/
layer_norm_grad_kernel.cu
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/
layer_norm_grad_kernel.cu
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// 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.
#include "paddle/phi/kernels/layer_norm_grad_kernel.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/layer_norm_impl.cu.h"
#include "paddle/phi/kernels/funcs/layer_norm_util.h"
namespace phi {
template <typename T, typename Context>
void LayerNormGradKernel(const Context &dev_ctx,
const DenseTensor &x,
const paddle::optional<DenseTensor> &scale_opt,
const paddle::optional<DenseTensor> &bias_opt,
const DenseTensor &mean,
const DenseTensor &variance,
const DenseTensor &out_grad,
float epsilon,
int begin_norm_axis,
DenseTensor *x_grad,
DenseTensor *scale_grad,
DenseTensor *bias_grad) {
using U = phi::funcs::LayerNormParamType<T>;
// d_x, d_scale, d_bias may be nullptr
auto *d_x = x_grad;
auto *d_scale = scale_grad;
auto *d_bias = bias_grad;
auto *scale = scale_opt.get_ptr();
auto *bias = bias_opt.get_ptr();
auto *d_y = &out_grad;
const auto &x_dims = x.dims();
auto matrix_dim = common::flatten_to_2d(x_dims, begin_norm_axis);
int64_t batch_size = static_cast<int64_t>(matrix_dim[0]);
int64_t feature_size = static_cast<int64_t>(matrix_dim[1]);
auto *x_data = x.data<T>();
auto *d_y_data = d_y->data<T>();
auto *mean_data = mean.data<U>();
auto *var_data = variance.data<U>();
auto *d_x_data = (d_x == nullptr ? nullptr : dev_ctx.template Alloc<T>(d_x));
auto x_dtype = x.dtype();
phi::DataType scale_bias_dtype;
if (scale != nullptr) {
scale_bias_dtype = scale->dtype();
} else {
// FIXME(zengjinle): do not find a better way to get the right
// data type of the d_scale and d_bias if scale == nullptr.
if (bias != nullptr) {
scale_bias_dtype = bias->dtype();
} else {
scale_bias_dtype = x_dtype;
}
}
#define PADDLE_LAUNCH_LAYERNORM_BWD(ScaleBiasT, IsScaleBiasSameDTypeWithX) \
do { \
auto *scale_data = \
(scale == nullptr ? nullptr : scale->data<ScaleBiasT>()); \
auto *d_scale_data = \
(d_scale == nullptr ? nullptr \
: dev_ctx.template Alloc<ScaleBiasT>(d_scale)); \
auto *d_bias_data = \
(d_bias == nullptr ? nullptr \
: dev_ctx.template Alloc<ScaleBiasT>(d_bias)); \
auto *d_x_data = \
(d_x == nullptr ? nullptr : dev_ctx.template Alloc<T>(d_x)); \
phi::funcs::LayerNormBackward<T, U, IsScaleBiasSameDTypeWithX>( \
x_data, \
d_y_data, \
scale_data, \
mean_data, \
var_data, \
d_x_data, \
d_scale_data, \
d_bias_data, \
epsilon, \
batch_size, \
feature_size, \
dev_ctx); \
} while (0)
if (scale_bias_dtype == x_dtype) {
PADDLE_LAUNCH_LAYERNORM_BWD(T, true);
} else {
PADDLE_LAUNCH_LAYERNORM_BWD(U, false);
}
#undef PADDLE_LAUNCH_LAYERNORM_BWD
}
} // namespace phi
#ifdef PADDLE_WITH_HIP
// MIOPEN do not support double
PD_REGISTER_KERNEL(layer_norm_grad,
GPU,
ALL_LAYOUT,
phi::LayerNormGradKernel,
float,
phi::dtype::float16) {
if (kernel_key.dtype() == phi::DataType::FLOAT16) {
kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32);
kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32);
}
}
#elif CUDNN_VERSION_MIN(8, 1, 0)
PD_REGISTER_KERNEL(layer_norm_grad,
GPU,
ALL_LAYOUT,
phi::LayerNormGradKernel,
float,
double,
phi::dtype::float16,
phi::dtype::bfloat16) {
if (kernel_key.dtype() == phi::DataType::FLOAT16) {
kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32);
kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32);
}
}
#else
PD_REGISTER_KERNEL(layer_norm_grad,
GPU,
ALL_LAYOUT,
phi::LayerNormGradKernel,
float,
double,
phi::dtype::float16) {
if (kernel_key.dtype() == phi::DataType::FLOAT16) {
kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32);
kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32);
}
}
#endif