mirror of https://github.com/llvm/torch-mlir
42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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# Also available under a BSD-style license. See LICENSE.
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# RUN: %PYTHON %s | FileCheck %s
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import torch
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import torch_mlir
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class TwoMethodsModule(torch.nn.Module):
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def sin(self, x):
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return torch.ops.aten.sin(x)
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def cos(self, x):
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return torch.ops.aten.cos(x)
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example_args = torch_mlir.ExampleArgs()
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example_args.add_method("sin", torch.ones(2, 3))
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example_args.add_method("cos", torch.ones(2, 4))
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# Note: Due to https://github.com/pytorch/pytorch/issues/88735 we need to
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# check the `use_tracing` case first.
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print(torch_mlir.compile(TwoMethodsModule(), example_args, use_tracing=True))
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# CHECK: module
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# CHECK-DAG: func.func @sin
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# CHECK-DAG: func.func @cos
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# As a convenience, we do the equivalent of calling `torch.jit.export` on all
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# methods indicated in `example_args` before calling `torch.jit.script`.
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# Otherwise the user would have to do this manually, which is tedious. This
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# technically mutates the user input model which is not great but probably okay
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# for this kind of API sugar. Users can always take full control of the process
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# by scripting the model themselves before passing it to `torch_mlir.compile`.
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print(torch_mlir.compile(TwoMethodsModule(), example_args))
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# CHECK: module
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# CHECK-DAG: func.func @sin
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# CHECK-DAG: func.func @cos
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