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