Source code for mindspore.nn.optim.optimizer

# Copyright 2020 Huawei Technologies Co., Ltd
# 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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# ============================================================================
from typing import Iterable

import numpy as np

import mindspore
from mindspore.ops import functional as F, composite as C, operations as P
from mindspore.nn.cell import Cell
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.common.initializer import initializer
import mindspore.common.dtype as mstype
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from mindspore.common.tensor import Tensor
from mindspore import log as logger

__all__ = ['Optimizer']

[docs]class Optimizer(Cell): """ Base class for all optimizers. This class defines the API to add Ops to train a model. Note: This class defines the API to add Ops to train a model. Never use this class directly, but instead instantiate one of its subclasses. Args: learning_rate (float): A floating point value for the learning rate. Should be greater than 0. parameters (list): A list of parameter, which will be updated. The element in `parameters` should be class mindspore.Parameter. weight_decay (float): A floating point value for the weight decay. If the type of `weight_decay` input is int, it will be convertd to float. Default: 0.0. loss_scale (float): A floating point value for the loss scale. It should be greater than 0. If the type of `loss_scale` input is int, it will be convertd to float. Default: 1.0. decay_filter (Function): A function to determine whether to apply weight decay on parameters. Default: lambda x: 'beta' not in and 'gamma' not in Raises: ValueError: If the learning_rate is a Tensor, but the dims of tensor is greater than 1. TypeError: If the learning_rate is not any of the three types: float, Tensor, Iterable. """ def __init__(self, learning_rate, parameters, weight_decay=0.0, loss_scale=1.0, decay_filter=lambda x: 'beta' not in and 'gamma' not in super(Optimizer, self).__init__(auto_prefix=False) if isinstance(learning_rate, float): self.dynamic_lr = False self.gather = None self.assignadd = None self.global_step = None validator.check_number_range("learning rate", learning_rate, 0.0, float("inf"), Rel.INC_LEFT, self.cls_name) learning_rate = Tensor(learning_rate, mstype.float32) else: self.dynamic_lr = True self.gather = P.GatherV2() self.assignadd = P.AssignAdd() self.global_step = Parameter(initializer(0, [1], mindspore.int32), name='global_step') if isinstance(learning_rate, Iterable): learning_rate = Tensor(np.array(list(learning_rate)).astype(np.float32)) elif isinstance(learning_rate, Tensor): if learning_rate.dim() > 1: raise ValueError("Learning rate should be a 0 or 1 dim `Tensor`," f"but got {learning_rate.dim()}.") if learning_rate.dim() == 1 and learning_rate.size() < 2: logger.warning("If want to use the dynamic learning rate, please make sure that the number " "of elements in the list, tuple or tensor passed is greater than 1.") else: raise TypeError("Learning rate should be float, Tensor or Iterable.") if isinstance(weight_decay, int): weight_decay = float(weight_decay) validator.check_float_legal_value('weight_decay', weight_decay, None) if isinstance(loss_scale, int): loss_scale = float(loss_scale) validator.check_float_legal_value('loss_scale', loss_scale, None) if loss_scale <= 0.0: raise ValueError("Loss scale should be greater than 0, but got {}".format(loss_scale)) self.loss_scale = loss_scale if weight_decay < 0.0: raise ValueError("Weight decay should be equal or greater than 0, but got {}".format(weight_decay)) self.learning_rate = Parameter(learning_rate, name="learning_rate") self.parameters = ParameterTuple(parameters) self.reciprocal_scale = 1.0 / loss_scale self.weight_decay = weight_decay * loss_scale self.decay_flags = tuple(decay_filter(x) for x in self.parameters) if not self.parameters: raise ValueError("optimizer got an empty parameter list.")
[docs] def decay_weight(self, gradients): """ Weight decay. An approach to reduce the overfitting of a deep learning neural network model. Args: gradients (tuple[Tensor]): The gradients of `self.parameters`, and have the same shape with `self.parameters`. Returns: tuple[Tensor], The gradients after weight decay. """ if self.weight_decay > 0: params = self.parameters gradients = self.hyper_map(F.partial(apply_decay, self.weight_decay), self.decay_flags, params, gradients) return gradients
[docs] def scale_grad(self, gradients): """ Loss scale for mixed precision. An approach of mixed precision training to improve the speed and energy efficiency of training deep neural network. Args: gradients (tuple[Tensor]): The gradients of `self.parameters`, and have the same shape with `self.parameters`. Returns: tuple[Tensor], The gradients after loss scale. """ if self.reciprocal_scale != 1.0: gradients = self.hyper_map(F.partial(grad_scale, self.reciprocal_scale), gradients) return gradients
[docs] def get_lr(self): """ Get the learning rate of current step. Returns: float, the learning rate of current step. """ lr = self.learning_rate if self.dynamic_lr: lr = self.gather(self.learning_rate, self.global_step, 0) F.control_depend(lr, self.assignadd(self.global_step, 1)) return lr
def construct(self, *hyper_params): raise NotImplementedError
op_add = P.AddN() apply_decay = C.MultitypeFuncGraph("apply_decay") @apply_decay.register("Number", "Bool", "Tensor", "Tensor") def _tensor_apply_decay(weight_decay, if_apply, weight, gradient): """Get grad with weight_decay.""" if if_apply: return op_add((weight * weight_decay, gradient)) return gradient grad_scale = C.MultitypeFuncGraph("grad_scale") @grad_scale.register("Number", "Tensor") def tensor_grad_scale(scale, grad): """Get grad with scale.""" if scale == 1.0: return grad cast_op = P.Cast() type_op = P.DType() return grad * cast_op(F.scalar_to_array(scale), type_op(grad))