github LeelaChessZero/lc0 v0.31.0-rc1

latest release: v0.31.0-rc2
pre-release28 days ago

In this version:

  • The blas, cuda, eigen, metal and onnx backends now have support for multihead network architecture and can run BT3/BT4 nets.
  • Updated the internal Elo model to better align with regular Elo for human players.
  • There is a new XLA backend that uses OpenXLA compiler to produce code to execute the neural network. See https://github.com/LeelaChessZero/lc0/wiki/XLA-backend for details. Related are new leela2onnx options to output the HLO format that XLA understands.
  • There is a vastly simplified lc0 interface available by renaming the executable to lc0simple.
  • The backends can now suggest a minibatch size to the search, this is enabled by --minibatch-size=0 (the new default).
  • If the cudnn backend detected an unsupported network architecture it will switch to the cuda backend.
  • Two new selfplay options enable value and policy tournaments. A policy tournament is using a single node policy to select the move to play, while a value tournament searches all possible moves at depth 1 to select the one with the best q.
  • While it is easy to get a single node policy evaluation (go nodes 1 using uci), there was no simple way to get the effect of a value only evaluation, so the --value-only option was added.
  • Button uci options were implemented and a button to clear the tree was added (as hidden option).
  • Support for the uci go mate option was added.
  • The rescorer can now be built from the lc0 code base instead of a separate branch.
  • A dicrete onnx layernorm implementation was added to get around a onnxruntime bug with directml - this has some overhead so it is only enabled for onnx-dml and can be switched off with the alt_layernorm=false backend option.
  • The --onnx2pytoch option was added to leela2onnx to generate pytorch compatible models.
  • There is a cuda min_batch backend option to reduce non-determinism with small batches.
  • New options were added to onnx2leela to fix tf exported onnx models.
  • The onnx backend can now be built for amd's rocm.
  • Fixed a bug where the Contempt effect on eval was too low for nets with natively higher draw rates.
  • Made the WDL Rescale sharpness limit configurable via the --wdl-max-s hidden option.
  • The search task workers can be set automatically, to either 0 for cpu backends or up to 4 depending on the number of cpu cores. This is enabled by --task-workers=-1 (the new default).
  • Several assorted fixes and code cleanups.

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