| // RUN: iree-dialects-opt --iree-linalg-ext-tile --split-input-file --verify-diagnostics -cse %s | FileCheck %s |
| |
| func.func @scatter_tiling( |
| %original: tensor<?x?xf32>, %indices: tensor<?x1xi32>, |
| %update : tensor<?x?xf32>) -> tensor<?x?xf32> { |
| %0 = iree_linalg_ext.scatter |
| {__internal_linalg_transform__ = "tiling_input"} |
| dimension_map = [0] |
| unique_indices(true) |
| ins(%update, %indices : tensor<?x?xf32>, tensor<?x1xi32>) |
| outs(%original : tensor<?x?xf32>) { |
| ^bb0(%arg1: f32, %arg2: f32): |
| %1 = arith.addf %arg1, %arg2 : f32 |
| iree_linalg_ext.yield %1 : f32 |
| } -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (20, -d0 + s1)> |
| // CHECK: func.func @scatter_tiling( |
| // CHECK-SAME: %[[ORIGINAL:[a-zA-Z0-9_]+]]: tensor<?x?xf32> |
| // CHECK-SAME: %[[INDICES:[a-zA-Z0-9_]+]]: tensor<?x1xi32> |
| // CHECK-SAME: %[[UPDATES:[a-zA-Z0-9_]+]]: tensor<?x?xf32> |
| // CHECK-DAG: %[[TILESIZEY:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[TILESIZEX:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[D0:.+]] = tensor.dim %[[UPDATES]], %[[C0]] |
| // CHECK-DAG: %[[D1:.+]] = tensor.dim %[[UPDATES]], %[[C1]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:.+]] = %[[C0]] to %[[D0]] step %[[TILESIZEY]] |
| // CHECK-SAME: iter_args(%[[INITY:.+]] = %[[ORIGINAL]]) |
| // CHECK-DAG: %[[USED_TILESIZEY:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[TILESIZEY]], %[[D0]]] |
| // CHECK: %[[RESULT_INNER:.+]] = scf.for %[[IV1:.+]] = %[[C0]] to %[[D1]] step %[[TILESIZEX]] |
| // CHECK-SAME: iter_args(%[[INITX:.+]] = %[[INITY]]) |
| // CHECK: %[[USED_TILESIZEX:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[TILESIZEX]], %[[D1]]] |
| // CHECK: %[[UPDATE_SLICE:.+]] = tensor.extract_slice %[[UPDATES]][%[[IV0]], %[[IV1]]] |
| // CHECK-SAME: [%[[USED_TILESIZEY]], %[[USED_TILESIZEX]]] |
| // CHECK: %[[INDEX_SLICE:.+]] = tensor.extract_slice %[[INDICES]][%[[IV0]], 0] |
| // CHECK-SAME: [%[[USED_TILESIZEY]], 1] |
| // CHECK: %[[SCATTER_DIM:.+]] = tensor.dim %[[ORIGINAL]], %[[C0]] |
| // CHECK: %[[ORIGINAL_SLICE:.+]] = tensor.extract_slice %[[ORIGINAL]][0, %[[IV1]]] |
| // CHECK-SAME: [%[[SCATTER_DIM]], %[[USED_TILESIZEX]]] |
| // CHECK: %[[SCATTER_TILE:.+]] = iree_linalg_ext.scatter |
| // CHECK-SAME: __internal_linalg_transform__ = "tiling_output" |
| // CHECK-SAME: unique_indices(true) |
| // CHECK-SAME: ins(%[[UPDATE_SLICE]], %[[INDEX_SLICE]] |
| // CHECK-SAME: outs(%[[ORIGINAL_SLICE]] |
| // CHECK: %[[YIELD:.+]] = tensor.insert_slice %[[SCATTER_TILE]] into %[[INITX]][0, %[[IV1]]] |
| // CHECK-SAME: [%[[SCATTER_DIM]], %[[USED_TILESIZEX]]] |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: scf.yield %[[RESULT_INNER]] |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func.func @scatter_tiling_memref( |
| %original: memref<?x?xf32>, %indices: memref<?x1xi32>, |
| %update : memref<?x?xf32>) { |
| iree_linalg_ext.scatter |
| {__internal_linalg_transform__ = "tiling_input"} |
| dimension_map = [0] |
| unique_indices(true) |
| ins(%update, %indices : memref<?x?xf32>, memref<?x1xi32>) |
| outs(%original : memref<?x?xf32>) { |
| ^bb0(%arg1: f32, %arg2: f32): |
| %1 = arith.addf %arg1, %arg2 : f32 |
| iree_linalg_ext.yield %1 : f32 |
| } |
| return |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (20, -d0 + s1)> |
| // CHECK: func.func @scatter_tiling_memref( |
| // CHECK-SAME: %[[ORIGINAL:[a-zA-Z0-9_]+]]: memref<?x?xf32> |
| // CHECK-SAME: %[[INDICES:[a-zA-Z0-9_]+]]: memref<?x1xi32> |
| // CHECK-SAME: %[[UPDATES:[a-zA-Z0-9_]+]]: memref<?x?xf32> |
| // CHECK-DAG: %[[TILESIZEY:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[TILESIZEX:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[D0:.+]] = memref.dim %[[UPDATES]], %[[C0]] |
| // CHECK-DAG: %[[D1:.+]] = memref.dim %[[UPDATES]], %[[C1]] |
| // CHECK: scf.for %[[IV0:.+]] = %[[C0]] to %[[D0]] step %[[TILESIZEY]] |
| // CHECK-DAG: %[[USED_TILESIZEY:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[TILESIZEY]], %[[D0]]] |
| // CHECK: scf.for %[[IV1:.+]] = %[[C0]] to %[[D1]] step %[[TILESIZEX]] |
| // CHECK-DAG: %[[USED_TILESIZEX:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[TILESIZEX]], %[[D1]]] |
| // CHECK: %[[UPDATE_SLICE:.+]] = memref.subview %[[UPDATES]][%[[IV0]], %[[IV1]]] |
| // CHECK-SAME: [%[[USED_TILESIZEY]], %[[USED_TILESIZEX]]] |
| // CHECK: %[[INDEX_SLICE:.+]] = memref.subview %[[INDICES]][%[[IV0]], 0] |
| // CHECK-SAME: [%[[USED_TILESIZEY]], 1] |
| // CHECK: %[[SCATTER_DIM:.+]] = memref.dim %[[ORIGINAL]], %[[C0]] |
| // CHECK: %[[ORIGINAL_SLICE:.+]] = memref.subview %[[ORIGINAL]][0, %[[IV1]] |
| // CHECK-SAME: [%[[SCATTER_DIM]], %[[USED_TILESIZEX]]] |
| // CHECK: iree_linalg_ext.scatter |
| // CHECK-SAME: __internal_linalg_transform__ = "tiling_output" |
| // CHECK-SAME: unique_indices(true) |
| // CHECK-SAME: ins(%[[UPDATE_SLICE]], %[[INDEX_SLICE]] |
| // CHECK-SAME: outs(%[[ORIGINAL_SLICE]] |
| |
| // ----- |
| |
| func.func @scatter_no_tiling( |
| %original: tensor<?x?xf32>, %indices: tensor<?x1xi32>, |
| %update : tensor<?x?xf32>) -> tensor<?x?xf32> { |
| %0 = iree_linalg_ext.scatter |
| {__internal_linalg_transform__ = "no_tiling_input"} |
| dimension_map = [0] |
| unique_indices(true) |
| ins(%update, %indices : tensor<?x?xf32>, tensor<?x1xi32>) |
| outs(%original : tensor<?x?xf32>) { |
| ^bb0(%arg1: f32, %arg2: f32): |
| %1 = arith.addf %arg1, %arg2 : f32 |
| iree_linalg_ext.yield %1 : f32 |
| } -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| // CHECK: func.func @scatter_no_tiling |
| // CHECK-SAME: %[[ORIGINAL:[a-zA-Z0-9_]+]]: tensor<?x?xf32> |
| // CHECK-SAME: %[[INDICES:[a-zA-Z0-9_]+]]: tensor<?x1xi32> |
| // CHECK-SAME: %[[UPDATES:[a-zA-Z0-9_]+]]: tensor<?x?xf32> |
| // CHECK: %[[RESULT:.+]] = iree_linalg_ext.scatter |
| // CHECK-SAME: __internal_linalg_transform__ = "no_tiling_output" |
| // CHECK-SAME: unique_indices(true) |
| // CHECK-SAME: ins(%[[UPDATES]], %[[INDICES]] |
| // CHECK-SAME: outs(%[[ORIGINAL]] |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func.func @scatter_repeated_indices_tiling( |
| %original: tensor<?x?xf32>, %indices: tensor<?x1xi32>, |
| %update : tensor<?x?xf32>) -> tensor<?x?xf32> { |
| %0 = iree_linalg_ext.scatter |
| {__internal_linalg_transform__ = "tiling_repeated_indices_scatter_input"} |
| dimension_map = [0] |
| unique_indices(false) |
| ins(%update, %indices : tensor<?x?xf32>, tensor<?x1xi32>) |
| outs(%original : tensor<?x?xf32>) { |
| ^bb0(%arg1: f32, %arg2: f32): |
| %1 = arith.addf %arg1, %arg2 : f32 |
| iree_linalg_ext.yield %1 : f32 |
| } -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| // CHECK-DAG: #[[MAP:.+]] = affine_map<(d0)[s0, s1] -> (20, -d0 + s1)> |
| // CHECK: func.func @scatter_repeated_indices_tiling |
| // CHECK-SAME: %[[ORIGINAL:[a-zA-Z0-9_]+]]: tensor<?x?xf32> |
| // CHECK-SAME: %[[INDICES:[a-zA-Z0-9_]+]]: tensor<?x1xi32> |
| // CHECK-SAME: %[[UPDATES:[a-zA-Z0-9_]+]]: tensor<?x?xf32> |
| // CHECK-DAG: %[[TILESIZE:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[D0:.+]] = tensor.dim %[[UPDATES]], %[[C0]] |
| // CHECK-DAG: %[[D1:.+]] = tensor.dim %[[UPDATES]], %[[C1]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[I:.+]] = %[[C0]] to %[[D1]] step %[[TILESIZE]] |
| // CHECK-SAME: iter_args(%[[ITER:.+]] = %[[ORIGINAL]]) |
| // CHECK: %[[SZ:.+]] = affine.min #[[MAP]](%[[I]])[%[[TILESIZE]], %[[D1]]] |
| // CHECK: %[[UPDATES_TILE:.+]] = tensor.extract_slice |
| // CHECK-SAME: %[[UPDATES]][0, %[[I]]] [%[[D0]], %[[SZ]]] [1, 1] |
| // CHECK: %[[INDICES_TILE:.+]] = tensor.extract_slice |
| // CHECK-SAME: %[[INDICES]][0, 0] [%[[D0]], 1] [1, 1] |
| // CHECK: %[[ORIGINAL_D0:.+]] = tensor.dim %[[ORIGINAL]], %[[C0]] |
| // CHECK: %[[ORIGINAL_TILE:.+]] = tensor.extract_slice |
| // CHECK-SAME: %[[ORIGINAL]][0, %[[I]]] [%[[ORIGINAL_D0]], %[[SZ]]] [1, 1] |
| // CHECK: %[[SCATTER:.+]] = iree_linalg_ext.scatter |
| // CHECK-SAME: __internal_linalg_transform__ = "tiling_repeated_indices_scatter_output" |
| // CHECK-SAME: unique_indices(false) |
| // CHECK-SAME: ins(%[[UPDATES_TILE]], %[[INDICES_TILE]] |
| // CHECK-SAME: outs(%[[ORIGINAL_TILE]] |
| // CHECK: %[[RES:.+]] = tensor.insert_slice %[[SCATTER]] into |
| // CHECK-SAME: %[[ITER]][0, %[[I]]] [%[[ORIGINAL_D0]], %[[SZ]]] [1, 1] |
| // CHECK: scf.yield %[[RES]] |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func.func @scatter_repeated_indices_no_tiling( |
| %original: tensor<?x?xf32>, %indices: tensor<?x1xi32>, |
| %update : tensor<?x?xf32>) -> tensor<?x?xf32> { |
| // expected-error @+1 {{unimplemented tiling of non-parallel loop iterator type}} |
| %0 = iree_linalg_ext.scatter |
| {__internal_linalg_transform__ = "tiling_input"} |
| dimension_map = [0] |
| unique_indices(false) |
| ins(%update, %indices : tensor<?x?xf32>, tensor<?x1xi32>) |
| outs(%original : tensor<?x?xf32>) { |
| ^bb0(%arg1: f32, %arg2: f32): |
| %1 = arith.addf %arg1, %arg2 : f32 |
| iree_linalg_ext.yield %1 : f32 |
| } -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| // ----- |
| |
| func.func @sort_1d(%arg0: tensor<?xi32>) -> tensor<?xi32> { |
| %0 = iree_linalg_ext.sort |
| {__internal_linalg_transform__ = "outer_reduce_input"} |
| dimension(0) |
| outs(%arg0 : tensor<?xi32>) { |
| ^bb0(%arg2: i32, %arg3: i32): // no predecessors |
| %0 = arith.cmpi sgt, %arg2, %arg3 : i32 |
| iree_linalg_ext.yield %0 : i1 |
| } -> tensor<?xi32> |
| return %0 : tensor<?xi32> |
| } |
| // CHECK: func.func @sort_1d( |
| // CHECK-SAME: %[[OPERAND:.+]]: tensor<?xi32> |
| // CHECK: %[[RESULT:.+]] = iree_linalg_ext.sort |
| // CHECK-SAME: {__internal_linalg_transform__ = "outer_reduce_output"} |
| // CHECK-SAME: outs(%[[OPERAND]] : |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func.func @sort_2d(%arg0: tensor<?x?xi32>) -> tensor<?x?xi32> { |
| %0 = iree_linalg_ext.sort |
| {__internal_linalg_transform__ = "inner_reduce_input"} |
| dimension(1) |
| outs(%arg0 : tensor<?x?xi32>) { |
| ^bb0(%arg2: i32, %arg3: i32): // no predecessors |
| %0 = arith.cmpi sgt, %arg2, %arg3 : i32 |
| iree_linalg_ext.yield %0 : i1 |
| } -> tensor<?x?xi32> |
| return %0 : tensor<?x?xi32> |
| } |
| // CHECK: #[[MAP:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK: func.func @sort_2d( |
| // CHECK-SAME: %[[OPERAND:.+]]: tensor<?x?xi32> |
| // CHECK-DAG: %[[TILESIZE:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[D0:.+]] = tensor.dim %[[OPERAND]], %[[C0]] |
| // CHECK-DAG: %[[D1:.+]] = tensor.dim %[[OPERAND]], %[[C1]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV:.+]] = %[[C0]] to %[[D0]] step %[[TILESIZE]] |
| // CHECK-SAME: iter_args(%[[INIT:.+]] = %[[OPERAND]]) |
| // CHECK-DAG: %[[USED_TILESIZE:.+]] = affine.min #[[MAP]](%[[IV]])[%[[TILESIZE]], %[[D0]]] |
| // CHECK: %[[OPERAND_SLICE:.+]] = tensor.extract_slice %[[OPERAND]][%[[IV]], 0] |
| // CHECK-SAME: [%[[USED_TILESIZE]], %[[D1]]] |
| // CHECK: %[[SORT_TILE:.+]] = iree_linalg_ext.sort |
| // CHECK-SAME: __internal_linalg_transform__ = "inner_reduce_output" |
| // CHECK-SAME: outs(%[[OPERAND_SLICE]] |
| // CHECK: %[[YIELD:.+]] = tensor.insert_slice %[[SORT_TILE]] into %[[INIT]][%[[IV]], 0] |
| // CHECK-SAME: [%[[USED_TILESIZE]], %[[D1]]] |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func.func @sort_2d_inner_parallel(%arg0: tensor<?x?xi32>) -> tensor<?x?xi32> { |
| %0 = iree_linalg_ext.sort |
| {__internal_linalg_transform__ = "outer_reduce_input"} |
| dimension(0) |
| outs(%arg0 : tensor<?x?xi32>) { |
| ^bb0(%arg2: i32, %arg3: i32): // no predecessors |
| %0 = arith.cmpi sgt, %arg2, %arg3 : i32 |
| iree_linalg_ext.yield %0 : i1 |
| } -> tensor<?x?xi32> |
| return %0 : tensor<?x?xi32> |
| } |
| // CHECK: #[[MAP:.+]] = affine_map<(d0)[s0, s1] -> (20, -d0 + s1)> |
| // CHECK: func.func @sort_2d_inner_parallel( |
| // CHECK-SAME: %[[OPERAND:.+]]: tensor<?x?xi32> |
| // CHECK-DAG: %[[TILESIZE:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[D0:.+]] = tensor.dim %[[OPERAND]], %[[C0]] |
| // CHECK-DAG: %[[D1:.+]] = tensor.dim %[[OPERAND]], %[[C1]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV:.+]] = %[[C0]] to %[[D1]] step %[[TILESIZE]] |
| // CHECK-SAME: iter_args(%[[INIT:.+]] = %[[OPERAND]]) |
| // CHECK-DAG: %[[USED_TILESIZE:.+]] = affine.min #[[MAP]](%[[IV]])[%[[TILESIZE]], %[[D1]]] |
| // CHECK: %[[OPERAND_SLICE:.+]] = tensor.extract_slice %[[OPERAND]][0, %[[IV]]] |
| // CHECK-SAME: [%[[D0]], %[[USED_TILESIZE]]] |
| // CHECK: %[[SORT_TILE:.+]] = iree_linalg_ext.sort |
| // CHECK-SAME: __internal_linalg_transform__ = "outer_reduce_output" |
| // CHECK-SAME: outs(%[[OPERAND_SLICE]] |
| // CHECK: %[[YIELD:.+]] = tensor.insert_slice %[[SORT_TILE]] into %[[INIT]][0, %[[IV]]] |
| // CHECK-SAME: [%[[D0]], %[[USED_TILESIZE]]] |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func.func @sort_2d_multi_result( |
| %arg0: tensor<?x?xi32>, %arg1: tensor<?x?xf32>) |
| -> (tensor<?x?xi32>, tensor<?x?xf32>) { |
| %0:2 = iree_linalg_ext.sort |
| {__internal_linalg_transform__ = "inner_reduce_input"} |
| dimension(1) |
| outs(%arg0, %arg1 : tensor<?x?xi32>, tensor<?x?xf32>) { |
| ^bb0(%arg2: i32, %arg3: i32, %arg4 : f32, %arg5 : f32): // no predecessors |
| %1 = arith.cmpf ogt, %arg4, %arg5 : f32 |
| iree_linalg_ext.yield %1 : i1 |
| } -> tensor<?x?xi32>, tensor<?x?xf32> |
| return %0#0, %0#1 : tensor<?x?xi32>, tensor<?x?xf32> |
| } |
| // CHECK: #[[MAP:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK: func.func @sort_2d_multi_result( |
| // CHECK-SAME: %[[OPERAND1:.+]]: tensor<?x?xi32> |
| // CHECK-SAME: %[[OPERAND2:.+]]: tensor<?x?xf32> |
| // CHECK-DAG: %[[TILESIZE:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[D0:.+]] = tensor.dim %[[OPERAND1]], %[[C0]] |
| // CHECK-DAG: %[[D1:.+]] = tensor.dim %[[OPERAND1]], %[[C1]] |
| // CHECK: %[[RESULT:.+]]:2 = scf.for %[[IV:.+]] = %[[C0]] to %[[D0]] step %[[TILESIZE]] |
| // CHECK-SAME: iter_args(%[[INIT1:.+]] = %[[OPERAND1]], %[[INIT2:.+]] = %[[OPERAND2]]) |
| // CHECK-DAG: %[[USED_TILESIZE:.+]] = affine.min #[[MAP]](%[[IV]])[%[[TILESIZE]], %[[D0]]] |
| // CHECK: %[[OPERAND1_SLICE:.+]] = tensor.extract_slice %[[OPERAND1]][%[[IV]], 0] |
| // CHECK-SAME: [%[[USED_TILESIZE]], %[[D1]]] |
| // CHECK: %[[OPERAND2_SLICE:.+]] = tensor.extract_slice %[[OPERAND2]][%[[IV]], 0] |
| // CHECK-SAME: [%[[USED_TILESIZE]], %[[D1]]] |
| // CHECK: %[[SORT_TILE:.+]]:2 = iree_linalg_ext.sort |
| // CHECK-SAME: __internal_linalg_transform__ = "inner_reduce_output" |
| // CHECK-SAME: outs(%[[OPERAND1_SLICE]], %[[OPERAND2_SLICE]] |
| // CHECK: %[[YIELD1:.+]] = tensor.insert_slice %[[SORT_TILE]]#0 into %[[INIT1]][%[[IV]], 0] |
| // CHECK-SAME: [%[[USED_TILESIZE]], %[[D1]]] |
| // CHECK: %[[YIELD2:.+]] = tensor.insert_slice %[[SORT_TILE]]#1 into %[[INIT2]][%[[IV]], 0] |
| // CHECK-SAME: [%[[USED_TILESIZE]], %[[D1]]] |
| // CHECK: scf.yield %[[YIELD1]], %[[YIELD2]] |
| // CHECK: return %[[RESULT]]#0, %[[RESULT]]#1 |
| |
| // ----- |
| |
| func.func @sort_2d_multi_result_memref( |
| %arg0: memref<?x?xi32>, %arg1: memref<?x?xf32>) { |
| iree_linalg_ext.sort |
| {__internal_linalg_transform__ = "outer_reduce_input"} |
| dimension(0) |
| outs(%arg0, %arg1 : memref<?x?xi32>, memref<?x?xf32>) { |
| ^bb0(%arg2: i32, %arg3: i32, %arg4 : f32, %arg5 : f32): // no predecessors |
| %0 = arith.cmpf ogt, %arg4, %arg5 : f32 |
| iree_linalg_ext.yield %0 : i1 |
| } |
| return |
| } |
| // CHECK: #[[MAP:.+]] = affine_map<(d0)[s0, s1] -> (20, -d0 + s1)> |
| // CHECK: func.func @sort_2d_multi_result_memref( |
| // CHECK-SAME: %[[OPERAND1:.+]]: memref<?x?xi32> |
| // CHECK-SAME: %[[OPERAND2:.+]]: memref<?x?xf32> |
| // CHECK-DAG: %[[TILESIZE:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[D0:.+]] = memref.dim %[[OPERAND1]], %[[C0]] |
| // CHECK-DAG: %[[D1:.+]] = memref.dim %[[OPERAND1]], %[[C1]] |
| // CHECK: scf.for %[[IV:.+]] = %[[C0]] to %[[D1]] step %[[TILESIZE]] |
| // CHECK-DAG: %[[USED_TILESIZE:.+]] = affine.min #[[MAP]](%[[IV]])[%[[TILESIZE]], %[[D1]]] |
| // CHECK: %[[OPERAND1_SLICE:.+]] = memref.subview %[[OPERAND1]][0, %[[IV]]] |
| // CHECK-SAME: [%[[D0]], %[[USED_TILESIZE]]] |
| // CHECK: %[[OPERAND2_SLICE:.+]] = memref.subview %[[OPERAND2]][0, %[[IV]]] |
| // CHECK-SAME: [%[[D0]], %[[USED_TILESIZE]]] |
| // CHECK: iree_linalg_ext.sort |
| // CHECK-SAME: __internal_linalg_transform__ = "outer_reduce_output" |
| // CHECK-SAME: outs(%[[OPERAND1_SLICE]], %[[OPERAND2_SLICE]] |
| |
| // ----- |
| |
| func.func @fft_1d_stage_5(%arg0: tensor<1024xf32>, %arg1: tensor<1024xf32>, |
| %arg2: tensor<16xf32>, %arg3: tensor<16xf32>) -> (tensor<1024xf32>, tensor<1024xf32>) { |
| %cst1 = arith.constant 5 : index |
| %0:2 = iree_linalg_ext.fft |
| {__internal_linalg_transform__ = "tiling_1d_stage5_fft_input"} |
| ins(%cst1, %arg2, %arg3: index, tensor<16xf32>, tensor<16xf32>) |
| outs(%arg0, %arg1: tensor<1024xf32>, tensor<1024xf32>) |
| : tensor<1024xf32>, tensor<1024xf32> |
| return %0#0, %0#1 : tensor<1024xf32>, tensor<1024xf32> |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (32, -d0 + s1)> |
| // CHECK: func.func @fft_1d_stage_5( |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]] |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]] |
| // CHECK-SAME: %[[COEF_REAL:[a-zA-Z0-9_]+]] |
| // CHECK-SAME: %[[COEF_IMAG:[a-zA-Z0-9_]+]] |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C5:.+]] = arith.constant 5 : index |
| // CHECK-DAG: %[[C32:.+]] = arith.constant 32 : index |
| // CHECK-DAG: %[[C1024:.+]] = arith.constant 1024 : index |
| // CHECK: %[[RES:.+]]:2 = scf.for %[[I:.+]] = %[[C0]] to %[[C1024]] step %[[C32]] |
| // CHECK-SAME: iter_args(%[[ARG5:.+]] = %[[ARG0]], %[[ARG6:.+]] = %[[ARG1]]) |
| // CHECK-SAME: -> (tensor<1024xf32>, tensor<1024xf32>) { |
| // CHECK: %[[SIZE:.+]] = affine.min #[[MAP0]](%[[I]])[%[[C32]], %[[C1024]]] |
| // CHECK: %[[SLICE1:.+]] = tensor.extract_slice %[[ARG0]][%[[I]]] [%[[SIZE]]] [1] : tensor<1024xf32> to tensor<?xf32> |
| // CHECK: %[[SLICE2:.+]] = tensor.extract_slice %[[ARG1]][%[[I]]] [%[[SIZE]]] [1] : tensor<1024xf32> to tensor<?xf32> |
| // CHECK: %[[FFT:.+]]:2 = iree_linalg_ext.fft |
| // CHECK-SAME: {__internal_linalg_transform__ = "tiling_1d_stage5_fft_output"} |
| // CHECK-SAME: ins(%[[C5]], %[[COEF_REAL]], %[[COEF_IMAG]] : index, tensor<16xf32>, tensor<16xf32>) |
| // CHECK-SAME: outs(%[[SLICE1]], %[[SLICE2]] : tensor<?xf32>, tensor<?xf32>) |
| // CHECK: %[[INSERT1:.+]] = tensor.insert_slice %[[FFT]]#0 into %[[ARG5]][%[[I]]] [%[[SIZE]]] [1] : tensor<?xf32> into tensor<1024xf32> |
| // CHECK: %[[INSERT2:.+]] = tensor.insert_slice %[[FFT]]#1 into %[[ARG6]][%[[I]]] [%[[SIZE]]] [1] : tensor<?xf32> into tensor<1024xf32> |
| // CHECK: scf.yield %[[INSERT1]], %[[INSERT2]] |
| // CHECK: return %[[RES]]#0, %[[RES]]#1 : tensor<1024xf32>, tensor<1024xf32> |
| |
| // ----- |
| |
| func.func @fft_2d_stage_5(%arg0: tensor<3x1024xf32>, %arg1: tensor<3x1024xf32>, |
| %arg2: tensor<16xf32>, %arg3: tensor<16xf32>) -> (tensor<3x1024xf32>, tensor<3x1024xf32>) { |
| %cst1 = arith.constant 5 : index |
| %0:2 = iree_linalg_ext.fft |
| {__internal_linalg_transform__ = "tiling_2d_stage5_fft_input"} |
| ins(%cst1, %arg2, %arg3: index, tensor<16xf32>, tensor<16xf32>) |
| outs(%arg0, %arg1: tensor<3x1024xf32>, tensor<3x1024xf32>) |
| : tensor<3x1024xf32>, tensor<3x1024xf32> |
| return %0#0, %0#1 : tensor<3x1024xf32>, tensor<3x1024xf32> |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (32, -d0 + s1)> |
| // CHECK: func.func @fft_2d_stage_5( |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]] |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]] |
| // CHECK-SAME: %[[COEF_REAL:[a-zA-Z0-9_]+]] |
| // CHECK-SAME: %[[COEF_IMAG:[a-zA-Z0-9_]+]] |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C3:.+]] = arith.constant 3 : index |
| // CHECK-DAG: %[[C5:.+]] = arith.constant 5 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C32:.+]] = arith.constant 32 : index |
| // CHECK-DAG: %[[C1024:.+]] = arith.constant 1024 : index |
| // CHECK: %[[RES:.+]]:2 = scf.for %[[I:.+]] = %[[C0]] to %[[C3]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[ARG5:.+]] = %[[ARG0]], %[[ARG6:.+]] = %[[ARG1]]) |
| // CHECK-SAME: -> (tensor<3x1024xf32>, tensor<3x1024xf32>) { |
| // CHECK: %[[SZ1:.+]] = affine.min #[[MAP0]](%[[I]])[%[[C10]], %[[C3]]] |
| // CHECK: %{{.+}} = scf.for %[[J:.+]] = %[[C0]] to %[[C1024]] step %[[C32]] |
| // CHECK-SAME: iter_args(%[[ARG8:.+]] = %[[ARG5]], %[[ARG9:.+]] = %[[ARG6]]) -> (tensor<3x1024xf32>, tensor<3x1024xf32>) { |
| // CHECK: %[[SZ2:.+]] = affine.min #[[MAP1]](%[[J]])[%[[C32]], %[[C1024]]] |
| // CHECK: %[[SLICE1:.+]] = tensor.extract_slice %[[ARG0]][%[[I]], %[[J]]] [%[[SZ1]], %[[SZ2]]] [1, 1] |
| // CHECK: %[[SLICE2:.+]] = tensor.extract_slice %[[ARG1]][%[[I]], %[[J]]] [%[[SZ1]], %[[SZ2]]] [1, 1] |
| // CHECK: %[[FFT:.+]]:2 = iree_linalg_ext.fft |
| // CHECK-SAME: {__internal_linalg_transform__ = "tiling_2d_stage5_fft_output"} |
| // CHECK-SAME: ins(%[[C5]], %[[COEF_REAL]], %[[COEF_IMAG]] : index, tensor<16xf32>, tensor<16xf32>) |
| // CHECK-SAME: outs(%[[SLICE1]], %[[SLICE2]] : tensor<?x?xf32>, tensor<?x?xf32>) |
| // CHECK: %[[INSERT1:.+]] = tensor.insert_slice %[[FFT]]#0 into %[[ARG8]][%[[I]], %[[J]]] [%[[SZ1]], %[[SZ2]]] [1, 1] |
| // CHECK: %[[INSERT2:.+]] = tensor.insert_slice %[[FFT]]#1 into %[[ARG9]][%[[I]], %[[J]]] [%[[SZ1]], %[[SZ2]]] [1, 1] |
| // CHECK: scf.yield %[[INSERT1]], %[[INSERT2]] : tensor<3x1024xf32>, tensor<3x1024xf32> |
| |
| // ----- |
| |
| func.func @fft_1d_stage_5_memref(%arg0: memref<1024xf32>, %arg1: memref<1024xf32>, |
| %arg2: memref<16xf32>, %arg3: memref<16xf32>) { |
| %cst1 = arith.constant 5 : index |
| iree_linalg_ext.fft |
| {__internal_linalg_transform__ = "tiling_1d_stage5_fft_input"} |
| ins(%cst1, %arg2, %arg3: index, memref<16xf32>, memref<16xf32>) |
| outs(%arg0, %arg1: memref<1024xf32>, memref<1024xf32>) |
| return |
| } |
| // CHECK: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (32, -d0 + s1)> |
| // CHECK: func.func @fft_1d_stage_5_memref( |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]] |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]] |
| // CHECK-SAME: %[[COEF_REAL:[a-zA-Z0-9_]+]] |
| // CHECK-SAME: %[[COEF_IMAG:[a-zA-Z0-9_]+]] |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C5:.+]] = arith.constant 5 : index |
| // CHECK-DAG: %[[C32:.+]] = arith.constant 32 : index |
| // CHECK-DAG: %[[C1024:.+]] = arith.constant 1024 : index |
| // CHECK: scf.for %[[I:.+]] = %[[C0]] to %[[C1024]] step %[[C32]] { |
| // CHECK: %[[SZ:.+]] = affine.min #[[MAP0]](%[[I]])[%[[C32]], %[[C1024]]] |
| // CHECK: %[[SUB1:.+]] = memref.subview %[[ARG0]][%[[I]]] [%[[SZ]]] [1] : memref<1024xf32> to memref<?xf32, strided<[1], offset: ?>> |
| // CHECK: %[[SUB2:.+]] = memref.subview %[[ARG1]][%[[I]]] [%[[SZ]]] [1] : memref<1024xf32> to memref<?xf32, strided<[1], offset: ?>> |
| // CHECK: iree_linalg_ext.fft |
| // CHECK-SAME: {__internal_linalg_transform__ = "tiling_1d_stage5_fft_output"} |
| // CHECK-SAME: ins(%[[C5]], %[[COEF_REAL]], %[[COEF_IMAG]] : index, memref<16xf32>, memref<16xf32>) |
| // CHECK-SAME: outs(%[[SUB1]], %[[SUB2]] : memref<?xf32, strided<[1], offset: ?>>, memref<?xf32, strided<[1], offset: ?>>) |
| |
| // ----- |
| |
| func.func @reverse_memref(%arg0: memref<?xi32>, %arg1: memref<?xi32>) { |
| iree_linalg_ext.reverse |
| {__internal_linalg_transform__ = "tiling_input"} |
| dimensions(dense<0> : tensor<1xi64>) |
| ins(%arg0: memref<?xi32>) |
| outs(%arg1: memref<?xi32>) |
| return |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-DAG: #[[MAP2:.+]] = affine_map<()[s0, s1, s2] -> (s0 - s1 - s2)> |
| // CHECK: func.func @reverse_memref( |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]] |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]] |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[D0:.+]] = memref.dim %[[ARG0]], %[[C0]] : memref<?xi32> |
| // CHECK: scf.for %[[I:.+]] = %[[C0]] to %[[D0]] step %[[C10]] { |
| // CHECK-DAG: %[[SIZE:.+]] = affine.min #[[MAP0]](%[[I]])[%[[C10]], %[[D0]]] |
| // CHECK-DAG: %[[IDX:.+]] = affine.apply #[[MAP2]]()[%[[D0]], %[[I]], %[[SIZE]]] |
| // CHECK-DAG: %[[SUB_IN:.+]] = memref.subview %[[ARG0]][%[[I]]] [%[[SIZE]]] [1] |
| // CHECK-DAG: %[[SUB_OUT:.+]] = memref.subview %[[ARG1]][%[[IDX]]] [%[[SIZE]]] [1] |
| // CHECK: iree_linalg_ext.reverse |
| // CHECK-SAME: {__internal_linalg_transform__ = "tiling_output"} |
| // CHECK-SAME: dimensions(dense<0> : tensor<1xi64>) |
| // CHECK-SAME: ins(%[[SUB_IN]] |
| // CHECK-SAME: outs(%[[SUB_OUT]] |
| |
| // ----- |
| |
| func.func @reverse_tensor_multi_dim(%arg0: tensor<?x?xi32>) -> tensor<?x?xi32> { |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : index |
| %d0 = tensor.dim %arg0, %c0 : tensor<?x?xi32> |
| %d1 = tensor.dim %arg0, %c1 : tensor<?x?xi32> |
| %init = tensor.empty(%d0, %d1) : tensor<?x?xi32> |
| %0 = iree_linalg_ext.reverse |
| {__internal_linalg_transform__ = "tiling_input"} |
| dimensions(dense<[0, 1]> : tensor<2xi64>) |
| ins(%arg0: tensor<?x?xi32>) |
| outs(%init: tensor<?x?xi32>) : tensor<?x?xi32> |
| return %0 : tensor<?x?xi32> |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (20, -d0 + s1)> |
| // CHECK-DAG: #[[MAP2:.+]] = affine_map<()[s0, s1, s2] -> (s0 - s1 - s2)> |
| // CHECK: func.func @reverse_tensor_multi_dim( |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]] |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[D0:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xi32> |
| // CHECK-DAG: %[[D1:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?xi32> |
| // CHECK: %[[INIT:.+]] = tensor.empty(%[[D0]], %[[D1]]) : tensor<?x?xi32> |
| // CHECK: %[[RES:.+]] = scf.for %[[I:.+]] = %[[C0]] to %[[D0]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[INIT2:.+]] = %[[INIT]]) -> (tensor<?x?xi32>) { |
| // CHECK: %[[SIZE_I:.+]] = affine.min #[[MAP0]](%[[I]])[%[[C10]], %[[D0]]] |
| // CHECK: %[[RES2:.+]] = scf.for %[[J:.+]] = %[[C0]] to %[[D1]] step %[[C20]] |
| // CHECK-SAME: iter_args(%[[INIT3:.+]] = %[[INIT2]]) -> (tensor<?x?xi32>) { |
| // CHECK-DAG: %[[SIZE_J:.+]] = affine.min #[[MAP1]](%[[J]])[%[[C20]], %[[D1]]] |
| // CHECK-DAG: %[[IDX0:.+]] = affine.apply #[[MAP2]]()[%[[D0]], %[[I]], %[[SIZE_I]]] |
| // CHECK-DAG: %[[IDX1:.+]] = affine.apply #[[MAP2]]()[%[[D1]], %[[J]], %[[SIZE_J]]] |
| // CHECK: %[[SUB_IN:.+]] = tensor.extract_slice |
| // CHECK-SAME: %[[ARG0]][%[[I]], %[[J]]] [%[[SIZE_I]], %[[SIZE_J]]] [1, 1] |
| // CHECK: %[[SUB_INIT:.+]] = tensor.extract_slice |
| // CHECK-SAME: %[[INIT]][%[[IDX0]], %[[IDX1]]] [%[[SIZE_I]], %[[SIZE_J]]] [1, 1] |
| // CHECK: %[[REV:.+]] = iree_linalg_ext.reverse |
| // CHECK-SAME: {__internal_linalg_transform__ = "tiling_output"} |
| // CHECK-SAME: dimensions(dense<[0, 1]> : tensor<2xi64>) |
| // CHECK-SAME: ins(%[[SUB_IN]] |
| // CHECK-SAME: outs(%[[SUB_INIT]] |
| // CHECK: %[[RES3:.+]] = tensor.insert_slice %[[REV]] into |
| // CHECK-SAME: %[[INIT3]][%[[IDX0]], %[[IDX1]]] [%[[SIZE_I]], %[[SIZE_J]]] [1, 1] |
| // CHECK: scf.yield %[[RES3]] |
| // CHECK: scf.yield %[[RES2]] |
| // CHECK: return %[[RES]] |
| |
| // ----- |
| |
| func.func @scan_1d(%0: tensor<128xi32>) -> tensor<128xi32> { |
| %c0 = tensor.empty() : tensor<i32> |
| %1 = tensor.empty() : tensor<128xi32> |
| %2:2 = iree_linalg_ext.scan |
| {__internal_linalg_transform__ = "outer_reduce_input"} |
| dimension(0) inclusive(true) |
| ins(%0 : tensor<128xi32>) outs(%1, %c0 : tensor<128xi32>, tensor<i32>) { |
| ^bb0(%arg0 : i32, %arg1 : i32): |
| %sum = arith.addi %arg0, %arg1 : i32 |
| iree_linalg_ext.yield %sum : i32 |
| } -> tensor<128xi32>, tensor<i32> |
| return %2#0 : tensor<128xi32> |
| } |
| // CHECK: func.func @scan_1d( |
| // CHECK-SAME: %[[OPERAND:.+]]: tensor<128xi32> |
| // CHECK: %[[ACC:.+]] = tensor.empty() : tensor<i32> |
| // CHECK: %[[OUTPUT:.+]] = tensor.empty() : tensor<128xi32> |
| // CHECK: %[[RESULT:.+]]:2 = iree_linalg_ext.scan |
| // CHECK-SAME: __internal_linalg_transform__ = "outer_reduce_output" |
| // CHECK-SAME: ins(%[[OPERAND]] : |
| // CHECK-SAME: outs(%[[OUTPUT]], %[[ACC]] : |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func.func @scan_2d(%0: tensor<16x32xi32>) -> tensor<16x32xi32> { |
| %c0 = tensor.empty() : tensor<32xi32> |
| %1 = tensor.empty() : tensor<16x32xi32> |
| %2:2 = iree_linalg_ext.scan |
| {__internal_linalg_transform__ = "outer_reduce_input"} |
| dimension(0) inclusive(true) |
| ins(%0 : tensor<16x32xi32>) outs(%1, %c0 : tensor<16x32xi32>, tensor<32xi32>) { |
| ^bb0(%arg0 : i32, %arg1 : i32): |
| %sum = arith.addi %arg0, %arg1 : i32 |
| iree_linalg_ext.yield %sum : i32 |
| } -> tensor<16x32xi32>, tensor<32xi32> |
| return %2#0 : tensor<16x32xi32> |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (20, -d0 + s1)> |
| // CHECK: func.func @scan_2d( |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]] |
| // CHECK: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK: %[[C16:.+]] = arith.constant 16 : index |
| // CHECK: %[[C32:.+]] = arith.constant 32 : index |
| // CHECK: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK: %[[ACC:.+]] = tensor.empty() : tensor<32xi32> |
| // CHECK: %[[OUTPUT:.+]] = tensor.empty() : tensor<16x32xi32> |
| // CHECK: %[[RESULT:.+]]:2 = scf.for %[[I:.+]] = %[[C0]] to %[[C32]] step %[[C20]] |
| // CHECK-SAME: iter_args(%[[ARG2:.+]] = %[[OUTPUT]], %[[ARG3:.+]] = %[[ACC]]) |
| // CHECK: %[[SIZE:.+]] = affine.min #[[MAP0]](%[[I]])[%[[C20]], %[[C32]]] |
| // CHECK: %[[UPDATE_SLICE_IN:.+]] = tensor.extract_slice %[[ARG0]][0, %[[I]]] [%[[C16]], %[[SIZE]]] |
| // CHECK: %[[UPDATE_SLICE_OUT:.+]] = tensor.extract_slice %[[OUTPUT]][0, %[[I]]] [%[[C16]], %[[SIZE]]] |
| // CHECK: %[[UPDATE_SLICE_ACC:.+]] = tensor.extract_slice %[[ACC]][%[[I]]] [%[[SIZE]]] |
| // CHECK: %[[SCAN_TILE:.+]]:2 = iree_linalg_ext.scan |
| // CHECK-SAME: {__internal_linalg_transform__ = "outer_reduce_output"} |
| // CHECK-SAME: dimension(0) inclusive(true) |
| // CHECK-SAME: ins(%[[UPDATE_SLICE_IN]] |
| // CHECK-SAME: outs(%[[UPDATE_SLICE_OUT]], %[[UPDATE_SLICE_ACC]] |
| // CHECK: %[[YIELD:.+]] = tensor.insert_slice %[[SCAN_TILE]]#0 into %[[ARG2]][0, %[[I]]] |
| // CHECK-SAME: [%[[C16]], %[[SIZE]]] |
| // CHECK: %[[ACC_YIELD:.+]] = tensor.insert_slice %[[SCAN_TILE]]#1 into %[[ARG3]][%[[I]]] |
| // CHECK-SAME: [%[[SIZE]]] |
| // CHECK: scf.yield %[[YIELD]], %[[ACC_YIELD]] : tensor<16x32xi32>, tensor<32xi32> |
| // CHECK: return %[[RESULT]]#0 |
| |
| // ----- |
| |
| func.func @scan_2d_memref(%0: memref<16x32xi32>, %1: memref<16x32xi32>) { |
| %c0 = memref.alloc() : memref<32xi32> |
| iree_linalg_ext.scan |
| {__internal_linalg_transform__ = "outer_reduce_input"} |
| dimension(0) inclusive(true) |
| ins(%0 : memref<16x32xi32>) outs(%1, %c0 : memref<16x32xi32>, memref<32xi32>) { |
| ^bb0(%arg0 : i32, %arg1 : i32): |
| %sum = arith.addi %arg0, %arg1 : i32 |
| iree_linalg_ext.yield %sum : i32 |
| } |
| return |
| } |
| // CHECK: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (20, -d0 + s1)> |
| // CHECK: func.func @scan_2d_memref( |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]] |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]] |
| // CHECK: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK: %[[C16:.+]] = arith.constant 16 : index |
| // CHECK: %[[C32:.+]] = arith.constant 32 : index |
| // CHECK: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK: %[[ACC:.+]] = memref.alloc() : memref<32xi32> |
| // CHECK: scf.for %[[I:.+]] = %[[C0]] to %[[C32]] step %[[C20]] |
| // CHECK: %[[SIZE:.+]] = affine.min #[[MAP0]](%[[I]])[%[[C20]], %[[C32]]] |
| // CHECK: %[[UPDATE_SLICE_IN:.+]] = memref.subview %[[ARG0]][0, %[[I]]] [%[[C16]], %[[SIZE]]] |
| // CHECK: %[[UPDATE_SLICE_OUT:.+]] = memref.subview %[[ARG1]][0, %[[I]]] [%[[C16]], %[[SIZE]]] |
| // CHECK: %[[UPDATE_SLICE_ACC:.+]] = memref.subview %[[ACC]][%[[I]]] [%[[SIZE]]] |
| // CHECK: iree_linalg_ext.scan |
| // CHECK-SAME: {__internal_linalg_transform__ = "outer_reduce_output"} |
| // CHECK-SAME: dimension(0) inclusive(true) |
| // CHECK-SAME: ins(%[[UPDATE_SLICE_IN]] |
| // CHECK-SAME: outs(%[[UPDATE_SLICE_OUT]], %[[UPDATE_SLICE_ACC]] |
| // CHECK: return |
| |
| // ----- |
| |
| func.func @topk_tile_tensor(%input_values: tensor<?x?xf32>, %input_indices: tensor<?x?xi32>, %out_values: tensor<?x3xf32> , %out_indices: tensor<?x3xi32>) -> (tensor<?x3xf32>, tensor<?x3xi32>) { |
| %0:2 = iree_linalg_ext.topk |
| {__internal_linalg_transform__ = "inner_reduce_input"} |
| dimension(1) |
| ins(%input_values, %input_indices : tensor<?x?xf32> , tensor<?x?xi32>) |
| outs(%out_values, %out_indices : tensor<?x3xf32>, tensor<?x3xi32>) { |
| ^bb0(%arg0: f32, %arg1: f32): // no predecessors |
| %0 = arith.cmpf ogt, %arg0, %arg1 : f32 |
| iree_linalg_ext.yield %0 : i1 |
| } -> tensor<?x3xf32>, tensor<?x3xi32> |
| return %0#0, %0#1 : tensor<?x3xf32>, tensor<?x3xi32> |
| } |
| |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-LABEL: func.func @topk_tile_tensor |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]] |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]] |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]] |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]] |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK: %[[D0:.+]] = tensor.dim %[[ARG0:.+]], %[[C0]] |
| // CHECK: %[[D1:.+]] = tensor.dim %[[ARG0:.+]], %[[C1]] |
| // CHECK: %[[RESULT:.+]]:2 = scf.for %[[ARG4:.+]] = %[[C0]] to %[[D0]] step %[[C10]] iter_args(%[[ARG5:.+]] = %[[ARG2]], %[[ARG6:.+]] = %[[ARG3]]) |
| // CHECK: %[[D3:.+]] = affine.min #[[MAP0]](%[[ARG4]])[%[[C10]], %[[D0]]] |
| // CHECK: %[[D4:.+]] = tensor.extract_slice %[[ARG0]][%[[ARG4]], 0] [%[[D3]], %[[D1]]] [1, 1] |
| // CHECK: %[[D5:.+]] = tensor.extract_slice %[[ARG1]][%[[ARG4]], 0] [%[[D3]], %[[D1]]] [1, 1] |
| // CHECK: %[[D6:.+]] = tensor.extract_slice %[[ARG2]][%[[ARG4]], 0] [%[[D3]], 3] [1, 1] |
| // CHECK: %[[D7:.+]] = tensor.extract_slice %[[ARG3]][%[[ARG4]], 0] [%[[D3]], 3] [1, 1] |
| // CHECK: %[[D8:.+]]:2 = iree_linalg_ext.topk {__internal_linalg_transform__ = "inner_reduce_output"} |
| // CHECK-SAME: dimension(1) |
| // CHECK-SAME: ins(%[[D4]], %[[D5]] |
| // CHECK-SAME: outs(%[[D6]], %[[D7]] |
| // CHECK: %[[D9:.+]] = tensor.insert_slice %[[D8]]#0 into %[[ARG5]][%[[ARG4]], 0] [%[[D3]], 3] [1, 1] |
| // CHECK: %[[D10:.+]] = tensor.insert_slice %[[D8]]#1 into %[[ARG6]][%[[ARG4]], 0] [%[[D3]], 3] [1, 1] |
| // CHECK: scf.yield %[[D9]], %[[D10]] |
| // CHECK: return %[[RESULT]]#0, %[[RESULT]]#1 |
| |
| |
| // ----- |
| |
| func.func @topk_tile_memref(%input_values: memref<?x?xf32>, %input_indices: memref<?x?xi32>, %out_values: memref<?x3xf32>, %out_indices: memref<?x3xi32>) { |
| iree_linalg_ext.topk |
| {__internal_linalg_transform__ = "inner_reduce_input"} |
| dimension(1) |
| ins(%input_values, %input_indices : memref<?x?xf32> , memref<?x?xi32>) |
| outs(%out_values, %out_indices : memref<?x3xf32>, memref<?x3xi32>) { |
| ^bb0(%arg0: f32, %arg1: f32): // no predecessors |
| %0 = arith.cmpf ogt, %arg0, %arg1 : f32 |
| iree_linalg_ext.yield %0 : i1 |
| } |
| return |
| } |
| |
| // CHECK: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-LABEL: func.func @topk_tile_memref |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]] |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]] |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]] |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]] |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK: %[[D0:.+]] = memref.dim %[[ARG0:.+]], %[[C0]] |
| // CHECK: %[[D1:.+]] = memref.dim %[[ARG0:.+]], %[[C1]] |
| // CHECK: scf.for %[[ARG4:.+]] = %[[C0]] to %[[D0]] step %[[C10]] |
| // CHECK: %[[D2:.+]] = affine.min #[[MAP0]](%[[ARG4]])[%[[C10]], %[[D0]]] |
| // CHECK: %[[D3:.+]] = memref.subview %[[ARG0]][%[[ARG4]], 0] [%[[D2]], %[[D1]]] [1, 1] |
| // CHECK: %[[D4:.+]] = memref.subview %[[ARG1]][%[[ARG4]], 0] [%[[D2]], %[[D1]]] [1, 1] |
| // CHECK: %[[D5:.+]] = memref.subview %[[ARG2]][%[[ARG4]], 0] [%[[D2]], 3] [1, 1] |
| // CHECK: %[[D6:.+]] = memref.subview %[[ARG3]][%[[ARG4]], 0] [%[[D2]], 3] [1, 1] |
| // CHECK: iree_linalg_ext.topk {__internal_linalg_transform__ = "inner_reduce_output"} |
| // CHECK-SAME: dimension(1) |
| // CHECK-SAME: ins(%[[D3]], %[[D4]] |
| // CHECK-SAME: outs(%[[D5]], %[[D6]] |
| // CHECK: return |
| |
| // ----- |
| |
| func.func @topk_tile_tensor_optional(%input_values: tensor<20x10xf32>, %out_values: tensor<20x3xf32> , %out_indices: tensor<20x3xi32>) -> (tensor<20x3xf32>, tensor<20x3xi32>) { |
| %0:2 = iree_linalg_ext.topk |
| {__internal_linalg_transform__ = "inner_reduce_input"} |
| dimension(1) |
| ins(%input_values : tensor<20x10xf32>) |
| outs(%out_values, %out_indices : tensor<20x3xf32>, tensor<20x3xi32>) { |
| ^bb0(%arg0: f32, %arg1: f32): // no predecessors |
| %0 = arith.cmpf ogt, %arg0, %arg1 : f32 |
| iree_linalg_ext.yield %0 : i1 |
| } -> tensor<20x3xf32>, tensor<20x3xi32> |
| return %0#0, %0#1 : tensor<20x3xf32>, tensor<20x3xi32> |
| } |
| |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-LABEL: func.func @topk_tile_tensor_optional |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]] |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]] |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]] |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK: %[[RESULT:.+]]:2 = scf.for %[[ARG3:.+]] = %[[C0]] to %[[C20]] step %[[C10]] iter_args(%[[ARG4:.+]] = %[[ARG1]], %[[ARG5:.+]] = %[[ARG2]]) |
| // CHECK: %[[D1:.+]] = affine.min #[[MAP0]](%[[ARG3]])[%[[C10]], %[[C20]]] |
| // CHECK: %[[D2:.+]] = tensor.extract_slice %[[ARG0]][%[[ARG3]], 0] [%[[D1]], %[[C10]]] [1, 1] |
| // CHECK: %[[D3:.+]] = tensor.extract_slice %[[ARG1]][%[[ARG3]], 0] [%[[D1]], 3] [1, 1] |
| // CHECK: %[[D4:.+]] = tensor.extract_slice %[[ARG2]][%[[ARG3]], 0] [%[[D1]], 3] [1, 1] |
| // CHECK: %[[D5:.+]]:2 = iree_linalg_ext.topk {__internal_linalg_transform__ = "inner_reduce_output"} |
| // CHECK-SAME: dimension(1) |
| // CHECK-SAME: ins(%[[D2]] |
| // CHECK-SAME: outs(%[[D3]], %[[D4]] |
| // CHECK: %[[D6:.+]] = tensor.insert_slice %[[D5]]#0 into %[[ARG4]][%[[ARG3]], 0] [%[[D1]], 3] [1, 1] |
| // CHECK: %[[D7:.+]] = tensor.insert_slice %[[D5]]#1 into %[[ARG5]][%[[ARG3]], 0] [%[[D1]], 3] [1, 1] |
| // CHECK: scf.yield %[[D6]], %[[D7]] |
| // CHECK: return %[[RESULT]]#0, %[[RESULT]]#1 |
| |
| // ----- |
| |
| func.func @winograd_input_transform(%arg0: tensor<1x10x10x1280xf32>) -> tensor<8x8x1x2x2x1280xf32> { |
| %0 = tensor.empty() : tensor<8x8x1x2x2x1280xf32> |
| %1 = iree_linalg_ext.winograd.input_transform {__internal_linalg_transform__ = "tiling_winograd_input_nhwc"} |
| output_tile_size(6) kernel_size(3) image_dimensions([1, 2]) |
| ins(%arg0 : tensor<1x10x10x1280xf32>) outs(%0 : tensor<8x8x1x2x2x1280xf32>) -> tensor<8x8x1x2x2x1280xf32> |
| return %1 : tensor<8x8x1x2x2x1280xf32> |
| } |
| // CHECK-DAG: #[[MAP:.+]] = affine_map<(d0)[s0, s1] -> (1, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (32, -d0 + s1)> |
| // CHECK: func.func @winograd_input_transform(%[[ARG0:[a-zA-Z0-9_]+]]: tensor<1x10x10x1280xf32>) -> |
| // CHECK-SAME: tensor<8x8x1x2x2x1280xf32> { |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C1280:.+]] = arith.constant 1280 : index |
| // CHECK-DAG: %[[C32:.+]] = arith.constant 32 : index |
| // CHECK: %[[D0:.+]] = tensor.empty() : tensor<8x8x1x2x2x1280xf32> |
| // CHECK: %[[D1:.+]] = scf.for %[[ARG1:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C1]] step %[[C1]] |
| // CHECK-SAME: iter_args(%[[ARG2:[a-zA-Z0-9_]+]] = %[[D0]]) -> (tensor<8x8x1x2x2x1280xf32>) { |
| // CHECK-DAG: %[[D2:.+]] = affine.min #[[MAP]](%[[ARG1]])[%[[C1]], %[[C1]]] |
| // CHECK: %[[D3:.+]] = scf.for %[[ARG3:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C1280]] step %[[C32]] |
| // CHECK-SAME: iter_args(%[[ARG4:[a-zA-Z0-9_]+]] = %[[ARG2]]) -> (tensor<8x8x1x2x2x1280xf32>) { |
| // CHECK-DAG: %[[D4:.+]] = affine.min #[[MAP1]](%[[ARG3]])[%[[C32]], %[[C1280]]] |
| // CHECK: %[[EXTRACTED_SLICE:.+]] = tensor.extract_slice %[[ARG0]][%[[ARG1]], 0, 0, %[[ARG3]]] [%[[D2]], 10, |
| // CHECK-SAME: 10, %[[D4]]] [1, 1, 1, 1] : tensor<1x10x10x1280xf32> to tensor<?x10x10x?xf32> |
| // CHECK: %[[EXTRACTED_SLICE_0:.+]] = tensor.extract_slice %[[D0]][0, 0, %[[ARG1]], 0, 0, %[[ARG3]]] [8, 8, |
| // CHECK-SAME: %[[D2]], 2, 2, %[[D4]]] [1, 1, 1, 1, 1, 1] : tensor<8x8x1x2x2x1280xf32> to |
| // CHECK-SAME: tensor<8x8x?x2x2x?xf32> |
| // CHECK: %[[D5:.+]] = iree_linalg_ext.winograd.input_transform output_tile_size(6) kernel_size(3) |
| // CHECK-SAME: image_dimensions([1, 2]) ins(%[[EXTRACTED_SLICE]] : tensor<?x10x10x?xf32>) |
| // CHECK-SAME: outs(%[[EXTRACTED_SLICE]]_0 : tensor<8x8x?x2x2x?xf32>) -> tensor<8x8x?x2x2x?xf32> |
| // CHECK: %[[INSERTED_SLICE:.+]] = tensor.insert_slice %[[D5]] into %[[ARG4]][0, 0, %[[ARG1]], 0, 0, |
| // CHECK-SAME: %[[ARG3]]] [8, 8, %[[D2]], 2, 2, %[[D4]]] [1, 1, 1, 1, 1, 1] : tensor<8x8x?x2x2x?xf32> into |
| // CHECK-SAME: tensor<8x8x1x2x2x1280xf32> |
| // CHECK: scf.yield %[[INSERTED_SLICE]] : tensor<8x8x1x2x2x1280xf32> |
| // CHECK: } |
| // CHECK: scf.yield %[[D3]] : tensor<8x8x1x2x2x1280xf32> |
| // CHECK: } |
| // CHECK: return %[[D1]] : tensor<8x8x1x2x2x1280xf32> |
| // CHECK: } |
| |
| // ----- |
| |
| func.func @winograd_input_transform_memref(%arg0: memref<1x10x10x1280xf32>, %arg1: memref<8x8x1x2x2x1280xf32>) { |
| iree_linalg_ext.winograd.input_transform {__internal_linalg_transform__ = "tiling_winograd_input_nhwc"} |
| output_tile_size(6) kernel_size(3) image_dimensions([1, 2]) |
| ins(%arg0 : memref<1x10x10x1280xf32>) outs(%arg1 : memref<8x8x1x2x2x1280xf32>) |
| return |
| } |
| // CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0, s1] -> (1, -d0 + s1)> |
| // CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0)[s0, s1] -> (32, -d0 + s1)> |
| // CHECK: func.func @winograd_input_transform_memref(%[[ARG0:[a-zA-Z0-9_]+]]: memref<1x10x10x1280xf32>, |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: memref<8x8x1x2x2x1280xf32>) { |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C1280:.+]] = arith.constant 1280 : index |
| // CHECK-DAG: %[[C32:.+]] = arith.constant 32 : index |
| // CHECK: scf.for %[[ARG2:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C1]] step %[[C1]] { |
| // CHECK-DAG: %[[D0:.+]] = affine.min #[[MAP2]](%[[ARG2]])[%[[C1]], %[[C1]]] |
| // CHECK: scf.for %[[ARG3:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C1280]] step %[[C32]] { |
| // CHECK-DAG: %[[D1:.+]] = affine.min #[[MAP3]](%[[ARG3]])[%[[C32]], %[[C1280]]] |
| // CHECK: %[[SUBVIEW:.+]] = memref.subview %[[ARG0]][%[[ARG2]], 0, 0, %[[ARG3]]] [%[[D0]], 10, 10, %[[D1]]] |
| // CHECK-SAME: [1, 1, 1, 1] : memref<1x10x10x1280xf32> to memref<?x10x10x?xf32, strided<[128000, 12800, 1280, |
| // CHECK-SAME: 1], offset: ?>> |
| // CHECK: %[[SUBVIEW_0:.+]] = memref.subview %[[ARG1]][0, 0, %[[ARG2]], 0, 0, %[[ARG3]]] [8, 8, %[[D0]], 2, |
| // CHECK-SAME: 2, %[[D1]]] [1, 1, 1, 1, 1, 1] : memref<8x8x1x2x2x1280xf32> to memref<8x8x?x2x2x?xf32, |
| // CHECK-SAME: strided<[40960, 5120, 5120, 2560, 1280, 1], offset: ?>> |
| // CHECK: iree_linalg_ext.winograd.input_transform output_tile_size(6) kernel_size(3) image_dimensions([1, |
| // CHECK-SAME: 2]) ins(%[[SUBVIEW]] : memref<?x10x10x?xf32, strided<[128000, 12800, 1280, 1], offset: ?>>) |
| // CHECK-SAME: outs(%[[SUBVIEW]]_0 : memref<8x8x?x2x2x?xf32, strided<[40960, 5120, 5120, 2560, 1280, 1], offset: |
| // CHECK-SAME: ?>>) |
| // CHECK: } |
| // CHECK: } |
| // CHECK: return |
| // CHECK: } |
| |
| // ----- |
| |
| func.func @winograd_output_transform(%arg0: tensor<8x8x1x2x2x32xf32>) -> tensor<1x12x12x32xf32> { |
| %0 = tensor.empty() : tensor<1x12x12x32xf32> |
| %1 = iree_linalg_ext.winograd.output_transform {__internal_linalg_transform__ = "tiling_winograd_input_nhwc"} |
| output_tile_size(6) kernel_size(3) image_dimensions([1, 2]) |
| ins(%arg0 : tensor<8x8x1x2x2x32xf32>) outs(%0 : tensor<1x12x12x32xf32>) -> tensor<1x12x12x32xf32> |
| return %1 : tensor<1x12x12x32xf32> |
| } |
| // CHECK-DAG: #[[MAP:.+]] = affine_map<(d0)[s0, s1] -> (1, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (32, -d0 + s1)> |
| // CHECK: func.func @winograd_output_transform(%[[ARG0:[a-zA-Z0-9_]+]]: tensor<8x8x1x2x2x32xf32>) -> |
| // CHECK-SAME: tensor<1x12x12x32xf32> { |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C32:.+]] = arith.constant 32 : index |
| // CHECK: %[[D0:.+]] = tensor.empty() : tensor<1x12x12x32xf32> |
| // CHECK: %[[D1:.+]] = scf.for %[[ARG1:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C1]] step %[[C1]] |
| // CHECK-SAME: iter_args(%[[ARG2:[a-zA-Z0-9_]+]] = %[[D0]]) -> (tensor<1x12x12x32xf32>) { |
| // CHECK-DAG: %[[D2:.+]] = affine.min #[[MAP]](%[[ARG1]])[%[[C1]], %[[C1]]] |
| // CHECK: %[[D3:.+]] = scf.for %[[ARG3:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C32]] step %[[C32]] |
| // CHECK-SAME: iter_args(%[[ARG4:[a-zA-Z0-9_]+]] = %[[ARG2]]) -> (tensor<1x12x12x32xf32>) { |
| // CHECK-DAG: %[[D4:.+]] = affine.min #[[MAP1]](%[[ARG3]])[%[[C32]], %[[C32]]] |
| // CHECK: %[[EXTRACTED_SLICE:.+]] = tensor.extract_slice %[[ARG0]][0, 0, %[[ARG1]], 0, 0, %[[ARG3]]] [8, 8, |
| // CHECK-SAME: %[[D2]], 2, 2, %[[D4]]] [1, 1, 1, 1, 1, 1] : tensor<8x8x1x2x2x32xf32> to tensor<8x8x?x2x2x?xf32> |
| // CHECK: %[[EXTRACTED_SLICE_0:.+]] = tensor.extract_slice %[[D0]][%[[ARG1]], 0, 0, %[[ARG3]]] [%[[D2]], 12, |
| // CHECK-SAME: 12, %[[D4]]] [1, 1, 1, 1] : tensor<1x12x12x32xf32> to tensor<?x12x12x?xf32> |
| // CHECK: %[[D5:.+]] = iree_linalg_ext.winograd.output_transform output_tile_size(6) kernel_size(3) |
| // CHECK-SAME: image_dimensions([1, 2]) ins(%[[EXTRACTED_SLICE]] : tensor<8x8x?x2x2x?xf32>) |
| // CHECK-SAME: outs(%[[EXTRACTED_SLICE]]_0 : tensor<?x12x12x?xf32>) -> tensor<?x12x12x?xf32> |
| // CHECK: %[[INSERTED_SLICE:.+]] = tensor.insert_slice %[[D5]] into %[[ARG4]][%[[ARG1]], 0, 0, %[[ARG3]]] |
| // CHECK-SAME: [%[[D2]], 12, 12, %[[D4]]] [1, 1, 1, 1] : tensor<?x12x12x?xf32> into tensor<1x12x12x32xf32> |
| // CHECK: scf.yield %[[INSERTED_SLICE]] : tensor<1x12x12x32xf32> |
| // CHECK: } |
| // CHECK: scf.yield %[[D3]] : tensor<1x12x12x32xf32> |
| // CHECK: } |
| // CHECK: return %[[D1]] : tensor<1x12x12x32xf32> |
| // CHECK: } |
| |
| // ----- |
| |
| func.func @winograd_output_transform_memref(%arg0: memref<8x8x1x2x2x32xf32>, %arg1: memref<1x12x12x32xf32>) { |
| iree_linalg_ext.winograd.output_transform {__internal_linalg_transform__ = "tiling_winograd_input_nhwc"} |
| output_tile_size(6) kernel_size(3) image_dimensions([1, 2]) |
| ins(%arg0 : memref<8x8x1x2x2x32xf32>) outs(%arg1 : memref<1x12x12x32xf32>) |
| return |
| } |
| // CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0, s1] -> (1, -d0 + s1)> |
| // CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0)[s0, s1] -> (32, -d0 + s1)> |
| // CHECK: func.func @winograd_output_transform_memref(%[[ARG0:[a-zA-Z0-9_]+]]: memref<8x8x1x2x2x32xf32>, |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: memref<1x12x12x32xf32>) { |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C32:.+]] = arith.constant 32 : index |
| // CHECK: scf.for %[[ARG2:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C1]] step %[[C1]] { |
| // CHECK-DAG: %[[D0:.+]] = affine.min #[[MAP2]](%[[ARG2]])[%[[C1]], %[[C1]]] |
| // CHECK: scf.for %[[ARG3:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C32]] step %[[C32]] { |
| // CHECK-DAG: %[[D1:.+]] = affine.min #[[MAP3]](%[[ARG3]])[%[[C32]], %[[C32]]] |
| // CHECK: %[[SUBVIEW:.+]] = memref.subview %[[ARG0]][0, 0, %[[ARG2]], 0, 0, %[[ARG3]]] [8, 8, %[[D0]], 2, 2, |
| // CHECK-SAME: %[[D1]]] [1, 1, 1, 1, 1, 1] : memref<8x8x1x2x2x32xf32> to memref<8x8x?x2x2x?xf32, strided<[1024, |
| // CHECK-SAME: 128, 128, 64, 32, 1], offset: ?>> |
| // CHECK: %[[SUBVIEW_0:.+]] = memref.subview %[[ARG1]][%[[ARG2]], 0, 0, %[[ARG3]]] [%[[D0]], 12, 12, %[[D1]]] |
| // CHECK-SAME: [1, 1, 1, 1] : memref<1x12x12x32xf32> to memref<?x12x12x?xf32, strided<[4608, 384, 32, 1], |
| // CHECK-SAME: offset: ?>> |
| // CHECK: iree_linalg_ext.winograd.output_transform output_tile_size(6) kernel_size(3) image_dimensions([1, |
| // CHECK-SAME: 2]) ins(%[[SUBVIEW]] : memref<8x8x?x2x2x?xf32, strided<[1024, 128, 128, 64, 32, 1], offset: ?>>) |
| // CHECK-SAME: outs(%[[SUBVIEW]]_0 : memref<?x12x12x?xf32, strided<[4608, 384, 32, 1], offset: ?>>) |
| // CHECK: } |
| // CHECK: } |
| // CHECK: return |
| // CHECK: } |
| |
| // ----- |
| |
| func.func @winograd_input_transform_nchw(%arg0: tensor<1x1280x10x10xf32>) -> tensor<8x8x1x2x2x1280xf32> { |
| %0 = tensor.empty() : tensor<8x8x1x2x2x1280xf32> |
| %1 = iree_linalg_ext.winograd.input_transform {__internal_linalg_transform__ = "tiling_winograd_input_nhwc"} |
| output_tile_size(6) kernel_size(3) image_dimensions([2, 3]) |
| ins(%arg0 : tensor<1x1280x10x10xf32>) outs(%0 : tensor<8x8x1x2x2x1280xf32>) -> tensor<8x8x1x2x2x1280xf32> |
| return %1 : tensor<8x8x1x2x2x1280xf32> |
| } |
| // CHECK-DAG: #[[MAP:.+]] = affine_map<(d0)[s0, s1] -> (1, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (32, -d0 + s1)> |
| // CHECK: func.func @winograd_input_transform_nchw(%[[ARG0:[a-zA-Z0-9_]+]]: tensor<1x1280x10x10xf32>) -> |
| // CHECK-SAME: tensor<8x8x1x2x2x1280xf32> { |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C1280:.+]] = arith.constant 1280 : index |
| // CHECK-DAG: %[[C32:.+]] = arith.constant 32 : index |
| // CHECK: %[[D0:.+]] = tensor.empty() : tensor<8x8x1x2x2x1280xf32> |
| // CHECK: %[[D1:.+]] = scf.for %[[ARG1:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C1]] step %[[C1]] |
| // CHECK-SAME: iter_args(%[[ARG2:[a-zA-Z0-9_]+]] = %[[D0]]) -> (tensor<8x8x1x2x2x1280xf32>) { |
| // CHECK-DAG: %[[D2:.+]] = affine.min #[[MAP]](%[[ARG1]])[%[[C1]], %[[C1]]] |
| // CHECK: %[[D3:.+]] = scf.for %[[ARG3:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C1280]] step %[[C32]] |
| // CHECK-SAME: iter_args(%[[ARG4:[a-zA-Z0-9_]+]] = %[[ARG2]]) -> (tensor<8x8x1x2x2x1280xf32>) { |
| // CHECK-DAG: %[[D4:.+]] = affine.min #[[MAP1]](%[[ARG3]])[%[[C32]], %[[C1280]]] |
| // CHECK: %[[EXTRACTED_SLICE:.+]] = tensor.extract_slice %[[ARG0]][%[[ARG1]], %[[ARG3]], 0, 0] [%[[D2]], |
| // CHECK-SAME: %[[D4]], 10, 10] [1, 1, 1, 1] : tensor<1x1280x10x10xf32> to tensor<?x?x10x10xf32> |
| // CHECK: %[[EXTRACTED_SLICE_0:.+]] = tensor.extract_slice %[[D0]][0, 0, %[[ARG1]], 0, 0, %[[ARG3]]] [8, 8, |
| // CHECK-SAME: %[[D2]], 2, 2, %[[D4]]] [1, 1, 1, 1, 1, 1] : tensor<8x8x1x2x2x1280xf32> to |
| // CHECK-SAME: tensor<8x8x?x2x2x?xf32> |
| // CHECK: %[[D5:.+]] = iree_linalg_ext.winograd.input_transform output_tile_size(6) kernel_size(3) |
| // CHECK-SAME: image_dimensions([2, 3]) ins(%[[EXTRACTED_SLICE]] : tensor<?x?x10x10xf32>) |
| // CHECK-SAME: outs(%[[EXTRACTED_SLICE]]_0 : tensor<8x8x?x2x2x?xf32>) -> tensor<8x8x?x2x2x?xf32> |
| // CHECK: %[[INSERTED_SLICE:.+]] = tensor.insert_slice %[[D5]] into %[[ARG4]][0, 0, %[[ARG1]], 0, 0, |
| // CHECK-SAME: %[[ARG3]]] [8, 8, %[[D2]], 2, 2, %[[D4]]] [1, 1, 1, 1, 1, 1] : tensor<8x8x?x2x2x?xf32> into |
| // CHECK-SAME: tensor<8x8x1x2x2x1280xf32> |
| // CHECK: scf.yield %[[INSERTED_SLICE]] : tensor<8x8x1x2x2x1280xf32> |
| // CHECK: } |
| // CHECK: scf.yield %[[D3]] : tensor<8x8x1x2x2x1280xf32> |
| // CHECK: } |
| // CHECK: return %[[D1]] : tensor<8x8x1x2x2x1280xf32> |
| // CHECK: } |
| // CHECK: } |
| |
| // ----- |
| |
| func.func @winograd_output_transform_nchw(%arg0: tensor<8x8x1x2x2x32xf32>) -> tensor<1x32x12x12xf32> { |
| %0 = tensor.empty() : tensor<1x32x12x12xf32> |
| %1 = iree_linalg_ext.winograd.output_transform {__internal_linalg_transform__ = "tiling_winograd_input_nhwc"} |
| output_tile_size(6) kernel_size(3) image_dimensions([2, 3]) |
| ins(%arg0 : tensor<8x8x1x2x2x32xf32>) outs(%0 : tensor<1x32x12x12xf32>) -> tensor<1x32x12x12xf32> |
| return %1 : tensor<1x32x12x12xf32> |
| } |
| // CHECK-DAG: #[[MAP:.+]] = affine_map<(d0)[s0, s1] -> (1, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (32, -d0 + s1)> |
| // CHECK: func.func @winograd_output_transform_nchw(%[[ARG0:[a-zA-Z0-9_]+]]: tensor<8x8x1x2x2x32xf32>) -> |
| // CHECK-SAME: tensor<1x32x12x12xf32> { |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C32:.+]] = arith.constant 32 : index |
| // CHECK: %[[D0:.+]] = tensor.empty() : tensor<1x32x12x12xf32> |
| // CHECK: %[[D1:.+]] = scf.for %[[ARG1:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C1]] step %[[C1]] |
| // CHECK-SAME: iter_args(%[[ARG2:[a-zA-Z0-9_]+]] = %[[D0]]) -> (tensor<1x32x12x12xf32>) { |
| // CHECK-DAG: %[[D2:.+]] = affine.min #[[MAP]](%[[ARG1]])[%[[C1]], %[[C1]]] |
| // CHECK: %[[D3:.+]] = scf.for %[[ARG3:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C32]] step %[[C32]] |
| // CHECK-SAME: iter_args(%[[ARG4:[a-zA-Z0-9_]+]] = %[[ARG2]]) -> (tensor<1x32x12x12xf32>) { |
| // CHECK-DAG: %[[D4:.+]] = affine.min #[[MAP1]](%[[ARG3]])[%[[C32]], %[[C32]]] |
| // CHECK: %[[EXTRACTED_SLICE:.+]] = tensor.extract_slice %[[ARG0]][0, 0, %[[ARG1]], 0, 0, %[[ARG3]]] [8, 8, |
| // CHECK-SAME: %[[D2]], 2, 2, %[[D4]]] [1, 1, 1, 1, 1, 1] : tensor<8x8x1x2x2x32xf32> to tensor<8x8x?x2x2x?xf32> |
| // CHECK: %[[EXTRACTED_SLICE_0:.+]] = tensor.extract_slice %[[D0]][%[[ARG1]], %[[ARG3]], 0, 0] [%[[D2]], |
| // CHECK-SAME: %[[D4]], 12, 12] [1, 1, 1, 1] : tensor<1x32x12x12xf32> to tensor<?x?x12x12xf32> |
| // CHECK: %[[D5:.+]] = iree_linalg_ext.winograd.output_transform output_tile_size(6) kernel_size(3) |
| // CHECK-SAME: image_dimensions([2, 3]) ins(%[[EXTRACTED_SLICE]] : tensor<8x8x?x2x2x?xf32>) |
| // CHECK-SAME: outs(%[[EXTRACTED_SLICE]]_0 : tensor<?x?x12x12xf32>) -> tensor<?x?x12x12xf32> |
| // CHECK: %[[INSERTED_SLICE:.+]] = tensor.insert_slice %[[D5]] into %[[ARG4]][%[[ARG1]], %[[ARG3]], 0, 0] |
| // CHECK-SAME: [%[[D2]], %[[D4]], 12, 12] [1, 1, 1, 1] : tensor<?x?x12x12xf32> into tensor<1x32x12x12xf32> |
| // CHECK: scf.yield %[[INSERTED_SLICE]] : tensor<1x32x12x12xf32> |
| // CHECK: } |
| // CHECK: scf.yield %[[D3]] : tensor<1x32x12x12xf32> |
| // CHECK: } |
| // CHECK: return %[[D1]] : tensor<1x32x12x12xf32> |
| // CHECK: } |
| // CHECK: } |
| |
| // ----- |
| |
| func.func @attention(%query: tensor<192x1024x64xf32>, %key: tensor<192x1024x64xf32>, %value: tensor<192x1024x64xf32>) -> tensor<192x1024x64xf32> { |
| %0 = tensor.empty() : tensor<192x1024x64xf32> |
| %1 = iree_linalg_ext.attention {__internal_linalg_transform__ = "tiling_attention"} ins(%query, %key, %value : tensor<192x1024x64xf32>, tensor<192x1024x64xf32>, tensor<192x1024x64xf32>) outs(%0 : tensor<192x1024x64xf32>) -> tensor<192x1024x64xf32> |
| return %1 : tensor<192x1024x64xf32> |
| } |
| // CHECK-DAG: #[[MAP:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (30, -d0 + s1)> |
| // CHECK: func.func @attention(%[[ARG0:[a-zA-Z0-9_]+]]: tensor<192x1024x64xf32>, %[[ARG1:[a-zA-Z0-9_]+]]: |
| // CHECK-SAME: tensor<192x1024x64xf32>, %[[ARG2:[a-zA-Z0-9_]+]]: tensor<192x1024x64xf32>) -> tensor<192x1024x64xf32> |
| // CHECK-SAME: { |
| // CHECK: %[[C30:.+]] = arith.constant 30 : index |
| // CHECK: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK: %[[C192:.+]] = arith.constant 192 : index |
| // CHECK: %[[C1024:.+]] = arith.constant 1024 : index |
| // CHECK: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK: %[[D0:.+]] = tensor.empty() : tensor<192x1024x64xf32> |
| // CHECK: %[[D1:.+]] = scf.for %[[ARG3:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C192]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[ARG4:[a-zA-Z0-9_]+]] = %[[D0]]) -> (tensor<192x1024x64xf32>) { |
| // CHECK-DAG: %[[D2:.+]] = affine.min #[[MAP]](%[[ARG3]])[%[[C10]], %[[C192]]] |
| // CHECK: %[[D3:.+]] = scf.for %[[ARG5:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C1024]] step %[[C30]] |
| // CHECK-SAME: iter_args(%[[ARG6:[a-zA-Z0-9_]+]] = %[[ARG4]]) -> (tensor<192x1024x64xf32>) { |
| // CHECK-DAG: %[[D4:.+]] = affine.min #[[MAP1]](%[[ARG5]])[%[[C30]], %[[C1024]]] |
| // CHECK: %[[EXTRACTED_SLICE:.+]] = tensor.extract_slice %[[ARG0]][%[[ARG3]], %[[ARG5]], 0] [%[[D2]], |
| // CHECK-SAME: %[[D4]], 64] [1, 1, 1] : tensor<192x1024x64xf32> to tensor<?x?x64xf32> |
| // CHECK: %[[EXTRACTED_SLICE_0:.+]] = tensor.extract_slice %[[ARG1]][%[[ARG3]], 0, 0] [%[[D2]], 1024, 64] [1, |
| // CHECK-SAME: 1, 1] : tensor<192x1024x64xf32> to tensor<?x1024x64xf32> |
| // CHECK: %[[EXTRACTED_SLICE_1:.+]] = tensor.extract_slice %[[ARG2]][%[[ARG3]], 0, 0] [%[[D2]], 1024, 64] [1, |
| // CHECK-SAME: 1, 1] : tensor<192x1024x64xf32> to tensor<?x1024x64xf32> |
| // CHECK: %[[EXTRACTED_SLICE_2:.+]] = tensor.extract_slice %[[D0]][%[[ARG3]], %[[ARG5]], 0] [%[[D2]], |
| // CHECK-SAME: %[[D4]], 64] [1, 1, 1] : tensor<192x1024x64xf32> to tensor<?x?x64xf32> |
| // CHECK: %[[D5:.+]] = iree_linalg_ext.attention ins(%[[EXTRACTED_SLICE]], %[[EXTRACTED_SLICE_0]], |
| // CHECK-SAME: %[[EXTRACTED_SLICE_1]] : tensor<?x?x64xf32>, tensor<?x1024x64xf32>, tensor<?x1024x64xf32>) |
| // CHECK-SAME: outs(%[[EXTRACTED_SLICE_2]] : tensor<?x?x64xf32>) -> tensor<?x?x64xf32> |
| // CHECK: %[[INSERTED_SLICE:.+]] = tensor.insert_slice %[[D5]] into %[[ARG6]][%[[ARG3]], %[[ARG5]], 0] |
| // CHECK-SAME: [%[[D2]], %[[D4]], 64] [1, 1, 1] : tensor<?x?x64xf32> into tensor<192x1024x64xf32> |
| // CHECK: scf.yield %[[INSERTED_SLICE]] : tensor<192x1024x64xf32> |
| // CHECK: } |
| // CHECK: scf.yield %[[D3]] : tensor<192x1024x64xf32> |
| // CHECK: } |
| // CHECK: return %[[D1]] : tensor<192x1024x64xf32> |
| // CHECK: } |
| |
| // ----- |
| |
| func.func @attention_memref(%query: memref<192x1024x64xf32>, %key: memref<192x1024x64xf32>, %value: memref<192x1024x64xf32>, %output: memref<192x1024x64xf32>) { |
| iree_linalg_ext.attention {__internal_linalg_transform__ = "tiling_attention"} ins(%query, %key, %value : memref<192x1024x64xf32>, memref<192x1024x64xf32>, memref<192x1024x64xf32>) outs(%output : memref<192x1024x64xf32>) |
| return |
| } |
| // CHECK-DAG: #[[MAP:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (30, -d0 + s1)> |
| // CHECK: func.func @attention_memref(%[[ARG0:[a-zA-Z0-9_]+]]: memref<192x1024x64xf32>, %[[ARG1:[a-zA-Z0-9_]+]]: |
| // CHECK-SAME: memref<192x1024x64xf32>, %[[ARG2:[a-zA-Z0-9_]+]]: memref<192x1024x64xf32>, %[[ARG3:[a-zA-Z0-9_]+]]: |
| // CHECK-SAME: memref<192x1024x64xf32>) { |
| // CHECK: %[[C30:.+]] = arith.constant 30 : index |
| // CHECK: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK: %[[C192:.+]] = arith.constant 192 : index |
| // CHECK: %[[C1024:.+]] = arith.constant 1024 : index |
| // CHECK: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK: scf.for %[[ARG4:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C192]] step %[[C10]] { |
| // CHECK-DAG: %[[D0:.+]] = affine.min #[[MAP]](%[[ARG4]])[%[[C10]], %[[C192]]] |
| // CHECK: scf.for %[[ARG5:[a-zA-Z0-9_]+]] = %[[C0]] to %[[C1024]] step %[[C30]] { |
| // CHECK-DAG: %[[D1:.+]] = affine.min #[[MAP1]](%[[ARG5]])[%[[C30]], %[[C1024]]] |
| // CHECK: %[[SUBVIEW:.+]] = memref.subview %[[ARG0]][%[[ARG4]], %[[ARG5]], 0] [%[[D0]], %[[D1]], 64] [1, 1, |
| // CHECK-SAME: 1] : memref<192x1024x64xf32> to memref<?x?x64xf32, strided<[65536, 64, 1], offset: ?>> |
| // CHECK: %[[SUBVIEW_0:.+]] = memref.subview %[[ARG1]][%[[ARG4]], 0, 0] [%[[D0]], 1024, 64] [1, 1, 1] : |
| // CHECK-SAME: memref<192x1024x64xf32> to memref<?x1024x64xf32, strided<[65536, 64, 1], offset: ?>> |
| // CHECK: %[[SUBVIEW_1:.+]] = memref.subview %[[ARG2]][%[[ARG4]], 0, 0] [%[[D0]], 1024, 64] [1, 1, 1] : |
| // CHECK-SAME: memref<192x1024x64xf32> to memref<?x1024x64xf32, strided<[65536, 64, 1], offset: ?>> |
| // CHECK: %[[SUBVIEW_2:.+]] = memref.subview %[[ARG3]][%[[ARG4]], %[[ARG5]], 0] [%[[D0]], %[[D1]], 64] [1, 1, |
| // CHECK-SAME: 1] : memref<192x1024x64xf32> to memref<?x?x64xf32, strided<[65536, 64, 1], offset: ?>> |
| // CHECK: iree_linalg_ext.attention ins(%[[SUBVIEW]], %[[SUBVIEW_0]], %[[SUBVIEW_1]] : memref<?x?x64xf32, |
| // CHECK-SAME: strided<[65536, 64, 1], offset: ?>>, memref<?x1024x64xf32, strided<[65536, 64, 1], offset: ?>>, |
| // CHECK-SAME: memref<?x1024x64xf32, strided<[65536, 64, 1], offset: ?>>) outs(%[[SUBVIEW_2]] : |
| // CHECK-SAME: memref<?x?x64xf32, strided<[65536, 64, 1], offset: ?>>) |
| // CHECK: } |
| // CHECK: } |
| // CHECK: return |
| // CHECK: } |
