Add DeepSeek V2 Model into Transformers (#36400)

* add initial structure

* doc fixes, add model base logic

* update init files

* some fixes to config and modular

* some improvements for attention

* format

* remove unused attn

* some fixes for moe layer and for decoder

* adapt _compute_yarn_parameters for deepseek

* format

* small fix

* fix for decoder forward

* add tests, small refactoring

* fix dummies

* fix init

* fix doc

* fix config docs

* add sequce doc, fix init for gate

* fix issues in tests

* fix config doc

* remove unused args

* some fixes and refactoring after review

* fix doc for config

* small fixes for config args

* revert config refactoring

* small refactoring

* minor fixes after rebase

* small fix after merge

* fix modular

* remove rotaryembd from public init

* small test fix

* some rotary pos calculation improvement

* fix format

* some improvements and fixes

* fix config

* some refactoring

* adjust some unit tests

* skip test

* small fixes and tests adjustment

* reapply modular

* fix all tests except Integration

* fix integration testzs

* cleanup BC stuff

* rope

* fix integrations tests based on a10

* style

---------

Co-authored-by: Cyril Vallez <cyril.vallez@huggingface.co>
Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>

docs/source/en/model_doc/deepseek_v2.md

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+
+# DeepSeek-V2
+
+## Overview
+
+The DeepSeek-V2 model was proposed in [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://arxiv.org/abs/2405.04434) by DeepSeek-AI Team.
+
+The abstract from the paper is the following:
+We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation. Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unlock its potential. Evaluation results show that, even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models.
+
+This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber).
+The original code can be found [here](https://huggingface.co/deepseek-ai/DeepSeek-V2).
+
+### Usage tips
+The model uses Multi-head Latent Attention (MLA) and DeepSeekMoE architectures for efficient inference and cost-effective training. It employs an auxiliary-loss-free strategy for load balancing and multi-token prediction training objective. The model can be used for various language tasks after being pre-trained on 14.8 trillion tokens and going through Supervised Fine-Tuning and Reinforcement Learning stages.
+
+## DeepseekV2Config
+
+[[autodoc]] DeepseekV2Config
+
+## DeepseekV2Model
+
+[[autodoc]] DeepseekV2Model
+    - forward
+
+## DeepseekV2ForCausalLM
+
+[[autodoc]] DeepseekV2ForCausalLM
+    - forward
+
+## DeepseekV2ForSequenceClassification
+
+[[autodoc]] DeepseekV2ForSequenceClassification
+    - forward