add warning to let the user know that the `__call__` method is faster than...

add warning to let the user know that the `__call__` method is faster than `encode` + `pad` for a fast tokenizer (#18693)

* add warning to let the user know that the  method is slower that  for a fast tokenizer

* user warnings

* fix layoutlmv2

* fix layout*

* change warnings into logger.warning

src/transformers/tokenization_utils_base.py

@@ -2821,7 +2821,10 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
         in the batch.
 
         Padding side (left/right) padding token ids are defined at the tokenizer level (with `self.padding_side`,
-        `self.pad_token_id` and `self.pad_token_type_id`)
+        `self.pad_token_id` and `self.pad_token_type_id`).
+
+        Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the
+        text followed by a call to the `pad` method to get a padded encoding.
 
         <Tip>
 
@@ -2871,6 +2874,15 @@ class PreTrainedTokenizerBase(SpecialTokensMixin, PushToHubMixin):
             verbose (`bool`, *optional*, defaults to `True`):
                 Whether or not to print more information and warnings.
         """
+        if self.__class__.__name__.endswith("Fast"):
+            if not self.deprecation_warnings.get("Asking-to-pad-a-fast-tokenizer", False):
+                logger.warning_advice(
+                    f"You're using a {self.__class__.__name__} tokenizer. Please note that with a fast tokenizer,"
+                    " using the `__call__` method is faster than using a method to encode the text followed by a call"
+                    " to the `pad` method to get a padded encoding."
+                )
+                self.deprecation_warnings["Asking-to-pad-a-fast-tokenizer"] = True
+
         # If we have a list of dicts, let's convert it in a dict of lists
         # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
         if isinstance(encoded_inputs, (list, tuple)) and isinstance(encoded_inputs[0], Mapping):

tests/models/layoutlmv2/test_tokenization_layoutlmv2.py

@@ -21,7 +21,14 @@ import tempfile
 import unittest
 from typing import List
 
-from transformers import AddedToken, LayoutLMv2TokenizerFast, SpecialTokensMixin, is_tf_available, is_torch_available
+from transformers import (
+    AddedToken,
+    LayoutLMv2TokenizerFast,
+    SpecialTokensMixin,
+    is_tf_available,
+    is_torch_available,
+    logging,
+)
 from transformers.models.layoutlmv2.tokenization_layoutlmv2 import (
     VOCAB_FILES_NAMES,
     BasicTokenizer,
@@ -41,6 +48,9 @@ from ...test_tokenization_common import (
 )
 
 
+logger = logging.get_logger(__name__)
+
+
 @require_tokenizers
 @require_pandas
 class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
@@ -788,6 +798,49 @@ class LayoutLMv2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
 
                 self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
 
+    def test_padding_warning_message_fast_tokenizer(self):
+        if not self.test_rust_tokenizer:
+            return
+
+        words, boxes = self.get_words_and_boxes_batch()
+
+        tokenizer_fast = self.get_rust_tokenizer()
+
+        encoding_fast = tokenizer_fast(
+            words,
+            boxes=boxes,
+        )
+
+        with self.assertLogs("transformers", level="WARNING") as cm:
+            tokenizer_fast.pad(encoding_fast)
+        self.assertEqual(len(cm.records), 1)
+        self.assertIn(
+            "Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to"
+            " encode the text followed by a call to the `pad` method to get a padded encoding.",
+            cm.records[0].message,
+        )
+
+        if not self.test_slow_tokenizer:
+            return
+
+        tokenizer_slow = self.get_tokenizer()
+
+        encoding_slow = tokenizer_slow(
+            words,
+            boxes=boxes,
+        )
+
+        with self.assertLogs(level="WARNING") as cm:
+            # We want to assert there are no warnings, but the 'assertLogs' method does not support that.
+            # Therefore, we are adding a dummy warning, and then we will assert it is the only warning.
+            logger.warning("Dummy warning")
+            tokenizer_slow.pad(encoding_slow)
+        self.assertEqual(len(cm.records), 1)
+        self.assertIn(
+            "Dummy warning",
+            cm.records[0].message,
+        )
+
     def test_call(self):
         # Tests that all call wrap to encode_plus and batch_encode_plus
         tokenizers = self.get_tokenizers(do_lower_case=False)

tests/models/layoutlmv3/test_tokenization_layoutlmv3.py

@@ -22,13 +22,23 @@ import tempfile
 import unittest
 from typing import List
 
-from transformers import AddedToken, LayoutLMv3TokenizerFast, SpecialTokensMixin, is_tf_available, is_torch_available
+from transformers import (
+    AddedToken,
+    LayoutLMv3TokenizerFast,
+    SpecialTokensMixin,
+    is_tf_available,
+    is_torch_available,
+    logging,
+)
 from transformers.models.layoutlmv3.tokenization_layoutlmv3 import VOCAB_FILES_NAMES, LayoutLMv3Tokenizer
 from transformers.testing_utils import is_pt_tf_cross_test, require_pandas, require_tokenizers, require_torch, slow
 
 from ...test_tokenization_common import SMALL_TRAINING_CORPUS, TokenizerTesterMixin, merge_model_tokenizer_mappings
 
 
+logger = logging.get_logger(__name__)
+
+
 @require_tokenizers
 @require_pandas
 class LayoutLMv3TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
@@ -668,6 +678,49 @@ class LayoutLMv3TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
 
                 self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
 
+    def test_padding_warning_message_fast_tokenizer(self):
+        if not self.test_rust_tokenizer:
+            return
+
+        words, boxes = self.get_words_and_boxes_batch()
+
+        tokenizer_fast = self.get_rust_tokenizer()
+
+        encoding_fast = tokenizer_fast(
+            words,
+            boxes=boxes,
+        )
+
+        with self.assertLogs("transformers", level="WARNING") as cm:
+            tokenizer_fast.pad(encoding_fast)
+        self.assertEqual(len(cm.records), 1)
+        self.assertIn(
+            "Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to"
+            " encode the text followed by a call to the `pad` method to get a padded encoding.",
+            cm.records[0].message,
+        )
+
+        if not self.test_slow_tokenizer:
+            return
+
+        tokenizer_slow = self.get_tokenizer()
+
+        encoding_slow = tokenizer_slow(
+            words,
+            boxes=boxes,
+        )
+
+        with self.assertLogs(level="WARNING") as cm:
+            # We want to assert there are no warnings, but the 'assertLogs' method does not support that.
+            # Therefore, we are adding a dummy warning, and then we will assert it is the only warning.
+            logger.warning("Dummy warning")
+            tokenizer_slow.pad(encoding_slow)
+        self.assertEqual(len(cm.records), 1)
+        self.assertIn(
+            "Dummy warning",
+            cm.records[0].message,
+        )
+
     def test_call(self):
         # Tests that all call wrap to encode_plus and batch_encode_plus
         tokenizers = self.get_tokenizers(do_lower_case=False)

tests/models/layoutxlm/test_tokenization_layoutxlm.py

@@ -19,7 +19,14 @@ import tempfile
 import unittest
 from typing import List
 
-from transformers import AddedToken, LayoutXLMTokenizerFast, SpecialTokensMixin, is_tf_available, is_torch_available
+from transformers import (
+    AddedToken,
+    LayoutXLMTokenizerFast,
+    SpecialTokensMixin,
+    is_tf_available,
+    is_torch_available,
+    logging,
+)
 from transformers.models.layoutxlm.tokenization_layoutxlm import LayoutXLMTokenizer
 from transformers.testing_utils import (
     get_tests_dir,
@@ -40,6 +47,7 @@ from ...test_tokenization_common import (
 )
 
 
+logger = logging.get_logger(__name__)
 SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
 
 
@@ -697,6 +705,49 @@ class LayoutXLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
 
                 self.assert_batch_padded_input_match(input_r, input_p, max_length, pad_token_id)
 
+    def test_padding_warning_message_fast_tokenizer(self):
+        if not self.test_rust_tokenizer:
+            return
+
+        words, boxes = self.get_words_and_boxes_batch()
+
+        tokenizer_fast = self.get_rust_tokenizer()
+
+        encoding_fast = tokenizer_fast(
+            words,
+            boxes=boxes,
+        )
+
+        with self.assertLogs("transformers", level="WARNING") as cm:
+            tokenizer_fast.pad(encoding_fast)
+        self.assertEqual(len(cm.records), 1)
+        self.assertIn(
+            "Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to"
+            " encode the text followed by a call to the `pad` method to get a padded encoding.",
+            cm.records[0].message,
+        )
+
+        if not self.test_slow_tokenizer:
+            return
+
+        tokenizer_slow = self.get_tokenizer()
+
+        encoding_slow = tokenizer_slow(
+            words,
+            boxes=boxes,
+        )
+
+        with self.assertLogs(level="WARNING") as cm:
+            # We want to assert there are no warnings, but the 'assertLogs' method does not support that.
+            # Therefore, we are adding a dummy warning, and then we will assert it is the only warning.
+            logger.warning("Dummy warning")
+            tokenizer_slow.pad(encoding_slow)
+        self.assertEqual(len(cm.records), 1)
+        self.assertIn(
+            "Dummy warning",
+            cm.records[0].message,
+        )
+
     def test_call(self):
         # Tests that all call wrap to encode_plus and batch_encode_plus
         tokenizers = self.get_tokenizers(do_lower_case=False)

tests/test_tokenization_common.py

@@ -48,6 +48,7 @@ from transformers import (
     is_tf_available,
     is_tokenizers_available,
     is_torch_available,
+    logging,
 )
 from transformers.testing_utils import (
     TOKEN,
@@ -81,6 +82,8 @@ if is_tokenizers_available():
     from test_module.custom_tokenization_fast import CustomTokenizerFast
 
 
+logger = logging.get_logger(__name__)
+
 NON_ENGLISH_TAGS = ["chinese", "dutch", "french", "finnish", "german", "multilingual"]
 
 SMALL_TRAINING_CORPUS = [
@@ -1834,6 +1837,47 @@ class TokenizerTesterMixin:
                     self.assertEqual(attention_mask + [0] * padding_size, right_padded_attention_mask)
                     self.assertEqual([0] * padding_size + attention_mask, left_padded_attention_mask)
 
+    def test_padding_warning_message_fast_tokenizer(self):
+        if not self.test_rust_tokenizer:
+            return
+
+        sequence = "This is a text"
+
+        tokenizer_fast = self.get_rust_tokenizer()
+        # check correct behaviour if no pad_token_id exists and add it eventually
+        self._check_no_pad_token_padding(tokenizer_fast, sequence)
+
+        encoding_fast = tokenizer_fast(sequence)
+
+        with self.assertLogs("transformers", level="WARNING") as cm:
+            tokenizer_fast.pad(encoding_fast)
+        self.assertEqual(len(cm.records), 1)
+        self.assertIn(
+            "Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to"
+            " encode the text followed by a call to the `pad` method to get a padded encoding.",
+            cm.records[0].message,
+        )
+
+        if not self.test_slow_tokenizer:
+            return
+
+        tokenizer_slow = self.get_tokenizer()
+        # check correct behaviour if no pad_token_id exists and add it eventually
+        self._check_no_pad_token_padding(tokenizer_slow, sequence)
+
+        encoding_slow = tokenizer_slow(sequence)
+
+        with self.assertLogs(level="WARNING") as cm:
+            # We want to assert there are no warnings, but the 'assertLogs' method does not support that.
+            # Therefore, we are adding a dummy warning, and then we will assert it is the only warning.
+            logger.warning("Dummy warning")
+            tokenizer_slow.pad(encoding_slow)
+        self.assertEqual(len(cm.records), 1)
+        self.assertIn(
+            "Dummy warning",
+            cm.records[0].message,
+        )
+
     def test_separate_tokenizers(self):
         # This tests that tokenizers don't impact others. Unfortunately the case where it fails is when
         # we're loading an S3 configuration from a pre-trained identifier, and we have no way of testing those today.