1// RUN: mlir-opt -test-linalg-elementwise-fusion-patterns=control-fusion-by-expansion %s -split-input-file | FileCheck %s 2 3func.func @control_producer_reshape_fusion(%arg0 : tensor<?x?x?xf32>, %arg1 : tensor<?xf32>) -> tensor<?x?xf32> { 4 %c0 = arith.constant 0 : index 5 %c1 = arith.constant 1 : index 6 %0 = tensor.collapse_shape %arg0 [[0, 1], [2]] : tensor<?x?x?xf32> into tensor<?x?xf32> 7 %d0 = tensor.dim %0, %c0 : tensor<?x?xf32> 8 %d1 = tensor.dim %0, %c1 : tensor<?x?xf32> 9 %init = tensor.empty(%d0, %d1) : tensor<?x?xf32> 10 %1 = linalg.generic { 11 indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0, d1)>], 12 iterator_types = ["parallel", "parallel"]} 13 ins(%0, %arg1 : tensor<?x?xf32>, tensor<?xf32>) 14 outs(%init : tensor<?x?xf32>) { 15 ^bb0(%arg2 : f32, %arg3:f32, %arg4 : f32): 16 %2 = arith.addf %arg2, %arg3 : f32 17 linalg.yield %2 : f32 18 } -> tensor<?x?xf32> 19 return %1 : tensor<?x?xf32> 20} 21// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1) -> (d0, d1)> 22// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1) -> (d1)> 23// CHECK: func @control_producer_reshape_fusion 24// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?x?xf32> 25// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?xf32> 26// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index 27// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index 28// CHECK: %[[RESHAPE:.+]] = tensor.collapse_shape %[[ARG0]] 29// CHECK-SAME: {{\[}}[0, 1], [2]{{\]}} : tensor<?x?x?xf32> into tensor<?x?xf32> 30// CHECK: %[[RESULT:.+]] = linalg.generic 31// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP0]]] 32// CHECK-SAME: ins(%[[RESHAPE]], %[[ARG1]] : tensor<?x?xf32>, tensor<?xf32>) 33// CHECK: return %[[RESULT]] 34 35// ----- 36 37func.func @control_consumer_reshape_fusion(%arg0 : tensor<1x?x?xf32>, %arg1 : tensor<1x?x?xf32>) -> tensor<1x?x?xf32> { 38 %c1 = arith.constant 1 : index 39 %c2 = arith.constant 2 : index 40 %cst = arith.constant 0.0 : f32 41 %d0 = tensor.dim %arg0, %c1 : tensor<1x?x?xf32> 42 %d1 = tensor.dim %arg1, %c2 : tensor<1x?x?xf32> 43 %init = tensor.empty(%d0, %d1) : tensor<?x?xf32> 44 %fill = linalg.generic { 45 indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>], 46 iterator_types = ["parallel", "parallel"]} 47 outs(%init : tensor<?x?xf32>) { 48 ^bb0(%arg2: f32): 49 linalg.yield %cst : f32 50 } -> tensor<?x?xf32> 51 %0 = tensor.expand_shape %fill [[0, 1], [2]] output_shape [1, %d0, %d1] : tensor<?x?xf32> into tensor<1x?x?xf32> 52 %1 = linalg.batch_matmul ins(%arg0, %arg1 : tensor<1x?x?xf32>, tensor<1x?x?xf32>) 53 outs(%0 : tensor<1x?x?xf32>) -> tensor<1x?x?xf32> 54 return %1 : tensor<1x?x?xf32> 55} 56// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2) 57// CHECK: func @control_consumer_reshape_fusion 58// CHECK: %[[FILL:.+]] = linalg.generic 59// CHECK-SAME: indexing_maps = [#[[MAP]]] 60// CHECK-SAME: outs(%{{.+}} : tensor<1x?x?xf32>) 61// CHECK: linalg.batch_matmul 62// CHECK-SAME: outs(%[[FILL]] : tensor<1x?x?xf32>) 63