mirror of https://github.com/llvm/torch-mlir
28 lines
1.0 KiB
Python
28 lines
1.0 KiB
Python
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# -*- 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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# RUN: %PYTHON %s | npcomp-opt -aten-recognize-kernels -numpy-public-functions-to-tensor -canonicalize | FileCheck %s
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# TODO: This test should go away or become part of an e2e test suite. It is
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# preserved right now as a stop-gap.
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import torch
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import torch_mlir
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t0 = torch.randn((1,4))
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t1 = torch.randn((4,1))
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mb = torch_mlir.ModuleBuilder()
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with mb.capture_function("foobar", [t0, t1]) as f:
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result = t0 + t1
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f.returns([result])
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# CHECK-LABEL: func @foobar(
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# CHECK-SAME: %[[VAL_0:.*]]: tensor<1x4xf32>,
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# CHECK-SAME: %[[VAL_1:.*]]: tensor<4x1xf32>) -> tensor<4x4xf32> {
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# CHECK: %[[VAL_2:.*]] = constant 1 : i64
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# CHECK: %[[VAL_3:.*]] = "aten.add"(%[[VAL_0]], %[[VAL_1]], %[[VAL_2]]) : (tensor<1x4xf32>, tensor<4x1xf32>, i64) -> tensor<4x4xf32>
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# CHECK: return %[[VAL_3]] : tensor<4x4xf32>
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# CHECK: }
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mb.module.operation.print(large_elements_limit=2)
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