wehub-resource-sync
116 lines
4.1 KiB
C++
116 lines
4.1 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/batch_norm_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_dnn.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/empty_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void BatchNormInferKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& mean,
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const DenseTensor& variance,
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const DenseTensor& scale,
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const DenseTensor& bias,
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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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DenseTensor* y,
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DenseTensor* mean_out,
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DenseTensor* variance_out) {
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// Since saved_mean and saved_variance are used regardless of whether
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// they are in test mode, temporary variables need to be created here
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// to be compatible
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auto saved_mean = EmptyLike<T, Context>(dev_ctx, *mean_out);
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auto saved_variance = EmptyLike<T, Context>(dev_ctx, *variance_out);
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BatchNormKernel<T, Context>(dev_ctx,
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x,
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mean,
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variance,
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scale,
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bias,
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/*is_test=*/true,
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momentum,
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epsilon,
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data_layout,
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/*use_global_stats=*/false,
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/*trainable_statistics=*/false,
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y,
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mean_out,
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variance_out,
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&saved_mean,
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&saved_variance,
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/*reserve_space=*/nullptr);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(batch_norm_infer,
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CPU,
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ALL_LAYOUT,
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phi::BatchNormInferKernel,
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float,
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double) {}
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#ifdef PADDLE_WITH_CUDA
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#if CUDNN_VERSION_MIN(8, 1, 0)
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PD_REGISTER_KERNEL(batch_norm_infer,
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GPU,
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ALL_LAYOUT,
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phi::BatchNormInferKernel,
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float,
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double,
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phi::bfloat16,
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phi::float16) {
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if (kernel_key.dtype() == phi::DataType::FLOAT16 ||
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kernel_key.dtype() == phi::DataType::BFLOAT16) {
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kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32);
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kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32);
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}
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}
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#else
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PD_REGISTER_KERNEL(batch_norm_infer,
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GPU,
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ALL_LAYOUT,
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phi::BatchNormInferKernel,
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float,
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double,
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phi::float16) {
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if (kernel_key.dtype() == phi::DataType::FLOAT16) {
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kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32);
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kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32);
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}
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}
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#endif
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#endif
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#ifdef PADDLE_WITH_HIP
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PD_REGISTER_KERNEL(batch_norm_infer,
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GPU,
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ALL_LAYOUT,
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phi::BatchNormInferKernel,
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float,
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phi::float16) {}
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#endif
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#ifdef PADDLE_WITH_XPU
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PD_REGISTER_KERNEL(batch_norm_infer,
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XPU,
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ALL_LAYOUT,
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phi::BatchNormInferKernel,
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float,
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phi::float16) {}
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#endif
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