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// RUN: mlir-opt %s --test-transform-dialect-interpreter --split-input-file | FileCheck %s
// CHECK-LABEL: func.func @eliminate_tensor_empty(
// CHECK-SAME: %[[arg0:.*]]: tensor<50x91xf32>,
// CHECK-NOT: tensor.empty
// CHECK: %[[filled:.*]] = linalg.fill {{.*}} outs(%[[arg0]]
// CHECK: %[[matmul:.*]] = linalg.matmul {{.*}} outs(%[[filled]]
// CHECK: %[[generic:.*]] = linalg.generic {{.*}} outs(%[[matmul]]
// CHECK: return %[[generic]]
func.func @eliminate_tensor_empty(
%arg0: tensor<50x91xf32>, %arg1: tensor<91xf32>, %arg2: tensor<50x1280xf32>,
%arg3: tensor<1280x91xf32>) -> tensor<50x91xf32>
{
%cst = arith.constant 0.0 : f32
%0 = tensor.empty() : tensor<50x91xf32>
%1 = linalg.fill ins(%cst : f32)
outs(%0 : tensor<50x91xf32>) -> tensor<50x91xf32>
%2 = linalg.matmul
ins(%arg2, %arg3 : tensor<50x1280xf32>, tensor<1280x91xf32>)
outs(%1 : tensor<50x91xf32>) -> tensor<50x91xf32>
%3 = linalg.generic
{indexing_maps = [affine_map<(d0, d1) -> (d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%arg1, %2 : tensor<91xf32>, tensor<50x91xf32>)
outs(%arg0 : tensor<50x91xf32>) {
^bb0(%in: f32, %in_0: f32, %out: f32):
%16 = arith.addf %in, %in_0 : f32
linalg.yield %16 : f32
} -> tensor<50x91xf32>
return %3 : tensor<50x91xf32>
}
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.any_op
transform.structured.eliminate_empty_tensors %0 : !transform.any_op
transform.apply_patterns to %0 {
transform.apply_patterns.linalg.erase_unnecessary_inputs
} : !transform.any_op
}
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