Source code for mindspore.nn.optim.adam

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"""adam"""
import numpy as np

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

_adam_opt = C.MultitypeFuncGraph("adam_opt")
_scaler_one = Tensor(1, mstype.int32)
_scaler_ten = Tensor(10, mstype.float32)


@_adam_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tensor", "Tensor",
                    "Tensor", "Bool", "Bool")
def _update_run_op(beta1, beta2, eps, lr, weight_decay, param, m, v, gradient, decay_flag, optim_filter):
    """
    Update parameters.

    Args:
        beta1 (Tensor): The exponential decay rate for the 1st moment estimations. Should be in range (0.0, 1.0).
        beta2 (Tensor): The exponential decay rate for the 2nd moment estimations. Should be in range (0.0, 1.0).
        eps (Tensor): Term added to the denominator to improve numerical stability. Should be greater than 0.
        lr (Tensor): Learning rate.
        weight_decay (numbers.Number): Weight decay. Should be equal to or greater than 0.
        param (Tensor): Parameters.
        m (Tensor): m value of parameters.
        v (Tensor): v value of parameters.
        gradient (Tensor): Gradient of parameters.
        decay_flag (bool): Applies weight decay or not.
        optim_filter (bool): Applies parameter update or not.

    Returns:
        Tensor, the new value of v after updating.
    """
    op_cast = P.Cast()
    if optim_filter:
        op_mul = P.Mul()
        op_square = P.Square()
        op_sqrt = P.Sqrt()
        op_cast = P.Cast()
        op_reshape = P.Reshape()
        op_shape = P.Shape()
        param_fp32 = op_cast(param, mstype.float32)
        m_fp32 = op_cast(m, mstype.float32)
        v_fp32 = op_cast(v, mstype.float32)
        gradient_fp32 = op_cast(gradient, mstype.float32)

        next_m = op_mul(beta1, m_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32)
                                                - beta1, gradient_fp32)

        next_v = op_mul(beta2, v_fp32) + op_mul(op_cast(F.tuple_to_array((1.0,)), mstype.float32)
                                                - beta2, op_square(gradient_fp32))

        update = next_m / (eps + op_sqrt(next_v))
        if decay_flag:
            update = op_mul(weight_decay, param_fp32) + update

        update_with_lr = op_mul(lr, update)
        next_param = param_fp32 - op_reshape(update_with_lr, op_shape(param_fp32))

        next_param = F.depend(next_param, F.assign(param, op_cast(next_param, F.dtype(param))))
        next_param = F.depend(next_param, F.assign(m, op_cast(next_m, F.dtype(m))))
        next_param = F.depend(next_param, F.assign(v, op_cast(next_v, F.dtype(v))))

        return op_cast(next_param, F.dtype(param))
    return op_cast(gradient, F.dtype(param))


@_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, param, m, v, ps_parameter, cache_enable):
    """Apply sparse 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(param), 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), param))
        return success

    if not target:
        success = F.depend(success, sparse_opt(param, m, v, beta1_power, beta2_power, lr, beta1, beta2,
                                               eps, values, indices))
    else:
        op_mul = P.Mul()
        op_square = P.Square()
        op_sqrt = P.Sqrt()
        scatter_add = P.ScatterAdd(use_locking)

        success = F.depend(success, F.assign(m, op_mul(beta1, m)))
        success = F.depend(success, F.assign(v, op_mul(beta2, v)))

        grad_indices = gradient.indices
        grad_value = gradient.values

        next_m = scatter_add(m,
                             grad_indices,
                             op_mul(F.tuple_to_array((1.0,)) - beta1, grad_value))

        next_v = scatter_add(v,
                             grad_indices,
                             op_mul(F.tuple_to_array((1.0,)) - beta2, op_square(grad_value)))

        if use_nesterov:
            m_temp = next_m * _scaler_ten
            F.assign(m, op_mul(beta1, next_m))
            div_value = scatter_add(m,
                                    op_mul(grad_indices, _scaler_one),
                                    op_mul(F.tuple_to_array((1.0,)) - beta1, grad_value))
            param_update = div_value / (op_sqrt(next_v) + eps)
            F.assign(m, m_temp / _scaler_ten)
        else:
            param_update = next_m / (op_sqrt(next_v) + eps)

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

        success = F.depend(success, F.assign(param, next_param))
        success = F.depend(success, F.assign(m, next_m))
        success = F.depend(success, F.assign(v, next_v))

    return success


@_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, param,
                             moment1, moment2, ps_parameter, cache_enable):
    """Apply 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(param), op_shape(moment1), op_shape(moment2))), param))
    else:
        success = F.depend(success, opt(param, moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2,
                                        eps, gradient))
    return success


@_adam_opt.register("Function", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Tensor",
                    "Tensor", "Tensor")
def _run_off_load_opt(opt, beta1_power, beta2_power, beta1, beta2, eps, lr, gradient, param, moment1, moment2):
    """Apply AdamOffload optimizer to the weight parameter using Tensor."""
    success = True
    delat_param = opt(moment1, moment2, beta1_power, beta2_power, lr, beta1, beta2, eps, gradient)
    success = F.depend(success, F.assign_add(param, delat_param))
    return success


def _check_param_value(beta1, beta2, eps, 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_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)


[docs]class Adam(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>`_. 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 updating 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: When separating parameter groups, the weight decay in each group will be applied on the parameters if the weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive. When separating parameter groups, if you want to centralize the gradient, set grad_centralization to True, but the gradient centralization can only be applied to the parameters of the convolution layer. If the parameters of the non convolution layer are set to True, an error will be reported. To improve parameter groups performance, the customized order of parameters is supported. The sparse strategy is applied while the SparseGatherV2 operator is used for forward network. The sparse feature is under continuous development. If the sparse strategy wants to be executed on the host, set the target to the CPU. Args: params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated, the element in `params` must be class `Parameter`. When the `params` is a list of `dict`, the "params", "lr", "weight_decay" and "order_params" are the keys can be parsed. - params: Required. The value must be a list of `Parameter`. - lr: Optional. If "lr" is in the keys, the value of the corresponding learning rate will be used. If not, the `learning_rate` in the API will be used. - weight_decay: Optional. If "weight_decay" is in the keys, the value of the corresponding weight decay will be used. If not, the `weight_decay` in the API will be used. - order_params: Optional. If "order_params" is in the keys, the value must be the order of parameters and the order will be followed in the optimizer. There are no other keys in the `dict` and the parameters which in the 'order_params' must be in one of group parameters. - grad_centralization: Optional. The data type of "grad_centralization" is Bool. If "grad_centralization" is in the keys, the set value will be used. If not, the `grad_centralization` is False by default. This parameter only works on the convolution layer. learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or a graph for the learning rate. When the learning_rate is an Iterable or a Tensor in a 1D dimension, use the dynamic learning rate, then the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule, use dynamic learning rate, the i-th learning rate will be calculated during the process of training according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor in a zero dimension, use fixed learning rate. Other cases are not supported. The float learning rate must be equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float. Default: 1e-3. 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 variable tensors from being updated. If true, updates of the var, 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 (float): Weight decay (L2 penalty). It must be equal to or greater than 0. Default: 0.0. loss_scale (float): A floating point value for the loss scale. Should be greater than 0. 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: >>> net = Net() >>> #1) All parameters use the same learning rate and weight decay >>> optim = nn.Adam(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.Adam(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(Adam, self).__init__(learning_rate, params, weight_decay, loss_scale) _check_param_value(beta1, beta2, eps, 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.moment1 = self.parameters.clone(prefix="moment1", init='zeros') self.moment2 = self.parameters.clone(prefix="moment2", init='zeros') self._is_device = True self.opt = P.Adam(use_locking, use_nesterov) self.sparse_opt = P.FusedSparseAdam(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): params = self.parameters moment1 = self.moment1 moment2 = self.moment2 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() beta1_power = self.beta1_power * self.beta1 self.beta1_power = beta1_power beta2_power = self.beta2_power * self.beta2 self.beta2_power = beta2_power if self.is_group_lr: success = self.map_(F.partial(_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull, self.use_locking, self.use_nesterov, self._is_device, beta1_power, beta2_power, self.beta1, self.beta2, self.eps), lr, gradients, params, moment1, moment2, self.ps_parameters, self.cache_enable) else: success = self.map_(F.partial(_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull, self.use_locking, self.use_nesterov, self._is_device, beta1_power, beta2_power, self.beta1, self.beta2, self.eps, lr), gradients, params, moment1, 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)
[docs]class AdamWeightDecay(Optimizer): r""" Implements the Adam algorithm to fix the weight decay. .. 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 \\ update = \frac{m_{t+1}}{\sqrt{v_{t+1}} + eps} \\ update = \begin{cases} update + weight\_decay * w_{t} & \text{ if } weight\_decay > 0 \\ update & \text{ otherwise } \end{cases} \\ w_{t+1} = w_{t} - lr * update \end{array} :math:`m` represents the 1st moment vector `moment1`, :math:`v` represents the 2nd moment vector `moment2`, :math:`g` represents `gradients`, :math:`lr` represents `learning_rate`, :math:`\beta_1, \beta_2` represent `beta1` and `beta2`, :math:`t` represents updating step while :math:`w` represents `params`. Note: When separating parameter groups, the weight decay in each group will be applied on the parameters if the weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive. To improve parameter groups performance, the customized order of parameters can be supported. There is usually no connection between a optimizer and mixed precision. But when `FixedLossScaleManager` is used and `drop_overflow_update` in `FixedLossScaleManager` is set to False, optimizer needs to set the 'loss_scale'. As this optimizer has no argument of `loss_scale`, so `loss_scale` needs to be processed by other means, refer document `LossScale <https://www.mindspore.cn/docs/programming_guide/zh-CN/r1.5/lossscale.html>`_ to process `loss_scale` correctly. Args: params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated, the element in `params` must be class `Parameter`. When the `params` is a list of `dict`, the "params", "lr", "weight_decay" and "order_params" are the keys can be parsed. - params: Required. The value must be a list of `Parameter`. - lr: Optional. If "lr" is in the keys, the value of the corresponding learning rate will be used. If not, the `learning_rate` in the API will be used. - weight_decay: Optional. If "weight_decay" is in the keys, the value of the corresponding weight decay will be used. If not, the `weight_decay` in the API will be used. - order_params: Optional. If "order_params" is in the keys, the value must be the order of parameters and the order will be followed in the optimizer. There are no other keys in the `dict` and the parameters which in the 'order_params' must be in one of group parameters. learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or a graph for the learning rate. When the learning_rate is an Iterable or a Tensor in a 1D dimension, use the dynamic learning rate, then the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule, use dynamic learning rate, the i-th learning rate will be calculated during the process of training according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor in a zero dimension, use fixed learning rate. Other cases are not supported. The float learning rate must be equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float. Default: 1e-3. beta1 (float): The exponential decay rate for the 1st moment estimations. Default: 0.9. Should be in range (0.0, 1.0). beta2 (float): The exponential decay rate for the 2nd moment estimations. Default: 0.999. Should be in range (0.0, 1.0). eps (float): Term added to the denominator to improve numerical stability. Default: 1e-6. Should be greater than 0. weight_decay (float): Weight decay (L2 penalty). It must be equal to or greater than 0. Default: 0.0. Inputs: - **gradients** (tuple[Tensor]) - The gradients of `params`, the shape is the same as `params`. Outputs: tuple[bool], all elements are 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` or `eps` is not a float. TypeError: If `weight_decay` is neither float nor int. ValueError: If `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: >>> net = Net() >>> #1) All parameters use the same learning rate and weight decay >>> optim = nn.AdamWeightDecay(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}, ... {'params': no_conv_params, 'lr': 0.01}, ... {'order_params': net.trainable_params()}] >>> optim = nn.AdamWeightDecay(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. >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0. >>> # 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) """ def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0): super(AdamWeightDecay, self).__init__(learning_rate, params, weight_decay) _check_param_value(beta1, beta2, eps, self.cls_name) self.beta1 = Tensor(np.array([beta1]).astype(np.float32)) self.beta2 = Tensor(np.array([beta2]).astype(np.float32)) self.eps = Tensor(np.array([eps]).astype(np.float32)) self.moments1 = self.parameters.clone(prefix="adam_m", init='zeros') self.moments2 = self.parameters.clone(prefix="adam_v", init='zeros') def construct(self, gradients): lr = self.get_lr() if self.is_group: if self.is_group_lr: optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps), lr, self.weight_decay, self.parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr), self.weight_decay, self.parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr, self.weight_decay), self.parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) if self.use_parallel: self.broadcast_params(optim_result) return optim_result
[docs]class AdamOffload(Optimizer): r""" This optimizer will offload Adam optimizer to host CPU and keep parameters being updated on the device, to minimize the memory cost. Although that would bring about an increase of performance overhead, the optimizer could be used to run a larger model. The Adam algorithm is proposed in `Adam: A Method for Stochastic Optimization <https://arxiv.org/abs/1412.6980>`_. 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 updating 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: This optimizer only supports `GRAPH_MODE` currently. When separating parameter groups, the weight decay in each group will be applied on the parameters if the weight decay is positive. When not separating parameter groups, the `weight_decay` in the API will be applied on the parameters without 'beta' or 'gamma' in their names if `weight_decay` is positive. To improve parameter groups performance, the customized order of parameters is supported. Args: params (Union[list[Parameter], list[dict]]): When the `params` is a list of `Parameter` which will be updated, the element in `params` must be class `Parameter`. When the `params` is a list of `dict`, the "params", "lr", "weight_decay" and "order_params" are the keys can be parsed. - params: Required. The value must be a list of `Parameter`. - lr: Optional. If "lr" is in the keys, the value of the corresponding learning rate will be used. If not, the `learning_rate` in the API will be used. - weight_decay: Optional. If "weight_decay" is in the keys, the value of the corresponding weight decay will be used. If not, the `weight_decay` in the API will be used. - order_params: Optional. If "order_params" is in the keys, the value must be the order of parameters and the order will be followed in the optimizer. There are no other keys in the `dict` and the parameters which in the 'order_params' must be in one of group parameters. learning_rate (Union[float, Tensor, Iterable, LearningRateSchedule]): A value or a graph for the learning rate. When the learning_rate is an Iterable or a Tensor in a 1D dimension, use the dynamic learning rate, then the i-th step will take the i-th value as the learning rate. When the learning_rate is LearningRateSchedule, use dynamic learning rate, the i-th learning rate will be calculated during the process of training according to the formula of LearningRateSchedule. When the learning_rate is a float or a Tensor in a zero dimension, use fixed learning rate. Other cases are not supported. The float learning rate must be equal to or greater than 0. If the type of `learning_rate` is int, it will be converted to float. Default: 1e-3. 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 variable tensors from being updated. If true, updates of the var, 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 (float): Weight decay (L2 penalty). It must be equal to or greater than 0. Default: 0.0. loss_scale (float): A floating point value for the loss scale. Should be greater than 0. 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: >>> net = Net() >>> #1) All parameters use the same learning rate and weight decay >>> optim = nn.AdamOffload(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}, ... {'params': no_conv_params, 'lr': 0.01}, ... {'order_params': net.trainable_params()}] >>> optim = nn.AdamOffload(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. >>> # The no_conv_params's parameters will use learning rate of 0.01 and default weight decay of 0.0. >>> # 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) """ 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(AdamOffload, self).__init__(learning_rate, params, weight_decay, loss_scale) _check_param_value(beta1, beta2, eps, 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.moment1 = self.parameters.clone(prefix="moment1", init='zeros') self.moment2 = self.parameters.clone(prefix="moment2", init='zeros') self.opt = P.AdamNoUpdateParam(use_locking, use_nesterov) self.opt.add_prim_attr("primitive_target", "CPU") def construct(self, gradients): params = self.parameters moment1 = self.moment1 moment2 = self.moment2 gradients = self.decay_weight(gradients) gradients = self.scale_grad(gradients) lr = self.get_lr() beta1_power = self.beta1_power * self.beta1 self.beta1_power = beta1_power beta2_power = self.beta2_power * self.beta2 self.beta2_power = beta2_power if self.is_group_lr: success = self.map_reverse(F.partial(_adam_opt, self.opt, beta1_power, beta2_power, self.beta1, self.beta2, self.eps), lr, gradients, params, moment1, moment2) else: success = self.map_reverse(F.partial(_adam_opt, self.opt, beta1_power, beta2_power, self.beta1, self.beta2, self.eps, lr), gradients, params, moment1, moment2) return success