This is a technical preview for oneDNN Graph API based on oneDNN v2.3.2.
oneDNN Graph API extends oneDNN with a unified, high-level graph API for multiple AI hardware classes (CPU, GPU, accelerators). The graph interface integrates with the deep learning frameworks and inference engines to maximize opportunities for performance optimizations across a variety of hardware targets. This preview has full support for the oneAPI Graph programming model and partial support of the operations in oneDNN Graph API specification v0.7.
Learn more about oneDNN Graph API:
Supported Functionality
- C++ and DPC++ API.
- Graph partition and compilation API.
- Operations and fusions targeting fp32 inference for CNNs, MLPs, and transformer neural networks.
Performance Optimizations
Backend implementation relies on oneDNN and includes performance optimizations for Intel Architecture processors with Intel SSE4.1, Intel AVX, Intel AVX2, or Intel AVX512 instruction set.
Validation
- Gtest suite is available for basic functional testing.
- Comprehensive functional and performance validation is covered by the extended version of benchdnn.
Known Issues and Limitations
- Some subgraphs might not be recognized as a partition even if it matches the general pattern description due to internal implementation.
- The weight’s opaque layout can be queried only from a compiled partition, which requires that tensor shapes must be known at compilation time.
- Binary operation with scalar and tensor inputs is not optimized.
Thanks to the Contributors
This release contains contributions from the project core teams as well as Jiong Gong, Pinzhen Xu, Chunyuan Wu, Jianping Chen, Scott Cyphers, Nishant Patel, Yiqiang Li, Yang Sheng, Kiefer Kuah, Adam Straw, Tim Zerrell, Namrata Choudhury and others.