batch_norm_grad_kernel.h

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed 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.

#pragma once

#include <string>
#include "paddle/phi/core/dense_tensor.h"

namespace phi {

template <typename T, typename Context>
void BatchNormGradRawKernel(const Context& dev_ctx,
                            const DenseTensor& x,
                            const DenseTensor& scale,
                            const DenseTensor& bias,
                            paddle::optional<const DenseTensor&> mean,
                            paddle::optional<const DenseTensor&> variance,
                            const DenseTensor& saved_mean,
                            const DenseTensor& saved_variance,
                            paddle::optional<const DenseTensor&> reserve_space,
                            const DenseTensor& y_grad,
                            float momentum,
                            float epsilon,
                            const std::string& data_layout,
                            bool is_test,
                            bool use_global_stats,
                            bool trainable_statistics,
                            bool fuse_with_relu,
                            bool is_inplace,
                            DenseTensor* x_grad,
                            DenseTensor* scale_grad,
                            DenseTensor* bias_grad);

template <typename T, typename Context>
void BatchNormGradKernel(const Context& dev_ctx,
                         const DenseTensor& x,
                         const DenseTensor& scale,
                         const DenseTensor& bias,
                         paddle::optional<const DenseTensor&> mean,
                         paddle::optional<const DenseTensor&> variance,
                         const DenseTensor& saved_mean,
                         const DenseTensor& saved_variance,
                         paddle::optional<const DenseTensor&> reserve_space,
                         const DenseTensor& y_grad,
                         float momentum,
                         float epsilon,
                         const std::string& data_layout,
                         bool is_test,
                         bool use_global_stats,
                         bool trainable_statistics,
                         bool fuse_with_relu,
                         DenseTensor* x_grad,
                         DenseTensor* scale_grad,
                         DenseTensor* bias_grad);

template <typename T, typename Context>
void BatchNormDoubleGradKernel(const Context& dev_ctx,
                               const DenseTensor& x_grad_grad,
                               const DenseTensor& scale_grad_grad,
                               const DenseTensor& bias_grad_grad,
                               const DenseTensor& y_grad,
                               const DenseTensor& x,
                               const DenseTensor& scale,
                               const DenseTensor& saved_mean,
                               const DenseTensor& saved_variance,
                               paddle::optional<const DenseTensor&> mean,
                               paddle::optional<const DenseTensor&> variance,
                               float momentum,
                               float epsilon,
                               const std::string& data_layout,
                               bool is_test,
                               bool use_global_stats,
                               bool trainable_statistics,
                               bool fuse_with_relu,
                               DenseTensor* x_grad,
                               DenseTensor* scale_grad,
                               DenseTensor* y_grad_grad);

}  // namespace phi