''' @File : train_rlhf.py @Time : 2023/03/08 15:23:19 @Author : Lu Xin @Contact : luxin@csdn.net ''' # here put the import lib ######################################################################################################## # The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM ######################################################################################################## if __name__ == "__main__": from argparse import ArgumentParser from pytorch_lightning import Trainer from pytorch_lightning.utilities import rank_zero_info, rank_zero_only rank_zero_info("########## work in progress ##########") ######################################################################################################## # # example: train a simple L12-D768 RWKV on dummy data # # python train.py --load_model "" --wandb "" --proj_dir "out" \ # --data_file "" --data_type "dummy" --vocab_size 0 \ # --ctx_len 128 --epoch_steps 1000 --epoch_count 20 --epoch_begin 0 --epoch_save 10 \ # --micro_bsz 16 --n_layer 12 --n_embd 768 --pre_ffn 0 --head_qk 0 \ # --lr_init 6e-4 --lr_final 1e-5 --warmup_steps 0 --beta1 0.9 --beta2 0.99 --adam_eps 1e-8 \ # --accelerator gpu --devices 1 --precision bf16 --strategy ddp_find_unused_parameters_false --grad_cp 0 # example: train a simple L6-D512 RWKV from scratch on enwik8 # # python train.py --load_model "" --wandb "" --proj_dir "out" \ # --data_file "../data/enwik8" --data_type "utf-8" --vocab_size 0 \ # --ctx_len 512 --epoch_steps 5000 --epoch_count 500 --epoch_begin 0 --epoch_save 5 \ # --micro_bsz 12 --n_layer 6 --n_embd 512 --pre_ffn 0 --head_qk 0 \ # --lr_init 8e-4 --lr_final 1e-5 --warmup_steps 0 --beta1 0.9 --beta2 0.99 --adam_eps 1e-8 \ # --accelerator gpu --devices 1 --precision bf16 --strategy ddp_find_unused_parameters_false --grad_cp 0 # example: fine-tune RWKV 1.5B using 8xA100 40G = 1.76it/s = 115k token/s, VRAM 37477M # # python train.py --load_model "/fsx/BlinkDL/CODE/FP16/out_1b2/all-8040.pth" --wandb "" --proj_dir "out" \ # --data_file "../data/train.npy" --data_type "numpy" --vocab_size 50277 \ # --ctx_len 1024 --epoch_steps 1000 --epoch_count 1000 --epoch_begin 0 --epoch_save 5 \ # --micro_bsz 8 --n_layer 24 --n_embd 2048 --pre_ffn 0 --head_qk 0 \ # --lr_init 1e-5 --lr_final 1e-5 --warmup_steps 0 --beta1 0.9 --beta2 0.999 --adam_eps 1e-8 \ # --accelerator gpu --devices 8 --precision bf16 --strategy deepspeed_stage_2 --grad_cp 0 # example: fine-tune RWKV 1.5B using 1 GPU fp16 (VRAM 16G) NOTE: fp16 might overflow # # python train.py --load_model "/fsx/BlinkDL/CODE/FP16/out_1b2/all-8040.pth" --wandb "" --proj_dir "out" \ # --data_file "../data/train.npy" --data_type "numpy" --vocab_size 50277 \ # --ctx_len 1024 --epoch_steps 200 --epoch_count 1000 --epoch_begin 0 --epoch_save 1 \ # --micro_bsz 11 --n_layer 24 --n_embd 2048 --pre_ffn 0 --head_qk 0 \ # --lr_init 1e-5 --lr_final 1e-5 --warmup_steps 0 --beta1 0.9 --beta2 0.999 --adam_eps 1e-8 \ # --accelerator gpu --devices 1 --precision fp16 --strategy deepspeed_stage_2_offload --grad_cp 1 parser = ArgumentParser() parser.add_argument("--load_sft_model", default="", type=str) # full path, with .pth parser.add_argument("--load_rm_model", default="", type=str) # full path, with .pth parser.add_argument("--wandb", default="", type=str) # wandb project name. if "" then don't use wandb parser.add_argument("--proj_dir", default="out", type=str) parser.add_argument("--random_seed", default="-1", type=int) parser.add_argument("--data_file", default="", type=str) parser.add_argument("--data_type", default="utf-8", type=str) parser.add_argument("--vocab_size", default=0, type=int) # vocab_size = 0 means auto (for char-level LM and .txt data) parser.add_argument("--ctx_len", default=1024, type=int) parser.add_argument("--epoch_steps", default=1000, type=int) # a mini "epoch" has [epoch_steps] steps parser.add_argument("--epoch_count", default=500, type=int) # train for this many "epochs". will continue afterwards with lr = lr_final parser.add_argument("--epoch_begin", default=0, type=int) # if you load a model trained for x "epochs", set epoch_begin = x parser.add_argument("--epoch_save", default=5, type=int) # save the model every [epoch_save] "epochs" parser.add_argument("--micro_bsz", default=12, type=int) # micro batch size (batch size per GPU) parser.add_argument("--n_layer", default=6, type=int) parser.add_argument("--n_embd", default=512, type=int) parser.add_argument("--dim_att", default=0, type=int) parser.add_argument("--dim_ffn", default=0, type=int) parser.add_argument("--pre_ffn", default=0, type=int) # replace first att layer by ffn (sometimes better) parser.add_argument("--head_qk", default=0, type=int) # my headQK trick parser.add_argument("--tiny_att_dim", default=0, type=int) # tiny attention dim parser.add_argument("--tiny_att_layer", default=-999, type=int) # tiny attention @ which layer parser.add_argument("--lr_init", default=6e-4, type=float) # 6e-4 for L12-D768, 4e-4 for L24-D1024, 3e-4 for L24-D2048 parser.add_argument("--lr_final", default=1e-5, type=float) parser.add_argument("--warmup_steps", default=0, type=int) # try 50 if you load a model parser.add_argument("--beta1", default=0.9, type=float) parser.add_argument("--beta2", default=0.99, type=float) # use 0.999 when your model is close to convergence parser.add_argument("--adam_eps", default=1e-8, type=float) parser.add_argument("--grad_cp", default=0, type=int) # gradient checkpt: saves VRAM, but slower parser.add_argument("--my_pile_stage", default=0, type=int) # my special pile mode parser.add_argument("--my_pile_shift", default=-1, type=int) # my special pile mode - text shift parser.add_argument("--my_pile_edecay", default=0, type=int) parser.add_argument("--layerwise_lr", default=1, type=int) # layerwise lr for faster convergence (but slower it/s) parser.add_argument("--ds_bucket_mb", default=200, type=int) # deepspeed bucket size in MB. 200 seems enough # parser.add_argument("--cuda_cleanup", default=0, type=int) # extra cuda cleanup (sometimes helpful) parser.add_argument("--my_img_version", default=0, type=str) parser.add_argument("--my_img_size", default=0, type=int) parser.add_argument("--my_img_bit", default=0, type=int) parser.add_argument("--my_img_clip", default='x', type=str) parser.add_argument("--my_img_clip_scale", default=1, type=float) parser.add_argument("--my_img_l1_scale", default=0, type=float) parser.add_argument("--my_img_encoder", default='x', type=str) # parser.add_argument("--my_img_noise_scale", default=0, type=float) parser.add_argument("--my_sample_len", default=0, type=int) parser.add_argument("--my_ffn_shift", default=1, type=int) parser.add_argument("--my_att_shift", default=1, type=int) parser.add_argument("--my_pos_emb", default=0, type=int) parser.add_argument("--load_partial", default=0, type=int) parser.add_argument("--magic_prime", default=0, type=int) parser.add_argument("--my_qa_mask", default=0, type=int) parser.add_argument("--my_testing", default='', type=str) # PPO model parameters parser.add_argument("--critic_pooled_values", default=True, type=bool) parser.add_argument("--max_norm", default=None, type=float) parser.add_argument("--kl_div_loss_weight", default=0.1, type=float) # between old action probs and new action probs - not sure what the right value is parser.add_argument("--eps_clip", default=0.2, type=float) parser.add_argument("--value_clip", default=0.4, type=float) parser.add_argument("--beta_s", default=0.01, type=float) parser.add_argument("--actor_lr", default=1e-4, type=float) parser.add_argument("--critic_lr", default=1e-4, type=float) parser.add_argument("--actor_wd", default=0., type=float) parser.add_argument("--critic_wd", default=0., type=float) parser.add_argument("--actor_adam_eps", default=1e-7, type=float) parser.add_argument("--critic_adam_eps", default=1e-7, type=float) parser.add_argument("--pad_value", default=1, type=float) # token pad value parser.add_argument("--use_lion", default=False, type=bool) parser.add_argument("--num_episodes", default=50000, type=int) parser.add_argument("--max_timesteps", default=500, type=int) parser.add_argument("--update_timesteps", default=5000, type=int) parser = Trainer.add_argparse_args(parser) args = parser.parse_args() ######################################################################################################## import os, warnings, math, datetime, sys, time import numpy as np import torch from torch.utils.data import DataLoader import deepspeed import pytorch_lightning as pl from pytorch_lightning import seed_everything if args.random_seed >= 0: print(f"########## WARNING: GLOBAL SEED {args.random_seed} THIS WILL AFFECT MULTIGPU SAMPLING ##########\n" * 3) seed_everything(args.random_seed) np.set_printoptions(precision=4, suppress=True, linewidth=200) warnings.filterwarnings("ignore", ".*Consider increasing the value of the `num_workers` argument*") warnings.filterwarnings("ignore", ".*The progress bar already tracks a metric with the*") # os.environ["WDS_SHOW_SEED"] = "1" os.environ["TOKENIZERS_PARALLELISM"] = "false" args.my_timestamp = datetime.datetime.today().strftime("%Y-%m-%d-%H-%M-%S") args.enable_checkpointing = False args.replace_sampler_ddp = False args.logger = False args.gradient_clip_val = 1.0 args.num_sanity_val_steps = 0 args.check_val_every_n_epoch = int(1e20) args.log_every_n_steps = int(1e20) args.max_epochs = -1 # continue forever args.betas = (args.beta1, args.beta2) args.real_bsz = int(args.num_nodes) * int(args.devices) * args.micro_bsz os.environ["RWKV_T_MAX"] = str(args.ctx_len) os.environ["RWKV_MY_TESTING"] = args.my_testing if args.dim_att <= 0: args.dim_att = args.n_embd if args.dim_ffn <= 0: args.dim_ffn = args.n_embd * 4 args.run_name = f"{args.vocab_size} ctx{args.ctx_len} L{args.n_layer} D{args.n_embd}" if not os.path.exists(args.proj_dir): os.makedirs(args.proj_dir) samples_per_epoch = args.epoch_steps * args.real_bsz tokens_per_epoch = samples_per_epoch * args.ctx_len rank_zero_info( f""" ############################################################################ # # RWKV-4 {args.precision.upper()} on {args.num_nodes}x{args.devices} {args.accelerator.upper()}, bsz {args.num_nodes}x{args.devices}x{args.micro_bsz}={args.real_bsz}, {args.strategy} {'with grad_cp' if args.grad_cp > 0 else ''} # # Data = {args.data_file} ({args.data_type}), ProjDir = {args.proj_dir} # # Epoch = {args.epoch_begin} to {args.epoch_begin + args.epoch_count - 1} (will continue afterwards), save every {args.epoch_save} epoch # # Each "epoch" = {args.epoch_steps} steps, {samples_per_epoch} samples, {tokens_per_epoch} tokens # # Model = {args.n_layer} n_layer, {args.n_embd} n_embd, {args.ctx_len} ctx_len # # Adam = lr {args.lr_init} to {args.lr_final}, warmup {args.warmup_steps} steps, beta {args.betas}, eps {args.adam_eps} # # Found torch {torch.__version__}, recommend 1.12.1+cu116 or newer # Found deepspeed {deepspeed.__version__}, recommend 0.7.0 (faster than newer versions) # Found pytorch_lightning {pl.__version__}, recommend 1.7.4 or newer # ############################################################################ """ ) rank_zero_info(str(vars(args)) + "\n") assert args.data_type in ["utf-8", "utf-16le", "numpy", "binidx", "dummy", "wds_img", "uint16"] if args.lr_final == 0 or args.lr_init == 0: rank_zero_info("\n\nNote: lr_final = 0 or lr_init = 0. Using linear LR schedule instead.\n\n") assert args.precision in ["fp32", "tf32", "fp16", "bf16"] os.environ["RWKV_FLOAT_MODE"] = args.precision if args.precision == "fp32": rank_zero_info("\n\nNote: you are using fp32 (very slow). Try bf16 / tf32 for faster training.\n\n") if args.precision == "fp16": rank_zero_info("\n\nNote: you are using fp16 (might overflow). Try bf16 / tf32 for stable training.\n\n") os.environ["RWKV_JIT_ON"] = "1" if "deepspeed_stage_3" in args.strategy: os.environ["RWKV_JIT_ON"] = "0" torch.backends.cudnn.benchmark = True torch.backends.cudnn.enabled = True if args.precision == "fp32": torch.backends.cudnn.allow_tf32 = False torch.backends.cuda.matmul.allow_tf32 = False else: torch.backends.cudnn.allow_tf32 = True torch.backends.cuda.matmul.allow_tf32 = True if "32" in args.precision: args.precision = 32 elif args.precision == "fp16": args.precision = 16 else: args.precision = "bf16" ######################################################################################################## from tqdm import tqdm from collections import deque, namedtuple from einops import rearrange from src.dataset import PPODataset, load_prompt_data_4_ppo from src.rlhf.ppo import RLHF from src.trainer import rlhf_train_callback from src.model import RWKV from src.rlhf.reward import RewardModel # 用于 PPO 训练的数据，需要与 environment 交互获得 memory = [] # 读入训练数据集 prompts = load_prompt_data_4_ppo(args) # 加载 RWKV 模型 rwkv = RWKV(args) if len(args.load_sft_model) == 0: rank_zero_info(f"SFT must load model, please input ") exit(1) rank_zero_info(f"########## Loading {args.load_sft_model}... ##########") try: load_dict = torch.load(args.load_sft_model, map_location="cpu") except: rank_zero_info(f"Bad checkpoint {args.load_sft_model}") exit(1) if args.load_partial == 1: load_keys = load_dict.keys() for k in rwkv.state_dict(): if k not in load_keys: load_dict[k] = rwkv.state_dict()[k] rwkv.load_state_dict(load_dict) # 加载 reward_model reward_model = RewardModel(args) reward_model.load(args.load_rm_model) # PPO 模型 rlhf_model = RLHF(args, rwkv, reward_model) # 模型训练 # trainer trainer = Trainer.from_argparse_args( args, callbacks=[rlhf_train_callback(args)], ) time_cnt = 0 for eps in tqdm(range(args.num_episodes), desc = 'episodes'): for timestep in range(args.max_timesteps): time_cnt += 1 # 生成 ppo 模型的训练数据 experience_data = rlhf_model.make_experience(prompts, eos_token=0) memory.append(experience_data) # learn from the stored memories if time_cnt % args.update_timesteps == 0: if trainer.global_rank == 0: for n in rlhf_model.state_dict(): shape = rlhf_model.state_dict()[n].shape shape = [i for i in shape if i != 1] if len(shape) > 1: print(f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {n}") else: print(f"{str(shape[0]).ljust(5)} {n}") if "deepspeed" in args.strategy: trainer.strategy.config["zero_optimization"]["allgather_bucket_size"] = args.ds_bucket_mb * 1000 * 1000 trainer.strategy.config["zero_optimization"]["reduce_bucket_size"] = args.ds_bucket_mb * 1000 * 1000 train_data = PPODataset(memory) data_loader = DataLoader(train_data, shuffle=False, pin_memory=True, batch_size=args.micro_bsz, num_workers=1, persistent_workers=False, drop_last=True) trainer.fit(rlhf_model, data_loader) print('rlhf training complete')