训练时梯度累积有问题？

runner.py中的launch_training_task()中，先清空了梯度再更新参数，这不会导致梯度累计失去作用吗？
model, optimizer, dataloader, scheduler = accelerator.prepare(model, optimizer, dataloader, scheduler)
initialize_deepspeed_gradient_checkpointing(accelerator)
for epoch_id in range(num_epochs):
for data in tqdm(dataloader):
with accelerator.accumulate(model):
optimizer.zero_grad()
if dataset.load_from_cache:
loss = model({}, inputs=data)
else:
loss = model(data)
accelerator.backward(loss)
optimizer.step()
model_logger.on_step_end(accelerator, model, save_steps, loss=loss)
scheduler.step()
if save_steps is None:
model_logger.on_epoch_end(accelerator, model, epoch_id)
model_logger.on_training_end(accelerator, model, save_steps)
是否应该改为：
for epoch_id in range(num_epochs):
for data in tqdm(dataloader):
with accelerator.accumulate(model):
loss = model(data)
accelerator.backward(loss)
if accelerator.sync_gradients and max_grad_norm > 0:
accelerator.clip_grad_norm_(model.parameters(), max_grad_norm)
optimizer.step()
model_logger.on_step_end(accelerator, model, save_steps, loss=loss)
scheduler.step()
optimizer.zero_grad()
if save_steps is None:
model_logger.on_epoch_end(accelerator, model, epoch_id)
model_logger.on_training_end(accelerator, model, save_steps)
先更新参数后清空梯度

Start in runner.py at launch_training_task() and inspect how accelerator.accumulate(model) handles gradient synchronization, optimizer updates, and gradient clearing. Compare the current loop with the proposed ordering, then verify that accumulation and scheduler behavior remain correct during training; the issue is done when the confirmed ordering is implemented and validated.