mirror of https://github.com/llvm/torch-mlir
28 lines
1.4 KiB
MLIR
28 lines
1.4 KiB
MLIR
// RUN: npcomp-opt -split-input-file %s -canonicalize | FileCheck --dump-input=fail %s
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// CHECK-LABEL: func @elideCreateRedundantArrayFromTensor
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func @elideCreateRedundantArrayFromTensor() -> tensor<2xf64> {
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// CHECK: %[[CST:.*]] = constant
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// CHECK-NOT: numpy.create_array_from_tensor
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// CHECK-NOT: numpy.copy_to_tensor
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%cst = constant dense<[1.000000e+00, 2.000000e+00]> : tensor<2xf64>
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%0 = numpy.create_array_from_tensor %cst : (tensor<2xf64>) -> !numpy.ndarray<[2]:f64>
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%1 = numpy.copy_to_tensor %0 : (!numpy.ndarray<[2]:f64>) -> tensor<2xf64>
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// CHECK: return %[[CST]]
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return %1 : tensor<2xf64>
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}
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// This test verifies that the very trivial elision is not overly aggressive.
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// Note that in this example, it is still safe to remove the copy, but the
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// analysis has not yet been written to do that safely.
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// CHECK-LABEL: func @elideCreateRedundantArrayFromTensorNonTrivial
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func @elideCreateRedundantArrayFromTensorNonTrivial() -> (tensor<2xf64>, tensor<2xf64>) {
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// CHECK: numpy.create_array_from_tensor
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// CHECK: numpy.copy_to_tensor
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%cst = constant dense<[1.000000e+00, 2.000000e+00]> : tensor<2xf64>
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%0 = numpy.create_array_from_tensor %cst : (tensor<2xf64>) -> !numpy.ndarray<[2]:f64>
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%1 = numpy.copy_to_tensor %0 : (!numpy.ndarray<[2]:f64>) -> tensor<2xf64>
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%2 = numpy.copy_to_tensor %0 : (!numpy.ndarray<[2]:f64>) -> tensor<2xf64>
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return %1, %2 : tensor<2xf64>, tensor<2xf64>
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}
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