base on # :robot: MegaBlocks MegaBlocks is a light-weight library for mixture-of-experts (MoE) training. The core of the system is efficient "dropless-MoE" ([dMoE](megablocks/layers/dmoe.py), [paper](https://arxiv.org/abs/2211.15841)) and standard [MoE](megablocks/layers/moe.py) layers. MegaBlocks is integrated with [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), where we support data, expert and pipeline parallel training of MoEs. Stay tuned for tighter integration with Databricks libraries and tools! # :rocket: Performance  MegaBlocks dMoEs outperform MoEs trained with [Tutel](https://github.com/microsoft/tutel) by up to **40%** compared to Tutel's best performing `capacity_factor` configuration. MegaBlocks dMoEs use a reformulation of MoEs in terms of block-sparse operations, which allows us to avoid token dropping without sacrificing hardware efficiency. In addition to being faster, MegaBlocks simplifies MoE training by removing the `capacity_factor` hyperparameter altogether. Compared to dense Transformers trained with [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), MegaBlocks dMoEs can accelerate training by as much as **2.4x**. Check out our [paper](https://arxiv.org/abs/2211.15841) for more details! # :building_construction: Installation NOTE: This assumes you have `numpy` and `torch` installed. **Training models with Megatron-LM:** We recommend using NGC's [`nvcr.io/nvidia/pytorch:23.09-py3`](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch/tags) PyTorch container. The [Dockerfile](Dockerfile) builds on this image with additional dependencies. To build the image, run `docker build . -t megablocks-dev` and then `bash docker.sh` to launch the container. Once inside the container, install MegaBlocks with `pip install .`. See [Usage](#steam_locomotive-usage) for instructions on training MoEs with MegaBlocks + Megatron-LM. **Using MegaBlocks in other packages:** To install the MegaBlocks package for use in other frameworks, run `pip install megablocks`. For example, [Mixtral-8x7B](https://mistral.ai/news/mixtral-of-experts/) can be run with [vLLM](https://github.com/vllm-project/vllm) + MegaBlocks with this installation method. **Extras:** MegaBlocks has optional dependencies that enable additional features. Installing `megablocks[gg]` enables dMoE computation with grouped GEMM. This feature is enabled by setting the `mlp_impl` argument to `grouped`. This is currently our recommended path for Hopper-generation GPUs. Installing `megablocks[dev]` allows you to contribute to MegaBlocks and test locally. Installing `megablocks[testing]` allows you to test via Github Actions. If you've installed megablocks[dev], you can run pre-commit install to configure the pre-commit hook to automatically format the code. MegaBlocks can be installed with all dependencies (except for `testing`) via the `megablocks[all]` package. # :steam_locomotive: Usage We provide scripts for pre-training Transformer MoE and dMoE language models under the [top-level directory](megablocks/). The quickest way to get started is to use one of the [experiment launch scripts](exp/). These scripts require a dataset in Megatron-LM's format, which can be created by following their [instructions](https://github.com/NVIDIA/Megatron-LM#data-preprocessing). # :writing_hand: Citation ``` @article{megablocks, title={{MegaBlocks: Efficient Sparse Training with Mixture-of-Experts}}, author={Trevor Gale and Deepak Narayanan and Cliff Young and Matei Zaharia}, journal={Proceedings of Machine Learning and Systems}, volume={5}, year={2023} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"5802","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"