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
34 lines
1.5 KiB
MLIR
34 lines
1.5 KiB
MLIR
// RUN: mlir-opt %s -linalg-bufferize -std-bufferize \
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// RUN: -tensor-constant-bufferize -tensor-bufferize -func-bufferize \
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// RUN: -finalizing-bufferize \
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// RUN: -convert-linalg-to-loops -convert-scf-to-std -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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%const = constant dense<[[[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]]]> : tensor<1x2x3xf32>
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%dynamic = tensor.cast %const: tensor<1x2x3xf32> to tensor<1x?x3xf32>
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%offset = constant 2 : index
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%cst = constant 2.3 : f32
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%c0 = constant 0 : index
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%out = linalg.pad_tensor %dynamic low[%c0, %offset, %c0] high[%c0, %c0, %offset] {
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^bb0(%gen_arg1: index, %gen_arg2: index, %gen_arg3: index): // no predecessors
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linalg.yield %cst : f32
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} : tensor<1x?x3xf32> to tensor<1x?x?xf32>
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%unranked = tensor.cast %out: tensor<1x?x?xf32> 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 = 3 offset = 0 sizes = [1, 4, 5] strides = [20, 5, 1] data =
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// CHECK-NEXT{LITERAL}: [[[2.3, 2.3, 2.3, 2.3, 2.3],
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// CHECK-NEXT: [2.3, 2.3, 2.3, 2.3, 2.3],
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// CHECK-NEXT: [1, 2, 3, 2.3, 2.3],
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// CHECK-NEXT: [2, 3, 4, 2.3, 2.3]]]
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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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