1 /* Copyright 2015 The TensorFlow Authors. All Rights Reserved. 2 3 Licensed under the Apache License, Version 2.0 (the "License"); 4 you may not use this file except in compliance with the License. 5 You may obtain a copy of the License at 6 7 http://www.apache.org/licenses/LICENSE-2.0 8 9 Unless required by applicable law or agreed to in writing, software 10 distributed under the License is distributed on an "AS IS" BASIS, 11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 See the License for the specific language governing permissions and 13 limitations under the License. 14 ==============================================================================*/ 15 16 #ifndef TENSORFLOW_KERNELS_SCATTER_FUNCTOR_GPU_CU_H_ 17 #define TENSORFLOW_KERNELS_SCATTER_FUNCTOR_GPU_CU_H_ 18 19 #if GOOGLE_CUDA 20 21 #define EIGEN_USE_GPU 22 23 #include "tensorflow/core/framework/tensor_types.h" 24 #include "tensorflow/core/kernels/scatter_functor.h" 25 #include "tensorflow/core/platform/types.h" 26 #include "tensorflow/core/util/cuda_kernel_helper.h" 27 28 namespace tensorflow { 29 30 typedef Eigen::GpuDevice GPUDevice; 31 32 template <typename T, typename Index, scatter_op::UpdateOp op> 33 __global__ void ScatterOpCustomKernel(T* params, const T* updates, 34 const Index* indices, 35 Index first_dim_size, Index updates_size, 36 Index indices_size) { 37 Index update_block = updates_size / indices_size; 38 CUDA_1D_KERNEL_LOOP(i, updates_size) { 39 int indices_i = i / update_block; 40 int updates_i = i; 41 int param_first_index = indices[indices_i]; 42 if (!(param_first_index >= 0 && param_first_index < first_dim_size)) { 43 // Ignore indices that are out of range. 44 continue; 45 } 46 int params_i = param_first_index * update_block + (i % update_block); 47 switch (op) { 48 case scatter_op::UpdateOp::ASSIGN: { 49 params[params_i] = ldg(updates + updates_i); 50 break; 51 } 52 case scatter_op::UpdateOp::ADD: { 53 CudaAtomicAdd(params + params_i, ldg(updates + updates_i)); 54 break; 55 } 56 case scatter_op::UpdateOp::SUB: { 57 CudaAtomicSub(params + params_i, ldg(updates + updates_i)); 58 break; 59 } 60 case scatter_op::UpdateOp::MUL: { 61 CudaAtomicMul(params + params_i, ldg(updates + updates_i)); 62 break; 63 } 64 case scatter_op::UpdateOp::DIV: { 65 CudaAtomicDiv(params + params_i, ldg(updates + updates_i)); 66 break; 67 } 68 } 69 } 70 } 71 72 namespace functor { 73 // Specialization for a GPU device. 74 template <typename T, typename Index, scatter_op::UpdateOp op> 75 struct ScatterFunctor<GPUDevice, T, Index, op> { 76 Index operator()(OpKernelContext* c, const GPUDevice& d, 77 typename TTypes<T>::Matrix params, 78 typename TTypes<T>::ConstMatrix updates, 79 typename TTypes<Index>::ConstFlat indices) { 80 // TODO(b/31801742): Implement indices range check. The hardest part is 81 // with returning a value after the range check, as we do not want to do 82 // device to host memcpy during a stream. 83 const Index first_dim_size = params.dimension(0); 84 const Index indices_size = indices.size(); 85 const Index updates_size = updates.size(); 86 CudaLaunchConfig config = GetCudaLaunchConfig(updates_size, d); 87 ScatterOpCustomKernel<T, Index, op> 88 <<<config.block_count, config.thread_per_block, 0, d.stream()>>>( 89 params.data(), updates.data(), indices.data(), first_dim_size, 90 updates_size, indices_size); 91 return -1; 92 } 93 }; 94 95 } // namespace functor 96 } // namespace tensorflow 97 98 #endif // GOOGLE_CUDA 99 100 #endif // TENSORFLOW_KERNELS_SCATTER_FUNCTOR_GPU_CU_H_ 101
