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from pathlib import Path
import torch
from peft import PeftModel
import modules.shared as shared
from modules.logging_colors import logger
from modules.models import reload_model
def add_lora_to_model(lora_names):
if 'GPTQForCausalLM' in shared.model.__class__.__name__ or shared.args.loader == 'AutoGPTQ':
add_lora_autogptq(lora_names)
elif shared.model.__class__.__name__ in ['ExllamaModel', 'ExllamaHF'] or shared.args.loader == 'ExLlama':
add_lora_exllama(lora_names)
else:
add_lora_transformers(lora_names)
def add_lora_exllama(lora_names):
try:
from exllama.lora import ExLlamaLora
except:
try:
from repositories.exllama.lora import ExLlamaLora
except:
logger.error("Could not find the file repositories/exllama/lora.py. Make sure that exllama is cloned inside repositories/ and is up to date.")
return
if len(lora_names) == 0:
if shared.model.__class__.__name__ == 'ExllamaModel':
shared.model.generator.lora = None
else:
shared.model.lora = None
shared.lora_names = []
return
else:
if len(lora_names) > 1:
logger.warning('ExLlama can only work with 1 LoRA at the moment. Only the first one in the list will be loaded.')
lora_path = Path(f"{shared.args.lora_dir}/{lora_names[0]}")
lora_config_path = lora_path / "adapter_config.json"
lora_adapter_path = lora_path / "adapter_model.bin"
logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join([lora_names[0]])))
if shared.model.__class__.__name__ == 'ExllamaModel':
lora = ExLlamaLora(shared.model.model, str(lora_config_path), str(lora_adapter_path))
shared.model.generator.lora = lora
else:
lora = ExLlamaLora(shared.model.ex_model, str(lora_config_path), str(lora_adapter_path))
shared.model.lora = lora
shared.lora_names = [lora_names[0]]
return
# Adapted from https://github.com/Ph0rk0z/text-generation-webui-testing
def add_lora_autogptq(lora_names):
try:
from auto_gptq import get_gptq_peft_model
from auto_gptq.utils.peft_utils import GPTQLoraConfig
except:
logger.error("This version of AutoGPTQ does not support LoRA. You need to install from source or wait for a new release.")
return
if len(lora_names) == 0:
reload_model()
shared.lora_names = []
return
else:
if len(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.')
if not shared.args.no_inject_fused_attention:
logger.warning('Fused Atttention + AutoGPTQ may break Lora loading. Disable it.')
peft_config = GPTQLoraConfig(
inference_mode=True,
)
lora_path = Path(f"{shared.args.lora_dir}/{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)
shared.lora_names = [lora_names[0]]
return
def add_lora_transformers(lora_names):
prior_set = set(shared.lora_names)
added_set = set(lora_names) - prior_set
removed_set = prior_set - set(lora_names)
# 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 may no longer be PeftModel
if hasattr(shared.model, 'disable_adapter'):
shared.model.disable_adapter()
shared.model = shared.model.base_model.model
if len(lora_names) > 0:
params = {}
if not shared.args.cpu:
if shared.args.load_in_4bit or shared.args.load_in_8bit:
params['peft_type'] = shared.model.dtype
else:
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()}
logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names)))
shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), adapter_name=lora_names[0], **params)
for lora in lora_names[1:]:
shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
shared.lora_names = lora_names
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.backends.mps.is_available():
device = torch.device('mps')
shared.model = shared.model.to(device)
else:
shared.model = shared.model.cuda()
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