Bringup tt-torch models in forge

env/core_requirements.txt

@@ -51,3 +51,4 @@ pytorch_forecasting==1.0.0
 patool
 openpyxl==3.1.5
 GitPython==3.1.44
+kornia==0.8.0

forge/test/models/pytorch/text/albert/test_albert.py

@@ -5,8 +5,11 @@
 import torch
 from transformers import (
     AlbertForMaskedLM,
+    AlbertForQuestionAnswering,
+    AlbertForSequenceClassification,
     AlbertForTokenClassification,
     AlbertTokenizer,
+    AutoTokenizer,
 )
 
 import forge
@@ -162,3 +165,72 @@ def test_albert_token_classification_pytorch(record_forge_property, size, varian
 
     print(f"Context: {sample_text}")
     print(f"Answer: {predicted_tokens_classes}")
+
+
+@pytest.mark.nightly
+def test_albert_question_answering_pytorch(record_forge_property):
+
+    # Build Module Name
+    module_name = build_module_name(
+        framework=Framework.PYTORCH,
+        model="albert",
+        task=Task.QA,
+        source=Source.HUGGINGFACE,
+    )
+
+    # Record Forge Property
+    record_forge_property("model_name", module_name)
+
+    # Load Albert tokenizer and model from HuggingFace
+    tokenizer = download_model(AutoTokenizer.from_pretrained, "twmkn9/albert-base-v2-squad2")
+    framework_model = download_model(
+        AlbertForQuestionAnswering.from_pretrained, "twmkn9/albert-base-v2-squad2", return_dict=False
+    )
+
+    # Load data sample
+    question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
+
+    # Data preprocessing
+    input_tokens = tokenizer(question, text, return_tensors="pt")
+    inputs = [input_tokens["input_ids"], input_tokens["attention_mask"]]
+
+    # Forge compile framework model
+    compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
+
+    # Model Verification
+    verify(inputs, framework_model, compiled_model)
+
+
+@pytest.mark.nightly
+@pytest.mark.push
+def test_albert_sequence_classification_pytorch(record_forge_property):
+
+    # Build Module Name
+    module_name = build_module_name(
+        framework=Framework.PYTORCH,
+        model="albert",
+        task=Task.SEQUENCE_CLASSIFICATION,
+        source=Source.HUGGINGFACE,
+    )
+
+    # Record Forge Property
+    record_forge_property("model_name", module_name)
+
+    # Load Albert tokenizer and model from HuggingFace
+    tokenizer = download_model(AlbertTokenizer.from_pretrained, "textattack/albert-base-v2-imdb")
+    framework_model = download_model(
+        AlbertForSequenceClassification.from_pretrained, "textattack/albert-base-v2-imdb", return_dict=False
+    )
+
+    # Load data sample
+    input_text = "Hello, my dog is cute."
+
+    # Data preprocessing
+    input_tokens = tokenizer(input_text, return_tensors="pt")
+    inputs = [input_tokens["input_ids"], input_tokens["attention_mask"]]
+
+    # Forge compile framework model
+    compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
+
+    # Model Verification
+    verify(inputs, framework_model, compiled_model)

forge/test/models/pytorch/text/bloom/test_bloom.py

@@ -0,0 +1,47 @@
+# SPDX-FileCopyrightText: (c) 2024 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+import torch
+
+import forge
+from forge.verify.verify import verify
+
+from test.models.pytorch.text.bloom.utils.utils import load_input, load_model
+from test.models.utils import Framework, Source, Task, build_module_name
+
+
+# Wrapper to get around past key values
+class Wrapper(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, input_ids, attention_mask):
+        output = self.model(input_ids, None, attention_mask)
+        return output
+
+
+@pytest.mark.nightly
+def test_bloom(record_forge_property):
+
+    # Build Module Name
+    module_name = build_module_name(
+        framework=Framework.PYTORCH,
+        model="bloom",
+        source=Source.HUGGINGFACE,
+        task=Task.CAUSAL_LM,
+    )
+
+    # Record Forge Property
+    record_forge_property("model_name", module_name)
+
+    # Load model and input
+    model = load_model()
+    framework_model = Wrapper(model)
+    inputs = load_input()
+
+    # Forge compile framework model
+    compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
+
+    # Model Verification
+    verify(inputs, framework_model, compiled_model)

forge/test/models/pytorch/text/bloom/utils/utils.py

@@ -0,0 +1,25 @@
+# SPDX-FileCopyrightText: (c) 2024 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+def load_model():
+    model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-1b1")
+    model.config.use_cache = False
+    model.eval()
+    return model
+
+
+def load_input():
+    test_input = "This is a sample text from "
+    tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-1b1", padding_side="left")
+    inputs = tokenizer.encode_plus(
+        test_input,
+        return_tensors="pt",
+        max_length=32,
+        padding="max_length",
+        add_special_tokens=True,
+        truncation=True,
+    )
+    return [inputs["input_ids"], inputs["attention_mask"]]

forge/test/models/pytorch/text/mamba/test_mamba.py

@@ -0,0 +1,58 @@
+# SPDX-FileCopyrightText: (c) 2024 Tenstorrent AI ULC
+
+# SPDX-License-Identifier: Apache-2.0
+# Reference: https://huggingface.co/state-spaces/mamba-2.8b-hf
+
+import pytest
+import torch
+
+import forge
+from forge.verify.verify import verify
+
+from test.models.pytorch.text.mamba.utils.utils import load_input, load_model
+from test.models.utils import Framework, Source, Task, build_module_name
+
+
+# Wrapper to return only the output tensor, excluding cache or additional outputs
+class Wrapper(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(self, input_ids):
+        output = self.model(input_ids)
+        return output[0]
+
+
+variants = [
+    "state-spaces/mamba-790m-hf",
+    "state-spaces/mamba-2.8b-hf",
+    "state-spaces/mamba-1.4b-hf",
+    "state-spaces/mamba-370m-hf",
+]
+
+
+@pytest.mark.nightly
+@pytest.mark.parametrize("variant", variants)
+def test_mamba(record_forge_property, variant):
+    if variant != "state-spaces/mamba-790m-hf":
+        pytest.skip("Skipping this variant; only testing the base model (mamba-790m-hf) for now.")
+
+    # Build Module Name
+    module_name = build_module_name(
+        framework=Framework.PYTORCH, model="mamba", variant=variant, task=Task.CAUSAL_LM, source=Source.HUGGINGFACE
+    )
+
+    # Record Forge Property
+    record_forge_property("model_name", module_name)
+
+    # Load model and input
+    model = load_model(variant)
+    framework_model = Wrapper(model)
+    inputs = load_input(variant)
+
+    # Forge compile framework model
+    compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
+
+    # Model Verification
+    verify(inputs, framework_model, compiled_model)

forge/test/models/pytorch/text/mamba/utils/utils.py

@@ -0,0 +1,19 @@
+# SPDX-FileCopyrightText: (c) 2024 Tenstorrent AI ULC
+
+# SPDX-License-Identifier: Apache-2.0
+# Reference: https://huggingface.co/state-spaces/mamba-2.8b-hf
+
+from transformers import AutoTokenizer, MambaForCausalLM
+
+
+def load_model(variant):
+    model = MambaForCausalLM.from_pretrained(variant)
+    model.eval()
+    return model
+
+
+def load_input(variant):
+    prompt = "Hey how are you doing?"
+    tokenizer = AutoTokenizer.from_pretrained(variant)
+    input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"]
+    return [input_ids]

forge/test/models/pytorch/text/qwen/test_qwen_v2.py

@@ -2,7 +2,11 @@
 #
 # SPDX-License-Identifier: Apache-2.0
 import pytest
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    Qwen2ForTokenClassification,
+)
 
 import forge
 from forge.verify.verify import verify
@@ -57,3 +61,31 @@ def test_qwen_clm(record_forge_property, variant):
 
     # Model Verification
     verify(inputs, framework_model, compiled_model)
+
+
+@pytest.mark.nightly
+def test_qwen2_token_classification(record_forge_property):
+
+    # Build Module Name
+    module_name = build_module_name(
+        framework=Framework.PYTORCH, model="qwen_v2", task=Task.TOKEN_CLASSIFICATION, source=Source.HUGGINGFACE
+    )
+
+    # Record Forge Property
+    record_forge_property("model_name", module_name)
+
+    # Load model and tokenizer
+    framework_model = Qwen2ForTokenClassification.from_pretrained("Qwen/Qwen2-7B")
+    tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B")
+
+    # Prepare input
+    text = "HuggingFace is a company based in Paris and New York."
+    model_inputs = tokenizer(text, add_special_tokens=False, return_tensors="pt")
+
+    inputs = [model_inputs["input_ids"], model_inputs["attention_mask"]]
+
+    # Forge compile framework model
+    compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
+
+    # Model Verification
+    verify(inputs, framework_model, compiled_model)

forge/test/models/pytorch/vision/beit/test_beit.py

@@ -0,0 +1,39 @@
+# SPDX-FileCopyrightText: (c) 2024 Tenstorrent AI ULC
+
+# SPDX-License-Identifier: Apache-2.0
+import pytest
+
+import forge
+from forge.verify.verify import verify
+
+from test.models.pytorch.vision.beit.utils.utils import load_input, load_model
+from test.models.utils import Framework, Source, Task, build_module_name
+
+variants = ["microsoft/beit-base-patch16-224", "microsoft/beit-large-patch16-224"]
+
+
+@pytest.mark.nightly
+@pytest.mark.parametrize("variant", variants)
+def test_beit_image_classification(record_forge_property, variant):
+
+    # Build Module Name
+    module_name = build_module_name(
+        framework=Framework.PYTORCH,
+        model="beit",
+        variant=variant,
+        source=Source.HUGGINGFACE,
+        task=Task.IMAGE_CLASSIFICATION,
+    )
+
+    # Record Forge Property
+    record_forge_property("model_name", module_name)
+
+    # Load model and input
+    framework_model = load_model(variant)
+    inputs = load_input(variant)
+
+    # Forge compile framework model
+    compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
+
+    # Model Verification
+    verify(inputs, framework_model, compiled_model)

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

@@ -0,0 +1,20 @@
+# SPDX-FileCopyrightText: (c) 2024 Tenstorrent AI ULC
+#
+# SPDX-License-Identifier: Apache-2.0
+import requests
+from PIL import Image
+from transformers import BeitForImageClassification, BeitImageProcessor
+
+
+def load_model(variant):
+    model = BeitForImageClassification.from_pretrained(variant)
+    model.eval()
+    return model
+
+
+def load_input(variant):
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    image = Image.open(requests.get(url, stream=True).raw)
+    processor = BeitImageProcessor.from_pretrained(variant)
+    inputs = processor(images=image, return_tensors="pt")
+    return [inputs["pixel_values"]]

forge/test/models/pytorch/vision/glpn_kitti/test_glpn_kitti.py

@@ -0,0 +1,33 @@
+# SPDX-FileCopyrightText: (c) 2024 Tenstorrent AI ULC
+
+# SPDX-License-Identifier: Apache-2.0
+import forge
+from forge.verify.verify import verify
+
+from test.models.pytorch.vision.glpn_kitti.utils.utils import load_input, load_model
+from test.models.utils import Framework, Source, Task, build_module_name
+
+
+@pytest.mark.nightly
+def test_glpn_kitti(record_forge_property):
+
+    # Build Module Name
+    module_name = build_module_name(
+        framework=Framework.PYTORCH,
+        model="glpn_kitti",
+        source=Source.HUGGINGFACE,
+        task=Task.DEPTH_ESTIMATION,
+    )
+
+    # Record Forge Property
+    record_forge_property("model_name", module_name)
+
+    # Load model and input
+    framework_model = load_model()
+    inputs = load_input()
+
+    # Forge compile framework model
+    compiled_model = forge.compile(framework_model, sample_inputs=inputs, module_name=module_name)
+
+    # Model Verification
+    verify(inputs, framework_model, compiled_model)