295 lines
11 KiB
Python
295 lines
11 KiB
Python
import io
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import os
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import sys
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import platform
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import argparse
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import threading
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import time
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import math
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import warnings
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import pandas as pd
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import torch
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import torch.distributed as dist
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from torch import optim, nn
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from torch._C._profiler import ActiveProfilerType
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from torch.nn.parallel import DistributedDataParallel
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from torch.nn.utils.rnn import pad_sequence
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from torch.optim.lr_scheduler import CosineAnnealingLR
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from torch.utils.data import DataLoader, DistributedSampler
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from contextlib import nullcontext
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from transformers import AutoTokenizer, PreTrainedTokenizerFast
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from mem_utils import log_memory_snapshot, ActivationMemoryTracker
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from model.model import MiniMindLM
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from model.LMConfig import LMConfig
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from model.dataset import PretrainDataset
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warnings.filterwarnings('ignore')
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def Logger(content):
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if not ddp or dist.get_rank() == 0:
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print(content)
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def get_dynamic_lr(cur_step, warmup_steps, decay_steps, lr):
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min_lr = lr / 10
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if cur_step < warmup_steps:
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return lr * (cur_step / warmup_steps)
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if cur_step > decay_steps:
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return min_lr
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step_ratio = (cur_step - warmup_steps)/(decay_steps-warmup_steps)
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cos_scope = 0.5 * (1 + math.cos(math.pi * step_ratio))
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return min_lr + (lr - min_lr) * cos_scope
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def get_lr(current_step, total_steps, lr):
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return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps))
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def train_epoch(epoch, wandb):
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# 初始化跟踪器
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# tracker = ActivationMemoryTracker()
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# tracker.track_activations(model)
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loss_fct = nn.CrossEntropyLoss(reduction='none')
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step_base = epoch * iter_per_epoch
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total_steps = iter_per_epoch * args.epochs
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warmup_steps = int(total_steps * 0.005)
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decay_steps = int(total_steps * 0.9)
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start_time = time.time()
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for step, (X, Y, loss_mask) in enumerate(train_loader):
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X = X.to(args.device)
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Y = Y.to(args.device)
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loss_mask = loss_mask.to(args.device)
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lr = get_dynamic_lr(step_base + step, warmup_steps, decay_steps, args.learning_rate)
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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with ctx:
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res = model(X)
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loss = loss_fct(
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res.logits.view(-1, res.logits.size(-1)),
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Y.view(-1)
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).view(Y.size())
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loss = (loss * loss_mask).sum() / loss_mask.sum()
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loss += res.aux_loss
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loss = loss / args.accumulation_steps
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scaler.scale(loss).backward()
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if (step + 1) % args.accumulation_steps == 0:
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scaler.unscale_(optimizer)
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torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
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scaler.step(optimizer)
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scaler.update()
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optimizer.zero_grad(set_to_none=True)
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if step % args.log_interval == 0:
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spend_time = time.time() - start_time
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Logger(
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'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.12f} spend:{}min remain:{}min'.format(
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epoch + 1,
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args.epochs,
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step,
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iter_per_epoch,
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loss.item() * args.accumulation_steps,
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optimizer.param_groups[-1]['lr'],
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spend_time // 60,
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spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
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if (wandb is not None) and (not ddp or dist.get_rank() == 0):
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wandb.log({"loss": loss.item() * args.accumulation_steps,
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"lr": optimizer.param_groups[-1]['lr'],
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"remain": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
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if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
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model.eval()
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'{args.save_dir}/pretrain_{lm_config.dim}{moe_path}.pth'
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if isinstance(model, torch.nn.parallel.DistributedDataParallel):
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state_dict = model.module.state_dict()
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else:
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state_dict = model.state_dict()
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buffer = io.BytesIO()
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torch.save(state_dict, buffer)
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threading.Thread(target=save_checkpoint, args=(buffer, ckp)).start()
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model.train()
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# 内存调试
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# log_memory_snapshot(model, optimizer, step)
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# # 输出激活值显存统计
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# print("中间激活值显存占用：")
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# total = 0
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# for name, info in tracker.activation_info.items():
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# print(f"Layer: {name} | {info}")
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# total += info["size"]
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# print(f"中间激活值显存总占用: {total:.2f} MB")
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# print(torch.cuda.memory_summary())
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# return
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def save_checkpoint(buffer: io.BytesIO, path):
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with open(path, 'wb') as f:
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f.write(buffer.getvalue())
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def init_model(cli_args):
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lm_config = LMConfig(
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dim=cli_args.dim,
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n_layers=cli_args.n_layers,
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n_heads=cli_args.n_heads,
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n_kv_heads=cli_args.n_kv_heads,
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max_seq_len=cli_args.max_seq_len,
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use_moe=cli_args.use_moe,
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use_mla=cli_args.use_mla,
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torch_dtype=cli_args.dtype,
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)
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tokenizer = AutoTokenizer.from_pretrained(f'./model/{cli_args.vocab}_tokenizer')
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# tokenizer = AutoTokenizer.from_pretrained('./deepseek_v3_tokenizer')
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lm_config.vocab_size = tokenizer.vocab_size
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model = MiniMindLM(lm_config)
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Logger(f'LLM总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
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return model, tokenizer
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def init_distributed_mode():
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if not ddp: return
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global ddp_local_rank, DEVICE
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dist.init_process_group(backend="nccl")
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ddp_rank = int(os.environ["RANK"])
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ddp_local_rank = int(os.environ["LOCAL_RANK"])
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ddp_world_size = int(os.environ["WORLD_SIZE"])
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DEVICE = f"cuda:{ddp_local_rank}"
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torch.cuda.set_device(DEVICE)
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# torchrun --nproc_per_node 2 1-pretrain.py
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="MiniMind Pretraining")
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parser.add_argument("--save_dir", type=str, default="out")
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# 若要以最快速度实现zero则epochs设置为1轮；否则应当利用有限的数据训练2~6个epochs。
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parser.add_argument("--epochs", type=int, default=1)
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parser.add_argument("--batch_size", type=int, default=32)
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parser.add_argument("--learning_rate", type=float, default=5e-4)
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parser.add_argument('--dim', default=512, type=int)
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parser.add_argument('--n_layers', default=8, type=int)
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parser.add_argument('--n_heads', default=8, type=int)
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parser.add_argument('--n_kv_heads', default=2, type=int)
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parser.add_argument('--max_seq_len', default=512, type=int)
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parser.add_argument('--use_mla', default=False, action='store_true')
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parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu")
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parser.add_argument("--dtype", type=str, default="bfloat16")
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parser.add_argument("--use_wandb", action="store_true")
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parser.add_argument("--wandb_project", type=str, default="MiniMind-Pretrain")
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parser.add_argument("--num_workers", type=int, default=1)
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parser.add_argument("--ddp", action="store_true")
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parser.add_argument("--accumulation_steps", type=int, default=8)
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parser.add_argument("--grad_clip", type=float, default=1.0)
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parser.add_argument("--warmup_iters", type=int, default=0)
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parser.add_argument("--log_interval", type=int, default=500)
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parser.add_argument("--save_interval", type=int, default=200)
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parser.add_argument('--local_rank', type=int, default=-1)
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parser.add_argument('--vocab', default='minimind', type=str)
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parser.add_argument('--use_moe', default=False, type=bool)
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parser.add_argument("--data_paths", type=str, nargs="+", default=["./dataset/pretrain_hq.jsonl"])
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parser.add_argument("--ckpt_path", type=str, default=None)
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args = parser.parse_args()
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args.log_interval = args.save_interval
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os.makedirs(args.save_dir, exist_ok=True)
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torch.manual_seed(1337)
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device_type = "cuda" if "cuda" in args.device else "cpu"
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args.wandb_run_name = f"MiniMind-Pretrain-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"
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if device_type == "cpu":
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ctx = nullcontext()
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else:
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# 映射dtype字符串到torch类型
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amp_dtype = torch.bfloat16 if args.dtype == 'bfloat16' else torch.float16
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ctx = torch.cuda.amp.autocast(dtype=amp_dtype)
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ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run?
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ddp_local_rank, DEVICE = 0, "cuda:0"
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if ddp:
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init_distributed_mode()
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args.device = torch.device(DEVICE)
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if args.use_wandb and (not ddp or ddp_local_rank == 0):
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import wandb
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wandb.init(project=args.wandb_project, name=args.wandb_run_name)
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else:
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wandb = None
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model, tokenizer = init_model(args)
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if args.ckpt_path is not None:
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# 接着训练
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state_dict = torch.load(args.ckpt_path, map_location=args.device)
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model.load_state_dict({k: v for k, v in state_dict.items() if 'mask' not in k}, strict=True)
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model.to(args.device)
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train_ds = PretrainDataset(args.data_paths, tokenizer, max_length=args.max_seq_len)
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def collate_fn(batch):
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# 对 batch 中的每个样本进行 padding，动态padding到每个batch的最大长度
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batch = pad_sequence(batch, batch_first=True, padding_value=tokenizer.pad_token_id)
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# 以下是padding到最大长度，方便调试显存占用情况
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# max_len = args.max_seq_len
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# processed = []
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# for input_ids in batch:
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# # 截断超过 max_len 的部分
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# if len(input_ids) > max_len:
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# processed.append(input_ids[:max_len])
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# else:
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# # 不足则填充到 max_len
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# padded = torch.full(
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# (max_len,),
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# tokenizer.pad_token_id,
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# dtype=input_ids.dtype
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# )
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# padded[:len(input_ids)] = input_ids
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# processed.append(padded)
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# batch = torch.stack(processed) # 直接堆叠已等长的样本
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# 直接通过切片生成输入和目标序列
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bx = batch[:, :-1] # 输入序列（去掉最后一个token）
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by = batch[:, 1:] # 目标序列（去掉第一个token）
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# 生成attention mask（去掉第一个token的位置）
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bl = (batch != tokenizer.pad_token_id)[:, 1:]
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return bx, by, bl
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train_sampler = DistributedSampler(train_ds) if ddp else None
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train_loader = DataLoader(
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train_ds,
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batch_size=args.batch_size,
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pin_memory=True,
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drop_last=False,
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shuffle=True,
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num_workers=args.num_workers,
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sampler=train_sampler,
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collate_fn=collate_fn,
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)
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scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
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optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
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if ddp:
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model._ddp_params_and_buffers_to_ignore = {"pos_cis"}
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model = DistributedDataParallel(model, device_ids=[ddp_local_rank])
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iter_per_epoch = len(train_loader)
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for epoch in range(args.epochs):
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train_epoch(epoch, wandb)
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