| // RUN: mlir-opt -linalg-bufferize -canonicalize -cse -split-input-file %s | FileCheck %s |
| |
| #map0 = affine_map<(d0) -> (d0)> |
| |
| // In-depth checking of a basic case, this is testing |
| // - bufferization.to_memref / bufferization.to_tensor materializations are |
| // properly inserted |
| // - payload is correctly carried over |
| // - affine maps are correctly carried over |
| // Later tests will not check all these details. |
| |
| // CHECK: #map = affine_map<(d0) -> (d0)> |
| // CHECK-LABEL: func @basic( |
| // CHECK-SAME: %[[TENSOR:.*]]: tensor<4xf32>) -> tensor<4xf32> { |
| // CHECK-DAG: %[[MEMREF:.*]] = bufferization.to_memref %[[TENSOR]] : memref<4xf32> |
| // CHECK-DAG: %[[RESULT_MEMREF:.*]] = memref.alloc() {{.*}} : memref<4xf32> |
| // CHECK: linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} |
| // CHECK-SAME: ins(%[[MEMREF]] : memref<4xf32>) |
| // CHECK-SAME: outs(%[[RESULT_MEMREF]] : memref<4xf32>) { |
| // CHECK: ^bb0(%[[RESULT1:.*]]: f32, %[[UNUSED:.*]]: f32): |
| // CHECK: %[[DIM1:.*]] = math.exp %[[RESULT1]] : f32 |
| // CHECK: linalg.yield %[[DIM1]] : f32 |
| // CHECK: } |
| // CHECK: %[[RESULT:.*]] = bufferization.to_tensor %[[RESULT_MEMREF]] : memref<4xf32> |
| // CHECK: return %[[RESULT]] : tensor<4xf32> |
| func.func @basic(%arg0: tensor<4xf32>) -> tensor<4xf32> { |
| %0 = linalg.generic { |
| indexing_maps = [#map0, #map0], |
| iterator_types = ["parallel"] |
| } ins(%arg0 : tensor<4xf32>) |
| outs(%arg0 : tensor<4xf32>) { |
| ^bb0(%gen_arg1: f32, %out: f32): |
| %tmp1 = math.exp %gen_arg1 : f32 |
| linalg.yield %tmp1 : f32 |
| } -> tensor<4xf32> |
| return %0 : tensor<4xf32> |
| } |
| |
| |
| // ----- |
| |
| #map0 = affine_map<(d0) -> (d0)> |
| |
| // Same as above but with tensor.empty op. |
| |
| // CHECK: #map = affine_map<(d0) -> (d0)> |
| // CHECK-LABEL: func @empty_tensor( |
| // CHECK-SAME: %[[IN:.*]]: tensor<?xf32>, %[[SIZE:.*]]: index) |
| // CHECK-DAG: %[[MEMREF:.*]] = bufferization.to_memref %[[IN]] : memref<?xf32> |
| // CHECK-DAG: %[[OUT_BUF:.*]] = memref.alloc(%[[SIZE]]) {{.*}} : memref<?xf32> |
| // CHECK: linalg.generic |
| // CHECK-SAME: ins(%[[MEMREF]] : memref<?xf32>) |
| // CHECK-SAME: outs(%[[OUT_BUF]] : memref<?xf32>) { |
| func.func @empty_tensor(%in : tensor<?xf32>, %size: index) -> tensor<?xf32> { |
| %init = tensor.empty(%size) : tensor<?xf32> |
| %0 = linalg.generic { |
| indexing_maps = [#map0, #map0], |
| iterator_types = ["parallel"] |
| } ins(%in : tensor<?xf32>) |
| outs(%init : tensor<?xf32>) { |
| ^bb0(%gen_arg1: f32, %out: f32): |
| %tmp1 = math.exp %gen_arg1 : f32 |
| linalg.yield %tmp1 : f32 |
| } -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| } |
| |
| |
| // ----- |
| |
| #map0 = affine_map<(d0) -> (d0)> |
| |
| // CHECK-LABEL: func @multiple_results |
| // CHECK: %[[RESULT0:.*]] = memref.alloc() {{.*}} : memref<4xf32> |
| // CHECK: %[[RESULT1:.*]] = memref.alloc() {{.*}} : memref<4xf32> |
| // CHECK: linalg.generic |
| // CHECK-SAME: ins(%{{.*}} : memref<4xf32>) |
| // CHECK-SAME: outs(%[[RESULT0]], %[[RESULT1]] : memref<4xf32>, memref<4xf32>) |
| // CHECK-NEXT: ^bb0(%{{.*}}: f32, %{{.*}}: f32, %{{.*}}: f32): |
| func.func @multiple_results(%arg0: tensor<4xf32>) -> (tensor<4xf32>, tensor<4xf32>) { |
| %0, %1 = linalg.generic { |
| indexing_maps = [#map0, #map0, #map0], |
| iterator_types = ["parallel"] |
| } ins(%arg0 : tensor<4xf32>) |
| outs (%arg0, %arg0 : tensor<4xf32>, tensor<4xf32>) { |
| ^bb0(%gen_arg1: f32, %out1: f32, %out2: f32): |
| %tmp1 = math.exp %gen_arg1 : f32 |
| linalg.yield %tmp1, %tmp1 : f32, f32 |
| } -> (tensor<4xf32>, tensor<4xf32>) |
| return %0, %1 : tensor<4xf32>, tensor<4xf32> |
| } |
| |
| // ----- |
| |
| #map_2d = affine_map<(d0, d1) -> (d0, d1)> |
| |
| // Check that the allocs properly consider the different shapes of the output |
| // operands. The permuted indexing maps translate to different output shapes. |
| |
| // CHECK-LABEL: func @dynamic_results( |
| // CHECK-SAME: %[[ARG:.*]]: tensor<?x?xf32> |
| // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[DIM0:.*]] = tensor.dim %[[ARG]], %[[C0]] : tensor<?x?xf32> |
| // CHECK-DAG: %[[DIM1:.*]] = tensor.dim %[[ARG]], %[[C1]] : tensor<?x?xf32> |
| // CHECK-DAG: %[[RESULT0:.*]] = memref.alloc(%[[DIM0]], %[[DIM1]]) {{.*}} : memref<?x?xf32> |
| // CHECK-DAG: %[[RESULT1:.*]] = memref.alloc(%[[DIM0]], %[[DIM1]]) {{.*}} : memref<?x?xf32> |
| // CHECK-DAG: %[[MEMREF_ARG:.*]] = bufferization.to_memref %[[ARG]] : memref<?x?xf32> |
| // CHECK: linalg.generic |
| // CHECK-SAME: ins(%[[MEMREF_ARG]] : memref<?x?xf32>) |
| // CHECK-SAME: outs(%[[RESULT0]], %[[RESULT1]] : memref<?x?xf32>, memref<?x?xf32>) |
| func.func @dynamic_results(%arg0: tensor<?x?xf32>) |
| -> (tensor<?x?xf32>, tensor<?x?xf32>) { |
| %0, %1 = linalg.generic { |
| indexing_maps = [#map_2d, #map_2d, #map_2d], |
| iterator_types = ["parallel", "parallel"] |
| } ins(%arg0 : tensor<?x?xf32>) |
| outs (%arg0, %arg0 : tensor<?x?xf32>, tensor<?x?xf32>) { |
| ^bb0(%gen_arg1: f32, %out1: f32, %out2: f32): |
| %tmp1 = math.exp %gen_arg1 : f32 |
| linalg.yield %tmp1, %tmp1 : f32, f32 |
| } -> (tensor<?x?xf32>, tensor<?x?xf32>) |
| return %0, %1 : tensor<?x?xf32>, tensor<?x?xf32> |
| } |
| |
| // ----- |
| |
| #accesses = [ |
| affine_map<(i, j, k) -> (j, i, k)>, |
| affine_map<(i, j, k) -> (i, j)> |
| ] |
| |
| #trait = { |
| indexing_maps = #accesses, |
| iterator_types = ["parallel", "parallel", "reduction"] |
| } |
| |
| // Check the bufferization of init tensors. |
| |
| // CHECK-LABEL: func @generic_with_init_tensor( |
| // CHECK-SAME: %[[ARG0_TENSOR:.*]]: tensor<2x3x4xvector<3x4xi4>>, |
| // CHECK-SAME: %[[ARG1_TENSOR:.*]]: tensor<3x2xf32>) -> tensor<3x2xf32> { |
| // CHECK-DAG: %[[INIT_BUFFER:.*]] = memref.alloc() {{.*}} : memref<3x2xf32> |
| // CHECK-DAG: %[[ARG0_MEMREF:.*]] = bufferization.to_memref %[[ARG0_TENSOR]] : memref<2x3x4xvector<3x4xi4>> |
| // CHECK-DAG: %[[ARG1_MEMREF:.*]] = bufferization.to_memref %[[ARG1_TENSOR]] : memref<3x2xf32> |
| // CHECK: memref.copy %[[ARG1_MEMREF]], %[[INIT_BUFFER]] : memref<3x2xf32> to memref<3x2xf32> |
| // CHECK: linalg.generic |
| // CHECK-SAME: ins(%[[ARG0_MEMREF]] : memref<2x3x4xvector<3x4xi4>>) |
| // CHECK-SAME: outs(%[[INIT_BUFFER]] : memref<3x2xf32>) { |
| func.func @generic_with_init_tensor(%arg0: tensor<2x3x4xvector<3x4xi4>>, |
| %arg1: tensor<3x2xf32>) -> (tensor<3x2xf32>) { |
| |
| %0 = linalg.generic #trait |
| ins(%arg0 : tensor<2x3x4xvector<3x4xi4>>) |
| outs(%arg1 : tensor<3x2xf32>) { |
| ^bb(%v0: vector<3x4xi4>, %v1: f32) : |
| linalg.yield %v1 : f32 |
| } -> tensor<3x2xf32> |
| |
| return %0 : tensor<3x2xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @bufferize_fill( |
| // CHECK-SAME: %[[IN:.*]]: tensor<?xf32> |
| func.func @bufferize_fill(%arg0: tensor<?xf32>) -> tensor<?xf32> { |
| %c0 = arith.constant 0.0 : f32 |
| // CHECK: %[[ALLOC:.*]] = memref.alloc |
| // CHECK: linalg.fill ins(%cst : f32) outs(%[[ALLOC]] : memref<?xf32>) |
| // CHECK: %[[TENSOR:.*]] = bufferization.to_tensor %[[ALLOC]] : memref<?xf32> |
| // CHECK: return %[[TENSOR]] |
| %0 = linalg.fill ins(%c0 : f32) outs(%arg0 : tensor<?xf32>) -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func @bufferize_dot |
| func.func @bufferize_dot(%in: tensor<4xf32>, %out: tensor<f32>) -> tensor<f32> { |
| %dot = linalg.dot ins(%in, %in : tensor<4xf32>, tensor<4xf32>) |
| outs(%out : tensor<f32>) -> tensor<f32> |
| return %dot : tensor<f32> |
| // CHECK: %[[ALLOC:.*]] = memref.alloc |
| // TODO: The copy is not necessary. |
| // CHECK: memref.copy {{.*}}, %[[ALLOC]] |
| // CHECK: linalg.dot ins(%{{.*}}, %{{.*}} : memref<4xf32>, memref<4xf32>) |
| // CHECK-SAME: outs(%[[ALLOC:.*]] : memref<f32>) |
| // CHECK: %[[OUT_TENSOR:.*]] = bufferization.to_tensor %[[ALLOC]] : memref<f32> |
| // CHECK: return %[[OUT_TENSOR]] |
| } |
| |
| // ----- |
| |
| // This is a regression test. The linalg-bufferize pass should ignore all func |
| // dialect ops. |
| |
| // CHECK-LABEL: func private @csum(tensor<6xi64>) -> tensor<6xi64> |
| func.func private @csum(%arg0: tensor<6xi64>) -> tensor<6xi64> |
| |
| // CHECK: func public @main(%[[arg0:.*]]: tensor<2x3xi1>) |
| // CHECK: %[[collapse:.*]] = tensor.collapse_shape %[[arg0]] |
| // CHECK: %[[collapse_m:.*]] = bufferization.to_memref %[[collapse]] |
| // CHECK: %[[alloc:.*]] = memref.alloc() |
| // CHECK: linalg.generic {{.*}} ins(%[[collapse_m]] : memref<6xi1>) outs(%[[alloc]] : memref<6xi64>) |
| // CHECK: %[[generic_t:.*]] = bufferization.to_tensor %[[alloc]] |
| // CHECK: %[[call:.*]] = call @csum(%[[generic_t]]) |
| // CHECK: return %[[call]] |
| func.func public @main(%arg0: tensor<2x3xi1>) -> tensor<6xi64> { |
| %0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<2x3xi1> into tensor<6xi1> |
| %1 = tensor.empty() : tensor<6xi64> |
| %2 = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> (d0)>], iterator_types = ["parallel"]} ins(%0 : tensor<6xi1>) outs(%1 : tensor<6xi64>) { |
| ^bb0(%arg1: i1, %arg2: i64): |
| %4 = arith.extui %arg1 : i1 to i64 |
| linalg.yield %4 : i64 |
| } -> tensor<6xi64> |
| %3 = func.call @csum(%2) : (tensor<6xi64>) -> tensor<6xi64> |
| return %3 : tensor<6xi64> |
| } |
