mirror of https://github.com/llvm/torch-mlir
42 lines
2.3 KiB
MLIR
42 lines
2.3 KiB
MLIR
// RUN: npcomp-opt -split-input-file %s | npcomp-opt | FileCheck --dump-input=fail %s
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// -----
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// CHECK-LABEL: @builtin_ufunc
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func @builtin_ufunc(%arg0 : tensor<3xf64>, %arg1 : tensor<3xf64>) -> tensor<3xf64> {
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%0 = numpy.builtin_ufunc_call<"numpy.add"> (%arg0, %arg1) : (tensor<3xf64>, tensor<3xf64>) -> tensor<3xf64>
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return %0 : tensor<3xf64>
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}
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// CHECK-LABEL: @ndarray_tensor_bridging
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func @ndarray_tensor_bridging(%arg0: !numpy.ndarray<[2,3]:f32>, %arg1: !numpy.ndarray<[2,3]:f32>, %arg2: tensor<2x3xf32>) {
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// CHECK-NEXT: numpy.copy_to_tensor
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%t = numpy.copy_to_tensor %arg1 : (!numpy.ndarray<[2,3]:f32>) -> tensor<2x3xf32>
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// CHECK-NEXT: numpy.create_array_from_tensor
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%a = numpy.create_array_from_tensor %arg2 : (tensor<2x3xf32>) -> !numpy.ndarray<[2,3]:f32>
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// CHECK-NEXT: numpy.overwrite_array
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numpy.overwrite_array %arg2 overwrites %arg0 : tensor<2x3xf32>, !numpy.ndarray<[2,3]:f32>
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return
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}
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// CHECK-LABEL: @static_info_cast
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func @static_info_cast(%arg0: !numpy.ndarray<[2,3]:f32>, %arg1: !numpy.ndarray<[?,3]:f32>, %arg2: !numpy.ndarray<*:f32>) {
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// CHECK-NEXT: numpy.static_info_cast %arg0 : !numpy.ndarray<[2,3]:f32> to !numpy.ndarray<*:!numpy.any_dtype>
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%0 = numpy.static_info_cast %arg0 : !numpy.ndarray<[2,3]:f32> to !numpy.ndarray<*:!numpy.any_dtype>
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// CHECK-NEXT: numpy.static_info_cast %arg1 : !numpy.ndarray<[?,3]:f32> to !numpy.ndarray<[7,3]:f32>
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%1 = numpy.static_info_cast %arg1 : !numpy.ndarray<[?,3]:f32> to !numpy.ndarray<[7,3]:f32>
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// CHECK-NEXT: numpy.static_info_cast %arg2 : !numpy.ndarray<*:f32> to !numpy.ndarray<[?,?]:f32>
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%2 = numpy.static_info_cast %arg2 : !numpy.ndarray<*:f32> to !numpy.ndarray<[?,?]:f32>
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return
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}
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// CHECK-LABEL: @tensor_static_info_cast
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func @tensor_static_info_cast(%arg0: tensor<2x3xf32>, %arg1: tensor<?x3xf32>, %arg2: tensor<*xf32>) {
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// CHECK-NEXT: numpy.tensor_static_info_cast %arg0 : tensor<2x3xf32> to tensor<*x!numpy.any_dtype>
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%0 = numpy.tensor_static_info_cast %arg0 : tensor<2x3xf32> to tensor<*x!numpy.any_dtype>
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// CHECK-NEXT: numpy.tensor_static_info_cast %arg1 : tensor<?x3xf32> to tensor<7x3xf32>
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%1 = numpy.tensor_static_info_cast %arg1 : tensor<?x3xf32> to tensor<7x3xf32>
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// CHECK-NEXT: numpy.tensor_static_info_cast %arg2 : tensor<*xf32> to tensor<?x?xf32>
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%2 = numpy.tensor_static_info_cast %arg2 : tensor<*xf32> to tensor<?x?xf32>
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return
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}
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