Source code for mindspore.nn.optim.lars

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"""lars optimizer"""
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore.ops import functional as F
from mindspore._checkparam import Validator as validator
from mindspore.common import Tensor, Parameter, dtype as mstype
from .optimizer import _grad_scale, Optimizer

_lars_opt = C.MultitypeFuncGraph("lars_opt")


@_lars_opt.register("Function", "Tensor", "Number", "Tensor", "Tensor", "Bool", "Bool")
def _tensor_run_opt(lars, 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, 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 with LARSUpdate Operator. 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} \\ \lambda = \frac{\theta \text{ * } || \omega || } \\ {|| g_{t} || \text{ + } \delta \text{ * } || \omega || } \\ \lambda = \begin{cases} \min(\frac{\lambda}{\alpha }, 1) & \text{ if } clip = True \\ \lambda & \text{ otherwise } \end{cases}\\ g_{t+1} = \lambda * (g_{t} + \delta * \omega) \end{array} :math:`\theta` represents `coefficient`, :math:`\omega` represents `parameters`, :math:`g` represents `gradients`, :math:`t` represents updating step, :math:`\delta` represents `weight_decay`, :math:`\alpha` represents `learning_rate`, :math:`clip` represents `use_clip`. Args: optimizer (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 whether apply the 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. Outputs: Union[Tensor[bool], tuple[Parameter]], it depends on the output of `optimizer`. Supported Platforms: ``Ascend`` Examples: >>> net = Net() >>> 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 = Model(net, loss_fn=loss, optimizer=opt_lars, metrics=None) """ 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.parameters = optimizer.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.hyper_map = C.HyperMap() self.lars = P.LARSUpdate(epsilon, coefficient, use_clip) self.cast = P.Cast() if use_clip: self.is_group_lr = optimizer.is_group_lr self.dynamic_lr = optimizer.dynamic_lr self.origin_learning_rate = optimizer.learning_rate self.global_step = optimizer.global_step if self.is_group_lr and self.dynamic_lr: raise ValueError('Grouped dynamic learning rate is currently not supported for the inputs optimizer ' \ 'of lars.') if self.is_group: self.weight_decay = tuple(map(lambda x: x / optimizer.loss_scale, optimizer.weight_decay)) optimizer.weight_decay = tuple(map(lambda x: 0.0, optimizer.weight_decay)) else: self.weight_decay = optimizer.weight_decay / optimizer.loss_scale optimizer.weight_decay = 0.0 optimizer.decay_flags = tuple(map(lambda x: False, self.decay_flags)) 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 def construct(self, gradients): params = self.parameters if self.use_clip: lr = self._get_lr() else: lr = self.learning_rate 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), lr, self.weight_decay, gradients, params, self.decay_flags, self.lars_flag) else: gradients = self.hyper_map(F.partial(_lars_opt, self.lars, lr), self.weight_decay, gradients, params, self.decay_flags, self.lars_flag) else: gradients = self.hyper_map(F.partial(_lars_opt, self.lars, lr, self.weight_decay), gradients, params, self.decay_flags, self.lars_flag) success = self.opt(gradients) return success