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 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, str2bool) 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-') shared.is_LLaMA = model_name.lower().startswith('llama-') # Default settings if not (shared.args.cpu or shared.args.load_in_8bit or shared.args.auto_devices or shared.args.disk or shared.args.gpu_memory is not None or shared.args.cpu_memory is not None or shared.args.deepspeed or shared.args.flexgen or shared.is_RWKV or shared.is_LLaMA): 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 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") return model, None # LLaMA model (not on HuggingFace) elif shared.is_LLaMA: import modules.LLaMA from modules.LLaMA import LLaMAModel model = LLaMAModel.from_pretrained(Path(f'models/{model_name}')) return model, None # Custom else: command = "AutoModelForCausalLM.from_pretrained" params = ["low_cpu_mem_usage=True"] if not shared.args.cpu and not torch.cuda.is_available(): print("Warning: no GPU has been detected.\nFalling back to CPU mode.\n") shared.args.cpu = True if shared.args.cpu: params.append("low_cpu_mem_usage=True") params.append("torch_dtype=torch.float32") else: params.append("device_map='auto'") params.append("load_in_8bit=True" if shared.args.load_in_8bit else "torch_dtype=torch.bfloat16" if shared.args.bf16 else "torch_dtype=torch.float16") if shared.args.gpu_memory: memory_map = shared.args.gpu_memory max_memory = f"max_memory={{0: '{memory_map[0]}GiB'" for i in range(1, len(memory_map)): max_memory += (f", {i}: '{memory_map[i]}GiB'") max_memory += (f", 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}") params.append(max_memory) elif not shared.args.load_in_8bit: 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") params.append(f"max_memory={{0: '{suggestion}GiB', 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}") if shared.args.disk: params.append(f"offload_folder='{shared.args.disk_cache_dir}'") command = f"{command}(Path(f'models/{shared.model_name}'), {', '.join(set(params))})" model = eval(command) # 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