aligned training task and CE related changes

Summary:
This diff adds:

1. Aligned training task specifically for doing cross entropy criterion training using prod data and prod like models
2. Few changes to correctly register the task and criterions.
3. Changes to trainer code for propogating accuracy metrics which we care about for training.

Couple of things are hacky right now:
- The reporting is not modular (this needs to be thought about in general for fairseq).

- The get dummy batch could be specific to task instead of specific for dataset.

Reviewed By: myleott

Differential Revision: D14670482

fbshipit-source-id: dc077247b2ae9d26a8e842a386ec5faa5771e836

Author: Jay Mahadeokar

fairseq/trainer.py

@@ -271,6 +271,10 @@ def train_step(self, samples, dummy_batch=False):
                 1. if grad_norm > self.args.clip_norm and self.args.clip_norm > 0 else 0.
             )
             self.meters['train_loss'].update(logging_output.get('loss', 0), sample_size)
+            if 'train_acc' in self.meters:
+                self.meters['train_acc'].update(
+                    logging_output.get('acc', 0), sample_size)
+
             if 'nll_loss' in logging_output:
                 self.meters['train_nll_loss'].update(logging_output.get('nll_loss', 0), ntokens)
         except OverflowError as e:
@@ -340,6 +344,10 @@ def valid_step(self, sample, raise_oom=False):
         # update meters for validation
         ntokens = logging_output.get('ntokens', 0)
         self.meters['valid_loss'].update(logging_output.get('loss', 0), sample_size)
+        if 'valid_acc' in self.meters:
+            self.meters['valid_acc'].update(
+                logging_output.get('acc', 0), sample_size)
+
         if 'nll_loss' in logging_output:
             self.meters['valid_nll_loss'].update(logging_output.get('nll_loss', 0), ntokens)