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1 2// RUN: mlir-opt --transform-interpreter -cse -canonicalize -split-input-file -verify-diagnostics %s | FileCheck %s3 4#map = affine_map<()[s0] -> (-s0 + 12, 7)>5 6// CHECK-LABEL: func @pad_to_memory_space(7// CHECK-SAME: %[[arg0:.*]]: memref<24x12xf32, strided<[?, ?], offset: ?>>,8// CHECK-SAME: %[[arg1:.*]]: memref<12x25xf32, strided<[?, ?], offset: ?>>,9// CHECK-SAME: %[[arg2:.*]]: memref<24x25xf32, strided<[?, ?], offset: ?>>,10func.func @pad_to_memory_space(%arg0: tensor<24x12xf32>,11 %arg1: tensor<12x25xf32>,12 %arg2: tensor<24x25xf32>,13 %iv0 : index, %iv1 : index,14 %iv2 : index) -> tensor<24x25xf32> {15 %0 = affine.min #map()[%iv2]16 17 // CHECK: %[[s0:.*]] = memref.subview %[[arg0]]18 %1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>19 // CHECK: %[[s1:.*]] = memref.subview %[[arg1]]20 %2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>21 // CHECK: %[[s2:.*]] = memref.subview %[[arg2]]22 %3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>23 24 // CHECK: %[[alloc0:.*]] = memref.alloc() : memref<4x7xf32, 3>25 // CHECK: linalg.fill {{.*}} outs(%[[alloc0]]26 // CHECK: %[[alloc0_view:.*]] = memref.subview %[[alloc0]][0, 0] [4, %{{.*}}] [1, 1]27 // CHECK: memref.copy %[[s0]], %[[alloc0_view]]28 29 // CHECK: %[[alloc1:.*]] = memref.alloc() : memref<7x5xf32, 3>30 // CHECK: linalg.fill {{.*}} outs(%[[alloc1]]31 // CHECK: %[[alloc1_view:.*]] = memref.subview %[[alloc1]][0, 0] [%{{.*}}, 5] [1, 1]32 // CHECK: memref.copy %[[s1]], %[[alloc1_view]]33 34 // CHECK: %[[alloc2:.*]] = memref.alloc() : memref<4x5xf32, 3>35 // CHECK-NOT: linalg.fill {{.*}} outs(%[[alloc2]]36 // No subview because there is 0 padding37 // CHECK: memref.copy %[[s2]], %[[alloc2]]38 39 // CHECK: linalg.matmul ins(%[[alloc0]], %[[alloc1]] : {{.*}}) outs(%[[alloc2]] : {{.*}})40 // Copy back result.41 // CHECK: memref.copy %[[alloc2]], %[[s2]]42 %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>43 44 // insert_slice bufferizes to a no-op.45 %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>46 func.return %5 : tensor<24x25xf32>47}48 49module attributes {transform.with_named_sequence} {50 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.consumed}) {51 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op52 %padded, %pad, %copy_back = transform.structured.pad %0 {53 padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],54 padding_dimensions=[0, 1, 2],55 nofold_flags=[1, 1, 1]56 } : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)57 %buffer, %new_ops = transform.structured.bufferize_to_allocation %pad {memory_space = 3, emit_dealloc} : !transform.any_op58 %2 = transform.bufferization.one_shot_bufferize %arg1 {bufferize_function_boundaries=true} : (!transform.any_op) -> !transform.any_op59 60 transform.yield61 }62}63 64// -----65 66#map = affine_map<()[s0] -> (-s0 + 12, 7)>67 68// CHECK-LABEL: func @vectorize_and_bufferize_pad(69// CHECK-SAME: %[[arg0:.*]]: memref<24x12xf32, strided<[?, ?], offset: ?>>,70// CHECK-SAME: %[[arg1:.*]]: memref<12x25xf32, strided<[?, ?], offset: ?>>,71// CHECK-SAME: %[[arg2:.*]]: memref<24x25xf32, strided<[?, ?], offset: ?>>,72func.func @vectorize_and_bufferize_pad(%arg0: tensor<24x12xf32>,73 %arg1: tensor<12x25xf32>,74 %arg2: tensor<24x25xf32>,75 %iv0 : index, %iv1 : index,76 %iv2 : index) -> tensor<24x25xf32> {77 %0 = affine.min #map()[%iv2]78 79 // CHECK: %[[s0:.*]] = memref.subview %[[arg0]]80 %1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32>81 // CHECK: %[[s1:.*]] = memref.subview %[[arg1]]82 %2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32>83 // CHECK: %[[s2:.*]] = memref.subview %[[arg2]]84 %3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>85 86 // CHECK: %[[v0:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s0]]87 // CHECK: %[[alloc0:.*]] = memref.alloc() : memref<4x7xf32, 3>88 // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v0]], %[[alloc0]]89 90 // CHECK: %[[v1:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s1]]91 // CHECK: %[[alloc1:.*]] = memref.alloc() : memref<7x5xf32, 3>92 // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v1]], %[[alloc1]]93 94 // CHECK: %[[v2:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s2]]95 // CHECK: %[[alloc2:.*]] = memref.alloc() : memref<4x5xf32, 3>96 // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v2]], %[[alloc0]]97 98 // CHECK: linalg.matmul ins(%[[alloc0]], %[[alloc1]] : {{.*}}) outs(%[[alloc2]] : {{.*}})99 // Copy back result.100 // CHECK: memref.copy %[[alloc2]], %[[s2]]101 %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32>102 103 // insert_slice bufferizes to a no-op.104 %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>105 func.return %5 : tensor<24x25xf32>106}107 108module attributes {transform.with_named_sequence} {109 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.consumed}) {110 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op111 %padded, %pad, %copy_back = transform.structured.pad %0 {112 padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],113 padding_dimensions=[0, 1, 2],114 nofold_flags=[1, 1, 1]115 } : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)116 transform.structured.vectorize %pad vector_sizes [10, 12] : !transform.any_op117 %vector_write = transform.structured.match ops{["vector.transfer_write"]} in %arg1 : (!transform.any_op) -> !transform.any_op118 %mask_op = transform.get_parent_op %vector_write {op_name = "vector.mask"} : (!transform.any_op) -> !transform.any_op119 %buffer, %new_ops = transform.structured.bufferize_to_allocation %mask_op {memory_space = 3, emit_dealloc} : !transform.any_op120 %2 = transform.bufferization.one_shot_bufferize %arg1 {bufferize_function_boundaries=true} : (!transform.any_op) -> !transform.any_op121 transform.yield122 }123}124