v0.31.2

In this version:

• Updated the WDL_mu centipawn fallback.
• Fix for build issues with newer Linux c++ libraries.
• Fix for an XLA Mish bug.
• Minor README.md update.

v0.31.1

In this version:

• Make WDL_mu score type work as intended.
• Fix macos CI builds.

v0.31.0

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 evalaution, 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).
• Changed cuda compilation options to use -arch=native or -arch=all-major if no specific version is requested, with fallback for older cuda that don't support those options.
• Updated android builds to use openblas 0.3.27.
• The WDLDrawRateTarget option now accepts the value 0 (new default) to retain raw WDL values if WDLCalibrationElo is set to 0 (default).
• Improvements to the verbose move stats if `WDLEvalObjectivity is used.
• The centipawn score is displayed by default for old nets without WDL output.
• Several assorted fixes and code cleanups.

v0.31.0-rc3

In this version:

• The WDLDrawRateTarget option now accepts the value 0 (new default) to retain raw WDL values if WDLCalibrationElo is set to 0 (default).
• Improvements to the verbose move stats if `WDLEvalObjectivity is used.
• The centipawn score is displayed by default for old nets without WDL output.
• Some build system improvements.

v0.31.0-rc2

In this version:

• Changed cuda compilation options to use -arch=native or -arch=all-major if no specific version is requested, with fallback for older cuda that don't support those options.
• Updated android builds to use openblas 0.3.27.
• A few small fixes.

v0.31.0-rc1

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.

v0.30.0

In this version:

• Support for networks with attention body and smolgen added to blas, cuda, metal and onnx backends.
• WDL conversion for more realistic WDL score and contempt. Adds an Elo based WDL transformation of the NN value head output. Helps with more accurate play at high level (WDL sharpening), more aggressive play against weaker opponents and draw avoiding openings (contempt), piece odds play. For details on how it works see https://lczero.org/blog/2023/07/the-lc0-v0.30.0-wdl-rescale/contempt-implementation/.
• A new score type WDL_mu which follows the new eval convention, where +1.00 means 50% white win chance.
• Changed mlh threshold effect to create a smooth transition.
• WDL_mu score type is now the default and the --moves-left-threshold default was changed from 0 to 0.8.
• Simplified to a single --draw-score parameter, adjusting the draw score from white's perspective: 0 gives standard scoring, -1 gives Armageddon scoring.
• Updated describenet for new net architectures.
• Added a first-move-bonus option to the legacy time manager, to accompany book-ply-bonus for shallow openings.
• Persistent L2 cache optimization for the cuda backend. Use the cache_opt=true backend option to turn it on.
• Some performance improvements for the cuda, onnx and blas backends.
• Added the threads backend option to onnx, defaults to 0 (let the onnxruntime decide) except for onnx-cpu that defaults to 1.
• The onnx-dml package now includes a directml.dll installation script.
• Some users experienced memory issues with onnx-dml, so the defaults were changed. This may affect performance, in which case you can use the steps=8 backend option to get the old behavior.
• The Python bindings are available as a package, see the README for instructions.
• Revised 'simple' time manager.
• A new spinlock implementation (selected with --search-spin-backoff) to help with many cpu threads (e.g. 128 threads), obviously for cpu backends only.
• Fixes for contempt with infinite search/pondering and for the wdl display when pondering.
• Some assorted fixes and code cleanups.

v0.30.0-rc2

In this release:

• WDL conversion for more realistic WDL score and contempt. Adds an Elo based
  WDL transformation of the NN value head output. Helps with more accurate play
  at high level (WDL sharpening), more aggressive play against weaker opponents
  and draw avoiding openings (contempt), piece odds play. There will be a blog
  post soon explaining in detail how it works.
• A new score type WDL_mu which follows the new eval convention, where +1.00
  means 50% white win chance.
• Simplified to a single --draw-score parameter, adjusting the draw score from
  white's perspective: 0 gives standard scoring, -1 gives Armageddon scoring.
• Updated describenet for new net architectures.
• Added a first-move-bonus option to the legacy time manager, to accompany
  book-ply-bonus for shallow openings.
• Changed mlh threshold effect to create a smooth transition.
• Revised 'simple' time manager.
• A new spinlock implementation (selected with --search-spin-backoff) to help
  with many cpu threads (e.g. 128 threads), obviously for cpu backends only.
• Some assorted fixes and code cleanups.

v0.30.0-rc1

In this release:

• Support for networks with attention body and smolgen added to blas, cuda, metal and onnx backends.
• Persistent L2 cache optimization for the cuda backend. Use the cache_opt=true backend option to turn it on.
• Some performance improvements for the cuda, onnx and blas backends.
• Added the threads backend option to onnx, defaults to 0 (let the onnxruntime decide) except for onnx-cpu that defaults to 1.
• The onnx-dml package now includes a directml.dll installation script.
• Some users experienced memory issues with onnx-dml, so the defaults were changed. This may affect performance, in which case you can use the steps=8 backend option to get the old behavior.
• The Python bindings are available as a package, see the README for instructions.
• Some assorted fixes and code cleanups.

v0.29.0

In this release:

• New metal backend for apple systems. This is now the default backend for macos builds.
• New onnx-dml backend to use DirectML under windows, has better net compatibility than dx12 and is faster than opencl. See the README for use instructions, a separate download of the DirectML dll is required.
• Full attention policy support in cuda, cudnn, metal, onnx, blas, dnnl, and eigen backends.
• Partial attention policy support in onednn backend (good enough for T79).
• Non multigather (legacy) search code and --multigather option are removed.
• Now the onnx backends can use fp16 when running with a network file (not with .onnx model files). This is the default for onnx-cuda and onnx-dml, can be switched on or off with by setting the fp16 backend option to true or false respectively.
• The onednn package comes with the latest dnnl compiled to allow running on an intel gpu by adding gpu=0 to the backend options.
• The default net is now 791556 for most backends except opencl and dx12 that get 753723 (as they lack attention policy support).
• Support for using pgn book with long lines in training: selfplay can start at a random point in the book.
• New "simple" time manager.
• Support for double Fischer random chess (dfrc).
• Added TC-dependent output to the backendbench assistant.
• Starting with this version, the check backend compares policy for valid moves after softmax.
• The onnx backend now allows selecting gpu to use.
• Improved error messages for unsupported network files.
• Some assorted fixes and code cleanups.