437 lines
18 KiB
Python
Vendored
437 lines
18 KiB
Python
Vendored
########################################################################################################
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# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
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########################################################################################################
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import logging
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logging.basicConfig(level=logging.INFO)
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if __name__ == "__main__":
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from argparse import ArgumentParser
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from pytorch_lightning import Trainer
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from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
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import pytorch_lightning as pl
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rank_zero_info("########## work in progress ##########")
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parser = ArgumentParser()
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parser.add_argument("--load_model", default="", type=str) # full path, with .pth
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parser.add_argument(
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"--wandb", default="", type=str
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) # wandb project name. if "" then don't use wandb
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parser.add_argument("--proj_dir", default="out", type=str)
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parser.add_argument("--random_seed", default="-1", type=int)
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parser.add_argument("--data_file", default="", type=str)
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parser.add_argument("--data_type", default="utf-8", type=str)
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parser.add_argument(
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"--vocab_size", default=0, type=int
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) # vocab_size = 0 means auto (for char-level LM and .txt data)
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parser.add_argument("--ctx_len", default=1024, type=int)
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parser.add_argument(
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"--epoch_steps", default=1000, type=int
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) # a mini "epoch" has [epoch_steps] steps
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parser.add_argument(
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"--epoch_count", default=500, type=int
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) # train for this many "epochs". will continue afterwards with lr = lr_final
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parser.add_argument(
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"--epoch_begin", default=0, type=int
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) # if you load a model trained for x "epochs", set epoch_begin = x
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parser.add_argument(
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"--epoch_save", default=5, type=int
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) # save the model every [epoch_save] "epochs"
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parser.add_argument(
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"--micro_bsz", default=12, type=int
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) # micro batch size (batch size per GPU)
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parser.add_argument("--n_layer", default=6, type=int)
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parser.add_argument("--n_embd", default=512, type=int)
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parser.add_argument("--dim_att", default=0, type=int)
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parser.add_argument("--dim_ffn", default=0, type=int)
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parser.add_argument(
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"--pre_ffn", default=0, type=int
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) # replace first att layer by ffn (sometimes better)
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parser.add_argument("--head_qk", default=0, type=int) # my headQK trick
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parser.add_argument("--tiny_att_dim", default=0, type=int) # tiny attention dim
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parser.add_argument(
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"--tiny_att_layer", default=-999, type=int
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) # tiny attention @ which layer
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parser.add_argument(
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"--lr_init", default=6e-4, type=float
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) # 6e-4 for L12-D768, 4e-4 for L24-D1024, 3e-4 for L24-D2048
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parser.add_argument("--lr_final", default=1e-5, type=float)
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parser.add_argument(
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"--warmup_steps", default=-1, type=int
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) # try 50 if you load a model
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parser.add_argument("--beta1", default=0.9, type=float)
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parser.add_argument(
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"--beta2", default=0.99, type=float
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) # use 0.999 when your model is close to convergence
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parser.add_argument("--adam_eps", default=1e-8, type=float)
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parser.add_argument(
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"--grad_cp", default=0, type=int
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) # gradient checkpt: saves VRAM, but slower
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parser.add_argument(
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"--dropout", default=0, type=float
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) # try 0.01 / 0.02 / 0.05 / 0.1
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parser.add_argument(
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"--weight_decay", default=0, type=float
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) # try 0.1 / 0.01 / 0.001
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parser.add_argument("--weight_decay_final", default=-1, type=float)
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parser.add_argument(
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"--my_pile_version", default=1, type=int
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) # my special pile version
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parser.add_argument("--my_pile_stage", default=0, type=int) # my special pile mode
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parser.add_argument(
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"--my_pile_shift", default=-1, type=int
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) # my special pile mode - text shift
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parser.add_argument("--my_pile_edecay", default=0, type=int)
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parser.add_argument(
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"--layerwise_lr", default=1, type=int
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) # layerwise lr for faster convergence (but slower it/s)
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parser.add_argument(
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"--ds_bucket_mb", default=200, type=int
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) # deepspeed bucket size in MB. 200 seems enough
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# parser.add_argument("--cuda_cleanup", default=0, type=int) # extra cuda cleanup (sometimes helpful)
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parser.add_argument("--my_sample_len", default=0, type=int)
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parser.add_argument("--my_ffn_shift", default=1, type=int)
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parser.add_argument("--my_att_shift", default=1, type=int)
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parser.add_argument(
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"--head_size_a", default=64, type=int
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) # can try larger values for larger models
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parser.add_argument("--head_size_divisor", default=8, type=int)
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parser.add_argument("--my_pos_emb", default=0, type=int)
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parser.add_argument("--load_partial", default=0, type=int)
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parser.add_argument("--magic_prime", default=0, type=int)
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parser.add_argument("--my_qa_mask", default=0, type=int)
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parser.add_argument("--my_random_steps", default=0, type=int)
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parser.add_argument("--my_testing", default="", type=str)
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parser.add_argument("--my_exit", default=99999999, type=int)
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parser.add_argument("--my_exit_tokens", default=0, type=int)
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# LORA
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parser.add_argument("--emb", action="store_true")
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parser.add_argument("--lora", action="store_true")
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parser.add_argument("--lora_load", default="", type=str)
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parser.add_argument("--lora_r", default=8, type=int)
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parser.add_argument("--lora_alpha", default=32, type=float)
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parser.add_argument("--lora_dropout", default=0.01, type=float)
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parser.add_argument("--lora_parts", default="att,ln,time", type=str)
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if pl.__version__[0] == "2":
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parser.add_argument("--accelerator", default="gpu", type=str)
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parser.add_argument("--strategy", default="auto", type=str)
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parser.add_argument("--devices", default=1, type=int)
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parser.add_argument("--num_nodes", default=1, type=int)
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parser.add_argument("--precision", default="fp16", type=str)
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parser.add_argument("--accumulate_grad_batches", default=1, type=int)
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else:
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parser = Trainer.add_argparse_args(parser)
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args = parser.parse_args()
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########################################################################################################
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import os, warnings, math, datetime, sys, time
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import numpy as np
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import torch
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from torch.utils.data import DataLoader
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if "deepspeed" in args.strategy:
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import deepspeed
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from pytorch_lightning import seed_everything
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if args.random_seed >= 0:
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print(
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f"########## WARNING: GLOBAL SEED {args.random_seed} THIS WILL AFFECT MULTIGPU SAMPLING ##########\n"
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* 3
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)
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seed_everything(args.random_seed)
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np.set_printoptions(precision=4, suppress=True, linewidth=200)
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warnings.filterwarnings(
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"ignore", ".*Consider increasing the value of the `num_workers` argument*"
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)
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warnings.filterwarnings(
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"ignore", ".*The progress bar already tracks a metric with the*"
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)
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# os.environ["WDS_SHOW_SEED"] = "1"
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args.my_timestamp = datetime.datetime.today().strftime("%Y-%m-%d-%H-%M-%S")
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args.enable_checkpointing = False
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args.replace_sampler_ddp = False
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args.logger = False
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args.gradient_clip_val = 1.0
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args.num_sanity_val_steps = 0
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args.check_val_every_n_epoch = int(1e20)
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args.log_every_n_steps = int(1e20)
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args.max_epochs = args.epoch_count # -1 continue forever
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args.betas = (args.beta1, args.beta2)
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args.real_bsz = int(args.num_nodes) * int(args.devices) * args.micro_bsz
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os.environ["RWKV_MY_TESTING"] = args.my_testing
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os.environ["RWKV_HEAD_SIZE_A"] = str(args.head_size_a)
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if args.dim_att <= 0:
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args.dim_att = args.n_embd
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if args.dim_ffn <= 0:
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args.dim_ffn = int((args.n_embd * 3.5) // 32 * 32) # default = 3.5x emb size
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if args.data_type == "wds_img":
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args.run_name = f"v{args.my_img_version}-{args.my_img_size}-{args.my_img_bit}bit-{args.my_img_clip}x{args.my_img_clip_scale}"
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args.proj_dir = f"{args.proj_dir}-{args.run_name}"
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else:
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args.run_name = (
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f"{args.vocab_size} ctx{args.ctx_len} L{args.n_layer} D{args.n_embd}"
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)
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if not os.path.exists(args.proj_dir):
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os.makedirs(args.proj_dir)
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if args.my_pile_stage > 0:
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magic_prime_bak = args.magic_prime
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if args.my_pile_shift < 0:
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args.my_pile_shift = 0
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if magic_prime_bak > 0:
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args.magic_prime = magic_prime_bak
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if args.my_qa_mask == 2:
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args.epoch_count = 2 * args.magic_prime // 40320
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else:
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args.epoch_count = args.magic_prime // 40320
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args.epoch_steps = 40320 // args.real_bsz
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assert args.epoch_steps * args.real_bsz == 40320
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# if args.my_pile_stage == 2:
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# assert args.lr_final == args.lr_init
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if args.my_pile_stage >= 2: # find latest saved model
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list_p = []
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for p in os.listdir(args.proj_dir):
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if p.startswith("rwkv") and p.endswith(".pth"):
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p = ((p.split("-"))[1].split("."))[0]
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if p != "final":
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if p == "init":
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p = -1
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else:
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p = int(p)
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list_p += [p]
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list_p.sort()
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max_p = list_p[-1]
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if len(list_p) > 1:
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args.my_pile_prev_p = list_p[-2] # in case max_p is corrupted
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if max_p == -1:
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args.load_model = f"{args.proj_dir}/rwkv-init.pth"
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else:
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args.load_model = f"{args.proj_dir}/rwkv-{max_p}.pth"
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if args.warmup_steps < 0:
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if args.my_pile_stage == 2:
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args.warmup_steps = 10
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else:
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args.warmup_steps = 30
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args.epoch_begin = max_p + 1
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samples_per_epoch = args.epoch_steps * args.real_bsz
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tokens_per_epoch = samples_per_epoch * args.ctx_len
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try:
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deepspeed_version = deepspeed.__version__
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except:
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deepspeed_version = None
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pass
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rank_zero_info(
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f"""
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############################################################################
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#
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# RWKV-5 {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 ''}
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#
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# Data = {args.data_file} ({args.data_type}), ProjDir = {args.proj_dir}
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#
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# Epoch = {args.epoch_begin} to {args.epoch_begin + args.epoch_count - 1}, save every {args.epoch_save} epoch
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#
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# Each "epoch" = {args.epoch_steps} steps, {samples_per_epoch} samples, {tokens_per_epoch} tokens
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#
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# Model = {args.n_layer} n_layer, {args.n_embd} n_embd, {args.ctx_len} ctx_len
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#
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# Adam = lr {args.lr_init} to {args.lr_final}, warmup {args.warmup_steps} steps, beta {args.betas}, eps {args.adam_eps}
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#
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# Found torch {torch.__version__}, recommend 1.13.1+cu117 or newer
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# Found deepspeed {deepspeed_version}, recommend 0.7.0 (faster than newer versions)
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# Found pytorch_lightning {pl.__version__}, recommend 1.9.5
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#
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############################################################################
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"""
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)
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rank_zero_info(str(vars(args)) + "\n")
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assert args.data_type in ["utf-8", "utf-16le", "numpy", "binidx", "dummy", "uint16"]
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if args.lr_final == 0 or args.lr_init == 0:
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rank_zero_info(
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"\n\nNote: lr_final = 0 or lr_init = 0. Using linear LR schedule instead.\n\n"
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)
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assert args.precision in ["fp32", "tf32", "fp16", "bf16"]
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os.environ["RWKV_FLOAT_MODE"] = args.precision
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if args.precision == "fp32":
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for i in range(10):
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rank_zero_info(
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"\n\nNote: you are using fp32 (very slow). Try bf16 / tf32 for faster training.\n\n"
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)
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if args.precision == "fp16":
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rank_zero_info(
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"\n\nNote: you are using fp16 (might overflow). Try bf16 / tf32 for stable training.\n\n"
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)
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os.environ["RWKV_JIT_ON"] = "0"
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if "deepspeed_stage_3" in args.strategy:
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os.environ["RWKV_JIT_ON"] = "0"
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torch.backends.cudnn.benchmark = True
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torch.backends.cudnn.enabled = True
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if args.precision == "fp32":
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torch.backends.cudnn.allow_tf32 = False
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torch.backends.cuda.matmul.allow_tf32 = False
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else:
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torch.backends.cudnn.allow_tf32 = True
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torch.backends.cuda.matmul.allow_tf32 = True
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if "32" in args.precision:
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args.precision = 32
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elif args.precision == "fp16":
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args.precision = 16
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else:
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args.precision = "bf16"
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########################################################################################################
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from src.trainer import train_callback, generate_init_weight
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from src.dataset import MyDataset
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train_data = MyDataset(args)
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args.vocab_size = train_data.vocab_size
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from src.model import RWKV, LORA_CONFIG, LoraLinear
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if args.lora:
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assert args.lora_r > 0, "LoRA should have its `r` > 0"
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LORA_CONFIG["r"] = args.lora_r
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LORA_CONFIG["alpha"] = args.lora_alpha
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LORA_CONFIG["dropout"] = args.lora_dropout
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LORA_CONFIG["parts"] = set(str(args.lora_parts).split(","))
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enable_time_finetune = "time" in LORA_CONFIG["parts"]
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enable_ln_finetune = "ln" in LORA_CONFIG["parts"]
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model = RWKV(args)
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# only train lora parameters
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if args.lora:
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model.requires_grad_(False)
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for name, module in model.named_modules():
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if any(n.startswith("lora_") for n, _ in module.named_parameters()):
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print(f" LoRA additionally training module {name}")
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for pname, param in module.named_parameters():
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param.requires_grad = "lora_" in pname
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elif enable_ln_finetune and ".ln" in name:
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print(f" LoRA additionally training module {name}")
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for param in module.parameters():
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param.requires_grad = True
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elif enable_time_finetune and any(
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n.startswith("time") for n, _ in module.named_parameters()
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):
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for pname, param in module.named_parameters():
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if pname.startswith("time"):
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print(f" LoRA additionally training parameter {pname}")
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param.requires_grad = True
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if (
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len(args.load_model) == 0 or args.my_pile_stage == 1
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): # shall we build the initial weights?
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init_weight_name = f"{args.proj_dir}/rwkv-init.pth"
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generate_init_weight(model, init_weight_name) # save initial weights
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args.load_model = init_weight_name
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rank_zero_info(f"########## Loading {args.load_model}... ##########")
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try:
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load_dict = torch.load(args.load_model, map_location="cpu")
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load_keys = list(load_dict.keys())
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for k in load_keys:
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if k.startswith("_forward_module."):
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load_dict[k.replace("_forward_module.", "")] = load_dict[k]
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del load_dict[k]
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except:
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rank_zero_info(f"Bad checkpoint {args.load_model}")
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if args.my_pile_stage >= 2: # try again using another checkpoint
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max_p = args.my_pile_prev_p
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if max_p == -1:
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args.load_model = f"{args.proj_dir}/rwkv-init.pth"
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else:
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args.load_model = f"{args.proj_dir}/rwkv-{max_p}.pth"
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args.epoch_begin = max_p + 1
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rank_zero_info(f"Trying {args.load_model}")
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load_dict = torch.load(args.load_model, map_location="cpu")
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if args.load_partial == 1:
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load_keys = load_dict.keys()
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for k in model.state_dict():
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if k not in load_keys:
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load_dict[k] = model.state_dict()[k]
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# model.load_state_dict(load_dict)
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model.load_state_dict(load_dict, strict=(not args.lora))
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if os.path.isfile(args.lora_load):
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model.load_state_dict(
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torch.load(args.lora_load, map_location="cpu"), strict=False
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)
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if pl.__version__[0] == "2":
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trainer = Trainer(
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accelerator=args.accelerator,
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strategy=args.strategy,
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devices=args.devices,
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num_nodes=args.num_nodes,
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precision=args.precision,
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logger=args.logger,
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callbacks=[train_callback(args)],
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max_epochs=args.max_epochs,
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check_val_every_n_epoch=args.check_val_every_n_epoch,
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num_sanity_val_steps=args.num_sanity_val_steps,
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log_every_n_steps=args.log_every_n_steps,
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enable_checkpointing=args.enable_checkpointing,
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accumulate_grad_batches=args.accumulate_grad_batches,
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gradient_clip_val=args.gradient_clip_val,
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)
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else:
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trainer = Trainer.from_argparse_args(
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args,
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callbacks=[train_callback(args)],
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)
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if trainer.global_rank == 0:
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for n in model.state_dict():
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shape = model.state_dict()[n].shape
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shape = [i for i in shape if i != 1]
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if len(shape) > 1:
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print(f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {n}")
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|
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
|
|
)
|
|
|
|
# must set shuffle=False, persistent_workers=False (because worker is in another thread)
|
|
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(model, data_loader)
|