SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Add recurrent gemma (#30143)

Browse Source 
* Fork.

* RecurrentGemma initial commit.

* Updating __init__.py.

* Minor modification to how we initialize the cache.
Changing how the config specifies the architecture.

* Reformat code to 4 spaces.
Fixed a few typos.

* Fixed the forward pass.
Still unclear on the cache?

* Fixed the RecurrentGemmaForCausalLM

* Minor comment that we might not need attention_mask and output_attention arguments.

* Now cache should work as well.

* Adding a temporary example to check whether the model generation works.

* Adding the tests and updating imports.

* Adding the example file missing in the previous commit.

* First working example.

* Removing .gitignore and reverting parts of __init__.

* Re-add .gitignore.

* Addressing comments for configuration.

* Move mask creation to `_prepare_inputs_for_generation`.

* First try at integration tests:
1. AttributeError: 'GriffinCausalLMOutput' object has no attribute 'attentions'.
2. `cache_position` not passed

* Transfoering between machines.

* Running normal tests.

* Minor fix.

* More fixes.

* Addressing more comments.

* Minor fixes.

* first stab at cleanup

* more refactoring

* fix copies and else

* renaming and get init to work

* fix causal mask creation

* update

* nit

* fix a hell lot of things

* updates

* update conversion script

* make all keys importable

* nits

* add auto mappings

* properly convert ffw_up and down

* add scaling

* fix generations

* for recurrent dtype

* update

* fix going beyong window

* fixup

* add missing files

* current updates to remove last einops

* finish modeling refactor

* TADA

* fix compile

* fix most failing testt ? ?

* update tests

* refactor and update

* update

* nits, fixup and update tests

* more fixup

* nits

* fix imports

* test format

* fixups

* nits

* tuple typing

* fix code quality

* add model card

* fix doc

* skip most generation tests

* nits

* style

* doc fixes

* fix pr and check_copies?

* last nit

* oupsy

* Apply suggestions from code review

Co-authored-by: Lysandre Debut <hi@lysand.re>

* update

* Update src/transformers/models/recurrent_gemma/convert_recurrent_gemma_to_hf.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/models/recurrent_gemma/test_modeling_recurrent_gemma.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* update based on review

* doc nit

* fix quality

* quality

* fix slow test model path

* update default dype

* ignore attributes that can be safely ignored in check config attributes

* 0lallalala come on

* save nit

* style

* remove to dict update

* make sure we can also run in float16

* style

---------

Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>
Co-authored-by: Aleksandar Botev <botev@google.com>
Co-authored-by: Leonard Berrada <lberrada@users.noreply.github.com>
Co-authored-by: anushanf <anushanf@google.com>
Co-authored-by: botev <botevmg@gmail.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
This commit is contained in:
Arthur
2024-04-10 16:59:13 +02:00
committed by GitHub
parent 33bca5419c
commit 0fe44059ae
32 changed files with 2001 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
docs/source/en/_toctree.yml
View File

@@ -468,6 +468,8 @@
title: RAG
- local: model_doc/realm
title: REALM
- local: model_doc/recurrent_gemma
title: RecurrentGemma
- local: model_doc/reformer
title: Reformer
- local: model_doc/rembert

1
docs/source/en/index.md
View File

@@ -243,6 +243,7 @@ Flax), PyTorch, and/or TensorFlow.
| [Qwen2MoE](model_doc/qwen2_moe) | ✅ | ❌ | ❌ |
| [RAG](model_doc/rag) | ✅ | ✅ | ❌ |
| [REALM](model_doc/realm) | ✅ | ❌ | ❌ |
| [RecurrentGemma](model_doc/recurrent_gemma) | ✅ | ❌ | ❌ |
| [Reformer](model_doc/reformer) | ✅ | ❌ | ❌ |
| [RegNet](model_doc/regnet) | ✅ | ✅ | ✅ |
| [RemBERT](model_doc/rembert) | ✅ | ✅ | ❌ |

48
docs/source/en/model_doc/recurrent_gemma.md Normal file
View File

@@ -0,0 +1,48 @@
<!--Copyright 2024 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.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# RecurrentGemma
## Overview
The Recurrent Gemma model was proposed in [RecurrentGemma: Moving Past Transformers for Efficient Open Language Models](https://storage.googleapis.com/deepmind-media/gemma/recurrentgemma-report.pdf) by the Griffin, RLHF and Gemma Teams of Google.
The abstract from the paper is the following:
*We introduce RecurrentGemma, an open language model which uses Google’s novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide a pre-trained model with 2B non-embedding parameters, and an instruction tuned variant. Both models achieve comparable performance to Gemma-2B despite being trained on fewer tokens.*
Tips:
- The original checkpoints can be converted using the conversion script `src/transformers/models/recurrent_gemma/convert_recurrent_gemma_weights_to_hf.py`
This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ).
## RecurrentGemmaConfig
[[autodoc]] RecurrentGemmaConfig
## RecurrentGemmaModel
[[autodoc]] RecurrentGemmaModel
- forward
## RecurrentGemmaForCausalLM
[[autodoc]] RecurrentGemmaForCausalLM
- forward

2
docs/source/en/tasks/language_modeling.md
View File

@@ -37,7 +37,7 @@ You can finetune other architectures for causal language modeling following the
Choose one of the following architectures:
<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
[BART](../model_doc/bart), [BERT](../model_doc/bert), [Bert Generation](../model_doc/bert-generation), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CodeLlama](../model_doc/code_llama), [CodeGen](../model_doc/codegen), [Cohere](../model_doc/cohere), [CPM-Ant](../model_doc/cpmant), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [Falcon](../model_doc/falcon), [Fuyu](../model_doc/fuyu), [Gemma](../model_doc/gemma), [GIT](../model_doc/git), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT NeoX Japanese](../model_doc/gpt_neox_japanese), [GPT-J](../model_doc/gptj), [LLaMA](../model_doc/llama), [Mamba](../model_doc/mamba), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [Mistral](../model_doc/mistral), [Mixtral](../model_doc/mixtral), [MPT](../model_doc/mpt), [MusicGen](../model_doc/musicgen), [MusicGen Melody](../model_doc/musicgen_melody), [MVP](../model_doc/mvp), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Pegasus](../model_doc/pegasus), [Persimmon](../model_doc/persimmon), [Phi](../model_doc/phi), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [QDQBert](../model_doc/qdqbert), [Qwen2](../model_doc/qwen2), [Qwen2MoE](../model_doc/qwen2_moe), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [RWKV](../model_doc/rwkv), [Speech2Text2](../model_doc/speech_to_text_2), [StableLm](../model_doc/stablelm), [Starcoder2](../model_doc/starcoder2), [Transformer-XL](../model_doc/transfo-xl), [TrOCR](../model_doc/trocr), [Whisper](../model_doc/whisper), [XGLM](../model_doc/xglm), [XLM](../model_doc/xlm), [XLM-ProphetNet](../model_doc/xlm-prophetnet), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod)
[BART](../model_doc/bart), [BERT](../model_doc/bert), [Bert Generation](../model_doc/bert-generation), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CodeLlama](../model_doc/code_llama), [CodeGen](../model_doc/codegen), [Cohere](../model_doc/cohere), [CPM-Ant](../model_doc/cpmant), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [Falcon](../model_doc/falcon), [Fuyu](../model_doc/fuyu), [Gemma](../model_doc/gemma), [GIT](../model_doc/git), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT NeoX Japanese](../model_doc/gpt_neox_japanese), [GPT-J](../model_doc/gptj), [LLaMA](../model_doc/llama), [Mamba](../model_doc/mamba), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [Mistral](../model_doc/mistral), [Mixtral](../model_doc/mixtral), [MPT](../model_doc/mpt), [MusicGen](../model_doc/musicgen), [MusicGen Melody](../model_doc/musicgen_melody), [MVP](../model_doc/mvp), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Pegasus](../model_doc/pegasus), [Persimmon](../model_doc/persimmon), [Phi](../model_doc/phi), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [QDQBert](../model_doc/qdqbert), [Qwen2](../model_doc/qwen2), [Qwen2MoE](../model_doc/qwen2_moe), [RecurrentGemma](../model_doc/recurrent_gemma), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [RWKV](../model_doc/rwkv), [Speech2Text2](../model_doc/speech_to_text_2), [StableLm](../model_doc/stablelm), [Starcoder2](../model_doc/starcoder2), [Transformer-XL](../model_doc/transfo-xl), [TrOCR](../model_doc/trocr), [Whisper](../model_doc/whisper), [XGLM](../model_doc/xglm), [XLM](../model_doc/xlm), [XLM-ProphetNet](../model_doc/xlm-prophetnet), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod)