# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
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__()