Add Perceiver IO

src/transformers/models/perceiver/tokenization_perceiver.py

@@ -56,12 +56,12 @@ class PerceiverTokenizer(PreTrainedTokenizer):
 
     def __init__(
         self,
-        pad_token="<pad>",
-        bos_token="<s>",
-        eos_token="</s>",
-        mask_token="<mask>",
-        cls_token="<cls>",
-        sep_token="<sep>",
+        pad_token="[PAD]",
+        bos_token="[BOS]",
+        eos_token="[EOS]",
+        mask_token="[MASK]",
+        cls_token="[CLS]",
+        sep_token="[SEP]",
         model_max_length=2048,
         **kwargs
     ) -> None:
@@ -127,7 +127,7 @@ def get_special_tokens_mask(
 
         # normal case: some special tokens
         if token_ids_1 is None:
-            return ([0] * len(token_ids_0)) + [1]
+            return [0] * len(token_ids_0)
         return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
 
     def build_inputs_with_special_tokens(
@@ -138,7 +138,7 @@ def build_inputs_with_special_tokens(
         following format:
 
         - single sequence: ``X``
-        - pair of sequences: ``A </s> B </s>``
+        - pair of sequences: ``A [SEP] B [SEP]``
 
         Args:
             token_ids_0 (:obj:`List[int]`):
@@ -152,7 +152,7 @@ def build_inputs_with_special_tokens(
         if token_ids_1 is None:
             return token_ids_0
         else:
-            return token_ids_0 + token_ids_1
+            return token_ids_0 + [self.sep_token_id] + token_ids_1 + [self.sep_token_id]
 
     def _tokenize(self, text: str) -> List[str]:
         """Take as input a string and return a list of strings (tokens) for words/sub-words"""

tests/test_tokenization_perceiver.py

@@ -0,0 +1,284 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+import re
+import shutil
+import tempfile
+import unittest
+from typing import Tuple
+
+from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
+from transformers.file_utils import cached_property, is_tf_available, is_torch_available
+
+from .test_tokenization_common import TokenizerTesterMixin
+
+
+if is_torch_available():
+    FRAMEWORK = "pt"
+elif is_tf_available():
+    FRAMEWORK = "tf"
+else:
+    FRAMEWORK = "jax"
+
+
+class PerceiverTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+
+    tokenizer_class = PerceiverTokenizer
+    test_rust_tokenizer = False
+
+    def setUp(self):
+        super().setUp()
+        tokenizer = PerceiverTokenizer()
+        tokenizer.save_pretrained(self.tmpdirname)
+
+    @cached_property
+    def perceiver_tokenizer(self):
+        return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver")
+
+    def get_tokenizer(self, **kwargs) -> PerceiverTokenizer:
+        return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_clean_sequence(self, tokenizer, with_prefix_space=False, max_length=20, min_length=5) -> Tuple[str, list]:
+        # XXX The default common tokenizer tests assume that every ID is decodable on its own.
+        # This assumption is invalid for Perceiver because single bytes might not be
+        # valid utf-8 (byte 128 for instance).
+        # Here we're overriding the smallest possible method to provide
+        # a clean sequence without making the same assumption.
+
+        toks = []
+        for i in range(len(tokenizer)):
+            try:
+                tok = tokenizer.decode([i], clean_up_tokenization_spaces=False)
+            except UnicodeDecodeError:
+                pass
+            toks.append((i, tok))
+
+        toks = list(filter(lambda t: re.match(r"^[ a-zA-Z]+$", t[1]), toks))
+        toks = list(filter(lambda t: [t[0]] == tokenizer.encode(t[1], add_special_tokens=False), toks))
+        if max_length is not None and len(toks) > max_length:
+            toks = toks[:max_length]
+        if min_length is not None and len(toks) < min_length and len(toks) > 0:
+            while len(toks) < min_length:
+                toks = toks + toks
+        # toks_str = [t[1] for t in toks]
+        toks_ids = [t[0] for t in toks]
+
+        # Ensure consistency
+        output_txt = tokenizer.decode(toks_ids, clean_up_tokenization_spaces=False)
+        if " " not in output_txt and len(toks_ids) > 1:
+            output_txt = (
+                tokenizer.decode([toks_ids[0]], clean_up_tokenization_spaces=False)
+                + " "
+                + tokenizer.decode(toks_ids[1:], clean_up_tokenization_spaces=False)
+            )
+        if with_prefix_space:
+            output_txt = " " + output_txt
+        output_ids = tokenizer.encode(output_txt, add_special_tokens=False)
+        return output_txt, output_ids
+
+    def test_multibytes_char(self):
+        tokenizer = self.perceiver_tokenizer
+        src_text = "Unicode €."
+        encoded = tokenizer(src_text)
+        encoded_ids = [91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52]
+        self.assertEqual(encoded["input_ids"], encoded_ids)
+
+        # decoding
+        decoded = tokenizer.decode(encoded_ids)
+        self.assertEqual(decoded, "Unicode €.")
+
+        encoded = tokenizer("e è é ê ë")
+        encoded_ids = [107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177]
+        self.assertEqual(encoded["input_ids"], encoded_ids)
+        # decoding
+        decoded = tokenizer.decode(encoded_ids)
+        self.assertEqual(decoded, "e è é ê ë")
+
+        # encode/decode, but with `encode` instead of `__call__`
+        self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë")), "e è é ê ë")
+
+    def test_prepare_batch_integration(self):
+        tokenizer = self.perceiver_tokenizer
+        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
+        # fmt: off
+        expected_src_tokens = [71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 0]
+        # fmt: on
+        batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
+        self.assertIsInstance(batch, BatchEncoding)
+
+        if FRAMEWORK != "jax":
+            result = list(batch.input_ids.numpy()[0])
+        else:
+            result = list(batch.input_ids.tolist()[0])
+
+        self.assertListEqual(expected_src_tokens, result)
+
+        self.assertEqual((2, 36), batch.input_ids.shape)
+        self.assertEqual((2, 36), batch.attention_mask.shape)
+
+    def test_empty_target_text(self):
+        tokenizer = self.perceiver_tokenizer
+        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
+        batch = tokenizer(src_text, padding=True, return_tensors=FRAMEWORK)
+        # 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_length_integration(self):
+        tokenizer = self.perceiver_tokenizer
+        tgt_text = [
+            "Summary of the text.",
+            "Another summary.",
+        ]
+        with tokenizer.as_target_tokenizer():
+            targets = tokenizer(
+                tgt_text, max_length=32, padding="max_length", truncation=True, return_tensors=FRAMEWORK
+            )
+        self.assertEqual(32, targets["input_ids"].shape[1])
+
+    # cannot use default save_and_load_tokenzier test method because tokenzier has no vocab
+    def test_save_and_load_tokenizer(self):
+        # safety check on max_len default value so we are sure the test works
+        tokenizers = self.get_tokenizers()
+        for tokenizer in tokenizers:
+            with self.subTest(f"{tokenizer.__class__.__name__}"):
+                self.assertNotEqual(tokenizer.model_max_length, 42)
+
+        # Now let's start the test
+        tokenizers = self.get_tokenizers()
+        for tokenizer in tokenizers:
+            with self.subTest(f"{tokenizer.__class__.__name__}"):
+                # Isolate this from the other tests because we save additional tokens/etc
+                tmpdirname = tempfile.mkdtemp()
+
+                sample_text = " He is very happy, UNwant\u00E9d,running"
+                before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
+                tokenizer.save_pretrained(tmpdirname)
+
+                after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
+                after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
+                self.assertListEqual(before_tokens, after_tokens)
+
+                shutil.rmtree(tmpdirname)
+
+        tokenizers = self.get_tokenizers(model_max_length=42)
+        for tokenizer in tokenizers:
+            with self.subTest(f"{tokenizer.__class__.__name__}"):
+                # Isolate this from the other tests because we save additional tokens/etc
+                tmpdirname = tempfile.mkdtemp()
+
+                sample_text = " He is very happy, UNwant\u00E9d,running"
+                tokenizer.add_tokens(["bim", "bambam"])
+                additional_special_tokens = tokenizer.additional_special_tokens
+                additional_special_tokens.append("new_additional_special_token")
+                tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
+                before_tokens = tokenizer.encode(sample_text, add_special_tokens=False)
+                tokenizer.save_pretrained(tmpdirname)
+
+                after_tokenizer = tokenizer.__class__.from_pretrained(tmpdirname)
+                after_tokens = after_tokenizer.encode(sample_text, add_special_tokens=False)
+                self.assertListEqual(before_tokens, after_tokens)
+                self.assertIn("new_additional_special_token", after_tokenizer.additional_special_tokens)
+                self.assertEqual(after_tokenizer.model_max_length, 42)
+
+                tokenizer = tokenizer.__class__.from_pretrained(tmpdirname, model_max_length=43)
+                self.assertEqual(tokenizer.model_max_length, 43)
+
+                shutil.rmtree(tmpdirname)
+
+    # There is a conflict between the default value of extra_ids and adding a new special token through additional_special_tokens
+    # We need to add the extra_ids in the list of the arg additional_special_tokens
+    def test_special_tokens_initialization_with_non_empty_additional_special_tokens(self):
+        tokenizer_list = []
+        if self.test_slow_tokenizer:
+            tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
+
+        if self.test_rust_tokenizer:
+            tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
+
+        for tokenizer_class, tokenizer_utils in tokenizer_list:
+            with tempfile.TemporaryDirectory() as tmp_dir:
+                tokenizer_utils.save_pretrained(tmp_dir)
+
+                with open(os.path.join(tmp_dir, "special_tokens_map.json"), encoding="utf-8") as json_file:
+                    special_tokens_map = json.load(json_file)
+
+                with open(os.path.join(tmp_dir, "tokenizer_config.json"), encoding="utf-8") as json_file:
+                    tokenizer_config = json.load(json_file)
+
+                added_tokens_extra_ids = [f"<extra_id_{i}>" for i in range(125)]
+
+                special_tokens_map["additional_special_tokens"] = added_tokens_extra_ids + [
+                    "an_additional_special_token"
+                ]
+                tokenizer_config["additional_special_tokens"] = added_tokens_extra_ids + [
+                    "an_additional_special_token"
+                ]
+
+                with open(os.path.join(tmp_dir, "special_tokens_map.json"), "w", encoding="utf-8") as outfile:
+                    json.dump(special_tokens_map, outfile)
+                with open(os.path.join(tmp_dir, "tokenizer_config.json"), "w", encoding="utf-8") as outfile:
+                    json.dump(tokenizer_config, outfile)
+
+                # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
+                # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
+                # "special_tokens_map.json" files
+                tokenizer_without_change_in_init = tokenizer_class.from_pretrained(
+                    tmp_dir,
+                )
+                self.assertIn(
+                    "an_additional_special_token", tokenizer_without_change_in_init.additional_special_tokens
+                )
+                self.assertEqual(
+                    ["an_additional_special_token"],
+                    tokenizer_without_change_in_init.convert_ids_to_tokens(
+                        tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"])
+                    ),
+                )
+
+                # Now we test that we can change the value of additional_special_tokens in the from_pretrained
+                new_added_tokens = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token", lstrip=True)]
+                tokenizer = tokenizer_class.from_pretrained(
+                    tmp_dir,
+                    additional_special_tokens=new_added_tokens,
+                )
+
+                self.assertIn("a_new_additional_special_token", tokenizer.additional_special_tokens)
+                self.assertEqual(
+                    ["a_new_additional_special_token"],
+                    tokenizer.convert_ids_to_tokens(
+                        tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"])
+                    ),
+                )
+
+    # tokenizer can be instantiated without any pretrained files, so no need for pretrained tokenizer list
+    def test_pretrained_model_lists(self):
+        pass
+
+    # tokenizer does not have vocabulary
+    def test_get_vocab(self):
+        pass
+
+    # inputs cannot be pretokenized since ids depend on whole input string and not just on single characters
+    def test_pretokenized_inputs(self):
+        pass
+
+    # tests all ids in vocab => vocab doesn't exist so unnecessary to test
+    def test_conversion_reversible(self):
+        pass