mirror of https://github.com/llvm/torch-mlir
40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
# -*- Python -*-
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# This file is licensed under a pytorch-style license
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# See frontends/pytorch/LICENSE for license information.
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import typing
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import torch
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import torch_mlir
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# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
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mb = torch_mlir.ModuleBuilder()
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class TestModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x, y):
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return x * y
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# The symbol name of the function is NOT load-bearing and cannot be relied upon.
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# CHECK-LABEL: func private
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# CHECK-SAME: @[[SYMNAME:.*]](
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# CHECK-SAME: %[[SELF:.*]]: !torch.nn.Module,
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# CHECK-SAME: %[[X:.*]]: !numpy.ndarray<*:!numpy.any_dtype>,
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# CHECK-SAME: %[[Y:.*]]: !numpy.ndarray<*:!numpy.any_dtype>) -> !numpy.ndarray<*:!numpy.any_dtype> {
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# CHECK: %[[RET:.*]] = torch.kernel_call "aten::mul" %[[X]], %[[Y]]
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# CHECK: return %[[RET]] : !numpy.ndarray<*:!numpy.any_dtype>
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# CHECK: %[[ROOT:.*]] = torch.nn_module {
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# CHECK: torch.method "forward", @[[SYMNAME]]
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# CHECK: }
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test_module = TestModule()
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recursivescriptmodule = torch.jit.script(test_module)
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# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
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mb.import_module(recursivescriptmodule._c)
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mb.module.operation.print()
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