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import os
import sys
import gc
import json
import re
import torch
from transformers import (
AutoModelForCausalLM, AutoModel,
AutoTokenizer, LlamaTokenizer
)
from peft import PeftModel
from .globals import Global
from .lib.get_device import get_device
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()
model_class = AutoModelForCausalLM
from_tf = False
force_download = False
has_tried_force_download = False
while True:
try:
model = _get_model_from_pretrained(
model_class, base_model_name, from_tf=from_tf, force_download=force_download)
break
except Exception as e:
if 'from_tf' in str(e):
print(
f"Got error while loading model {base_model_name} with AutoModelForCausalLM: {e}.")
print("Retrying with from_tf=True...")
from_tf = True
force_download = False
elif model_class == AutoModelForCausalLM:
print(
f"Got error while loading model {base_model_name} with AutoModelForCausalLM: {e}.")
print("Retrying with AutoModel...")
model_class = AutoModel
force_download = False
else:
if has_tried_force_download:
raise e
print(
f"Got error while loading model {base_model_name}: {e}.")
print("Retrying with force_download=True...")
model_class = AutoModelForCausalLM
from_tf = False
force_download = True
has_tried_force_download = True
tokenizer = get_tokenizer(base_model_name)
if re.match("[^/]+/llama", base_model_name):
model.config.pad_token_id = tokenizer.pad_token_id = 0
model.config.bos_token_id = tokenizer.bos_token_id = 1
model.config.eos_token_id = tokenizer.eos_token_id = 2
return model
def _get_model_from_pretrained(model_class, model_name, from_tf=False, force_download=False):
device = get_device()
if device == "cuda":
return model_class.from_pretrained(
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},
from_tf=from_tf,
force_download=force_download,
trust_remote_code=Global.trust_remote_code
)
elif device == "mps":
return model_class.from_pretrained(
model_name,
device_map={"": device},
torch_dtype=torch.float16,
from_tf=from_tf,
force_download=force_download,
trust_remote_code=Global.trust_remote_code
)
else:
return model_class.from_pretrained(
model_name,
device_map={"": device},
low_cpu_mem_usage=True,
from_tf=from_tf,
force_download=force_download,
trust_remote_code=Global.trust_remote_code
)
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
try:
tokenizer = AutoTokenizer.from_pretrained(
base_model_name,
trust_remote_code=Global.trust_remote_code
)
except Exception as e:
if 'LLaMATokenizer' in str(e):
tokenizer = LlamaTokenizer.from_pretrained(
base_model_name,
trust_remote_code=Global.trust_remote_code
)
else:
raise e
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},
)
if re.match("[^/]+/llama", base_model_name):
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()
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