# 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 .optimizer import Optimizer

from ..fluid import core

from ..fluid import framework

from ..fluid.framework import Variable, _in_legacy_dygraph, in_dygraph_mode

from ..fluid import layers

from ..fluid import unique_name

from ..fluid.layer_helper import LayerHelper

import warnings

from ..fluid.dygraph import base as imperative_base

from collections import defaultdict

import numpy as np

import time

import paddle

from paddle import _C_ops

__all__ = []

class Adam(Optimizer):

r"""

The Adam optimizer uses an optimization described at the end

of section 2 of `Adam paper <https://arxiv.org/abs/1412.6980>`_ ,

it can dynamically adjusts the learning rate of each parameter using

the 1st moment estimates and the 2nd moment estimates of the gradient.

The parameter ``param_out`` update rule with gradient ``grad``:

.. math::

t & = t + 1

moment\_1\_out & = {\beta}_1 * moment\_1 + (1 - {\beta}_1) * grad

moment\_2\_out & = {\beta}_2 * moment\_2 + (1 - {\beta}_2) * grad * grad

learning\_rate & = learning\_rate * \

\frac{\sqrt{1 - {\beta}_2^t}}{1 - {\beta}_1^t}

param\_out & = param - learning\_rate * \frac{moment\_1}{\sqrt{moment\_2} + \epsilon}

Related paper: `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_

Args:

learning_rate (float|LRScheduler, optional): The learning rate used to update ``Parameter``.

It can be a float value or a LRScheduler. The default value is 0.001.

beta1 (float|Tensor, optional): The exponential decay rate for the 1st moment estimates.

It should be a float number or a Tensor with shape [1] and data type as float32.

The default value is 0.9.

beta2 (float|Tensor, optional): The exponential decay rate for the 2nd moment estimates.

It should be a float number or a Tensor with shape [1] and data type as float32.

The default value is 0.999.

epsilon (float|Tensor, optional): A small float value for numerical stability.

It should be a float number or a Tensor with shape [1] and data type as float32.

The default value is 1e-08.

parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. \

This parameter is required in dygraph mode. And you can specify different options for \

different parameter groups such as the learning rate, weight decay, etc, \

then the parameters are list of dict. Note that the learning_rate in paramter groups \

represents the scale of base learning_rate. \

The default value is None in static mode, at this time all parameters will be updated.

weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \

It canbe a float value as coeff of L2 regularization or \

: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.

lazy_mode (bool, optional): The official Adam algorithm has two moving-average accumulators.

The accumulators are updated at every step. Every element of the two moving-average

is updated in both dense mode and sparse mode. If the size of parameter is very large,

then the update may be very slow. The lazy mode only update the element that has

gradient in current mini-batch, so it will be much more faster. But this mode has

different semantics with the original Adam algorithm and may lead to different result.

The default value is False.

multi_precision (bool, optional): Whether to use multi-precision during weight updating. Default is false.

use_multi_tensor (bool, optional): Whether to use multi-tensor strategy to update all parameters at once . Default is false.

name (str, optional): Normally there is no need for user to set this property.

For more information, please refer to :ref:`api_guide_Name`.

The default value is None.

Examples:

.. code-block:: python

import paddle

linear = paddle.nn.Linear(10, 10)

inp = paddle.rand([10,10], dtype="float32")

out = linear(inp)

loss = paddle.mean(out)

adam = paddle.optimizer.Adam(learning_rate=0.1,

parameters=linear.parameters())

out.backward()

adam.step()

adam.clear_grad()

.. code-block:: python

# Adam with beta1/beta2 as Tensor and weight_decay as float

import paddle

linear = paddle.nn.Linear(10, 10)

inp = paddle.rand([10,10], dtype="float32")

out = linear(inp)

loss = paddle.mean(out)

beta1 = paddle.to_tensor([0.9], dtype="float32")

beta2 = paddle.to_tensor([0.99], dtype="float32")

adam = paddle.optimizer.Adam(learning_rate=0.1,

parameters=linear.parameters(),

beta1=beta1,

beta2=beta2,

weight_decay=0.01)

out.backward()

adam.step()

adam.clear_grad()

#Note that the learning_rate of linear_2 is 0.01.

linear_1 = paddle.nn.Linear(10, 10)

linear_2 = paddle.nn.Linear(10, 10)

inp = paddle.uniform(shape=[10, 10], min=-0.1, max=0.1)

out = linear_1(inp)

out = linear_2(out)

loss = paddle.mean(out)

adam = paddle.optimizer.Adam(

learning_rate=0.1,

parameters=[{

'params': linear_1.parameters()

}, {

'params': linear_2.parameters(),

'weight_decay': 0.001,

'learning_rate': 0.1,

'beta1': 0.8

}],

weight_decay=0.01,

beta1=0.9)

out.backward()

adam.step()

adam.clear_grad()

"""

_moment1_acc_str = "moment1"

_moment2_acc_str = "moment2"

_beta1_pow_acc_str = "beta1_pow_acc"

_beta2_pow_acc_str = "beta2_pow_acc"

def __init__(self,

learning_rate=0.001,

beta1=0.9,

beta2=0.999,

epsilon=1e-8,

parameters=None,

weight_decay=None,

grad_clip=None,

lazy_mode=False,

multi_precision=False,

use_multi_tensor=False,

name=None):

assert learning_rate is not None

assert beta1 is not None

assert beta2 is not None

assert epsilon is not None

if not isinstance(beta1, Variable):

if not 0 <= beta1 < 1:

raise ValueError(

"Invaild value of beta1, expect beta1 in [0,1).")

if not isinstance(beta2, Variable):

if not 0 <= beta2 < 1:

raise ValueError(

"Invaild value of beta2, expect beta2 in [0,1).")

if not isinstance(epsilon, Variable):

if not 0 <= epsilon:

raise ValueError(

"Invaild value of epsilon, expect epsilon >= 0.")

super(Adam, self).__init__(learning_rate=learning_rate,

parameters=parameters,

weight_decay=weight_decay,

grad_clip=grad_clip,

name=name)

self.type = "adam"

self._beta1 = beta1

self._beta2 = beta2

self._epsilon = epsilon

self._lazy_mode = lazy_mode

self._multi_precision = multi_precision

self._master_weights = {}

self._default_dict = {

'beta1': beta1,

'beta2': beta2,

'epsilon': epsilon,

'lazy_mode': lazy_mode,

}

self._use_multi_tensor = use_multi_tensor

if self._use_multi_tensor:

self._param_dict = {'FP32_LODTensor': [], 'FP16_LODTensor': []}

self._moment1_dict = {'FP32_LODTensor': [], 'FP16_LODTensor': []}

self._moment2_dict = {'FP32_LODTensor': [], 'FP16_LODTensor': []}

self._beta1_pow_acc_dict = {

'FP32_LODTensor': [],

'FP16_LODTensor': []

}

self._beta2_pow_acc_dict = {

'FP32_LODTensor': [],

'FP16_LODTensor': []

}

self._master_weight_dict = {

'FP32_LODTensor': None,

'FP16_LODTensor': []

}

def _create_master_weight(self, param):

if param.name in self._master_weights:

var = self._master_weights[param.name]

else:

assert isinstance(self.helper, LayerHelper)

var_name = param.name + "_fp32_master"

var_name = unique_name.generate(var_name)

var = layers.create_global_var(name=var_name,

shape=param.shape,

value=0,

dtype='float32',

persistable=True)

block = self.helper.startup_program.global_block()

block.append_op(type="cast",

inputs={"X": [param]},

outputs={"Out": [var]},

attrs={

"in_dtype": param.dtype,

"out_dtype": core.VarDesc.VarType.FP32

})

self._master_weights[param.name] = var

return var

def _get_accumulator(self, name, param):

"""Utility function to fetch an accumulator for a parameter

Args:

name: name of the accumulator

param: parameter variable for which accumulator is to be fetched

Returns:

accumulator variable for the parameter

"""

if self._name is not None:

name = self._name + "_" + name

find_master = self._multi_precision and param.dtype == core.VarDesc.VarType.FP16

target_param = self._master_weights[

param.name] if find_master else param

target_name = target_param.name

if (name not in self._accumulators

or target_name not in self._accumulators[name]):

raise Exception(

"Accumulator {} does not exist for parameter {}".format(

name, target_name))

return self._accumulators[name][target_name]

def _add_moments_pows(self, p):

acc_dtype = p.dtype

if acc_dtype == core.VarDesc.VarType.FP16:

acc_dtype = core.VarDesc.VarType.FP32

self._add_accumulator(self._moment1_acc_str, p, dtype=acc_dtype)

self._add_accumulator(self._moment2_acc_str, p, dtype=acc_dtype)

self._add_accumulator(

name=self._beta1_pow_acc_str,

param=p,

dtype=acc_dtype,

fill_value=0.9 if isinstance(self._beta1, Variable) \

else self._beta1,

shape=[1],

type=core.VarDesc.VarType.LOD_TENSOR, device='cpu')

self._add_accumulator(

name=self._beta2_pow_acc_str,

param=p,

dtype=acc_dtype,

fill_value=0.999 if isinstance(self._beta2, Variable) \

else self._beta2,

shape=[1],

type=core.VarDesc.VarType.LOD_TENSOR, device='cpu')

def _create_accumulators(self, block, parameters):

assert isinstance(block, framework.Block)

if isinstance(parameters, dict):

parameters = self._update_param_group(parameters)

# Create accumulator tensors for first and second moments

for p in parameters:

if self._multi_precision and p.dtype == core.VarDesc.VarType.FP16:

master_p = self._create_master_weight(p)

self._add_moments_pows(master_p)

continue

if p.dtype == core.VarDesc.VarType.FP16 and not self._multi_precision:

warnings.warn(

"Accumulating with FP16 in optimizer can lead to poor accuracy or slow convergence."

"Consider using multi_precision=True option of the Adam optimizer."

)

self._add_moments_pows(p)

def _append_optimize_op(self, block, param_and_grad):

assert isinstance(block, framework.Block)

if isinstance(param_and_grad, dict):

param_and_grad = self._update_param_group(param_and_grad)

moment1 = self._get_accumulator(self._moment1_acc_str,

param_and_grad[0])

moment2 = self._get_accumulator(self._moment2_acc_str,

param_and_grad[0])

beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,

param_and_grad[0])

beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,

param_and_grad[0])

find_master = self._multi_precision and param_and_grad[

0].dtype == core.VarDesc.VarType.FP16

master_weight = (self._master_weights[param_and_grad[0].name]

if find_master else None)

lr = self._create_param_lr(param_and_grad)

# create the adam optimize op

if framework.in_dygraph_mode():

found_inf = self._get_auxiliary_var('found_inf')

_beta1 = self._beta1 if not isinstance(

self._beta1, Variable) else self._beta1.numpy().item(0)

_beta2 = self._beta2 if not isinstance(

self._beta2, Variable) else self._beta2.numpy().item(0)

_, _, _, _, _, _ = _C_ops.final_state_adam_(

param_and_grad[0], param_and_grad[1], lr, moment1, moment2,

beta1_pow_acc, beta2_pow_acc, master_weight, found_inf, _beta1,

_beta2, self._epsilon, self._lazy_mode, 1000, find_master,

False)

return None

if framework._in_legacy_dygraph():

_beta1 = self._beta1 if not isinstance(

self._beta1, Variable) else self._beta1.numpy().item(0)

_beta2 = self._beta2 if not isinstance(

self._beta2, Variable) else self._beta2.numpy().item(0)

_, _, _, _, _, _ = _C_ops.adam(

param_and_grad[0], param_and_grad[1], lr, moment1, moment2,

beta1_pow_acc, beta2_pow_acc, master_weight, param_and_grad[0],

moment1, moment2, beta1_pow_acc, beta2_pow_acc, master_weight,

'epsilon', self._epsilon, 'lazy_mode', self._lazy_mode,

'min_row_size_to_use_multithread', 1000, 'beta1', _beta1,

'beta2', _beta2, 'multi_precision', find_master)

return None

inputs = {

"Param": [param_and_grad[0]],

"Grad": [param_and_grad[1]],

"LearningRate": [lr],

"Moment1": [moment1],

"Moment2": [moment2],

"Beta1Pow": [beta1_pow_acc],

"Beta2Pow": [beta2_pow_acc]

}

outputs = {

"ParamOut": [param_and_grad[0]],

"Moment1Out": [moment1],

"Moment2Out": [moment2],

"Beta1PowOut": [beta1_pow_acc],

"Beta2PowOut": [beta2_pow_acc],

}

attrs = {

"lazy_mode": self._lazy_mode,

"min_row_size_to_use_multithread": 1000,

"multi_precision": find_master

}

if isinstance(self._beta1, Variable):

inputs['Beta1Tensor'] = self._beta1

else:

attrs['beta1'] = self._beta1

if isinstance(self._beta2, Variable):

inputs['Beta2Tensor'] = self._beta2

else:

attrs['beta2'] = self._beta2

if isinstance(self._epsilon, Variable):

inputs['EpsilonTensor'] = self._epsilon

else:

attrs['epsilon'] = self._epsilon

if find_master:

inputs["MasterParam"] = master_weight

outputs["MasterParamOut"] = master_weight

adam_op = block.append_op(type=self.type,

inputs=inputs,

outputs=outputs,

attrs=attrs,

stop_gradient=True)

return adam_op

@imperative_base.no_grad

@framework.dygraph_only

def step(self):

"""

Execute the optimizer and update parameters once.

Returns:

None

Examples:

.. code-block:: python

import paddle

a = paddle.rand([2,13], dtype="float32")

linear = paddle.nn.Linear(13, 5)

# This can be any optimizer supported by dygraph.

adam = paddle.optimizer.Adam(learning_rate = 0.01,

parameters = linear.parameters())

out = linear(a)

out.backward()

adam.step()

adam.clear_grad()

"""

if not isinstance(self._parameter_list[0], dict):

params_grads = []

for param in self._parameter_list:

if param.stop_gradient:

continue

if param._grad_ivar() is not None:

grad_var = param._grad_ivar()

if in_dygraph_mode():

if hasattr(grad_var, "is_selected_rows"

) and grad_var.is_selected_rows(

) and self.regularization is not None:

raise RuntimeError(

"Adam don't support weight_decay with sparse parameters, please set it to None."

)

else:

if hasattr(

grad_var, "_is_sparse") and grad_var._is_sparse(

) and self.regularization is not None:

raise RuntimeError(

"Adam don't support weight_decay with sparse parameters, please set it to None."

)

params_grads.append((param, grad_var))

optimize_ops = self._apply_optimize(loss=None,

startup_program=None,

params_grads=params_grads)

else:

# optimize parameters in groups

for param_group in self._param_groups:

params_grads = defaultdict(lambda: list())

for param in param_group['params']:

if param.stop_gradient:

continue

if param._grad_ivar() is not None:

grad_var = param._grad_ivar()

params_grads['params'].append((param, grad_var))

params_grads.update(

{k: v

for k, v in param_group.items() if k != 'params'})

self._apply_optimize(loss=None,

startup_program=None,

params_grads=params_grads)

def _multi_tensor_init(self, target_block, parameters):

"""

All parameters used for optimizer (such as: parameters, master_weight, velocity_acc for momentum) calculations are grouped into a python list by data type (float16, float32).

This function will be overridden in the corresponding optimizer file.

Args:

target_block: the block in which the loss tensor is present

parameters: list of parameter tensors for the optimizer

"""

self._create_accumulators(target_block, parameters)

for param in parameters:

moment1 = self._get_accumulator(self._moment1_acc_str, param)

moment2 = self._get_accumulator(self._moment2_acc_str, param)

beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,

param)

beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,

param)

if param.dtype == paddle.float32:

self._param_dict['FP32_LODTensor'].append(param)

self._moment1_dict['FP32_LODTensor'].append(moment1)

self._moment2_dict['FP32_LODTensor'].append(moment2)

self._beta1_pow_acc_dict['FP32_LODTensor'].append(beta1_pow_acc)

self._beta2_pow_acc_dict['FP32_LODTensor'].append(beta2_pow_acc)

elif param.dtype == paddle.float16:

self._param_dict['FP16_LODTensor'].append(param)

self._moment1_dict['FP16_LODTensor'].append(moment1)

self._moment2_dict['FP16_LODTensor'].append(moment2)

self._beta1_pow_acc_dict['FP16_LODTensor'].append(beta1_pow_acc)

self._beta2_pow_acc_dict['FP16_LODTensor'].append(beta2_pow_acc)

if self._multi_precision:

self._master_weight_dict['FP16_LODTensor'].append(

self._master_weights[param.name])

else:

self._master_weight_dict['FP16_LODTensor'] = None

else:

raise ValueError(

"Now multi_tensor_momentum only support fp32 and fp16 parameters and grad is LOD_TENSOR."

)

def _append_optimize_multi_tensor_op(self, target_block,

parameters_and_grads):

"""

For Multi Tensor, append optimize merged_operator to block.

"""

assert isinstance(target_block, framework.Block)

grad_dict = {'FP32_LODTensor': [], 'FP16_LODTensor': []}

lr_dict = {'FP32_LODTensor': [], 'FP16_LODTensor': []}

if isinstance(parameters_and_grads, list):

for param_and_grad in parameters_and_grads:

if param_and_grad[1] is None:

continue

if param_and_grad[0].stop_gradient is False:

if param_and_grad[

0].dtype == paddle.float32 and param_and_grad[

1].type == core.VarDesc.VarType.LOD_TENSOR:

grad_dict['FP32_LODTensor'].append(param_and_grad[1])

lr = self._create_param_lr(param_and_grad)

lr_dict['FP32_LODTensor'].append(lr)

elif param_and_grad[

0].dtype == paddle.float16 and param_and_grad[

1].type == core.VarDesc.VarType.LOD_TENSOR:

grad_dict['FP16_LODTensor'].append(param_and_grad[1])

lr = self._create_param_lr(param_and_grad)

lr_dict['FP16_LODTensor'].append(lr)

else:

for param_and_grad in parameters_and_grads['params']:

if param_and_grad[1] is None:

continue

if param_and_grad[0].stop_gradient is False:

param_grad_dict = dict()

param_grad_dict['params'] = param_and_grad

param_grad_dict.update({

k: v

for k, v in parameters_and_grads.items()

if k != 'params'

})

param_and_grad = self._update_param_group(param_grad_dict)

if param_and_grad[

0].dtype == paddle.float32 and param_and_grad[

1].type == core.VarDesc.VarType.LOD_TENSOR:

grad_dict['FP32_LODTensor'].append(param_and_grad[1])

lr = self._create_param_lr(param_and_grad)

lr_dict['FP32_LODTensor'].append(lr)

elif param_and_grad[

0].dtype == paddle.float16 and param_and_grad[

1].type == core.VarDesc.VarType.LOD_TENSOR:

grad_dict['FP16_LODTensor'].append(param_and_grad[1])

lr = self._create_param_lr(param_and_grad)

lr_dict['FP16_LODTensor'].append(lr)

multi_tensor_list = ['FP32_LODTensor', 'FP16_LODTensor']

for key in multi_tensor_list:

if len(self._param_dict[key]) > 0:

find_master = self._multi_precision and key == 'FP16_LODTensor'

_beta1 = self._beta1 if not isinstance(

self._beta1, Variable) else self._beta1.numpy().item(0)

_beta2 = self._beta2 if not isinstance(

self._beta2, Variable) else self._beta2.numpy().item(0)

if framework._non_static_mode():

_, _, _, _, _, _ = _C_ops.merged_adam(

self._param_dict[key], grad_dict[key], lr_dict[key],

self._moment1_dict[key], self._moment2_dict[key],

self._beta1_pow_acc_dict[key],

self._beta2_pow_acc_dict[key],

self._master_weight_dict[key], self._param_dict[key],

self._moment1_dict[key], self._moment2_dict[key],

self._beta1_pow_acc_dict[key],

self._beta2_pow_acc_dict[key],

self._master_weight_dict[key], 'epsilon', self._epsilon,

'beta1', _beta1, 'beta2', _beta2, 'multi_precision',

find_master)

else:

inputs = {

"Param": self._param_dict[key],

"Grad": grad_dict[key],

"LearningRate": lr_dict[key],

"Moment1": self._moment1_dict[key],

"Moment2": self._moment2_dict[key],

"Beta1Pow": self._beta1_pow_acc_dict[key],

"Beta2Pow": self._beta2_pow_acc_dict[key]

}

outputs = {

"ParamOut": self._param_dict[key],

"Moment1Out": self._moment1_dict[key],

"Moment2Out": self._moment2_dict[key],

"Beta1PowOut": self._beta1_pow_acc_dict[key],

"Beta2PowOut": self._beta2_pow_acc_dict[key]

}

attrs = {

"epsilon": self._epsilon,

"beta1": _beta1,

"beta2": _beta2

}

if find_master:

inputs["MasterParam"] = self._master_weight_dict[key]

outputs["MasterParamOut"] = self._master_weight_dict[

key]

attrs["multi_precision"] = find_master

target_block.append_op(type="merged_adam",

inputs=inputs,

outputs=outputs,

attrs=attrs,

stop_gradient=True)

return None

def _update_param_group(self, parameters):

self._beta1 = parameters.get('beta1', self._default_dict['beta1'])

self._beta2 = parameters.get('beta2', self._default_dict['beta2'])

self._epsilon = parameters.get('epsilon', self._default_dict['epsilon'])

self._lazy_mode = parameters.get('lazy_mode',

self._default_dict['lazy_mode'])

parameters = parameters.get('params')

return parameters