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from pathlib import Path
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import modules.shared as shared
from modules.logging_colors import logger
from modules.models import get_max_memory_dict
def load_quantized(model_name):
path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
pt_path = None
# Find the model checkpoint
if shared.args.checkpoint:
pt_path = Path(shared.args.checkpoint)
else:
for ext in ['.safetensors', '.pt', '.bin']:
found = list(path_to_model.glob(f"*{ext}"))
if len(found) > 0:
if len(found) > 1:
logger.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.')
pt_path = found[-1]
break
if pt_path is None:
logger.error("The model could not be loaded because its checkpoint file in .bin/.pt/.safetensors format could not be located.")
return
use_safetensors = pt_path.suffix == '.safetensors'
if not (path_to_model / "quantize_config.json").exists():
quantize_config = BaseQuantizeConfig(
bits=bits if (bits := shared.args.wbits) > 0 else 4,
group_size=gs if (gs := shared.args.groupsize) > 0 else -1,
desc_act=shared.args.desc_act
)
else:
quantize_config = None
# Define the params for AutoGPTQForCausalLM.from_quantized
params = {
'model_basename': pt_path.stem,
'device': "cuda:0" if not shared.args.cpu else "cpu",
'use_triton': shared.args.triton,
'inject_fused_attention': not shared.args.no_inject_fused_attention,
'inject_fused_mlp': not shared.args.no_inject_fused_mlp,
'use_safetensors': use_safetensors,
'trust_remote_code': shared.args.trust_remote_code,
'max_memory': get_max_memory_dict(),
'quantize_config': quantize_config,
'use_cuda_fp16': not shared.args.no_use_cuda_fp16,
}
logger.info(f"The AutoGPTQ params are: {params}")
model = AutoGPTQForCausalLM.from_quantized(path_to_model, **params)
# These lines fix the multimodal extension when used with AutoGPTQ
if hasattr(model, 'model'):
if not hasattr(model, 'dtype'):
if hasattr(model.model, 'dtype'):
model.dtype = model.model.dtype
if hasattr(model.model, 'model') and hasattr(model.model.model, 'embed_tokens'):
if not hasattr(model, 'embed_tokens'):
model.embed_tokens = model.model.model.embed_tokens
if not hasattr(model.model, 'embed_tokens'):
model.model.embed_tokens = model.model.model.embed_tokens
return model
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