mirror of https://github.com/llvm/torch-mlir
50 lines
2.3 KiB
Python
50 lines
2.3 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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# Interesting test case, where a function calls a method.
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# CHECK-LABEL: func private @__torch__.TestModule.forward
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# CHECK-SAME: (%[[ARG0:.*]]: !torch.nn.Module<"__torch__.TestModule">, %[[ARG1:.*]]: !numpy.ndarray<*:!numpy.any_dtype>) -> !basicpy.NoneType {
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# CHECK: %[[F:.*]] = constant @__torch__.calls_method : (!torch.nn.Module<"__torch__.TestModule">, !numpy.ndarray<*:!numpy.any_dtype>) -> !basicpy.NoneType
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# CHECK: %[[RET:.*]] = call_indirect %[[F]](%[[ARG0]], %[[ARG1]]) : (!torch.nn.Module<"__torch__.TestModule">, !numpy.ndarray<*:!numpy.any_dtype>) -> !basicpy.NoneType
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# CHECK: return %[[RET]] : !basicpy.NoneType
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# CHECK: }
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# CHECK-LABEL: func private @__torch__.TestModule.method
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# CHECK-SAME: (%[[ARG0:.*]]: !torch.nn.Module<"__torch__.TestModule">, %[[ARG1:.*]]: !numpy.ndarray<*:!numpy.any_dtype>) -> !basicpy.NoneType {
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# CHECK: %[[RET:.*]] = basicpy.singleton : !basicpy.NoneType
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# CHECK: return %[[RET]] : !basicpy.NoneType
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# CHECK: }
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# CHECK-LABEL: func private @__torch__.calls_method
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# CHECK-SAME: (%[[ARG0:.*]]: !torch.nn.Module<"__torch__.TestModule">, %[[ARG1:.*]]: !numpy.ndarray<*:!numpy.any_dtype>) -> !basicpy.NoneType {
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# CHECK: %[[RET:.*]] = torch.prim.CallMethod %[[ARG0]]["method"] (%[[ARG1]]) : !torch.nn.Module<"__torch__.TestModule">, (!numpy.ndarray<*:!numpy.any_dtype>) -> !basicpy.NoneType
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# CHECK: return %[[RET]] : !basicpy.NoneType
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# CHECK: }
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def calls_method(c: 'TestModule', x):
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return c.method(x)
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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 calls_method(self, x)
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@torch.jit.export # Needed so that scripting sees it.
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def method(self, x):
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return
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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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