mirror of https://github.com/llvm/torch-mlir
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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# Also available under a BSD-style license. See LICENSE.
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# RUN: %PYTHON %s | FileCheck %s
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import torch
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from torch_mlir_e2e_test.annotations import annotate_args, export
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from torch_mlir.dialects.torch.importer.jit_ir import ClassAnnotator
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from torch_mlir.dialects.torch.importer.jit_ir.torchscript_annotations import extract_annotations
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class MmModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args([
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None,
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([3, 4], torch.float32, False),
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([4, 5], torch.float32, True),
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])
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def forward(self, lhs, rhs):
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return torch.mm(lhs, rhs)
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module = MmModule()
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annotator = ClassAnnotator()
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extract_annotations(module, torch.jit.script(module), annotator)
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print(annotator)
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# CHECK: ClassAnnotator {
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# CHECK: ClassAnnotation('{{.*}}.MmModule') {
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# CHECK: MethodAnnotation('forward') {
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# CHECK: isExported = true
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# CHECK: argAnnotations =
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# CHECK: ArgAnnotation(0) {
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# CHECK: dtype = <none>
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# CHECK: shape = <none>
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# CHECK: }
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# CHECK: ArgAnnotation(1) {
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# CHECK: dtype = Float
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# CHECK: shape = [3, 4]
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# CHECK: hasValueSemantics = false
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# CHECK: }
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# CHECK: ArgAnnotation(2) {
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# CHECK: dtype = Float
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# CHECK: shape = [4, 5]
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# CHECK: hasValueSemantics = true
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# CHECK: }
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# CHECK: }
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# CHECK: }
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# CHECK: }
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