mirror of https://github.com/llvm/torch-mlir
43 lines
1.6 KiB
Python
43 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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class TestModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.t1 = torch.ones(1)
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self.t2 = torch.ones(1)
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# CHECK-LABEL: func{{.*}}TestModule.forward{{.*}}(
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# CHECK-SAME: %[[SELF:.*]]: !torch.nn.Module) -> !basicpy.NoneType {
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def forward(self):
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# CHECK: %[[T2:.*]] = torch.prim.GetAttr %[[SELF]]["t2"]
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# CHECK: torch.prim.SetAttr %[[SELF]]["t1"] = %[[T2]]
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self.t1 = self.t2
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# CHECK: torch.prim.CallMethod %[[SELF]]["callee"] (%{{.*}}, %{{.*}})
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self.callee(self.t1, self.t2)
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# CHECK-LABEL: func{{.*}}TestModule.callee{{.*}}(
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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>
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def callee(self, x, y):
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# CHECK: %[[BYTES:.*]] = basicpy.bytes_constant "x"
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# CHECK: torch.prim.Print(%[[BYTES]], %[[X]]) : !basicpy.BytesType, !numpy.ndarray<*:!numpy.any_dtype>
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print("x", x)
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pass
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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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