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1// RUN: mlir-opt %s -transform-interpreter -split-input-file | FileCheck %s2 3// CHECK-LABEL: func.func @fill(4// CHECK-SAME: %[[ARG0:.*]]: f32,5// CHECK-SAME: %[[ARG1:.*]]: memref<32x7xf32>6// CHECK-NEXT: %[[FLATTENED:.*]] = memref.collapse_shape %[[ARG1]] {{\[}}[0, 1]]7// CHECK-NEXT: linalg.fill ins(%[[ARG0]] : f32) outs(%[[FLATTENED]] : memref<224xf32>)8func.func @fill(%cst: f32, %arg: memref<32x7xf32>) {9 linalg.fill ins(%cst: f32) outs(%arg: memref<32x7xf32>)10 return11}12 13module attributes {transform.with_named_sequence} {14 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {15 %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op16 %flattened = transform.structured.flatten_elementwise %017 : (!transform.any_op) -> !transform.any_op18 transform.yield19 }20}21 22// -----23 24// CHECK-LABEL: func.func @fill_tensor(25// CHECK-SAME: %[[ARG0:.*]]: f32,26// CHECK-SAME: %[[ARG1:.*]]: tensor<32x7xf32>27// CHECK-NEXT: %[[FLATTENED:.*]] = tensor.collapse_shape %[[ARG1]] {{\[}}[0, 1]]28// CHECK-NEXT: %[[FLATTENED_RESULT:.*]] = linalg.fill ins(%[[ARG0]] : f32) outs(%[[FLATTENED]] : tensor<224xf32>)29// CHECK-NEXT: %[[RESULT:.*]] = tensor.expand_shape %[[FLATTENED_RESULT]] {{\[}}[0, 1]] output_shape [32, 7] : tensor<224xf32> into tensor<32x7xf32>30func.func @fill_tensor(%cst: f32, %arg: tensor<32x7xf32>) -> tensor<32x7xf32> {31 %0 = linalg.fill ins(%cst: f32) outs(%arg: tensor<32x7xf32>) -> tensor<32x7xf32>32 return %0 : tensor<32x7xf32>33}34 35module attributes {transform.with_named_sequence} {36 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {37 %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op38 %flattened = transform.structured.flatten_elementwise %039 : (!transform.any_op) -> !transform.any_op40 transform.yield41 }42}43 44// -----45 46// CHECK-LABEL: func.func @map(47// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: memref<32x7xf32>48// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: memref<32x7xf32>49// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]*]]: memref<32x7xf32>50// CHECK-NEXT: %[[FLATTENED_0:.*]] = memref.collapse_shape %[[ARG0]] {{\[}}[0, 1]]51// CHECK-NEXT: %[[FLATTENED_1:.*]] = memref.collapse_shape %[[ARG1]] {{\[}}[0, 1]]52// CHECK-NEXT: %[[FLATTENED_2:.*]] = memref.collapse_shape %[[ARG2]] {{\[}}[0, 1]]53// CHECK-NEXT: linalg.map { arith.addf } ins(%[[FLATTENED_0]], %[[FLATTENED_1]] : memref<224xf32>, memref<224xf32>) outs(%[[FLATTENED_2]] : memref<224xf32>)54func.func @map(%arg0: memref<32x7xf32>, %arg1: memref<32x7xf32>, %arg2: memref<32x7xf32>) {55 linalg.map {arith.addf} ins(%arg0, %arg1: memref<32x7xf32>, memref<32x7xf32>) outs(%arg2: memref<32x7xf32>)56 return57}58 59module attributes {transform.with_named_sequence} {60 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {61 %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op62 %flattened = transform.structured.flatten_elementwise %063 : (!transform.any_op) -> !transform.any_op64 transform.yield65 }66}67 68// -----69 70// CHECK-LABEL: func.func @map_already_flat(71// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: memref<32xf32>72// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: memref<32xf32>73// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]*]]: memref<32xf32>74// CHECK-NEXT: linalg.map { arith.addf } ins(%[[ARG0]], %[[ARG1]] : memref<32xf32>, memref<32xf32>) outs(%[[ARG2]] : memref<32xf32>)75func.func @map_already_flat(%arg0: memref<32xf32>, %arg1: memref<32xf32>, %arg2: memref<32xf32>) {76 linalg.map {arith.addf} ins(%arg0, %arg1: memref<32xf32>, memref<32xf32>) outs(%arg2: memref<32xf32>)77 return78}79 80module attributes {transform.with_named_sequence} {81 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {82 %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op83 %flattened = transform.structured.flatten_elementwise %084 : (!transform.any_op) -> !transform.any_op85 transform.yield86 }87}88 89// -----90 91// CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)>92// CHECK-LABEL: func.func @generic93// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: memref<32x7xf32>94// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: memref<32x7xf32>95// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]*]]: memref<32x7xf32>96// CHECK-NEXT: %[[FLATTENED_0:.*]] = memref.collapse_shape %[[ARG0]] {{\[}}[0, 1]]97// CHECK-NEXT: %[[FLATTENED_1:.*]] = memref.collapse_shape %[[ARG1]] {{\[}}[0, 1]]98// CHECK-NEXT: %[[FLATTENED_2:.*]] = memref.collapse_shape %[[ARG2]] {{\[}}[0, 1]]99// CHECK-NEXT: linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%[[FLATTENED_0]], %[[FLATTENED_1]] : memref<224xf32>, memref<224xf32>) outs(%[[FLATTENED_2]] : memref<224xf32>)100// CHECK-NEXT: ^bb0(%[[A:.*]]: f32, %[[B:.*]]: f32, %[[C:.*]]: f32)101// CHECK-NEXT: %[[SUM:.*]] = arith.addf %[[A]], %[[B]]102// CHECK-NEXT: linalg.yield %[[SUM]]103#map = affine_map<(d0, d1) -> (d0, d1)>104func.func @generic( %arg0: memref<32x7xf32>, %arg1: memref<32x7xf32>, %arg2: memref<32x7xf32>) {105 linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1: memref<32x7xf32>, memref<32x7xf32>) outs(%arg2: memref<32x7xf32>) {106 ^bb0(%a: f32, %b: f32, %c: f32):107 %0 = arith.addf %a, %b : f32108 linalg.yield %0 : f32109 }110 return111}112 113module attributes {transform.with_named_sequence} {114 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {115 %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op116 %flattened = transform.structured.flatten_elementwise %0117 : (!transform.any_op) -> !transform.any_op118 transform.yield119 }120}121