import gc import os import re import time from pathlib import Path import hashlib import torch import transformers from accelerate import infer_auto_device_map, init_empty_weights from transformers import ( AutoConfig, AutoModel, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, BitsAndBytesConfig, ) import modules.shared as shared from modules import llama_attn_hijack, sampler_hijack from modules.logging_colors import logger from modules.models_settings import infer_loader transformers.logging.set_verbosity_error() local_rank = None if shared.args.deepspeed: import deepspeed from transformers.deepspeed import ( HfDeepSpeedConfig, is_deepspeed_zero3_enabled ) from modules.deepspeed_parameters import generate_ds_config # Distributed setup local_rank = shared.args.local_rank if shared.args.local_rank is not None else int(os.getenv("LOCAL_RANK", "0")) world_size = int(os.getenv("WORLD_SIZE", "1")) torch.cuda.set_device(local_rank) deepspeed.init_distributed() ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir) dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration sampler_hijack.hijack_samplers() def load_model(model_name, loader=None): logger.info(f"Loading {model_name}...") t0 = time.time() shared.is_seq2seq = False load_func_map = { 'Transformers': huggingface_loader, 'AutoGPTQ': AutoGPTQ_loader, 'GPTQ-for-LLaMa': GPTQ_loader, 'llama.cpp': llamacpp_loader, 'FlexGen': flexgen_loader, 'RWKV': RWKV_loader, 'ExLlama': ExLlama_loader, 'ExLlama_HF': ExLlama_HF_loader } p = Path(model_name) if p.exists(): model_name = p.parts[-1] if loader is None: if shared.args.loader is not None: loader = shared.args.loader else: loader = infer_loader(model_name) if loader is None: logger.error('The path to the model does not exist. Exiting.') return None, None shared.args.loader = loader output = load_func_map[loader](model_name) if type(output) is tuple: model, tokenizer = output else: model = output if model is None: return None, None else: tokenizer = load_tokenizer(model_name, model) # Hijack attention with xformers if any((shared.args.xformers, shared.args.sdp_attention)): llama_attn_hijack.hijack_llama_attention() logger.info(f"Loaded the model in {(time.time()-t0):.2f} seconds.\n") return model, tokenizer def load_tokenizer(model_name, model): tokenizer = None path_to_model = Path(f"{shared.args.model_dir}/{model_name}/") if any(s in model_name.lower() for s in ['gpt-4chan', 'gpt4chan']) and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists(): tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/")) elif path_to_model.exists(): try: tokenizer = AutoTokenizer.from_pretrained( path_to_model, trust_remote_code=shared.args.trust_remote_code, use_fast=False ) except ValueError: tokenizer = AutoTokenizer.from_pretrained( path_to_model, trust_remote_code=shared.args.trust_remote_code, use_fast=True ) if tokenizer.__class__.__name__ == 'LlamaTokenizer': pairs = [ ['tokenizer_config.json', '516c6167c884793a738c440e29ccb80c15e1493ffc965affc69a1a8ddef4572a'], ['special_tokens_map.json', 'ff3b4a612c4e447acb02d40071bddd989fe0da87eb5b7fe0dbadfc4f74de7531'] ] for pair in pairs: p = path_to_model / pair[0] if p.exists(): with open(p, "rb") as f: bytes = f.read() file_hash = hashlib.sha256(bytes).hexdigest() if file_hash != pair[1]: logger.warning(f"{p} is different from the original LlamaTokenizer file. It is either customized or outdated.") return tokenizer def huggingface_loader(model_name): path_to_model = Path(f'{shared.args.model_dir}/{model_name}') if 'chatglm' in model_name.lower(): LoaderClass = AutoModel else: config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code) if config.to_dict().get("is_encoder_decoder", False): LoaderClass = AutoModelForSeq2SeqLM shared.is_seq2seq = True else: LoaderClass = AutoModelForCausalLM # Load the model in simple 16-bit mode by default if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.load_in_4bit, shared.args.auto_devices, shared.args.disk, shared.args.deepspeed, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None]): model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16, trust_remote_code=shared.args.trust_remote_code) if torch.has_mps: device = torch.device('mps') model = model.to(device) else: model = model.cuda() # DeepSpeed ZeRO-3 elif shared.args.deepspeed: model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16) model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0] model.module.eval() # Inference logger.info(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}") # Custom else: params = { "low_cpu_mem_usage": True, "trust_remote_code": shared.args.trust_remote_code } if not any((shared.args.cpu, torch.cuda.is_available(), torch.has_mps)): logger.warning("torch.cuda.is_available() returned False. This means that no GPU has been detected. Falling back to CPU mode.") shared.args.cpu = True if shared.args.cpu: params["torch_dtype"] = torch.float32 else: params["device_map"] = 'auto' if shared.args.load_in_4bit: # See https://github.com/huggingface/transformers/pull/23479/files # and https://huggingface.co/blog/4bit-transformers-bitsandbytes quantization_config_params = { 'load_in_4bit': True, 'bnb_4bit_compute_dtype': eval("torch.{}".format(shared.args.compute_dtype)) if shared.args.compute_dtype in ["bfloat16", "float16", "float32"] else None, 'bnb_4bit_quant_type': shared.args.quant_type, 'bnb_4bit_use_double_quant': shared.args.use_double_quant, } logger.warning("Using the following 4-bit params: " + str(quantization_config_params)) params['quantization_config'] = BitsAndBytesConfig(**quantization_config_params) elif shared.args.load_in_8bit and any((shared.args.auto_devices, shared.args.gpu_memory)): params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True) elif shared.args.load_in_8bit: params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True) elif shared.args.bf16: params["torch_dtype"] = torch.bfloat16 else: params["torch_dtype"] = torch.float16 params['max_memory'] = get_max_memory_dict() if shared.args.disk: params["offload_folder"] = shared.args.disk_cache_dir checkpoint = Path(f'{shared.args.model_dir}/{model_name}') if shared.args.load_in_8bit and params.get('max_memory', None) is not None and params['device_map'] == 'auto': config = AutoConfig.from_pretrained(checkpoint, trust_remote_code=shared.args.trust_remote_code) with init_empty_weights(): model = LoaderClass.from_config(config, trust_remote_code=shared.args.trust_remote_code) model.tie_weights() params['device_map'] = infer_auto_device_map( model, dtype=torch.int8, max_memory=params['max_memory'], no_split_module_classes=model._no_split_modules ) model = LoaderClass.from_pretrained(checkpoint, **params) return model def flexgen_loader(model_name): from flexgen.flex_opt import CompressionConfig, ExecutionEnv, OptLM, Policy # Initialize environment env = ExecutionEnv.create(shared.args.disk_cache_dir) # Offloading policy policy = Policy(1, 1, shared.args.percent[0], shared.args.percent[1], shared.args.percent[2], shared.args.percent[3], shared.args.percent[4], shared.args.percent[5], overlap=True, sep_layer=True, pin_weight=shared.args.pin_weight, cpu_cache_compute=False, attn_sparsity=1.0, compress_weight=shared.args.compress_weight, comp_weight_config=CompressionConfig( num_bits=4, group_size=64, group_dim=0, symmetric=False), compress_cache=False, comp_cache_config=CompressionConfig( num_bits=4, group_size=64, group_dim=2, symmetric=False)) model = OptLM(f"facebook/{model_name}", env, shared.args.model_dir, policy) return model def RWKV_loader(model_name): from modules.RWKV import RWKVModel, RWKVTokenizer model = RWKVModel.from_pretrained(Path(f'{shared.args.model_dir}/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda") tokenizer = RWKVTokenizer.from_pretrained(Path(shared.args.model_dir)) return model, tokenizer def llamacpp_loader(model_name): from modules.llamacpp_model import LlamaCppModel path = Path(f'{shared.args.model_dir}/{model_name}') if path.is_file(): model_file = path else: model_file = list(Path(f'{shared.args.model_dir}/{model_name}').glob('*ggml*.bin'))[0] logger.info(f"llama.cpp weights detected: {model_file}\n") model, tokenizer = LlamaCppModel.from_pretrained(model_file) return model, tokenizer def GPTQ_loader(model_name): # Monkey patch if shared.args.monkey_patch: logger.warning("Applying the monkey patch for using LoRAs with GPTQ models. It may cause undefined behavior outside its intended scope.") from modules.monkey_patch_gptq_lora import load_model_llama model, _ = load_model_llama(model_name) # No monkey patch else: import modules.GPTQ_loader model = modules.GPTQ_loader.load_quantized(model_name) return model def AutoGPTQ_loader(model_name): import modules.AutoGPTQ_loader return modules.AutoGPTQ_loader.load_quantized(model_name) def ExLlama_loader(model_name): from modules.exllama import ExllamaModel model, tokenizer = ExllamaModel.from_pretrained(model_name) return model, tokenizer def ExLlama_HF_loader(model_name): from modules.exllama_hf import ExllamaHF return ExllamaHF.from_pretrained(model_name) def get_max_memory_dict(): max_memory = {} if shared.args.gpu_memory: memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory)) for i in range(len(memory_map)): max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i] max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB' max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory # If --auto-devices is provided standalone, try to get a reasonable value # for the maximum memory of device :0 elif shared.args.auto_devices: total_mem = (torch.cuda.get_device_properties(0).total_memory / (1024 * 1024)) suggestion = round((total_mem - 1000) / 1000) * 1000 if total_mem - suggestion < 800: suggestion -= 1000 suggestion = int(round(suggestion / 1000)) logger.warning(f"Auto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors. You can manually set other values.") max_memory = {0: f'{suggestion}GiB', 'cpu': f'{shared.args.cpu_memory or 99}GiB'} return max_memory if len(max_memory) > 0 else None def clear_torch_cache(): gc.collect() if not shared.args.cpu: torch.cuda.empty_cache() def unload_model(): shared.model = shared.tokenizer = None shared.lora_names = [] shared.model_dirty_from_training = False clear_torch_cache() def reload_model(): unload_model() shared.model, shared.tokenizer = load_model(shared.model_name)