Add MVP model (#17787)

* Add MVP model

* Update README

* Remove useless module

* Update docs

* Fix bugs in tokenizer

* Remove useless test

* Remove useless module

* Update vocab

* Remove specifying

* Remove specifying

* Add #Copied ... statement

* Update paper link

* Remove useless TFMvp

* Add #Copied ... statement

* Fix style in test mvp model

* Fix some typos

* Fix properties of unset special tokens in non verbose mode

* Update paper link

* Update MVP doc

* Update MVP doc

* Fix README

* Fix typos in docs

* Update docs

src/transformers/utils/dummy_tokenizers_objects.py

@@ -262,6 +262,13 @@ class MT5TokenizerFast(metaclass=DummyObject):
         requires_backends(self, ["tokenizers"])
 
 
+class MvpTokenizerFast(metaclass=DummyObject):
+    _backends = ["tokenizers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tokenizers"])
+
+
 class OpenAIGPTTokenizerFast(metaclass=DummyObject):
     _backends = ["tokenizers"]
 

tests/models/mvp/test_tokenization_mvp.py

+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# 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 unittest
+
+from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
+from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
+from transformers.testing_utils import require_tokenizers, require_torch
+from transformers.utils import cached_property
+
+from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
+
+
+@require_tokenizers
+class TestTokenizationMvp(TokenizerTesterMixin, unittest.TestCase):
+    tokenizer_class = MvpTokenizer
+    rust_tokenizer_class = MvpTokenizerFast
+    test_rust_tokenizer = True
+    from_pretrained_filter = filter_roberta_detectors
+    # from_pretrained_kwargs = {'add_prefix_space': True}
+
+    def setUp(self):
+        super().setUp()
+        vocab = [
+            "l",
+            "o",
+            "w",
+            "e",
+            "r",
+            "s",
+            "t",
+            "i",
+            "d",
+            "n",
+            "\u0120",
+            "\u0120l",
+            "\u0120n",
+            "\u0120lo",
+            "\u0120low",
+            "er",
+            "\u0120lowest",
+            "\u0120newer",
+            "\u0120wider",
+            "<unk>",
+        ]
+        vocab_tokens = dict(zip(vocab, range(len(vocab))))
+        merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
+        self.special_tokens_map = {"unk_token": "<unk>"}
+
+        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
+        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
+        with open(self.vocab_file, "w", encoding="utf-8") as fp:
+            fp.write(json.dumps(vocab_tokens) + "\n")
+        with open(self.merges_file, "w", encoding="utf-8") as fp:
+            fp.write("\n".join(merges))
+
+    def get_tokenizer(self, **kwargs):
+        kwargs.update(self.special_tokens_map)
+        return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_rust_tokenizer(self, **kwargs):
+        kwargs.update(self.special_tokens_map)
+        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
+
+    def get_input_output_texts(self, tokenizer):
+        return "lower newer", "lower newer"
+
+    @cached_property
+    def default_tokenizer(self):
+        return MvpTokenizer.from_pretrained("RUCAIBox/mvp")
+
+    @cached_property
+    def default_tokenizer_fast(self):
+        return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp")
+
+    @require_torch
+    def test_prepare_batch(self):
+        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
+        expected_src_tokens = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]
+
+        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
+            batch = tokenizer(src_text, max_length=len(expected_src_tokens), padding=True, return_tensors="pt")
+            self.assertIsInstance(batch, BatchEncoding)
+
+            self.assertEqual((2, 9), batch.input_ids.shape)
+            self.assertEqual((2, 9), batch.attention_mask.shape)
+            result = batch.input_ids.tolist()[0]
+            self.assertListEqual(expected_src_tokens, result)
+            # Test that special tokens are reset
+
+    @require_torch
+    def test_prepare_batch_empty_target_text(self):
+        src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."]
+        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
+            batch = tokenizer(src_text, padding=True, return_tensors="pt")
+            # check if input_ids are returned and no labels
+            self.assertIn("input_ids", batch)
+            self.assertIn("attention_mask", batch)
+            self.assertNotIn("labels", batch)
+            self.assertNotIn("decoder_attention_mask", batch)
+
+    @require_torch
+    def test_as_target_tokenizer_target_length(self):
+        tgt_text = [
+            "Summary of the text.",
+            "Another summary.",
+        ]
+        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
+            with tokenizer.as_target_tokenizer():
+                targets = tokenizer(tgt_text, max_length=32, padding="max_length", return_tensors="pt")
+            self.assertEqual(32, targets["input_ids"].shape[1])
+
+    @require_torch
+    def test_prepare_batch_not_longer_than_maxlen(self):
+        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
+            batch = tokenizer(
+                ["I am a small frog" * 1024, "I am a small frog"], padding=True, truncation=True, return_tensors="pt"
+            )
+            self.assertIsInstance(batch, BatchEncoding)
+            self.assertEqual(batch.input_ids.shape, (2, 1024))
+
+    @require_torch
+    def test_special_tokens(self):
+
+        src_text = ["A long paragraph for summarization."]
+        tgt_text = [
+            "Summary of the text.",
+        ]
+        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
+            inputs = tokenizer(src_text, return_tensors="pt")
+            with tokenizer.as_target_tokenizer():
+                targets = tokenizer(tgt_text, return_tensors="pt")
+            input_ids = inputs["input_ids"]
+            labels = targets["input_ids"]
+            self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
+            self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
+            self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
+            self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())
+
+    def test_pretokenized_inputs(self):
+        pass
+
+    def test_embeded_special_tokens(self):
+        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
+            with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
+                tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+                tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
+                sentence = "A, <mask> AllenNLP sentence."
+                tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
+                tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True)
+
+                # token_type_ids should put 0 everywhere
+                self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"]))
+
+                # attention_mask should put 1 everywhere, so sum over length should be 1
+                self.assertEqual(
+                    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]),
+                    sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]),
+                )
+
+                tokens_r_str = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
+                tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])
+
+                # Rust correctly handles the space before the mask while python doesnt
+                self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
+                self.assertSequenceEqual(tokens_r["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
+
+                self.assertSequenceEqual(
+                    tokens_p_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
+                )
+                self.assertSequenceEqual(
+                    tokens_r_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
+                )

utils/check_repo.py

@@ -78,6 +78,8 @@ IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [
     "MegatronBertEncoder",  # Building part of bigger (tested) model.
     "MegatronBertDecoder",  # Building part of bigger (tested) model.
     "MegatronBertDecoderWrapper",  # Building part of bigger (tested) model.
+    "MvpDecoderWrapper",  # Building part of bigger (tested) model.
+    "MvpEncoder",  # Building part of bigger (tested) model.
     "PegasusEncoder",  # Building part of bigger (tested) model.
     "PegasusDecoderWrapper",  # Building part of bigger (tested) model.
     "DPREncoder",  # Building part of bigger (tested) model.