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未验证 提交 871e3329 编写于 作者: P pangyoki 提交者： GitHub
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[PHI] move layer_norm/layer_norm_grad xpu kernel to phi (#45524) 

* move layer_norm xpu kernel to phi, test=kunlun

* fix, test=kunlun
上级 56eedf27
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Showing with 146 addition and 140 deletion
paddle/fluid/operators/layer_norm_op_xpu.cc 已删除 100644 → 0
浏览文件 @ 56eedf27
/* 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. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device/device_wrapper.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using DDim = framework::DDim;
template <typename DeviceContext, typename T>
class LayerNormXPUKernel : public framework::OpKernel<T> {
using XPUType = typename XPUTypeTrait<T>::Type;
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
const auto epsilon = ctx.Attr<float>("epsilon");
const auto* x = ctx.Input<Tensor>("X");
const auto& x_dims = x->dims();
auto matrix_dim = phi::flatten_to_2d(x_dims, begin_norm_axis);
int left = static_cast<int>(matrix_dim[0]);
int right = static_cast<int>(matrix_dim[1]);
const auto* scale = ctx.Input<Tensor>("Scale");
const auto* bias = ctx.Input<Tensor>("Bias");
auto* y = ctx.Output<Tensor>("Y");
auto* mean = ctx.Output<Tensor>("Mean");
auto* variance = ctx.Output<Tensor>("Variance");
const auto* x_data = x->data<T>();
const auto* scale_data =
(scale == nullptr ? nullptr : scale->data<float>());
const auto* bias_data = (bias == nullptr ? nullptr : bias->data<float>());
auto* y_data = y->mutable_data<T>(ctx.GetPlace());
auto* mean_data = mean->mutable_data<float>(ctx.GetPlace());
auto* variance_data = variance->mutable_data<float>(ctx.GetPlace());
auto& dev_ctx = ctx.template device_context<DeviceContext>();
// int layer_norm(Context* ctx, const T* x, T* y, int m, int n, float eps,
// const float* scale, const float* bias, float* mean, float* var);
int r = xpu::layer_norm(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x_data),
reinterpret_cast<XPUType*>(y_data),
left,
right,
epsilon,
scale_data,
bias_data,
mean_data,
variance_data);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "layer_norm");
}
};
template <typename DeviceContext, typename T>
class LayerNormGradXPUKernel : public framework::OpKernel<T> {
using XPUType = typename XPUTypeTrait<T>::Type;
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
const auto epsilon = ctx.Attr<float>("epsilon");
const auto* x = ctx.Input<Tensor>("X");
const auto& x_dims = x->dims();
auto matrix_dim = phi::flatten_to_2d(x_dims, begin_norm_axis);
int left = static_cast<int>(matrix_dim[0]);
int right = static_cast<int>(matrix_dim[1]);
const auto* mean = ctx.Input<Tensor>("Mean");
const auto* variance = ctx.Input<Tensor>("Variance");
const auto* scale = ctx.Input<Tensor>("Scale");
const auto* dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* dscale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto* dbias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
const auto* x_data = x->data<T>();
const auto* dy_data = dy->data<T>();
const auto* mean_data = mean->data<float>();
const auto* variance_data = variance->data<float>();
const auto* scale_data =
(scale == nullptr ? nullptr : scale->data<float>());
auto* dscale_data =
(dscale == nullptr ? nullptr
: dscale->mutable_data<float>(ctx.GetPlace()));
auto* dbias_data =
(dbias == nullptr ? nullptr
: dbias->mutable_data<float>(ctx.GetPlace()));
auto* dx_data =
(dx == nullptr ? nullptr : dx->mutable_data<T>(ctx.GetPlace()));
auto& dev_ctx = ctx.template device_context<DeviceContext>();
// int layer_norm_grad(Context* ctx, const T* x, const T* dy, T* dx, int m,
// int n, float eps, const float* scale, const float* mean, const float*
// var, float* dscale, float* dbias);
int r = xpu::layer_norm_grad(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(x_data),
reinterpret_cast<const XPUType*>(dy_data),
reinterpret_cast<XPUType*>(dx_data),
left,
right,
epsilon,
scale_data,
mean_data,
variance_data,
dscale_data,
dbias_data);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "layer_norm_grad");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(
layer_norm,
ops::LayerNormXPUKernel<paddle::platform::XPUDeviceContext, float>,
ops::LayerNormXPUKernel<paddle::platform::XPUDeviceContext,
paddle::platform::float16>);
REGISTER_OP_XPU_KERNEL(
layer_norm_grad,
ops::LayerNormGradXPUKernel<paddle::platform::XPUDeviceContext, float>,
ops::LayerNormGradXPUKernel<paddle::platform::XPUDeviceContext,
paddle::platform::float16>);
#endif // PADDLE_WITH_XPU
paddle/phi/kernels/xpu/layer_norm_grad_kernel.cc 0 → 100644
浏览文件 @ 871e3329
// 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/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
template <typename T, typename Context>
void LayerNormGradKernel(const Context& ctx,
const DenseTensor& x,
const paddle::optional<DenseTensor>& scale,
const paddle::optional<DenseTensor>& bias,
const DenseTensor& mean,
const DenseTensor& variance,
const DenseTensor& out_grad,
float epsilon,
int begin_norm_axis,
bool is_test,
DenseTensor* x_grad,
DenseTensor* scale_grad,
DenseTensor* bias_grad) {
using XPUType = typename XPUTypeTrait<T>::Type;
const auto& x_dims = x.dims();
auto matrix_dim = phi::flatten_to_2d(x_dims, begin_norm_axis);
int left = static_cast<int>(matrix_dim[0]);
int right = static_cast<int>(matrix_dim[1]);
const auto* x_data = x.data<T>();
const auto* out_grad_data = out_grad.data<T>();
const auto* mean_data = mean.data<float>();
const auto* variance_data = variance.data<float>();
const auto* scale_data =
(scale.get_ptr() == nullptr ? nullptr : scale.get_ptr()->data<float>());
auto* scale_grad_data =
(scale_grad == nullptr ? nullptr : ctx.template Alloc<float>(scale_grad));
auto* bias_grad_data =
(bias_grad == nullptr ? nullptr : ctx.template Alloc<float>(bias_grad));
auto* x_grad_data =
(x_grad == nullptr ? nullptr : ctx.template Alloc<T>(x_grad));
// int layer_norm_grad(Context* ctx, const T* x, const T* dy, T* dx, int m,
// int n, float eps, const float* scale, const float* mean, const float*
// var, float* dscale, float* dbias);
int r = xpu::layer_norm_grad(ctx.x_context(),
reinterpret_cast<const XPUType*>(x_data),
reinterpret_cast<const XPUType*>(out_grad_data),
reinterpret_cast<XPUType*>(x_grad_data),
left,
right,
epsilon,
scale_data,
mean_data,
variance_data,
scale_grad_data,
bias_grad_data);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "layer_norm_grad");
}
} // namespace phi
PD_REGISTER_KERNEL(layer_norm_grad,
XPU,
ALL_LAYOUT,
phi::LayerNormGradKernel,
float,
phi::dtype::float16) {}
paddle/phi/kernels/xpu/layer_norm_kernel.cc 0 → 100644
浏览文件 @ 871e3329
// 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_kernel.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
template <typename T, typename Context>
void LayerNormKernel(const Context& ctx,
const DenseTensor& x,
const paddle::optional<DenseTensor>& scale,
const paddle::optional<DenseTensor>& bias,
float epsilon,
int begin_norm_axis,
bool is_test,
DenseTensor* out,
DenseTensor* mean,
DenseTensor* variance) {
using XPUType = typename XPUTypeTrait<T>::Type;
const auto& x_dims = x.dims();
auto matrix_dim = phi::flatten_to_2d(x_dims, begin_norm_axis);
int left = static_cast<int>(matrix_dim[0]);
int right = static_cast<int>(matrix_dim[1]);
const auto* x_data = x.data<T>();
const auto* scale_data =
(scale.get_ptr() == nullptr ? nullptr : scale.get_ptr()->data<float>());
const auto* bias_data =
(bias.get_ptr() == nullptr ? nullptr : bias.get_ptr()->data<float>());
auto* out_data = ctx.template Alloc<T>(out);
auto* mean_data = ctx.template Alloc<float>(mean);
auto* variance_data = ctx.template Alloc<float>(variance);
// int layer_norm(Context* ctx, const T* x, T* y, int m, int n, float eps,
// const float* scale, const float* bias, float* mean, float* var);
int r = xpu::layer_norm(ctx.x_context(),
reinterpret_cast<const XPUType*>(x_data),
reinterpret_cast<XPUType*>(out_data),
left,
right,
epsilon,
scale_data,
bias_data,
mean_data,
variance_data);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "layer_norm");
}
} // namespace phi
PD_REGISTER_KERNEL(layer_norm,
XPU,
ALL_LAYOUT,
phi::LayerNormKernel,
float,
phi::dtype::float16) {}
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