mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-22 22:02:51 +08:00
102 lines
4.3 KiB
Python
102 lines
4.3 KiB
Python
from typing import TYPE_CHECKING, Any, Dict, List, Optional
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import torch
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from transformers import PreTrainedModel
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from ..data import get_template_and_fix_tokenizer
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from ..extras.callbacks import LogCallback
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from ..extras.logging import get_logger
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from ..hparams import get_infer_args, get_train_args
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from ..model import load_model, load_tokenizer
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from .dpo import run_dpo
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from .orpo import run_orpo
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from .ppo import run_ppo
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from .pt import run_pt
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from .rm import run_rm
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from .sft import run_sft
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if TYPE_CHECKING:
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from transformers import TrainerCallback
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logger = get_logger(__name__)
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def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: List["TrainerCallback"] = []) -> None:
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model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args)
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callbacks.append(LogCallback(training_args.output_dir))
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if finetuning_args.stage == "pt":
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run_pt(model_args, data_args, training_args, finetuning_args, callbacks)
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elif finetuning_args.stage == "sft":
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run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
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elif finetuning_args.stage == "rm":
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run_rm(model_args, data_args, training_args, finetuning_args, callbacks)
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elif finetuning_args.stage == "ppo":
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run_ppo(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
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elif finetuning_args.stage == "dpo":
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run_dpo(model_args, data_args, training_args, finetuning_args, callbacks)
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elif finetuning_args.stage == "orpo":
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run_orpo(model_args, data_args, training_args, finetuning_args, callbacks)
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else:
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raise ValueError("Unknown task.")
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def export_model(args: Optional[Dict[str, Any]] = None) -> None:
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model_args, data_args, finetuning_args, _ = get_infer_args(args)
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if model_args.export_dir is None:
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raise ValueError("Please specify `export_dir` to save model.")
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if model_args.adapter_name_or_path is not None and model_args.export_quantization_bit is not None:
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raise ValueError("Please merge adapters before quantizing the model.")
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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processor = tokenizer_module["processor"]
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get_template_and_fix_tokenizer(tokenizer, data_args.template)
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model = load_model(tokenizer, model_args, finetuning_args) # must after fixing tokenizer to resize vocab
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if getattr(model, "quantization_method", None) and model_args.adapter_name_or_path is not None:
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raise ValueError("Cannot merge adapters to a quantized model.")
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if not isinstance(model, PreTrainedModel):
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raise ValueError("The model is not a `PreTrainedModel`, export aborted.")
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if getattr(model, "quantization_method", None) is None: # cannot convert dtype of a quantized model
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output_dtype = getattr(model.config, "torch_dtype", torch.float16)
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setattr(model.config, "torch_dtype", output_dtype)
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model = model.to(output_dtype)
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else:
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setattr(model.config, "torch_dtype", torch.float16)
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model.save_pretrained(
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save_directory=model_args.export_dir,
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max_shard_size="{}GB".format(model_args.export_size),
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safe_serialization=(not model_args.export_legacy_format),
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)
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if model_args.export_hub_model_id is not None:
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model.push_to_hub(
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model_args.export_hub_model_id,
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token=model_args.hf_hub_token,
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max_shard_size="{}GB".format(model_args.export_size),
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safe_serialization=(not model_args.export_legacy_format),
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)
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try:
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tokenizer.padding_side = "left" # restore padding side
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tokenizer.init_kwargs["padding_side"] = "left"
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tokenizer.save_pretrained(model_args.export_dir)
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if model_args.export_hub_model_id is not None:
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tokenizer.push_to_hub(model_args.export_hub_model_id, token=model_args.hf_hub_token)
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if model_args.visual_inputs and processor is not None:
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getattr(processor, "image_processor").save_pretrained(model_args.export_dir)
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if model_args.export_hub_model_id is not None:
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getattr(processor, "image_processor").push_to_hub(
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model_args.export_hub_model_id, token=model_args.hf_hub_token
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)
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except Exception:
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logger.warning("Cannot save tokenizer, please copy the files manually.")
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