# Copyright (c) ModelScope Contributors. All rights reserved. import os import torch import torch.distributed as dist from dataclasses import dataclass from typing import Literal, Optional from swift.utils import HfConfigFactory, get_logger, init_process_group, set_default_ddp_config, to_abspath from .base_args import BaseArguments from .merge_args import MergeArguments logger = get_logger() @dataclass class ExportArguments(MergeArguments, BaseArguments): """ExportArguments is a dataclass that inherits from BaseArguments and MergeArguments. Args: output_dir (Optional[str]): Directory to save the exported results. Defaults to None, which automatically sets a path with an appropriate suffix. quant_method (Optional[str]): The quantization method. Can be 'awq', 'gptq', 'bnb', 'fp8', or 'gptq_v2'. Defaults to None. See examples for more details. quant_n_samples (int): Number of samples for GPTQ/AWQ calibration. Defaults to 256. quant_batch_size (int): The batch size for quantization. Defaults to 1. group_size (int): The group size for quantization. Defaults to 128. to_cached_dataset (bool): Whether to tokenize and export the dataset in advance as a cached dataset. Defaults to False. Note: You can specify the validation set content through `--split_dataset_ratio` or `--val_dataset`. to_ollama (bool): Whether to generate the `Modelfile` required by Ollama. Defaults to False. to_mcore (bool): Whether to convert Hugging Face format weights to Megatron-Core format. Defaults to False. to_hf (bool): Whether to convert Megatron-Core format weights to Hugging Face format. Defaults to False. mcore_model (Optional[str]): The path to the Megatron-Core format model. Defaults to None. mcore_adapter (Optional[str]): A list of adapter paths for the Megatron-Core format model. Defaults to []. thread_count (Optional[int]): The number of model shards when `to_mcore` is True. Defaults to None, which automatically sets the number based on the model size to keep the largest shard under 10GB. test_convert_precision (bool): Whether to test the precision error of weight conversion between Hugging Face and Megatron-Core formats. Defaults to False. test_convert_dtype (str): The dtype to use for the conversion precision test. Defaults to 'float32'. push_to_hub (bool): Whether to push the output to the Model Hub. Defaults to False. See examples for more details. hub_model_id (Optional[str]): The model ID for pushing to the Hub (e.g., 'user_name/repo_name' or 'repo_name'). Defaults to None. hub_private_repo (bool): Whether the Hub repository is private. Defaults to False. commit_message (str): The commit message for pushing to the Hub. Defaults to 'update files'. to_peft_format (bool): Whether to export in PEFT format. This argument is for compatibility and currently has no effect. Defaults to False. exist_ok (bool): If the output_dir exists, do not raise an exception and overwrite its contents. Defaults to False. """ output_dir: Optional[str] = None # awq/gptq quant_method: Literal['awq', 'gptq', 'bnb', 'fp8', 'gptq_v2'] = None quant_n_samples: int = 256 quant_batch_size: int = 1 group_size: int = 128 # cached_dataset to_cached_dataset: bool = False template_mode: Literal['train', 'rlhf', 'kto'] = 'train' # ollama to_ollama: bool = False # megatron to_mcore: bool = False to_hf: bool = False mcore_model: Optional[str] = None mcore_adapter: Optional[str] = None thread_count: Optional[int] = None test_convert_precision: bool = False test_convert_dtype: str = 'float32' # push to ms hub push_to_hub: bool = False # 'user_name/repo_name' or 'repo_name' hub_model_id: Optional[str] = None hub_private_repo: bool = False commit_message: str = 'update files' # compat to_peft_format: bool = False exist_ok: bool = False def load_args_from_ckpt(self) -> None: if self.to_cached_dataset: return super().load_args_from_ckpt() def _init_output_dir(self): if self.output_dir is None: ckpt_dir = self.ckpt_dir or f'./{self.model_suffix}' ckpt_dir, ckpt_name = os.path.split(ckpt_dir) if self.to_peft_format: suffix = 'peft' elif self.quant_method: suffix = f'{self.quant_method}' if self.quant_bits is not None: suffix += f'-int{self.quant_bits}' elif self.to_ollama: suffix = 'ollama' elif self.merge_lora: suffix = 'merged' elif self.to_mcore: suffix = 'mcore' elif self.to_hf: suffix = 'hf' elif self.to_cached_dataset: suffix = 'cached_dataset' else: return self.output_dir = os.path.join(ckpt_dir, f'{ckpt_name}-{suffix}') self.output_dir = to_abspath(self.output_dir) if not self.exist_ok and os.path.exists(self.output_dir): raise FileExistsError(f'args.output_dir: `{self.output_dir}` already exists.') logger.info(f'args.output_dir: `{self.output_dir}`') def __post_init__(self): if self.quant_batch_size == -1: self.quant_batch_size = None if self.quant_bits and self.quant_method is None: raise ValueError('Please specify the quantization method using `--quant_method awq/gptq/bnb`.') if self.quant_method and self.quant_bits is None and self.quant_method != 'fp8': raise ValueError('Please specify `--quant_bits`.') if self.quant_method in {'gptq', 'awq'} and self.torch_dtype is None: self.torch_dtype = torch.float16 if self.to_mcore or self.to_hf: if self.merge_lora: self.merge_lora = False logger.warning('`swift export --to_mcore/to_hf` does not support the `--merge_lora` parameter. ' 'To export LoRA delta weights, please use `megatron export`') self.mcore_model = to_abspath(self.mcore_model, check_path_exist=True) if not dist.is_initialized(): set_default_ddp_config() init_process_group(backend=self.ddp_backend, timeout=self.ddp_timeout) BaseArguments.__post_init__(self) self._init_output_dir() self.test_convert_dtype = HfConfigFactory.to_torch_dtype(self.test_convert_dtype) if self.quant_method in {'gptq', 'awq'} and len(self.dataset) == 0: raise ValueError(f'self.dataset: {self.dataset}, Please input the quant dataset.') if self.to_cached_dataset: self.lazy_tokenize = False if self.packing: raise ValueError('Packing will be handled during training; here we only perform tokenization ' 'in advance, so you do not need to set up packing separately.') assert not self.streaming, 'not supported'