Source code for mindspore.nn.optim.lazyadam

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"""lazy adam"""
from mindspore.common import dtype as mstype
from mindspore.common.initializer import initializer
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore.ops import functional as F
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from .optimizer import Optimizer
from .optimizer import opt_init_args_register

_lazy_adam_opt = C.MultitypeFuncGraph("lazy_adam_opt")


@_lazy_adam_opt.register("Function", "Function", "Function", "Function", "Bool", "Bool", "Bool", "Tensor", "Tensor",
                         "Tensor", "Tensor", "Tensor", "Tensor", "RowTensor", "Tensor", "Tensor", "Tensor", "Bool",
                         "Bool")
def _run_opt_with_sparse(opt, sparse_opt, push, pull, use_locking, use_nesterov, target, beta1_power, beta2_power,
                         beta1, beta2, eps, lr, gradient, params, m, v, ps_parameter, cache_enable):
    """Apply sparse lazy adam optimizer to the weight parameter when the gradient is sparse."""
    success = True
    indices = gradient.indices
    values = gradient.values
    if ps_parameter and not cache_enable:
        op_shape = P.Shape()
        shapes = (op_shape(params), op_shape(m), op_shape(v),
                  op_shape(beta1_power), op_shape(beta2_power), op_shape(lr), op_shape(beta1),
                  op_shape(beta2), op_shape(eps), op_shape(values), op_shape(indices))
        success = F.depend(success, pull(push((beta1_power, beta2_power, lr, beta1, beta2,
                                               eps, values, indices), shapes), params))
        return success

    if not target:
        success = F.depend(success, sparse_opt(params, m, v, beta1_power, beta2_power, lr, beta1, beta2,
                                               eps, values, indices))
    else:
        op_gather = P.Gather()
        op_sqrt = P.Sqrt()
        scatter_add = P.ScatterAdd(use_locking)
        scatter_update = P.ScatterUpdate(use_locking)

        m_slice = op_gather(m, indices, 0)
        v_slice = op_gather(v, indices, 0)

        next_m = m_slice * beta1 + values * (1 - beta1)
        next_v = v_slice * beta2 + values * values * (1 - beta2)

        lr_t = lr * op_sqrt(1 - beta2_power) / (1 - beta1_power)

        if use_nesterov:
            m_temp = beta1 * next_m + values * (1 - beta1)
            param_update = m_temp / (op_sqrt(next_v) + eps)
        else:
            param_update = next_m / (op_sqrt(next_v) + eps)

        success = F.depend(success, scatter_add(params, indices, - lr_t * param_update))
        success = F.depend(success, scatter_update(m, indices, next_m))
        success = F.depend(success, scatter_update(v, indices, next_v))

    return success


@_lazy_adam_opt.register("Function", "Function", "Function", "Function", "Bool", "Bool", "Bool", "Tensor", "Tensor",
                         "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Bool", "Bool")
def _run_opt_with_one_number(opt, sparse_opt, push, pull, use_locking, use_nesterov, target, beta1_power, beta2_power,
                             beta1, beta2, eps, lr, gradient, params, moment1, moment2, ps_parameter, cache_enable):
    """Apply lazy adam optimizer to the weight parameter using Tensor."""
    success = True
    if ps_parameter and not cache_enable:
        op_shape = P.Shape()
        success = F.depend(success, pull(push((beta1_power, beta2_power, lr, beta1, beta2, eps, gradient),
                                              (op_shape(params), op_shape(moment1), op_shape(moment2))), params))
    else:
        success = F.depend(success, opt(params, moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2,
                                        eps, gradient))
    return success


def _check_param_value(beta1, beta2, eps, weight_decay, prim_name):
    """Check the type of inputs."""
    validator.check_value_type("beta1", beta1, [float], prim_name)
    validator.check_value_type("beta2", beta2, [float], prim_name)
    validator.check_value_type("eps", eps, [float], prim_name)
    validator.check_value_type("weight_dacay", weight_decay, [float], prim_name)
    validator.check_float_range(beta1, 0.0, 1.0, Rel.INC_NEITHER, "beta1", prim_name)
    validator.check_float_range(beta2, 0.0, 1.0, Rel.INC_NEITHER, "beta2", prim_name)
    validator.check_positive_float(eps, "eps", prim_name)
    validator.check_non_negative_float(weight_decay, "weight_decay", prim_name)


[docs]class LazyAdam(Optimizer): r""" Updates gradients by the Adaptive Moment Estimation (Adam) algorithm. The Adam algorithm is proposed in `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_. This optimizer will apply a lazy adam algorithm when gradient is sparse. The updating formulas are as follows, .. math:: \begin{array}{ll} \\ m_{t+1} = \beta_1 * m_{t} + (1 - \beta_1) * g \\ v_{t+1} = \beta_2 * v_{t} + (1 - \beta_2) * g * g \\ l = \alpha * \frac{\sqrt{1-\beta_2^t}}{1-\beta_1^t} \\ w_{t+1} = w_{t} - l * \frac{m_{t+1}}{\sqrt{v_{t+1}} + \epsilon} \end{array} :math:`m` represents the 1st moment vector `moment1`, :math:`v` represents the 2nd moment vector `moment2`, :math:`g` represents `gradients`, :math:`l` represents scaling factor, :math:`\beta_1, \beta_2` represent `beta1` and `beta2`, :math:`t` represents the current step while :math:`beta_1^t` and :math:`beta_2^t` represent `beta1_power` and `beta2_power`, :math:`\alpha` represents `learning_rate`, :math:`w` represents `params`, :math:`\epsilon` represents `eps`. Note: The sparse strategy is applied while the SparseGatherV2 operator is used for forward network. If the sparse strategy wants to be executed on the host, set the target to the CPU. Please note, the sparse behavior is not equivalent to the original Adam algorithm, as only the current indices parames will be updated. The sparse feature is under continuous development. If parameters are not grouped, the `weight_decay` in optimizer will be applied on the network parameters without 'beta' or 'gamma' in their names. Users can group parameters to change the strategy of decaying weight. When parameters are grouped, each group can set `weight_decay`, if not, the `weight_decay` in optimizer will be applied. Args: params (Union[list[Parameter], list[dict]]): Must be list of `Parameter` or list of `dict`. When the `params` is a list of `dict`, the string "params", "lr", "weight_decay", "grad_centralization" and "order_params" are the keys can be parsed. - params: Required. Parameters in current group. The value must be a list of `Parameter`. - lr: Optional. If "lr" in the keys, the value of corresponding learning rate will be used. If not, the `learning_rate` in optimizer will be used. Fixed and dynamic learning rate are supported. - weight_decay: Optional. If "weight_decay" in the keys, the value of corresponding weight decay will be used. If not, the `weight_decay` in the optimizer will be used. - grad_centralization: Optional. Must be Boolean. If "grad_centralization" is in the keys, the set value will be used. If not, the `grad_centralization` is False by default. This configuration only works on the convolution layer. - order_params: Optional. When parameters is grouped, this usually is used to maintain the order of parameters that appeared in the network to improve performance. The value should be parameters whose order will be followed in optimizer. If `order_params` in the keys, other keys will be ignored and the element of 'order_params' must be in one group of `params`. learning_rate (Union[float, int, Tensor, Iterable, LearningRateSchedule]): Default: 1e-3. - float: The fixed learning rate value. Must be equal to or greater than 0. - int: The fixed learning rate value. Must be equal to or greater than 0. It will be converted to float. - Tensor: Its value should be a scalar or a 1-D vector. For scalar, fixed learning rate will be applied. For vector, learning rate is dynamic, then the i-th step will take the i-th value as the learning rate. - Iterable: Learning rate is dynamic. The i-th step will take the i-th value as the learning rate. - LearningRateSchedule: Learning rate is dynamic. During training, the optimizer calls the instance of LearningRateSchedule with step as the input to get the learning rate of current step. beta1 (float): The exponential decay rate for the 1st moment estimations. Should be in range (0.0, 1.0). Default: 0.9. beta2 (float): The exponential decay rate for the 2nd moment estimations. Should be in range (0.0, 1.0). Default: 0.999. eps (float): Term added to the denominator to improve numerical stability. Should be greater than 0. Default: 1e-8. use_locking (bool): Whether to enable a lock to protect the updating process of variable tensors. If true, updates of the `w`, `m`, and `v` tensors will be protected by a lock. If false, the result is unpredictable. Default: False. use_nesterov (bool): Whether to use Nesterov Accelerated Gradient (NAG) algorithm to update the gradients. If true, update the gradients using NAG. If false, update the gradients without using NAG. Default: False. weight_decay (Union[float, int]): Weight decay (L2 penalty). Default: 0.0. loss_scale (float): A floating point value for the loss scale. Should be equal to or greater than 1. In general, use the default value. Only when `FixedLossScaleManager` is used for training and the `drop_overflow_update` in `FixedLossScaleManager` is set to False, then this value needs to be the same as the `loss_scale` in `FixedLossScaleManager`. Refer to class :class:`mindspore.FixedLossScaleManager` for more details. Default: 1.0. Inputs: - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`. Outputs: Tensor[bool], the value is True. Raises: TypeError: If `learning_rate` is not one of int, float, Tensor, Iterable, LearningRateSchedule. TypeError: If element of `parameters` is neither Parameter nor dict. TypeError: If `beta1`, `beta2`, `eps` or `loss_scale` is not a float. TypeError: If `weight_decay` is neither float nor int. TypeError: If `use_locking` or `use_nesterov` is not a bool. ValueError: If `loss_scale` or `eps` is less than or equal to 0. ValueError: If `beta1`, `beta2` is not in range (0.0, 1.0). ValueError: If `weight_decay` is less than 0. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> from mindspore import nn, Model >>> >>> net = Net() >>> #1) All parameters use the same learning rate and weight decay >>> optim = nn.LazyAdam(params=net.trainable_params()) >>> >>> #2) Use parameter groups and set different values >>> conv_params = list(filter(lambda x: 'conv' in x.name, net.trainable_params())) >>> no_conv_params = list(filter(lambda x: 'conv' not in x.name, net.trainable_params())) >>> group_params = [{'params': conv_params, 'weight_decay': 0.01, 'grad_centralization':True}, ... {'params': no_conv_params, 'lr': 0.01}, ... {'order_params': net.trainable_params()}] >>> optim = nn.LazyAdam(group_params, learning_rate=0.1, weight_decay=0.0) >>> # The conv_params's parameters will use default learning rate of 0.1 and weight decay of 0.01 and grad >>> # centralization of True. >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0 and grad >>> # centralization of False. >>> # The final parameters order in which the optimizer will be followed is the value of 'order_params'. >>> >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> model = Model(net, loss_fn=loss, optimizer=optim) """ @opt_init_args_register def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False, use_nesterov=False, weight_decay=0.0, loss_scale=1.0): super(LazyAdam, self).__init__(learning_rate, params, weight_decay, loss_scale) _check_param_value(beta1, beta2, eps, weight_decay, self.cls_name) validator.check_value_type("use_locking", use_locking, [bool], self.cls_name) validator.check_value_type("use_nesterov", use_nesterov, [bool], self.cls_name) self.beta1 = Tensor(beta1, mstype.float32) self.beta2 = Tensor(beta2, mstype.float32) self.beta1_power = Parameter(initializer(1, [1], mstype.float32), name="beta1_power") self.beta2_power = Parameter(initializer(1, [1], mstype.float32), name="beta2_power") self.eps = Tensor(eps, mstype.float32) self.use_nesterov = use_nesterov self.use_locking = use_locking self._is_device = True self.moment1 = self.parameters.clone(prefix="moment1", init='zeros') self.moment2 = self.parameters.clone(prefix="moment2", init='zeros') self.opt = P.Adam(use_locking, use_nesterov) self.sparse_opt = P.FusedSparseLazyAdam(use_locking, use_nesterov) self.sparse_opt.add_prim_attr("primitive_target", "CPU") self._ps_pull = P.Pull() self._ps_push = P.Push("Adam", [0, 1, 2]) self._ps_push.add_prim_attr("use_nesterov", use_nesterov) def construct(self, gradients): gradients = self.decay_weight(gradients) gradients = self.gradients_centralization(gradients) gradients = self.scale_grad(gradients) gradients = self._grad_sparse_indices_deduplicate(gradients) lr = self.get_lr() self.beta1_power = self.beta1_power * self.beta1 self.beta2_power = self.beta2_power * self.beta2 if self.is_group_lr: success = self.map_reverse(F.partial(_lazy_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull, self.use_locking, self.use_nesterov, self._is_device, self.beta1_power, self.beta2_power, self.beta1, self.beta2, self.eps), lr, gradients, self.parameters, self.moment1, self.moment2, self.ps_parameters, self.cache_enable) else: success = self.map_reverse(F.partial(_lazy_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull, self.use_locking, self.use_nesterov, self._is_device, self.beta1_power, self.beta2_power, self.beta1, self.beta2, self.eps, lr), gradients, self.parameters, self.moment1, self.moment2, self.ps_parameters, self.cache_enable) return success @Optimizer.target.setter def target(self, value): """ If the input value is set to "CPU", the parameters will be updated on the host using the Fused optimizer operation. """ self._set_base_target(value)