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tinygrad 0.9.0

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@wozeparrot wozeparrot released this 28 May 18:48
· 716 commits to master since this release
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Close to the new line limit of 8000 lines, sitting at 7958 lines.
tinygrad is much more usable now.

Just over 1200 commits since 0.8.0.

Release Highlights

  • New documentation: https://docs.tinygrad.org
  • gpuctypes has been brought in tree and is no longer an external dependency. [#3253]
  • AMD=1 and NV=1 experimental backends for not requiring any userspace runtime components like ROCm or CUDA.
    • These backends should reduce the amount of python time, and specifically with multi-gpu use cases.
  • PTX=1 for rendering directly to ptx instead of cuda. [#3139] [#3623] [#3775]
  • Nvidia tensor core support. [#3544]
  • THREEFRY=1 for numpy-less random number generation using threefry2x32. [#2601] [#3785]
  • More stabilized multi-tensor API.
  • Core tinygrad has been refactored into 4 pieces, read more about it here.
  • Linearizer and codegen has support for generating kernels with multiple outputs.
  • Lots of progress towards greater kernel fusion in the scheduler.
    • Fusing of ReduceOps with their elementwise children. This trains mnist and gpt2 with ~20% less kernels and makes llama inference faster.
    • New LoadOps.ASSIGN allows fusing optimizer updates with grad.
    • Schedule kernels in BFS order. This improves resnet and llama speed.
    • W.I.P. for fusing multiple reduces: [#4259] [#4208]
  • MLPerf ResNet and BERT with a W.I.P. UNet3D
  • Llama 3 support with a new llama3.py that provides an OpenAI compatible API. [#4576]
  • NF4 quantization support in Llama examples. [#4540]
  • label_smoothing has been added to sparse_categorical_crossentropy. [#3568]

Known Issues

  • Using tinygrad in a conda env on macOS is known to cause problems with the METAL backend. See #2226.

See the full changelog: v0.8.0...v0.9.0

See the known issues: https://github.com/tinygrad/tinygrad/issues?q=is%3Aissue+is%3Aopen+label%3Abug+sort%3Aupdated-desc

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