momentum.py

# 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.

import warnings

from .optimizer import Optimizer
from ..fluid import core
from ..fluid import framework
from ..fluid.framework import Variable, name_scope
from ..fluid.layer_helper import LayerHelper
from ..fluid import unique_name
from ..fluid import layers
import paddle.fluid as fluid
from paddle.fluid.regularizer import L2DecayRegularizer
from paddle import _C_ops
import paddle

__all__ = []


class Momentum(Optimizer):
    r"""

    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|Tensor|LearningRateDecay, optional): The learning rate used to update ``Parameter``.
            It can be a float value, a ``Tensor`` with a float type or a LearningRateDecay. The default value is 0.001.
        momentum (float): Momentum factor. The default value is 0.9.
        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.
        multi_precision (bool, optional): Whether to use multi-precision during weight updating. Default is false.
        rescale_grad (float, optional): Multiply the gradient with `rescale_grad` before updating. \
            Often choose to be ``1.0/batch_size``.
        use_multi_tensor (bool, optional): Whether to use multi-tensor strategy to update all parameters at once . Default is false.
        name (str, optional): The default value is None. Normally there is no need for user
                to set this property. For more information, please refer to
                :ref:`api_guide_Name` .

    Examples:
        .. code-block:: python

            import paddle
            import numpy as np
            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)
            beta1 = paddle.to_tensor([0.9], dtype="float32")
            beta2 = paddle.to_tensor([0.99], dtype="float32")
            momentum = paddle.optimizer.Momentum(learning_rate=0.1, parameters=linear.parameters(), weight_decay=0.01)
            back = out.backward()
            momentum.step()
            momentum.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)
            momentum = paddle.optimizer.Momentum(
                learning_rate=0.1,
                parameters=[{
                    'params': linear_1.parameters()
                }, {
                    'params': linear_2.parameters(),
                    'weight_decay': 0.001,
                    'learning_rate': 0.1
                }],
                weight_decay=0.01,
                momentum=0.9)                   
            out.backward()
            momentum.step()
            momentum.clear_grad()

    """
    _velocity_acc_str = "velocity"

    def __init__(self,
                 learning_rate=0.001,
                 momentum=0.9,
                 parameters=None,
                 use_nesterov=False,
                 weight_decay=None,
                 grad_clip=None,
                 multi_precision=False,
                 rescale_grad=1.0,
                 use_multi_tensor=False,
                 name=None):
        if learning_rate is None:
            raise ValueError("learning_rate is not set")
        if momentum is None:
            raise ValueError("momentum is not set")

        predicate = lambda regular: isinstance(regular, (L2DecayRegularizer, float))
        if isinstance(parameters, list):
            if isinstance(parameters[0], dict):
                for param_group in parameters:
                    decay = param_group[
                        'weight_decay'] if 'weight_decay' in param_group else weight_decay
                    reg_method, reg_coeff = self._update_regularization(decay)
                    param_group['regularization_method'] = reg_method
                    param_group['regularization_coeff'] = reg_coeff
                    py_regular = None if predicate(decay) else decay
                    param_group['weight_decay'] = py_regular

        py_regular = None if predicate(weight_decay) else weight_decay
        super(Momentum, self).__init__(
            learning_rate=learning_rate,
            parameters=parameters,
            weight_decay=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 = self._update_regularization(
            weight_decay)
        self._multi_precision = multi_precision
        self._rescale_grad = rescale_grad
        self._master_weights = {}

        self._default_dict = {
            'momentum': momentum,
            'use_nesterov': use_nesterov,
            'rescale_grad': rescale_grad,
            'regularization_method': self._regularization_method,
            'regularization_coeff': self._regularization_coeff,
        }
        self._use_multi_tensor = use_multi_tensor
        if self._use_multi_tensor:
            self._param_dict = {'FP32_LODTensor': [], 'FP16_LODTensor': []}
            self._velocity_dict = {'FP32_LODTensor': [], 'FP16_LODTensor': []}
            self._master_weight_dict = {
                'FP32_LODTensor': None,
                'FP16_LODTensor': []
            }
            self._regularization_method_dict = {
                'FP32_LODTensor': [],
                'FP16_LODTensor': []
            }
            self._regularization_coeff_dict = {
                'FP32_LODTensor': [],
                'FP16_LODTensor': []
            }

    def _update_regularization(self, weight_decay):
        reg_method = ""
        reg_coeff = 0.0

        if (isinstance(weight_decay, L2DecayRegularizer)):
            reg_method = "l2_decay"
            reg_coeff = weight_decay._regularization_coeff
        if (isinstance(weight_decay, float)):
            reg_method = "l2_decay"
            reg_coeff = weight_decay
        return reg_method, reg_coeff

    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 _create_accumulators(self, block, parameters):
        '''
        if framework._non_static_mode():
            return
        '''
        assert isinstance(block, framework.Block)

        if isinstance(parameters, dict):
            parameters = self._update_param_group(parameters)

        for p in parameters:
            if self._multi_precision and p.dtype == core.VarDesc.VarType.FP16:
                master_p = self._create_master_weight(p)
                self._add_accumulator(self._velocity_acc_str, 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 Momentum optimizer."
                )
            self._add_accumulator(self._velocity_acc_str, p)

    def _create_regularization_of_grad(self, param, grad, regularization=None):
        """ Create and add backward regularization Operators
    
        Function helper of append_regularization_ops.
        """
        # If ParamAttr is set to L2Decay, we skip doing regularization here. And then we fused
        # L2Decay with momentum which can refer to _append_optimize_op below.
        if hasattr(param, 'regularizer') and isinstance(param.regularizer,
                                                        L2DecayRegularizer):
            return grad
        return super(Momentum, self)._create_regularization_of_grad(
            param, grad, regularization)

    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)

        velocity_acc = self._get_accumulator(self._velocity_acc_str,
                                             param_and_grad[0])
        lr = self._create_param_lr(param_and_grad)

        # For fusion of momentum and l2decay 
        param = param_and_grad[0]
        regularization_method = self._regularization_method
        regularization_coeff = self._regularization_coeff
        if hasattr(param, 'regularizer'):
            # we skip param's l2decay before, so fuse it with momentum here.
            if isinstance(param.regularizer, L2DecayRegularizer):
                regularization_method = "l2_decay"
                regularization_coeff = param.regularizer._regularization_coeff
            # the param's regularization has been done before, we avoid do l2decay in momentum.
            elif param.regularizer is not None:
                regularization_method = ""
                regularization_coeff = 0.0

        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)

        if framework._non_static_mode():
            if isinstance(param_and_grad, dict):
                self._update_regularization(param_and_grad['weight_decay'])
            _, _, _ = _C_ops.momentum(
                param_and_grad[0], param_and_grad[1], velocity_acc, lr,
                master_weight, param_and_grad[0], velocity_acc, master_weight,
                'mu', self._momentum, 'use_nesterov', self._use_nesterov,
                'regularization_method', regularization_method,
                'regularization_coeff', regularization_coeff, 'multi_precision',
                find_master)

            return None

        attrs = {
            "mu": self._momentum,
            "use_nesterov": self._use_nesterov,
            "regularization_method": regularization_method,
            "regularization_coeff": regularization_coeff,
            "multi_precision": find_master,
            "rescale_grad": self._rescale_grad
        }

        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]
        }

        if find_master:
            inputs["MasterParam"] = master_weight
            outputs["MasterParamOut"] = master_weight

        # 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

    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:
            velocity_acc = self._get_accumulator(self._velocity_acc_str, param)
            regularization_method = self._regularization_method
            regularization_coeff = self._regularization_coeff
            if hasattr(param, 'regularizer'):
                # we skip param's l2decay before, so fuse it with momentum here.
                if isinstance(param.regularizer, L2DecayRegularizer):
                    regularization_method = "l2_decay"
                    regularization_coeff = param.regularizer._regularization_coeff
                elif param.regularizer is not None:
                    regularization_method = ""
                    regularization_coeff = 0.0
            if param.dtype == paddle.float32:
                self._param_dict['FP32_LODTensor'].append(param)
                self._velocity_dict['FP32_LODTensor'].append(velocity_acc)
                # fp32 no master weight
                self._regularization_method_dict['FP32_LODTensor'].append(
                    regularization_method)
                self._regularization_coeff_dict['FP32_LODTensor'].append(
                    regularization_coeff)
            elif param.dtype == paddle.float16:
                self._param_dict['FP16_LODTensor'].append(param)
                self._velocity_dict['FP16_LODTensor'].append(velocity_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
                self._regularization_method_dict['FP16_LODTensor'].append(
                    regularization_method)
                self._regularization_coeff_dict['FP16_LODTensor'].append(
                    regularization_coeff)
            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'

                if framework._non_static_mode():
                    _, _, _ = _C_ops.merged_momentum(
                        self._param_dict[key], grad_dict[key],
                        self._velocity_dict[key], lr_dict[key],
                        self._master_weight_dict[key], self._param_dict[key],
                        self._velocity_dict[key], self._master_weight_dict[key],
                        'mu', self._momentum, 'use_nesterov',
                        self._use_nesterov, 'regularization_method',
                        self._regularization_method_dict[key],
                        'regularization_coeff',
                        self._regularization_coeff_dict[key], 'multi_precision',
                        find_master)
                else:
                    inputs = {
                        "Param": self._param_dict[key],
                        "Grad": grad_dict[key],
                        "Velocity": self._velocity_dict[key],
                        "LearningRate": lr_dict[key],
                    }
                    outputs = {
                        "ParamOut": self._param_dict[key],
                        "VelocityOut": self._velocity_dict[key],
                    }
                    attrs = {
                        "mu": self._momentum,
                        "use_nesterov": self._use_nesterov,
                        "regularization_method":
                        self._regularization_method_dict[key],
                        "regularization_coeff":
                        self._regularization_coeff_dict[key],
                    }
                    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_momentum",
                        inputs=inputs,
                        outputs=outputs,
                        attrs=attrs,
                        stop_gradient=True)
        return None

    def _update_param_group(self, parameters):
        self._momentum = parameters.get('momentum',
                                        self._default_dict['momentum'])
        self._use_nesterov = parameters.get('use_nesterov',
                                            self._default_dict['use_nesterov'])
        self._rescale_grad = parameters.get('rescale_grad',
                                            self._default_dict['rescale_grad'])
        self._regularization_method = parameters.get(
            'regularization_method',
            self._default_dict['regularization_method'])
        self._regularization_coeff = parameters.get(
            'regularization_coeff', self._default_dict['regularization_coeff'])
        parameters = parameters.get('params')
        return parameters