Add new model RoFormer (use rotary position embedding ) (#11684)

* add roformer

* Update docs/source/model_doc/roformer.rst

Co-authored-by: Suraj Patil <surajp815@gmail.com>

* Update docs/source/model_doc/roformer.rst

Co-authored-by: Suraj Patil <surajp815@gmail.com>

* update

* add TFRoFormerSinusoidalPositionalEmbedding and fix TFMarianSinusoidalPositionalEmbedding

* update docs

* make style and make quality

* roback

* unchanged

* rm copies from , this is a error in TFMarianSinusoidalPositionalEmbedding

* update Copyright year

* move # Add modeling imports here to the correct position

* max_position_embeddings can be set to 1536

* # Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->RoFormer

* # Copied from transformers.models.bert.modeling_bert.BertLayer.__init__ with Bert->RoFormer

* update tokenization_roformer

* make style

* add staticmethod apply_rotary_position_embeddings

* add TF staticmethod apply_rotary_position_embeddings

* update torch apply_rotary_position_embeddings

* fix tf apply_rotary_position_embeddings error

* make style

* add pytorch RoFormerSelfAttentionRotaryPositionEmbeddingTest

* add TF rotary_position_embeddings test

* update test_modeling_rofomer

* Update docs/source/model_doc/roformer.rst

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/__init__.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/__init__.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/__init__.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/__init__.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/roformer/convert_roformer_original_tf_checkpoint_to_pytorch.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/roformer/modeling_roformer.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/roformer/modeling_roformer.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/roformer/modeling_tf_roformer.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* refact roformer tokenizer

* add RoFormerTokenizerFast

* add RoFormerTokenizationTest

* add require_jieba

* update Copyright

* update tokenizer & add copy from

* add option rotary_value

* use rust jieba

* use rjieba

* use rust jieba

* fix test_alignement_methods

* slice normalized_string is too slow

* add config.embedding_size when embedding_size!=hidden_size

* fix pickle tokenizer

* Update docs/source/model_doc/roformer.rst

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* make style and make quality

Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

README.md

@@ -243,6 +243,7 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 1. **[ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
 1. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
 1. **[RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
+1. **[RoFormer](https://huggingface.co/transformers/model_doc/roformer.html)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
 1. **[SpeechToTextTransformer](https://huggingface.co/transformers/model_doc/speech_to_text.html)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
 1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
 1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.

docs/source/index.rst

@@ -231,41 +231,44 @@ Supported models
 45. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
     Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar
     Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
-46. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
+46. :doc:`RoFormer <model_doc/roformer>` (from ZhuiyiTechnology), released together with the paper a `RoFormer:
+    Enhanced Transformer with Rotary Position Embedding <https://arxiv.org/pdf/2104.09864v1.pdf>`__ by Jianlin Su and
+    Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+47. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
     `fairseq S2T: Fast Speech-to-Text Modeling with fairseq <https://arxiv.org/abs/2010.05171>`__ by Changhan Wang, Yun
     Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
-47. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+48. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
     about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi
     Krishna, and Kurt W. Keutzer.
-48. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+49. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
     Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
     Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-49. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
+50. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
     Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
     Francesco Piccinno and Julian Martin Eisenschlos.
-50. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+51. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
     Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
     Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-51. :doc:`Vision Transformer (ViT) <model_doc/vit>` (from Google AI) released with the paper `An Image is Worth 16x16
+52. :doc:`Vision Transformer (ViT) <model_doc/vit>` (from Google AI) released with the paper `An Image is Worth 16x16
     Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`__ by Alexey Dosovitskiy,
     Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias
     Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
-52. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for
+53. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for
     Self-Supervised Learning of Speech Representations <https://arxiv.org/abs/2006.11477>`__ by Alexei Baevski, Henry
     Zhou, Abdelrahman Mohamed, Michael Auli.
-53. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+54. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
     Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
-54. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+55. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
     Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
     Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-55. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+56. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
     Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
     Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
     Zettlemoyer and Veselin Stoyanov.
-56. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
+57. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
     Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
     Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-57. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
+58. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
     Cross-Lingual Representation Learning For Speech Recognition <https://arxiv.org/abs/2006.13979>`__ by Alexis
     Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
 
@@ -369,6 +372,8 @@ Flax), PyTorch, and/or TensorFlow.
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
+|          RoFormer           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
++-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         Speech2Text         |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 +-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
 |         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
@@ -520,6 +525,7 @@ Flax), PyTorch, and/or TensorFlow.
     model_doc/reformer
     model_doc/retribert
     model_doc/roberta
+    model_doc/roformer
     model_doc/speech_to_text
     model_doc/squeezebert
     model_doc/t5

docs/source/model_doc/roformer.rst

@@ -0,0 +1,161 @@
+.. 
+    Copyright 2021 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.
+
+RoFormer
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The RoFormer model was proposed in `RoFormer: Enhanced Transformer with Rotary Position Embedding
+<https://arxiv.org/pdf/2104.09864v1.pdf>`__ by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
+
+The abstract from the paper is the following:
+
+*Position encoding in transformer architecture provides supervision for dependency modeling between elements at
+different positions in the sequence. We investigate various methods to encode positional information in
+transformer-based language models and propose a novel implementation named Rotary Position Embedding(RoPE). The
+proposed RoPE encodes absolute positional information with rotation matrix and naturally incorporates explicit relative
+position dependency in self-attention formulation. Notably, RoPE comes with valuable properties such as flexibility of
+being expand to any sequence lengths, decaying inter-token dependency with increasing relative distances, and
+capability of equipping the linear self-attention with relative position encoding. As a result, the enhanced
+transformer with rotary position embedding, or RoFormer, achieves superior performance in tasks with long texts. We
+release the theoretical analysis along with some preliminary experiment results on Chinese data. The undergoing
+experiment for English benchmark will soon be updated.*
+
+Tips:
+
+- RoFormer is a BERT-like autoencoding model with rotary position embeddings. Rotary position embeddings have shown
+  improved performance on classification tasks with long texts.
+
+
+This model was contributed by `junnyu <https://huggingface.co/junnyu>`__. The original code can be found `here
+<https://github.com/ZhuiyiTechnology/roformer>`__.
+
+RoFormerConfig
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RoFormerConfig
+    :members:
+
+
+RoFormerTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RoFormerTokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+RobertaTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RoFormerTokenizerFast
+    :members: build_inputs_with_special_tokens
+
+
+RoFormerModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RoFormerModel
+    :members: forward
+
+
+RoFormerForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RoFormerForCausalLM
+    :members: forward
+
+
+RoFormerForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RoFormerForMaskedLM
+    :members: forward
+
+
+RoFormerForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RoFormerForSequenceClassification
+    :members: forward
+
+
+RoFormerForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RoFormerForMultipleChoice
+    :members: forward
+
+
+RoFormerForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RoFormerForTokenClassification
+    :members: forward
+
+
+RoFormerForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.RoFormerForQuestionAnswering
+    :members: forward
+
+
+TFRoFormerModel
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRoFormerModel
+    :members: call
+
+
+TFRoFormerForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRoFormerForMaskedLM
+    :members: call
+
+
+TFRoFormerForCausalLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRoFormerForCausalLM
+    :members: call
+
+
+TFRoFormerForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRoFormerForSequenceClassification
+    :members: call
+
+
+TFRoFormerForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRoFormerForMultipleChoice
+    :members: call
+
+
+TFRoFormerForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRoFormerForTokenClassification
+    :members: call
+
+
+TFRoFormerForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.TFRoFormerForQuestionAnswering
+    :members: call

src/transformers/__init__.py

@@ -218,6 +218,7 @@ _import_structure = {
     "models.reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"],
     "models.retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig", "RetriBertTokenizer"],
     "models.roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaTokenizer"],
+    "models.roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerTokenizer"],
     "models.speech_to_text": [
         "SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
         "Speech2TextConfig",
@@ -322,6 +323,7 @@ else:
 # tokenizers-backed objects
 if is_tokenizers_available():
     # Fast tokenizers
+    _import_structure["models.roformer"].append("RoFormerTokenizerFast")
     _import_structure["models.clip"].append("CLIPTokenizerFast")
     _import_structure["models.convbert"].append("ConvBertTokenizerFast")
     _import_structure["models.albert"].append("AlbertTokenizerFast")
@@ -927,6 +929,21 @@ if is_torch_available():
             "RobertaModel",
         ]
     )
+    _import_structure["models.roformer"].extend(
+        [
+            "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "RoFormerForCausalLM",
+            "RoFormerForMaskedLM",
+            "RoFormerForMultipleChoice",
+            "RoFormerForQuestionAnswering",
+            "RoFormerForSequenceClassification",
+            "RoFormerForTokenClassification",
+            "RoFormerLayer",
+            "RoFormerModel",
+            "RoFormerPreTrainedModel",
+            "load_tf_weights_in_roformer",
+        ]
+    )
     _import_structure["models.speech_to_text"].extend(
         [
             "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -1339,6 +1356,20 @@ if is_tf_available():
             "TFRobertaPreTrainedModel",
         ]
     )
+    _import_structure["models.roformer"].extend(
+        [
+            "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFRoFormerForCausalLM",
+            "TFRoFormerForMaskedLM",
+            "TFRoFormerForMultipleChoice",
+            "TFRoFormerForQuestionAnswering",
+            "TFRoFormerForSequenceClassification",
+            "TFRoFormerForTokenClassification",
+            "TFRoFormerLayer",
+            "TFRoFormerModel",
+            "TFRoFormerPreTrainedModel",
+        ]
+    )
     _import_structure["models.t5"].extend(
         [
             "TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -1641,6 +1672,7 @@ if TYPE_CHECKING:
     from .models.reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig
     from .models.retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig, RetriBertTokenizer
     from .models.roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaTokenizer
+    from .models.roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerTokenizer
     from .models.speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, Speech2TextConfig
     from .models.squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig, SqueezeBertTokenizer
     from .models.t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
@@ -1767,6 +1799,7 @@ if TYPE_CHECKING:
         from .models.reformer import ReformerTokenizerFast
         from .models.retribert import RetriBertTokenizerFast
         from .models.roberta import RobertaTokenizerFast
+        from .models.roformer import RoFormerTokenizerFast
         from .models.squeezebert import SqueezeBertTokenizerFast
         from .models.t5 import T5TokenizerFast
         from .models.xlm_roberta import XLMRobertaTokenizerFast
@@ -2232,6 +2265,19 @@ if TYPE_CHECKING:
             RobertaForTokenClassification,
             RobertaModel,
         )
+        from .models.roformer import (
+            ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
+            RoFormerForCausalLM,
+            RoFormerForMaskedLM,
+            RoFormerForMultipleChoice,
+            RoFormerForQuestionAnswering,
+            RoFormerForSequenceClassification,
+            RoFormerForTokenClassification,
+            RoFormerLayer,
+            RoFormerModel,
+            RoFormerPreTrainedModel,
+            load_tf_weights_in_roformer,
+        )
         from .models.speech_to_text import (
             SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
             Speech2TextForConditionalGeneration,
@@ -2575,6 +2621,18 @@ if TYPE_CHECKING:
             TFRobertaModel,
             TFRobertaPreTrainedModel,
         )
+        from .models.roformer import (
+            TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFRoFormerForCausalLM,
+            TFRoFormerForMaskedLM,
+            TFRoFormerForMultipleChoice,
+            TFRoFormerForQuestionAnswering,
+            TFRoFormerForSequenceClassification,
+            TFRoFormerForTokenClassification,
+            TFRoFormerLayer,
+            TFRoFormerModel,
+            TFRoFormerPreTrainedModel,
+        )
         from .models.t5 import (
             TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFT5EncoderModel,

src/transformers/convert_slow_tokenizer.py

@@ -25,6 +25,7 @@ from tokenizers import Regex, Tokenizer, decoders, normalizers, pre_tokenizers,
 from tokenizers.models import BPE, Unigram, WordPiece
 
 from .file_utils import requires_backends
+from .models.roformer.tokenization_utils import JiebaPreTokenizer
 
 
 class SentencePieceExtractor:
@@ -296,6 +297,43 @@ class RobertaConverter(Converter):
         return tokenizer
 
 
+class RoFormerConverter(Converter):
+    def converted(self) -> Tokenizer:
+        vocab = self.original_tokenizer.vocab
+        tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
+
+        strip_accents = False
+        do_lower_case = False
+        if hasattr(self.original_tokenizer, "basic_tokenizer"):
+            strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents
+            do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case
+
+        tokenizer.normalizer = normalizers.BertNormalizer(
+            clean_text=True,
+            handle_chinese_chars=False,
+            strip_accents=strip_accents,
+            lowercase=do_lower_case,
+        )
+        tokenizer.pre_tokenizer = pre_tokenizers.PreTokenizer.custom(JiebaPreTokenizer(vocab))
+
+        cls = str(self.original_tokenizer.cls_token)
+        sep = str(self.original_tokenizer.sep_token)
+        cls_token_id = self.original_tokenizer.cls_token_id
+        sep_token_id = self.original_tokenizer.sep_token_id
+
+        tokenizer.post_processor = processors.TemplateProcessing(
+            single=f"{cls}:0 $A:0 {sep}:0",
+            pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1",
+            special_tokens=[
+                (cls, cls_token_id),
+                (sep, sep_token_id),
+            ],
+        )
+        tokenizer.decoder = decoders.WordPiece(prefix="##")
+
+        return tokenizer
+
+
 class DebertaConverter(Converter):
     def converted(self) -> Tokenizer:
         ot = self.original_tokenizer
@@ -755,6 +793,7 @@ SLOW_TO_FAST_CONVERTERS = {
     "ReformerTokenizer": ReformerConverter,
     "RetriBertTokenizer": BertConverter,
     "RobertaTokenizer": RobertaConverter,
+    "RoFormerTokenizer": RoFormerConverter,
     "SqueezeBertTokenizer": BertConverter,
     "T5Tokenizer": T5Converter,
     "XLMRobertaTokenizer": XLMRobertaConverter,

src/transformers/models/__init__.py

@@ -68,6 +68,7 @@ from . import (
     reformer,
     retribert,
     roberta,
+    roformer,
     speech_to_text,
     squeezebert,
     t5,

src/transformers/models/auto/configuration_auto.py

@@ -68,6 +68,7 @@ from ..rag.configuration_rag import RagConfig
 from ..reformer.configuration_reformer import ReformerConfig
 from ..retribert.configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
 from ..roberta.configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
+from ..roformer.configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig
 from ..speech_to_text.configuration_speech_to_text import (
     SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     Speech2TextConfig,
@@ -91,6 +92,7 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
     (key, value)
     for pretrained_map in [
         # Add archive maps here
+        ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -146,6 +148,7 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
 CONFIG_MAPPING = OrderedDict(
     [
         # Add configs here
+        ("roformer", RoFormerConfig),
         ("clip", CLIPConfig),
         ("bigbird_pegasus", BigBirdPegasusConfig),
         ("deit", DeiTConfig),
@@ -207,6 +210,7 @@ CONFIG_MAPPING = OrderedDict(
 MODEL_NAMES_MAPPING = OrderedDict(
     [
         # Add full (and cased) model names here
+        ("roformer", "RoFormer"),
         ("clip", "CLIP"),
         ("bigbird_pegasus", "BigBirdPegasus"),
         ("deit", "DeiT"),

src/transformers/models/auto/modeling_auto.py

@@ -240,6 +240,15 @@ from ..roberta.modeling_roberta import (
     RobertaForTokenClassification,
     RobertaModel,
 )
+from ..roformer.modeling_roformer import (
+    RoFormerForCausalLM,
+    RoFormerForMaskedLM,
+    RoFormerForMultipleChoice,
+    RoFormerForQuestionAnswering,
+    RoFormerForSequenceClassification,
+    RoFormerForTokenClassification,
+    RoFormerModel,
+)
 from ..speech_to_text.modeling_speech_to_text import Speech2TextForConditionalGeneration, Speech2TextModel
 from ..squeezebert.modeling_squeezebert import (
     SqueezeBertForMaskedLM,
@@ -334,6 +343,7 @@ from .configuration_auto import (
     ReformerConfig,
     RetriBertConfig,
     RobertaConfig,
+    RoFormerConfig,
     Speech2TextConfig,
     SqueezeBertConfig,
     T5Config,
@@ -354,6 +364,7 @@ logger = logging.get_logger(__name__)
 MODEL_MAPPING = OrderedDict(
     [
         # Base model mapping
+        (RoFormerConfig, RoFormerModel),
         (CLIPConfig, CLIPModel),
         (BigBirdPegasusConfig, BigBirdPegasusModel),
         (DeiTConfig, DeiTModel),
@@ -451,6 +462,7 @@ MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
 MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
     [
         # Model with LM heads mapping
+        (RoFormerConfig, RoFormerForMaskedLM),
         (BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration),
         (GPTNeoConfig, GPTNeoForCausalLM),
         (BigBirdConfig, BigBirdForMaskedLM),
@@ -498,6 +510,7 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
 MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
     [
         # Model for Causal LM mapping
+        (RoFormerConfig, RoFormerForCausalLM),
         (BigBirdPegasusConfig, BigBirdPegasusForCausalLM),
         (GPTNeoConfig, GPTNeoForCausalLM),
         (BigBirdConfig, BigBirdForCausalLM),
@@ -539,6 +552,7 @@ MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING = OrderedDict(
 MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
     [
         # Model for Masked LM mapping
+        (RoFormerConfig, RoFormerForMaskedLM),
         (BigBirdConfig, BigBirdForMaskedLM),
         (Wav2Vec2Config, Wav2Vec2ForMaskedLM),
         (ConvBertConfig, ConvBertForMaskedLM),
@@ -592,6 +606,7 @@ MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = OrderedDict(
 MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
     [
         # Model for Sequence Classification mapping
+        (RoFormerConfig, RoFormerForSequenceClassification),
         (BigBirdPegasusConfig, BigBirdPegasusForSequenceClassification),
         (BigBirdConfig, BigBirdForSequenceClassification),
         (ConvBertConfig, ConvBertForSequenceClassification),
@@ -630,6 +645,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
 MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
     [
         # Model for Question Answering mapping
+        (RoFormerConfig, RoFormerForQuestionAnswering),
         (BigBirdPegasusConfig, BigBirdPegasusForQuestionAnswering),
         (BigBirdConfig, BigBirdForQuestionAnswering),
         (ConvBertConfig, ConvBertForQuestionAnswering),
@@ -670,6 +686,7 @@ MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING = OrderedDict(
 MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
     [
         # Model for Token Classification mapping
+        (RoFormerConfig, RoFormerForTokenClassification),
         (BigBirdConfig, BigBirdForTokenClassification),
         (ConvBertConfig, ConvBertForTokenClassification),
         (LayoutLMConfig, LayoutLMForTokenClassification),
@@ -699,6 +716,7 @@ MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
 MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
     [
         # Model for Multiple Choice mapping
+        (RoFormerConfig, RoFormerForMultipleChoice),
         (BigBirdConfig, BigBirdForMultipleChoice),
         (ConvBertConfig, ConvBertForMultipleChoice),
         (CamembertConfig, CamembertForMultipleChoice),

src/transformers/models/auto/modeling_tf_auto.py

@@ -148,6 +148,15 @@ from ..roberta.modeling_tf_roberta import (
     TFRobertaForTokenClassification,
     TFRobertaModel,
 )
+from ..roformer.modeling_tf_roformer import (
+    TFRoFormerForCausalLM,
+    TFRoFormerForMaskedLM,
+    TFRoFormerForMultipleChoice,
+    TFRoFormerForQuestionAnswering,
+    TFRoFormerForSequenceClassification,
+    TFRoFormerForTokenClassification,
+    TFRoFormerModel,
+)
 from ..t5.modeling_tf_t5 import TFT5ForConditionalGeneration, TFT5Model
 from ..transfo_xl.modeling_tf_transfo_xl import (
     TFTransfoXLForSequenceClassification,
@@ -206,6 +215,7 @@ from .configuration_auto import (
     OpenAIGPTConfig,
     PegasusConfig,
     RobertaConfig,
+    RoFormerConfig,
     T5Config,
     TransfoXLConfig,
     XLMConfig,
@@ -220,6 +230,7 @@ logger = logging.get_logger(__name__)
 TF_MODEL_MAPPING = OrderedDict(
     [
         # Base model mapping
+        (RoFormerConfig, TFRoFormerModel),
         (ConvBertConfig, TFConvBertModel),
         (LEDConfig, TFLEDModel),
         (LxmertConfig, TFLxmertModel),
@@ -285,6 +296,7 @@ TF_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
 TF_MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
     [
         # Model with LM heads mapping
+        (RoFormerConfig, TFRoFormerForMaskedLM),
         (ConvBertConfig, TFConvBertForMaskedLM),
         (LEDConfig, TFLEDForConditionalGeneration),
         (T5Config, TFT5ForConditionalGeneration),
@@ -315,6 +327,7 @@ TF_MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
 TF_MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
     [
         # Model for Causal LM mapping
+        (RoFormerConfig, TFRoFormerForCausalLM),
         (BertConfig, TFBertLMHeadModel),
         (OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
         (GPT2Config, TFGPT2LMHeadModel),
@@ -331,6 +344,7 @@ TF_MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
 TF_MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
     [
         # Model for Masked LM mapping
+        (RoFormerConfig, TFRoFormerForMaskedLM),
         (ConvBertConfig, TFConvBertForMaskedLM),
         (DistilBertConfig, TFDistilBertForMaskedLM),
         (AlbertConfig, TFAlbertForMaskedLM),
@@ -368,6 +382,7 @@ TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = OrderedDict(
 TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
     [
         # Model for Sequence Classification mapping
+        (RoFormerConfig, TFRoFormerForSequenceClassification),
         (ConvBertConfig, TFConvBertForSequenceClassification),
         (DistilBertConfig, TFDistilBertForSequenceClassification),
         (AlbertConfig, TFAlbertForSequenceClassification),
@@ -394,6 +409,7 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
 TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
     [
         # Model for Question Answering mapping
+        (RoFormerConfig, TFRoFormerForQuestionAnswering),
         (ConvBertConfig, TFConvBertForQuestionAnswering),
         (DistilBertConfig, TFDistilBertForQuestionAnswering),
         (AlbertConfig, TFAlbertForQuestionAnswering),
@@ -415,6 +431,7 @@ TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
 TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
     [
         # Model for Token Classification mapping
+        (RoFormerConfig, TFRoFormerForTokenClassification),
         (ConvBertConfig, TFConvBertForTokenClassification),
         (DistilBertConfig, TFDistilBertForTokenClassification),
         (AlbertConfig, TFAlbertForTokenClassification),
@@ -437,6 +454,7 @@ TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
 TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
     [
         # Model for Multiple Choice mapping
+        (RoFormerConfig, TFRoFormerForMultipleChoice),
         (ConvBertConfig, TFConvBertForMultipleChoice),
         (CamembertConfig, TFCamembertForMultipleChoice),
         (XLMConfig, TFXLMForMultipleChoice),

src/transformers/models/auto/tokenization_auto.py

@@ -51,6 +51,7 @@ from ..prophetnet.tokenization_prophetnet import ProphetNetTokenizer
 from ..rag.tokenization_rag import RagTokenizer
 from ..retribert.tokenization_retribert import RetriBertTokenizer
 from ..roberta.tokenization_roberta import RobertaTokenizer
+from ..roformer.tokenization_roformer import RoFormerTokenizer
 from ..squeezebert.tokenization_squeezebert import SqueezeBertTokenizer
 from ..tapas.tokenization_tapas import TapasTokenizer
 from ..transfo_xl.tokenization_transfo_xl import TransfoXLTokenizer
@@ -98,6 +99,7 @@ from .configuration_auto import (
     ReformerConfig,
     RetriBertConfig,
     RobertaConfig,
+    RoFormerConfig,
     Speech2TextConfig,
     SqueezeBertConfig,
     T5Config,
@@ -228,6 +230,7 @@ logger = logging.get_logger(__name__)
 TOKENIZER_MAPPING = OrderedDict(
     [
         (RetriBertConfig, (RetriBertTokenizer, RetriBertTokenizerFast)),
+        (RoFormerConfig, (RoFormerTokenizer, None)),
         (T5Config, (T5Tokenizer, T5TokenizerFast)),
         (MT5Config, (MT5Tokenizer, MT5TokenizerFast)),
         (MobileBertConfig, (MobileBertTokenizer, MobileBertTokenizerFast)),

src/transformers/models/roformer/__init__.py

@@ -0,0 +1,115 @@
+# flake8: noqa
+# There's no way to ignore "F401 '...' imported but unused" warnings in this
+# module, but to preserve other warnings. So, don't check this module at all.
+
+# Copyright 2021 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.
+from typing import TYPE_CHECKING
+
+from ...file_utils import _BaseLazyModule, is_tf_available, is_tokenizers_available, is_torch_available
+
+
+_import_structure = {
+    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig"],
+    "tokenization_roformer": ["RoFormerTokenizer"],
+}
+
+if is_tokenizers_available():
+    _import_structure["tokenization_roformer_fast"] = ["RoFormerTokenizerFast"]
+
+if is_torch_available():
+    _import_structure["modeling_roformer"] = [
+        "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "RoFormerForCausalLM",
+        "RoFormerForMaskedLM",
+        "RoFormerForMultipleChoice",
+        "RoFormerForQuestionAnswering",
+        "RoFormerForSequenceClassification",
+        "RoFormerForTokenClassification",
+        "RoFormerLayer",
+        "RoFormerModel",
+        "RoFormerPreTrainedModel",
+        "load_tf_weights_in_roformer",
+    ]
+
+
+if is_tf_available():
+    _import_structure["modeling_tf_roformer"] = [
+        "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFRoFormerForCausalLM",
+        "TFRoFormerForMaskedLM",
+        "TFRoFormerForMultipleChoice",
+        "TFRoFormerForQuestionAnswering",
+        "TFRoFormerForSequenceClassification",
+        "TFRoFormerForTokenClassification",
+        "TFRoFormerLayer",
+        "TFRoFormerModel",
+        "TFRoFormerPreTrainedModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig
+    from .tokenization_roformer import RoFormerTokenizer
+
+    if is_tokenizers_available():
+        from .tokenization_roformer_fast import RoFormerTokenizerFast
+
+    if is_torch_available():
+        from .modeling_roformer import (
+            ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
+            RoFormerForCausalLM,
+            RoFormerForMaskedLM,
+            RoFormerForMultipleChoice,
+            RoFormerForQuestionAnswering,
+            RoFormerForSequenceClassification,
+            RoFormerForTokenClassification,
+            RoFormerLayer,
+            RoFormerModel,
+            RoFormerPreTrainedModel,
+            load_tf_weights_in_roformer,
+        )
+
+    if is_tf_available():
+        from .modeling_tf_roformer import (
+            TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFRoFormerForCausalLM,
+            TFRoFormerForMaskedLM,
+            TFRoFormerForMultipleChoice,
+            TFRoFormerForQuestionAnswering,
+            TFRoFormerForSequenceClassification,
+            TFRoFormerForTokenClassification,
+            TFRoFormerLayer,
+            TFRoFormerModel,
+            TFRoFormerPreTrainedModel,
+        )
+
+
+else:
+    import importlib
+    import os
+    import sys
+
+    class _LazyModule(_BaseLazyModule):
+        """
+        Module class that surfaces all objects but only performs associated imports when the objects are requested.
+        """
+
+        __file__ = globals()["__file__"]
+        __path__ = [os.path.dirname(__file__)]
+
+        def _get_module(self, module_name: str):
+            return importlib.import_module("." + module_name, self.__name__)
+
+    sys.modules[__name__] = _LazyModule(__name__, _import_structure)

src/transformers/models/roformer/configuration_roformer.py

@@ -0,0 +1,134 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. 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.
+""" RoFormer model configuration """
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
+    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json"
+    # See all RoFormer models at https://huggingface.co/models?filter=roformer
+}
+
+
+class RoFormerConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a :class:`~transformers.RoFormerModel`. It is used to
+    instantiate an RoFormer model according to the specified arguments, defining the model architecture. Instantiating
+    a configuration with the defaults will yield a similar configuration to that of the RoFormer
+    `junnyu/roformer_chinese_base <https://huggingface.co/junnyu/roformer_chinese_base>`__ architecture.
+
+    Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
+    outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
+
+
+    Args:
+        vocab_size (:obj:`int`, `optional`, defaults to 50000):
+            Vocabulary size of the RoFormer model. Defines the number of different tokens that can be represented by
+            the :obj:`inputs_ids` passed when calling :class:`~transformers.RoFormerModel` or
+            :class:`~transformers.TFRoFormerModel`.
+        embedding_size (:obj:`int`, `optional`, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        hidden_size (:obj:`int`, `optional`, defaults to 768):
+            Dimension of the encoder layers and the pooler layer.
+        num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (:obj:`int`, `optional`, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (:obj:`int`, `optional`, defaults to 3072):
+            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string,
+            :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
+        hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (:obj:`int`, `optional`, defaults to 1536):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 1536).
+        type_vocab_size (:obj:`int`, `optional`, defaults to 2):
+            The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.RoFormerModel`
+            or :class:`~transformers.TFRoFormerModel`.
+        initializer_range (:obj:`float`, `optional`, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if ``config.is_decoder=True``.
+        rotary_value (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            Whether or not apply rotary position embeddings on value layer.
+        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
+            If :obj:`True`, use gradient checkpointing to save memory at the expense of slower backward pass.
+
+        Example::
+
+        >>> from transformers import RoFormerModel, RoFormerConfig
+
+        >>> # Initializing a RoFormer junnyu/roformer_chinese_base style configuration
+        >>> configuration = RoFormerConfig()
+
+        >>> # Initializing a model from the junnyu/roformer_chinese_base style configuration
+        >>> model = RoFormerModel(configuration)
+
+        >>> # Accessing the model configuration
+        >>> configuration = model.config
+    """
+    model_type = "roformer"
+
+    def __init__(
+        self,
+        vocab_size=50000,
+        embedding_size=768,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=1536,
+        type_vocab_size=2,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        pad_token_id=0,
+        gradient_checkpointing=False,
+        rotary_value=False,
+        use_cache=True,
+        **kwargs
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+
+        self.vocab_size = vocab_size
+        self.embedding_size = embedding_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.gradient_checkpointing = gradient_checkpointing
+        self.rotary_value = rotary_value
+        self.use_cache = use_cache

src/transformers/models/roformer/convert_roformer_original_tf_checkpoint_to_pytorch.py

@@ -0,0 +1,61 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. 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.
+"""Convert RoFormer checkpoint."""
+
+
+import argparse
+
+import torch
+
+from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
+from transformers.utils import logging
+
+
+logging.set_verbosity_info()
+
+
+def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
+    # Initialise PyTorch model
+    config = RoFormerConfig.from_json_file(bert_config_file)
+    print(f"Building PyTorch model from configuration: {config}")
+    model = RoFormerForMaskedLM(config)
+
+    # Load weights from tf checkpoint
+    load_tf_weights_in_roformer(model, config, tf_checkpoint_path)
+
+    # Save pytorch-model
+    print(f"Save PyTorch model to {pytorch_dump_path}")
+    torch.save(model.state_dict(), pytorch_dump_path, _use_new_zipfile_serialization=False)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    # Required parameters
+    parser.add_argument(
+        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
+    )
+    parser.add_argument(
+        "--bert_config_file",
+        default=None,
+        type=str,
+        required=True,
+        help="The config json file corresponding to the pre-trained BERT model. \n"
+        "This specifies the model architecture.",
+    )
+    parser.add_argument(
+        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
+    )
+    args = parser.parse_args()
+    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)

src/transformers/models/roformer/tokenization_roformer.py

@@ -0,0 +1,317 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. 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.
+"""Tokenization classes for RoFormer."""
+
+import collections
+import os
+from typing import List, Optional, Tuple
+
+from ...tokenization_utils import PreTrainedTokenizer
+from ...utils import logging
+from ..bert.tokenization_bert import BasicTokenizer, WordpieceTokenizer, load_vocab
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
+        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
+    }
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"junnyu/roformer_chinese_small": 1536, "junnyu/roformer_chinese_base": 1536}
+
+
+PRETRAINED_INIT_CONFIGURATION = {
+    "junnyu/roformer_chinese_small": {"do_lower_case": True},
+    "junnyu/roformer_chinese_base": {"do_lower_case": True},
+}
+
+
+class RoFormerTokenizer(PreTrainedTokenizer):
+    r"""
+    Construct a RoFormer tokenizer. Based on `Rust Jieba <https://pypi.org/project/rjieba/>`.
+
+    This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
+    Users should refer to this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (:obj:`str`):
+            File containing the vocabulary.
+        do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to lowercase the input when tokenizing.
+        do_basic_tokenize (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to do basic tokenization before WordPiece.
+        never_split (:obj:`Iterable`, `optional`):
+            Collection of tokens which will never be split during tokenization. Only has an effect when
+            :obj:`do_basic_tokenize=True`
+        unk_token (:obj:`str`, `optional`, defaults to :obj:`"[UNK]"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        sep_token (:obj:`str`, `optional`, defaults to :obj:`"[SEP]"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        pad_token (:obj:`str`, `optional`, defaults to :obj:`"[PAD]"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        cls_token (:obj:`str`, `optional`, defaults to :obj:`"[CLS]"`):
+            The classifier token which is used when doing sequence classification (classification of the whole sequence
+            instead of per-token classification). It is the first token of the sequence when built with special tokens.
+        mask_token (:obj:`str`, `optional`, defaults to :obj:`"[MASK]"`):
+            The token used for masking values. This is the token used when training this model with masked language
+            modeling. This is the token which the model will try to predict.
+        tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not to tokenize Chinese characters.
+
+            This should likely be deactivated for Japanese (see this `issue
+            <https://github.com/huggingface/transformers/issues/328>`__).
+        strip_accents: (:obj:`bool`, `optional`):
+            Whether or not to strip all accents. If this option is not specified, then it will be determined by the
+            value for :obj:`lowercase` (as in the original BERT).
+
+    Example::
+
+        >>> from transformers import RoFormerTokenizer
+        >>> tokenizer = RoFormerTokenizer.from_pretrained('junnyu/roformer_chinese_base')
+        >>> tokenizer.tokenize("今天天气非常好。")
+        # ['今', '天', '天', '气', '非常', '好', '。']
+
+    """
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+
+    def __init__(
+        self,
+        vocab_file,
+        do_lower_case=True,
+        do_basic_tokenize=True,
+        never_split=None,
+        unk_token="[UNK]",
+        sep_token="[SEP]",
+        pad_token="[PAD]",
+        cls_token="[CLS]",
+        mask_token="[MASK]",
+        tokenize_chinese_chars=True,
+        strip_accents=None,
+        **kwargs
+    ):
+        super().__init__(
+            do_lower_case=do_lower_case,
+            do_basic_tokenize=do_basic_tokenize,
+            never_split=never_split,
+            unk_token=unk_token,
+            sep_token=sep_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            mask_token=mask_token,
+            tokenize_chinese_chars=tokenize_chinese_chars,
+            strip_accents=strip_accents,
+            **kwargs,
+        )
+
+        if not os.path.isfile(vocab_file):
+            raise ValueError(
+                f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
+                "model use `tokenizer = RoFormerTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
+            )
+        self.vocab = load_vocab(vocab_file)
+        self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
+        self.do_basic_tokenize = do_basic_tokenize
+        if do_basic_tokenize:
+            self.basic_tokenizer = BasicTokenizer(
+                do_lower_case=do_lower_case,
+                never_split=never_split,
+                tokenize_chinese_chars=tokenize_chinese_chars,
+                strip_accents=strip_accents,
+            )
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)
+        try:
+            import rjieba
+        except ImportError:
+            raise ImportError(
+                "You need to install rjieba to use RoFormerTokenizer."
+                "See https://pypi.org/project/rjieba/ for installation."
+            )
+        self.jieba = rjieba
+
+    @property
+    def do_lower_case(self):
+        return self.basic_tokenizer.do_lower_case
+
+    @property
+    def vocab_size(self):
+        return len(self.vocab)
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["jieba"] = None
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+        try:
+            import rjieba
+        except ImportError:
+            raise ImportError(
+                "You need to install rjieba to use RoFormerTokenizer."
+                "See https://pypi.org/project/rjieba/ for installation."
+            )
+        self.jieba = rjieba
+
+    def get_vocab(self):
+        return dict(self.vocab, **self.added_tokens_encoder)
+
+    def _tokenize(self, text, use_jieba=True):
+        split_tokens = []
+        if use_jieba:
+            for wholword in self.jieba.cut(text, False):
+                if wholword in self.vocab:
+                    split_tokens.append(wholword)
+                else:
+                    # use bert tokenizer to _tokenize
+                    char_list = self._tokenize(wholword, use_jieba=False)
+                    split_tokens.extend(char_list)
+        else:
+            if self.do_basic_tokenize:
+                for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
+                    # If the token is part of the never_split set
+                    if token in self.basic_tokenizer.never_split:
+                        split_tokens.append(token)
+                    else:
+                        split_tokens += self.wordpiece_tokenizer.tokenize(token)
+            else:
+                split_tokens = self.wordpiece_tokenizer.tokenize(text)
+        return split_tokens
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.vocab.get(token, self.vocab.get(self.unk_token))
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.ids_to_tokens.get(index, self.unk_token)
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        out_string = " ".join(tokens).replace(" ##", "").strip()
+        return out_string
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A RoFormer sequence has the following format:
+
+        - single sequence: ``[CLS] X [SEP]``
+        - pair of sequences: ``[CLS] A [SEP] B [SEP]``
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
+        """
+        if token_ids_1 is None:
+            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
+        cls = [self.cls_token_id]
+        sep = [self.sep_token_id]
+        return cls + token_ids_0 + sep + token_ids_1 + sep
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer ``prepare_for_model`` method.
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is not None:
+            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer
+        sequence pair mask has the following format:
+
+        ::
+
+            0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+            | first sequence    | second sequence |
+
+        If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
+            sequence(s).
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        index = 0
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+            )
+        else:
+            vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
+        with open(vocab_file, "w", encoding="utf-8") as writer:
+            for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
+                if index != token_index:
+                    logger.warning(
+                        f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
+                        " Please check that the vocabulary is not corrupted!"
+                    )
+                    index = token_index
+                writer.write(token + "\n")
+                index += 1
+        return (vocab_file,)

src/transformers/models/roformer/tokenization_roformer_fast.py

@@ -0,0 +1,191 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. 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.
+"""Tokenization classes for RoFormer."""
+import json
+from typing import List, Optional, Tuple
+
+from tokenizers import normalizers
+from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
+
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
+from ...utils import logging
+from .tokenization_roformer import RoFormerTokenizer
+from .tokenization_utils import JiebaPreTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
+        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
+    }
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"junnyu/roformer_chinese_small": 1536, "junnyu/roformer_chinese_base": 1536}
+
+
+PRETRAINED_INIT_CONFIGURATION = {
+    "junnyu/roformer_chinese_small": {"do_lower_case": True},
+    "junnyu/roformer_chinese_base": {"do_lower_case": True},
+}
+
+
+class RoFormerTokenizerFast(PreTrainedTokenizerFast):
+    r"""
+    Construct a "fast" RoFormer tokenizer (backed by HuggingFace's `tokenizers` library).
+
+    :class:`~transformers.RoFormerTokenizerFast` is almost identical to :class:`~transformers.BertTokenizerFast` and
+    runs end-to-end tokenization: punctuation splitting and wordpiece. There are some difference between them when
+    tokenizing Chinese.
+
+    This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the main
+    methods. Users should refer to this superclass for more information regarding those methods.
+
+    Example::
+
+        >>> from transformers import RoFormerTokenizerFast
+        >>> tokenizer = RoFormerTokenizerFast.from_pretrained('junnyu/roformer_chinese_base')
+        >>> tokenizer.tokenize("今天天气非常好。")
+        # ['今', '天', '天', '气', '非常', '好', '。']
+
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+    slow_tokenizer_class = RoFormerTokenizer
+
+    def __init__(
+        self,
+        vocab_file,
+        tokenizer_file=None,
+        do_lower_case=True,
+        unk_token="[UNK]",
+        sep_token="[SEP]",
+        pad_token="[PAD]",
+        cls_token="[CLS]",
+        mask_token="[MASK]",
+        tokenize_chinese_chars=True,
+        strip_accents=None,
+        **kwargs
+    ):
+        super().__init__(
+            vocab_file,
+            tokenizer_file=tokenizer_file,
+            do_lower_case=do_lower_case,
+            unk_token=unk_token,
+            sep_token=sep_token,
+            pad_token=pad_token,
+            cls_token=cls_token,
+            mask_token=mask_token,
+            tokenize_chinese_chars=tokenize_chinese_chars,
+            strip_accents=strip_accents,
+            **kwargs,
+        )
+
+        pre_tok_state = json.loads(self.backend_tokenizer.normalizer.__getstate__())
+        if (
+            pre_tok_state.get("lowercase", do_lower_case) != do_lower_case
+            or pre_tok_state.get("strip_accents", strip_accents) != strip_accents
+        ):
+            pre_tok_class = getattr(normalizers, pre_tok_state.pop("type"))
+            pre_tok_state["lowercase"] = do_lower_case
+            pre_tok_state["strip_accents"] = strip_accents
+            self.backend_tokenizer.normalizer = pre_tok_class(**pre_tok_state)
+
+        self.do_lower_case = do_lower_case
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["_tokenizer"].pre_tokenizer = BertPreTokenizer()
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+        vocab = self.__dict__["_tokenizer"].get_vocab()
+        self.__dict__["_tokenizer"].pre_tokenizer = PreTokenizer.custom(JiebaPreTokenizer(vocab))
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. A RoFormer sequence has the following format:
+
+        - single sequence: ``[CLS] X [SEP]``
+        - pair of sequences: ``[CLS] A [SEP] B [SEP]``
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
+        """
+        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
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer
+        sequence pair mask has the following format:
+
+        ::
+
+            0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+            | first sequence    | second sequence |
+
+        If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
+
+        Args:
+            token_ids_0 (:obj:`List[int]`):
+                List of IDs.
+            token_ids_1 (:obj:`List[int]`, `optional`):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
+            sequence(s).
+        """
+        sep = [self.sep_token_id]
+        cls = [self.cls_token_id]
+        if token_ids_1 is None:
+            return len(cls + token_ids_0 + sep) * [0]
+        return len(cls + token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        files = self._tokenizer.model.save(save_directory, name=filename_prefix)
+        return tuple(files)
+
+    def save_pretrained(
+        self,
+        save_directory,
+        legacy_format=None,
+        filename_prefix=None,
+        push_to_hub=False,
+        **kwargs,
+    ):
+        self.backend_tokenizer.pre_tokenizer = BertPreTokenizer()
+        return super().save_pretrained(save_directory, legacy_format, filename_prefix, push_to_hub, **kwargs)

src/transformers/models/roformer/tokenization_utils.py

@@ -0,0 +1,68 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. 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.
+"""Tokenization utils for RoFormer."""
+
+from typing import List
+
+from tokenizers import NormalizedString, PreTokenizedString, normalizers
+
+
+class JiebaPreTokenizer:
+    def __init__(self, vocab) -> None:
+        self.vocab = vocab
+        self.normalizers = normalizers.BertNormalizer(
+            clean_text=False,
+            handle_chinese_chars=True,
+            strip_accents=False,
+            lowercase=False,
+        )
+        try:
+            import rjieba
+        except ImportError:
+            raise ImportError(
+                "You need to install rjieba to use RoFormerTokenizer."
+                "See https://pypi.org/project/rjieba/ for installation."
+            )
+        self.jieba = rjieba
+
+    def jieba_split(self, i: int, normalized_string: NormalizedString) -> List[NormalizedString]:
+        splits = []
+
+        # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
+        # for token, start, end in self.jieba.tokenize(str(normalized_string), hmm=False):
+        #     if token in self.vocab:
+        #         splits.append(normalized_string.slice((start, end)))
+        #     else:
+        #         token_list = self.normalizers.normalize_str(token).split()
+        #         for token in token_list:
+        #             if token:
+        #                 end = start + len(token)
+        #                 splits.append(normalized_string.slice((start, end)))
+        #                 start = end
+
+        # this code test_alignement_methods can't pass but fast (300ms)
+        for token in self.jieba.cut(str(normalized_string), False):
+            if token in self.vocab:
+                splits.append(NormalizedString(token))
+            else:
+                token_list = self.normalizers.normalize_str(token).split()
+                for token in token_list:
+                    if token:
+                        splits.append(NormalizedString(token))
+
+        return splits
+
+    def pre_tokenize(self, pretok: PreTokenizedString):
+        pretok.split(self.jieba_split)

src/transformers/utils/dummy_pt_objects.py

@@ -2553,6 +2553,86 @@ class RobertaModel:
         requires_backends(self, ["torch"])
 
 
+ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class RoFormerForCausalLM:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class RoFormerForMaskedLM:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class RoFormerForMultipleChoice:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class RoFormerForQuestionAnswering:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class RoFormerForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class RoFormerForTokenClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class RoFormerLayer:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class RoFormerModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class RoFormerPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+def load_tf_weights_in_roformer(*args, **kwargs):
+    requires_backends(load_tf_weights_in_roformer, ["torch"])
+
+
 SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

src/transformers/utils/dummy_tf_objects.py

@@ -1479,6 +1479,82 @@ class TFRobertaPreTrainedModel:
         requires_backends(self, ["tf"])
 
 
+TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFRoFormerForCausalLM:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFRoFormerForMaskedLM:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFRoFormerForMultipleChoice:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFRoFormerForQuestionAnswering:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFRoFormerForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFRoFormerForTokenClassification:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFRoFormerLayer:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFRoFormerModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFRoFormerPreTrainedModel:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

src/transformers/utils/dummy_tokenizers_objects.py

@@ -281,6 +281,15 @@ class RobertaTokenizerFast:
         requires_backends(self, ["tokenizers"])
 
 
+class RoFormerTokenizerFast:
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tokenizers"])
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_backends(self, ["tokenizers"])
+
+
 class SqueezeBertTokenizerFast:
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["tokenizers"])

src/transformers/utils/modeling_auto_mapping.py

@@ -6,6 +6,7 @@ from collections import OrderedDict
 
 MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
     [
+        ("RoFormerConfig", "RoFormerForQuestionAnswering"),
         ("BigBirdPegasusConfig", "BigBirdPegasusForQuestionAnswering"),
         ("BigBirdConfig", "BigBirdForQuestionAnswering"),
         ("ConvBertConfig", "ConvBertForQuestionAnswering"),

tests/test_modeling_roformer.py

@@ -0,0 +1,556 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. 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.
+""" Testing suite for the PyTorch RoFormer model. """
+
+
+import unittest
+
+from tests.test_modeling_common import floats_tensor
+from transformers import is_torch_available
+from transformers.testing_utils import require_torch, slow, torch_device
+
+from .test_configuration_common import ConfigTester
+from .test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        RoFormerConfig,
+        RoFormerForCausalLM,
+        RoFormerForMaskedLM,
+        RoFormerForMultipleChoice,
+        RoFormerForQuestionAnswering,
+        RoFormerForSequenceClassification,
+        RoFormerForTokenClassification,
+        RoFormerModel,
+    )
+    from transformers.models.roformer.modeling_roformer import (
+        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
+        RoFormerSelfAttention,
+        RoFormerSinusoidalPositionalEmbedding,
+    )
+
+
+class RoFormerModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_token_type_ids = use_token_type_ids
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = RoFormerConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+        )
+
+        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def prepare_config_and_inputs_for_decoder(self):
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = self.prepare_config_and_inputs()
+
+        config.is_decoder = True
+        encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
+        encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
+
+        return (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        )
+
+    def create_and_check_model(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = RoFormerModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+        result = model(input_ids, token_type_ids=token_type_ids)
+        result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_model_as_decoder(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+        encoder_hidden_states,
+        encoder_attention_mask,
+    ):
+        config.add_cross_attention = True
+        model = RoFormerModel(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+        )
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_for_causal_lm(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+        encoder_hidden_states,
+        encoder_attention_mask,
+    ):
+        model = RoFormerForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_for_masked_lm(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = RoFormerForMaskedLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_decoder_model_past_large_inputs(
+        self,
+        config,
+        input_ids,
+        token_type_ids,
+        input_mask,
+        sequence_labels,
+        token_labels,
+        choice_labels,
+        encoder_hidden_states,
+        encoder_attention_mask,
+    ):
+        config.is_decoder = True
+        config.add_cross_attention = True
+        model = RoFormerForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        # first forward pass
+        outputs = model(
+            input_ids,
+            attention_mask=input_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            use_cache=True,
+        )
+        past_key_values = outputs.past_key_values
+
+        # create hypothetical multiple next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
+        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
+
+        # append to next input_ids and
+        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
+
+        output_from_no_past = model(
+            next_input_ids,
+            attention_mask=next_attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_hidden_states=True,
+        )["hidden_states"][0]
+        output_from_past = model(
+            next_tokens,
+            attention_mask=next_attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            past_key_values=past_key_values,
+            output_hidden_states=True,
+        )["hidden_states"][0]
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
+        output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
+        output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
+
+        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
+    def create_and_check_for_question_answering(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = RoFormerForQuestionAnswering(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            start_positions=sequence_labels,
+            end_positions=sequence_labels,
+        )
+        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
+        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
+
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = RoFormerForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_for_token_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = RoFormerForTokenClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
+    def create_and_check_for_multiple_choice(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_choices = self.num_choices
+        model = RoFormerForMultipleChoice(config=config)
+        model.to(torch_device)
+        model.eval()
+        multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
+        result = model(
+            multiple_choice_inputs_ids,
+            attention_mask=multiple_choice_input_mask,
+            token_type_ids=multiple_choice_token_type_ids,
+            labels=choice_labels,
+        )
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class RoFormerModelTest(ModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (
+            RoFormerModel,
+            RoFormerForMaskedLM,
+            RoFormerForCausalLM,
+            RoFormerForMultipleChoice,
+            RoFormerForQuestionAnswering,
+            RoFormerForSequenceClassification,
+            RoFormerForTokenClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+    all_generative_model_classes = (RoFormerForCausalLM,) if is_torch_available() else ()
+
+    def setUp(self):
+        self.model_tester = RoFormerModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=RoFormerConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_for_masked_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
+
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
+
+    def test_decoder_model_past_with_large_inputs(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
+        self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
+
+    def test_for_question_answering(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
+
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
+
+    def test_model_as_decoder(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
+        self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
+
+    def test_model_as_decoder_with_default_input_mask(self):
+        # This regression test was failing with PyTorch < 1.3
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        ) = self.model_tester.prepare_config_and_inputs_for_decoder()
+
+        input_mask = None
+
+        self.model_tester.create_and_check_model_as_decoder(
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            encoder_hidden_states,
+            encoder_attention_mask,
+        )
+
+    @slow
+    def test_model_from_pretrained(self):
+        for model_name in ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
+            model = RoFormerModel.from_pretrained(model_name)
+            self.assertIsNotNone(model)
+
+
+@require_torch
+class RoFormerModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_masked_lm(self):
+        model = RoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
+        input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
+        output = model(input_ids)[0]
+
+        # TODO Replace vocab size
+        vocab_size = 50000
+
+        expected_shape = torch.Size((1, 6, vocab_size))
+        self.assertEqual(output.shape, expected_shape)
+
+        # TODO Replace values below with what was printed above.
+        expected_slice = torch.tensor(
+            [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]
+        )
+
+        self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
+
+
+@require_torch
+class RoFormerSinusoidalPositionalEmbeddingTest(unittest.TestCase):
+    tolerance = 1e-4
+
+    def test_basic(self):
+        input_ids = torch.tensor([[4, 10]], dtype=torch.long, device=torch_device)
+        emb1 = RoFormerSinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6).to(torch_device)
+        emb = emb1(input_ids.shape)
+        desired_weights = torch.tensor(
+            [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]
+        ).to(torch_device)
+        self.assertTrue(
+            torch.allclose(emb, desired_weights, atol=self.tolerance),
+            msg=f"\nexp:\n{desired_weights}\ngot:\n{emb[0]}\n",
+        )
+
+    def test_positional_emb_weights_against_roformer(self):
+
+        desired_weights = torch.tensor(
+            [
+                [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
+                [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
+                [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
+            ]
+        ).to(torch_device)
+        emb1 = RoFormerSinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512).to(torch_device)
+        weights = emb1.weight.data[:3, :5].to(torch_device)
+
+        self.assertTrue(
+            torch.allclose(weights, desired_weights, atol=self.tolerance),
+            msg=f"\nexp:\n{desired_weights}\ngot:\n{weights}\n",
+        )
+
+
+@require_torch
+class RoFormerSelfAttentionRotaryPositionEmbeddingTest(unittest.TestCase):
+    tolerance = 1e-4
+
+    def test_apply_rotary_position_embeddings(self):
+        # 2,12,16,64
+        query_layer = (
+            torch.arange(2 * 12 * 16 * 64, dtype=torch.float, device=torch_device).reshape(2, 12, 16, 64) / 100
+        ).to(torch_device)
+        key_layer = (
+            -torch.arange(2 * 12 * 16 * 64, dtype=torch.float, device=torch_device).reshape(2, 12, 16, 64) / 100
+        ).to(torch_device)
+        embed_positions = RoFormerSinusoidalPositionalEmbedding(num_positions=32, embedding_dim=64).to(torch_device)
+        sinusoidal_pos = embed_positions([2, 16, 768])[None, None, :, :]
+
+        query_layer, key_layer = RoFormerSelfAttention.apply_rotary_position_embeddings(
+            sinusoidal_pos, query_layer, key_layer
+        )
+
+        desired_query_layer = torch.tensor(
+            [
+                [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
+                [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
+                [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
+                [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
+                [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
+                [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
+            ]
+        ).to(torch_device)
+        desired_key_layer = torch.tensor(
+            [
+                [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
+                [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
+                [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
+                [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
+                [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
+                [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
+            ]
+        ).to(torch_device)
+
+        self.assertTrue(
+            torch.allclose(query_layer[0, 0, :6, :8], desired_query_layer, atol=self.tolerance),
+            msg=f"\nexp:\n{desired_query_layer}\ngot:\n{query_layer}\n",
+        )
+        self.assertTrue(
+            torch.allclose(key_layer[0, 0, :6, :8], desired_key_layer, atol=self.tolerance),
+            msg=f"\nexp:\n{desired_key_layer}\ngot:\n{key_layer}\n",
+        )

tests/test_modeling_tf_roformer.py

@@ -0,0 +1,401 @@
+# coding=utf-8
+# Copyright 2021 The HuggingFace Inc. 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 unittest
+
+from transformers import RoFormerConfig, is_tf_available
+from transformers.testing_utils import require_tf, slow
+
+from .test_configuration_common import ConfigTester
+from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import (
+        TFRoFormerForCausalLM,
+        TFRoFormerForMaskedLM,
+        TFRoFormerForMultipleChoice,
+        TFRoFormerForQuestionAnswering,
+        TFRoFormerForSequenceClassification,
+        TFRoFormerForTokenClassification,
+        TFRoFormerModel,
+    )
+    from transformers.models.roformer.modeling_tf_roformer import (
+        TFRoFormerSelfAttention,
+        TFRoFormerSinusoidalPositionalEmbedding,
+    )
+
+
+class TFRoFormerModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=5,
+        num_attention_heads=4,
+        intermediate_size=37,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = 13
+        self.seq_length = 7
+        self.is_training = True
+        self.use_input_mask = True
+        self.use_token_type_ids = True
+        self.use_labels = True
+        self.vocab_size = 99
+        self.hidden_size = 32
+        self.num_hidden_layers = 5
+        self.num_attention_heads = 4
+        self.intermediate_size = 37
+        self.hidden_act = "gelu"
+        self.hidden_dropout_prob = 0.1
+        self.attention_probs_dropout_prob = 0.1
+        self.max_position_embeddings = 512
+        self.type_vocab_size = 16
+        self.type_sequence_label_size = 2
+        self.initializer_range = 0.02
+        self.num_labels = 3
+        self.num_choices = 4
+        self.scope = None
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
+
+        token_type_ids = None
+        if self.use_token_type_ids:
+            token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
+
+        sequence_labels = None
+        token_labels = None
+        choice_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+            choice_labels = ids_tensor([self.batch_size], self.num_choices)
+
+        config = RoFormerConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            max_position_embeddings=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            initializer_range=self.initializer_range,
+            return_dict=True,
+        )
+
+        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def create_and_check_model(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = TFRoFormerModel(config=config)
+        inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
+
+        inputs = [input_ids, input_mask]
+        result = model(inputs)
+
+        result = model(input_ids)
+
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_lm_head(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.is_decoder = True
+        model = TFRoFormerForCausalLM(config=config)
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+        }
+        prediction_scores = model(inputs)["logits"]
+        self.parent.assertListEqual(
+            list(prediction_scores.numpy().shape), [self.batch_size, self.seq_length, self.vocab_size]
+        )
+
+    def create_and_check_for_masked_lm(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = TFRoFormerForMaskedLM(config=config)
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+        }
+        result = model(inputs)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = TFRoFormerForSequenceClassification(config=config)
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+        }
+
+        result = model(inputs)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_for_multiple_choice(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_choices = self.num_choices
+        model = TFRoFormerForMultipleChoice(config=config)
+        multiple_choice_inputs_ids = tf.tile(tf.expand_dims(input_ids, 1), (1, self.num_choices, 1))
+        multiple_choice_input_mask = tf.tile(tf.expand_dims(input_mask, 1), (1, self.num_choices, 1))
+        multiple_choice_token_type_ids = tf.tile(tf.expand_dims(token_type_ids, 1), (1, self.num_choices, 1))
+        inputs = {
+            "input_ids": multiple_choice_inputs_ids,
+            "attention_mask": multiple_choice_input_mask,
+            "token_type_ids": multiple_choice_token_type_ids,
+        }
+        result = model(inputs)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
+
+    def create_and_check_for_token_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = TFRoFormerForTokenClassification(config=config)
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+        }
+        result = model(inputs)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
+    def create_and_check_for_question_answering(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = TFRoFormerForQuestionAnswering(config=config)
+        inputs = {
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "token_type_ids": token_type_ids,
+        }
+
+        result = model(inputs)
+        self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
+        self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_tf
+class TFRoFormerModelTest(TFModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (
+            TFRoFormerModel,
+            TFRoFormerForCausalLM,
+            TFRoFormerForMaskedLM,
+            TFRoFormerForQuestionAnswering,
+            TFRoFormerForSequenceClassification,
+            TFRoFormerForTokenClassification,
+            TFRoFormerForMultipleChoice,
+        )
+        if is_tf_available()
+        else ()
+    )
+
+    test_head_masking = False
+    test_onnx = False
+
+    def setUp(self):
+        self.model_tester = TFRoFormerModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=RoFormerConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_for_masked_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
+
+    def test_for_causal_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_lm_head(*config_and_inputs)
+
+    def test_for_multiple_choice(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
+
+    def test_for_question_answering(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
+
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model = TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base")
+        self.assertIsNotNone(model)
+
+
+@require_tf
+class TFRoFormerModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference_masked_lm(self):
+        model = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
+        input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
+        output = model(input_ids)[0]
+
+        # TODO Replace vocab size
+        vocab_size = 50000
+
+        expected_shape = [1, 6, vocab_size]
+        self.assertEqual(output.shape, expected_shape)
+
+        print(output[:, :3, :3])
+
+        # TODO Replace values below with what was printed above.
+        expected_slice = tf.constant(
+            [
+                [
+                    [-0.12053341, -1.0264901, 0.29221946],
+                    [-1.5133783, 0.197433, 0.15190607],
+                    [-5.0135403, -3.900256, -0.84038764],
+                ]
+            ]
+        )
+        tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
+
+
+@require_tf
+class TFRoFormerSinusoidalPositionalEmbeddingTest(unittest.TestCase):
+    tolerance = 1e-4
+
+    def test_basic(self):
+        input_ids = tf.constant([[4, 10]])
+        emb1 = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6, embedding_dim=6)
+
+        emb = emb1(input_ids.shape)
+        desired_weights = tf.constant(
+            [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]
+        )
+
+        tf.debugging.assert_near(emb, desired_weights, atol=self.tolerance)
+
+    def test_positional_emb_weights_against_roformer(self):
+
+        desired_weights = tf.constant(
+            [
+                [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
+                [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
+                [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
+            ]
+        )
+        emb1 = TFRoFormerSinusoidalPositionalEmbedding(num_positions=512, embedding_dim=512)
+        emb1([2, 16, 512])
+        weights = emb1.weight[:3, :5]
+
+        tf.debugging.assert_near(weights, desired_weights, atol=self.tolerance)
+
+
+@require_tf
+class TFRoFormerSelfAttentionRotaryPositionEmbeddingTest(unittest.TestCase):
+    tolerance = 1e-4
+
+    def test_apply_rotary_position_embeddings(self):
+        # 2,12,16,64
+        query_layer = tf.reshape(tf.range(2 * 12 * 16 * 64, dtype=tf.float32), shape=(2, 12, 16, 64)) / 100
+
+        key_layer = -tf.reshape(tf.range(2 * 12 * 16 * 64, dtype=tf.float32), shape=(2, 12, 16, 64)) / 100
+
+        embed_positions = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32, embedding_dim=64)
+        sinusoidal_pos = embed_positions([2, 16, 768])[None, None, :, :]
+
+        query_layer, key_layer = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
+            sinusoidal_pos, query_layer, key_layer
+        )
+
+        desired_query_layer = tf.constant(
+            [
+                [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
+                [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
+                [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
+                [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
+                [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
+                [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
+            ]
+        )
+        desired_key_layer = tf.constant(
+            [
+                [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
+                [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
+                [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
+                [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
+                [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
+                [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
+            ]
+        )
+
+        tf.debugging.assert_near(query_layer[0, 0, :6, :8], desired_query_layer, atol=self.tolerance)
+        tf.debugging.assert_near(key_layer[0, 0, :6, :8], desired_key_layer, atol=self.tolerance)

tests/test_tokenization_roformer.py

@@ -0,0 +1,84 @@
+# coding=utf-8
+# Copyright 2021 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 importlib
+import unittest
+
+from transformers import RoFormerTokenizer, RoFormerTokenizerFast
+from transformers.testing_utils import require_tokenizers
+
+from .test_tokenization_common import TokenizerTesterMixin
+
+
+def is_rjieba_available():
+    return importlib.util.find_spec("rjieba") is not None
+
+
+def require_rjieba(test_case):
+    """
+    Decorator marking a test that requires Jieba. These tests are skipped when Jieba isn't installed.
+    """
+    if not is_rjieba_available():
+        return unittest.skip("test requires rjieba")(test_case)
+    else:
+        return test_case
+
+
+@require_rjieba
+@require_tokenizers
+class RoFormerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
+
+    tokenizer_class = RoFormerTokenizer
+    rust_tokenizer_class = RoFormerTokenizerFast
+    space_between_special_tokens = True
+    test_rust_tokenizer = True
+
+    def setUp(self):
+        super().setUp()
+
+    def get_tokenizer(self, **kwargs):
+        return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base", **kwargs)
+
+    def get_rust_tokenizer(self, **kwargs):
+        return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base", **kwargs)
+
+    def get_chinese_input_output_texts(self):
+        input_text = "永和服装饰品有限公司,今天天气非常好"
+        output_text = "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
+        return input_text, output_text
+
+    def test_tokenizer(self):
+        tokenizer = self.get_tokenizer()
+        input_text, output_text = self.get_chinese_input_output_texts()
+        tokens = tokenizer.tokenize(input_text)
+
+        self.assertListEqual(tokens, output_text.split())
+
+        input_tokens = tokens + [tokenizer.unk_token]
+        exp_tokens = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
+        self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), exp_tokens)
+
+    def test_rust_tokenizer(self):
+        tokenizer = self.get_rust_tokenizer()
+        input_text, output_text = self.get_chinese_input_output_texts()
+        tokens = tokenizer.tokenize(input_text)
+        self.assertListEqual(tokens, output_text.split())
+        input_tokens = tokens + [tokenizer.unk_token]
+        exp_tokens = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
+        self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), exp_tokens)
+
+    # due to custom pre_tokenize , char_to_token may be error
+    def test_alignement_methods(self):
+        pass