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Add AutoGPTQ LoRA support

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oobabooga 2023-06-05 23:29:29 -03:00
parent 3a5cfe96f0
commit 11f38b5c2b
1 changed files with 61 additions and 33 deletions
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94
modules/LoRA.py
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@ -1,10 +1,13 @@
from pathlib import Path from pathlib import Path
import torch import torch
from auto_gptq import get_gptq_peft_model
from auto_gptq.utils.peft_utils import GPTQLoraConfig
from peft import PeftModel from peft import PeftModel
import modules.shared as shared import modules.shared as shared
from modules.logging_colors import logger from modules.logging_colors import logger
from modules.models import reload_model
def add_lora_to_model(lora_names): def add_lora_to_model(lora_names):
@ -13,43 +16,68 @@ def add_lora_to_model(lora_names):
removed_set = prior_set - set(lora_names) removed_set = prior_set - set(lora_names)
shared.lora_names = list(lora_names) shared.lora_names = list(lora_names)
# If no LoRA needs to be added or removed, exit is_autogptq = 'GPTQForCausalLM' in shared.model.__class__.__name__
if len(added_set) == 0 and len(removed_set) == 0:
return
# Add a LoRA when another LoRA is already present # AutoGPTQ case. It doesn't use the peft functions.
if len(removed_set) == 0 and len(prior_set) > 0: # Copied from https://github.com/Ph0rk0z/text-generation-webui-testing
logger.info(f"Adding the LoRA(s) named {added_set} to the model...") if is_autogptq:
for lora in added_set: if len(prior_set) > 0:
shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora) reload_model()
return 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')
# If any LoRA needs to be removed, start over peft_config = GPTQLoraConfig(
if len(removed_set) > 0: inference_mode=True,
shared.model.disable_adapter() )
shared.model = shared.model.base_model.model
if len(lora_names) > 0: 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))) logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join([lora_names[0]])))
params = {} shared.model = get_gptq_peft_model(shared.model, peft_config, lora_path)
if not shared.args.cpu: return
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]}"), **params) # Transformers case
else:
# If no LoRA needs to be added or removed, exit
if len(added_set) == 0 and len(removed_set) == 0:
return
for lora in lora_names[1:]: # Add a LoRA when another LoRA is already present
shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora) 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)
if not shared.args.load_in_8bit and not shared.args.cpu: return
shared.model.half()
if not hasattr(shared.model, "hf_device_map"): # If any LoRA needs to be removed, start over
if torch.has_mps: if len(removed_set) > 0:
device = torch.device('mps') shared.model.disable_adapter()
shared.model = shared.model.to(device) shared.model = shared.model.base_model.model
else:
shared.model = shared.model.cuda() 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]}"), **params)
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()