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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# ============================================================================
"""lars optimizer"""
from __future__ import absolute_import
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore.ops import functional as F
from mindspore import _checkparam as validator
from mindspore.common import Tensor, Parameter, dtype as mstype
from mindspore.common.api import jit
from mindspore.nn.optim.optimizer import _grad_scale, Optimizer
from mindspore.nn.optim.optimizer import opt_init_args_register
_lars_opt = C.MultitypeFuncGraph("lars_opt")
@_lars_opt.register("Function", "Number", "Tensor", "Tensor", "Tensor", "Tensor", "Bool", "Bool")
def _tensor_run_opt(lars, loss_scale, learning_rate, weight_decay, gradient, weight, decay_flag, lars_flag):
"""Apply lars optimizer to the weight parameter."""
if lars_flag:
op_reduce_sum = P.SquareSumAll()
w_square_sum, grad_square_sum = op_reduce_sum(weight, gradient)
if decay_flag:
grad_t = lars(weight, gradient, w_square_sum, grad_square_sum, weight_decay / loss_scale, learning_rate)
else:
num_zero = 0.0
grad_t = lars(weight, gradient, w_square_sum, grad_square_sum, num_zero, learning_rate)
return grad_t
return gradient
def _check_param_value(optimizer, epsilon, coefficient, use_clip, prim_name):
validator.check_value_type("optimizer", optimizer, Optimizer, prim_name)
validator.check_value_type("epsilon", epsilon, [float], prim_name)
validator.check_value_type("coefficient", coefficient, [float], prim_name)
validator.check_value_type("use_clip", use_clip, [bool], prim_name)
[docs]class LARS(Optimizer):
r"""
Implements the LARS algorithm.
LARS is an optimization algorithm employing a large batch optimization technique. Refer to paper `LARGE BATCH
TRAINING OF CONVOLUTIONAL NETWORKS <https://arxiv.org/abs/1708.03888>`_.
The updating formulas are as follows,
.. math::
\begin{array}{ll} \\
&\newline
&\hline \\
&\textbf{Parameters}: \text{base learning rate } \gamma_{0} , \text{ momentum m}, \text{ weight decay }
\lambda , \\
&\hspace{5mm}\text{ LARS coefficient } \eta , \text{ number of steps } T \\
&\textbf{Init}: \text{ t=0, v=0, init weight } w_{0}^{l} \text{ for each layer } l \\[-1.ex]
&\newline
&\hline \\
&\textbf{while} \text{ t<T for each layer } l \textbf{ do} \\
&\hspace{5mm}g_{t}^{l} \leftarrow \nabla L\left(w_{t}^{l}\right) \\
&\hspace{5mm}\gamma_{t} \leftarrow \gamma_{0} *\left(1-\frac{t}{T}\right)^{2} \\
&\hspace{5mm}\gamma^{l} \leftarrow \eta *\frac{\left\|w_{t}^{l}\right\|}{\left\|g_{t}^{l}\right\|+
\lambda\left\|w_{t}^{l}\right\|} \text{(compute the local LR } \gamma^{ l)} \\
&\hspace{5mm}v_{t+1}^{l} \leftarrow m v_{t}^{l}+\gamma_{t+1} * \gamma^{l} *\left(g_{t}^{l}+\lambda
w_{t}^{l}\right) \\
&\hspace{5mm}w_{t+1}^{l} \leftarrow w_{t}^{l}-v_{t+1}^{l} \\
&\textbf{ end while } \\[-1.ex]
&\newline
&\hline \\[-1.ex]
\end{array}
:math:`w` represents the network's params, :math:`g` represents `gradients`,
:math:`t` represents the current step, :math:`\lambda` represents `weight_decay` in `optimizer`,
:math:`\gamma` represents `learning_rate` in `optimizer`, :math:`\eta` represents `coefficient`.
Args:
optimizer (:class:`mindspore.nn.Optimizer`): MindSpore optimizer for which to wrap and modify gradients.
epsilon (float): Term added to the denominator to improve numerical stability. Default: ``1e-05`` .
coefficient (float): Trust coefficient for calculating the local learning rate. Default: ``0.001`` .
use_clip (bool): Whether to use clip operation for calculating the local learning rate. Default: ``False`` .
lars_filter (Function): A function to determine which of the network parameters to use LARS algorithm. Default:
``lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name``.
Inputs:
- **gradients** (tuple[Tensor]) - The gradients of `params` in the optimizer, the shape is the
as same as the `params` in the optimizer.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore as ms
>>> from mindspore import nn
>>>
>>> # Define the network structure of LeNet5. Refer to
>>> # https://gitee.com/mindspore/docs/blob/master/docs/mindspore/code/lenet.py
>>> net = LeNet5()
>>> loss = nn.SoftmaxCrossEntropyWithLogits()
>>> opt = nn.Momentum(net.trainable_params(), 0.1, 0.9)
>>> opt_lars = nn.LARS(opt, epsilon=1e-08, coefficient=0.02)
>>> model = ms.train.Model(net, loss_fn=loss, optimizer=opt_lars, metrics=None)
"""
@opt_init_args_register
def __init__(self, optimizer, epsilon=1e-05, coefficient=0.001, use_clip=False,
lars_filter=lambda x: 'LayerNorm' not in x.name and 'bias' not in x.name):
super(LARS, self).__init__(0.0, [Parameter(Tensor(0.0), name="fake_param")])
_check_param_value(optimizer, epsilon, coefficient, use_clip, self.cls_name)
self.opt = optimizer
self.dynamic_decay_flags = optimizer.dynamic_decay_flags
self.dynamic_weight_decay = optimizer.dynamic_weight_decay
self.weight_decay = optimizer.weight_decay
self.global_step = optimizer.global_step
self.parameters = optimizer.parameters
if optimizer._use_flattened_params: # pylint: disable=W0212
self.opt._use_flattened_params = False # pylint: disable=W0212
self._user_parameters += [param.name for param in self.parameters]
self.use_clip = use_clip
self.lars_flag = tuple(lars_filter(x) for x in self.parameters)
self.is_group = optimizer.is_group
self.learning_rate = Parameter(Tensor(0.0, dtype=mstype.float32), name="fake_lr")
self.decay_flags = optimizer.decay_flags
self.reciprocal_scale = optimizer.reciprocal_scale
self.need_scale = optimizer.need_scale
self.lars = P.LARSUpdate(epsilon, coefficient, use_clip)
self.cast = P.Cast()
self.loss_scale = optimizer.loss_scale
if use_clip:
self.is_group_lr = optimizer.is_group_lr
self.dynamic_lr = optimizer.dynamic_lr
self.origin_learning_rate = optimizer.learning_rate
if self.is_group_lr and self.dynamic_lr:
raise ValueError("For 'LARS', if the argument 'use_clip' is set to True, then the dynamic "
"learning rate and group learning rate cannot both be true.")
if self.is_group:
optimizer.dynamic_decay_flags = tuple(map(lambda x: False, self.dynamic_decay_flags))
else:
optimizer.dynamic_decay_flags = False
optimizer.decay_flags = tuple(map(lambda x: False, self.decay_flags))
optimizer.dynamic_weight_decay = False
optimizer.reciprocal_scale = 1.0
optimizer.exec_weight_decay = False
def _get_lr(self):
"""Get the learning rate of current step."""
lr = self.origin_learning_rate
if self.dynamic_lr:
if self.is_group_lr:
lr = ()
for learning_rate in self.origin_learning_rate:
current_dynamic_lr = learning_rate(self.global_step)
lr += (current_dynamic_lr,)
else:
lr = self.origin_learning_rate(self.global_step)
return lr
@jit
def construct(self, gradients):
params = self.parameters
gradients = self.flatten_gradients(gradients)
if self.use_clip:
lr = self._get_lr()
else:
lr = self.learning_rate
weight_decay = self.get_weight_decay()
if self.need_scale:
gradients = self.hyper_map(F.partial(_grad_scale, self.reciprocal_scale), gradients)
if self.is_group:
if self.is_group_lr:
gradients = self.hyper_map(F.partial(_lars_opt, self.lars, self.loss_scale), lr, weight_decay,
gradients, params, self.decay_flags, self.lars_flag)
else:
gradients = self.hyper_map(F.partial(_lars_opt, self.lars, self.loss_scale, lr), weight_decay,
gradients, params, self.decay_flags, self.lars_flag)
else:
gradients = self.hyper_map(F.partial(_lars_opt, self.lars, self.loss_scale, lr, weight_decay),
gradients, params, self.decay_flags, self.lars_flag)
success = self.opt(gradients)
if self._is_dynamic_lr_or_weight_decay() and not self.opt.dynamic_lr and not self.opt.dynamic_weight_decay:
self.assignadd(self.global_step, self.global_step_increase_tensor)
return success