mirror of https://github.com/llvm/torch-mlir
46 lines
2.1 KiB
MLIR
46 lines
2.1 KiB
MLIR
// RUN: npcomp-opt -split-input-file %s -verify-diagnostics -allow-unregistered-dialect -numpy-public-functions-to-tensor | FileCheck --dump-input=fail %s
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// CHECK-LABEL: legalConversion
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module @legalConversion {
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// CHECK: @f(%arg0: tensor<3x?xf32>, %arg1: i32, %arg2: tensor<*xf32>) -> (i32, tensor<3x?xf32>, tensor<*xf32>)
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func @f(%arg0: !numpy.ndarray<[3,?]:f32>, %arg1: i32, %arg2: !numpy.ndarray<*:f32>) ->
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(i32, !numpy.ndarray<[3,?]:f32>, !numpy.ndarray<*:f32>) {
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// CHECK: %[[CREATE0:.+]] = numpy.create_array_from_tensor %arg0
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// CHECK: %[[CREATE1:.+]] = numpy.create_array_from_tensor %arg2
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// CHECK: %[[R0:.+]] = "unfoldable_arg0"(%[[CREATE0]]) : (!numpy.ndarray<[3,?]:f32>) -> !numpy.ndarray<[3,?]:f32>
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// CHECK: %[[R1:.+]] = "unfoldable_arg1"(%[[CREATE1]]) : (!numpy.ndarray<*:f32>) -> !numpy.ndarray<*:f32>
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%0 = "unfoldable_arg0"(%arg0) : (!numpy.ndarray<[3,?]:f32>) -> !numpy.ndarray<[3,?]:f32>
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%1 = "unfoldable_arg1"(%arg2) : (!numpy.ndarray<*:f32>) -> !numpy.ndarray<*:f32>
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// CHECK: %[[COPY0:.+]] = numpy.copy_to_tensor %[[R0]]
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// CHECK: %[[COPY1:.+]] = numpy.copy_to_tensor %[[R1]]
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// CHECK: return %arg1, %[[COPY0]], %[[COPY1]] : i32, tensor<3x?xf32>, tensor<*xf32>
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return %arg1, %0, %1 : i32, !numpy.ndarray<[3,?]:f32>, !numpy.ndarray<*:f32>
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}
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}
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// -----
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// CHECK-LABEL: @nonPublic
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module @nonPublic {
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// CHECK: @f(%arg0: !numpy.ndarray<[3,?]:f32>) -> !numpy.ndarray<[3,?]:f32>
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func @f(%arg0: !numpy.ndarray<[3,?]:f32>) -> (!numpy.ndarray<[3,?]:f32>)
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attributes { sym_visibility = "private" } {
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return %arg0 : !numpy.ndarray<[3,?]:f32>
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}
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}
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// -----
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// CHECK-LABEL: @called
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module @called {
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// CHECK: @f(%arg0: !numpy.ndarray<*:f32>) -> !numpy.ndarray<*:f32>
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// expected-warning @+1 {{unimplemented: cannot convert}}
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func @f(%arg0: !numpy.ndarray<*:f32>) -> !numpy.ndarray<*:f32> {
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return %arg0 : !numpy.ndarray<*:f32>
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}
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func @caller(%arg0: !numpy.ndarray<*:f32>) -> !numpy.ndarray<*:f32>
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attributes { sym_visibility = "private" } {
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%0 = call @f(%arg0) : (!numpy.ndarray<*:f32>) -> !numpy.ndarray<*:f32>
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return %0 : !numpy.ndarray<*:f32>
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}
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}
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