96 lines
5.1 KiB
Python
96 lines
5.1 KiB
Python
from dataclasses import dataclass, field
|
|
from typing import Optional
|
|
|
|
|
|
@dataclass
|
|
class DataArguments:
|
|
eval_dataset_size: int = field(
|
|
default=512, metadata={"help": "Size of validation dataset."}
|
|
)
|
|
source_max_len: int = field(
|
|
default=512,
|
|
metadata={"help": "Maximum source sequence length. Sequences will be right padded (and possibly truncated)."},
|
|
)
|
|
train_on_source: Optional[bool] = field(
|
|
default=False,
|
|
metadata={"help": "Wether to train on the input in addition to the target text when in s2s mode."}
|
|
)
|
|
target_max_len: int = field(
|
|
default=256,
|
|
metadata={"help": "Maximum target sequence length. Sequences will be right padded (and possibly truncated)."},
|
|
)
|
|
dataset: str = field(
|
|
default=None,
|
|
metadata={"help": "A json file (s2s) or text file with the dataset to train on"}
|
|
)
|
|
block_size: int = field(
|
|
default=512,
|
|
metadata={"help": "size of the blocks the text is split into for training"},
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class ModelArguments:
|
|
model_name_or_path: Optional[str] = field(
|
|
default="EleutherAI/pythia-12b"
|
|
)
|
|
tokenizer: Optional[str] = field(
|
|
default=None
|
|
)
|
|
trust_remote_code: Optional[bool] = field(
|
|
default=False,
|
|
metadata={"help": "Enable unpickling of arbitrary code in AutoModelForCausalLM#from_pretrained."}
|
|
)
|
|
max_instant_params: int = field(
|
|
default=0,
|
|
metadata={"help": "Maximum amount of paramters to optimize per step in millions"}
|
|
)
|
|
noresize: Optional[bool] = field(
|
|
default=False,
|
|
metadata={"help": "Never resize tokenizer embeddings"}
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class TrainingArguments():
|
|
cache_dir: Optional[str] = field(
|
|
default=None
|
|
)
|
|
adam8bit: bool = field(
|
|
default=False,
|
|
metadata={"help": "Use 8-bit adam."}
|
|
)
|
|
report_to: str = field(
|
|
default='none',
|
|
metadata={"help": "To use wandb or something else for reporting."}
|
|
)
|
|
resume: bool = field(default=False, metadata={"help": 'Resume from previous checkpoint'})
|
|
ddp_find_unused_parameters: bool = field(default=True, metadata={"help": 'set if trainer should try to find unused parameters'})
|
|
output_dir: str = field(default='./output', metadata={"help": 'The output dir for logs and checkpoints'})
|
|
per_device_train_batch_size: int = field(default=1, metadata={"help": 'The training batch size per GPU. Increase for better speed.'})
|
|
gradient_accumulation_steps: int = field(default=16, metadata={"help": 'How many gradients to accumulate before to perform an optimizer step'})
|
|
epochs: int = field(default=3, metadata={"help": 'How many epochs to train for'})
|
|
weight_decay: float = field(default=0.0, metadata={"help": 'The L2 weight decay rate of AdamW'})
|
|
learning_rate: float = field(default=0.0002, metadata={"help": 'The learnign rate'})
|
|
adam_epsilon: float = field(default=1e-7, metadata={"help": 'Adam epsilon'})
|
|
remove_unused_columns: bool = field(default=False, metadata={"help": 'Removed unused columns. Needed to make this codebase work.'})
|
|
max_grad_norm: float = field(default=0.3, metadata={"help": 'Gradient clipping max norm. This is tuned and works well for all models tested.'})
|
|
gradient_checkpointing: bool = field(default=True, metadata={"help": 'Use gradient checkpointing. You want to use this.'})
|
|
fp16: bool = field(default=False, metadata={"help": 'Train in 16 bit mixed precision'})
|
|
do_train: bool = field(default=True, metadata={"help": 'To train or not to train, that is the question?'})
|
|
do_eval: bool = field(default=False, metadata={"help": 'To eval or not to eval, that is the question?'})
|
|
lr_scheduler_type: str = field(default='constant',
|
|
metadata={"help": 'Learning rate schedule. Constant a bit better than cosine, and has advantage for analysis'})
|
|
warmup_steps: float = field(default=0, metadata={"help": 'number of steps to do a warmup for'})
|
|
logging_steps: int = field(default=10, metadata={"help": 'The frequency of update steps after which to log the loss'})
|
|
group_by_length: bool = field(default=False,
|
|
metadata={"help": 'Group sequences into batches with same length. Saves memory and speeds up training considerably.'})
|
|
storage_fp16: bool = field(default=False, metadata={"help": 'Store untrained layers in 16bit'})
|
|
save_steps: int = field(default=250, metadata={"help": 'How often to save a model'})
|
|
max_checkpoints: int = field(default=0, metadata={"help": 'the maximum amount of checkpoints to save'})
|
|
save_total_limit: int = field(default=40, metadata={"help": 'How many checkpoints to save before the oldest is overwritten'})
|
|
primary_device: str = field(default="cuda:0", metadata={"help": 'The primary device to use'})
|
|
secondary_device: str = field(default="cuda:0", metadata={"help": 'The secondary device to use'})
|
|
train_non_linear_layers: str = field(default=False, metadata={"help": 'train non linear layers'})
|
|
flush_allocator: bool = field(default=False, metadata={"help": 'flush torches allocator on eatch iteration'})
|