train.py

########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
import os

import logging
logging.basicConfig(level=logging.INFO)

if __name__ == "__main__":
    from argparse import ArgumentParser
    from pytorch_lightning import Trainer
    from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
    import pytorch_lightning as pl

    rank_zero_info("########## work in progress ##########")

    parser = ArgumentParser()
    
    parser.add_argument("--op", default="train", type=str) # train or eval

    parser.add_argument("--load_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_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=-1, 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("--dropout", default=0, type=float) # try 0.01 / 0.02 / 0.05 / 0.1
    parser.add_argument("--weight_decay", default=0, type=float) # try 0.1 / 0.01 / 0.001
    parser.add_argument("--weight_decay_final", default=-1, type=float)

    parser.add_argument("--my_pile_version", default=1, type=int)  # my special pile version
    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_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("--head_size_a", default=64, type=int) # can try larger values for larger models
    parser.add_argument("--head_size_divisor", default=8, 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_random_steps", default=0, type=int)
    parser.add_argument("--my_testing", default='x052', type=str)
    parser.add_argument("--my_exit", default=99999999, type=int)
    parser.add_argument("--my_exit_tokens", default=0, type=int)

    #LORA
    parser.add_argument("--emb", action="store_true")
    parser.add_argument("--lora", action="store_true")
    parser.add_argument("--lora_load", default="", type=str)
    parser.add_argument("--lora_r", default=8, type=int)
    parser.add_argument("--lora_alpha", default=32, type=float)
    parser.add_argument("--lora_dropout", default=0.01, type=float)
    parser.add_argument("--lora_parts", default="att,ln,time", type=str)

    #LISA
    parser.add_argument("--LISA", action="store_true")
    parser.add_argument("--lisa_r", default=2, type=int)
    parser.add_argument("--lisa_k", default=100, type=int)

    #PISSA
    parser.add_argument("--PISSA", action="store_true")
    parser.add_argument("--svd_niter", default=4, type=int)
    parser.add_argument("--pissa_load", default="", type=str)
    parser.add_argument("--pissa_init", default="", type=str)

    #quant
    parser.add_argument("--quant", default="none", type=str)

    #dataset
    parser.add_argument("--dataload", default="get", type=str)

    #state tuning
    parser.add_argument("--state_tune", action="store_true")


    parser.add_argument("--chunk_ctx", default=512, type=int)
    #fla
    parser.add_argument("--fla", action="store_true")
    parser.add_argument("--train_type", default="none", type=str)

    #loss_mask
    parser.add_argument("--loss_mask", action="store_true")
    parser.add_argument("--file_path", default="none", type=str)

    if pl.__version__[0]=='2':
        parser.add_argument("--accelerator", default="gpu", type=str)
        parser.add_argument("--strategy", default="auto", type=str)
        parser.add_argument("--devices", default=1, type=int)
        parser.add_argument("--num_nodes", default=1, type=int)
        parser.add_argument("--precision", default="fp16", type=str)
        parser.add_argument("--accumulate_grad_batches", default=4, type=int)
    else:
        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
    if "deepspeed" in args.strategy:
        import deepspeed
    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"

    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 = 10.0
    args.gradient_accumulation_steps = 4
    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
    if args.dataload!='get':
        args.max_epochs = args.epoch_count
    args.betas = (args.beta1, args.beta2)
    args.real_bsz = int(args.num_nodes) * int(args.devices) * args.micro_bsz
    os.environ["RWKV_MY_TESTING"] = args.my_testing
    os.environ["RWKV_CTXLEN"] = str(args.ctx_len)
    os.environ["RWKV_HEAD_SIZE_A"] = str(args.head_size_a)
    ######state tuning
    os.environ["RWKV_TRAIN_TYPE"]=''
    if args.train_type=='state':
        os.environ["RWKV_TRAIN_TYPE"]='states'

    os.environ["WKV"]='fla' if args.fla else ''
    if args.dim_att <= 0:
        args.dim_att = args.n_embd
    if args.dim_ffn <= 0:
        args.dim_ffn = int((args.n_embd * 3.5) // 32 * 32) # default = 3.5x emb size

    if args.data_type == "wds_img":
        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}"
        args.proj_dir = f"{args.proj_dir}-{args.run_name}"
    else:
        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)

    if args.my_pile_stage > 0:
        magic_prime_bak = args.magic_prime

        if args.my_pile_shift < 0:
            args.my_pile_shift = 0

        if magic_prime_bak > 0:
            args.magic_prime = magic_prime_bak
        if args.my_qa_mask == 2:
            args.epoch_count = 2 * args.magic_prime // 40320
        else:
            args.epoch_count = args.magic_prime // 40320

        args.epoch_steps = 40320 // args.real_bsz
        assert args.epoch_steps * args.real_bsz == 40320
        # if args.my_pile_stage == 2:
        #     assert args.lr_final == args.lr_init
        if args.my_pile_stage >= 2:  # find latest saved model
            list_p = []
            for p in os.listdir(args.proj_dir):
                if p.startswith("rwkv") and p.endswith(".pth"):
                    p = ((p.split("-"))[1].split("."))[0]
                    if p != "final":
                        if p == "init":
                            p = -1
                        else:
                            p = int(p)
                        list_p += [p]
            list_p.sort()
            max_p = list_p[-1]
            if len(list_p) > 1:
                args.my_pile_prev_p = list_p[-2]  # in case max_p is corrupted
            if max_p == -1:
                args.load_model = f"{args.proj_dir}/rwkv-init.pth"
            else:
                args.load_model = f"{args.proj_dir}/rwkv-{max_p}.pth"
                if args.warmup_steps < 0:
                    if args.my_pile_stage == 2:
                        args.warmup_steps = 10
                    else:
                        args.warmup_steps = 30
            args.epoch_begin = max_p + 1

    samples_per_epoch = args.epoch_steps * args.real_bsz
    tokens_per_epoch = samples_per_epoch * args.ctx_len
    try:
        deepspeed_version = deepspeed.__version__
    except:
        deepspeed_version = None
        pass
    rank_zero_info(
        f"""
############################################################################
#
# 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 ''}
#
# Data = ({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 1.13.1+cu117 or newer
# Found deepspeed {deepspeed_version}, recommend 0.7.0 (faster than newer versions)
# Found pytorch_lightning {pl.__version__}, recommend 1.9.5
#
############################################################################
"""
    )
    rank_zero_info(str(vars(args)) + "\n")

    assert args.data_type in ["utf-8", "utf-16le", "numpy", "binidx", "dummy", "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":
        for i in range(10):
            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"] = "0"
    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 src.trainer import train_callback, generate_init_weight
    from src.dataset2 import MyDataset

    # train_data = MyDataset(args)
    # args.vocab_size = train_data.vocab_size
    from src.rwkvLinear import LORA_CONFIG, LoraLinear
    from src.model import RWKV
    
    if args.quant!='none':
        LORA_CONFIG["quant"]=True
    model = RWKV(args)
    freeze=False
    if args.lora or args.LISA or args.train_type=='state':
        model.requires_grad_(False)
        freeze=True
    
    if args.state_tune or args.train_type=='state':
        for name, module in model.named_modules():
            for pname, param in module.named_parameters():
                if 'state' in pname :
                    param.requires_grad = True
            break

    if len(args.load_model) == 0 or args.my_pile_stage == 1:  # shall we build the initial weights?
        init_weight_name = f"{args.proj_dir}/rwkv-init.pth"
        generate_init_weight(model, init_weight_name)  # save initial weights
        args.load_model = init_weight_name

    rank_zero_info(f"########## Loading {args.load_model}... ##########")

    model.load_state_dict(torch.load(args.load_model, map_location="cpu"), strict=(not freeze))

    


    if pl.__version__[0]=='2':
        trainer = Trainer(accelerator=args.accelerator,strategy=args.strategy,devices=args.devices,num_nodes=args.num_nodes,precision=args.precision,
        logger=args.logger,callbacks=[train_callback(args)],max_epochs=args.max_epochs,check_val_every_n_epoch=args.check_val_every_n_epoch,num_sanity_val_steps=args.num_sanity_val_steps,
        log_every_n_steps=args.log_every_n_steps,enable_checkpointing=args.enable_checkpointing,accumulate_grad_batches=args.accumulate_grad_batches,gradient_clip_val=args.gradient_clip_val)
    else:
        trainer = Trainer.from_argparse_args(
            args,
            callbacks=[train_callback(args)],
        )

    if trainer.global_rank == 100:
        for n in model.state_dict():
            shape = 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
    
    from src.asr import SLAM_ASR
    Total_model = SLAM_ASR(
        args,
        # "facebook/hubert-large-ls960-ft", # SHOULD NOT BE USED, THIS IS A FINETUNED VERSION.
        # "microsoft/wavlm-base-plus",
        "microsoft/wavlm-large",
        # "facebook/hubert-large-ll60k",
        model,
    )
    
    import glob
    file_paths = glob.glob('output/rwkv-adapter*.pth')
    # file_paths = glob.glob('output/rwkv*.pth')
    # 检查是否找到了文件
    if file_paths:
        file_path = file_paths[0]
        Total_model.load_state_dict(torch.load(file_path), strict=False)
        print(f"Loaded model from {file_path}")
    else:
        print("No weights found. Create origin model.")
    
    
    from datasets import load_from_disk,load_dataset, concatenate_datasets
    if(args.op == "train"):# training
        
        
        dataset = load_dataset('librispeech_asr','clean',split='train.100')
        dataset2 = load_dataset('librispeech_asr','clean',split='train.360')
        dataset3 = load_dataset('librispeech_asr','other',split='train.500')
        
        
        dataset = concatenate_datasets([dataset, dataset2, dataset3]).shuffle()
        dataset = MyDataset(args, dataset)
        data_loader = DataLoader(dataset, shuffle=True, pin_memory=True, batch_size=args.micro_bsz, num_workers=8, persistent_workers=False, drop_last=True, collate_fn=lambda x: x)
        print("train starting...")
        trainer.fit(Total_model, data_loader)
        
    # elif(args.op == "eval"):#prediction
        
    #     dataset = load_dataset('librispeech_asr','clean',split='train.100')
    #     dataset = dataset.select(range(100))
        
    #     tokenizer = Total_model.return_tokenizer()
    #     Total_model.to("cuda", dtype=torch.bfloat16)
        
    #     for data in dataset:
    #         import librosa
            
    #         output= Total_model.generate(data['audio']['array'])
    #         output = ''.join(output)
            
    #         print(f"output:\n{output}")
    #         print(f"answer:\n{data['text'].lower()}")
    #         print("\n\n")
    elif(args.op == "eval"):#wer
        from datasets import load_dataset
        ds1 = load_dataset("librispeech_asr","clean",split="test")
        ds2 = load_dataset("librispeech_asr","other",split="test")
        dss = [ds1,ds2]
        tokenizer = Total_model.return_tokenizer()
        Total_model.to("cuda", dtype=torch.bfloat16)
        
        from jiwer import wer
        def calculate_wer(predictions, references):
            total_wer = 0.0
            for pred, ref in zip(predictions, references):
                total_wer += wer(ref, pred)
            average_wer = total_wer / len(predictions)
            return average_wer
        
        from tqdm import tqdm
        for ds in dss:
            predictions = []
            references = []
            for i in tqdm(range(len(ds))):
                x = ds[i]["audio"]["array"]
                z = ds[i]["text"].lower()
                # asr(x)
                # print(f"Audio length:{len(x)/16000} s")
                with torch.no_grad():
                    output = Total_model.generate(x) 
                output = ''.join(output)
                predictions.append(output)
                references.append(z)
            
            average_wer = calculate_wer(predictions, references)
            # print(ds)
            print(f"Average WER for {ds} is: {average_wer}") 
    elif(args.op == 'predict'):
        
        import librosa
        import time
        
        audio, sr = librosa.load(args.file_path, sr=None)
        audio = librosa.resample(audio, orig_sr=sr, target_sr=16000)
        Total_model = Total_model.to("cuda", dtype=torch.bfloat16)
        
        start_time = time.time()
        output= Total_model.generate(audio)
        output = ''.join(output)
        end_time = time.time()
        
        print(f"audio: {args.file_path}")
        print(f"predict: {output}")
        print(f"Response time: {end_time - start_time} seconds")