mirror of https://github.com/llvm/torch-mlir
44 lines
1.6 KiB
Python
44 lines
1.6 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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# Function names in the Torch compilation unit are systematic -- they
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# are effectively Python dotted paths. E.g. a Python module "foo" with a class
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# "bar" with a method "baz" will result in a function in the compilation unit
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# called "foo.bar.baz" when it gets `torch.jit.script`'ed.
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# (with the exception that `__main__` is replaced with `__torch__`).
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#
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# Given how systematic this is, we don't treat the symbol names as opaque (i.e.
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# we don't need to capture their names when FileCheck testing).
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# CHECK-LABEL: func private @__torch__.TestModule.forward
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# CHECK-SAME: (%[[SELF:.*]]: !torch.nn.Module<"__torch__.TestModule">, %[[X:.*]]: !numpy.ndarray<*:!numpy.any_dtype>) -> !numpy.ndarray<*:!numpy.any_dtype> {
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# CHECK: return %[[X]] : !numpy.ndarray<*:!numpy.any_dtype>
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# CHECK: }
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#
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# CHECK-LABEL: torch.class_type @__torch__.TestModule {
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# CHECK: torch.method "forward", @__torch__.TestModule.forward
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# CHECK: }
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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):
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return x
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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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