| // RUN: iree-dialects-opt -iree-linalg-ext-tile -split-input-file %s | FileCheck %s |
| |
| 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"} |
| 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 @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 %[[INITX]][0, %[[IV1]]] |
| // CHECK-SAME: [%[[SCATTER_DIM]], %[[USED_TILESIZEX]]] |
| // CHECK: %[[SCATTER_TILE:.+]] = iree_linalg_ext.scatter |
| // CHECK-SAME: __internal_linalg_transform__ = "tiling_output" |
| // 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 @scatter_tiling_memref( |
| %original: memref<?x?xf32>, %indices: memref<?x1xi32>, |
| %update : memref<?x?xf32>) { |
| iree_linalg_ext.scatter |
| {__internal_linalg_transform__ = "tiling_input"} |
| 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 @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: ins(%[[UPDATE_SLICE]], %[[INDEX_SLICE]] |
| // CHECK-SAME: outs(%[[ORIGINAL_SLICE]] |
| |
| // ----- |
| |
| func @scatter_tiling_distribution( |
| %original: tensor<?x?xf32>, %indices: tensor<?x1xi32>, |
| %update : tensor<?x?xf32>) -> tensor<?x?xf32> { |
| %0 = iree_linalg_ext.scatter |
| {__internal_linalg_transform__ = "distribute_input"} |
| 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<()[s0] -> (s0 * 10)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK: func @scatter_tiling_distribution( |
| // 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: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[TILESIZE:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[D0:.+]] = tensor.dim %[[UPDATES]], %[[C0]] |
| // CHECK-DAG: %[[D1:.+]] = tensor.dim %[[UPDATES]], %[[C1]] |
| // CHECK-DAG: %[[ID:.+]] = iree_input.dispatch.workgroup.id[0] |
| // CHECK-DAG: %[[COUNT:.+]] = iree_input.dispatch.workgroup.count[0] |
| // CHECK-DAG: %[[OFFSET:.+]] = affine.apply #[[MAP0]]()[%[[ID]]] |
| // CHECK-DAG: %[[STEP:.+]] = affine.apply #[[MAP0]]()[%[[COUNT]]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV:.+]] = %[[OFFSET]] to %[[D0]] step %[[STEP]] |
| // CHECK-SAME: iter_args(%[[INIT:.+]] = %[[ORIGINAL]]) |
| // CHECK: %[[USED_TILESIZE:.+]] = affine.min #[[MAP1]](%[[IV]])[%[[TILESIZE]], %[[D0]]] |
| // CHECK: %[[UPDATE_SLICE:.+]] = tensor.extract_slice %[[UPDATES]][%[[IV]], 0] |
| // CHECK-SAME: [%[[USED_TILESIZE]], %[[D1]]] |
| // CHECK: %[[INDEX_SLICE:.+]] = tensor.extract_slice %[[INDICES]][%[[IV]], 0] |
| // CHECK-SAME: [%[[USED_TILESIZE]], 1] |
| // CHECK: %[[D2:.+]] = tensor.dim %[[ORIGINAL]], %[[C0]] |
| // CHECK: %[[ORIGINAL_SLICE:.+]] = tensor.extract_slice %[[INIT]][0, 0] |
| // CHECK-SAME: [%[[D2]], %[[D1]]] |
| // CHECK: %[[SCATTER_TILE:.+]] = iree_linalg_ext.scatter |
| // CHECK-SAME: __internal_linalg_transform__ = "distribute_output" |
| // CHECK-SAME: ins(%[[UPDATE_SLICE]], %[[INDEX_SLICE]] |
| // CHECK-SAME: outs(%[[ORIGINAL_SLICE]] |
| // CHECK: %[[YIELD:.+]] = tensor.insert_slice %[[SCATTER_TILE]] into %[[INIT]][0, 0] |
| // CHECK-SAME: [%[[D2]], %[[D1]]] |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| 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"} |
| 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 @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: ins(%[[UPDATES]], %[[INDICES]] |
| // CHECK-SAME: outs(%[[ORIGINAL]] |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func @sort_1d(%arg0: tensor<?xi32>) -> tensor<?xi32> { |
| %0 = iree_linalg_ext.sort |
| {__internal_linalg_transform__ = "outer_reduce_input"} |
| 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 @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 @sort_2d(%arg0: tensor<?x?xi32>) -> tensor<?x?xi32> { |
| %0 = iree_linalg_ext.sort dimension(1) |
| {__internal_linalg_transform__ = "inner_reduce_input"} |
| 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 @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 %[[INIT]][%[[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 @sort_2d_inner_parallel(%arg0: tensor<?x?xi32>) -> tensor<?x?xi32> { |
| %0 = iree_linalg_ext.sort dimension(0) |
| {__internal_linalg_transform__ = "outer_reduce_input"} |
| 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 @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 %[[INIT]][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 @sort_2d_multi_result( |
| %arg0: tensor<?x?xi32>, %arg1: tensor<?x?xf32>) |
| -> (tensor<?x?xi32>, tensor<?x?xf32>) { |
| %0:2 = iree_linalg_ext.sort dimension(1) |
| {__internal_linalg_transform__ = "inner_reduce_input"} |
| 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 @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 %[[INIT1]][%[[IV]], 0] |
| // CHECK-SAME: [%[[USED_TILESIZE]], %[[D1]]] |
| // CHECK: %[[OPERAND2_SLICE:.+]] = tensor.extract_slice %[[INIT2]][%[[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 @sort_2d_multi_result_memref( |
| %arg0: memref<?x?xi32>, %arg1: memref<?x?xf32>) { |
| iree_linalg_ext.sort dimension(0) |
| {__internal_linalg_transform__ = "outer_reduce_input"} |
| 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 @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 @sort_3d_multi_result_distribute( |
| %arg0: tensor<?x?x?xi32>, %arg1 : tensor<?x?x?xf32>) |
| -> (tensor<?x?x?xi32>, tensor<?x?x?xf32>) { |
| %0, %1 = iree_linalg_ext.sort dimension(1) |
| {__internal_linalg_transform__ = "distribute_input"} |
| outs(%arg0, %arg1 : tensor<?x?x?xi32>, tensor<?x?x?xf32>) { |
| ^bb0(%arg2: i32, %arg3: i32, %arg4 : f32, %arg5 : f32): // no predecessors |
| %2 = arith.cmpf ogt, %arg4, %arg5 : f32 |
| iree_linalg_ext.yield %2 : i1 |
| } -> tensor<?x?x?xi32>, tensor<?x?x?xf32> |
| return %0, %1 : tensor<?x?x?xi32>, tensor<?x?x?xf32> |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<()[s0] -> (s0 * 10)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-DAG: #[[MAP2:.+]] = affine_map<()[s0] -> (s0 * 30)> |
| // CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0)[s0, s1] -> (30, -d0 + s1)> |
| // CHECK: func @sort_3d_multi_result_distribute( |
| // CHECK-SAME: %[[OPERAND1:[a-zA-Z0-9_]+]]: tensor<?x?x?xi32> |
| // CHECK-SAME: %[[OPERAND2:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32> |
| // CHECK-DAG: %[[TILESIZE1:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[TILESIZE2:.+]] = arith.constant 30 : index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index |
| // CHECK-DAG: %[[D0:.+]] = tensor.dim %[[OPERAND1]], %[[C0]] |
| // CHECK-DAG: %[[D1:.+]] = tensor.dim %[[OPERAND1]], %[[C1]] |
| // CHECK-DAG: %[[D2:.+]] = tensor.dim %[[OPERAND1]], %[[C2]] |
| // CHECK-DAG: %[[IDX:.+]] = iree_input.dispatch.workgroup.id[0] |
| // CHECK-DAG: %[[COUNTX:.+]] = iree_input.dispatch.workgroup.count[0] |
| // CHECK-DAG: %[[IDY:.+]] = iree_input.dispatch.workgroup.id[1] |
| // CHECK-DAG: %[[COUNTY:.+]] = iree_input.dispatch.workgroup.count[1] |
| // CHECK-DAG: %[[OFFSETY:.+]] = affine.apply #[[MAP0]]()[%[[IDY]]] |
| // CHECK-DAG: %[[STEPY:.+]] = affine.apply #[[MAP0]]()[%[[COUNTY]]] |
| // CHECK: %[[RESULT:.+]]:2 = scf.for %[[IV0:.+]] = %[[OFFSETY]] to %[[D0]] step %[[STEPY]] |
| // CHECK-SAME: iter_args(%[[INIT1:.+]] = %[[OPERAND1]], %[[INIT2:.+]] = %[[OPERAND2]]) |
| // CHECK-DAG: %[[USED_TILESIZE1:.+]] = affine.min #[[MAP1]](%[[IV0]])[%[[TILESIZE1]], %[[D0]]] |
| // CHECK-DAG: %[[OFFSETX:.+]] = affine.apply #[[MAP2]]()[%[[IDX]]] |
| // CHECK-DAG: %[[STEPX:.+]] = affine.apply #[[MAP2]]()[%[[COUNTX]]] |
| // CHECK: %[[RESULT_INNER:.+]]:2 = scf.for %[[IV1:.+]] = %[[OFFSETX]] to %[[D2]] step %[[STEPX]] |
| // CHECK-SAME: iter_args(%[[INIT3:.+]] = %[[INIT1]], %[[INIT4:.+]] = %[[INIT2]]) |
| // CHECK-DAG: %[[USED_TILESIZE2:.+]] = affine.min #[[MAP3]](%[[IV1]])[%[[TILESIZE2]], %[[D2]]] |
| // CHECK: %[[OPERAND1_SLICE:.+]] = tensor.extract_slice %[[INIT3]][%[[IV0]], 0, %[[IV1]]] |
| // CHECK-SAME: [%[[USED_TILESIZE1]], %[[D1]], %[[USED_TILESIZE2]]] |
| // CHECK: %[[OPERAND2_SLICE:.+]] = tensor.extract_slice %[[INIT4]][%[[IV0]], 0, %[[IV1]]] |
| // CHECK-SAME: [%[[USED_TILESIZE1]], %[[D1]], %[[USED_TILESIZE2]]] |
| // CHECK: %[[SORT_SLICE:.+]]:2 = iree_linalg_ext.sort |
| // CHECK-SAME: __internal_linalg_transform__ = "distribute_output" |
| // CHECK-SAME: outs(%[[OPERAND1_SLICE]], %[[OPERAND2_SLICE]] |
| // CHECK: %[[YIELD1:.+]] = tensor.insert_slice %[[SORT_SLICE]]#0 |
| // CHECK-SAME: into %[[INIT3]][%[[IV0]], 0, %[[IV1]]] |
| // CHECK: %[[YIELD2:.+]] = tensor.insert_slice %[[SORT_SLICE]]#1 |
| // CHECK-SAME: into %[[INIT4]][%[[IV0]], 0, %[[IV1]]] |
| // CHECK: scf.yield %[[YIELD1]], %[[YIELD2]] |
| // CHECK: scf.yield %[[RESULT_INNER]]#0, %[[RESULT_INNER]]#1 |
| // CHECK: return %[[RESULT]]#0, %[[RESULT]]#1 |
| |
| // ----- |
| |
| func @sort_3d_multi_result_distribute_memref( |
| %arg0: memref<?x?x?xi32>, %arg1 : memref<?x?x?xf32>) { |
| iree_linalg_ext.sort dimension(1) |
| {__internal_linalg_transform__ = "distribute_input"} |
| outs(%arg0, %arg1 : memref<?x?x?xi32>, memref<?x?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-DAG: #[[MAP0:.+]] = affine_map<()[s0] -> (s0 * 10)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-DAG: #[[MAP2:.+]] = affine_map<()[s0] -> (s0 * 30)> |
| // CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0)[s0, s1] -> (30, -d0 + s1)> |
| // CHECK: func @sort_3d_multi_result_distribute_memref( |
| // CHECK-SAME: %[[OPERAND1:[a-zA-Z0-9_]+]]: memref<?x?x?xi32> |
| // CHECK-SAME: %[[OPERAND2:[a-zA-Z0-9_]+]]: memref<?x?x?xf32> |
| // CHECK-DAG: %[[TILESIZE1:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[TILESIZE2:.+]] = arith.constant 30 : index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index |
| // CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index |
| // CHECK-DAG: %[[D0:.+]] = memref.dim %[[OPERAND1]], %[[C0]] |
| // CHECK-DAG: %[[D1:.+]] = memref.dim %[[OPERAND1]], %[[C1]] |
| // CHECK-DAG: %[[D2:.+]] = memref.dim %[[OPERAND1]], %[[C2]] |
| // CHECK-DAG: %[[IDX:.+]] = iree_input.dispatch.workgroup.id[0] |
| // CHECK-DAG: %[[COUNTX:.+]] = iree_input.dispatch.workgroup.count[0] |
| // CHECK-DAG: %[[IDY:.+]] = iree_input.dispatch.workgroup.id[1] |
| // CHECK-DAG: %[[COUNTY:.+]] = iree_input.dispatch.workgroup.count[1] |
| // CHECK-DAG: %[[OFFSETY:.+]] = affine.apply #[[MAP0]]()[%[[IDY]]] |
| // CHECK-DAG: %[[STEPY:.+]] = affine.apply #[[MAP0]]()[%[[COUNTY]]] |
| // CHECK: scf.for %[[IV0:.+]] = %[[OFFSETY]] to %[[D0]] step %[[STEPY]] |
| // CHECK-DAG: %[[USED_TILESIZE1:.+]] = affine.min #[[MAP1]](%[[IV0]])[%[[TILESIZE1]], %[[D0]]] |
| // CHECK-DAG: %[[OFFSETX:.+]] = affine.apply #[[MAP2]]()[%[[IDX]]] |
| // CHECK-DAG: %[[STEPX:.+]] = affine.apply #[[MAP2]]()[%[[COUNTX]]] |
| // CHECK: scf.for %[[IV1:.+]] = %[[OFFSETX]] to %[[D2]] step %[[STEPX]] |
| // CHECK-DAG: %[[USED_TILESIZE2:.+]] = affine.min #[[MAP3]](%[[IV1]])[%[[TILESIZE2]], %[[D2]]] |
| // CHECK: %[[OPERAND1_SLICE:.+]] = memref.subview %[[OPERAND1]][%[[IV0]], 0, %[[IV1]]] |
| // CHECK-SAME: [%[[USED_TILESIZE1]], %[[D1]], %[[USED_TILESIZE2]]] |
| // CHECK: %[[OPERAND2_SLICE:.+]] = memref.subview %[[OPERAND2]][%[[IV0]], 0, %[[IV1]]] |
| // CHECK-SAME: [%[[USED_TILESIZE1]], %[[D1]], %[[USED_TILESIZE2]]] |
| // CHECK: iree_linalg_ext.sort |
| // CHECK-SAME: __internal_linalg_transform__ = "distribute_output" |
| // CHECK-SAME: outs(%[[OPERAND1_SLICE]], %[[OPERAND2_SLICE]] |
| |
| // ----- |
| |
| func @slice_insert(%source :tensor<?x?xf32>, %dest: tensor<?x?xf32>, |
| %idx0 : index, %idx1 : index) -> tensor<?x?xf32> { |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : index |
| %0 = tensor.dim %source, %c0 : tensor<?x?xf32> |
| %1 = tensor.dim %source, %c1 : tensor<?x?xf32> |
| %2 = tensor.insert_slice %source into %dest[%idx0, %idx1] [%0, %1] [1, 1] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<?x?xf32> into tensor<?x?xf32> |
| return %2 : 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-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0] -> (d0 + s0)> |
| // CHECK: func @slice_insert( |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?xf32> |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32> |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9_]+]]: index |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:[a-zA-Z0-9]+]] = |
| // CHECK: %[[YIELD1:.+]] = scf.for %[[IV1:[a-zA-Z0-9]+]] = |
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]][%[[IV0]], %[[IV1]]] |
| // CHECK: %[[OFFSET0:.+]] = affine.apply #[[MAP2]](%[[IV0]])[%[[ARG2]]] |
| // CHECK: %[[OFFSET1:.+]] = affine.apply #[[MAP2]](%[[IV1]])[%[[ARG3]]] |
| // CHECK: %[[UPDATE:.+]] = tensor.insert_slice %[[SLICE]] |
| // CHECK-SAME: into %{{.+}}[%[[OFFSET0]], %[[OFFSET1]]] |
| // CHECK: scf.yield %[[UPDATE]] |
| // CHECK: scf.yield %[[YIELD1]] |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func @slice_insert_rank_reduce(%source :tensor<?x?xf32>, %dest: tensor<?x?x?xf32>, |
| %idx0 : index, %idx1 : index) -> tensor<?x?x?xf32> { |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : index |
| %0 = tensor.dim %source, %c0 : tensor<?x?xf32> |
| %1 = tensor.dim %source, %c1 : tensor<?x?xf32> |
| %2 = tensor.insert_slice %source into %dest[%idx0, 0, %idx1] [%0, 1, %1] [1, 1, 1] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<?x?xf32> into tensor<?x?x?xf32> |
| return %2 : tensor<?x?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-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0] -> (d0 + s0)> |
| // CHECK: func @slice_insert_rank_reduce( |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?xf32> |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32> |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9_]+]]: index |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:[a-zA-Z0-9]+]] = |
| // CHECK: %[[YIELD1:.+]] = scf.for %[[IV1:[a-zA-Z0-9]+]] = |
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]][%[[IV0]], %[[IV1]]] |
| // CHECK: %[[OFFSET0:.+]] = affine.apply #[[MAP2]](%[[IV0]])[%[[ARG2]]] |
| // CHECK: %[[OFFSET1:.+]] = affine.apply #[[MAP2]](%[[IV1]])[%[[ARG3]]] |
| // CHECK: %[[UPDATE:.+]] = tensor.insert_slice %[[SLICE]] |
| // CHECK-SAME: into %{{.+}}[%[[OFFSET0]], 0, %[[OFFSET1]]] |
| // CHECK: scf.yield %[[UPDATE]] |
| // CHECK: scf.yield %[[YIELD1]] |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| 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 @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 %[[ARG5]][%[[I]]] [%[[SIZE]]] [1] : tensor<1024xf32> to tensor<?xf32> |
| // CHECK: %[[SLICE2:.+]] = tensor.extract_slice %[[ARG6]][%[[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 @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 @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 %[[ARG8]][%[[I]], %[[J]]] [%[[SZ1]], %[[SZ2]]] [1, 1] |
| // CHECK: %[[SLICE2:.+]] = tensor.extract_slice %[[ARG9]][%[[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 @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-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (32, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0] -> (d0 + s0)> |
| // CHECK: 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, #[[MAP1]]> |
| // CHECK: %[[SUB2:.+]] = memref.subview %[[ARG1]][%[[I]]] [%[[SZ]]] [1] : memref<1024xf32> to memref<?xf32, #[[MAP1]]> |
| // 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, #[[MAP1]]>, memref<?xf32, #[[MAP1]]>) |
| |
| // ----- |
| |
| func @reverse_memref(%arg0: memref<?xi32>, %arg1: memref<?xi32>) { |
| iree_linalg_ext.reverse |
| dimensions(dense<0> : tensor<1xi64>) |
| {__internal_linalg_transform__ = "tiling_input"} |
| ins(%arg0: memref<?xi32>) |
| outs(%arg1: memref<?xi32>) |
| return |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0] -> (d0 + s0)> |
| // CHECK-DAG: #[[MAP2:.+]] = affine_map<()[s0, s1, s2] -> (s0 - s1 - s2)> |
| // CHECK: 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: %[[SIZE:.+]] = affine.min #[[MAP0]](%[[I]])[%[[C10]], %[[D0]]] |
| // CHECK: %[[SUB_IN:.+]] = memref.subview %[[ARG0]][%[[I]]] [%[[SIZE]]] [1] |
| // CHECK: %[[T0:.+]] = memref.dim %[[ARG0]], %[[C0]] : memref<?xi32> |
| // CHECK: %[[IDX:.+]] = affine.apply #[[MAP2]]()[%[[T0]], %[[I]], %[[SIZE]]] |
| // CHECK: %[[SUB_OUT:.+]] = memref.subview %[[ARG1]][%[[IDX]]] [%[[SIZE]]] [1] |
| // CHECK: iree_linalg_ext.reverse |
| // CHECK-SAME: dimensions(dense<0> : tensor<1xi64>) |
| // CHECK-SAME: {__internal_linalg_transform__ = "tiling_output"} |
| // CHECK-SAME: ins(%[[SUB_IN]] |
| // CHECK-SAME: outs(%[[SUB_OUT]] |
| |
| // ----- |
| |
| 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 = linalg.init_tensor [%d0, %d1] : tensor<?x?xi32> |
| %0 = iree_linalg_ext.reverse |
| dimensions(dense<[0, 1]> : tensor<2xi64>) |
| {__internal_linalg_transform__ = "tiling_input"} |
| 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 @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:.+]] = linalg.init_tensor [%[[D0]], %[[D1]]] : tensor<?x?xi32> |
| // CHECK-DAG: %[[D0:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xi32> |
| // CHECK-DAG: %[[D1:.+]] = tensor.dim %[[ARG0]], %[[C1]] : 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: %[[SIZE_J:.+]] = affine.min #[[MAP1]](%[[J]])[%[[C20]], %[[D1]]] |
| // CHECK: %[[SUB_IN:.+]] = tensor.extract_slice |
| // CHECK-SAME: %[[ARG0]][%[[I]], %[[J]]] [%[[SIZE_I]], %[[SIZE_J]]] [1, 1] |
| // CHECK: %[[T0:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xi32> |
| // CHECK: %[[IDX0:.+]] = affine.apply #[[MAP2]]()[%[[T0]], %[[I]], %[[SIZE_I]]] |
| // CHECK: %[[T1:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?xi32> |
| // CHECK: %[[IDX1:.+]] = affine.apply #[[MAP2]]()[%[[T1]], %[[J]], %[[SIZE_J]]] |
| // 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: dimensions(dense<[0, 1]> : tensor<2xi64>) |
| // CHECK-SAME: {__internal_linalg_transform__ = "tiling_output"} |
| // 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 @dynamic_insert_slice(%arg0 : tensor<?xf32>, %arg1 : tensor<?x?xf32>, |
| %arg2 : index, %arg3 : index) -> tensor<?x?xf32> { |
| %c0 = arith.constant 0 : index |
| %d0 = tensor.dim %arg0, %c0 : tensor<?xf32> |
| %0 = tensor.insert_slice %arg0 into %arg1[%arg2, %arg3] [1, %d0] [1, 1] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<?xf32> into 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] -> (d0 + s0)> |
| // CHECK: func @dynamic_insert_slice( |
| // CHECK-SAME: %[[ARG0:.+]]: tensor<?xf32> |
| // CHECK-SAME: %[[ARG1:.+]]: tensor<?x?xf32> |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9_]+]]: index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK: %[[D0:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?xf32> |
| // CHECK: %[[RESULT:.+]] = scf.for %[[ARG4:.+]] = %[[C0]] to %[[D0]] |
| // CHECK-SAME: step %[[C10]] iter_args(%[[ARG5:.+]] = %[[ARG1]]) |
| // CHECK: %[[TILESIZE:.+]] = affine.min #[[MAP0]](%[[ARG4]])[%[[C10]], %[[D0]]] |
| // CHECK: %[[EXTRACT:.+]] = tensor.extract_slice %[[ARG0]][%[[ARG4]]] [%[[TILESIZE]]] |
| // CHECK: %[[OFFSET:.+]] = affine.apply #[[MAP1]](%[[ARG4]])[%[[ARG3]]] |
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[EXTRACT]] into %[[ARG5]] |
| // CHECK-SAME: [%[[ARG2]], %[[OFFSET]]] [1, %[[TILESIZE]]] |
| // CHECK: scf.yield %[[INSERT]] |
| // CHECK: return %[[RESULT]] |
| |
| |
| // ----- |
| |
| func @insert_slice_rank_reduced_inner(%arg0 : tensor<?xf32>, |
| %arg1 : tensor<?x?x?xf32>, %arg2: index, %arg3 : index, %arg4 : index) -> tensor<?x?x?xf32> { |
| %c0 = arith.constant 0 : index |
| %d0 = tensor.dim %arg0, %c0 : tensor<?xf32> |
| %0 = tensor.insert_slice %arg0 into %arg1[%arg2, %arg3, %arg4] [1, %d0, 1] [1, 1, 1] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<?xf32> into tensor<?x?x?xf32> |
| return %0 : tensor<?x?x?xf32> |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0)[s0, s1] -> (10, -d0 + s1)> |
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0)[s0] -> (d0 + s0)> |
| // CHECK: func @insert_slice_rank_reduced_inner( |
| // CHECK-SAME: %[[ARG0:.+]]: tensor<?xf32> |
| // CHECK-SAME: %[[ARG1:.+]]: tensor<?x?x?xf32> |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9_]+]]: index |
| // CHECK-SAME: %[[ARG4:[a-zA-Z0-9_]+]]: index |
| // CHECK-DAG: %[[LB:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[STEP:.+]] = arith.constant 10 : index |
| // CHECK: %[[UB:.+]] = tensor.dim %[[ARG0]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:[a-zA-Z0-9_]+]] = %[[LB]] |
| // CHECK-SAME: to %[[D0]] step %[[STEP]] iter_args(%[[ARG6:.+]] = %[[ARG1]]) |
| // CHECK: %[[TILESIZE:.+]] = affine.min #[[MAP0]](%[[ARG5]])[%[[STEP]], %[[UB]]] |
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]][%[[IV0]]] [%[[TILESIZE]]] |
| // CHECK: %[[APPLY:.+]] = affine.apply #[[MAP1]](%[[IV0]])[%[[ARG3]]] |
| // CHECK: %[[YIELD:.+]] = tensor.insert_slice %[[SLICE]] into %[[ARG6]] |
| // CHECK-SAME: [%[[ARG2]], %[[APPLY]], %[[ARG4]]] [1, %[[TILESIZE]], 1] |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func @extract_slice(%arg0 : tensor<?x?xf32>, %arg1: index, |
| %arg2 : index, %arg3 : index, %arg4 : index, %arg5 : index, |
| %arg6 : index) -> tensor<?x?xf32> { |
| %0 = tensor.extract_slice %arg0[%arg1, %arg2] [%arg3, %arg4] [%arg5, %arg6] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<?x?xf32> to 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-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0, s1] -> (d0 * s0 + s1)> |
| // CHECK: func @extract_slice |
| // CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32> |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG4:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG5:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG6:[a-zA-Z0-9]+]]: index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[INIT:.+]] = linalg.init_tensor [%[[ARG3]], %[[ARG4]]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG3]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[ITER1:.+]] = %[[INIT]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[ARG3]]] |
| // CHECK: %[[YIELD:.+]] = scf.for %[[IV1:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG4]] step %[[C20]] |
| // CHECK-SAME: iter_args(%[[ITER2:.+]] = %[[ITER1]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[ARG4]]] |
| // CHECK-DAG: %[[OFFSET_Y:.+]] = affine.apply #[[MAP2]](%[[IV0]])[%[[ARG5]], %[[ARG1]]] |
| // CHECK-DAG: %[[OFFSET_X:.+]] = affine.apply #[[MAP2]](%[[IV1]])[%[[ARG6]], %[[ARG2]]] |
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]] |
| // CHECK-SAME: [%[[OFFSET_Y]], %[[OFFSET_X]]] [%[[TILE_Y]], %[[TILE_X]]] [%[[ARG5]], %[[ARG6]]] |
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[SLICE]] into %[[ITER2]] |
| // CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TILE_Y]], %[[TILE_X]]] [1, 1] |
| // CHECK: scf.yield %[[INSERT]] |
| // CHECK: } |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: } |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func @extract_slice_static(%arg0 : tensor<50x60xf32>) -> tensor<20x30xf32> { |
| %0 = tensor.extract_slice %arg0[2, 3] [20, 30] [5, 6] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<50x60xf32> to tensor<20x30xf32> |
| return %0 : tensor<20x30xf32> |
| } |
| // 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<(d0)[s0, s1] -> (d0 * s0 + s1)> |
| // CHECK: func @extract_slice_static |
| // CHECK-SAME: %[[ARG0:.+]]: tensor<50x60xf32> |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index |
| // CHECK-DAG: %[[C3:.+]] = arith.constant 3 : index |
| // CHECK-DAG: %[[C5:.+]] = arith.constant 5 : index |
| // CHECK-DAG: %[[C6:.+]] = arith.constant 6 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[C30:.+]] = arith.constant 30 : index |
| // CHECK-DAG: %[[INIT:.+]] = linalg.init_tensor [20, 30] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[C20]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[ITER1:.+]] = %[[INIT]]) -> (tensor<20x30xf32>) { |
| // CHECK: %[[TILE_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[C20]]] |
| // CHECK: %[[YIELD:.+]] = scf.for %[[IV1:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[C30]] step %[[C20]] |
| // CHECK-SAME: iter_args(%[[ITER2:.+]] = %[[ITER1]]) -> (tensor<20x30xf32>) { |
| // CHECK: %[[TILE_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[C30]]] |
| // CHECK-DAG: %[[OFFSET_Y:.+]] = affine.apply #[[MAP2]](%[[IV0]])[%[[C5]], %[[C2]]] |
| // CHECK-DAG: %[[OFFSET_X:.+]] = affine.apply #[[MAP2]](%[[IV1]])[%[[C6]], %[[C3]]] |
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]] |
| // CHECK-SAME: [%[[OFFSET_Y]], %[[OFFSET_X]]] [%[[TILE_Y]], %[[TILE_X]]] [5, 6] |
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[SLICE]] into %[[ITER2]] |
| // CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TILE_Y]], %[[TILE_X]]] [1, 1] |
| // CHECK: scf.yield %[[INSERT]] |
| // CHECK: } |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: } |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func @extract_slice_reduced_rank_outer(%arg0 : tensor<?x?x?xf32>, %arg1 : index, |
| %arg2 : index, %arg3 : index, %arg4 : index, %arg5 : index, %arg6 : index, |
| %arg7 : index, %arg8 : index) -> tensor<?x?xf32> { |
| %0 = tensor.extract_slice %arg0[%arg1, %arg2, %arg3] [1, %arg4, %arg5] [%arg6, %arg7, %arg8] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<?x?x?xf32> to 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-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0, s1] -> (d0 * s0 + s1)> |
| // CHECK: func @extract_slice_reduced_rank_outer |
| // CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?xf32> |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG4:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG5:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG6:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG7:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG8:[a-zA-Z0-9]+]]: index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[INIT:.+]] = linalg.init_tensor [%[[ARG4]], %[[ARG5]]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG4]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[ITER1:.+]] = %[[INIT]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[ARG4]]] |
| // CHECK: %[[YIELD:.+]] = scf.for %[[IV1:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG5]] step %[[C20]] |
| // CHECK-SAME: iter_args(%[[ITER2:.+]] = %[[ITER1]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[ARG5]]] |
| // CHECK-DAG: %[[OFFSET_Y:.+]] = affine.apply #[[MAP2]](%[[IV0]])[%[[ARG7]], %[[ARG2]]] |
| // CHECK-DAG: %[[OFFSET_X:.+]] = affine.apply #[[MAP2]](%[[IV1]])[%[[ARG8]], %[[ARG3]]] |
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]] |
| // CHECK-SAME: [%[[ARG1]], %[[OFFSET_Y]], %[[OFFSET_X]]] |
| // CHECK-SAME: [1, %[[TILE_Y]], %[[TILE_X]]] [%[[ARG6]], %[[ARG7]], %[[ARG8]]] |
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[SLICE]] into %[[ITER2]] |
| // CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TILE_Y]], %[[TILE_X]]] [1, 1] |
| // CHECK: scf.yield %[[INSERT]] |
| // CHECK: } |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: } |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func @extract_slice_reduced_rank_middle(%arg0 : tensor<?x?x?xf32>, %arg1 : index, |
| %arg2 : index, %arg3 : index, %arg4 : index, %arg5 : index, %arg6 : index, |
| %arg7 : index, %arg8 : index) -> tensor<?x?xf32> { |
| %0 = tensor.extract_slice %arg0[%arg1, %arg2, %arg3] [%arg4, 1, %arg5] [%arg6, %arg7, %arg8] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<?x?x?xf32> to 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-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0, s1] -> (d0 * s0 + s1)> |
| // CHECK: func @extract_slice_reduced_rank_middle |
| // CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?xf32> |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG4:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG5:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG6:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG7:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG8:[a-zA-Z0-9]+]]: index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[INIT:.+]] = linalg.init_tensor [%[[ARG4]], %[[ARG5]]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG4]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[ITER1:.+]] = %[[INIT]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[ARG4]]] |
| // CHECK: %[[YIELD:.+]] = scf.for %[[IV1:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG5]] step %[[C20]] |
| // CHECK-SAME: iter_args(%[[ITER2:.+]] = %[[ITER1]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[ARG5]]] |
| // CHECK-DAG: %[[OFFSET_Y:.+]] = affine.apply #[[MAP2]](%[[IV0]])[%[[ARG6]], %[[ARG1]]] |
| // CHECK-DAG: %[[OFFSET_X:.+]] = affine.apply #[[MAP2]](%[[IV1]])[%[[ARG8]], %[[ARG3]]] |
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]] |
| // CHECK-SAME: [%[[OFFSET_Y]], %[[ARG2]], %[[OFFSET_X]]] |
| // CHECK-SAME: [%[[TILE_Y]], 1, %[[TILE_X]]] [%[[ARG6]], %[[ARG7]], %[[ARG8]]] |
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[SLICE]] into %[[ITER2]] |
| // CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TILE_Y]], %[[TILE_X]]] [1, 1] |
| // CHECK: scf.yield %[[INSERT]] |
| // CHECK: } |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: } |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func @extract_slice_reduced_rank_inner(%arg0 : tensor<?x?x?xf32>, %arg1 : index, |
| %arg2 : index, %arg3 : index, %arg4 : index, %arg5 : index, %arg6 : index, |
| %arg7 : index, %arg8 : index) -> tensor<?x?xf32> { |
| %0 = tensor.extract_slice %arg0[%arg1, %arg2, %arg3] [%arg4, %arg5, 1] [%arg6, %arg7, %arg8] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<?x?x?xf32> to 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-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0, s1] -> (d0 * s0 + s1)> |
| // CHECK: func @extract_slice_reduced_rank_inner |
| // CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?xf32> |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG4:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG5:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG6:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG7:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG8:[a-zA-Z0-9]+]]: index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[INIT:.+]] = linalg.init_tensor [%[[ARG4]], %[[ARG5]]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG4]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[ITER1:.+]] = %[[INIT]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[ARG4]]] |
| // CHECK: %[[YIELD:.+]] = scf.for %[[IV1:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG5]] step %[[C20]] |
| // CHECK-SAME: iter_args(%[[ITER2:.+]] = %[[ITER1]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[ARG5]]] |
| // CHECK-DAG: %[[OFFSET_Y:.+]] = affine.apply #[[MAP2]](%[[IV0]])[%[[ARG6]], %[[ARG1]]] |
| // CHECK-DAG: %[[OFFSET_X:.+]] = affine.apply #[[MAP2]](%[[IV1]])[%[[ARG7]], %[[ARG2]]] |
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]] |
| // CHECK-SAME: [%[[OFFSET_Y]], %[[OFFSET_X]], %[[ARG3]]] |
| // CHECK-SAME: [%[[TILE_Y]], %[[TILE_X]], 1] [%[[ARG6]], %[[ARG7]], %[[ARG8]] |
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[SLICE]] into %[[ITER2]] |
| // CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TILE_Y]], %[[TILE_X]]] [1, 1] |
| // CHECK: scf.yield %[[INSERT]] |
| // CHECK: } |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: } |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func @extract_slice_reduced_rank_two_dims_1(%arg0 : tensor<?x?x?x?xf32>, %arg1 : index, |
| %arg2 : index, %arg3 : index, %arg4 : index, %arg5 : index, %arg6 : index, |
| %arg7 : index, %arg8 : index, %arg9 : index, %arg10 : index) -> tensor<?x?xf32> { |
| %0 = tensor.extract_slice %arg0[%arg1, %arg2, %arg3, %arg4] [%arg5, 1, %arg6, 1] [%arg7, %arg8, %arg9, %arg10] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<?x?x?x?xf32> to 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-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0, s1] -> (d0 * s0 + s1)> |
| // CHECK: func @extract_slice_reduced_rank_two_dims_1 |
| // CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?x?xf32> |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG4:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG5:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG6:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG7:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG8:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG9:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG10:[a-zA-Z0-9]+]]: index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[INIT:.+]] = linalg.init_tensor [%[[ARG5]], %[[ARG6]]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG5]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[ITER1:.+]] = %[[INIT]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[ARG5]]] |
| // CHECK: %[[YIELD:.+]] = scf.for %[[IV1:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG6]] step %[[C20]] |
| // CHECK-SAME: iter_args(%[[ITER2:.+]] = %[[ITER1]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[ARG6]]] |
| // CHECK-DAG: %[[OFFSET_Y:.+]] = affine.apply #[[MAP2]](%[[IV0]])[%[[ARG7]], %[[ARG1]]] |
| // CHECK-DAG: %[[OFFSET_X:.+]] = affine.apply #[[MAP2]](%[[IV1]])[%[[ARG9]], %[[ARG3]]] |
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]] |
| // CHECK-SAME: [%[[OFFSET_Y]], %[[ARG2]], %[[OFFSET_X]], %[[ARG4]]] |
| // CHECK-SAME: [%[[TILE_Y]], 1, %[[TILE_X]], 1] |
| // CHECK-SAME: [%[[ARG7]], %[[ARG8]], %[[ARG9]], %[[ARG10]]] |
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[SLICE]] into %[[ITER2]] |
| // CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TILE_Y]], %[[TILE_X]]] [1, 1] |
| // CHECK: scf.yield %[[INSERT]] |
| // CHECK: } |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: } |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func @extract_slice_reduced_rank_two_dims_2(%arg0 : tensor<?x?x?x?xf32>, %arg1 : index, |
| %arg2 : index, %arg3 : index, %arg4 : index, %arg5 : index, %arg6 : index, |
| %arg7 : index, %arg8 : index, %arg9 : index, %arg10 : index) -> tensor<?x?xf32> { |
| %0 = tensor.extract_slice %arg0[%arg1, %arg2, %arg3, %arg4] [%arg5, 1, 1, %arg6] [%arg7, %arg8, %arg9, %arg10] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<?x?x?x?xf32> to 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-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0, s1] -> (d0 * s0 + s1)> |
| // CHECK: func @extract_slice_reduced_rank_two_dims_2 |
| // CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?x?xf32> |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG4:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG5:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG6:[a-zA-Z0-9]+]]: index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[INIT:.+]] = linalg.init_tensor [%[[ARG5]], %[[ARG6]]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG5]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[ITER1:.+]] = %[[INIT]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[ARG5]]] |
| // CHECK: %[[YIELD:.+]] = scf.for %[[IV1:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG6]] step %[[C20]] |
| // CHECK-SAME: iter_args(%[[ITER2:.+]] = %[[ITER1]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[ARG6]]] |
| // CHECK-DAG: %[[OFFSET_Y:.+]] = affine.apply #[[MAP2]](%[[IV0]])[%[[ARG7]], %[[ARG1]]] |
| // CHECK-DAG: %[[OFFSET_X:.+]] = affine.apply #[[MAP2]](%[[IV1]])[%[[ARG10]], %[[ARG4]]] |
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]] |
| // CHECK-SAME: [%[[OFFSET_Y]], %[[ARG2]], %[[ARG3]], %[[OFFSET_X]]] |
| // CHECK-SAME: [%[[TILE_Y]], 1, 1, %[[TILE_X]]] |
| // CHECK-SAME: [%[[ARG7]], %[[ARG8]], %[[ARG9]], %[[ARG10]]] |
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[SLICE]] into %[[ITER2]] |
| // CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TILE_Y]], %[[TILE_X]]] [1, 1] |
| // CHECK: scf.yield %[[INSERT]] |
| // CHECK: } |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: } |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func @extract_slice_reduced_rank_two_dims_3(%arg0 : tensor<?x?x?x?xf32>, %arg1 : index, |
| %arg2 : index, %arg3 : index, %arg4 : index, %arg5 : index, %arg6 : index, |
| %arg7 : index, %arg8 : index, %arg9 : index, %arg10 : index) -> tensor<?x?xf32> { |
| %0 = tensor.extract_slice %arg0[%arg1, %arg2, %arg3, %arg4] [1, %arg5, 1, %arg6] [%arg7, %arg8, %arg9, %arg10] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<?x?x?x?xf32> to 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-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0, s1] -> (d0 * s0 + s1)> |
| // CHECK: func @extract_slice_reduced_rank_two_dims_3 |
| // CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?x?xf32> |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG4:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG5:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG6:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG7:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG8:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG9:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG10:[a-zA-Z0-9]+]]: index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[INIT:.+]] = linalg.init_tensor [%[[ARG5]], %[[ARG6]]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG5]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[ITER1:.+]] = %[[INIT]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[ARG5]]] |
| // CHECK: %[[YIELD:.+]] = scf.for %[[IV1:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG6]] step %[[C20]] |
| // CHECK-SAME: iter_args(%[[ITER2:.+]] = %[[ITER1]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[ARG6]]] |
| // CHECK-DAG: %[[OFFSET_Y:.+]] = affine.apply #[[MAP2]](%[[IV0]])[%[[ARG8]], %[[ARG2]]] |
| // CHECK-DAG: %[[OFFSET_X:.+]] = affine.apply #[[MAP2]](%[[IV1]])[%[[ARG10]], %[[ARG4]]] |
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]] |
| // CHECK-SAME: [%[[ARG1]], %[[OFFSET_Y]], %[[ARG3]], %[[OFFSET_X]]] |
| // CHECK-SAME: [1, %[[TILE_Y]], 1, %[[TILE_X]]] |
| // CHECK-SAME: [%[[ARG7]], %[[ARG8]], %[[ARG9]], %[[ARG10]]] |
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[SLICE]] into %[[ITER2]] |
| // CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TILE_Y]], %[[TILE_X]]] [1, 1] |
| // CHECK: scf.yield %[[INSERT]] |
| // CHECK: } |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: } |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func @extract_slice_reduced_rank_two_dims_4(%arg0 : tensor<?x?x?x?xf32>, %arg1 : index, |
| %arg2 : index, %arg3 : index, %arg4 : index, %arg5 : index, %arg6 : index, |
| %arg7 : index, %arg8 : index, %arg9 : index, %arg10 : index) -> tensor<?x?xf32> { |
| %0 = tensor.extract_slice %arg0[%arg1, %arg2, %arg3, %arg4] [%arg5, %arg6, 1, 1] [%arg7, %arg8, %arg9, %arg10] |
| {__internal_linalg_transform__ = "tiling_input"} : tensor<?x?x?x?xf32> to 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-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0, s1] -> (d0 * s0 + s1)> |
| // CHECK: func @extract_slice_reduced_rank_two_dims_4 |
| // CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?x?xf32> |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG4:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG5:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG6:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG7:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG8:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG9:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG10:[a-zA-Z0-9]+]]: index |
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index |
| // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index |
| // CHECK-DAG: %[[INIT:.+]] = linalg.init_tensor [%[[ARG5]], %[[ARG6]]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG5]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[ITER1:.+]] = %[[INIT]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[ARG5]]] |
| // CHECK: %[[YIELD:.+]] = scf.for %[[IV1:.+]] = %[[C0]] |
| // CHECK-SAME: to %[[ARG6]] step %[[C20]] |
| // CHECK-SAME: iter_args(%[[ITER2:.+]] = %[[ITER1]]) -> (tensor<?x?xf32>) { |
| // CHECK: %[[TILE_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[ARG6]]] |
| // CHECK-DAG: %[[OFFSET_Y:.+]] = affine.apply #[[MAP2]](%[[IV0]])[%[[ARG7]], %[[ARG1]]] |
| // CHECK-DAG: %[[OFFSET_X:.+]] = affine.apply #[[MAP2]](%[[IV1]])[%[[ARG8]], %[[ARG2]]] |
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]] |
| // CHECK-SAME: [%[[OFFSET_Y]], %[[OFFSET_X]], %[[ARG3]], %[[ARG4]]] |
| // CHECK-SAME: [%[[TILE_Y]], %[[TILE_X]], 1, 1] |
| // CHECK-SAME: [%[[ARG7]], %[[ARG8]], %[[ARG9]], %[[ARG10]]] |
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[SLICE]] into %[[ITER2]] |
| // CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TILE_Y]], %[[TILE_X]]] [1, 1] |
| // CHECK: scf.yield %[[INSERT]] |
| // CHECK: } |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: } |
| // CHECK: return %[[RESULT]] |
| |
| // ----- |
| |
| func @pad_tensor(%arg0 : tensor<?x?xf32>, %arg1 : index, %arg2 : index, |
| %arg3 : index, %arg4 : index, %arg5 : f32) -> tensor<?x?xf32> { |
| %0 = linalg.pad_tensor %arg0 low[%arg1, %arg2] high[%arg3, %arg4] { |
| ^bb0(%arg6 : index, %arg7 : index): |
| linalg.yield %arg5 : f32 |
| } {__internal_linalg_transform__ = "tiling_input"} |
| : tensor<?x?xf32> to tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<()[s0, s1, s2] -> (s2 + s0 + s1)> |
| // CHECK: func @pad_tensor |
| // CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<?x?xf32> |
| // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG4:[a-zA-Z0-9]+]]: index |
| // CHECK-SAME: %[[ARG5:[a-zA-Z0-9]+]]: f32 |
| // 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: %[[INIT:.+]] = linalg.init_tensor |
| // CHECK: %[[D0:.+]] = tensor.dim %[[ARG0]], %[[C0]] |
| // CHECK: %[[UBY:.+]] = affine.apply #[[MAP0]]()[%[[ARG1]], %[[ARG3]], %[[D0]]] |
| // CHECK: %[[D1:.+]] = tensor.dim %[[ARG0]], %[[C1]] |
| // CHECK: %[[UBX:.+]] = affine.apply #[[MAP0]]()[%[[ARG2]], %[[ARG4]], %[[D1]]] |
| // CHECK: %[[RESULT:.+]] = scf.for %[[IV0:[a-zA-Z0-9]+]] = %[[C0]] to %[[UBY]] step %[[C10]] |
| // CHECK-SAME: iter_args(%[[ARG7:.+]] = %[[INIT]]) |
| // CHECK: %[[YIELD:.+]] = scf.for %[[IV1:[a-zA-Z0-9]+]] = %[[C0]] to %[[UBX]] step %[[C20]] |
| // CHECK-SAME: iter_args(%[[ARG9:.+]] = %[[ARG7]]) |
| // CHECK: %[[PAD_TILE:.+]] = scf.if |
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[PAD_TILE]] into %[[ARG9]] |
| // CHECK-SAME: [%[[IV0]], %[[IV1]]] |
| // CHECK: scf.yield %[[INSERT]] |
| // CHECK: scf.yield %[[YIELD]] |
| // CHECK: return %[[RESULT]] |
