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[WIP] Adding GPT-NeoX-20B (#16659)

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* initial

* first try

* working 20B

* 20B tokenizers

* Docs

* Import fixes for missing classes

* Update docs, fixup

* black formatting

* isort

* flake

* dummy objects

* documentation

* Documentation yml

* more docs

* tweaks for tests

* tokenization auto

* fix neox tests

* test

* test

* einsum

* address PR feedback

* Documentation

* Update README.md

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

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

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

* Update src/transformers/models/gpt_neox/configuration_gpt_neox.py

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

* Apply suggestions from code review

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

* Remove undefined LaTeX syntax

* Update to full url to avoid confusion about if that's supposed to refer to the Hub

* fix auto

* move tests

* documentation fix

* more doc fixes

* test refactor

* fix import

* fix import

* fix import

* fix import

* fix import

* style fixes

* More modeling fixes

Co-authored-by: Jason Phang <zp489@gr057.hpc.nyu.edu>
Co-authored-by: Stella Biderman <stellabiderman@gmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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# GPT-NeoX
## Overview
We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will
be made freely and openly available to the public through a permissive license. It is, to the best of our knowledge,
the largest dense autoregressive model that has publicly available weights at the time of submission. In this work,
we describe GPT-NeoX-20B's architecture and training and evaluate its performance on a range of language-understanding,
mathematics, and knowledge-based tasks. We find that GPT-NeoX-20B is a particularly powerful few-shot reasoner and
gains far more in performance when evaluated five-shot than similarly sized GPT-3 and FairSeq models. We open-source
the training and evaluation code, as well as the model weights, at [https://github.com/EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox).
Development of the model was led by Sid Black, Stella Biderman and Eric Hallahan, and the model was trained with
generous the support of [CoreWeave](https://www.coreweave.com/).
GPT-NeoX-20B was trained with fp16, thus it is recommended to initialize the model as follows:
```python
model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b").half().cuda()
```
GPT-NeoX-20B also has a different tokenizer from the one used in GPT-J-6B and GPT-Neo. The new tokenizer allocates
additional tokens to whitespace characters, making the model more suitable for certain tasks like code generation.
### Generation
The `generate()` method can be used to generate text using GPT Neo model.
```python
>>> from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast
>>> model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b")
>>> tokenizer = GPTNeoXTokenizerFast.from_pretrained("EleutherAI/gpt-neox-20b")
>>> prompt = "GPTNeoX20B is a 20B-parameter autoregressive Transformer model developed by EleutherAI."
>>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
>>> gen_tokens = model.generate(
... input_ids,
... do_sample=True,
... temperature=0.9,
... max_length=100,
... )
>>> gen_text = tokenizer.batch_decode(gen_tokens)[0]
```
## GPTNeoXConfig
[[autodoc]] GPTNeoXConfig
## GPTNeoXTokenizerFast
[[autodoc]] GPTNeoXTokenizerFast
## GPTNeoXModel
[[autodoc]] GPTNeoXModel
- forward
## GPTNeoXForCausalLM
[[autodoc]] GPTNeoXForCausalLM
- forward