import re import sys from pathlib import Path import accelerate import torch import modules.shared as shared sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa"))) import llama import llama_inference_offload import opt def load_quantized(model_name): if not shared.args.model_type: # Try to determine model type from model name model_type = model_name.split('-')[0].lower() if model_type not in ('llama', 'opt'): print("Can't determine model type from model name. Please specify it manually using --gptq-model-type " "argument") exit() else: model_type = shared.args.model_type.lower() if model_type == 'llama': if not shared.args.pre_layer: load_quant = llama.load_quant else: load_quant = llama_inference_offload.load_quant elif model_type == 'opt': load_quant = opt.load_quant else: print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported") exit() path_to_model = Path(f'models/{model_name}') if path_to_model.name.lower().startswith('llama-7b'): pt_model = f'llama-7b-{shared.args.wbits}bit.pt' elif path_to_model.name.lower().startswith('llama-13b'): pt_model = f'llama-13b-{shared.args.wbits}bit.pt' elif path_to_model.name.lower().startswith('llama-30b'): pt_model = f'llama-30b-{shared.args.wbits}bit.pt' elif path_to_model.name.lower().startswith('llama-65b'): pt_model = f'llama-65b-{shared.args.wbits}bit.pt' else: pt_model = f'{model_name}-{shared.args.wbits}bit.pt' # Try to find the .pt both in models/ and in the subfolder pt_path = None for path in [Path(p) for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]: if path.exists(): pt_path = path if not pt_path: print(f"Could not find {pt_model}, exiting...") exit() # qwopqwop200's offload if shared.args.pre_layer: model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.pre_layer) else: model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits) # accelerate offload (doesn't work properly) if shared.args.gpu_memory: memory_map = list(map(lambda x : x.strip(), shared.args.gpu_memory)) max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB' max_memory = {} for i in range(len(memory_map)): max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i] max_memory['cpu'] = max_cpu_memory device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"]) print("Using the following device map for the 4-bit model:", device_map) # https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True) # No offload elif not shared.args.cpu: model = model.to(torch.device('cuda:0')) return model