format
This commit is contained in:
josc146
parent
e930eb5967
commit
d8c70453ec
198
finetune/lora/train.py
vendored
198
finetune/lora/train.py
vendored
@ -50,52 +50,84 @@ if __name__ == "__main__":
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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("--wandb", default="", type=str) # wandb project name. if "" then don't use wandb
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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("--vocab_size", default=0, type=int) # vocab_size = 0 means auto (for char-level LM and .txt data)
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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("--epoch_steps", default=1000, type=int) # a mini "epoch" has [epoch_steps] steps
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parser.add_argument("--epoch_count", default=500, type=int) # train for this many "epochs". will continue afterwards with lr = lr_final
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parser.add_argument("--epoch_begin", default=0, type=int) # if you load a model trained for x "epochs", set epoch_begin = x
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parser.add_argument("--epoch_save", default=5, type=int) # save the model every [epoch_save] "epochs"
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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("--micro_bsz", default=12, type=int) # micro batch size (batch size per GPU)
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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("--pre_ffn", default=0, type=int) # replace first att layer by ffn (sometimes better)
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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("--tiny_att_layer", default=-999, type=int) # tiny attention @ which layer
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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("--lr_init", default=6e-4, type=float) # 6e-4 for L12-D768, 4e-4 for L24-D1024, 3e-4 for L24-D2048
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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("--warmup_steps", default=0, type=int) # try 50 if you load a model
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parser.add_argument(
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"--warmup_steps", default=0, 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("--beta2", default=0.99, type=float) # use 0.999 when your model is close to convergence
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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("--grad_cp", default=0, type=int) # gradient checkpt: saves VRAM, but slower
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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("--my_pile_stage", default=0, type=int) # my special pile mode
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parser.add_argument("--my_pile_shift", default=-1, type=int) # my special pile mode - text shift
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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("--layerwise_lr", default=1, type=int) # layerwise lr for faster convergence (but slower it/s)
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parser.add_argument("--ds_bucket_mb", default=200, type=int) # deepspeed bucket size in MB. 200 seems enough
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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_img_version", default=0, type=str)
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parser.add_argument("--my_img_size", default=0, type=int)
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parser.add_argument("--my_img_bit", default=0, type=int)
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parser.add_argument("--my_img_clip", default='x', type=str)
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parser.add_argument("--my_img_clip", default="x", type=str)
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parser.add_argument("--my_img_clip_scale", default=1, type=float)
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parser.add_argument("--my_img_l1_scale", default=0, type=float)
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parser.add_argument("--my_img_encoder", default='x', type=str)
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parser.add_argument("--my_img_encoder", default="x", type=str)
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# parser.add_argument("--my_img_noise_scale", default=0, type=float)
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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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@ -104,7 +136,7 @@ if __name__ == "__main__":
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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_testing", default='', type=str)
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parser.add_argument("--my_testing", default="", type=str)
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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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@ -122,18 +154,26 @@ if __name__ == "__main__":
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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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import pytorch_lightning as pl
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from pytorch_lightning import seed_everything
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if args.random_seed >= 0:
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print(f"########## WARNING: GLOBAL SEED {args.random_seed} THIS WILL AFFECT MULTIGPU SAMPLING ##########\n" * 3)
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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("ignore", ".*Consider increasing the value of the `num_workers` argument*")
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warnings.filterwarnings("ignore", ".*The progress bar already tracks a metric with the*")
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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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@ -158,7 +198,9 @@ if __name__ == "__main__":
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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 = f"{args.vocab_size} ctx{args.ctx_len} L{args.n_layer} D{args.n_embd}"
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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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@ -240,24 +282,40 @@ if __name__ == "__main__":
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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", "wds_img", "uint16"]
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assert args.data_type in [
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"utf-8",
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"utf-16le",
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"numpy",
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"binidx",
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"dummy",
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"wds_img",
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"uint16",
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]
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if args.lr_final == 0 or args.lr_init == 0:
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rank_zero_info("\n\nNote: lr_final = 0 or lr_init = 0. Using linear LR schedule instead.\n\n")
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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("\n\nNote: you are using fp32 (very slow). Try bf16 / tf32 for faster training.\n\n")
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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("\n\nNote: you are using fp16 (might overflow). Try bf16 / tf32 for stable training.\n\n")
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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"] = "1"
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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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if args.lora and args.grad_cp == 1:
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print('!!!!! LoRA Warning: Gradient Checkpointing requires JIT off, disabling it')
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print(
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"!!!!! LoRA Warning: Gradient Checkpointing requires JIT off, disabling it"
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)
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os.environ["RWKV_JIT_ON"] = "0"
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torch.backends.cudnn.benchmark = True
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@ -284,20 +342,22 @@ if __name__ == "__main__":
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train_data = MyDataset(args)
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args.vocab_size = train_data.vocab_size
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if args.data_type == 'wds_img':
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if args.data_type == "wds_img":
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from src.model_img import RWKV_IMG
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assert args.lora, "LoRA not yet supported for RWKV_IMG"
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model = RWKV_IMG(args)
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else:
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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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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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@ -305,20 +365,24 @@ if __name__ == "__main__":
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for name, module in model.named_modules():
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# have to check param name since it may have been wrapped by torchscript
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if any(n.startswith("lora_") for n, _ in module.named_parameters()):
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print(f' LoRA training module {name}')
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print(f" LoRA 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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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(n.startswith("time") for n, _ in module.named_parameters()):
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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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print(f" LoRA additionally training parameter {pname}")
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param.requires_grad = True
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if len(args.load_model) == 0 or args.my_pile_stage == 1: # shall we build the initial weights?
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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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@ -346,27 +410,39 @@ if __name__ == "__main__":
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# If using LoRA, the LoRA keys might be missing in the original model
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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(torch.load(args.lora_load, map_location="cpu"),
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strict=False)
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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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trainer: 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 (args.lr_init > 1e-4 or trainer.world_size * args.micro_bsz * trainer.accumulate_grad_batches < 8):
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if 'I_KNOW_WHAT_IM_DOING' in os.environ:
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if (
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args.lr_init > 1e-4
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or trainer.world_size * args.micro_bsz * trainer.accumulate_grad_batches < 8
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):
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if "I_KNOW_WHAT_IM_DOING" in os.environ:
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if trainer.global_rank == 0:
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print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
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print(f' WARNING: you are using too large LR ({args.lr_init} > 1e-4) or too small global batch size ({trainer.world_size} * {args.micro_bsz} * {trainer.accumulate_grad_batches} < 8)')
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print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
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print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
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print(
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f" WARNING: you are using too large LR ({args.lr_init} > 1e-4) or too small global batch size ({trainer.world_size} * {args.micro_bsz} * {trainer.accumulate_grad_batches} < 8)"
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)
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print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
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else:
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if trainer.global_rank == 0:
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print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
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print(f' ERROR: you are using too large LR ({args.lr_init} > 1e-4) or too small global batch size ({trainer.world_size} * {args.micro_bsz} * {trainer.accumulate_grad_batches} < 8)')
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print(f' Unless you are sure this is what you want, adjust them accordingly')
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print(f' (to suppress this, set environment variable "I_KNOW_WHAT_IM_DOING")')
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print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
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print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
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print(
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f" ERROR: you are using too large LR ({args.lr_init} > 1e-4) or too small global batch size ({trainer.world_size} * {args.micro_bsz} * {trainer.accumulate_grad_batches} < 8)"
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)
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print(
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f" Unless you are sure this is what you want, adjust them accordingly"
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)
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print(
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f' (to suppress this, set environment variable "I_KNOW_WHAT_IM_DOING")'
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)
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print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
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exit(0)
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if trainer.global_rank == 0:
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@ -379,10 +455,22 @@ if __name__ == "__main__":
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print(f"{str(shape[0]).ljust(5)} {n}")
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if "deepspeed" in args.strategy:
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trainer.strategy.config["zero_optimization"]["allgather_bucket_size"] = args.ds_bucket_mb * 1000 * 1000
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trainer.strategy.config["zero_optimization"]["reduce_bucket_size"] = args.ds_bucket_mb * 1000 * 1000
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trainer.strategy.config["zero_optimization"]["allgather_bucket_size"] = (
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args.ds_bucket_mb * 1000 * 1000
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)
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trainer.strategy.config["zero_optimization"]["reduce_bucket_size"] = (
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args.ds_bucket_mb * 1000 * 1000
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)
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# must set shuffle=False, persistent_workers=False (because worker is in another thread)
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data_loader = DataLoader(train_data, shuffle=False, pin_memory=True, batch_size=args.micro_bsz, num_workers=1, persistent_workers=False, drop_last=True)
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data_loader = DataLoader(
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train_data,
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shuffle=False,
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pin_memory=True,
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batch_size=args.micro_bsz,
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num_workers=1,
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persistent_workers=False,
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drop_last=True,
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)
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trainer.fit(model, data_loader)
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