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import os
import sys
import gc
import torch
import transformers
from peft import PeftModel
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
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
device = get_device()
def get_base_model():
load_base_model()
return Global.loaded_base_model
def get_model_with_lora(lora_weights: str = "tloen/alpaca-lora-7b"):
Global.model_has_been_used = True
if device == "cuda":
model = PeftModel.from_pretrained(
get_base_model(),
lora_weights,
torch_dtype=torch.float16,
device_map={'': 0}, # ? https://github.com/tloen/alpaca-lora/issues/21
)
elif device == "mps":
model = PeftModel.from_pretrained(
get_base_model(),
lora_weights,
device_map={"": device},
torch_dtype=torch.float16,
)
else:
model = PeftModel.from_pretrained(
get_base_model(),
lora_weights,
device_map={"": device},
)
model.config.pad_token_id = get_tokenizer().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)
return model
def get_tokenizer():
load_base_model()
return Global.loaded_tokenizer
def load_base_model():
if Global.ui_dev_mode:
return
if Global.loaded_tokenizer is None:
Global.loaded_tokenizer = LlamaTokenizer.from_pretrained(
Global.base_model
)
if Global.loaded_base_model is None:
if device == "cuda":
Global.loaded_base_model = LlamaForCausalLM.from_pretrained(
Global.base_model,
load_in_8bit=Global.load_8bit,
torch_dtype=torch.float16,
# device_map="auto",
device_map={'': 0}, # ? https://github.com/tloen/alpaca-lora/issues/21
)
elif device == "mps":
Global.loaded_base_model = LlamaForCausalLM.from_pretrained(
Global.base_model,
device_map={"": device},
torch_dtype=torch.float16,
)
else:
Global.loaded_base_model = LlamaForCausalLM.from_pretrained(
Global.base_model, device_map={"": device}, low_cpu_mem_usage=True
)
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():
del Global.loaded_base_model
Global.loaded_base_model = None
del Global.loaded_tokenizer
Global.loaded_tokenizer = None
clear_cache()
Global.model_has_been_used = False
def unload_models_if_already_used():
if Global.model_has_been_used:
unload_models()
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