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529d89f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 | # Copyright (c) ModelScope Contributors. All rights reserved.
import torch
from dataclasses import dataclass
from typing import Literal, Optional
from swift.model import get_model_processor
from swift.utils import HfConfigFactory, get_modules_to_not_convert
@dataclass
class QuantizeArguments:
"""A dataclass that holds the configuration for model quantization.
Args:
quant_method (Optional[str]): The quantization method to use when loading the model. Can be one of {'bnb',
'hqq', 'eetq', 'quanto', 'fp8'}. Note: This is not required for QLoRA training on pre-quantized AWQ/GPTQ
models. Defaults to None.
quant_bits (Optional[Union[int, str]]): The number of bits for quantization, e.g., {1, 2, 3, 4, 8, 'float8'}.
Defaults to None.
hqq_axis (Optional[int]): The quantization axis for HQQ quantization. Defaults to None.
bnb_4bit_compute_dtype (Optional[str]): The compute data type for 4-bit BNB quantization. Can be one of {
'float16', 'bfloat16', 'float32'}. Defaults to None, which will use the model's `torch_dtype`.
bnb_4bit_quant_type (str): The quantization type for 4-bit BNB quantization. Can be one of {'fp4', 'nf4'}.
Defaults to 'nf4'.
bnb_4bit_use_double_quant (bool): Whether to use double quantization for 4-bit BNB quantization.
Defaults to True.
bnb_4bit_quant_storage (Optional[str]): The storage type for packing quantized 4-bit parameters in BNB.
Defaults to None.
"""
# awq, gptq, and aqlm need to be pre-quantized models.
# It can be detected automatically, without the need to pass in.
# while bnb, hqq, and eetq can be quantized during SFT using the original models.
quant_method: Literal['bnb', 'hqq', 'eetq', 'quanto', 'fp8'] = None
# bnb: 4,8; hqq: 1,2,3,4,8'; eetq: 8
# awq: 4; gptq: 2,3,4,8
quant_bits: Literal[1, 2, 3, 4, 8, 'float8'] = None
# hqq
hqq_axis: Optional[int] = None
# bnb
bnb_4bit_compute_dtype: Literal['float16', 'bfloat16', 'float32', None] = None
bnb_4bit_quant_type: Literal['fp4', 'nf4'] = 'nf4'
bnb_4bit_use_double_quant: bool = True
bnb_4bit_quant_storage: Optional[str] = None
def get_quantization_config(self):
if self.quant_method is None or self.quant_method in {'awq', 'gptq', 'gptq_v2'}:
return None
assert self.quant_method in {'bnb', 'hqq', 'eetq', 'quanto', 'fp8'}
if self.quant_method != 'fp8' and self.quant_bits is None:
raise ValueError(f'Please set the quant_bits. args.quant_bits: {self.quant_bits}')
if self.quant_method == 'bnb':
if self.quant_bits == 4:
load_in_4bit, load_in_8bit = True, False
elif self.quant_bits == 8:
load_in_4bit, load_in_8bit = False, True
else:
raise ValueError(f'bnb not support quant_bits: {self.quant_bits}')
from transformers import BitsAndBytesConfig
llm_int8_skip_modules = self.get_modules_to_not_convert()
quantization_config = BitsAndBytesConfig(
load_in_4bit=load_in_4bit,
load_in_8bit=load_in_8bit,
bnb_4bit_compute_dtype=self.bnb_4bit_compute_dtype,
bnb_4bit_quant_type=self.bnb_4bit_quant_type,
bnb_4bit_use_double_quant=self.bnb_4bit_use_double_quant,
bnb_4bit_quant_storage=self.bnb_4bit_quant_storage,
llm_int8_skip_modules=llm_int8_skip_modules)
elif self.quant_method == 'fp8':
if not hasattr(self, 'model_info'):
return
from transformers import FineGrainedFP8Config
with torch.device('meta'):
hf_model, _ = get_model_processor(self.model_dir, model_type=self.model_type, return_dummy_model=True)
modules_to_not_convert = get_modules_to_not_convert(hf_model)
quantization_config = FineGrainedFP8Config(modules_to_not_convert=modules_to_not_convert)
elif self.quant_method == 'hqq':
from transformers import HqqConfig
quantization_config = HqqConfig(nbits=self.quant_bits, axis=self.hqq_axis)
elif self.quant_method == 'quanto':
from transformers import QuantoConfig
if self.quant_bits == 8:
weights = 'int8'
elif self.quant_bits == 'float8':
weights = 'float8'
elif self.quant_bits == 4:
weights = 'int4'
elif self.quant_bits == 2:
weights = 'int2'
else:
raise ValueError('quanto quantization only support quant bits 2/4/8/float8')
quantization_config = QuantoConfig(weights=weights)
else: # 'eetq'
from transformers import EetqConfig
quantization_config = EetqConfig(f'int{self.quant_bits}')
return quantization_config
def get_modules_to_not_convert(self):
if not hasattr(self, 'model_meta') or not hasattr(self, 'model_info'):
return None
model_arch = self.model_meta.model_arch
res = []
if self.model_info.is_moe_model:
res += ['mlp.gate', 'mlp.shared_expert_gate']
if model_arch is not None:
for key in ['vision_tower', 'aligner']:
value = getattr(model_arch, key, None)
if value:
res += value
if not res:
return None
res.append('lm_head')
return res
def __post_init__(self):
if self.bnb_4bit_compute_dtype is None:
if self.torch_dtype in {torch.float16, torch.float32}:
self.bnb_4bit_compute_dtype = torch.float32
elif self.torch_dtype == torch.bfloat16:
self.bnb_4bit_compute_dtype = torch.bfloat16
self.bnb_4bit_compute_dtype: torch.dtype = HfConfigFactory.to_torch_dtype(self.bnb_4bit_compute_dtype)
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