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