| // RUN: mlir-opt %s -split-input-file -linalg-generalize-named-ops | FileCheck %s |
| |
| func @generalize_conv(%input : memref<1x225x225x3xf32>, %filter: memref<3x3x3x32xf32>, %output: memref<1x112x112x32xf32>) { |
| linalg.conv(%filter, %input, %output) {dilations = [2, 3], strides = [4, 5]} : memref<3x3x3x32xf32>, memref<1x225x225x3xf32>, memref<1x112x112x32xf32> |
| return |
| } |
| |
| // CHECK: #[[FILTER_MAP:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d5, d6, d4, d3)> |
| // CHECK: #[[INPUT_MAP:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1 * 4 + d5 * 2, d2 * 5 + d6 * 3, d4)> |
| // CHECK: #[[OUTPUT_MAP:.+]] = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3)> |
| |
| // CHECK: func @generalize_conv |
| // CHECK-SAME: %[[INPUT:.+]]: memref<1x225x225x3xf32> |
| // CHECK-SAME: %[[FILTER:.+]]: memref<3x3x3x32xf32> |
| // CHECK-SAME: %[[OUTPUT:.+]]: memref<1x112x112x32xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK-SAME: indexing_maps = [#[[FILTER_MAP]], #[[INPUT_MAP]], #[[OUTPUT_MAP]]] |
| // CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction", "window", "window"] |
| // CHECK-SAME: ins(%[[FILTER]], %[[INPUT]] |
| // CHECK-SAME: outs(%[[OUTPUT]] |
| |
| // CHECK: ^{{.*}}(%[[FILTER_ARG:.+]]: f32, %[[INPUT_ARG:.+]]: f32, %[[OUTPUT_ARG:.+]]: f32) |
| // CHECK: %[[MUL:.+]] = mulf %[[FILTER_ARG]], %[[INPUT_ARG]] |
| // CHECK: %[[ADD:.+]] = addf %[[MUL]], %[[OUTPUT_ARG]] |
| // CHECK: linalg.yield %[[ADD]] |
| |
| // ----- |
| |
| func @generalize_matmul_buffer(%A : memref<16x8xf32>, %B: memref<8x32xf32>, %C: memref<16x32xf32>) { |
| linalg.matmul ins(%A, %B: memref<16x8xf32>, memref<8x32xf32>) |
| outs(%C: memref<16x32xf32>) |
| return |
| } |
| |
| |
| // CHECK: #[[A_MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d2)> |
| // CHECK: #[[B_MAP:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)> |
| // CHECK: #[[C_MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)> |
| |
| // CHECK: func @generalize_matmul_buffer |
| // CHECK-SAME: %[[A:.+]]: memref<16x8xf32> |
| // CHECK-SAME: %[[B:.+]]: memref<8x32xf32> |
| // CHECK-SAME: %[[C:.+]]: memref<16x32xf32> |
| |
| // CHECK: linalg.generic |
| // CHECK-SAME: indexing_maps = [#[[A_MAP]], #[[B_MAP]], #[[C_MAP]]] |
| // CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"] |
| // CHECK-SAME: ins(%[[A]], %[[B]] |
| // CHECK-SAME: outs(%[[C]] |
| |
| // CHECK: ^{{.*}}(%[[A_ARG:.+]]: f32, %[[B_ARG:.+]]: f32, %[[C_ARG:.+]]: f32) |
| // CHECK: %[[MUL:.+]] = mulf %[[A_ARG]], %[[B_ARG]] : f32 |
| // CHECK: %[[ADD:.+]] = addf %[[C_ARG]], %[[MUL]] : f32 |
| // CHECK: linalg.yield %[[ADD]] : f32 |
| |
| // ----- |
| |
| func @generalize_matmul_tensor(%A : tensor<16x8xf32>, %B: tensor<8x32xf32>, %C: tensor<16x32xf32>) -> tensor<16x32xf32> { |
| %0 = linalg.matmul ins(%A, %B: tensor<16x8xf32>, tensor<8x32xf32>) |
| outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32> |
| return %0: tensor<16x32xf32> |
| } |
| |
| // CHECK: func @generalize_matmul_tensor |
| |
| // CHECK: linalg.generic |
| // CHECK-SAME: ins(%{{.+}}, %{{.+}} : tensor<16x8xf32>, tensor<8x32xf32>) |
| // CHECK-SAME: outs(%{{.+}} : tensor<16x32xf32>) |
| |
| // CHECK: ^{{.*}}(%[[A_ARG:.+]]: f32, %[[B_ARG:.+]]: f32, %[[C_ARG:.+]]: f32) |
| // CHECK-NEXT: %[[MUL:.+]] = mulf %[[A_ARG]], %[[B_ARG]] : f32 |
| // CHECK-NEXT: %[[ADD:.+]] = addf %[[C_ARG]], %[[MUL]] : f32 |
| // CHECK-NEXT: linalg.yield %[[ADD]] : f32 |
| // CHECK-NEXT: -> tensor<16x32xf32> |
