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BladeDISC is an end-to-end DynamIc Shape Compiler project for machine learning workloads.
| Date | Stars |
|---|---|
| 2026-07-24 | 932 |
| 2026-07-25 | 932 |
| 2026-07-28 | 932 |
| 2026-07-30 | 932 |
| 2026-07-31 | 931 |
| 2026-08-06 | 931 |
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# BladeDISC Introduction <!-- omit in toc -->
## We're hiring!🔥🔥🔥
We're always looking for candicates to join dev team. Your're the one we're searching for long:
* 🥷 If you are an compiler or AI enthusiasts.
* ⭐️ or if you are experienced in optimization on CPUs and GPUs.
* ⚙️ or if you wanna build an unified and automated compiler to optimize both inference and training workload.
* 🤿 or if you are using BladeDISC in production or research projects, and wanna have a deeper dive into it.
* ✄ or you wanna build cutting-edge infrastructure in the AIGC era.
Please contact us via email or Dingtalk at the bottom of page.⬇️⬇️⬇️
- [What's New](#whats-new)
- [Overview](#overview)
- [Features and Roadmap](#features-and-roadmap)
- [Frontend Framework Support Matrix](#frontend-framework-support-matrix)
- [Backend Support Matrix](#backend-support-matrix)
- [Deployment Solutions](#deployment-solutions)
- [Numbers of Typical Workloads](#numbers-of-typical-workloads)
- [Advantage in Dynamic Shape Workloads](#advantage-in-dynamic-shape-workloads)
- [API QuickView](#api-quickview)
- [For TensorFlow Users](#for-tensorflow-users)
- [For PyTorch Users](#for-pytorch-users)
- [Setup and Examples](#setup-and-examples)
- [Publications](#publications)
- [Tutorials and Documents for Developers](#tutorials-and-documents-for-developers)
- [Presentations and Talks](#presentations-and-talks)
- [How to Contribute](#how-to-contribute)
- [Building Status](#building-status)
- [FAQ](#faq)
- [Roadmap with mlir-hlo Project](#roadmap-with-mlir-hlo-project)
- [Roadmap with Torch-MLIR Project](#roadmap-with-torch-mlir-project)
- [Contact Us](#contact-us)
## What's New
+ [🔥 2023.03.17] BladeDISC v0.4.0: [Massive performance and feature updates](https://github.com/alibaba/BladeDISC/releases/tag/v0.4.0)
+ [2022.12.08] BladeDISC v0.3.0:
[Announce PyTorch 2.0 Compilation Support](https://github.com/alibaba/BladeDISC/releases/tag/v0.3.0)
## Overview
BladeDISC is an end-to-end **DynamIc Shape Compiler** project for machine
learning workloads, which is one of the key components of Alibaba's
[PAI-Blade](https://www.aliyun.com/activity/bigdata/blade). BladeDISC provides
general, transparent, and ease-of-use performance optimization for
TensorFlow/PyTorch workloads on GPGPU and CPU backends. The architecture
natively supports dynamic shape workloads, with many considerations in the
performance of both static and dynamic shape scenarios. It also supports
multiple and flexible deployment solutions, including both Plugin Mode inside
TensorFlow/PyTorch runtime, and Standalone Mode for AOT standalone execution.
The project is based on [MLIR](https://mlir.llvm.org/) and highly related to
[mlir-hlo](https://github.com/tensorflow/mlir-hlo) project.
Refer to [our website](https://alibaba.github.io/BladeDISC/) for more
information, including the setup tutorial, developer guide, demo examples and
documents for developers.
### Features and Roadmap
#### Frontend Framework Support Matrix
| | TensorFlow [1] | PyTorch [2] |
|---------- | -------------- | ------------ |
| Inference | Yes | Yes |
| Training | Yes [3] | Ongoing |
[1] TensorFlow 1.12, 1.15, 2.4 & 2.5 are supported and fully verified. For other
versions, some slight work on adaptation might be needed.
[2] PyTorch version >= 1.6.0 has been fully verified.
[3] Although supported, there's much room for improvement on Op coverage for
training workloads.
#### Backend Support Matrix
| | Status |
|----------- | ------------- |
| Nvidia GPU | Yes [1] |
| AMD GPU | Yes |
| Hygon DCU | Yes |
| X86 | Yes |
| AArch64 | Yes |
[1] Support for CUDA below 11.0 has been deprecated officially since Aug 2022.
#### Deployment Solutions
* Plugin Mode - BladeDISC works as a plugin of TensorFlow or PyTorch. Only the
supported Ops are clustered and compiled, and the unsupported ones wilExcerpt of 12,894 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:923768eccb2351f6, topic:deep-learning, topic:neural-network, topic:pytorch