[BartTokenizer] add prepare s2s batch (#6212)

Co-authored-by: sgugger <sylvain.gugger@gmail.com>

tests/test_modeling_bart.py

@@ -18,7 +18,8 @@ import unittest
 
 import timeout_decorator  # noqa
 
-from transformers import is_torch_available
+from transformers import BatchEncoding, is_torch_available
+from transformers.file_utils import cached_property
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -416,6 +417,10 @@ TOLERANCE = 1e-4
 
 @require_torch
 class BartModelIntegrationTests(unittest.TestCase):
+    @cached_property
+    def default_tokenizer(self):
+        return BartTokenizer.from_pretrained("facebook/bart-large")
+
     @slow
     def test_inference_no_head(self):
         model = BartModel.from_pretrained("facebook/bart-large").to(torch_device)
@@ -559,6 +564,76 @@ class BartModelIntegrationTests(unittest.TestCase):
         # TODO(SS): run fairseq again with num_beams=2, min_len=20.
         # TODO(SS): add test case that hits max_length
 
+    def test_prepare_seq2seq_batch(self):
+        tokenizer = self.default_tokenizer
+        src_text = ["A long paragraph for summrization.", "Another paragraph for summrization."]
+        tgt_text = [
+            "Summary of the text.",
+            "Another summary.",
+        ]
+        expected_src_tokens = [0, 250, 251, 17818, 13, 32933, 21645, 1258, 4, 2]
+        batch = tokenizer.prepare_seq2seq_batch(
+            src_text, tgt_texts=tgt_text, max_length=len(expected_src_tokens), return_tensors="pt"
+        )
+        self.assertIsInstance(batch, BatchEncoding)
+
+        self.assertEqual((2, 10), batch.input_ids.shape)
+        self.assertEqual((2, 10), batch.attention_mask.shape)
+        result = batch.input_ids.tolist()[0]
+        self.assertListEqual(expected_src_tokens, result)
+        # Test that special tokens are reset
+
+    def test_empty_target_text(self):
+        tokenizer = self.default_tokenizer
+        src_text = ["A long paragraph for summrization.", "Another paragraph for summrization."]
+        batch = tokenizer.prepare_seq2seq_batch(src_text, return_tensors="pt")
+        # check if input_ids are returned and no decoder_input_ids
+        self.assertIn("input_ids", batch)
+        self.assertIn("attention_mask", batch)
+        self.assertNotIn("decoder_input_ids", batch)
+        self.assertNotIn("decoder_attention_mask", batch)
+
+    def test_max_target_length(self):
+        tokenizer = self.default_tokenizer
+        src_text = ["A long paragraph for summrization.", "Another paragraph for summrization."]
+        tgt_text = [
+            "Summary of the text.",
+            "Another summary.",
+        ]
+        batch = tokenizer.prepare_seq2seq_batch(
+            src_text, tgt_texts=tgt_text, max_target_length=32, padding="max_length", return_tensors="pt"
+        )
+        self.assertEqual(32, batch["decoder_input_ids"].shape[1])
+        self.assertEqual(32, batch["decoder_attention_mask"].shape[1])
+
+        # test None max_target_length
+        batch = tokenizer.prepare_seq2seq_batch(
+            src_text, tgt_texts=tgt_text, max_length=32, padding="max_length", return_tensors="pt"
+        )
+        self.assertEqual(32, batch["decoder_input_ids"].shape[1])
+        self.assertEqual(32, batch["decoder_attention_mask"].shape[1])
+
+    def test_outputs_not_longer_than_maxlen(self):
+        tokenizer = self.default_tokenizer
+
+        batch = tokenizer.prepare_seq2seq_batch(["I am a small frog" * 1024, "I am a small frog"], return_tensors="pt")
+        self.assertIsInstance(batch, BatchEncoding)
+        self.assertEqual(batch.input_ids.shape, (2, 1024))
+
+    def test_special_tokens(self):
+        tokenizer = self.default_tokenizer
+        src_text = ["A long paragraph for summrization."]
+        tgt_text = [
+            "Summary of the text.",
+        ]
+        batch = tokenizer.prepare_seq2seq_batch(src_text, tgt_texts=tgt_text, return_tensors="pt")
+        input_ids = batch["input_ids"]
+        decoder_input_ids = batch["decoder_input_ids"]
+        self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
+        self.assertTrue((decoder_input_ids[:, 0] == tokenizer.bos_token_id).all().item())
+        self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
+        self.assertTrue((decoder_input_ids[:, -1] == tokenizer.eos_token_id).all().item())
+
 
 @require_torch
 class TestSinusoidalPositionalEmbeddings(unittest.TestCase):