4a58a63280
This patch removes the `test-linalg-to-vector-patterns` option from the `-test-linalg-transform-patterns=` test flag. It was only used in one test, where a more specialized transform dialect op can be used instead: * `transform.apply_patterns.linalg.pad_vectorization` While we could preserve `test-linalg-to-vector-patterns`, it's better to rely on finer-grained transformations — this way, we know exactly what is being run and tested. Now that its only use has been removed, it feels natural to delete `test-linalg-to-vector-patterns`.
49 lines
2.1 KiB
MLIR
49 lines
2.1 KiB
MLIR
// RUN: mlir-opt %s -transform-interpreter -test-transform-dialect-erase-schedule \
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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-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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// TODO: Use TD for vectorization and remove `test-linalg-to-vector-patterns`
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// that's otherwise not required.
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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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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%func_op = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.op<"func.func">
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transform.apply_patterns to %func_op {
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transform.apply_patterns.linalg.pad_vectorization
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} : !transform.op<"func.func">
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transform.yield
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}
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}
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func.func private @printMemrefF32(%ptr : tensor<*xf32>)
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