| |
| // RUN: mlir-opt --transform-interpreter -cse -canonicalize -split-input-file -verify-diagnostics %s | FileCheck %s |
| |
| #map = affine_map<()[s0] -> (-s0 + 12, 7)> |
| |
| // CHECK-LABEL: func @pad_to_memory_space( |
| // CHECK-SAME: %[[arg0:.*]]: memref<24x12xf32, strided<[?, ?], offset: ?>>, |
| // CHECK-SAME: %[[arg1:.*]]: memref<12x25xf32, strided<[?, ?], offset: ?>>, |
| // CHECK-SAME: %[[arg2:.*]]: memref<24x25xf32, strided<[?, ?], offset: ?>>, |
| func.func @pad_to_memory_space(%arg0: tensor<24x12xf32>, |
| %arg1: tensor<12x25xf32>, |
| %arg2: tensor<24x25xf32>, |
| %iv0 : index, %iv1 : index, |
| %iv2 : index) -> tensor<24x25xf32> { |
| %0 = affine.min #map()[%iv2] |
| |
| // CHECK: %[[s0:.*]] = memref.subview %[[arg0]] |
| %1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32> |
| // CHECK: %[[s1:.*]] = memref.subview %[[arg1]] |
| %2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32> |
| // CHECK: %[[s2:.*]] = memref.subview %[[arg2]] |
| %3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32> |
| |
| // CHECK: %[[alloc0:.*]] = memref.alloc() : memref<4x7xf32, 3> |
| // CHECK: linalg.fill {{.*}} outs(%[[alloc0]] |
| // CHECK: %[[alloc0_view:.*]] = memref.subview %[[alloc0]][0, 0] [4, %{{.*}}] [1, 1] |
| // CHECK: memref.copy %[[s0]], %[[alloc0_view]] |
| |
| // CHECK: %[[alloc1:.*]] = memref.alloc() : memref<7x5xf32, 3> |
| // CHECK: linalg.fill {{.*}} outs(%[[alloc1]] |
| // CHECK: %[[alloc1_view:.*]] = memref.subview %[[alloc1]][0, 0] [%{{.*}}, 5] [1, 1] |
| // CHECK: memref.copy %[[s1]], %[[alloc1_view]] |
| |
| // CHECK: %[[alloc2:.*]] = memref.alloc() : memref<4x5xf32, 3> |
| // CHECK-NOT: linalg.fill {{.*}} outs(%[[alloc2]] |
| // No subview because there is 0 padding |
| // CHECK: memref.copy %[[s2]], %[[alloc2]] |
| |
| // CHECK: linalg.matmul ins(%[[alloc0]], %[[alloc1]] : {{.*}}) outs(%[[alloc2]] : {{.*}}) |
| // Copy back result. |
| // CHECK: memref.copy %[[alloc2]], %[[s2]] |
| %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32> |
| |
| // insert_slice bufferizes to a no-op. |
| %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32> |
| func.return %5 : tensor<24x25xf32> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.consumed}) { |
| %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| %padded, %pad, %copy_back = transform.structured.pad %0 { |
| padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32], |
| padding_dimensions=[0, 1, 2], |
| pack_paddings=[1, 1, 1] |
| } : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op) |
| %buffer, %new_ops = transform.structured.bufferize_to_allocation %pad {memory_space = 3, emit_dealloc} : !transform.any_op |
| %2 = transform.bufferization.one_shot_bufferize %arg1 {bufferize_function_boundaries=true} : (!transform.any_op) -> !transform.any_op |
| |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| #map = affine_map<()[s0] -> (-s0 + 12, 7)> |
| |
| // CHECK-LABEL: func @vectorize_and_bufferize_pad( |
| // CHECK-SAME: %[[arg0:.*]]: memref<24x12xf32, strided<[?, ?], offset: ?>>, |
| // CHECK-SAME: %[[arg1:.*]]: memref<12x25xf32, strided<[?, ?], offset: ?>>, |
| // CHECK-SAME: %[[arg2:.*]]: memref<24x25xf32, strided<[?, ?], offset: ?>>, |
| func.func @vectorize_and_bufferize_pad(%arg0: tensor<24x12xf32>, |
| %arg1: tensor<12x25xf32>, |
| %arg2: tensor<24x25xf32>, |
| %iv0 : index, %iv1 : index, |
| %iv2 : index) -> tensor<24x25xf32> { |
| %0 = affine.min #map()[%iv2] |
| |
| // CHECK: %[[s0:.*]] = memref.subview %[[arg0]] |
| %1 = tensor.extract_slice %arg0[%iv0, %iv2] [4, %0] [1, 1] : tensor<24x12xf32> to tensor<4x?xf32> |
| // CHECK: %[[s1:.*]] = memref.subview %[[arg1]] |
| %2 = tensor.extract_slice %arg1[%iv2, %iv1] [%0, 5] [1, 1] : tensor<12x25xf32> to tensor<?x5xf32> |
| // CHECK: %[[s2:.*]] = memref.subview %[[arg2]] |
| %3 = tensor.extract_slice %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32> |
| |
| // CHECK: %[[v0:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s0]] |
| // CHECK: %[[alloc0:.*]] = memref.alloc() : memref<4x7xf32, 3> |
| // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v0]], %[[alloc0]] |
| |
| // CHECK: %[[v1:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s1]] |
| // CHECK: %[[alloc1:.*]] = memref.alloc() : memref<7x5xf32, 3> |
| // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v1]], %[[alloc1]] |
| |
| // CHECK: %[[v2:.*]] = vector.mask {{.*}} { vector.transfer_read %[[s2]] |
| // CHECK: %[[alloc2:.*]] = memref.alloc() : memref<4x5xf32, 3> |
| // CHECK: vector.mask {{.*}} { vector.transfer_write %[[v2]], %[[alloc0]] |
| |
| // CHECK: linalg.matmul ins(%[[alloc0]], %[[alloc1]] : {{.*}}) outs(%[[alloc2]] : {{.*}}) |
| // Copy back result. |
| // CHECK: memref.copy %[[alloc2]], %[[s2]] |
| %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32> |
| |
| // insert_slice bufferizes to a no-op. |
| %5 = tensor.insert_slice %4 into %arg2[%iv0, %iv1] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32> |
| func.return %5 : tensor<24x25xf32> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.consumed}) { |
| %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| %padded, %pad, %copy_back = transform.structured.pad %0 { |
| padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32], |
| padding_dimensions=[0, 1, 2], |
| pack_paddings=[1, 1, 1] |
| } : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op) |
| transform.structured.vectorize %pad vector_sizes [10, 12] : !transform.any_op |
| %vector_write = transform.structured.match ops{["vector.transfer_write"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| %mask_op = transform.get_parent_op %vector_write {op_name = "vector.mask"} : (!transform.any_op) -> !transform.any_op |
| %buffer, %new_ops = transform.structured.bufferize_to_allocation %mask_op {memory_space = 3, emit_dealloc} : !transform.any_op |
| %2 = transform.bufferization.one_shot_bufferize %arg1 {bufferize_function_boundaries=true} : (!transform.any_op) -> !transform.any_op |
| transform.yield |
| } |
| } |