mirror of https://github.com/llvm/torch-mlir
25 lines
1.6 KiB
MLIR
25 lines
1.6 KiB
MLIR
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// RUN: npcomp-opt -split-input-file %s -numpy-array-to-tensor | FileCheck --dump-input=fail %s
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// Basic case that can be resolved with local reasoning.
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// This pass will eventually need to learn about aliasing relationships.
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//
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// This is taken from a test case from an e2e spike, and isn't intended to be
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// particularly minimal or specifically test one thing, since the pass is
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// currently just a handful of canonicalization patterns that are already
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// tested elsewhere.
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// CHECK-LABEL: func @local(
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// CHECK-SAME: %[[ARG:.*]]: tensor<2x3x?xf32>) -> tensor<*x!numpy.any_dtype> {
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// CHECK: %[[ERASED:.*]] = numpy.tensor_static_info_cast %[[ARG]] : tensor<2x3x?xf32> to tensor<*x!numpy.any_dtype>
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// CHECK: %[[RET:.*]] = "aten.tanh"(%[[ERASED]]) : (tensor<*x!numpy.any_dtype>) -> tensor<*x!numpy.any_dtype>
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// CHECK: return %[[RET]] : tensor<*x!numpy.any_dtype>
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func @local(%arg0: tensor<2x3x?xf32>) -> tensor<*x!numpy.any_dtype> {
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%0 = numpy.create_array_from_tensor %arg0 : (tensor<2x3x?xf32>) -> !numpy.ndarray<[2,3,?]:f32>
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%1 = numpy.static_info_cast %0 : !numpy.ndarray<[2,3,?]:f32> to !numpy.ndarray<*:!numpy.any_dtype>
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%2 = numpy.copy_to_tensor %1 : (!numpy.ndarray<*:!numpy.any_dtype>) -> tensor<*x!numpy.any_dtype>
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%3 = "aten.tanh"(%2) : (tensor<*x!numpy.any_dtype>) -> tensor<*x!numpy.any_dtype>
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%4 = numpy.create_array_from_tensor %3 : (tensor<*x!numpy.any_dtype>) -> !numpy.ndarray<*:!numpy.any_dtype>
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%5 = numpy.copy_to_tensor %4 : (!numpy.ndarray<*:!numpy.any_dtype>) -> tensor<*x!numpy.any_dtype>
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return %5 : tensor<*x!numpy.any_dtype>
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}
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