[s2s] Delete useless method, log tokens_per_batch (#6081)

examples/seq2seq/finetune.py

@@ -160,9 +160,16 @@ def _step(self, batch: dict) -> Tuple:
             )
         return (loss,)
 
+    @property
+    def pad(self) -> int:
+        return self.tokenizer.pad_token_id
+
     def training_step(self, batch, batch_idx) -> Dict:
         loss_tensors = self._step(batch)
+
         logs = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
+        # tokens per batch
+        logs["tpb"] = batch["input_ids"].ne(self.pad).sum() + batch["decoder_input_ids"].ne(self.pad).sum()
         return {"loss": loss_tensors[0], "log": logs}
 
     def validation_step(self, batch, batch_idx) -> Dict:
@@ -172,7 +179,7 @@ def validation_epoch_end(self, outputs, prefix="val") -> Dict:
         self.step_count += 1
         losses = {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
         loss = losses["loss"]
-        rouges = {k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "summ_len"]}
+        rouges = {k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "gen_len"]}
         rouge_tensor: torch.FloatTensor = torch.tensor(rouges[self.val_metric]).type_as(loss)
         rouges.update({k: v.item() for k, v in losses.items()})
         losses.update(rouges)
@@ -190,23 +197,21 @@ def calc_generative_metrics(self, preds, target) -> Dict:
         return calculate_rouge(preds, target)
 
     def _generative_step(self, batch: dict) -> dict:
-        pad_token_id = self.tokenizer.pad_token_id
-        source_ids, source_mask, y = Seq2SeqDataset.trim_seq2seq_batch(batch, pad_token_id)
         t0 = time.time()
         generated_ids = self.model.generate(
-            input_ids=source_ids,
-            attention_mask=source_mask,
+            batch["input_ids"],
+            attention_mask=batch["attention_mask"],
             use_cache=True,
             decoder_start_token_id=self.decoder_start_token_id,
         )
-        gen_time = (time.time() - t0) / source_ids.shape[0]
-        preds = self.ids_to_clean_text(generated_ids)
-        target = self.ids_to_clean_text(y)
+        gen_time = (time.time() - t0) / batch["input_ids"].shape[0]
+        preds: List[str] = self.ids_to_clean_text(generated_ids)
+        target: List[str] = self.ids_to_clean_text(batch["decoder_input_ids"])
         loss_tensors = self._step(batch)
         base_metrics = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
         rouge: Dict = self.calc_generative_metrics(preds, target)
         summ_len = np.mean(lmap(len, generated_ids))
-        base_metrics.update(gen_time=gen_time, summ_len=summ_len, preds=preds, target=target, **rouge)
+        base_metrics.update(gen_time=gen_time, gen_len=summ_len, preds=preds, target=target, **rouge)
         return base_metrics
 
     def test_step(self, batch, batch_idx):

examples/seq2seq/utils.py

@@ -128,12 +128,6 @@ def __getitem__(self, index) -> Dict[str, torch.Tensor]:
     def get_char_lens(data_file):
         return [len(x) for x in Path(data_file).open().readlines()]
 
-    @staticmethod
-    def trim_seq2seq_batch(batch, pad_token_id) -> tuple:
-        y = trim_batch(batch["decoder_input_ids"], pad_token_id)
-        source_ids, source_mask = trim_batch(batch["input_ids"], pad_token_id, attention_mask=batch["attention_mask"])
-        return source_ids, source_mask, y
-
     def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
         input_ids = torch.stack([x["input_ids"] for x in batch])
         masks = torch.stack([x["attention_mask"] for x in batch])