| // RUN: mlir-opt %s -transform-interpreter -split-input-file | FileCheck %s |
| |
| func.func @vectorize_dynamic_identity(%arg0: tensor<?xf32>, |
| %arg1: tensor<?xf32>, |
| %arg2: tensor<?xf32>) -> tensor<?xf32> { |
| %0 = linalg.generic { indexing_maps = [affine_map<(d0) -> (d0)>, |
| affine_map<(d0) -> (d0)>, |
| affine_map<(d0) -> (d0)>], |
| iterator_types = ["parallel"] } |
| ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>) |
| outs(%arg2 : tensor<?xf32>) { |
| ^bb(%in0: f32, %in1: f32, %out: f32) : |
| %0 = arith.addf %in0, %in1 : f32 |
| linalg.yield %0 : f32 |
| } -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| } |
| |
| // CHECK-LABEL: @vectorize_dynamic_identity |
| // CHECK: %[[VAL_3:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?xf32> |
| // CHECK: %[[VAL_7:.*]] = vector.create_mask %[[VAL_4]] : vector<[4]xi1> |
| // CHECK: %[[VAL_8:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32> |
| // CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32> |
| // CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32> |
| // CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<[4]xf32> |
| // CHECK: %[[VAL_14:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %{{.*}} {in_bounds = [true]} : vector<[4]xf32>, tensor<?xf32> } : vector<[4]xi1> -> tensor<?xf32> |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [[4]] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @vectorize_partial_dynamic_identity(%arg0: tensor<8x?xf32>, |
| %arg1: tensor<8x?xf32>, |
| %arg2: tensor<8x?xf32>) -> tensor<8x?xf32> { |
| %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, |
| affine_map<(d0, d1) -> (d0, d1)>, |
| affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "parallel"] } |
| ins(%arg0, %arg1 : tensor<8x?xf32>, tensor<8x?xf32>) |
| outs(%arg2 : tensor<8x?xf32>) { |
| ^bb(%in0: f32, %in1: f32, %out: f32) : |
| %0 = arith.addf %in0, %in1 : f32 |
| linalg.yield %0 : f32 |
| } -> tensor<8x?xf32> |
| return %0 : tensor<8x?xf32> |
| } |
| |
| // CHECK-LABEL: func.func @vectorize_partial_dynamic_identity( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<8x?xf32>, %[[VAL_1:.*]]: tensor<8x?xf32>, %[[VAL_2:.*]]: tensor<8x?xf32>) -> tensor<8x?xf32> { |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_4:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<8x?xf32> |
| // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 8 : index |
| // CHECK: %[[VAL_8:.*]] = vector.create_mask %[[VAL_7]], %[[VAL_4]] : vector<8x[32]xi1> |
| // CHECK: %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_0]][%[[VAL_5]], %[[VAL_5]]], %[[VAL_6]] {in_bounds = [true, true]} : tensor<8x?xf32>, vector<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32> |
| // CHECK: %[[VAL_10:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_1]][%[[VAL_5]], %[[VAL_5]]], %[[VAL_10]] {in_bounds = [true, true]} : tensor<8x?xf32>, vector<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32> |
| // CHECK: %[[VAL_12:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_2]][%[[VAL_5]], %[[VAL_5]]], %[[VAL_12]] {in_bounds = [true, true]} : tensor<8x?xf32>, vector<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32> |
| // CHECK: %[[VAL_14:.*]] = arith.addf %[[VAL_9]], %[[VAL_11]] : vector<8x[32]xf32> |
| // CHECK: %[[VAL_15:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_16:.*]] = vector.mask %[[VAL_8]] { vector.transfer_write %[[VAL_14]], %[[VAL_2]][%[[VAL_15]], %[[VAL_15]]] {in_bounds = [true, true]} : vector<8x[32]xf32>, tensor<8x?xf32> } : vector<8x[32]xi1> -> tensor<8x?xf32> |
| |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [8, [32]] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @vectorize_static_shape_with_mask(%arg0: tensor<8x30xf32>, |
| %arg1: tensor<8x30xf32>, |
| %arg2: tensor<8x30xf32>) -> tensor<8x30xf32> { |
| %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, |
| affine_map<(d0, d1) -> (d0, d1)>, |
| affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "parallel"] } |
| ins(%arg0, %arg1 : tensor<8x30xf32>, tensor<8x30xf32>) |
| outs(%arg2 : tensor<8x30xf32>) { |
| ^bb(%in0: f32, %in1: f32, %out: f32) : |
| %0 = arith.addf %in0, %in1 : f32 |
| linalg.yield %0 : f32 |
| } -> tensor<8x30xf32> |
| return %0 : tensor<8x30xf32> |
| } |
| |
| // CHECK-LABEL: func.func @vectorize_static_shape_with_mask( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<8x30xf32>, %[[VAL_1:.*]]: tensor<8x30xf32>, %[[VAL_2:.*]]: tensor<8x30xf32>) -> tensor<8x30xf32> { |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index |
| // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 30 : index |
| // CHECK: %[[VAL_7:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_6]] : vector<8x[32]xi1> |
| // CHECK: %[[VAL_8:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %[[VAL_0]][%[[VAL_3]], %[[VAL_3]]], %[[VAL_4]] {in_bounds = [true, true]} : tensor<8x30xf32>, vector<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32> |
| // CHECK: %[[VAL_9:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %[[VAL_1]][%[[VAL_3]], %[[VAL_3]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<8x30xf32>, vector<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32> |
| // CHECK: %[[VAL_11:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %[[VAL_2]][%[[VAL_3]], %[[VAL_3]]], %[[VAL_11]] {in_bounds = [true, true]} : tensor<8x30xf32>, vector<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32> |
| // CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<8x[32]xf32> |
| // CHECK: %[[VAL_14:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %[[VAL_13]], %[[VAL_2]][%[[VAL_14]], %[[VAL_14]]] {in_bounds = [true, true]} : vector<8x[32]xf32>, tensor<8x30xf32> } : vector<8x[32]xi1> -> tensor<8x30xf32> |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [8, [32]] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @vectorize_dynamic_fill(%A : tensor<?x?xf32>, %arg0 : f32) -> tensor<?x?xf32> { |
| %0 = linalg.fill ins(%arg0 : f32) outs(%A : tensor<?x?xf32>) -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| // CHECK-LABEL: func.func @vectorize_dynamic_fill |
| // CHECK: %[[DIM0:.*]] = tensor.dim |
| // CHECK: %[[DIM1:.*]] = tensor.dim |
| // CHECK: %[[MASK:.*]] = vector.create_mask %[[DIM0]], %[[DIM1]] : vector<8x[16]xi1> |
| // CHECK: %[[BCAST:.*]] = vector.broadcast %{{.*}} : f32 to vector<8x[16]xf32> |
| // CHECK: vector.mask %[[MASK]] { vector.transfer_write %[[BCAST]], {{.*}} {in_bounds = [true, true]} : vector<8x[16]xf32>, tensor<?x?xf32> } : vector<8x[16]xi1> |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [8, [16]] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
