| /* |
| * 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. |
| */ |
| |
| |
| /*! |
| * Copyright (c) 2015 by Contributors |
| * \file activation.cc |
| * \brief activation op |
| * \author Bing Xu, Da Zheng |
| */ |
| #include "./activation-inl.h" |
| #include "../mshadow_op.h" |
| #include "../tensor/elemwise_unary_op.h" |
| #if MXNET_USE_MKLDNN == 1 |
| #include "./mkldnn/mkldnn_base-inl.h" |
| #include "./mkldnn/mkldnn_ops-inl.h" |
| #endif // MXNET_USE_MKLDNN == 1 |
| #include "../operator_common.h" |
| #include "../../common/utils.h" |
| |
| namespace mxnet { |
| namespace op { |
| |
| namespace activation { |
| |
| int GradNumInputs(int act_type) { |
| // check activation.cu \sa ActivationGradCompute |
| switch (act_type) { |
| case kReLU: |
| return 2; |
| case kSoftReLU: |
| case kSoftSign: |
| case kTanh: |
| case kSigmoid: |
| return 3; |
| default: |
| CHECK(false) << "missing activation type"; |
| } |
| // unreachable |
| return -1; |
| } |
| |
| } // namespace activation |
| |
| DMLC_REGISTER_PARAMETER(ActivationParam); |
| |
| // This will determine the order of the inputs for backward computation. |
| struct ActivationGrad { |
| const char *op_name; |
| std::vector<nnvm::NodeEntry> operator()(const nnvm::NodePtr& n, |
| const std::vector<nnvm::NodeEntry>& ograds) const { |
| // ograds, output... |
| std::vector<nnvm::NodeEntry> heads(ograds.begin(), ograds.end()); |
| heads.emplace_back(nnvm::NodeEntry{n, activation::kOut, 0}); |
| |
| const NodeAttrs& attrs = n->attrs; |
| using namespace activation; |
| int act_type = dmlc::get<ActivationParam>(attrs.parsed).act_type; |
| // for ReLU, no need to pass input data. This enables inplace optimization during the |
| // forward pass. |
| // check activation.cu \sa ActivationGradCompute |
| switch (act_type) { |
| case kReLU: |
| break; |
| case kSoftReLU: |
| case kSoftSign: |
| case kTanh: |
| case kSigmoid: |
| heads.push_back(n->inputs[activation::kData]); |
| break; |
| default: |
| CHECK(false) << "missing activation type"; |
| } |
| return MakeGradNode(op_name, n, heads, n->attrs.dict); |
| } |
| }; |
| |
| #if MXNET_USE_MKLDNN == 1 |
| static void ActivationComputeExCPU(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs) { |
| const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed); |
| CHECK_EQ(inputs.size(), 1U); |
| CHECK_EQ(outputs.size(), 1U); |
| if (SupportMKLDNNAct(param, inputs[0])) { |
| MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs); |
| MKLDNNActivationForward(attrs, ctx, inputs[0], req[0], outputs[0]); |
| MKLDNN_OPCHECK_RUN(ActivationCompute<cpu>, attrs, ctx, inputs, req, outputs); |
| return; |
| } |
| FallBackCompute(ActivationComputeImpl<cpu>, attrs, ctx, inputs, req, outputs); |
| } |
| |
| void ActivationGradComputeExCPU(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs) { |
| const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed); |
| CHECK_EQ(inputs.size(), activation::GradNumInputs(param.act_type)); |
| if (SupportMKLDNNAct(param, inputs[0])) { |
| MKLDNN_OPCHECK_INIT(true, outputs.size(), inputs, outputs); |
| // XXX: for y = relu(x), y is passed as "in_data" to Backward() |
| const bool relu = param.act_type == activation::kReLU; |
| MKLDNNActivationBackward(attrs, ctx, inputs.at(0), relu ? inputs.at(1) : inputs.at(2), req[0], |
| outputs[0]); |
| MKLDNN_OPCHECK_RUN(ActivationGradCompute<cpu>, attrs, ctx, inputs, req, outputs); |
| return; |
| } |
| FallBackCompute(ActivationGradComputeImpl<cpu>, attrs, ctx, inputs, req, outputs); |
| } |
| |
| inline static bool ActivationStorageType(const nnvm::NodeAttrs& attrs, |
| const int dev_mask, |
| DispatchMode* dispatch_mode, |
| std::vector<int> *in_attrs, |
| std::vector<int> *out_attrs) { |
| CHECK_EQ(in_attrs->size(), 1); |
| CHECK_EQ(out_attrs->size(), 1); |
| const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed); |
| return MKLDNNStorageType(attrs, dev_mask, SupportMKLDNNAct(param), |
| dispatch_mode, in_attrs, out_attrs); |
| } |
| |
| inline static bool BackwardActStorageType(const nnvm::NodeAttrs& attrs, |
| const int dev_mask, |
| DispatchMode* dispatch_mode, |
| std::vector<int> *in_attrs, |
| std::vector<int> *out_attrs) { |
| const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed); |
| CHECK_EQ(in_attrs->size(), activation::GradNumInputs(param.act_type)); |
| return MKLDNNStorageType(attrs, dev_mask, SupportMKLDNNAct(param), |
| dispatch_mode, in_attrs, out_attrs); |
| } |
| #endif // MXNET_USE_MKLDNN == 1 |
| |
| |
| MXNET_OPERATOR_REGISTER_UNARY(Activation) |
| .describe(R"code(Applies an activation function element-wise to the input. |
| |
| The following activation functions are supported: |
| |
| - `relu`: Rectified Linear Unit, :math:`y = max(x, 0)` |
| - `sigmoid`: :math:`y = \frac{1}{1 + exp(-x)}` |
| - `tanh`: Hyperbolic tangent, :math:`y = \frac{exp(x) - exp(-x)}{exp(x) + exp(-x)}` |
| - `softrelu`: Soft ReLU, or SoftPlus, :math:`y = log(1 + exp(x))` |
| - `softsign`: :math:`y = \frac{x}{1 + abs(x)}` |
| |
| )code" ADD_FILELINE) |
| .set_attr_parser(ParamParser<ActivationParam>) |
| #if MXNET_USE_MKLDNN == 1 |
| .set_attr<FInferStorageType>("FInferStorageType", ActivationStorageType) |
| #endif |
| .set_attr<nnvm::FListOutputNames>("FListOutputNames", |
| [](const NodeAttrs& attrs) { |
| return std::vector<std::string>{"output"}; |
| }) |
| .set_attr<FCompute>("FCompute<cpu>", ActivationCompute<cpu>) |
| #if MXNET_USE_MKLDNN == 1 |
| .set_attr<bool>("TIsMKLDNN", true) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", ActivationComputeExCPU) |
| #endif |
| .set_attr<nnvm::FGradient>("FGradient", ActivationGrad{"_backward_Activation"}) |
| .add_arguments(ActivationParam::__FIELDS__()); |
| |
| NNVM_REGISTER_OP(_backward_Activation) |
| .set_num_inputs([](const nnvm::NodeAttrs& attrs) { |
| const int act_type = dmlc::get<ActivationParam>(attrs.parsed).act_type; |
| return activation::GradNumInputs(act_type); |
| }) |
| .set_num_outputs(1) |
| .set_attr<nnvm::TIsBackward>("TIsBackward", true) |
| #if MXNET_USE_MKLDNN == 1 |
| .set_attr<FInferStorageType>("FInferStorageType", BackwardActStorageType) |
| #endif |
| .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<-1, 1>) |
| .set_attr<nnvm::FInferType>("FInferType", ElemwiseType<-1, 1>) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", [](const NodeAttrs& attrs){ |
| return std::vector<std::pair<int, int> >{{0, 0}}; |
| }) |
| #if MXNET_USE_MKLDNN == 1 |
| .set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) { |
| return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; |
| }) |
| #endif |
| .set_attr_parser(ParamParser<ActivationParam>) |
| #if MXNET_USE_MKLDNN == 1 |
| .set_attr<bool>("TIsMKLDNN", true) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", ActivationGradComputeExCPU) |
| #endif |
| .set_attr<FCompute>("FCompute<cpu>", ActivationGradCompute<cpu>); |
| |
| } // namespace op |
| } // namespace mxnet |
