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apache / tvm / b40a02c265ad029a6dec2eef808b48945e39c31b
commitb40a02c265ad029a6dec2eef808b48945e39c31b[log] [tgz]
authorSiyuan Feng <Hzfengsy@sjtu.edu.cn>Fri Aug 09 21:44:14 2024 +0800
committerGitHub <noreply@github.com>Fri Aug 09 09:44:14 2024 -0400
tree5d9cb40ac5344eee312d6b03ce9509e14d8acd8a
parent77391714ab714afcc849fde1378a5a0c62d99c2e [diff]
[Relax] Add KVCache Interface for Relax NNModule (#17261)

Introduce kv cache interface for Relax NNModule to support paged attention.
Note that the implementation is migrated from MLC-llm

Co-authored-by: Bohan Hou <bohanhou@andrew.cmu.edu>
Co-authored-by: Ruihang Lai <ruihangl@cs.cmu.edu>
Co-authored-by: Hongyi Jin <hongyij@andrew.cmu.edu>
Co-authored-by: krishnaraj36 <quic_kvegiraj@quicinc.com>
5 files changed
tree: 5d9cb40ac5344eee312d6b03ce9509e14d8acd8a
  1. .github/
  2. 3rdparty/
  3. apps/
  4. ci/
  5. cmake/
  6. conda/
  7. configs/
  8. docker/
  9. docs/
  10. gallery/
  11. golang/
  12. include/
  13. jvm/
  14. licenses/
  15. python/
  16. rust/
  17. src/
  18. tests/
  19. vta/
  20. web/
  21. .asf.yaml
  22. .clang-format
  23. .gitattributes
  24. .gitignore
  25. .gitmodules
  26. .pre-commit-config.yaml
  27. CMakeLists.txt
  28. conftest.py
  29. CONTRIBUTORS.md
  30. KEYS
  31. LICENSE
  32. Makefile
  33. mypy.ini
  34. NEWS.md
  35. NOTICE
  36. pyproject.toml
  37. README.md
  38. version.py
README.md

Open Deep Learning Compiler Stack

Documentation | Contributors | Community | Release Notes

Build Status WinMacBuild

Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends.

License

TVM is licensed under the Apache-2.0 license.

Getting Started

Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM tutorial is a great place to start.

Contribute to TVM

TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Check out the Contributor Guide.

Acknowledgement

We learned a lot from the following projects when building TVM.

  • Halide: Part of TVM's TIR and arithmetic simplification module originates from Halide. We also learned and adapted some part of lowering pipeline from Halide.
  • Loopy: use of integer set analysis and its loop transformation primitives.
  • Theano: the design inspiration of symbolic scan operator for recurrence.