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/*
* 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.cu
* \brief
* \author Bing Xu
*/
#include "./activation-inl.h"
#include "../mshadow_op.h"
#if MXNET_USE_CUDNN == 1
#include "./cudnn/cudnn_activation-inl.h"
#endif
namespace mxnet {
namespace op {
#if MXNET_USE_CUDNN == 1
template<typename DType>
static CuDNNActivationOp<DType> &get_cudnn_op(const ActivationParam& param) {
#if DMLC_CXX11_THREAD_LOCAL
static thread_local CuDNNActivationOp<DType> cudnn_op;
#else
static MX_THREAD_LOCAL CuDNNActivationOp<DType> cudnn_op;
#endif
cudnn_op.Init(param);
return cudnn_op;
}
template<>
void ActivationCompute<gpu>(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
CHECK_EQ(inputs.size(), 1U);
CHECK_EQ(outputs.size(), 1U);
const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);
const int act_type = param.act_type;
// SoftReLU and kSoftSign are both not supported by CUDNN yet
if (act_type == activation::kSoftReLU) {
ActivationForward<gpu, mshadow_op::softrelu, mshadow_op::softrelu_grad>(ctx,
inputs[0], req[0], outputs[0]);
} else if (act_type == activation::kSoftSign) {
ActivationForward<gpu, mshadow_op::softsign, mshadow_op::softsign_grad>(ctx,
inputs[0], req[0], outputs[0]);
} else {
MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
get_cudnn_op<DType>(param).Forward(ctx, inputs[0], req[0], outputs[0]);
});
}
}
template<>
void ActivationGradCompute<gpu>(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType>& req,
const std::vector<TBlob>& outputs) {
const ActivationParam& param = nnvm::get<ActivationParam>(attrs.parsed);
const int act_type = param.act_type;
CHECK_EQ(inputs.size(), activation::GradNumInputs(act_type));
CHECK_EQ(outputs.size(), 1U);
CHECK_EQ(req.size(), 1U);
// both SoftReLU and SoftSign not supported by CUDNN yet
if (act_type == activation::kSoftReLU) {
ActivationBackward<gpu, mshadow_op::softrelu, mshadow_op::softrelu_grad>(
ctx, inputs.at(0), inputs.at(1), req[0], outputs[0]);
} else if (act_type == activation::kSoftSign) {
ActivationBackward<gpu, mshadow_op::softsign, mshadow_op::softsign_grad>(
ctx, inputs.at(0), inputs.at(2), req[0], outputs[0]);
} else if (act_type == activation::kReLU) {
MSHADOW_REAL_TYPE_SWITCH(inputs.at(0).type_flag_, DType, {
// XXX: for y = relu(x), y is passed as "in_data" to Backward()
get_cudnn_op<DType>(param).Backward(ctx, inputs.at(0), inputs.at(1),
inputs.at(1), req[0], outputs[0]);
});
} else {
MSHADOW_REAL_TYPE_SWITCH(inputs.at(0).type_flag_, DType, {
get_cudnn_op<DType>(param).Backward(ctx, inputs.at(0), inputs.at(2),
inputs.at(1), req[0], outputs[0]);
});
}
}
#endif
NNVM_REGISTER_OP(Activation)
.set_attr<FCompute>("FCompute<gpu>", ActivationCompute<gpu>);
NNVM_REGISTER_OP(_backward_Activation)
.set_attr<FCompute>("FCompute<gpu>", ActivationGradCompute<gpu>);
} // namespace op
} // namespace mxnet