Override build_inputs_with_special_tokens for fast tokenizers (#2912)

* Override build_inputs_with_special_tokens for fast impl + unittest.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

* Quality + format.
Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>

src/transformers/tokenization_bert.py

@@ -572,3 +572,11 @@ class BertTokenizerFast(PreTrainedTokenizerFast):
         )
 
         self.do_lower_case = do_lower_case
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        output = [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+
+        if token_ids_1:
+            output += token_ids_1 + [self.sep_token_id]
+
+        return output

src/transformers/tokenization_roberta.py

@@ -210,3 +210,10 @@ class RobertaTokenizerFast(GPT2TokenizerFast):
         # We need to recompute max_len according to the newly register post_processor to get real values.
         self.max_len_single_sentence = self.max_len - self.num_added_tokens(False)  # take into account special tokens
         self.max_len_sentences_pair = self.max_len - self.num_added_tokens(True)  # take into account special tokens
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        output = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
+        if token_ids_1 is None:
+            return output
+
+        return output + [self.eos_token_id] + token_ids_1 + [self.eos_token_id]

src/transformers/tokenization_utils.py

@@ -1669,6 +1669,12 @@ class PreTrainedTokenizerFast(PreTrainedTokenizer):
         self._update_special_tokens()
         return added
 
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if token_ids_1 is None:
+            return token_ids_0
+        else:
+            return token_ids_0 + token_ids_1
+
     def num_added_tokens(self, pair=False):
         return self.tokenizer.num_special_tokens_to_add(pair)
 

tests/test_tokenization_fast.py

@@ -172,6 +172,35 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             self.assertEqual(len(tokens[key].shape), 2)
             self.assertEqual(tokens[key].shape[-1], 6)
 
+    def assert_build_inputs_with_special_tokens(self, tokenizer_r, tokenizer_p):
+        # Input string
+        input_simple = tokenizer_p.tokenize("This is a sample input")
+        input_pair = tokenizer_p.tokenize("This is a sample pair")
+
+        # Generate output
+        output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple)
+        output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple)
+        self.assertEqual(output_p, output_r)
+
+        # Generate pair output
+        output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple, input_pair)
+        output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple, input_pair)
+        self.assertEqual(output_p, output_r)
+
+        # Input tokens id
+        input_simple = tokenizer_p.encode("This is a sample input")
+        input_pair = tokenizer_p.encode("This is a sample pair")
+
+        # Generate output
+        output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple)
+        output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple)
+        self.assertEqual(output_p, output_r)
+
+        # Generate pair output
+        output_r = tokenizer_r.build_inputs_with_special_tokens(input_simple, input_pair)
+        output_p = tokenizer_p.build_inputs_with_special_tokens(input_simple, input_pair)
+        self.assertEqual(output_p, output_r)
+
     def test_bert(self):
         for tokenizer_name in BertTokenizer.pretrained_vocab_files_map["vocab_file"].keys():
             tokenizer_p = BertTokenizer.from_pretrained(tokenizer_name)
@@ -204,6 +233,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             # Check for dynamic encoding sequence handling in batch_encode_plus
             self.assert_batch_encode_dynamic_overflowing(tokenizer_r)
 
+            # Check alignment for build_inputs_with_special_tokens
+            self.assert_build_inputs_with_special_tokens(tokenizer_r, tokenizer_p)
+
     @require_torch
     def test_transfoxl(self):
         for tokenizer_name in TransfoXLTokenizer.pretrained_vocab_files_map["pretrained_vocab_file"].keys():
@@ -237,6 +269,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             # Check for dynamic encoding sequence handling in batch_encode_plus
             self.assertRaises(ValueError, self.assert_batch_encode_dynamic_overflowing, tokenizer_r)
 
+            # Check alignment for build_inputs_with_special_tokens
+            self.assert_build_inputs_with_special_tokens(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)
@@ -270,6 +305,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             # Check for dynamic encoding sequence handling in batch_encode_plus
             self.assert_batch_encode_dynamic_overflowing(tokenizer_r)
 
+            # Check alignment for build_inputs_with_special_tokens
+            self.assert_build_inputs_with_special_tokens(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)
@@ -302,6 +340,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             # Check for dynamic encoding sequence handling in batch_encode_plus
             self.assertRaises(ValueError, self.assert_batch_encode_dynamic_overflowing, tokenizer_r)
 
+            # Check alignment for build_inputs_with_special_tokens
+            self.assert_build_inputs_with_special_tokens(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)
@@ -334,6 +375,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             # Check for dynamic encoding sequence handling in batch_encode_plus
             self.assert_batch_encode_dynamic_overflowing(tokenizer_r)
 
+            # Check alignment for build_inputs_with_special_tokens
+            self.assert_build_inputs_with_special_tokens(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)
@@ -366,6 +410,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
             # Check for dynamic encoding sequence handling in batch_encode_plus
             self.assertRaises(ValueError, self.assert_batch_encode_dynamic_overflowing, tokenizer_r)
 
+            # Check alignment for build_inputs_with_special_tokens
+            self.assert_build_inputs_with_special_tokens(tokenizer_r, tokenizer_p)
+
 
 if __name__ == "__main__":
     unittest.main()