Add pytorch model demos(port from tt-forge-fe) & add tt-forge-models as submodule

.gitmodules

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+[submodule "third_party/tt_forge_models"]
+	path = third_party/tt_forge_models
+	url = https://github.com/tenstorrent/tt-forge-models.git

demos/tt-forge-fe/cnn/densenet_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# Densenet Demo Script
+
+import forge
+from ....third_party.tt_forge_models.densenet.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+inputs = ModelLoader.load_inputs()
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=[inputs])
+
+# Run inference on Tenstorrent device
+output = compiled_model(inputs)
+
+# Post-process the output
+# ModelLoader.print_cls_results(output)

demos/tt-forge-fe/cnn/efficientnet_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# Efficientnet Demo Script
+
+import forge
+from third_party.tt_forge_models.efficientnet.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+inputs = ModelLoader.load_inputs()
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=[inputs])
+
+# Run inference on Tenstorrent device
+output = compiled_model(inputs)
+
+# Post-process the output
+# ModelLoader.print_cls_results(output)

demos/tt-forge-fe/cnn/ghostnet_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# Ghostnet Demo Script
+
+import forge
+from ....third_party.tt_forge_models.ghostnet.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+inputs = ModelLoader.load_inputs()
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=[inputs])
+
+# Run inference on Tenstorrent device
+output = compiled_model(inputs)
+
+# Post-process the output
+# ModelLoader.print_cls_results(output)

demos/tt-forge-fe/cnn/regnet_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# Regnet Demo Script
+
+import forge
+from ....third_party.tt_forge_models.regnet.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+inputs = ModelLoader.load_inputs()
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=[inputs])
+
+# Run inference on Tenstorrent device
+output = compiled_model(inputs)
+
+# Post-process the output
+# ModelLoader.print_cls_results(output)

demos/tt-forge-fe/cnn/resnext_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# Resnext Demo Script
+
+import forge
+from ....third_party.tt_forge_models.resnext.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+inputs = ModelLoader.load_inputs()
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=[inputs])
+
+# Run inference on Tenstorrent device
+output = compiled_model(inputs)
+
+# Post-process the output
+# ModelLoader.print_cls_results(output)

demos/tt-forge-fe/cnn/swin_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# Swin Demo Script
+
+import forge
+from ....third_party.tt_forge_models.swin.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+inputs = ModelLoader.load_inputs()
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=[inputs])
+
+# Run inference on Tenstorrent device
+output = compiled_model(inputs)
+
+# Post-process the output
+# ModelLoader.print_cls_results(output)

demos/tt-forge-fe/cnn/vgg_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# Vgg Demo Script
+
+import forge
+from ....third_party.tt_forge_models.vgg.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+inputs = ModelLoader.load_inputs()
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=[inputs])
+
+# Run inference on Tenstorrent device
+output = compiled_model(inputs)
+
+# Post-process the output
+# ModelLoader.print_cls_results(output)

demos/tt-forge-fe/cnn/wideresnet_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# Wideresnet Demo Script
+
+import forge
+from ....third_party.tt_forge_models.wide_resnet.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+inputs = ModelLoader.load_inputs()
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=[inputs])
+
+# Run inference on Tenstorrent device
+output = compiled_model(inputs)
+
+# Post-process the output
+# ModelLoader.print_cls_results(output)

demos/tt-forge-fe/cnn/xception_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# Xception Demo Script
+
+import forge
+from ....third_party.tt_forge_models.xception.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+inputs = ModelLoader.load_inputs()
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=[inputs])
+
+# Run inference on Tenstorrent device
+output = compiled_model(inputs)
+
+# Post-process the output
+# ModelLoader.print_cls_results(output)

demos/tt-forge-fe/nlp/dpr_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# DPR Demo Script
+
+import forge
+from ....third_party.tt_forge_models.dpr.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+input_tokens = ModelLoader.load_inputs()
+
+inputs = [input_tokens["input_ids"], input_tokens["attention_mask"], input_tokens["token_type_ids"]]
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=inputs)
+
+# Run inference on Tenstorrent device
+output = compiled_model(*inputs)

demos/tt-forge-fe/nlp/mamba_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# Mamba Demo Script
+
+import forge
+from ....third_party.tt_forge_models.mamba.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+input_tokens = ModelLoader.load_inputs()
+
+# prepare input
+inputs = [input_tokens["input_ids"]]
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=inputs)
+
+# Run inference on Tenstorrent device
+output = compiled_model(*inputs)

demos/tt-forge-fe/nlp/roberta_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# RoBERTa Demo Script
+
+import forge
+from ....third_party.tt_forge_models.roberta.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+inputs = ModelLoader.load_inputs()
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=[inputs])
+
+# Run inference on Tenstorrent device
+output = compiled_model(inputs)

demos/tt-forge-fe/nlp/squeezebert_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# SqueezeBERT Demo Script
+
+import forge
+from ....third_party.tt_forge_models.squeezebert.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+inputs = ModelLoader.load_inputs()
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=[inputs])
+
+# Run inference on Tenstorrent device
+output = compiled_model(inputs)

demos/tt-forge-fe/nlp/xglm_demo.py

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+# SPDX-FileCopyrightText: © 2025 Tenstorrent AI ULC
+# SPDX-License-Identifier: Apache-2.0
+
+# XGLM Demo Script
+
+import forge
+from ....third_party.tt_forge_models.xglm.pytorch import ModelLoader
+
+# Load model and input
+model = ModelLoader.load_model()
+input_tokens = ModelLoader.load_inputs()
+
+# Prepare input
+inputs = [input_tokens["input_ids"], input_tokens["attention_mask"]]
+
+# Compile the model using Forge
+compiled_model = forge.compile(model, sample_inputs=inputs)
+
+# Run inference on Tenstorrent device
+output = compiled_model(inputs)