29 lines
1.5 KiB
MLIR
29 lines
1.5 KiB
MLIR
// RUN: mlir-opt --transform-interpreter %s | FileCheck %s
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// CHECK-LABEL: func.func @matmul_split
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func.func @matmul_split(%A : tensor<?x256xf32>, %B: tensor<256x32xf32>, %C: tensor<?x32xf32>) -> tensor<?x32xf32> {
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// CHECK: bufferization.alloc_tensor({{.*}}) : tensor<?x32x64xf32>
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// CHECK: linalg.generic
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// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction"]
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// CHECK-SAME: ins(%{{[a-zA-Z0-9]*}}, %{{[a-zA-Z0-9]*}}, %{{[a-zA-Z0-9]*}} : tensor<?x256xf32>, tensor<256x32xf32>, tensor<64x4xi1>)
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// CHECK-SAME: outs(%{{[a-zA-Z0-9]*}} : tensor<?x32x64xf32>) {
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// CHECK: linalg.generic
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// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]
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// CHECK-SAME: ins(%{{[a-zA-Z0-9]*}} : tensor<?x32x64xf32>)
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// CHECK-SAME: outs(%{{[a-zA-Z0-9]*}} : tensor<?x32xf32>) {
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%0 = linalg.matmul ins(%A, %B: tensor<?x256xf32>, tensor<256x32xf32>)
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outs(%C: tensor<?x32xf32>) -> tensor<?x32xf32>
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return %0: tensor<?x32xf32>
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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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%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
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%1:4 = transform.structured.split_reduction %0
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{ split_factor = 4, insert_split_dimension = 2, use_scaling_algorithm, use_alloc}
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: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
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transform.yield
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}
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}
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