5a7b919409
The current StandardToLLVM conversion patterns only really handle the Func dialect. The pass itself adds patterns for Arithmetic/CFToLLVM, but those should be/will be split out in a followup. This commit focuses solely on being an NFC rename. Aside from the directory change, the pattern and pass creation API have been renamed: * populateStdToLLVMFuncOpConversionPattern -> populateFuncToLLVMFuncOpConversionPattern * populateStdToLLVMConversionPatterns -> populateFuncToLLVMConversionPatterns * createLowerToLLVMPass -> createConvertFuncToLLVMPass Differential Revision: https://reviews.llvm.org/D120778
40 lines
2.0 KiB
MLIR
40 lines
2.0 KiB
MLIR
// UNSUPPORTED: asan
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// RUN: mlir-opt %s -linalg-bufferize -arith-bufferize \
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// RUN: -tensor-bufferize -func-bufferize -finalizing-bufferize -buffer-deallocation -convert-linalg-to-loops -convert-scf-to-cf \
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// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
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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_c_runner_utils%shlibext,%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
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// RUN: | FileCheck %s
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// RUN: mlir-opt %s -linalg-tile="tile-sizes=1,2,3" -linalg-bufferize \
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// RUN: -scf-bufferize -arith-bufferize -tensor-bufferize \
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// RUN: -func-bufferize \
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// RUN: -finalizing-bufferize -convert-linalg-to-loops -convert-scf-to-cf -convert-scf-to-cf \
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// RUN: -convert-linalg-to-llvm -lower-affine -convert-scf-to-cf --convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
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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_c_runner_utils%shlibext,%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 = arith.constant dense<[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]> : tensor<2x3xf32>
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%B = arith.constant dense<[[1.0, 2.0, 3.0, 4.0],
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[5.0, 6.0, 7.0, 8.0],
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[9.0, 10.0, 11.0, 12.0]]> : tensor<3x4xf32>
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%C = arith.constant dense<1000.0> : tensor<2x4xf32>
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%D = linalg.matmul ins(%A, %B: tensor<2x3xf32>, tensor<3x4xf32>)
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outs(%C: tensor<2x4xf32>) -> tensor<2x4xf32>
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%unranked = tensor.cast %D : tensor<2x4xf32> 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 = 2 offset = 0 sizes = [2, 4] strides = [4, 1] data =
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// CHECK-NEXT: [1038, 1044, 1050, 1056]
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// CHECK-NEXT: [1083, 1098, 1113, 1128]
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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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