derek.holloway
7502bf64a4
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62 lines
2.8 KiB
MLIR
62 lines
2.8 KiB
MLIR
// RUN: mlir-opt -slice-analysis-test -split-input-file %s | FileCheck %s
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func.func @slicing_linalg_op(%arg0 : index, %arg1 : index, %arg2 : index) {
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%a = memref.alloc(%arg0, %arg2) : memref<?x?xf32>
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%b = memref.alloc(%arg2, %arg1) : memref<?x?xf32>
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%c = memref.alloc(%arg0, %arg1) : memref<?x?xf32>
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%d = memref.alloc(%arg0, %arg1) : memref<?x?xf32>
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linalg.matmul ins(%a, %b : memref<?x?xf32>, memref<?x?xf32>)
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outs(%c : memref<?x?xf32>)
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linalg.matmul ins(%a, %b : memref<?x?xf32>, memref<?x?xf32>)
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outs(%d : memref<?x?xf32>)
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memref.dealloc %c : memref<?x?xf32>
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memref.dealloc %b : memref<?x?xf32>
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memref.dealloc %a : memref<?x?xf32>
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memref.dealloc %d : memref<?x?xf32>
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return
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}
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// CHECK-LABEL: func @slicing_linalg_op__backward_slice__0
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// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: index
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// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: index
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// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: index
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// CHECK-DAG: %[[A:.+]] = memref.alloc(%[[ARG0]], %[[ARG2]]) : memref<?x?xf32>
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// CHECK-DAG: %[[B:.+]] = memref.alloc(%[[ARG2]], %[[ARG1]]) : memref<?x?xf32>
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// CHECK-DAG: %[[C:.+]] = memref.alloc(%[[ARG0]], %[[ARG1]]) : memref<?x?xf32>
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// CHECK: return
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// CHECK-LABEL: func @slicing_linalg_op__backward_slice__1
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// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: index
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// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: index
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// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: index
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// CHECK-DAG: %[[A:.+]] = memref.alloc(%[[ARG0]], %[[ARG2]]) : memref<?x?xf32>
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// CHECK-DAG: %[[B:.+]] = memref.alloc(%[[ARG2]], %[[ARG1]]) : memref<?x?xf32>
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// CHECK-DAG: %[[C:.+]] = memref.alloc(%[[ARG0]], %[[ARG1]]) : memref<?x?xf32>
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// CHECK: return
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// -----
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#map = affine_map<(d0, d1) -> (d0, d1)>
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func.func @slice_use_from_above(%arg0: tensor<5x5xf32>, %arg1: tensor<5x5xf32>) {
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%0 = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0 : tensor<5x5xf32>) outs(%arg1 : tensor<5x5xf32>) {
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^bb0(%in: f32, %out: f32):
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%2 = arith.addf %in, %in : f32
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linalg.yield %2 : f32
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} -> tensor<5x5xf32>
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%collapsed = tensor.collapse_shape %0 [[0, 1]] : tensor<5x5xf32> into tensor<25xf32>
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%1 = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel"]} ins(%0 : tensor<5x5xf32>) outs(%arg1 : tensor<5x5xf32>) {
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^bb0(%in: f32, %out: f32):
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%c2 = arith.constant 2 : index
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%extracted = tensor.extract %collapsed[%c2] : tensor<25xf32>
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%2 = arith.addf %extracted, %extracted : f32
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linalg.yield %2 : f32
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} -> tensor<5x5xf32>
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return
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}
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// CHECK-LABEL: func @slice_use_from_above__backward_slice__0
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// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor
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// CHECK: %[[A:.+]] = linalg.generic {{.*}} ins(%[[ARG0]]
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// CHECK: %[[B:.+]] = tensor.collapse_shape %[[A]]
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// CHECK: return
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