30 lines
1.3 KiB
MLIR
30 lines
1.3 KiB
MLIR
// RUN: mlir-opt %s -test-linalg-elementwise-fusion-patterns=fuse-generic-ops-control -split-input-file | FileCheck %s
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#map = affine_map<(d0, d1) -> (d0, d1)>
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func.func @drop_unused_producer_result(%arg0 : tensor<?x?xf32>,
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%arg1 : 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>) outs(%arg0, %arg0 : 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, %b0 : f32
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%2 = arith.mulf %b0, %b0 : f32
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linalg.yield %1, %2 : f32, f32
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} -> (tensor<?x?xf32>, tensor<?x?xf32>)
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%3 = 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#0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%arg0 : tensor<?x?xf32>) {
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^bb0(%b0: f32, %b1: f32, %b2: f32):
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%4 = arith.subf %b0, %b1 : f32
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linalg.yield %4 : f32
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} -> tensor<?x?xf32>
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return %3 : tensor<?x?xf32>
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}
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// CHECK-LABEL: func @drop_unused_producer_result
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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: %[[FUSED_OP:.+]] = linalg.generic
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// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] :
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// CHECK: return %[[FUSED_OP]]
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