eb6c4197d5
Do not run `cf-to-llvm` as part of `func-to-llvm`. This commit fixes https://github.com/llvm/llvm-project/issues/70982. This commit changes the way how `func.func` ops are lowered to LLVM. Previously, the signature of the entire region (i.e., entry block and all other blocks in the `func.func` op) was converted as part of the `func.func` lowering pattern. Now, only the entry block is converted. The remaining block signatures are converted together with `cf.br` and `cf.cond_br` as part of `cf-to-llvm`. All unstructured control flow is not converted as part of a single pass (`cf-to-llvm`). `func-to-llvm` no longer deals with unstructured control flow. Also add more test cases for control flow dialect ops. Note: This PR is in preparation of #120431, which adds an additional GPU-specific lowering for `cf.assert`. This was a problem because `cf.assert` used to be converted as part of `func-to-llvm`. Note for LLVM integration: If you see failures, add `-convert-cf-to-llvm` to your pass pipeline.
35 lines
1.6 KiB
MLIR
35 lines
1.6 KiB
MLIR
// RUN: mlir-opt %s -test-linalg-transform-patterns=test-linalg-to-vector-patterns \
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// RUN: -one-shot-bufferize="bufferize-function-boundaries" \
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// RUN: -buffer-deallocation-pipeline -convert-bufferization-to-memref \
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// RUN: -convert-linalg-to-loops -convert-scf-to-cf -expand-strided-metadata \
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// RUN: -lower-affine -convert-arith-to-llvm -finalize-memref-to-llvm -convert-func-to-llvm -convert-cf-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_c_runner_utils,%mlir_runner_utils \
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// RUN: | FileCheck %s
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func.func @main() {
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%const = arith.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 = arith.constant 2 : index
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%cst = arith.constant 2.3 : f32
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%c0 = arith.constant 0 : index
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%out = tensor.pad %dynamic low[%c0, %offset, %c0] high[%c0, %c0, %offset] {
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^bb0(%gen_arg1: index, %gen_arg2: index, %gen_arg3: index):
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tensor.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 @printMemrefF32(%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.func private @printMemrefF32(%ptr : tensor<*xf32>)
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