wehub-resource-sync
123 lines
5.9 KiB
Python
123 lines
5.9 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import torch
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from dataclasses import dataclass
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from typing import Literal, Optional
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from swift.model import get_model_processor
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from swift.utils import HfConfigFactory, get_modules_to_not_convert
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@dataclass
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class QuantizeArguments:
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"""A dataclass that holds the configuration for model quantization.
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Args:
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quant_method (Optional[str]): The quantization method to use when loading the model. Can be one of {'bnb',
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'hqq', 'eetq', 'quanto', 'fp8'}. Note: This is not required for QLoRA training on pre-quantized AWQ/GPTQ
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models. Defaults to None.
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quant_bits (Optional[Union[int, str]]): The number of bits for quantization, e.g., {1, 2, 3, 4, 8, 'float8'}.
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Defaults to None.
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hqq_axis (Optional[int]): The quantization axis for HQQ quantization. Defaults to None.
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bnb_4bit_compute_dtype (Optional[str]): The compute data type for 4-bit BNB quantization. Can be one of {
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'float16', 'bfloat16', 'float32'}. Defaults to None, which will use the model's `torch_dtype`.
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bnb_4bit_quant_type (str): The quantization type for 4-bit BNB quantization. Can be one of {'fp4', 'nf4'}.
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Defaults to 'nf4'.
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bnb_4bit_use_double_quant (bool): Whether to use double quantization for 4-bit BNB quantization.
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Defaults to True.
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bnb_4bit_quant_storage (Optional[str]): The storage type for packing quantized 4-bit parameters in BNB.
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Defaults to None.
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"""
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# awq, gptq, and aqlm need to be pre-quantized models.
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# It can be detected automatically, without the need to pass in.
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# while bnb, hqq, and eetq can be quantized during SFT using the original models.
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quant_method: Literal['bnb', 'hqq', 'eetq', 'quanto', 'fp8'] = None
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# bnb: 4,8; hqq: 1,2,3,4,8'; eetq: 8
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# awq: 4; gptq: 2,3,4,8
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quant_bits: Literal[1, 2, 3, 4, 8, 'float8'] = None
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# hqq
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hqq_axis: Optional[int] = None
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# bnb
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bnb_4bit_compute_dtype: Literal['float16', 'bfloat16', 'float32', None] = None
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bnb_4bit_quant_type: Literal['fp4', 'nf4'] = 'nf4'
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bnb_4bit_use_double_quant: bool = True
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bnb_4bit_quant_storage: Optional[str] = None
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def get_quantization_config(self):
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if self.quant_method is None or self.quant_method in {'awq', 'gptq', 'gptq_v2'}:
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return None
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assert self.quant_method in {'bnb', 'hqq', 'eetq', 'quanto', 'fp8'}
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if self.quant_method != 'fp8' and self.quant_bits is None:
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raise ValueError(f'Please set the quant_bits. args.quant_bits: {self.quant_bits}')
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if self.quant_method == 'bnb':
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if self.quant_bits == 4:
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load_in_4bit, load_in_8bit = True, False
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elif self.quant_bits == 8:
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load_in_4bit, load_in_8bit = False, True
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else:
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raise ValueError(f'bnb not support quant_bits: {self.quant_bits}')
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from transformers import BitsAndBytesConfig
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llm_int8_skip_modules = self.get_modules_to_not_convert()
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quantization_config = BitsAndBytesConfig(
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load_in_4bit=load_in_4bit,
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load_in_8bit=load_in_8bit,
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bnb_4bit_compute_dtype=self.bnb_4bit_compute_dtype,
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bnb_4bit_quant_type=self.bnb_4bit_quant_type,
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bnb_4bit_use_double_quant=self.bnb_4bit_use_double_quant,
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bnb_4bit_quant_storage=self.bnb_4bit_quant_storage,
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llm_int8_skip_modules=llm_int8_skip_modules)
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elif self.quant_method == 'fp8':
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if not hasattr(self, 'model_info'):
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return
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from transformers import FineGrainedFP8Config
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with torch.device('meta'):
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hf_model, _ = get_model_processor(self.model_dir, model_type=self.model_type, return_dummy_model=True)
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modules_to_not_convert = get_modules_to_not_convert(hf_model)
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quantization_config = FineGrainedFP8Config(modules_to_not_convert=modules_to_not_convert)
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elif self.quant_method == 'hqq':
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from transformers import HqqConfig
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quantization_config = HqqConfig(nbits=self.quant_bits, axis=self.hqq_axis)
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elif self.quant_method == 'quanto':
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from transformers import QuantoConfig
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if self.quant_bits == 8:
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weights = 'int8'
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elif self.quant_bits == 'float8':
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weights = 'float8'
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elif self.quant_bits == 4:
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weights = 'int4'
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elif self.quant_bits == 2:
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weights = 'int2'
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else:
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raise ValueError('quanto quantization only support quant bits 2/4/8/float8')
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quantization_config = QuantoConfig(weights=weights)
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else: # 'eetq'
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from transformers import EetqConfig
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quantization_config = EetqConfig(f'int{self.quant_bits}')
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return quantization_config
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def get_modules_to_not_convert(self):
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if not hasattr(self, 'model_meta') or not hasattr(self, 'model_info'):
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return None
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model_arch = self.model_meta.model_arch
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res = []
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if self.model_info.is_moe_model:
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res += ['mlp.gate', 'mlp.shared_expert_gate']
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if model_arch is not None:
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for key in ['vision_tower', 'aligner']:
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value = getattr(model_arch, key, None)
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if value:
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res += value
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if not res:
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return None
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res.append('lm_head')
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return res
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def __post_init__(self):
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if self.bnb_4bit_compute_dtype is None:
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if self.torch_dtype in {torch.float16, torch.float32}:
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self.bnb_4bit_compute_dtype = torch.float32
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elif self.torch_dtype == torch.bfloat16:
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self.bnb_4bit_compute_dtype = torch.bfloat16
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self.bnb_4bit_compute_dtype: torch.dtype = HfConfigFactory.to_torch_dtype(self.bnb_4bit_compute_dtype)
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