1// RUN: mlir-opt %s -split-input-file -linalg-generalize-named-ops | FileCheck %s 2 3func @generalize_conv(%input : memref<1x225x225x3xf32>, %filter: memref<3x3x3x32xf32>, %output: memref<1x112x112x32xf32>) { 4 linalg.conv(%filter, %input, %output) {dilations = [2, 3], strides = [4, 5]} : memref<3x3x3x32xf32>, memref<1x225x225x3xf32>, memref<1x112x112x32xf32> 5 return 6} 7 8// CHECK: #[[FILTER_MAP:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d5, d6, d4, d3)> 9// CHECK: #[[INPUT_MAP:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1 * 4 + d5 * 2, d2 * 5 + d6 * 3, d4)> 10// CHECK: #[[OUTPUT_MAP:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3)> 11 12// CHECK: func @generalize_conv 13// CHECK-SAME: %[[INPUT:.+]]: memref<1x225x225x3xf32> 14// CHECK-SAME: %[[FILTER:.+]]: memref<3x3x3x32xf32> 15// CHECK-SAME: %[[OUTPUT:.+]]: memref<1x112x112x32xf32> 16 17// CHECK: linalg.generic 18// CHECK-SAME: indexing_maps = [#[[FILTER_MAP]], #[[INPUT_MAP]], #[[OUTPUT_MAP]]] 19// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction", "window", "window"] 20// CHECK-SAME: ins(%[[FILTER]], %[[INPUT]] 21// CHECK-SAME: outs(%[[OUTPUT]] 22 23// CHECK: ^{{.*}}(%[[FILTER_ARG:.+]]: f32, %[[INPUT_ARG:.+]]: f32, %[[OUTPUT_ARG:.+]]: f32) 24// CHECK: %[[MUL:.+]] = mulf %[[FILTER_ARG]], %[[INPUT_ARG]] 25// CHECK: %[[ADD:.+]] = addf %[[MUL]], %[[OUTPUT_ARG]] 26// CHECK: linalg.yield %[[ADD]] 27 28// ----- 29 30func @generalize_matmul_buffer(%A : memref<16x8xf32>, %B: memref<8x32xf32>, %C: memref<16x32xf32>) { 31 linalg.matmul ins(%A, %B: memref<16x8xf32>, memref<8x32xf32>) outs(%C: memref<16x32xf32>) 32 return 33} 34 35 36// CHECK: #[[A_MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d2)> 37// CHECK: #[[B_MAP:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)> 38// CHECK: #[[C_MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)> 39 40// CHECK: func @generalize_matmul_buffer 41// CHECK-SAME: %[[A:.+]]: memref<16x8xf32> 42// CHECK-SAME: %[[B:.+]]: memref<8x32xf32> 43// CHECK-SAME: %[[C:.+]]: memref<16x32xf32> 44 45// CHECK: linalg.generic 46// CHECK-SAME: indexing_maps = [#[[A_MAP]], #[[B_MAP]], #[[C_MAP]]] 47// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"] 48// CHECK-SAME: ins(%[[A]], %[[B]] 49// CHECK-SAME: outs(%[[C]] 50 51// CHECK: ^{{.*}}(%[[A_ARG:.+]]: f32, %[[B_ARG:.+]]: f32, %[[C_ARG:.+]]: f32) 52// CHECK: %[[MUL:.+]] = mulf %[[A_ARG]], %[[B_ARG]] : f32 53// CHECK: %[[ADD:.+]] = addf %[[C_ARG]], %[[MUL]] : f32 54// CHECK: linalg.yield %[[ADD]] : f32 55 56// ----- 57 58func @generalize_matmul_tensor(%A : tensor<16x8xf32>, %B: tensor<8x32xf32>, %C: tensor<16x32xf32>) -> tensor<16x32xf32> { 59 %0 = linalg.matmul ins(%A, %B: tensor<16x8xf32>, tensor<8x32xf32>) init(%C: tensor<16x32xf32>) -> tensor<16x32xf32> 60 return %0: tensor<16x32xf32> 61} 62 63// CHECK: func @generalize_matmul_tensor 64 65// CHECK: linalg.generic 66// CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<16x8xf32>, tensor<8x32xf32>) 67// CHECK-SAME: init(%{{.+}} : tensor<16x32xf32>) 68 69// CHECK: ^{{.*}}(%[[A_ARG:.+]]: f32, %[[B_ARG:.+]]: f32, %[[C_ARG:.+]]: f32) 70// CHECK-NEXT: %[[MUL:.+]] = mulf %[[A_ARG]], %[[B_ARG]] : f32 71// CHECK-NEXT: %[[ADD:.+]] = addf %[[C_ARG]], %[[MUL]] : f32 72// CHECK-NEXT: linalg.yield %[[ADD]] : f32 73// CHECK-NEXT: -> tensor<16x32xf32> 74