Add Nystromformer (#14659)

* Initial commit

* Config and modelling changes

Added Nystromformer-specific attributes to config and removed all decoder functionality from modelling.

* Modelling and test changes

Added Nystrom approximation and removed decoder tests.

* Code quality fixes

* Modeling changes and conversion script

Initial commits to conversion script, modeling changes.

* Minor modeling changes and conversion script

* Modeling changes

* Correct modeling, add tests and documentation

* Code refactor

* Remove tokenizers

* Code refactor

* Update __init__.py

* Fix bugs

* Update src/transformers/__init__.py

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* Update src/transformers/__init__.py

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* Update src/transformers/models/nystromformer/__init__.py

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* Update docs/source/model_doc/nystromformer.mdx

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* Update src/transformers/models/nystromformer/configuration_nystromformer.py

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* Update src/transformers/models/nystromformer/configuration_nystromformer.py

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* Update src/transformers/models/nystromformer/configuration_nystromformer.py

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* Update src/transformers/models/nystromformer/configuration_nystromformer.py

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* Update src/transformers/models/nystromformer/convert_nystromformer_original_pytorch_checkpoint_to_pytorch.py

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* Update src/transformers/models/nystromformer/configuration_nystromformer.py

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* Update modeling and test_modeling

* Code refactor

* .rst to .mdx

* doc changes

* Doc changes

* Update modeling_nystromformer.py

* Doc changes

* Fix copies

* Apply suggestions from code review

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* Apply suggestions from code review

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* Update configuration_nystromformer.py

* Fix copies

* Update tests/test_modeling_nystromformer.py

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* Update test_modeling_nystromformer.py

* Apply suggestions from code review

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Fix code style

* Update modeling_nystromformer.py

* Update modeling_nystromformer.py

* Fix code style

* Reformat modeling file

* Update modeling_nystromformer.py

* Modify NystromformerForMultipleChoice

* Fix code quality

* Apply suggestions from code review

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

* Code style changes and torch.no_grad()

* make style

* Apply suggestions from code review

Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/model_doc/nystromformer.mdx

@@ -0,0 +1,68 @@
+<!--Copyright 2022 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Nyströmformer
+
+## Overview
+
+The Nyströmformer model was proposed in [*Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention*](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn
+Fung, Yin Li, and Vikas Singh.
+
+The abstract from the paper is the following:
+
+*Transformers have emerged as a powerful tool for a broad range of natural language processing tasks. A key component
+that drives the impressive performance of Transformers is the self-attention mechanism that encodes the influence or
+dependence of other tokens on each specific token. While beneficial, the quadratic complexity of self-attention on the
+input sequence length has limited its application to longer sequences -- a topic being actively studied in the
+community. To address this limitation, we propose Nyströmformer -- a model that exhibits favorable scalability as a
+function of sequence length. Our idea is based on adapting the Nyström method to approximate standard self-attention
+with O(n) complexity. The scalability of Nyströmformer enables application to longer sequences with thousands of
+tokens. We perform evaluations on multiple downstream tasks on the GLUE benchmark and IMDB reviews with standard
+sequence length, and find that our Nyströmformer performs comparably, or in a few cases, even slightly better, than
+standard self-attention. On longer sequence tasks in the Long Range Arena (LRA) benchmark, Nyströmformer performs
+favorably relative to other efficient self-attention methods. Our code is available at this https URL.*
+
+This model was contributed by [novice03](https://huggingface.co/novice03). The original code can be found [here](https://github.com/mlpen/Nystromformer).
+
+## NystromformerConfig
+
+[[autodoc]] NystromformerConfig
+
+## NystromformerModel
+
+[[autodoc]] NystromformerModel
+    - forward
+
+## NystromformerForMaskedLM
+
+[[autodoc]] NystromformerForMaskedLM
+    - forward
+
+## NystromformerForSequenceClassification
+
+[[autodoc]] NystromformerForSequenceClassification
+    - forward
+
+## NystromformerForMultipleChoice
+
+[[autodoc]] NystromformerForMultipleChoice
+    - forward
+
+## NystromformerForTokenClassification
+
+[[autodoc]] NystromformerForTokenClassification
+    - forward
+
+## NystromformerForQuestionAnswering
+
+[[autodoc]] NystromformerForQuestionAnswering
+    - forward