When using gradient accumulation, does the order of optimizer.zero_grad() affect training?

if I use accelerate+deepspeed to train a model, and I set
deepspeed_config: gradient_accumulation_steps: 8 offload_optimizer_device: cpu offload_param_device: cpu zero3_init_flag: false zero_stage: 2

does the order of the order of backward(), step(), zero_grad() affect training?
For example:
for batch in training_dataloader: with accelerator.accumulate(model): inputs, targets = batch outputs = model(inputs) loss = loss_function(outputs, targets) accelerator.backward(loss) optimizer.step() scheduler.step() optimizer.zero_grad()

and
for batch in training_dataloader: with accelerator.accumulate(model): optimizer.zero_grad() inputs, targets = batch outputs = model(inputs) loss = loss_function(outputs, targets) accelerator.backward(loss) optimizer.step() scheduler.step()

I want to know whether the two situations will yield the same result. During gradient accumulation training, when the model needs to update the parameters and accelerate.sync_gradients=True, will using the second method clear the gradients, causing the gradient accumulation to be incorrect, so that at this point there is only one sample?

Start with the two training-loop variants in the issue, focusing on accelerator.accumulate(model), accelerator.backward(loss), optimizer.step(), scheduler.step(), and optimizer.zero_grad(). Compare their behavior when gradient accumulation is enabled and determine whether both loops preserve the intended accumulated gradients and produce equivalent updates.