| /* |
| * 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. |
| */ |
| |
| /*! |
| * \file dnnl_ops-inl.h |
| * \brief |
| * \author Da Zheng |
| */ |
| |
| #ifndef MXNET_OPERATOR_NN_DNNL_DNNL_OPS_INL_H_ |
| #define MXNET_OPERATOR_NN_DNNL_DNNL_OPS_INL_H_ |
| |
| #include <dmlc/logging.h> |
| #include <dmlc/optional.h> |
| #include <mxnet/base.h> |
| #include <mxnet/io.h> |
| #include <mxnet/ndarray.h> |
| #include <mxnet/operator.h> |
| #include <mxnet/operator_util.h> |
| |
| #include <vector> |
| |
| #if MXNET_USE_ONEDNN == 1 |
| #include <dnnl.hpp> |
| |
| namespace mxnet { |
| namespace op { |
| |
| /* For fully connected. */ |
| void DNNLFCForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& in_data, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& out_data); |
| void DNNLFCBackward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs); |
| |
| /* For convolution. */ |
| void DNNLConvolutionForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& in_data, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& out_data); |
| void DNNLConvolutionBackward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs); |
| |
| /* For deconvolution */ |
| void DNNLDeconvolutionForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& in_data, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& out_data); |
| void DNNLDeconvolutionBackward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs); |
| |
| /* For activation */ |
| void DNNLActivationForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const NDArray& in_data, |
| const OpReqType& req, |
| const NDArray& out_data); |
| void DNNLActivationBackward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs); |
| |
| void DNNLLeakyReluForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const NDArray& in_data, |
| const OpReqType& req, |
| const NDArray& out_data); |
| void DNNLLeakyReluBackward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs); |
| |
| /* For softmax */ |
| void DNNLSoftmaxForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const NDArray& in_data, |
| const OpReqType& req, |
| const NDArray& out_data); |
| void DNNLSoftmaxBackward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& in_data, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& out_data); |
| |
| /* For log_softmax */ |
| void DNNLLogSoftmaxForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const NDArray& in_data, |
| const OpReqType& req, |
| const NDArray& out_data); |
| void DNNLLogSoftmaxBackward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& in_data, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& out_data); |
| |
| /* For softmax_output */ |
| void DNNLSoftmaxOutputForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& in_data, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& out_data); |
| |
| /* For sum */ |
| void DNNLSumForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs); |
| |
| /* For copy */ |
| void DNNLCopy(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const NDArray& in_data, |
| const OpReqType& req, |
| const NDArray& out_data); |
| |
| /* For concat */ |
| void DNNLConcatForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& in_data, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& out_data); |
| void DNNLConcatBackward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs); |
| |
| /* For batch dot */ |
| template <bool subgraph> |
| void DNNLBatchDotForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs); |
| |
| /* For layer normalization */ |
| void DNNLLayerNormForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs); |
| void DNNLLayerNormBackward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs); |
| |
| void DNNLSum(const dnnl::memory& arr1, const dnnl::memory& arr2, const dnnl::memory& out); |
| |
| void DNNLStackForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& in_data, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& out_data); |
| |
| template <class ParamType> |
| void DNNLTransposeForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const NDArray& data, |
| const OpReqType& req, |
| const NDArray& output); |
| |
| void DNNLReshapeForward(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const NDArray& input, |
| const OpReqType& req, |
| const NDArray& output); |
| } // namespace op |
| } // namespace mxnet |
| |
| #endif // MXNET_USE_ONEDNN == 1 |
| #endif // MXNET_OPERATOR_NN_DNNL_DNNL_OPS_INL_H_ |
