Add Switch transformers (#19323)
* first commit * add more comments * add router v1 * clean up - remove `tf` modeling files * clean up - remove `tf` modeling files * clean up * v0 routers * added more router - Implemented `ExpertsChooseMaskedRouter` - added tests - 2 more routers to implement * last router * improved docstring - completed the docstring in `router.py` - added more args in the config * v0 sparse mlp * replace wrong naming * forward pass run * update MOE layer * small router update * fixup * consistency * remove scatter router * remove abstract layer * update test and model for integration testing * v1 conversion * update * hardcode hack * all keys match * add gin conversion, without additional libraries * update conversion sctipy * delete router file * update tests wrt router deletion * fix router issues * update expert code * update, logits match, code needsREFACTORING * Refactor code Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com> * add generate tests Co-authored-by: younesbelkada <younesbelkada@gmail.com> * add support for router loss Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com> * fix forward error * refactor a bit * remove `FlaxSwitchTransformers` modules * more tests pass * Update code Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com> * fixup * fix tests * fix doc * fix doc + tokenization * fix tokenizer test * fix test * fix loss output * update code for backward pass * add loss support * update documentation * fix documentation, clean tokenizer * more doc fix, cleanup example_switch * fix failing test * fix test * fix test * fix loss issue * move layer * update doc and fix router capacity usage * fixup * add sparse mlp index for documentation on hub * fixup * test sparse mix architecture * Apply suggestions from code review * Update docs/source/en/model_doc/switch_transformers.mdx * fixup on update * fix tests * fix another test * attempt fix * Update src/transformers/models/switch_transformers/configuration_switch_transformers.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/switch_transformers/convert_switch_transformers_original_flax_checkpoint_to_pytorch.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * try * all tests pass * fix jitter noise * Apply suggestions from code review * doc tests pass * Update src/transformers/models/switch_transformers/modeling_switch_transformers.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/switch_transformers/modeling_switch_transformers.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * remove assert * change config order * fix readme japanese * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * remove parallelizable tests + add one liners * remove ONNX config * fix nits - add `T5Tokenizer` in auto mapping - remove `Switch Transformers` from ONNX supported models * remove `_get_router` * remove asserts * add check in test for `router_dtype` * add `SwitchTransformersConfig` in `run_pipeline_test` * Update tests/pipelines/test_pipelines_summarization.py * add huge model conversion script * fix slow tests - add better casting for `Linear8bitLt` - remove `torchscript` tests * add make dir * style on new script * fix nits - doctest - remove `_keys_to_ignore_on_load_unexpected` * Update src/transformers/models/switch_transformers/configuration_switch_transformers.py * add google as authors * fix year * remove last `assert` statements * standardize vertical spaces * fix failing import * fix another failing test * Remove strange àuthorized_keys` * removing todo and padding that is never used Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com> Co-authored-by: ybelkada <younes@huggingface.co> Co-authored-by: Younes Belkada <younesbelkada@users.noreply.github.com> Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Arthur Zucker <arthur@huggingface.co>
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Younes Belkada
@@ -351,6 +351,8 @@
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title: Splinter
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- local: model_doc/squeezebert
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title: SqueezeBERT
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- local: model_doc/switch_transformers
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title: SwitchTransformers
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- local: model_doc/t5
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title: T5
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- local: model_doc/t5v1.1
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@@ -167,6 +167,7 @@ The documentation is organized into five sections:
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1. **[SqueezeBERT](model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
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1. **[Swin Transformer](model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
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1. **[Swin Transformer V2](model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
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1. **[SwitchTransformers](model_doc/switch_transformers)** (from Google) released with the paper [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961) by William Fedus, Barret Zoph, Noam Shazeer.
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1. **[T5](model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
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1. **[T5v1.1](model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
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1. **[Table Transformer](model_doc/table-transformer)** (from Microsoft Research) released with the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Brandon Smock, Rohith Pesala, Robin Abraham.
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@@ -322,6 +323,7 @@ Flax), PyTorch, and/or TensorFlow.
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| SqueezeBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
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| Swin Transformer | ❌ | ❌ | ✅ | ✅ | ❌ |
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| Swin Transformer V2 | ❌ | ❌ | ✅ | ❌ | ❌ |
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| SwitchTransformers | ❌ | ❌ | ✅ | ❌ | ❌ |
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| T5 | ✅ | ✅ | ✅ | ✅ | ✅ |
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| Table Transformer | ❌ | ❌ | ✅ | ❌ | ❌ |
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| TAPAS | ✅ | ❌ | ✅ | ✅ | ❌ |
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64
docs/source/en/model_doc/switch_transformers.mdx
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docs/source/en/model_doc/switch_transformers.mdx
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<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# SwitchTransformers
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## Overview
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The SwitchTransformers model was proposed in [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961) by William Fedus, Barret Zoph, Noam Shazeer.
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The Switch Transformer model uses a sparse T5 encoder-decoder architecure, where the MLP are replaced by a Mixture of Experts (MoE). A routing mechanism (top 1 in this case) associates each token to one of the expert, where each expert is a dense MLP. While switch transformers have a lot more weights than their equivalent dense models, the sparsity allows better scaling and better finetuning performance at scale.
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During a forward pass, only a fraction of the weights are used. The routing mecanism allows the model to select relevant weights on the fly which increases the model capacity without increasing the number of operations.
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The abstract from the paper is the following:
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*In deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) defies this and instead selects different parameters for each incoming example. The result is a sparsely-activated model -- with outrageous numbers of parameters -- but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs and training instability -- we address these with the Switch Transformer. We simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. Our proposed training techniques help wrangle the instabilities and we show large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. We design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings where we measure gains over the mT5-Base version across all 101 languages. Finally, we advance the current scale of language models by pre-training up to trillion parameter models on the "Colossal Clean Crawled Corpus" and achieve a 4x speedup over the T5-XXL model.*
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Tips:
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- SwitchTransformers uses the [`T5Tokenizer`], which can be loaded directly from each model's repository.
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- The released weights are pretrained on English [Masked Language Modeling](https://moon-ci-docs.huggingface.co/docs/transformers/pr_19323/en/glossary#general-terms) task, and should be finetuned.
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This model was contributed by [Younes Belkada](https://huggingface.co/ybelkada) and [Arthur Zucker](https://huggingface.co/ArtZucker) .
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The original code can be found [here](https://github.com/google/flaxformer/tree/main/flaxformer/architectures/moe).
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## SwitchTransformersConfig
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[[autodoc]] SwitchTransformersConfig
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## SwitchTransformersTop1Router
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[[autodoc]] SwitchTransformersTop1Router
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- _compute_router_probabilities
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- forward
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## SwitchTransformersSparseMLP
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[[autodoc]] SwitchTransformersSparseMLP
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- forward
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## SwitchTransformersModel
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[[autodoc]] SwitchTransformersModel
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- forward
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## SwitchTransformersForConditionalGeneration
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[[autodoc]] SwitchTransformersForConditionalGeneration
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- forward
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## SwitchTransformersEncoderModel
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[[autodoc]] SwitchTransformersEncoderModel
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- forward
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