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93 lines
3.6 KiB
93 lines
3.6 KiB
/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/batch_norm_op.h"
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#include "paddle/fluid/operators/inplace_abn_op.h"
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#include "paddle/fluid/operators/sync_batch_norm_op.cu.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class InplaceABNKernel
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: public paddle::operators::SyncBatchNormKernel<DeviceContext, T>,
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public paddle::operators::BatchNormKernel<DeviceContext, T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* y = ctx.Output<Tensor>("Y");
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auto* x = ctx.Input<Tensor>("X");
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PADDLE_ENFORCE_EQ(x, y, platform::errors::InvalidArgument(
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"X and Y not inplaced in inplace mode"));
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auto activation =
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GetInplaceABNActivationType(ctx.Attr<std::string>("activation"));
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auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
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if (ctx.Attr<bool>("use_sync_bn")) {
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SyncBatchNormKernel<DeviceContext, T>::Compute(ctx);
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} else {
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BatchNormKernel<DeviceContext, T>::Compute(ctx);
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}
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auto cur_y = EigenVector<T>::Flatten(*y);
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InplaceABNActivation<DeviceContext, T> functor;
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functor.Compute(ctx, activation, place, cur_y, cur_y);
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}
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};
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// Deriving the Gradient for the Backward Pass of Batch Normalization
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// https://kevinzakka.github.io/2016/09/14/batch_normalization/
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template <typename DeviceContext, typename T>
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class InplaceABNGradKernel
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: public paddle::operators::SyncBatchNormGradKernel<DeviceContext, T>,
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public paddle::operators::BatchNormGradKernel<DeviceContext, T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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const auto* y = ctx.Input<Tensor>("Y");
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auto* d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
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auto* d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
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PADDLE_ENFORCE_EQ(d_x, d_y,
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platform::errors::InvalidArgument(
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"X@GRAD and Y@GRAD not inplaced in inplace mode"));
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auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
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auto activation =
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GetInplaceABNActivationType(ctx.Attr<std::string>("activation"));
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auto py = *y;
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auto pd_y = *d_y;
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auto cur_y = EigenVector<T>::Flatten(py);
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auto cur_dy = EigenVector<T>::Flatten(pd_y);
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InplaceABNActivation<DeviceContext, T> functor;
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functor.GradCompute(ctx, activation, place, cur_y, cur_y, cur_dy, cur_dy);
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if (ctx.Attr<bool>("use_sync_bn")) {
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SyncBatchNormGradKernel<DeviceContext, T>::Compute(ctx);
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} else {
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BatchNormGradKernel<DeviceContext, T>::Compute(ctx);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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namespace plat = paddle::platform;
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REGISTER_OP_CUDA_KERNEL(inplace_abn,
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ops::InplaceABNKernel<plat::CUDADeviceContext, float>,
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ops::InplaceABNKernel<plat::CUDADeviceContext, double>);
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REGISTER_OP_CUDA_KERNEL(
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inplace_abn_grad, ops::InplaceABNGradKernel<plat::CUDADeviceContext, float>,
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ops::InplaceABNGradKernel<plat::CUDADeviceContext, double>);
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