Simplify train step in Conformer RNN-T LibriSpeech recipe (#2981)

Summary:
In the Conformer RNN-T LibriSpeech recipe, there's no need to perform manual optimization. This PR modifies the recipe to use automatic optimization instead.

Pull Request resolved: https://github.com/pytorch/audio/pull/2981

Reviewed By: mthrok

Differential Revision: D42507228

Pulled By: hwangjeff

fbshipit-source-id: 9712add951eba356e39f7e8c8dc3bf584ba48309

examples/asr/librispeech_conformer_rnnt/lightning.py

@@ -95,8 +95,6 @@ class ConformerRNNTModule(LightningModule):
         self.optimizer = torch.optim.Adam(self.model.parameters(), lr=8e-4, betas=(0.9, 0.98), eps=1e-9)
         self.warmup_lr_scheduler = WarmupLR(self.optimizer, 40, 120, 0.96)
 
-        self.automatic_optimization = False
-
     def _step(self, batch, _, step_type):
         if batch is None:
             return None
@@ -145,25 +143,13 @@ class ConformerRNNTModule(LightningModule):
         - Update parameters on each GPU.
 
         Doing so allows us to account for the variability in batch sizes that
-        variable-length sequential data commonly yields.
+        variable-length sequential data yield.
         """
-
-        opt = self.optimizers()
-        opt.zero_grad()
         loss = self._step(batch, batch_idx, "train")
         batch_size = batch.features.size(0)
         batch_sizes = self.all_gather(batch_size)
         self.log("Gathered batch size", batch_sizes.sum(), on_step=True, on_epoch=True)
         loss *= batch_sizes.size(0) / batch_sizes.sum()  # world size / batch size
-        self.manual_backward(loss)
-        torch.nn.utils.clip_grad_norm_(self.model.parameters(), 10.0)
-        opt.step()
-
-        # step every epoch
-        sch = self.lr_schedulers()
-        if self.trainer.is_last_batch:
-            sch.step()
-
         return loss
 
     def validation_step(self, batch, batch_idx):

examples/asr/librispeech_conformer_rnnt/train.py

@@ -44,6 +44,7 @@ def run_train(args):
         strategy=DDPPlugin(find_unused_parameters=False),
         callbacks=callbacks,
         reload_dataloaders_every_n_epochs=1,
+        gradient_clip_val=10.0,
     )
 
     sp_model = spm.SentencePieceProcessor(model_file=str(args.sp_model_path))