[s2s] save first batch to json for debugging purposes (#6810)

examples/seq2seq/finetune.py

@@ -33,6 +33,7 @@ from utils import (
     lmap,
     pickle_save,
     save_git_info,
+    save_json,
     use_task_specific_params,
 )
 
@@ -105,6 +106,7 @@ class SummarizationModule(BaseTransformer):
         self.dataset_class = (
             Seq2SeqDataset if hasattr(self.tokenizer, "prepare_seq2seq_batch") else LegacySeq2SeqDataset
         )
+        self.already_saved_batch = False
         self.eval_beams = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
         if self.hparams.eval_max_gen_length is not None:
             self.eval_max_length = self.hparams.eval_max_gen_length
@@ -112,6 +114,17 @@ class SummarizationModule(BaseTransformer):
             self.eval_max_length = self.model.config.max_length
         self.val_metric = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
 
+    def save_readable_batch(self, batch: Dict[str, torch.Tensor]) -> Dict[str, List[str]]:
+        """A debugging utility"""
+        readable_batch = {
+            k: self.tokenizer.batch_decode(v.tolist()) if "mask" not in k else v.shape for k, v in batch.items()
+        }
+        save_json(readable_batch, Path(self.output_dir) / "text_batch.json")
+        save_json({k: v.tolist() for k, v in batch.items()}, Path(self.output_dir) / "tok_batch.json")
+
+        self.already_saved_batch = True
+        return readable_batch
+
     def forward(self, input_ids, **kwargs):
         return self.model(input_ids, **kwargs)
 
@@ -129,6 +142,9 @@ class SummarizationModule(BaseTransformer):
             decoder_input_ids = self.model._shift_right(tgt_ids)
         else:
             decoder_input_ids = shift_tokens_right(tgt_ids, pad_token_id)
+        if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
+            batch["decoder_input_ids"] = decoder_input_ids
+            self.save_readable_batch(batch)
 
         outputs = self(src_ids, attention_mask=src_mask, decoder_input_ids=decoder_input_ids, use_cache=False)
         lm_logits = outputs[0]

examples/seq2seq/test_seq2seq_examples.py

@@ -422,6 +422,10 @@ def test_finetune(model):
         assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
         assert bart.decoder.embed_tokens == bart.shared
 
+    example_batch = load_json(module.output_dir / "text_batch.json")
+    assert isinstance(example_batch, dict)
+    assert len(example_batch) >= 4
+
 
 def test_finetune_extra_model_args():
     args_d: dict = CHEAP_ARGS.copy()