Source code for cerebras.modelzoo.config_manager.config_classes.base.optimizer_config

# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""
Config classes of Optimizer Based Configs

"""

import copy
from dataclasses import asdict, dataclass
from typing import List, Optional, Union

from cerebras.modelzoo.config_manager.config_classes.base.base_config import (
    BaseConfig,
    config_field,
    required,
)
from cerebras.modelzoo.config_manager.config_validators import LossScalingFactor
from cerebras.pytorch.optim import (
    configure_optimizer_params,
    configure_scheduler_params,
)


[docs]@dataclass class OptimizerConfig(BaseConfig): optimizer_type: str = required """ Optimizer to be used. See supported optimizers - https://docs.cerebras.net/en/latest/pytorch-docs/pytorch-ops/supported-pytorch-optimizers.html) """ weight_decay: float = 0.0 log_summaries: bool = False """ Flag to log per layer gradient norm in Tensorboard. Defaults to False """ loss_scaling_factor: Union[str, float] = config_field( default=1.0, constraint=LossScalingFactor, ) learning_rate: Optional[Union[float, List[dict]]] = None """ Learning rate scheduler to be used. See [supported LR schedulers] (https://docs.cerebras.net/en/latest/pytorch-docs/pytorch-ops/ supported-pt-learning-rate-schedulers.html). optional, defaults to None) """ optim_params: Optional[dict] = None """ A dictionary created internally that holds the optimizer specific params. The params that are part of specific optimizers get collapsed into this dictionary and validated against optimizer class signature. Yaml/config class object can pass these arguements as part of optimizer directly. They are all collapsed under optim_params internally """ max_gradient_norm: Optional[float] = None """ Max norm of the gradients for learnable parameters. Used for gradient clipping. Default=None """ adjust_learning_rate: Optional[dict] = None # Custom init for optimizer, where we want to capture all fixed optimizer params as members. # optim params is a dict that is populated by checking all additional params supplied to us. # These are optimizer specific and we use signature of that optimizer to validate these. def __init__(self, **kwargs): for field_name, field_type in self.__annotations__.items(): if field_name in kwargs: setattr(self, field_name, kwargs.pop(field_name)) self.optim_params = {key: value for key, value in kwargs.items()} if self.adjust_learning_rate is None: self.adjust_learning_rate = {} super().__init__() def __post_init__(self): # convert to a List[LearningRateConfig] if its only a float value float_lr = None if isinstance(self.learning_rate, float): float_lr = self.learning_rate self.learning_rate = [ {"scheduler": "constant", "learning_rate": self.learning_rate} ] if isinstance(self.learning_rate, list): for lr in self.learning_rate: lr_params = copy.deepcopy(lr) # Main scheduler isnt expected for signature checks lr_params.pop("main_scheduler", None) configure_scheduler_params(lr_params) elif isinstance(self.learning_rate, dict): lr_params = copy.deepcopy(self.learning_rate) lr_params.pop("main_scheduler", None) configure_scheduler_params(lr_params) if float_lr: self.learning_rate = float_lr optimizer_param_dict = copy.deepcopy(asdict(self)) # We get a bunch of args in optimizer config but arent used for optim signature # As this function is called for signature validation also, these may not be stripped out # These arent optimizer specific and we dont need to throw error based on sign for these include_list = [ 'learning_rate', 'optim_params', ] optimizer_param_dict = { k: v for k, v in optimizer_param_dict.items() if k in include_list } configure_optimizer_params( optimizer_type=self.optimizer_type, kwargs=optimizer_param_dict ) super().__post_init__()