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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() | |