ddp
hi:
When I try to reproduce it, it comes up with the following error.Do you guys have any good suggestions, please?


Error executing job with overrides: []
    self._run(model, ckpt_path=ckpt_path)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 935, in _run
    results = self._run_stage()
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 978, in _run_stage
    self.fit_loop.run()
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 201, in run
    self.advance()
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 354, in advance
    self.epoch_loop.run(self._data_fetcher)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 133, in run
    self.advance(data_fetcher)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 218, in advance
    batch_output = self.automatic_optimization.run(trainer.optimizers[0], kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 185, in run
    self._optimizer_step(kwargs.get("batch_idx", 0), closure)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 261, in _optimizer_step
    call._call_lightning_module_hook(
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 142, in _call_lightning_module_hook
    output = fn(*args, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/core/module.py", line 1266, in optimizer_step
    optimizer.step(closure=optimizer_closure)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/core/optimizer.py", line 158, in step
    step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 257, in optimizer_step
    optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/strategy.py", line 224, in optimizer_step
    return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 114, in optimizer_step
    return optimizer.step(closure=closure, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 69, in wrapper
    return wrapped(*args, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/optim/optimizer.py", line 280, in wrapper
    out = func(*args, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/optim/optimizer.py", line 33, in _use_grad
    ret = func(self, *args, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/optim/adamw.py", line 148, in step
    loss = closure()
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 101, in _wrap_closure
    closure_result = closure()
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 140, in __call__
    self._result = self.closure(*args, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 126, in closure
    step_output = self._step_fn()
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 308, in _training_step
    training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/trainer/call.py", line 288, in _call_strategy_hook
    output = fn(*args, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/pytorch_lightning/strategies/ddp.py", line 329, in training_step
    return self.model(*args, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/zhengbeida/anaconda3/envs/nemo/lib/python3.10/site-packages/torch/nn/parallel/distributed.py", line 1139, in forward
    if torch.is_grad_enabled() and self.reducer._rebuild_buckets():
RuntimeError: It looks like your LightningModule has parameters that were not used in producing the loss returned by training_step. If this is intentional, you must enable the detection of unused parameters in DDP, either by setting the string value `strategy='ddp_find_unused_parameters_true'` or by setting the flag in the strategy with `strategy=DDPStrategy(find_unused_parameters=True)`.
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
