wehub-resource-sync
116 lines
4.7 KiB
C++
116 lines
4.7 KiB
C++
/* Copyright (c) 2022 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/phi/kernels/sparse/batch_norm_grad_kernel.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/batch_norm_grad_kernel.h"
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#include "paddle/phi/kernels/empty_kernel.h"
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#include "paddle/phi/kernels/sparse/empty_kernel.h"
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namespace phi::sparse {
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template <typename T, typename Context>
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void BatchNormCooGradKernel(const Context& dev_ctx,
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const SparseCooTensor& x,
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const DenseTensor& scale,
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const DenseTensor& bias,
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const optional<DenseTensor>& mean,
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const optional<DenseTensor>& variance,
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const DenseTensor& saved_mean,
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const DenseTensor& saved_variance,
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const optional<DenseTensor>& reserve_space,
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const SparseCooTensor& y_grad,
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float momentum,
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float epsilon,
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const std::string& data_layout,
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bool is_test,
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bool use_global_stats,
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bool trainable_statistics,
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SparseCooTensor* x_grad,
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DenseTensor* scale_grad,
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DenseTensor* bias_grad) {
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EmptyLikeCooKernel<T, Context>(dev_ctx, x, x_grad);
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// TODO(umiswing): add check for parameter freezing automatically
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PADDLE_ENFORCE_EQ((scale_grad == nullptr && bias_grad == nullptr) ||
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(scale_grad != nullptr && bias_grad != nullptr),
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true,
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common::errors::InvalidArgument(
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"Weight and bias's stop_gradient of BatchNorm must be "
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"True or False at the same time."));
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if (scale_grad && bias_grad) {
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*scale_grad = EmptyLike<T, Context>(dev_ctx, scale);
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*bias_grad = EmptyLike<T, Context>(dev_ctx, bias);
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}
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phi::BatchNormGradKernel<T, Context>(dev_ctx,
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x.values(),
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scale,
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bias,
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mean,
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variance,
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saved_mean,
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saved_variance,
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reserve_space,
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y_grad.values(),
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momentum,
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epsilon,
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data_layout,
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is_test,
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use_global_stats,
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trainable_statistics,
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x_grad->mutable_values(),
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scale_grad,
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bias_grad);
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}
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} // namespace phi::sparse
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PD_REGISTER_KERNEL(batch_norm_coo_grad,
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CPU,
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ALL_LAYOUT,
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phi::sparse::BatchNormCooGradKernel,
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float,
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double) {
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kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
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}
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#if defined(PADDLE_WITH_HIP)
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PD_REGISTER_KERNEL(batch_norm_coo_grad,
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GPU,
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ALL_LAYOUT,
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phi::sparse::BatchNormCooGradKernel,
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float,
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phi::float16) {
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kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
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}
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#endif
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#if defined(PADDLE_WITH_CUDA)
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PD_REGISTER_KERNEL(batch_norm_coo_grad,
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GPU,
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ALL_LAYOUT,
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phi::sparse::BatchNormCooGradKernel,
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float,
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double,
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phi::float16) {
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kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
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if (kernel_key.dtype() == phi::DataType::FLOAT16) {
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kernel->OutputAt(0).SetDataType(phi::DataType::FLOAT32); // x_grad
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kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32); // scale_grad
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kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32); // bias_grad
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}
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}
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#endif
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