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8f6e73f068
(1) Enable experimental exporter logic to dynamo_export (2) Refine dynamic shapes and retry export in export strategies (3) Delete `torch_export_graph_extractor` and use the new export logic (4) Disable ExportedProgram test in `test_fx_onnx_with_onnxruntime.py`, as ONNXProgram is different now. Fixes https://github.com/pytorch/pytorch/issues/126479 Fixes #135183 Pull Request resolved: https://github.com/pytorch/pytorch/pull/134976 Approved by: https://github.com/justinchuby
77 lines
3.0 KiB
Python
77 lines
3.0 KiB
Python
# Owner(s): ["module: onnx"]
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from __future__ import annotations
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import onnx
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import onnx.inliner
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import pytorch_test_common
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import torch
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from torch.testing._internal import common_utils
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def assert_op_in_onnx_model(model: onnx.ModelProto, op_type: str):
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inlined = onnx.inliner.inline_local_functions(model)
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for node in inlined.graph.node:
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if node.op_type == op_type:
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return
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raise AssertionError(f"Op {op_type} not found in model")
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class TestDynamoExportDecompSkip(pytorch_test_common.ExportTestCase):
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def test_upsample_bilinear2d(self):
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class TestModel(torch.nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.upsample = torch.nn.Upsample(scale_factor=2, mode="bilinear")
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def forward(self, x):
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return self.upsample(x)
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onnx_program = torch.onnx.dynamo_export(TestModel(), torch.randn(1, 1, 2, 2))
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# If decomposition is skipped, the model will contain a Resize op instead of fine grained subgraph.
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assert_op_in_onnx_model(onnx_program.model_proto, "Resize")
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def test_upsample_bilinear2d_output_size(self):
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def func(x: torch.Tensor):
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return torch.nn.functional.interpolate(x, size=(4, 4), mode="bilinear")
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onnx_program = torch.onnx.dynamo_export(func, torch.randn(1, 1, 2, 2))
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# If decomposition is skipped, the model will contain a Resize op instead of fine grained subgraph.
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assert_op_in_onnx_model(onnx_program.model_proto, "Resize")
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def test_upsample_trilinear3d(self):
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class TestModel(torch.nn.Module):
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def __init__(self) -> None:
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super().__init__()
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self.upsample = torch.nn.Upsample(scale_factor=2, mode="trilinear")
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def forward(self, x):
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return self.upsample(x)
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onnx_program = torch.onnx.dynamo_export(TestModel(), torch.randn(1, 1, 2, 2, 3))
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# If decomposition is skipped, the model will contain a Resize op instead of fine grained subgraph.
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assert_op_in_onnx_model(onnx_program.model_proto, "Resize")
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def test_upsample_trilinear3d_output_size(self):
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def func(x: torch.Tensor):
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return torch.nn.functional.interpolate(x, size=(4, 4, 4), mode="trilinear")
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onnx_program = torch.onnx.dynamo_export(func, torch.randn(1, 1, 2, 2, 3))
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# If decomposition is skipped, the model will contain a Resize op instead of fine grained subgraph.
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assert_op_in_onnx_model(onnx_program.model_proto, "Resize")
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def test_instance_norm(self):
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class TestModel(torch.nn.Module):
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def forward(self, x):
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return torch.nn.functional.instance_norm(x)
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onnx_program = torch.onnx.dynamo_export(TestModel(), torch.randn(1, 1, 2, 2))
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# If decomposition is skipped, the model will contain an InstanceNormalization op
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# instead of BatchNormalization op w/ training=True.
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assert_op_in_onnx_model(onnx_program.model_proto, "InstanceNormalization")
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if __name__ == "__main__":
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common_utils.run_tests()
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