129d6e554e
This is almost entirely mechanical. Differential Revision: https://reviews.llvm.org/D93357
37 lines
1.6 KiB
MLIR
37 lines
1.6 KiB
MLIR
// RUN: mlir-opt %s -tensor-constant-bufferize -std-bufferize -linalg-bufferize \
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// RUN: -tensor-bufferize -func-bufferize -finalizing-bufferize -convert-linalg-to-loops \
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// RUN: -convert-linalg-to-llvm -convert-std-to-llvm | \
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// RUN: mlir-cpu-runner -e main -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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func @foo() -> tensor<4xf32> {
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%0 = constant dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32>
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return %0 : tensor<4xf32>
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}
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func @main() {
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%0 = call @foo() : () -> tensor<4xf32>
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// Instead of relying on tensor_store which introduces aliasing, we rely on
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// the conversion of print_memref_f32(tensor<*xf32>) to
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// print_memref_f32(memref<*xf32>).
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// Note that this is skipping a step and we would need at least some function
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// attribute to declare that this conversion is valid (e.g. when we statically
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// know that things will play nicely at the C ABI boundary).
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%unranked = tensor.cast %0 : tensor<4xf32> to tensor<*xf32>
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call @print_memref_f32(%unranked) : (tensor<*xf32>) -> ()
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// CHECK: Unranked Memref base@ = {{0x[-9a-f]*}}
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// CHECK-SAME: rank = 1 offset = 0 sizes = [4] strides = [1] data =
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// CHECK-NEXT: [1, 2, 3, 4]
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return
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}
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// This gets converted to a function operating on memref<*xf32>.
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// Note that this is skipping a step and we would need at least some function
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// attribute to declare that this conversion is valid (e.g. when we statically
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// know that things will play nicely at the C ABI boundary).
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func private @print_memref_f32(%ptr : tensor<*xf32>)
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