未验证 提交 d7807806 编写于 作者: W WangZhen 提交者: GitHub

Move XPU momentum to phi (#45565)

* Move XPU momentum to phi, test=kunlun

* Fix mu type, test=kunlun
上级 7db017b0
/* Copyright (c) 2020 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 <string>
#include "paddle/fluid/operators/optimizers/sgd_op.h"
#include "paddle/fluid/platform/device/device_wrapper.h"
namespace paddle {
namespace operators {
template <typename DeviceContext, typename T>
class MomentumOpXPUKernel : public framework::OpKernel<T> {
using XPUType = typename XPUTypeTrait<T>::Type;
public:
void Compute(const framework::ExecutionContext& ctx) const override {
T mu = static_cast<T>(ctx.Attr<float>("mu"));
bool use_nesterov = ctx.Attr<bool>("use_nesterov");
auto learning_rate = ctx.Input<framework::Tensor>("LearningRate");
auto param = ctx.Input<framework::Tensor>("Param");
auto param_out = ctx.Output<framework::Tensor>("ParamOut");
auto* velocity = ctx.Input<framework::Tensor>("Velocity");
auto velocity_out = ctx.Output<framework::Tensor>("VelocityOut");
param_out->mutable_data<T>(ctx.GetPlace());
velocity_out->mutable_data<T>(ctx.GetPlace());
auto* lr = learning_rate->data<float>();
auto regularization_method = ctx.Attr<std::string>("regularization_method");
auto regularization_coeff = ctx.Attr<float>("regularization_coeff");
if (regularization_method != "l2_decay") {
// only support l2_decay
regularization_coeff = 0.0f;
}
auto* grad_var = ctx.InputVar("Grad");
PADDLE_ENFORCE_EQ(grad_var->IsType<framework::LoDTensor>(),
true,
platform::errors::PermissionDenied(
"Unsupported Variable Type of Param & Grad in "
"MomentumOp-XPU. Excepted "
"LodTensor, But received [%s] and [%s]",
paddle::framework::ToTypeName(grad_var->Type())));
auto grad = ctx.Input<framework::Tensor>("Grad");
auto& dev_ctx = ctx.template device_context<DeviceContext>();
// int momentum(Context* ctx, const T* param, const T* velocity, const T*
// grad, T* param_out, T* velocity_out, int len, const float* lr, int
// use_nesterov, float mu, float l2_weight_decay);
int r = xpu::momentum(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(param->data<T>()),
reinterpret_cast<const XPUType*>(velocity->data<T>()),
reinterpret_cast<const XPUType*>(grad->data<T>()),
reinterpret_cast<XPUType*>(param_out->data<T>()),
reinterpret_cast<XPUType*>(velocity_out->data<T>()),
param_out->numel(),
lr,
use_nesterov,
mu,
regularization_coeff);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "momentum");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_XPU_KERNEL(
momentum,
ops::MomentumOpXPUKernel<paddle::platform::XPUDeviceContext, float>,
ops::MomentumOpXPUKernel<paddle::platform::XPUDeviceContext,
paddle::platform::float16>);
#endif
// 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/momentum_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 MomentumDenseKernel(const Context& dev_ctx,
const DenseTensor& param,
const DenseTensor& grad,
const DenseTensor& velocity,
const DenseTensor& learning_rate,
const paddle::optional<DenseTensor>& master_param,
float mu,
bool use_nesterov,
const std::string& regularization_method,
float regularization_coeff,
bool multi_precision,
float rescale_grad,
DenseTensor* param_out,
DenseTensor* velocity_out,
DenseTensor* master_param_out) {
using XPUType = typename XPUTypeTrait<T>::Type;
dev_ctx.template Alloc<T>(param_out);
dev_ctx.template Alloc<T>(velocity_out);
auto* lr = learning_rate.data<float>();
if (regularization_method != "l2_decay") {
// only support l2_decay
regularization_coeff = 0.0f;
}
// int momentum(Context* ctx, const T* param, const T* velocity, const T*
// grad, T* param_out, T* velocity_out, int len, const float* lr, int
// use_nesterov, float mu, float l2_weight_decay);
int r = xpu::momentum(dev_ctx.x_context(),
reinterpret_cast<const XPUType*>(param.data<T>()),
reinterpret_cast<const XPUType*>(velocity.data<T>()),
reinterpret_cast<const XPUType*>(grad.data<T>()),
reinterpret_cast<XPUType*>(param_out->data<T>()),
reinterpret_cast<XPUType*>(velocity_out->data<T>()),
param_out->numel(),
lr,
use_nesterov,
static_cast<T>(mu),
regularization_coeff);
PADDLE_ENFORCE_XDNN_SUCCESS(r, "momentum");
}
} // namespace phi
PD_REGISTER_KERNEL(momentum,
XPU,
ALL_LAYOUT,
phi::MomentumDenseKernel,
float,
phi::dtype::float16) {}
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