Add exception rules for the unknown compiler component and fix the fo…

…rge test models failures (#1217)

1. Analysed latest ran [model analysis
pipeline](https://github.com/tenstorrent/tt-forge-fe/actions/runs/13226767422)
artifacts (i.e
[script-outputs](https://github.com/tenstorrent/tt-forge-fe/actions/runs/13226767422/artifacts/2576589360)
) and found out there are some failed model test cases which includes
skipped model variants and fixed the model test cases failures in
`forge/test/models/pytorch` directory path
Note: In the model analysis pipeline, we are running all the models in
the `forge/test/models/pytorch` directory path which includes skipped
model test cases too.

2. Added new exception rules for the UNKNOWN Compiler component failures
present in the
[PR](#1210) for updating
model_analysis_docs with the help of
[unique-ops-logs](https://github.com/tenstorrent/tt-forge-fe/actions/runs/13226767422/artifacts/2576589743)
artifacts

forge/forge/tvm_unique_op_generation.py

@@ -569,7 +569,7 @@ def extract_and_generate_unique_ops_tests(
         named_buffers_file_name = os.path.join(module_directory, str(current_module_name) + "_named_buffers.pt")
         torch.save(named_buffers, named_buffers_file_name)
     else:
-        if os.path.exists(param_file_name):
+        if param_file_name is not None and os.path.exists(param_file_name):
             os.remove(param_file_name)
             param_file_name = None
 

forge/test/models/pytorch/text/mistral/test_mistral.py

@@ -8,7 +8,7 @@
 import forge
 from forge.verify.verify import verify
 
-from test.models.utils import Framework, build_module_name
+from test.models.utils import Framework, Source, Task, build_module_name
 
 variants = ["mistralai/Mistral-7B-v0.1"]
 

forge/test/models/pytorch/text/t5/test_t5.py

@@ -3,7 +3,7 @@
 # SPDX-License-Identifier: Apache-2.0
 import pytest
 import torch
-from transformers import T5Config, T5ForConditionalGeneration
+from transformers import AutoTokenizer, T5Config, T5ForConditionalGeneration
 
 import forge
 from forge.verify.verify import verify

forge/test/models/pytorch/vision/densenet/test_densenet.py

@@ -120,7 +120,13 @@ def test_densenet_201_pytorch(record_forge_property, variant):
     pytest.skip("Insufficient host DRAM to run this model (requires a more than 32 GB during compile time)")
 
     # Build Module Name
-    module_name = build_module_name(framework=Framework.PYTORCH, model="densenet", variant=variant)
+    module_name = build_module_name(
+        framework=Framework.PYTORCH,
+        model="densenet",
+        variant=variant,
+        task=Task.IMAGE_CLASSIFICATION,
+        source=Source.TORCHVISION,
+    )
 
     # Record Forge Property
     record_forge_property("model_name", module_name)

forge/test/models/pytorch/vision/mlp_mixer/test_mlp_mixer.py

@@ -49,7 +49,7 @@ def test_mlp_mixer_timm_pytorch(record_forge_property, variant):
     record_forge_property("model_name", module_name)
 
     framework_model = download_model(timm.create_model, variant, pretrained=True)
-    config = resolve_data_config({}, model=model)
+    config = resolve_data_config({}, model=framework_model)
     transform = create_transform(**config)
 
     try:

forge/test/models/pytorch/vision/monodepth2/utils/depth_decoder.py

@@ -15,6 +15,12 @@
 import torch
 import torch.nn as nn
 
+from test.models.pytorch.vision.monodepth2.utils.layers import (
+    Conv3x3,
+    ConvBlock,
+    upsample,
+)
+
 
 class DepthDecoder(nn.Module):
     def __init__(self, num_ch_enc, scales=range(4), num_output_channels=1, use_skips=True):

forge/test/models/pytorch/vision/resnext/test_resnext.py

@@ -10,7 +10,8 @@
 from forge.verify.verify import verify
 
 from test.models.pytorch.vision.resnext.utils.utils import (
-    load_resnext_model,
+    get_image_tensor,
+    get_resnext_model_and_input,
     post_processing,
 )
 from test.models.utils import Framework, Source, Task, build_module_name
@@ -34,7 +35,7 @@ def test_resnext_50_torchhub_pytorch(record_forge_property, variant):
     record_forge_property("model_name", module_name)
 
     # Load the model and prepare input data
-    framework_model, inputs = load_resnext_model(variant)
+    framework_model, inputs = get_resnext_model_and_input("pytorch/vision:v0.10.0", variant)
 
     # Forge compile framework model
     compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
@@ -65,12 +66,8 @@ def test_resnext_101_torchhub_pytorch(record_forge_property, variant):
     # Record Forge Property
     record_forge_property("model_name", module_name)
 
-    # STEP 2: Create Forge module from PyTorch model
-    framework_model = download_model(torch.hub.load, "pytorch/vision:v0.10.0", variant, pretrained=True)
-    framework_model.eval()
-
     # Load the model and prepare input data
-    framework_model, inputs = load_resnext_model(variant)
+    framework_model, inputs = get_resnext_model_and_input("pytorch/vision:v0.10.0", variant)
 
     # Forge compile framework model
     compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
@@ -102,8 +99,7 @@ def test_resnext_101_32x8d_fb_wsl_pytorch(record_forge_property, variant):
     # Record Forge Property
     record_forge_property("model_name", module_name)
 
-    # STEP 2: Create Forge module from PyTorch model
-    # 4 variants
+    # Load the model and prepare input data
     framework_model = download_model(torch.hub.load, "facebookresearch/WSL-Images", variant)
     framework_model.eval()
 
@@ -134,7 +130,7 @@ def test_resnext_14_osmr_pytorch(record_forge_property, variant):
     # Record Forge Property
     record_forge_property("model_name", module_name)
 
-    # STEP 2: Create Forge module from PyTorch model
+    # Load the model and prepare input data
     framework_model = download_model(ptcv_get_model, variant, pretrained=True)
     framework_model.eval()
 

forge/test/models/pytorch/vision/resnext/utils/utils.py

@@ -11,12 +11,7 @@
 from test.utils import download_model
 
 
-def load_resnext_model(model_name):
-
-    # Create model
-    model = download_model(torch.hub.load, "pytorch/vision:v0.10.0", model_name, pretrained=True)
-    model.eval()
-
+def get_image_tensor():
     # Load data sample
     url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
     urllib.request.urlretrieve(url, filename)
@@ -33,6 +28,16 @@ def load_resnext_model(model_name):
     )
     input_tensor = preprocess(input_image)
     input_batch = input_tensor.unsqueeze(0)
+    return input_batch
+
+
+def get_resnext_model_and_input(repo_or_dir, model_name):
+
+    # Create model
+    model = download_model(torch.hub.load, repo_or_dir, model_name, pretrained=True)
+    model.eval()
+
+    input_batch = get_image_tensor()
 
     return model, [input_batch]
 

forge/test/models/pytorch/vision/swin/test_swin.py

@@ -3,8 +3,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # STEP 0: import Forge library
 import pytest
-import timm
 from transformers import (
+    SwinForImageClassification,
     Swinv2ForImageClassification,
     Swinv2ForMaskedImageModeling,
     Swinv2Model,
@@ -16,7 +16,6 @@
 
 from test.models.pytorch.vision.swin.utils.image_utils import load_image
 from test.models.utils import Framework, Source, Task, build_module_name
-from test.utils import download_model
 
 
 @pytest.mark.nightly
@@ -36,8 +35,7 @@ def test_swin_v1_tiny_4_224_hf_pytorch(record_forge_property, variant):
 
     # STEP 1: Create Forge module from PyTorch model
     feature_extractor = ViTImageProcessor.from_pretrained(variant)
-    # model = SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224", torchscript=True)
-    framework_model = download_model(timm.create_model, variant, pretrained=True)
+    framework_model = SwinForImageClassification.from_pretrained(variant)
     framework_model.eval()
 
     # STEP 2: Prepare input samples

forge/test/models/pytorch/vision/vovnet/test_vovnet.py

@@ -84,7 +84,7 @@ def test_vovnet_v1_39_stigma_pytorch(record_forge_property):
     verify(inputs, framework_model, compiled_model)
 
 
-def generate_model_vovnet57_imgcls_stigma_pytorch(variant):
+def generate_model_vovnet57_imgcls_stigma_pytorch():
     # STEP 2: Create Forge module from PyTorch model
     model, image_tensor = download_model(preprocess_steps, vovnet57)
 

forge/test/models/pytorch/vision/vovnet/utils/src_vovnet_stigma.py

@@ -102,7 +102,7 @@ def __init__(self, in_ch, stage_ch, concat_ch, block_per_stage, layer_per_block,
             self.add_module("Pooling", nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True))
 
         module_name = f"OSA{stage_num}_1"
-        self.add_module(module_name, _OSA_module(in_ch, stage_ch, concat_ch, layer_per_blockmodule_name=module_name))
+        self.add_module(module_name, _OSA_module(in_ch, stage_ch, concat_ch, layer_per_block, module_name))
         for i in range(block_per_stage - 1):
             module_name = f"OSA{stage_num}_{i+2}"
             self.add_module(

forge/test/models/pytorch/vision/wideresnet/test_wideresnet.py

@@ -5,6 +5,7 @@
 
 import pytest
 import timm
+from PIL import Image
 from timm.data import resolve_data_config
 from timm.data.transforms_factory import create_transform
 

scripts/model_analysis/exception_rules.py

@@ -122,6 +122,33 @@
                     ", compiled_model",
                 ],
             ),
+            MatchingExceptionRule(
+                "tt-metal multi-paged buffer",
+                [
+                    "RuntimeError",
+                    "tt_metal/impl/buffers/dispatch.cpp",
+                    "num_pages == 1",
+                    "TODO: add support for multi-paged buffer with page size > 64KB",
+                ],
+            ),
+            MatchingExceptionRule(
+                "tt-metal buffer allocation",
+                [
+                    "RuntimeError",
+                    "tt_metal/impl/allocator/bank_manager.cpp",
+                    "Out of Memory: Not enough space to allocate",
+                    "DRAM buffer",
+                ],
+            ),
+            MatchingExceptionRule(
+                "tt-metal kernel",
+                [
+                    "RuntimeError",
+                    "tt-metal/tt_metal/impl/kernels/kernel.cpp",
+                    "unique+common runtime args targeting kernel",
+                    "are too large",
+                ],
+            ),
             MatchingExceptionRule(
                 "ttnn.tilize validation",
                 [
@@ -327,6 +354,14 @@
                     "input_shape[3] == 16",
                 ],
             ),
+            MatchingExceptionRule(
+                "ttnn conv2d",
+                [
+                    "RuntimeError",
+                    "tt-metal/ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op_sharded_program_factory.cpp",
+                    "act_block_w_datums == round_up(conv_act_size_c * filter_w, TILE_WIDTH)",
+                ],
+            ),
         ],
     ),
 ]