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210 lines
9.0 KiB
Python
210 lines
9.0 KiB
Python
########################################################################################################
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# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
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########################################################################################################
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if __name__ == "__main__":
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print()
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import os, warnings, math, datetime
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import numpy as np
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from argparse import ArgumentParser
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import pytorch_lightning as pl
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from pytorch_lightning import Trainer
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from pytorch_lightning import seed_everything
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from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
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from pytorch_lightning.callbacks import TQDMProgressBar
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from pytorch_lightning import Callback
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seed_everything(42)
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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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########################################################################################################
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parser = ArgumentParser()
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parser = Trainer.add_argparse_args(parser)
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parser.add_argument("--wandb", default="", type=str)
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parser.add_argument("--proj_dir", default="out", type=str)
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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("--pre_ffn", default=0, type=int)
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parser.add_argument("--head_qk", default=0, type=int)
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parser.add_argument("--lr_init", default=6e-4, type=float)
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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)
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parser.add_argument("--epoch_steps", default=1000, type=int)
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parser.add_argument("--epoch_bias", default=0, type=int)
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parser.add_argument("--epoch_save", default=5, type=int)
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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)
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parser.add_argument("--adam_eps", default=1e-8, type=float)
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parser.add_argument("--ctx_len", default=1024, type=int)
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parser.add_argument("--micro_bsz", default=12, type=int)
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parser.add_argument("--data_workers", default=1, type=int)
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parser.add_argument("--grad_cp", default=0, type=int)
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parser.add_argument("--load_model", default="", type=str)
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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)
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args = parser.parse_args()
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args.enable_checkpointing = 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.betas = (args.beta1, args.beta2)
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args.proj_dir = args.proj_dir.strip().strip("\\/")
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samples_per_epoch = args.epoch_steps * int(args.devices) * args.micro_bsz
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tokens_per_epoch = samples_per_epoch * args.ctx_len
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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-4 {args.precision.upper()} on {args.devices} x {args.accelerator.upper()} {args.strategy.upper()} {'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_bias} to {args.epoch_bias + args.max_epochs - 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, β {args.betas}, eps {args.adam_eps}
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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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if not os.path.exists(args.proj_dir):
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os.makedirs(args.proj_dir)
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assert args.data_type in ["utf-8", "utf-16le", "numpy", "binidx"]
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assert len(args.data_file) > 0
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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.\n\n")
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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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rank_zero_info("\n\nNote: you are using fp32 (very slow). Try bf16 / tf32 for faster training.\n\n")
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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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import torch
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torch.backends.cudnn.benchmark = 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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class train_callback(pl.Callback):
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def __init__(self, args):
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super().__init__()
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self.args = args
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def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
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args = self.args
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g_step = trainer.global_step
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# logging
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if trainer.global_rank == 0:
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if g_step == 0:
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trainer.my_log = open(args.proj_dir + "/train_log.txt", "a")
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trainer.my_log.write(f"NEW RUN {datetime.datetime.now()}\n{vars(self.args)}\n")
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trainer.my_log.flush()
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if len(args.wandb) > 0:
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print("Login to wandb...")
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import wandb
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model_name = str(args.vocab_size) + "-" + str(args.ctx_len) + "-" + str(args.n_layer) + "-" + str(args.n_embd)
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wandb.init(project=args.wandb, name=model_name + "-" + datetime.datetime.today().strftime("%Y-%m-%d-%H-%M-%S"), config=args, save_code=False)
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trainer.my_wandb = wandb
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# LR schedule
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w_step = args.warmup_steps
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if g_step < w_step:
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lr = args.lr_init * (g_step / w_step)
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else:
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progress = (g_step - w_step) / (args.max_epochs * args.epoch_steps - w_step - 1)
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progress = min(1, max(0, progress))
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if args.lr_final == 0 or args.lr_init == 0: # linear decay
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lr = args.lr_init + (args.lr_final - args.lr_init) * progress
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else: # exp decay
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lr = args.lr_init * math.exp(math.log(args.lr_final / args.lr_init) * pow(progress, 1))
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for param_group in trainer.optimizers[0].param_groups:
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param_group["lr"] = lr
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trainer.my_lr = lr
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# rank_zero_info(f"{g_step} {lr}")
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def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
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args = self.args
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# logging
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if trainer.global_rank == 0:
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if len(args.wandb) > 0:
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trainer.my_wandb.log({"loss": trainer.my_loss, "lr": trainer.my_lr}, step=trainer.global_step)
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def on_train_epoch_end(self, trainer, pl_module):
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args = self.args
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if trainer.current_epoch % args.epoch_save == 0 or trainer.current_epoch == args.max_epochs - 1:
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torch.save(pl_module.state_dict(), f"{args.proj_dir}/rwkv-{args.epoch_bias + trainer.current_epoch}.pth")
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trainer.my_log.write(f"{args.epoch_bias + trainer.current_epoch} {trainer.my_epoch_loss:.6f} {math.exp(trainer.my_epoch_loss):.4f} {trainer.my_lr:.8f} {datetime.datetime.now()} {trainer.current_epoch}\n")
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trainer.my_log.flush()
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@rank_zero_only
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def generate_init_weight(model, temp_name):
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try:
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os.remove(temp_name)
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except:
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pass
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mm = model.generate_init_weight()
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print(f"Saving to {temp_name}...")
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torch.save(mm, temp_name)
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########################################################################################################
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from torch.utils.data import DataLoader
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from src.dataset import MyDataset
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from src.model import RWKV
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train_data = MyDataset(args)
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args.vocab_size = train_data.vocab_size
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model = RWKV(args)
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if len(args.load_model) == 0:
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args.load_model = f"{args.proj_dir}/rwkv-init.pth" # init weights to tmp file
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generate_init_weight(model, args.load_model)
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else:
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args.load_model = f"{args.proj_dir}/{args.load_model}"
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print(f"\nLoading {args.load_model}...\n")
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load_dict = torch.load(args.load_model, map_location="cpu")
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model.load_state_dict(load_dict)
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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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train_loader = DataLoader(train_data, batch_size=args.micro_bsz, num_workers=args.data_workers)
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trainer.fit(model, train_loader)
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