// RUN: mlir-opt -test-tiling-interface=tile-using-scf-for -split-input-file %s | FileCheck %s func.func @simple_matmul(%arg0 : tensor, %arg1 : tensor, %arg2 : tensor) -> tensor { %0 = linalg.matmul {__internal_linalg_transform__ = "simple_gemm"} ins(%arg0, %arg1 : tensor, tensor) outs(%arg2 : tensor) -> tensor return %0 : tensor } // 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 @simple_matmul( // CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor // 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: %[[M:.+]] = tensor.dim %[[ARG0]], %[[C0]] // CHECK-DAG: %[[K:.+]] = tensor.dim %[[ARG0]], %[[C1]] // CHECK-DAG: %[[N:.+]] = tensor.dim %[[ARG1]], %[[C1]] // CHECK: %[[OUTER:[a-zA-Z0-9]+]] = scf.for %[[IV0:[a-zA-Z0-9]+]] = %[[C0]] to %[[M]] step %[[C10]] // CHECK-SAME: iter_args(%[[INIT0:.+]] = %[[ARG2]]) // CHECK: %[[TS_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[M]]] // CHECK: %[[INNER:[a-zA-Z0-9]+]] = scf.for %[[IV1:[a-zA-Z0-9]+]] = %[[C0]] to %[[N]] step %[[C20]] // CHECK-SAME: iter_args(%[[INIT1:.+]] = %[[INIT0]]) // CHECK: %[[TS_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[N]]] // CHECK-DAG: %[[LHS_TILE:.+]] = tensor.extract_slice %[[ARG0]] // CHECK-SAME: [%[[IV0]], 0] [%[[TS_Y]], %[[K]]] [1, 1] // CHECK-DAG: %[[RHS_TILE:.+]] = tensor.extract_slice %[[ARG1]] // CHECK-SAME: [0, %[[IV1]]] [%[[K]], %[[TS_X]]] [1, 1] // CHECK-DAG: %[[INIT_TILE:.+]] = tensor.extract_slice %[[INIT1]] // CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TS_Y]], %[[TS_X]]] [1, 1] // CHECK: %[[GEMM_TILE:.+]] = linalg.matmul // CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] : // CHECK-SAME: outs(%[[INIT_TILE]] : // CHECK: %[[UPDATE:.+]] = tensor.insert_slice %[[GEMM_TILE]] into %[[INIT1]] // CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TS_Y]], %[[TS_X]]] [1, 1] // CHECK: scf.yield %[[UPDATE]] // CHECK: scf.yield %[[INNER]] // CHECK: return %[[OUTER]] // ----- func.func @simple_matmul_memref(%arg0 : memref, %arg1 : memref, %arg2 : memref) { linalg.matmul {__internal_linalg_transform__ = "simple_gemm_memref"} ins(%arg0, %arg1 : memref, memref) outs(%arg2 : memref) return } // 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] -> (30, -d0 + s1)> // CHECK: func.func @simple_matmul_memref( // CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: memref // CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: memref // CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: memref // 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: %[[C30:.+]] = arith.constant 30 : index // CHECK-DAG: %[[M:.+]] = memref.dim %[[ARG0]], %[[C0]] // CHECK-DAG: %[[K:.+]] = memref.dim %[[ARG0]], %[[C1]] // CHECK-DAG: %[[N:.+]] = memref.dim %[[ARG1]], %[[C1]] // CHECK: scf.for %[[IV0:[a-zA-Z0-9]+]] = %[[C0]] to %[[M]] step %[[C10]] // CHECK: %[[TS_M:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[M]]] // CHECK: scf.for %[[IV1:[a-zA-Z0-9]+]] = %[[C0]] to %[[N]] step %[[C20]] // CHECK: %[[TS_N:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[N]]] // CHECK: scf.for %[[IV2:[a-zA-Z0-9]+]] = %[[C0]] to %[[K]] step %[[C30]] // CHECK: %[[TS_K:.+]] = affine.min #[[MAP2]](%[[IV2]])[%[[C30]], %[[K]]] // CHECK-DAG: %[[LHS_TILE:.+]] = memref.subview %[[ARG0]] // CHECK-SAME: [%[[IV0]], %[[IV2]]] [%[[TS_M]], %[[TS_K]]] [1, 1] // CHECK-DAG: %[[RHS_TILE:.+]] = memref.subview %[[ARG1]] // CHECK-SAME: [%[[IV2]], %[[IV1]]] [%[[TS_K]], %[[TS_N]]] [1, 1] // CHECK-DAG: %[[OUT_TILE:.+]] = memref.subview %[[ARG2]] // CHECK-SAME: [%[[IV0]], %[[IV1]]] [%[[TS_M]], %[[TS_N]]] [1, 1] // CHECK: linalg.matmul // CHECK-SAME: ins(%[[LHS_TILE]], %[[RHS_TILE]] : // CHECK-SAME: outs(%[[OUT_TILE]] : // ----- #map0 = affine_map<(d0, d1, d2) -> (d0, d1, d2)> #map1 = affine_map<(d0, d1, d2) -> (d0, d2, d1)> #map2 = affine_map<(d0, d1, d2) -> (d2, d0, d1)> func.func @multi_result(%arg0 : tensor<128x200x300xf32>) -> (tensor<128x300x200xf32>, tensor<300x128x200xf32>) { %init0 = linalg.init_tensor [128, 300, 200] : tensor<128x300x200xf32> %init1 = linalg.init_tensor [300, 128, 200] : tensor<300x128x200xf32> %0:2 = linalg.generic { indexing_maps = [#map0, #map1, #map2], iterator_types = ["parallel", "parallel", "parallel"]} {__internal_linalg_transform__ = "parallel_generic_transpose"} ins(%arg0 : tensor<128x200x300xf32>) outs(%init0, %init1 : tensor<128x300x200xf32>, tensor<300x128x200xf32>) { ^bb0(%b0 : f32, %b1 : f32, %b2 : f32): linalg.yield %b0, %b0 : f32, f32 } -> (tensor<128x300x200xf32>, tensor<300x128x200xf32>) return %0#0, %0#1 : tensor<128x300x200xf32>, tensor<300x128x200xf32> } // 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 @multi_result( // CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<128x200x300xf32>) // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index // CHECK-DAG: %[[C128:.+]] = arith.constant 128 : index // CHECK-DAG: %[[C300:.+]] = arith.constant 300 : index // CHECK-DAG: %[[INIT0:.+]] = linalg.init_tensor [128, 300, 200] // CHECK-DAG: %[[INIT1:.+]] = linalg.init_tensor [300, 128, 200] // CHECK: %[[OUTER:[a-zA-Z0-9]+]]:2 = scf.for %[[IV0:[a-zA-Z0-9]+]] = %[[C0]] to %[[C128]] step %[[C10]] // CHECK-SAME: iter_args(%[[ARG1:[a-zA-Z0-9]+]] = %[[INIT0]], %[[ARG2:[a-zA-Z0-9]+]] = %[[INIT1]]) // CHECK: %[[TS_Y:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[C128]]] // CHECK: %[[INNER:[a-zA-Z0-9]+]]:2 = scf.for %[[IV1:[a-zA-Z0-9]+]] = %[[C0]] to %[[C300]] step %[[C20]] // CHECK-SAME: iter_args(%[[ARG3:[a-zA-Z0-9]+]] = %[[ARG1]], %[[ARG4:[a-zA-Z0-9]+]] = %[[ARG2]]) // CHECK: %[[TS_X:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[C300]]] // CHECK-DAG: %[[ARG_TILE:.+]] = tensor.extract_slice %[[ARG0]] // CHECK-SAME: [%[[IV0]], 0, %[[IV1]]] [%[[TS_Y]], 200, %[[TS_X]]] [1, 1, 1] // CHECK-DAG: %[[INIT0_TILE:.+]] = tensor.extract_slice %[[ARG3]] // CHECK-SAME: [%[[IV0]], %[[IV1]], 0] [%[[TS_Y]], %[[TS_X]], 200] [1, 1, 1] // CHECK-DAG: %[[INIT1_TILE:.+]] = tensor.extract_slice %[[ARG4]] // CHECK-SAME: [%[[IV1]], %[[IV0]], 0] [%[[TS_X]], %[[TS_Y]], 200] [1, 1, 1] // CHECK: %[[RESULT_TILE:.+]]:2 = linalg.generic // CHECK-SAME: ins(%[[ARG_TILE]] : // CHECK-SAME: outs(%[[INIT0_TILE]], %[[INIT1_TILE]] : // CHECK: %[[UPDATE0:.+]] = tensor.insert_slice %[[RESULT_TILE]]#0 into %[[ARG3]] // CHECK-SAME: [%[[IV0]], %[[IV1]], 0] [%[[TS_Y]], %[[TS_X]], 200] [1, 1, 1] // CHECK: %[[UPDATE1:.+]] = tensor.insert_slice %[[RESULT_TILE]]#1 into %[[ARG4]] // CHECK-SAME: [%[[IV1]], %[[IV0]], 0] [%[[TS_X]], %[[TS_Y]], 200] [1, 1, 1] // CHECK: scf.yield %[[UPDATE0]], %[[UPDATE1]] // CHECK: scf.yield %[[INNER]]#0, %[[INNER]]#1 // CHECK: return %[[OUTER]]#0, %[[OUTER]]#1 // ----- func.func @conv2D(%arg0 : tensor, %arg1 : tensor, %arg2 : tensor) -> tensor { %0 = linalg.conv_2d_nhwc_hwcf { strides = dense<[2, 3]> : tensor<2xi64>, dilation = dense<[4, 5]> : tensor<2xi64>, __internal_linalg_transform__ = "simple_conv"} ins(%arg0, %arg1 : tensor, tensor) outs(%arg2 : tensor) -> tensor return %0 : tensor } // 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] -> (30, -d0 + s1)> // CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0)[s0] -> (d0 + s0 * 2 - 2)> // CHECK-DAG: #[[MAP4:.+]] = affine_map<(d0)[s0] -> (d0 + s0 * 3 - 3)> // CHECK: func.func @conv2D( // CHECK-SAME: %[[INPUT:[a-zA-Z0-9]+]]: tensor // CHECK-SAME: %[[FILTER:[a-zA-Z0-9]+]]: tensor // CHECK-SAME: %[[INIT:[a-zA-Z0-9]+]]: tensor // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index // CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index // CHECK-DAG: %[[C3:.+]] = arith.constant 3 : index // CHECK-DAG: %[[C10:.+]] = arith.constant 10 : index // CHECK-DAG: %[[C20:.+]] = arith.constant 20 : index // CHECK-DAG: %[[C30:.+]] = arith.constant 30 : index // CHECK-DAG: %[[N:.+]] = tensor.dim %[[INPUT]], %[[C0]] // CHECK-DAG: %[[C:.+]] = tensor.dim %[[INPUT]], %[[C3]] // CHECK-DAG: %[[P:.+]] = tensor.dim %[[FILTER]], %[[C0]] // CHECK-DAG: %[[Q:.+]] = tensor.dim %[[FILTER]], %[[C1]] // CHECK-DAG: %[[F:.+]] = tensor.dim %[[FILTER]], %[[C3]] // CHECK-DAG: %[[R:.+]] = tensor.dim %[[INIT]], %[[C1]] // CHECK-DAG: %[[S:.+]] = tensor.dim %[[INIT]], %[[C2]] // CHECK: scf.for %[[IV0:[a-zA-Z0-9]+]] = %[[C0]] to %[[P]] step %[[C10]] // CHECK-SAME: iter_args(%[[INIT0:.+]] = %[[INIT]]) // CHECK: %[[TS_P:.+]] = affine.min #[[MAP0]](%[[IV0]])[%[[C10]], %[[P]]] // CHECK: scf.for %[[IV1:[a-zA-Z0-9]+]] = %[[C0]] to %[[Q]] step %[[C20]] // CHECK-SAME: iter_args(%[[INIT1:.+]] = %[[INIT0]]) // CHECK: %[[TS_Q:.+]] = affine.min #[[MAP1]](%[[IV1]])[%[[C20]], %[[Q]]] // CHECK: scf.for %[[IV2:[a-zA-Z0-9]+]] = %[[C0]] to %[[C]] step %[[C30]] // CHECK-SAME: iter_args(%[[INIT2:.+]] = %[[INIT1]]) // CHECK-DAG: %[[TS_C:.+]] = affine.min #[[MAP2]](%[[IV2]])[%[[C30]], %[[C]]] // CHECK-DAG: %[[TS_H:.+]] = affine.apply #[[MAP3]](%[[TS_P]])[%[[R]]] // CHECK-DAG: %[[TS_W:.+]] = affine.apply #[[MAP4]](%[[TS_Q]])[%[[S]]] // CHECK-DAG: %[[INPUT_TILE:.+]] = tensor.extract_slice %[[INPUT]] // CHECK-SAME: [0, %[[IV0]], %[[IV1]], %[[IV2]]] [%[[N]], %[[TS_H]], %[[TS_W]], %[[TS_C]]] // CHECK-DAG: %[[FILTER_TILE:.+]] = tensor.extract_slice %[[FILTER]] // CHECK-SAME: [%[[IV0]], %[[IV1]], %[[IV2]], 0] [%[[TS_P]], %[[TS_Q]], %[[TS_C]], %[[F]]] // CHECK-DAG: %[[INIT_TILE:.+]] = tensor.extract_slice %[[INIT2]] // CHECK-SAME: [0, 0, 0, 0] [%[[N]], %[[R]], %[[S]], %[[F]]] // CHECK: %[[CONV_TILE:.+]] = linalg.conv_2d_nhwc_hwcf // CHECK-SAME: dilation = dense<[4, 5]> : tensor<2xi64>, strides = dense<[2, 3]> : tensor<2xi64> // CHECK-SAME: ins(%[[INPUT_TILE]], %[[FILTER_TILE]] : // CHECK-SAME: outs(%[[INIT_TILE]] : // CHECK: tensor.insert_slice %[[CONV_TILE]] into %[[INIT2]] // CHECK-SAME: [0, 0, 0, 0] [%[[N]], %[[R]], %[[S]], %[[F]]]