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