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from pathlib import Path | |
import torch | |
from peft import PeftModel | |
from transformers import is_torch_xpu_available | |
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) | |
elif shared.model.__class__.__name__ in ['Exllamav2Model', 'Exllamav2HF'] or shared.args.loader == ['ExLlamav2', 'ExLlamav2_HF']: | |
add_lora_exllamav2(lora_names) | |
else: | |
add_lora_transformers(lora_names) | |
def get_lora_path(lora_name): | |
p = Path(lora_name) | |
if p.exists(): | |
lora_name = p.parts[-1] | |
return Path(f"{shared.args.lora_dir}/{lora_name}") | |
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 = get_lora_path(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 | |
def add_lora_exllamav2(lora_names): | |
from exllamav2 import ExLlamaV2Lora | |
if isinstance(shared.model.loras, list): | |
for lora in shared.model.loras: | |
lora.unload() | |
if len(lora_names) > 0: | |
logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names))) | |
shared.model.loras = [] | |
for lora_name in lora_names: | |
lora_path = get_lora_path(lora_name) | |
if shared.model.__class__.__name__ == 'Exllamav2Model': | |
lora = ExLlamaV2Lora.from_directory(shared.model.model, str(lora_path)) | |
else: | |
lora = ExLlamaV2Lora.from_directory(shared.model.ex_model, str(lora_path)) | |
shared.model.loras.append(lora) | |
shared.lora_names = lora_names | |
else: | |
shared.lora_names = [] | |
shared.model.loras = None | |
def add_lora_autogptq(lora_names): | |
''' | |
Adapted from https://github.com/Ph0rk0z/text-generation-webui-testing | |
''' | |
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 = get_lora_path(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 and "__merged" not in shared.model.peft_config.keys(): | |
logger.info(f"Adding the LoRA(s) named {added_set} to the model...") | |
for lora in added_set: | |
shared.model.load_adapter(get_lora_path(lora), lora) | |
if len(lora_names) > 1: | |
merge_loras() | |
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, get_lora_path(lora_names[0]), adapter_name=lora_names[0], **params) | |
for lora in lora_names[1:]: | |
shared.model.load_adapter(get_lora_path(lora), lora) | |
if len(lora_names) > 1: | |
merge_loras() | |
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) | |
elif is_torch_xpu_available(): | |
device = torch.device("xpu:0") | |
shared.model = shared.model.to(device) | |
else: | |
shared.model = shared.model.cuda() | |
def merge_loras(): | |
if len(list({shared.model.peft_config[adapter].r for adapter in shared.model.peft_config.keys()})) > 1: | |
logger.warning("The loaded LoRAs cannot be merged, as they have dissimilar ranks. Only the first one will be active.") | |
return | |
shared.model.add_weighted_adapter(shared.lora_names, [1] * len(shared.lora_names), "__merged") | |
shared.model.set_adapter("__merged") | |