torch-mlir/test/Dialect/Numpy/canonicalizers.mlir

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