File size: 8,350 Bytes
ae81e0f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 |
"""
Classes for loading pretrained models
"""
from os.path import join
from omegaconf import OmegaConf
import torch
import torch.nn as nn
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer
# from transformers import BitsAndBytesConfig
from peft import prepare_model_for_kbit_training
def get_pretrained_loader(pretrained_model_name_or_path: str,
huggingface_token: str = None,
**model_kwargs: any):
"""
Return the appropriate loader for the pretrained model
"""
if 'lama' in pretrained_model_name_or_path: # Llama or llama
return PretrainedLlamaLoader(
pretrained_model_name_or_path=pretrained_model_name_or_path,
huggingface_token=huggingface_token,
**model_kwargs,
)
elif 'istral' in pretrained_model_name_or_path: # Mistral or mistral;
return PretrainedMistralLoader(
pretrained_model_name_or_path=pretrained_model_name_or_path,
huggingface_token=huggingface_token,
**model_kwargs,
)
else:
print(f'-> {pretrained_model_name_or_path} using default pretrained model loader')
return PretrainedModelLoader(
pretrained_model_name_or_path=pretrained_model_name_or_path,
huggingface_token=huggingface_token,
**model_kwargs,
)
class PretrainedModelLoader():
"""
Class for loading a pretrained model.
Example:
model_loader = PretrainedModelLoader(**model_kwargs)
model = model_loader.load()
"""
def __init__(self,
pretrained_model_name_or_path: str,
cache_dir: str = None,
return_dict: bool = True, # False
device_map: str = 'auto',
low_cpu_mem_usage: bool = True,
torch_dtype: str = 'bfloat16',
rope_theta: float = 10000.,
attn_implementation: str = 'sdpa', # eager
load_in_8bit: bool = False,
load_in_4bit: bool = False,
huggingface_token: str = None,
peft_id: str = None,
rope_scaling: dict = None,
**other_kwargs: any) -> None:
print(f'-> Using {attn_implementation} attention')
self.loading_kwargs = {
'pretrained_model_name_or_path': pretrained_model_name_or_path,
'cache_dir': cache_dir,
'return_dict': return_dict,
'load_in_8bit': load_in_8bit,
'load_in_4bit': load_in_4bit,
'device_map': device_map,
'low_cpu_mem_usage': low_cpu_mem_usage,
'torch_dtype': getattr(torch, torch_dtype),
'rope_theta': rope_theta,
'attn_implementation': attn_implementation,
}
if rope_scaling is not None: # Llama 3.1 patch
rope_scaling = OmegaConf.to_container(rope_scaling)
self.loading_kwargs['rope_scaling'] = rope_scaling
for k, v in other_kwargs.items():
self.loading_kwargs[k] = v
self.quantization = load_in_8bit or load_in_4bit
self.peft_id = peft_id
self.gradient_checkpointing = False
if huggingface_token is not None: # for gated models, e.g., Llama 3
self.loading_kwargs['token'] = huggingface_token
if self.quantization:
raise NotImplementedError
# bnb_config = BitsAndBytesConfig(
# load_in_8bit=load_in_8bit,
# load_in_4bit=load_in_4bit,
# bnb_4bit_compute_dtype=torch.bfloat16,
# bnb_4bit_use_double_quant=True,
# bnb_4bit_quant_type="nf4",
# )
# del self.loading_kwargs['load_in_8bit']
# del self.loading_kwargs['load_in_4bit']
# self.loading_kwargs['quantization_config'] = bnb_config
def load(self) -> nn.Module:
"""
Load pretrained model
"""
model = AutoModelForCausalLM.from_pretrained(**self.loading_kwargs)
if self.quantization:
model = prepare_model_for_kbit_training(
model, use_gradient_checkpointing=self.gradient_checkpointing,
gradient_checkpointing_kwargs={'use_reentrant': False},
)
return model
def load_tokenizer(self):
"""
Load pretrained tokenizer
"""
try:
return AutoTokenizer.from_pretrained(**self.loading_kwargs)
except Exception as e:
print("-> Error with `AutoTokenizer.from_pretrained(**self.loading_kwargs)`:", e)
print("-> Trying `LlamaTokenizer.from_pretrained(**self.loading_kwargs)`")
# MZ 6/1: Mistral-7B-Instruct-v0.3 in Transformers v4.36 doesn't work with the above
return LlamaTokenizer.from_pretrained(**self.loading_kwargs)
class PretrainedLlamaLoader(PretrainedModelLoader):
def load(self, model_type: str = None, ):
llama3_1 = float('.'.join(transformers.__version__.split('.')[:2])) > 4.42 # 'Meta-Llama-3.1' in self.loading_kwargs['pretrained_model_name_or_path']
if model_type is None:
from transformers import LlamaForCausalLM as model_class
elif 'lolcats_llama_sharded' in model_type:
from .modeling_llama_sharded import ShardedLolcatsLlamaForCausalLM as model_class
elif 'lolcats_long_llama' in model_type:
from .modeling_llama import LooooolcatsLlamaForCausalLM as model_class
elif 'lolcats_llama' in model_type:
from .modeling_llama import LolcatsLlamaForCausalLM as model_class
else:
if model_type == 'flash_attention_2':
self.loading_kwargs['attn_implementation'] = model_type
from transformers import AutoModelForCausalLM as model_class
print('-> Loading from AutoModelForCausalLM')
model = model_class.from_pretrained(**self.loading_kwargs)
if self.peft_id is not None:
from peft import PeftModel
print('-> Loading PEFT checkpoint')
model = PeftModel.from_pretrained(
model,
self.peft_id,
torch_dtype=self.loading_kwargs['torch_dtype'],
device_map='auto',
cache_dir=self.loading_kwargs['cache_dir']
).merge_and_unload()
if self.quantization:
model = prepare_model_for_kbit_training(
model, use_gradient_checkpointing=self.gradient_checkpointing,
gradient_checkpointing_kwargs={'use_reentrant': False},
)
return model
def load_tokenizer(self):
return AutoTokenizer.from_pretrained(**self.loading_kwargs)
class PretrainedMistralLoader(PretrainedModelLoader):
def load(self, model_type: str = None):
if model_type is None:
from transformers import MistralForCausalLM as model_class
elif 'lolcats_long_llama' in model_type:
from .modeling_mistral import LooooolcatsMistralForCausalLM as model_class
elif 'lolcats_llama' in model_type:
from .modeling_mistral import LolcatsMistralForCausalLM as model_class
else:
if model_type == 'flash_attention_2':
self.loading_kwargs['attn_implementation'] = model_type
from transformers import AutoModelForCausalLM as model_class
print('-> Loading from AutoModelForCausalLM')
model = model_class.from_pretrained(**self.loading_kwargs)
if self.peft_id is not None:
from peft import PeftModel
model = PeftModel.from_pretrained(
model,
self.peft_id,
torch_dtype=self.loading_kwargs['torch_dtype'],
device_map='auto',
cache_dir=self.loading_kwargs['cache_dir'],
).merge_and_unload()
if self.quantization:
model = prepare_model_for_kbit_training(
model, use_gradient_checkpointing=self.gradient_checkpointing,
gradient_checkpointing_kwargs={'use_reentrant': False},
)
return model
|