Source code for mindspore.nn.optim.lamb

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"""lamb"""
import numpy as np
from mindspore import context
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 .. import layer


num_one = Tensor(np.ones([1]), mstype.float32)

_lamb_opt = C.MultitypeFuncGraph("lamb_opt")


@_lamb_opt.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tensor", "Tensor",
                    "Tensor", "Bool", "Bool")
def _update_run_op(beta1, beta2, eps, global_step, 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 (Number): Weight decay. Should be equal to or greater than 0.
        global_step (Tensor): Global step.
        param (Tensor): Parameters.
        m (Tensor): m value of parameters.
        v (Tensor): v value of parameters.
        gradient (Tensor): Gradient of parameters.
        decay_flag (bool): Specifies whether param update with weight decay.
        optim_filter(bool): Applies parameter update or not.

    Returns:
        Tensor, the new value of v after updating.
    """
    if optim_filter:
        op_mul = P.Mul()
        op_sqrt = P.Sqrt()
        op_rsqrt = P.Rsqrt()
        op_square = P.Square()
        op_cast = P.Cast()
        op_reshape = P.Reshape()
        op_shape = P.Shape()
        op_pow = P.Pow()
        op_norm = layer.Norm()
        op_select = P.Select()
        op_greater = P.Greater()
        op_fill = P.Fill()
        op_dtype = P.DType()

        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(num_one, mstype.float32) - beta1, gradient_fp32)

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

        next_mm = next_m / (op_cast(num_one, mstype.float32)
                            - op_pow(beta1, op_cast(global_step + num_one, mstype.float32)))
        next_vv = next_v / (op_cast(num_one, mstype.float32) -
                            op_pow(beta2, op_cast(global_step + num_one, mstype.float32)))
        w_norm = op_norm(param_fp32)
        g_norm = op_norm(gradient_fp32)

        g_norm_hat = op_norm(op_mul(next_mm, op_rsqrt(next_vv + eps)) + weight_decay * param_fp32)
        zeros = F.zeros_like(w_norm)
        ones = op_fill(op_dtype(w_norm), op_shape(w_norm), 1.0)
        trust_ratio = op_select(
            op_greater(w_norm, zeros),
            op_select(op_greater(g_norm, zeros), w_norm / g_norm_hat, ones),
            ones)
        tens = op_fill(op_dtype(trust_ratio), op_shape(trust_ratio), 10.0)
        trust_ratio = C.clip_by_value(trust_ratio, zeros, tens)
        update = next_mm / (op_sqrt(next_vv) + eps)

        if decay_flag:
            update = update + op_mul(weight_decay, param_fp32)

        update_with_lr = op_mul(op_mul(trust_ratio, 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 gradient

_lamb_opt_ascend = C.MultitypeFuncGraph("lamb_opt_ascend")


@_lamb_opt_ascend.register("Tensor", "Tensor", "Tensor", "Tensor", "Tensor", "Number", "Tensor", "Tensor", "Tensor",
                           "Tensor", "Bool", "Bool")
def _update_run_op_ascend(beta1, beta2, eps, global_step, lr, weight_decay, param, m, v, gradient, decay_flag,
                          optim_filter):
    """
    Update parameters function when device target is ascend.

    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 (Number): Weight decay. Should be equal to or greater than 0.
        global_step (Tensor): Global step.
        param (Tensor): Parameters.
        m (Tensor): m value of parameters.
        v (Tensor): v value of parameters.
        gradient (Tensor): Gradient of parameters.
        decay_flag (bool): Specifies whether param update with weight decay.
        optim_filter(bool): Applies parameter update or not.

    Returns:
        Tensor, the new value of v after updating.
    """
    if optim_filter:
        op_cast = P.Cast()
        op_norm = layer.Norm()
        op_lamb_apply_optimizer_assign = P.LambApplyOptimizerAssign()
        op_lamb_apply_weight_assign = P.LambApplyWeightAssign()

        param_fp32 = op_cast(param, mstype.float32)
        gradient_fp32 = op_cast(gradient, mstype.float32)
        new_global_step = op_cast(global_step + num_one, mstype.float32)
        weight_decay_flag = op_cast(decay_flag, mstype.float32)

        update, _, _ = op_lamb_apply_optimizer_assign(gradient_fp32, v, m, param_fp32,
                                                      beta1, 1.0 - beta1, beta2, 1.0 - beta2, eps,
                                                      new_global_step, weight_decay_flag, weight_decay)
        w_norm = op_norm(param_fp32)
        g_norm = op_norm(update)
        update = F.depend(update, op_lamb_apply_weight_assign(w_norm, g_norm, lr, update, param))
        return update
    return gradient


def _check_param_value(beta1, beta2, eps, prim_name):
    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 Lamb(Optimizer): """ Lamb Dynamic Learning Rate. LAMB is an optimization algorithm employing a layerwise adaptive large batch optimization technique. Refer to the paper `LARGE BATCH OPTIMIZATION FOR DEEP LEARNING: TRAINING BERT IN 76 MINUTES <https://arxiv.org/abs/1904.00962>`_. 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 can be 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" in the keys, the value of corresponding learning rate will be used. If not, the `learning_rate` in the API will be used. - 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 API will be used. - order_params: Optional. If "order_params" in the keys, the value must be the order of parameters and the order will be followed in optimizer. There are no other keys in the `dict` and the parameters which in the value of '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 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. 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). Default: 0.0. Should be equal to or greater than 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`` Examples: >>> net = Net() >>> #1) All parameters use the same learning rate and weight decay >>> optim = nn.Lamb(params=net.trainable_params(), learning_rate=0.1) >>> >>> #2) Use parameter groups and set different values >>> poly_decay_lr = learning_rate_schedule.PolynomialDecayLR(learning_rate=0.1, end_learning_rate=0.01, ... decay_steps=4, power = 0.5) >>> 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': poly_decay_lr}, ... {'order_params': net.trainable_params(0.01)}] >>> optim = nn.Lamb(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 dynamic learning rate of poly decay learning rate 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) """ def __init__(self, params, learning_rate, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0): super(Lamb, self).__init__(learning_rate, params, weight_decay) _check_param_value(beta1, beta2, eps, self.cls_name) # turn them to scalar when me support scalar/tensor mix operations 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.params = self.parameters self.moments1 = self.params.clone(prefix="lamb_m", init='zeros') self.moments2 = self.params.clone(prefix="lamb_v", init='zeros') if not self.dynamic_lr: self.global_step = Parameter(initializer(0, [1]), name='global_step') self.assignadd = P.AssignAdd() self.hyper_map = C.HyperMap() self.device_ascend = context.get_context("device_target") == "Ascend" def construct(self, gradients): lr = self.get_lr() lamb_opt = _lamb_opt_ascend if self.device_ascend else _lamb_opt gradients = self.gradients_centralization(gradients) if self.is_group: if self.is_group_lr: optim_result = self.hyper_map(F.partial(lamb_opt, self.beta1, self.beta2, self.eps, self.global_step), lr, self.weight_decay, self.params, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map(F.partial(lamb_opt, self.beta1, self.beta2, self.eps, self.global_step, lr), self.weight_decay, self.params, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map(F.partial(lamb_opt, self.beta1, self.beta2, self.eps, self.global_step, lr, self.weight_decay), self.params, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) if self.use_parallel: optim_result = F.depend(optim_result, self.broadcast_params(optim_result)) if not self.dynamic_lr: optim_result = F.depend(optim_result, self.assignadd(self.global_step, 1)) return optim_result