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// RUN: mlir-opt %s -transform-interpreter -test-transform-dialect-erase-schedule -one-shot-bufferize="bufferize-function-boundaries" -buffer-deallocation-pipeline -lower-vector-mask --test-lower-to-llvm | \
// RUN: mlir-runner -e main -entry-point-result=void --shared-libs=%mlir_c_runner_utils,%mlir_runner_utils | \
// RUN: FileCheck %s
func.func private @printMemrefF32(%ptr : tensor<*xf32>)
func.func @main() {
%c4 = arith.constant 4 : index
%c8 = arith.constant 8 : index
%A = arith.constant dense<[
[ 1.1, 2.1 ],
[ 1.2, 2.2 ],
[ 1.3, 2.3 ],
[ 1.4, 2.4 ],
[ 1.5, 2.5 ],
[ 1.6, 2.6 ],
[ 1.7, 2.7 ],
[ 1.8, 2.8 ]
]> : tensor<8x2xf32>
%B = arith.constant dense<[
[ 10.1, 11.1, 12.1, 13.1 ],
[ 10.2, 11.2, 12.2, 13.2 ]
]> : tensor<2x4xf32>
%C_dyn = bufferization.alloc_tensor(%c8, %c4) : tensor<?x?xf32>
%A_dyn = tensor.cast %A : tensor<8x2xf32> to tensor<?x?xf32>
%B_dyn = tensor.cast %B : tensor<2x4xf32> to tensor<?x?xf32>
%c0_i32 = arith.constant 0 : i32
%C_init = linalg.fill ins(%c0_i32 : i32) outs(%C_dyn : tensor<?x?xf32>) -> tensor<?x?xf32>
%res = linalg.matmul ins(%A_dyn, %B_dyn: tensor<?x?xf32>, tensor<?x?xf32>)
outs(%C_init: tensor<?x?xf32>) -> tensor<?x?xf32>
%xf = tensor.cast %res : tensor<?x?xf32> to tensor<*xf32>
// CHECK: {{\[}}[32.53, 35.73, 38.93, 42.13],
// CHECK-NEXT: [34.56, 37.96, 41.36, 44.76],
// CHECK-NEXT: [36.59, 40.19, 43.79, 47.39],
// CHECK-NEXT: [38.62, 42.42, 46.22, 50.02],
// CHECK-NEXT: [0, 0, 0, 0],
// CHECK-NEXT: [0, 0, 0, 0],
// CHECK-NEXT: [0, 0, 0, 0],
// CHECK-NEXT: [0, 0, 0, 0]]
call @printMemrefF32(%xf) : (tensor<*xf32>) -> ()
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%func_op = transform.get_parent_op %0 : (!transform.any_op) -> !transform.op<"func.func">
transform.structured.vectorize %0 vector_sizes [4, 4, 2] : !transform.any_op
transform.apply_patterns to %func_op {
transform.apply_patterns.vector.lower_multi_reduction lowering_strategy = "innerreduction"
} : !transform.op<"func.func">
transform.yield
}
}
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