| // RUN: mlir-opt %s -transform-interpreter -split-input-file | FileCheck %s |
| |
| func.func @masked_static_vectorize_nd_tensor_extract_with_affine_apply_contiguous(%6: tensor<80x16xf32>, %arg0: index, %extracted_slice : tensor<1x3xf32>) -> tensor<1x3xf32> { |
| %c79 = arith.constant 79 : index |
| %1 = linalg.generic { |
| indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "parallel"] |
| } outs(%extracted_slice : tensor<1x3xf32>) { |
| ^bb0(%out: f32): |
| %2 = linalg.index 1 : index |
| %3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %arg0) |
| %extracted = tensor.extract %6[%c79, %3] : tensor<80x16xf32> |
| linalg.yield %extracted : f32 |
| } -> tensor<1x3xf32> |
| return %1 : tensor<1x3xf32> |
| } |
| |
| // CHECK-LABEL: func.func @masked_static_vectorize_nd_tensor_extract_with_affine_apply_contiguous |
| // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 3 : index |
| // CHECK: %[[VAL_8:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_5]] : vector<1x4xi1> |
| // CHECK: %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<1x3xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32> |
| // CHECK: %[[VAL_11:.*]] = vector.broadcast {{.*}} : index to vector<4xindex> |
| // CHECK: %[[VAL_12:.*]] = arith.addi {{.*}} : vector<4xindex> |
| // CHECK: %[[VAL_20:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<80x16xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32> |
| // CHECK: %[[VAL_22:.*]] = vector.mask %[[VAL_8]] { vector.transfer_write {{.*}} {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x3xf32> } : vector<1x4xi1> -> tensor<1x3xf32> |
| |
| 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 [1, 4] vectorize_nd_extract : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_contiguous(%6: tensor<?x?xf32>, %arg0: index, %extracted_slice : tensor<?x?xf32>) -> tensor<?x?xf32> { |
| %c79 = arith.constant 79 : index |
| %1 = linalg.generic { |
| indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "parallel"] |
| } outs(%extracted_slice : tensor<?x?xf32>) { |
| ^bb0(%out: f32): |
| %2 = linalg.index 1 : index |
| %3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %arg0) |
| %extracted = tensor.extract %6[%c79, %3] : tensor<?x?xf32> |
| linalg.yield %extracted : f32 |
| } -> tensor<?x?xf32> |
| return %1 : tensor<?x?xf32> |
| } |
| |
| // CHECK-LABEL: func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_contiguous( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>, |
| // CHECK-SAME: %[[VAL_1:.*]]: index, |
| // CHECK-SAME: %[[VAL_2:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> { |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 79 : index |
| // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_2]], %[[VAL_4]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_6:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_2]], %[[VAL_6]] : tensor<?x?xf32> |
| // CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_9:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_7]] : vector<1x4xi1> |
| // CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_2]]{{\[}}%[[VAL_8]], %[[VAL_8]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32> |
| // CHECK: %[[VAL_12:.*]] = arith.constant dense<[0, 1, 2, 3]> : vector<4xindex> |
| // CHECK: %[[VAL_13:.*]] = vector.broadcast %[[VAL_1]] : index to vector<4xindex> |
| // CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_13]] : vector<4xindex> |
| // CHECK-DAG: %[[VAL_15:.*]] = arith.constant dense<true> : vector<1x4xi1> |
| // CHECK-DAG: %[[VAL_16:.*]] = arith.constant dense<0.000000e+00> : vector<1x4xf32> |
| // CHECK-DAG: %[[VAL_17:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_18:.*]] = arith.constant dense<79> : vector<1x4xindex> |
| // CHECK-DAG: %[[VAL_19:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_20:.*]] = tensor.dim %[[VAL_0]], %[[VAL_19]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_21:.*]] = vector.broadcast %[[VAL_20]] : index to vector<1x4xindex> |
| // CHECK: %[[VAL_22:.*]] = arith.muli %[[VAL_18]], %[[VAL_21]] : vector<1x4xindex> |
| // CHECK: %[[VAL_23:.*]] = vector.broadcast %[[VAL_14]] : vector<4xindex> to vector<1x4xindex> |
| // CHECK: %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_22]] : vector<1x4xindex> |
| // CHECK: %[[VAL_25:.*]] = vector.mask %[[VAL_10]] { vector.gather %[[VAL_0]]{{\[}}%[[VAL_17]], %[[VAL_17]]] {{\[}}%[[VAL_24]]], %[[VAL_15]], %[[VAL_16]] : tensor<?x?xf32>, vector<1x4xindex>, vector<1x4xi1>, vector<1x4xf32> into vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32> |
| // CHECK: %[[VAL_26:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_27:.*]] = vector.mask %[[VAL_10]] { vector.transfer_write %[[VAL_25]], %[[VAL_2]]{{\[}}%[[VAL_26]], %[[VAL_26]]] {in_bounds = [true, true]} : vector<1x4xf32>, tensor<?x?xf32> } : vector<1x4xi1> -> tensor<?x?xf32> |
| // CHECK: return %[[VAL_27]] : tensor<?x?xf32> |
| // CHECK: } |
| |
| 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 [1, 4] vectorize_nd_extract : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @masked_vectorize_nd_tensor_extract_with_affine_apply_gather(%6: tensor<80x16xf32>, %arg0: index, %extracted_slice : tensor<1x3xf32>) -> tensor<1x3xf32> { |
| %c16 = arith.constant 16 : index |
| %1 = linalg.generic { |
| indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "parallel"] |
| } outs(%extracted_slice : tensor<1x3xf32>) { |
| ^bb0(%out: f32): |
| %2 = linalg.index 1 : index |
| %3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %arg0) |
| %extracted = tensor.extract %6[%3, %c16] : tensor<80x16xf32> |
| linalg.yield %extracted : f32 |
| } -> tensor<1x3xf32> |
| return %1 : tensor<1x3xf32> |
| } |
| |
| // CHECK-LABEL: func.func @masked_vectorize_nd_tensor_extract_with_affine_apply_gather |
| // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 3 : index |
| // CHECK: %[[VAL_8:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_5]] : vector<1x4xi1> |
| // CHECK: %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<1x3xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32> |
| // CHECK: %[[VAL_11:.*]] = vector.broadcast {{.*}} : index to vector<4xindex> |
| // CHECK: %[[VAL_12:.*]] = arith.addi {{.*}} : vector<4xindex> |
| // CHECK: %[[VAL_16:.*]] = vector.broadcast {{.*}} : vector<4xindex> to vector<1x4xindex> |
| // CHECK: %[[VAL_18:.*]] = tensor.dim {{.*}} : tensor<80x16xf32> |
| // CHECK: %[[VAL_19:.*]] = vector.broadcast {{.*}} : index to vector<1x4xindex> |
| // CHECK: %[[VAL_20:.*]] = arith.muli {{.*}} : vector<1x4xindex> |
| // CHECK: %[[VAL_22:.*]] = arith.addi {{.*}} : vector<1x4xindex> |
| // CHECK: %[[VAL_23:.*]] = vector.mask %[[VAL_8]] { vector.gather {{.*}} : tensor<80x16xf32>, vector<1x4xindex>, vector<1x4xi1>, vector<1x4xf32> into vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32> |
| // CHECK: %[[VAL_25:.*]] = vector.mask %[[VAL_8]] { vector.transfer_write {{.*}} {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x3xf32> } : vector<1x4xi1> -> tensor<1x3xf32> |
| |
| 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 [1, 4] vectorize_nd_extract : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_gather(%6: tensor<?x?xf32>, %arg0: index, %extracted_slice : tensor<?x?xf32>) -> tensor<?x?xf32> { |
| %c16 = arith.constant 16 : index |
| %1 = linalg.generic { |
| indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "parallel"] |
| } outs(%extracted_slice : tensor<?x?xf32>) { |
| ^bb0(%out: f32): |
| %2 = linalg.index 1 : index |
| %3 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %arg0) |
| %extracted = tensor.extract %6[%3, %c16] : tensor<?x?xf32> |
| linalg.yield %extracted : f32 |
| } -> tensor<?x?xf32> |
| return %1 : tensor<?x?xf32> |
| } |
| |
| // CHECK-LABEL: func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_gather( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>, |
| // CHECK-SAME: %[[VAL_1:.*]]: index, |
| // CHECK-SAME: %[[VAL_2:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> { |
| // CHECK: %[[VAL_3:.*]] = arith.constant 16 : index |
| // CHECK: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_2]], %[[VAL_4]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_6:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_2]], %[[VAL_6]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_8:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_9:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_7]] : vector<1x4xi1> |
| // CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_2]]{{\[}}%[[VAL_8]], %[[VAL_8]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32> |
| // CHECK: %[[VAL_12:.*]] = arith.constant dense<[0, 1, 2, 3]> : vector<4xindex> |
| // CHECK: %[[VAL_13:.*]] = vector.broadcast %[[VAL_1]] : index to vector<4xindex> |
| // CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_13]] : vector<4xindex> |
| // CHECK: %[[VAL_15:.*]] = arith.constant dense<true> : vector<1x4xi1> |
| // CHECK: %[[VAL_16:.*]] = arith.constant dense<0.000000e+00> : vector<1x4xf32> |
| // CHECK: %[[VAL_17:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_18:.*]] = vector.broadcast %[[VAL_14]] : vector<4xindex> to vector<1x4xindex> |
| // CHECK: %[[VAL_19:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_20:.*]] = tensor.dim %[[VAL_0]], %[[VAL_19]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_21:.*]] = vector.broadcast %[[VAL_20]] : index to vector<1x4xindex> |
| // CHECK: %[[VAL_22:.*]] = arith.muli %[[VAL_18]], %[[VAL_21]] : vector<1x4xindex> |
| // CHECK: %[[VAL_23:.*]] = arith.constant dense<16> : vector<1x4xindex> |
| // CHECK: %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_22]] : vector<1x4xindex> |
| // CHECK: %[[VAL_25:.*]] = vector.mask %[[VAL_10]] { vector.gather %[[VAL_0]]{{\[}}%[[VAL_17]], %[[VAL_17]]] {{\[}}%[[VAL_24]]], %[[VAL_15]], %[[VAL_16]] : tensor<?x?xf32>, vector<1x4xindex>, vector<1x4xi1>, vector<1x4xf32> into vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32> |
| // CHECK: %[[VAL_26:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_27:.*]] = vector.mask %[[VAL_10]] { vector.transfer_write %[[VAL_25]], %[[VAL_2]]{{\[}}%[[VAL_26]], %[[VAL_26]]] {in_bounds = [true, true]} : vector<1x4xf32>, tensor<?x?xf32> } : vector<1x4xi1> -> tensor<?x?xf32> |
| // CHECK: return %[[VAL_27]] : tensor<?x?xf32> |
| // CHECK: } |
| |
| 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 [1, 4] vectorize_nd_extract : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| #map1 = affine_map<(d0, d1) -> (d0, d1)> |
| func.func @extract_masked_vectorize(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> { |
| %c0 = arith.constant 1 : index |
| %c1 = arith.constant 2 : index |
| %2 = linalg.generic { |
| indexing_maps = [#map1], |
| iterator_types = ["parallel", "parallel"] |
| } outs(%arg1 : tensor<?x?xf32>) { |
| ^bb0(%arg3: f32): |
| %7 = tensor.extract %arg0[%c0, %c1] : tensor<?x?xf32> |
| linalg.yield %7 : f32 |
| } -> tensor<?x?xf32> |
| return %2 : tensor<?x?xf32> |
| } |
| |
| // CHECK-LABEL: func.func @extract_masked_vectorize( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> { |
| // CHECK: %[[VAL_2:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_3:.*]] = arith.constant 2 : index |
| // CHECK: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_6:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_6]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_8:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_9:.*]] = arith.constant 0.000000e+00 : f32 |
| // CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_7]] : vector<3x3xi1> |
| // CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_1]]{{\[}}%[[VAL_8]], %[[VAL_8]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<3x3xf32> } : vector<3x3xi1> -> vector<3x3xf32> |
| // CHECK: %[[VAL_12:.*]] = arith.constant dense<true> : vector<3x3xi1> |
| // CHECK: %[[VAL_13:.*]] = arith.constant dense<0.000000e+00> : vector<3x3xf32> |
| // CHECK: %[[VAL_14:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_15:.*]] = arith.constant dense<1> : vector<3x3xindex> |
| // CHECK: %[[VAL_16:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_17:.*]] = tensor.dim %[[VAL_0]], %[[VAL_16]] : tensor<?x?xf32> |
| // CHECK: %[[VAL_18:.*]] = vector.broadcast %[[VAL_17]] : index to vector<3x3xindex> |
| // CHECK: %[[VAL_19:.*]] = arith.muli %[[VAL_15]], %[[VAL_18]] : vector<3x3xindex> |
| // CHECK: %[[VAL_20:.*]] = arith.constant dense<2> : vector<3x3xindex> |
| // CHECK: %[[VAL_21:.*]] = arith.addi %[[VAL_20]], %[[VAL_19]] : vector<3x3xindex> |
| // CHECK: %[[VAL_22:.*]] = vector.mask %[[VAL_10]] { vector.gather %[[VAL_0]]{{\[}}%[[VAL_14]], %[[VAL_14]]] {{\[}}%[[VAL_21]]], %[[VAL_12]], %[[VAL_13]] : tensor<?x?xf32>, vector<3x3xindex>, vector<3x3xi1>, vector<3x3xf32> into vector<3x3xf32> } : vector<3x3xi1> -> vector<3x3xf32> |
| // CHECK: %[[VAL_23:.*]] = arith.constant 0 : index |
| // CHECK: %[[VAL_24:.*]] = vector.mask %[[VAL_10]] { vector.transfer_write %[[VAL_22]], %[[VAL_1]]{{\[}}%[[VAL_23]], %[[VAL_23]]] {in_bounds = [true, true]} : vector<3x3xf32>, tensor<?x?xf32> } : vector<3x3xi1> -> 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 [3, 3] vectorize_nd_extract : !transform.any_op |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| #map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> |
| func.func @tensor_extract_dynamic_shape(%arg1: tensor<123x321xf32>, %arg2: tensor<1x?x8xf32>) -> tensor<1x?x8xf32> { |
| %c0 = arith.constant 1 : index |
| %c1 = arith.constant 2 : index |
| %2 = linalg.generic { |
| indexing_maps = [#map1], |
| iterator_types = ["parallel", "parallel", "parallel"] |
| } outs(%arg2 : tensor<1x?x8xf32>) |
| { |
| ^bb0(%arg3: f32): |
| %idx_0 = linalg.index 0 : index |
| %idx_1 = linalg.index 1 : index |
| %idx = arith.addi %idx_0, %idx_1 : index |
| %7 = tensor.extract %arg1[%c0, %idx] : tensor<123x321xf32> |
| linalg.yield %7 : f32 |
| } -> tensor<1x?x8xf32> |
| return %2 : tensor<1x?x8xf32> |
| } |
| |
| // TODO: Make sure that this is vectorized as "scalar broadcast" when only |
| // vectorising the 2nd dimension. |
| // CHECK-LABEL: func.func @tensor_extract_dynamic_shape( |
| // CHECK-SAME: %[[ARG_1:.*]]: tensor<123x321xf32>, |
| // CHECK-SAME: %[[ARG_2:.*]]: tensor<1x?x8xf32>) -> tensor<1x?x8xf32> { |
| // CHECK: %[[C2:.*]] = arith.constant 2 : index |
| // CHECK: %[[C1_1:.*]] = arith.constant 1 : index |
| // CHECK: %[[C1_2:.*]] = arith.constant 1 : index |
| // CHECK: %[[DIM:.*]] = tensor.dim %[[ARG_2]], %[[C1_2]] : tensor<1x?x8xf32> |
| // CHECK: %[[C8:.*]] = arith.constant 8 : index |
| // CHECK: %[[MASK:.*]] = vector.create_mask %[[C1_1]], %[[DIM]], %[[C8]] : vector<1x3x8xi1> |
| // CHECK: %[[MASK_2:.*]] = arith.constant dense<true> : vector<1x3x8xi1> |
| // CHECK: %[[FALLTHROUGH:.*]] = arith.constant dense<0.000000e+00> : vector<1x3x8xf32> |
| // CHECK: %[[C0_1:.*]] = arith.constant 0 : index |
| // CHECK: vector.mask %[[MASK]] { vector.gather %[[ARG_1]][%[[C0_1]], %[[C0_1]]] [%{{.*}}], %[[MASK_2]], %[[FALLTHROUGH]] : tensor<123x321xf32>, vector<1x3x8xindex>, vector<1x3x8xi1>, vector<1x3x8xf32> into vector<1x3x8xf32> } : vector<1x3x8xi1> -> vector<1x3x8xf32> |
| |
| 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 [1, 3, 8] vectorize_nd_extract : !transform.any_op |
| transform.yield |
| } |
| } |
