| // 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<4xi1> |
| // CHECK: %[[VAL_8:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32> |
| // CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32> |
| // CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32> |
| // CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<4xf32> |
| // CHECK: %[[VAL_14:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %{{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> 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_dynamic_1d_broadcast(%arg0: tensor<?xf32>, |
| %arg1: tensor<?xf32>, |
| %arg2: tensor<?xf32>) -> tensor<?xf32> { |
| %0 = linalg.generic { indexing_maps = [affine_map<(d0) -> (0)>, |
| 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_1d_broadcast |
| // CHECK: %[[VAL_3:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?xf32> |
| // CHECK: %[[VAL_7:.*]] = vector.transfer_read %{{.*}} {permutation_map = #{{.*}}} : tensor<?xf32>, vector<4xf32> |
| // CHECK: %[[VAL_9:.*]] = vector.create_mask %[[VAL_4]] : vector<4xi1> |
| // CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_9]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32> |
| // CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_9]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32> |
| // CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_7]], %[[VAL_10]] : vector<4xf32> |
| // CHECK: %[[VAL_14:.*]] = vector.mask %{{.*}} { vector.transfer_write %[[VAL_13]], {{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> 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_dynamic_2d_transpose(%arg0: tensor<?x?xf32>, |
| %arg1: tensor<?x?xf32>, |
| %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> { |
| %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>, |
| affine_map<(d0, d1) -> (d0, d1)>, |
| affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "parallel"] } |
| ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) |
| outs(%arg2 : tensor<?x?xf32>) { |
| ^bb(%in0: f32, %in1: f32, %out: f32) : |
| %0 = arith.addf %in0, %in1 : f32 |
| linalg.yield %0 : f32 |
| } -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| // CHECK-LABEL: @vectorize_dynamic_2d_transpose |
| // CHECK: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_5:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_6:.*]] = tensor.dim %{{.*}}, %[[VAL_5]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_9:.*]] = vector.create_mask %[[VAL_6]], %[[VAL_4]] : vector<8x4xi1> |
| // CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_9]] { vector.transfer_read %{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : tensor<?x?xf32>, vector<4x8xf32> } : vector<8x4xi1> -> vector<4x8xf32> |
| // CHECK: %[[VAL_12:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]] : vector<4x8xi1> |
| // CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_12]] { vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32> |
| // CHECK: %[[VAL_14:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_12]] { vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32> |
| // CHECK: %[[VAL_16:.*]] = arith.addf %[[VAL_10]], %[[VAL_13]] : vector<4x8xf32> |
| // CHECK: %[[VAL_17:.*]] = vector.mask %[[VAL_12]] { vector.transfer_write %[[VAL_16]], %{{.*}} {in_bounds = [true, true]} : vector<4x8xf32>, tensor<?x?xf32> } : vector<4x8xi1> -> tensor<?x?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, 8] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @vectorize_dynamic_generic_2d_broadcast(%arg0: tensor<?x?xf32>, |
| %arg1: tensor<?x?xf32>, |
| %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> { |
| %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (0, d1)>, |
| affine_map<(d0, d1) -> (d0, d1)>, |
| affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "parallel"] } |
| ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>) |
| outs(%arg2 : tensor<?x?xf32>) { |
| ^bb(%in0: f32, %in1: f32, %out: f32) : |
| %0 = arith.addf %in0, %in1 : f32 |
| linalg.yield %0 : f32 |
| } -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| // CHECK-LABEL: @vectorize_dynamic_generic_2d_broadcast |
| // CHECK: %[[VAL_3:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_6:.*]] = tensor.dim %{{.*}}, %[[VAL_5]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_9:.*]] = vector.create_mask %[[VAL_6]] : vector<8xi1> |
| // CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_9]] { vector.transfer_read %{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : tensor<?x?xf32>, vector<4x8xf32> } : vector<8xi1> -> vector<4x8xf32> |
| // CHECK: %[[VAL_12:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]] : vector<4x8xi1> |
| // CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_12]] { vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32> |
| // CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_12]] { vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32> |
| // CHECK: %[[VAL_16:.*]] = arith.addf %[[VAL_10]], %[[VAL_13]] : vector<4x8xf32> |
| // CHECK: %[[VAL_18:.*]] = vector.mask %[[VAL_12]] { vector.transfer_write %{{.*}} {in_bounds = [true, true]} : vector<4x8xf32>, tensor<?x?xf32> } : vector<4x8xi1> -> tensor<?x?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, 8] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @vectorize_dynamic_reduction(%arg0: tensor<?x?xf32>, |
| %arg1: tensor<?xf32>) -> tensor<?xf32> { |
| %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, |
| affine_map<(d0, d1) -> (d0)>], |
| iterator_types = ["parallel", "reduction"] } |
| ins(%arg0 : tensor<?x?xf32>) |
| outs(%arg1 : tensor<?xf32>) { |
| ^bb(%in: f32, %out: f32) : |
| %0 = arith.addf %in, %out : f32 |
| linalg.yield %0 : f32 |
| } -> tensor<?xf32> |
| return %0 : 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, 8] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // CHECK-LABEL: @vectorize_dynamic_reduction( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<?xf32>) -> tensor<?xf32> { |
| // CHECK: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_3:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_4:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_8:.*]] = vector.create_mask %[[VAL_3]], %[[VAL_5]] : vector<4x8xi1> |
| // CHECK: %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_0]]{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32> |
| // CHECK: %[[VAL_11:.*]] = vector.create_mask %[[VAL_3]] : vector<4xi1> |
| // CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_11]] { vector.transfer_read %[[VAL_1]]{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32> |
| // CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_8]] { vector.multi_reduction <add>, %[[VAL_9]], %[[VAL_12]] [1] : vector<4x8xf32> to vector<4xf32> } : vector<4x8xi1> -> vector<4xf32> |
| // CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_11]] { vector.transfer_write %[[VAL_13]], %[[VAL_1]]{{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32> |
| // CHECK: return %[[VAL_15]] : tensor<?xf32> |
| // CHECK: } |
| |
| // ----- |
| |
| func.func @vectorize_dynamic_transpose_reduction(%arg0: tensor<?x?x?xf32>, |
| %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> { |
| %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, |
| affine_map<(d0, d1, d2) -> (d2, d1)>], |
| iterator_types = ["reduction", "parallel", "parallel"] } |
| ins(%arg0 : tensor<?x?x?xf32>) |
| outs(%arg1 : tensor<?x?xf32>) { |
| ^bb(%in: f32, %out: f32) : |
| %0 = arith.addf %in, %out : f32 |
| linalg.yield %0 : f32 |
| } -> tensor<?x?xf32> |
| return %0 : tensor<?x?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, 8, 16] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // CHECK-LABEL: @vectorize_dynamic_transpose_reduction( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> { |
| // CHECK: %[[VAL_2:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_3:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xf32> |
| // CHECK: %[[VAL_4:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32> |
| // CHECK: %[[VAL_6:.*]] = arith.constant 2 : index |
| // CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_6]] : tensor<?x?x?xf32> |
| // CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_3]], %[[VAL_5]], %[[VAL_7]] : vector<4x8x16xi1> |
| // CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_0]]{{.*}} {in_bounds = [true, true, true]} : tensor<?x?x?xf32>, vector<4x8x16xf32> } : vector<4x8x16xi1> -> vector<4x8x16xf32> |
| // CHECK: %[[VAL_13:.*]] = vector.create_mask %[[VAL_7]], %[[VAL_5]] : vector<16x8xi1> |
| // CHECK: %[[VAL_14:.*]] = vector.mask %[[VAL_13]] { vector.transfer_read %[[VAL_1]]{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : tensor<?x?xf32>, vector<8x16xf32> } : vector<16x8xi1> -> vector<8x16xf32> |
| // CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_10]] { vector.multi_reduction <add>, %[[VAL_11]], %[[VAL_14]] [0] : vector<4x8x16xf32> to vector<8x16xf32> } : vector<4x8x16xi1> -> vector<8x16xf32> |
| // CHECK: %[[VAL_17:.*]] = vector.mask %[[VAL_13]] { vector.transfer_write %[[VAL_15]], %{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : vector<8x16xf32>, tensor<?x?xf32> } : vector<16x8xi1> -> tensor<?x?xf32> |
| |
| // ----- |
| |
| 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<8x32xi1> |
| // 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<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32> |
| // 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<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32> |
| // 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<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32> |
| // CHECK: %[[VAL_14:.*]] = arith.addf %[[VAL_9]], %[[VAL_11]] : vector<8x32xf32> |
| // 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<8x32xf32>, tensor<8x?xf32> } : vector<8x32xi1> -> 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 @do_not_generate_masks(%arg0: tensor<8x32xf32>, |
| %arg1: tensor<8x32xf32>, |
| %arg2: tensor<8x32xf32>) -> tensor<8x32xf32> { |
| %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<8x32xf32>, tensor<8x32xf32>) |
| outs(%arg2 : tensor<8x32xf32>) { |
| ^bb(%in0: f32, %in1: f32, %out: f32) : |
| %0 = arith.addf %in0, %in1 : f32 |
| linalg.yield %0 : f32 |
| } -> tensor<8x32xf32> |
| return %0 : tensor<8x32xf32> |
| } |
| |
| // CHECK-LABEL: func.func @do_not_generate_masks |
| // CHECK-NOT: vector.mask |
| |
| 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<8x32xi1> |
| // 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<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32> |
| // 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<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32> |
| // 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<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32> |
| // CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<8x32xf32> |
| // 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<8x32xf32>, tensor<8x30xf32> } : vector<8x32xi1> -> 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<8x16xi1> |
| // CHECK: %[[BCAST:.*]] = vector.broadcast %{{.*}} : f32 to vector<8x16xf32> |
| // CHECK: vector.mask %[[MASK]] { vector.transfer_write %[[BCAST]], {{.*}} {in_bounds = [true, true]} : vector<8x16xf32>, tensor<?x?xf32> } : vector<8x16xi1> |
| |
| 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 |
| } |
| } |
| |
| // ----- |
| |
| // CHECK: #[[MAP:.*]] = affine_map<(d0, d1) -> (d1, d0)> |
| // CHECK: func @test_masked_vectorize_linalg_transpose |
| func.func @test_masked_vectorize_linalg_transpose(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> { |
| // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[D0:.*]] = tensor.dim %arg0, %[[C0]] |
| // CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[D1:.*]] = tensor.dim %arg0, %[[C1]] |
| // CHECK: %[[MASK0:.*]] = vector.create_mask %[[D0]], %[[D1]] |
| // CHECK: %[[LOAD:.*]] = vector.mask %[[MASK0]] { vector.transfer_read %arg0{{.+}} permutation_map = #[[MAP]]{{.+}} } |
| // CHECK-SAME: vector<4x2xi1> -> vector<2x4xf32> |
| // CHECK: %[[MASK1:.*]] = vector.create_mask %[[D1]], %[[D0]] |
| // CHECK: %[[WRITE:.*]] = vector.mask %[[MASK1]] { vector.transfer_write %[[LOAD]], %arg1{{.+}} } |
| // CHECK-SAME: vector<2x4xi1> -> tensor<?x?xf32> |
| // CHECK: return %[[WRITE]] |
| %0 = linalg.transpose ins(%arg0 : tensor<?x?xf32>) outs(%arg1 : tensor<?x?xf32>) permutation = [1, 0] |
| return %0 : tensor<?x?xf32> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.transpose"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @test_masked_vectorize_linalg_copy |
| func.func @test_masked_vectorize_linalg_copy(%A : memref<?x?xf32>, %B : memref<?x?xf32>) { |
| // CHECK: %[[c0:.*]] = arith.constant 0 : index |
| // CHECK: %[[d0:.*]] = memref.dim %{{.*}}, %[[c0]] : memref<?x?xf32> |
| // CHECK: %[[c1:.*]] = arith.constant 1 : index |
| // CHECK: %[[d1:.*]] = memref.dim %{{.*}}, %[[c1]] : memref<?x?xf32> |
| // CHECK: %[[mask:.*]] = vector.create_mask %[[d0]], %[[d1]] : vector<2x4xi1> |
| // CHECK: vector.mask %[[mask]] {{.*}} vector.transfer_read %{{.*}} {in_bounds = [true, true]} : memref<?x?xf32>, vector<2x4xf32> } : vector<2x4xi1> -> vector<2x4xf32> |
| // CHECK: vector.mask %[[mask]] {{.*}} vector.transfer_write %{{.*}} {in_bounds = [true, true]} : vector<2x4xf32>, memref<?x?xf32> } : vector<2x4xi1> |
| linalg.copy ins(%A : memref<?x?xf32>) outs(%B : memref<?x?xf32>) |
| return |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @test_masked_vectorize_pad |
| func.func @test_masked_vectorize_pad( |
| %0 : tensor<?x?xf32>, %h0 : index, %h1 : index) |
| -> tensor<2x4xf32> |
| { |
| // CHECK-DAG: %[[c42:.*]] = arith.constant 4.243000e+01 : f32 |
| // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[c0_0:.*]] = arith.constant 0 : index |
| // CHECK: %[[d0:.*]] = tensor.dim {{.*}} : tensor<?x?xf32> |
| // CHECK: %[[d1:.*]] = tensor.dim {{.*}} : tensor<?x?xf32> |
| // CHECK: %[[mask:.*]] = vector.create_mask %[[d0]], %[[d1]] : vector<2x4xi1> |
| // CHECK: %[[masked_read:.*]] = vector.mask %[[mask]] { |
| // CHECK-SAME: vector.transfer_read %{{.*}}[%[[c0_0]], %[[c0_0]]], %[[c42]] |
| // CHECK-SAME: {in_bounds = [true, true]} : tensor<?x?xf32>, vector<2x4xf32> |
| // CHECK-SAME: } : vector<2x4xi1> -> vector<2x4xf32> |
| // CHECK-DAG: %[[c0_1:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<2x4xf32> |
| // CHECK: vector.transfer_write %[[masked_read]], %[[empty]][%[[c0_1]], %[[c0_1]]] |
| // CHECK-SAME: {in_bounds = [true, true]} : vector<2x4xf32>, tensor<2x4xf32> |
| %cst = arith.constant 42.43 : f32 |
| %c0 = arith.constant 0 : index |
| %1 = tensor.pad %0 low[0, %c0] high[%h0, %h1] { |
| ^bb0(%hh1: index, %hh2: index): |
| tensor.yield %cst : f32 |
| } : tensor<?x?xf32> to tensor<2x4xf32> |
| return %1: tensor<2x4xf32> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK: #[[MAP:.+]] = affine_map<()[s0, s1] -> (s0 + s1)> |
| // CHECK: func @test_masked_vectorize_dynamic_pad |
| func.func @test_masked_vectorize_dynamic_pad( |
| %0 : tensor<?x?xf32>, %h0 : index, %h1 : index) |
| -> tensor<?x?xf32> |
| { |
| // CHECK-DAG: %[[c42:.*]] = arith.constant 4.243000e+01 : f32 |
| // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[res_d0:.+]] = affine.apply #[[MAP]]() |
| // CHECK-DAG: %[[res_d1:.+]] = affine.apply #[[MAP]]() |
| // CHECK: %[[c0_2:.*]] = arith.constant 0 : index |
| // CHECK: %[[d0:.*]] = tensor.dim {{.*}} : tensor<?x?xf32> |
| // CHECK: %[[d1:.*]] = tensor.dim {{.*}} : tensor<?x?xf32> |
| // CHECK: %[[mask:.*]] = vector.create_mask %[[d0]], %[[d1]] : vector<2x4xi1> |
| // CHECK: %[[masked_read:.*]] = vector.mask %[[mask]] { |
| // CHECK-SAME: vector.transfer_read %{{.*}}[%[[c0_2]], %[[c0_2]]], %[[c42]] |
| // CHECK-SAME: {in_bounds = [true, true]} : tensor<?x?xf32>, vector<2x4xf32> |
| // CHECK-SAME: } : vector<2x4xi1> -> vector<2x4xf32> |
| // CHECK-DAG: %[[empty:.*]] = tensor.empty(%[[res_d0]], %[[res_d1]]) : tensor<?x?xf32> |
| // CHECK-DAG: %[[c0_3:.*]] = arith.constant 0 : index |
| // CHECK: %[[mask_2:.*]] = vector.create_mask %[[res_d0]], %[[res_d1]] : vector<2x4xi1> |
| // CHECK: %[[masked_write:.*]] = vector.mask %[[mask_2]] { |
| // CHECK-SAME: vector.transfer_write %[[masked_read]], %[[empty]][%[[c0_3]], %[[c0_3]]] |
| // CHECK-SAME: {in_bounds = [true, true]} : vector<2x4xf32>, tensor<?x?xf32> |
| // CHECK: return %[[masked_write]] : tensor<?x?xf32> |
| %cst = arith.constant 42.43 : f32 |
| %c0 = arith.constant 0 : index |
| %1 = tensor.pad %0 low[0, %c0] high[%h0, %h1] { |
| ^bb0(%hh1: index, %hh2: index): |
| tensor.yield %cst : f32 |
| } : tensor<?x?xf32> to tensor<?x?xf32> |
| return %1: tensor<?x?xf32> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 |
| : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @test_vectorize_pack(%arg0: tensor<32x8x16xf32>, %arg1: tensor<4x1x32x16x2xf32>) -> tensor<4x1x32x16x2xf32> { |
| %pack = tensor.pack %arg0 outer_dims_perm = [1, 2, 0] inner_dims_pos = [2, 1] inner_tiles = [16, 2] into %arg1 : tensor<32x8x16xf32> -> tensor<4x1x32x16x2xf32> |
| return %pack : tensor<4x1x32x16x2xf32> |
| } |
| // CHECK-DAG: %[[cst:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index |
| // CHECK: %[[read:.*]] = vector.transfer_read %{{.*}}[%[[c0]], %[[c0]], %[[c0]]], %[[cst]] |
| // CHECK-SAME: {in_bounds = [true, true, true]} : tensor<32x8x16xf32>, vector<32x8x16xf32> |
| // CHECK: %[[shape_cast:.*]] = vector.shape_cast %[[read]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32> |
| // CHECK: %[[transpose:.*]] = vector.transpose %[[shape_cast]], [1, 3, 0, 4, 2] : vector<32x4x2x1x16xf32> to vector<4x1x32x16x2xf32> |
| // CHECK-DAG: %[[c0_1:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<4x1x32x16x2xf32> |
| // CHECK: %[[write:.*]] = vector.transfer_write %[[transpose]], %[[empty]][%[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]]] |
| // CHECK-SAME: {in_bounds = [true, true, true, true, true]} : vector<4x1x32x16x2xf32>, tensor<4x1x32x16x2xf32> |
| // CHECK: return %[[write]] : tensor<4x1x32x16x2xf32> |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pack"]} in %arg0 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [4, 1, 32] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @test_vectorize_padded_pack(%arg0: tensor<32x7x15xf32>, %arg1: tensor<32x4x1x16x2xf32>) -> tensor<32x4x1x16x2xf32> { |
| %pad = arith.constant 0.000000e+00 : f32 |
| %pack = tensor.pack %arg0 padding_value(%pad : f32) inner_dims_pos = [2, 1] inner_tiles = [16, 2] into %arg1 : tensor<32x7x15xf32> -> tensor<32x4x1x16x2xf32> |
| return %pack : tensor<32x4x1x16x2xf32> |
| } |
| // CHECK-DAG: %[[cst:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[c32:.*]] = arith.constant 32 : index |
| // CHECK-DAG: %[[c7:.*]] = arith.constant 7 : index |
| // CHECK-DAG: %[[c15:.*]] = arith.constant 15 : index |
| // CHECK: %[[mask:.*]] = vector.create_mask %[[c32]], %[[c7]], %[[c15]] : vector<32x8x16xi1> |
| // CHECK: %[[masked_read:.*]] = vector.mask %[[mask]] { |
| // CHECK-SAME: vector.transfer_read %{{.*}}[%[[c0]], %[[c0]], %[[c0]]], %[[cst]] |
| // CHECK-SAME: {in_bounds = [true, true, true]} : tensor<32x7x15xf32>, vector<32x8x16xf32> |
| // CHECK-SAME: } : vector<32x8x16xi1> -> vector<32x8x16xf32> |
| // CHECK: %[[shape_cast:.*]] = vector.shape_cast %[[masked_read]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32> |
| // CHECK: %[[transpose:.*]] = vector.transpose %[[shape_cast]], [0, 1, 3, 4, 2] : vector<32x4x2x1x16xf32> to vector<32x4x1x16x2xf32> |
| // CHECK-DAG: %[[c0_1:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<32x4x1x16x2xf32> |
| // CHECK: %[[write:.*]] = vector.transfer_write %[[transpose]], %[[empty]][%[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]]] |
| // CHECK-SAME: {in_bounds = [true, true, true, true, true]} : vector<32x4x1x16x2xf32>, tensor<32x4x1x16x2xf32> |
| // CHECK: return %[[write]] : tensor<32x4x1x16x2xf32> |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pack"]} in %arg0 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [32, 4, 1] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @test_vectorize_dynamic_pack(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?x16x2xf32>) -> tensor<?x?x16x2xf32> { |
| %pack = tensor.pack %arg0 inner_dims_pos = [1, 0] inner_tiles = [16, 2] into %arg1 : tensor<?x?xf32> -> tensor<?x?x16x2xf32> |
| return %pack : tensor<?x?x16x2xf32> |
| } |
| // CHECK-DAG: %[[cst:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[d0:.*]] = tensor.dim {{.*}} %[[c0]] : tensor<?x?x16x2xf32> |
| // CHECK-DAG: %[[d1:.*]] = tensor.dim {{.*}} %[[c1]] : tensor<?x?x16x2xf32> |
| // CHECK-DAG: %[[c0_1:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[c0_0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[c1_0:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[d0_0:.*]] = tensor.dim {{.*}} %[[c0_0]] : tensor<?x?xf32> |
| // CHECK-DAG: %[[d1_0:.*]] = tensor.dim {{.*}} %[[c1_0]] : tensor<?x?xf32> |
| // CHECK: %[[mask:.*]] = vector.create_mask %[[d0_0]], %[[d1_0]] : vector<8x16xi1> |
| // CHECK: %[[masked_read:.*]] = vector.mask %[[mask]] { |
| // CHECK-SAME: vector.transfer_read %{{.*}}[%[[c0_1]], %[[c0_1]]], %[[cst]] |
| // CHECK-SAME: {in_bounds = [true, true]} : tensor<?x?xf32>, vector<8x16xf32> |
| // CHECK-SAME: } : vector<8x16xi1> -> vector<8x16xf32> |
| // CHECK: %[[shape_cast:.*]] = vector.shape_cast %[[masked_read]] : vector<8x16xf32> to vector<4x2x1x16xf32> |
| // CHECK: %[[transpose:.*]] = vector.transpose %[[shape_cast]], [0, 2, 3, 1] : vector<4x2x1x16xf32> to vector<4x1x16x2xf32> |
| // CHECK-DAG: %[[c0_2:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[c16:.*]] = arith.constant 16 : index |
| // CHECK-DAG: %[[c2:.*]] = arith.constant 2 : index |
| // CHECK-DAG: %[[empty:.*]] = tensor.empty(%[[d0]], %[[d1]]) : tensor<?x?x16x2xf32> |
| // CHECK: %[[mask_0:.*]] = vector.create_mask %[[d0]], %[[d1]], %[[c16]], %[[c2]] : vector<4x1x16x2xi1> |
| // CHECK: %[[masked_write:.*]] = vector.mask %[[mask_0]] { |
| // CHECK-SAME: vector.transfer_write %[[transpose]], %[[empty]][%[[c0_2]], %[[c0_2]], %[[c0_2]], %[[c0_2]]] |
| // CHECK-SAME: {in_bounds = [true, true, true, true]} : vector<4x1x16x2xf32>, tensor<?x?x16x2xf32> |
| // CHECK: return %[[masked_write]] : tensor<?x?x16x2xf32> |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pack"]} in %arg0 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [4, 1] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @matmul(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>) { |
| linalg.matmul ins(%A, %B: memref<?x?xf32>, memref<?x?xf32>) |
| outs(%C: memref<?x?xf32>) |
| return |
| } |
| |
| // CHECK-LABEL: func.func @matmul( |
| // CHECK-SAME: %[[A:.*]]: memref<?x?xf32>, %[[B:.*]]: memref<?x?xf32>, %[[C:.*]]: memref<?x?xf32>) { |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_4:.*]] = memref.dim %[[A]], %[[VAL_3]] : memref<?x?xf32> |
| // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_6:.*]] = memref.dim %[[B]], %[[VAL_5]] : memref<?x?xf32> |
| // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_8:.*]] = memref.dim %[[A]], %[[VAL_7]] : memref<?x?xf32> |
| // CHECK: %[[MASK_A:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_8]] : vector<8x4xi1> |
| // CHECK: %[[LOAD_A:.*]] = vector.mask %[[MASK_A]] { vector.transfer_read %[[A]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true, true], permutation_map = #{{.*}}} : memref<?x?xf32>, vector<8x16x4xf32> } : vector<8x4xi1> -> vector<8x16x4xf32> |
| // CHECK: %[[MASK_B:.*]] = vector.create_mask %[[VAL_8]], %[[VAL_6]] : vector<4x16xi1> |
| // CHECK: %[[LOAD_B:.*]] = vector.mask %[[MASK_B]] { vector.transfer_read %[[B]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true, true], permutation_map = #{{.*}}} : memref<?x?xf32>, vector<8x16x4xf32> } : vector<4x16xi1> -> vector<8x16x4xf32> |
| // CHECK: %[[MASK_C:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]] : vector<8x16xi1> |
| // CHECK: %[[LOAD_C:.*]] = vector.mask %[[MASK_C]] { vector.transfer_read %[[C]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true]} : memref<?x?xf32>, vector<8x16xf32> } : vector<8x16xi1> -> vector<8x16xf32> |
| // CHECK: %[[MULF:.*]] = arith.mulf %[[LOAD_A]], %[[LOAD_B]] : vector<8x16x4xf32> |
| // CHECK: %[[MASK_MULIT_RED:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]], %[[VAL_8]] : vector<8x16x4xi1> |
| // CHECK: %[[MULTI_RED:.*]] = vector.mask %[[MASK_MULIT_RED]] { vector.multi_reduction <add>, %[[MULF]], %[[LOAD_C]] [2] : vector<8x16x4xf32> to vector<8x16xf32> } : vector<8x16x4xi1> -> vector<8x16xf32> |
| // CHECK: %[[C2:.*]] = arith.constant 0 : index |
| // CHECK: vector.mask %[[MASK_C]] { vector.transfer_write %[[MULTI_RED]], %[[C]]{{\[}}%[[C2]], %[[C2]]] {in_bounds = [true, true]} : vector<8x16xf32>, memref<?x?xf32> } : vector<8x16xi1> |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %matmul vector_sizes [8, 16, 4] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @mmt4d(%A: memref<16x16x8x1xf32>, %B: memref<16x16x8x1xf32>, %C_in: memref<16x16x8x8xf32>) { |
| linalg.mmt4d ins(%A, %B: memref<16x16x8x1xf32>, memref<16x16x8x1xf32>) |
| outs(%C_in: memref<16x16x8x8xf32>) |
| return |
| } |
| |
| // CHECK-LABEL: func.func @mmt4d( |
| // CHECK-SAME: %[[A:.*]]: memref<16x16x8x1xf32>, %[[B:.*]]: memref<16x16x8x1xf32>, %[[C:.*]]: memref<16x16x8x8xf32>) { |
| // CHECK: %[[VEC_A:.*]] = vector.transfer_read %[[A]]{{.*}} : memref<16x16x8x1xf32>, vector<16x16x16x8x8x1xf32> |
| // CHECK: %[[VEC_B:.*]] = vector.transfer_read %[[B]]{{.*}} : memref<16x16x8x1xf32>, vector<16x16x16x8x8x1xf32> |
| // CHECK: %[[VEC_C:.*]] = vector.transfer_read %[[C]]{{.*}} : memref<16x16x8x8xf32>, vector<16x16x8x8xf32> |
| // CHECK: %[[MUL:.*]] = arith.mulf %[[VEC_A]], %[[VEC_B]] : vector<16x16x16x8x8x1xf32> |
| // CHECK: %[[RED:.*]] = vector.multi_reduction <add>, %[[MUL]], %[[VEC_C]] [2, 5] : vector<16x16x16x8x8x1xf32> to vector<16x16x8x8xf32> |
| // CHECK: vector.transfer_write %[[RED]], %[[C]]{{.*}} : vector<16x16x8x8xf32>, memref<16x16x8x8xf32> |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %mmt4d = transform.structured.match ops{["linalg.mmt4d"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %mmt4d : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @matmul_scalable(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>) { |
| linalg.matmul ins(%A, %B: memref<?x?xf32>, memref<?x?xf32>) |
| outs(%C: memref<?x?xf32>) |
| return |
| } |
| |
| // CHECK-LABEL: func.func @matmul_scalable( |
| // CHECK-SAME: %[[A:.*]]: memref<?x?xf32>, %[[B:.*]]: memref<?x?xf32>, %[[C:.*]]: memref<?x?xf32>) { |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_4:.*]] = memref.dim %[[A]], %[[VAL_3]] : memref<?x?xf32> |
| // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_6:.*]] = memref.dim %[[B]], %[[VAL_5]] : memref<?x?xf32> |
| // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_8:.*]] = memref.dim %[[A]], %[[VAL_7]] : memref<?x?xf32> |
| // CHECK: %[[MASK_A:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_8]] : vector<8x4xi1> |
| // CHECK: %[[LOAD_A:.*]] = vector.mask %[[MASK_A]] { vector.transfer_read %[[A]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true, true], permutation_map = #{{.*}}} : memref<?x?xf32>, vector<8x[16]x4xf32> } : vector<8x4xi1> -> vector<8x[16]x4xf32> |
| // CHECK: %[[MASK_B:.*]] = vector.create_mask %[[VAL_8]], %[[VAL_6]] : vector<4x[16]xi1> |
| // CHECK: %[[LOAD_B:.*]] = vector.mask %[[MASK_B]] { vector.transfer_read %[[B]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true, true], permutation_map = #{{.*}}} : memref<?x?xf32>, vector<8x[16]x4xf32> } : vector<4x[16]xi1> -> vector<8x[16]x4xf32> |
| // CHECK: %[[MASK_C:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]] : vector<8x[16]xi1> |
| // CHECK: %[[LOAD_C:.*]] = vector.mask %[[MASK_C]] { vector.transfer_read %[[C]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true]} : memref<?x?xf32>, vector<8x[16]xf32> } : vector<8x[16]xi1> -> vector<8x[16]xf32> |
| // CHECK: %[[MULF:.*]] = arith.mulf %[[LOAD_A]], %[[LOAD_B]] : vector<8x[16]x4xf32> |
| // CHECK: %[[MASK_MULIT_RED:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]], %[[VAL_8]] : vector<8x[16]x4xi1> |
| // CHECK: %[[MULTI_RED:.*]] = vector.mask %[[MASK_MULIT_RED]] { vector.multi_reduction <add>, %[[MULF]], %[[LOAD_C]] [2] : vector<8x[16]x4xf32> to vector<8x[16]xf32> } : vector<8x[16]x4xi1> -> vector<8x[16]xf32> |
| // CHECK: %[[C2:.*]] = arith.constant 0 : index |
| // CHECK: vector.mask %[[MASK_C]] { vector.transfer_write %[[MULTI_RED]], %[[C]]{{\[}}%[[C2]], %[[C2]]] {in_bounds = [true, true]} : vector<8x[16]xf32>, memref<?x?xf32> } : vector<8x[16]xi1> |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %matmul vector_sizes [8, [16], 4] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @test_vectorize_dynamic_shapes_unpack |
| func.func @test_vectorize_dynamic_shapes_unpack(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?x16x2xf32>) -> tensor<?x?xf32> { |
| // CHECK: %[[C0:.*]] = arith.constant 0 |
| // CHECK: %[[DIM:.*]] = tensor.dim %arg0, %[[C0]] : tensor<?x?xf32> |
| // CHECK: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK: %[[DIM0:.*]] = tensor.dim %arg0, %[[C1]] : tensor<?x?xf32> |
| // CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 |
| // CHECK: %[[C01:.*]] = arith.constant 0 |
| // CHECK: %[[C02:.*]] = arith.constant 0 |
| // CHECK: %[[DIM4:.*]] = tensor.dim %arg1, %[[C02]] : tensor<?x?x16x2xf32> |
| // CHECK: %[[CNST14:.*]] = arith.constant 1 |
| // CHECK: %[[DIM6:.*]] = tensor.dim %arg1, %[[CNST14]] : tensor<?x?x16x2xf32> |
| // CHECK: %[[CNST16:.*]] = arith.constant 16 : index |
| // CHECK: %[[CNST2:.*]] = arith.constant 2 : index |
| // CHECK: %[[readMsk0:.*]] = vector.create_mask %[[DIM4]], %[[DIM6]], %[[CNST16]], %[[CNST2]] : vector<2x1x16x2xi1> |
| // CHECK: %[[read0:.*]] = vector.mask %[[readMsk0]] {{.*}} vector.transfer_read %{{.*}} : tensor<?x?x16x2xf32>, vector<2x1x16x2xf32> } : vector<2x1x16x2xi1> -> vector<2x1x16x2xf32> |
| // CHECK: %[[trans0:.*]] = vector.transpose %[[read0]], [0, 3, 1, 2] : vector<2x1x16x2xf32> to vector<2x2x1x16xf32> |
| // CHECK: %[[sc0:.*]] = vector.shape_cast %[[trans0]] : vector<2x2x1x16xf32> to vector<4x16xf32> |
| // CHECK: %[[empt0:.*]] = tensor.empty |
| // CHECK: %[[writeMsk0:.*]] = vector.create_mask {{.*}} : vector<4x16xi1> |
| // CHECK: %[[write0:.*]] = vector.mask %[[writeMsk0:.*]] {{.*}} vector.transfer_write %[[sc0]], %[[empt0]] |
| // CHECK: return %[[write0]] |
| %ret = tensor.unpack %arg1 inner_dims_pos = [1, 0] inner_tiles = [16, 2] into %arg0 : tensor<?x?x16x2xf32> -> tensor<?x?xf32> |
| return %ret : tensor<?x?xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.unpack"]} in %arg0 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [4, 16] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @test_vectorize_unpack |
| func.func @test_vectorize_unpack(%source: tensor<8x8x32x16xf32>, %dest: tensor<256x128xf32>) -> tensor<256x128xf32> { |
| // CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK: %[[C0:.*]]= arith.constant 0 : index |
| // CHECK: %[[C8:.*]] = arith.constant 8 : index |
| // CHECK: %[[C80:.*]] = arith.constant 8 : index |
| // CHECK: %[[C32:.*]] = arith.constant 32 : index |
| // CHECK: %[[C16:.*]] = arith.constant 16 : index |
| // CHECK: %[[MSK0:.*]] = vector.create_mask %[[C8]], %[[C80]], %[[C32]], %[[C16]] : vector<16x8x32x16xi1> |
| // CHECK: %[[READ0:.*]] = vector.mask %[[MSK0]] {{.*}} : vector<16x8x32x16xi1> -> vector<16x8x32x16xf32> |
| // CHECK: %[[TRANSP0:.*]] = vector.transpose %[[READ0]], [0, 2, 1, 3] : vector<16x8x32x16xf32> to vector<16x32x8x16xf32> |
| // CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP0]] : vector<16x32x8x16xf32> to vector<512x128xf32> |
| // CHECK: %[[EMPT:.*]] = tensor.empty() : tensor<256x128xf32> |
| // CHECK: %[[C01:.*]] = arith.constant 0 : index |
| // CHECK: %[[C256:.*]] = arith.constant 256 : index |
| // CHECK: %[[C128:.*]] = arith.constant 128 : index |
| // CHECK: %[[WRITEMSK:.*]] = vector.create_mask %[[C256]], %[[C128]] : vector<512x128xi1> |
| // CHECK: %[[WRIT:.*]] = vector.mask %[[WRITEMSK]] {{.*}} : vector<512x128xi1> -> tensor<256x128xf32> |
| // CHECK: return %[[WRIT]] : tensor<256x128xf32> |
| %0 = tensor.unpack %source inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %dest : tensor<8x8x32x16xf32> -> tensor<256x128xf32> |
| return %0 : tensor<256x128xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.unpack"]} in %arg0 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [512, 128] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @test_vectorize_unpack_no_masks |
| func.func @test_vectorize_unpack_no_masks(%source: tensor<8x8x32x16xf32>, %dest: tensor<256x128xf32>) -> tensor<256x128xf32> { |
| // CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK: %[[READ:.*]] = vector.transfer_read {{.*}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32> |
| // CHECK: %[[TRANSP:.*]] = vector.transpose %[[READ]], [0, 2, 1, 3] : vector<8x8x32x16xf32> to vector<8x32x8x16xf32> |
| // CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP]] : vector<8x32x8x16xf32> to vector<256x128xf32> |
| // CHECK: %[[EMPT:.*]] = tensor.empty() : tensor<256x128xf32> |
| // CHECK: %[[C00:.*]] = arith.constant 0 : index |
| // CHECK: %[[WRIT:.*]] = vector.transfer_write %[[SHAPC]], {{.*}} : vector<256x128xf32>, tensor<256x128xf32> |
| // CHECK: return %[[WRIT]] : tensor<256x128xf32> |
| %0 = tensor.unpack %source inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %dest : tensor<8x8x32x16xf32> -> tensor<256x128xf32> |
| return %0 : tensor<256x128xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.unpack"]} in %arg0 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [256, 128] : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: test_vectorize_unpack_with_outer_perm |
| func.func @test_vectorize_unpack_with_outer_perm(%source: tensor<8x8x32x16xf32>, %dest: tensor<256x128xf32>) -> tensor<256x128xf32> { |
| // CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK: %[[READ:.*]] = vector.transfer_read {{.*}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32> |
| // CHECK: %[[TRANSP:.*]] = vector.transpose %[[READ]], [1, 2, 0, 3] : vector<8x8x32x16xf32> to vector<8x32x8x16xf32> |
| // CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP]] : vector<8x32x8x16xf32> to vector<256x128xf32> |
| // CHECK: %[[EMPT:.*]] = tensor.empty() : tensor<256x128xf32> |
| // CHECK: %[[C00:.*]] = arith.constant 0 : index |
| // CHECK: %[[WRIT:.*]] = vector.transfer_write %[[SHAPC]], {{.*}} : vector<256x128xf32>, tensor<256x128xf32> |
| // CHECK: return %[[WRIT]] : tensor<256x128xf32> |
| %0 = tensor.unpack %source outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %dest : tensor<8x8x32x16xf32> -> tensor<256x128xf32> |
| return %0 : tensor<256x128xf32> |
| } |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.unpack"]} in %arg0 : (!transform.any_op) -> !transform.any_op |
| transform.structured.vectorize %0 vector_sizes [256, 128] : !transform.any_op |
| transform.yield |
| } |
| } |
