213 lines
6.9 KiB
Python
213 lines
6.9 KiB
Python
import os
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import platform
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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.nn.functional as F
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import torch.distributed as dist
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from contextlib import nullcontext
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from torch import optim
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from torch.nn.parallel import DistributedDataParallel
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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 transformers import AutoTokenizer, AutoModel
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from model.model import Transformer
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from model.LMConfig import LMConfig
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from model.dataset import SFTDataset
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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_lr(it, all):
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warmup_iters = 0
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lr_decay_iters = all
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min_lr = learning_rate / epochs
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if it < warmup_iters:
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return learning_rate * it / warmup_iters
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if it > lr_decay_iters:
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return min_lr
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decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
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assert 0 <= decay_ratio <= 1
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coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
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return min_lr + coeff * (learning_rate - min_lr)
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# ------------------------------------------------------------------------------
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def train_epoch(epoch):
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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(device)
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Y = Y.to(device)
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loss_mask = loss_mask.to(device)
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lr = get_lr(epoch * iter_per_epoch + step, epochs * iter_per_epoch)
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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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logits = model(X, Y).logits
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loss = F.cross_entropy(logits.view(-1, logits.size(-1)), Y.view(-1), ignore_index=0, reduction='none')
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loss_mask = loss_mask.view(-1)
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loss = torch.sum(loss * loss_mask) / loss_mask.sum()
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# Backward pass
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scaler.scale(loss).backward()
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# Unscale gradients and clip them
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scaler.unscale_(optimizer)
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
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# Update parameters
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scaler.step(optimizer)
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scaler.update()
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# Zero the gradients
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optimizer.zero_grad(set_to_none=True)
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# 打印日志
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if step % 100 == 0:
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spend_time = time.time() - start_time
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Logger(
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'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.8f} epoch_Time:{}min:'.format(
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epoch,
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epochs,
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step,
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iter_per_epoch,
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loss,
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optimizer.param_groups[-1]['lr'],
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spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
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if (step + 1) % 1000 == 0 and (not ddp or dist.get_rank() == 0):
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model.eval()
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# torch.save(model.state_dict(), '{}/sft_iter_{}.pth'.format(save_dir, int(step + epoch * iter_per_epoch)))
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'{save_dir}/full_sft_{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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torch.save(state_dict, ckp)
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model.train()
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def init_model(lm_config):
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tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
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model_from = 1 # 1从权重，2用transformers
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def count_parameters(model):
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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if model_from == 1:
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'./out/single_chat/full_sft_{lm_config.dim}{moe_path}.pth'
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model = Transformer(lm_config)
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state_dict = torch.load(ckp, map_location=device)
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unwanted_prefix = '_orig_mod.'
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for k, v in list(state_dict.items()):
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if k.startswith(unwanted_prefix):
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state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
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model.load_state_dict(state_dict, strict=False)
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else:
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model = AutoModel.from_pretrained('./minimind', trust_remote_code=True)
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Logger(f'LLM总参数量：{count_parameters(model) / 1e6:.3f} 百万')
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model = model.to(device)
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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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# I/O
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if __name__ == "__main__":
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# -----------------------------------------------------------------------------
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lm_config = LMConfig()
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max_seq_len = lm_config.max_seq_len
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out_dir = 'out'
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epochs = 19
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gradient_accumulation_steps = 1
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batch_size = 48
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learning_rate = 2e-8
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device = 'cuda:0'
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dtype = 'bfloat16'
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# dtype = 'float16'
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save_dir = os.path.join(out_dir)
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os.makedirs(save_dir, exist_ok=True)
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tokens_per_iter = gradient_accumulation_steps * batch_size * max_seq_len
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os.makedirs(out_dir, exist_ok=True)
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torch.manual_seed(1337)
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device_type = device if "cuda" in device else "cpu"
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ctx = (
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nullcontext()
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if device_type == "cpu"
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else torch.cuda.amp.autocast()
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)
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### ddp config
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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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device = torch.device(DEVICE)
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# -----------------------------------------------------------------------------
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model, tokenizer = init_model(lm_config)
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# -----init dataloader------
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df = pd.read_csv('./dataset/sft_data.csv')
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df = df.sample(frac=1.0)
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train_ds = SFTDataset(df, tokenizer, max_length=max_seq_len)
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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=batch_size,
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pin_memory=False,
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drop_last=False,
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shuffle=False,
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num_workers=8,
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sampler=train_sampler
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)
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scaler = torch.cuda.amp.GradScaler(enabled=(dtype == dtype))
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# optimizer
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optimizer = optim.Adam(model.parameters(), lr=learning_rate)
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iter_per_epoch = len(train_loader)
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# compile the model
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if False and not lm_config.use_moe and platform.system() != 'Windows' and float(
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torch.__version__.split('.')[0]) >= 2:
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Logger("compiling the model... (takes a ~minute)")
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unoptimized_model = model
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model = torch.compile(model) # requires PyTorch 2.0
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if ddp:
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# Ignore the pos_cis buffer so that DDP does not broadcast it at
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# construction time since NCCL does not support ComplexFloat
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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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# training loop
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for epoch in range(epochs):
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train_epoch(epoch)
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