Spaces:
Paused
Paused
File size: 5,560 Bytes
ff8f4ba |
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 |
import os
import sys
import gc
import json
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
from peft import PeftModel
from .globals import Global
def get_device():
if torch.cuda.is_available():
return "cuda"
else:
return "cpu"
try:
if torch.backends.mps.is_available():
return "mps"
except: # noqa: E722
pass
def get_new_base_model(base_model_name):
if Global.ui_dev_mode:
return
if Global.new_base_model_that_is_ready_to_be_used:
if Global.name_of_new_base_model_that_is_ready_to_be_used == base_model_name:
model = Global.new_base_model_that_is_ready_to_be_used
Global.new_base_model_that_is_ready_to_be_used = None
Global.name_of_new_base_model_that_is_ready_to_be_used = None
return model
else:
Global.new_base_model_that_is_ready_to_be_used = None
Global.name_of_new_base_model_that_is_ready_to_be_used = None
clear_cache()
device = get_device()
if device == "cuda":
model = LlamaForCausalLM.from_pretrained(
base_model_name,
load_in_8bit=Global.load_8bit,
torch_dtype=torch.float16,
# device_map="auto",
# ? https://github.com/tloen/alpaca-lora/issues/21
device_map={'': 0},
)
elif device == "mps":
model = LlamaForCausalLM.from_pretrained(
base_model_name,
device_map={"": device},
torch_dtype=torch.float16,
)
else:
model = LlamaForCausalLM.from_pretrained(
base_model_name, device_map={"": device}, low_cpu_mem_usage=True
)
model.config.pad_token_id = get_tokenizer(base_model_name).pad_token_id = 0
model.config.bos_token_id = 1
model.config.eos_token_id = 2
return model
def get_tokenizer(base_model_name):
if Global.ui_dev_mode:
return
loaded_tokenizer = Global.loaded_tokenizers.get(base_model_name)
if loaded_tokenizer:
return loaded_tokenizer
tokenizer = LlamaTokenizer.from_pretrained(base_model_name)
Global.loaded_tokenizers.set(base_model_name, tokenizer)
return tokenizer
def get_model(
base_model_name,
peft_model_name=None):
if Global.ui_dev_mode:
return
if peft_model_name == "None":
peft_model_name = None
model_key = base_model_name
if peft_model_name:
model_key = f"{base_model_name}//{peft_model_name}"
loaded_model = Global.loaded_models.get(model_key)
if loaded_model:
return loaded_model
peft_model_name_or_path = peft_model_name
if peft_model_name:
lora_models_directory_path = os.path.join(Global.data_dir, "lora_models")
possible_lora_model_path = os.path.join(
lora_models_directory_path, peft_model_name)
if os.path.isdir(possible_lora_model_path):
peft_model_name_or_path = possible_lora_model_path
possible_model_info_json_path = os.path.join(possible_lora_model_path, "info.json")
if os.path.isfile(possible_model_info_json_path):
try:
with open(possible_model_info_json_path, "r") as file:
json_data = json.load(file)
possible_hf_model_name = json_data.get("hf_model_name")
if possible_hf_model_name and json_data.get("load_from_hf"):
peft_model_name_or_path = possible_hf_model_name
except Exception as e:
raise ValueError("Error reading model info from {possible_model_info_json_path}: {e}")
Global.loaded_models.prepare_to_set()
clear_cache()
model = get_new_base_model(base_model_name)
if peft_model_name:
device = get_device()
if device == "cuda":
model = PeftModel.from_pretrained(
model,
peft_model_name_or_path,
torch_dtype=torch.float16,
# ? https://github.com/tloen/alpaca-lora/issues/21
device_map={'': 0},
)
elif device == "mps":
model = PeftModel.from_pretrained(
model,
peft_model_name_or_path,
device_map={"": device},
torch_dtype=torch.float16,
)
else:
model = PeftModel.from_pretrained(
model,
peft_model_name_or_path,
device_map={"": device},
)
model.config.pad_token_id = get_tokenizer(base_model_name).pad_token_id = 0
model.config.bos_token_id = 1
model.config.eos_token_id = 2
if not Global.load_8bit:
model.half() # seems to fix bugs for some users.
model.eval()
if torch.__version__ >= "2" and sys.platform != "win32":
model = torch.compile(model)
Global.loaded_models.set(model_key, model)
clear_cache()
return model
def prepare_base_model(base_model_name=Global.default_base_model_name):
Global.new_base_model_that_is_ready_to_be_used = get_new_base_model(base_model_name)
Global.name_of_new_base_model_that_is_ready_to_be_used = base_model_name
def clear_cache():
gc.collect()
# if not shared.args.cpu: # will not be running on CPUs anyway
with torch.no_grad():
torch.cuda.empty_cache()
def unload_models():
Global.loaded_models.clear()
Global.loaded_tokenizers.clear()
clear_cache()
|