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((20, 1, 64), torch.float32), ((20, 64, 1), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((20, 1, 1), torch.float32), ((20, 1, 64), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 1, 1280), torch.float32), ((1280, 1280), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1500, 1280), torch.float32), ((1280, 1280), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_large_speech_recognition_hf", + "pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((20, 1500, 64), torch.float32), ((20, 64, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((20, 1500, 1500), torch.float32), ((20, 1500, 64), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 1500, 1280), torch.float32), ((1280, 5120), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 1500, 5120), torch.float32), ((5120, 1280), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((20, 1, 64), torch.float32), ((20, 64, 1500), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((20, 1, 1500), torch.float32), ((20, 1500, 64), torch.float32)], + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 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"pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((256, 512), torch.float32), ((512, 512), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((8, 256, 64), torch.float32), ((8, 64, 256), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((8, 256, 256), torch.float32), ((8, 256, 64), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((1, 256, 512), torch.float32), ((512, 2048), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((1, 256, 2048), torch.float32), ((2048, 512), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((1, 256, 512), torch.float32), ((512, 768), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((1, 1024, 320), torch.float32), ((320, 768), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((1, 4096, 128), torch.float32), ((128, 768), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((1, 16384, 64), torch.float32), ((64, 768), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((1, 256, 256), torch.float32), ((256, 256), torch.float32)], + {"model_name": ["pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 1024, 160), torch.float32), ((160, 256), torch.float32)], + {"model_name": ["pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 16384, 32), torch.float32), ((32, 256), torch.float32)], + {"model_name": ["pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 256, 512), torch.float32), ((512, 256), torch.float32)], + {"model_name": ["pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 1024, 320), torch.float32), ((320, 256), torch.float32)], + {"model_name": ["pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 4096, 128), torch.float32), ((128, 256), torch.float32)], + {"model_name": ["pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((3136, 96), torch.float32), ((96, 96), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((192, 49, 32), torch.float32), ((192, 32, 49), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((192, 49, 49), torch.float32), ((192, 49, 32), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 3136, 96), torch.float32), ((96, 384), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 3136, 384), torch.float32), ((384, 96), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 784, 384), torch.float32), ((384, 192), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((784, 192), torch.float32), ((192, 192), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((96, 49, 32), torch.float32), ((96, 32, 49), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((96, 49, 49), torch.float32), ((96, 49, 32), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 784, 192), torch.float32), ((192, 768), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 784, 768), torch.float32), ((768, 192), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 196, 768), torch.float32), ((768, 384), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((196, 384), torch.float32), ((384, 384), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((48, 49, 32), torch.float32), ((48, 32, 49), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((48, 49, 49), torch.float32), ((48, 49, 32), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 196, 384), torch.float32), ((384, 1536), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 196, 1536), torch.float32), ((1536, 384), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 49, 1536), torch.float32), ((1536, 768), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((49, 768), torch.float32), ((768, 768), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((24, 49, 32), torch.float32), ((24, 32, 49), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((24, 49, 49), torch.float32), ((24, 49, 32), torch.float32)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 25088), torch.float32), ((25088, 4096), torch.float32)], + { + "model_name": [ + "pt_vgg_bn_vgg19_obj_det_osmr", + "pt_vgg_19_obj_det_hf", + "pt_vgg_vgg19_bn_obj_det_torchhub", + "pt_vgg_vgg11_obj_det_osmr", + "pt_vgg_bn_vgg19b_obj_det_osmr", + "pt_vgg_vgg16_obj_det_osmr", + "pt_vgg_vgg19_obj_det_osmr", + "pt_vgg_vgg13_obj_det_osmr", + ], + "pcc": 0.99, + }, + ), + ( + Matmul0, + [((197, 1024), torch.float32), ((1024, 1024), torch.float32)], + {"model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((16, 197, 64), torch.float32), ((16, 64, 197), torch.float32)], + {"model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((16, 197, 197), torch.float32), ((16, 197, 64), torch.float32)], + {"model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 197, 1024), torch.float32), ((1024, 4096), torch.float32)], + {"model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], "pcc": 0.99}, + ), + ( + Matmul0, + [((1, 197, 4096), torch.float32), ((4096, 1024), torch.float32)], + {"model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], "pcc": 0.99}, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Matmul") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_max.py b/forge/test/models_ops/test_max.py new file mode 100644 index 000000000..d8b4a2546 --- /dev/null +++ b/forge/test/models_ops/test_max.py @@ -0,0 +1,103 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Max0(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("max0_const_1", shape=(1,), dtype=torch.float32) + + def forward(self, max_input_0): + max_output_1 = forge.op.Max("", max_input_0, self.get_constant("max0_const_1")) + return max_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + (Max0, [((1, 32, 256, 256), torch.float32)], {"model_name": ["pt_opt_facebook_opt_1_3b_clm_hf"], "pcc": 0.99}), + ( + Max0, + [((1, 32, 32, 32), torch.float32)], + {"model_name": ["pt_opt_facebook_opt_1_3b_seq_cls_hf", "pt_opt_facebook_opt_1_3b_qa_hf"], "pcc": 0.99}, + ), + ( + Max0, + [((1, 16, 32, 32), torch.float32)], + {"model_name": ["pt_opt_facebook_opt_350m_qa_hf", "pt_opt_facebook_opt_350m_seq_cls_hf"], "pcc": 0.99}, + ), + ( + Max0, + [((1, 12, 32, 32), torch.float32)], + {"model_name": ["pt_opt_facebook_opt_125m_seq_cls_hf", "pt_opt_facebook_opt_125m_qa_hf"], "pcc": 0.99}, + ), + (Max0, [((1, 12, 256, 256), torch.float32)], {"model_name": ["pt_opt_facebook_opt_125m_clm_hf"], "pcc": 0.99}), + ( + Max0, + [((1, 16, 256, 256), torch.float32)], + { + "model_name": [ + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Max") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_maxpool2d.py b/forge/test/models_ops/test_maxpool2d.py new file mode 100644 index 000000000..47a2557ff --- /dev/null +++ b/forge/test/models_ops/test_maxpool2d.py @@ -0,0 +1,1874 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Maxpool2D0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, maxpool2d_input_0): + maxpool2d_output_1 = forge.op.MaxPool2d( + "", + maxpool2d_input_0, + kernel_size=3, + stride=2, + padding=[0, 0, 0, 0], + dilation=1, + ceil_mode=False, + channel_last=0, + ) + return maxpool2d_output_1 + + +class Maxpool2D1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, maxpool2d_input_0): + maxpool2d_output_1 = forge.op.MaxPool2d( + "", + maxpool2d_input_0, + kernel_size=3, + stride=2, + padding=[0, 0, 0, 0], + dilation=1, + ceil_mode=True, + channel_last=0, + ) + return maxpool2d_output_1 + + +class Maxpool2D2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, maxpool2d_input_0): + maxpool2d_output_1 = forge.op.MaxPool2d( + "", + maxpool2d_input_0, + kernel_size=2, + stride=2, + padding=[0, 0, 0, 0], + dilation=1, + ceil_mode=False, + channel_last=0, + ) + return maxpool2d_output_1 + + +class Maxpool2D3(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, maxpool2d_input_0): + maxpool2d_output_1 = forge.op.MaxPool2d( + "", + maxpool2d_input_0, + kernel_size=3, + stride=2, + padding=[1, 1, 1, 1], + dilation=1, + ceil_mode=False, + channel_last=0, + ) + return maxpool2d_output_1 + + +class Maxpool2D4(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, maxpool2d_input_0): + maxpool2d_output_1 = forge.op.MaxPool2d( + "", + maxpool2d_input_0, + kernel_size=3, + stride=1, + padding=[1, 1, 1, 1], + dilation=1, + ceil_mode=True, + channel_last=0, + ) + return maxpool2d_output_1 + + +class Maxpool2D5(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, maxpool2d_input_0): + maxpool2d_output_1 = forge.op.MaxPool2d( + "", + maxpool2d_input_0, + kernel_size=1, + stride=2, + padding=[0, 0, 0, 0], + dilation=1, + ceil_mode=False, + channel_last=0, + ) + return maxpool2d_output_1 + + +class Maxpool2D6(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, maxpool2d_input_0): + maxpool2d_output_1 = forge.op.MaxPool2d( + "", + maxpool2d_input_0, + kernel_size=2, + stride=2, + padding=[0, 0, 0, 0], + dilation=1, + ceil_mode=True, + channel_last=0, + ) + return maxpool2d_output_1 + + +class Maxpool2D7(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, maxpool2d_input_0): + maxpool2d_output_1 = forge.op.MaxPool2d( + "", + maxpool2d_input_0, + kernel_size=5, + stride=1, + padding=[2, 2, 2, 2], + dilation=1, + ceil_mode=False, + channel_last=0, + ) + return maxpool2d_output_1 + + +class Maxpool2D8(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, maxpool2d_input_0): + maxpool2d_output_1 = forge.op.MaxPool2d( + "", + maxpool2d_input_0, + kernel_size=9, + stride=1, + padding=[4, 4, 4, 4], + dilation=1, + ceil_mode=False, + channel_last=0, + ) + return maxpool2d_output_1 + + +class Maxpool2D9(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, maxpool2d_input_0): + maxpool2d_output_1 = forge.op.MaxPool2d( + "", + maxpool2d_input_0, + kernel_size=13, + stride=1, + padding=[6, 6, 6, 6], + dilation=1, + ceil_mode=False, + channel_last=0, + ) + return maxpool2d_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Maxpool2D0, + [((1, 64, 55, 55), torch.float32)], + { + "model_name": ["pt_alexnet_alexnet_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D0, + [((1, 192, 27, 27), torch.float32)], + { + "model_name": ["pt_alexnet_alexnet_img_cls_torchhub", "pt_rcnn_base_obj_det_torchvision_rect_0"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D0, + [((1, 256, 13, 13), torch.float32)], + { + "model_name": ["pt_alexnet_alexnet_img_cls_torchhub", "pt_rcnn_base_obj_det_torchvision_rect_0"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D1, + [((1, 256, 13, 13), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D1, + [((1, 96, 54, 54), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D1, + [((1, 256, 27, 27), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D2, + [((1, 16, 28, 28), torch.float32)], + { + "model_name": ["pt_autoencoder_conv_img_enc_github"], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D2, + [((1, 4, 14, 14), torch.float32)], + { + "model_name": ["pt_autoencoder_conv_img_enc_github"], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D3, + [((1, 96, 112, 112), torch.float32)], + { + "model_name": ["pt_densenet_densenet161_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D3, + [((1, 64, 112, 112), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "ResNetForImageClassification", + "ResNet", + "pt_resnet_50_img_cls_timm", + "pt_resnext_resnext26_32x4d_img_cls_osmr", + "pt_resnext_resnext101_64x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_torchhub", + "pt_resnext_resnext14_32x4d_img_cls_osmr", + "pt_resnext_resnext101_32x8d_img_cls_torchhub", + "pt_resnext_resnext101_32x8d_wsl_img_cls_torchhub", + "pt_unet_qubvel_img_seg_torchhub", + "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D1, + [((1, 64, 112, 112), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D0, + [((1, 64, 112, 112), torch.float32)], + { + "model_name": ["pt_perceiverio_deepmind_vision_perceiver_conv_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D2, + [((1, 32, 112, 112), torch.float32)], + { + "model_name": [ + "pt_dla_dla34_visual_bb_torchvision", + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla102x_visual_bb_torchvision", + "pt_dla_dla46x_c_visual_bb_torchvision", + "pt_dla_dla60x_c_visual_bb_torchvision", + "pt_dla_dla46_c_visual_bb_torchvision", + "pt_dla_dla102x2_visual_bb_torchvision", + "pt_dla_dla60x_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + "pt_monodle_base_obj_det_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D2, + [((1, 64, 56, 56), torch.float32)], + { + "model_name": [ + "pt_dla_dla34_visual_bb_torchvision", + "pt_dla_dla46x_c_visual_bb_torchvision", + "pt_dla_dla60x_c_visual_bb_torchvision", + "pt_dla_dla46_c_visual_bb_torchvision", + "pt_monodle_base_obj_det_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D0, + [((1, 64, 56, 56), torch.float32)], + { + "model_name": ["pt_rcnn_base_obj_det_torchvision_rect_0"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D2, + [((1, 128, 28, 28), torch.float32)], + { + "model_name": ["pt_dla_dla34_visual_bb_torchvision", "pt_monodle_base_obj_det_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D2, + [((1, 256, 14, 14), torch.float32)], + { + "model_name": ["pt_dla_dla34_visual_bb_torchvision", "pt_monodle_base_obj_det_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D2, + [((1, 128, 56, 56), torch.float32)], + { + "model_name": [ + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla102x_visual_bb_torchvision", + "pt_dla_dla102x2_visual_bb_torchvision", + "pt_dla_dla60x_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D1, + [((1, 128, 56, 56), torch.float32)], + { + "model_name": ["pt_vovnet_vovnet27s_obj_det_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D2, + [((1, 256, 28, 28), torch.float32)], + { + "model_name": [ + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla102x_visual_bb_torchvision", + "pt_dla_dla102x2_visual_bb_torchvision", + "pt_dla_dla60x_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D4, + [((1, 256, 28, 28), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "1", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D1, + [((1, 256, 28, 28), torch.float32)], + { + "model_name": ["pt_vovnet_vovnet27s_obj_det_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D2, + [((1, 512, 14, 14), torch.float32)], + { + "model_name": [ + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla102x_visual_bb_torchvision", + "pt_dla_dla102x2_visual_bb_torchvision", + "pt_dla_dla60x_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + "pt_vgg_bn_vgg19_obj_det_osmr", + "pt_vgg_19_obj_det_hf", + "pt_vgg_vgg19_bn_obj_det_torchhub", + "pt_vgg_vgg11_obj_det_osmr", + "pt_vgg_vgg19_bn_obj_det_timm", + "pt_vgg_bn_vgg19b_obj_det_osmr", + "pt_vgg_vgg16_obj_det_osmr", + "pt_vgg_vgg19_obj_det_osmr", + "pt_vgg_vgg13_obj_det_osmr", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D4, + [((1, 512, 14, 14), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "1", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D2, + [((1, 64, 28, 28), torch.float32)], + { + "model_name": [ + "pt_dla_dla46x_c_visual_bb_torchvision", + "pt_dla_dla60x_c_visual_bb_torchvision", + "pt_dla_dla46_c_visual_bb_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D2, + [((1, 128, 14, 14), torch.float32)], + { + "model_name": [ + "pt_dla_dla46x_c_visual_bb_torchvision", + "pt_dla_dla60x_c_visual_bb_torchvision", + "pt_dla_dla46_c_visual_bb_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D5, + [((1, 256, 8, 8), torch.float32)], + { + "model_name": ["pt_fpn_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "1", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D1, + [((1, 192, 56, 56), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D4, + [((1, 192, 28, 28), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "1", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D1, + [((1, 480, 28, 28), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D4, + [((1, 480, 14, 14), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "1", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D4, + [((1, 528, 14, 14), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "1", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D6, + [((1, 832, 14, 14), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D4, + [((1, 832, 7, 7), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "1", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D0, + [((1, 64, 147, 147), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D0, + [((1, 192, 71, 71), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D0, + [((1, 384, 35, 35), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D0, + [((1, 1024, 17, 17), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D3, + [((1, 64, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D3, + [((1, 64, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": 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( + Maxpool2D7, + [((1, 256, 15, 15), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 384, 10, 10), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_320x320"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 128, 10, 10), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_320x320"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 128, 20, 20), torch.float32)], + { + "model_name": 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"pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 512, 15, 15), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 128, 15, 15), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 256, 10, 10), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_320x320"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 384, 20, 20), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", "pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D8, + [((1, 384, 20, 20), torch.float32)], + { + "model_name": ["pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "9", + "stride": "1", + "padding": "[4, 4, 4, 4]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D9, + [((1, 384, 20, 20), torch.float32)], + { + "model_name": ["pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "13", + "stride": "1", + "padding": "[6, 6, 6, 6]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 256, 14, 20), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 512, 14, 20), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 128, 14, 20), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 384, 14, 20), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 192, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D8, + [((1, 192, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "9", + "stride": "1", + "padding": "[4, 4, 4, 4]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D9, + [((1, 192, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "13", + "stride": "1", + "padding": "[6, 6, 6, 6]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D7, + [((1, 128, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "5", + "stride": "1", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D8, + [((1, 128, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "9", + "stride": "1", + "padding": "[4, 4, 4, 4]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), + ( + Maxpool2D9, + [((1, 128, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "kernel_size": "13", + "stride": "1", + "padding": "[6, 6, 6, 6]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "MaxPool2d") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_multiply.py b/forge/test/models_ops/test_multiply.py new file mode 100644 index 000000000..dbb3c4fa3 --- /dev/null +++ b/forge/test/models_ops/test_multiply.py @@ -0,0 +1,18957 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Multiply0(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply0_const_1", shape=(1,), dtype=torch.float32) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_constant("multiply0_const_1")) + return multiply_output_1 + + +class Multiply1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, multiply_input_0, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, multiply_input_1) + return multiply_output_1 + + +class Multiply2(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply2.weight_0", forge.Parameter(*(768,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_parameter("multiply2.weight_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply3(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply3_const_0", shape=(2, 1, 1, 13), dtype=torch.float32) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_constant("multiply3_const_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply4(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply4_const_0", shape=(2, 1, 7, 7), dtype=torch.float32) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_constant("multiply4_const_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply5(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply5.weight_0", + forge.Parameter(*(4096,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_parameter("multiply5.weight_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply6(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply6_const_0", shape=(1, 1, 256, 256), dtype=torch.float32) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_constant("multiply6_const_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply7(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply7_const_1", shape=(1, 256, 1, 32), dtype=torch.float32) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_constant("multiply7_const_1")) + return multiply_output_1 + + +class Multiply8(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply8_const_0", shape=(1, 12, 128, 128), dtype=torch.float32) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_constant("multiply8_const_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply9(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply9_const_0", shape=(1, 12, 384, 384), dtype=torch.float32) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_constant("multiply9_const_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply10(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply10.weight_0", + forge.Parameter(*(3072,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_parameter("multiply10.weight_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply11(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply11.weight_0", + forge.Parameter(*(2048,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_parameter("multiply11.weight_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply12(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply12_const_0", shape=(4096,), dtype=torch.float32) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_constant("multiply12_const_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply13(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply13_const_0", shape=(1, 1, 32, 32), dtype=torch.float32) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_constant("multiply13_const_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply14(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply14.weight_0", + forge.Parameter(*(1024,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_parameter("multiply14.weight_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply15(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply15.weight_0", + forge.Parameter(*(3584,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_parameter("multiply15.weight_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply16(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply16.weight_0", + forge.Parameter(*(1536,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_parameter("multiply16.weight_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply17(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply17.weight_0", + forge.Parameter(*(896,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_parameter("multiply17.weight_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply18(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply18.weight_0", + forge.Parameter(*(512,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_parameter("multiply18.weight_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply19(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply19_const_0", shape=(1,), dtype=torch.float32) + + def forward(self, multiply_input_1): + multiply_output_1 = forge.op.Multiply("", self.get_constant("multiply19_const_0"), multiply_input_1) + return multiply_output_1 + + +class Multiply20(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply20.weight_1", forge.Parameter(*(96,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply20.weight_1")) + return multiply_output_1 + + +class Multiply21(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply21.weight_1", + forge.Parameter(*(192,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply21.weight_1")) + return multiply_output_1 + + +class Multiply22(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply22.weight_1", + forge.Parameter(*(144,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply22.weight_1")) + return multiply_output_1 + + +class Multiply23(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply23.weight_1", + forge.Parameter(*(240,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply23.weight_1")) + return multiply_output_1 + + +class Multiply24(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply24.weight_1", + forge.Parameter(*(288,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply24.weight_1")) + return multiply_output_1 + + +class Multiply25(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply25.weight_1", + forge.Parameter(*(336,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply25.weight_1")) + return multiply_output_1 + + +class Multiply26(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply26.weight_1", + forge.Parameter(*(384,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply26.weight_1")) + return multiply_output_1 + + +class Multiply27(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply27.weight_1", + forge.Parameter(*(432,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply27.weight_1")) + return multiply_output_1 + + +class Multiply28(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply28.weight_1", + forge.Parameter(*(480,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply28.weight_1")) + return multiply_output_1 + + +class Multiply29(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply29.weight_1", + forge.Parameter(*(528,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply29.weight_1")) + return multiply_output_1 + + +class Multiply30(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply30.weight_1", + forge.Parameter(*(576,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply30.weight_1")) + return multiply_output_1 + + +class Multiply31(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply31.weight_1", + forge.Parameter(*(624,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply31.weight_1")) + return multiply_output_1 + + +class Multiply32(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply32.weight_1", + forge.Parameter(*(672,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply32.weight_1")) + return multiply_output_1 + + +class Multiply33(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply33.weight_1", + forge.Parameter(*(720,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply33.weight_1")) + return multiply_output_1 + + +class Multiply34(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply34.weight_1", + forge.Parameter(*(768,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply34.weight_1")) + return multiply_output_1 + + +class Multiply35(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply35.weight_1", + forge.Parameter(*(816,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply35.weight_1")) + return multiply_output_1 + + +class Multiply36(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply36.weight_1", + forge.Parameter(*(864,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply36.weight_1")) + return multiply_output_1 + + +class Multiply37(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply37.weight_1", + forge.Parameter(*(912,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply37.weight_1")) + return multiply_output_1 + + +class Multiply38(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply38.weight_1", + forge.Parameter(*(960,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply38.weight_1")) + return multiply_output_1 + + +class Multiply39(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply39.weight_1", + forge.Parameter(*(1008,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply39.weight_1")) + return multiply_output_1 + + +class Multiply40(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply40.weight_1", + forge.Parameter(*(1056,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply40.weight_1")) + return multiply_output_1 + + +class Multiply41(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply41.weight_1", + forge.Parameter(*(1104,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply41.weight_1")) + return multiply_output_1 + + +class Multiply42(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply42.weight_1", + forge.Parameter(*(1152,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply42.weight_1")) + return multiply_output_1 + + +class Multiply43(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply43.weight_1", + forge.Parameter(*(1200,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply43.weight_1")) + return multiply_output_1 + + +class Multiply44(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply44.weight_1", + forge.Parameter(*(1248,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply44.weight_1")) + return multiply_output_1 + + +class Multiply45(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply45.weight_1", + forge.Parameter(*(1296,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply45.weight_1")) + return multiply_output_1 + + +class Multiply46(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply46.weight_1", + forge.Parameter(*(1344,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply46.weight_1")) + return multiply_output_1 + + +class Multiply47(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply47.weight_1", + forge.Parameter(*(1392,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply47.weight_1")) + return multiply_output_1 + + +class Multiply48(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply48.weight_1", + forge.Parameter(*(1440,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply48.weight_1")) + return multiply_output_1 + + +class Multiply49(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply49.weight_1", + forge.Parameter(*(1488,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply49.weight_1")) + return multiply_output_1 + + +class Multiply50(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply50.weight_1", + forge.Parameter(*(1536,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply50.weight_1")) + return multiply_output_1 + + +class Multiply51(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply51.weight_1", + forge.Parameter(*(1584,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply51.weight_1")) + return multiply_output_1 + + +class Multiply52(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply52.weight_1", + forge.Parameter(*(1632,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply52.weight_1")) + return multiply_output_1 + + +class Multiply53(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply53.weight_1", + forge.Parameter(*(1680,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply53.weight_1")) + return multiply_output_1 + + +class Multiply54(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply54.weight_1", + forge.Parameter(*(1728,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply54.weight_1")) + return multiply_output_1 + + +class Multiply55(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply55.weight_1", + forge.Parameter(*(1776,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply55.weight_1")) + return multiply_output_1 + + +class Multiply56(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply56.weight_1", + forge.Parameter(*(1824,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply56.weight_1")) + return multiply_output_1 + + +class Multiply57(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply57.weight_1", + forge.Parameter(*(1872,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply57.weight_1")) + return multiply_output_1 + + +class Multiply58(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply58.weight_1", + forge.Parameter(*(1920,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply58.weight_1")) + return multiply_output_1 + + +class Multiply59(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply59.weight_1", + forge.Parameter(*(1968,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply59.weight_1")) + return multiply_output_1 + + +class Multiply60(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply60.weight_1", + forge.Parameter(*(2016,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply60.weight_1")) + return multiply_output_1 + + +class Multiply61(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply61.weight_1", + forge.Parameter(*(2064,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply61.weight_1")) + return multiply_output_1 + + +class Multiply62(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply62.weight_1", + forge.Parameter(*(2112,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply62.weight_1")) + return multiply_output_1 + + +class Multiply63(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply63.weight_1", + forge.Parameter(*(2160,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply63.weight_1")) + return multiply_output_1 + + +class Multiply64(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply64.weight_1", + forge.Parameter(*(2208,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply64.weight_1")) + return multiply_output_1 + + +class Multiply65(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply65.weight_1", forge.Parameter(*(64,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply65.weight_1")) + return multiply_output_1 + + +class Multiply66(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply66.weight_1", + forge.Parameter(*(128,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply66.weight_1")) + return multiply_output_1 + + +class Multiply67(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply67.weight_1", + forge.Parameter(*(160,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply67.weight_1")) + return multiply_output_1 + + +class Multiply68(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply68.weight_1", + forge.Parameter(*(224,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply68.weight_1")) + return multiply_output_1 + + +class Multiply69(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply69.weight_1", + forge.Parameter(*(256,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply69.weight_1")) + return multiply_output_1 + + +class Multiply70(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply70.weight_1", + forge.Parameter(*(320,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply70.weight_1")) + return multiply_output_1 + + +class Multiply71(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply71.weight_1", + forge.Parameter(*(352,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply71.weight_1")) + return multiply_output_1 + + +class Multiply72(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply72.weight_1", + forge.Parameter(*(416,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply72.weight_1")) + return multiply_output_1 + + +class Multiply73(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply73.weight_1", + forge.Parameter(*(448,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply73.weight_1")) + return multiply_output_1 + + +class Multiply74(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply74.weight_1", + forge.Parameter(*(512,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply74.weight_1")) + return multiply_output_1 + + +class Multiply75(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply75.weight_1", + forge.Parameter(*(544,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply75.weight_1")) + return multiply_output_1 + + +class Multiply76(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply76.weight_1", + forge.Parameter(*(608,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply76.weight_1")) + return multiply_output_1 + + +class Multiply77(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply77.weight_1", + forge.Parameter(*(640,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply77.weight_1")) + return multiply_output_1 + + +class Multiply78(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply78.weight_1", + forge.Parameter(*(704,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply78.weight_1")) + return multiply_output_1 + + +class Multiply79(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply79.weight_1", + forge.Parameter(*(736,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply79.weight_1")) + return multiply_output_1 + + +class Multiply80(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply80.weight_1", + forge.Parameter(*(800,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply80.weight_1")) + return multiply_output_1 + + +class Multiply81(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply81.weight_1", + forge.Parameter(*(832,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply81.weight_1")) + return multiply_output_1 + + +class Multiply82(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply82.weight_1", + forge.Parameter(*(896,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply82.weight_1")) + return multiply_output_1 + + +class Multiply83(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply83.weight_1", + forge.Parameter(*(928,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply83.weight_1")) + return multiply_output_1 + + +class Multiply84(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply84.weight_1", + forge.Parameter(*(992,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply84.weight_1")) + return multiply_output_1 + + +class Multiply85(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply85.weight_1", + forge.Parameter(*(1024,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply85.weight_1")) + return multiply_output_1 + + +class Multiply86(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply86.weight_1", + forge.Parameter(*(1088,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply86.weight_1")) + return multiply_output_1 + + +class Multiply87(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply87.weight_1", + forge.Parameter(*(1120,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply87.weight_1")) + return multiply_output_1 + + +class Multiply88(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply88.weight_1", + forge.Parameter(*(1184,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply88.weight_1")) + return multiply_output_1 + + +class Multiply89(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply89.weight_1", + forge.Parameter(*(1216,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply89.weight_1")) + return multiply_output_1 + + +class Multiply90(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply90.weight_1", + forge.Parameter(*(1280,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply90.weight_1")) + return multiply_output_1 + + +class Multiply91(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply91.weight_1", + forge.Parameter(*(1312,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply91.weight_1")) + return multiply_output_1 + + +class Multiply92(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply92.weight_1", + forge.Parameter(*(1376,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply92.weight_1")) + return multiply_output_1 + + +class Multiply93(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply93.weight_1", + forge.Parameter(*(1408,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply93.weight_1")) + return multiply_output_1 + + +class Multiply94(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply94.weight_1", + forge.Parameter(*(1472,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply94.weight_1")) + return multiply_output_1 + + +class Multiply95(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply95.weight_1", + forge.Parameter(*(1504,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply95.weight_1")) + return multiply_output_1 + + +class Multiply96(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply96.weight_1", + forge.Parameter(*(1568,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply96.weight_1")) + return multiply_output_1 + + +class Multiply97(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply97.weight_1", + forge.Parameter(*(1600,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply97.weight_1")) + return multiply_output_1 + + +class Multiply98(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply98.weight_1", + forge.Parameter(*(1664,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply98.weight_1")) + return multiply_output_1 + + +class Multiply99(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply99.weight_1", + forge.Parameter(*(1696,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply99.weight_1")) + return multiply_output_1 + + +class Multiply100(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply100.weight_1", + forge.Parameter(*(1760,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply100.weight_1")) + return multiply_output_1 + + +class Multiply101(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply101.weight_1", + forge.Parameter(*(1792,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply101.weight_1")) + return multiply_output_1 + + +class Multiply102(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply102.weight_1", + forge.Parameter(*(1856,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply102.weight_1")) + return multiply_output_1 + + +class Multiply103(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply103.weight_1", + forge.Parameter(*(1888,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply103.weight_1")) + return multiply_output_1 + + +class Multiply104(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply104.weight_1", + forge.Parameter(*(16,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply104.weight_1")) + return multiply_output_1 + + +class Multiply105(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply105.weight_1", + forge.Parameter(*(32,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply105.weight_1")) + return multiply_output_1 + + +class Multiply106(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply106.weight_1", + forge.Parameter(*(2048,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply106.weight_1")) + return multiply_output_1 + + +class Multiply107(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply107.weight_1", + forge.Parameter(*(48,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply107.weight_1")) + return multiply_output_1 + + +class Multiply108(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply108.weight_1", + forge.Parameter(*(24,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply108.weight_1")) + return multiply_output_1 + + +class Multiply109(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply109.weight_1", + forge.Parameter(*(56,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply109.weight_1")) + return multiply_output_1 + + +class Multiply110(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply110.weight_1", + forge.Parameter(*(112,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply110.weight_1")) + return multiply_output_1 + + +class Multiply111(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply111.weight_1", + forge.Parameter(*(272,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply111.weight_1")) + return multiply_output_1 + + +class Multiply112(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply112.weight_1", + forge.Parameter(*(2688,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply112.weight_1")) + return multiply_output_1 + + +class Multiply113(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply113.weight_1", + forge.Parameter(*(40,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply113.weight_1")) + return multiply_output_1 + + +class Multiply114(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply114.weight_1", + forge.Parameter(*(80,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply114.weight_1")) + return multiply_output_1 + + +class Multiply115(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply115.weight_1", forge.Parameter(*(8,), requires_grad=True, dev_data_format=forge.DataFormat.Float32) + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply115.weight_1")) + return multiply_output_1 + + +class Multiply116(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply116.weight_1", + forge.Parameter(*(12,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply116.weight_1")) + return multiply_output_1 + + +class Multiply117(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply117.weight_1", + forge.Parameter(*(36,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply117.weight_1")) + return multiply_output_1 + + +class Multiply118(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply118.weight_1", + forge.Parameter(*(72,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply118.weight_1")) + return multiply_output_1 + + +class Multiply119(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply119.weight_1", + forge.Parameter(*(20,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply119.weight_1")) + return multiply_output_1 + + +class Multiply120(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply120.weight_1", + forge.Parameter(*(60,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply120.weight_1")) + return multiply_output_1 + + +class Multiply121(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply121.weight_1", + forge.Parameter(*(120,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply121.weight_1")) + return multiply_output_1 + + +class Multiply122(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply122.weight_1", + forge.Parameter(*(100,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply122.weight_1")) + return multiply_output_1 + + +class Multiply123(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply123.weight_1", + forge.Parameter(*(92,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply123.weight_1")) + return multiply_output_1 + + +class Multiply124(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply124.weight_1", + forge.Parameter(*(208,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply124.weight_1")) + return multiply_output_1 + + +class Multiply125(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply125.weight_1", + forge.Parameter(*(18,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply125.weight_1")) + return multiply_output_1 + + +class Multiply126(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply126.weight_1", + forge.Parameter(*(44,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply126.weight_1")) + return multiply_output_1 + + +class Multiply127(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply127.weight_1", + forge.Parameter(*(88,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply127.weight_1")) + return multiply_output_1 + + +class Multiply128(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply128.weight_1", + forge.Parameter(*(176,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply128.weight_1")) + return multiply_output_1 + + +class Multiply129(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply129.weight_1", + forge.Parameter(*(30,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply129.weight_1")) + return multiply_output_1 + + +class Multiply130(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply130.weight_1", + forge.Parameter(*(200,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply130.weight_1")) + return multiply_output_1 + + +class Multiply131(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply131.weight_1", + forge.Parameter(*(184,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply131.weight_1")) + return multiply_output_1 + + +class Multiply132(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_parameter( + "multiply132.weight_1", + forge.Parameter(*(728,), requires_grad=True, dev_data_format=forge.DataFormat.Float32), + ) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_parameter("multiply132.weight_1")) + return multiply_output_1 + + +class Multiply133(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply133_const_1", shape=(1, 255, 160, 160), dtype=torch.float32) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_constant("multiply133_const_1")) + return multiply_output_1 + + +class Multiply134(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply134_const_1", shape=(1, 255, 80, 80), dtype=torch.float32) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_constant("multiply134_const_1")) + return multiply_output_1 + + +class Multiply135(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply135_const_1", shape=(1, 255, 40, 40), dtype=torch.float32) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_constant("multiply135_const_1")) + return multiply_output_1 + + +class Multiply136(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply136_const_1", shape=(1, 255, 20, 20), dtype=torch.float32) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_constant("multiply136_const_1")) + return multiply_output_1 + + +class Multiply137(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply137_const_1", shape=(1, 255, 60, 60), dtype=torch.float32) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_constant("multiply137_const_1")) + return multiply_output_1 + + +class Multiply138(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply138_const_1", shape=(1, 255, 30, 30), dtype=torch.float32) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_constant("multiply138_const_1")) + return multiply_output_1 + + +class Multiply139(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply139_const_1", shape=(1, 255, 15, 15), dtype=torch.float32) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_constant("multiply139_const_1")) + return multiply_output_1 + + +class Multiply140(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply140_const_1", shape=(1, 255, 10, 10), dtype=torch.float32) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_constant("multiply140_const_1")) + return multiply_output_1 + + +class Multiply141(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("multiply141_const_1", shape=(5880, 1), dtype=torch.float32) + + def forward(self, multiply_input_0): + multiply_output_1 = forge.op.Multiply("", multiply_input_0, self.get_constant("multiply141_const_1")) + return multiply_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Multiply0, + [((2, 1, 2048), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Multiply1, + [((2, 13, 768), torch.float32), ((2, 13, 768), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((2, 13, 768), torch.float32), ((2, 13, 1), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Multiply2, + [((2, 13, 768), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Multiply0, + [((2, 1, 1, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((2, 13, 2048), torch.float32), ((2, 13, 1), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + ( + Multiply1, + [((2, 1, 1, 13), torch.float32), ((2, 1, 1, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Multiply3, + [((2, 1, 1, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Multiply0, + [((2, 1, 1536), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Multiply1, + [((2, 13, 1536), torch.float32), ((2, 13, 1), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + ( + Multiply0, + [((2, 1, 1024), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Multiply1, + [((2, 13, 1024), torch.float32), ((2, 13, 1), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + ( + Multiply0, + [((1, 1, 1024), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf", 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"pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((64,), torch.float32), ((1,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_dla_dla34_visual_bb_torchvision", + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla46x_c_visual_bb_torchvision", + "pt_dla_dla60x_c_visual_bb_torchvision", + "pt_dla_dla46_c_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w44_pose_estimation_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w32_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w64_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w64_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w44_pose_estimation_osmr", + "pt_hrnet_hrnet_w40_pose_estimation_timm", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w40_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_hrnet_hrnetv2_w32_pose_estimation_osmr", + "pt_inception_v4_img_cls_timm", + "pt_inception_v4_img_cls_osmr", + "pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf", + "pt_mobilenet_v1_basic_img_cls_torchvision", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + 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"pt_resnet_50_img_cls_timm", + "pt_resnext_resnext26_32x4d_img_cls_osmr", + "pt_resnext_resnext101_64x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_torchhub", + "pt_resnext_resnext14_32x4d_img_cls_osmr", + "pt_resnext_resnext101_32x8d_img_cls_torchhub", + "pt_resnext_resnext101_32x8d_wsl_img_cls_torchhub", + "pt_retinanet_retinanet_rn101fpn_obj_det_hf", + "pt_retinanet_retinanet_rn152fpn_obj_det_hf", + "pt_retinanet_retinanet_rn34fpn_obj_det_hf", + "pt_retinanet_retinanet_rn50fpn_obj_det_hf", + "pt_retinanet_retinanet_rn18fpn_obj_det_hf", + "pt_ssd300_resnet50_base_img_cls_torchhub", + "pt_unet_base_img_seg_torchhub", + "pt_unet_cityscape_img_seg_osmr", + "pt_unet_qubvel_img_seg_torchhub", + "pt_vgg_bn_vgg19_obj_det_osmr", + "pt_vgg_vgg19_bn_obj_det_torchhub", + "pt_vgg_vgg19_bn_obj_det_timm", + "pt_vgg_bn_vgg19b_obj_det_osmr", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + "pt_xception_xception_img_cls_timm", + "pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + "pt_yolox_yolox_nano_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((64,), torch.float32), ((64,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_dla_dla34_visual_bb_torchvision", + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla46x_c_visual_bb_torchvision", + "pt_dla_dla60x_c_visual_bb_torchvision", + "pt_dla_dla46_c_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w44_pose_estimation_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w32_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w64_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w64_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w44_pose_estimation_osmr", + "pt_hrnet_hrnet_w40_pose_estimation_timm", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w40_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_hrnet_hrnetv2_w32_pose_estimation_osmr", + "pt_inception_v4_img_cls_timm", + "pt_inception_v4_img_cls_osmr", + "pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf", + "pt_mobilenet_v1_basic_img_cls_torchvision", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + 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"pt_retinanet_retinanet_rn50fpn_obj_det_hf", + "pt_retinanet_retinanet_rn18fpn_obj_det_hf", + "pt_ssd300_resnet50_base_img_cls_torchhub", + "pt_unet_base_img_seg_torchhub", + "pt_unet_cityscape_img_seg_osmr", + "pt_unet_qubvel_img_seg_torchhub", + "pt_vgg_bn_vgg19_obj_det_osmr", + "pt_vgg_vgg19_bn_obj_det_torchhub", + "pt_vgg_vgg19_bn_obj_det_timm", + "pt_vgg_bn_vgg19b_obj_det_osmr", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + "pt_xception_xception_img_cls_timm", + "pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + "pt_yolox_yolox_nano_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((1, 64, 56, 56), torch.float32), ((64, 1, 1), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_dla_dla34_visual_bb_torchvision", + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla46x_c_visual_bb_torchvision", + "pt_dla_dla60x_c_visual_bb_torchvision", + "pt_dla_dla46_c_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w44_pose_estimation_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w32_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w64_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w64_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w44_pose_estimation_osmr", + "pt_hrnet_hrnet_w40_pose_estimation_timm", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w40_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_hrnet_hrnetv2_w32_pose_estimation_osmr", + "pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf", + "pt_mobilenet_v1_basic_img_cls_torchvision", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + "pt_monodle_base_obj_det_torchvision", + "ResNetForImageClassification", + "ResNet", + "pt_resnet_50_img_cls_timm", + "pt_unet_qubvel_img_seg_torchhub", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Multiply19, + [((128,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_dla_dla34_visual_bb_torchvision", + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla102x_visual_bb_torchvision", + "pt_dla_dla46x_c_visual_bb_torchvision", + "pt_dla_dla60x_c_visual_bb_torchvision", + "pt_dla_dla46_c_visual_bb_torchvision", + "pt_dla_dla102x2_visual_bb_torchvision", + "pt_dla_dla60x_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w44_pose_estimation_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w32_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w64_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w64_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w44_pose_estimation_osmr", + "pt_hrnet_hrnet_w40_pose_estimation_timm", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w40_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_hrnet_hrnetv2_w32_pose_estimation_osmr", + "pt_inception_v4_img_cls_timm", + "pt_inception_v4_img_cls_osmr", + "pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf", + "pt_mobilenet_v1_basic_img_cls_torchvision", + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + "pt_monodle_base_obj_det_torchvision", + "pt_regnet_facebook_regnet_y_040_img_cls_hf", + "ResNetForImageClassification", + "ResNet", + "pt_resnet_50_img_cls_timm", + "pt_resnext_resnext26_32x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_torchhub", + "pt_resnext_resnext14_32x4d_img_cls_osmr", + "pt_retinanet_retinanet_rn101fpn_obj_det_hf", + "pt_retinanet_retinanet_rn152fpn_obj_det_hf", + "pt_retinanet_retinanet_rn34fpn_obj_det_hf", + "pt_retinanet_retinanet_rn50fpn_obj_det_hf", + "pt_retinanet_retinanet_rn18fpn_obj_det_hf", + "pt_ssd300_resnet50_base_img_cls_torchhub", + "pt_unet_base_img_seg_torchhub", + "pt_unet_cityscape_img_seg_osmr", + "pt_unet_qubvel_img_seg_torchhub", + "pt_vgg_bn_vgg19_obj_det_osmr", + "pt_vgg_vgg19_bn_obj_det_torchhub", + "pt_vgg_vgg19_bn_obj_det_timm", + "pt_vgg_bn_vgg19b_obj_det_osmr", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + "pt_xception_xception_img_cls_timm", + "pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + "pt_yolox_yolox_nano_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Multiply66, + [((128,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_dla_dla34_visual_bb_torchvision", + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla102x_visual_bb_torchvision", + "pt_dla_dla46x_c_visual_bb_torchvision", + "pt_dla_dla60x_c_visual_bb_torchvision", + "pt_dla_dla46_c_visual_bb_torchvision", + "pt_dla_dla102x2_visual_bb_torchvision", + "pt_dla_dla60x_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w44_pose_estimation_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w32_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w64_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w64_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w44_pose_estimation_osmr", + "pt_hrnet_hrnet_w40_pose_estimation_timm", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w40_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_hrnet_hrnetv2_w32_pose_estimation_osmr", + "pt_inception_v4_img_cls_timm", + "pt_inception_v4_img_cls_osmr", + "pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf", + "pt_mobilenet_v1_basic_img_cls_torchvision", + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + "pt_monodle_base_obj_det_torchvision", + "pt_regnet_facebook_regnet_y_040_img_cls_hf", + "ResNetForImageClassification", + "ResNet", + "pt_resnet_50_img_cls_timm", + "pt_resnext_resnext26_32x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_torchhub", + "pt_resnext_resnext14_32x4d_img_cls_osmr", + "pt_retinanet_retinanet_rn101fpn_obj_det_hf", + "pt_retinanet_retinanet_rn152fpn_obj_det_hf", + "pt_retinanet_retinanet_rn34fpn_obj_det_hf", + "pt_retinanet_retinanet_rn50fpn_obj_det_hf", + "pt_retinanet_retinanet_rn18fpn_obj_det_hf", + "pt_ssd300_resnet50_base_img_cls_torchhub", + "pt_unet_base_img_seg_torchhub", + "pt_unet_cityscape_img_seg_osmr", + "pt_unet_qubvel_img_seg_torchhub", + "pt_vgg_bn_vgg19_obj_det_osmr", + "pt_vgg_vgg19_bn_obj_det_torchhub", + "pt_vgg_vgg19_bn_obj_det_timm", + "pt_vgg_bn_vgg19b_obj_det_osmr", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + "pt_xception_xception_img_cls_timm", + "pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + "pt_yolox_yolox_nano_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((1, 128, 56, 56), torch.float32), ((128, 1, 1), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla102x_visual_bb_torchvision", + "pt_dla_dla102x2_visual_bb_torchvision", + "pt_dla_dla60x_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w44_pose_estimation_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w32_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w64_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w64_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w44_pose_estimation_osmr", + "pt_hrnet_hrnet_w40_pose_estimation_timm", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w40_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_hrnet_hrnetv2_w32_pose_estimation_osmr", + "pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf", + "pt_mobilenet_v1_basic_img_cls_torchvision", + "pt_regnet_facebook_regnet_y_040_img_cls_hf", + "ResNetForImageClassification", + "ResNet", + "pt_resnet_50_img_cls_timm", + "pt_resnext_resnext26_32x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_torchhub", + "pt_resnext_resnext14_32x4d_img_cls_osmr", + "pt_unet_cityscape_img_seg_osmr", + "pt_unet_qubvel_img_seg_torchhub", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((128,), torch.float32), ((1,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_dla_dla34_visual_bb_torchvision", + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla102x_visual_bb_torchvision", + "pt_dla_dla46x_c_visual_bb_torchvision", + "pt_dla_dla60x_c_visual_bb_torchvision", + "pt_dla_dla46_c_visual_bb_torchvision", + "pt_dla_dla102x2_visual_bb_torchvision", + "pt_dla_dla60x_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w44_pose_estimation_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w32_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w64_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w64_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w44_pose_estimation_osmr", + "pt_hrnet_hrnet_w40_pose_estimation_timm", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w40_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_hrnet_hrnetv2_w32_pose_estimation_osmr", + "pt_inception_v4_img_cls_timm", + "pt_inception_v4_img_cls_osmr", + "pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf", + "pt_mobilenet_v1_basic_img_cls_torchvision", + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + "pt_monodle_base_obj_det_torchvision", + "pt_regnet_facebook_regnet_y_040_img_cls_hf", + "ResNetForImageClassification", + "ResNet", + "pt_resnet_50_img_cls_timm", + "pt_resnext_resnext26_32x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_torchhub", + "pt_resnext_resnext14_32x4d_img_cls_osmr", + "pt_retinanet_retinanet_rn101fpn_obj_det_hf", + "pt_retinanet_retinanet_rn152fpn_obj_det_hf", + "pt_retinanet_retinanet_rn34fpn_obj_det_hf", + "pt_retinanet_retinanet_rn50fpn_obj_det_hf", + "pt_retinanet_retinanet_rn18fpn_obj_det_hf", + "pt_ssd300_resnet50_base_img_cls_torchhub", + "pt_unet_base_img_seg_torchhub", + "pt_unet_cityscape_img_seg_osmr", + "pt_unet_qubvel_img_seg_torchhub", + "pt_vgg_bn_vgg19_obj_det_osmr", + "pt_vgg_vgg19_bn_obj_det_torchhub", + "pt_vgg_vgg19_bn_obj_det_timm", + "pt_vgg_bn_vgg19b_obj_det_osmr", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + "pt_xception_xception_img_cls_timm", + "pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + "pt_yolox_yolox_nano_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((128,), torch.float32), ((128,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + 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"pt_resnext_resnext50_32x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_torchhub", + "pt_resnext_resnext14_32x4d_img_cls_osmr", + "pt_resnext_resnext101_32x8d_img_cls_torchhub", + "pt_resnext_resnext101_32x8d_wsl_img_cls_torchhub", + "pt_retinanet_retinanet_rn101fpn_obj_det_hf", + "pt_retinanet_retinanet_rn152fpn_obj_det_hf", + "pt_retinanet_retinanet_rn34fpn_obj_det_hf", + "pt_retinanet_retinanet_rn50fpn_obj_det_hf", + "pt_retinanet_retinanet_rn18fpn_obj_det_hf", + "pt_ssd300_resnet50_base_img_cls_torchhub", + "pt_unet_base_img_seg_torchhub", + "pt_unet_cityscape_img_seg_osmr", + "pt_unet_qubvel_img_seg_torchhub", + "pt_vgg_bn_vgg19_obj_det_osmr", + "pt_vgg_vgg19_bn_obj_det_torchhub", + "pt_vgg_vgg19_bn_obj_det_timm", + "pt_vgg_bn_vgg19b_obj_det_osmr", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + 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((1,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_dla_dla34_visual_bb_torchvision", + "pt_dla_dla102_visual_bb_torchvision", + "pt_dla_dla102x_visual_bb_torchvision", + "pt_dla_dla102x2_visual_bb_torchvision", + "pt_dla_dla60x_visual_bb_torchvision", + "pt_dla_dla60_visual_bb_torchvision", + "pt_dla_dla169_visual_bb_torchvision", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w44_pose_estimation_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w32_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w64_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + 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"pt_unet_base_img_seg_torchhub", + "pt_unet_cityscape_img_seg_osmr", + "pt_unet_qubvel_img_seg_torchhub", + "pt_vgg_bn_vgg19_obj_det_osmr", + "pt_vgg_vgg19_bn_obj_det_torchhub", + "pt_vgg_vgg19_bn_obj_det_timm", + "pt_vgg_bn_vgg19b_obj_det_osmr", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((512,), torch.float32), ((512,), torch.float32)], + { + "model_name": [ + 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["pt_yolox_yolox_l_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 128, 160, 160), torch.float32), ((128, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_l_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 64, 160, 160), torch.float32), ((64, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((1, 256, 80, 80), torch.float32), ((256, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_l_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 128, 80, 80), torch.float32), ((128, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((1, 512, 40, 40), torch.float32), ((512, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_l_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 256, 40, 40), torch.float32), ((256, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((1, 1024, 20, 20), torch.float32), ((1024, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_l_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 512, 20, 20), torch.float32), ((512, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((1, 256, 20, 20), torch.float32), ((256, 1, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Multiply1, + [((1, 48, 320, 320), torch.float32), ((48, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 96, 160, 160), torch.float32), ((96, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 384, 40, 40), torch.float32), ((384, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 768, 20, 20), torch.float32), ((768, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 384, 20, 20), torch.float32), ((384, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 192, 20, 20), torch.float32), ((192, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 32, 320, 320), torch.float32), ((32, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_s_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 32, 160, 160), torch.float32), ((32, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_s_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 64, 80, 80), torch.float32), ((64, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_s_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 128, 40, 40), torch.float32), ((128, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_s_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 128, 20, 20), torch.float32), ((128, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_s_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 24, 208, 208), torch.float32), ((24, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 24, 208, 208), torch.float32), ((1, 24, 208, 208), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 48, 104, 104), torch.float32), ((48, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 48, 104, 104), torch.float32), ((1, 48, 104, 104), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 24, 104, 104), torch.float32), ((24, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 24, 104, 104), torch.float32), ((1, 24, 104, 104), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 96, 52, 52), torch.float32), ((96, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 96, 52, 52), torch.float32), ((1, 96, 52, 52), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 48, 52, 52), torch.float32), ((48, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 48, 52, 52), torch.float32), ((1, 48, 52, 52), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 192, 26, 26), torch.float32), ((192, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 192, 26, 26), torch.float32), ((1, 192, 26, 26), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 96, 26, 26), torch.float32), ((96, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 96, 26, 26), torch.float32), ((1, 96, 26, 26), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 384, 13, 13), torch.float32), ((384, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 384, 13, 13), torch.float32), ((1, 384, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 192, 13, 13), torch.float32), ((192, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 192, 13, 13), torch.float32), ((1, 192, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 96, 13, 13), torch.float32), ((96, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 96, 13, 13), torch.float32), ((1, 96, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 80, 320, 320), torch.float32), ((80, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 160, 160, 160), torch.float32), ((160, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 80, 160, 160), torch.float32), ((80, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 320, 80, 80), torch.float32), ((320, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 160, 80, 80), torch.float32), ((160, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 640, 40, 40), torch.float32), ((640, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 320, 40, 40), torch.float32), ((320, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 1280, 20, 20), torch.float32), ((1280, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 640, 20, 20), torch.float32), ((640, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 320, 20, 20), torch.float32), ((320, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 32, 640, 640), torch.float32), ((32, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 16, 208, 208), torch.float32), ((16, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 16, 208, 208), torch.float32), ((1, 16, 208, 208), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 16, 104, 104), torch.float32), ((16, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 16, 104, 104), torch.float32), ((1, 16, 104, 104), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 32, 104, 104), torch.float32), ((32, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 32, 104, 104), torch.float32), ((1, 32, 104, 104), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 32, 52, 52), torch.float32), ((32, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 32, 52, 52), torch.float32), ((1, 32, 52, 52), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 64, 52, 52), torch.float32), ((64, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 64, 52, 52), torch.float32), ((1, 64, 52, 52), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 64, 26, 26), torch.float32), ((64, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 64, 26, 26), torch.float32), ((1, 64, 26, 26), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 128, 26, 26), torch.float32), ((128, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 128, 26, 26), torch.float32), ((1, 128, 26, 26), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 128, 13, 13), torch.float32), ((128, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 128, 13, 13), torch.float32), ((1, 128, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 256, 13, 13), torch.float32), ((256, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 256, 13, 13), torch.float32), ((1, 256, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 64, 13, 13), torch.float32), ((64, 1, 1), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Multiply1, + [((1, 64, 13, 13), torch.float32), ((1, 64, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Multiply") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_notequal.py b/forge/test/models_ops/test_notequal.py new file mode 100644 index 000000000..2ae5ff7d8 --- /dev/null +++ b/forge/test/models_ops/test_notequal.py @@ -0,0 +1,85 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Notequal0(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("notequal0_const_1", shape=(1,), dtype=torch.int64) + + def forward(self, notequal_input_0): + notequal_output_1 = forge.op.NotEqual("", notequal_input_0, self.get_constant("notequal0_const_1")) + return notequal_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Notequal0, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "NotEqual") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_pad.py b/forge/test/models_ops/test_pad.py new file mode 100644 index 000000000..134997ae5 --- /dev/null +++ b/forge/test/models_ops/test_pad.py @@ -0,0 +1,390 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Pad0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, pad_input_0): + pad_output_1 = forge.op.Pad("", pad_input_0, pad=(0, 0, 2, 2), mode="constant", channel_last=True) + return pad_output_1 + + +class Pad1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, pad_input_0): + pad_output_1 = forge.op.Pad("", pad_input_0, pad=(1, 1, 1, 1), mode="reflect", channel_last=False) + return pad_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Pad0, + [((1, 1, 96, 54, 54), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": {"pad": "(0, 0, 2, 2)", "mode": '"constant"', "channel_last": "True"}, + }, + ), + ( + Pad0, + [((1, 1, 256, 27, 27), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": {"pad": "(0, 0, 2, 2)", "mode": '"constant"', "channel_last": "True"}, + }, + ), + ( + Pad1, + [((1, 512, 10, 32), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 512, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 256, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 256, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 128, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 128, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 64, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 96, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 32, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 16, 320, 1024), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 512, 6, 20), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 512, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 256, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 256, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 128, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 128, 48, 160), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 64, 48, 160), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 96, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 32, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + ( + Pad1, + [((1, 16, 192, 640), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Pad") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_reciprocal.py b/forge/test/models_ops/test_reciprocal.py new file mode 100644 index 000000000..45dd5fe91 --- /dev/null +++ b/forge/test/models_ops/test_reciprocal.py @@ -0,0 +1,2075 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Reciprocal0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reciprocal_input_0): + reciprocal_output_1 = forge.op.Reciprocal("", reciprocal_input_0) + return reciprocal_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Reciprocal0, + [((2, 13, 1), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((1, 39, 1), torch.float32)], + { + "model_name": [ + "pt_deepseek_deepseek_math_7b_instruct_qa_hf", + "DeepSeekWrapper_decoder", + "pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((1, 10, 1), torch.float32)], + { + "model_name": [ + "pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_1b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf", + ], + "pcc": 0.99, + }, + ), + (Reciprocal0, [((1, 7, 1), torch.float32)], {"model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], "pcc": 0.99}), + ( + Reciprocal0, + [((1, 4, 1), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((1, 256, 1), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + "pt_phi3_microsoft_phi_3_mini_4k_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((1, 128, 1), torch.float32)], + {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99}, + ), + ( + Reciprocal0, + [((1, 13, 1), torch.float32)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_token_cls_hf"], "pcc": 0.99}, + ), + ( + Reciprocal0, + [((1, 5, 1), torch.float32)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf"], "pcc": 0.99}, + ), + (Reciprocal0, [((1, 6, 1), torch.float32)], {"model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_clm_hf"], "pcc": 0.99}), + ( + Reciprocal0, + [((1, 29, 1), torch.float32)], + { + "model_name": [ + "pt_qwen1_5_qwen_qwen1_5_0_5b_chat_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_0_5b_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((1, 35, 1), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((1, 1, 1), torch.float32)], + { + "model_name": [ + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + "pt_t5_t5_small_text_gen_hf", + "pt_t5_google_flan_t5_small_text_gen_hf", + "pt_t5_t5_base_text_gen_hf", + "pt_t5_google_flan_t5_base_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((1, 61, 1), torch.float32)], + { + "model_name": [ + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + "pt_t5_t5_small_text_gen_hf", + "pt_t5_google_flan_t5_small_text_gen_hf", + "pt_t5_t5_base_text_gen_hf", + "pt_t5_google_flan_t5_base_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + (Reciprocal0, [((1, 96, 54, 54), torch.float32)], {"model_name": ["pt_alexnet_base_img_cls_osmr"], "pcc": 0.99}), + (Reciprocal0, [((1, 256, 27, 27), torch.float32)], {"model_name": ["pt_alexnet_base_img_cls_osmr"], "pcc": 0.99}), + ( + Reciprocal0, + [((96,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_inception_v4_img_cls_timm", + "pt_inception_v4_img_cls_osmr", + "pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_tiny_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((192,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_timm", + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_inception_v4_img_cls_timm", + "pt_inception_v4_img_cls_osmr", + "pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + "pt_regnet_facebook_regnet_y_040_img_cls_hf", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_tiny_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((144,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_timm", + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((240,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((288,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_googlenet_base_img_cls_torchvision", + "pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((336,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_timm", + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((384,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_inception_v4_img_cls_timm", + "pt_inception_v4_img_cls_osmr", + "pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_tiny_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((432,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((480,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((528,), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Reciprocal0, + [((576,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + ( + 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( + Reciprocal0, + [((768,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf", + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((816,), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Reciprocal0, + [((864,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((912,), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Reciprocal0, + [((960,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_timm", + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + 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"pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((184,), torch.float32)], + { + "model_name": [ + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Reciprocal0, + [((728,), torch.float32)], + { + "model_name": [ + "pt_xception_xception_img_cls_timm", + "pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Reciprocal") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_reduceavg.py b/forge/test/models_ops/test_reduceavg.py new file mode 100644 index 000000000..58b948c58 --- /dev/null +++ b/forge/test/models_ops/test_reduceavg.py @@ -0,0 +1,1169 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Reduceavg0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reduceavg_input_0): + reduceavg_output_1 = forge.op.ReduceAvg("", reduceavg_input_0, dim=-1, keep_dim=True) + return reduceavg_output_1 + + +class Reduceavg1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reduceavg_input_0): + reduceavg_output_1 = forge.op.ReduceAvg("", reduceavg_input_0, dim=-2, keep_dim=True) + return reduceavg_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Reduceavg0, + [((2, 13, 768), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 39, 4096), torch.float32)], + { + "model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf", "DeepSeekWrapper_decoder"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 10, 3072), torch.float32)], + { + "model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 10, 2048), torch.float32)], + { + "model_name": ["pt_falcon3_tiiuae_falcon3_1b_base_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 7, 2048), torch.float32)], + { + "model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 4, 2048), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 256, 2048), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 4, 4096), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 128, 4096), torch.float32)], + { + "model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 256, 3072), torch.float32)], + { + "model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 13, 3072), torch.float32)], + { + "model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_token_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 5, 3072), torch.float32)], + { + "model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 6, 1024), torch.float32)], + { + "model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 29, 1024), torch.float32)], + { + "model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_chat_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 35, 3584), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 35, 1536), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 35, 2048), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 35, 896), torch.float32)], + { + "model_name": ["pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 29, 1536), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 39, 1536), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 39, 3584), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 29, 3584), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 29, 2048), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 39, 2048), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 29, 896), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 39, 896), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 1, 1024), torch.float32)], + { + "model_name": ["pt_t5_google_flan_t5_large_text_gen_hf", "pt_t5_t5_large_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 61, 1024), torch.float32)], + { + "model_name": ["pt_t5_google_flan_t5_large_text_gen_hf", "pt_t5_t5_large_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 1, 512), torch.float32)], + { + "model_name": ["pt_t5_t5_small_text_gen_hf", "pt_t5_google_flan_t5_small_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 61, 512), torch.float32)], + { + "model_name": ["pt_t5_t5_small_text_gen_hf", "pt_t5_google_flan_t5_small_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 1, 768), torch.float32)], + { + "model_name": ["pt_t5_t5_base_text_gen_hf", "pt_t5_google_flan_t5_base_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 61, 768), torch.float32)], + { + "model_name": ["pt_t5_t5_base_text_gen_hf", "pt_t5_google_flan_t5_base_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 48, 160, 160), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 48, 1, 160), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 24, 160, 160), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 24, 1, 160), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 144, 80, 80), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 144, 1, 80), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 192, 80, 80), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 192, 1, 80), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 192, 40, 40), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 192, 1, 40), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 336, 40, 40), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 336, 1, 40), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 336, 20, 20), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 336, 1, 20), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 672, 20, 20), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 672, 1, 20), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 960, 20, 20), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 960, 1, 20), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 960, 10, 10), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 960, 1, 10), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 1632, 10, 10), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 1632, 1, 10), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 2688, 10, 10), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 2688, 1, 10), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b4_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 32, 112, 112), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 32, 1, 112), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 96, 56, 56), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 96, 1, 56), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 144, 56, 56), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 144, 1, 56), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 144, 28, 28), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 144, 1, 28), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 240, 28, 28), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 240, 1, 28), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 240, 14, 14), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 240, 1, 14), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 480, 14, 14), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 480, 1, 14), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 672, 14, 14), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 672, 1, 14), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 672, 7, 7), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 672, 1, 7), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 1152, 7, 7), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 1152, 1, 7), torch.float32)], + { + "model_name": ["pt_efficientnet_efficientnet_b0_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 72, 28, 28), torch.float32)], + { + "model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm", "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 72, 1, 28), torch.float32)], + { + "model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm", "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 120, 28, 28), torch.float32)], + { + "model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm", "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 120, 1, 28), torch.float32)], + { + "model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm", "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 960, 7, 7), torch.float32)], + { + "model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm", "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 960, 1, 7), torch.float32)], + { + "model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm", "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 49, 768), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_b32_224_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 196, 512), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_s16_224_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 49, 512), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_s32_224_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 196, 768), torch.float32)], + { + "model_name": [ + "pt_mlp_mixer_mixer_b16_224_miil_in21k_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_miil_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_in21k_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 49, 1024), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_l32_224_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 196, 1024), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_l16_224_in21k_img_cls_timm", "pt_mlp_mixer_mixer_l16_224_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 16, 56, 56), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 16, 1, 56), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 96, 14, 14), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 96, 1, 14), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 120, 14, 14), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 120, 1, 14), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 144, 14, 14), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 144, 1, 14), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 288, 7, 7), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 288, 1, 7), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 576, 7, 7), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 576, 1, 7), torch.float32)], + { + "model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 256, 256), torch.float32)], + { + "model_name": ["pt_segformer_nvidia_mit_b0_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 256, 512), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 256, 56, 56), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 256, 1, 56), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 512, 28, 28), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 512, 1, 28), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 768, 14, 14), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 768, 1, 14), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + ( + Reduceavg1, + [((1, 1024, 7, 7), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "keep_dim": "True"}, + }, + ), + ( + Reduceavg0, + [((1, 1024, 1, 7), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "ReduceAvg") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_relu.py b/forge/test/models_ops/test_relu.py new file mode 100644 index 000000000..a7a8b7eeb --- /dev/null +++ b/forge/test/models_ops/test_relu.py @@ -0,0 +1,3920 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Relu0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, relu_input_0): + relu_output_1 = forge.op.Relu("", relu_input_0) + return relu_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Relu0, + [((2, 13, 3072), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 768), torch.float32)], + {"model_name": ["pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf"], "pcc": 0.99}, + ), + (Relu0, [((1, 334, 16384), torch.float32)], {"model_name": ["pt_fuyu_adept_fuyu_8b_qa_hf"], "pcc": 0.99}), + (Relu0, [((256, 8192), torch.float32)], {"model_name": ["pt_opt_facebook_opt_1_3b_clm_hf"], "pcc": 0.99}), + ( + Relu0, + [((32, 8192), torch.float32)], + {"model_name": ["pt_opt_facebook_opt_1_3b_seq_cls_hf", "pt_opt_facebook_opt_1_3b_qa_hf"], "pcc": 0.99}, + ), + ( + Relu0, + [((32, 4096), torch.float32)], + {"model_name": ["pt_opt_facebook_opt_350m_qa_hf", "pt_opt_facebook_opt_350m_seq_cls_hf"], "pcc": 0.99}, + ), + ( + Relu0, + [((32, 3072), torch.float32)], + {"model_name": ["pt_opt_facebook_opt_125m_seq_cls_hf", "pt_opt_facebook_opt_125m_qa_hf"], "pcc": 0.99}, + ), + (Relu0, [((256, 3072), torch.float32)], {"model_name": ["pt_opt_facebook_opt_125m_clm_hf"], "pcc": 0.99}), + (Relu0, [((256, 4096), torch.float32)], {"model_name": ["pt_opt_facebook_opt_350m_clm_hf"], "pcc": 0.99}), + (Relu0, [((1, 61, 4096), torch.float32)], {"model_name": ["pt_t5_t5_large_text_gen_hf"], "pcc": 0.99}), + (Relu0, [((1, 1, 4096), torch.float32)], {"model_name": ["pt_t5_t5_large_text_gen_hf"], "pcc": 0.99}), + (Relu0, [((1, 61, 2048), torch.float32)], {"model_name": ["pt_t5_t5_small_text_gen_hf"], "pcc": 0.99}), + (Relu0, [((1, 1, 2048), torch.float32)], {"model_name": ["pt_t5_t5_small_text_gen_hf"], "pcc": 0.99}), + (Relu0, [((1, 61, 3072), torch.float32)], {"model_name": ["pt_t5_t5_base_text_gen_hf"], "pcc": 0.99}), + (Relu0, [((1, 1, 3072), torch.float32)], {"model_name": ["pt_t5_t5_base_text_gen_hf"], "pcc": 0.99}), + (Relu0, [((1024, 2048), torch.float32)], {"model_name": ["pt_nbeats_seasionality_basis_clm_hf"], "pcc": 0.99}), + (Relu0, [((1024, 256), torch.float32)], {"model_name": ["pt_nbeats_trend_basis_clm_hf"], "pcc": 0.99}), + (Relu0, [((1024, 512), torch.float32)], {"model_name": ["pt_nbeats_generic_basis_clm_hf"], "pcc": 0.99}), + (Relu0, [((1, 64, 55, 55), torch.float32)], {"model_name": ["pt_alexnet_alexnet_img_cls_torchhub"], "pcc": 0.99}), + ( + Relu0, + [((1, 192, 27, 27), torch.float32)], + {"model_name": ["pt_alexnet_alexnet_img_cls_torchhub", "pt_rcnn_base_obj_det_torchvision_rect_0"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 384, 13, 13), torch.float32)], + { + "model_name": [ + "pt_alexnet_alexnet_img_cls_torchhub", + "pt_alexnet_base_img_cls_osmr", + "pt_rcnn_base_obj_det_torchvision_rect_0", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 256, 13, 13), torch.float32)], + { + "model_name": [ + "pt_alexnet_alexnet_img_cls_torchhub", + "pt_alexnet_base_img_cls_osmr", + "pt_rcnn_base_obj_det_torchvision_rect_0", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 4096), torch.float32)], + { + "model_name": [ + "pt_alexnet_alexnet_img_cls_torchhub", + "pt_alexnet_base_img_cls_osmr", + "pt_rcnn_base_obj_det_torchvision_rect_0", + "pt_vgg_bn_vgg19_obj_det_osmr", + "pt_vgg_19_obj_det_hf", + "pt_vgg_vgg19_bn_obj_det_torchhub", + "pt_vgg_vgg11_obj_det_osmr", + "pt_vgg_bn_vgg19b_obj_det_osmr", + "pt_vgg_vgg16_obj_det_osmr", + "pt_vgg_vgg19_obj_det_osmr", + "pt_vgg_vgg13_obj_det_osmr", + ], + "pcc": 0.99, + }, + ), + (Relu0, [((1, 96, 54, 54), torch.float32)], {"model_name": ["pt_alexnet_base_img_cls_osmr"], "pcc": 0.99}), + (Relu0, [((1, 256, 27, 27), torch.float32)], {"model_name": ["pt_alexnet_base_img_cls_osmr"], "pcc": 0.99}), + (Relu0, [((1, 128), torch.float32)], {"model_name": ["pt_autoencoder_linear_img_enc_github"], "pcc": 0.99}), + (Relu0, [((1, 64), torch.float32)], {"model_name": ["pt_autoencoder_linear_img_enc_github"], "pcc": 0.99}), + (Relu0, [((1, 12), torch.float32)], {"model_name": ["pt_autoencoder_linear_img_enc_github"], "pcc": 0.99}), + ( + Relu0, + [((1, 16, 28, 28), torch.float32)], + { + "model_name": [ + "pt_autoencoder_conv_img_enc_github", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + ], + "pcc": 0.99, + }, + ), + (Relu0, [((1, 4, 14, 14), torch.float32)], {"model_name": ["pt_autoencoder_conv_img_enc_github"], "pcc": 0.99}), + ( + Relu0, + [((1, 16, 14, 14), torch.float32)], + { + "model_name": [ + "pt_autoencoder_conv_img_enc_github", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 96, 112, 112), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 96, 56, 56), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 192, 56, 56), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_googlenet_base_img_cls_torchvision", + "pt_regnet_facebook_regnet_y_040_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 144, 56, 56), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 240, 56, 56), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 288, 56, 56), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 336, 56, 56), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 384, 56, 56), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 192, 28, 28), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_googlenet_base_img_cls_torchvision", + "pt_regnet_facebook_regnet_y_040_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 240, 28, 28), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 288, 28, 28), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_googlenet_base_img_cls_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 336, 28, 28), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 384, 28, 28), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 432, 28, 28), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 480, 28, 28), torch.float32)], + { + "model_name": 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["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 384, 14, 14), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_vovnet_vovnet27s_obj_det_osmr", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 192, 14, 14), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 432, 14, 14), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + 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torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 912, 14, 14), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 960, 14, 14), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 1008, 14, 14), torch.float32)], + {"model_name": ["pt_densenet_densenet161_img_cls_torchvision"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 1056, 14, 14), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + 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"pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 1536, 10, 10), torch.float32)], + { + "model_name": [ + "pt_xception_xception_img_cls_timm", + "pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 2048, 10, 10), torch.float32)], + { + "model_name": [ + "pt_xception_xception_img_cls_timm", + "pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 32, 150, 150), torch.float32)], + { + "model_name": [ + "pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 128, 150, 150), torch.float32)], + { + "model_name": [ + 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torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 128, 56, 80), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 256, 28, 40), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 512, 14, 20), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 256, 14, 20), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 128, 14, 20), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub", "pt_yolo_v6_yolov6n_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 128, 28, 40), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Relu0, + [((1, 64, 28, 40), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub", "pt_yolo_v6_yolov6n_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Relu0, + [((1, 64, 56, 80), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + (Relu0, [((1, 256, 56, 80), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], "pcc": 0.99}), + ( + Relu0, + [((1, 128, 112, 160), torch.float32)], + {"model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], "pcc": 0.99}, + ), + (Relu0, [((1, 16, 224, 320), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 32, 112, 160), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 64, 14, 20), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 32, 28, 40), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 32, 56, 80), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 48, 224, 320), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 96, 112, 160), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 192, 56, 80), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 384, 28, 40), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 768, 14, 20), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 384, 14, 20), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 192, 14, 20), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 192, 28, 40), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 96, 28, 40), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}), + (Relu0, [((1, 96, 56, 80), torch.float32)], {"model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], "pcc": 0.99}), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Relu") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_remainder.py b/forge/test/models_ops/test_remainder.py new file mode 100644 index 000000000..1dd4d23ea --- /dev/null +++ b/forge/test/models_ops/test_remainder.py @@ -0,0 +1,86 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Remainder0(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("remainder0_const_1", shape=(1,), dtype=torch.int32) + + def forward(self, remainder_input_0): + remainder_output_1 = forge.op.Remainder("", remainder_input_0, self.get_constant("remainder0_const_1")) + return remainder_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Remainder0, + [((1,), torch.int32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Remainder") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_repeatinterleave.py b/forge/test/models_ops/test_repeatinterleave.py new file mode 100644 index 000000000..f9f716b97 --- /dev/null +++ b/forge/test/models_ops/test_repeatinterleave.py @@ -0,0 +1,943 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Repeatinterleave0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, repeatinterleave_input_0): + repeatinterleave_output_1 = forge.op.RepeatInterleave("", repeatinterleave_input_0, repeats=1, dim=1) + return repeatinterleave_output_1 + + +class Repeatinterleave1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, repeatinterleave_input_0): + repeatinterleave_output_1 = forge.op.RepeatInterleave("", repeatinterleave_input_0, repeats=1, dim=2) + return repeatinterleave_output_1 + + +class Repeatinterleave2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, repeatinterleave_input_0): + repeatinterleave_output_1 = forge.op.RepeatInterleave("", repeatinterleave_input_0, repeats=7, dim=2) + return repeatinterleave_output_1 + + +class Repeatinterleave3(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, repeatinterleave_input_0): + repeatinterleave_output_1 = forge.op.RepeatInterleave("", repeatinterleave_input_0, repeats=1, dim=0) + return repeatinterleave_output_1 + + +class Repeatinterleave4(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, repeatinterleave_input_0): + repeatinterleave_output_1 = forge.op.RepeatInterleave("", repeatinterleave_input_0, repeats=256, dim=2) + return repeatinterleave_output_1 + + +class Repeatinterleave5(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, repeatinterleave_input_0): + repeatinterleave_output_1 = forge.op.RepeatInterleave("", repeatinterleave_input_0, repeats=3, dim=2) + return repeatinterleave_output_1 + + +class Repeatinterleave6(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, repeatinterleave_input_0): + repeatinterleave_output_1 = forge.op.RepeatInterleave("", repeatinterleave_input_0, repeats=2, dim=2) + return repeatinterleave_output_1 + + +class Repeatinterleave7(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, repeatinterleave_input_0): + repeatinterleave_output_1 = forge.op.RepeatInterleave("", repeatinterleave_input_0, repeats=8, dim=2) + return repeatinterleave_output_1 + + +class Repeatinterleave8(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, repeatinterleave_input_0): + repeatinterleave_output_1 = forge.op.RepeatInterleave("", repeatinterleave_input_0, repeats=4, dim=2) + return repeatinterleave_output_1 + + +class Repeatinterleave9(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, repeatinterleave_input_0): + repeatinterleave_output_1 = forge.op.RepeatInterleave("", repeatinterleave_input_0, repeats=32, dim=2) + return repeatinterleave_output_1 + + +class Repeatinterleave10(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, repeatinterleave_input_0): + repeatinterleave_output_1 = forge.op.RepeatInterleave("", repeatinterleave_input_0, repeats=6, dim=2) + return repeatinterleave_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Repeatinterleave0, + [((2, 1, 1, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "1"}, + }, + ), + ( + Repeatinterleave1, + [((2, 1, 1, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "2"}, + }, + ), + ( + Repeatinterleave0, + [((2, 1, 1, 7), torch.int64)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "1"}, + }, + ), + ( + Repeatinterleave2, + [((2, 1, 1, 7), torch.int64)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"repeats": "7", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 64, 1), torch.float32)], + { + "model_name": [ + "pt_deepseek_deepseek_math_7b_instruct_qa_hf", + "DeepSeekWrapper_decoder", + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + "pt_mistral_mistralai_mistral_7b_v0_1_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave1, + [((1, 64, 1), torch.float32)], + { + "model_name": [ + "pt_deepseek_deepseek_math_7b_instruct_qa_hf", + "DeepSeekWrapper_decoder", + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + "pt_mistral_mistralai_mistral_7b_v0_1_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave3, + [((1, 1, 1, 256), torch.int64)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave0, + [((1, 1, 1, 256), torch.int64)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "1"}, + }, + ), + ( + Repeatinterleave4, + [((1, 1, 1, 256), torch.int64)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "256", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 384), torch.int64)], + { + "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave3, + [((1, 32, 1), torch.float32)], + { + "model_name": [ + "pt_falcon_tiiuae_falcon_7b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + "pt_qwen1_5_qwen_qwen1_5_0_5b_clm_hf", + "pt_qwen1_5_qwen_qwen1_5_0_5b_chat_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_0_5b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave1, + [((1, 32, 1), torch.float32)], + { + "model_name": [ + "pt_falcon_tiiuae_falcon_7b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + "pt_qwen1_5_qwen_qwen1_5_0_5b_clm_hf", + "pt_qwen1_5_qwen_qwen1_5_0_5b_chat_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_0_5b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 128, 1), torch.float32)], + { + "model_name": [ + "pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_1b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf", + "pt_gemma_google_gemma_2b_text_gen_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave1, + [((1, 128, 1), torch.float32)], + { + "model_name": [ + "pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_1b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf", + "pt_gemma_google_gemma_2b_text_gen_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 4, 1, 10, 256), torch.float32)], + { + "model_name": [ + "pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_1b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave5, + [((1, 4, 1, 10, 256), torch.float32)], + { + "model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "3", "dim": "2"}, + }, + ), + ( + Repeatinterleave6, + [((1, 4, 1, 10, 256), torch.float32)], + { + "model_name": ["pt_falcon3_tiiuae_falcon3_1b_base_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "2", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 16, 1), torch.float32)], + { + "model_name": [ + "pt_fuyu_adept_fuyu_8b_qa_hf", + "pt_phi2_microsoft_phi_2_pytdml_token_cls_hf", + "pt_phi2_microsoft_phi_2_clm_hf", + "pt_phi2_microsoft_phi_2_token_cls_hf", + "pt_phi2_microsoft_phi_2_seq_cls_hf", + "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf", + "pt_phi2_microsoft_phi_2_pytdml_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave1, + [((1, 16, 1), torch.float32)], + { + "model_name": [ + "pt_fuyu_adept_fuyu_8b_qa_hf", + "pt_phi2_microsoft_phi_2_pytdml_token_cls_hf", + "pt_phi2_microsoft_phi_2_clm_hf", + "pt_phi2_microsoft_phi_2_token_cls_hf", + "pt_phi2_microsoft_phi_2_seq_cls_hf", + "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf", + "pt_phi2_microsoft_phi_2_pytdml_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 1, 1, 7, 256), torch.float32)], + { + "model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave0, + [((1, 1, 1, 7, 256), torch.float32)], + { + "model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "1"}, + }, + ), + ( + Repeatinterleave7, + [((1, 1, 1, 7, 256), torch.float32)], + { + "model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], + "pcc": 0.99, + "op_params": {"repeats": "8", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 8, 1, 4, 64), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave8, + [((1, 8, 1, 4, 64), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "4", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 8, 1, 256, 64), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave8, + [((1, 8, 1, 256, 64), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "4", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 8, 1, 4, 128), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave8, + [((1, 8, 1, 4, 128), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "4", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 8, 1, 128, 128), torch.float32)], + { + "model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave8, + [((1, 8, 1, 128, 128), torch.float32)], + { + "model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "4", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 1, 1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave0, + [((1, 1, 1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "1"}, + }, + ), + ( + Repeatinterleave9, + [((1, 1, 1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "32", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 48, 1), torch.float32)], + { + "model_name": [ + "pt_phi3_microsoft_phi_3_mini_4k_instruct_clm_hf", + "pt_phi3_microsoft_phi_3_mini_4k_instruct_token_cls_hf", + "pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave1, + [((1, 48, 1), torch.float32)], + { + "model_name": [ + "pt_phi3_microsoft_phi_3_mini_4k_instruct_clm_hf", + "pt_phi3_microsoft_phi_3_mini_4k_instruct_token_cls_hf", + "pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 4, 1, 35, 128), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave2, + [((1, 4, 1, 35, 128), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "7", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 2, 1, 35, 128), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave10, + [((1, 2, 1, 35, 128), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "6", "dim": "2"}, + }, + ), + ( + Repeatinterleave7, + [((1, 2, 1, 35, 128), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "8", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 2, 1, 35, 64), torch.float32)], + { + "model_name": ["pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave2, + [((1, 2, 1, 35, 64), torch.float32)], + { + "model_name": ["pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "7", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 2, 1, 29, 128), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf", "pt_qwen_v2_qwen_qwen2_5_3b_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave10, + [((1, 2, 1, 29, 128), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf"], "pcc": 0.99, "op_params": {"repeats": "6", "dim": "2"}}, + ), + ( + Repeatinterleave7, + [((1, 2, 1, 29, 128), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_clm_hf"], "pcc": 0.99, "op_params": {"repeats": "8", "dim": "2"}}, + ), + ( + Repeatinterleave3, + [((1, 2, 1, 39, 128), torch.float32)], + { + "model_name": [ + "pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave10, + [((1, 2, 1, 39, 128), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "6", "dim": "2"}, + }, + ), + ( + Repeatinterleave7, + [((1, 2, 1, 39, 128), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "8", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 4, 1, 39, 128), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave2, + [((1, 4, 1, 39, 128), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "7", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 4, 1, 29, 128), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_clm_hf"], "pcc": 0.99, "op_params": {"repeats": "1", "dim": "0"}}, + ), + ( + Repeatinterleave2, + [((1, 4, 1, 29, 128), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_clm_hf"], "pcc": 0.99, "op_params": {"repeats": "7", "dim": "2"}}, + ), + ( + Repeatinterleave3, + [((1, 2, 1, 29, 64), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_clm_hf"], "pcc": 0.99, "op_params": {"repeats": "1", "dim": "0"}}, + ), + ( + Repeatinterleave2, + [((1, 2, 1, 29, 64), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_clm_hf"], "pcc": 0.99, "op_params": {"repeats": "7", "dim": "2"}}, + ), + ( + Repeatinterleave3, + [((1, 2, 1, 39, 64), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave2, + [((1, 2, 1, 39, 64), torch.float32)], + { + "model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf"], + "pcc": 0.99, + "op_params": {"repeats": "7", "dim": "2"}, + }, + ), + ( + Repeatinterleave3, + [((1, 1, 768), torch.float32)], + { + "model_name": [ + "pt_deit_facebook_deit_base_patch16_224_img_cls_hf", + "pt_deit_facebook_deit_base_distilled_patch16_224_img_cls_hf", + "pt_vit_google_vit_base_patch16_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave3, + [((1, 1, 192), torch.float32)], + { + "model_name": ["pt_deit_facebook_deit_tiny_patch16_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave3, + [((1, 1, 384), torch.float32)], + { + "model_name": ["pt_deit_facebook_deit_small_patch16_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave3, + [((1, 1, 1024), torch.float32)], + { + "model_name": [ + "pt_perceiverio_deepmind_vision_perceiver_conv_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_fourier_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave3, + [((1, 512, 1024), torch.float32)], + { + "model_name": [ + "pt_perceiverio_deepmind_vision_perceiver_conv_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_fourier_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave3, + [((1, 50176, 256), torch.float32)], + { + "model_name": ["pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + ( + Repeatinterleave3, + [((1, 1, 1024), torch.float32)], + { + "model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "RepeatInterleave") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_reshape.py b/forge/test/models_ops/test_reshape.py new file mode 100644 index 000000000..75ca7e844 --- /dev/null +++ b/forge/test/models_ops/test_reshape.py @@ -0,0 +1,22668 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Reshape0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 4, 1)) + return reshape_output_1 + + +class Reshape1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1)) + return reshape_output_1 + + +class Reshape2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2048)) + return reshape_output_1 + + +class Reshape3(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 2048)) + return reshape_output_1 + + +class Reshape4(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 2048)) + return reshape_output_1 + + +class Reshape5(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1, 32, 64)) + return reshape_output_1 + + +class Reshape6(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1, 2048)) + return reshape_output_1 + + +class Reshape7(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 1, 64)) + return reshape_output_1 + + +class Reshape8(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 32, 1, 64)) + return reshape_output_1 + + +class Reshape9(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 13)) + return reshape_output_1 + + +class Reshape10(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(26, 768)) + return reshape_output_1 + + +class Reshape11(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 13, 12, 64)) + return reshape_output_1 + + +class Reshape12(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 13, 768)) + return reshape_output_1 + + +class Reshape13(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(24, 13, 64)) + return reshape_output_1 + + +class Reshape14(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 12, 13, 13)) + return reshape_output_1 + + +class Reshape15(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(24, 13, 13)) + return reshape_output_1 + + +class Reshape16(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(24, 64, 13)) + return reshape_output_1 + + +class Reshape17(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 12, 13, 64)) + return reshape_output_1 + + +class Reshape18(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 13, 3072)) + return reshape_output_1 + + +class Reshape19(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(26, 3072)) + return reshape_output_1 + + +class Reshape20(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 13, 2048)) + return reshape_output_1 + + +class Reshape21(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 13, 32, 64)) + return reshape_output_1 + + +class Reshape22(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(26, 2048)) + return reshape_output_1 + + +class Reshape23(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 13, 64)) + return reshape_output_1 + + +class Reshape24(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 32, 1, 13)) + return reshape_output_1 + + +class Reshape25(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 1, 13)) + return reshape_output_1 + + +class Reshape26(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1, 8192)) + return reshape_output_1 + + +class Reshape27(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 8192)) + return reshape_output_1 + + +class Reshape28(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 1, 2048)) + return reshape_output_1 + + +class Reshape29(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1536)) + return reshape_output_1 + + +class Reshape30(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1536)) + return reshape_output_1 + + +class Reshape31(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1, 24, 64)) + return reshape_output_1 + + +class Reshape32(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1, 1536)) + return reshape_output_1 + + +class Reshape33(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(48, 1, 64)) + return reshape_output_1 + + +class Reshape34(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 24, 1, 64)) + return reshape_output_1 + + +class Reshape35(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 13, 1536)) + return reshape_output_1 + + +class Reshape36(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 13, 24, 64)) + return reshape_output_1 + + +class Reshape37(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(26, 1536)) + return reshape_output_1 + + +class Reshape38(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(48, 13, 64)) + return reshape_output_1 + + +class Reshape39(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 24, 1, 13)) + return reshape_output_1 + + +class Reshape40(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(48, 1, 13)) + return reshape_output_1 + + +class Reshape41(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1, 6144)) + return reshape_output_1 + + +class Reshape42(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 6144)) + return reshape_output_1 + + +class Reshape43(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024)) + return reshape_output_1 + + +class Reshape44(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 1024)) + return reshape_output_1 + + +class Reshape45(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 16, 64)) + return reshape_output_1 + + +class Reshape46(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1024)) + return reshape_output_1 + + +class Reshape47(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1, 16, 64)) + return reshape_output_1 + + +class Reshape48(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1, 1024)) + return reshape_output_1 + + +class Reshape49(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 1, 64)) + return reshape_output_1 + + +class Reshape50(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 16, 1, 64)) + return reshape_output_1 + + +class Reshape51(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 13, 1024)) + return reshape_output_1 + + +class Reshape52(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 13, 16, 64)) + return reshape_output_1 + + +class Reshape53(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(26, 1024)) + return reshape_output_1 + + +class Reshape54(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 13, 64)) + return reshape_output_1 + + +class Reshape55(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 16, 1, 13)) + return reshape_output_1 + + +class Reshape56(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 1, 13)) + return reshape_output_1 + + +class Reshape57(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1, 4096)) + return reshape_output_1 + + +class Reshape58(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 4096)) + return reshape_output_1 + + +class Reshape59(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1)) + return reshape_output_1 + + +class Reshape60(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 1, 1024)) + return reshape_output_1 + + +class Reshape61(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 1, 64)) + return reshape_output_1 + + +class Reshape62(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 1, 1)) + return reshape_output_1 + + +class Reshape63(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 1, 1)) + return reshape_output_1 + + +class Reshape64(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 4)) + return reshape_output_1 + + +class Reshape65(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 64, 1)) + return reshape_output_1 + + +class Reshape66(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 1, 64)) + return reshape_output_1 + + +class Reshape67(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 80, 3000, 1)) + return reshape_output_1 + + +class Reshape68(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1024, 80, 3, 1)) + return reshape_output_1 + + +class Reshape69(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 3000)) + return reshape_output_1 + + +class Reshape70(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 3000, 1)) + return reshape_output_1 + + +class Reshape71(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1024, 1024, 3, 1)) + return reshape_output_1 + + +class Reshape72(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 1500)) + return reshape_output_1 + + +class Reshape73(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1500, 1024)) + return reshape_output_1 + + +class Reshape74(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1500, 16, 64)) + return reshape_output_1 + + +class Reshape75(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1500, 1024)) + return reshape_output_1 + + +class Reshape76(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 1500, 64)) + return reshape_output_1 + + +class Reshape77(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 1500, 1500)) + return reshape_output_1 + + +class Reshape78(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 1500, 1500)) + return reshape_output_1 + + +class Reshape79(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 64, 1500)) + return reshape_output_1 + + +class Reshape80(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 1500, 64)) + return reshape_output_1 + + +class Reshape81(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 1, 1500)) + return reshape_output_1 + + +class Reshape82(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 1, 1500)) + return reshape_output_1 + + +class Reshape83(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1280)) + return reshape_output_1 + + +class Reshape84(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 20, 64)) + return reshape_output_1 + + +class Reshape85(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 1280)) + return reshape_output_1 + + +class Reshape86(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 1, 64)) + return reshape_output_1 + + +class Reshape87(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 20, 1, 1)) + return reshape_output_1 + + +class Reshape88(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 1, 1)) + return reshape_output_1 + + +class Reshape89(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 64, 1)) + return reshape_output_1 + + +class Reshape90(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 20, 1, 64)) + return reshape_output_1 + + +class Reshape91(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1280, 80, 3, 1)) + return reshape_output_1 + + +class Reshape92(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1280, 3000)) + return reshape_output_1 + + +class Reshape93(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1280, 3000, 1)) + return reshape_output_1 + + +class Reshape94(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1280, 1280, 3, 1)) + return reshape_output_1 + + +class Reshape95(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1280, 1500)) + return reshape_output_1 + + +class Reshape96(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1500, 1280)) + return reshape_output_1 + + +class Reshape97(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1500, 20, 64)) + return reshape_output_1 + + +class Reshape98(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1500, 1280)) + return reshape_output_1 + + +class Reshape99(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 1500, 64)) + return reshape_output_1 + + +class Reshape100(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 20, 1500, 1500)) + return reshape_output_1 + + +class Reshape101(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 1500, 1500)) + return reshape_output_1 + + +class Reshape102(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 64, 1500)) + return reshape_output_1 + + +class Reshape103(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 20, 1500, 64)) + return reshape_output_1 + + +class Reshape104(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 20, 1, 1500)) + return reshape_output_1 + + +class Reshape105(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 1, 1500)) + return reshape_output_1 + + +class Reshape106(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 384)) + return reshape_output_1 + + +class Reshape107(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 6, 64)) + return reshape_output_1 + + +class Reshape108(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 384)) + return reshape_output_1 + + +class Reshape109(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 1, 64)) + return reshape_output_1 + + +class Reshape110(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 1, 64)) + return reshape_output_1 + + +class Reshape111(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 1, 1)) + return reshape_output_1 + + +class Reshape112(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 1, 1)) + return reshape_output_1 + + +class Reshape113(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 64, 1)) + return reshape_output_1 + + +class Reshape114(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 64)) + return reshape_output_1 + + +class Reshape115(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(384, 80, 3, 1)) + return reshape_output_1 + + +class Reshape116(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 384, 3000)) + return reshape_output_1 + + +class Reshape117(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 384, 3000, 1)) + return reshape_output_1 + + +class Reshape118(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(384, 384, 3, 1)) + return reshape_output_1 + + +class Reshape119(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 384, 1500)) + return reshape_output_1 + + +class Reshape120(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1500, 384)) + return reshape_output_1 + + +class Reshape121(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1500, 6, 64)) + return reshape_output_1 + + +class Reshape122(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1500, 384)) + return reshape_output_1 + + +class Reshape123(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 1500, 64)) + return reshape_output_1 + + +class Reshape124(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 1500, 1500)) + return reshape_output_1 + + +class Reshape125(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 1500, 1500)) + return reshape_output_1 + + +class Reshape126(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 64, 1500)) + return reshape_output_1 + + +class Reshape127(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 1500, 64)) + return reshape_output_1 + + +class Reshape128(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 1, 1500)) + return reshape_output_1 + + +class Reshape129(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 1, 1500)) + return reshape_output_1 + + +class Reshape130(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512)) + return reshape_output_1 + + +class Reshape131(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 8, 64)) + return reshape_output_1 + + +class Reshape132(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 1, 512)) + return reshape_output_1 + + +class Reshape133(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 512)) + return reshape_output_1 + + +class Reshape134(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 1, 64)) + return reshape_output_1 + + +class Reshape135(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 1, 1)) + return reshape_output_1 + + +class Reshape136(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 1, 1)) + return reshape_output_1 + + +class Reshape137(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 64, 1)) + return reshape_output_1 + + +class Reshape138(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 1, 64)) + return reshape_output_1 + + +class Reshape139(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(512, 80, 3, 1)) + return reshape_output_1 + + +class Reshape140(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 3000)) + return reshape_output_1 + + +class Reshape141(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 3000, 1)) + return reshape_output_1 + + +class Reshape142(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(512, 512, 3, 1)) + return reshape_output_1 + + +class Reshape143(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 1500)) + return reshape_output_1 + + +class Reshape144(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1500, 512)) + return reshape_output_1 + + +class Reshape145(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1500, 8, 64)) + return reshape_output_1 + + +class Reshape146(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1500, 512)) + return reshape_output_1 + + +class Reshape147(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 1500, 64)) + return reshape_output_1 + + +class Reshape148(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 1500, 1500)) + return reshape_output_1 + + +class Reshape149(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 1500, 1500)) + return reshape_output_1 + + +class Reshape150(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 64, 1500)) + return reshape_output_1 + + +class Reshape151(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 1500, 64)) + return reshape_output_1 + + +class Reshape152(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 1, 1500)) + return reshape_output_1 + + +class Reshape153(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 1, 1500)) + return reshape_output_1 + + +class Reshape154(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768)) + return reshape_output_1 + + +class Reshape155(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 12, 64)) + return reshape_output_1 + + +class Reshape156(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 768)) + return reshape_output_1 + + +class Reshape157(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 1, 64)) + return reshape_output_1 + + +class Reshape158(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 1, 1)) + return reshape_output_1 + + +class Reshape159(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 1, 1)) + return reshape_output_1 + + +class Reshape160(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 64, 1)) + return reshape_output_1 + + +class Reshape161(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 1, 64)) + return reshape_output_1 + + +class Reshape162(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(768, 80, 3, 1)) + return reshape_output_1 + + +class Reshape163(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 3000)) + return reshape_output_1 + + +class Reshape164(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 3000, 1)) + return reshape_output_1 + + +class Reshape165(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(768, 768, 3, 1)) + return reshape_output_1 + + +class Reshape166(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 1500)) + return reshape_output_1 + + +class Reshape167(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1500, 768)) + return reshape_output_1 + + +class Reshape168(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1500, 12, 64)) + return reshape_output_1 + + +class Reshape169(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1500, 768)) + return reshape_output_1 + + +class Reshape170(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 1500, 64)) + return reshape_output_1 + + +class Reshape171(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 1500, 1500)) + return reshape_output_1 + + +class Reshape172(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 1500, 1500)) + return reshape_output_1 + + +class Reshape173(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 64, 1500)) + return reshape_output_1 + + +class Reshape174(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 1500, 64)) + return reshape_output_1 + + +class Reshape175(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 1, 1500)) + return reshape_output_1 + + +class Reshape176(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 1, 1500)) + return reshape_output_1 + + +class Reshape177(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2)) + return reshape_output_1 + + +class Reshape178(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 1280)) + return reshape_output_1 + + +class Reshape179(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2, 20, 64)) + return reshape_output_1 + + +class Reshape180(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2, 1280)) + return reshape_output_1 + + +class Reshape181(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 2, 64)) + return reshape_output_1 + + +class Reshape182(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 20, 2, 2)) + return reshape_output_1 + + +class Reshape183(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 2, 2)) + return reshape_output_1 + + +class Reshape184(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 64, 2)) + return reshape_output_1 + + +class Reshape185(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 20, 2, 64)) + return reshape_output_1 + + +class Reshape186(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 20, 2, 1500)) + return reshape_output_1 + + +class Reshape187(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 2, 1500)) + return reshape_output_1 + + +class Reshape188(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 7)) + return reshape_output_1 + + +class Reshape189(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(14, 512)) + return reshape_output_1 + + +class Reshape190(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 7, 8, 64)) + return reshape_output_1 + + +class Reshape191(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 7, 512)) + return reshape_output_1 + + +class Reshape192(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 7, 64)) + return reshape_output_1 + + +class Reshape193(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 8, 7, 7)) + return reshape_output_1 + + +class Reshape194(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 7, 7)) + return reshape_output_1 + + +class Reshape195(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 8, 7, 64)) + return reshape_output_1 + + +class Reshape196(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 7, 2048)) + return reshape_output_1 + + +class Reshape197(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(14, 2048)) + return reshape_output_1 + + +class Reshape198(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(39, 4096)) + return reshape_output_1 + + +class Reshape199(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 32, 128)) + return reshape_output_1 + + +class Reshape200(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 4096)) + return reshape_output_1 + + +class Reshape201(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 39, 128)) + return reshape_output_1 + + +class Reshape202(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 39, 39)) + return reshape_output_1 + + +class Reshape203(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 39, 39)) + return reshape_output_1 + + +class Reshape204(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 128, 39)) + return reshape_output_1 + + +class Reshape205(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 39, 128)) + return reshape_output_1 + + +class Reshape206(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 11008)) + return reshape_output_1 + + +class Reshape207(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(204, 768)) + return reshape_output_1 + + +class Reshape208(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 204, 12, 64)) + return reshape_output_1 + + +class Reshape209(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 204, 768)) + return reshape_output_1 + + +class Reshape210(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 204, 64)) + return reshape_output_1 + + +class Reshape211(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 204, 204)) + return reshape_output_1 + + +class Reshape212(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 204, 204)) + return reshape_output_1 + + +class Reshape213(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 64, 204)) + return reshape_output_1 + + +class Reshape214(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 204, 64)) + return reshape_output_1 + + +class Reshape215(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(201, 768)) + return reshape_output_1 + + +class Reshape216(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 201, 12, 64)) + return reshape_output_1 + + +class Reshape217(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 201, 768)) + return reshape_output_1 + + +class Reshape218(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 201, 64)) + return reshape_output_1 + + +class Reshape219(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 201, 201)) + return reshape_output_1 + + +class Reshape220(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 201, 201)) + return reshape_output_1 + + +class Reshape221(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 64, 201)) + return reshape_output_1 + + +class Reshape222(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 201, 64)) + return reshape_output_1 + + +class Reshape223(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(128, 4096)) + return reshape_output_1 + + +class Reshape224(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 64, 64)) + return reshape_output_1 + + +class Reshape225(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 4096)) + return reshape_output_1 + + +class Reshape226(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 32, 128)) + return reshape_output_1 + + +class Reshape227(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 128, 64)) + return reshape_output_1 + + +class Reshape228(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 64, 128, 128)) + return reshape_output_1 + + +class Reshape229(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 128, 128)) + return reshape_output_1 + + +class Reshape230(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 64, 16384, 1)) + return reshape_output_1 + + +class Reshape231(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 64, 128)) + return reshape_output_1 + + +class Reshape232(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 64, 128, 64)) + return reshape_output_1 + + +class Reshape233(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 4096, 1)) + return reshape_output_1 + + +class Reshape234(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(128, 768)) + return reshape_output_1 + + +class Reshape235(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 12, 64)) + return reshape_output_1 + + +class Reshape236(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 768)) + return reshape_output_1 + + +class Reshape237(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 128, 64)) + return reshape_output_1 + + +class Reshape238(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 128, 128)) + return reshape_output_1 + + +class Reshape239(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 128, 128)) + return reshape_output_1 + + +class Reshape240(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 64, 128)) + return reshape_output_1 + + +class Reshape241(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 128, 1)) + return reshape_output_1 + + +class Reshape242(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 128, 64)) + return reshape_output_1 + + +class Reshape243(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 768, 1)) + return reshape_output_1 + + +class Reshape244(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(128, 1024)) + return reshape_output_1 + + +class Reshape245(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 16, 64)) + return reshape_output_1 + + +class Reshape246(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 1024)) + return reshape_output_1 + + +class Reshape247(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 8, 128)) + return reshape_output_1 + + +class Reshape248(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 128, 64)) + return reshape_output_1 + + +class Reshape249(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 128, 128)) + return reshape_output_1 + + +class Reshape250(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 128, 128)) + return reshape_output_1 + + +class Reshape251(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 64, 128)) + return reshape_output_1 + + +class Reshape252(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 128, 64)) + return reshape_output_1 + + +class Reshape253(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 1024, 1)) + return reshape_output_1 + + +class Reshape254(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(128, 2048)) + return reshape_output_1 + + +class Reshape255(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 16, 128)) + return reshape_output_1 + + +class Reshape256(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 2048)) + return reshape_output_1 + + +class Reshape257(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 2048, 1)) + return reshape_output_1 + + +class Reshape258(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 1024)) + return reshape_output_1 + + +class Reshape259(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 16, 64)) + return reshape_output_1 + + +class Reshape260(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 32, 32)) + return reshape_output_1 + + +class Reshape261(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 1024)) + return reshape_output_1 + + +class Reshape262(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 4, 256)) + return reshape_output_1 + + +class Reshape263(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 256, 64)) + return reshape_output_1 + + +class Reshape264(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 256, 256)) + return reshape_output_1 + + +class Reshape265(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 256, 256)) + return reshape_output_1 + + +class Reshape266(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 256, 64)) + return reshape_output_1 + + +class Reshape267(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256)) + return reshape_output_1 + + +class Reshape268(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(384, 1024)) + return reshape_output_1 + + +class Reshape269(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 384, 16, 64)) + return reshape_output_1 + + +class Reshape270(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 384, 1024)) + return reshape_output_1 + + +class Reshape271(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 384, 64)) + return reshape_output_1 + + +class Reshape272(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 384, 384)) + return reshape_output_1 + + +class Reshape273(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 384, 384)) + return reshape_output_1 + + +class Reshape274(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 64, 384)) + return reshape_output_1 + + +class Reshape275(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 384, 64)) + return reshape_output_1 + + +class Reshape276(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 384, 1)) + return reshape_output_1 + + +class Reshape277(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 16, 32, 1)) + return reshape_output_1 + + +class Reshape278(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 64, 256)) + return reshape_output_1 + + +class Reshape279(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 4096)) + return reshape_output_1 + + +class Reshape280(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 1, 128)) + return reshape_output_1 + + +class Reshape281(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(384, 768)) + return reshape_output_1 + + +class Reshape282(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 384, 12, 64)) + return reshape_output_1 + + +class Reshape283(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 384, 768)) + return reshape_output_1 + + +class Reshape284(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 384, 64)) + return reshape_output_1 + + +class Reshape285(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 384, 384)) + return reshape_output_1 + + +class Reshape286(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 1, 384)) + return reshape_output_1 + + +class Reshape287(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 384, 384)) + return reshape_output_1 + + +class Reshape288(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 64, 384)) + return reshape_output_1 + + +class Reshape289(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 384, 64)) + return reshape_output_1 + + +class Reshape290(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 1)) + return reshape_output_1 + + +class Reshape291(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128)) + return reshape_output_1 + + +class Reshape292(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1,)) + return reshape_output_1 + + +class Reshape293(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 4544)) + return reshape_output_1 + + +class Reshape294(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 18176)) + return reshape_output_1 + + +class Reshape295(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 73, 64)) + return reshape_output_1 + + +class Reshape296(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 71, 6, 64)) + return reshape_output_1 + + +class Reshape297(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(71, 6, 64)) + return reshape_output_1 + + +class Reshape298(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 71, 6, 6)) + return reshape_output_1 + + +class Reshape299(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(71, 6, 6)) + return reshape_output_1 + + +class Reshape300(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 4544)) + return reshape_output_1 + + +class Reshape301(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(10, 3072)) + return reshape_output_1 + + +class Reshape302(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 10, 12, 256)) + return reshape_output_1 + + +class Reshape303(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 10, 3072)) + return reshape_output_1 + + +class Reshape304(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 10, 256)) + return reshape_output_1 + + +class Reshape305(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 10, 4, 256)) + return reshape_output_1 + + +class Reshape306(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 10, 256)) + return reshape_output_1 + + +class Reshape307(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 10, 10)) + return reshape_output_1 + + +class Reshape308(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 10, 10)) + return reshape_output_1 + + +class Reshape309(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 256, 10)) + return reshape_output_1 + + +class Reshape310(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 10, 9216)) + return reshape_output_1 + + +class Reshape311(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(10, 2048)) + return reshape_output_1 + + +class Reshape312(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 10, 8, 256)) + return reshape_output_1 + + +class Reshape313(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 10, 2048)) + return reshape_output_1 + + +class Reshape314(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 10, 256)) + return reshape_output_1 + + +class Reshape315(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 10, 256)) + return reshape_output_1 + + +class Reshape316(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 10, 10)) + return reshape_output_1 + + +class Reshape317(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 10, 10)) + return reshape_output_1 + + +class Reshape318(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 256, 10)) + return reshape_output_1 + + +class Reshape319(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 10, 8192)) + return reshape_output_1 + + +class Reshape320(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 10, 23040)) + return reshape_output_1 + + +class Reshape321(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 334, 64, 3, 64)) + return reshape_output_1 + + +class Reshape322(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 334, 64, 64)) + return reshape_output_1 + + +class Reshape323(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 334, 64)) + return reshape_output_1 + + +class Reshape324(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 64, 334, 334)) + return reshape_output_1 + + +class Reshape325(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 334, 334)) + return reshape_output_1 + + +class Reshape326(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 64, 334)) + return reshape_output_1 + + +class Reshape327(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 64, 334, 64)) + return reshape_output_1 + + +class Reshape328(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(334, 4096)) + return reshape_output_1 + + +class Reshape329(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 334, 4096)) + return reshape_output_1 + + +class Reshape330(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(7, 2048)) + return reshape_output_1 + + +class Reshape331(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 7, 8, 256)) + return reshape_output_1 + + +class Reshape332(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 7, 2048)) + return reshape_output_1 + + +class Reshape333(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 7, 256)) + return reshape_output_1 + + +class Reshape334(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 7, 1, 256)) + return reshape_output_1 + + +class Reshape335(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 7, 256)) + return reshape_output_1 + + +class Reshape336(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 7, 7)) + return reshape_output_1 + + +class Reshape337(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 7, 7)) + return reshape_output_1 + + +class Reshape338(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 256, 7)) + return reshape_output_1 + + +class Reshape339(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 7, 16384)) + return reshape_output_1 + + +class Reshape340(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 768)) + return reshape_output_1 + + +class Reshape341(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 12, 64)) + return reshape_output_1 + + +class Reshape342(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 768)) + return reshape_output_1 + + +class Reshape343(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 256, 64)) + return reshape_output_1 + + +class Reshape344(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 256, 256)) + return reshape_output_1 + + +class Reshape345(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 256, 256)) + return reshape_output_1 + + +class Reshape346(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 64, 256)) + return reshape_output_1 + + +class Reshape347(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 256, 64)) + return reshape_output_1 + + +class Reshape348(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 3072)) + return reshape_output_1 + + +class Reshape349(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 3072)) + return reshape_output_1 + + +class Reshape350(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 32, 96)) + return reshape_output_1 + + +class Reshape351(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 2560)) + return reshape_output_1 + + +class Reshape352(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 32, 80)) + return reshape_output_1 + + +class Reshape353(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 20, 128)) + return reshape_output_1 + + +class Reshape354(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 2560)) + return reshape_output_1 + + +class Reshape355(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 256, 128)) + return reshape_output_1 + + +class Reshape356(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 20, 256, 256)) + return reshape_output_1 + + +class Reshape357(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 256, 256)) + return reshape_output_1 + + +class Reshape358(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 128, 256)) + return reshape_output_1 + + +class Reshape359(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 20, 256, 128)) + return reshape_output_1 + + +class Reshape360(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 2048)) + return reshape_output_1 + + +class Reshape361(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 32, 64)) + return reshape_output_1 + + +class Reshape362(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 16, 128)) + return reshape_output_1 + + +class Reshape363(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 2048)) + return reshape_output_1 + + +class Reshape364(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 256, 128)) + return reshape_output_1 + + +class Reshape365(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 128, 256)) + return reshape_output_1 + + +class Reshape366(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 256, 128)) + return reshape_output_1 + + +class Reshape367(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 768)) + return reshape_output_1 + + +class Reshape368(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 12, 64)) + return reshape_output_1 + + +class Reshape369(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 768)) + return reshape_output_1 + + +class Reshape370(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 32, 64)) + return reshape_output_1 + + +class Reshape371(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 32, 32)) + return reshape_output_1 + + +class Reshape372(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 32, 32)) + return reshape_output_1 + + +class Reshape373(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 64, 32)) + return reshape_output_1 + + +class Reshape374(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 32, 64)) + return reshape_output_1 + + +class Reshape375(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 2560)) + return reshape_output_1 + + +class Reshape376(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 20, 128)) + return reshape_output_1 + + +class Reshape377(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 2560)) + return reshape_output_1 + + +class Reshape378(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 32, 128)) + return reshape_output_1 + + +class Reshape379(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 20, 32, 32)) + return reshape_output_1 + + +class Reshape380(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 32, 32)) + return reshape_output_1 + + +class Reshape381(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 128, 32)) + return reshape_output_1 + + +class Reshape382(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 20, 32, 128)) + return reshape_output_1 + + +class Reshape383(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 2048)) + return reshape_output_1 + + +class Reshape384(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 32, 64)) + return reshape_output_1 + + +class Reshape385(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 16, 128)) + return reshape_output_1 + + +class Reshape386(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 2048)) + return reshape_output_1 + + +class Reshape387(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 32, 128)) + return reshape_output_1 + + +class Reshape388(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 32, 32)) + return reshape_output_1 + + +class Reshape389(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 32, 32)) + return reshape_output_1 + + +class Reshape390(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 128, 32)) + return reshape_output_1 + + +class Reshape391(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 32, 128)) + return reshape_output_1 + + +class Reshape392(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(4, 2048)) + return reshape_output_1 + + +class Reshape393(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 32, 64)) + return reshape_output_1 + + +class Reshape394(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 2048)) + return reshape_output_1 + + +class Reshape395(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 4, 64)) + return reshape_output_1 + + +class Reshape396(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 8, 64)) + return reshape_output_1 + + +class Reshape397(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 4, 64)) + return reshape_output_1 + + +class Reshape398(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 4, 4)) + return reshape_output_1 + + +class Reshape399(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 4, 4)) + return reshape_output_1 + + +class Reshape400(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 64, 4)) + return reshape_output_1 + + +class Reshape401(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 8192)) + return reshape_output_1 + + +class Reshape402(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 256, 64)) + return reshape_output_1 + + +class Reshape403(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 8, 64)) + return reshape_output_1 + + +class Reshape404(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 512)) + return reshape_output_1 + + +class Reshape405(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 512)) + return reshape_output_1 + + +class Reshape406(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 256, 64)) + return reshape_output_1 + + +class Reshape407(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 256, 256)) + return reshape_output_1 + + +class Reshape408(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 256, 256)) + return reshape_output_1 + + +class Reshape409(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 64, 256)) + return reshape_output_1 + + +class Reshape410(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 8192)) + return reshape_output_1 + + +class Reshape411(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(4, 4096)) + return reshape_output_1 + + +class Reshape412(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 32, 128)) + return reshape_output_1 + + +class Reshape413(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 4096)) + return reshape_output_1 + + +class Reshape414(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 4, 128)) + return reshape_output_1 + + +class Reshape415(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 8, 128)) + return reshape_output_1 + + +class Reshape416(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 4, 128)) + return reshape_output_1 + + +class Reshape417(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 128, 4)) + return reshape_output_1 + + +class Reshape418(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 14336)) + return reshape_output_1 + + +class Reshape419(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 128, 128)) + return reshape_output_1 + + +class Reshape420(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 16384, 1)) + return reshape_output_1 + + +class Reshape421(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 128, 128)) + return reshape_output_1 + + +class Reshape422(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 14336)) + return reshape_output_1 + + +class Reshape423(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 7)) + return reshape_output_1 + + +class Reshape424(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(7, 768)) + return reshape_output_1 + + +class Reshape425(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 7, 12, 64)) + return reshape_output_1 + + +class Reshape426(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 7, 768)) + return reshape_output_1 + + +class Reshape427(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 7, 64)) + return reshape_output_1 + + +class Reshape428(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 7, 7)) + return reshape_output_1 + + +class Reshape429(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 7, 7)) + return reshape_output_1 + + +class Reshape430(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 64, 7)) + return reshape_output_1 + + +class Reshape431(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 7, 64)) + return reshape_output_1 + + +class Reshape432(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 7, 3072)) + return reshape_output_1 + + +class Reshape433(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(7, 3072)) + return reshape_output_1 + + +class Reshape434(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32)) + return reshape_output_1 + + +class Reshape435(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 32, 64)) + return reshape_output_1 + + +class Reshape436(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 32, 32)) + return reshape_output_1 + + +class Reshape437(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 32, 32)) + return reshape_output_1 + + +class Reshape438(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 2)) + return reshape_output_1 + + +class Reshape439(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 1)) + return reshape_output_1 + + +class Reshape440(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 1024)) + return reshape_output_1 + + +class Reshape441(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 16, 64)) + return reshape_output_1 + + +class Reshape442(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 1024)) + return reshape_output_1 + + +class Reshape443(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 32, 64)) + return reshape_output_1 + + +class Reshape444(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 32, 64)) + return reshape_output_1 + + +class Reshape445(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 512)) + return reshape_output_1 + + +class Reshape446(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 50272)) + return reshape_output_1 + + +class Reshape447(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 2560)) + return reshape_output_1 + + +class Reshape448(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 32, 80)) + return reshape_output_1 + + +class Reshape449(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 2560)) + return reshape_output_1 + + +class Reshape450(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 12, 80)) + return reshape_output_1 + + +class Reshape451(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 12, 12)) + return reshape_output_1 + + +class Reshape452(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 12, 12)) + return reshape_output_1 + + +class Reshape453(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 80, 12)) + return reshape_output_1 + + +class Reshape454(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 12, 80)) + return reshape_output_1 + + +class Reshape455(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 10240)) + return reshape_output_1 + + +class Reshape456(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 256, 80)) + return reshape_output_1 + + +class Reshape457(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 80, 256)) + return reshape_output_1 + + +class Reshape458(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 256, 80)) + return reshape_output_1 + + +class Reshape459(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 10240)) + return reshape_output_1 + + +class Reshape460(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(11, 2560)) + return reshape_output_1 + + +class Reshape461(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 11, 32, 80)) + return reshape_output_1 + + +class Reshape462(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 11, 2560)) + return reshape_output_1 + + +class Reshape463(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 11, 80)) + return reshape_output_1 + + +class Reshape464(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 11, 11)) + return reshape_output_1 + + +class Reshape465(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 11, 11)) + return reshape_output_1 + + +class Reshape466(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 80, 11)) + return reshape_output_1 + + +class Reshape467(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 11, 80)) + return reshape_output_1 + + +class Reshape468(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 11, 10240)) + return reshape_output_1 + + +class Reshape469(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 256, 96)) + return reshape_output_1 + + +class Reshape470(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 96, 256)) + return reshape_output_1 + + +class Reshape471(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 256, 96)) + return reshape_output_1 + + +class Reshape472(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 13, 32, 96)) + return reshape_output_1 + + +class Reshape473(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(13, 3072)) + return reshape_output_1 + + +class Reshape474(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 13, 96)) + return reshape_output_1 + + +class Reshape475(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 13, 13)) + return reshape_output_1 + + +class Reshape476(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 13, 13)) + return reshape_output_1 + + +class Reshape477(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 96, 13)) + return reshape_output_1 + + +class Reshape478(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 13, 96)) + return reshape_output_1 + + +class Reshape479(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 13, 3072)) + return reshape_output_1 + + +class Reshape480(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 13, 8192)) + return reshape_output_1 + + +class Reshape481(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 5, 32, 96)) + return reshape_output_1 + + +class Reshape482(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(5, 3072)) + return reshape_output_1 + + +class Reshape483(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 5, 96)) + return reshape_output_1 + + +class Reshape484(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 5, 5)) + return reshape_output_1 + + +class Reshape485(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 5, 5)) + return reshape_output_1 + + +class Reshape486(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 96, 5)) + return reshape_output_1 + + +class Reshape487(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 5, 96)) + return reshape_output_1 + + +class Reshape488(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 5, 3072)) + return reshape_output_1 + + +class Reshape489(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 5, 8192)) + return reshape_output_1 + + +class Reshape490(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 1024)) + return reshape_output_1 + + +class Reshape491(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 16, 64)) + return reshape_output_1 + + +class Reshape492(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 1024)) + return reshape_output_1 + + +class Reshape493(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 6, 64)) + return reshape_output_1 + + +class Reshape494(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 6, 6)) + return reshape_output_1 + + +class Reshape495(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 6, 6)) + return reshape_output_1 + + +class Reshape496(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 64, 6)) + return reshape_output_1 + + +class Reshape497(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 6, 64)) + return reshape_output_1 + + +class Reshape498(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 2816)) + return reshape_output_1 + + +class Reshape499(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(29, 1024)) + return reshape_output_1 + + +class Reshape500(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 16, 64)) + return reshape_output_1 + + +class Reshape501(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 1024)) + return reshape_output_1 + + +class Reshape502(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 29, 64)) + return reshape_output_1 + + +class Reshape503(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 29, 29)) + return reshape_output_1 + + +class Reshape504(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 29, 29)) + return reshape_output_1 + + +class Reshape505(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 64, 29)) + return reshape_output_1 + + +class Reshape506(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 29, 64)) + return reshape_output_1 + + +class Reshape507(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 2816)) + return reshape_output_1 + + +class Reshape508(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(35, 3584)) + return reshape_output_1 + + +class Reshape509(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 28, 128)) + return reshape_output_1 + + +class Reshape510(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 3584)) + return reshape_output_1 + + +class Reshape511(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(28, 35, 128)) + return reshape_output_1 + + +class Reshape512(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 512)) + return reshape_output_1 + + +class Reshape513(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 4, 128)) + return reshape_output_1 + + +class Reshape514(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 28, 35, 128)) + return reshape_output_1 + + +class Reshape515(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 28, 35, 35)) + return reshape_output_1 + + +class Reshape516(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(28, 35, 35)) + return reshape_output_1 + + +class Reshape517(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(28, 128, 35)) + return reshape_output_1 + + +class Reshape518(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 18944)) + return reshape_output_1 + + +class Reshape519(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(35, 1536)) + return reshape_output_1 + + +class Reshape520(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 12, 128)) + return reshape_output_1 + + +class Reshape521(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 1536)) + return reshape_output_1 + + +class Reshape522(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 35, 128)) + return reshape_output_1 + + +class Reshape523(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 256)) + return reshape_output_1 + + +class Reshape524(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 2, 128)) + return reshape_output_1 + + +class Reshape525(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 35, 128)) + return reshape_output_1 + + +class Reshape526(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 35, 35)) + return reshape_output_1 + + +class Reshape527(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 35, 35)) + return reshape_output_1 + + +class Reshape528(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 128, 35)) + return reshape_output_1 + + +class Reshape529(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 8960)) + return reshape_output_1 + + +class Reshape530(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(35, 2048)) + return reshape_output_1 + + +class Reshape531(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 16, 128)) + return reshape_output_1 + + +class Reshape532(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 2048)) + return reshape_output_1 + + +class Reshape533(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 35, 128)) + return reshape_output_1 + + +class Reshape534(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 35, 128)) + return reshape_output_1 + + +class Reshape535(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 35, 35)) + return reshape_output_1 + + +class Reshape536(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 35, 35)) + return reshape_output_1 + + +class Reshape537(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 128, 35)) + return reshape_output_1 + + +class Reshape538(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 11008)) + return reshape_output_1 + + +class Reshape539(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(35, 896)) + return reshape_output_1 + + +class Reshape540(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 14, 64)) + return reshape_output_1 + + +class Reshape541(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 896)) + return reshape_output_1 + + +class Reshape542(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(14, 35, 64)) + return reshape_output_1 + + +class Reshape543(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 128)) + return reshape_output_1 + + +class Reshape544(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 2, 64)) + return reshape_output_1 + + +class Reshape545(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 14, 35, 64)) + return reshape_output_1 + + +class Reshape546(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 14, 35, 35)) + return reshape_output_1 + + +class Reshape547(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(14, 35, 35)) + return reshape_output_1 + + +class Reshape548(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(14, 64, 35)) + return reshape_output_1 + + +class Reshape549(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 35, 4864)) + return reshape_output_1 + + +class Reshape550(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(29, 1536)) + return reshape_output_1 + + +class Reshape551(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 12, 128)) + return reshape_output_1 + + +class Reshape552(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 1536)) + return reshape_output_1 + + +class Reshape553(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 29, 128)) + return reshape_output_1 + + +class Reshape554(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 256)) + return reshape_output_1 + + +class Reshape555(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 2, 128)) + return reshape_output_1 + + +class Reshape556(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 29, 128)) + return reshape_output_1 + + +class Reshape557(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 29, 29)) + return reshape_output_1 + + +class Reshape558(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 29, 29)) + return reshape_output_1 + + +class Reshape559(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 128, 29)) + return reshape_output_1 + + +class Reshape560(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 8960)) + return reshape_output_1 + + +class Reshape561(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(39, 1536)) + return reshape_output_1 + + +class Reshape562(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 12, 128)) + return reshape_output_1 + + +class Reshape563(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 1536)) + return reshape_output_1 + + +class Reshape564(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 39, 128)) + return reshape_output_1 + + +class Reshape565(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 256)) + return reshape_output_1 + + +class Reshape566(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 2, 128)) + return reshape_output_1 + + +class Reshape567(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 39, 128)) + return reshape_output_1 + + +class Reshape568(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 39, 39)) + return reshape_output_1 + + +class Reshape569(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 39, 39)) + return reshape_output_1 + + +class Reshape570(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 128, 39)) + return reshape_output_1 + + +class Reshape571(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 8960)) + return reshape_output_1 + + +class Reshape572(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(39, 3584)) + return reshape_output_1 + + +class Reshape573(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 28, 128)) + return reshape_output_1 + + +class Reshape574(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 3584)) + return reshape_output_1 + + +class Reshape575(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(28, 39, 128)) + return reshape_output_1 + + +class Reshape576(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 512)) + return reshape_output_1 + + +class Reshape577(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 4, 128)) + return reshape_output_1 + + +class Reshape578(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 28, 39, 128)) + return reshape_output_1 + + +class Reshape579(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 28, 39, 39)) + return reshape_output_1 + + +class Reshape580(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(28, 39, 39)) + return reshape_output_1 + + +class Reshape581(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(28, 128, 39)) + return reshape_output_1 + + +class Reshape582(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 18944)) + return reshape_output_1 + + +class Reshape583(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(29, 3584)) + return reshape_output_1 + + +class Reshape584(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 28, 128)) + return reshape_output_1 + + +class Reshape585(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 3584)) + return reshape_output_1 + + +class Reshape586(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(28, 29, 128)) + return reshape_output_1 + + +class Reshape587(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 512)) + return reshape_output_1 + + +class Reshape588(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 4, 128)) + return reshape_output_1 + + +class Reshape589(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 28, 29, 128)) + return reshape_output_1 + + +class Reshape590(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 28, 29, 29)) + return reshape_output_1 + + +class Reshape591(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(28, 29, 29)) + return reshape_output_1 + + +class Reshape592(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(28, 128, 29)) + return reshape_output_1 + + +class Reshape593(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 18944)) + return reshape_output_1 + + +class Reshape594(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(29, 2048)) + return reshape_output_1 + + +class Reshape595(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 16, 128)) + return reshape_output_1 + + +class Reshape596(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 2048)) + return reshape_output_1 + + +class Reshape597(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 29, 128)) + return reshape_output_1 + + +class Reshape598(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 29, 128)) + return reshape_output_1 + + +class Reshape599(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 128, 29)) + return reshape_output_1 + + +class Reshape600(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 11008)) + return reshape_output_1 + + +class Reshape601(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(39, 2048)) + return reshape_output_1 + + +class Reshape602(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 16, 128)) + return reshape_output_1 + + +class Reshape603(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 2048)) + return reshape_output_1 + + +class Reshape604(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 39, 128)) + return reshape_output_1 + + +class Reshape605(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 39, 128)) + return reshape_output_1 + + +class Reshape606(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 39, 39)) + return reshape_output_1 + + +class Reshape607(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 39, 39)) + return reshape_output_1 + + +class Reshape608(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 128, 39)) + return reshape_output_1 + + +class Reshape609(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(29, 896)) + return reshape_output_1 + + +class Reshape610(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 14, 64)) + return reshape_output_1 + + +class Reshape611(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 896)) + return reshape_output_1 + + +class Reshape612(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(14, 29, 64)) + return reshape_output_1 + + +class Reshape613(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 128)) + return reshape_output_1 + + +class Reshape614(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 2, 64)) + return reshape_output_1 + + +class Reshape615(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 14, 29, 64)) + return reshape_output_1 + + +class Reshape616(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 14, 29, 29)) + return reshape_output_1 + + +class Reshape617(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(14, 29, 29)) + return reshape_output_1 + + +class Reshape618(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(14, 64, 29)) + return reshape_output_1 + + +class Reshape619(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 29, 4864)) + return reshape_output_1 + + +class Reshape620(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(39, 896)) + return reshape_output_1 + + +class Reshape621(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 14, 64)) + return reshape_output_1 + + +class Reshape622(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 896)) + return reshape_output_1 + + +class Reshape623(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(14, 39, 64)) + return reshape_output_1 + + +class Reshape624(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 128)) + return reshape_output_1 + + +class Reshape625(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 2, 64)) + return reshape_output_1 + + +class Reshape626(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 14, 39, 64)) + return reshape_output_1 + + +class Reshape627(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 14, 39, 39)) + return reshape_output_1 + + +class Reshape628(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(14, 39, 39)) + return reshape_output_1 + + +class Reshape629(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(14, 64, 39)) + return reshape_output_1 + + +class Reshape630(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 39, 4864)) + return reshape_output_1 + + +class Reshape631(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 64, 128)) + return reshape_output_1 + + +class Reshape632(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(768, 768, 1, 1)) + return reshape_output_1 + + +class Reshape633(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 128)) + return reshape_output_1 + + +class Reshape634(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 61)) + return reshape_output_1 + + +class Reshape635(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(61, 1024)) + return reshape_output_1 + + +class Reshape636(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 61, 16, 64)) + return reshape_output_1 + + +class Reshape637(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 61, 1024)) + return reshape_output_1 + + +class Reshape638(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 61, 64)) + return reshape_output_1 + + +class Reshape639(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 61, 61)) + return reshape_output_1 + + +class Reshape640(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 61, 61)) + return reshape_output_1 + + +class Reshape641(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 64, 61)) + return reshape_output_1 + + +class Reshape642(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 61, 64)) + return reshape_output_1 + + +class Reshape643(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 61, 2816)) + return reshape_output_1 + + +class Reshape644(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 1, 61)) + return reshape_output_1 + + +class Reshape645(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 1, 61)) + return reshape_output_1 + + +class Reshape646(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 2816)) + return reshape_output_1 + + +class Reshape647(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(61, 512)) + return reshape_output_1 + + +class Reshape648(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 61, 8, 64)) + return reshape_output_1 + + +class Reshape649(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 61, 512)) + return reshape_output_1 + + +class Reshape650(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 61, 64)) + return reshape_output_1 + + +class Reshape651(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 61, 61)) + return reshape_output_1 + + +class Reshape652(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 61, 61)) + return reshape_output_1 + + +class Reshape653(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 64, 61)) + return reshape_output_1 + + +class Reshape654(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 61, 64)) + return reshape_output_1 + + +class Reshape655(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 1, 61)) + return reshape_output_1 + + +class Reshape656(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 1, 61)) + return reshape_output_1 + + +class Reshape657(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 61, 6, 64)) + return reshape_output_1 + + +class Reshape658(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 61, 64)) + return reshape_output_1 + + +class Reshape659(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 61, 61)) + return reshape_output_1 + + +class Reshape660(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 61, 61)) + return reshape_output_1 + + +class Reshape661(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 64, 61)) + return reshape_output_1 + + +class Reshape662(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 61, 64)) + return reshape_output_1 + + +class Reshape663(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(61, 384)) + return reshape_output_1 + + +class Reshape664(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 1, 61)) + return reshape_output_1 + + +class Reshape665(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 1, 61)) + return reshape_output_1 + + +class Reshape666(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(61, 768)) + return reshape_output_1 + + +class Reshape667(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 61, 12, 64)) + return reshape_output_1 + + +class Reshape668(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 61, 768)) + return reshape_output_1 + + +class Reshape669(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 61, 64)) + return reshape_output_1 + + +class Reshape670(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 61, 61)) + return reshape_output_1 + + +class Reshape671(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 61, 61)) + return reshape_output_1 + + +class Reshape672(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 64, 61)) + return reshape_output_1 + + +class Reshape673(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 61, 64)) + return reshape_output_1 + + +class Reshape674(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 1, 61)) + return reshape_output_1 + + +class Reshape675(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 1, 61)) + return reshape_output_1 + + +class Reshape676(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 61, 2048)) + return reshape_output_1 + + +class Reshape677(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 9216, 1, 1)) + return reshape_output_1 + + +class Reshape678(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 9216)) + return reshape_output_1 + + +class Reshape679(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 96, 54, 54)) + return reshape_output_1 + + +class Reshape680(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 96, 54, 54)) + return reshape_output_1 + + +class Reshape681(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 256, 27, 27)) + return reshape_output_1 + + +class Reshape682(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 27, 27)) + return reshape_output_1 + + +class Reshape683(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 196, 1)) + return reshape_output_1 + + +class Reshape684(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(197, 768)) + return reshape_output_1 + + +class Reshape685(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 197, 12, 64)) + return reshape_output_1 + + +class Reshape686(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 197, 768)) + return reshape_output_1 + + +class Reshape687(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 197, 64)) + return reshape_output_1 + + +class Reshape688(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 197, 197)) + return reshape_output_1 + + +class Reshape689(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 197, 197)) + return reshape_output_1 + + +class Reshape690(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 64, 197)) + return reshape_output_1 + + +class Reshape691(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 12, 197, 64)) + return reshape_output_1 + + +class Reshape692(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 192, 196, 1)) + return reshape_output_1 + + +class Reshape693(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(197, 192)) + return reshape_output_1 + + +class Reshape694(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 197, 3, 64)) + return reshape_output_1 + + +class Reshape695(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 197, 192)) + return reshape_output_1 + + +class Reshape696(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(3, 197, 64)) + return reshape_output_1 + + +class Reshape697(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3, 197, 197)) + return reshape_output_1 + + +class Reshape698(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(3, 197, 197)) + return reshape_output_1 + + +class Reshape699(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(3, 64, 197)) + return reshape_output_1 + + +class Reshape700(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3, 197, 64)) + return reshape_output_1 + + +class Reshape701(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 192)) + return reshape_output_1 + + +class Reshape702(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 384, 196, 1)) + return reshape_output_1 + + +class Reshape703(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(197, 384)) + return reshape_output_1 + + +class Reshape704(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 197, 6, 64)) + return reshape_output_1 + + +class Reshape705(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 197, 384)) + return reshape_output_1 + + +class Reshape706(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 197, 64)) + return reshape_output_1 + + +class Reshape707(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 197, 197)) + return reshape_output_1 + + +class Reshape708(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 197, 197)) + return reshape_output_1 + + +class Reshape709(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(6, 64, 197)) + return reshape_output_1 + + +class Reshape710(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 6, 197, 64)) + return reshape_output_1 + + +class Reshape711(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2208, 1, 1)) + return reshape_output_1 + + +class Reshape712(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1920, 1, 1)) + return reshape_output_1 + + +class Reshape713(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1664, 1, 1)) + return reshape_output_1 + + +class Reshape714(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 1, 1)) + return reshape_output_1 + + +class Reshape715(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1000)) + return reshape_output_1 + + +class Reshape716(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(48, 1, 3, 3)) + return reshape_output_1 + + +class Reshape717(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(24, 1, 3, 3)) + return reshape_output_1 + + +class Reshape718(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(144, 1, 3, 3)) + return reshape_output_1 + + +class Reshape719(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(192, 1, 3, 3)) + return reshape_output_1 + + +class Reshape720(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(192, 1, 5, 5)) + return reshape_output_1 + + +class Reshape721(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(336, 1, 5, 5)) + return reshape_output_1 + + +class Reshape722(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(336, 1, 3, 3)) + return reshape_output_1 + + +class Reshape723(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(672, 1, 3, 3)) + return reshape_output_1 + + +class Reshape724(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(672, 1, 5, 5)) + return reshape_output_1 + + +class Reshape725(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(960, 1, 5, 5)) + return reshape_output_1 + + +class Reshape726(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1632, 1, 5, 5)) + return reshape_output_1 + + +class Reshape727(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1632, 1, 3, 3)) + return reshape_output_1 + + +class Reshape728(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2688, 1, 3, 3)) + return reshape_output_1 + + +class Reshape729(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1792, 1, 1)) + return reshape_output_1 + + +class Reshape730(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(32, 1, 3, 3)) + return reshape_output_1 + + +class Reshape731(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(96, 1, 3, 3)) + return reshape_output_1 + + +class Reshape732(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(144, 1, 5, 5)) + return reshape_output_1 + + +class Reshape733(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(240, 1, 5, 5)) + return reshape_output_1 + + +class Reshape734(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(240, 1, 3, 3)) + return reshape_output_1 + + +class Reshape735(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(480, 1, 3, 3)) + return reshape_output_1 + + +class Reshape736(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(480, 1, 5, 5)) + return reshape_output_1 + + +class Reshape737(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1152, 1, 5, 5)) + return reshape_output_1 + + +class Reshape738(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1152, 1, 3, 3)) + return reshape_output_1 + + +class Reshape739(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1280, 1, 1)) + return reshape_output_1 + + +class Reshape740(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 1, 3, 3)) + return reshape_output_1 + + +class Reshape741(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(12, 1, 3, 3)) + return reshape_output_1 + + +class Reshape742(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 1, 3, 3)) + return reshape_output_1 + + +class Reshape743(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(36, 1, 3, 3)) + return reshape_output_1 + + +class Reshape744(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(72, 1, 5, 5)) + return reshape_output_1 + + +class Reshape745(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(20, 1, 3, 3)) + return reshape_output_1 + + +class Reshape746(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(24, 1, 5, 5)) + return reshape_output_1 + + +class Reshape747(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(60, 1, 3, 3)) + return reshape_output_1 + + +class Reshape748(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(120, 1, 3, 3)) + return reshape_output_1 + + +class Reshape749(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(40, 1, 3, 3)) + return reshape_output_1 + + +class Reshape750(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(100, 1, 3, 3)) + return reshape_output_1 + + +class Reshape751(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(92, 1, 3, 3)) + return reshape_output_1 + + +class Reshape752(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(56, 1, 3, 3)) + return reshape_output_1 + + +class Reshape753(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(80, 1, 3, 3)) + return reshape_output_1 + + +class Reshape754(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(112, 1, 5, 5)) + return reshape_output_1 + + +class Reshape755(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 224, 224)) + return reshape_output_1 + + +class Reshape756(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2048, 1, 1)) + return reshape_output_1 + + +class Reshape757(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1536, 1, 1)) + return reshape_output_1 + + +class Reshape758(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 49, 1)) + return reshape_output_1 + + +class Reshape759(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 196, 1)) + return reshape_output_1 + + +class Reshape760(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 49, 1)) + return reshape_output_1 + + +class Reshape761(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 25088)) + return reshape_output_1 + + +class Reshape762(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 25088, 1, 1)) + return reshape_output_1 + + +class Reshape763(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 49, 1)) + return reshape_output_1 + + +class Reshape764(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 196, 1)) + return reshape_output_1 + + +class Reshape765(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(384, 1, 3, 3)) + return reshape_output_1 + + +class Reshape766(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(768, 1, 3, 3)) + return reshape_output_1 + + +class Reshape767(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 1, 1)) + return reshape_output_1 + + +class Reshape768(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 1, 3, 3)) + return reshape_output_1 + + +class Reshape769(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(128, 1, 3, 3)) + return reshape_output_1 + + +class Reshape770(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 1, 3, 3)) + return reshape_output_1 + + +class Reshape771(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(512, 1, 3, 3)) + return reshape_output_1 + + +class Reshape772(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1024, 1, 3, 3)) + return reshape_output_1 + + +class Reshape773(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(576, 1, 3, 3)) + return reshape_output_1 + + +class Reshape774(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(960, 1, 3, 3)) + return reshape_output_1 + + +class Reshape775(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(288, 1, 3, 3)) + return reshape_output_1 + + +class Reshape776(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(432, 1, 3, 3)) + return reshape_output_1 + + +class Reshape777(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(720, 1, 3, 3)) + return reshape_output_1 + + +class Reshape778(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(72, 1, 3, 3)) + return reshape_output_1 + + +class Reshape779(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(88, 1, 3, 3)) + return reshape_output_1 + + +class Reshape780(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(96, 1, 5, 5)) + return reshape_output_1 + + +class Reshape781(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(120, 1, 5, 5)) + return reshape_output_1 + + +class Reshape782(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(288, 1, 5, 5)) + return reshape_output_1 + + +class Reshape783(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(576, 1, 5, 5)) + return reshape_output_1 + + +class Reshape784(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 576, 1, 1)) + return reshape_output_1 + + +class Reshape785(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(200, 1, 3, 3)) + return reshape_output_1 + + +class Reshape786(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(184, 1, 3, 3)) + return reshape_output_1 + + +class Reshape787(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 960, 1, 1)) + return reshape_output_1 + + +class Reshape788(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 1, 322)) + return reshape_output_1 + + +class Reshape789(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3025, 64)) + return reshape_output_1 + + +class Reshape790(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(3025, 322)) + return reshape_output_1 + + +class Reshape791(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3025, 1, 322)) + return reshape_output_1 + + +class Reshape792(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3025, 322)) + return reshape_output_1 + + +class Reshape793(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 512, 3025)) + return reshape_output_1 + + +class Reshape794(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 3025)) + return reshape_output_1 + + +class Reshape795(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 322, 3025)) + return reshape_output_1 + + +class Reshape796(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(512, 1024)) + return reshape_output_1 + + +class Reshape797(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 8, 128)) + return reshape_output_1 + + +class Reshape798(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 1, 1024)) + return reshape_output_1 + + +class Reshape799(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 1024)) + return reshape_output_1 + + +class Reshape800(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 512, 128)) + return reshape_output_1 + + +class Reshape801(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 512, 512)) + return reshape_output_1 + + +class Reshape802(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 512, 512)) + return reshape_output_1 + + +class Reshape803(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 128, 512)) + return reshape_output_1 + + +class Reshape804(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 512, 128)) + return reshape_output_1 + + +class Reshape805(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 512)) + return reshape_output_1 + + +class Reshape806(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 1, 261)) + return reshape_output_1 + + +class Reshape807(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 50176, 3)) + return reshape_output_1 + + +class Reshape808(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(50176, 261)) + return reshape_output_1 + + +class Reshape809(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 50176, 1, 261)) + return reshape_output_1 + + +class Reshape810(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 50176, 261)) + return reshape_output_1 + + +class Reshape811(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 512, 50176)) + return reshape_output_1 + + +class Reshape812(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 50176)) + return reshape_output_1 + + +class Reshape813(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 261, 50176)) + return reshape_output_1 + + +class Reshape814(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 1, 512)) + return reshape_output_1 + + +class Reshape815(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 50176, 256)) + return reshape_output_1 + + +class Reshape816(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(50176, 512)) + return reshape_output_1 + + +class Reshape817(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 50176, 1, 512)) + return reshape_output_1 + + +class Reshape818(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 50176, 512)) + return reshape_output_1 + + +class Reshape819(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1088, 1, 1)) + return reshape_output_1 + + +class Reshape820(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16384, 1, 32)) + return reshape_output_1 + + +class Reshape821(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 128, 32)) + return reshape_output_1 + + +class Reshape822(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 256)) + return reshape_output_1 + + +class Reshape823(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 32)) + return reshape_output_1 + + +class Reshape824(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 1, 32)) + return reshape_output_1 + + +class Reshape825(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 32)) + return reshape_output_1 + + +class Reshape826(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 16384, 256)) + return reshape_output_1 + + +class Reshape827(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16384, 256)) + return reshape_output_1 + + +class Reshape828(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 128, 128)) + return reshape_output_1 + + +class Reshape829(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 16384, 1)) + return reshape_output_1 + + +class Reshape830(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 64, 4096, 1)) + return reshape_output_1 + + +class Reshape831(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4096, 2, 32)) + return reshape_output_1 + + +class Reshape832(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 64, 64, 64)) + return reshape_output_1 + + +class Reshape833(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 4096, 32)) + return reshape_output_1 + + +class Reshape834(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 64, 256)) + return reshape_output_1 + + +class Reshape835(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 64)) + return reshape_output_1 + + +class Reshape836(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 2, 32)) + return reshape_output_1 + + +class Reshape837(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 1, 64)) + return reshape_output_1 + + +class Reshape838(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 64)) + return reshape_output_1 + + +class Reshape839(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 256, 32)) + return reshape_output_1 + + +class Reshape840(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2, 4096, 256)) + return reshape_output_1 + + +class Reshape841(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 4096, 256)) + return reshape_output_1 + + +class Reshape842(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 32, 256)) + return reshape_output_1 + + +class Reshape843(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2, 4096, 32)) + return reshape_output_1 + + +class Reshape844(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(4096, 64)) + return reshape_output_1 + + +class Reshape845(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4096, 64)) + return reshape_output_1 + + +class Reshape846(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 64, 64)) + return reshape_output_1 + + +class Reshape847(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 4096, 1)) + return reshape_output_1 + + +class Reshape848(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 160, 1024, 1)) + return reshape_output_1 + + +class Reshape849(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 5, 32)) + return reshape_output_1 + + +class Reshape850(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 32, 160)) + return reshape_output_1 + + +class Reshape851(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(5, 1024, 32)) + return reshape_output_1 + + +class Reshape852(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 160, 32, 32)) + return reshape_output_1 + + +class Reshape853(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 160, 256)) + return reshape_output_1 + + +class Reshape854(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 160)) + return reshape_output_1 + + +class Reshape855(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 5, 32)) + return reshape_output_1 + + +class Reshape856(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 160)) + return reshape_output_1 + + +class Reshape857(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(5, 256, 32)) + return reshape_output_1 + + +class Reshape858(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 5, 1024, 256)) + return reshape_output_1 + + +class Reshape859(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(5, 1024, 256)) + return reshape_output_1 + + +class Reshape860(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(5, 32, 256)) + return reshape_output_1 + + +class Reshape861(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 5, 1024, 32)) + return reshape_output_1 + + +class Reshape862(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1024, 160)) + return reshape_output_1 + + +class Reshape863(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 160)) + return reshape_output_1 + + +class Reshape864(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 640, 32, 32)) + return reshape_output_1 + + +class Reshape865(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(640, 1, 3, 3)) + return reshape_output_1 + + +class Reshape866(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 640, 1024, 1)) + return reshape_output_1 + + +class Reshape867(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 256, 1)) + return reshape_output_1 + + +class Reshape868(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 256)) + return reshape_output_1 + + +class Reshape869(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 8, 32)) + return reshape_output_1 + + +class Reshape870(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 256)) + return reshape_output_1 + + +class Reshape871(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 16, 256)) + return reshape_output_1 + + +class Reshape872(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 16, 16)) + return reshape_output_1 + + +class Reshape873(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 256, 32)) + return reshape_output_1 + + +class Reshape874(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 256, 256)) + return reshape_output_1 + + +class Reshape875(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 256, 256)) + return reshape_output_1 + + +class Reshape876(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 32, 256)) + return reshape_output_1 + + +class Reshape877(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 256, 32)) + return reshape_output_1 + + +class Reshape878(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 16, 16)) + return reshape_output_1 + + +class Reshape879(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 256, 1)) + return reshape_output_1 + + +class Reshape880(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16384, 1, 64)) + return reshape_output_1 + + +class Reshape881(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 128, 64)) + return reshape_output_1 + + +class Reshape882(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 128, 128)) + return reshape_output_1 + + +class Reshape883(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 16384, 1)) + return reshape_output_1 + + +class Reshape884(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4096, 2, 64)) + return reshape_output_1 + + +class Reshape885(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 64, 64, 128)) + return reshape_output_1 + + +class Reshape886(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 4096, 64)) + return reshape_output_1 + + +class Reshape887(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 128, 256)) + return reshape_output_1 + + +class Reshape888(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 128)) + return reshape_output_1 + + +class Reshape889(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 2, 64)) + return reshape_output_1 + + +class Reshape890(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 128)) + return reshape_output_1 + + +class Reshape891(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 256, 64)) + return reshape_output_1 + + +class Reshape892(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2, 64, 256)) + return reshape_output_1 + + +class Reshape893(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2, 4096, 64)) + return reshape_output_1 + + +class Reshape894(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(4096, 128)) + return reshape_output_1 + + +class Reshape895(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4096, 128)) + return reshape_output_1 + + +class Reshape896(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 64, 64)) + return reshape_output_1 + + +class Reshape897(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 4096, 1)) + return reshape_output_1 + + +class Reshape898(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 320, 1024, 1)) + return reshape_output_1 + + +class Reshape899(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 5, 64)) + return reshape_output_1 + + +class Reshape900(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 32, 32, 320)) + return reshape_output_1 + + +class Reshape901(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(5, 1024, 64)) + return reshape_output_1 + + +class Reshape902(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 320, 32, 32)) + return reshape_output_1 + + +class Reshape903(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 320, 256)) + return reshape_output_1 + + +class Reshape904(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(256, 320)) + return reshape_output_1 + + +class Reshape905(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 5, 64)) + return reshape_output_1 + + +class Reshape906(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 256, 320)) + return reshape_output_1 + + +class Reshape907(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(5, 256, 64)) + return reshape_output_1 + + +class Reshape908(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(5, 64, 256)) + return reshape_output_1 + + +class Reshape909(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 5, 1024, 64)) + return reshape_output_1 + + +class Reshape910(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1024, 320)) + return reshape_output_1 + + +class Reshape911(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1024, 320)) + return reshape_output_1 + + +class Reshape912(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1280, 32, 32)) + return reshape_output_1 + + +class Reshape913(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1280, 1, 3, 3)) + return reshape_output_1 + + +class Reshape914(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1280, 1024, 1)) + return reshape_output_1 + + +class Reshape915(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 512, 256, 1)) + return reshape_output_1 + + +class Reshape916(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 16, 512)) + return reshape_output_1 + + +class Reshape917(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 256, 64)) + return reshape_output_1 + + +class Reshape918(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(8, 64, 256)) + return reshape_output_1 + + +class Reshape919(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 256, 64)) + return reshape_output_1 + + +class Reshape920(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2048, 16, 16)) + return reshape_output_1 + + +class Reshape921(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2048, 1, 3, 3)) + return reshape_output_1 + + +class Reshape922(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2048, 256, 1)) + return reshape_output_1 + + +class Reshape923(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 16, 16)) + return reshape_output_1 + + +class Reshape924(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 32, 32)) + return reshape_output_1 + + +class Reshape925(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 64, 64)) + return reshape_output_1 + + +class Reshape926(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 128, 128)) + return reshape_output_1 + + +class Reshape927(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 5776)) + return reshape_output_1 + + +class Reshape928(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 2166)) + return reshape_output_1 + + +class Reshape929(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 600)) + return reshape_output_1 + + +class Reshape930(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 150)) + return reshape_output_1 + + +class Reshape931(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 36)) + return reshape_output_1 + + +class Reshape932(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 81, 5776)) + return reshape_output_1 + + +class Reshape933(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 81, 2166)) + return reshape_output_1 + + +class Reshape934(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 81, 600)) + return reshape_output_1 + + +class Reshape935(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 81, 150)) + return reshape_output_1 + + +class Reshape936(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 81, 36)) + return reshape_output_1 + + +class Reshape937(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 81, 4)) + return reshape_output_1 + + +class Reshape938(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 96, 3136, 1)) + return reshape_output_1 + + +class Reshape939(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 7, 8, 7, 96)) + return reshape_output_1 + + +class Reshape940(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 56, 56, 96)) + return reshape_output_1 + + +class Reshape941(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(3136, 96)) + return reshape_output_1 + + +class Reshape942(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 49, 96)) + return reshape_output_1 + + +class Reshape943(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 49, 3, 32)) + return reshape_output_1 + + +class Reshape944(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 8, 8, 7, 7, 96)) + return reshape_output_1 + + +class Reshape945(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(192, 49, 32)) + return reshape_output_1 + + +class Reshape946(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 3, 49, 49)) + return reshape_output_1 + + +class Reshape947(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(2401,)) + return reshape_output_1 + + +class Reshape948(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(49, 49, 3)) + return reshape_output_1 + + +class Reshape949(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(192, 49, 49)) + return reshape_output_1 + + +class Reshape950(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 64, 3, 49, 49)) + return reshape_output_1 + + +class Reshape951(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(192, 32, 49)) + return reshape_output_1 + + +class Reshape952(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(64, 3, 49, 32)) + return reshape_output_1 + + +class Reshape953(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3136, 96)) + return reshape_output_1 + + +class Reshape954(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 784, 384)) + return reshape_output_1 + + +class Reshape955(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 7, 4, 7, 192)) + return reshape_output_1 + + +class Reshape956(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 28, 28, 192)) + return reshape_output_1 + + +class Reshape957(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(784, 192)) + return reshape_output_1 + + +class Reshape958(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 49, 192)) + return reshape_output_1 + + +class Reshape959(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 49, 6, 32)) + return reshape_output_1 + + +class Reshape960(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 4, 7, 7, 192)) + return reshape_output_1 + + +class Reshape961(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(96, 49, 32)) + return reshape_output_1 + + +class Reshape962(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 6, 49, 49)) + return reshape_output_1 + + +class Reshape963(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(49, 49, 6)) + return reshape_output_1 + + +class Reshape964(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(96, 49, 49)) + return reshape_output_1 + + +class Reshape965(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 6, 49, 49)) + return reshape_output_1 + + +class Reshape966(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(96, 32, 49)) + return reshape_output_1 + + +class Reshape967(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 6, 49, 32)) + return reshape_output_1 + + +class Reshape968(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 784, 192)) + return reshape_output_1 + + +class Reshape969(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 196, 768)) + return reshape_output_1 + + +class Reshape970(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2, 7, 2, 7, 384)) + return reshape_output_1 + + +class Reshape971(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 14, 14, 384)) + return reshape_output_1 + + +class Reshape972(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(196, 384)) + return reshape_output_1 + + +class Reshape973(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(4, 49, 384)) + return reshape_output_1 + + +class Reshape974(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(4, 49, 12, 32)) + return reshape_output_1 + + +class Reshape975(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2, 2, 7, 7, 384)) + return reshape_output_1 + + +class Reshape976(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(48, 49, 32)) + return reshape_output_1 + + +class Reshape977(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(4, 12, 49, 49)) + return reshape_output_1 + + +class Reshape978(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(49, 49, 12)) + return reshape_output_1 + + +class Reshape979(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(48, 49, 49)) + return reshape_output_1 + + +class Reshape980(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 12, 49, 49)) + return reshape_output_1 + + +class Reshape981(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(48, 32, 49)) + return reshape_output_1 + + +class Reshape982(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(4, 12, 49, 32)) + return reshape_output_1 + + +class Reshape983(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 196, 384)) + return reshape_output_1 + + +class Reshape984(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 49, 1536)) + return reshape_output_1 + + +class Reshape985(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(49, 768)) + return reshape_output_1 + + +class Reshape986(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 49, 24, 32)) + return reshape_output_1 + + +class Reshape987(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 49, 768)) + return reshape_output_1 + + +class Reshape988(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(24, 49, 32)) + return reshape_output_1 + + +class Reshape989(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 24, 49, 49)) + return reshape_output_1 + + +class Reshape990(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(49, 49, 24)) + return reshape_output_1 + + +class Reshape991(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(24, 49, 49)) + return reshape_output_1 + + +class Reshape992(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(24, 32, 49)) + return reshape_output_1 + + +class Reshape993(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 24, 49, 32)) + return reshape_output_1 + + +class Reshape994(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 768, 1)) + return reshape_output_1 + + +class Reshape995(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4096, 1, 1)) + return reshape_output_1 + + +class Reshape996(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(197, 1024)) + return reshape_output_1 + + +class Reshape997(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 197, 16, 64)) + return reshape_output_1 + + +class Reshape998(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 197, 1024)) + return reshape_output_1 + + +class Reshape999(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 197, 64)) + return reshape_output_1 + + +class Reshape1000(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 197, 197)) + return reshape_output_1 + + +class Reshape1001(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 197, 197)) + return reshape_output_1 + + +class Reshape1002(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(16, 64, 197)) + return reshape_output_1 + + +class Reshape1003(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 16, 197, 64)) + return reshape_output_1 + + +class Reshape1004(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(160, 1, 3, 3)) + return reshape_output_1 + + +class Reshape1005(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(224, 1, 3, 3)) + return reshape_output_1 + + +class Reshape1006(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(728, 1, 3, 3)) + return reshape_output_1 + + +class Reshape1007(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1536, 1, 3, 3)) + return reshape_output_1 + + +class Reshape1008(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 255, 160, 160)) + return reshape_output_1 + + +class Reshape1009(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 255, 25600)) + return reshape_output_1 + + +class Reshape1010(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3, 85, 25600)) + return reshape_output_1 + + +class Reshape1011(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 76800, 85)) + return reshape_output_1 + + +class Reshape1012(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 255, 80, 80)) + return reshape_output_1 + + +class Reshape1013(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 255, 6400)) + return reshape_output_1 + + +class Reshape1014(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3, 85, 6400)) + return reshape_output_1 + + +class Reshape1015(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 19200, 85)) + return reshape_output_1 + + +class Reshape1016(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 255, 40, 40)) + return reshape_output_1 + + +class Reshape1017(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 255, 1600)) + return reshape_output_1 + + +class Reshape1018(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3, 85, 1600)) + return reshape_output_1 + + +class Reshape1019(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4800, 85)) + return reshape_output_1 + + +class Reshape1020(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 255, 20, 20)) + return reshape_output_1 + + +class Reshape1021(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 255, 400)) + return reshape_output_1 + + +class Reshape1022(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3, 85, 400)) + return reshape_output_1 + + +class Reshape1023(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1200, 85)) + return reshape_output_1 + + +class Reshape1024(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 255, 60, 60)) + return reshape_output_1 + + +class Reshape1025(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 255, 3600)) + return reshape_output_1 + + +class Reshape1026(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3, 85, 3600)) + return reshape_output_1 + + +class Reshape1027(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 10800, 85)) + return reshape_output_1 + + +class Reshape1028(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 255, 30, 30)) + return reshape_output_1 + + +class Reshape1029(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 255, 900)) + return reshape_output_1 + + +class Reshape1030(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3, 85, 900)) + return reshape_output_1 + + +class Reshape1031(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 2700, 85)) + return reshape_output_1 + + +class Reshape1032(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 255, 15, 15)) + return reshape_output_1 + + +class Reshape1033(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 255, 225)) + return reshape_output_1 + + +class Reshape1034(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3, 85, 225)) + return reshape_output_1 + + +class Reshape1035(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 675, 85)) + return reshape_output_1 + + +class Reshape1036(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 255, 10, 10)) + return reshape_output_1 + + +class Reshape1037(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 1, 255, 100)) + return reshape_output_1 + + +class Reshape1038(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 3, 85, 100)) + return reshape_output_1 + + +class Reshape1039(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 300, 85)) + return reshape_output_1 + + +class Reshape1040(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 4480)) + return reshape_output_1 + + +class Reshape1041(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 1120)) + return reshape_output_1 + + +class Reshape1042(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 280)) + return reshape_output_1 + + +class Reshape1043(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 80, 4480)) + return reshape_output_1 + + +class Reshape1044(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 80, 1120)) + return reshape_output_1 + + +class Reshape1045(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 80, 280)) + return reshape_output_1 + + +class Reshape1046(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 17, 4480)) + return reshape_output_1 + + +class Reshape1047(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 17, 1120)) + return reshape_output_1 + + +class Reshape1048(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 4, 17, 280)) + return reshape_output_1 + + +class Reshape1049(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 85, 6400, 1)) + return reshape_output_1 + + +class Reshape1050(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 85, 1600, 1)) + return reshape_output_1 + + +class Reshape1051(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 85, 400, 1)) + return reshape_output_1 + + +class Reshape1052(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 85, 2704, 1)) + return reshape_output_1 + + +class Reshape1053(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 85, 676, 1)) + return reshape_output_1 + + +class Reshape1054(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, reshape_input_0): + reshape_output_1 = forge.op.Reshape("", reshape_input_0, shape=(1, 85, 169, 1)) + return reshape_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Reshape0, + [((8, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(2, 4, 1)"}, + }, + ), + ( + Reshape1, + [((2, 1, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(2, 1)"}, + }, + ), + ( + Reshape2, + [((1, 2048), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(1, 2048)"}, + }, + ), + ( + Reshape3, + [((1, 2048), torch.float32)], + {"model_name": ["pt_t5_google_flan_t5_base_text_gen_hf"], "pcc": 0.99, "op_params": {"shape": "(1, 1, 2048)"}}, + ), + ( + Reshape4, + [((2, 1, 2048), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 2048)"}, + }, + ), + ( + Reshape5, + [((2, 1, 2048), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 1, 32, 64)"}, + }, + ), + ( + Reshape6, + [((2, 2048), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(2, 1, 2048)"}, + }, + ), + ( + Reshape5, + [((2, 2048), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 1, 32, 64)"}, + }, + ), + ( + Reshape7, + [((2, 32, 1, 64), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(64, 1, 64)"}, + }, + ), + ( + Reshape8, + [((64, 1, 64), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 32, 1, 64)"}, + }, + ), + ( + Reshape4, + [((2, 1, 32, 64), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 2048)"}, + }, + ), + ( + Reshape9, + [((2, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(2, 13)"}, + }, + ), + ( + Reshape10, + [((2, 13, 768), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(26, 768)"}, + }, + ), + ( + Reshape11, + [((26, 768), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(2, 13, 12, 64)"}, + }, + ), + ( + Reshape12, + [((26, 768), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(2, 13, 768)"}, + }, + ), + ( + Reshape13, + [((2, 12, 13, 64), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(24, 13, 64)"}, + }, + ), + ( + Reshape14, + [((24, 13, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(2, 12, 13, 13)"}, + }, + ), + ( + Reshape15, + [((2, 12, 13, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(24, 13, 13)"}, + }, + ), + ( + Reshape16, + [((2, 12, 64, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(24, 64, 13)"}, + }, + ), + ( + Reshape17, + [((24, 13, 64), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(2, 12, 13, 64)"}, + }, + ), + ( + Reshape10, + [((2, 13, 12, 64), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(26, 768)"}, + }, + ), + ( + Reshape18, + [((26, 3072), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(2, 13, 3072)"}, + }, + ), + ( + Reshape19, + [((2, 13, 3072), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(26, 3072)"}, + }, + ), + ( + Reshape20, + [((26, 2048), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 13, 2048)"}, + }, + ), + ( + Reshape21, + [((26, 2048), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 13, 32, 64)"}, + }, + ), + ( + Reshape22, + [((2, 13, 2048), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(26, 2048)"}, + }, + ), + ( + Reshape23, + [((2, 32, 13, 64), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(64, 13, 64)"}, + }, + ), + ( + Reshape24, + [((64, 1, 13), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 32, 1, 13)"}, + }, + ), + ( + Reshape25, + [((2, 32, 1, 13), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(64, 1, 13)"}, + }, + ), + ( + Reshape26, + [((2, 8192), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 1, 8192)"}, + }, + ), + ( + Reshape27, + [((2, 1, 8192), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 8192)"}, + }, + ), + ( + Reshape28, + [((2, 4, 1, 2048), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(8, 1, 2048)"}, + }, + ), + ( + Reshape29, + [((1, 1536), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(1, 1536)"}, + }, + ), + ( + Reshape30, + [((2, 1, 1536), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 1536)"}, + }, + ), + ( + Reshape31, + [((2, 1, 1536), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 1, 24, 64)"}, + }, + ), + ( + Reshape32, + [((2, 1536), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 1, 1536)"}, + }, + ), + ( + Reshape31, + [((2, 1536), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 1, 24, 64)"}, + }, + ), + ( + Reshape33, + [((2, 24, 1, 64), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(48, 1, 64)"}, + }, + ), + ( + Reshape34, + [((48, 1, 64), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 24, 1, 64)"}, + }, + ), + ( + Reshape30, + [((2, 1, 24, 64), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 1536)"}, + }, + ), + ( + Reshape35, + [((26, 1536), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 13, 1536)"}, + }, + ), + ( + Reshape36, + [((26, 1536), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 13, 24, 64)"}, + }, + ), + ( + Reshape37, + [((2, 13, 1536), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(26, 1536)"}, + }, + ), + ( + Reshape38, + [((2, 24, 13, 64), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(48, 13, 64)"}, + }, + ), + ( + Reshape39, + [((48, 1, 13), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 24, 1, 13)"}, + }, + ), + ( + Reshape40, + [((2, 24, 1, 13), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(48, 1, 13)"}, + }, + ), + ( + Reshape41, + [((2, 6144), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 1, 6144)"}, + }, + ), + ( + Reshape42, + [((2, 1, 6144), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2, 6144)"}, + }, + ), + ( + Reshape43, + [((1, 1024), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], + "pcc": 0.99, + "op_params": {"shape": "(1, 1024)"}, + }, + ), + ( + Reshape44, + [((1, 1024), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + "pt_t5_google_flan_t5_small_text_gen_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(1, 1, 1024)"}, + }, + ), + ( + Reshape45, + [((1, 1024), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + ], + "pcc": 0.99, + "op_params": {"shape": 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[((1, 80, 56, 80), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"shape": "(1, 80, 4480)"}, + }, + ), + ( + Reshape1044, + [((1, 80, 28, 40), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"shape": "(1, 80, 1120)"}, + }, + ), + ( + Reshape1045, + [((1, 80, 14, 20), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"shape": "(1, 80, 280)"}, + }, + ), + ( + Reshape1046, + [((1, 68, 56, 80), torch.float32)], + { + 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"pcc": 0.99, + "op_params": {"shape": "(1, 4, 17, 280)"}, + }, + ), + ( + Reshape1042, + [((1, 1, 4, 280), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"shape": "(1, 4, 280)"}, + }, + ), + ( + Reshape1049, + [((1, 85, 80, 80), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"shape": "(1, 85, 6400, 1)"}, + }, + ), + ( + Reshape1050, + [((1, 85, 40, 40), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"shape": "(1, 85, 1600, 1)"}, + }, + ), + ( + Reshape1051, + [((1, 85, 20, 20), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"shape": "(1, 85, 400, 1)"}, + }, + ), + ( + Reshape1052, + [((1, 85, 52, 52), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"shape": "(1, 85, 2704, 1)"}, + }, + ), + ( + Reshape1053, + [((1, 85, 26, 26), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"shape": "(1, 85, 676, 1)"}, + }, + ), + ( + Reshape1054, + [((1, 85, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"shape": "(1, 85, 169, 1)"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Reshape") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_resize2d.py b/forge/test/models_ops/test_resize2d.py new file mode 100644 index 000000000..10a7d97c0 --- /dev/null +++ b/forge/test/models_ops/test_resize2d.py @@ -0,0 +1,2089 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Resize2D0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[16, 16], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[64, 64], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[128, 128], method="linear", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D3(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[56, 56], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D4(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[28, 28], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D5(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[14, 14], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D6(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[28, 28], method="linear", align_corners=True, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D7(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[20, 64], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D8(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[40, 128], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D9(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[80, 256], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D10(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[160, 512], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D11(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[320, 1024], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D12(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[12, 40], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D13(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[24, 80], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D14(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[48, 160], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D15(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[96, 320], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D16(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[192, 640], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D17(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[30, 40], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D18(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[60, 80], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D19(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[56, 56], method="linear", align_corners=True, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D20(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[112, 112], method="linear", align_corners=True, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D21(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[224, 224], method="linear", align_corners=True, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D22(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[112, 112], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D23(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[224, 224], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D24(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[80, 80], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D25(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[160, 160], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D26(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[40, 40], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D27(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[30, 30], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D28(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[60, 60], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D29(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[20, 20], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D30(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[26, 26], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +class Resize2D31(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, resize2d_input_0): + resize2d_output_1 = forge.op.Resize2d( + "", resize2d_input_0, sizes=[52, 52], method="nearest_neighbor", align_corners=False, channel_last=0 + ) + return resize2d_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Resize2D0, + [((1, 256, 8, 8), torch.float32)], + { + "model_name": ["pt_fpn_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "sizes": "[16, 16]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D1, + [((1, 256, 16, 16), torch.float32)], + { + "model_name": ["pt_fpn_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "sizes": "[64, 64]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D2, + [((1, 256, 16, 16), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + "op_params": {"sizes": "[128, 128]", "method": '"linear"', "align_corners": "False", "channel_last": "0"}, + }, + ), + ( + Resize2D3, + [((1, 18, 28, 28), torch.float32)], + { + "model_name": [ + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + ], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D3, + [((1, 18, 14, 14), torch.float32)], + { + "model_name": [ + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + ], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D4, + [((1, 36, 14, 14), torch.float32)], + { + "model_name": [ + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + ], + "pcc": 0.99, + "op_params": { + "sizes": "[28, 28]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D3, + [((1, 18, 7, 7), torch.float32)], + { + "model_name": [ + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + ], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D4, + [((1, 36, 7, 7), torch.float32)], + { + "model_name": [ + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + ], + "pcc": 0.99, + "op_params": { + "sizes": "[28, 28]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D5, + [((1, 72, 7, 7), torch.float32)], + { + "model_name": [ + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + ], + "pcc": 0.99, + "op_params": { + "sizes": "[14, 14]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D3, + [((1, 44, 28, 28), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D3, + [((1, 44, 14, 14), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D4, + [((1, 88, 14, 14), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], + "pcc": 0.99, + "op_params": { + "sizes": "[28, 28]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D3, + [((1, 44, 7, 7), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D4, + [((1, 88, 7, 7), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], + "pcc": 0.99, + "op_params": { + "sizes": "[28, 28]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D5, + [((1, 176, 7, 7), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], + "pcc": 0.99, + "op_params": { + "sizes": "[14, 14]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D3, + [((1, 30, 28, 28), torch.float32)], + { + "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D3, + [((1, 30, 14, 14), torch.float32)], + { + "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D4, + [((1, 60, 14, 14), torch.float32)], + { + "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], + "pcc": 0.99, + "op_params": { + "sizes": "[28, 28]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D3, + [((1, 30, 7, 7), torch.float32)], + { + "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D4, + [((1, 60, 7, 7), torch.float32)], + { + "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], + "pcc": 0.99, + "op_params": { + "sizes": "[28, 28]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D5, + [((1, 120, 7, 7), torch.float32)], + { + "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], + "pcc": 0.99, + "op_params": { + "sizes": "[14, 14]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D3, + [((1, 32, 28, 28), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w32_pose_estimation_timm", "pt_hrnet_hrnetv2_w32_pose_estimation_osmr"], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D3, + [((1, 32, 14, 14), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w32_pose_estimation_timm", "pt_hrnet_hrnetv2_w32_pose_estimation_osmr"], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D4, + [((1, 32, 14, 14), torch.float32)], + { + "model_name": [ + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + ], + "pcc": 0.99, + "op_params": { + "sizes": "[28, 28]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D4, + [((1, 64, 14, 14), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w32_pose_estimation_timm", "pt_hrnet_hrnetv2_w32_pose_estimation_osmr"], + "pcc": 0.99, + "op_params": { + "sizes": "[28, 28]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D3, + [((1, 64, 14, 14), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w64_pose_estimation_timm", "pt_hrnet_hrnetv2_w64_pose_estimation_osmr"], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D3, + [((1, 32, 7, 7), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w32_pose_estimation_timm", "pt_hrnet_hrnetv2_w32_pose_estimation_osmr"], + "pcc": 0.99, + "op_params": { + "sizes": "[56, 56]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D4, + [((1, 32, 7, 7), torch.float32)], + { + "model_name": [ + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + ], + "pcc": 0.99, + "op_params": { + "sizes": "[28, 28]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D4, + [((1, 64, 7, 7), torch.float32)], + { + "model_name": 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"op_params": { + "sizes": "[112, 112]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D23, + [((1, 32, 112, 112), torch.float32)], + { + "model_name": ["pt_unet_qubvel_img_seg_torchhub"], + "pcc": 0.99, + "op_params": { + "sizes": "[224, 224]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D24, + [((1, 256, 40, 40), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280", + "pt_yolo_v5_yolov5l_imgcls_torchhub_640x640", + "pt_yolox_yolox_l_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": { + "sizes": "[80, 80]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D25, + [((1, 128, 80, 80), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280"], + "pcc": 0.99, + "op_params": { + "sizes": "[160, 160]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D26, + [((1, 640, 20, 20), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640", "pt_yolox_yolox_x_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "sizes": "[40, 40]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D24, + [((1, 320, 40, 40), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640", "pt_yolox_yolox_x_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "sizes": "[80, 80]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D27, + [((1, 640, 15, 15), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "sizes": "[30, 30]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D28, + [((1, 320, 30, 30), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "sizes": "[60, 60]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D27, + [((1, 256, 15, 15), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "sizes": "[30, 30]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D28, + [((1, 128, 30, 30), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "sizes": "[60, 60]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D29, + [((1, 384, 10, 10), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_320x320"], + "pcc": 0.99, + "op_params": { + "sizes": "[20, 20]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D26, + [((1, 192, 20, 20), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_320x320"], + "pcc": 0.99, + "op_params": { + "sizes": "[40, 40]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D29, + [((1, 128, 10, 10), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_320x320"], + "pcc": 0.99, + "op_params": { + "sizes": "[20, 20]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D26, + [((1, 64, 20, 20), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_320x320"], + "pcc": 0.99, + "op_params": { + "sizes": "[40, 40]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D26, + [((1, 128, 20, 20), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_640x640", "pt_yolo_v5_yolov5s_imgcls_torchhub_320x320"], + "pcc": 0.99, + "op_params": { + "sizes": "[40, 40]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D24, + [((1, 64, 40, 40), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "sizes": "[80, 80]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D26, + [((1, 256, 20, 20), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5s_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5l_imgcls_torchhub_320x320", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": { + "sizes": "[40, 40]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D24, + [((1, 128, 40, 40), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5s_imgcls_torchhub_640x640", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": { + "sizes": "[80, 80]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D27, + [((1, 384, 15, 15), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "sizes": "[30, 30]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D28, + [((1, 192, 30, 30), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "sizes": "[60, 60]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D26, + [((1, 512, 20, 20), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_640x640", "pt_yolox_yolox_l_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "sizes": "[40, 40]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D29, + [((1, 512, 10, 10), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_320x320"], + "pcc": 0.99, + "op_params": { + "sizes": "[20, 20]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D29, + [((1, 640, 10, 10), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_320x320"], + "pcc": 0.99, + "op_params": { + "sizes": "[20, 20]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D26, + [((1, 320, 20, 20), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_320x320"], + "pcc": 0.99, + "op_params": { + "sizes": "[40, 40]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D27, + [((1, 512, 15, 15), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "sizes": "[30, 30]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D28, + [((1, 256, 30, 30), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "sizes": "[60, 60]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D27, + [((1, 128, 15, 15), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "sizes": "[30, 30]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D28, + [((1, 64, 30, 30), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "sizes": "[60, 60]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D29, + [((1, 256, 10, 10), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_320x320"], + "pcc": 0.99, + "op_params": { + "sizes": "[20, 20]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D26, + [((1, 384, 20, 20), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", "pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "sizes": "[40, 40]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D24, + [((1, 192, 40, 40), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", "pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "sizes": "[80, 80]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D30, + [((1, 192, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "sizes": "[26, 26]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D31, + [((1, 96, 26, 26), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "sizes": "[52, 52]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D30, + [((1, 128, 13, 13), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "sizes": "[26, 26]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + ( + Resize2D31, + [((1, 64, 26, 26), torch.float32)], + { + "model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "sizes": "[52, 52]", + "method": '"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Resize2d") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_sigmoid.py b/forge/test/models_ops/test_sigmoid.py new file mode 100644 index 000000000..d65db8dca --- /dev/null +++ b/forge/test/models_ops/test_sigmoid.py @@ -0,0 +1,2049 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Sigmoid0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, sigmoid_input_0): + sigmoid_output_1 = forge.op.Sigmoid("", sigmoid_input_0) + return sigmoid_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Sigmoid0, + [((2, 7, 2048), torch.float32)], + {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 39, 11008), torch.float32)], + { + "model_name": [ + "pt_deepseek_deepseek_math_7b_instruct_qa_hf", + "DeepSeekWrapper_decoder", + "pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sigmoid0, + [((1, 10, 9216), torch.float32)], + {"model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 10, 8192), torch.float32)], + {"model_name": ["pt_falcon3_tiiuae_falcon3_1b_base_clm_hf"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 10, 23040), torch.float32)], + {"model_name": ["pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 4, 8192), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sigmoid0, + [((1, 256, 8192), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + 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"pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 96, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 1, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 80, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 16, 208, 208), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 16, 104, 104), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 32, 104, 104), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 32, 52, 52), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 64, 52, 52), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 64, 26, 26), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 128, 26, 26), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 128, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 256, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), + ( + Sigmoid0, + [((1, 64, 13, 13), torch.float32)], + {"model_name": ["pt_yolox_yolox_nano_obj_det_torchhub"], "pcc": 0.99}, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Sigmoid") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_sine.py b/forge/test/models_ops/test_sine.py new file mode 100644 index 000000000..63a490c16 --- /dev/null +++ b/forge/test/models_ops/test_sine.py @@ -0,0 +1,230 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Sine0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, sine_input_0): + sine_output_1 = forge.op.Sine("", sine_input_0) + return sine_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Sine0, + [((1, 39, 128), torch.float32)], + { + "model_name": [ + "pt_deepseek_deepseek_math_7b_instruct_qa_hf", + "DeepSeekWrapper_decoder", + "pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sine0, + [((1, 6, 64), torch.float32)], + { + "model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf", "pt_qwen1_5_qwen_qwen1_5_0_5b_clm_hf"], + "pcc": 0.99, + }, + ), + ( + Sine0, + [((1, 10, 256), torch.float32)], + { + "model_name": [ + "pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_1b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf", + ], + "pcc": 0.99, + }, + ), + (Sine0, [((1, 334, 32), torch.float32)], {"model_name": ["pt_fuyu_adept_fuyu_8b_qa_hf"], "pcc": 0.99}), + (Sine0, [((1, 7, 256), torch.float32)], {"model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], "pcc": 0.99}), + ( + Sine0, + [((1, 4, 64), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sine0, + [((1, 256, 64), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sine0, + [((1, 4, 128), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sine0, + [((1, 128, 128), torch.float32)], + {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99}, + ), + ( + Sine0, + [((1, 12, 32), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_pytdml_token_cls_hf", "pt_phi2_microsoft_phi_2_token_cls_hf"], + "pcc": 0.99, + }, + ), + ( + Sine0, + [((1, 256, 32), torch.float32)], + {"model_name": ["pt_phi2_microsoft_phi_2_clm_hf", "pt_phi2_microsoft_phi_2_pytdml_clm_hf"], "pcc": 0.99}, + ), + ( + Sine0, + [((1, 11, 32), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_seq_cls_hf", "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf"], + "pcc": 0.99, + }, + ), + ( + Sine0, + [((1, 256, 96), torch.float32)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_clm_hf"], "pcc": 0.99}, + ), + ( + Sine0, + [((1, 13, 96), torch.float32)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_token_cls_hf"], "pcc": 0.99}, + ), + ( + Sine0, + [((1, 5, 96), torch.float32)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf"], "pcc": 0.99}, + ), + ( + Sine0, + [((1, 29, 64), torch.float32)], + { + "model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_chat_clm_hf", "pt_qwen_v2_qwen_qwen2_5_0_5b_clm_hf"], + "pcc": 0.99, + }, + ), + ( + Sine0, + [((1, 35, 128), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sine0, + [((1, 35, 64), torch.float32)], + {"model_name": ["pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf"], "pcc": 0.99}, + ), + ( + Sine0, + [((1, 29, 128), torch.float32)], + { + "model_name": [ + "pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sine0, + [((1, 39, 64), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf"], "pcc": 0.99}, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Sine") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_softmax.py b/forge/test/models_ops/test_softmax.py new file mode 100644 index 000000000..8a7cbc76f --- /dev/null +++ b/forge/test/models_ops/test_softmax.py @@ -0,0 +1,990 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Softmax0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, softmax_input_0): + softmax_output_1 = forge.op.Softmax("", softmax_input_0, dim=-1) + return softmax_output_1 + + +class Softmax1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, softmax_input_0): + softmax_output_1 = forge.op.Softmax("", softmax_input_0, dim=1) + return softmax_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Softmax0, + [((64, 1, 1), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((2, 12, 13, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((64, 1, 13), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((48, 1, 1), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((48, 1, 13), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((32, 1, 1), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((32, 1, 13), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 16, 1, 1), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 16, 1500, 1500), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 16, 1, 1500), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 20, 1, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 20, 1500, 1500), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 20, 1, 1500), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 6, 1, 1), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_tiny_speech_recognition_hf", + "pt_t5_google_flan_t5_small_text_gen_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 6, 1500, 1500), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 6, 1, 1500), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 8, 1, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf", "pt_t5_t5_small_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 8, 1500, 1500), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 8, 1, 1500), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 12, 1, 1), torch.float32)], + { + 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"pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 8, 256, 256), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((64, 3, 49, 49), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((16, 6, 49, 49), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((4, 12, 49, 49), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 24, 49, 49), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Softmax0, + [((1, 16, 197, 197), torch.float32)], + {"model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], "pcc": 0.99, "op_params": {"dim": "-1"}}, + ), + ( + Softmax1, + [((1, 17, 4, 4480), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Softmax1, + [((1, 17, 4, 1120), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Softmax1, + [((1, 17, 4, 280), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Softmax") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_sqrt.py b/forge/test/models_ops/test_sqrt.py new file mode 100644 index 000000000..c6ad5c232 --- /dev/null +++ b/forge/test/models_ops/test_sqrt.py @@ -0,0 +1,1979 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Sqrt0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, sqrt_input_0): + sqrt_output_1 = forge.op.Sqrt("", sqrt_input_0) + return sqrt_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Sqrt0, + [((2, 13, 1), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((1, 39, 1), torch.float32)], + { + "model_name": [ + "pt_deepseek_deepseek_math_7b_instruct_qa_hf", + "DeepSeekWrapper_decoder", + "pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((1, 10, 1), torch.float32)], + { + "model_name": [ + "pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_1b_base_clm_hf", + "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf", + ], + "pcc": 0.99, + }, + ), + (Sqrt0, [((1, 7, 1), torch.float32)], {"model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], "pcc": 0.99}), + ( + Sqrt0, + [((1, 4, 1), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((1, 256, 1), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + "pt_phi3_microsoft_phi_3_mini_4k_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((1, 128, 1), torch.float32)], + {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99}, + ), + ( + Sqrt0, + [((1, 13, 1), torch.float32)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_token_cls_hf"], "pcc": 0.99}, + ), + ( + Sqrt0, + [((1, 5, 1), torch.float32)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf"], "pcc": 0.99}, + ), + (Sqrt0, [((1, 6, 1), torch.float32)], {"model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_clm_hf"], "pcc": 0.99}), + ( + Sqrt0, + [((1, 29, 1), torch.float32)], + { + "model_name": [ + "pt_qwen1_5_qwen_qwen1_5_0_5b_chat_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_0_5b_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((1, 35, 1), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((1, 1, 1), torch.float32)], + { + "model_name": [ + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + "pt_t5_t5_small_text_gen_hf", + "pt_t5_google_flan_t5_small_text_gen_hf", + "pt_t5_t5_base_text_gen_hf", + "pt_t5_google_flan_t5_base_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((1, 61, 1), torch.float32)], + { + "model_name": [ + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + "pt_t5_t5_small_text_gen_hf", + "pt_t5_google_flan_t5_small_text_gen_hf", + "pt_t5_t5_base_text_gen_hf", + "pt_t5_google_flan_t5_base_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + (Sqrt0, [((1, 96, 54, 54), torch.float32)], {"model_name": ["pt_alexnet_base_img_cls_osmr"], "pcc": 0.99}), + (Sqrt0, [((1, 256, 27, 27), torch.float32)], {"model_name": ["pt_alexnet_base_img_cls_osmr"], "pcc": 0.99}), + ( + Sqrt0, + [((96,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_inception_v4_img_cls_timm", + "pt_inception_v4_img_cls_osmr", + "pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_tiny_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((192,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_timm", + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_inception_v4_img_cls_timm", + "pt_inception_v4_img_cls_osmr", + "pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + "pt_regnet_facebook_regnet_y_040_img_cls_hf", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_tiny_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((144,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_timm", + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_googlenet_base_img_cls_torchvision", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf", + "pt_mobilenetv2_basic_img_cls_torchhub", + "pt_mobilenetv2_mobilenetv2_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((240,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((288,), torch.float32)], + { + "model_name": [ + "pt_densenet_densenet161_img_cls_torchvision", + "pt_densenet_densenet201_img_cls_torchvision", + "pt_densenet_densenet169_img_cls_torchvision", + "pt_densenet_densenet121_img_cls_torchvision", + "pt_googlenet_base_img_cls_torchvision", + 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"pt_yolox_yolox_tiny_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((56,), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b4_img_cls_timm", + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((112,), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b4_img_cls_timm", + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_googlenet_base_img_cls_torchvision", + "pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + "pt_vovnet_vovnet27s_obj_det_osmr", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((272,), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b4_img_cls_timm", + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((2688,), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b4_img_cls_timm", + "pt_efficientnet_efficientnet_b4_img_cls_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((40,), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_hrnet_hrnet_w40_pose_estimation_timm", + "pt_hrnet_hrnetv2_w40_pose_estimation_osmr", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((80,), torch.float32)], + { + "model_name": [ + "pt_efficientnet_efficientnet_b0_img_cls_torchvision", + "pt_efficientnet_efficientnet_b0_img_cls_timm", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_hrnet_hrnet_w40_pose_estimation_timm", + "pt_hrnet_hrnetv2_w40_pose_estimation_osmr", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + "pt_vovnet_vovnet27s_obj_det_osmr", + "pt_yolox_yolox_x_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((8,), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf", + ], + "pcc": 0.99, + }, + ), + (Sqrt0, [((12,), torch.float32)], {"model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm"], "pcc": 0.99}), + ( + Sqrt0, + [((36,), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((72,), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + (Sqrt0, [((20,), torch.float32)], {"model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm"], "pcc": 0.99}), + ( + Sqrt0, + [((60,), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((120,), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + (Sqrt0, [((100,), torch.float32)], {"model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm"], "pcc": 0.99}), + (Sqrt0, [((92,), torch.float32)], {"model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm"], "pcc": 0.99}), + (Sqrt0, [((208,), torch.float32)], {"model_name": ["pt_googlenet_base_img_cls_torchvision"], "pcc": 0.99}), + ( + Sqrt0, + [((18,), torch.float32)], + { + "model_name": [ + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((44,), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((88,), torch.float32)], + { + "model_name": [ + "pt_hrnet_hrnet_w44_pose_estimation_timm", + "pt_hrnet_hrnetv2_w44_pose_estimation_osmr", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((176,), torch.float32)], + { + "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((30,), torch.float32)], + { + "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((200,), torch.float32)], + { + "model_name": [ + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((184,), torch.float32)], + { + "model_name": [ + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Sqrt0, + [((728,), torch.float32)], + { + "model_name": [ + "pt_xception_xception_img_cls_timm", + "pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Sqrt") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_squeeze.py b/forge/test/models_ops/test_squeeze.py new file mode 100644 index 000000000..f1687f7bc --- /dev/null +++ b/forge/test/models_ops/test_squeeze.py @@ -0,0 +1,1178 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Squeeze0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, squeeze_input_0): + squeeze_output_1 = forge.op.Squeeze("", squeeze_input_0, dim=-1) + return squeeze_output_1 + + +class Squeeze1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, squeeze_input_0): + squeeze_output_1 = forge.op.Squeeze("", squeeze_input_0, dim=-2) + return squeeze_output_1 + + +class Squeeze2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, squeeze_input_0): + squeeze_output_1 = forge.op.Squeeze("", squeeze_input_0, dim=-4) + return squeeze_output_1 + + +class Squeeze3(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, squeeze_input_0): + squeeze_output_1 = forge.op.Squeeze("", squeeze_input_0, dim=-3) + return squeeze_output_1 + + +class Squeeze4(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, squeeze_input_0): + squeeze_output_1 = forge.op.Squeeze("", squeeze_input_0, dim=1) + return squeeze_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Squeeze0, + [((1, 128, 4096, 1), torch.float32)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 128, 768, 1), torch.float32)], + { + "model_name": [ + "pt_albert_base_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_base_v2_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 128, 1024, 1), torch.float32)], + { + "model_name": [ + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_large_v1_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 128, 2048, 1), torch.float32)], + { + "model_name": [ + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 384, 1), torch.float32)], + { + "model_name": [ + "pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf", + "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 256, 16, 32, 1), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 128, 1), torch.float32)], + { + "model_name": [ + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 1), torch.int32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 32, 1), torch.float32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze1, + [((1, 768, 1, 128), torch.float32)], + { + "model_name": ["pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2"}, + }, + ), + ( + Squeeze1, + [((1, 3072, 1, 128), torch.float32)], + { + "model_name": ["pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2"}, + }, + ), + ( + Squeeze2, + [((1, 1, 1024, 1), torch.float32)], + { + "model_name": [ + "pt_nbeats_seasionality_basis_clm_hf", + "pt_nbeats_trend_basis_clm_hf", + "pt_nbeats_generic_basis_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-4"}, + }, + ), + ( + Squeeze3, + [((1, 1024, 1), torch.float32)], + { + "model_name": [ + "pt_nbeats_seasionality_basis_clm_hf", + "pt_nbeats_trend_basis_clm_hf", + "pt_nbeats_generic_basis_clm_hf", + ], + "pcc": 0.99, + 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"pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2"}, + }, + ), + ( + Squeeze0, + [((1, 256, 16384, 1), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 512, 4096, 1), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 320, 1024, 1), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 1280, 1024, 1), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 512, 256, 1), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 2048, 256, 1), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 96, 3136, 1), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze1, + [((1, 25088, 1, 1), torch.float32)], + { + "model_name": ["pt_vgg_19_obj_det_hf", "pt_vgg_vgg19_bn_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "-2"}, + }, + ), + ( + Squeeze0, + [((1, 25088, 1), torch.float32)], + { + "model_name": ["pt_vgg_19_obj_det_hf", "pt_vgg_vgg19_bn_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze1, + [((1, 4096, 1, 1), torch.float32)], + {"model_name": ["pt_vgg_vgg19_bn_obj_det_timm"], "pcc": 0.99, "op_params": {"dim": "-2"}}, + ), + ( + Squeeze0, + [((1, 4096, 1), torch.float32)], + {"model_name": ["pt_vgg_vgg19_bn_obj_det_timm"], "pcc": 0.99, "op_params": {"dim": "-1"}}, + ), + ( + Squeeze0, + [((1, 85, 6400, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 85, 1600, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 85, 400, 1), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 85, 2704, 1), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 85, 676, 1), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + ( + Squeeze0, + [((1, 85, 169, 1), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Squeeze") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_stack.py b/forge/test/models_ops/test_stack.py new file mode 100644 index 000000000..23aa4f28b --- /dev/null +++ b/forge/test/models_ops/test_stack.py @@ -0,0 +1,113 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Stack0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, stack_input_0, stack_input_1, stack_input_2, stack_input_3): + stack_output_1 = forge.op.Stack("", stack_input_0, stack_input_1, stack_input_2, stack_input_3, axis=-3) + return stack_output_1 + + +class Stack1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, stack_input_0, stack_input_1): + stack_output_1 = forge.op.Stack("", stack_input_0, stack_input_1, axis=-1) + return stack_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Stack0, + [ + ((2, 1, 2048), torch.float32), + ((2, 1, 2048), torch.float32), + ((2, 1, 2048), torch.float32), + ((2, 1, 2048), torch.float32), + ], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-3"}, + }, + ), + ( + Stack1, + [((1, 256, 16, 16), torch.float32), ((1, 256, 16, 16), torch.float32)], + { + "model_name": [ + "pt_codegen_salesforce_codegen_350m_multi_clm_hf", + "pt_codegen_salesforce_codegen_350m_mono_clm_hf", + "pt_codegen_salesforce_codegen_350m_nl_clm_hf", + ], + "pcc": 0.99, + "op_params": {"axis": "-1"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Stack") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_subtract.py b/forge/test/models_ops/test_subtract.py new file mode 100644 index 000000000..c35c6fcd0 --- /dev/null +++ b/forge/test/models_ops/test_subtract.py @@ -0,0 +1,853 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Subtract0(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract0_const_0", shape=(1,), dtype=torch.float32) + + def forward(self, subtract_input_1): + subtract_output_1 = forge.op.Subtract("", self.get_constant("subtract0_const_0"), subtract_input_1) + return subtract_output_1 + + +class Subtract1(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract1_const_0", shape=(1,), dtype=torch.int32) + + def forward(self, subtract_input_1): + subtract_output_1 = forge.op.Subtract("", self.get_constant("subtract1_const_0"), subtract_input_1) + return subtract_output_1 + + +class Subtract2(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract2_const_1", shape=(1,), dtype=torch.int64) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract2_const_1")) + return subtract_output_1 + + +class Subtract3(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract3_const_1", shape=(1,), dtype=torch.int32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract3_const_1")) + return subtract_output_1 + + +class Subtract4(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, subtract_input_0, subtract_input_1): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, subtract_input_1) + return subtract_output_1 + + +class Subtract5(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract5_const_1", shape=(1,), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract5_const_1")) + return subtract_output_1 + + +class Subtract6(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract6_const_1", shape=(1, 256, 10, 32), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract6_const_1")) + return subtract_output_1 + + +class Subtract7(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract7_const_1", shape=(1, 256, 20, 64), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract7_const_1")) + return subtract_output_1 + + +class Subtract8(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract8_const_1", shape=(1, 128, 20, 64), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract8_const_1")) + return subtract_output_1 + + +class Subtract9(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract9_const_1", shape=(1, 128, 40, 128), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract9_const_1")) + return subtract_output_1 + + +class Subtract10(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract10_const_1", shape=(1, 64, 40, 128), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract10_const_1")) + return subtract_output_1 + + +class Subtract11(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract11_const_1", shape=(1, 64, 80, 256), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract11_const_1")) + return subtract_output_1 + + +class Subtract12(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract12_const_1", shape=(1, 32, 80, 256), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract12_const_1")) + return subtract_output_1 + + +class Subtract13(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract13_const_1", shape=(1, 32, 160, 512), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract13_const_1")) + return subtract_output_1 + + +class Subtract14(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract14_const_1", shape=(1, 16, 160, 512), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract14_const_1")) + return subtract_output_1 + + +class Subtract15(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract15_const_1", shape=(1, 16, 320, 1024), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract15_const_1")) + return subtract_output_1 + + +class Subtract16(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract16_const_1", shape=(1, 256, 6, 20), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract16_const_1")) + return subtract_output_1 + + +class Subtract17(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract17_const_1", shape=(1, 256, 12, 40), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract17_const_1")) + return subtract_output_1 + + +class Subtract18(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract18_const_1", shape=(1, 128, 12, 40), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract18_const_1")) + return subtract_output_1 + + +class Subtract19(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract19_const_1", shape=(1, 128, 24, 80), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract19_const_1")) + return subtract_output_1 + + +class Subtract20(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract20_const_1", shape=(1, 64, 24, 80), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract20_const_1")) + return subtract_output_1 + + +class Subtract21(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract21_const_1", shape=(1, 64, 48, 160), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract21_const_1")) + return subtract_output_1 + + +class Subtract22(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract22_const_1", shape=(1, 32, 48, 160), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract22_const_1")) + return subtract_output_1 + + +class Subtract23(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract23_const_1", shape=(1, 32, 96, 320), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract23_const_1")) + return subtract_output_1 + + +class Subtract24(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract24_const_1", shape=(1, 16, 96, 320), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract24_const_1")) + return subtract_output_1 + + +class Subtract25(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract25_const_1", shape=(1, 16, 192, 640), dtype=torch.float32) + + def forward(self, subtract_input_0): + subtract_output_1 = forge.op.Subtract("", subtract_input_0, self.get_constant("subtract25_const_1")) + return subtract_output_1 + + +class Subtract26(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("subtract26_const_0", shape=(5880, 2), dtype=torch.float32) + + def forward(self, subtract_input_1): + subtract_output_1 = forge.op.Subtract("", self.get_constant("subtract26_const_0"), subtract_input_1) + return subtract_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Subtract0, + [((2, 1, 1, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + ( + Subtract0, + [((2, 1, 7, 7), torch.float32)], + {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99}, + ), + ( + Subtract0, + [((1, 1, 1, 204), torch.float32)], + {"model_name": ["pt_vilt_dandelin_vilt_b32_mlm_mlm_hf"], "pcc": 0.99}, + ), + ( + Subtract0, + [((1, 1, 1, 201), torch.float32)], + {"model_name": ["pt_vilt_dandelin_vilt_b32_finetuned_vqa_qa_hf"], "pcc": 0.99}, + ), + ( + Subtract0, + [((1, 1, 1, 128), torch.float32)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_roberta_xlm_roberta_base_mlm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Subtract0, + [((1, 1, 256, 256), torch.float32)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Subtract1, + [((1, 128), torch.int32)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Subtract0, + [((1, 12, 128, 128), torch.float32)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Subtract1, + [((1, 384), torch.int32)], + {"model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], "pcc": 0.99}, + ), + ( + Subtract0, + [((1, 12, 384, 384), torch.float32)], + {"model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], "pcc": 0.99}, + ), + (Subtract0, [((1, 1, 1, 256), torch.float32)], {"model_name": ["pt_gpt2_gpt2_text_gen_hf"], "pcc": 0.99}), + ( + Subtract1, + [((1, 1, 256, 256), torch.int32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Subtract0, + [((1, 1, 1, 7), torch.float32)], + {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99}, + ), + ( + Subtract2, + [((1, 256), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Subtract2, + [((1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + }, + ), + ( + Subtract0, + [((1, 1, 32, 32), torch.float32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + }, + ), + ( + Subtract3, + [((1,), torch.int32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Subtract4, + [((1024, 72), torch.float32), ((1024, 72), torch.float32)], + { + "model_name": [ + "pt_nbeats_seasionality_basis_clm_hf", + "pt_nbeats_trend_basis_clm_hf", + "pt_nbeats_generic_basis_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Subtract5, + [((1, 3, 320, 1024), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract6, + [((1, 256, 10, 32), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract7, + [((1, 256, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract8, + [((1, 128, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract9, + [((1, 128, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract10, + [((1, 64, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract11, + [((1, 64, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract12, + [((1, 32, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract13, + [((1, 32, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract14, + [((1, 16, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract15, + [((1, 16, 320, 1024), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract5, + [((1, 3, 192, 640), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract16, + [((1, 256, 6, 20), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract17, + [((1, 256, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract18, + [((1, 128, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract19, + [((1, 128, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract20, + [((1, 64, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract21, + [((1, 64, 48, 160), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract22, + [((1, 32, 48, 160), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract23, + [((1, 32, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract24, + [((1, 16, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract25, + [((1, 16, 192, 640), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Subtract26, + [((1, 5880, 2), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + ( + Subtract4, + [((1, 5880, 2), torch.float32), ((1, 5880, 2), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Subtract") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_tanh.py b/forge/test/models_ops/test_tanh.py new file mode 100644 index 000000000..2571db386 --- /dev/null +++ b/forge/test/models_ops/test_tanh.py @@ -0,0 +1,87 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Tanh0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, tanh_input_0): + tanh_output_1 = forge.op.Tanh("", tanh_input_0) + return tanh_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Tanh0, + [((1, 768), torch.float32)], + { + "model_name": [ + "pt_vilt_dandelin_vilt_b32_mlm_mlm_hf", + "pt_vilt_dandelin_vilt_b32_finetuned_vqa_qa_hf", + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + "pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Tanh") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_transpose.py b/forge/test/models_ops/test_transpose.py new file mode 100644 index 000000000..6587ab6f3 --- /dev/null +++ b/forge/test/models_ops/test_transpose.py @@ -0,0 +1,9540 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Transpose0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, transpose_input_0): + transpose_output_1 = forge.op.Transpose("", transpose_input_0, dim0=-2, dim1=-1) + return transpose_output_1 + + +class Transpose1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, transpose_input_0): + transpose_output_1 = forge.op.Transpose("", transpose_input_0, dim0=-3, dim1=-2) + return transpose_output_1 + + +class Transpose2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, transpose_input_0): + transpose_output_1 = forge.op.Transpose("", transpose_input_0, dim0=-3, dim1=-1) + return transpose_output_1 + + +class Transpose3(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, transpose_input_0): + transpose_output_1 = forge.op.Transpose("", transpose_input_0, dim0=-4, dim1=-3) + return transpose_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Transpose0, + [((2048, 2048), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + "pt_falcon3_tiiuae_falcon3_1b_base_clm_hf", + "pt_gemma_google_gemma_2b_text_gen_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_nbeats_seasionality_basis_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose1, + [((2, 1, 32, 64), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim0": "-3", "dim1": "-2"}, + }, + ), + ( + Transpose0, + [((64, 1, 64), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((64, 64, 1), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose1, + [((2, 32, 1, 64), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim0": "-3", "dim1": "-2"}, + }, + ), + ( + Transpose0, + [((768, 768), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + "pt_whisper_openai_whisper_small_speech_recognition_hf", + "pt_vilt_dandelin_vilt_b32_mlm_mlm_hf", + "pt_vilt_dandelin_vilt_b32_finetuned_vqa_qa_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_125m_qa_hf", + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + "pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf", + "pt_t5_t5_base_text_gen_hf", + "pt_t5_google_flan_t5_base_text_gen_hf", + "pt_deit_facebook_deit_base_patch16_224_img_cls_hf", + "pt_deit_facebook_deit_base_distilled_patch16_224_img_cls_hf", + "pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf", + "pt_vit_google_vit_base_patch16_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose1, + [((2, 13, 12, 64), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim0": "-3", "dim1": "-2"}, + }, + ), + ( + Transpose0, + [((24, 13, 64), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose2, + [((13, 13, 12), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim0": "-3", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((12, 13, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((2, 12, 13, 64), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose1, + [((2, 12, 13, 64), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim0": "-3", "dim1": "-2"}, + }, + ), + ( + Transpose0, + [((24, 64, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((3072, 768), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + "pt_whisper_openai_whisper_small_speech_recognition_hf", + "pt_vilt_dandelin_vilt_b32_mlm_mlm_hf", + "pt_vilt_dandelin_vilt_b32_finetuned_vqa_qa_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_125m_qa_hf", + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + "pt_t5_t5_base_text_gen_hf", + "pt_deit_facebook_deit_base_patch16_224_img_cls_hf", + "pt_deit_facebook_deit_base_distilled_patch16_224_img_cls_hf", + "pt_mlp_mixer_mixer_b32_224_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_miil_in21k_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_miil_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_in21k_img_cls_timm", + "pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf", + "pt_vit_google_vit_base_patch16_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((768, 3072), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + "pt_whisper_openai_whisper_small_speech_recognition_hf", + "pt_vilt_dandelin_vilt_b32_mlm_mlm_hf", + "pt_vilt_dandelin_vilt_b32_finetuned_vqa_qa_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_125m_clm_hf", + 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"pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((1, 3, 85, 1600), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280", + "pt_yolo_v5_yolov5x_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5m_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5n_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5n_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5s_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5l_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5l_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5x_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5s_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((1, 3, 85, 400), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5x_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5m_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5n_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5n_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5s_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5l_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5l_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5x_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5s_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((1, 3, 85, 3600), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5x_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5s_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5m_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5l_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5n_imgcls_torchhub_480x480", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((1, 3, 85, 900), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5x_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5s_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5m_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5l_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5n_imgcls_torchhub_480x480", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((1, 3, 85, 225), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5x_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5s_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5m_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5l_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5n_imgcls_torchhub_480x480", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((1, 3, 85, 100), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5m_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5n_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5l_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5x_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5s_imgcls_torchhub_320x320", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((1, 4, 5880), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((1, 80, 5880), torch.float32)], + { + "model_name": [ + "pt_yolo_v6_yolov6s_obj_det_torchhub", + "pt_yolo_v6_yolov6l_obj_det_torchhub", + "pt_yolo_v6_yolov6n_obj_det_torchhub", + "pt_yolo_v6_yolov6m_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose1, + [((1, 4, 17, 4480), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim0": "-3", "dim1": "-2"}, + }, + ), + ( + Transpose1, + [((1, 4, 17, 1120), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim0": "-3", "dim1": "-2"}, + }, + ), + ( + Transpose1, + [((1, 4, 17, 280), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim0": "-3", "dim1": "-2"}, + }, + ), + ( + Transpose0, + [((1, 85, 8400), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + ( + Transpose0, + [((1, 85, 3549), torch.float32)], + { + "model_name": ["pt_yolox_yolox_tiny_obj_det_torchhub", "pt_yolox_yolox_nano_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Transpose") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_unsqueeze.py b/forge/test/models_ops/test_unsqueeze.py new file mode 100644 index 000000000..ed3e11489 --- /dev/null +++ b/forge/test/models_ops/test_unsqueeze.py @@ -0,0 +1,6764 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Unsqueeze0(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, unsqueeze_input_0): + unsqueeze_output_1 = forge.op.Unsqueeze("", unsqueeze_input_0, dim=0) + return unsqueeze_output_1 + + +class Unsqueeze1(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, unsqueeze_input_0): + unsqueeze_output_1 = forge.op.Unsqueeze("", unsqueeze_input_0, dim=1) + return unsqueeze_output_1 + + +class Unsqueeze2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, unsqueeze_input_0): + unsqueeze_output_1 = forge.op.Unsqueeze("", unsqueeze_input_0, dim=2) + return unsqueeze_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Unsqueeze0, + [((12, 13, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "0"}, + }, + ), + ( + Unsqueeze1, + [((2, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze2, + [((2, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "2"}, + }, + ), + ( + Unsqueeze2, + [((2, 1, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "2"}, + }, + ), + ( + Unsqueeze1, + [((1024,), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w44_pose_estimation_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w32_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w64_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w64_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w44_pose_estimation_osmr", + "pt_hrnet_hrnet_w40_pose_estimation_timm", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w40_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_hrnet_hrnetv2_w32_pose_estimation_osmr", + "pt_mlp_mixer_mixer_l32_224_img_cls_timm", + "pt_mlp_mixer_mixer_l16_224_in21k_img_cls_timm", + "pt_mlp_mixer_mixer_l16_224_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + "pt_vit_google_vit_large_patch16_224_img_cls_hf", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((1280,), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_large_speech_recognition_hf", + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((384,), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_tiny_speech_recognition_hf", + "pt_alexnet_alexnet_img_cls_torchhub", + "pt_alexnet_base_img_cls_osmr", + "pt_deit_facebook_deit_small_patch16_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((512,), torch.float32)], + { + "model_name": [ + "pt_whisper_openai_whisper_base_speech_recognition_hf", + "pt_hrnet_hrnet_w18_pose_estimation_timm", + "pt_hrnet_hrnet_w44_pose_estimation_timm", + "pt_hrnet_hrnetv2_w30_pose_estimation_osmr", + "pt_hrnet_hrnet_w32_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", + "pt_hrnet_hrnet_w30_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_pose_estimation_timm", + "pt_hrnet_hrnet_w64_pose_estimation_timm", + "pt_hrnet_hrnet_w18_small_v1_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w48_pose_estimation_osmr", + "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w64_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w44_pose_estimation_osmr", + "pt_hrnet_hrnet_w40_pose_estimation_timm", + "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", + "pt_hrnet_hrnetv2_w40_pose_estimation_osmr", + "pt_hrnet_hrnet_w48_pose_estimation_timm", + "pt_hrnet_hrnetv2_w32_pose_estimation_osmr", + "pt_mlp_mixer_mixer_s16_224_img_cls_timm", + "pt_mlp_mixer_mixer_s32_224_img_cls_timm", + "pt_regnet_facebook_regnet_y_040_img_cls_hf", + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + "pt_unet_cityscape_img_seg_osmr", + "pt_vgg_19_obj_det_hf", + "pt_vgg_vgg19_bn_obj_det_torchhub", + "pt_vgg_vgg11_obj_det_osmr", + "pt_vgg_vgg19_bn_obj_det_timm", + "pt_vgg_bn_vgg19b_obj_det_osmr", + "pt_vgg_vgg16_obj_det_osmr", + "pt_vgg_vgg19_obj_det_osmr", + "pt_vgg_vgg13_obj_det_osmr", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + 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"pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((512,), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280", + "pt_yolo_v5_yolov5s_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5s_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5l_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5l_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5l_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5s_imgcls_torchhub_320x320", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((255,), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280", + "pt_yolo_v5_yolov5x_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5x_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5s_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5m_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5n_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5n_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5s_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5m_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5l_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5l_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5x_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5l_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5n_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5s_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((255, 1), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280", + "pt_yolo_v5_yolov5x_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5x_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5s_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5m_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5n_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5n_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5s_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5m_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5l_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5l_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5x_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5l_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5n_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5s_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((80,), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5x_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5x_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5x_imgcls_torchhub_320x320", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((160,), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5x_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5x_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5x_imgcls_torchhub_320x320", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((320,), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5x_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5x_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5x_imgcls_torchhub_320x320", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((640,), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5x_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5x_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5x_imgcls_torchhub_320x320", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((1280,), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5x_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5x_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5x_imgcls_torchhub_320x320", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((96,), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5m_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5m_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((768,), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5m_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5m_imgcls_torchhub_480x480", + "pt_yolo_v5_yolov5m_imgcls_torchhub_640x640", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((1024,), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5l_imgcls_torchhub_640x640", + "pt_yolo_v5_yolov5l_imgcls_torchhub_320x320", + "pt_yolo_v5_yolov5l_imgcls_torchhub_480x480", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + ( + Unsqueeze1, + [((80,), torch.float32)], + { + "model_name": [ + "pt_yolox_yolox_l_obj_det_torchhub", + "pt_yolox_yolox_m_obj_det_torchhub", + "pt_yolox_yolox_s_obj_det_torchhub", + "pt_yolox_yolox_tiny_obj_det_torchhub", + "pt_yolox_yolox_x_obj_det_torchhub", + "pt_yolox_yolox_darknet_obj_det_torchhub", + "pt_yolox_yolox_nano_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Unsqueeze") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) diff --git a/forge/test/models_ops/test_where.py b/forge/test/models_ops/test_where.py new file mode 100644 index 000000000..d565e4650 --- /dev/null +++ b/forge/test/models_ops/test_where.py @@ -0,0 +1,856 @@ +# SPDX-FileCopyrightText: (c) 2025 Tenstorrent AI ULC +# +# SPDX-License-Identifier: Apache-2.0 +import forge +import forge.op +from forge import ForgeModule + +from loguru import logger +import torch + +from forge import Tensor, compile +from forge.verify.compare import compare_with_golden +from forge.verify.verify import verify +from forge.verify.value_checkers import AutomaticValueChecker +from forge.verify.config import VerifyConfig +import pytest + + +class Where0(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where0_const_1", shape=(1,), dtype=torch.float32) + self.add_constant("where0_const_2", shape=(1,), dtype=torch.float32) + + def forward(self, where_input_0): + where_output_1 = forge.op.Where( + "", where_input_0, self.get_constant("where0_const_1"), self.get_constant("where0_const_2") + ) + return where_output_1 + + +class Where1(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where1_const_2", shape=(1, 256, 10, 32), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where1_const_2")) + return where_output_1 + + +class Where2(ForgeModule): + def __init__(self, name): + super().__init__(name) + + def forward(self, where_input_0, where_input_1, where_input_2): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, where_input_2) + return where_output_1 + + +class Where3(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where3_const_2", shape=(1, 256, 20, 64), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where3_const_2")) + return where_output_1 + + +class Where4(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where4_const_2", shape=(1, 128, 20, 64), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where4_const_2")) + return where_output_1 + + +class Where5(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where5_const_2", shape=(1, 128, 40, 128), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where5_const_2")) + return where_output_1 + + +class Where6(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where6_const_2", shape=(1, 64, 40, 128), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where6_const_2")) + return where_output_1 + + +class Where7(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where7_const_2", shape=(1, 64, 80, 256), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where7_const_2")) + return where_output_1 + + +class Where8(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where8_const_2", shape=(1, 32, 80, 256), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where8_const_2")) + return where_output_1 + + +class Where9(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where9_const_2", shape=(1, 32, 160, 512), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where9_const_2")) + return where_output_1 + + +class Where10(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where10_const_2", shape=(1, 16, 160, 512), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where10_const_2")) + return where_output_1 + + +class Where11(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where11_const_2", shape=(1, 16, 320, 1024), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where11_const_2")) + return where_output_1 + + +class Where12(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where12_const_2", shape=(1, 256, 6, 20), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where12_const_2")) + return where_output_1 + + +class Where13(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where13_const_2", shape=(1, 256, 12, 40), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where13_const_2")) + return where_output_1 + + +class Where14(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where14_const_2", shape=(1, 128, 12, 40), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where14_const_2")) + return where_output_1 + + +class Where15(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where15_const_2", shape=(1, 128, 24, 80), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where15_const_2")) + return where_output_1 + + +class Where16(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where16_const_2", shape=(1, 64, 24, 80), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where16_const_2")) + return where_output_1 + + +class Where17(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where17_const_2", shape=(1, 64, 48, 160), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where17_const_2")) + return where_output_1 + + +class Where18(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where18_const_2", shape=(1, 32, 48, 160), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where18_const_2")) + return where_output_1 + + +class Where19(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where19_const_2", shape=(1, 32, 96, 320), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where19_const_2")) + return where_output_1 + + +class Where20(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where20_const_2", shape=(1, 16, 96, 320), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where20_const_2")) + return where_output_1 + + +class Where21(ForgeModule): + def __init__(self, name): + super().__init__(name) + self.add_constant("where21_const_2", shape=(1, 16, 192, 640), dtype=torch.float32) + + def forward(self, where_input_0, where_input_1): + where_output_1 = forge.op.Where("", where_input_0, where_input_1, self.get_constant("where21_const_2")) + return where_output_1 + + +def ids_func(param): + forge_module = param[0] + shapes_dtypes = param[1] + return str(forge_module.__name__) + "-" + str(shapes_dtypes) + + +forge_modules_and_shapes_dtypes_list = [ + ( + Where0, + [((1, 1, 256, 256), torch.bool)], + { + "model_name": [ + "pt_gpt2_gpt2_text_gen_hf", + "pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf", + "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", + ], + "pcc": 0.99, + }, + ), + ( + Where0, + [((1, 1, 32, 32), torch.bool)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + ( + Where0, + [((1, 1, 7, 7), torch.bool)], + {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99}, + ), + ( + Where1, + [((1, 256, 10, 32), torch.bool), ((1, 256, 10, 32), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 256, 10, 32), torch.bool), ((1, 256, 10, 32), torch.float32), ((1, 256, 10, 32), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where3, + [((1, 256, 20, 64), torch.bool), ((1, 256, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 256, 20, 64), torch.bool), ((1, 256, 20, 64), torch.float32), ((1, 256, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where4, + [((1, 128, 20, 64), torch.bool), ((1, 128, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 128, 20, 64), torch.bool), ((1, 128, 20, 64), torch.float32), ((1, 128, 20, 64), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where5, + [((1, 128, 40, 128), torch.bool), ((1, 128, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 128, 40, 128), torch.bool), ((1, 128, 40, 128), torch.float32), ((1, 128, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where6, + [((1, 64, 40, 128), torch.bool), ((1, 64, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 64, 40, 128), torch.bool), ((1, 64, 40, 128), torch.float32), ((1, 64, 40, 128), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where7, + [((1, 64, 80, 256), torch.bool), ((1, 64, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 64, 80, 256), torch.bool), ((1, 64, 80, 256), torch.float32), ((1, 64, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where8, + [((1, 32, 80, 256), torch.bool), ((1, 32, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 32, 80, 256), torch.bool), ((1, 32, 80, 256), torch.float32), ((1, 32, 80, 256), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where9, + [((1, 32, 160, 512), torch.bool), ((1, 32, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 32, 160, 512), torch.bool), ((1, 32, 160, 512), torch.float32), ((1, 32, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where10, + [((1, 16, 160, 512), torch.bool), ((1, 16, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 16, 160, 512), torch.bool), ((1, 16, 160, 512), torch.float32), ((1, 16, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where11, + [((1, 16, 320, 1024), torch.bool), ((1, 16, 320, 1024), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 16, 320, 1024), torch.bool), ((1, 16, 320, 1024), torch.float32), ((1, 16, 320, 1024), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where12, + [((1, 256, 6, 20), torch.bool), ((1, 256, 6, 20), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 256, 6, 20), torch.bool), ((1, 256, 6, 20), torch.float32), ((1, 256, 6, 20), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where13, + [((1, 256, 12, 40), torch.bool), ((1, 256, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 256, 12, 40), torch.bool), ((1, 256, 12, 40), torch.float32), ((1, 256, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where14, + [((1, 128, 12, 40), torch.bool), ((1, 128, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 128, 12, 40), torch.bool), ((1, 128, 12, 40), torch.float32), ((1, 128, 12, 40), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where15, + [((1, 128, 24, 80), torch.bool), ((1, 128, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 128, 24, 80), torch.bool), ((1, 128, 24, 80), torch.float32), ((1, 128, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where16, + [((1, 64, 24, 80), torch.bool), ((1, 64, 24, 80), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + 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"pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 64, 48, 160), torch.bool), ((1, 64, 48, 160), torch.float32), ((1, 64, 48, 160), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where18, + [((1, 32, 48, 160), torch.bool), ((1, 32, 48, 160), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + 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"pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where20, + [((1, 16, 96, 320), torch.bool), ((1, 16, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 16, 96, 320), torch.bool), ((1, 16, 96, 320), torch.float32), ((1, 16, 96, 320), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where21, + [((1, 16, 192, 640), torch.bool), ((1, 16, 192, 640), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), + ( + Where2, + [((1, 16, 192, 640), torch.bool), ((1, 16, 192, 640), torch.float32), ((1, 16, 192, 640), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + }, + ), +] + + +@pytest.mark.push +@pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) +def test_module(forge_module_and_shapes_dtypes, record_forge_property): + record_forge_property("op_name", "Where") + + forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes + + pcc = metadata.pop("pcc") + + for metadata_name, metadata_value in metadata.items(): + record_forge_property(metadata_name, metadata_value) + + max_int = 1000 + inputs = [ + Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) + for operand_shape, operand_dtype in operand_shapes_dtypes + ] + + framework_model = forge_module(forge_module.__name__) + framework_model.process_framework_parameters() + + for name, parameter in framework_model._parameters.items(): + parameter_tensor = Tensor.create_torch_tensor( + shape=parameter.shape.get_pytorch_shape(), dtype=parameter.pt_data_format, max_int=max_int + ) + framework_model.set_parameter(name, parameter_tensor) + + for name, constant in framework_model._constants.items(): + constant_tensor = Tensor.create_torch_tensor( + shape=constant.shape.get_pytorch_shape(), dtype=constant.pt_data_format, max_int=max_int + ) + framework_model.set_constant(name, constant_tensor) + + compiled_model = compile(framework_model, sample_inputs=inputs) + + verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) From cd2c71d99de54b93e9aafb86a6a3d5b682c4670f Mon Sep 17 00:00:00 2001 From: chandrasekaranpradeep Date: Tue, 18 Feb 2025 13:19:03 +0000 Subject: [PATCH 2/8] Add skip marker with reason for avgpool1d, avgpool3d, conv2d, pad and reshape ops test due to segmentation fault in CI --- forge/test/models_ops/test_avgpool1d.py | 29 +- forge/test/models_ops/test_avgpool3d.py | 70 +- forge/test/models_ops/test_conv2d.py | 816 ++++++++++++++---------- forge/test/models_ops/test_pad.py | 87 +-- forge/test/models_ops/test_reshape.py | 38 +- 5 files changed, 620 insertions(+), 420 deletions(-) diff --git a/forge/test/models_ops/test_avgpool1d.py b/forge/test/models_ops/test_avgpool1d.py index 2d3bf57c7..8c6522306 100644 --- a/forge/test/models_ops/test_avgpool1d.py +++ b/forge/test/models_ops/test_avgpool1d.py @@ -40,20 +40,23 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Avgpool1D0, - [((1, 768, 49), torch.float32)], - { - "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "kernel_size": "[49]", - "stride": "[49]", - "padding": "[0, 0]", - "ceil_mode": "False", - "count_include_pad": "True", + pytest.param( + ( + Avgpool1D0, + [((1, 768, 49), torch.float32)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[49]", + "stride": "[49]", + "padding": "[0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + }, }, - }, + ), + marks=[pytest.mark.skip(reason="Segmentation fault at Run mlir compile stage")], ), ] diff --git a/forge/test/models_ops/test_avgpool3d.py b/forge/test/models_ops/test_avgpool3d.py index ee3fbc30d..428f64f75 100644 --- a/forge/test/models_ops/test_avgpool3d.py +++ b/forge/test/models_ops/test_avgpool3d.py @@ -41,37 +41,51 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Avgpool3D0, - [((1, 1, 100, 54, 54), torch.float32)], - { - "model_name": ["pt_alexnet_base_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "kernel_size": "[5, 1, 1]", - "stride": "[1, 1, 1]", - "padding": "[0, 0, 0, 0, 0, 0]", - "ceil_mode": "False", - "count_include_pad": "True", - "channel_last": "0", + pytest.param( + ( + Avgpool3D0, + [((1, 1, 100, 54, 54), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[5, 1, 1]", + "stride": "[1, 1, 1]", + "padding": "[0, 0, 0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="[mlir::AffineMap collapsedLinearAffineMap] Assertion Dim does not participate in AffineMap RHS" + ) + ], ), - ( - Avgpool3D0, - [((1, 1, 260, 27, 27), torch.float32)], - { - "model_name": ["pt_alexnet_base_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "kernel_size": "[5, 1, 1]", - "stride": "[1, 1, 1]", - "padding": "[0, 0, 0, 0, 0, 0]", - "ceil_mode": "False", - "count_include_pad": "True", - "channel_last": "0", + pytest.param( + ( + Avgpool3D0, + [((1, 1, 260, 27, 27), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[5, 1, 1]", + "stride": "[1, 1, 1]", + "padding": "[0, 0, 0, 0, 0, 0]", + "ceil_mode": "False", + "count_include_pad": "True", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="[mlir::AffineMap collapsedLinearAffineMap] Assertion Dim does not participate in AffineMap RHS" + ) + ], ), ] diff --git a/forge/test/models_ops/test_conv2d.py b/forge/test/models_ops/test_conv2d.py index 6738c103d..b95c9e40b 100644 --- a/forge/test/models_ops/test_conv2d.py +++ b/forge/test/models_ops/test_conv2d.py @@ -28954,24 +28954,31 @@ def ids_func(param): }, }, ), - ( - Conv2D256, - [((1, 3, 224, 224), torch.float32)], - { - "model_name": [ - "pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf", - "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", - "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", - }, - }, + pytest.param( + ( + Conv2D256, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": [ + "pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf", + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, + }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D257, @@ -29097,23 +29104,30 @@ def ids_func(param): }, }, ), - ( - Conv2D263, - [((1, 96, 112, 112), torch.float32), ((96, 1, 3, 3), torch.float32)], - { - "model_name": [ - "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", - "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "96", - "channel_last": "0", + pytest.param( + ( + Conv2D263, + [((1, 96, 112, 112), torch.float32), ((96, 1, 3, 3), torch.float32)], + { + "model_name": [ + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "96", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D264, @@ -29232,23 +29246,30 @@ def ids_func(param): }, }, ), - ( - Conv2D268, - [((1, 144, 56, 56), torch.float32), ((144, 1, 3, 3), torch.float32)], - { - "model_name": [ - "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", - "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "144", - "channel_last": "0", + pytest.param( + ( + Conv2D268, + [((1, 144, 56, 56), torch.float32), ((144, 1, 3, 3), torch.float32)], + { + "model_name": [ + "pt_mobilnetv2_google_deeplabv3_mobilenet_v2_1_0_513_img_cls_hf", + "pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "144", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D269, @@ -35900,20 +35921,27 @@ def ids_func(param): }, }, ), - ( - Conv2D549, - [((1, 3, 192, 192), torch.float32)], - { - "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D549, + [((1, 3, 192, 192), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D205, @@ -35945,20 +35973,27 @@ def ids_func(param): }, }, ), - ( - Conv2D551, - [((1, 48, 96, 96), torch.float32), ((48, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "48", - "channel_last": "0", + pytest.param( + ( + Conv2D551, + [((1, 48, 96, 96), torch.float32), ((48, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "48", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D552, @@ -35990,20 +36025,27 @@ def ids_func(param): }, }, ), - ( - Conv2D263, - [((1, 96, 48, 48), torch.float32), ((96, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "96", - "channel_last": "0", + pytest.param( + ( + Conv2D263, + [((1, 96, 48, 48), torch.float32), ((96, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "96", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D554, @@ -36050,20 +36092,27 @@ def ids_func(param): }, }, ), - ( - Conv2D555, - [((1, 192, 24, 24), torch.float32), ((192, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "192", - "channel_last": "0", + pytest.param( + ( + Conv2D555, + [((1, 192, 24, 24), torch.float32), ((192, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "192", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D22, @@ -36110,20 +36159,27 @@ def ids_func(param): }, }, ), - ( - Conv2D558, - [((1, 384, 12, 12), torch.float32), ((384, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "384", - "channel_last": "0", + pytest.param( + ( + Conv2D558, + [((1, 384, 12, 12), torch.float32), ((384, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_0_75_192_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "384", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D559, @@ -36185,20 +36241,27 @@ def ids_func(param): }, }, ), - ( - Conv2D563, - [((1, 64, 112, 112), torch.float32), ((64, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "64", - "channel_last": "0", + pytest.param( + ( + Conv2D563, + [((1, 64, 112, 112), torch.float32), ((64, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "64", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D564, @@ -36254,20 +36317,27 @@ def ids_func(param): }, }, ), - ( - Conv2D567, - [((1, 128, 56, 56), torch.float32), ((128, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "128", - "channel_last": "0", + pytest.param( + ( + Conv2D567, + [((1, 128, 56, 56), torch.float32), ((128, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "128", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D568, @@ -36302,20 +36372,27 @@ def ids_func(param): }, }, ), - ( - Conv2D570, - [((1, 256, 28, 28), torch.float32), ((256, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "256", - "channel_last": "0", + pytest.param( + ( + Conv2D570, + [((1, 256, 28, 28), torch.float32), ((256, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "256", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D571, @@ -36350,20 +36427,27 @@ def ids_func(param): }, }, ), - ( - Conv2D573, - [((1, 512, 14, 14), torch.float32), ((512, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "512", - "channel_last": "0", + pytest.param( + ( + Conv2D573, + [((1, 512, 14, 14), torch.float32), ((512, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilnet_v1_google_mobilenet_v1_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "512", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D574, @@ -36398,20 +36482,27 @@ def ids_func(param): }, }, ), - ( - Conv2D576, - [((1, 3, 96, 96), torch.float32)], - { - "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D576, + [((1, 3, 96, 96), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D302, @@ -36458,20 +36549,27 @@ def ids_func(param): }, }, ), - ( - Conv2D551, - [((1, 48, 48, 48), torch.float32), ((48, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "48", - "channel_last": "0", + pytest.param( + ( + Conv2D551, + [((1, 48, 48, 48), torch.float32), ((48, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "48", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D578, @@ -36518,20 +36616,27 @@ def ids_func(param): }, }, ), - ( - Conv2D551, - [((1, 48, 24, 24), torch.float32), ((48, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "48", - "channel_last": "0", + pytest.param( + ( + Conv2D551, + [((1, 48, 24, 24), torch.float32), ((48, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "48", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D579, @@ -36578,20 +36683,27 @@ def ids_func(param): }, }, ), - ( - Conv2D263, - [((1, 96, 12, 12), torch.float32), ((96, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "96", - "channel_last": "0", + pytest.param( + ( + Conv2D263, + [((1, 96, 12, 12), torch.float32), ((96, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "96", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D580, @@ -36713,20 +36825,27 @@ def ids_func(param): }, }, ), - ( - Conv2D555, - [((1, 192, 6, 6), torch.float32), ((192, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "192", - "channel_last": "0", + pytest.param( + ( + Conv2D555, + [((1, 192, 6, 6), torch.float32), ((192, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_35_96_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "192", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D218, @@ -36908,20 +37027,27 @@ def ids_func(param): }, }, ), - ( - Conv2D555, - [((1, 192, 28, 28), torch.float32), ((192, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "192", - "channel_last": "0", + pytest.param( + ( + Conv2D555, + [((1, 192, 28, 28), torch.float32), ((192, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "192", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D583, @@ -37071,20 +37197,27 @@ def ids_func(param): }, }, ), - ( - Conv2D589, - [((1, 576, 14, 14), torch.float32), ((576, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "576", - "channel_last": "0", + pytest.param( + ( + Conv2D589, + [((1, 576, 14, 14), torch.float32), ((576, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_1_0_224_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "576", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D590, @@ -37183,20 +37316,27 @@ def ids_func(param): }, }, ), - ( - Conv2D549, - [((1, 3, 160, 160), torch.float32)], - { - "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D549, + [((1, 3, 160, 160), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D205, @@ -37243,20 +37383,27 @@ def ids_func(param): }, }, ), - ( - Conv2D263, - [((1, 96, 80, 80), torch.float32), ((96, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "96", - "channel_last": "0", + pytest.param( + ( + Conv2D263, + [((1, 96, 80, 80), torch.float32), ((96, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "96", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D266, @@ -37303,20 +37450,27 @@ def ids_func(param): }, }, ), - ( - Conv2D268, - [((1, 144, 40, 40), torch.float32), ((144, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "144", - "channel_last": "0", + pytest.param( + ( + Conv2D268, + [((1, 144, 40, 40), torch.float32), ((144, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "144", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D269, @@ -37378,20 +37532,27 @@ def ids_func(param): }, }, ), - ( - Conv2D268, - [((1, 144, 20, 20), torch.float32), ((144, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "144", - "channel_last": "0", + pytest.param( + ( + Conv2D268, + [((1, 144, 20, 20), torch.float32), ((144, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "144", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D595, @@ -37498,20 +37659,27 @@ def ids_func(param): }, }, ), - ( - Conv2D602, - [((1, 432, 10, 10), torch.float32), ((432, 1, 3, 3), torch.float32)], - { - "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 1, 0, 1]", - "dilation": "1", - "groups": "432", - "channel_last": "0", - }, - }, + pytest.param( + ( + Conv2D602, + [((1, 432, 10, 10), torch.float32), ((432, 1, 3, 3), torch.float32)], + { + "model_name": ["pt_mobilenetv2_google_mobilenet_v2_0_75_160_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 1, 0, 1]", + "dilation": "1", + "groups": "432", + "channel_last": "0", + }, + }, + ), + marks=[ + pytest.mark.skip( + reason="TTNN only supports padding height/width attributes. Thus, padding_top must equal padding_bottom for the op to execute as expected." + ) + ], ), ( Conv2D603, diff --git a/forge/test/models_ops/test_pad.py b/forge/test/models_ops/test_pad.py index 134997ae5..28e3d495d 100644 --- a/forge/test/models_ops/test_pad.py +++ b/forge/test/models_ops/test_pad.py @@ -150,31 +150,37 @@ def ids_func(param): "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, }, ), - ( - Pad1, - [((1, 96, 160, 512), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, - }, + pytest.param( + ( + Pad1, + [((1, 96, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + marks=[pytest.mark.skip(reason="Segmentation fault occurs while executing ttnn binary")], ), - ( - Pad1, - [((1, 32, 160, 512), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, - }, + pytest.param( + ( + Pad1, + [((1, 32, 160, 512), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + marks=[pytest.mark.skip(reason="Segmentation fault occurs while executing ttnn binary")], ), ( Pad1, @@ -333,21 +339,24 @@ def ids_func(param): "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, }, ), - ( - Pad1, - [((1, 16, 192, 640), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, - }, + pytest.param( + ( + Pad1, + [((1, 16, 192, 640), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + marks=[pytest.mark.skip(reason="Segmentation fault occurs while executing ttnn binary")], ), ] diff --git a/forge/test/models_ops/test_reshape.py b/forge/test/models_ops/test_reshape.py index 75ca7e844..28ff24867 100644 --- a/forge/test/models_ops/test_reshape.py +++ b/forge/test/models_ops/test_reshape.py @@ -10363,14 +10363,17 @@ def ids_func(param): "op_params": {"shape": "(1, 1024, 3000, 1)"}, }, ), - ( - Reshape71, - [((1024, 1024, 3), torch.float32)], - { - "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], - "pcc": 0.99, - "op_params": {"shape": "(1024, 1024, 3, 1)"}, - }, + pytest.param( + ( + Reshape71, + [((1024, 1024, 3), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"shape": "(1024, 1024, 3, 1)"}, + }, + ), + marks=[pytest.mark.skip(reason="Segmentation fault while executing the TTNN binary")], ), ( Reshape72, @@ -10606,14 +10609,17 @@ def ids_func(param): "op_params": {"shape": "(1, 1280, 3000, 1)"}, }, ), - ( - Reshape94, - [((1280, 1280, 3), torch.float32)], - { - "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], - "pcc": 0.99, - "op_params": {"shape": "(1280, 1280, 3, 1)"}, - }, + pytest.param( + ( + Reshape94, + [((1280, 1280, 3), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": {"shape": "(1280, 1280, 3, 1)"}, + }, + ), + marks=[pytest.mark.skip(reason="Segmentation fault while executing the TTNN binary")], ), ( Reshape95, From 06a3f2376887e5c7cd7294014bd1d990e8419eb6 Mon Sep 17 00:00:00 2001 From: chandrasekaranpradeep Date: Fri, 21 Feb 2025 06:23:41 +0000 Subject: [PATCH 3/8] Skip the transpose test cases which takes 30 GB memory --- forge/test/models_ops/test_transpose.py | 101 ++++++++++++++---------- 1 file changed, 60 insertions(+), 41 deletions(-) diff --git a/forge/test/models_ops/test_transpose.py b/forge/test/models_ops/test_transpose.py index 6587ab6f3..b4581ab42 100644 --- a/forge/test/models_ops/test_transpose.py +++ b/forge/test/models_ops/test_transpose.py @@ -1715,14 +1715,17 @@ def ids_func(param): "op_params": {"dim0": "-2", "dim1": "-1"}, }, ), - ( - Transpose0, - [((102400, 4096), torch.float32)], - { - "model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf"], - "pcc": 0.99, - "op_params": {"dim0": "-2", "dim1": "-1"}, - }, + pytest.param( + ( + Transpose0, + [((102400, 4096), torch.float32)], + { + "model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf"], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + marks=[pytest.mark.skip(reason="Insufficient host DRAM to run this test (requires around 30 GB)")], ), ( Transpose1, @@ -2866,14 +2869,17 @@ def ids_func(param): "op_params": {"dim0": "-2", "dim1": "-1"}, }, ), - ( - Transpose0, - [((131072, 3072), torch.float32)], - { - "model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], - "pcc": 0.99, - "op_params": {"dim0": "-2", "dim1": "-1"}, - }, + pytest.param( + ( + Transpose0, + [((131072, 3072), torch.float32)], + { + "model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + marks=[pytest.mark.skip(reason="Insufficient host DRAM to run this test (requires around 30 GB)")], ), ( Transpose1, @@ -3087,14 +3093,17 @@ def ids_func(param): "op_params": {"dim0": "-2", "dim1": "-1"}, }, ), - ( - Transpose0, - [((256000, 2048), torch.float32)], - { - "model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], - "pcc": 0.99, - "op_params": {"dim0": "-2", "dim1": "-1"}, - }, + pytest.param( + ( + Transpose0, + [((256000, 2048), torch.float32)], + { + "model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + marks=[pytest.mark.skip(reason="Insufficient host DRAM to run this test (requires around 30 GB)")], ), ( Transpose0, @@ -4742,19 +4751,22 @@ def ids_func(param): "op_params": {"dim0": "-2", "dim1": "-1"}, }, ), - ( - Transpose0, - [((152064, 3584), torch.float32)], - { - "model_name": [ - "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", - "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", - "pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf", - "pt_qwen_v2_qwen_qwen2_5_7b_clm_hf", - ], - "pcc": 0.99, - "op_params": {"dim0": "-2", "dim1": "-1"}, - }, + pytest.param( + ( + Transpose0, + [((152064, 3584), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf", + "pt_qwen_v2_qwen_qwen2_5_7b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + marks=[pytest.mark.skip(reason="Insufficient host DRAM to run this test (requires around 30 GB)")], ), ( Transpose1, @@ -5853,10 +5865,17 @@ def ids_func(param): "op_params": {"dim0": "-2", "dim1": "-1"}, }, ), - ( - Transpose0, - [((256008, 2048), torch.float32)], - {"model_name": ["pt_xglm_facebook_xglm_1_7b_clm_hf"], "pcc": 0.99, "op_params": {"dim0": "-2", "dim1": "-1"}}, + pytest.param( + ( + Transpose0, + [((256008, 2048), torch.float32)], + { + "model_name": ["pt_xglm_facebook_xglm_1_7b_clm_hf"], + "pcc": 0.99, + "op_params": {"dim0": "-2", "dim1": "-1"}, + }, + ), + marks=[pytest.mark.skip(reason="Insufficient host DRAM to run this test (requires around 30 GB)")], ), ( Transpose0, From 7c09c6ef5600ea0d351f5f20ceb20b912761a548 Mon Sep 17 00:00:00 2001 From: chandrasekaranpradeep Date: Mon, 24 Feb 2025 14:46:00 +0000 Subject: [PATCH 4/8] Reimplement the models ops tests failure updation script --- .../models_ops_test_failure_update.py | 979 +++++++++++++----- scripts/model_analysis/utils.py | 11 +- 2 files changed, 753 insertions(+), 237 deletions(-) diff --git a/scripts/model_analysis/models_ops_test_failure_update.py b/scripts/model_analysis/models_ops_test_failure_update.py index 40bff7539..34569dd61 100644 --- a/scripts/model_analysis/models_ops_test_failure_update.py +++ b/scripts/model_analysis/models_ops_test_failure_update.py @@ -7,7 +7,9 @@ from loguru import logger import argparse from utils import check_path, run_precommit -from typing import List, Dict +from typing import Dict, Optional, List +from forge.utils import create_excel_file +import pandas as pd class ErrorMessageUpdater: @@ -85,364 +87,869 @@ def read_file(file_path: str): return lines -def extract_failed_models_ops_tests_with_failure_reason(log_files: List[str], models_ops_test_dir_path: str): +def rename_marker(marker: str) -> str: """ - Extracts failed test cases and their corresponding failure reasons from pytest log files. + Standardizes the test marker name to either 'xfail' or 'skip'. + + Args: + marker (str): The original marker name. + + Returns: + str: Normalized marker ('xfail' or 'skip') if it matches known values, + otherwise returns the lowercase version of the input. + """ + marker_lower = marker.lower() + if marker_lower in ["xfail", "failed"]: + return "xfail" + elif marker_lower in ["skip", "skipped"]: + return "skip" + else: + return marker_lower + +def extract_test_file_path_and_test_case_func(test_case: str): + """ + Extracts the test file path and test case function name from a given test case string. + + Args: + test_case (str): A test case string in the format 'file_path::test_function'. + + Returns: + Tuple[Optional[str], Optional[str]]: A tuple containing: + - test_file_path (str or None): The extracted file path if present. + - test_case_func (str or None): The extracted test case function name if present. + """ + test_file_path = None + test_case_func = None - The function processes the provided log files to identify failed tests that belong to a specified - directory (models_ops_test_dir_path). It first scans the short test summary for any failed tests, - and then it parses the detailed failure section to extract the error messages associated with those tests. + # Check if test case contains '::' separator + if "::" in test_case: + test_file_path, test_case_func = test_case.split("::", 1) # Splitting into two parts + + return test_file_path, test_case_func + +def extract_unique_test_file_paths(test_cases: List[str]) -> set: + """ + Extracts unique test file paths from a list of test cases. + + Args: + test_cases (List[str]): A list of test case strings, each in the format 'file_path::test_function'. + + Returns: + set: A set of unique test file paths. + """ + unique_test_file_paths = set() + + for test_case in test_cases: + test_file_path, _ = extract_test_file_path_and_test_case_func(test_case) + + # Add file path to the set if it exists + if test_file_path is not None: + unique_test_file_paths.add(test_file_path) + + return unique_test_file_paths + + +def extract_models_ops_test_params(file_path: str, return_marker_with_reason: bool = False): + """ + Extract test parameter tuples and (optionally) their associated marker information from a Python file. + + This function reads a Python file, parses its content into an Abstract Syntax Tree (AST), and searches for an assignment + to the variable 'forge_modules_and_shapes_dtypes_list'. It extracts test parameters defined either as direct tuples or via a call + to pytest.param(...). When pytest.param is used, the function also collects marker information (e.g., 'skip', 'xfail') and any + associated reason provided with the marker. + + Args: + file_path (str): The path to the Python file containing test parameter definitions. + return_marker_with_reason (bool, optional): If True, returns a tuple containing both the list of test parameter tuples and a + dictionary mapping marker names (with reasons) to lists of corresponding test parameter tuples. + Defaults to False. + + Returns: + list: A list of test parameter tuples, where each tuple is represented as a list of source code strings. + dict (optional): If return_marker_with_reason is True, also returns a dictionary with marker information. The dictionary maps + each marker name to another dictionary that maps a reason (or "No_Reason" if unspecified) to a list of test parameter tuples. + """ + # Open and read the file content + with open(file_path, "r") as file: + content = file.read() + + # Parse the file content into an Abstract Syntax Tree (AST) + tree = ast.parse(content) + + # List to hold the extracted test parameter tuples + models_ops_test_params = [] + + # Dictionary to hold marker names along with their reasons and associated test parameter tuples + marker_with_reason_and_params = {} + + # Iterate over each top-level node in the AST + for node in tree.body: + # Check if the node is an assignment (e.g., variable assignment) + if isinstance(node, ast.Assign): + # Loop through each target of the assignment (handle cases with multiple targets) + for target in node.targets: + # Look for the specific variable name 'forge_modules_and_shapes_dtypes_list' + if isinstance(target, ast.Name) and target.id == "forge_modules_and_shapes_dtypes_list": + # Iterate over each element in the assigned list + for elt in node.value.elts: + # Initialize the variable to hold the test parameter tuple + param_tuple = None + # Flag to indicate if marker(s) were found for this element + marks_found = False + # Dictionary to collect markers and their reasons for the current element + marks_dict = {} + + # Check if the element is a call (e.g., a call to pytest.param(...)) + if ( + isinstance(elt, ast.Call) + and isinstance(elt.func, ast.Attribute) + and elt.func.attr == "param" + ): + # Extract the first argument from the pytest.param(...) call, if available + param_tuple = elt.args[0] if elt.args else None + + # Look for a 'marks' keyword in the pytest.param call + for kw in elt.keywords: + if kw.arg == "marks": + marks_found = True + # The marks can be provided as a list/tuple or as a single marker + mark_nodes = kw.value.elts if isinstance(kw.value, (ast.List, ast.Tuple)) else [kw.value] + + # Process each marker node + for mark_node in mark_nodes: + # Initialize variables for marker name and reason + marker_name = None + reason = None + + # Check if the marker is a function call (e.g., pytest.mark.skip(reason="...")) + if isinstance(mark_node, ast.Call) and isinstance(mark_node.func, ast.Attribute): + # Extract the marker name from the function call's attribute (e.g., 'skip' in pytest.mark.skip) + marker_name = mark_node.func.attr + + # Look for a 'reason' keyword argument in the marker call + for m_kw in mark_node.keywords: + if m_kw.arg == "reason": + # Unparse the reason AST node back to source code and strip surrounding quotes + reason = ast.unparse(m_kw.value).strip().strip('"').strip("'") + break + + # If the marker is not a call but a simple attribute (e.g., pytest.mark.skip) + elif isinstance(mark_node, ast.Attribute): + marker_name = mark_node.attr + + # If a marker name was successfully extracted, record it in marks_dict. + # Note: If the same marker appears multiple times, the latest reason (or None) will overwrite previous entries. + if marker_name: + marks_dict[marker_name] = reason + + + # If the element is not a pytest.param call, check if it's directly a tuple + elif isinstance(elt, ast.Tuple): + param_tuple = elt + else: + # If the element doesn't match expected patterns, skip processing it + param_tuple = None + + # If a valid test parameter tuple was found, process it further + if param_tuple: + # Convert each element of the tuple back to source code (as strings) + tuple_elements = [ast.unparse(item) for item in param_tuple.elts] + # Append the test parameter tuple to the list + models_ops_test_params.append(tuple_elements) + + # If markers were found, update the marker information dictionary accordingly + if marks_found and marks_dict: + for marker_name, reason in marks_dict.items(): + # Use a default reason if none is provided + if reason is None: + reason = "No_Reason" + # Update the dictionary for the current marker and reason + if marker_name in marker_with_reason_and_params.keys(): + if reason in marker_with_reason_and_params[marker_name].keys(): + marker_with_reason_and_params[marker_name][reason].append(tuple_elements) + else: + marker_with_reason_and_params[marker_name][reason] = [tuple_elements] + else: + marker_with_reason_and_params[marker_name] = {reason: [tuple_elements]} + + # Return both test parameters and marker info if requested + if return_marker_with_reason: + return models_ops_test_params, marker_with_reason_and_params + + # Otherwise, return only the list of extracted test parameter tuples + return models_ops_test_params + + +def extract_test_cases_and_status( + log_files: List[str], + target_dirs: Optional[List[str]] = None, + target_statuses: Optional[List[str]] = None +) -> Dict[str, Dict[str, str]]: + """ + Extract test cases and their statuses from provided log files. + + This function processes each log file and extracts test case names along with their statuses + using a regular expression pattern. It supports filtering by target directories paths and target statuses. + The extraction halts upon encountering the "==== FAILURES ====" marker in a log file. + + Args: + log_files (List[str]): A list of file paths to the log files that need to be processed. + target_dirs (Optional[List[str]]): A list of directory paths to filter test cases. + Only test cases whose paths contain one of these directory paths will be included. + Defaults to None, which means no directory filtering is applied. + target_statuses (Optional[List[str]]): A list of statuses to filter test cases. + Only test cases with one of these statuses will be included. + Defaults to None, meaning no status filtering is applied. + + Returns: + Dict[str, Dict[str, str]]: A dictionary where keys are test statuses (e.g., 'PASSED', 'FAILED', 'SKIP', 'XFAIL') + and values are dictionaries mapping test case names (as strings) to their associated reason (as a string). + An empty string is used as a placeholder for the reason if none is provided. + """ + # Initialize a dictionary to map statuses to their corresponding test cases and reasons. + status_to_tests_and_reason = {} + + # Compile a regular expression pattern to capture the test case and status from each line. + # The pattern explanation: + # ^(.*?) - Capture any characters from the beginning as the test case name. + # \s+ - One or more whitespace characters. + # (PASSED|FAILED|SKIPPED|XFAIL) - Capture one of the specified statuses. + # .* - Followed by any characters (the remainder of the line). + pattern = re.compile(r"^(.*?)\s+(PASSED|FAILED|SKIPPED|XFAIL).*") + + # Process each log file provided in the log_files list. + for log_file in log_files: + # Check if the log file exists using the check_path function. + if check_path(log_file): + # Read all lines from the file using the read_file function. + lines = read_file(log_file) + for line in lines: + # Attempt to match the line with the regex pattern. + match = pattern.match(line) + if match: + # Extract the test case name and status from the regex match groups. + test_case = match.group(1).strip() + status = match.group(2).strip() + + # convert 'SKIPPED' to 'SKIP'. + if status == "SKIPPED": + status = "SKIP" + + # Filter by target directories if provided. + # Include the test case if target_dirs is None or if it contains any specified directory. + if target_dirs is None or any(dir_path in test_case for dir_path in target_dirs): + # Filter by target statuses if provided. + # Include the test case if target_statuses is None or if the status is in the provided list. + if target_statuses is None or status in target_statuses: + # If the status is already in the dictionary, add the test case to its inner dictionary. + if status in status_to_tests_and_reason: + status_to_tests_and_reason[status][test_case] = "" + else: + # Otherwise, create a new entry for this status with the test case. + status_to_tests_and_reason[status] = {test_case: ""} + + # Stop processing further lines once the failures section is reached. + if "==== FAILURES ====" in line: + break + else: + # Log a warning if the provided log file path does not exist. + logger.warning(f"Provided path {log_file} does not exist!") + + return status_to_tests_and_reason + + +def update_reason_for_xfailed_skipped_test(status_to_tests_and_reason: Dict[str, Dict[str, str]]) -> Dict[str, Dict[str, str]]: + """ + Update the reasons for test cases marked as 'XFAIL' or 'SKIP' based on marker details extracted from test files. + + This function updates the given mapping of test statuses to test case strings and their associated reasons. + It works as follows: + 1. Collects all test case strings from both 'SKIP' and 'XFAIL' entries. + 2. Extracts unique test file paths from these test case strings. + 3. For each unique test file, it extracts marker information (marker name, reason, and associated parameters) + using the `extract_models_ops_test_params` function. + 4. For each marker found in the test file, if the corresponding test case matches the parameters from the marker, + update the test case's reason in the input dictionary. + + Args: + status_to_tests_and_reason (Dict[str, Dict[str, str]]): + A dictionary mapping statuses (e.g., 'SKIP', 'XFAIL') to dictionaries of test case strings and their reasons. + + Returns: + Dict[str, Dict[str, str]]: The updated dictionary with reasons for test cases modified based on the marker data. + """ + test_cases = [] # Accumulate test case strings for 'SKIP' and 'XFAIL' + + # Collect test cases for 'SKIP' status if present. + if "SKIP" in status_to_tests_and_reason.keys(): + test_cases.extend(status_to_tests_and_reason["SKIP"]) + + # Collect test cases for 'XFAIL' status if present. + if "XFAIL" in status_to_tests_and_reason.keys(): + test_cases.extend(status_to_tests_and_reason["XFAIL"]) + + # Remove duplicate test cases. + test_cases = list(set(test_cases)) + + # Extract unique file paths from the collected test case strings. + unique_test_file_paths = extract_unique_test_file_paths(test_cases=test_cases) + + # For each unique test file path, extract marker information along with reasons. + for test_file_path in unique_test_file_paths: + # The function returns a tuple; we are interested in the marker information dictionary. + _, marker_with_reason_and_params = extract_models_ops_test_params( + file_path=test_file_path, + return_marker_with_reason=True + ) + # Iterate over each marker (e.g., 'skip', 'xfail') and its associated reason and parameter list. + for marker, reason_with_params in marker_with_reason_and_params.items(): + for reason, params in reason_with_params.items(): + for param in params: + # Check if the uppercase version of the marker exists in our input dictionary. + if marker.upper() in status_to_tests_and_reason.keys(): + # Iterate through test cases for this marker. + for test in status_to_tests_and_reason[marker.upper()].keys(): + # If both parts of the test parameter (e.g., module and function names) are found in the test string, + # update the reason for that test case. + if param[0] in test and param[1] in test: + status_to_tests_and_reason[marker.upper()][test] = reason + break # Once updated, no need to check further for this test case. + return status_to_tests_and_reason + +def extract_xfailed_and_skipped_tests_with_reason(log_files: List[str], models_ops_test_dir_path: str) -> Dict[str, Dict[str, str]]: + """ + Extract and update test cases with statuses 'XFAIL' and 'SKIP' from log files, including detailed reasons. + + This function performs the following steps: + 1. Uses `extract_test_cases_and_status` to retrieve test cases and their statuses from the provided log files, + filtering by the target directory paths and statuses ('XFAIL' and 'SKIP'). + 2. If any test cases are found, it updates these entries with detailed reason information by calling + `update_reason_for_xfailed_skipped_test`. + + Args: + log_files (List[str]): A list of log file paths containing test execution information. + models_ops_test_dir_path (str): The directory path to filter test cases (typically where the tests reside). + + Returns: + Dict[str, Dict[str, str]]: A dictionary mapping statuses ('XFAIL', 'SKIP') to dictionaries that map test case + strings to their corresponding reasons. + """ + # Extract test cases with statuses 'XFAIL' and 'SKIP' from the log files, + # filtering for those test cases within the specified directory. + status_to_tests_and_reason = extract_test_cases_and_status( + log_files=log_files, + target_dirs=[models_ops_test_dir_path], + target_statuses=["XFAIL", "SKIP"] + ) + + # If test cases were found, update them with the marker-based reason information. + if len(status_to_tests_and_reason) != 0: + status_to_tests_and_reason = update_reason_for_xfailed_skipped_test( + status_to_tests_and_reason=status_to_tests_and_reason + ) + + return status_to_tests_and_reason + + +def extract_failed_tests_with_failure_reason(log_files: List[str], target_dirs: Optional[List[str]] = None) -> Dict[str, str]: + """ + Extract failed test cases along with their failure reasons from pytest log files. + + This function processes one or more pytest log files to identify failed tests and extract their + corresponding error messages. It operates in two main phases: + + 1. Short Test Summary Extraction: + - Scans each log file for the "short test summary info" section. + - Identifies test cases marked as FAILED and filters them based on the provided target + directories (if any). + - Stores these failed test cases in a dictionary with an empty string as a placeholder + for the failure reason. + + 2. Detailed Failure Reason Extraction: + - After the summary, the function parses the "==== FAILURES ====" section. + - It uses a regular expression pattern to locate the test function names within the failure details. + - For lines starting with "E " (indicating error messages), it collects up to a specified maximum + number of consecutive error lines to form a comprehensive error message. + - An external helper, `ErrorMessageUpdater`, is used to further refine or update the error message + with additional context from subsequent lines. + - The refined error message is then matched with the corresponding failed test case (using substring + checks) and updated in the dictionary. Args: log_files (List[str]): A list of file paths to pytest log files. - models_ops_test_dir_path (str): The directory path that contains model ops tests; - used to filter which failures to consider. + target_dirs (Optional[List[str]], optional): A list of directory paths to filter the failed tests. + Only test cases whose identifiers include one of these directory strings will be processed. + If None, all failed tests from the log files are considered. Returns: - Dict[str, str]: A dictionary where the keys correponds to the failed test cases and - the values are the corresponding failure reasons (error messages). + Dict[str, str]: A dictionary mapping each failed test case (as a string) to its corresponding + failure reason (error message). If a test case's error message could not be determined, its value + remains an empty string. """ - # Instantiate a helper object to update/refine error messages with additional context. + # Instantiate the helper object for refining error messages with additional context. error_message_updater = ErrorMessageUpdater() - # Regular expression pattern to match the test function name + # Regular expression to capture the test function name from lines in the detailed failure section. test_func_pattern = r"^_+\s+(test_[a-zA-Z0-9_]+\[.*\])\s+_+$" - # Dictionary to store the mapping from failed test cases to their failure messages. - failed_models_ops_tests = {} + # Dictionary to store mapping from each failed test case to its failure message. + failed_tests_with_reason: Dict[str, str] = {} - # Define the maximum number of consecutive error lines to consider when assembling the error message. + # Define the maximum number of consecutive error lines to consider for assembling a complete error message. maximum_error_lines = 3 + # Process each log file provided. for log_file in log_files: - if check_path(log_file): - + # Read all lines from the log file. lines = read_file(log_file) - # Flag to indicate that the "short test summary info" section has started. - collect_failed_models_ops_tests = False + # Flag indicating the start of the "short test summary info" section. + collect_failed_tests = False + + # First pass: Extract test cases marked as FAILED from the summary section. for line in lines: - # Detect the start of the short test summary section. + # Start collecting when the short test summary section begins. if "==== short test summary info ====" in line: - collect_failed_models_ops_tests = True + collect_failed_tests = True - # Once in the summary section, collect lines that indicate a test failure. - elif collect_failed_models_ops_tests and "FAILED" in line: - # Remove the "FAILED" tag and trim whitespace/newlines to get the test identifier. + # Within the summary section, identify lines that indicate a test failure. + elif collect_failed_tests and "FAILED" in line: + # Remove the "FAILED" tag and trim any whitespace/newlines. failed_test = line.replace("FAILED", "").strip("\n").strip() - # Only consider tests from the specified directory and ensure no duplicates. - if models_ops_test_dir_path in failed_test and failed_test not in failed_models_ops_tests: - failed_models_ops_tests[failed_test] = "" + # If target_dirs filter is provided, only include tests that match the directory criteria. + if target_dirs is None or any(dir_path in failed_test for dir_path in target_dirs): + failed_tests_with_reason[failed_test] = "" - # If no failed tests were found in the current log file, log a warning and skip further processing. - if len(failed_models_ops_tests) == 0: + # If no failed tests were found in the current log file, log a warning and skip detailed extraction. + if len(failed_tests_with_reason) == 0: logger.warning(f"There is no failure in the {log_file}") continue - # Sort the failed models ops test dict - failed_models_ops_tests = dict(sorted(failed_models_ops_tests.items(), key=lambda kv: (kv[1], kv[0]))) + # Optionally sort the dictionary (here by value and then key) for consistency. + failed_tests_with_reason = dict(sorted(failed_tests_with_reason.items(), key=lambda kv: (kv[1], kv[0]))) - # Variables to track the current test case function and to mark the start of the detailed failure section. - test_case_func = "" - collect_failure_reason = False + # Initialize variables for the detailed extraction phase. + test_case_func = "" # To store the current test function name. + collect_failure_reason = False # Flag indicating that the detailed failure section has begun. - # Iterate over each line of the log file to extract detailed failure messages. + # Second pass: Extract detailed failure messages from the "==== FAILURES ====" section. for current_line, line in enumerate(lines): - # Detect the beginning of the detailed failures section. + # Detect the start of the detailed failures section. if "==== FAILURES ====" in line: collect_failure_reason = True elif collect_failure_reason: - # If we haven't yet identified the current test function, look for its name. + # If the test function name hasn't been captured yet, try to extract it. if len(test_case_func) == 0: match = re.search(test_func_pattern, line) if match: - # Capture the test function name (including any parameterized details). + # Capture the test function name (including parameterized details). test_case_func = match.group(1) else: - # Once a test function has been identified, look for lines that start with "E ", - # which indicate error message lines. + # When the test function is identified, look for lines starting with "E " which denote error messages. if line.startswith("E "): - # Check if the next few lines (up to maximum_error_lines) all start with "E ". + # Check if subsequent lines (up to maximum_error_lines) are also error lines. if all( error_line.startswith("E ") for error_line in lines[current_line : current_line + maximum_error_lines] ): - # Extract and clean up each of the consecutive error lines. + # Extract and clean each of the consecutive error lines. error_message = [ error_line.replace("E ", "").strip("\n").strip() for error_line in lines[current_line : current_line + maximum_error_lines] ] - # Combine the multiple lines into a single error message string. + # Combine these lines into a single error message string. error_message = " ".join(error_message) else: - # If not all the subsequent lines are error lines, use the current line as the error message. + # If not all subsequent lines are error lines, use the current line as the error message. error_message = line.replace("E ", "").strip("\n").strip() - # Enhance the error message with additional context by using the updater helper. + # Update/enhance the error message with additional context from the following lines. error_message = error_message_updater.update( error_message, lines[current_line + 1 : current_line + 11] ) - # For each recorded failed test, check if the current test function name is part of its identifier. - for failed_test in failed_models_ops_tests.keys(): + # Match the current test function with each failed test from the summary. + for failed_test in failed_tests_with_reason.keys(): if test_case_func in failed_test: - failed_models_ops_tests[failed_test] = error_message + # Update the failure reason for the matching test case. + failed_tests_with_reason[failed_test] = error_message - # Reset the test function tracker to allow extraction of the next failure. + # Reset the test function tracker for processing the next failure. test_case_func = "" else: - logger.warning(f"Provided {log_file} path doesn't exists!!") + logger.warning(f"Provided {log_file} path doesn't exist!") - return failed_models_ops_tests + return failed_tests_with_reason -def extract_failed_models_ops_tests_config(failed_models_ops_tests: Dict[str, str]): +def update_params(models_ops_test_params, marker_with_test_config, marker_with_reason_and_params): """ - Extracts and organizes configuration details from failed model operation tests. - - This function processes a dictionary mapping failed test identifiers to their error messages. - It extracts configuration details such as the module name and operand shapes and dtypes from the test case - identifier (using a regular expression) and organizes this information under the corresponding test file path. + Update test parameters by appending marker configuration and reason information. + + This function iterates over each test parameter tuple in `models_ops_test_params` and updates it + based on two sources of marker data: + + 1. `marker_with_test_config`: A dictionary mapping marker names to lists of configuration dictionaries. + Each configuration is expected to contain: + - "module_name": the module name associated with the test parameter. + - "shapes_and_dtypes": the corresponding shapes and dtypes string. + - "reason": the reason to be applied for the marker. + If a test parameter matches a configuration (i.e. its module name and shapes_and_dtypes match the config), + a marker string (with reason) is created and appended. + + 2. `marker_with_reason_and_params`: Additional marker information, where for each marker, a set of reasons + and associated parameter tuples is provided. If the current test parameter matches one of these tuples, + an additional marker string is appended. If the reason is not "No_Reason", it is included in the marker string. + + The updated test parameters are formatted as strings using the pytest.param syntax when marker information is present, + otherwise they remain as a simple tuple string. Args: - failed_models_ops_tests (Dict[str, str]): A dictionary where each key is a string in the format - "::[module_name-[shapes_and_dtypes]]" and each value is - the associated error message. + models_ops_test_params: A list of test parameter tuples (each tuple is typically a list of strings) that define the tests. + marker_with_test_config: A dictionary where keys are marker names (e.g., "skip", "xfail") and values are lists of + configuration dictionaries. Each configuration dictionary must include "module_name", + "shapes_and_dtypes", and "reason". + marker_with_reason_and_params: Additional marker information containing reason details and associated parameter tuples. + This is typically structured as an iterable of pairs where each pair consists of a marker + name and a nested structure mapping reasons to lists of parameter tuples. Returns: - Dict[str, List[Dict[str, str]]]: A dictionary mapping test file paths to a list of configuration - dictionaries. Each configuration dictionary contains the extracted error message, module name, - and operand shapes and dtypes. + A list of strings representing the updated test parameter definitions, with marker annotations added where applicable. """ + new_models_ops_test_params = [] # List to store updated test parameter definitions - # Dictionary to store organized configuration information by test file path - failed_models_ops_tests_info = {} - - # Regular expression to capture module name and operand shapes and dtypes from the test case identifier. - regex = r"\[([^\[]+)-\[(.*)\]\]" - - for idx, (failed_test, error_message) in enumerate(failed_models_ops_tests.items()): - - failed_test_path, failed_test_cases = failed_test.split("::") - - # Attempt to extract module name and operand shapes/dtypes using the regular expression - match = re.search(regex, failed_test_cases) - - # Initialize a configuration dictionary for the current failed test and save the error message in the configuration dictionary - failed_test_config = {} - failed_test_config["error_message"] = error_message - - # If the regex matches, extract the module name and operand shapes/dtypes - if match: - module_name = match.group(1) - shapes_and_dtypes = match.group(2) - failed_test_config["module_name"] = module_name - failed_test_config["shapes_and_dtypes"] = f"[{shapes_and_dtypes}]" + # Process each test parameter tuple + for param in models_ops_test_params: + # Convert the test parameter tuple into a comma-separated string representation + param_str = ", ".join(param) - # Organize the configuration details under the appropriate test file path in the result dictionary - if failed_test_path in failed_models_ops_tests_info.keys(): - failed_models_ops_tests_info[failed_test_path].append(failed_test_config) + markers_str = [] # List to collect marker strings for this parameter + + # Check marker configurations from marker_with_test_config + for marker, configs in marker_with_test_config.items(): + for config in configs: + # If the test parameter matches the configuration based on module_name and shapes_and_dtypes... + if param[0] == config["module_name"] and param[1] == config["shapes_and_dtypes"]: + reason = config["reason"] + # Append the marker with its reason using the rename_marker helper function + markers_str.append(f'pytest.mark.{rename_marker(marker)}(reason="{reason}")') + break # Stop processing further configs for this marker if a match is found + if len(markers_str) != 0: + break # If a marker is already found, exit the outer loop + + # Process additional marker reason information if provided + if len(marker_with_reason_and_params) != 0: + for marker_name, reason_with_params in marker_with_reason_and_params.items(): + for reason, params_list in reason_with_params.items(): + for param_tuple in params_list: + # Check if the current test parameter matches the parameter tuple from marker reason info + if param_tuple[0] == param[0] and param_tuple[1] == param[1]: + # Build an additional marker string. Note: using marker_name might be intended instead of marker. + additional_param_str = f'pytest.mark.{rename_marker(marker_name)}' + if reason != "No_Reason": + additional_param_str += f'(reason="{reason}")' + markers_str.append(additional_param_str) + + # Format the updated test parameter with marker annotations if any markers were added. + if len(markers_str) != 0: + # Join all marker strings into a single comma-separated string + markers_str = ", ".join(markers_str) + new_models_ops_test_params.append(f"pytest.param(({param_str}), marks=[{markers_str}])") else: - failed_models_ops_tests_info[failed_test_path] = [failed_test_config] - - return failed_models_ops_tests_info + # If no markers apply, retain the original test parameter tuple format. + new_models_ops_test_params.append(f"({param_str})") + + return new_models_ops_test_params -def extract_models_ops_test_params(file_path: str): +def update_models_ops_tests(models_ops_test_update_info: Dict[str, Dict[str, List[Dict[str, str]]]]): """ - Extracts model operation test parameters from a Python file. - - This function reads the content of the specified file and parses it into an Abstract Syntax Tree (AST). - It then locates the assignment to the variable 'forge_modules_and_shapes_dtypes_list' and extracts - test parameters from the assigned list. Test parameters can be specified either directly as tuples or as - calls to pytest.param() where the first argument is a tuple. Each test parameter tuple is converted - back to its source code representation using ast.unparse, and the resulting tuple elements are stored - as a list of strings. + Update test files with new test parameters that include marker configurations and failure reasons. + + For each test file specified in the `models_ops_test_update_info` dictionary, this function: + + 1. Extracts the current test parameters and marker reason information by calling `extract_models_ops_test_params` + with `return_marker_with_reason=True`. + 2. Removes any existing "skip" or "xfail" entries from the marker reason information. + 3. Calls `update_params` to generate updated test parameter definitions that include the correct marker annotations. + 4. Reads the test file's content, locates the block containing the test parameter definitions (identified by the variable + "forge_modules_and_shapes_dtypes_list"), and replaces that block with the updated definitions. + 5. Writes the updated content back to the file, effectively updating the tests in place. Args: - file_path (str): The path to the Python file containing the model operation test parameters. + models_ops_test_update_info (Dict[str, Dict[str, List[Dict[str, str]]]]): + A dictionary where each key is a test file path and each value is a dictionary mapping marker names to a list + of configuration dictionaries. Each configuration dictionary should include information such as "module_name", + "shapes_and_dtypes", and "reason". Returns: - List[List[str]]: A list of test parameter tuples, with each tuple represented as a list of strings. + None. The function updates the test files directly. """ + # Iterate over each test file path and its corresponding marker test configuration + for idx, (test_file_path, marker_with_test_config) in enumerate(models_ops_test_update_info.items()): + # Extract current test parameters and marker reason information from the test file + models_ops_test_params, marker_with_reason_and_params = extract_models_ops_test_params( + test_file_path, return_marker_with_reason=True + ) + + # Remove 'skip' and 'xfail' markers from marker reason information if they exist + if "skip" in marker_with_reason_and_params.keys(): + marker_with_reason_and_params.pop("skip") + if "xfail" in marker_with_reason_and_params.keys(): + marker_with_reason_and_params.pop("xfail") + + # Generate updated test parameter definitions with the new marker information + new_models_ops_test_params = update_params( + models_ops_test_params, marker_with_test_config, marker_with_reason_and_params + ) + + # Read the entire content of the test file + lines = read_file(test_file_path) + + new_lines = [] # Will store the updated file content + is_pytest_params = False # Flag to indicate if we are inside the old test parameter block + + # Process each line of the file + for line in lines: + # When encountering the marker "@pytest.mark.push", insert the updated test parameters before it + if "@pytest.mark.push" in line: + new_lines.append("forge_modules_and_shapes_dtypes_list = [\n") + # Append each updated test parameter (indented for formatting) + for test_param in new_models_ops_test_params: + new_lines.append(f"\t{test_param},\n") + new_lines.append("]\n") + new_lines.append("\n") + new_lines.append("\n") + # Append the marker line itself + new_lines.append(line) + is_pytest_params = False # End the parameter block insertion + # Skip lines that are part of the old test parameter block + elif "forge_modules_and_shapes_dtypes_list = [" in line or is_pytest_params: + is_pytest_params = True + else: + # Retain lines that are not part of the test parameter block + new_lines.append(line) - with open(file_path, "r") as file: - content = file.read() + # Write the updated file content back to the test file + with open(test_file_path, "w") as file: + file.writelines(new_lines) - # Parse the file content into an AST (Abstract Syntax Tree). - tree = ast.parse(content) + +def create_report(report_file_path: str, + failed_models_ops_tests: Dict[str, str], + status_to_tests_and_reason: Optional[Dict[str, Dict[str, str]]] = None): + """ + Create an Excel report summarizing failed tests and tests with specific markers and reasons. - # List to hold the extracted test parameter tuples. - models_ops_test_params = [] + This function generates an Excel report that consolidates: + 1. Failed test cases (from `failed_models_ops_tests`), each marked as "FAILED". + 2. Additional test cases with marker information (from `status_to_tests_and_reason`, if provided). + For these tests, the corresponding marker (e.g., "SKIP", "XFAIL") and the reason are included. - # Iterate over each top-level node in the AST. - for node in tree.body: - # Check if the node is an assignment (e.g., a variable assignment). - if isinstance(node, ast.Assign): - # Loop through each target of the assignment (in case of multiple targets). - for target in node.targets: - # Look for the specific variable name 'forge_modules_and_shapes_dtypes_list'. - if isinstance(target, ast.Name) and target.id == "forge_modules_and_shapes_dtypes_list": - # Iterate over each element in the assigned list. - for elt in node.value.elts: - # Initialize the variable to hold the test parameter tuple. - param_tuple = None - # Check if the element is a call (e.g., a call to pytest.param(...)). - if ( - isinstance(elt, ast.Call) - and isinstance(elt.func, ast.Attribute) - and elt.func.attr == "param" - ): - # Extract the first argument from the pytest.param(...) call, if available. - param_tuple = elt.args[0] if elt.args else None - # Otherwise, check if the element is directly a tuple. - elif isinstance(elt, ast.Tuple): - param_tuple = elt - else: - # If the element doesn't match the expected patterns, skip it. - param_tuple = None + The report is written to the file specified by `report_file_path` using the `create_excel_file` helper. - # If a valid test parameter tuple was found, unparse each element back to source code. - if param_tuple: - tuple_elements = [ast.unparse(item) for item in param_tuple.elts] - models_ops_test_params.append(tuple_elements) + Args: + report_file_path (str): The file path where the Excel report will be saved. + failed_models_ops_tests (Dict[str, str]): A dictionary mapping failed test cases to their failure reasons. + status_to_tests_and_reason (Optional[Dict[str, Dict[str, str]]], optional): + A dictionary mapping marker names to a dictionary of test cases and their associated reasons. + Defaults to None, meaning only failed tests will be reported. - # Return the list of extracted test parameter tuples. - return models_ops_test_params + Returns: + None. The function writes the report to the specified file. + """ + sheet_title = "model_ops_test" # Title of the Excel sheet + headers = ["TestCases", "Marker", "Reason"] # Column headers for the Excel report + data = [] # List to hold rows of data for the report + + # Add each failed test case to the report data with a "FAILED" marker. + for failed_test, failure_reason in failed_models_ops_tests.items(): + data.append([failed_test, "FAILED", failure_reason]) + + # If additional test status information is provided, add those test cases to the report. + # Note: The check ensures that the dictionary is not None and not empty. + if status_to_tests_and_reason is not None and len(status_to_tests_and_reason) != 0: + for marker, test_with_reason in status_to_tests_and_reason.items(): + for test, reason in test_with_reason.items(): + data.append([test, marker, reason]) + + # Create the Excel report using the provided helper function. + create_excel_file( + title=sheet_title, + headers=headers, + rows=data, + output_file_path=report_file_path, + ) -def update_params(models_ops_test_params, failed_test_configs): +def extract_data_from_report(report_file_path: str): """ - Update test parameters by adding xfail marks for tests that have failed. - - This function iterates over the list of model operation test parameters (each represented as a list of strings) - and checks if the first two elements of the parameter (assumed to be the module name and shapes/dtypes) - match any of the failed test configurations. For matching parameters, a new parameter string is created using - a pytest.param call with an xfail mark and the associated error message. If no match is found, the original - parameter string is retained. + Extract test update configuration information from an Excel report. + This function reads an Excel report that contains test case details, markers, and reasons, + then parses and organizes the data to generate a mapping of test file paths to marker configuration + details for updating model ops tests. + Args: - models_ops_test_params (List[List[str]]): A list of test parameter tuples (each tuple is a list of strings). - failed_test_configs (List[Dict[str, str]]): A list of dictionaries where each dictionary contains - the keys "module_name", "shapes_and_dtypes", and "error_message" representing a failed test. + report_file_path (str): The file path to the Excel report. Returns: - List[str]: A list of updated test parameter strings. Each string is either a plain tuple or a pytest.param - call with an xfail mark. + Dict[str, Dict[str, List[Dict[str, str]]]]: + A dictionary mapping each test file path to another dictionary that maps markers + to lists of test configuration dictionaries. Each configuration dictionary contains: + - "reason": The associated failure reason. + - "module_name": The extracted module name. + - "shapes_and_dtypes": The extracted shapes and dtypes information. """ - new_models_ops_test_params = [] + # Read the Excel report into a DataFrame, selecting only the required columns. + report_df = pd.read_excel( + report_file_path, + header=0, + usecols=["TestCases", "Marker", "Reason"], + ) - for param in models_ops_test_params: + # Regex pattern to extract module name and shapes/dtypes from the test case function string. + regex = r"\[([^\[]+)-\[(.*)\]\]" - param_str = ", ".join(param) - matched = False + # Dictionary to hold the update configuration info. + models_ops_test_update_info = {} + + # Iterate over each row in the DataFrame. + for index, row in report_df.iterrows(): + # Clean and extract the test case string. + test_cases = str(row.TestCases).strip("\n") + # Normalize the marker using the helper function. + marker = rename_marker(str(row.Marker).strip("\n")) + # Extract the reason. + reason = str(row.Reason).strip("\n") + + # Extract the test file path and test case function using a helper function. + test_file_path, test_case_func = extract_test_file_path_and_test_case_func(test_cases) + # Skip processing if either the test file path or test case function is missing. + if test_file_path is None or test_case_func is None: + continue + + # Initialize a dictionary to hold test configuration details. + test_config = {} + test_config["reason"] = reason + + # Use regex to extract module name and shapes/dtypes from the test case function string. + match = re.search(regex, test_case_func) + if match: + module_name = match.group(1) + shapes_and_dtypes = match.group(2) + test_config["module_name"] = module_name + test_config["shapes_and_dtypes"] = f"[{shapes_and_dtypes}]" + + # Update the configuration dictionary for the test file. + # If the test file path already exists, update the marker configuration list. + if test_file_path in models_ops_test_update_info.keys(): + if marker in models_ops_test_update_info[test_file_path].keys(): + models_ops_test_update_info[test_file_path][marker].append(test_config) + else: + models_ops_test_update_info[test_file_path][marker] = [test_config] + else: + # Create a new entry for the test file path with the marker and configuration. + models_ops_test_update_info[test_file_path] = {marker: [test_config]} - for config in failed_test_configs: + return models_ops_test_update_info - if param[0] == config["module_name"] and param[1] == config["shapes_and_dtypes"]: - error_message = config["error_message"] - new_models_ops_test_params.append( - f'pytest.param(({param_str}), marks=[pytest.mark.xfail(reason="{error_message}")])' - ) - matched = True - break - if not matched: - new_models_ops_test_params.append(f"({param_str})") - return new_models_ops_test_params -def update_models_ops_tests_failures(failed_models_ops_tests_info: Dict[str, List[Dict[str, str]]]): +#python scripts/model_analysis/models_ops_test_failure_update.py --log_files ci_logs/pytest_1.log ci_logs/pytest_2.log ci_logs/pytest_3.log ci_logs/pytest_4.log +#python scripts/model_analysis/models_ops_test_failure_update.py --report_file_path model_ops_tests_report.xlsx --use_report +def main(): """ - Update test files by modifying their test parameter lists to include xfail marks for failed tests. + Main function to update model ops tests based on pytest log failures. - This function processes each test file (specified by the keys in failed_models_ops_tests_info), extracts - the current test parameters using extract_models_ops_test_params, and updates these parameters using the - failed test configurations. The updated parameter list is then injected back into the file, replacing the - old list. + This utility can operate in two modes: + 1. Use an existing Excel report (via --use_report flag) to update tests. + 2. Parse provided pytest log files directly to extract failed tests and test markers, + then generate an Excel report and update tests accordingly. - Args: - failed_models_ops_tests_info (Dict[str, List[Dict[str, str]]]): A dictionary mapping test file paths to a - list of failed test configuration dictionaries. Each configuration should contain "module_name", - "shapes_and_dtypes", and "error_message". + Pre-commit checks are run on the models ops test directory both before and after updates. """ - for idx, (failed_test_path, failed_test_config) in enumerate(failed_models_ops_tests_info.items()): - # Extract the current list of test parameters from the file. - models_ops_test_params = extract_models_ops_test_params(failed_test_path) - # Update the test parameters with failure information. - new_models_ops_test_params = update_params(models_ops_test_params, failed_test_config) - - lines = read_file(failed_test_path) - - new_lines = [] - is_pytest_params = False # Flag to track if we are within the test parameter block. - - for line in lines: - # When the marker "@pytest.mark.push" is encountered, insert the updated test parameters before it. - if "@pytest.mark.push" in line: - new_lines.append("forge_modules_and_shapes_dtypes_list = [\n") - # Append each updated test parameter. - for test_param in new_models_ops_test_params: - new_lines.append(f"\t{test_param},\n") - new_lines.append("]\n") - new_lines.append("\n") - new_lines.append("\n") - # Append the line containing the marker. - new_lines.append(line) - is_pytest_params = False # End of the parameter block. - # Skip the old test parameter block lines. - elif "forge_modules_and_shapes_dtypes_list = [" in line or is_pytest_params: - is_pytest_params = True - else: - # Retain other lines unchanged. - new_lines.append(line) - - # Write the updated content back to the test file. - with open(failed_test_path, "w") as file: - file.writelines(new_lines) - - -def main(): parser = argparse.ArgumentParser( - description="Update models ops tests with xfail marks based on pytest log failures." + description=( + "Update model ops tests with xfail marks based on pytest log failures. " + ) + ) + parser.add_argument( + "--log_files", + nargs="+", + type=str, + required=False, + help=( + "A space-separated list of paths to pytest log files containing test execution output. " + "These logs are used to extract information about failed tests and tests marked as XFAIL/SKIP." + ), ) - parser.add_argument("--log_files", nargs="+", type=str, required=True, help="List of pytest log files") parser.add_argument( "--models_ops_test_dir_path", type=str, default="forge/test/models_ops/", required=False, - help="Specify the directory path that contains generated models ops tests", + help=( + "The directory path that contains the generated model ops tests. " + "Defaults to 'forge/test/models_ops/'." + ), + ) + parser.add_argument( + "--report_file_path", + type=str, + default=os.path.join(os.getcwd(), "model_ops_tests_report.xlsx"), + required=False, + help=( + "The file path to the Excel report containing test case details (test cases, markers, and reasons). " + "When used with the '--use_report' flag, this report is used to update the tests. " + "Defaults to 'model_ops_tests_report.xlsx' in the current working directory." + ), + ) + parser.add_argument( + "--use_report", + action="store_true", + help=( + "If set, the utility will use the provided Excel report file to update model ops tests. " + "If not set, the utility will parse the provided pytest log files directly to extract failure and marker information." + ), ) args = parser.parse_args() log_files = args.log_files models_ops_test_dir_path = args.models_ops_test_dir_path + report_file_path = args.report_file_path + use_report = args.use_report run_precommit(directory_path=models_ops_test_dir_path) - # Extract failed tests and their failure reasons from the provided log files. - failed_models_ops_tests = extract_failed_models_ops_tests_with_failure_reason( - log_files=log_files, models_ops_test_dir_path=models_ops_test_dir_path - ) - - if len(failed_models_ops_tests) == 0: - log_files_str = ", ".join(log_files) - logger.error(f"There is no failures in the provided {log_files_str} log files") - - # Extract and organize failure configuration details from the failed tests. - failed_models_ops_tests_info = extract_failed_models_ops_tests_config( - failed_models_ops_tests=failed_models_ops_tests - ) - - # Update the test files with the new parameters (including xfail marks) based on the failures. - update_models_ops_tests_failures(failed_models_ops_tests_info=failed_models_ops_tests_info) + if use_report and check_path(report_file_path): + models_ops_test_update_info = extract_data_from_report(report_file_path=report_file_path) + update_models_ops_tests(models_ops_test_update_info=models_ops_test_update_info) + run_precommit(directory_path=models_ops_test_dir_path) + else: + status_to_tests_and_reason = extract_xfailed_and_skipped_tests_with_reason( + log_files=log_files, models_ops_test_dir_path=models_ops_test_dir_path + ) + + failed_models_ops_tests = extract_failed_tests_with_failure_reason( + log_files=log_files, target_dirs=[models_ops_test_dir_path] + ) + + if len(failed_models_ops_tests) == 0: + log_files_str = ", ".join(log_files) + logger.error(f"There are no failures in the provided {log_files_str} log files") + + create_report( + report_file_path=report_file_path, + failed_models_ops_tests=failed_models_ops_tests, + status_to_tests_and_reason=status_to_tests_and_reason, + ) - run_precommit(directory_path=models_ops_test_dir_path) if __name__ == "__main__": diff --git a/scripts/model_analysis/utils.py b/scripts/model_analysis/utils.py index 7b16530fc..9ca2afd09 100644 --- a/scripts/model_analysis/utils.py +++ b/scripts/model_analysis/utils.py @@ -258,7 +258,16 @@ def run_command(command: str): def run_precommit(directory_path: str): - run_command("pip install pre-commit") + """Checks if pre-commit is installed and runs it on all files in the given directory.""" + + # Check if pre-commit is installed + if shutil.which("pre-commit") is None: + logger.info("pre-commit is not installed. Installing...") + run_command("pip install pre-commit") + else: + logger.info("pre-commit is already installed.") + + # Run pre-commit on all files in the directory run_command(f"pre-commit run --files $(find {directory_path} -type f)") From 51b98f70fac1483ddaeeb8d09112c5f1c66ddb16 Mon Sep 17 00:00:00 2001 From: chandrasekaranpradeep Date: Tue, 25 Feb 2025 07:45:04 +0000 Subject: [PATCH 5/8] Add max_int parameter for adv_index ops test --- forge/test/models_ops/test_advindex.py | 26 +++++++++++++++----------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/forge/test/models_ops/test_advindex.py b/forge/test/models_ops/test_advindex.py index 28f5a7a98..e110d5715 100644 --- a/forge/test/models_ops/test_advindex.py +++ b/forge/test/models_ops/test_advindex.py @@ -97,32 +97,32 @@ def ids_func(param): ( Advindex0, [((448, 1024), torch.float32), ((1, 1), torch.int64)], - {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99}, + {"model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], "pcc": 0.99, "max_int": 447}, ), ( Advindex0, [((448, 1280), torch.float32), ((1, 1), torch.int64)], - {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99}, + {"model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], "pcc": 0.99, "max_int": 447}, ), ( Advindex0, [((448, 384), torch.float32), ((1, 1), torch.int64)], - {"model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], "pcc": 0.99}, + {"model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], "pcc": 0.99, "max_int": 447}, ), ( Advindex0, [((448, 512), torch.float32), ((1, 1), torch.int64)], - {"model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], "pcc": 0.99}, + {"model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], "pcc": 0.99, "max_int": 447}, ), ( Advindex0, [((448, 768), torch.float32), ((1, 1), torch.int64)], - {"model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], "pcc": 0.99}, + {"model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], "pcc": 0.99, "max_int": 447}, ), ( Advindex0, [((448, 1280), torch.float32), ((1, 2), torch.int64)], - {"model_name": ["pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf"], "pcc": 0.99}, + {"model_name": ["pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf"], "pcc": 0.99, "max_int": 447}, ), ( Advindex1, @@ -143,6 +143,7 @@ def ids_func(param): "pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf", ], "pcc": 0.99, + "max_int": 0, }, ), ( @@ -155,6 +156,7 @@ def ids_func(param): "pt_opt_facebook_opt_350m_seq_cls_hf", ], "pcc": 0.99, + "max_int": 31, }, ), ( @@ -167,27 +169,28 @@ def ids_func(param): "pt_nbeats_generic_basis_clm_hf", ], "pcc": 0.99, + "max_int": 0, }, ), ( Advindex2, [((2401,), torch.int64)], - {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99, "max_int": 168}, ), ( Advindex3, [((2401,), torch.int64)], - {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99, "max_int": 168}, ), ( Advindex4, [((2401,), torch.int64)], - {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99, "max_int": 168}, ), ( Advindex5, [((2401,), torch.int64)], - {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99, "max_int": 168}, ), ] @@ -200,11 +203,11 @@ def test_module(forge_module_and_shapes_dtypes, record_forge_property): forge_module, operand_shapes_dtypes, metadata = forge_module_and_shapes_dtypes pcc = metadata.pop("pcc") + max_int = metadata.pop("max_int") for metadata_name, metadata_value in metadata.items(): record_forge_property(metadata_name, metadata_value) - max_int = 1000 inputs = [ Tensor.create_from_shape(operand_shape, operand_dtype, max_int=max_int) for operand_shape, operand_dtype in operand_shapes_dtypes @@ -228,3 +231,4 @@ def test_module(forge_module_and_shapes_dtypes, record_forge_property): compiled_model = compile(framework_model, sample_inputs=inputs) verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) + From 22fc43336b39ea1ce059aeef934d84686eebf40b Mon Sep 17 00:00:00 2001 From: chandrasekaranpradeep Date: Tue, 25 Feb 2025 13:13:49 +0000 Subject: [PATCH 6/8] Change marker from push to nightly_models_ops --- forge/test/models_ops/test_abs.py | 2 +- forge/test/models_ops/test_add.py | 2 +- forge/test/models_ops/test_advindex.py | 2 +- forge/test/models_ops/test_argmax.py | 2 +- forge/test/models_ops/test_avgpool1d.py | 2 +- forge/test/models_ops/test_avgpool2d.py | 2 +- forge/test/models_ops/test_avgpool3d.py | 2 +- forge/test/models_ops/test_broadcast.py | 2 +- forge/test/models_ops/test_cast.py | 2 +- forge/test/models_ops/test_clip.py | 2 +- forge/test/models_ops/test_concatenate.py | 2 +- forge/test/models_ops/test_conv2d.py | 2 +- forge/test/models_ops/test_conv2dtranspose.py | 2 +- forge/test/models_ops/test_cosine.py | 2 +- forge/test/models_ops/test_cumsum.py | 2 +- forge/test/models_ops/test_embedding.py | 2 +- forge/test/models_ops/test_equal.py | 2 +- forge/test/models_ops/test_exp.py | 2 +- forge/test/models_ops/test_gelu.py | 2 +- forge/test/models_ops/test_greater.py | 2 +- forge/test/models_ops/test_identity.py | 2 +- forge/test/models_ops/test_index.py | 2 +- forge/test/models_ops/test_layernorm.py | 2 +- forge/test/models_ops/test_leakyrelu.py | 2 +- forge/test/models_ops/test_less.py | 2 +- forge/test/models_ops/test_matmul.py | 2 +- forge/test/models_ops/test_max.py | 2 +- forge/test/models_ops/test_maxpool2d.py | 2 +- forge/test/models_ops/test_multiply.py | 2 +- forge/test/models_ops/test_notequal.py | 2 +- forge/test/models_ops/test_pad.py | 2 +- forge/test/models_ops/test_reciprocal.py | 2 +- forge/test/models_ops/test_reduceavg.py | 2 +- forge/test/models_ops/test_relu.py | 2 +- forge/test/models_ops/test_remainder.py | 2 +- .../test/models_ops/test_repeatinterleave.py | 2 +- forge/test/models_ops/test_reshape.py | 2 +- forge/test/models_ops/test_resize2d.py | 2 +- forge/test/models_ops/test_sigmoid.py | 2 +- forge/test/models_ops/test_sine.py | 2 +- forge/test/models_ops/test_softmax.py | 2 +- forge/test/models_ops/test_sqrt.py | 2 +- forge/test/models_ops/test_squeeze.py | 2 +- forge/test/models_ops/test_stack.py | 2 +- forge/test/models_ops/test_subtract.py | 2 +- forge/test/models_ops/test_tanh.py | 2 +- forge/test/models_ops/test_transpose.py | 2 +- forge/test/models_ops/test_unsqueeze.py | 2 +- forge/test/models_ops/test_where.py | 2 +- .../models_ops_test_failure_update.py | 143 +++++++++--------- 50 files changed, 122 insertions(+), 119 deletions(-) diff --git a/forge/test/models_ops/test_abs.py b/forge/test/models_ops/test_abs.py index 1814e5b97..b79f5dfd8 100644 --- a/forge/test/models_ops/test_abs.py +++ b/forge/test/models_ops/test_abs.py @@ -103,7 +103,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Abs") diff --git a/forge/test/models_ops/test_add.py b/forge/test/models_ops/test_add.py index 1d9202afe..0a1a23f01 100644 --- a/forge/test/models_ops/test_add.py +++ b/forge/test/models_ops/test_add.py @@ -18523,7 +18523,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Add") diff --git a/forge/test/models_ops/test_advindex.py b/forge/test/models_ops/test_advindex.py index e110d5715..6ad5a959e 100644 --- a/forge/test/models_ops/test_advindex.py +++ b/forge/test/models_ops/test_advindex.py @@ -195,7 +195,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "AdvIndex") diff --git a/forge/test/models_ops/test_argmax.py b/forge/test/models_ops/test_argmax.py index c8059d5bc..09b610974 100644 --- a/forge/test/models_ops/test_argmax.py +++ b/forge/test/models_ops/test_argmax.py @@ -48,7 +48,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Argmax") diff --git a/forge/test/models_ops/test_avgpool1d.py b/forge/test/models_ops/test_avgpool1d.py index 8c6522306..824d17959 100644 --- a/forge/test/models_ops/test_avgpool1d.py +++ b/forge/test/models_ops/test_avgpool1d.py @@ -61,7 +61,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "AvgPool1d") diff --git a/forge/test/models_ops/test_avgpool2d.py b/forge/test/models_ops/test_avgpool2d.py index 2c870ac63..7efdb2f5f 100644 --- a/forge/test/models_ops/test_avgpool2d.py +++ b/forge/test/models_ops/test_avgpool2d.py @@ -1434,7 +1434,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "AvgPool2d") diff --git a/forge/test/models_ops/test_avgpool3d.py b/forge/test/models_ops/test_avgpool3d.py index 428f64f75..de51c3457 100644 --- a/forge/test/models_ops/test_avgpool3d.py +++ b/forge/test/models_ops/test_avgpool3d.py @@ -90,7 +90,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "AvgPool3d") diff --git a/forge/test/models_ops/test_broadcast.py b/forge/test/models_ops/test_broadcast.py index a3b881522..f30c53b9c 100644 --- a/forge/test/models_ops/test_broadcast.py +++ b/forge/test/models_ops/test_broadcast.py @@ -134,7 +134,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Broadcast") diff --git a/forge/test/models_ops/test_cast.py b/forge/test/models_ops/test_cast.py index ac7c97865..5532126cf 100644 --- a/forge/test/models_ops/test_cast.py +++ b/forge/test/models_ops/test_cast.py @@ -464,7 +464,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Cast") diff --git a/forge/test/models_ops/test_clip.py b/forge/test/models_ops/test_clip.py index 5138ef3c5..339225613 100644 --- a/forge/test/models_ops/test_clip.py +++ b/forge/test/models_ops/test_clip.py @@ -1072,7 +1072,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Clip") diff --git a/forge/test/models_ops/test_concatenate.py b/forge/test/models_ops/test_concatenate.py index 4b8927502..e02c25b43 100644 --- a/forge/test/models_ops/test_concatenate.py +++ b/forge/test/models_ops/test_concatenate.py @@ -12920,7 +12920,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Concatenate") diff --git a/forge/test/models_ops/test_conv2d.py b/forge/test/models_ops/test_conv2d.py index b95c9e40b..f528f5687 100644 --- a/forge/test/models_ops/test_conv2d.py +++ b/forge/test/models_ops/test_conv2d.py @@ -56748,7 +56748,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Conv2d") diff --git a/forge/test/models_ops/test_conv2dtranspose.py b/forge/test/models_ops/test_conv2dtranspose.py index 46e4d807d..f308d1c26 100644 --- a/forge/test/models_ops/test_conv2dtranspose.py +++ b/forge/test/models_ops/test_conv2dtranspose.py @@ -503,7 +503,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Conv2dTranspose") diff --git a/forge/test/models_ops/test_cosine.py b/forge/test/models_ops/test_cosine.py index 2a74bb54c..2436744e1 100644 --- a/forge/test/models_ops/test_cosine.py +++ b/forge/test/models_ops/test_cosine.py @@ -192,7 +192,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Cosine") diff --git a/forge/test/models_ops/test_cumsum.py b/forge/test/models_ops/test_cumsum.py index 8f087200d..18d0e6b8f 100644 --- a/forge/test/models_ops/test_cumsum.py +++ b/forge/test/models_ops/test_cumsum.py @@ -76,7 +76,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "CumSum") diff --git a/forge/test/models_ops/test_embedding.py b/forge/test/models_ops/test_embedding.py index 2b60e9dda..706dab2be 100644 --- a/forge/test/models_ops/test_embedding.py +++ b/forge/test/models_ops/test_embedding.py @@ -1652,7 +1652,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Embedding") diff --git a/forge/test/models_ops/test_equal.py b/forge/test/models_ops/test_equal.py index 61f84b91e..dc3f71324 100644 --- a/forge/test/models_ops/test_equal.py +++ b/forge/test/models_ops/test_equal.py @@ -48,7 +48,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Equal") diff --git a/forge/test/models_ops/test_exp.py b/forge/test/models_ops/test_exp.py index 4bc10a021..58813f8ce 100644 --- a/forge/test/models_ops/test_exp.py +++ b/forge/test/models_ops/test_exp.py @@ -305,7 +305,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Exp") diff --git a/forge/test/models_ops/test_gelu.py b/forge/test/models_ops/test_gelu.py index c389827d4..dc3a61a82 100644 --- a/forge/test/models_ops/test_gelu.py +++ b/forge/test/models_ops/test_gelu.py @@ -919,7 +919,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Gelu") diff --git a/forge/test/models_ops/test_greater.py b/forge/test/models_ops/test_greater.py index ef40a6249..a3f9e34af 100644 --- a/forge/test/models_ops/test_greater.py +++ b/forge/test/models_ops/test_greater.py @@ -574,7 +574,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Greater") diff --git a/forge/test/models_ops/test_identity.py b/forge/test/models_ops/test_identity.py index eb2d67156..bfb31bed0 100644 --- a/forge/test/models_ops/test_identity.py +++ b/forge/test/models_ops/test_identity.py @@ -1748,7 +1748,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Identity") diff --git a/forge/test/models_ops/test_index.py b/forge/test/models_ops/test_index.py index a31c2d78b..b6f4d9a3e 100644 --- a/forge/test/models_ops/test_index.py +++ b/forge/test/models_ops/test_index.py @@ -5648,7 +5648,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Index") diff --git a/forge/test/models_ops/test_layernorm.py b/forge/test/models_ops/test_layernorm.py index 1e3af4161..3a55fcb43 100644 --- a/forge/test/models_ops/test_layernorm.py +++ b/forge/test/models_ops/test_layernorm.py @@ -1630,7 +1630,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Layernorm") diff --git a/forge/test/models_ops/test_leakyrelu.py b/forge/test/models_ops/test_leakyrelu.py index 01b5b7ec6..990e1bc4d 100644 --- a/forge/test/models_ops/test_leakyrelu.py +++ b/forge/test/models_ops/test_leakyrelu.py @@ -152,7 +152,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "LeakyRelu") diff --git a/forge/test/models_ops/test_less.py b/forge/test/models_ops/test_less.py index a57ac5a94..1c595dc0a 100644 --- a/forge/test/models_ops/test_less.py +++ b/forge/test/models_ops/test_less.py @@ -496,7 +496,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Less") diff --git a/forge/test/models_ops/test_matmul.py b/forge/test/models_ops/test_matmul.py index b394108ba..e072b8db9 100644 --- a/forge/test/models_ops/test_matmul.py +++ b/forge/test/models_ops/test_matmul.py @@ -5648,7 +5648,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Matmul") diff --git a/forge/test/models_ops/test_max.py b/forge/test/models_ops/test_max.py index d8b4a2546..0644a2c08 100644 --- a/forge/test/models_ops/test_max.py +++ b/forge/test/models_ops/test_max.py @@ -65,7 +65,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Max") diff --git a/forge/test/models_ops/test_maxpool2d.py b/forge/test/models_ops/test_maxpool2d.py index 47a2557ff..0b787d207 100644 --- a/forge/test/models_ops/test_maxpool2d.py +++ b/forge/test/models_ops/test_maxpool2d.py @@ -1836,7 +1836,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "MaxPool2d") diff --git a/forge/test/models_ops/test_multiply.py b/forge/test/models_ops/test_multiply.py index dbb3c4fa3..6b8de1481 100644 --- a/forge/test/models_ops/test_multiply.py +++ b/forge/test/models_ops/test_multiply.py @@ -18919,7 +18919,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Multiply") diff --git a/forge/test/models_ops/test_notequal.py b/forge/test/models_ops/test_notequal.py index 2ae5ff7d8..da43b1fc6 100644 --- a/forge/test/models_ops/test_notequal.py +++ b/forge/test/models_ops/test_notequal.py @@ -47,7 +47,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "NotEqual") diff --git a/forge/test/models_ops/test_pad.py b/forge/test/models_ops/test_pad.py index 28e3d495d..233cedb49 100644 --- a/forge/test/models_ops/test_pad.py +++ b/forge/test/models_ops/test_pad.py @@ -361,7 +361,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Pad") diff --git a/forge/test/models_ops/test_reciprocal.py b/forge/test/models_ops/test_reciprocal.py index 45dd5fe91..2106e9a8f 100644 --- a/forge/test/models_ops/test_reciprocal.py +++ b/forge/test/models_ops/test_reciprocal.py @@ -2037,7 +2037,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Reciprocal") diff --git a/forge/test/models_ops/test_reduceavg.py b/forge/test/models_ops/test_reduceavg.py index 58b948c58..3033c6ab1 100644 --- a/forge/test/models_ops/test_reduceavg.py +++ b/forge/test/models_ops/test_reduceavg.py @@ -1131,7 +1131,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "ReduceAvg") diff --git a/forge/test/models_ops/test_relu.py b/forge/test/models_ops/test_relu.py index a7a8b7eeb..a311b750e 100644 --- a/forge/test/models_ops/test_relu.py +++ b/forge/test/models_ops/test_relu.py @@ -3882,7 +3882,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Relu") diff --git a/forge/test/models_ops/test_remainder.py b/forge/test/models_ops/test_remainder.py index 1dd4d23ea..396a28dde 100644 --- a/forge/test/models_ops/test_remainder.py +++ b/forge/test/models_ops/test_remainder.py @@ -48,7 +48,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Remainder") diff --git a/forge/test/models_ops/test_repeatinterleave.py b/forge/test/models_ops/test_repeatinterleave.py index f9f716b97..3da779a36 100644 --- a/forge/test/models_ops/test_repeatinterleave.py +++ b/forge/test/models_ops/test_repeatinterleave.py @@ -905,7 +905,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "RepeatInterleave") diff --git a/forge/test/models_ops/test_reshape.py b/forge/test/models_ops/test_reshape.py index 28ff24867..af4d7e8e1 100644 --- a/forge/test/models_ops/test_reshape.py +++ b/forge/test/models_ops/test_reshape.py @@ -22636,7 +22636,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Reshape") diff --git a/forge/test/models_ops/test_resize2d.py b/forge/test/models_ops/test_resize2d.py index 10a7d97c0..46af1bf62 100644 --- a/forge/test/models_ops/test_resize2d.py +++ b/forge/test/models_ops/test_resize2d.py @@ -2051,7 +2051,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Resize2d") diff --git a/forge/test/models_ops/test_sigmoid.py b/forge/test/models_ops/test_sigmoid.py index d65db8dca..fd8192328 100644 --- a/forge/test/models_ops/test_sigmoid.py +++ b/forge/test/models_ops/test_sigmoid.py @@ -2011,7 +2011,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Sigmoid") diff --git a/forge/test/models_ops/test_sine.py b/forge/test/models_ops/test_sine.py index 63a490c16..2400f59e3 100644 --- a/forge/test/models_ops/test_sine.py +++ b/forge/test/models_ops/test_sine.py @@ -192,7 +192,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Sine") diff --git a/forge/test/models_ops/test_softmax.py b/forge/test/models_ops/test_softmax.py index 8a7cbc76f..8e11f91ee 100644 --- a/forge/test/models_ops/test_softmax.py +++ b/forge/test/models_ops/test_softmax.py @@ -952,7 +952,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Softmax") diff --git a/forge/test/models_ops/test_sqrt.py b/forge/test/models_ops/test_sqrt.py index c6ad5c232..e283cdb80 100644 --- a/forge/test/models_ops/test_sqrt.py +++ b/forge/test/models_ops/test_sqrt.py @@ -1941,7 +1941,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Sqrt") diff --git a/forge/test/models_ops/test_squeeze.py b/forge/test/models_ops/test_squeeze.py index f1687f7bc..f2d8e0480 100644 --- a/forge/test/models_ops/test_squeeze.py +++ b/forge/test/models_ops/test_squeeze.py @@ -1140,7 +1140,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Squeeze") diff --git a/forge/test/models_ops/test_stack.py b/forge/test/models_ops/test_stack.py index 23aa4f28b..32d378eab 100644 --- a/forge/test/models_ops/test_stack.py +++ b/forge/test/models_ops/test_stack.py @@ -75,7 +75,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Stack") diff --git a/forge/test/models_ops/test_subtract.py b/forge/test/models_ops/test_subtract.py index c35c6fcd0..093519882 100644 --- a/forge/test/models_ops/test_subtract.py +++ b/forge/test/models_ops/test_subtract.py @@ -815,7 +815,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Subtract") diff --git a/forge/test/models_ops/test_tanh.py b/forge/test/models_ops/test_tanh.py index 2571db386..0f5862ab8 100644 --- a/forge/test/models_ops/test_tanh.py +++ b/forge/test/models_ops/test_tanh.py @@ -49,7 +49,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Tanh") diff --git a/forge/test/models_ops/test_transpose.py b/forge/test/models_ops/test_transpose.py index b4581ab42..6ca7321a0 100644 --- a/forge/test/models_ops/test_transpose.py +++ b/forge/test/models_ops/test_transpose.py @@ -9521,7 +9521,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Transpose") diff --git a/forge/test/models_ops/test_unsqueeze.py b/forge/test/models_ops/test_unsqueeze.py index ed3e11489..185f18778 100644 --- a/forge/test/models_ops/test_unsqueeze.py +++ b/forge/test/models_ops/test_unsqueeze.py @@ -6726,7 +6726,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Unsqueeze") diff --git a/forge/test/models_ops/test_where.py b/forge/test/models_ops/test_where.py index d565e4650..b28ea9cec 100644 --- a/forge/test/models_ops/test_where.py +++ b/forge/test/models_ops/test_where.py @@ -818,7 +818,7 @@ def ids_func(param): ] -@pytest.mark.push +@pytest.mark.nightly_models_ops @pytest.mark.parametrize("forge_module_and_shapes_dtypes", forge_modules_and_shapes_dtypes_list, ids=ids_func) def test_module(forge_module_and_shapes_dtypes, record_forge_property): record_forge_property("op_name", "Where") diff --git a/scripts/model_analysis/models_ops_test_failure_update.py b/scripts/model_analysis/models_ops_test_failure_update.py index 34569dd61..c76637092 100644 --- a/scripts/model_analysis/models_ops_test_failure_update.py +++ b/scripts/model_analysis/models_ops_test_failure_update.py @@ -90,12 +90,12 @@ def read_file(file_path: str): def rename_marker(marker: str) -> str: """ Standardizes the test marker name to either 'xfail' or 'skip'. - + Args: marker (str): The original marker name. - + Returns: - str: Normalized marker ('xfail' or 'skip') if it matches known values, + str: Normalized marker ('xfail' or 'skip') if it matches known values, otherwise returns the lowercase version of the input. """ marker_lower = marker.lower() @@ -106,6 +106,7 @@ def rename_marker(marker: str) -> str: else: return marker_lower + def extract_test_file_path_and_test_case_func(test_case: str): """ Extracts the test file path and test case function name from a given test case string. @@ -127,6 +128,7 @@ def extract_test_file_path_and_test_case_func(test_case: str): return test_file_path, test_case_func + def extract_unique_test_file_paths(test_cases: List[str]) -> set: """ Extracts unique test file paths from a list of test cases. @@ -141,7 +143,7 @@ def extract_unique_test_file_paths(test_cases: List[str]) -> set: for test_case in test_cases: test_file_path, _ = extract_test_file_path_and_test_case_func(test_case) - + # Add file path to the set if it exists if test_file_path is not None: unique_test_file_paths.add(test_file_path) @@ -213,8 +215,10 @@ def extract_models_ops_test_params(file_path: str, return_marker_with_reason: bo if kw.arg == "marks": marks_found = True # The marks can be provided as a list/tuple or as a single marker - mark_nodes = kw.value.elts if isinstance(kw.value, (ast.List, ast.Tuple)) else [kw.value] - + mark_nodes = ( + kw.value.elts if isinstance(kw.value, (ast.List, ast.Tuple)) else [kw.value] + ) + # Process each marker node for mark_node in mark_nodes: # Initialize variables for marker name and reason @@ -222,10 +226,12 @@ def extract_models_ops_test_params(file_path: str, return_marker_with_reason: bo reason = None # Check if the marker is a function call (e.g., pytest.mark.skip(reason="...")) - if isinstance(mark_node, ast.Call) and isinstance(mark_node.func, ast.Attribute): + if isinstance(mark_node, ast.Call) and isinstance( + mark_node.func, ast.Attribute + ): # Extract the marker name from the function call's attribute (e.g., 'skip' in pytest.mark.skip) marker_name = mark_node.func.attr - + # Look for a 'reason' keyword argument in the marker call for m_kw in mark_node.keywords: if m_kw.arg == "reason": @@ -242,7 +248,6 @@ def extract_models_ops_test_params(file_path: str, return_marker_with_reason: bo if marker_name: marks_dict[marker_name] = reason - # If the element is not a pytest.param call, check if it's directly a tuple elif isinstance(elt, ast.Tuple): param_tuple = elt @@ -256,7 +261,7 @@ def extract_models_ops_test_params(file_path: str, return_marker_with_reason: bo tuple_elements = [ast.unparse(item) for item in param_tuple.elts] # Append the test parameter tuple to the list models_ops_test_params.append(tuple_elements) - + # If markers were found, update the marker information dictionary accordingly if marks_found and marks_dict: for marker_name, reason in marks_dict.items(): @@ -281,23 +286,21 @@ def extract_models_ops_test_params(file_path: str, return_marker_with_reason: bo def extract_test_cases_and_status( - log_files: List[str], - target_dirs: Optional[List[str]] = None, - target_statuses: Optional[List[str]] = None + log_files: List[str], target_dirs: Optional[List[str]] = None, target_statuses: Optional[List[str]] = None ) -> Dict[str, Dict[str, str]]: """ Extract test cases and their statuses from provided log files. - This function processes each log file and extracts test case names along with their statuses + This function processes each log file and extracts test case names along with their statuses using a regular expression pattern. It supports filtering by target directories paths and target statuses. The extraction halts upon encountering the "==== FAILURES ====" marker in a log file. Args: log_files (List[str]): A list of file paths to the log files that need to be processed. - target_dirs (Optional[List[str]]): A list of directory paths to filter test cases. + target_dirs (Optional[List[str]]): A list of directory paths to filter test cases. Only test cases whose paths contain one of these directory paths will be included. Defaults to None, which means no directory filtering is applied. - target_statuses (Optional[List[str]]): A list of statuses to filter test cases. + target_statuses (Optional[List[str]]): A list of statuses to filter test cases. Only test cases with one of these statuses will be included. Defaults to None, meaning no status filtering is applied. @@ -316,7 +319,7 @@ def extract_test_cases_and_status( # (PASSED|FAILED|SKIPPED|XFAIL) - Capture one of the specified statuses. # .* - Followed by any characters (the remainder of the line). pattern = re.compile(r"^(.*?)\s+(PASSED|FAILED|SKIPPED|XFAIL).*") - + # Process each log file provided in the log_files list. for log_file in log_files: # Check if the log file exists using the check_path function. @@ -330,7 +333,7 @@ def extract_test_cases_and_status( # Extract the test case name and status from the regex match groups. test_case = match.group(1).strip() status = match.group(2).strip() - + # convert 'SKIPPED' to 'SKIP'. if status == "SKIPPED": status = "SKIP" @@ -347,18 +350,20 @@ def extract_test_cases_and_status( else: # Otherwise, create a new entry for this status with the test case. status_to_tests_and_reason[status] = {test_case: ""} - + # Stop processing further lines once the failures section is reached. if "==== FAILURES ====" in line: break else: # Log a warning if the provided log file path does not exist. logger.warning(f"Provided path {log_file} does not exist!") - + return status_to_tests_and_reason -def update_reason_for_xfailed_skipped_test(status_to_tests_and_reason: Dict[str, Dict[str, str]]) -> Dict[str, Dict[str, str]]: +def update_reason_for_xfailed_skipped_test( + status_to_tests_and_reason: Dict[str, Dict[str, str]] +) -> Dict[str, Dict[str, str]]: """ Update the reasons for test cases marked as 'XFAIL' or 'SKIP' based on marker details extracted from test files. @@ -372,9 +377,9 @@ def update_reason_for_xfailed_skipped_test(status_to_tests_and_reason: Dict[str, update the test case's reason in the input dictionary. Args: - status_to_tests_and_reason (Dict[str, Dict[str, str]]): + status_to_tests_and_reason (Dict[str, Dict[str, str]]): A dictionary mapping statuses (e.g., 'SKIP', 'XFAIL') to dictionaries of test case strings and their reasons. - + Returns: Dict[str, Dict[str, str]]: The updated dictionary with reasons for test cases modified based on the marker data. """ @@ -383,23 +388,22 @@ def update_reason_for_xfailed_skipped_test(status_to_tests_and_reason: Dict[str, # Collect test cases for 'SKIP' status if present. if "SKIP" in status_to_tests_and_reason.keys(): test_cases.extend(status_to_tests_and_reason["SKIP"]) - + # Collect test cases for 'XFAIL' status if present. if "XFAIL" in status_to_tests_and_reason.keys(): test_cases.extend(status_to_tests_and_reason["XFAIL"]) - + # Remove duplicate test cases. test_cases = list(set(test_cases)) - + # Extract unique file paths from the collected test case strings. unique_test_file_paths = extract_unique_test_file_paths(test_cases=test_cases) - + # For each unique test file path, extract marker information along with reasons. for test_file_path in unique_test_file_paths: # The function returns a tuple; we are interested in the marker information dictionary. _, marker_with_reason_and_params = extract_models_ops_test_params( - file_path=test_file_path, - return_marker_with_reason=True + file_path=test_file_path, return_marker_with_reason=True ) # Iterate over each marker (e.g., 'skip', 'xfail') and its associated reason and parameter list. for marker, reason_with_params in marker_with_reason_and_params.items(): @@ -416,7 +420,10 @@ def update_reason_for_xfailed_skipped_test(status_to_tests_and_reason: Dict[str, break # Once updated, no need to check further for this test case. return status_to_tests_and_reason -def extract_xfailed_and_skipped_tests_with_reason(log_files: List[str], models_ops_test_dir_path: str) -> Dict[str, Dict[str, str]]: + +def extract_xfailed_and_skipped_tests_with_reason( + log_files: List[str], models_ops_test_dir_path: str +) -> Dict[str, Dict[str, str]]: """ Extract and update test cases with statuses 'XFAIL' and 'SKIP' from log files, including detailed reasons. @@ -437,42 +444,42 @@ def extract_xfailed_and_skipped_tests_with_reason(log_files: List[str], models_o # Extract test cases with statuses 'XFAIL' and 'SKIP' from the log files, # filtering for those test cases within the specified directory. status_to_tests_and_reason = extract_test_cases_and_status( - log_files=log_files, - target_dirs=[models_ops_test_dir_path], - target_statuses=["XFAIL", "SKIP"] + log_files=log_files, target_dirs=[models_ops_test_dir_path], target_statuses=["XFAIL", "SKIP"] ) - + # If test cases were found, update them with the marker-based reason information. if len(status_to_tests_and_reason) != 0: status_to_tests_and_reason = update_reason_for_xfailed_skipped_test( status_to_tests_and_reason=status_to_tests_and_reason ) - + return status_to_tests_and_reason -def extract_failed_tests_with_failure_reason(log_files: List[str], target_dirs: Optional[List[str]] = None) -> Dict[str, str]: +def extract_failed_tests_with_failure_reason( + log_files: List[str], target_dirs: Optional[List[str]] = None +) -> Dict[str, str]: """ Extract failed test cases along with their failure reasons from pytest log files. - This function processes one or more pytest log files to identify failed tests and extract their + This function processes one or more pytest log files to identify failed tests and extract their corresponding error messages. It operates in two main phases: - + 1. Short Test Summary Extraction: - Scans each log file for the "short test summary info" section. - - Identifies test cases marked as FAILED and filters them based on the provided target + - Identifies test cases marked as FAILED and filters them based on the provided target directories (if any). - - Stores these failed test cases in a dictionary with an empty string as a placeholder + - Stores these failed test cases in a dictionary with an empty string as a placeholder for the failure reason. 2. Detailed Failure Reason Extraction: - After the summary, the function parses the "==== FAILURES ====" section. - It uses a regular expression pattern to locate the test function names within the failure details. - - For lines starting with "E " (indicating error messages), it collects up to a specified maximum + - For lines starting with "E " (indicating error messages), it collects up to a specified maximum number of consecutive error lines to form a comprehensive error message. - - An external helper, `ErrorMessageUpdater`, is used to further refine or update the error message + - An external helper, `ErrorMessageUpdater`, is used to further refine or update the error message with additional context from subsequent lines. - - The refined error message is then matched with the corresponding failed test case (using substring + - The refined error message is then matched with the corresponding failed test case (using substring checks) and updated in the dictionary. Args: @@ -482,8 +489,8 @@ def extract_failed_tests_with_failure_reason(log_files: List[str], target_dirs: If None, all failed tests from the log files are considered. Returns: - Dict[str, str]: A dictionary mapping each failed test case (as a string) to its corresponding - failure reason (error message). If a test case's error message could not be determined, its value + Dict[str, str]: A dictionary mapping each failed test case (as a string) to its corresponding + failure reason (error message). If a test case's error message could not be determined, its value remains an empty string. """ # Instantiate the helper object for refining error messages with additional context. @@ -591,7 +598,7 @@ def update_params(models_ops_test_params, marker_with_test_config, marker_with_r This function iterates over each test parameter tuple in `models_ops_test_params` and updates it based on two sources of marker data: - + 1. `marker_with_test_config`: A dictionary mapping marker names to lists of configuration dictionaries. Each configuration is expected to contain: - "module_name": the module name associated with the test parameter. @@ -599,7 +606,7 @@ def update_params(models_ops_test_params, marker_with_test_config, marker_with_r - "reason": the reason to be applied for the marker. If a test parameter matches a configuration (i.e. its module name and shapes_and_dtypes match the config), a marker string (with reason) is created and appended. - + 2. `marker_with_reason_and_params`: Additional marker information, where for each marker, a set of reasons and associated parameter tuples is provided. If the current test parameter matches one of these tuples, an additional marker string is appended. If the reason is not "No_Reason", it is included in the marker string. @@ -648,11 +655,11 @@ def update_params(models_ops_test_params, marker_with_test_config, marker_with_r # Check if the current test parameter matches the parameter tuple from marker reason info if param_tuple[0] == param[0] and param_tuple[1] == param[1]: # Build an additional marker string. Note: using marker_name might be intended instead of marker. - additional_param_str = f'pytest.mark.{rename_marker(marker_name)}' + additional_param_str = f"pytest.mark.{rename_marker(marker_name)}" if reason != "No_Reason": additional_param_str += f'(reason="{reason}")' markers_str.append(additional_param_str) - + # Format the updated test parameter with marker annotations if any markers were added. if len(markers_str) != 0: # Join all marker strings into a single comma-separated string @@ -661,7 +668,7 @@ def update_params(models_ops_test_params, marker_with_test_config, marker_with_r else: # If no markers apply, retain the original test parameter tuple format. new_models_ops_test_params.append(f"({param_str})") - + return new_models_ops_test_params @@ -670,7 +677,7 @@ def update_models_ops_tests(models_ops_test_update_info: Dict[str, Dict[str, Lis Update test files with new test parameters that include marker configurations and failure reasons. For each test file specified in the `models_ops_test_update_info` dictionary, this function: - + 1. Extracts the current test parameters and marker reason information by calling `extract_models_ops_test_params` with `return_marker_with_reason=True`. 2. Removes any existing "skip" or "xfail" entries from the marker reason information. @@ -680,7 +687,7 @@ def update_models_ops_tests(models_ops_test_update_info: Dict[str, Dict[str, Lis 5. Writes the updated content back to the file, effectively updating the tests in place. Args: - models_ops_test_update_info (Dict[str, Dict[str, List[Dict[str, str]]]]): + models_ops_test_update_info (Dict[str, Dict[str, List[Dict[str, str]]]]): A dictionary where each key is a test file path and each value is a dictionary mapping marker names to a list of configuration dictionaries. Each configuration dictionary should include information such as "module_name", "shapes_and_dtypes", and "reason". @@ -709,13 +716,13 @@ def update_models_ops_tests(models_ops_test_update_info: Dict[str, Dict[str, Lis # Read the entire content of the test file lines = read_file(test_file_path) - new_lines = [] # Will store the updated file content + new_lines = [] # Will store the updated file content is_pytest_params = False # Flag to indicate if we are inside the old test parameter block # Process each line of the file for line in lines: - # When encountering the marker "@pytest.mark.push", insert the updated test parameters before it - if "@pytest.mark.push" in line: + # When encountering the marker "@pytest.mark.nightly_models_ops", insert the updated test parameters before it + if "@pytest.mark.nightly_models_ops" in line: new_lines.append("forge_modules_and_shapes_dtypes_list = [\n") # Append each updated test parameter (indented for formatting) for test_param in new_models_ops_test_params: @@ -737,10 +744,12 @@ def update_models_ops_tests(models_ops_test_update_info: Dict[str, Dict[str, Lis with open(test_file_path, "w") as file: file.writelines(new_lines) - -def create_report(report_file_path: str, - failed_models_ops_tests: Dict[str, str], - status_to_tests_and_reason: Optional[Dict[str, Dict[str, str]]] = None): + +def create_report( + report_file_path: str, + failed_models_ops_tests: Dict[str, str], + status_to_tests_and_reason: Optional[Dict[str, Dict[str, str]]] = None, +): """ Create an Excel report summarizing failed tests and tests with specific markers and reasons. @@ -792,7 +801,7 @@ def extract_data_from_report(report_file_path: str): This function reads an Excel report that contains test case details, markers, and reasons, then parses and organizes the data to generate a mapping of test file paths to marker configuration details for updating model ops tests. - + Args: report_file_path (str): The file path to the Excel report. @@ -858,10 +867,8 @@ def extract_data_from_report(report_file_path: str): return models_ops_test_update_info - - -#python scripts/model_analysis/models_ops_test_failure_update.py --log_files ci_logs/pytest_1.log ci_logs/pytest_2.log ci_logs/pytest_3.log ci_logs/pytest_4.log -#python scripts/model_analysis/models_ops_test_failure_update.py --report_file_path model_ops_tests_report.xlsx --use_report +# python scripts/model_analysis/models_ops_test_failure_update.py --log_files ci_logs/pytest_1.log ci_logs/pytest_2.log ci_logs/pytest_3.log ci_logs/pytest_4.log +# python scripts/model_analysis/models_ops_test_failure_update.py --report_file_path model_ops_tests_report.xlsx --use_report def main(): """ Main function to update model ops tests based on pytest log failures. @@ -875,9 +882,7 @@ def main(): """ parser = argparse.ArgumentParser( - description=( - "Update model ops tests with xfail marks based on pytest log failures. " - ) + description=("Update model ops tests with xfail marks based on pytest log failures. ") ) parser.add_argument( "--log_files", @@ -895,8 +900,7 @@ def main(): default="forge/test/models_ops/", required=False, help=( - "The directory path that contains the generated model ops tests. " - "Defaults to 'forge/test/models_ops/'." + "The directory path that contains the generated model ops tests. " "Defaults to 'forge/test/models_ops/'." ), ) parser.add_argument( @@ -951,6 +955,5 @@ def main(): ) - if __name__ == "__main__": main() From ccaf56a4fa95289da71cd5aa6b95f3530d87f3de Mon Sep 17 00:00:00 2001 From: chandrasekaranpradeep Date: Wed, 26 Feb 2025 08:52:51 +0000 Subject: [PATCH 7/8] Update failed tests with xfail marker and reason --- forge/test/models_ops/test_add.py | 506 +- forge/test/models_ops/test_advindex.py | 60 +- forge/test/models_ops/test_argmax.py | 27 +- forge/test/models_ops/test_avgpool2d.py | 103 +- forge/test/models_ops/test_broadcast.py | 150 +- forge/test/models_ops/test_cast.py | 806 ++-- forge/test/models_ops/test_concatenate.py | 27 +- forge/test/models_ops/test_conv2d.py | 4252 ++++++++++------- forge/test/models_ops/test_conv2dtranspose.py | 391 +- forge/test/models_ops/test_cumsum.py | 93 +- forge/test/models_ops/test_embedding.py | 1564 +++--- forge/test/models_ops/test_equal.py | 29 +- forge/test/models_ops/test_greater.py | 166 +- forge/test/models_ops/test_index.py | 536 ++- forge/test/models_ops/test_layernorm.py | 61 +- forge/test/models_ops/test_matmul.py | 655 +-- forge/test/models_ops/test_max.py | 92 +- forge/test/models_ops/test_maxpool2d.py | 503 +- forge/test/models_ops/test_multiply.py | 467 +- forge/test/models_ops/test_notequal.py | 27 +- forge/test/models_ops/test_pad.py | 69 +- forge/test/models_ops/test_reciprocal.py | 31 +- forge/test/models_ops/test_reduceavg.py | 57 +- .../test/models_ops/test_repeatinterleave.py | 442 +- forge/test/models_ops/test_reshape.py | 372 +- forge/test/models_ops/test_resize2d.py | 4188 +++++++++------- forge/test/models_ops/test_softmax.py | 102 +- forge/test/models_ops/test_subtract.py | 91 +- forge/test/models_ops/test_unsqueeze.py | 477 +- forge/test/models_ops/test_where.py | 1271 ++--- 30 files changed, 10291 insertions(+), 7324 deletions(-) diff --git a/forge/test/models_ops/test_add.py b/forge/test/models_ops/test_add.py index 0a1a23f01..dbd4786ae 100644 --- a/forge/test/models_ops/test_add.py +++ b/forge/test/models_ops/test_add.py @@ -2208,10 +2208,13 @@ def ids_func(param): [((2, 1, 2048), torch.float32), ((2, 1, 2048), torch.float32)], {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, ), - ( - Add0, - [((2, 1, 2048), torch.float32), ((1, 2048), torch.float32)], - {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + pytest.param( + ( + Add0, + [((2, 1, 2048), torch.float32), ((1, 2048), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add1, @@ -2288,10 +2291,13 @@ def ids_func(param): [((2, 1, 1536), torch.float32), ((2, 1, 1536), torch.float32)], {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, ), - ( - Add0, - [((2, 1, 1536), torch.float32), ((1, 1536), torch.float32)], - {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + pytest.param( + ( + Add0, + [((2, 1, 1536), torch.float32), ((1, 1536), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add3, @@ -2308,10 +2314,13 @@ def ids_func(param): [((2, 1, 1024), torch.float32), ((2, 1, 1024), torch.float32)], {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, ), - ( - Add0, - [((2, 1, 1024), torch.float32), ((1, 1024), torch.float32)], - {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + pytest.param( + ( + Add0, + [((2, 1, 1024), torch.float32), ((1, 1024), torch.float32)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add4, @@ -3912,17 +3921,24 @@ def ids_func(param): [((7, 3072), torch.float32)], {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99}, ), - ( - Add46, - [((1, 256), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add46, + [((1, 256), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), (Add25, [((256, 8192), torch.float32)], {"model_name": ["pt_opt_facebook_opt_1_3b_clm_hf"], "pcc": 0.99}), (Add2, [((256, 2048), torch.float32)], {"model_name": ["pt_opt_facebook_opt_1_3b_clm_hf"], "pcc": 0.99}), @@ -3931,20 +3947,27 @@ def ids_func(param): [((256, 2048), torch.float32), ((256, 2048), torch.float32)], {"model_name": ["pt_opt_facebook_opt_1_3b_clm_hf"], "pcc": 0.99}, ), - ( - Add46, - [((1, 32), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add46, + [((1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Add0, @@ -4665,16 +4688,23 @@ def ids_func(param): [((1, 39, 896), torch.float32), ((1, 39, 896), torch.float32)], {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf"], "pcc": 0.99}, ), - ( - Add46, - [((1, 128), torch.int64)], - { - "model_name": [ - "pt_roberta_xlm_roberta_base_mlm_hf", - "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add46, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), (Add56, [((1, 128, 250002), torch.float32)], {"model_name": ["pt_roberta_xlm_roberta_base_mlm_hf"], "pcc": 0.99}), ( @@ -12161,17 +12191,20 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Add1, - [((1, 72, 1, 1), torch.float32)], - { - "model_name": [ - "pt_ghostnet_ghostnet_100_img_cls_timm", - "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", - "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 72, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add0, @@ -12246,19 +12279,22 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Add1, - [((1, 120, 1, 1), torch.float32)], - { - "model_name": [ - "pt_ghostnet_ghostnet_100_img_cls_timm", - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", - "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 120, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add0, @@ -12344,17 +12380,20 @@ def ids_func(param): [((1, 92, 14, 14), torch.float32), ((92, 1, 1), torch.float32)], {"model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm"], "pcc": 0.99}, ), - ( - Add1, - [((1, 480, 1, 1), torch.float32)], - { - "model_name": [ - "pt_ghostnet_ghostnet_100_img_cls_timm", - "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", - "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 480, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add0, @@ -12373,17 +12412,20 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Add1, - [((1, 672, 1, 1), torch.float32)], - { - "model_name": [ - "pt_ghostnet_ghostnet_100_img_cls_timm", - "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", - "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 672, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add0, @@ -12438,17 +12480,20 @@ def ids_func(param): [((1, 480, 7, 7), torch.float32), ((480, 1, 1), torch.float32)], {"model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm"], "pcc": 0.99}, ), - ( - Add1, - [((1, 960, 1, 1), torch.float32)], - { - "model_name": [ - "pt_ghostnet_ghostnet_100_img_cls_timm", - "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", - "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 960, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add0, @@ -13940,16 +13985,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Add1, - [((1, 16, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 16, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add0, @@ -13997,16 +14045,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Add1, - [((1, 96, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 96, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add1, @@ -14032,16 +14083,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Add1, - [((1, 240, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 240, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add0, @@ -14087,16 +14141,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Add1, - [((1, 144, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 144, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add0, @@ -14153,16 +14210,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Add1, - [((1, 288, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 288, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add0, @@ -14197,16 +14257,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Add1, - [((1, 576, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 576, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add0, @@ -14242,18 +14305,21 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Add1, - [((1, 1024, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - "pt_vovnet_ese_vovnet39b_obj_det_torchhub", - "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", - "pt_vovnet_ese_vovnet99b_obj_det_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 1024, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add1, @@ -14398,10 +14464,13 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Add1, - [((1, 1280, 1, 1), torch.float32)], - {"model_name": ["pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm"], "pcc": 0.99}, + pytest.param( + ( + Add1, + [((1, 1280, 1, 1), torch.float32)], + {"model_name": ["pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add7, @@ -16649,29 +16718,35 @@ def ids_func(param): [((1, 197, 4096), torch.float32)], {"model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], "pcc": 0.99}, ), - ( - Add1, - [((1, 256, 1, 1), torch.float32)], - { - "model_name": [ - "pt_vovnet_ese_vovnet39b_obj_det_torchhub", - "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", - "pt_vovnet_ese_vovnet99b_obj_det_torchhub", - ], - "pcc": 0.99, - }, - ), - ( - Add1, - [((1, 512, 1, 1), torch.float32)], - { - "model_name": [ - "pt_vovnet_ese_vovnet39b_obj_det_torchhub", - "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", - "pt_vovnet_ese_vovnet99b_obj_det_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 256, 1, 1), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Add1, + [((1, 512, 1, 1), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add0, @@ -16685,17 +16760,20 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Add1, - [((1, 768, 1, 1), torch.float32)], - { - "model_name": [ - "pt_vovnet_ese_vovnet39b_obj_det_torchhub", - "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", - "pt_vovnet_ese_vovnet99b_obj_det_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Add1, + [((1, 768, 1, 1), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Add0, diff --git a/forge/test/models_ops/test_advindex.py b/forge/test/models_ops/test_advindex.py index 6ad5a959e..fb472b8e4 100644 --- a/forge/test/models_ops/test_advindex.py +++ b/forge/test/models_ops/test_advindex.py @@ -119,10 +119,17 @@ def ids_func(param): [((448, 768), torch.float32), ((1, 1), torch.int64)], {"model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], "pcc": 0.99, "max_int": 447}, ), - ( - Advindex0, - [((448, 1280), torch.float32), ((1, 2), torch.int64)], - {"model_name": ["pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf"], "pcc": 0.99, "max_int": 447}, + pytest.param( + ( + Advindex0, + [((448, 1280), torch.float32), ((1, 2), torch.int64)], + { + "model_name": ["pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf"], + "pcc": 0.99, + "max_int": 447, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Advindex1, @@ -172,25 +179,37 @@ def ids_func(param): "max_int": 0, }, ), - ( - Advindex2, - [((2401,), torch.int64)], - {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99, "max_int": 168}, + pytest.param( + ( + Advindex2, + [((2401,), torch.int64)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99, "max_int": 168}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Advindex3, - [((2401,), torch.int64)], - {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99, "max_int": 168}, + pytest.param( + ( + Advindex3, + [((2401,), torch.int64)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99, "max_int": 168}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Advindex4, - [((2401,), torch.int64)], - {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99, "max_int": 168}, + pytest.param( + ( + Advindex4, + [((2401,), torch.int64)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99, "max_int": 168}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Advindex5, - [((2401,), torch.int64)], - {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99, "max_int": 168}, + pytest.param( + ( + Advindex5, + [((2401,), torch.int64)], + {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99, "max_int": 168}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ] @@ -231,4 +250,3 @@ def test_module(forge_module_and_shapes_dtypes, record_forge_property): compiled_model = compile(framework_model, sample_inputs=inputs) verify(inputs, framework_model, compiled_model, VerifyConfig(value_checker=AutomaticValueChecker(pcc=pcc))) - diff --git a/forge/test/models_ops/test_argmax.py b/forge/test/models_ops/test_argmax.py index 09b610974..b39a79207 100644 --- a/forge/test/models_ops/test_argmax.py +++ b/forge/test/models_ops/test_argmax.py @@ -32,18 +32,21 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Argmax0, - [((1, 32), torch.int32)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-1"}, - }, + pytest.param( + ( + Argmax0, + [((1, 32), torch.int32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Generated MLIR module failed verification.")], ), ] diff --git a/forge/test/models_ops/test_avgpool2d.py b/forge/test/models_ops/test_avgpool2d.py index 7efdb2f5f..efbc61f9d 100644 --- a/forge/test/models_ops/test_avgpool2d.py +++ b/forge/test/models_ops/test_avgpool2d.py @@ -1103,53 +1103,62 @@ def ids_func(param): }, }, ), - ( - Avgpool2D9, - [((1, 384, 35, 35), torch.float32)], - { - "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "kernel_size": "[3, 3]", - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "ceil_mode": "False", - "count_include_pad": "False", - "channel_last": "0", - }, - }, - ), - ( - Avgpool2D9, - [((1, 1024, 17, 17), torch.float32)], - { - "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "kernel_size": "[3, 3]", - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "ceil_mode": "False", - "count_include_pad": "False", - "channel_last": "0", - }, - }, - ), - ( - Avgpool2D9, - [((1, 1536, 8, 8), torch.float32)], - { - "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "kernel_size": "[3, 3]", - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "ceil_mode": "False", - "count_include_pad": "False", - "channel_last": "0", - }, - }, + pytest.param( + ( + Avgpool2D9, + [((1, 384, 35, 35), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[3, 3]", + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "ceil_mode": "False", + "count_include_pad": "False", + "channel_last": "0", + }, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 1 - stride mismatch: expected [1225, 1], got [0, 0]")], + ), + pytest.param( + ( + Avgpool2D9, + [((1, 1024, 17, 17), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[3, 3]", + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "ceil_mode": "False", + "count_include_pad": "False", + "channel_last": "0", + }, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 1 - stride mismatch: expected [289, 1], got [0, 0]")], + ), + pytest.param( + ( + Avgpool2D9, + [((1, 1536, 8, 8), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "[3, 3]", + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "ceil_mode": "False", + "count_include_pad": "False", + "channel_last": "0", + }, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 1 - stride mismatch: expected [64, 1], got [0, 0]")], ), ( Avgpool2D10, diff --git a/forge/test/models_ops/test_broadcast.py b/forge/test/models_ops/test_broadcast.py index f30c53b9c..fd7dcb9b0 100644 --- a/forge/test/models_ops/test_broadcast.py +++ b/forge/test/models_ops/test_broadcast.py @@ -59,77 +59,95 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Broadcast0, - [((1, 1, 1, 128), torch.bool)], - { - "model_name": [ - "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", - "pt_distilbert_distilbert_base_cased_mlm_hf", - "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", - "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", - "pt_distilbert_distilbert_base_uncased_mlm_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-3", "shape": "12"}, - }, + pytest.param( + ( + Broadcast0, + [((1, 1, 1, 128), torch.bool)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-3", "shape": "12"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Generated MLIR module failed verification.")], ), - ( - Broadcast1, - [((1, 1, 1, 128), torch.bool)], - { - "model_name": [ - "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", - "pt_distilbert_distilbert_base_cased_mlm_hf", - "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", - "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", - "pt_distilbert_distilbert_base_uncased_mlm_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-4", "shape": "1"}, - }, + pytest.param( + ( + Broadcast1, + [((1, 1, 1, 128), torch.bool)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-4", "shape": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Broadcast2, - [((1, 12, 1, 128), torch.bool)], - { - "model_name": [ - "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", - "pt_distilbert_distilbert_base_cased_mlm_hf", - "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", - "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", - "pt_distilbert_distilbert_base_uncased_mlm_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-2", "shape": "128"}, - }, + pytest.param( + ( + Broadcast2, + [((1, 12, 1, 128), torch.bool)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "shape": "128"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Generated MLIR module failed verification.")], ), - ( - Broadcast0, - [((1, 1, 1, 384), torch.bool)], - { - "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], - "pcc": 0.99, - "op_params": {"dim": "-3", "shape": "12"}, - }, + pytest.param( + ( + Broadcast0, + [((1, 1, 1, 384), torch.bool)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-3", "shape": "12"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Generated MLIR module failed verification.")], ), - ( - Broadcast1, - [((1, 1, 1, 384), torch.bool)], - { - "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], - "pcc": 0.99, - "op_params": {"dim": "-4", "shape": "1"}, - }, + pytest.param( + ( + Broadcast1, + [((1, 1, 1, 384), torch.bool)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-4", "shape": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Broadcast3, - [((1, 12, 1, 384), torch.bool)], - { - "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], - "pcc": 0.99, - "op_params": {"dim": "-2", "shape": "384"}, - }, + pytest.param( + ( + Broadcast3, + [((1, 12, 1, 384), torch.bool)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "shape": "384"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Generated MLIR module failed verification.")], ), ] diff --git a/forge/test/models_ops/test_cast.py b/forge/test/models_ops/test_cast.py index 5532126cf..dcd413aad 100644 --- a/forge/test/models_ops/test_cast.py +++ b/forge/test/models_ops/test_cast.py @@ -59,202 +59,249 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Cast0, - [((2, 1, 1, 13), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((2, 1, 1, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Cast0, - [((2, 13, 1), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((2, 13, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Cast0, - [((2, 1, 1, 13), torch.bool)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((2, 1, 1, 13), torch.bool)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast0, - [((2, 1, 7, 7), torch.int64)], - { - "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((2, 1, 7, 7), torch.int64)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Cast0, - [((2, 1, 7, 7), torch.bool)], - { - "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((2, 1, 7, 7), torch.bool)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast0, - [((1, 1, 1, 128), torch.int64)], - { - "model_name": [ - "pt_albert_xxlarge_v1_token_cls_hf", - "pt_albert_base_v2_token_cls_hf", - "pt_albert_xxlarge_v2_token_cls_hf", - "pt_albert_large_v1_token_cls_hf", - "pt_albert_large_v2_token_cls_hf", - "pt_albert_base_v1_token_cls_hf", - "pt_albert_base_v1_mlm_hf", - "pt_albert_xlarge_v2_token_cls_hf", - "pt_albert_xxlarge_v2_mlm_hf", - "pt_albert_large_v2_mlm_hf", - "pt_albert_base_v2_mlm_hf", - "pt_albert_xlarge_v1_token_cls_hf", - "pt_albert_xlarge_v1_mlm_hf", - "pt_albert_large_v1_mlm_hf", - "pt_albert_xxlarge_v1_mlm_hf", - "pt_albert_xlarge_v2_mlm_hf", - "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", - "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", - "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", - "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", - "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", - "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", - "pt_roberta_xlm_roberta_base_mlm_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((1, 1, 1, 128), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_roberta_xlm_roberta_base_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Cast0, - [((1, 1, 256, 256), torch.int64)], - { - "model_name": [ - "pt_bart_facebook_bart_large_mnli_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - "pt_xglm_facebook_xglm_1_7b_clm_hf", - "pt_xglm_facebook_xglm_564m_clm_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((1, 1, 256, 256), torch.int64)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Cast0, - [((1, 1, 256, 256), torch.bool)], - { - "model_name": [ - "pt_bart_facebook_bart_large_mnli_seq_cls_hf", - "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", - "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - "pt_xglm_facebook_xglm_1_7b_clm_hf", - "pt_xglm_facebook_xglm_564m_clm_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((1, 1, 256, 256), torch.bool)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast1, - [((1, 1, 256, 256), torch.bool)], - { - "model_name": [ - "pt_gpt2_gpt2_text_gen_hf", - "pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf", - "pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf", - "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.bool"}, - }, + pytest.param( + ( + Cast1, + [((1, 1, 256, 256), torch.bool)], + { + "model_name": [ + "pt_gpt2_gpt2_text_gen_hf", + "pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf", + "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast2, - [((1, 1, 256, 256), torch.bool)], - { - "model_name": [ - "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", - "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.int32"}, - }, + pytest.param( + ( + Cast2, + [((1, 1, 256, 256), torch.bool)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.int32"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast1, - [((1, 128), torch.int64)], - { - "model_name": [ - "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", - "pt_distilbert_distilbert_base_cased_mlm_hf", - "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", - "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", - "pt_distilbert_distilbert_base_uncased_mlm_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.bool"}, - }, + pytest.param( + ( + Cast1, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int8, compiled_model.dtype=torch.uint8" + ) + ], ), - ( - Cast2, - [((1, 128), torch.bool)], - { - "model_name": [ - "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", - "pt_distilbert_distilbert_base_cased_mlm_hf", - "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", - "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", - "pt_distilbert_distilbert_base_uncased_mlm_hf", - "pt_roberta_xlm_roberta_base_mlm_hf", - "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.int32"}, - }, + pytest.param( + ( + Cast2, + [((1, 128), torch.bool)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.int32"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast1, - [((1, 128), torch.int32)], - { - "model_name": [ - "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", - "pt_distilbert_distilbert_base_cased_mlm_hf", - "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", - "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", - "pt_distilbert_distilbert_base_uncased_mlm_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.bool"}, - }, + pytest.param( + ( + Cast1, + [((1, 128), torch.int32)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int8, compiled_model.dtype=torch.uint8" + ) + ], ), ( Cast3, @@ -268,156 +315,211 @@ def ids_func(param): "op_params": {"dtype": "torch.int64"}, }, ), - ( - Cast0, - [((1, 12, 128, 128), torch.bool)], - { - "model_name": [ - "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", - "pt_distilbert_distilbert_base_cased_mlm_hf", - "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", - "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", - "pt_distilbert_distilbert_base_uncased_mlm_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((1, 12, 128, 128), torch.bool)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast1, - [((1, 384), torch.int64)], - { - "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], - "pcc": 0.99, - "op_params": {"dtype": "torch.bool"}, - }, + pytest.param( + ( + Cast1, + [((1, 384), torch.int64)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int8, compiled_model.dtype=torch.uint8" + ) + ], ), - ( - Cast2, - [((1, 384), torch.bool)], - { - "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], - "pcc": 0.99, - "op_params": {"dtype": "torch.int32"}, - }, + pytest.param( + ( + Cast2, + [((1, 384), torch.bool)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dtype": "torch.int32"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast1, - [((1, 384), torch.int32)], - { - "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], - "pcc": 0.99, - "op_params": {"dtype": "torch.bool"}, - }, + pytest.param( + ( + Cast1, + [((1, 384), torch.int32)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int8, compiled_model.dtype=torch.uint8" + ) + ], ), - ( - Cast0, - [((1, 12, 384, 384), torch.bool)], - { - "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((1, 12, 384, 384), torch.bool)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast1, - [((1, 1, 32, 32), torch.bool)], - { - "model_name": [ - "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", - "pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf", - "pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.bool"}, - }, + pytest.param( + ( + Cast1, + [((1, 1, 32, 32), torch.bool)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast0, - [((1, 1, 32, 32), torch.bool)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((1, 1, 32, 32), torch.bool)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast1, - [((1, 1, 256, 256), torch.float32)], - { - "model_name": [ - "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", - "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.bool"}, - }, + pytest.param( + ( + Cast1, + [((1, 1, 256, 256), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int8, compiled_model.dtype=torch.uint8" + ) + ], ), - ( - Cast1, - [((1, 1, 256, 256), torch.int32)], - { - "model_name": [ - "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", - "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.bool"}, - }, + pytest.param( + ( + Cast1, + [((1, 1, 256, 256), torch.int32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int8, compiled_model.dtype=torch.uint8" + ) + ], ), - ( - Cast1, - [((1, 1, 7, 7), torch.bool)], - { - "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], - "pcc": 0.99, - "op_params": {"dtype": "torch.bool"}, - }, + pytest.param( + ( + Cast1, + [((1, 1, 7, 7), torch.bool)], + { + "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast0, - [((1, 1, 1, 7), torch.int64)], - { - "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((1, 1, 1, 7), torch.int64)], + { + "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Cast0, - [((1, 1, 32, 32), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.float32"}, - }, + pytest.param( + ( + Cast0, + [((1, 1, 32, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.float32"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Cast2, - [((1, 32), torch.bool)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.int32"}, - }, + pytest.param( + ( + Cast2, + [((1, 32), torch.bool)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.int32"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), ( Cast3, @@ -432,34 +534,40 @@ def ids_func(param): "op_params": {"dtype": "torch.int64"}, }, ), - ( - Cast1, - [((1, 16, 320, 1024), torch.bool)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.bool"}, - }, + pytest.param( + ( + Cast1, + [((1, 16, 320, 1024), torch.bool)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), - ( - Cast1, - [((1, 16, 192, 640), torch.bool)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": {"dtype": "torch.bool"}, - }, + pytest.param( + ( + Cast1, + [((1, 16, 192, 640), torch.bool)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"dtype": "torch.bool"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), ] diff --git a/forge/test/models_ops/test_concatenate.py b/forge/test/models_ops/test_concatenate.py index e02c25b43..c60e85a09 100644 --- a/forge/test/models_ops/test_concatenate.py +++ b/forge/test/models_ops/test_concatenate.py @@ -10740,14 +10740,25 @@ def ids_func(param): "op_params": {"axis": "-3"}, }, ), - ( - Concatenate52, - [((256, 1536, 1, 1), torch.float32), ((384, 1536, 1, 1), torch.float32), ((384, 1536, 1, 1), torch.float32)], - { - "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], - "pcc": 0.99, - "op_params": {"axis": "-4"}, - }, + pytest.param( + ( + Concatenate52, + [ + ((256, 1536, 1, 1), torch.float32), + ((384, 1536, 1, 1), torch.float32), + ((384, 1536, 1, 1), torch.float32), + ], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": {"axis": "-4"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 6442450944 B DRAM buffer across 12 banks, where each bank needs to store 536870912 B" + ) + ], ), ( Concatenate5, diff --git a/forge/test/models_ops/test_conv2d.py b/forge/test/models_ops/test_conv2d.py index f528f5687..4d8666d82 100644 --- a/forge/test/models_ops/test_conv2d.py +++ b/forge/test/models_ops/test_conv2d.py @@ -21645,35 +21645,45 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Conv2D0, - [((1, 80, 3000, 1), torch.float32), ((1024, 80, 3, 1), torch.float32)], - { - "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D0, + [((1, 80, 3000, 1), torch.float32), ((1024, 80, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op_sharded_program_factory.cpp:1180: act_block_w_datums == round_up(conv_act_size_c * filter_w, TILE_WIDTH) info: Error" + ) + ], ), - ( - Conv2D1, - [((1, 1024, 3000, 1), torch.float32), ((1024, 1024, 3, 1), torch.float32)], - { - "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 1]", - "padding": "[0, 0, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D1, + [((1, 1024, 3000, 1), torch.float32), ((1024, 1024, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_medium_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D0, @@ -21690,110 +21700,155 @@ def ids_func(param): }, }, ), - ( - Conv2D1, - [((1, 1280, 3000, 1), torch.float32), ((1280, 1280, 3, 1), torch.float32)], - { - "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 1]", - "padding": "[0, 0, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D1, + [((1, 1280, 3000, 1), torch.float32), ((1280, 1280, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_large_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D0, - [((1, 80, 3000, 1), torch.float32), ((384, 80, 3, 1), torch.float32)], - { - "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D0, + [((1, 80, 3000, 1), torch.float32), ((384, 80, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op_sharded_program_factory.cpp:1180: act_block_w_datums == round_up(conv_act_size_c * filter_w, TILE_WIDTH) info: Error" + ) + ], ), - ( - Conv2D1, - [((1, 384, 3000, 1), torch.float32), ((384, 384, 3, 1), torch.float32)], - { - "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 1]", - "padding": "[0, 0, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D1, + [((1, 384, 3000, 1), torch.float32), ((384, 384, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_tiny_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op_sharded_program_factory.cpp:1180: act_block_w_datums == round_up(conv_act_size_c * filter_w, TILE_WIDTH) info: Error" + ) + ], ), - ( - Conv2D0, - [((1, 80, 3000, 1), torch.float32), ((512, 80, 3, 1), torch.float32)], - { - "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D0, + [((1, 80, 3000, 1), torch.float32), ((512, 80, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op_sharded_program_factory.cpp:1180: act_block_w_datums == round_up(conv_act_size_c * filter_w, TILE_WIDTH) info: Error" + ) + ], ), - ( - Conv2D1, - [((1, 512, 3000, 1), torch.float32), ((512, 512, 3, 1), torch.float32)], - { - "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 1]", - "padding": "[0, 0, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D1, + [((1, 512, 3000, 1), torch.float32), ((512, 512, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_base_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op_sharded_program_factory.cpp:1180: act_block_w_datums == round_up(conv_act_size_c * filter_w, TILE_WIDTH) info: Error" + ) + ], ), - ( - Conv2D0, - [((1, 80, 3000, 1), torch.float32), ((768, 80, 3, 1), torch.float32)], - { - "model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D0, + [((1, 80, 3000, 1), torch.float32), ((768, 80, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op_sharded_program_factory.cpp:1180: act_block_w_datums == round_up(conv_act_size_c * filter_w, TILE_WIDTH) info: Error" + ) + ], ), - ( - Conv2D1, - [((1, 768, 3000, 1), torch.float32), ((768, 768, 3, 1), torch.float32)], - { - "model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 1]", - "padding": "[0, 0, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D1, + [((1, 768, 3000, 1), torch.float32), ((768, 768, 3, 1), torch.float32)], + { + "model_name": ["pt_whisper_openai_whisper_small_speech_recognition_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 1]", + "padding": "[0, 0, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op_sharded_program_factory.cpp:1180: act_block_w_datums == round_up(conv_act_size_c * filter_w, TILE_WIDTH) info: Error" + ) + ], ), ( Conv2D2, @@ -21840,35 +21895,45 @@ def ids_func(param): }, }, ), - ( - Conv2D2, - [((1, 3072, 1, 128), torch.float32), ((768, 768, 1, 1), torch.float32)], - { - "model_name": ["pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "4", - "channel_last": "0", + pytest.param( + ( + Conv2D2, + [((1, 3072, 1, 128), torch.float32), ((768, 768, 1, 1), torch.float32)], + { + "model_name": ["pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "4", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D4, - [((1, 3, 224, 224), torch.float32)], - { - "model_name": ["pt_alexnet_alexnet_img_cls_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[4, 4]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D4, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_alexnet_alexnet_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[4, 4]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 1062 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=5)]. L1 buffer allocated at 947328 and static circular buffer region ends at 1180896" + ) + ], ), ( Conv2D5, @@ -21930,20 +21995,27 @@ def ids_func(param): }, }, ), - ( - Conv2D9, - [((1, 3, 224, 224), torch.float32)], - { - "model_name": ["pt_alexnet_base_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "stride": "[4, 4]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D9, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[4, 4]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:896: tt::exception info: Statically allocated circular buffers on core range [(x=0,y=0) - (x=7,y=5)] grow to 1676512 B which is beyond max L1 size of 1499136 B" + ) + ], ), ( Conv2D10, @@ -22020,58 +22092,79 @@ def ids_func(param): }, }, ), - ( - Conv2D15, - [((1, 3, 224, 224), torch.float32)], - { - "model_name": [ - "pt_deit_facebook_deit_base_patch16_224_img_cls_hf", - "pt_deit_facebook_deit_base_distilled_patch16_224_img_cls_hf", - "pt_mlp_mixer_mixer_b16_224_miil_in21k_img_cls_timm", - "pt_mlp_mixer_mixer_b16_224_miil_img_cls_timm", - "pt_mlp_mixer_mixer_b16_224_img_cls_timm", - "pt_mlp_mixer_mixer_b16_224_in21k_img_cls_timm", - "pt_vit_google_vit_base_patch16_224_img_cls_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[16, 16]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D15, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": [ + "pt_deit_facebook_deit_base_patch16_224_img_cls_hf", + "pt_deit_facebook_deit_base_distilled_patch16_224_img_cls_hf", + "pt_mlp_mixer_mixer_b16_224_miil_in21k_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_miil_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_img_cls_timm", + "pt_mlp_mixer_mixer_b16_224_in21k_img_cls_timm", + "pt_vit_google_vit_base_patch16_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[16, 16]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 12500992 B L1 buffer across 7 banks, where each bank needs to store 1785856 B" + ) + ], ), - ( - Conv2D16, - [((1, 3, 224, 224), torch.float32)], - { - "model_name": ["pt_deit_facebook_deit_tiny_patch16_224_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[16, 16]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D16, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_deit_facebook_deit_tiny_patch16_224_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[16, 16]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 12500992 B L1 buffer across 7 banks, where each bank needs to store 1785856 B" + ) + ], ), - ( - Conv2D17, - [((1, 3, 224, 224), torch.float32)], - { - "model_name": ["pt_deit_facebook_deit_small_patch16_224_img_cls_hf"], - "pcc": 0.99, - "op_params": { - "stride": "[16, 16]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D17, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_deit_facebook_deit_small_patch16_224_img_cls_hf"], + "pcc": 0.99, + "op_params": { + "stride": "[16, 16]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 12500992 B L1 buffer across 7 banks, where each bank needs to store 1785856 B" + ) + ], ), ( Conv2D18, @@ -35317,20 +35410,23 @@ def ids_func(param): }, }, ), - ( - Conv2D516, - [((1, 64, 147, 147), torch.float32)], - { - "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D516, + [((1, 64, 147, 147), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D517, @@ -35347,20 +35443,23 @@ def ids_func(param): }, }, ), - ( - Conv2D518, - [((1, 64, 73, 73), torch.float32)], - { - "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D518, + [((1, 64, 73, 73), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D519, @@ -35392,20 +35491,23 @@ def ids_func(param): }, }, ), - ( - Conv2D521, - [((1, 192, 71, 71), torch.float32)], - { - "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D521, + [((1, 192, 71, 71), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D3, @@ -35467,20 +35569,23 @@ def ids_func(param): }, }, ), - ( - Conv2D524, - [((1, 384, 35, 35), torch.float32)], - { - "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D524, + [((1, 384, 35, 35), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D26, @@ -35512,20 +35617,23 @@ def ids_func(param): }, }, ), - ( - Conv2D526, - [((1, 224, 35, 35), torch.float32)], - { - "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D526, + [((1, 224, 35, 35), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D3, @@ -35647,20 +35755,23 @@ def ids_func(param): }, }, ), - ( - Conv2D521, - [((1, 192, 17, 17), torch.float32)], - { - "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D521, + [((1, 192, 17, 17), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D533, @@ -35707,20 +35818,23 @@ def ids_func(param): }, }, ), - ( - Conv2D536, - [((1, 320, 17, 17), torch.float32)], - { - "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D536, + [((1, 320, 17, 17), torch.float32)], + { + "model_name": ["pt_inception_v4_img_cls_timm", "pt_inception_v4_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D3, @@ -35842,84 +35956,119 @@ def ids_func(param): }, }, ), - ( - Conv2D544, - [((1, 3, 224, 224), torch.float32)], - { - "model_name": ["pt_mlp_mixer_mixer_b32_224_img_cls_timm"], - "pcc": 0.99, - "op_params": { - "stride": "[32, 32]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D544, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_b32_224_img_cls_timm"], + "pcc": 0.99, + "op_params": { + "stride": "[32, 32]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 6422528 B L1 buffer across 2 banks, where each bank needs to store 3211264 B" + ) + ], ), - ( - Conv2D545, - [((1, 3, 224, 224), torch.float32)], - { - "model_name": ["pt_mlp_mixer_mixer_s16_224_img_cls_timm"], - "pcc": 0.99, - "op_params": { - "stride": "[16, 16]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D545, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_s16_224_img_cls_timm"], + "pcc": 0.99, + "op_params": { + "stride": "[16, 16]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 12500992 B L1 buffer across 7 banks, where each bank needs to store 1785856 B" + ) + ], ), - ( - Conv2D546, - [((1, 3, 224, 224), torch.float32)], - { - "model_name": ["pt_mlp_mixer_mixer_s32_224_img_cls_timm"], - "pcc": 0.99, - "op_params": { - "stride": "[32, 32]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D546, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_s32_224_img_cls_timm"], + "pcc": 0.99, + "op_params": { + "stride": "[32, 32]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 6422528 B L1 buffer across 2 banks, where each bank needs to store 3211264 B" + ) + ], ), - ( - Conv2D547, - [((1, 3, 224, 224), torch.float32)], - { - "model_name": ["pt_mlp_mixer_mixer_l32_224_img_cls_timm"], - "pcc": 0.99, - "op_params": { - "stride": "[32, 32]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D547, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": ["pt_mlp_mixer_mixer_l32_224_img_cls_timm"], + "pcc": 0.99, + "op_params": { + "stride": "[32, 32]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 6422528 B L1 buffer across 2 banks, where each bank needs to store 3211264 B" + ) + ], ), - ( - Conv2D548, - [((1, 3, 224, 224), torch.float32)], - { - "model_name": [ - "pt_mlp_mixer_mixer_l16_224_in21k_img_cls_timm", - "pt_mlp_mixer_mixer_l16_224_img_cls_timm", - "pt_vit_google_vit_large_patch16_224_img_cls_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[16, 16]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D548, + [((1, 3, 224, 224), torch.float32)], + { + "model_name": [ + "pt_mlp_mixer_mixer_l16_224_in21k_img_cls_timm", + "pt_mlp_mixer_mixer_l16_224_img_cls_timm", + "pt_vit_google_vit_large_patch16_224_img_cls_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[16, 16]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 12500992 B L1 buffer across 7 banks, where each bank needs to store 1785856 B" + ) + ], ), pytest.param( ( @@ -39031,24 +39180,31 @@ def ids_func(param): }, }, ), - ( - Conv2D66, - [((1, 3, 320, 1024), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[3, 3, 3, 3]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D66, + [((1, 3, 320, 1024), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[3, 3, 3, 3]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 88612864 B L1 buffer across 64 banks, where each bank needs to store 1384576 B" + ) + ], ), ( Conv2D135, @@ -39240,157 +39396,185 @@ def ids_func(param): }, }, ), - ( - Conv2D654, - [((1, 512, 12, 34), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D654, + [((1, 512, 12, 34), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D654, - [((1, 512, 22, 66), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D654, + [((1, 512, 22, 66), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D655, - [((1, 256, 22, 66), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D655, + [((1, 256, 22, 66), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D655, - [((1, 256, 42, 130), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D655, + [((1, 256, 42, 130), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D656, - [((1, 128, 42, 130), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D656, + [((1, 128, 42, 130), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D656, - [((1, 128, 82, 258), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D656, + [((1, 128, 82, 258), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D657, - [((1, 64, 82, 258), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D657, + [((1, 64, 82, 258), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D658, - [((1, 96, 162, 514), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D658, + [((1, 96, 162, 514), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 56868864 B L1 buffer across 64 banks, where each bank needs to store 888576 B" + ) + ], ), ( Conv2D659, @@ -39411,43 +39595,57 @@ def ids_func(param): }, }, ), - ( - Conv2D660, - [((1, 16, 322, 1026), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D660, + [((1, 16, 322, 1026), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 58834944 B L1 buffer across 64 banks, where each bank needs to store 919296 B" + ) + ], ), - ( - Conv2D661, - [((1, 16, 322, 1026), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D661, + [((1, 16, 322, 1026), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 58834944 B L1 buffer across 64 banks, where each bank needs to store 919296 B" + ) + ], ), ( Conv2D66, @@ -39691,181 +39889,205 @@ def ids_func(param): }, }, ), - ( - Conv2D654, - [((1, 512, 8, 22), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D654, + [((1, 512, 8, 22), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D654, - [((1, 512, 14, 42), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D654, + [((1, 512, 14, 42), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D655, - [((1, 256, 14, 42), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D655, + [((1, 256, 14, 42), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D655, - [((1, 256, 26, 82), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D655, + [((1, 256, 26, 82), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D656, - [((1, 128, 26, 82), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D656, + [((1, 128, 26, 82), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D656, - [((1, 128, 50, 162), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D656, + [((1, 128, 50, 162), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D657, - [((1, 64, 50, 162), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D657, + [((1, 64, 50, 162), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D658, - [((1, 96, 98, 322), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D658, + [((1, 96, 98, 322), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", + "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D659, @@ -40098,20 +40320,27 @@ def ids_func(param): }, }, ), - ( - Conv2D670, - [((1, 3, 227, 227), torch.float32)], - { - "model_name": ["pt_rcnn_base_obj_det_torchvision_rect_0"], - "pcc": 0.99, - "op_params": { - "stride": "[4, 4]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D670, + [((1, 3, 227, 227), torch.float32)], + { + "model_name": ["pt_rcnn_base_obj_det_torchvision_rect_0"], + "pcc": 0.99, + "op_params": { + "stride": "[4, 4]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 3727 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=6)]. L1 buffer allocated at 1031040 and static circular buffer region ends at 1180896" + ) + ], ), ( Conv2D671, @@ -40838,36 +41067,39 @@ def ids_func(param): }, }, ), - ( - Conv2D698, - [((1, 1024, 14, 14), torch.float32)], - { - "model_name": [ - "ResNetForImageClassification", - "ResNet", - "pt_resnet_50_img_cls_timm", - "pt_resnext_resnext26_32x4d_img_cls_osmr", - "pt_resnext_resnext101_64x4d_img_cls_osmr", - "pt_resnext_resnext50_32x4d_img_cls_osmr", - "pt_resnext_resnext50_32x4d_img_cls_torchhub", - "pt_resnext_resnext14_32x4d_img_cls_osmr", - "pt_resnext_resnext101_32x8d_img_cls_torchhub", - "pt_resnext_resnext101_32x8d_wsl_img_cls_torchhub", - "pt_unet_qubvel_img_seg_torchhub", - "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", - "pt_wideresnet_wide_resnet50_2_img_cls_timm", - "pt_wideresnet_wide_resnet101_2_img_cls_timm", - "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D698, + [((1, 1024, 14, 14), torch.float32)], + { + "model_name": [ + "ResNetForImageClassification", + "ResNet", + "pt_resnet_50_img_cls_timm", + "pt_resnext_resnext26_32x4d_img_cls_osmr", + "pt_resnext_resnext101_64x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_osmr", + "pt_resnext_resnext50_32x4d_img_cls_torchhub", + "pt_resnext_resnext14_32x4d_img_cls_osmr", + "pt_resnext_resnext101_32x8d_img_cls_torchhub", + "pt_resnext_resnext101_32x8d_wsl_img_cls_torchhub", + "pt_unet_qubvel_img_seg_torchhub", + "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D195, @@ -41080,26 +41312,33 @@ def ids_func(param): }, }, ), - ( - Conv2D66, - [((1, 3, 480, 640), torch.float32)], - { - "model_name": [ - "pt_retinanet_retinanet_rn101fpn_obj_det_hf", - "pt_retinanet_retinanet_rn152fpn_obj_det_hf", - "pt_retinanet_retinanet_rn34fpn_obj_det_hf", - "pt_retinanet_retinanet_rn50fpn_obj_det_hf", - "pt_retinanet_retinanet_rn18fpn_obj_det_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[3, 3, 3, 3]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D66, + [((1, 3, 480, 640), torch.float32)], + { + "model_name": [ + "pt_retinanet_retinanet_rn101fpn_obj_det_hf", + "pt_retinanet_retinanet_rn152fpn_obj_det_hf", + "pt_retinanet_retinanet_rn34fpn_obj_det_hf", + "pt_retinanet_retinanet_rn50fpn_obj_det_hf", + "pt_retinanet_retinanet_rn18fpn_obj_det_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[3, 3, 3, 3]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 73080832 B L1 buffer across 64 banks, where each bank needs to store 1141888 B" + ) + ], ), ( Conv2D174, @@ -41451,24 +41690,27 @@ def ids_func(param): }, }, ), - ( - Conv2D159, - [((1, 512, 30, 40), torch.float32)], - { - "model_name": [ - "pt_retinanet_retinanet_rn101fpn_obj_det_hf", - "pt_retinanet_retinanet_rn152fpn_obj_det_hf", - "pt_retinanet_retinanet_rn50fpn_obj_det_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D159, + [((1, 512, 30, 40), torch.float32)], + { + "model_name": [ + "pt_retinanet_retinanet_rn101fpn_obj_det_hf", + "pt_retinanet_retinanet_rn152fpn_obj_det_hf", + "pt_retinanet_retinanet_rn50fpn_obj_det_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D192, @@ -41489,24 +41731,27 @@ def ids_func(param): }, }, ), - ( - Conv2D698, - [((1, 1024, 30, 40), torch.float32)], - { - "model_name": [ - "pt_retinanet_retinanet_rn101fpn_obj_det_hf", - "pt_retinanet_retinanet_rn152fpn_obj_det_hf", - "pt_retinanet_retinanet_rn50fpn_obj_det_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D698, + [((1, 1024, 30, 40), torch.float32)], + { + "model_name": [ + "pt_retinanet_retinanet_rn101fpn_obj_det_hf", + "pt_retinanet_retinanet_rn152fpn_obj_det_hf", + "pt_retinanet_retinanet_rn50fpn_obj_det_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D156, @@ -41651,24 +41896,27 @@ def ids_func(param): }, }, ), - ( - Conv2D703, - [((1, 2048, 15, 20), torch.float32)], - { - "model_name": [ - "pt_retinanet_retinanet_rn101fpn_obj_det_hf", - "pt_retinanet_retinanet_rn152fpn_obj_det_hf", - "pt_retinanet_retinanet_rn50fpn_obj_det_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D703, + [((1, 2048, 15, 20), torch.float32)], + { + "model_name": [ + "pt_retinanet_retinanet_rn101fpn_obj_det_hf", + "pt_retinanet_retinanet_rn152fpn_obj_det_hf", + "pt_retinanet_retinanet_rn50fpn_obj_det_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D8, @@ -42030,41 +42278,51 @@ def ids_func(param): }, }, ), - ( - Conv2D706, - [((1, 3, 512, 512), torch.float32)], - { - "model_name": [ - "pt_segformer_nvidia_mit_b0_img_cls_hf", - "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[4, 4]", - "padding": "[3, 3, 3, 3]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D706, + [((1, 3, 512, 512), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[4, 4]", + "padding": "[3, 3, 3, 3]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 3984 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=7)]. L1 buffer allocated at 213120 and static circular buffer region ends at 623904" + ) + ], ), - ( - Conv2D707, - [((1, 32, 128, 128), torch.float32)], - { - "model_name": [ - "pt_segformer_nvidia_mit_b0_img_cls_hf", - "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[8, 8]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D707, + [((1, 32, 128, 128), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[8, 8]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D566, @@ -42117,23 +42375,26 @@ def ids_func(param): }, }, ), - ( - Conv2D708, - [((1, 64, 64, 64), torch.float32)], - { - "model_name": [ - "pt_segformer_nvidia_mit_b0_img_cls_hf", - "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[4, 4]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D708, + [((1, 64, 64, 64), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[4, 4]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D569, @@ -42171,23 +42432,26 @@ def ids_func(param): }, }, ), - ( - Conv2D710, - [((1, 160, 32, 32), torch.float32)], - { - "model_name": [ - "pt_segformer_nvidia_mit_b0_img_cls_hf", - "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D710, + [((1, 160, 32, 32), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_mit_b0_img_cls_hf", + "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D711, @@ -42243,55 +42507,69 @@ def ids_func(param): }, }, ), - ( - Conv2D713, - [((1, 3, 512, 512), torch.float32)], - { - "model_name": [ - "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_mit_b1_img_cls_hf", - "pt_segformer_nvidia_mit_b5_img_cls_hf", - "pt_segformer_nvidia_mit_b4_img_cls_hf", - "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_mit_b2_img_cls_hf", - "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_mit_b3_img_cls_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[4, 4]", - "padding": "[3, 3, 3, 3]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D713, + [((1, 3, 512, 512), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[4, 4]", + "padding": "[3, 3, 3, 3]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 4004 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=7)]. L1 buffer allocated at 213120 and static circular buffer region ends at 623904" + ) + ], ), - ( - Conv2D714, - [((1, 64, 128, 128), torch.float32)], - { - "model_name": [ - "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_mit_b1_img_cls_hf", - "pt_segformer_nvidia_mit_b5_img_cls_hf", - "pt_segformer_nvidia_mit_b4_img_cls_hf", - "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_mit_b2_img_cls_hf", - "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_mit_b3_img_cls_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[8, 8]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D714, + [((1, 64, 128, 128), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[8, 8]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 3976 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=0)]. L1 buffer allocated at 417792 and static circular buffer region ends at 624032" + ) + ], ), ( Conv2D569, @@ -42343,30 +42621,33 @@ def ids_func(param): }, }, ), - ( - Conv2D715, - [((1, 128, 64, 64), torch.float32)], - { - "model_name": [ - "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_mit_b1_img_cls_hf", - "pt_segformer_nvidia_mit_b5_img_cls_hf", - "pt_segformer_nvidia_mit_b4_img_cls_hf", - "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_mit_b2_img_cls_hf", - "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_mit_b3_img_cls_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[4, 4]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D715, + [((1, 128, 64, 64), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[4, 4]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D572, @@ -42418,30 +42699,33 @@ def ids_func(param): }, }, ), - ( - Conv2D717, - [((1, 320, 32, 32), torch.float32)], - { - "model_name": [ - "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_mit_b1_img_cls_hf", - "pt_segformer_nvidia_mit_b5_img_cls_hf", - "pt_segformer_nvidia_mit_b4_img_cls_hf", - "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_mit_b2_img_cls_hf", - "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_mit_b3_img_cls_hf", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D717, + [((1, 320, 32, 32), torch.float32)], + { + "model_name": [ + "pt_segformer_nvidia_segformer_b2_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b1_img_cls_hf", + "pt_segformer_nvidia_mit_b5_img_cls_hf", + "pt_segformer_nvidia_mit_b4_img_cls_hf", + "pt_segformer_nvidia_segformer_b3_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b2_img_cls_hf", + "pt_segformer_nvidia_segformer_b4_finetuned_ade_512_512_sem_seg_hf", + "pt_segformer_nvidia_mit_b3_img_cls_hf", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D718, @@ -42832,20 +43116,23 @@ def ids_func(param): }, }, ), - ( - Conv2D724, - [((1, 1024, 38, 38), torch.float32)], - { - "model_name": ["pt_ssd300_resnet50_base_img_cls_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D724, + [((1, 1024, 38, 38), torch.float32)], + { + "model_name": ["pt_ssd300_resnet50_base_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D145, @@ -42982,20 +43269,23 @@ def ids_func(param): }, }, ), - ( - Conv2D727, - [((1, 128, 5, 5), torch.float32)], - { - "model_name": ["pt_ssd300_resnet50_base_img_cls_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D727, + [((1, 128, 5, 5), torch.float32)], + { + "model_name": ["pt_ssd300_resnet50_base_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D728, @@ -43027,20 +43317,23 @@ def ids_func(param): }, }, ), - ( - Conv2D727, - [((1, 128, 3, 3), torch.float32)], - { - "model_name": ["pt_ssd300_resnet50_base_img_cls_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D727, + [((1, 128, 3, 3), torch.float32)], + { + "model_name": ["pt_ssd300_resnet50_base_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D728, @@ -43057,20 +43350,23 @@ def ids_func(param): }, }, ), - ( - Conv2D729, - [((1, 1024, 38, 38), torch.float32)], - { - "model_name": ["pt_ssd300_resnet50_base_img_cls_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D729, + [((1, 1024, 38, 38), torch.float32)], + { + "model_name": ["pt_ssd300_resnet50_base_img_cls_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D730, @@ -43297,20 +43593,23 @@ def ids_func(param): }, }, ), - ( - Conv2D664, - [((1, 512, 32, 32), torch.float32)], - { - "model_name": ["pt_unet_base_img_seg_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D664, + [((1, 512, 32, 32), torch.float32)], + { + "model_name": ["pt_unet_base_img_seg_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D663, @@ -43561,65 +43860,81 @@ def ids_func(param): }, }, ), - ( - Conv2D738, - [((1, 1024, 28, 28), torch.float32)], - { - "model_name": ["pt_unet_cityscape_img_seg_osmr"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D738, + [((1, 1024, 28, 28), torch.float32)], + { + "model_name": ["pt_unet_cityscape_img_seg_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D739, - [((1, 512, 56, 56), torch.float32)], - { - "model_name": ["pt_unet_cityscape_img_seg_osmr"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D739, + [((1, 512, 56, 56), torch.float32)], + { + "model_name": ["pt_unet_cityscape_img_seg_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D452, - [((1, 256, 112, 112), torch.float32)], - { - "model_name": ["pt_unet_cityscape_img_seg_osmr"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D452, + [((1, 256, 112, 112), torch.float32)], + { + "model_name": ["pt_unet_cityscape_img_seg_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D662, - [((1, 128, 224, 224), torch.float32)], - { - "model_name": ["pt_unet_cityscape_img_seg_osmr"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D662, + [((1, 128, 224, 224), torch.float32)], + { + "model_name": ["pt_unet_cityscape_img_seg_osmr"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 4310 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=6)]. L1 buffer allocated at 410624 and static circular buffer region ends at 509600" + ) + ], ), ( Conv2D740, @@ -43636,35 +43951,41 @@ def ids_func(param): }, }, ), - ( - Conv2D741, - [((1, 3072, 14, 14), torch.float32)], - { - "model_name": ["pt_unet_qubvel_img_seg_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D741, + [((1, 3072, 14, 14), torch.float32)], + { + "model_name": ["pt_unet_qubvel_img_seg_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D742, - [((1, 768, 28, 28), torch.float32)], - { - "model_name": ["pt_unet_qubvel_img_seg_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D742, + [((1, 768, 28, 28), torch.float32)], + { + "model_name": ["pt_unet_qubvel_img_seg_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D743, @@ -43726,20 +44047,23 @@ def ids_func(param): }, }, ), - ( - Conv2D746, - [((1, 512, 7, 7), torch.float32)], - { - "model_name": ["pt_vgg_vgg19_bn_obj_det_timm"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D746, + [((1, 512, 7, 7), torch.float32)], + { + "model_name": ["pt_vgg_vgg19_bn_obj_det_timm"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D747, @@ -44033,25 +44357,28 @@ def ids_func(param): }, }, ), - ( - Conv2D757, - [((1, 1024, 7, 7), torch.float32)], - { - "model_name": [ - "pt_vovnet_ese_vovnet39b_obj_det_torchhub", - "pt_vovnet_vovnet39_obj_det_osmr", - "pt_vovnet_vovnet57_obj_det_osmr", - "pt_vovnet_ese_vovnet99b_obj_det_torchhub", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D757, + [((1, 1024, 7, 7), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D758, @@ -44467,45 +44794,51 @@ def ids_func(param): }, }, ), - ( - Conv2D779, - [((1, 1024, 14, 14), torch.float32)], - { - "model_name": [ - "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", - "pt_wideresnet_wide_resnet50_2_img_cls_timm", - "pt_wideresnet_wide_resnet101_2_img_cls_timm", - "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D779, + [((1, 1024, 14, 14), torch.float32)], + { + "model_name": [ + "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D780, - [((1, 1024, 7, 7), torch.float32)], - { - "model_name": [ - "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", - "pt_wideresnet_wide_resnet50_2_img_cls_timm", - "pt_wideresnet_wide_resnet101_2_img_cls_timm", - "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", - ], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D780, + [((1, 1024, 7, 7), torch.float32)], + { + "model_name": [ + "pt_wideresnet_wide_resnet101_2_img_cls_torchvision", + "pt_wideresnet_wide_resnet50_2_img_cls_timm", + "pt_wideresnet_wide_resnet101_2_img_cls_timm", + "pt_wideresnet_wide_resnet50_2_img_cls_torchvision", + ], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D781, @@ -44807,25 +45140,28 @@ def ids_func(param): }, }, ), - ( - Conv2D787, - [((1, 728, 19, 19), torch.float32)], - { - "model_name": [ - "pt_xception_xception_img_cls_timm", - "pt_xception_xception41_img_cls_timm", - "pt_xception_xception65_img_cls_timm", - "pt_xception_xception71_img_cls_timm", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D787, + [((1, 728, 19, 19), torch.float32)], + { + "model_name": [ + "pt_xception_xception_img_cls_timm", + "pt_xception_xception41_img_cls_timm", + "pt_xception_xception65_img_cls_timm", + "pt_xception_xception71_img_cls_timm", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D575, @@ -45313,20 +45649,23 @@ def ids_func(param): }, }, ), - ( - Conv2D793, - [((1, 728, 38, 38), torch.float32)], - { - "model_name": ["pt_xception_xception71_img_cls_timm"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D793, + [((1, 728, 38, 38), torch.float32)], + { + "model_name": ["pt_xception_xception71_img_cls_timm"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D794, @@ -45385,35 +45724,49 @@ def ids_func(param): }, }, ), - ( - Conv2D796, - [((1, 3, 1280, 1280), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D796, + [((1, 3, 1280, 1280), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 209715200 B L1 buffer across 64 banks, where each bank needs to store 3276800 B" + ) + ], ), - ( - Conv2D797, - [((1, 32, 640, 640), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D797, + [((1, 32, 640, 640), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 57843712 B L1 buffer across 64 banks, where each bank needs to store 903808 B" + ) + ], ), ( Conv2D798, @@ -45475,23 +45828,30 @@ def ids_func(param): }, }, ), - ( - Conv2D802, - [((1, 64, 320, 320), torch.float32)], - { - "model_name": [ - "pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280", - "pt_yolo_v5_yolov5l_imgcls_torchhub_640x640", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D802, + [((1, 64, 320, 320), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280", + "pt_yolo_v5_yolov5l_imgcls_torchhub_640x640", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 4413 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=6)]. L1 buffer allocated at 413440 and static circular buffer region ends at 525728" + ) + ], ), ( Conv2D803, @@ -45566,23 +45926,26 @@ def ids_func(param): }, }, ), - ( - Conv2D806, - [((1, 128, 160, 160), torch.float32)], - { - "model_name": [ - "pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280", - "pt_yolo_v5_yolov5l_imgcls_torchhub_640x640", - ], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D806, + [((1, 128, 160, 160), torch.float32)], + { + "model_name": [ + "pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280", + "pt_yolo_v5_yolov5l_imgcls_torchhub_640x640", + ], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D807, @@ -45843,20 +46206,23 @@ def ids_func(param): }, }, ), - ( - Conv2D809, - [((1, 128, 160, 160), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D809, + [((1, 128, 160, 160), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_1280x1280"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D819, @@ -45912,35 +46278,49 @@ def ids_func(param): }, }, ), - ( - Conv2D821, - [((1, 3, 640, 640), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D821, + [((1, 3, 640, 640), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 73859072 B L1 buffer across 64 banks, where each bank needs to store 1154048 B" + ) + ], ), - ( - Conv2D822, - [((1, 80, 320, 320), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D822, + [((1, 80, 320, 320), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 58305792 B L1 buffer across 62 banks, where each bank needs to store 940416 B" + ) + ], ), ( Conv2D823, @@ -46002,20 +46382,23 @@ def ids_func(param): }, }, ), - ( - Conv2D827, - [((1, 160, 160, 160), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D827, + [((1, 160, 160, 160), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D828, @@ -46077,20 +46460,23 @@ def ids_func(param): }, }, ), - ( - Conv2D831, - [((1, 320, 80, 80), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D831, + [((1, 320, 80, 80), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D832, @@ -46167,20 +46553,23 @@ def ids_func(param): }, }, ), - ( - Conv2D836, - [((1, 640, 40, 40), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D836, + [((1, 640, 40, 40), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D837, @@ -46317,20 +46706,23 @@ def ids_func(param): }, }, ), - ( - Conv2D834, - [((1, 320, 80, 80), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D834, + [((1, 320, 80, 80), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D845, @@ -46347,20 +46739,23 @@ def ids_func(param): }, }, ), - ( - Conv2D839, - [((1, 640, 40, 40), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D839, + [((1, 640, 40, 40), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D846, @@ -46377,35 +46772,49 @@ def ids_func(param): }, }, ), - ( - Conv2D821, - [((1, 3, 480, 480), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_480x480"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D821, + [((1, 3, 480, 480), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 4581 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=6)]. L1 buffer allocated at 197632 and static circular buffer region ends at 570656" + ) + ], ), - ( - Conv2D822, - [((1, 80, 240, 240), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_480x480"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D822, + [((1, 80, 240, 240), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 4648 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=6)]. L1 buffer allocated at 409216 and static circular buffer region ends at 493088" + ) + ], ), ( Conv2D823, @@ -46467,20 +46876,23 @@ def ids_func(param): }, }, ), - ( - Conv2D827, - [((1, 160, 120, 120), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_480x480"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D827, + [((1, 160, 120, 120), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D828, @@ -46617,20 +47029,23 @@ def ids_func(param): }, }, ), - ( - Conv2D836, - [((1, 640, 30, 30), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_480x480"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D836, + [((1, 640, 30, 30), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D837, @@ -46782,20 +47197,23 @@ def ids_func(param): }, }, ), - ( - Conv2D839, - [((1, 640, 30, 30), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_480x480"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D839, + [((1, 640, 30, 30), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5x_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D846, @@ -46812,20 +47230,27 @@ def ids_func(param): }, }, ), - ( - Conv2D796, - [((1, 3, 480, 480), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_480x480"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D796, + [((1, 3, 480, 480), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 4677 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=6)]. L1 buffer allocated at 197632 and static circular buffer region ends at 570656" + ) + ], ), ( Conv2D797, @@ -48249,20 +48674,27 @@ def ids_func(param): }, }, ), - ( - Conv2D874, - [((1, 3, 640, 640), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D874, + [((1, 3, 640, 640), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 73859072 B L1 buffer across 64 banks, where each bank needs to store 1154048 B" + ) + ], ), ( Conv2D875, @@ -48658,20 +49090,27 @@ def ids_func(param): }, }, ), - ( - Conv2D796, - [((1, 3, 640, 640), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D796, + [((1, 3, 640, 640), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5s_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 73859072 B L1 buffer across 64 banks, where each bank needs to store 1154048 B" + ) + ], ), ( Conv2D797, @@ -48932,20 +49371,27 @@ def ids_func(param): }, }, ), - ( - Conv2D848, - [((1, 3, 480, 480), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_480x480"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D848, + [((1, 3, 480, 480), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 5019 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=6)]. L1 buffer allocated at 197632 and static circular buffer region ends at 570656" + ) + ], ), ( Conv2D849, @@ -49367,35 +49813,45 @@ def ids_func(param): }, }, ), - ( - Conv2D881, - [((1, 3, 640, 640), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D881, + [((1, 3, 640, 640), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 73859072 B L1 buffer across 64 banks, where each bank needs to store 1154048 B" + ) + ], ), - ( - Conv2D882, - [((1, 512, 40, 40), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D882, + [((1, 512, 40, 40), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D883, @@ -49472,20 +49928,23 @@ def ids_func(param): }, }, ), - ( - Conv2D884, - [((1, 512, 40, 40), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D884, + [((1, 512, 40, 40), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D888, @@ -49997,20 +50456,27 @@ def ids_func(param): }, }, ), - ( - Conv2D881, - [((1, 3, 480, 480), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_480x480"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D881, + [((1, 3, 480, 480), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5l_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 5261 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=6)]. L1 buffer allocated at 197632 and static circular buffer region ends at 570656" + ) + ], ), ( Conv2D802, @@ -50327,20 +50793,27 @@ def ids_func(param): }, }, ), - ( - Conv2D874, - [((1, 3, 480, 480), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_480x480"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D874, + [((1, 3, 480, 480), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5n_imgcls_torchhub_480x480"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 5346 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=6)]. L1 buffer allocated at 197632 and static circular buffer region ends at 570656" + ) + ], ), ( Conv2D875, @@ -50732,35 +51205,49 @@ def ids_func(param): }, }, ), - ( - Conv2D848, - [((1, 3, 640, 640), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[2, 2, 2, 2]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D848, + [((1, 3, 640, 640), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[2, 2, 2, 2]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 73859072 B L1 buffer across 64 banks, where each bank needs to store 1154048 B" + ) + ], ), - ( - Conv2D849, - [((1, 48, 320, 320), torch.float32)], - { - "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D849, + [((1, 48, 320, 320), torch.float32)], + { + "model_name": ["pt_yolo_v5_yolov5m_imgcls_torchhub_640x640"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 5301 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=6)]. L1 buffer allocated at 413440 and static circular buffer region ends at 525728" + ) + ], ), ( Conv2D850, @@ -51092,20 +51579,27 @@ def ids_func(param): }, }, ), - ( - Conv2D889, - [((1, 3, 448, 640), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D889, + [((1, 3, 448, 640), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 5350 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=7)]. L1 buffer allocated at 194432 and static circular buffer region ends at 673056" + ) + ], ), ( Conv2D890, @@ -51678,20 +52172,27 @@ def ids_func(param): }, }, ), - ( - Conv2D918, - [((1, 3, 448, 640), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D918, + [((1, 3, 448, 640), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 5511 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=7)]. L1 buffer allocated at 194432 and static circular buffer region ends at 673056" + ) + ], ), ( Conv2D893, @@ -51828,20 +52329,23 @@ def ids_func(param): }, }, ), - ( - Conv2D919, - [((1, 512, 28, 40), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D919, + [((1, 512, 28, 40), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D920, @@ -52098,20 +52602,27 @@ def ids_func(param): }, }, ), - ( - Conv2D929, - [((1, 3, 448, 640), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D929, + [((1, 3, 448, 640), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 5557 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=7)]. L1 buffer allocated at 194432 and static circular buffer region ends at 673056" + ) + ], ), ( Conv2D930, @@ -52518,20 +53029,27 @@ def ids_func(param): }, }, ), - ( - Conv2D939, - [((1, 3, 448, 640), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D939, + [((1, 3, 448, 640), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 5603 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=7)]. L1 buffer allocated at 194432 and static circular buffer region ends at 673056" + ) + ], ), ( Conv2D940, @@ -53208,20 +53726,27 @@ def ids_func(param): }, }, ), - ( - Conv2D139, - [((1, 64, 320, 320), torch.float32)], - { - "model_name": ["pt_yolox_yolox_l_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D139, + [((1, 64, 320, 320), torch.float32)], + { + "model_name": ["pt_yolox_yolox_l_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 5718 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=6)]. L1 buffer allocated at 413440 and static circular buffer region ends at 525728" + ) + ], ), ( Conv2D138, @@ -53283,20 +53808,23 @@ def ids_func(param): }, }, ), - ( - Conv2D142, - [((1, 128, 160, 160), torch.float32)], - { - "model_name": ["pt_yolox_yolox_l_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D142, + [((1, 128, 160, 160), torch.float32)], + { + "model_name": ["pt_yolox_yolox_l_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D75, @@ -53467,20 +53995,23 @@ def ids_func(param): }, }, ), - ( - Conv2D399, - [((1, 512, 40, 40), torch.float32)], - { - "model_name": ["pt_yolox_yolox_l_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D399, + [((1, 512, 40, 40), torch.float32)], + { + "model_name": ["pt_yolox_yolox_l_obj_det_torchhub", "pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D130, @@ -53711,20 +54242,23 @@ def ids_func(param): }, }, ), - ( - Conv2D159, - [((1, 512, 40, 40), torch.float32)], - { - "model_name": ["pt_yolox_yolox_l_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D159, + [((1, 512, 40, 40), torch.float32)], + { + "model_name": ["pt_yolox_yolox_l_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D533, @@ -53820,20 +54354,27 @@ def ids_func(param): }, }, ), - ( - Conv2D475, - [((1, 48, 320, 320), torch.float32)], - { - "model_name": ["pt_yolox_yolox_m_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D475, + [((1, 48, 320, 320), torch.float32)], + { + "model_name": ["pt_yolox_yolox_m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:905: tt::exception info: Statically allocated circular buffers in program 5946 clash with L1 buffers on core range [(x=0,y=0) - (x=7,y=6)]. L1 buffer allocated at 413440 and static circular buffer region ends at 525728" + ) + ], ), ( Conv2D474, @@ -55455,20 +55996,27 @@ def ids_func(param): }, }, ), - ( - Conv2D499, - [((1, 80, 320, 320), torch.float32)], - { - "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D499, + [((1, 80, 320, 320), torch.float32)], + { + "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 58305792 B L1 buffer across 62 banks, where each bank needs to store 940416 B" + ) + ], ), ( Conv2D502, @@ -55530,20 +56078,23 @@ def ids_func(param): }, }, ), - ( - Conv2D364, - [((1, 160, 160, 160), torch.float32)], - { - "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D364, + [((1, 160, 160, 160), torch.float32)], + { + "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D997, @@ -55605,20 +56156,23 @@ def ids_func(param): }, }, ), - ( - Conv2D999, - [((1, 320, 80, 80), torch.float32)], - { - "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D999, + [((1, 320, 80, 80), torch.float32)], + { + "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D1000, @@ -55680,20 +56234,23 @@ def ids_func(param): }, }, ), - ( - Conv2D1002, - [((1, 640, 40, 40), torch.float32)], - { - "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D1002, + [((1, 640, 40, 40), torch.float32)], + { + "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D129, @@ -55800,35 +56357,41 @@ def ids_func(param): }, }, ), - ( - Conv2D504, - [((1, 320, 80, 80), torch.float32)], - { - "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D504, + [((1, 320, 80, 80), torch.float32)], + { + "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Conv2D1008, - [((1, 320, 80, 80), torch.float32)], - { - "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D1008, + [((1, 320, 80, 80), torch.float32)], + { + "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D1009, @@ -55920,20 +56483,23 @@ def ids_func(param): }, }, ), - ( - Conv2D1012, - [((1, 640, 40, 40), torch.float32)], - { - "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D1012, + [((1, 640, 40, 40), torch.float32)], + { + "model_name": ["pt_yolox_yolox_x_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Conv2D1006, @@ -56010,35 +56576,49 @@ def ids_func(param): }, }, ), - ( - Conv2D734, - [((1, 3, 640, 640), torch.float32)], - { - "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[1, 1]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D734, + [((1, 3, 640, 640), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[1, 1]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 63111168 B L1 buffer across 64 banks, where each bank needs to store 986112 B" + ) + ], ), - ( - Conv2D134, - [((1, 32, 640, 640), torch.float32)], - { - "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], - "pcc": 0.99, - "op_params": { - "stride": "[2, 2]", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "groups": "1", - "channel_last": "0", + pytest.param( + ( + Conv2D134, + [((1, 32, 640, 640), torch.float32)], + { + "model_name": ["pt_yolox_yolox_darknet_obj_det_torchhub"], + "pcc": 0.99, + "op_params": { + "stride": "[2, 2]", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "groups": "1", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/allocator/bank_manager.cpp:132: tt::exception info: Out of Memory: Not enough space to allocate 57843712 B L1 buffer across 64 banks, where each bank needs to store 903808 B" + ) + ], ), ( Conv2D182, diff --git a/forge/test/models_ops/test_conv2dtranspose.py b/forge/test/models_ops/test_conv2dtranspose.py index f308d1c26..6c8a16c8c 100644 --- a/forge/test/models_ops/test_conv2dtranspose.py +++ b/forge/test/models_ops/test_conv2dtranspose.py @@ -347,158 +347,277 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Conv2Dtranspose0, - [((1, 4, 7, 7), torch.float32)], - { - "model_name": ["pt_autoencoder_conv_img_enc_github"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose0, + [((1, 4, 7, 7), torch.float32)], + { + "model_name": ["pt_autoencoder_conv_img_enc_github"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose1, - [((1, 16, 14, 14), torch.float32)], - { - "model_name": ["pt_autoencoder_conv_img_enc_github"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose1, + [((1, 16, 14, 14), torch.float32)], + { + "model_name": ["pt_autoencoder_conv_img_enc_github"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose2, - [((1, 64, 28, 28), torch.float32)], - { - "model_name": ["pt_monodle_base_obj_det_torchvision"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "1", "dilation": "1", "groups": "64", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose2, + [((1, 64, 28, 28), torch.float32)], + { + "model_name": ["pt_monodle_base_obj_det_torchvision"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "1", "dilation": "1", "groups": "64", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose3, - [((1, 128, 14, 14), torch.float32)], - { - "model_name": ["pt_monodle_base_obj_det_torchvision"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "1", "dilation": "1", "groups": "128", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose3, + [((1, 128, 14, 14), torch.float32)], + { + "model_name": ["pt_monodle_base_obj_det_torchvision"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "1", "dilation": "1", "groups": "128", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose4, - [((1, 256, 7, 7), torch.float32)], - { - "model_name": ["pt_monodle_base_obj_det_torchvision"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "1", "dilation": "1", "groups": "256", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose4, + [((1, 256, 7, 7), torch.float32)], + { + "model_name": ["pt_monodle_base_obj_det_torchvision"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "1", "dilation": "1", "groups": "256", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose5, - [((1, 512, 16, 16), torch.float32)], - { - "model_name": ["pt_unet_base_img_seg_torchhub"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose5, + [((1, 512, 16, 16), torch.float32)], + { + "model_name": ["pt_unet_base_img_seg_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose6, - [((1, 256, 32, 32), torch.float32)], - { - "model_name": ["pt_unet_base_img_seg_torchhub"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose6, + [((1, 256, 32, 32), torch.float32)], + { + "model_name": ["pt_unet_base_img_seg_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose7, - [((1, 128, 64, 64), torch.float32)], - { - "model_name": ["pt_unet_base_img_seg_torchhub"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose7, + [((1, 128, 64, 64), torch.float32)], + { + "model_name": ["pt_unet_base_img_seg_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose8, - [((1, 64, 128, 128), torch.float32)], - { - "model_name": ["pt_unet_base_img_seg_torchhub"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose8, + [((1, 64, 128, 128), torch.float32)], + { + "model_name": ["pt_unet_base_img_seg_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose9, - [((1, 128, 14, 20), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose9, + [((1, 128, 14, 20), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose10, - [((1, 64, 28, 40), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose10, + [((1, 64, 28, 40), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6s_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose11, - [((1, 256, 14, 20), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose11, + [((1, 256, 14, 20), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose9, - [((1, 128, 28, 40), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose9, + [((1, 128, 28, 40), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose10, - [((1, 64, 14, 20), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose10, + [((1, 64, 14, 20), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose12, - [((1, 32, 28, 40), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose12, + [((1, 32, 28, 40), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6n_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose13, - [((1, 192, 14, 20), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose13, + [((1, 192, 14, 20), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), - ( - Conv2Dtranspose14, - [((1, 96, 28, 40), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], - "pcc": 0.99, - "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, - }, + pytest.param( + ( + Conv2Dtranspose14, + [((1, 96, 28, 40), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"stride": "2", "padding": "0", "dilation": "1", "groups": "1", "channel_last": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), ] diff --git a/forge/test/models_ops/test_cumsum.py b/forge/test/models_ops/test_cumsum.py index 18d0e6b8f..4305b52ed 100644 --- a/forge/test/models_ops/test_cumsum.py +++ b/forge/test/models_ops/test_cumsum.py @@ -32,46 +32,63 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Cumsum0, - [((1, 256), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "1"}, - }, + pytest.param( + ( + Cumsum0, + [((1, 256), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), - ( - Cumsum0, - [((1, 32), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "1"}, - }, + pytest.param( + ( + Cumsum0, + [((1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), - ( - Cumsum0, - [((1, 128), torch.int32)], - { - "model_name": [ - "pt_roberta_xlm_roberta_base_mlm_hf", - "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "1"}, - }, + pytest.param( + ( + Cumsum0, + [((1, 128), torch.int32)], + { + "model_name": [ + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ] diff --git a/forge/test/models_ops/test_embedding.py b/forge/test/models_ops/test_embedding.py index 706dab2be..c8719ff2c 100644 --- a/forge/test/models_ops/test_embedding.py +++ b/forge/test/models_ops/test_embedding.py @@ -783,46 +783,61 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Embedding0, - [((2, 1), torch.int64)], - {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99, "max_int": 2048}, - ), - ( - Embedding1, - [((2, 13), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "max_int": 32127, - }, - ), - ( - Embedding2, - [((13, 13), torch.int32), ((32, 12), torch.float32)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "max_int": 31, - }, - ), - ( - Embedding3, - [((2, 1), torch.int64)], - {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99, "max_int": 2048}, - ), - ( - Embedding4, - [((2, 1), torch.int64)], - {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99, "max_int": 2048}, + pytest.param( + ( + Embedding0, + [((2, 1), torch.int64)], + {"model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], "pcc": 0.99, "max_int": 2048}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding1, + [((2, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "max_int": 32127, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((13, 13), torch.int32), ((32, 12), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "max_int": 31, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding3, + [((2, 1), torch.int64)], + {"model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], "pcc": 0.99, "max_int": 2048}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding4, + [((2, 1), torch.int64)], + {"model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], "pcc": 0.99, "max_int": 2048}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding5, @@ -854,51 +869,63 @@ def ids_func(param): [((1, 2), torch.int32)], {"model_name": ["pt_whisper_openai_whisper_large_v3_turbo_speech_translate_hf"], "pcc": 0.99, "max_int": 51865}, ), - ( - Embedding11, - [((2, 7), torch.int64)], - {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99, "max_int": 49407}, - ), - ( - Embedding12, - [((1, 7), torch.int64)], - {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99, "max_int": 76}, - ), - ( - Embedding13, - [((1, 39), torch.int64)], - {"model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf"], "pcc": 0.99, "max_int": 102399}, + pytest.param( + ( + Embedding11, + [((2, 7), torch.int64)], + {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99, "max_int": 49407}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding12, + [((1, 7), torch.int64)], + {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99, "max_int": 76}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding13, + [((1, 39), torch.int64)], + {"model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf"], "pcc": 0.99, "max_int": 102399}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding13, [((1, 39), torch.int32)], {"model_name": ["DeepSeekWrapper_decoder"], "pcc": 0.99, "max_int": 102399}, ), - ( - Embedding14, - [((1, 128), torch.int64)], - { - "model_name": [ - "pt_albert_xxlarge_v1_token_cls_hf", - "pt_albert_base_v2_token_cls_hf", - "pt_albert_xxlarge_v2_token_cls_hf", - "pt_albert_large_v1_token_cls_hf", - "pt_albert_large_v2_token_cls_hf", - "pt_albert_base_v1_token_cls_hf", - "pt_albert_base_v1_mlm_hf", - "pt_albert_xlarge_v2_token_cls_hf", - "pt_albert_xxlarge_v2_mlm_hf", - "pt_albert_large_v2_mlm_hf", - "pt_albert_base_v2_mlm_hf", - "pt_albert_xlarge_v1_token_cls_hf", - "pt_albert_xlarge_v1_mlm_hf", - "pt_albert_large_v1_mlm_hf", - "pt_albert_xxlarge_v1_mlm_hf", - "pt_albert_xlarge_v2_mlm_hf", - ], - "pcc": 0.99, - "max_int": 29999, - }, + pytest.param( + ( + Embedding14, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + ], + "pcc": 0.99, + "max_int": 29999, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding15, @@ -926,61 +953,73 @@ def ids_func(param): "max_int": 1, }, ), - ( - Embedding16, - [((1, 128), torch.int64)], - { - "model_name": [ - "pt_albert_xxlarge_v1_token_cls_hf", - "pt_albert_base_v2_token_cls_hf", - "pt_albert_xxlarge_v2_token_cls_hf", - "pt_albert_large_v1_token_cls_hf", - "pt_albert_large_v2_token_cls_hf", - "pt_albert_base_v1_token_cls_hf", - "pt_albert_base_v1_mlm_hf", - "pt_albert_xlarge_v2_token_cls_hf", - "pt_albert_xxlarge_v2_mlm_hf", - "pt_albert_large_v2_mlm_hf", - "pt_albert_base_v2_mlm_hf", - "pt_albert_xlarge_v1_token_cls_hf", - "pt_albert_xlarge_v1_mlm_hf", - "pt_albert_large_v1_mlm_hf", - "pt_albert_xxlarge_v1_mlm_hf", - "pt_albert_xlarge_v2_mlm_hf", - ], - "pcc": 0.99, - "max_int": 511, - }, - ), - ( - Embedding17, - [((1, 256), torch.int64)], - {"model_name": ["pt_bart_facebook_bart_large_mnli_seq_cls_hf"], "pcc": 0.99, "max_int": 50264}, - ), - ( - Embedding2, - [((1, 256), torch.int64), ((1026, 1024), torch.float32)], - {"model_name": ["pt_bart_facebook_bart_large_mnli_seq_cls_hf"], "pcc": 0.99, "max_int": 1025}, - ), - ( - Embedding18, - [((1, 128), torch.int64)], - { - "model_name": [ - "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", - "pt_bert_bert_base_uncased_mlm_hf", - "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", - "pt_distilbert_distilbert_base_uncased_mlm_hf", - "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", - "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", - "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", - "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", - "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", - "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", - ], - "pcc": 0.99, - "max_int": 30521, - }, + pytest.param( + ( + Embedding16, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + ], + "pcc": 0.99, + "max_int": 511, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding17, + [((1, 256), torch.int64)], + {"model_name": ["pt_bart_facebook_bart_large_mnli_seq_cls_hf"], "pcc": 0.99, "max_int": 50264}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((1, 256), torch.int64), ((1026, 1024), torch.float32)], + {"model_name": ["pt_bart_facebook_bart_large_mnli_seq_cls_hf"], "pcc": 0.99, "max_int": 1025}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding18, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + ], + "pcc": 0.99, + "max_int": 30521, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding19, @@ -1000,38 +1039,44 @@ def ids_func(param): "max_int": 1, }, ), - ( - Embedding20, - [((1, 128), torch.int64)], - { - "model_name": [ - "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", - "pt_bert_bert_base_uncased_mlm_hf", - "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", - "pt_distilbert_distilbert_base_cased_mlm_hf", - "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", - "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", - "pt_distilbert_distilbert_base_uncased_mlm_hf", - "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", - "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", - "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", - "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", - "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", - "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", - "pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf", - ], - "pcc": 0.99, - "max_int": 511, - }, - ), - ( - Embedding21, - [((1, 384), torch.int64)], - { - "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], - "pcc": 0.99, - "max_int": 28995, - }, + pytest.param( + ( + Embedding20, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf", + ], + "pcc": 0.99, + "max_int": 511, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding21, + [((1, 384), torch.int64)], + { + "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], + "pcc": 0.99, + "max_int": 28995, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding22, @@ -1042,23 +1087,29 @@ def ids_func(param): "max_int": 1, }, ), - ( - Embedding23, - [((1, 384), torch.int64)], - { - "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], - "pcc": 0.99, - "max_int": 511, - }, - ), - ( - Embedding21, - [((1, 128), torch.int64)], - { - "model_name": ["pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf"], - "pcc": 0.99, - "max_int": 28995, - }, + pytest.param( + ( + Embedding23, + [((1, 384), torch.int64)], + { + "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], + "pcc": 0.99, + "max_int": 511, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding21, + [((1, 128), torch.int64)], + { + "model_name": ["pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf"], + "pcc": 0.99, + "max_int": 28995, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding22, @@ -1069,14 +1120,17 @@ def ids_func(param): "max_int": 1, }, ), - ( - Embedding23, - [((1, 128), torch.int64)], - { - "model_name": ["pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf"], - "pcc": 0.99, - "max_int": 511, - }, + pytest.param( + ( + Embedding23, + [((1, 128), torch.int64)], + { + "model_name": ["pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf"], + "pcc": 0.99, + "max_int": 511, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding24, @@ -1091,137 +1145,207 @@ def ids_func(param): "max_int": 51199, }, ), - ( - Embedding25, - [((1, 128), torch.int64)], - { - "model_name": [ - "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", - "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", - ], - "pcc": 0.99, - "max_int": 119546, - }, - ), - ( - Embedding26, - [((1, 384), torch.int64)], - {"model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], "pcc": 0.99, "max_int": 28995}, - ), - ( - Embedding20, - [((1, 384), torch.int64)], - {"model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], "pcc": 0.99, "max_int": 511}, - ), - ( - Embedding26, - [((1, 128), torch.int64)], - {"model_name": ["pt_distilbert_distilbert_base_cased_mlm_hf"], "pcc": 0.99, "max_int": 28995}, - ), - ( - Embedding27, - [((1, 6), torch.int64)], - {"model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf"], "pcc": 0.99, "max_int": 65023}, - ), - ( - Embedding28, - [((1, 10), torch.int64)], - { - "model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], - "pcc": 0.99, - "max_int": 131071, - }, - ), - ( - Embedding29, - [((1, 10), torch.int64)], - {"model_name": ["pt_falcon3_tiiuae_falcon3_1b_base_clm_hf"], "pcc": 0.99, "max_int": 131071}, - ), - ( - Embedding30, - [((1, 7), torch.int64)], - {"model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], "pcc": 0.99, "max_int": 255999}, - ), - ( - Embedding31, - [((1, 256), torch.int64)], - { - "model_name": ["pt_gpt2_gpt2_text_gen_hf", "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf"], - "pcc": 0.99, - "max_int": 50256, - }, - ), - ( - Embedding2, - [((1, 256), torch.int64), ((1024, 768), torch.float32)], - {"model_name": ["pt_gpt2_gpt2_text_gen_hf"], "pcc": 0.99, "max_int": 1023}, - ), - ( - Embedding32, - [((1, 256), torch.int64)], - {"model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf"], "pcc": 0.99, "max_int": 50256}, - ), - ( - Embedding2, - [((1, 256), torch.int64), ((2048, 2560), torch.float32)], - {"model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf"], "pcc": 0.99, "max_int": 2047}, - ), - ( - Embedding33, - [((1, 256), torch.int64)], - {"model_name": ["pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf"], "pcc": 0.99, "max_int": 50256}, - ), - ( - Embedding2, - [((1, 256), torch.int64), ((2048, 2048), torch.float32)], - {"model_name": ["pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf"], "pcc": 0.99, "max_int": 2047}, - ), - ( - Embedding31, - [((1, 32), torch.int64)], - {"model_name": ["pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf"], "pcc": 0.99, "max_int": 50256}, - ), - ( - Embedding2, - [((1, 32), torch.int64), ((2048, 768), torch.float32)], - {"model_name": ["pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf"], "pcc": 0.99, "max_int": 2047}, - ), - ( - Embedding32, - [((1, 32), torch.int64)], - {"model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf"], "pcc": 0.99, "max_int": 50256}, - ), - ( - Embedding2, - [((1, 32), torch.int64), ((2048, 2560), torch.float32)], - {"model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf"], "pcc": 0.99, "max_int": 2047}, - ), - ( - Embedding2, - [((1, 256), torch.int64), ((2048, 768), torch.float32)], - {"model_name": ["pt_gptneo_eleutherai_gpt_neo_125m_clm_hf"], "pcc": 0.99, "max_int": 2047}, - ), - ( - Embedding33, - [((1, 32), torch.int64)], - {"model_name": ["pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf"], "pcc": 0.99, "max_int": 50256}, - ), - ( - Embedding2, - [((1, 32), torch.int64), ((2048, 2048), torch.float32)], - {"model_name": ["pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf"], "pcc": 0.99, "max_int": 2047}, - ), - ( - Embedding34, - [((1, 4), torch.int64)], - { - "model_name": [ - "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", - "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", - ], - "pcc": 0.99, - "max_int": 128255, - }, + pytest.param( + ( + Embedding25, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + ], + "pcc": 0.99, + "max_int": 119546, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding26, + [((1, 384), torch.int64)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "max_int": 28995, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding20, + [((1, 384), torch.int64)], + {"model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], "pcc": 0.99, "max_int": 511}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding26, + [((1, 128), torch.int64)], + {"model_name": ["pt_distilbert_distilbert_base_cased_mlm_hf"], "pcc": 0.99, "max_int": 28995}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding27, + [((1, 6), torch.int64)], + {"model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf"], "pcc": 0.99, "max_int": 65023}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding28, + [((1, 10), torch.int64)], + { + "model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf", "pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], + "pcc": 0.99, + "max_int": 131071, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding29, + [((1, 10), torch.int64)], + {"model_name": ["pt_falcon3_tiiuae_falcon3_1b_base_clm_hf"], "pcc": 0.99, "max_int": 131071}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding30, + [((1, 7), torch.int64)], + {"model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], "pcc": 0.99, "max_int": 255999}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding31, + [((1, 256), torch.int64)], + { + "model_name": ["pt_gpt2_gpt2_text_gen_hf", "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf"], + "pcc": 0.99, + "max_int": 50256, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((1, 256), torch.int64), ((1024, 768), torch.float32)], + {"model_name": ["pt_gpt2_gpt2_text_gen_hf"], "pcc": 0.99, "max_int": 1023}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding32, + [((1, 256), torch.int64)], + {"model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf"], "pcc": 0.99, "max_int": 50256}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((1, 256), torch.int64), ((2048, 2560), torch.float32)], + {"model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf"], "pcc": 0.99, "max_int": 2047}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding33, + [((1, 256), torch.int64)], + {"model_name": ["pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf"], "pcc": 0.99, "max_int": 50256}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((1, 256), torch.int64), ((2048, 2048), torch.float32)], + {"model_name": ["pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf"], "pcc": 0.99, "max_int": 2047}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding31, + [((1, 32), torch.int64)], + {"model_name": ["pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf"], "pcc": 0.99, "max_int": 50256}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((1, 32), torch.int64), ((2048, 768), torch.float32)], + {"model_name": ["pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf"], "pcc": 0.99, "max_int": 2047}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding32, + [((1, 32), torch.int64)], + {"model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf"], "pcc": 0.99, "max_int": 50256}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((1, 32), torch.int64), ((2048, 2560), torch.float32)], + {"model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf"], "pcc": 0.99, "max_int": 2047}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((1, 256), torch.int64), ((2048, 768), torch.float32)], + {"model_name": ["pt_gptneo_eleutherai_gpt_neo_125m_clm_hf"], "pcc": 0.99, "max_int": 2047}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding33, + [((1, 32), torch.int64)], + {"model_name": ["pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf"], "pcc": 0.99, "max_int": 50256}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((1, 32), torch.int64), ((2048, 2048), torch.float32)], + {"model_name": ["pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf"], "pcc": 0.99, "max_int": 2047}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding34, + [((1, 4), torch.int64)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + ], + "pcc": 0.99, + "max_int": 128255, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding34, @@ -1235,127 +1359,178 @@ def ids_func(param): "max_int": 128255, }, ), - ( - Embedding35, - [((1, 4), torch.int64)], - { - "model_name": [ - "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", - "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", - "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", - "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", - ], - "pcc": 0.99, - "max_int": 128255, - }, - ), - ( - Embedding36, - [((1, 128), torch.int64)], - {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99, "max_int": 31999}, - ), - ( - Embedding31, - [((1, 7), torch.int64)], - {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99, "max_int": 50256}, - ), - ( - Embedding2, - [((1, 7), torch.int64), ((1024, 768), torch.float32)], - {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99, "max_int": 1023}, - ), - ( - Embedding37, - [((1, 256), torch.int64)], - {"model_name": ["pt_opt_facebook_opt_1_3b_clm_hf"], "pcc": 0.99, "max_int": 50271}, - ), - ( - Embedding38, - [((1, 256), torch.int64)], - {"model_name": ["pt_opt_facebook_opt_1_3b_clm_hf"], "pcc": 0.99, "max_int": 2049}, - ), - ( - Embedding37, - [((1, 32), torch.int64)], - { - "model_name": ["pt_opt_facebook_opt_1_3b_seq_cls_hf", "pt_opt_facebook_opt_1_3b_qa_hf"], - "pcc": 0.99, - "max_int": 50271, - }, - ), - ( - Embedding38, - [((1, 32), torch.int64)], - { - "model_name": ["pt_opt_facebook_opt_1_3b_seq_cls_hf", "pt_opt_facebook_opt_1_3b_qa_hf"], - "pcc": 0.99, - "max_int": 2049, - }, - ), - ( - Embedding39, - [((1, 32), torch.int64)], - { - "model_name": ["pt_opt_facebook_opt_350m_qa_hf", "pt_opt_facebook_opt_350m_seq_cls_hf"], - "pcc": 0.99, - "max_int": 50271, - }, - ), - ( - Embedding40, - [((1, 32), torch.int64)], - { - "model_name": ["pt_opt_facebook_opt_350m_qa_hf", "pt_opt_facebook_opt_350m_seq_cls_hf"], - "pcc": 0.99, - "max_int": 2049, - }, - ), - ( - Embedding41, - [((1, 32), torch.int64)], - { - "model_name": ["pt_opt_facebook_opt_125m_seq_cls_hf", "pt_opt_facebook_opt_125m_qa_hf"], - "pcc": 0.99, - "max_int": 50271, - }, 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torch.int64)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + ], + "pcc": 0.99, + "max_int": 128255, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding36, + [((1, 128), torch.int64)], + {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99, "max_int": 31999}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding31, + [((1, 7), torch.int64)], + {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99, "max_int": 50256}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((1, 7), torch.int64), ((1024, 768), torch.float32)], + {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99, "max_int": 1023}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding37, + [((1, 256), torch.int64)], + {"model_name": ["pt_opt_facebook_opt_1_3b_clm_hf"], "pcc": 0.99, "max_int": 50271}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding38, + [((1, 256), torch.int64)], + {"model_name": ["pt_opt_facebook_opt_1_3b_clm_hf"], "pcc": 0.99, "max_int": 2049}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding37, + [((1, 32), torch.int64)], + { + "model_name": ["pt_opt_facebook_opt_1_3b_seq_cls_hf", "pt_opt_facebook_opt_1_3b_qa_hf"], + "pcc": 0.99, + "max_int": 50271, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding38, + [((1, 32), torch.int64)], + { + "model_name": ["pt_opt_facebook_opt_1_3b_seq_cls_hf", "pt_opt_facebook_opt_1_3b_qa_hf"], + "pcc": 0.99, + "max_int": 2049, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding39, + [((1, 32), torch.int64)], + { + "model_name": ["pt_opt_facebook_opt_350m_qa_hf", "pt_opt_facebook_opt_350m_seq_cls_hf"], + "pcc": 0.99, + "max_int": 50271, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding40, + [((1, 32), torch.int64)], + { + "model_name": ["pt_opt_facebook_opt_350m_qa_hf", "pt_opt_facebook_opt_350m_seq_cls_hf"], + "pcc": 0.99, + "max_int": 2049, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding41, + [((1, 32), torch.int64)], + { + "model_name": ["pt_opt_facebook_opt_125m_seq_cls_hf", "pt_opt_facebook_opt_125m_qa_hf"], + "pcc": 0.99, + "max_int": 50271, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding42, + [((1, 32), torch.int64)], + { + "model_name": ["pt_opt_facebook_opt_125m_seq_cls_hf", "pt_opt_facebook_opt_125m_qa_hf"], + "pcc": 0.99, + "max_int": 2049, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding41, + [((1, 256), torch.int64)], + {"model_name": ["pt_opt_facebook_opt_125m_clm_hf"], "pcc": 0.99, "max_int": 50271}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding42, + [((1, 256), torch.int64)], + {"model_name": ["pt_opt_facebook_opt_125m_clm_hf"], "pcc": 0.99, "max_int": 2049}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding39, + [((1, 256), torch.int64)], + {"model_name": ["pt_opt_facebook_opt_350m_clm_hf"], "pcc": 0.99, "max_int": 50271}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding40, + [((1, 256), torch.int64)], + {"model_name": ["pt_opt_facebook_opt_350m_clm_hf"], "pcc": 0.99, "max_int": 2049}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding43, + [((1, 12), torch.int64)], + { + "model_name": ["pt_phi2_microsoft_phi_2_pytdml_token_cls_hf", "pt_phi2_microsoft_phi_2_token_cls_hf"], + "pcc": 0.99, + "max_int": 51199, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding43, @@ -1366,125 +1541,182 @@ def ids_func(param): "max_int": 51199, }, ), - ( - Embedding43, - [((1, 11), torch.int64)], - { - "model_name": ["pt_phi2_microsoft_phi_2_seq_cls_hf", "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf"], - "pcc": 0.99, - "max_int": 51199, - }, - ), - ( - Embedding44, - [((1, 256), torch.int64)], - {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_clm_hf"], "pcc": 0.99, "max_int": 32063}, - ), - ( - Embedding44, - [((1, 13), torch.int64)], - {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_token_cls_hf"], "pcc": 0.99, "max_int": 32063}, - ), - ( - Embedding44, - [((1, 5), torch.int64)], - {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf"], "pcc": 0.99, "max_int": 32063}, - ), - ( - Embedding45, - [((1, 6), torch.int64)], - {"model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_clm_hf"], "pcc": 0.99, "max_int": 151935}, - ), - ( - Embedding45, - [((1, 29), torch.int64)], - {"model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_chat_clm_hf"], "pcc": 0.99, "max_int": 151935}, - ), - ( - Embedding46, - [((1, 35), torch.int64)], - { - "model_name": [ - "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", - "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", - ], - "pcc": 0.99, - "max_int": 152063, - }, - ), - ( - Embedding47, - [((1, 35), torch.int64)], - { - "model_name": [ - "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", - "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", - ], - "pcc": 0.99, - "max_int": 151935, - }, - ), - ( - Embedding48, - [((1, 35), torch.int64)], - { - "model_name": [ - "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", - "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", - ], - "pcc": 0.99, - "max_int": 151935, - }, - ), - ( - Embedding49, - [((1, 35), torch.int64)], - {"model_name": ["pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf"], "pcc": 0.99, "max_int": 151935}, - ), - ( - Embedding47, - [((1, 29), torch.int64)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf"], "pcc": 0.99, "max_int": 151935}, - ), - ( - Embedding47, - [((1, 39), torch.int64)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf"], "pcc": 0.99, "max_int": 151935}, - ), - ( - Embedding46, - [((1, 39), torch.int64)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf"], "pcc": 0.99, "max_int": 152063}, - ), - ( - Embedding46, - [((1, 29), torch.int64)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_clm_hf"], "pcc": 0.99, "max_int": 152063}, - ), - ( - Embedding48, - [((1, 29), torch.int64)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_clm_hf"], "pcc": 0.99, "max_int": 151935}, - ), - ( - Embedding48, - [((1, 39), torch.int64)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf"], "pcc": 0.99, "max_int": 151935}, - ), - ( - Embedding49, - [((1, 29), torch.int64)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_clm_hf"], "pcc": 0.99, "max_int": 151935}, - ), - ( - Embedding49, - [((1, 39), torch.int64)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf"], "pcc": 0.99, "max_int": 151935}, - ), - ( - Embedding50, - [((1, 128), torch.int64)], - {"model_name": ["pt_roberta_xlm_roberta_base_mlm_hf"], "pcc": 0.99, "max_int": 250001}, + pytest.param( + ( + Embedding43, + [((1, 11), torch.int64)], + { + "model_name": ["pt_phi2_microsoft_phi_2_seq_cls_hf", "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf"], + "pcc": 0.99, + "max_int": 51199, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding44, + [((1, 256), torch.int64)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_clm_hf"], "pcc": 0.99, "max_int": 32063}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding44, + [((1, 13), torch.int64)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_token_cls_hf"], "pcc": 0.99, "max_int": 32063}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding44, + [((1, 5), torch.int64)], + {"model_name": ["pt_phi3_microsoft_phi_3_mini_4k_instruct_seq_cls_hf"], "pcc": 0.99, "max_int": 32063}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding45, + [((1, 6), torch.int64)], + {"model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_clm_hf"], "pcc": 0.99, "max_int": 151935}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding45, + [((1, 29), torch.int64)], + {"model_name": ["pt_qwen1_5_qwen_qwen1_5_0_5b_chat_clm_hf"], "pcc": 0.99, "max_int": 151935}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding46, + [((1, 35), torch.int64)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + "max_int": 152063, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding47, + [((1, 35), torch.int64)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + ], + "pcc": 0.99, + "max_int": 151935, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding48, + [((1, 35), torch.int64)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + ], + "pcc": 0.99, + "max_int": 151935, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding49, + [((1, 35), torch.int64)], + {"model_name": ["pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf"], "pcc": 0.99, "max_int": 151935}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding47, + [((1, 29), torch.int64)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf"], "pcc": 0.99, "max_int": 151935}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding47, + [((1, 39), torch.int64)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf"], "pcc": 0.99, "max_int": 151935}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding46, + [((1, 39), torch.int64)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf"], "pcc": 0.99, "max_int": 152063}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding46, + [((1, 29), torch.int64)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_clm_hf"], "pcc": 0.99, "max_int": 152063}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding48, + [((1, 29), torch.int64)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_clm_hf"], "pcc": 0.99, "max_int": 151935}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding48, + [((1, 39), torch.int64)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf"], "pcc": 0.99, "max_int": 151935}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding49, + [((1, 29), torch.int64)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_clm_hf"], "pcc": 0.99, "max_int": 151935}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding49, + [((1, 39), torch.int64)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf"], "pcc": 0.99, "max_int": 151935}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding50, + [((1, 128), torch.int64)], + {"model_name": ["pt_roberta_xlm_roberta_base_mlm_hf"], "pcc": 0.99, "max_int": 250001}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding51, @@ -1498,31 +1730,40 @@ def ids_func(param): "max_int": 0, }, ), - ( - Embedding52, - [((1, 128), torch.int64)], - { - "model_name": [ - "pt_roberta_xlm_roberta_base_mlm_hf", - "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", - ], - "pcc": 0.99, - "max_int": 513, - }, - ), - ( - Embedding53, - [((1, 128), torch.int64)], - { - "model_name": ["pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf"], - "pcc": 0.99, - "max_int": 50264, - }, - ), - ( - Embedding54, - [((1, 128), torch.int64)], - {"model_name": ["pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf"], "pcc": 0.99, "max_int": 30527}, + pytest.param( + ( + Embedding52, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + "max_int": 513, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding53, + [((1, 128), torch.int64)], + { + "model_name": ["pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf"], + "pcc": 0.99, + "max_int": 50264, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding54, + [((1, 128), torch.int64)], + {"model_name": ["pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf"], "pcc": 0.99, "max_int": 30527}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding2, @@ -1547,23 +1788,29 @@ def ids_func(param): "max_int": 31, }, ), - ( - Embedding55, - [((1, 61), torch.int64)], - { - "model_name": ["pt_t5_google_flan_t5_large_text_gen_hf", "pt_t5_t5_large_text_gen_hf"], - "pcc": 0.99, - "max_int": 32127, - }, - ), - ( - Embedding2, - [((61, 61), torch.int32), ((32, 16), torch.float32)], - { - "model_name": ["pt_t5_google_flan_t5_large_text_gen_hf", "pt_t5_t5_large_text_gen_hf"], - "pcc": 0.99, - "max_int": 31, - }, + pytest.param( + ( + Embedding55, + [((1, 61), torch.int64)], + { + "model_name": ["pt_t5_google_flan_t5_large_text_gen_hf", "pt_t5_t5_large_text_gen_hf"], + "pcc": 0.99, + "max_int": 32127, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((61, 61), torch.int32), ((32, 16), torch.float32)], + { + "model_name": ["pt_t5_google_flan_t5_large_text_gen_hf", "pt_t5_t5_large_text_gen_hf"], + "pcc": 0.99, + "max_int": 31, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding56, @@ -1579,29 +1826,38 @@ def ids_func(param): [((1, 1), torch.int32), ((32, 8), torch.float32)], {"model_name": ["pt_t5_t5_small_text_gen_hf"], "pcc": 0.99, "max_int": 31}, ), - ( - Embedding56, - [((1, 61), torch.int64)], - { - "model_name": ["pt_t5_t5_small_text_gen_hf", "pt_t5_google_flan_t5_small_text_gen_hf"], - "pcc": 0.99, - "max_int": 32127, - }, - ), - ( - Embedding2, - [((61, 61), torch.int32), ((32, 8), torch.float32)], - {"model_name": ["pt_t5_t5_small_text_gen_hf"], "pcc": 0.99, "max_int": 31}, + pytest.param( + ( + Embedding56, + [((1, 61), torch.int64)], + { + "model_name": ["pt_t5_t5_small_text_gen_hf", "pt_t5_google_flan_t5_small_text_gen_hf"], + "pcc": 0.99, + "max_int": 32127, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((61, 61), torch.int32), ((32, 8), torch.float32)], + {"model_name": ["pt_t5_t5_small_text_gen_hf"], "pcc": 0.99, "max_int": 31}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding2, [((1, 1), torch.int32), ((32, 6), torch.float32)], {"model_name": ["pt_t5_google_flan_t5_small_text_gen_hf"], "pcc": 0.99, "max_int": 31}, ), - ( - Embedding2, - [((61, 61), torch.int32), ((32, 6), torch.float32)], - {"model_name": ["pt_t5_google_flan_t5_small_text_gen_hf"], "pcc": 0.99, "max_int": 31}, + pytest.param( + ( + Embedding2, + [((61, 61), torch.int32), ((32, 6), torch.float32)], + {"model_name": ["pt_t5_google_flan_t5_small_text_gen_hf"], "pcc": 0.99, "max_int": 31}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Embedding1, @@ -1621,33 +1877,45 @@ def ids_func(param): "max_int": 31, }, ), - ( - Embedding1, - [((1, 61), torch.int64)], - { - "model_name": ["pt_t5_t5_base_text_gen_hf", "pt_t5_google_flan_t5_base_text_gen_hf"], - "pcc": 0.99, - "max_int": 32127, - }, - ), - ( - Embedding2, - [((61, 61), torch.int32), ((32, 12), torch.float32)], - { - "model_name": ["pt_t5_t5_base_text_gen_hf", "pt_t5_google_flan_t5_base_text_gen_hf"], - "pcc": 0.99, - "max_int": 31, - }, - ), - ( - Embedding57, - [((1, 256), torch.int64)], - {"model_name": ["pt_xglm_facebook_xglm_1_7b_clm_hf"], "pcc": 0.99, "max_int": 256007}, - ), - ( - Embedding58, - [((1, 256), torch.int64)], - {"model_name": ["pt_xglm_facebook_xglm_564m_clm_hf"], "pcc": 0.99, "max_int": 256007}, + pytest.param( + ( + Embedding1, + [((1, 61), torch.int64)], + { + "model_name": ["pt_t5_t5_base_text_gen_hf", "pt_t5_google_flan_t5_base_text_gen_hf"], + "pcc": 0.99, + "max_int": 32127, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding2, + [((61, 61), torch.int32), ((32, 12), torch.float32)], + { + "model_name": ["pt_t5_t5_base_text_gen_hf", "pt_t5_google_flan_t5_base_text_gen_hf"], + "pcc": 0.99, + "max_int": 31, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding57, + [((1, 256), torch.int64)], + {"model_name": ["pt_xglm_facebook_xglm_1_7b_clm_hf"], "pcc": 0.99, "max_int": 256007}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Embedding58, + [((1, 256), torch.int64)], + {"model_name": ["pt_xglm_facebook_xglm_564m_clm_hf"], "pcc": 0.99, "max_int": 256007}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ] diff --git a/forge/test/models_ops/test_equal.py b/forge/test/models_ops/test_equal.py index dc3f71324..37ea9abcf 100644 --- a/forge/test/models_ops/test_equal.py +++ b/forge/test/models_ops/test_equal.py @@ -33,17 +33,24 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Equal0, - [((1, 32), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Equal0, + [((1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/broadcast_height_and_width_multi_core_program_factory.cpp:27: tt::exception info: BinaryOpType cannot be mapped to BcastOpMath" + ) + ], ), ] diff --git a/forge/test/models_ops/test_greater.py b/forge/test/models_ops/test_greater.py index a3f9e34af..fa1c9fed0 100644 --- a/forge/test/models_ops/test_greater.py +++ b/forge/test/models_ops/test_greater.py @@ -233,73 +233,115 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Greater0, - [((2, 1, 1, 13), torch.float32)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Greater0, + [((2, 1, 1, 13), torch.float32)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/broadcast_height_and_width_multi_core_program_factory.cpp:27: tt::exception info: BinaryOpType cannot be mapped to BcastOpMath" + ) + ], ), - ( - Greater0, - [((2, 1, 7, 7), torch.float32)], - {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99}, + pytest.param( + ( + Greater0, + [((2, 1, 7, 7), torch.float32)], + {"model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], "pcc": 0.99}, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/broadcast_height_and_width_multi_core_program_factory.cpp:27: tt::exception info: BinaryOpType cannot be mapped to BcastOpMath" + ) + ], ), - ( - Greater0, - [((1, 1, 256, 256), torch.float32)], - { - "model_name": [ - "pt_bart_facebook_bart_large_mnli_seq_cls_hf", - "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", - "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - "pt_xglm_facebook_xglm_1_7b_clm_hf", - "pt_xglm_facebook_xglm_564m_clm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Greater0, + [((1, 1, 256, 256), torch.float32)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/broadcast_height_and_width_multi_core_program_factory.cpp:27: tt::exception info: BinaryOpType cannot be mapped to BcastOpMath" + ) + ], ), - ( - Greater0, - [((1, 12, 128, 128), torch.float32)], - { - "model_name": [ - "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", - "pt_distilbert_distilbert_base_cased_mlm_hf", - "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", - "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", - "pt_distilbert_distilbert_base_uncased_mlm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Greater0, + [((1, 12, 128, 128), torch.float32)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/broadcast_height_and_width_multi_core_program_factory.cpp:27: tt::exception info: BinaryOpType cannot be mapped to BcastOpMath" + ) + ], ), - ( - Greater0, - [((1, 12, 384, 384), torch.float32)], - {"model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], "pcc": 0.99}, + pytest.param( + ( + Greater0, + [((1, 12, 384, 384), torch.float32)], + {"model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], "pcc": 0.99}, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/broadcast_height_and_width_multi_core_program_factory.cpp:27: tt::exception info: BinaryOpType cannot be mapped to BcastOpMath" + ) + ], ), - ( - Greater0, - [((1, 1, 32, 32), torch.float32)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Greater0, + [((1, 1, 32, 32), torch.float32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/broadcast_height_and_width_multi_core_program_factory.cpp:27: tt::exception info: BinaryOpType cannot be mapped to BcastOpMath" + ) + ], ), ( Greater1, diff --git a/forge/test/models_ops/test_index.py b/forge/test/models_ops/test_index.py index b6f4d9a3e..3810e2622 100644 --- a/forge/test/models_ops/test_index.py +++ b/forge/test/models_ops/test_index.py @@ -1904,57 +1904,69 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Index0, - [((2, 4, 1), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, - }, - ), - ( - Index1, - [((2, 4, 1), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-2", "start": "1", "stop": "2", "stride": "1"}, - }, - ), - ( - Index2, - [((2, 4, 1), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-2", "start": "2", "stop": "3", "stride": "1"}, - }, - ), - ( - Index3, - [((2, 4, 1), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-2", "start": "3", "stop": "4", "stride": "1"}, - }, + pytest.param( + ( + Index0, + [((2, 4, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 1 - data type mismatch: expected UInt32, got Float32")], + ), + pytest.param( + ( + Index1, + [((2, 4, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "1", "stop": "2", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 1 - data type mismatch: expected UInt32, got Float32")], + ), + pytest.param( + ( + Index2, + [((2, 4, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "2", "stop": "3", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 1 - data type mismatch: expected UInt32, got Float32")], + ), + pytest.param( + ( + Index3, + [((2, 4, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "3", "stop": "4", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 1 - data type mismatch: expected UInt32, got Float32")], ), ( Index0, @@ -1983,14 +1995,17 @@ def ids_func(param): "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, }, ), - ( - Index4, - [((1, 77), torch.int64)], - { - "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], - "pcc": 0.99, - "op_params": {"dim": "-1", "start": "0", "stop": "7", "stride": "1"}, - }, + pytest.param( + ( + Index4, + [((1, 77), torch.int64)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "7", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 1 - data type mismatch: expected UInt32, got Float32")], ), ( Index5, @@ -2028,58 +2043,64 @@ def ids_func(param): "op_params": {"dim": "-2", "start": "0", "stop": "1", "stride": "1"}, }, ), - ( - Index7, - [((1, 512), torch.int64)], - { - "model_name": [ - "pt_albert_xxlarge_v1_token_cls_hf", - "pt_albert_base_v2_token_cls_hf", - "pt_albert_xxlarge_v2_token_cls_hf", - "pt_albert_large_v1_token_cls_hf", - "pt_albert_large_v2_token_cls_hf", - "pt_albert_base_v1_token_cls_hf", - "pt_albert_base_v1_mlm_hf", - "pt_albert_xlarge_v2_token_cls_hf", - "pt_albert_xxlarge_v2_mlm_hf", - "pt_albert_large_v2_mlm_hf", - "pt_albert_base_v2_mlm_hf", - "pt_albert_xlarge_v1_token_cls_hf", - "pt_albert_xlarge_v1_mlm_hf", - "pt_albert_large_v1_mlm_hf", - "pt_albert_xxlarge_v1_mlm_hf", - "pt_albert_xlarge_v2_mlm_hf", - "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", - "pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf", - "pt_bert_bert_base_uncased_mlm_hf", - "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", - "pt_distilbert_distilbert_base_cased_mlm_hf", - "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", - "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", - "pt_distilbert_distilbert_base_uncased_mlm_hf", - "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", - "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", - "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", - "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", - "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", - "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", - "pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-1", "start": "0", "stop": "128", "stride": "1"}, - }, - ), - ( - Index8, - [((1, 512), torch.int64)], - { - "model_name": [ - "pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf", - "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-1", "start": "0", "stop": "384", "stride": "1"}, - }, + pytest.param( + ( + Index7, + [((1, 512), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_squeezebert_squeezebert_squeezebert_mnli_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "128", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 1 - data type mismatch: expected UInt32, got Float32")], + ), + pytest.param( + ( + Index8, + [((1, 512), torch.int64)], + { + "model_name": [ + "pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf", + "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "384", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 1 - data type mismatch: expected UInt32, got Float32")], ), ( Index0, @@ -2119,39 +2140,45 @@ def ids_func(param): "op_params": {"dim": "-2", "start": "1", "stop": "2", "stride": "1"}, }, ), - ( - Index9, - [((2,), torch.float32)], - { - "model_name": [ - "pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf", - "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", - "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", - "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-1", "start": "0", "stop": "1", "stride": "1"}, - }, - ), - ( - Index10, - [((2,), torch.float32)], - { - "model_name": [ - "pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf", - "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", - "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", - "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-1", "start": "1", "stop": "2", "stride": "1"}, - }, + pytest.param( + ( + Index9, + [((2,), torch.float32)], + { + "model_name": [ + "pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf", + "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "1", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Index10, + [((2,), torch.float32)], + { + "model_name": [ + "pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf", + "pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "1", "stop": "2", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Index11, @@ -2667,84 +2694,105 @@ def ids_func(param): "op_params": {"dim": "-1", "start": "1536", "stop": "2304", "stride": "1"}, }, ), - ( - Index11, - [((1, 1, 1024, 1024), torch.bool)], - { - "model_name": ["pt_gpt2_gpt2_text_gen_hf"], - "pcc": 0.99, - "op_params": {"dim": "-2", "start": "0", "stop": "256", "stride": "1"}, - }, - ), - ( - Index39, - [((1, 1, 1024, 1024), torch.bool)], - { - "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], - "pcc": 0.99, - "op_params": {"dim": "-2", "start": "0", "stop": "7", "stride": "1"}, - }, - ), - ( - Index40, - [((1, 1, 256, 1024), torch.bool)], - { - "model_name": ["pt_gpt2_gpt2_text_gen_hf"], - "pcc": 0.99, - "op_params": {"dim": "-1", "start": "0", "stop": "256", "stride": "1"}, - }, - ), - ( - Index11, - [((1, 1, 2048, 2048), torch.bool)], - { - "model_name": [ - "pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf", - "pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf", - "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-2", "start": "0", "stop": "256", "stride": "1"}, - }, - ), - ( - Index41, - [((1, 1, 2048, 2048), torch.bool)], - { - "model_name": [ - "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", - "pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf", - "pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-2", "start": "0", "stop": "32", "stride": "1"}, - }, - ), - ( - Index40, - [((1, 1, 256, 2048), torch.bool)], - { - "model_name": [ - "pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf", - "pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf", - "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-1", "start": "0", "stop": "256", "stride": "1"}, - }, - ), - ( - Index23, - [((1, 1, 32, 2048), torch.bool)], - { - "model_name": [ - "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", - "pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf", - "pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-1", "start": "0", "stop": "32", "stride": "1"}, - }, + pytest.param( + ( + Index11, + [((1, 1, 1024, 1024), torch.bool)], + { + "model_name": ["pt_gpt2_gpt2_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "256", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], + ), + pytest.param( + ( + Index39, + [((1, 1, 1024, 1024), torch.bool)], + { + "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "7", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], + ), + pytest.param( + ( + Index40, + [((1, 1, 256, 1024), torch.bool)], + { + "model_name": ["pt_gpt2_gpt2_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "256", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], + ), + pytest.param( + ( + Index11, + [((1, 1, 2048, 2048), torch.bool)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf", + "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "256", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], + ), + pytest.param( + ( + Index41, + [((1, 1, 2048, 2048), torch.bool)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-2", "start": "0", "stop": "32", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], + ), + pytest.param( + ( + Index40, + [((1, 1, 256, 2048), torch.bool)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf", + "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "256", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], + ), + pytest.param( + ( + Index23, + [((1, 1, 32, 2048), torch.bool)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf", + "pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "32", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), ( Index42, @@ -2963,14 +3011,17 @@ def ids_func(param): "op_params": {"dim": "-1", "start": "0", "stop": "64", "stride": "1"}, }, ), - ( - Index4, - [((1, 1, 7, 1024), torch.bool)], - { - "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], - "pcc": 0.99, - "op_params": {"dim": "-1", "start": "0", "stop": "7", "stride": "1"}, - }, + pytest.param( + ( + Index4, + [((1, 1, 7, 1024), torch.bool)], + { + "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "7", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), ( Index0, @@ -3749,17 +3800,20 @@ def ids_func(param): "op_params": {"dim": "-1", "start": "0", "stop": "32", "stride": "1"}, }, ), - ( - Index7, - [((1, 514), torch.int64)], - { - "model_name": [ - "pt_roberta_xlm_roberta_base_mlm_hf", - "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-1", "start": "0", "stop": "128", "stride": "1"}, - }, + pytest.param( + ( + Index7, + [((1, 514), torch.int64)], + { + "model_name": [ + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1", "start": "0", "stop": "128", "stride": "1"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 1 - data type mismatch: expected UInt32, got Float32")], ), ( Index53, diff --git a/forge/test/models_ops/test_layernorm.py b/forge/test/models_ops/test_layernorm.py index 3a55fcb43..c79f0bd32 100644 --- a/forge/test/models_ops/test_layernorm.py +++ b/forge/test/models_ops/test_layernorm.py @@ -743,32 +743,41 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Layernorm0, - [((2, 1, 2048), torch.float32)], - { - "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], - "pcc": 0.99, - "op_params": {"dim": "-1", "epsilon": "1e-05"}, - }, - ), - ( - Layernorm1, - [((2, 1, 1536), torch.float32)], - { - "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], - "pcc": 0.99, - "op_params": {"dim": "-1", "epsilon": "1e-05"}, - }, - ), - ( - Layernorm2, - [((2, 1, 1024), torch.float32)], - { - "model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], - "pcc": 0.99, - "op_params": {"dim": "-1", "epsilon": "1e-05"}, - }, + pytest.param( + ( + Layernorm0, + [((2, 1, 2048), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_large_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Layernorm1, + [((2, 1, 1536), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_medium_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], + ), + pytest.param( + ( + Layernorm2, + [((2, 1, 1024), torch.float32)], + { + "model_name": ["pt_stereo_facebook_musicgen_small_music_generation_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "epsilon": "1e-05"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Layernorm2, diff --git a/forge/test/models_ops/test_matmul.py b/forge/test/models_ops/test_matmul.py index e072b8db9..505eb9c7a 100644 --- a/forge/test/models_ops/test_matmul.py +++ b/forge/test/models_ops/test_matmul.py @@ -1011,10 +1011,13 @@ def ids_func(param): [((39, 4096), torch.float32), ((4096, 11008), torch.float32)], {"model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf", "DeepSeekWrapper_decoder"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 39, 11008), torch.float32), ((11008, 4096), torch.float32)], - {"model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf", "DeepSeekWrapper_decoder"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 39, 11008), torch.float32), ((11008, 4096), torch.float32)], + {"model_name": ["pt_deepseek_deepseek_math_7b_instruct_qa_hf", "DeepSeekWrapper_decoder"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -1160,18 +1163,21 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Matmul0, - [((1, 128, 16384), torch.float32), ((16384, 4096), torch.float32)], - { - "model_name": [ - "pt_albert_xxlarge_v1_token_cls_hf", - "pt_albert_xxlarge_v2_token_cls_hf", - "pt_albert_xxlarge_v2_mlm_hf", - "pt_albert_xxlarge_v1_mlm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 128, 16384), torch.float32), ((16384, 4096), torch.float32)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -1728,10 +1734,13 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Matmul0, - [((1, 128, 768), torch.float32), ((768, 119547), torch.float32)], - {"model_name": ["pt_distilbert_distilbert_base_multilingual_cased_mlm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 128, 768), torch.float32), ((768, 119547), torch.float32)], + {"model_name": ["pt_distilbert_distilbert_base_multilingual_cased_mlm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -1784,26 +1793,32 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Matmul0, - [((1, 768), torch.float32), ((768, 1), torch.float32)], - { - "model_name": [ - "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", - "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 768), torch.float32), ((768, 1), torch.float32)], + { + "model_name": [ + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, [((6, 4544), torch.float32), ((4544, 18176), torch.float32)], {"model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 6, 18176), torch.float32), ((18176, 4544), torch.float32)], - {"model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 6, 18176), torch.float32), ((18176, 4544), torch.float32)], + {"model_name": ["pt_falcon_tiiuae_falcon_7b_instruct_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -1887,10 +1902,13 @@ def ids_func(param): [((10, 3072), torch.float32), ((3072, 9216), torch.float32)], {"model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 10, 9216), torch.float32), ((9216, 3072), torch.float32)], - {"model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 10, 9216), torch.float32), ((9216, 3072), torch.float32)], + {"model_name": ["pt_falcon3_tiiuae_falcon3_3b_base_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -1940,10 +1958,13 @@ def ids_func(param): [((10, 3072), torch.float32), ((3072, 23040), torch.float32)], {"model_name": ["pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 10, 23040), torch.float32), ((23040, 3072), torch.float32)], - {"model_name": ["pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 10, 23040), torch.float32), ((23040, 3072), torch.float32)], + {"model_name": ["pt_falcon3_tiiuae_falcon3_7b_base_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -1971,10 +1992,13 @@ def ids_func(param): [((1, 334, 4096), torch.float32), ((4096, 16384), torch.float32)], {"model_name": ["pt_fuyu_adept_fuyu_8b_qa_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 334, 16384), torch.float32), ((16384, 4096), torch.float32)], - {"model_name": ["pt_fuyu_adept_fuyu_8b_qa_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 334, 16384), torch.float32), ((16384, 4096), torch.float32)], + {"model_name": ["pt_fuyu_adept_fuyu_8b_qa_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -2002,10 +2026,13 @@ def ids_func(param): [((7, 2048), torch.float32), ((2048, 16384), torch.float32)], {"model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 7, 16384), torch.float32), ((16384, 2048), torch.float32)], - {"model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 7, 16384), torch.float32), ((16384, 2048), torch.float32)], + {"model_name": ["pt_gemma_google_gemma_2b_text_gen_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -2083,17 +2110,20 @@ def ids_func(param): [((1, 256, 2560), torch.float32), ((2560, 10240), torch.float32)], {"model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 256, 10240), torch.float32), ((10240, 2560), torch.float32)], - { - "model_name": [ - "pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf", - "pt_phi2_microsoft_phi_2_clm_hf", - "pt_phi2_microsoft_phi_2_pytdml_clm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 256, 10240), torch.float32), ((10240, 2560), torch.float32)], + { + "model_name": [ + "pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf", + "pt_phi2_microsoft_phi_2_clm_hf", + "pt_phi2_microsoft_phi_2_pytdml_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -2218,10 +2248,13 @@ def ids_func(param): [((1, 32, 2560), torch.float32), ((2560, 10240), torch.float32)], {"model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 32, 10240), torch.float32), ((10240, 2560), torch.float32)], - {"model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 32, 10240), torch.float32), ((10240, 2560), torch.float32)], + {"model_name": ["pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -2341,16 +2374,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Matmul0, - [((1, 4, 8192), torch.float32), ((8192, 2048), torch.float32)], - { - "model_name": [ - "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", - "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 4, 8192), torch.float32), ((8192, 2048), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_2_1b_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -2421,16 +2457,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Matmul0, - [((1, 256, 2048), torch.float32), ((2048, 128256), torch.float32)], - { - "model_name": [ - "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", - "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 256, 2048), torch.float32), ((2048, 128256), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_2_1b_instruct_clm_hf", + "pt_llama3_meta_llama_llama_3_2_1b_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -2510,18 +2549,21 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Matmul0, - [((1, 4, 14336), torch.float32), ((14336, 4096), torch.float32)], - { - "model_name": [ - "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", - "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", - "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", - "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 4, 14336), torch.float32), ((14336, 4096), torch.float32)], + { + "model_name": [ + "pt_llama3_meta_llama_llama_3_1_8b_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_llama_3_1_8b_instruct_seq_cls_hf", + "pt_llama3_meta_llama_meta_llama_3_8b_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -2556,10 +2598,13 @@ def ids_func(param): [((128, 4096), torch.float32), ((4096, 14336), torch.float32)], {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 128, 14336), torch.float32), ((14336, 4096), torch.float32)], - {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 128, 14336), torch.float32), ((14336, 4096), torch.float32)], + {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -2776,13 +2821,16 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Matmul0, - [((1, 12, 10240), torch.float32), ((10240, 2560), torch.float32)], - { - "model_name": ["pt_phi2_microsoft_phi_2_pytdml_token_cls_hf", "pt_phi2_microsoft_phi_2_token_cls_hf"], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 12, 10240), torch.float32), ((10240, 2560), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_pytdml_token_cls_hf", "pt_phi2_microsoft_phi_2_token_cls_hf"], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -2857,13 +2905,16 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Matmul0, - [((1, 11, 10240), torch.float32), ((10240, 2560), torch.float32)], - { - "model_name": ["pt_phi2_microsoft_phi_2_seq_cls_hf", "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf"], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 11, 10240), torch.float32), ((10240, 2560), torch.float32)], + { + "model_name": ["pt_phi2_microsoft_phi_2_seq_cls_hf", "pt_phi2_microsoft_phi_2_pytdml_seq_cls_hf"], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3131,27 +3182,33 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Matmul0, - [((1, 35, 18944), torch.float32), ((18944, 3584), torch.float32)], - { - "model_name": [ - "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", - "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 35, 18944), torch.float32), ((18944, 3584), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Matmul0, - [((1, 35, 3584), torch.float32), ((3584, 152064), torch.float32)], - { - "model_name": [ - "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", - "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 35, 3584), torch.float32), ((3584, 152064), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_7b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_7b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3208,27 +3265,33 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Matmul0, - [((1, 35, 8960), torch.float32), ((8960, 1536), torch.float32)], - { - "model_name": [ - "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", - "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 35, 8960), torch.float32), ((8960, 1536), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Matmul0, - [((1, 35, 1536), torch.float32), ((1536, 151936), torch.float32)], - { - "model_name": [ - "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", - "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 35, 1536), torch.float32), ((1536, 151936), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_1_5b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3285,27 +3348,33 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Matmul0, - [((1, 35, 11008), torch.float32), ((11008, 2048), torch.float32)], - { - "model_name": [ - "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", - "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 35, 11008), torch.float32), ((11008, 2048), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Matmul0, - [((1, 35, 2048), torch.float32), ((2048, 151936), torch.float32)], - { - "model_name": [ - "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", - "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 35, 2048), torch.float32), ((2048, 151936), torch.float32)], + { + "model_name": [ + "pt_qwen_coder_qwen_qwen2_5_coder_3b_clm_hf", + "pt_qwen_coder_qwen_qwen2_5_coder_3b_instruct_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3342,10 +3411,13 @@ def ids_func(param): [((1, 35, 4864), torch.float32), ((4864, 896), torch.float32)], {"model_name": ["pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 35, 896), torch.float32), ((896, 151936), torch.float32)], - {"model_name": ["pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 35, 896), torch.float32), ((896, 151936), torch.float32)], + {"model_name": ["pt_qwen_coder_qwen_qwen2_5_coder_0_5b_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3384,10 +3456,13 @@ def ids_func(param): [((29, 1536), torch.float32), ((1536, 8960), torch.float32)], {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 29, 8960), torch.float32), ((8960, 1536), torch.float32)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 29, 8960), torch.float32), ((8960, 1536), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3419,15 +3494,21 @@ def ids_func(param): [((39, 1536), torch.float32), ((1536, 8960), torch.float32)], {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 39, 8960), torch.float32), ((8960, 1536), torch.float32)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 39, 8960), torch.float32), ((8960, 1536), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Matmul0, - [((1, 39, 1536), torch.float32), ((1536, 151936), torch.float32)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 39, 1536), torch.float32), ((1536, 151936), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_1_5b_instruct_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3454,15 +3535,21 @@ def ids_func(param): [((39, 3584), torch.float32), ((3584, 18944), torch.float32)], {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 39, 18944), torch.float32), ((18944, 3584), torch.float32)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 39, 18944), torch.float32), ((18944, 3584), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Matmul0, - [((1, 39, 3584), torch.float32), ((3584, 152064), torch.float32)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 39, 3584), torch.float32), ((3584, 152064), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_instruct_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3489,10 +3576,13 @@ def ids_func(param): [((29, 3584), torch.float32), ((3584, 18944), torch.float32)], {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 29, 18944), torch.float32), ((18944, 3584), torch.float32)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 29, 18944), torch.float32), ((18944, 3584), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_7b_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3524,10 +3614,13 @@ def ids_func(param): [((29, 2048), torch.float32), ((2048, 11008), torch.float32)], {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 29, 11008), torch.float32), ((11008, 2048), torch.float32)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 29, 11008), torch.float32), ((11008, 2048), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3559,15 +3652,21 @@ def ids_func(param): [((39, 2048), torch.float32), ((2048, 11008), torch.float32)], {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 39, 11008), torch.float32), ((11008, 2048), torch.float32)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 39, 11008), torch.float32), ((11008, 2048), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Matmul0, - [((1, 39, 2048), torch.float32), ((2048, 151936), torch.float32)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 39, 2048), torch.float32), ((2048, 151936), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_3b_instruct_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3639,15 +3738,21 @@ def ids_func(param): [((1, 39, 4864), torch.float32), ((4864, 896), torch.float32)], {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 39, 896), torch.float32), ((896, 151936), torch.float32)], - {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 39, 896), torch.float32), ((896, 151936), torch.float32)], + {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Matmul0, - [((1, 128, 768), torch.float32), ((768, 250002), torch.float32)], - {"model_name": ["pt_roberta_xlm_roberta_base_mlm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 128, 768), torch.float32), ((768, 250002), torch.float32)], + {"model_name": ["pt_roberta_xlm_roberta_base_mlm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3880,15 +3985,21 @@ def ids_func(param): [((1, 1, 2048), torch.float32), ((2048, 768), torch.float32)], {"model_name": ["pt_t5_google_flan_t5_base_text_gen_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 256, 2048), torch.float32), ((2048, 256008), torch.float32)], - {"model_name": ["pt_xglm_facebook_xglm_1_7b_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 256, 2048), torch.float32), ((2048, 256008), torch.float32)], + {"model_name": ["pt_xglm_facebook_xglm_1_7b_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), - ( - Matmul0, - [((1, 256, 1024), torch.float32), ((1024, 256008), torch.float32)], - {"model_name": ["pt_xglm_facebook_xglm_564m_clm_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 256, 1024), torch.float32), ((1024, 256008), torch.float32)], + {"model_name": ["pt_xglm_facebook_xglm_564m_clm_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -3939,17 +4050,20 @@ def ids_func(param): [((1024, 512), torch.float32), ((512, 96), torch.float32)], {"model_name": ["pt_nbeats_generic_basis_clm_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 9216), torch.float32), ((9216, 4096), torch.float32)], - { - "model_name": [ - "pt_alexnet_alexnet_img_cls_torchhub", - "pt_alexnet_base_img_cls_osmr", - "pt_rcnn_base_obj_det_torchvision_rect_0", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 9216), torch.float32), ((9216, 4096), torch.float32)], + { + "model_name": [ + "pt_alexnet_alexnet_img_cls_torchhub", + "pt_alexnet_base_img_cls_osmr", + "pt_rcnn_base_obj_det_torchvision_rect_0", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -4649,10 +4763,13 @@ def ids_func(param): [((1, 512, 261), torch.float32), ((1, 261, 50176), torch.float32)], {"model_name": ["pt_perceiverio_deepmind_vision_perceiver_fourier_img_cls_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 512, 50176), torch.float32), ((1, 50176, 261), torch.float32)], - {"model_name": ["pt_perceiverio_deepmind_vision_perceiver_fourier_img_cls_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 512, 50176), torch.float32), ((1, 50176, 261), torch.float32)], + {"model_name": ["pt_perceiverio_deepmind_vision_perceiver_fourier_img_cls_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -4679,10 +4796,13 @@ def ids_func(param): [((1, 512, 512), torch.float32), ((1, 512, 50176), torch.float32)], {"model_name": ["pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 512, 50176), torch.float32), ((1, 50176, 512), torch.float32)], - {"model_name": ["pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf"], "pcc": 0.99}, + pytest.param( + ( + Matmul0, + [((1, 512, 50176), torch.float32), ((1, 50176, 512), torch.float32)], + {"model_name": ["pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, @@ -5603,22 +5723,25 @@ def ids_func(param): [((24, 49, 49), torch.float32), ((24, 49, 32), torch.float32)], {"model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], "pcc": 0.99}, ), - ( - Matmul0, - [((1, 25088), torch.float32), ((25088, 4096), torch.float32)], - { - "model_name": [ - "pt_vgg_bn_vgg19_obj_det_osmr", - "pt_vgg_19_obj_det_hf", - "pt_vgg_vgg19_bn_obj_det_torchhub", - "pt_vgg_vgg11_obj_det_osmr", - "pt_vgg_bn_vgg19b_obj_det_osmr", - "pt_vgg_vgg16_obj_det_osmr", - "pt_vgg_vgg19_obj_det_osmr", - "pt_vgg_vgg13_obj_det_osmr", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Matmul0, + [((1, 25088), torch.float32), ((25088, 4096), torch.float32)], + { + "model_name": [ + "pt_vgg_bn_vgg19_obj_det_osmr", + "pt_vgg_19_obj_det_hf", + "pt_vgg_vgg19_bn_obj_det_torchhub", + "pt_vgg_vgg11_obj_det_osmr", + "pt_vgg_bn_vgg19b_obj_det_osmr", + "pt_vgg_vgg16_obj_det_osmr", + "pt_vgg_vgg19_obj_det_osmr", + "pt_vgg_vgg13_obj_det_osmr", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Matmul0, diff --git a/forge/test/models_ops/test_max.py b/forge/test/models_ops/test_max.py index 0644a2c08..60c2808ea 100644 --- a/forge/test/models_ops/test_max.py +++ b/forge/test/models_ops/test_max.py @@ -33,34 +33,76 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - (Max0, [((1, 32, 256, 256), torch.float32)], {"model_name": ["pt_opt_facebook_opt_1_3b_clm_hf"], "pcc": 0.99}), - ( - Max0, - [((1, 32, 32, 32), torch.float32)], - {"model_name": ["pt_opt_facebook_opt_1_3b_seq_cls_hf", "pt_opt_facebook_opt_1_3b_qa_hf"], "pcc": 0.99}, + pytest.param( + (Max0, [((1, 32, 256, 256), torch.float32)], {"model_name": ["pt_opt_facebook_opt_1_3b_clm_hf"], "pcc": 0.99}), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/binary_device_operation.cpp:102: tt::exception info: ttnn::operations::binary::BinaryDeviceOperation: unsupported broadcast" + ) + ], ), - ( - Max0, - [((1, 16, 32, 32), torch.float32)], - {"model_name": ["pt_opt_facebook_opt_350m_qa_hf", "pt_opt_facebook_opt_350m_seq_cls_hf"], "pcc": 0.99}, + pytest.param( + ( + Max0, + [((1, 32, 32, 32), torch.float32)], + {"model_name": ["pt_opt_facebook_opt_1_3b_seq_cls_hf", "pt_opt_facebook_opt_1_3b_qa_hf"], "pcc": 0.99}, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/binary_device_operation.cpp:102: tt::exception info: ttnn::operations::binary::BinaryDeviceOperation: unsupported broadcast" + ) + ], ), - ( - Max0, - [((1, 12, 32, 32), torch.float32)], - {"model_name": ["pt_opt_facebook_opt_125m_seq_cls_hf", "pt_opt_facebook_opt_125m_qa_hf"], "pcc": 0.99}, + pytest.param( + ( + Max0, + [((1, 16, 32, 32), torch.float32)], + {"model_name": ["pt_opt_facebook_opt_350m_qa_hf", "pt_opt_facebook_opt_350m_seq_cls_hf"], "pcc": 0.99}, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/binary_device_operation.cpp:102: tt::exception info: ttnn::operations::binary::BinaryDeviceOperation: unsupported broadcast" + ) + ], ), - (Max0, [((1, 12, 256, 256), torch.float32)], {"model_name": ["pt_opt_facebook_opt_125m_clm_hf"], "pcc": 0.99}), - ( - Max0, - [((1, 16, 256, 256), torch.float32)], - { - "model_name": [ - "pt_opt_facebook_opt_350m_clm_hf", - "pt_xglm_facebook_xglm_1_7b_clm_hf", - "pt_xglm_facebook_xglm_564m_clm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Max0, + [((1, 12, 32, 32), torch.float32)], + {"model_name": ["pt_opt_facebook_opt_125m_seq_cls_hf", "pt_opt_facebook_opt_125m_qa_hf"], "pcc": 0.99}, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/binary_device_operation.cpp:102: tt::exception info: ttnn::operations::binary::BinaryDeviceOperation: unsupported broadcast" + ) + ], + ), + pytest.param( + (Max0, [((1, 12, 256, 256), torch.float32)], {"model_name": ["pt_opt_facebook_opt_125m_clm_hf"], "pcc": 0.99}), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/binary_device_operation.cpp:102: tt::exception info: ttnn::operations::binary::BinaryDeviceOperation: unsupported broadcast" + ) + ], + ), + pytest.param( + ( + Max0, + [((1, 16, 256, 256), torch.float32)], + { + "model_name": [ + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/binary_device_operation.cpp:102: tt::exception info: ttnn::operations::binary::BinaryDeviceOperation: unsupported broadcast" + ) + ], ), ] diff --git a/forge/test/models_ops/test_maxpool2d.py b/forge/test/models_ops/test_maxpool2d.py index 0b787d207..529c80de0 100644 --- a/forge/test/models_ops/test_maxpool2d.py +++ b/forge/test/models_ops/test_maxpool2d.py @@ -267,21 +267,28 @@ def ids_func(param): }, }, ), - ( - Maxpool2D1, - [((1, 96, 54, 54), torch.float32)], - { - "model_name": ["pt_alexnet_base_img_cls_osmr"], - "pcc": 0.99, - "op_params": { - "kernel_size": "3", - "stride": "2", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "ceil_mode": "True", - "channel_last": "0", - }, - }, + pytest.param( + ( + Maxpool2D1, + [((1, 96, 54, 54), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/tensor/tensor_utils.cpp:50: new_volume == old_volume info: Invalid arguments to reshape" + ) + ], ), ( Maxpool2D1, @@ -315,21 +322,28 @@ def ids_func(param): }, }, ), - ( - Maxpool2D2, - [((1, 4, 14, 14), torch.float32)], - { - "model_name": ["pt_autoencoder_conv_img_enc_github"], - "pcc": 0.99, - "op_params": { - "kernel_size": "2", - "stride": "2", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "ceil_mode": "False", - "channel_last": "0", + pytest.param( + ( + Maxpool2D2, + [((1, 4, 14, 14), torch.float32)], + { + "model_name": ["pt_autoencoder_conv_img_enc_github"], + "pcc": 0.99, + "op_params": { + "kernel_size": "2", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/data_movement/sharded/interleaved_to_sharded/device/interleaved_to_sharded_op.cpp:23: (*this->output_mem_config.shard_spec).shape[1] * input_tensor.element_size() % hal.get_alignment(HalMemType::L1) == 0 info: Shard page size must currently have L1 aligned page size" + ) + ], ), ( Maxpool2D3, @@ -382,21 +396,28 @@ def ids_func(param): }, }, ), - ( - Maxpool2D1, - [((1, 64, 112, 112), torch.float32)], - { - "model_name": ["pt_googlenet_base_img_cls_torchvision"], - "pcc": 0.99, - "op_params": { - "kernel_size": "3", - "stride": "2", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "ceil_mode": "True", - "channel_last": "0", + pytest.param( + ( + Maxpool2D1, + [((1, 64, 112, 112), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/tensor/tensor_utils.cpp:50: new_volume == old_volume info: Invalid arguments to reshape" + ) + ], ), ( Maxpool2D0, @@ -535,21 +556,28 @@ def ids_func(param): }, }, ), - ( - Maxpool2D1, - [((1, 128, 56, 56), torch.float32)], - { - "model_name": ["pt_vovnet_vovnet27s_obj_det_osmr"], - "pcc": 0.99, - "op_params": { - "kernel_size": "3", - "stride": "2", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "ceil_mode": "True", - "channel_last": "0", + pytest.param( + ( + Maxpool2D1, + [((1, 128, 56, 56), torch.float32)], + { + "model_name": ["pt_vovnet_vovnet27s_obj_det_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/tensor/tensor_utils.cpp:50: new_volume == old_volume info: Invalid arguments to reshape" + ) + ], ), ( Maxpool2D2, @@ -590,21 +618,28 @@ def ids_func(param): }, }, ), - ( - Maxpool2D1, - [((1, 256, 28, 28), torch.float32)], - { - "model_name": ["pt_vovnet_vovnet27s_obj_det_osmr"], - "pcc": 0.99, - "op_params": { - "kernel_size": "3", - "stride": "2", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "ceil_mode": "True", - "channel_last": "0", + pytest.param( + ( + Maxpool2D1, + [((1, 256, 28, 28), torch.float32)], + { + "model_name": ["pt_vovnet_vovnet27s_obj_det_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/tensor/tensor_utils.cpp:50: new_volume == old_volume info: Invalid arguments to reshape" + ) + ], ), ( Maxpool2D2, @@ -710,21 +745,28 @@ def ids_func(param): }, }, ), - ( - Maxpool2D1, - [((1, 192, 56, 56), torch.float32)], - { - "model_name": ["pt_googlenet_base_img_cls_torchvision"], - "pcc": 0.99, - "op_params": { - "kernel_size": "3", - "stride": "2", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "ceil_mode": "True", - "channel_last": "0", + pytest.param( + ( + Maxpool2D1, + [((1, 192, 56, 56), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/tensor/tensor_utils.cpp:50: new_volume == old_volume info: Invalid arguments to reshape" + ) + ], ), ( Maxpool2D4, @@ -742,21 +784,28 @@ def ids_func(param): }, }, ), - ( - Maxpool2D1, - [((1, 480, 28, 28), torch.float32)], - { - "model_name": ["pt_googlenet_base_img_cls_torchvision"], - "pcc": 0.99, - "op_params": { - "kernel_size": "3", - "stride": "2", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "ceil_mode": "True", - "channel_last": "0", + pytest.param( + ( + Maxpool2D1, + [((1, 480, 28, 28), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/tensor/tensor_utils.cpp:50: new_volume == old_volume info: Invalid arguments to reshape" + ) + ], ), ( Maxpool2D4, @@ -774,21 +823,28 @@ def ids_func(param): }, }, ), - ( - Maxpool2D4, - [((1, 528, 14, 14), torch.float32)], - { - "model_name": ["pt_googlenet_base_img_cls_torchvision"], - "pcc": 0.99, - "op_params": { - "kernel_size": "3", - "stride": "1", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "ceil_mode": "True", - "channel_last": "0", + pytest.param( + ( + Maxpool2D4, + [((1, 528, 14, 14), torch.float32)], + { + "model_name": ["pt_googlenet_base_img_cls_torchvision"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "1", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/pool/generic/device/pool_op.cpp:37: (input_shape[3] % tt::constants::TILE_WIDTH == 0) || (input_shape[3] == 16) info: Input channels (528) should be padded to nearest TILE_WIDTH (32) or should be 16" + ) + ], ), ( Maxpool2D6, @@ -1112,27 +1168,34 @@ def ids_func(param): }, }, ), - ( - Maxpool2D1, - [((1, 256, 56, 56), torch.float32)], - { - "model_name": [ - "pt_vovnet_ese_vovnet39b_obj_det_torchhub", - "pt_vovnet_vovnet39_obj_det_osmr", - "pt_vovnet_vovnet57_obj_det_osmr", - "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", - "pt_vovnet_ese_vovnet99b_obj_det_torchhub", - ], - "pcc": 0.99, - "op_params": { - "kernel_size": "3", - "stride": "2", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "ceil_mode": "True", - "channel_last": "0", + pytest.param( + ( + Maxpool2D1, + [((1, 256, 56, 56), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/tensor/tensor_utils.cpp:50: new_volume == old_volume info: Invalid arguments to reshape" + ) + ], ), ( Maxpool2D2, @@ -1161,65 +1224,86 @@ def ids_func(param): }, }, ), - ( - Maxpool2D1, - [((1, 512, 28, 28), torch.float32)], - { - "model_name": [ - "pt_vovnet_ese_vovnet39b_obj_det_torchhub", - "pt_vovnet_vovnet39_obj_det_osmr", - "pt_vovnet_vovnet57_obj_det_osmr", - "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", - "pt_vovnet_ese_vovnet99b_obj_det_torchhub", - ], - "pcc": 0.99, - "op_params": { - "kernel_size": "3", - "stride": "2", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "ceil_mode": "True", - "channel_last": "0", - }, - }, - ), - ( - Maxpool2D1, - [((1, 768, 14, 14), torch.float32)], - { - "model_name": [ - "pt_vovnet_ese_vovnet39b_obj_det_torchhub", - "pt_vovnet_vovnet39_obj_det_osmr", - "pt_vovnet_vovnet57_obj_det_osmr", - "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", - "pt_vovnet_ese_vovnet99b_obj_det_torchhub", - ], - "pcc": 0.99, - "op_params": { - "kernel_size": "3", - "stride": "2", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "ceil_mode": "True", - "channel_last": "0", - }, - }, - ), - ( - Maxpool2D1, - [((1, 384, 14, 14), torch.float32)], - { - "model_name": ["pt_vovnet_vovnet27s_obj_det_osmr"], - "pcc": 0.99, - "op_params": { - "kernel_size": "3", - "stride": "2", - "padding": "[0, 0, 0, 0]", - "dilation": "1", - "ceil_mode": "True", - "channel_last": "0", - }, - }, + pytest.param( + ( + Maxpool2D1, + [((1, 512, 28, 28), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/tensor/tensor_utils.cpp:50: new_volume == old_volume info: Invalid arguments to reshape" + ) + ], + ), + pytest.param( + ( + Maxpool2D1, + [((1, 768, 14, 14), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_vovnet39_obj_det_osmr", + "pt_vovnet_vovnet57_obj_det_osmr", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/tensor/tensor_utils.cpp:50: new_volume == old_volume info: Invalid arguments to reshape" + ) + ], + ), + pytest.param( + ( + Maxpool2D1, + [((1, 384, 14, 14), torch.float32)], + { + "model_name": ["pt_vovnet_vovnet27s_obj_det_osmr"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[0, 0, 0, 0]", + "dilation": "1", + "ceil_mode": "True", + "channel_last": "0", + }, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/tensor/tensor_utils.cpp:50: new_volume == old_volume info: Invalid arguments to reshape" + ) + ], ), ( Maxpool2D3, @@ -1253,21 +1337,28 @@ def ids_func(param): }, }, ), - ( - Maxpool2D3, - [((1, 728, 37, 37), torch.float32)], - { - "model_name": ["pt_xception_xception_img_cls_timm"], - "pcc": 0.99, - "op_params": { - "kernel_size": "3", - "stride": "2", - "padding": "[1, 1, 1, 1]", - "dilation": "1", - "ceil_mode": "False", - "channel_last": "0", + pytest.param( + ( + Maxpool2D3, + [((1, 728, 37, 37), torch.float32)], + { + "model_name": ["pt_xception_xception_img_cls_timm"], + "pcc": 0.99, + "op_params": { + "kernel_size": "3", + "stride": "2", + "padding": "[1, 1, 1, 1]", + "dilation": "1", + "ceil_mode": "False", + "channel_last": "0", + }, }, - }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/pool/generic/device/pool_op.cpp:37: (input_shape[3] % tt::constants::TILE_WIDTH == 0) || (input_shape[3] == 16) info: Input channels (728) should be padded to nearest TILE_WIDTH (32) or should be 16" + ) + ], ), ( Maxpool2D3, diff --git a/forge/test/models_ops/test_multiply.py b/forge/test/models_ops/test_multiply.py index 6b8de1481..e7ea3178d 100644 --- a/forge/test/models_ops/test_multiply.py +++ b/forge/test/models_ops/test_multiply.py @@ -2879,37 +2879,51 @@ def ids_func(param): [((1, 1, 1, 7), torch.float32)], {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99}, ), - ( - Multiply1, - [((1, 256), torch.int64), ((1, 256), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply1, + [((1, 256), torch.int64), ((1, 256), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Multiply0, [((1, 256, 2048), torch.float32)], {"model_name": ["pt_opt_facebook_opt_1_3b_clm_hf", "pt_xglm_facebook_xglm_1_7b_clm_hf"], "pcc": 0.99}, ), - ( - Multiply1, - [((1, 32), torch.int64), ((1, 32), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply1, + [((1, 32), torch.int64), ((1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Multiply0, @@ -3810,16 +3824,19 @@ def ids_func(param): [((1, 39, 4864), torch.float32), ((1, 39, 4864), torch.float32)], {"model_name": ["pt_qwen_v2_qwen_qwen2_5_0_5b_instruct_clm_hf"], "pcc": 0.99}, ), - ( - Multiply1, - [((1, 128), torch.int32), ((1, 128), torch.int32)], - { - "model_name": [ - "pt_roberta_xlm_roberta_base_mlm_hf", - "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply1, + [((1, 128), torch.int32), ((1, 128), torch.int32)], + { + "model_name": [ + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -14457,17 +14474,20 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Multiply0, - [((1, 72, 1, 1), torch.float32)], - { - "model_name": [ - "pt_ghostnet_ghostnet_100_img_cls_timm", - "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", - "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 72, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -14566,19 +14586,22 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Multiply0, - [((1, 120, 1, 1), torch.float32)], - { - "model_name": [ - "pt_ghostnet_ghostnet_100_img_cls_timm", - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", - "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 120, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -14718,34 +14741,40 @@ def ids_func(param): [((92,), torch.float32), ((92,), torch.float32)], {"model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm"], "pcc": 0.99}, ), - ( - Multiply0, - [((1, 480, 1, 1), torch.float32)], - { - "model_name": [ - "pt_ghostnet_ghostnet_100_img_cls_timm", - "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", - "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 480, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, [((1, 56, 14, 14), torch.float32), ((56, 1, 1), torch.float32)], {"model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm"], "pcc": 0.99}, ), - ( - Multiply0, - [((1, 672, 1, 1), torch.float32)], - { - "model_name": [ - "pt_ghostnet_ghostnet_100_img_cls_timm", - "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", - "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 672, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -14785,17 +14814,20 @@ def ids_func(param): [((1, 480, 7, 7), torch.float32), ((480, 1, 1), torch.float32)], {"model_name": ["pt_ghostnet_ghostnet_100_img_cls_timm"], "pcc": 0.99}, ), - ( - Multiply0, - [((1, 960, 1, 1), torch.float32)], - { - "model_name": [ - "pt_ghostnet_ghostnet_100_img_cls_timm", - "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", - "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 960, 1, 1), torch.float32)], + { + "model_name": [ + "pt_ghostnet_ghostnet_100_img_cls_timm", + "pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm", + "pt_mobilenetv3_mobilenet_v3_large_img_cls_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -15849,16 +15881,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Multiply0, - [((1, 16, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 16, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -15922,16 +15957,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Multiply0, - [((1, 96, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 96, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -15951,16 +15989,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Multiply0, - [((1, 240, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 240, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply0, @@ -16022,16 +16063,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Multiply0, - [((1, 144, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 144, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -16093,16 +16137,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Multiply0, - [((1, 288, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 288, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -16142,16 +16189,19 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Multiply0, - [((1, 576, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 576, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilenetv3_mobilenet_v3_small_img_cls_torchhub", + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -16198,18 +16248,21 @@ def ids_func(param): [((1, 576, 7, 7), torch.float32), ((1, 576, 1, 1), torch.float32)], {"model_name": ["pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm"], "pcc": 0.99}, ), - ( - Multiply0, - [((1, 1024, 1, 1), torch.float32)], - { - "model_name": [ - "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", - "pt_vovnet_ese_vovnet39b_obj_det_torchhub", - "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", - "pt_vovnet_ese_vovnet99b_obj_det_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 1024, 1, 1), torch.float32)], + { + "model_name": [ + "pt_mobilnetv3_mobilenetv3_small_100_img_cls_timm", + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -16425,10 +16478,13 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Multiply0, - [((1, 1280, 1, 1), torch.float32)], - {"model_name": ["pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm"], "pcc": 0.99}, + pytest.param( + ( + Multiply0, + [((1, 1280, 1, 1), torch.float32)], + {"model_name": ["pt_mobilnetv3_mobilenetv3_large_100_img_cls_timm"], "pcc": 0.99}, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -17110,17 +17166,20 @@ def ids_func(param): [((1, 16, 197, 197), torch.float32)], {"model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], "pcc": 0.99}, ), - ( - Multiply0, - [((1, 256, 1, 1), torch.float32)], - { - "model_name": [ - "pt_vovnet_ese_vovnet39b_obj_det_torchhub", - "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", - "pt_vovnet_ese_vovnet99b_obj_det_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 256, 1, 1), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -17134,17 +17193,20 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Multiply0, - [((1, 512, 1, 1), torch.float32)], - { - "model_name": [ - "pt_vovnet_ese_vovnet39b_obj_det_torchhub", - "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", - "pt_vovnet_ese_vovnet99b_obj_det_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 512, 1, 1), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, @@ -17158,17 +17220,20 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Multiply0, - [((1, 768, 1, 1), torch.float32)], - { - "model_name": [ - "pt_vovnet_ese_vovnet39b_obj_det_torchhub", - "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", - "pt_vovnet_ese_vovnet99b_obj_det_torchhub", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Multiply0, + [((1, 768, 1, 1), torch.float32)], + { + "model_name": [ + "pt_vovnet_ese_vovnet39b_obj_det_torchhub", + "pt_vovnet_ese_vovnet19b_dw_obj_det_torchhub", + "pt_vovnet_ese_vovnet99b_obj_det_torchhub", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Multiply1, diff --git a/forge/test/models_ops/test_notequal.py b/forge/test/models_ops/test_notequal.py index da43b1fc6..8945d863d 100644 --- a/forge/test/models_ops/test_notequal.py +++ b/forge/test/models_ops/test_notequal.py @@ -33,16 +33,23 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Notequal0, - [((1, 128), torch.int64)], - { - "model_name": [ - "pt_roberta_xlm_roberta_base_mlm_hf", - "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Notequal0, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/eltwise/binary/device/broadcast_height_and_width_multi_core_program_factory.cpp:27: tt::exception info: BinaryOpType cannot be mapped to BcastOpMath" + ) + ], ), ] diff --git a/forge/test/models_ops/test_pad.py b/forge/test/models_ops/test_pad.py index 233cedb49..c39d5691d 100644 --- a/forge/test/models_ops/test_pad.py +++ b/forge/test/models_ops/test_pad.py @@ -41,23 +41,29 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Pad0, - [((1, 1, 96, 54, 54), torch.float32)], - { - "model_name": ["pt_alexnet_base_img_cls_osmr"], - "pcc": 0.99, - "op_params": {"pad": "(0, 0, 2, 2)", "mode": '"constant"', "channel_last": "True"}, - }, + pytest.param( + ( + Pad0, + [((1, 1, 96, 54, 54), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": {"pad": "(0, 0, 2, 2)", "mode": '"constant"', "channel_last": "True"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Generated MLIR module failed verification.")], ), - ( - Pad0, - [((1, 1, 256, 27, 27), torch.float32)], - { - "model_name": ["pt_alexnet_base_img_cls_osmr"], - "pcc": 0.99, - "op_params": {"pad": "(0, 0, 2, 2)", "mode": '"constant"', "channel_last": "True"}, - }, + pytest.param( + ( + Pad0, + [((1, 1, 256, 27, 27), torch.float32)], + { + "model_name": ["pt_alexnet_base_img_cls_osmr"], + "pcc": 0.99, + "op_params": {"pad": "(0, 0, 2, 2)", "mode": '"constant"', "channel_last": "True"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Generated MLIR module failed verification.")], ), ( Pad1, @@ -182,18 +188,25 @@ def ids_func(param): ), marks=[pytest.mark.skip(reason="Segmentation fault occurs while executing ttnn binary")], ), - ( - Pad1, - [((1, 16, 320, 1024), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, - }, + pytest.param( + ( + Pad1, + [((1, 16, 320, 1024), torch.float32)], + { + "model_name": [ + "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", + "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", + "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", + ], + "pcc": 0.99, + "op_params": {"pad": "(1, 1, 1, 1)", "mode": '"reflect"', "channel_last": "False"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: [enforce fail at alloc_cpu.cpp:91] err == 0. DefaultCPUAllocator: can't allocate memory: you tried to allocate 433025187840 bytes. Error code 12 (Cannot allocate memory)" + ) + ], ), ( Pad1, diff --git a/forge/test/models_ops/test_reciprocal.py b/forge/test/models_ops/test_reciprocal.py index 2106e9a8f..d15338480 100644 --- a/forge/test/models_ops/test_reciprocal.py +++ b/forge/test/models_ops/test_reciprocal.py @@ -145,20 +145,23 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Reciprocal0, - [((1, 1, 1), torch.float32)], - { - "model_name": [ - "pt_t5_google_flan_t5_large_text_gen_hf", - "pt_t5_t5_large_text_gen_hf", - "pt_t5_t5_small_text_gen_hf", - "pt_t5_google_flan_t5_small_text_gen_hf", - "pt_t5_t5_base_text_gen_hf", - "pt_t5_google_flan_t5_base_text_gen_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Reciprocal0, + [((1, 1, 1), torch.float32)], + { + "model_name": [ + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + "pt_t5_t5_small_text_gen_hf", + "pt_t5_google_flan_t5_small_text_gen_hf", + "pt_t5_t5_base_text_gen_hf", + "pt_t5_google_flan_t5_base_text_gen_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Reciprocal0, diff --git a/forge/test/models_ops/test_reduceavg.py b/forge/test/models_ops/test_reduceavg.py index 3033c6ab1..34a5e14ca 100644 --- a/forge/test/models_ops/test_reduceavg.py +++ b/forge/test/models_ops/test_reduceavg.py @@ -299,14 +299,17 @@ def ids_func(param): "op_params": {"dim": "-1", "keep_dim": "True"}, }, ), - ( - Reduceavg0, - [((1, 1, 1024), torch.float32)], - { - "model_name": ["pt_t5_google_flan_t5_large_text_gen_hf", "pt_t5_t5_large_text_gen_hf"], - "pcc": 0.99, - "op_params": {"dim": "-1", "keep_dim": "True"}, - }, + pytest.param( + ( + Reduceavg0, + [((1, 1, 1024), torch.float32)], + { + "model_name": ["pt_t5_google_flan_t5_large_text_gen_hf", "pt_t5_t5_large_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Reduceavg0, @@ -317,14 +320,17 @@ def ids_func(param): "op_params": {"dim": "-1", "keep_dim": "True"}, }, ), - ( - Reduceavg0, - [((1, 1, 512), torch.float32)], - { - "model_name": ["pt_t5_t5_small_text_gen_hf", "pt_t5_google_flan_t5_small_text_gen_hf"], - "pcc": 0.99, - "op_params": {"dim": "-1", "keep_dim": "True"}, - }, + pytest.param( + ( + Reduceavg0, + [((1, 1, 512), torch.float32)], + { + "model_name": ["pt_t5_t5_small_text_gen_hf", "pt_t5_google_flan_t5_small_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Reduceavg0, @@ -335,14 +341,17 @@ def ids_func(param): "op_params": {"dim": "-1", "keep_dim": "True"}, }, ), - ( - Reduceavg0, - [((1, 1, 768), torch.float32)], - { - "model_name": ["pt_t5_t5_base_text_gen_hf", "pt_t5_google_flan_t5_base_text_gen_hf"], - "pcc": 0.99, - "op_params": {"dim": "-1", "keep_dim": "True"}, - }, + pytest.param( + ( + Reduceavg0, + [((1, 1, 768), torch.float32)], + { + "model_name": ["pt_t5_t5_base_text_gen_hf", "pt_t5_google_flan_t5_base_text_gen_hf"], + "pcc": 0.99, + "op_params": {"dim": "-1", "keep_dim": "True"}, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Reduceavg0, diff --git a/forge/test/models_ops/test_repeatinterleave.py b/forge/test/models_ops/test_repeatinterleave.py index 3da779a36..1d7a4ca2c 100644 --- a/forge/test/models_ops/test_repeatinterleave.py +++ b/forge/test/models_ops/test_repeatinterleave.py @@ -122,49 +122,77 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Repeatinterleave0, - [((2, 1, 1, 13), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"repeats": "1", "dim": "1"}, - }, - ), - ( - Repeatinterleave1, - [((2, 1, 1, 13), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"repeats": "1", "dim": "2"}, - }, - ), - ( - Repeatinterleave0, - [((2, 1, 1, 7), torch.int64)], - { - "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], - "pcc": 0.99, - "op_params": {"repeats": "1", "dim": "1"}, - }, - ), - ( - Repeatinterleave2, - [((2, 1, 1, 7), torch.int64)], - { - "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], - "pcc": 0.99, - "op_params": {"repeats": "7", "dim": "2"}, - }, + pytest.param( + ( + Repeatinterleave0, + [((2, 1, 1, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Repeatinterleave1, + [((2, 1, 1, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "2"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Repeatinterleave0, + [((2, 1, 1, 7), torch.int64)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Repeatinterleave2, + [((2, 1, 1, 7), torch.int64)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"repeats": "7", "dim": "2"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Repeatinterleave3, @@ -224,95 +252,130 @@ def ids_func(param): "op_params": {"repeats": "1", "dim": "2"}, }, ), - ( - Repeatinterleave3, - [((1, 128), torch.int64)], - { - "model_name": [ - "pt_albert_xxlarge_v1_token_cls_hf", - "pt_albert_base_v2_token_cls_hf", - "pt_albert_xxlarge_v2_token_cls_hf", - "pt_albert_large_v1_token_cls_hf", - "pt_albert_large_v2_token_cls_hf", - "pt_albert_base_v1_token_cls_hf", - "pt_albert_base_v1_mlm_hf", - "pt_albert_xlarge_v2_token_cls_hf", - "pt_albert_xxlarge_v2_mlm_hf", - "pt_albert_large_v2_mlm_hf", - "pt_albert_base_v2_mlm_hf", - "pt_albert_xlarge_v1_token_cls_hf", - "pt_albert_xlarge_v1_mlm_hf", - "pt_albert_large_v1_mlm_hf", - "pt_albert_xxlarge_v1_mlm_hf", - "pt_albert_xlarge_v2_mlm_hf", - "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", - "pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf", - "pt_bert_bert_base_uncased_mlm_hf", - "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", - "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", - "pt_roberta_xlm_roberta_base_mlm_hf", - "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", - ], - "pcc": 0.99, - "op_params": {"repeats": "1", "dim": "0"}, - }, - ), - ( - Repeatinterleave3, - [((1, 1, 1, 256), torch.int64)], - { - "model_name": [ - "pt_bart_facebook_bart_large_mnli_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - "pt_xglm_facebook_xglm_1_7b_clm_hf", - "pt_xglm_facebook_xglm_564m_clm_hf", - ], - "pcc": 0.99, - "op_params": {"repeats": "1", "dim": "0"}, - }, - ), - ( - Repeatinterleave0, - [((1, 1, 1, 256), torch.int64)], - { - "model_name": [ - "pt_bart_facebook_bart_large_mnli_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - "pt_xglm_facebook_xglm_1_7b_clm_hf", - "pt_xglm_facebook_xglm_564m_clm_hf", - ], - "pcc": 0.99, - "op_params": {"repeats": "1", "dim": "1"}, - }, - ), - ( - Repeatinterleave4, - [((1, 1, 1, 256), torch.int64)], - { - "model_name": [ - "pt_bart_facebook_bart_large_mnli_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - "pt_xglm_facebook_xglm_1_7b_clm_hf", - "pt_xglm_facebook_xglm_564m_clm_hf", - ], - "pcc": 0.99, - "op_params": {"repeats": "256", "dim": "2"}, - }, - ), - ( - Repeatinterleave3, - [((1, 384), torch.int64)], - { - "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], - "pcc": 0.99, - "op_params": {"repeats": "1", "dim": "0"}, - }, + pytest.param( + ( + Repeatinterleave3, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + "pt_bert_textattack_bert_base_uncased_sst_2_seq_cls_hf", + "pt_bert_dbmdz_bert_large_cased_finetuned_conll03_english_token_cls_hf", + "pt_bert_bert_base_uncased_mlm_hf", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_roberta_xlm_roberta_base_mlm_hf", + "pt_roberta_cardiffnlp_twitter_roberta_base_sentiment_seq_cls_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Repeatinterleave3, + [((1, 1, 1, 256), torch.int64)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Repeatinterleave0, + [((1, 1, 1, 256), torch.int64)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Repeatinterleave4, + [((1, 1, 1, 256), torch.int64)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "256", "dim": "2"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/kernels/kernel.cpp:242: tt::exception info: 1283 unique+common runtime args targeting kernel reader_concat_stick_layout_interleaved_start_id on (x=0,y=0) are too large. Max allowable is 256" + ) + ], + ), + pytest.param( + ( + Repeatinterleave3, + [((1, 384), torch.int64)], + { + "model_name": ["pt_bert_bert_large_cased_whole_word_masking_finetuned_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Repeatinterleave3, @@ -568,53 +631,74 @@ def ids_func(param): "op_params": {"repeats": "4", "dim": "2"}, }, ), - ( - Repeatinterleave3, - [((1, 1, 1, 32), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - "op_params": {"repeats": "1", "dim": "0"}, - }, - ), - ( - Repeatinterleave0, - [((1, 1, 1, 32), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - "op_params": {"repeats": "1", "dim": "1"}, - }, - ), - ( - Repeatinterleave9, - [((1, 1, 1, 32), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - "op_params": {"repeats": "32", "dim": "2"}, - }, + pytest.param( + ( + Repeatinterleave3, + [((1, 1, 1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "0"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Repeatinterleave0, + [((1, 1, 1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "1", "dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Repeatinterleave9, + [((1, 1, 1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"repeats": "32", "dim": "2"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Repeatinterleave3, diff --git a/forge/test/models_ops/test_reshape.py b/forge/test/models_ops/test_reshape.py index af4d7e8e1..ef7ae66d0 100644 --- a/forge/test/models_ops/test_reshape.py +++ b/forge/test/models_ops/test_reshape.py @@ -9518,31 +9518,45 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Reshape0, - [((8, 1), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"shape": "(2, 4, 1)"}, - }, + pytest.param( + ( + Reshape0, + [((8, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(2, 4, 1)"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), - ( - Reshape1, - [((2, 1, 1), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"shape": "(2, 1)"}, - }, + pytest.param( + ( + Reshape1, + [((2, 1, 1), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(2, 1)"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Reshape2, @@ -9625,18 +9639,25 @@ def ids_func(param): "op_params": {"shape": "(2, 2048)"}, }, ), - ( - Reshape9, - [((2, 13), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"shape": "(2, 13)"}, - }, + pytest.param( + ( + Reshape9, + [((2, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(2, 13)"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Reshape10, @@ -10181,26 +10202,33 @@ def ids_func(param): "op_params": {"shape": "(2, 4096)"}, }, ), - ( - Reshape59, - [((1, 1), torch.int64)], - { - "model_name": [ - "pt_whisper_openai_whisper_medium_speech_recognition_hf", - "pt_whisper_openai_whisper_large_speech_recognition_hf", - "pt_whisper_openai_whisper_tiny_speech_recognition_hf", - "pt_whisper_openai_whisper_base_speech_recognition_hf", - "pt_whisper_openai_whisper_small_speech_recognition_hf", - "pt_t5_google_flan_t5_large_text_gen_hf", - "pt_t5_t5_large_text_gen_hf", - "pt_t5_t5_small_text_gen_hf", - "pt_t5_google_flan_t5_small_text_gen_hf", - "pt_t5_t5_base_text_gen_hf", - "pt_t5_google_flan_t5_base_text_gen_hf", - ], - "pcc": 0.99, - "op_params": {"shape": "(1, 1)"}, - }, + pytest.param( + ( + Reshape59, + [((1, 1), torch.int64)], + { + "model_name": [ + "pt_whisper_openai_whisper_medium_speech_recognition_hf", + "pt_whisper_openai_whisper_large_speech_recognition_hf", + "pt_whisper_openai_whisper_tiny_speech_recognition_hf", + "pt_whisper_openai_whisper_base_speech_recognition_hf", + "pt_whisper_openai_whisper_small_speech_recognition_hf", + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + "pt_t5_t5_small_text_gen_hf", + "pt_t5_google_flan_t5_small_text_gen_hf", + "pt_t5_t5_base_text_gen_hf", + "pt_t5_google_flan_t5_base_text_gen_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(1, 1)"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Reshape43, @@ -11725,14 +11753,21 @@ def ids_func(param): "op_params": {"shape": "(20, 2, 1500)"}, }, ), - ( - Reshape188, - [((2, 7), torch.int64)], - { - "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], - "pcc": 0.99, - "op_params": {"shape": "(2, 7)"}, - }, + pytest.param( + ( + Reshape188, + [((2, 7), torch.int64)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"shape": "(2, 7)"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Reshape189, @@ -12893,22 +12928,29 @@ def ids_func(param): "op_params": {"shape": "(256, 1024)"}, }, ), - ( - Reshape267, - [((1, 256), torch.int64)], - { - "model_name": [ - "pt_bart_facebook_bart_large_mnli_seq_cls_hf", - "pt_gpt2_gpt2_text_gen_hf", - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - "pt_xglm_facebook_xglm_1_7b_clm_hf", - "pt_xglm_facebook_xglm_564m_clm_hf", - ], - "pcc": 0.99, - "op_params": {"shape": "(1, 256)"}, - }, + pytest.param( + ( + Reshape267, + [((1, 256), torch.int64)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_gpt2_gpt2_text_gen_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(1, 256)"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Reshape268, @@ -13055,20 +13097,23 @@ def ids_func(param): "op_params": {"shape": "(1, 256, 4096)"}, }, ), - ( - Reshape280, - [((1, 128), torch.bool)], - { - "model_name": [ - "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", - "pt_distilbert_distilbert_base_cased_mlm_hf", - "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", - "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", - "pt_distilbert_distilbert_base_uncased_mlm_hf", - ], - "pcc": 0.99, - "op_params": {"shape": "(1, 1, 1, 128)"}, - }, + pytest.param( + ( + Reshape280, + [((1, 128), torch.bool)], + { + "model_name": [ + "pt_distilbert_distilbert_base_multilingual_cased_mlm_hf", + "pt_distilbert_distilbert_base_cased_mlm_hf", + "pt_distilbert_distilbert_base_uncased_finetuned_sst_2_english_seq_cls_hf", + "pt_distilbert_davlan_distilbert_base_multilingual_cased_ner_hrl_token_cls_hf", + "pt_distilbert_distilbert_base_uncased_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(1, 1, 1, 128)"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), ( Reshape281, @@ -13115,14 +13160,17 @@ def ids_func(param): "op_params": {"shape": "(1, 12, 384, 384)"}, }, ), - ( - Reshape286, - [((1, 384), torch.bool)], - { - "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], - "pcc": 0.99, - "op_params": {"shape": "(1, 1, 1, 384)"}, - }, + pytest.param( + ( + Reshape286, + [((1, 384), torch.bool)], + { + "model_name": ["pt_distilbert_distilbert_base_cased_distilled_squad_qa_hf"], + "pcc": 0.99, + "op_params": {"shape": "(1, 1, 1, 384)"}, + }, + ), + marks=[pytest.mark.xfail(reason="RuntimeError: Tensor 0 - data type mismatch: expected UInt8, got Float32")], ), ( Reshape287, @@ -14838,14 +14886,21 @@ def ids_func(param): "op_params": {"shape": "(1, 128, 14336)"}, }, ), - ( - Reshape423, - [((1, 7), torch.int64)], - { - "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], - "pcc": 0.99, - "op_params": {"shape": "(1, 7)"}, - }, + pytest.param( + ( + Reshape423, + [((1, 7), torch.int64)], + { + "model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], + "pcc": 0.99, + "op_params": {"shape": "(1, 7)"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Reshape424, @@ -14955,21 +15010,28 @@ def ids_func(param): "op_params": {"shape": "(7, 3072)"}, }, ), - ( - Reshape434, - [((1, 32), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - "op_params": {"shape": "(1, 32)"}, - }, + pytest.param( + ( + Reshape434, + [((1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(1, 32)"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Reshape435, @@ -17105,21 +17167,28 @@ def ids_func(param): "op_params": {"shape": "(1, 768, 128)"}, }, ), - ( - Reshape634, - [((1, 61), torch.int64)], - { - "model_name": [ - "pt_t5_google_flan_t5_large_text_gen_hf", - "pt_t5_t5_large_text_gen_hf", - "pt_t5_t5_small_text_gen_hf", - "pt_t5_google_flan_t5_small_text_gen_hf", - "pt_t5_t5_base_text_gen_hf", - "pt_t5_google_flan_t5_base_text_gen_hf", - ], - "pcc": 0.99, - "op_params": {"shape": "(1, 61)"}, - }, + pytest.param( + ( + Reshape634, + [((1, 61), torch.int64)], + { + "model_name": [ + "pt_t5_google_flan_t5_large_text_gen_hf", + "pt_t5_t5_large_text_gen_hf", + "pt_t5_t5_small_text_gen_hf", + "pt_t5_google_flan_t5_small_text_gen_hf", + "pt_t5_t5_base_text_gen_hf", + "pt_t5_google_flan_t5_base_text_gen_hf", + ], + "pcc": 0.99, + "op_params": {"shape": "(1, 61)"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Reshape635, @@ -21212,14 +21281,21 @@ def ids_func(param): "op_params": {"shape": "(64, 3, 49, 49)"}, }, ), - ( - Reshape947, - [((49, 49), torch.int64)], - { - "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], - "pcc": 0.99, - "op_params": {"shape": "(2401,)"}, - }, + pytest.param( + ( + Reshape947, + [((49, 49), torch.int64)], + { + "model_name": ["pt_swin_microsoft_swin_tiny_patch4_window7_224_img_cls_hf"], + "pcc": 0.99, + "op_params": {"shape": "(2401,)"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Reshape948, diff --git a/forge/test/models_ops/test_resize2d.py b/forge/test/models_ops/test_resize2d.py index 46af1bf62..64cc1e18f 100644 --- a/forge/test/models_ops/test_resize2d.py +++ b/forge/test/models_ops/test_resize2d.py @@ -375,1678 +375,2522 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Resize2D0, - [((1, 256, 8, 8), torch.float32)], - { - "model_name": ["pt_fpn_base_img_cls_torchvision"], - "pcc": 0.99, - "op_params": { - "sizes": "[16, 16]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D1, - [((1, 256, 16, 16), torch.float32)], - { - "model_name": ["pt_fpn_base_img_cls_torchvision"], - "pcc": 0.99, - "op_params": { - "sizes": "[64, 64]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D2, - [((1, 256, 16, 16), torch.float32)], - { - "model_name": [ - "pt_segformer_nvidia_segformer_b0_finetuned_ade_512_512_sem_seg_hf", - "pt_segformer_nvidia_segformer_b1_finetuned_ade_512_512_sem_seg_hf", - ], - "pcc": 0.99, - "op_params": {"sizes": "[128, 128]", "method": '"linear"', "align_corners": "False", "channel_last": "0"}, - }, - ), - ( - Resize2D3, - [((1, 18, 28, 28), torch.float32)], - { - "model_name": [ - "pt_hrnet_hrnet_w18_pose_estimation_timm", - "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", - "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", - "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", - ], - "pcc": 0.99, - "op_params": { - "sizes": "[56, 56]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D3, - [((1, 18, 14, 14), torch.float32)], - { - "model_name": [ - "pt_hrnet_hrnet_w18_pose_estimation_timm", - "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", - "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", - "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", - ], - "pcc": 0.99, - "op_params": { - "sizes": "[56, 56]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D4, - [((1, 36, 14, 14), torch.float32)], - { - "model_name": [ - "pt_hrnet_hrnet_w18_pose_estimation_timm", - "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", - "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", - "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", - ], - "pcc": 0.99, - "op_params": { - "sizes": "[28, 28]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D3, - [((1, 18, 7, 7), torch.float32)], - { - "model_name": [ - "pt_hrnet_hrnet_w18_pose_estimation_timm", - "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", - "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", - "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", - ], - "pcc": 0.99, - "op_params": { - "sizes": "[56, 56]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D4, - [((1, 36, 7, 7), torch.float32)], - { - "model_name": [ - "pt_hrnet_hrnet_w18_pose_estimation_timm", - "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", - "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", - "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", - ], - "pcc": 0.99, - "op_params": { - "sizes": "[28, 28]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D5, - [((1, 72, 7, 7), torch.float32)], - { - "model_name": [ - "pt_hrnet_hrnet_w18_pose_estimation_timm", - "pt_hrnet_hrnet_w18_small_v2_pose_estimation_timm", - "pt_hrnet_hrnet_w18_small_v2_pose_estimation_osmr", - "pt_hrnet_hrnetv2_w18_pose_estimation_osmr", - ], - "pcc": 0.99, - "op_params": { - "sizes": "[14, 14]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D3, - [((1, 44, 28, 28), torch.float32)], - { - "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], - "pcc": 0.99, - "op_params": { - "sizes": "[56, 56]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D3, - [((1, 44, 14, 14), torch.float32)], - { - "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], - "pcc": 0.99, - "op_params": { - "sizes": "[56, 56]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D4, - [((1, 88, 14, 14), torch.float32)], - { - "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], - "pcc": 0.99, - "op_params": { - "sizes": "[28, 28]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D3, - [((1, 44, 7, 7), torch.float32)], - { - "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], - "pcc": 0.99, - "op_params": { - "sizes": "[56, 56]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D4, - [((1, 88, 7, 7), torch.float32)], - { - "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], - "pcc": 0.99, - "op_params": { - "sizes": "[28, 28]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D5, - [((1, 176, 7, 7), torch.float32)], - { - "model_name": ["pt_hrnet_hrnet_w44_pose_estimation_timm", "pt_hrnet_hrnetv2_w44_pose_estimation_osmr"], - "pcc": 0.99, - "op_params": { - "sizes": "[14, 14]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D3, - [((1, 30, 28, 28), torch.float32)], - { - "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], - "pcc": 0.99, - "op_params": { - "sizes": "[56, 56]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D3, - [((1, 30, 14, 14), torch.float32)], - { - "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], - "pcc": 0.99, - "op_params": { - "sizes": "[56, 56]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D4, - [((1, 60, 14, 14), torch.float32)], - { - "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], - "pcc": 0.99, - "op_params": { - "sizes": "[28, 28]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D3, - [((1, 30, 7, 7), torch.float32)], - { - "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], - "pcc": 0.99, - "op_params": { - "sizes": "[56, 56]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D4, - [((1, 60, 7, 7), torch.float32)], - { - "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], - "pcc": 0.99, - "op_params": { - "sizes": "[28, 28]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D5, - [((1, 120, 7, 7), torch.float32)], - { - "model_name": ["pt_hrnet_hrnetv2_w30_pose_estimation_osmr", "pt_hrnet_hrnet_w30_pose_estimation_timm"], - "pcc": 0.99, - "op_params": { - "sizes": "[14, 14]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D3, - [((1, 32, 28, 28), torch.float32)], - { - "model_name": ["pt_hrnet_hrnet_w32_pose_estimation_timm", "pt_hrnet_hrnetv2_w32_pose_estimation_osmr"], - "pcc": 0.99, - "op_params": { - "sizes": "[56, 56]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D3, - [((1, 32, 14, 14), torch.float32)], - { - "model_name": ["pt_hrnet_hrnet_w32_pose_estimation_timm", "pt_hrnet_hrnetv2_w32_pose_estimation_osmr"], - "pcc": 0.99, - "op_params": { - "sizes": "[56, 56]", - "method": '"nearest_neighbor"', - "align_corners": "False", - "channel_last": "0", - }, - }, - ), - ( - Resize2D4, - [((1, 32, 14, 14), torch.float32)], - { - "model_name": [ - 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'"nearest_neighbor"', + "align_corners": "False", + "channel_last": "0", + }, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: Found Unsupported operations while lowering from TTForge to TTIR in forward graph" + ) + ], ), ] diff --git a/forge/test/models_ops/test_softmax.py b/forge/test/models_ops/test_softmax.py index 8e11f91ee..1e4235131 100644 --- a/forge/test/models_ops/test_softmax.py +++ b/forge/test/models_ops/test_softmax.py @@ -785,17 +785,24 @@ def ids_func(param): "op_params": {"dim": "-1"}, }, ), - ( - Softmax0, - [((1, 1, 512, 50176), torch.float32)], - { - "model_name": [ - "pt_perceiverio_deepmind_vision_perceiver_fourier_img_cls_hf", - "pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "-1"}, - }, + pytest.param( + ( + Softmax0, + [((1, 1, 512, 50176), torch.float32)], + { + "model_name": [ + "pt_perceiverio_deepmind_vision_perceiver_fourier_img_cls_hf", + "pt_perceiverio_deepmind_vision_perceiver_learned_img_cls_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "-1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_THROW @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/tt_metal/impl/program/program.cpp:896: tt::exception info: Statically allocated circular buffers on core range [(x=0,y=0) - (x=7,y=7)] grow to 3447968 B which is beyond max L1 size of 1499136 B" + ) + ], ), ( Softmax0, @@ -922,32 +929,53 @@ def ids_func(param): [((1, 16, 197, 197), torch.float32)], {"model_name": ["pt_vit_google_vit_large_patch16_224_img_cls_hf"], "pcc": 0.99, "op_params": {"dim": "-1"}}, ), - ( - Softmax1, - [((1, 17, 4, 4480), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], - "pcc": 0.99, - "op_params": {"dim": "1"}, - }, - ), - ( - Softmax1, - [((1, 17, 4, 1120), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], - "pcc": 0.99, - "op_params": {"dim": "1"}, - }, - ), - ( - Softmax1, - [((1, 17, 4, 280), torch.float32)], - { - "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], - "pcc": 0.99, - "op_params": {"dim": "1"}, - }, + pytest.param( + ( + Softmax1, + [((1, 17, 4, 4480), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/moreh/moreh_softmax/device/moreh_softmax_device_operation.cpp:92: input.get_dtype() == DataType::BFLOAT16 || input.get_dtype() == DataType::BFLOAT8_B info: Inputs must be of bfloat16 or bfloat8_b type" + ) + ], + ), + pytest.param( + ( + Softmax1, + [((1, 17, 4, 1120), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/moreh/moreh_softmax/device/moreh_softmax_device_operation.cpp:92: input.get_dtype() == DataType::BFLOAT16 || input.get_dtype() == DataType::BFLOAT8_B info: Inputs must be of bfloat16 or bfloat8_b type" + ) + ], + ), + pytest.param( + ( + Softmax1, + [((1, 17, 4, 280), torch.float32)], + { + "model_name": ["pt_yolo_v6_yolov6l_obj_det_torchhub", "pt_yolo_v6_yolov6m_obj_det_torchhub"], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="RuntimeError: TT_FATAL @ /__w/tt-forge-fe/tt-forge-fe/third_party/tt-mlir/third_party/tt-metal/src/tt-metal/ttnn/cpp/ttnn/operations/moreh/moreh_softmax/device/moreh_softmax_device_operation.cpp:92: input.get_dtype() == DataType::BFLOAT16 || input.get_dtype() == DataType::BFLOAT8_B info: Inputs must be of bfloat16 or bfloat8_b type" + ) + ], ), ] diff --git a/forge/test/models_ops/test_subtract.py b/forge/test/models_ops/test_subtract.py index 093519882..103a1789a 100644 --- a/forge/test/models_ops/test_subtract.py +++ b/forge/test/models_ops/test_subtract.py @@ -423,32 +423,46 @@ def ids_func(param): [((1, 1, 1, 7), torch.float32)], {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99}, ), - ( - Subtract2, - [((1, 256), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - ], - "pcc": 0.99, - }, - ), - ( - Subtract2, - [((1, 32), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Subtract2, + [((1, 256), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Subtract2, + [((1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Subtract0, @@ -465,17 +479,20 @@ def ids_func(param): "pcc": 0.99, }, ), - ( - Subtract3, - [((1,), torch.int32)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - ], - "pcc": 0.99, - }, + pytest.param( + ( + Subtract3, + [((1,), torch.int32)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + ], + "pcc": 0.99, + }, + ), + marks=[pytest.mark.xfail(reason="Data mismatch between framework output and compiled model output")], ), ( Subtract4, diff --git a/forge/test/models_ops/test_unsqueeze.py b/forge/test/models_ops/test_unsqueeze.py index 185f18778..55149d5e5 100644 --- a/forge/test/models_ops/test_unsqueeze.py +++ b/forge/test/models_ops/test_unsqueeze.py @@ -63,44 +63,65 @@ def ids_func(param): "op_params": {"dim": "0"}, }, ), - ( - Unsqueeze1, - [((2, 13), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "1"}, - }, - ), - ( - Unsqueeze2, - [((2, 13), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "2"}, - }, - ), - ( - Unsqueeze2, - [((2, 1, 13), torch.int64)], - { - "model_name": [ - "pt_stereo_facebook_musicgen_large_music_generation_hf", - "pt_stereo_facebook_musicgen_medium_music_generation_hf", - "pt_stereo_facebook_musicgen_small_music_generation_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "2"}, - }, + pytest.param( + ( + Unsqueeze1, + [((2, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Unsqueeze2, + [((2, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "2"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Unsqueeze2, + [((2, 1, 13), torch.int64)], + { + "model_name": [ + "pt_stereo_facebook_musicgen_large_music_generation_hf", + "pt_stereo_facebook_musicgen_medium_music_generation_hf", + "pt_stereo_facebook_musicgen_small_music_generation_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "2"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Unsqueeze1, @@ -253,23 +274,37 @@ def ids_func(param): "op_params": {"dim": "1"}, }, ), - ( - Unsqueeze1, - [((2, 7), torch.int64)], - { - "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], - "pcc": 0.99, - "op_params": {"dim": "1"}, - }, - ), - ( - Unsqueeze2, - [((2, 1, 7), torch.int64)], - { - "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], - "pcc": 0.99, - "op_params": {"dim": "2"}, - }, + pytest.param( + ( + Unsqueeze1, + [((2, 7), torch.int64)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Unsqueeze2, + [((2, 1, 7), torch.int64)], + { + "model_name": ["pt_clip_openai_clip_vit_base_patch32_text_gen_hf_text"], + "pcc": 0.99, + "op_params": {"dim": "2"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Unsqueeze0, @@ -385,103 +420,131 @@ def ids_func(param): [((1, 1, 201), torch.float32)], {"model_name": ["pt_vilt_dandelin_vilt_b32_finetuned_vqa_qa_hf"], "pcc": 0.99, "op_params": {"dim": "2"}}, ), - ( - Unsqueeze1, - [((1, 128), torch.int64)], - { - "model_name": [ - "pt_albert_xxlarge_v1_token_cls_hf", - "pt_albert_base_v2_token_cls_hf", - "pt_albert_xxlarge_v2_token_cls_hf", - "pt_albert_large_v1_token_cls_hf", - "pt_albert_large_v2_token_cls_hf", - "pt_albert_base_v1_token_cls_hf", - "pt_albert_base_v1_mlm_hf", - "pt_albert_xlarge_v2_token_cls_hf", - "pt_albert_xxlarge_v2_mlm_hf", - "pt_albert_large_v2_mlm_hf", - "pt_albert_base_v2_mlm_hf", - "pt_albert_xlarge_v1_token_cls_hf", - "pt_albert_xlarge_v1_mlm_hf", - "pt_albert_large_v1_mlm_hf", - "pt_albert_xxlarge_v1_mlm_hf", - "pt_albert_xlarge_v2_mlm_hf", - "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", - "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", - "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", - "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", - "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", - "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", - "pt_roberta_xlm_roberta_base_mlm_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "1"}, - }, - ), - ( - Unsqueeze2, - [((1, 1, 128), torch.int64)], - { - "model_name": [ - "pt_albert_xxlarge_v1_token_cls_hf", - "pt_albert_base_v2_token_cls_hf", - "pt_albert_xxlarge_v2_token_cls_hf", - "pt_albert_large_v1_token_cls_hf", - "pt_albert_large_v2_token_cls_hf", - "pt_albert_base_v1_token_cls_hf", - "pt_albert_base_v1_mlm_hf", - "pt_albert_xlarge_v2_token_cls_hf", - "pt_albert_xxlarge_v2_mlm_hf", - "pt_albert_large_v2_mlm_hf", - "pt_albert_base_v2_mlm_hf", - "pt_albert_xlarge_v1_token_cls_hf", - "pt_albert_xlarge_v1_mlm_hf", - "pt_albert_large_v1_mlm_hf", - "pt_albert_xxlarge_v1_mlm_hf", - "pt_albert_xlarge_v2_mlm_hf", - "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", - "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", - "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", - "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", - "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", - "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", - "pt_roberta_xlm_roberta_base_mlm_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "2"}, - }, - ), - ( - Unsqueeze1, - [((1, 256), torch.int64)], - { - "model_name": [ - "pt_bart_facebook_bart_large_mnli_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - "pt_xglm_facebook_xglm_1_7b_clm_hf", - "pt_xglm_facebook_xglm_564m_clm_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "1"}, - }, - ), - ( - Unsqueeze2, - [((1, 1, 256), torch.int64)], - { - "model_name": [ - "pt_bart_facebook_bart_large_mnli_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_clm_hf", - "pt_opt_facebook_opt_125m_clm_hf", - "pt_opt_facebook_opt_350m_clm_hf", - "pt_xglm_facebook_xglm_1_7b_clm_hf", - "pt_xglm_facebook_xglm_564m_clm_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "2"}, - }, + pytest.param( + ( + Unsqueeze1, + [((1, 128), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_roberta_xlm_roberta_base_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Unsqueeze2, + [((1, 1, 128), torch.int64)], + { + "model_name": [ + "pt_albert_xxlarge_v1_token_cls_hf", + "pt_albert_base_v2_token_cls_hf", + "pt_albert_xxlarge_v2_token_cls_hf", + "pt_albert_large_v1_token_cls_hf", + "pt_albert_large_v2_token_cls_hf", + "pt_albert_base_v1_token_cls_hf", + "pt_albert_base_v1_mlm_hf", + "pt_albert_xlarge_v2_token_cls_hf", + "pt_albert_xxlarge_v2_mlm_hf", + "pt_albert_large_v2_mlm_hf", + "pt_albert_base_v2_mlm_hf", + "pt_albert_xlarge_v1_token_cls_hf", + "pt_albert_xlarge_v1_mlm_hf", + "pt_albert_large_v1_mlm_hf", + "pt_albert_xxlarge_v1_mlm_hf", + "pt_albert_xlarge_v2_mlm_hf", + "pt_dpr_facebook_dpr_ctx_encoder_single_nq_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_question_encoder_single_nq_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_reader_single_nq_base_qa_hf_reader", + "pt_dpr_facebook_dpr_question_encoder_multiset_base_qa_hf_question_encoder", + "pt_dpr_facebook_dpr_ctx_encoder_multiset_base_qa_hf_context_encoder", + "pt_dpr_facebook_dpr_reader_multiset_base_qa_hf_reader", + "pt_roberta_xlm_roberta_base_mlm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "2"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Unsqueeze1, + [((1, 256), torch.int64)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Unsqueeze2, + [((1, 1, 256), torch.int64)], + { + "model_name": [ + "pt_bart_facebook_bart_large_mnli_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_clm_hf", + "pt_opt_facebook_opt_125m_clm_hf", + "pt_opt_facebook_opt_350m_clm_hf", + "pt_xglm_facebook_xglm_1_7b_clm_hf", + "pt_xglm_facebook_xglm_564m_clm_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "2"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Unsqueeze0, @@ -797,47 +860,75 @@ def ids_func(param): [((1, 8, 128, 128), torch.float32)], {"model_name": ["pt_mistral_mistralai_mistral_7b_v0_1_clm_hf"], "pcc": 0.99, "op_params": {"dim": "2"}}, ), - ( - Unsqueeze1, - [((1, 7), torch.int64)], - {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99, "op_params": {"dim": "1"}}, - ), - ( - Unsqueeze2, - [((1, 1, 7), torch.int64)], - {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99, "op_params": {"dim": "2"}}, - ), - ( - Unsqueeze1, - [((1, 32), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "1"}, - }, - ), - ( - Unsqueeze2, - [((1, 1, 32), torch.int64)], - { - "model_name": [ - "pt_opt_facebook_opt_1_3b_seq_cls_hf", - "pt_opt_facebook_opt_1_3b_qa_hf", - "pt_opt_facebook_opt_350m_qa_hf", - "pt_opt_facebook_opt_125m_seq_cls_hf", - "pt_opt_facebook_opt_350m_seq_cls_hf", - "pt_opt_facebook_opt_125m_qa_hf", - ], - "pcc": 0.99, - "op_params": {"dim": "2"}, - }, + pytest.param( + ( + Unsqueeze1, + [((1, 7), torch.int64)], + {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99, "op_params": {"dim": "1"}}, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Unsqueeze2, + [((1, 1, 7), torch.int64)], + {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99, "op_params": {"dim": "2"}}, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Unsqueeze1, + [((1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "1"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], + ), + pytest.param( + ( + Unsqueeze2, + [((1, 1, 32), torch.int64)], + { + "model_name": [ + "pt_opt_facebook_opt_1_3b_seq_cls_hf", + "pt_opt_facebook_opt_1_3b_qa_hf", + "pt_opt_facebook_opt_350m_qa_hf", + "pt_opt_facebook_opt_125m_seq_cls_hf", + "pt_opt_facebook_opt_350m_seq_cls_hf", + "pt_opt_facebook_opt_125m_qa_hf", + ], + "pcc": 0.99, + "op_params": {"dim": "2"}, + }, + ), + marks=[ + pytest.mark.xfail( + reason="TypeError: Dtype mismatch: framework_model.dtype=torch.int64, compiled_model.dtype=torch.int32" + ) + ], ), ( Unsqueeze1, diff --git a/forge/test/models_ops/test_where.py b/forge/test/models_ops/test_where.py index b28ea9cec..b59cc3a1f 100644 --- a/forge/test/models_ops/test_where.py +++ b/forge/test/models_ops/test_where.py @@ -245,575 +245,708 @@ def ids_func(param): forge_modules_and_shapes_dtypes_list = [ - ( - Where0, - [((1, 1, 256, 256), torch.bool)], - { - "model_name": [ - "pt_gpt2_gpt2_text_gen_hf", - "pt_gptneo_eleutherai_gpt_neo_2_7b_clm_hf", - "pt_gptneo_eleutherai_gpt_neo_1_3b_clm_hf", - "pt_gptneo_eleutherai_gpt_neo_125m_clm_hf", - ], - "pcc": 0.99, - }, - ), - ( - Where0, - [((1, 1, 32, 32), torch.bool)], - { - "model_name": [ - "pt_gptneo_eleutherai_gpt_neo_125m_seq_cls_hf", - "pt_gptneo_eleutherai_gpt_neo_2_7b_seq_cls_hf", - "pt_gptneo_eleutherai_gpt_neo_1_3b_seq_cls_hf", - ], - "pcc": 0.99, - }, - ), - ( - Where0, - [((1, 1, 7, 7), torch.bool)], - {"model_name": ["pt_nanogpt_financialsupport_nanogpt_text_gen_hf"], "pcc": 0.99}, - ), - ( - Where1, - [((1, 256, 10, 32), torch.bool), ((1, 256, 10, 32), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where2, - [((1, 256, 10, 32), torch.bool), ((1, 256, 10, 32), torch.float32), ((1, 256, 10, 32), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where3, - [((1, 256, 20, 64), torch.bool), ((1, 256, 20, 64), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where2, - [((1, 256, 20, 64), torch.bool), ((1, 256, 20, 64), torch.float32), ((1, 256, 20, 64), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where4, - [((1, 128, 20, 64), torch.bool), ((1, 128, 20, 64), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where2, - [((1, 128, 20, 64), torch.bool), ((1, 128, 20, 64), torch.float32), ((1, 128, 20, 64), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where5, - [((1, 128, 40, 128), torch.bool), ((1, 128, 40, 128), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where2, - [((1, 128, 40, 128), torch.bool), ((1, 128, 40, 128), torch.float32), ((1, 128, 40, 128), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where6, - [((1, 64, 40, 128), torch.bool), ((1, 64, 40, 128), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where2, - [((1, 64, 40, 128), torch.bool), ((1, 64, 40, 128), torch.float32), ((1, 64, 40, 128), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where7, - [((1, 64, 80, 256), torch.bool), ((1, 64, 80, 256), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where2, - [((1, 64, 80, 256), torch.bool), ((1, 64, 80, 256), torch.float32), ((1, 64, 80, 256), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where8, - [((1, 32, 80, 256), torch.bool), ((1, 32, 80, 256), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where2, - [((1, 32, 80, 256), torch.bool), ((1, 32, 80, 256), torch.float32), ((1, 32, 80, 256), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where9, - [((1, 32, 160, 512), torch.bool), ((1, 32, 160, 512), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where2, - [((1, 32, 160, 512), torch.bool), ((1, 32, 160, 512), torch.float32), ((1, 32, 160, 512), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where10, - [((1, 16, 160, 512), torch.bool), ((1, 16, 160, 512), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where2, - [((1, 16, 160, 512), torch.bool), ((1, 16, 160, 512), torch.float32), ((1, 16, 160, 512), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where11, - [((1, 16, 320, 1024), torch.bool), ((1, 16, 320, 1024), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where2, - [((1, 16, 320, 1024), torch.bool), ((1, 16, 320, 1024), torch.float32), ((1, 16, 320, 1024), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_1024x320_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_1024x320_depth_prediction_torchvision", - "pt_monodepth2_stereo_1024x320_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where12, - [((1, 256, 6, 20), torch.bool), ((1, 256, 6, 20), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - 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"pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where2, - [((1, 256, 12, 40), torch.bool), ((1, 256, 12, 40), torch.float32), ((1, 256, 12, 40), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where14, - [((1, 128, 12, 40), torch.bool), ((1, 128, 12, 40), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_640x192_depth_prediction_torchvision", - ], - "pcc": 0.99, - }, - ), - ( - Where2, - [((1, 128, 12, 40), torch.bool), ((1, 128, 12, 40), torch.float32), ((1, 128, 12, 40), torch.float32)], - { - "model_name": [ - "pt_monodepth2_mono_stereo_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_640x192_depth_prediction_torchvision", - "pt_monodepth2_stereo_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_no_pt_640x192_depth_prediction_torchvision", - "pt_monodepth2_mono_stereo_no_pt_640x192_depth_prediction_torchvision", - 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264b3a84d9cb5a5e52f42bc60dccf17cc2ee62e0 Mon Sep 17 00:00:00 2001 From: chandrasekaranpradeep Date: Wed, 26 Feb 2025 09:33:09 +0000 Subject: [PATCH 8/8] Schedule nightly models ops test and clean up --- .github/workflows/on-nightly-models-ops.yml | 7 ++----- forge/forge/tvm_unique_op_generation.py | 2 +- scripts/model_analysis/models_ops_test_failure_update.py | 2 -- scripts/model_analysis/utils.py | 2 +- 4 files changed, 4 insertions(+), 9 deletions(-) diff --git a/.github/workflows/on-nightly-models-ops.yml b/.github/workflows/on-nightly-models-ops.yml index a4880590c..24014eec4 100644 --- a/.github/workflows/on-nightly-models-ops.yml +++ b/.github/workflows/on-nightly-models-ops.yml @@ -2,11 +2,8 @@ name: On Nightly Models Ops on: workflow_dispatch: - # Temporarily disabled the scheduled run configuration because there are currently no tests - # marked with 'nightly_models_ops' in main branch. When model ops tests are added, uncomment the schedule below to - # automatically run them at 02:00 AM UTC every day. - # schedule: - # - cron: '0 2 * * *' # Runs at 02:00 AM UTC every day + schedule: + - cron: '0 2 * * *' # Runs at 02:00 AM UTC every day jobs: docker-build: diff --git a/forge/forge/tvm_unique_op_generation.py b/forge/forge/tvm_unique_op_generation.py index 50ebf5ddf..c6b9a6335 100644 --- a/forge/forge/tvm_unique_op_generation.py +++ b/forge/forge/tvm_unique_op_generation.py @@ -1094,7 +1094,7 @@ def generate_models_ops_test(unique_operations: UniqueOperations, models_ops_tes pytest_metadata_list.append(pytest_metadata) # List of marker that will added at the top of the test function - markers = ["push"] + markers = ["nightly_models_ops"] # To avoid recording pcc in record_property pytest fixture and add the pcc to the exclude metadata property list exclude_record_property = ["pcc"] diff --git a/scripts/model_analysis/models_ops_test_failure_update.py b/scripts/model_analysis/models_ops_test_failure_update.py index c76637092..74bbd19c6 100644 --- a/scripts/model_analysis/models_ops_test_failure_update.py +++ b/scripts/model_analysis/models_ops_test_failure_update.py @@ -867,8 +867,6 @@ def extract_data_from_report(report_file_path: str): return models_ops_test_update_info -# python scripts/model_analysis/models_ops_test_failure_update.py --log_files ci_logs/pytest_1.log ci_logs/pytest_2.log ci_logs/pytest_3.log ci_logs/pytest_4.log -# python scripts/model_analysis/models_ops_test_failure_update.py --report_file_path model_ops_tests_report.xlsx --use_report def main(): """ Main function to update model ops tests based on pytest log failures. diff --git a/scripts/model_analysis/utils.py b/scripts/model_analysis/utils.py index 9ca2afd09..22642d0ff 100644 --- a/scripts/model_analysis/utils.py +++ b/scripts/model_analysis/utils.py @@ -259,7 +259,7 @@ def run_command(command: str): def run_precommit(directory_path: str): """Checks if pre-commit is installed and runs it on all files in the given directory.""" - + # Check if pre-commit is installed if shutil.which("pre-commit") is None: logger.info("pre-commit is not installed. Installing...")