import inspect import logging import re import sys from pathlib import Path import accelerate import torch import transformers from transformers import AutoConfig, AutoModelForCausalLM import modules.shared as shared sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa"))) try: import llama_inference_offload except ImportError: logging.error('Failed to load GPTQ-for-LLaMa') logging.error('See https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md') sys.exit(-1) try: from modelutils import find_layers except ImportError: from utils import find_layers try: from quant import make_quant is_triton = False except ImportError: import quant is_triton = True # This function is a replacement for the load_quant function in the # GPTQ-for_LLaMa repository. It supports more models and branches. def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=None, kernel_switch_threshold=128, eval=True): exclude_layers = exclude_layers or ['lm_head'] def noop(*args, **kwargs): pass config = AutoConfig.from_pretrained(model, trust_remote_code=shared.args.trust_remote_code) torch.nn.init.kaiming_uniform_ = noop torch.nn.init.uniform_ = noop torch.nn.init.normal_ = noop torch.set_default_dtype(torch.half) transformers.modeling_utils._init_weights = False torch.set_default_dtype(torch.half) model = AutoModelForCausalLM.from_config(config, trust_remote_code=shared.args.trust_remote_code) torch.set_default_dtype(torch.float) if eval: model = model.eval() layers = find_layers(model) for name in exclude_layers: if name in layers: del layers[name] if not is_triton: gptq_args = inspect.getfullargspec(make_quant).args make_quant_kwargs = { 'module': model, 'names': layers, 'bits': wbits, } if 'groupsize' in gptq_args: make_quant_kwargs['groupsize'] = groupsize if 'faster' in gptq_args: make_quant_kwargs['faster'] = faster_kernel if 'kernel_switch_threshold' in gptq_args: make_quant_kwargs['kernel_switch_threshold'] = kernel_switch_threshold make_quant(**make_quant_kwargs) else: quant.make_quant_linear(model, layers, wbits, groupsize) del layers if checkpoint.endswith('.safetensors'): from safetensors.torch import load_file as safe_load model.load_state_dict(safe_load(checkpoint), strict=False) else: model.load_state_dict(torch.load(checkpoint), strict=False) if is_triton: if shared.args.quant_attn: quant.make_quant_attn(model) if eval and shared.args.fused_mlp: quant.make_fused_mlp(model) if shared.args.warmup_autotune: quant.autotune_warmup_linear(model, transpose=not eval) if eval and shared.args.fused_mlp: quant.autotune_warmup_fused(model) model.seqlen = 2048 return model # Used to locate the .pt/.safetensors quantized file def find_quantized_model_file(model_name): if shared.args.checkpoint: return Path(shared.args.checkpoint) path_to_model = Path(f'{shared.args.model_dir}/{model_name}') pt_path = None priority_name_list = [ Path(f'{shared.args.model_dir}/{model_name}{hyphen}{shared.args.wbits}bit{group}{ext}') for group in ([f'-{shared.args.groupsize}g', ''] if shared.args.groupsize > 0 else ['']) for ext in ['.safetensors', '.pt'] for hyphen in ['-', f'/{model_name}-', '/'] ] for path in priority_name_list: if path.exists(): pt_path = path break # If the model hasn't been found with a well-behaved name, pick the last .pt # or the last .safetensors found in its folder as a last resort if not pt_path: for ext in ['.pt', '.safetensors']: found = list(path_to_model.glob(f"*{ext}")) if len(found) > 0: if len(found) > 1: logging.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 return pt_path # The function that loads the model in modules/models.py def load_quantized(model_name): if shared.args.model_type is None: logging.error("The model could not be loaded because its type could not be inferred from its name.") logging.error("Please specify the type manually using the --model_type argument.") return None # Select the appropriate load_quant function model_type = shared.args.model_type.lower() if shared.args.pre_layer and model_type == 'llama': load_quant = llama_inference_offload.load_quant elif model_type in ('llama', 'opt', 'gptj'): if shared.args.pre_layer: logging.warning("Ignoring --pre_layer because it only works for llama model type.") load_quant = _load_quant else: logging.error("Unknown pre-quantized model type specified. Only 'llama', 'opt' and 'gptj' are supported") exit() # Find the quantized model weights file (.pt/.safetensors) path_to_model = Path(f'{shared.args.model_dir}/{model_name}') pt_path = find_quantized_model_file(model_name) if not pt_path: logging.error("Could not find the quantized model in .pt or .safetensors format, exiting...") exit() else: logging.info(f"Found the following quantized model: {pt_path}") # qwopqwop200's offload if model_type == 'llama' and shared.args.pre_layer: if len(shared.args.pre_layer) == 1: pre_layer = shared.args.pre_layer[0] else: pre_layer = shared.args.pre_layer model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, pre_layer) else: threshold = False if model_type == 'gptj' else 128 model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, kernel_switch_threshold=threshold) # accelerate offload (doesn't work properly) if shared.args.gpu_memory or torch.cuda.device_count() > 1: 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$', memory_map[i].lower()) else memory_map[i] max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory else: max_memory = accelerate.utils.get_balanced_memory(model) device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"]) logging.info("Using the following device map for the quantized 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