34 lines
1.6 KiB
MLIR
34 lines
1.6 KiB
MLIR
// RUN: mlir-opt -test-linalg-elementwise-fusion-patterns=fuse-multiuse-producer -split-input-file %s | FileCheck %s
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#map = affine_map<(d0, d1) -> (d0, d1)>
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func.func @multi_use_producer(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>,
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%arg2 : tensor<?x?xf32>, %arg3 : tensor<?x?xf32>, %arg4 : tensor<?x?xf32>)
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-> (tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>) {
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%0:2 = linalg.generic {
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indexing_maps = [#map, #map, #map],
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iterator_types = ["parallel", "parallel"]}
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ins(%arg0 : tensor<?x?xf32>)
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outs(%arg1, %arg2 : tensor<?x?xf32>, tensor<?x?xf32>) {
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^bb0(%b0: f32, %b1 : f32, %b2 : f32):
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%1 = arith.addf %b0, %b1 : f32
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linalg.yield %1, %1 : f32, f32
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} -> (tensor<?x?xf32>, tensor<?x?xf32>)
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%2 = linalg.generic {
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indexing_maps = [#map, #map, #map],
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iterator_types = ["parallel", "parallel"]}
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ins(%0#1, %arg3 : tensor<?x?xf32>, tensor<?x?xf32>)
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outs(%arg4 : tensor<?x?xf32>) {
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^bb0(%b0 : f32, %b1 : f32, %b2 : f32):
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%3 = arith.mulf %b0, %b1 : f32
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linalg.yield %3 : f32
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} -> tensor<?x?xf32>
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return %0#0, %0#1, %2 : tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>
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}
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// CHECK: func @multi_use_producer(
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// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32>
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// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<?x?xf32>
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// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<?x?xf32>
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// CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: tensor<?x?xf32>
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// CHECK-SAME: %[[ARG4:[a-zA-Z0-9]+]]: tensor<?x?xf32>)
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// CHECK: %[[RESULT:.+]]:3 = linalg.generic
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// CHECK: return %[[RESULT]]#0, %[[RESULT]]#1, %[[RESULT]]#2
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