75e5f0aac9
After the MemRef has been split out of the Standard dialect, the conversion to the LLVM dialect remained as a huge monolithic pass. This is undesirable for the same complexity management reasons as having a huge Standard dialect itself, and is even more confusing given the existence of a separate dialect. Extract the conversion of the MemRef dialect operations to LLVM into a separate library and a separate conversion pass. Reviewed By: herhut, silvas Differential Revision: https://reviews.llvm.org/D105625
20 lines
905 B
MLIR
20 lines
905 B
MLIR
// RUN: mlir-opt %s -convert-elementwise-to-linalg -std-bufferize -tensor-constant-bufferize -linalg-bufferize -tensor-bufferize -func-bufferize -convert-linalg-to-loops -convert-linalg-to-llvm --convert-memref-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 @main() {
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%a = constant dense<[1.0, 2.0, 3.0]> : tensor<3xf32>
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%b = constant dense<[10.0, 20.0, 30.0]> : tensor<3xf32>
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%addf = addf %a, %b : tensor<3xf32>
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%addf_unranked = tensor.cast %addf : tensor<3xf32> to tensor<*xf32>
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call @print_memref_f32(%addf_unranked) : (tensor<*xf32>) -> ()
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// CHECK: Unranked Memref base@ = {{.*}} rank = 1 offset = 0 sizes = [3] strides = [1] data =
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// CHECK-NEXT: [11, 22, 33]
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return
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}
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func private @print_memref_f32(%ptr : tensor<*xf32>)
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