import json import os import time import zipfile from pathlib import Path import numpy as np import torch import transformers from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoConfig from accelerate import infer_auto_device_map, init_empty_weights, load_checkpoint_and_dispatch import modules.shared as shared transformers.logging.set_verbosity_error() local_rank = None if shared.args.flexgen: from flexgen.flex_opt import CompressionConfig, ExecutionEnv, OptLM, Policy 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 def load_model(model_name): print(f"Loading {model_name}...") t0 = time.time() shared.is_RWKV = model_name.lower().startswith('rwkv-') # Default settings if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.gptq_bits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]): if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')): model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True) else: model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16).cuda() # FlexGen elif shared.args.flexgen: # 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/{shared.model_name}", env, "models", policy) # DeepSpeed ZeRO-3 elif shared.args.deepspeed: model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.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 print(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}") # RMKV model (not on HuggingFace) elif shared.is_RWKV: from modules.RWKV import RWKVModel, RWKVTokenizer model = RWKVModel.from_pretrained(Path(f'models/{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('models')) return model, tokenizer # Quantized model elif shared.args.gptq_bits > 0: from modules.GPTQ_loader import load_quantized model = load_quantized(model_name) # Custom else: params = {"low_cpu_mem_usage": True} if not shared.args.cpu and not torch.cuda.is_available(): print("Warning: torch.cuda.is_available() returned False.\nThis means that no GPU has been detected.\nFalling back to CPU mode.\n") shared.args.cpu = True if shared.args.cpu: params["torch_dtype"] = torch.float32 else: params["device_map"] = 'auto' if shared.args.load_in_8bit: params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True) elif shared.args.bf16: params["torch_dtype"] = torch.bfloat16 else: params["torch_dtype"] = torch.float16 if shared.args.gpu_memory: memory_map = shared.args.gpu_memory max_memory = { 0: f'{memory_map[0]}GiB' } for i in range(1, len(memory_map)): max_memory[i] = f'{memory_map[i]}GiB' max_memory['cpu'] = f'{shared.args.cpu_memory or 99}GiB' params['max_memory'] = max_memory else: 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)) print(f"\033[1;32;1mAuto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors.\nYou can manually set other values.\033[0;37;0m") max_memory = { 0: f'{suggestion}GiB', 'cpu': f'{shared.args.cpu_memory or 99}GiB' } params['max_memory'] = max_memory if shared.args.disk: params["offload_folder"] = shared.args.disk_cache_dir checkpoint = Path(f'models/{shared.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) with init_empty_weights(): model = AutoModelForCausalLM.from_config(config) 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 = AutoModelForCausalLM.from_pretrained(checkpoint, **params) # Loading the tokenizer if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path("models/gpt-j-6B/").exists(): tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/")) else: tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{shared.model_name}/")) tokenizer.truncation_side = 'left' print(f"Loaded the model in {(time.time()-t0):.2f} seconds.") return model, tokenizer 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()) print(f"\nLoading the softprompt \"{name}\".") for field in j: if field != 'name': if type(j[field]) is list: print(f"{field}: {', '.join(j[field])}") else: print(f"{field}: {j[field]}") print() 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