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未验证 提交 8ff35506 编写于 作者: F furnace 提交者： GitHub
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refactor momentum op to combine weight (#27414) 

* refactor momentum op to combine weight_decay (scale op and sum op)
上级 bd1d6d3b
隐藏空白更改
内联 并排
Showing with 646 addition and 135 deletion
paddle/fluid/operators/optimizers/momentum_op.cc
浏览文件 @ 8ff35506
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/optimizers/momentum_op.h"
#include "paddle/fluid/framework/op_version_registry.h"
namespace paddle {
namespace operators {
......@@ -61,6 +62,12 @@ void MomentumOpMaker::Make() {
"(bool, default false) "
"Use Nesterov Momentum")
.SetDefault(false);
AddAttr<std::string>(
"regularization_method",
"(string) regularization_method, right now only support l2decay or none")
.SetDefault("");
AddAttr<float>("regularization_coeff", "(float) regularization_coeff")
.SetDefault(0);
AddComment(R"DOC(
Momentum Optimizer.
......@@ -90,3 +97,16 @@ REGISTER_OPERATOR(
REGISTER_OP_CPU_KERNEL(
momentum, ops::MomentumOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::MomentumOpKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_VERSION(momentum)
.AddCheckpoint(
R"ROC(
Upgrade momentum add 2 attributes [regularization_method, regularization_coeff].
)ROC",
paddle::framework::compatible::OpVersionDesc()
.NewAttr("regularization_method",
"(string) regularization_method, right now only support "
"l2decay or none",
std::string(""))
.NewAttr("regularization_coeff", "(float) regularization_coeff",
0.0f));
paddle/fluid/operators/optimizers/momentum_op.h
浏览文件 @ 8ff35506
......@@ -29,6 +29,12 @@ using framework::SelectedRows;
struct NoNesterov;
struct UseNesterov;
enum class RegularizationType {
kNONE = 0,
kL1DECAY = 1, // do not need support right now
kL2DECAY = 2,
};
class MomentumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
......@@ -113,43 +119,60 @@ class MomentumOp : public framework::OperatorWithKernel {
template <typename T>
class CPUDenseMomentumFunctor {
private:
const Tensor* param;
const Tensor* grad;
const Tensor* velocity;
const Tensor* learning_rate;
const T mu;
const T use_nesterov;
Tensor* param_out;
Tensor* velocity_out;
const Tensor* param_;
const Tensor* grad_;
const Tensor* velocity_;
const Tensor* learning_rate_;
const T mu_;
const T use_nesterov_;
RegularizationType regularization_flag_;
const T regularization_coeff_;
Tensor* param_out_;
Tensor* velocity_out_;
public:
CPUDenseMomentumFunctor(const Tensor* param, const Tensor* grad,
const Tensor* velocity, const Tensor* learning_rate,
const T mu, const bool use_nesterov,
Tensor* param_out, Tensor* velocity_out)
: param(param),
grad(grad),
velocity(velocity),
learning_rate(learning_rate),
mu(mu),
use_nesterov(use_nesterov),
param_out(param_out),
velocity_out(velocity_out) {}
RegularizationType regularization_flag,
const T regularization_coeff, Tensor* param_out,
Tensor* velocity_out)
: param_(param),
grad_(grad),
velocity_(velocity),
learning_rate_(learning_rate),
mu_(mu),
use_nesterov_(use_nesterov),
regularization_flag_(regularization_flag),
regularization_coeff_(regularization_coeff),
param_out_(param_out),
velocity_out_(velocity_out) {}
inline void operator()() {
auto p_out = framework::EigenVector<T>::Flatten(*param_out);
auto v_out = framework::EigenVector<T>::Flatten(*velocity_out);
auto p = framework::EigenVector<T>::Flatten(*param);
auto v = framework::EigenVector<T>::Flatten(*velocity);
auto g = framework::EigenVector<T>::Flatten(*grad);
auto* lr = learning_rate->data<T>();
v_out = v * mu + g;
if (use_nesterov) {
p_out = p - (g + v_out * mu) * lr[0];
auto param_out = framework::EigenVector<T>::Flatten(*param_out_);
auto velocity_out = framework::EigenVector<T>::Flatten(*velocity_out_);
auto param = framework::EigenVector<T>::Flatten(*param_);
auto velocity = framework::EigenVector<T>::Flatten(*velocity_);
auto grad = framework::EigenVector<T>::Flatten(*grad_);
auto* lr = learning_rate_->data<T>();
if (regularization_flag_ == RegularizationType::kL2DECAY) {
velocity_out = velocity * mu_ + param * regularization_coeff_ + grad;
if (use_nesterov_) {
param_out =
param -
(param * regularization_coeff_ + grad + velocity_out * mu_) * lr[0];
} else {
param_out = param - lr[0] * velocity_out;
}
} else {
p_out = p - lr[0] * v_out;
velocity_out = velocity * mu_ + grad;
if (use_nesterov_) {
param_out = param - (grad + velocity_out * mu_) * lr[0];
} else {
param_out = param - lr[0] * velocity_out;
}
}
}
};
......@@ -163,76 +186,100 @@ class DenseMomentumFunctor;
template <typename T>
class DenseMomentumFunctor<T, UseNesterov> {
private:
const T* p_;
const T* g_;
const T* v_;
const T* param_;
const T* grad_;
const T* velocity_;
const T* lr_;
const T mu_;
const int64_t num_;
T* p_out_;
T* v_out_;
T* param_out_;
T* velocity_out_;
RegularizationType regularization_flag_;
const T regularization_coeff_;
public:
DenseMomentumFunctor(const T* p, const T* g, const T* v,
DenseMomentumFunctor(const T* param, const T* grad, const T* velocity,
const T* learning_rate, const T mu, const int64_t num,
T* p_out, T* v_out)
: p_(p),
g_(g),
v_(v),
RegularizationType regularization_flag,
const T regularization_coeff, T* param_out,
T* velocity_out)
: param_(param),
grad_(grad),
velocity_(velocity),
lr_(learning_rate),
mu_(mu),
num_(num),
p_out_(p_out),
v_out_(v_out) {}
param_out_(param_out),
velocity_out_(velocity_out),
regularization_flag_(regularization_flag),
regularization_coeff_(regularization_coeff) {}
inline HOSTDEVICE void operator()(size_t i) const {
// put memory access in register
const T p = p_[i];
const T g = g_[i];
const T param = param_[i];
T grad = grad_[i];
const T lr = lr_[0];
const T v = v_[i];
T v_out = v * mu_ + g;
T p_out = p - (g + v_out * mu_) * lr;
const T velocity = velocity_[i];
grad = regularization_flag_ == RegularizationType::kL2DECAY
? grad + regularization_coeff_ * param
: grad;
T velocity_out = velocity * mu_ + grad;
T param_out = param - (grad + velocity_out * mu_) * lr;
// write reigster to memory
v_out_[i] = v_out;
p_out_[i] = p_out;
velocity_out_[i] = velocity_out;
param_out_[i] = param_out;
}
};
template <typename T>
class DenseMomentumFunctor<T, NoNesterov> {
private:
const T* p_;
const T* g_;
const T* v_;
const T* param_;
const T* grad_;
const T* velocity_;
const T* lr_;
const T mu_;
const int64_t num_;
T* p_out_;
T* v_out_;
T* param_out_;
T* velocity_out_;
RegularizationType regularization_flag_;
const T regularization_coeff_;
public:
DenseMomentumFunctor(const T* p, const T* g, const T* v,
DenseMomentumFunctor(const T* param, const T* grad, const T* velocity,
const T* learning_rate, const T mu, const int64_t num,
T* p_out, T* v_out)
: p_(p),
g_(g),
v_(v),
RegularizationType regularization_flag,
const T regularization_coeff, T* param_out,
T* velocity_out)
: param_(param),
grad_(grad),
velocity_(velocity),
lr_(learning_rate),
mu_(mu),
num_(num),
p_out_(p_out),
v_out_(v_out) {}
param_out_(param_out),
velocity_out_(velocity_out),
regularization_flag_(regularization_flag),
regularization_coeff_(regularization_coeff) {}
inline HOSTDEVICE void operator()(size_t i) const {
// put memory access in register
const T p = p_[i];
const T g = g_[i];
const T param = param_[i];
T grad = grad_[i];
const T lr = lr_[0];
const T v = v_[i];
T v_out = v * mu_ + g;
T p_out = p - lr * v_out;
const T velocity = velocity_[i];
grad = regularization_flag_ == RegularizationType::kL2DECAY
? grad + regularization_coeff_ * param
: grad;
T velocity_out = velocity * mu_ + grad;
T param_out = param - lr * velocity_out;
// write reigster to memory
v_out_[i] = v_out;
p_out_[i] = p_out;
velocity_out_[i] = velocity_out;
param_out_[i] = param_out;
}
};
......@@ -242,92 +289,116 @@ class SparseMomentumFunctor;
template <typename T>
class SparseMomentumFunctor<T, UseNesterov> {
private:
const T* p_;
const T* g_;
const T* v_;
const T* param_;
const T* grad_;
const T* velocity_;
const T* lr_;
const T mu_;
const int64_t* rows_;
const int64_t row_numel_;
const int64_t row_height_;
T* p_out_;
T* v_out_;
T* param_out_;
T* velocity_out_;
RegularizationType regularization_flag_;
const T regularization_coeff_;
public:
SparseMomentumFunctor(const T* p, const T* g, const T* v, const T* lr,
const T mu, const int64_t* rows, int64_t row_numel,
int64_t row_height, T* p_out, T* v_out)
: p_(p),
g_(g),
v_(v),
SparseMomentumFunctor(const T* param, const T* grad, const T* velocity,
const T* lr, const T mu, const int64_t* rows,
int64_t row_numel, int64_t row_height,
RegularizationType regularization_flag,
const T regularization_coeff, T* param_out,
T* velocity_out)
: param_(param),
grad_(grad),
velocity_(velocity),
lr_(lr),
mu_(mu),
rows_(rows),
row_numel_(row_numel),
row_height_(row_height),
p_out_(p_out),
v_out_(v_out) {}
param_out_(param_out),
velocity_out_(velocity_out),
regularization_flag_(regularization_flag),
regularization_coeff_(regularization_coeff) {}
inline HOSTDEVICE void operator()(size_t i) {
auto row_idx =
math::BinarySearch<int64_t>(rows_, row_height_, i / row_numel_);
T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_]
: static_cast<T>(0);
T grad = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_]
: static_cast<T>(0);
// put memory access in register
const T p = p_[i];
const T param = param_[i];
const T lr = lr_[0];
const T v = v_[i];
T v_out = v * mu_ + g;
T p_out = p - (g + v_out * mu_) * lr;
const T velocity = velocity_[i];
grad = regularization_flag_ == RegularizationType::kL2DECAY
? grad + regularization_coeff_ * param
: grad;
T velocity_out = velocity * mu_ + grad;
T param_out = param - (grad + velocity_out * mu_) * lr;
// write reigster to memory
v_out_[i] = v_out;
p_out_[i] = p_out;
velocity_out_[i] = velocity_out;
param_out_[i] = param_out;
}
};
template <typename T>
class SparseMomentumFunctor<T, NoNesterov> {
private:
const T* p_;
const T* g_;
const T* v_;
const T* param_;
const T* grad_;
const T* velocity_;
const T* lr_;
const T mu_;
const int64_t* rows_;
const int64_t row_numel_;
const int64_t row_height_;
T* p_out_;
T* v_out_;
T* param_out_;
T* velocity_out_;
RegularizationType regularization_flag_;
const T regularization_coeff_;
public:
SparseMomentumFunctor(const T* p, const T* g, const T* v, const T* lr,
const T mu, const int64_t* rows, int64_t row_numel,
int64_t row_height, T* p_out, T* v_out)
: p_(p),
g_(g),
v_(v),
SparseMomentumFunctor(const T* param, const T* grad, const T* velocity,
const T* lr, const T mu, const int64_t* rows,
int64_t row_numel, int64_t row_height,
RegularizationType regularization_flag,
const T regularization_coeff, T* param_out,
T* velocity_out)
: param_(param),
grad_(grad),
velocity_(velocity),
lr_(lr),
mu_(mu),
rows_(rows),
row_numel_(row_numel),
row_height_(row_height),
p_out_(p_out),
v_out_(v_out) {}
param_out_(param_out),
velocity_out_(velocity_out),
regularization_flag_(regularization_flag),
regularization_coeff_(regularization_coeff) {}
inline HOSTDEVICE void operator()(size_t i) {
auto row_idx =
math::BinarySearch<int64_t>(rows_, row_height_, i / row_numel_);
T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_]
: static_cast<T>(0);
T grad = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_]
: static_cast<T>(0);
// put memory access in register
const T p = p_[i];
const T param = param_[i];
const T lr = lr_[0];
const T v = v_[i];
T v_out = v * mu_ + g;
T p_out = p - v_out * lr;
const T velocity = velocity_[i];
grad = regularization_flag_ == RegularizationType::kL2DECAY
? grad + regularization_coeff_ * param
: grad;
T velocity_out = velocity * mu_ + grad;
T param_out = param - velocity_out * lr;
// write reigster to memory
v_out_[i] = v_out;
p_out_[i] = p_out;
velocity_out_[i] = velocity_out;
param_out_[i] = param_out;
}
};
......@@ -335,6 +406,24 @@ template <typename DeviceContext, typename T>
class MomentumOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
std::string regularization_method =
ctx.Attr<std::string>("regularization_method");
if (regularization_method != "" || !regularization_method.empty()) {
PADDLE_ENFORCE_EQ("l2_decay", regularization_method,
platform::errors::InvalidArgument(
"if regularization_method is not null, "
"it should be l2_decay, but received %s",
regularization_method));
}
T regularization_coeff =
static_cast<T>(ctx.Attr<float>("regularization_coeff"));
RegularizationType regularization_flag{
RegularizationType::kNONE}; // disable regularization
if (regularization_method == "l2_decay") {
regularization_flag = RegularizationType::kL2DECAY;
}
T mu = static_cast<T>(ctx.Attr<float>("mu"));
bool use_nesterov = ctx.Attr<bool>("use_nesterov");
......@@ -343,6 +432,7 @@ class MomentumOpKernel : public framework::OpKernel<T> {
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());
......@@ -350,9 +440,9 @@ class MomentumOpKernel : public framework::OpKernel<T> {
if (grad_var->IsType<framework::LoDTensor>()) {
auto grad = ctx.Input<framework::Tensor>("Grad");
if (platform::is_cpu_place(ctx.GetPlace())) {
CPUDenseMomentumFunctor<T> functor(param, grad, velocity, learning_rate,
mu, use_nesterov, param_out,
velocity_out);
CPUDenseMomentumFunctor<T> functor(
param, grad, velocity, learning_rate, mu, use_nesterov,
regularization_flag, regularization_coeff, param_out, velocity_out);
functor();
} else if (platform::is_gpu_place(ctx.GetPlace())) {
platform::ForRange<DeviceContext> for_range(
......@@ -361,16 +451,16 @@ class MomentumOpKernel : public framework::OpKernel<T> {
if (use_nesterov) {
DenseMomentumFunctor<T, UseNesterov> functor(
param->data<T>(), grad->data<T>(), velocity->data<T>(),
learning_rate->data<T>(), mu, param->numel(),
param_out->mutable_data<T>(ctx.GetPlace()),
learning_rate->data<T>(), mu, param->numel(), regularization_flag,
regularization_coeff, param_out->mutable_data<T>(ctx.GetPlace()),
velocity_out->mutable_data<T>(ctx.GetPlace()));
for_range(functor);
} else {
DenseMomentumFunctor<T, NoNesterov> functor(
param->data<T>(), grad->data<T>(), velocity->data<T>(),
learning_rate->data<T>(), mu, param->numel(),
param_out->mutable_data<T>(ctx.GetPlace()),
learning_rate->data<T>(), mu, param->numel(), regularization_flag,
regularization_coeff, param_out->mutable_data<T>(ctx.GetPlace()),
velocity_out->mutable_data<T>(ctx.GetPlace()));
for_range(functor);
}
......@@ -403,6 +493,7 @@ class MomentumOpKernel : public framework::OpKernel<T> {
param->data<T>(), merged_grad->value().data<T>(),
velocity->data<T>(), learning_rate->data<T>(), mu, rows, row_numel,
static_cast<int64_t>(merged_grad->rows().size()),
regularization_flag, regularization_coeff,
param_out->mutable_data<T>(ctx.GetPlace()),
velocity_out->mutable_data<T>(ctx.GetPlace()));
for_range(functor);
......@@ -412,6 +503,7 @@ class MomentumOpKernel : public framework::OpKernel<T> {
param->data<T>(), merged_grad->value().data<T>(),
velocity->data<T>(), learning_rate->data<T>(), mu, rows, row_numel,
static_cast<int64_t>(merged_grad->rows().size()),
regularization_flag, regularization_coeff,
param_out->mutable_data<T>(ctx.GetPlace()),
velocity_out->mutable_data<T>(ctx.GetPlace()));
for_range(functor);
......
python/paddle/fluid/contrib/__init__.py
浏览文件 @ 8ff35506
......@@ -35,6 +35,7 @@ from . import mixed_precision
from .mixed_precision import *
from . import layers
from .layers import *
from . import optimizer
__all__ = []
__all__ += decoder.__all__
......@@ -46,3 +47,4 @@ __all__ += utils.__all__
__all__ += extend_optimizer.__all__
__all__ += ['mixed_precision']
__all__ += layers.__all__
__all__ += optimizer.__all__
python/paddle/fluid/contrib/optimizer.py 0 → 100644
浏览文件 @ 8ff35506
# 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.
from paddle.fluid.optimizer import Optimizer
from paddle.fluid.regularizer import L1DecayRegularizer
from paddle.fluid.regularizer import L2DecayRegularizer
from paddle.fluid.regularizer import append_regularization_ops
from paddle.fluid import framework
from paddle.fluid import core
from paddle.fluid.framework import program_guard
from paddle.fluid.clip import append_gradient_clip_ops
__all__ = ['Momentum']
class Momentum(Optimizer):
"""
Simple Momentum optimizer with velocity state
This optimizer has a flag for Nestrov Momentum.
The update equations are as follows:
.. math::
& velocity = mu * velocity + gradient
& if (use\_nesterov):
&\quad param = param - (gradient + mu * velocity) * learning\_rate
& else:
&\quad param = param - learning\_rate * velocity
Parameters:
learning_rate (float|Variable): The learning rate used to update parameters. \
Can be a float value or a Variable with one float value as data element.
momentum (float): Momentum factor
parameter_list (Iterable, optional): Iterable of ``Variable`` names to update to minimize ``loss``. \
This parameter is required in dygraph mode. \
The default value is None in static mode, at this time all parameters will be updated.
use_nesterov (bool, optional): Enables Nesterov momentum, default is false.
regularization (WeightDecayRegularizer, optional): The strategy of regularization. There are two method: \
:ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If a parameter has set \
regularizer using :ref:`api_fluid_ParamAttr` already, the regularization setting here in optimizer will be \
ignored for this parameter. Otherwise, the regularization setting here in optimizer will take effect. \
Default None, meaning there is no regularization.
grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of
some derived class of ``GradientClipBase`` . There are three cliping strategies
( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` ,
:ref:`api_fluid_clip_GradientClipByValue` ). Default None, meaning there is no gradient clipping.
name (str, optional): This parameter is used by developers to print debugging information. \
For details, please refer to :ref:`api_guide_Name`. Default is None.
Examples:
.. code-block:: python
import paddle
import paddle.fluid as fluid
import numpy as np
paddle.enable_static()
place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
x = paddle.static.data(name='x', shape=[1, 13], dtype='float32')
y = paddle.static.data(name='y', shape=[1], dtype='float32')
linear = paddle.nn.Linear(13, 1)
y_predict = linear(x)
cost = paddle.nn.functional.square_error_cost(input=y_predict, label=y)
avg_cost = paddle.mean(cost)
moment_optimizer = fluid.contrib.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
moment_optimizer.minimize(avg_cost)
fetch_list = [avg_cost]
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(paddle.static.default_startup_program())
for data in train_reader():
exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
"""
_velocity_acc_str = "velocity"
def __init__(self,
learning_rate,
momentum,
parameter_list=None,
use_nesterov=False,
regularization=None,
grad_clip=None,
name=None):
assert learning_rate is not None
assert momentum is not None
predicate = lambda regular: isinstance(regular, L2DecayRegularizer)
py_regular = None if predicate(regularization) else regularization
super(Momentum, self).__init__(
learning_rate=learning_rate,
parameter_list=parameter_list,
regularization=py_regular,
grad_clip=grad_clip,
name=name)
self.type = "momentum"
self._momentum = momentum
self._use_nesterov = bool(use_nesterov)
self._regularization_method = ""
self._regularization_coeff = 0
if (isinstance(regularization, L2DecayRegularizer)):
self._regularization_method = "l2_decay"
self._regularization_coeff = regularization._regularization_coeff
def _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
for p in parameters:
self._add_accumulator(self._velocity_acc_str, p)
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, framework.Block)
velocity_acc = self._get_accumulator(self._velocity_acc_str,
param_and_grad[0])
lr = self._create_param_lr(param_and_grad)
if framework.in_dygraph_mode():
_, _ = core.ops.momentum(
param_and_grad[0], param_and_grad[1], velocity_acc, lr,
param_and_grad[0], velocity_acc, 'mu', self._momentum,
'use_nesterov', self._use_nesterov, 'regularization_method',
self._regularization_method, 'regularization_coeff',
self._regularization_coeff)
return None
attrs = {
"mu": self._momentum,
"use_nesterov": self._use_nesterov,
"regularization_method": self._regularization_method,
"regularization_coeff": self._regularization_coeff
}
inputs = {
"Param": [param_and_grad[0]],
"Grad": [param_and_grad[1]],
"Velocity": [velocity_acc],
"LearningRate": [lr]
}
outputs = {
"ParamOut": [param_and_grad[0]],
"VelocityOut": [velocity_acc]
}
# create the momentum optimize op
momentum_op = block.append_op(
type=self.type,
inputs=inputs,
outputs=outputs,
attrs=attrs,
stop_gradient=True)
return momentum_op
python/paddle/fluid/tests/unittests/test_momentum_op.py
浏览文件 @ 8ff35506
......@@ -23,6 +23,33 @@ import paddle
import paddle.fluid as fluid
def calculate_momentum_by_numpy(param,
grad,
mu,
velocity,
use_nesterov,
learning_rate,
regularization_method=None,
regularization_coeff=1.0):
if regularization_method == "l2_decay":
grad = grad + regularization_coeff * param
velocity_out = mu * velocity + grad
if use_nesterov:
param_out = param - (grad + velocity_out * mu) * learning_rate
else:
param_out = param - learning_rate * velocity_out
else:
velocity_out = mu * velocity + grad
if use_nesterov:
param_out = param - grad * learning_rate - \
velocity_out * mu * learning_rate
else:
param_out = param - learning_rate * velocity_out
return param_out, velocity_out
class TestMomentumOp1(OpTest):
def setUp(self):
self.op_type = "momentum"
......@@ -45,12 +72,13 @@ class TestMomentumOp1(OpTest):
self.attrs = {'mu': mu}
velocity_out = mu * velocity + grad
if use_nesterov:
param_out = param - grad * learning_rate - \
velocity_out * mu * learning_rate
else:
param_out = param - learning_rate * velocity_out
param_out, velocity_out = calculate_momentum_by_numpy(
param=param,
grad=grad,
mu=mu,
velocity=velocity,
use_nesterov=use_nesterov,
learning_rate=learning_rate)
self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}
......@@ -92,12 +120,13 @@ class TestMomentumOp2(OpTest):
self.attrs = {'mu': mu, 'use_nesterov': use_nesterov}
velocity_out = mu * velocity + grad
if use_nesterov:
param_out = param - grad * learning_rate - \
velocity_out * mu * learning_rate
else:
param_out = param - learning_rate * velocity_out
param_out, velocity_out = calculate_momentum_by_numpy(
param=param,
grad=grad,
mu=mu,
velocity=velocity,
use_nesterov=use_nesterov,
learning_rate=learning_rate)
self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}
......@@ -141,12 +170,15 @@ class TestLarsMomentumOp(OpTest):
self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}
def test_check_output(self):
paddle.enable_static()
self.check_output()
class TestSparseMomentumOp(unittest.TestCase):
def setUp(self):
self.use_nesterov = False
self.regularization_method = ""
self.regularization_coeff = 1.0
def check_with_place(self, place):
self.init_kernel()
......@@ -157,6 +189,8 @@ class TestSparseMomentumOp(unittest.TestCase):
row_numel = 12
mu = 1.0
use_nesterov = self.use_nesterov
regularization_method = self.regularization_method
regularization_coeff = self.regularization_coeff
# create and initialize Param Variable
param = scope.var('Param').get_tensor()
......@@ -198,7 +232,9 @@ class TestSparseMomentumOp(unittest.TestCase):
VelocityOut='VelocityOut',
LearningRate='LearningRate',
mu=mu,
use_nesterov=use_nesterov)
use_nesterov=use_nesterov,
regularization_method=regularization_method,
regularization_coeff=regularization_coeff)
op.run(scope, place)
# get and compare result
......@@ -210,13 +246,19 @@ class TestSparseMomentumOp(unittest.TestCase):
_grad_np_array = np.full((height, row_numel), 0.0).astype("float32")
for i in range(len(rows)):
_grad_np_array[rows[i]] = grad_np_array[i]
_velocity_out = mu * velocity_np_array + _grad_np_array
_param = param_array
if use_nesterov:
_param_out = _param - (_grad_np_array + _velocity_out * mu
) * lr_array
else:
_param_out = _param - lr_array * _velocity_out
_param_out, _velocity_out = calculate_momentum_by_numpy(
param=_param,
grad=_grad_np_array,
mu=mu,
velocity=velocity_np_array,
use_nesterov=use_nesterov,
learning_rate=lr_array,
regularization_method=regularization_method,
regularization_coeff=regularization_coeff)
self.assertTrue((_velocity_out == velocity_out_np_array).all())
self.assertTrue((_param_out == param_out_np_array).all())
......@@ -251,6 +293,8 @@ class TestMomentumV2(unittest.TestCase):
adam.clear_gradients()
def test_momentum(self):
paddle.enable_static()
place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
......@@ -279,5 +323,183 @@ class TestMomentumV2(unittest.TestCase):
self.assertRaises(ValueError, paddle.optimizer.Momentum, momentum=None)
class TestMomentumOpWithDecay(OpTest):
def setUp(self):
self.op_type = "momentum"
self.dtype = np.float32
self.use_nesterov = True
self.regularization_method = 'l2_decay'
self.regularization_coeff = 0.9
self.init_config()
param = np.random.random((123, 321)).astype(self.dtype)
grad = np.random.random((123, 321)).astype(self.dtype)
velocity = np.zeros((123, 321)).astype(self.dtype)
learning_rate = np.array([0.001]).astype(self.dtype)
mu = 0.0001
use_nesterov = self.use_nesterov
regularization_method = self.regularization_method
regularization_coeff = self.regularization_coeff
self.inputs = {
'Param': param,
'Grad': grad,
'Velocity': velocity,
'LearningRate': learning_rate
}
self.attrs = {
'mu': mu,
'use_nesterov': use_nesterov,
'regularization_method': regularization_method,
'regularization_coeff': regularization_coeff
}
grad = grad + regularization_coeff * param
param_out, velocity_out = calculate_momentum_by_numpy(
param=param,
grad=grad,
mu=mu,
velocity=velocity,
use_nesterov=use_nesterov,
learning_rate=learning_rate)
self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}
def init_config(self):
pass
def test_check_output(self):
paddle.enable_static()
self.check_output()
class TestMomentumOpWithDecayFP16(TestMomentumOpWithDecay):
def init_config(self):
self.dtype = np.float16
def test_check_output(self):
paddle.enable_static()
self.check_output(atol=1e-3)
class TestMomentumOpWithDecay2(TestMomentumOpWithDecay):
def init_config(self):
self.use_nesterov = False
class TestSparseMomentumOpWithDecay(TestSparseMomentumOp):
def setUp(self):
self.use_nesterov = False
self.regularization_method = 'l2_decay'
self.regularization_coeff = 0.9
class TestSparseMomentumOpWithDecay2(TestSparseMomentumOpWithDecay):
def init_kernel(self):
self.use_nesterov = True
class TestMomentumOpWithDecayAPI(unittest.TestCase):
def _test_momentum_dygraph_common(self, regularization):
paddle.disable_static()
inp = np.random.uniform(-0.1, 0.1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
inp = paddle.to_tensor(inp)
out = linear(inp)
loss = paddle.mean(out)
# This can be any optimizer supported by dygraph.
momentum = paddle.fluid.contrib.optimizer.Momentum(
learning_rate=0.01,
momentum=0.9,
parameter_list=linear.parameters(),
regularization=regularization)
momentum.minimize(loss)
def test_momentum_dygraph_1(self):
self._test_momentum_dygraph_common(
regularization=paddle.fluid.regularizer.L2Decay(
regularization_coeff=0.1))
def test_momentum_static(self):
paddle.enable_static()
place = fluid.CPUPlace()
main = fluid.Program()
with fluid.program_guard(main):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
momentum_optimizer = paddle.fluid.contrib.optimizer.Momentum(
learning_rate=0.1, momentum=0.9)
momentum_optimizer.minimize(avg_cost)
fetch_list = [avg_cost]
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
for data in train_reader():
exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list)
class TestMomentumOpVsMomentumOpWithDecayAPI(unittest.TestCase):
def __update_params(self, momentum, linear):
for i in range(10):
inp = paddle.full(
shape=[2, 2], fill_value=i, dtype='float32').astype("float32")
inp = paddle.to_tensor(inp)
out = linear(inp)
loss = paddle.mean(out)
loss.backward()
momentum.minimize(loss)
def __test_vs(self, place=fluid.CPUPlace()):
paddle.disable_static(place=place)
linear_old = paddle.nn.Linear(
2,
2,
weight_attr=paddle.nn.initializer.Constant(value=2.0),
bias_attr=paddle.nn.initializer.Constant(value=2.0))
momentum_old = paddle.fluid.optimizer.Momentum(
learning_rate=0.01,
momentum=0.9,
parameter_list=linear_old.parameters(),
regularization=paddle.fluid.regularizer.L2Decay(
regularization_coeff=0.1))
self.__update_params(momentum=momentum_old, linear=linear_old)
linear_new = paddle.nn.Linear(
2,
2,
weight_attr=paddle.nn.initializer.Constant(value=2.0),
bias_attr=paddle.nn.initializer.Constant(value=2.0))
momentum_new = paddle.fluid.contrib.optimizer.Momentum(
learning_rate=0.01,
momentum=0.9,
parameter_list=linear_new.parameters(),
regularization=paddle.fluid.regularizer.L2Decay(
regularization_coeff=0.1))
self.__update_params(momentum=momentum_new, linear=linear_new)
self.assertEqual(
(linear_old.weight.numpy() == linear_new.weight.numpy()).all(),
True,
'the param weight updated by two Momentum optimizers should equal')
def test_vs(self, place=fluid.CPUPlace()):
places = [fluid.CPUPlace()]
if paddle.fluid.core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for place in places:
self.__test_vs(place=place)
if __name__ == "__main__":
unittest.main()
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