Fix max_length not taken into account when using pad_to_max_length on fast tokenizers (#2961)

* enable_padding should pad up to max_length if set.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Added more testing on padding.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

src/transformers/tokenization_utils.py

@@ -79,7 +79,7 @@ def truncate_and_pad(
 
     if pad_to_max_length and (pad_token and pad_token_id >= 0):
         tokenizer.enable_padding(
-            max_length=None,
+            max_length=max_length,
             direction=padding_side,
             pad_id=pad_token_id,
             pad_type_id=pad_token_type_id,

tests/test_tokenization_fast.py

@@ -76,6 +76,63 @@ class FastTokenizerMatchingTest(unittest.TestCase):
         for key in filter(lambda x: x in ["input_ids", "token_type_ids", "attention_mask"], input_p.keys()):
             self.assert_sequence_almost_equals(input_p[key], input_r[key], threshold)
 
+    def assert_padding(self, tokenizer_r, tokenizer_p):
+        # Simple input
+        input_r = tokenizer_r.encode("This is a simple input", max_length=15, pad_to_max_length=True)
+        input_p = tokenizer_p.encode("This is a simple input", max_length=15, pad_to_max_length=True)
+
+        self.assertSequenceEqual(input_r, input_p)
+
+        # Simple input
+        input_r = tokenizer_r.encode_plus("This is a simple input", max_length=15, pad_to_max_length=True)
+        input_p = tokenizer_p.encode_plus("This is a simple input", max_length=15, pad_to_max_length=True)
+
+        self.assertSequenceEqual(input_r, input_p)
+
+        # Simple input
+        # TODO: Re-enable this test when batch_encode_plus with padding correctly handles padding
+        # input_r = tokenizer_r.batch_encode_plus(
+        #     ["This is a simple input 1", "This is a simple input 2"], max_length=15, pad_to_max_length=True
+        # )
+        # input_p = tokenizer_p.batch_encode_plus(
+        #     ["This is a simple input 1", "This is a simple input 2"], max_length=15, pad_to_max_length=True
+        # )
+
+        # self.assertSequenceEqual(input_r, input_p)
+
+        # Pair input
+        input_r = tokenizer_r.encode("This is a simple input", "This is a pair", max_length=15, pad_to_max_length=True)
+        input_p = tokenizer_p.encode("This is a simple input", "This is a pair", max_length=15, pad_to_max_length=True)
+
+        self.assertSequenceEqual(input_r, input_p)
+
+        # Pair input
+        input_r = tokenizer_r.encode_plus(
+            "This is a simple input", "This is a pair", max_length=15, pad_to_max_length=True
+        )
+        input_p = tokenizer_p.encode_plus(
+            "This is a simple input", "This is a pair", max_length=15, pad_to_max_length=True
+        )
+
+        self.assertSequenceEqual(input_r, input_p)
+
+        # Pair input
+        # TODO: Re-enable this test when batch_encode_plus with padding correctly handles padding
+        # input_r = tokenizer_r.batch_encode_plus(
+        #     ["This is a simple input 1", "This is a simple input 2"],
+        #     ["This is a simple pair 1", "This is a simple pair 2"],
+        #     max_length=15,
+        #     pad_to_max_length=True,
+        # )
+        # input_p = tokenizer_p.batch_encode_plus(
+        #     ["This is a simple input 1", "This is a simple input 2"],
+        #     ["This is a simple pair 1", "This is a simple pair 2"],
+        #     max_length=15,
+        #     pad_to_max_length=True,
+        # )
+
+        # self.assertSequenceEqual(input_r, input_p)
+
     def assert_add_tokens(self, tokenizer_r):
         vocab_size = tokenizer_r.vocab_size
         self.assertEqual(tokenizer_r.add_tokens(""), 0)
@@ -239,6 +296,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             # Check the number of returned files for save_vocabulary
             self.assertEqual(len(tokenizer_r.save_vocabulary(".")), len(tokenizer_p.save_vocabulary(".")))
 
+            # Check for padding
+            self.assert_padding(tokenizer_r, tokenizer_p)
+
     @require_torch
     def test_transfoxl(self):
         for tokenizer_name in TransfoXLTokenizer.pretrained_vocab_files_map["pretrained_vocab_file"].keys():
@@ -278,6 +338,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             # Check the number of returned files for save_vocabulary
             self.assertEqual(len(tokenizer_r.save_vocabulary(".")), len(tokenizer_p.save_vocabulary(".")))
 
+            # Check for padding
+            self.assertRaises(ValueError, self.assert_padding, tokenizer_r, tokenizer_p)
+
     def test_distilbert(self):
         for tokenizer_name in DistilBertTokenizer.pretrained_vocab_files_map["vocab_file"].keys():
             tokenizer_p = DistilBertTokenizer.from_pretrained(tokenizer_name)
@@ -317,6 +380,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             # Check the number of returned files for save_vocabulary
             self.assertEqual(len(tokenizer_r.save_vocabulary(".")), len(tokenizer_p.save_vocabulary(".")))
 
+            # Check for padding
+            self.assert_padding(tokenizer_r, tokenizer_p)
+
     def test_gpt2(self):
         for tokenizer_name in GPT2Tokenizer.pretrained_vocab_files_map["vocab_file"].keys():
             tokenizer_p = GPT2Tokenizer.from_pretrained(tokenizer_name)
@@ -355,6 +421,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             # Check the number of returned files for save_vocabulary
             self.assertEqual(len(tokenizer_r.save_vocabulary(".")), len(tokenizer_p.save_vocabulary(".")))
 
+            # Check for padding
+            self.assertRaises(ValueError, self.assert_padding, tokenizer_r, tokenizer_p)
+
     def test_roberta(self):
         for tokenizer_name in RobertaTokenizer.pretrained_vocab_files_map["vocab_file"].keys():
             tokenizer_p = RobertaTokenizer.from_pretrained(tokenizer_name)
@@ -393,6 +462,10 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             # Check the number of returned files for save_vocabulary
             self.assertEqual(len(tokenizer_r.save_vocabulary(".")), len(tokenizer_p.save_vocabulary(".")))
 
+            # Check for padding
+            # TODO: Re-enable this test as soon as Roberta align with the python tokenizer.
+            # self.assert_padding(tokenizer_r, tokenizer_p)
+
     def test_openai(self):
         for tokenizer_name in OpenAIGPTTokenizer.pretrained_vocab_files_map["vocab_file"].keys():
             tokenizer_p = OpenAIGPTTokenizer.from_pretrained(tokenizer_name)
@@ -431,6 +504,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             # Check the number of returned files for save_vocabulary
             self.assertEqual(len(tokenizer_r.save_vocabulary(".")), len(tokenizer_p.save_vocabulary(".")))
 
+            # Check for padding
+            self.assertRaises(ValueError, self.assert_padding, tokenizer_r, tokenizer_p)
+
 
 if __name__ == "__main__":
     unittest.main()