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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, | |
| 'disable_exllama': shared.args.disable_exllama, | |
| } | |
| 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 | |