[s2s] dynamic batch size with --max_tokens_per_batch (#7030)

examples/seq2seq/save_len_file.py

@@ -0,0 +1,46 @@
+import fire
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from transformers import AutoTokenizer
+
+
+try:
+    from .utils import Seq2SeqDataset, pickle_save
+except ImportError:
+    from utils import Seq2SeqDataset, pickle_save
+
+
+def save_len_file(
+    tokenizer_name, data_dir, max_source_length=1024, max_target_length=1024, consider_target=False, **kwargs
+):
+    """Save max(src_len, tgt_len) for each example to allow dynamic batching."""
+    tok = AutoTokenizer.from_pretrained(tokenizer_name)
+    train_ds = Seq2SeqDataset(tok, data_dir, max_source_length, max_target_length, type_path="train", **kwargs)
+    pad = tok.pad_token_id
+
+    def get_lens(ds):
+        dl = tqdm(
+            DataLoader(ds, batch_size=512, num_workers=8, shuffle=False, collate_fn=ds.collate_fn),
+            desc=str(ds.len_file),
+        )
+        max_lens = []
+        for batch in dl:
+            src_lens = batch["input_ids"].ne(pad).sum(1).tolist()
+            tgt_lens = batch["labels"].ne(pad).sum(1).tolist()
+            if consider_target:
+                for src, tgt in zip(src_lens, tgt_lens):
+                    max_lens.append(max(src, tgt))
+            else:
+                max_lens.extend(src_lens)
+        return max_lens
+
+    train_lens = get_lens(train_ds)
+    val_ds = Seq2SeqDataset(tok, data_dir, max_source_length, max_target_length, type_path="val", **kwargs)
+    val_lens = get_lens(val_ds)
+    pickle_save(train_lens, train_ds.len_file)
+    pickle_save(val_lens, val_ds.len_file)
+
+
+if __name__ == "__main__":
+    fire.Fire(save_len_file)