[s2s] Delete useless method, log tokens_per_batch (#6081)
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@ -160,9 +160,16 @@ class SummarizationModule(BaseTransformer):
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)
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return (loss,)
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@property
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def pad(self) -> int:
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return self.tokenizer.pad_token_id
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def training_step(self, batch, batch_idx) -> Dict:
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loss_tensors = self._step(batch)
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logs = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
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# tokens per batch
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logs["tpb"] = batch["input_ids"].ne(self.pad).sum() + batch["decoder_input_ids"].ne(self.pad).sum()
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return {"loss": loss_tensors[0], "log": logs}
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def validation_step(self, batch, batch_idx) -> Dict:
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@ -172,7 +179,7 @@ class SummarizationModule(BaseTransformer):
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self.step_count += 1
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losses = {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
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loss = losses["loss"]
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rouges = {k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "summ_len"]}
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rouges = {k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "gen_len"]}
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rouge_tensor: torch.FloatTensor = torch.tensor(rouges[self.val_metric]).type_as(loss)
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rouges.update({k: v.item() for k, v in losses.items()})
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losses.update(rouges)
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@ -190,23 +197,21 @@ class SummarizationModule(BaseTransformer):
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return calculate_rouge(preds, target)
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def _generative_step(self, batch: dict) -> dict:
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pad_token_id = self.tokenizer.pad_token_id
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source_ids, source_mask, y = Seq2SeqDataset.trim_seq2seq_batch(batch, pad_token_id)
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t0 = time.time()
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generated_ids = self.model.generate(
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input_ids=source_ids,
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attention_mask=source_mask,
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batch["input_ids"],
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attention_mask=batch["attention_mask"],
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use_cache=True,
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decoder_start_token_id=self.decoder_start_token_id,
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)
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gen_time = (time.time() - t0) / source_ids.shape[0]
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preds = self.ids_to_clean_text(generated_ids)
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target = self.ids_to_clean_text(y)
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gen_time = (time.time() - t0) / batch["input_ids"].shape[0]
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preds: List[str] = self.ids_to_clean_text(generated_ids)
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target: List[str] = self.ids_to_clean_text(batch["decoder_input_ids"])
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loss_tensors = self._step(batch)
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base_metrics = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
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rouge: Dict = self.calc_generative_metrics(preds, target)
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summ_len = np.mean(lmap(len, generated_ids))
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base_metrics.update(gen_time=gen_time, summ_len=summ_len, preds=preds, target=target, **rouge)
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base_metrics.update(gen_time=gen_time, gen_len=summ_len, preds=preds, target=target, **rouge)
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return base_metrics
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def test_step(self, batch, batch_idx):
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@ -128,12 +128,6 @@ class Seq2SeqDataset(Dataset):
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def get_char_lens(data_file):
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return [len(x) for x in Path(data_file).open().readlines()]
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@staticmethod
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def trim_seq2seq_batch(batch, pad_token_id) -> tuple:
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y = trim_batch(batch["decoder_input_ids"], pad_token_id)
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source_ids, source_mask = trim_batch(batch["input_ids"], pad_token_id, attention_mask=batch["attention_mask"])
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return source_ids, source_mask, y
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def collate_fn(self, batch) -> Dict[str, torch.Tensor]:
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input_ids = torch.stack([x["input_ids"] for x in batch])
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masks = torch.stack([x["attention_mask"] for x in batch])
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