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Add onnx demos
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‎demos/tt-forge-onnx/onnx/cnn/alexnet_demo.py‎

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# SPDX-FileCopyrightText: (c) 2026 Tenstorrent AI ULC
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#
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# SPDX-License-Identifier: Apache-2.0
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import tempfile
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import forge
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from third_party.tt_forge_models.alexnet.image_classification.onnx import ModelLoader, ModelVariant
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def run_alexnet_demo_case(variant):
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loader = ModelLoader(variant=variant)
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with tempfile.TemporaryDirectory() as tmpdir:
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# Load model and input
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onnx_model = loader.load_model(onnx_tmp_path=tmpdir)
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inputs = loader.load_inputs().contiguous()
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framework_model = forge.OnnxModule(variant.value, onnx_model)
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# Compile the model using Forge
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compiled_model = forge.compile(framework_model, [inputs])
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# Run inference on Tenstorrent device
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output = compiled_model(inputs)
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# Print the results
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loader.print_cls_results(output)
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print("=" * 60, flush=True)
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if __name__ == "__main__":
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demo_cases = [
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ModelVariant.ALEXNET,
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ModelVariant.ALEXNET_OSMR_B,
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]
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# Run each demo case
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for variant in demo_cases:
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run_alexnet_demo_case(variant)

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

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# SPDX-FileCopyrightText: (c) 2026 Tenstorrent AI ULC
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#
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# SPDX-License-Identifier: Apache-2.0
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import tempfile
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import forge
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from third_party.tt_forge_models.densenet.image_classification.onnx import ModelLoader, ModelVariant
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def run_densenet_demo_case(variant):
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loader = ModelLoader(variant=variant)
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with tempfile.TemporaryDirectory() as tmpdir:
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# Load model and input
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onnx_model = loader.load_model(onnx_tmp_path=tmpdir)
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inputs = loader.load_inputs().contiguous()
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framework_model = forge.OnnxModule(variant.name.lower(), onnx_model)
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# Compile the model using Forge
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compiled_model = forge.compile(framework_model, [inputs])
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# Run inference on Tenstorrent device
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output = compiled_model(inputs)
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# Print the results
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loader.print_cls_results(output)
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print("=" * 60, flush=True)
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if __name__ == "__main__":
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demo_cases = [
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ModelVariant.DENSENET121,
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ModelVariant.DENSENET161,
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ModelVariant.DENSENET169,
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ModelVariant.DENSENET201,
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]
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# Run each demo case
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for variant in demo_cases:
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run_densenet_demo_case(variant)

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

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# SPDX-FileCopyrightText: (c) 2026 Tenstorrent AI ULC
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#
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# SPDX-License-Identifier: Apache-2.0
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import tempfile
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import forge
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from third_party.tt_forge_models.efficientnet.image_classification.onnx import ModelLoader, ModelVariant
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def run_efficientnet_demo_case(variant):
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loader = ModelLoader(variant=variant)
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with tempfile.TemporaryDirectory() as tmpdir:
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# Load model and input
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onnx_model = loader.load_model(onnx_tmp_path=tmpdir)
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inputs = loader.load_inputs().contiguous()
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framework_model = forge.OnnxModule(variant.value, onnx_model)
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# Compile the model using Forge
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compiled_model = forge.compile(framework_model, [inputs])
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# Run inference on Tenstorrent device
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output = compiled_model(inputs)
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# Print the results
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loader.print_cls_results(output)
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print("=" * 60, flush=True)
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if __name__ == "__main__":
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demo_cases = [
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ModelVariant.B0,
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ModelVariant.B1,
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ModelVariant.TIMM_EFFICIENTNET_B0,
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ModelVariant.TIMM_EFFICIENTNET_B4,
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ModelVariant.HF_TIMM_EFFICIENTNET_B0_RA_IN1K,
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ModelVariant.HF_TIMM_EFFICIENTNET_B4_RA2_IN1K,
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ModelVariant.HF_TIMM_EFFICIENTNET_B5_IN12K_FT_IN1K,
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ModelVariant.HF_TIMM_TF_EFFICIENTNET_B0_AA_IN1K,
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ModelVariant.HF_TIMM_EFFICIENTNETV2_RW_S_RA2_IN1K,
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ModelVariant.HF_TIMM_TF_EFFICIENTNETV2_S_IN21K,
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]
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# Run each demo case
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for variant in demo_cases:
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run_efficientnet_demo_case(variant)

‎demos/tt-forge-onnx/onnx/cnn/googlenet_demo.py‎

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# SPDX-FileCopyrightText: (c) 2026 Tenstorrent AI ULC
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#
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# SPDX-License-Identifier: Apache-2.0
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import tempfile
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import forge
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from third_party.tt_forge_models.googlenet.image_classification.onnx import ModelLoader, ModelVariant
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def run_googlenet_demo_case(variant):
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loader = ModelLoader(variant=variant)
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with tempfile.TemporaryDirectory() as tmpdir:
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# Load model and input
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onnx_model = loader.load_model(onnx_tmp_path=tmpdir)
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inputs = loader.load_inputs().contiguous()
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framework_model = forge.OnnxModule("googlenet", onnx_model)
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# Compile the model using Forge
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compiled_model = forge.compile(framework_model, [inputs])
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# Run inference on Tenstorrent device
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output = compiled_model(inputs)
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# Print the results
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loader.print_cls_results(output)
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print("=" * 60, flush=True)
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if __name__ == "__main__":
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demo_cases = [
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ModelVariant.GOOGLENET,
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]
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# Run each demo case
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for variant in demo_cases:
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run_googlenet_demo_case(variant)

‎demos/tt-forge-onnx/onnx/cnn/mobilenetv1_demo.py‎

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# SPDX-FileCopyrightText: (c) 2026 Tenstorrent AI ULC
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#
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# SPDX-License-Identifier: Apache-2.0
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import tempfile
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import forge
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from third_party.tt_forge_models.mobilenetv1.image_classification.onnx import ModelLoader, ModelVariant
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def run_mobilenetv1_demo_case(variant):
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loader = ModelLoader(variant=variant)
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with tempfile.TemporaryDirectory() as tmpdir:
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# Load model and input
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onnx_model = loader.load_model(onnx_tmp_path=tmpdir)
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inputs = loader.load_inputs().contiguous()
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framework_model = forge.OnnxModule(variant.value, onnx_model)
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# Compile the model using Forge
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compiled_model = forge.compile(framework_model, [inputs])
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# Run inference on Tenstorrent device
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output = compiled_model(inputs)
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# Print the results
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loader.print_cls_results(output)
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print("=" * 60, flush=True)
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if __name__ == "__main__":
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demo_cases = [
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ModelVariant.MOBILENET_V1_GITHUB,
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ModelVariant.MOBILENET_V1_075_192_HF,
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ModelVariant.MOBILENET_V1_100_224_HF,
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]
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# Run each demo case
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for variant in demo_cases:
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run_mobilenetv1_demo_case(variant)

‎demos/tt-forge-onnx/onnx/cnn/resnet_demo.py‎

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# SPDX-FileCopyrightText: (c) 2026 Tenstorrent AI ULC
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#
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# SPDX-License-Identifier: Apache-2.0
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import tempfile
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import forge
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from third_party.tt_forge_models.resnet.image_classification.onnx import ModelLoader, ModelVariant
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def run_resnet_onnx(variant):
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loader = ModelLoader(variant=variant)
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with tempfile.TemporaryDirectory() as tmpdir:
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# Load model and input
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onnx_model = loader.load_model(onnx_tmp_path=tmpdir)
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inputs = loader.load_inputs().contiguous()
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framework_model = forge.OnnxModule(variant.value, onnx_model)
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# Compile the model using Forge
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compiled_model = forge.compile(framework_model, [inputs])
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# Run inference on Tenstorrent device
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output = compiled_model(inputs)
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# Print the results
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loader.print_cls_results(output)
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print("=" * 60, flush=True)
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if __name__ == "__main__":
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demo_cases = [
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ModelVariant.RESNET_50_HF,
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ModelVariant.RESNET_50_HF_HIGH_RES,
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ModelVariant.RESNET_50_TIMM,
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ModelVariant.RESNET_50_TIMM_HIGH_RES,
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ModelVariant.RESNET_18,
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ModelVariant.RESNET_34,
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ModelVariant.RESNET_50,
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ModelVariant.RESNET_101,
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ModelVariant.RESNET_152,
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]
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# Run each demo case
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for variant in demo_cases:
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run_resnet_onnx(variant)

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

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# SPDX-FileCopyrightText: (c) 2026 Tenstorrent AI ULC
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#
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# SPDX-License-Identifier: Apache-2.0
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import tempfile
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import forge
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from third_party.tt_forge_models.roberta.sequence_classification.onnx import ModelLoader, ModelVariant
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def run_roberta_demo_case(variant):
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loader = ModelLoader(variant=variant)
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with tempfile.TemporaryDirectory() as tmpdir:
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# Load model and input
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onnx_model = loader.load_model(onnx_tmp_path=tmpdir)
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inputs = loader.load_inputs()
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framework_model = forge.OnnxModule(variant.value, onnx_model)
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# Compile the model using Forge
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compiled_model = forge.compile(framework_model, [inputs])
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# Run inference on Tenstorrent device
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output = compiled_model(inputs)
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# Decode the output
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loader.decode_output(output)
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print("=" * 60, flush=True)
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if __name__ == "__main__":
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demo_cases = [
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ModelVariant.ROBERTA_BASE_SENTIMENT,
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]
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for variant in demo_cases:
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run_roberta_demo_case(variant)

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

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# SPDX-FileCopyrightText: (c) 2026 Tenstorrent AI ULC
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#
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# SPDX-License-Identifier: Apache-2.0
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import tempfile
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import forge
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from third_party.tt_forge_models.squeezebert.sequence_classification.onnx import ModelLoader, ModelVariant
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def run_squeezebert_demo_case(variant):
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loader = ModelLoader(variant=variant)
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with tempfile.TemporaryDirectory() as tmpdir:
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# Load model and input
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onnx_model = loader.load_model(onnx_tmp_path=tmpdir)
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inputs = loader.load_inputs()
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framework_model = forge.OnnxModule(variant.value, onnx_model)
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# Compile the model using Forge
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compiled_model = forge.compile(framework_model, [inputs])
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# Run inference on Tenstorrent device
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output = compiled_model(inputs)
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# Decode the output
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loader.decode_output(output)
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print("=" * 60, flush=True)
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if __name__ == "__main__":
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demo_cases = [
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ModelVariant.MNLI,
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]
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for variant in demo_cases:
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run_squeezebert_demo_case(variant)

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