[WIP]NLLB-MoE Adds the moe model (#22024)

* Initial commit * update modeling code * update doc * add functions necessary * fix impotrs * revert changes * fixup * more styling to get going * remove standalone encoder * update code * styling * fix config and model * update code and some refactoring * make more tests pass * Adding NLLB-200 - MoE - 54.5B for no language left behind Fixes #21300 * fix mor common tests * styke * update testing file * update * update * Router2 doc * update check config with sparse layer * add dummy router * update current conversion script * create on the fly conversion script * Fixup * style * style 2 * fix empty return * fix return * Update default config sparse layers * easier to create sparse layers * update * update conversion script * update modeling * add to toctree * styling * make ruff happy * update docstring * update conversion script * update, will break tests but impelemting top2 * update * ❗local groups are supported here * ⚠️ Support for local groups is now removed ⚠️ This is because it has to work with model parallelism that we do not support * finish simplificaiton * Fix forward * style * fixup * Update modelling and test, refactoring * update tests * remove final layer)norm as it is done in the FF * routing works! Logits test added * nit in test * remove top1router * style * make sure sparse are tested. Had to change route_tokens a liottle bit * add support for unslip models when converting * fixup * style * update test s * update test * REFACTOR * encoder outputs match! * style * update testing * 🎉encoder and decoder logits match 🎉 * styleing * update tests * cleanup tests * fix router test and CIs * cleanup * cleanup test styling * fix tests * Finally the generation tests match! * cleanup * update test * style testing file * remove script * cleanup * more cleanup * nits * update * NLLB tokenizer is wrong and will be fixed soon * use LongTensors * update tests * revert some small changes * fix second expert sampling and batch prioritized routing * update tests * finish last tests * make ruff happy * update * ruff again * style * Update docs/source/en/model_doc/nllb-moe.mdx Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Updates based on review * style and fix import issue * nit * more nits * cleanup * styling * update test_seconde_expert_policy * fix name * last nit on the markdown examples --------- Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
2023-03-27 19:42:00 +02:00
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@@ -380,6 +380,7 @@ Número actual de puntos de control: ![](https://img.shields.io/endpoint?url=htt
 1. **[NAT](https://huggingface.co/docs/transformers/model_doc/nat)** (from SHI Labs) released with the paper [Neighborhood Attention Transformer](https://arxiv.org/abs/2204.07143) by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.
 1. **[Nezha](https://huggingface.co/docs/transformers/model_doc/nezha)** (from Huawei Noah’s Ark Lab) released with the paper [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) by Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
 1. **[NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
+1. **[NLLB-MOE](https://huggingface.co/docs/transformers/main/model_doc/nllb-moe)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
 1. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [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, Vikas Singh.
 1. **[OneFormer](https://huggingface.co/docs/transformers/model_doc/oneformer)** (from SHI Labs) released with the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi.
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.