我想用中文训练中文网络小说，请你作为nanogpt大师级别的人物，将下面的程序帮我修改成合适我的程序给我，请告诉我修改的原因。程序如下：# logging
if wandb_log and master_process:
import wandb
wandb.init(project=wandb_project, name=wandb_run_name, config=config)

# training loop
X, Y = get_batch(‘train’) # fetch the very first batch
t0 = time.time()
local_iter_num = 0 # number of iterations in the lifetime of this process
raw_model = model.module if ddp else model # unwrap DDP container if needed
running_mfu = -1.0
while True:

# determine and set the learning rate for this iteration
lr = get_lr(iter_num) if decay_lr else learning_rate
for param_group in optimizer.param_groups:
param_group[‘lr’] = lr

# evaluate the loss on train/val sets and write checkpoints
if iter_num % eval_interval == 0 and master_process:
losses = estimate_loss()
print(f"step {iter_num}: train loss {losses[‘train’]:.4f}, val loss {losses[‘val’]:.4f}“)
if wandb_log:
wandb.log({
“iter”: iter_num,
“train/loss”: losses[‘train’],
“val/loss”: losses[‘val’],
“lr”: lr,
“mfu”: running_mfu100, # convert to percentage
})
if losses[‘val’] < best_val_loss or always_save_checkpoint:
best_val_loss = losses[‘val’]
if iter_num > 0:
checkpoint = {
‘model’: raw_model.state_dict(),
‘optimizer’: optimizer.state_dict(),
‘model_args’: model_args,
‘iter_num’: iter_num,
‘best_val_loss’: best_val_loss,
‘config’: config,
}
print(f"saving checkpoint to {out_dir}")
torch.save(checkpoint, os.path.join(out_dir, ‘ckpt.pt’))
if iter_num == 0 and eval_only:
break

# forward backward update, with optional gradient accumulation to simulate larger batch size
# and using the GradScaler if data type is float16
for micro_step in range(gradient_accumulation_steps):
if ddp:
# in DDP training we only need to sync gradients at the last micro step.
# the official way to do this is with model.no_sync() context manager, but
# I really dislike that this bloats the code and forces us to repeat code
# looking at the source of that context manager, it just toggles this variable
model.require_backward_grad_sync = (micro_step == gradient_accumulation_steps - 1)
with ctx:
logits, loss = model(X, Y)
loss = loss / gradient_accumulation_steps # scale the loss to account for gradient accumulation
# immediately async prefetch next batch while model is doing the forward pass on the GPU
X, Y = get_batch(‘train’)
# backward pass, with gradient scaling if training in fp16
scaler.scale(loss).backward()
# clip the gradient
if grad_clip != 0.0:
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
# step the optimizer and scaler if training in fp16
scaler.step(optimizer)
scaler.update()
# flush the gradients as soon as we can, no need for this memory anymore
optimizer.zero_grad(set_to_none=True)

# timing and logging
t1 = time.time()
dt = t1 - t0
t0 = t1
if iter_num % log_interval == 0 and master_process:
# get loss as float. note: this is a CPU-GPU sync point
# scale up to undo the division above, approximating the true total loss (exact would have been a sum)
lossf = loss.item() * gradient_accumulation_steps
if local_iter_num >= 5: # let the training loop settle a bit
mfu = raw_model.estimate_mfu(batch_size * gradient_accumulation_steps, dt)
running_mfu = mfu if running_mfu == -1.0 else 0.9running_mfu + 0.1mfu
print(f"iter {iter_num}: loss {lossf:.4f}, time {dt1000:.2f}ms, mfu {running_mfu*100:.2f}%”)
iter_num += 1
local_iter_num += 1

# termination conditions
if iter_num > max_iters:
break

if ddp:
destroy_process_group()