| /* |
| * 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" |
| #include "./cudnn_softmax.h" |
| #ifdef USE_CUDNN |
| #include <cudnn.h> |
| #include "./cudnn_utils.h" |
| #include "singa/utils/logging.h" |
| namespace singa { |
| |
| RegisterLayerClass(cudnn_softmax, CudnnSoftmax); |
| CudnnSoftmax::~CudnnSoftmax() { |
| if (desc_ != nullptr) CUDNN_CHECK(cudnnDestroyTensorDescriptor(desc_)); |
| } |
| |
| void CudnnSoftmax::Setup(const Shape& in_sample, const LayerConf &conf) { |
| Softmax::Setup(in_sample, conf); |
| SoftmaxConf sft_conf = conf.softmax_conf(); |
| std::string algorithm = sft_conf.algorithm(); |
| CHECK(algorithm == "accurate" || algorithm == "fast" || algorithm == "log") |
| << "CudnnSoftmax only supports three algorithm preferences: " |
| << "accurate, fast and log."; |
| if (algorithm == "accurate") |
| algorithm_ = CUDNN_SOFTMAX_ACCURATE; |
| else if (algorithm == "fast") |
| algorithm_ = CUDNN_SOFTMAX_FAST; |
| else algorithm_ = CUDNN_SOFTMAX_LOG; |
| } |
| |
| void CudnnSoftmax::InitCudnn(Shape shape, DataType dtype) { |
| CHECK(!has_init_cudnn_); |
| CUDNN_CHECK(cudnnCreateTensorDescriptor(&desc_)); |
| |
| CHECK_LE(shape.size(), 2u) |
| << "Tensor shape should range from 1 to 2D;" |
| << "otherwise, add flatten layer to transform"; |
| if (shape.size() == 1u) |
| CUDNN_CHECK(cudnnSetTensor4dDescriptor( desc_, |
| CUDNN_TENSOR_NCHW, GetCudnnDataType(dtype), 1, shape[0], 1, 1)); |
| else |
| CUDNN_CHECK(cudnnSetTensor4dDescriptor( desc_, CUDNN_TENSOR_NCHW, |
| GetCudnnDataType(dtype), shape[0], shape[1], 1, 1)); |
| has_init_cudnn_ = true; |
| } |
| |
| const Tensor CudnnSoftmax::Forward(int flag, const Tensor& input) { |
| CHECK(buf_.empty()); |
| auto shape = input.shape(); |
| DataType dtype = input.data_type(); |
| if (!has_init_cudnn_) { |
| InitCudnn(shape, 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; |
| cudnnSoftmaxForward(ctx->cudnn_handle, this->algorithm_, |
| CUDNN_SOFTMAX_MODE_INSTANCE, |
| &alpha, this->desc_, inblock->data(), &beta, |
| this->desc_, outblock->mutable_data()); |
| }, {input.block()}, {output.block()}); |
| if (flag & kTrain) buf_.push(output); |
| return output; |
| } |
| |
| const std::pair<Tensor, vector<Tensor>> CudnnSoftmax::Backward( |
| int flag, const Tensor& grad) { |
| vector<Tensor> param_grad; |
| CHECK(!buf_.empty()); |
| Tensor dx, output = buf_.top(); |
| buf_.pop(); |
| dx.ResetLike(grad); |
| dx.device()->Exec([dx, grad, output, this](Context* ctx) { |
| Block* dyblock = grad.block(), * dxblock = dx.block(), |
| * yblock = output.block(); |
| float alpha = 1.0f, beta = 0.0f; |
| cudnnSoftmaxBackward(ctx->cudnn_handle, this->algorithm_, |
| CUDNN_SOFTMAX_MODE_INSTANCE, |
| &alpha, this->desc_, yblock->data(), this->desc_, |
| dyblock->data(), &beta, this->desc_, |
| dxblock->mutable_data()); |
| }, {grad.block(), output.block()}, {dx.block()}); |
| return std::make_pair(dx, param_grad); |
| } |
| } // namespace singa |
| #endif // USE_CUDNN |