mirror of https://github.com/llvm/torch-mlir
36 lines
1.3 KiB
Python
36 lines
1.3 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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# CHECK-LABEL: func private @__torch__.TestModule.forward(
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# CHECK-SAME: %[[SELF:.*]]: !torch.nn.Module<"__torch__.TestModule">) -> !torch.optional<i64> {
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# CHECK: %[[NONE:.*]] = basicpy.singleton : !basicpy.NoneType
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# CHECK: %[[DEREFINED:.*]] = torch.derefine %[[NONE]] : !basicpy.NoneType to !torch.optional<i64>
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# CHECK: %[[RET:.*]] = torch.prim.CallMethod %[[SELF]]["callee"] (%[[DEREFINED]]) : !torch.nn.Module<"__torch__.TestModule">, (!torch.optional<i64>) -> !torch.optional<i64>
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# CHECK: return %[[RET]] : !torch.optional<i64>
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def forward(self):
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return self.callee(None)
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def callee(self, o: typing.Optional[int]):
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return o
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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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