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from typing import TYPE_CHECKING, Any, Dict, List, Optional
import torch
from transformers import PreTrainedModel
from ..extras.callbacks import LogCallback
from ..extras.logging import get_logger
from ..hparams import get_infer_args, get_train_args
from ..model import load_model_and_tokenizer
from .dpo import run_dpo
from .ppo import run_ppo
from .pt import run_pt
from .rm import run_rm
from .sft import run_sft
if TYPE_CHECKING:
from transformers import TrainerCallback
logger = get_logger(__name__)
def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: Optional[List["TrainerCallback"]] = None):
model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args)
callbacks = [LogCallback()] if callbacks is None else callbacks
if finetuning_args.stage == "pt":
run_pt(model_args, data_args, training_args, finetuning_args, callbacks)
elif finetuning_args.stage == "sft":
run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
elif finetuning_args.stage == "rm":
run_rm(model_args, data_args, training_args, finetuning_args, callbacks)
elif finetuning_args.stage == "ppo":
run_ppo(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
elif finetuning_args.stage == "dpo":
run_dpo(model_args, data_args, training_args, finetuning_args, callbacks)
else:
raise ValueError("Unknown task.")
def export_model(args: Optional[Dict[str, Any]] = None):
model_args, _, finetuning_args, _ = get_infer_args(args)
if model_args.export_dir is None:
raise ValueError("Please specify `export_dir`.")
if model_args.adapter_name_or_path is not None and model_args.export_quantization_bit is not None:
raise ValueError("Please merge adapters before quantizing the model.")
model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
if getattr(model, "quantization_method", None) and model_args.adapter_name_or_path is not None:
raise ValueError("Cannot merge adapters to a quantized model.")
if not isinstance(model, PreTrainedModel):
raise ValueError("The model is not a `PreTrainedModel`, export aborted.")
if getattr(model, "quantization_method", None):
model = model.to("cpu")
elif hasattr(model.config, "torch_dtype"):
model = model.to(getattr(model.config, "torch_dtype")).to("cpu")
else:
model = model.to(torch.float16).to("cpu")
setattr(model.config, "torch_dtype", torch.float16)
model.save_pretrained(
save_directory=model_args.export_dir,
max_shard_size="{}GB".format(model_args.export_size),
safe_serialization=(not model_args.export_legacy_format),
)
if model_args.export_hub_model_id is not None:
model.push_to_hub(
model_args.export_hub_model_id,
token=model_args.hf_hub_token,
max_shard_size="{}GB".format(model_args.export_size),
safe_serialization=(not model_args.export_legacy_format),
)
try:
tokenizer.padding_side = "left" # restore padding side
tokenizer.init_kwargs["padding_side"] = "left"
tokenizer.save_pretrained(model_args.export_dir)
if model_args.export_hub_model_id is not None:
tokenizer.push_to_hub(model_args.export_hub_model_id, token=model_args.hf_hub_token)
except Exception:
logger.warning("Cannot save tokenizer, please copy the files manually.")
if __name__ == "__main__":
run_exp()