text-generation-webui/modules/LoRA.py

93 lines
3.5 KiB
Python
Raw Normal View History

2023-03-17 01:35:53 +01:00
from pathlib import Path
2023-03-25 05:18:32 +01:00
import torch
2023-03-30 03:50:58 +02:00
from peft import PeftModel
2023-03-25 05:18:32 +01:00
2023-03-17 01:35:53 +01:00
import modules.shared as shared
from modules.logging_colors import logger
2023-06-06 04:29:29 +02:00
from modules.models import reload_model
2023-03-24 01:56:26 +01:00
try:
from auto_gptq import get_gptq_peft_model
from auto_gptq.utils.peft_utils import GPTQLoraConfig
has_auto_gptq_peft = True
except:
has_auto_gptq_peft = False
def add_lora_to_model(lora_names):
prior_set = set(shared.lora_names)
added_set = set(lora_names) - prior_set
removed_set = prior_set - set(lora_names)
shared.lora_names = list(lora_names)
2023-03-17 01:35:53 +01:00
2023-06-06 04:29:29 +02:00
is_autogptq = 'GPTQForCausalLM' in shared.model.__class__.__name__
# AutoGPTQ case. It doesn't use the peft functions.
# Copied from https://github.com/Ph0rk0z/text-generation-webui-testing
if is_autogptq:
if not has_auto_gptq_peft:
logger.error("This version of AutoGPTQ does not support LoRA. You need to install from source or wait for a new release.")
return
2023-06-06 04:29:29 +02:00
if len(prior_set) > 0:
reload_model()
if len(shared.lora_names) == 0:
return
else:
if len(shared.lora_names) > 1:
logger.warning('AutoGPTQ can only work with 1 LoRA at the moment. Only the first one in the list will be loaded')
peft_config = GPTQLoraConfig(
inference_mode=True,
)
lora_path = Path(f"{shared.args.lora_dir}/{shared.lora_names[0]}")
logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join([lora_names[0]])))
shared.model = get_gptq_peft_model(shared.model, peft_config, lora_path)
return
# Transformers case
else:
# If no LoRA needs to be added or removed, exit
if len(added_set) == 0 and len(removed_set) == 0:
return
# Add a LoRA when another LoRA is already present
if len(removed_set) == 0 and len(prior_set) > 0:
logger.info(f"Adding the LoRA(s) named {added_set} to the model...")
for lora in added_set:
shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
return
# If any LoRA needs to be removed, start over
if len(removed_set) > 0:
shared.model.disable_adapter()
shared.model = shared.model.base_model.model
if len(lora_names) > 0:
logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names)))
params = {}
if not shared.args.cpu:
params['dtype'] = shared.model.dtype
if hasattr(shared.model, "hf_device_map"):
params['device_map'] = {"base_model.model." + k: v for k, v in shared.model.hf_device_map.items()}
elif shared.args.load_in_8bit:
params['device_map'] = {'': 0}
shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"),adapter_name=lora_names[0], **params)
2023-06-06 04:29:29 +02:00
for lora in lora_names[1:]:
shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
if not shared.args.load_in_8bit and not shared.args.cpu:
shared.model.half()
if not hasattr(shared.model, "hf_device_map"):
if torch.has_mps:
device = torch.device('mps')
shared.model = shared.model.to(device)
else:
shared.model = shared.model.cuda()