| /* |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| #include "singa/singa_config.h" |
| #ifdef USE_CUDNN |
| #include "./cudnn_activation.h" |
| #include <cudnn.h> |
| |
| #include "./cudnn_utils.h" |
| #include "singa/core/common.h" |
| #include "singa/utils/logging.h" |
| |
| namespace singa { |
| RegisterLayerClass(cudnn_relu, CudnnActivation); |
| RegisterLayerClass(cudnn_sigmoid, CudnnActivation); |
| RegisterLayerClass(cudnn_tanh, CudnnActivation); |
| CudnnActivation::~CudnnActivation() { |
| if (acti_desc_ != nullptr) |
| CUDNN_CHECK(cudnnDestroyActivationDescriptor(acti_desc_)); |
| if (desc_ != nullptr) CUDNN_CHECK(cudnnDestroyTensorDescriptor(desc_)); |
| } |
| |
| void CudnnActivation::InitCudnn(size_t size, DataType dtype) { |
| if (!has_init_cudnn_) { |
| CUDNN_CHECK(cudnnCreateTensorDescriptor(&desc_)); |
| CUDNN_CHECK(cudnnCreateActivationDescriptor(&acti_desc_)); |
| |
| if (mode_ == "sigmoid") |
| cudnn_mode_ = CUDNN_ACTIVATION_SIGMOID; |
| else if (mode_ == "tanh") |
| cudnn_mode_ = CUDNN_ACTIVATION_TANH; |
| else if (mode_ == "relu") |
| cudnn_mode_ = CUDNN_ACTIVATION_RELU; |
| else |
| LOG(FATAL) << "Unkown activation: " << mode_; |
| |
| CUDNN_CHECK(cudnnSetActivationDescriptor( |
| acti_desc_, cudnn_mode_, CUDNN_PROPAGATE_NAN, 0.0f)); |
| } |
| |
| CUDNN_CHECK(cudnnSetTensor4dDescriptor( |
| desc_, CUDNN_TENSOR_NCHW, GetCudnnDataType(dtype), 1, 1, 1, size)); |
| |
| has_init_cudnn_ = true; |
| } |
| |
| const Tensor CudnnActivation::Forward(int flag, const Tensor& input) { |
| CHECK(buf_.empty()); |
| auto size = input.Size(); |
| DataType dtype = input.data_type(); |
| if (!has_init_cudnn_) { |
| InitCudnn(size, dtype); |
| } else { |
| int n, c, h, w, s; |
| cudnnDataType_t type; |
| CUDNN_CHECK(cudnnGetTensor4dDescriptor(desc_, |
| &type, &n, &c, &h, &w, &s, &s, &s, &s)); |
| if (size != static_cast<size_t>(w)) |
| InitCudnn(size, dtype); |
| } |
| |
| Tensor output; |
| output.ResetLike(input); |
| output.device()->Exec([input, output, this](Context* ctx) { |
| Block* inblock = input.block(), * outblock = output.block(); |
| float alpha = 1.0f, beta = 0.0f; |
| CUDNN_CHECK(cudnnActivationForward( |
| ctx->cudnn_handle, this->acti_desc_, &alpha, this->desc_, |
| inblock->data(), &beta, this->desc_, outblock->mutable_data())); |
| }, {input.block()}, {output.block()}, "cudnnActivationForward"); |
| if (flag & kTrain) { |
| if (cudnn_mode_ == CUDNN_ACTIVATION_SIGMOID || |
| cudnn_mode_ == CUDNN_ACTIVATION_TANH) { |
| buf_.push(output); |
| } else if (cudnn_mode_ == CUDNN_ACTIVATION_RELU) { |
| buf_.push(input); |
| } |
| } |
| return output; |
| } |
| |
| const std::pair<Tensor, vector<Tensor>> CudnnActivation::Backward( |
| int flag, const Tensor& grad) { |
| vector<Tensor> param_grad; |
| Tensor dx; |
| CHECK(!buf_.empty()); |
| // inout means either used as input or output, only one is valid for one type |
| // of activation |
| Tensor inout = buf_.top(); |
| buf_.pop(); |
| dx.ResetLike(grad); |
| dx.device()->Exec([dx, grad, inout, this](Context* ctx) { |
| Block* dyblock = grad.block(), * dxblock = dx.block(), |
| * yblock = inout.block(), * xblock = inout.block(); |
| float alpha = 1.0f, beta = 0.0f; |
| CUDNN_CHECK(cudnnActivationBackward( |
| ctx->cudnn_handle, this->acti_desc_, &alpha, this->desc_, |
| yblock->data(), this->desc_, dyblock->data(), this->desc_, |
| xblock->data(), &beta, this->desc_, dxblock->mutable_data())); |
| }, {grad.block(), inout.block()}, {dx.block()}, "cudnnActivationBackward"); |
| return std::make_pair(dx, param_grad); |
| } |
| } // namespace singa |
| #endif // USE_CUDNN |