未验证 提交 b0c2ee26 编写于 作者: G gouzil 提交者: GitHub

[Fluid] move lars_momentum to phi (#55798)

* [Fluid] move lars_momentum to phi

* add sig

* fix optional Output

* off check_dygraph

* fix input

* fix operator[]

* fix

* try fix AllocateTmpTensor

* fix

* fix type

* Update paddle/phi/kernels/gpu/lars_momentum_kernel.cu

* fix type

* rollback

* Add Registration

* try fix win

* try fix win

* try use double

* try use operator *(float,const Derived &)

* try auto

* fix

* fix

* fix

* fix dtype

* fix type

* fix index
上级 6839a7b9
......@@ -12,7 +12,8 @@ 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/fluid/operators/optimizers/lars_momentum_op.h"
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
......@@ -233,6 +234,3 @@ REGISTER_OPERATOR(
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
ops::LarsMomentumOpVarTypeInference);
PD_REGISTER_STRUCT_KERNEL(
lars_momentum, CPU, ALL_LAYOUT, ops::LarsMomentumOpKernel, float, double) {}
/* Copyright (c) 2016 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. */
#pragma once
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename T, typename DeviceContext>
class LarsMomentumOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto param_out = ctx.MultiOutput<phi::DenseTensor>("ParamOut");
auto velocity_out = ctx.MultiOutput<phi::DenseTensor>("VelocityOut");
auto param = ctx.MultiInput<phi::DenseTensor>("Param");
auto velocity = ctx.MultiInput<phi::DenseTensor>("Velocity");
auto learning_rate = ctx.MultiInput<phi::DenseTensor>("LearningRate");
auto grad = ctx.MultiInput<phi::DenseTensor>("Grad");
auto weight_decay_arr = ctx.Attr<std::vector<float>>("lars_weight_decay");
T mu = static_cast<T>(ctx.Attr<float>("mu"));
T lars_coeff = ctx.Attr<float>("lars_coeff");
T epsilon = ctx.Attr<float>("epsilon");
T rescale_grad = ctx.Attr<float>("rescale_grad");
int op_num = param.size();
for (int i = 0; i < op_num; ++i) {
auto* lr = learning_rate[i]->data<T>();
T lars_weight_decay = weight_decay_arr[i];
param_out[i]->mutable_data<T>(ctx.GetPlace());
velocity_out[i]->mutable_data<T>(ctx.GetPlace());
auto p_out = framework::EigenVector<T>::Flatten(*(param_out[i]));
auto v_out = framework::EigenVector<T>::Flatten(*(velocity_out[i]));
auto p = framework::EigenVector<T>::Flatten(*(param[i]));
auto v = framework::EigenVector<T>::Flatten(*(velocity[i]));
auto g = framework::EigenVector<T>::Flatten(*(grad[i]));
auto rescale_g = rescale_grad * g;
phi::DenseTensor p_norm_t, g_norm_t;
p_norm_t.Resize({1});
g_norm_t.Resize({1});
p_norm_t.mutable_data<T>(ctx.GetPlace());
g_norm_t.mutable_data<T>(ctx.GetPlace());
auto ep_norm = framework::EigenScalar<T>::From(p_norm_t);
auto eg_norm = framework::EigenScalar<T>::From(g_norm_t);
ep_norm = p.square().sum().sqrt();
eg_norm = rescale_g.square().sum().sqrt();
T local_lr = lr[0];
if (lars_weight_decay > 0 && ep_norm(0) > 0 && eg_norm(0) > 0) {
local_lr = lr[0] * lars_coeff * ep_norm(0) /
(eg_norm(0) + lars_weight_decay * ep_norm(0) + epsilon);
}
v_out = v * mu + local_lr * (rescale_g + lars_weight_decay * p);
p_out = p - v_out;
}
}
};
} // namespace operators
} // namespace paddle
......@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#ifdef PADDLE_WITH_XPU
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/optimizers/lars_momentum_op.h"
#include "paddle/phi/backends/xpu/enforce_xpu.h"
namespace paddle {
......
// Copyright (c) 2023 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/lars_momentum_kernel.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/funcs/eigen/common.h"
namespace phi {
template <typename T, typename Context>
void LarsMomentumKernel(
const Context& dev_ctx,
const std::vector<const DenseTensor*>& param,
const std::vector<const DenseTensor*>& velocity,
const std::vector<const DenseTensor*>& learning_rate,
const std::vector<const DenseTensor*>& grad,
const paddle::optional<std::vector<const DenseTensor*>>& master_param,
const std::vector<float>& weight_decay_arr,
float mu,
float lars_coeff,
float epsilon,
bool multi_precision,
float rescale_grad,
std::vector<DenseTensor*> param_out,
std::vector<DenseTensor*> velocity_out,
std::vector<DenseTensor*> master_param_out) {
int op_num = param.size();
T mu_ = static_cast<T>(mu);
for (int i = 0; i < op_num; ++i) {
auto* lr = learning_rate[i]->data<T>();
T lars_weight_decay = weight_decay_arr[i];
dev_ctx.template Alloc<T>(param_out[i]);
dev_ctx.template Alloc<T>(velocity_out[i]);
auto p_out = phi::EigenVector<T>::Flatten(*(param_out[i]));
auto v_out = phi::EigenVector<T>::Flatten(*(velocity_out[i]));
auto p = phi::EigenVector<T>::Flatten(*(param[i]));
auto v = phi::EigenVector<T>::Flatten(*(velocity[i]));
Eigen::TensorMap<Eigen::Tensor<const T, 1, 1>> g =
phi::EigenVector<T>::Flatten(*(grad[i]));
auto rescale_g = static_cast<T>(rescale_grad) * g;
phi::DenseTensor p_norm_t, g_norm_t;
p_norm_t.Resize({1});
g_norm_t.Resize({1});
dev_ctx.template Alloc<T>(&p_norm_t);
dev_ctx.template Alloc<T>(&g_norm_t);
auto ep_norm = phi::EigenScalar<T>::From(p_norm_t);
auto eg_norm = phi::EigenScalar<T>::From(g_norm_t);
ep_norm = p.square().sum().sqrt();
eg_norm = rescale_g.square().sum().sqrt();
T local_lr = lr[0];
if (lars_weight_decay > 0 && ep_norm(0) > 0 && eg_norm(0) > 0) {
local_lr = lr[0] * lars_coeff * ep_norm(0) /
(eg_norm(0) + lars_weight_decay * ep_norm(0) + epsilon);
}
v_out = v * mu_ + local_lr * (rescale_g + lars_weight_decay * p);
p_out = p - v_out;
}
}
} // namespace phi
PD_REGISTER_KERNEL(
lars_momentum, CPU, ALL_LAYOUT, phi::LarsMomentumKernel, float, double) {}
// Copyright (c) 2023 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.
#pragma once
#include "paddle/phi/common/scalar.h"
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
template <typename T, typename Context>
void LarsMomentumKernel(
const Context& dev_ctx,
const std::vector<const DenseTensor*>& param,
const std::vector<const DenseTensor*>& velocity,
const std::vector<const DenseTensor*>& learning_rate,
const std::vector<const DenseTensor*>& grad,
const paddle::optional<std::vector<const DenseTensor*>>& master_param,
const std::vector<float>& weight_decay_arr,
float mu,
float lars_coeff,
float epsilon,
bool multi_precision,
float rescale_grad,
std::vector<DenseTensor*> param_out,
std::vector<DenseTensor*> velocity_out,
std::vector<DenseTensor*> master_param_out);
} // namespace phi
// Copyright (c) 2023 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/core/compat/op_utils.h"
namespace phi {
KernelSignature LarsMomentumOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature(
"lars_momentum",
{"Param", "Velocity", "LearningRate", "Grad", "MasterParam"},
{"lars_weight_decay",
"mu",
"lars_coeff",
"epsilon",
"multi_precision",
"rescale_grad"},
{"ParamOut", "VelocityOut", "MasterParamOut"});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(lars_momentum, phi::LarsMomentumOpArgumentMapping);
......@@ -312,7 +312,7 @@ class TestLarsMomentumOp(OpTest):
def test_check_output(self):
paddle.enable_static()
self.check_output()
self.check_output(check_dygraph=False)
def config(self):
self.params_num = 1
......
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