import gc import json import logging import os import re import time import zipfile from pathlib import Path import numpy as np import torch import transformers from accelerate import infer_auto_device_map, init_empty_weights from transformers import (AutoConfig, AutoModel, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, BitsAndBytesConfig, LlamaTokenizer) import modules.shared as shared from modules import llama_attn_hijack 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 # Some models require special treatment in various parts of the code. # This function detects those models def find_model_type(model_name): path_to_model = Path(f'{shared.args.model_dir}/{model_name}') if not path_to_model.exists(): return 'None' model_name_lower = model_name.lower() if 'rwkv-' in model_name_lower: return 'rwkv' elif len(list(path_to_model.glob('*ggml*.bin'))) > 0: return 'llamacpp' elif re.match('.*ggml.*\.bin', model_name_lower): return 'llamacpp' elif 'chatglm' in model_name_lower: return 'chatglm' elif 'galactica' in model_name_lower: return 'galactica' elif 'llava' in model_name_lower: return 'llava' elif 'oasst' in model_name_lower: return 'oasst' elif any((k in model_name_lower for k in ['gpt4chan', 'gpt-4chan'])): return 'gpt4chan' else: config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code) # Not a "catch all", but fairly accurate if config.to_dict().get("is_encoder_decoder", False): return 'HF_seq2seq' else: return 'HF_generic' def load_model(model_name): logging.info(f"Loading {model_name}...") t0 = time.time() shared.model_type = find_model_type(model_name) if shared.model_type == 'None': logging.error('The path to the model does not exist. Exiting.') return None, None if shared.args.autogptq: load_func = AutoGPTQ_loader elif shared.args.wbits > 0: load_func = GPTQ_loader elif shared.model_type == 'llamacpp': load_func = llamacpp_loader elif shared.model_type == 'rwkv': load_func = RWKV_loader elif shared.args.flexgen: load_func = flexgen_loader else: load_func = huggingface_loader output = load_func(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() logging.info(f"Loaded the model in {(time.time()-t0):.2f} seconds.\n") return model, tokenizer def load_tokenizer(model_name, model): tokenizer = None if shared.model_type == '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 type(model) is transformers.LlamaForCausalLM: # Try to load an universal LLaMA tokenizer if shared.model_type not in ['llava', 'oasst']: for p in [Path(f"{shared.args.model_dir}/llama-tokenizer/"), Path(f"{shared.args.model_dir}/oobabooga_llama-tokenizer/")]: if p.exists(): logging.info(f"Loading the universal LLaMA tokenizer from {p}...") tokenizer = LlamaTokenizer.from_pretrained(p, clean_up_tokenization_spaces=True) return tokenizer # Otherwise, load it from the model folder and hope that these # are not outdated tokenizer files. tokenizer = LlamaTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}/"), clean_up_tokenization_spaces=True) try: tokenizer.eos_token_id = 2 tokenizer.bos_token_id = 1 tokenizer.pad_token_id = 0 except: pass else: path_to_model = Path(f"{shared.args.model_dir}/{model_name}/") if path_to_model.exists(): tokenizer = AutoTokenizer.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code) return tokenizer def huggingface_loader(model_name): if shared.model_type == 'chatglm': LoaderClass = AutoModel elif shared.model_type == 'HF_seq2seq': LoaderClass = AutoModelForSeq2SeqLM 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.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 logging.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)): logging.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_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] logging.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: logging.warning("Applying the monkey patch for using LoRAs in 4-bit mode. 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 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)) logging.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 clear_torch_cache() def reload_model(): unload_model() shared.model, shared.tokenizer = load_model(shared.model_name) def load_soft_prompt(name): if name == 'None': shared.soft_prompt = False shared.soft_prompt_tensor = None else: with zipfile.ZipFile(Path(f'softprompts/{name}.zip')) as zf: zf.extract('tensor.npy') zf.extract('meta.json') j = json.loads(open('meta.json', 'r').read()) logging.info(f"\nLoading the softprompt \"{name}\".") for field in j: if field != 'name': if type(j[field]) is list: logging.info(f"{field}: {', '.join(j[field])}") else: logging.info(f"{field}: {j[field]}") tensor = np.load('tensor.npy') Path('tensor.npy').unlink() Path('meta.json').unlink() tensor = torch.Tensor(tensor).to(device=shared.model.device, dtype=shared.model.dtype) tensor = torch.reshape(tensor, (1, tensor.shape[0], tensor.shape[1])) shared.soft_prompt = True shared.soft_prompt_tensor = tensor return name