lora finetune (need to be refactored)
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josc146
203
finetune/lora/src/trainer.py
vendored
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203
finetune/lora/src/trainer.py
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import os, math, time, datetime, subprocess
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import torch
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from torch.utils.data import DataLoader
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import pytorch_lightning as pl
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from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
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from .model import LORA_CONFIG
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def my_save(dd, ff):
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if '14b-run1' not in ff:
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torch.save(dd, ff)
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else:
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fn = ff.split('/')[-1]
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fff = '/dev/shm/' + fn
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torch.save(dd, fff)
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subprocess.Popen(f" aws s3 mv {fff} s3://rwkv-14b-4k/{fn} --quiet", shell=True)
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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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# if args.cuda_cleanup > 0:
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# torch.cuda.empty_cache()
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real_step = trainer.global_step + args.epoch_begin * args.epoch_steps
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# LR schedule
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w_step = args.warmup_steps
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if args.lr_final == args.lr_init or args.epoch_count == 0:
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lr = args.lr_init
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else:
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decay_step = real_step - args.my_pile_edecay * args.epoch_steps
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decay_total = (args.epoch_count - args.my_pile_edecay) * args.epoch_steps
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progress = (decay_step - w_step + 1) / (decay_total - w_step)
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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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if trainer.global_step < w_step:
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lr = lr * (0.2 + 0.8 * trainer.global_step / w_step)
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# if trainer.is_global_zero:
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# print(trainer.global_step, decay_step, decay_total, w_step, progress, lr)
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for param_group in trainer.optimizers[0].param_groups:
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if args.layerwise_lr > 0:
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param_group["lr"] = lr * param_group["my_lr_scale"]
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# print(param_group["lr"], param_group["my_lr_scale"])
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else:
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param_group["lr"] = lr
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trainer.my_lr = lr
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# rank_zero_info(f"{real_step} {lr}")
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if trainer.global_step == 0:
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if trainer.is_global_zero: # logging
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trainer.my_loss_sum = 0
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trainer.my_loss_count = 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 {args.my_timestamp}\n{vars(self.args)}\n")
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try:
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print(f"\n{trainer.strategy.config}\n")
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trainer.my_log.write(f"{trainer.strategy.config}\n")
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except:
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pass
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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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wandb.init(
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project=args.wandb,
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name=args.run_name + " " + args.my_timestamp,
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config=args,
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save_code=False,
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)
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trainer.my_wandb = wandb
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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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if trainer.is_global_zero: # logging
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t_now = time.time_ns()
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token_per_step = args.ctx_len * args.real_bsz
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real_step = trainer.global_step + args.epoch_begin * args.epoch_steps
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kt_s = 0
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try:
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t_cost = (t_now - trainer.my_time_ns) / 1e9
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kt_s = token_per_step / t_cost / 1000
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self.log("REAL it/s", 1.0 / t_cost, prog_bar=True, on_step=True)
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self.log("Kt/s", kt_s, prog_bar=True, on_step=True)
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except:
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pass
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trainer.my_time_ns = t_now
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trainer.my_loss = trainer.my_loss_all.float().mean().item()
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trainer.my_loss_sum += trainer.my_loss
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trainer.my_loss_count += 1
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trainer.my_epoch_loss = trainer.my_loss_sum / trainer.my_loss_count
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self.log("lr", trainer.my_lr, prog_bar=True, on_step=True)
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self.log("loss", trainer.my_epoch_loss, prog_bar=True, on_step=True)
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# self.log("s", real_step, prog_bar=True, on_step=True)
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if len(args.wandb) > 0:
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lll = {"loss": trainer.my_loss, "lr": trainer.my_lr, "Gtokens": real_step * token_per_step / 1e9}
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if kt_s > 0:
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lll["kt/s"] = kt_s
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trainer.my_wandb.log(lll, step=int(real_step))
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if args.magic_prime > 0:
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expand_factor = 2 if args.my_qa_mask > 0 else 1
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if int(real_step) == int(args.magic_prime * expand_factor // args.real_bsz) - 1:
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to_save_dict = pl_module.state_dict()
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my_save(
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to_save_dict,
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f"{args.proj_dir}/rwkv-final.pth",
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)
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def on_train_epoch_start(self, trainer, pl_module):
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args = self.args
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dataset = trainer.train_dataloader.dataset.datasets
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assert "MyDataset" in str(dataset)
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dataset.global_rank = trainer.global_rank
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dataset.real_epoch = int(args.epoch_begin + trainer.current_epoch)
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dataset.world_size = trainer.world_size
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# print(f'########## world_size {dataset.world_size} global_rank {dataset.global_rank} real_epoch {dataset.real_epoch} ##########')
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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.is_global_zero: # logging & save state_dict
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if (args.epoch_save > 0 and trainer.current_epoch % args.epoch_save == 0) or trainer.current_epoch == args.epoch_count - 1:
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if args.data_type == 'wds_img':
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raw_dict = pl_module.state_dict()
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to_save_dict = {}
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for k in raw_dict:
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if k.startswith('encoder.') or k.startswith('decoder.'):
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to_save_dict[k] = raw_dict[k]
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else:
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to_save_dict = pl_module.state_dict()
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if args.lora:
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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_dict = {}
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for name, state in to_save_dict.items():
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if ('.lora_' in name
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or (enable_time_finetune and '.time_' in name)
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or (enable_ln_finetune and '.ln' in name)):
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lora_dict[name] = state
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to_save_dict = lora_dict
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try:
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my_save(
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to_save_dict,
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f"{args.proj_dir}/rwkv-{args.epoch_begin + trainer.current_epoch}.pth",
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)
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except Exception as e:
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print('Error\n\n', e, '\n\n')
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trainer.my_log.write(f"{args.epoch_begin + 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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trainer.my_loss_sum = 0
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trainer.my_loss_count = 0
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@rank_zero_only
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def generate_init_weight(model, init_weight_name):
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mm = model.generate_init_weight()
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if model.args.my_pile_stage == 1:
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if len(model.args.load_model) > 0:
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print(f"Combine weights from {model.args.load_model}...")
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load_dict = torch.load(model.args.load_model, map_location="cpu")
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for k in load_dict:
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assert k in mm
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src = load_dict[k]
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try:
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mm[k] = src.reshape(mm[k].shape)
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except:
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tmp = mm[k].squeeze().clone()
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print(k, src.shape, '-->', mm[k].shape)
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ss = src.shape[0]
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dd = tmp.shape[0]
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for i in range(dd):
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pos = i / dd * ss
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if pos >= ss - 1:
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tmp[i] = src[ss-1]
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else:
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p0 = int(math.floor(pos))
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ii = pos - p0
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tmp[i] = src[p0] * (1-ii) + src[p0+1] * (ii)
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mm[k] = tmp.reshape(mm[k].shape)
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sss = src.squeeze().float().cpu().numpy()
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print(sss[:10], '...', sss[-10:])
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mmm = mm[k].squeeze().float().cpu().numpy()
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print(mmm[:10], '...', mmm[-10:])
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print(f"Save to {init_weight_name}...")
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torch.save(mm, init_weight_name)
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if model.args.my_pile_stage == 1:
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print("Done. Now go for stage 2.")
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exit(0)
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