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// RUN: mlir-opt --test-transform-dialect-interpreter %s | FileCheck %s
// CHECK-LABEL: func.func @matmul_split
func.func @matmul_split(%A : tensor<?x256xf32>, %B: tensor<256x32xf32>, %C: tensor<?x32xf32>) -> tensor<?x32xf32> {
// CHECK: bufferization.alloc_tensor({{.*}}) : tensor<?x32x64xf32>
// CHECK: linalg.generic
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction"]
// CHECK-SAME: ins(%{{[a-zA-Z0-9]*}}, %{{[a-zA-Z0-9]*}}, %{{[a-zA-Z0-9]*}} : tensor<?x256xf32>, tensor<256x32xf32>, tensor<64x4xi1>)
// CHECK-SAME: outs(%{{[a-zA-Z0-9]*}} : tensor<?x32x64xf32>) {
// CHECK: linalg.generic
// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]
// CHECK-SAME: ins(%{{[a-zA-Z0-9]*}} : tensor<?x32x64xf32>)
// CHECK-SAME: outs(%{{[a-zA-Z0-9]*}} : tensor<?x32xf32>) {
%0 = linalg.matmul ins(%A, %B: tensor<?x256xf32>, tensor<256x32xf32>)
outs(%C: tensor<?x32xf32>) -> tensor<?x32xf32>
return %0: tensor<?x32xf32>
}
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1:4 = transform.structured.split_reduction %0
{ split_factor = 4, insert_split_dimension = 2, use_scaling_algorithm, use_alloc}
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
}
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