|
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" |
|
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() |
|
|
