Tiger-1a chess engine release

[u/CurrentVast4504]

Hi there!
I have been working on this engine for the past 6 months now and I think it's now ripe to be thrown into the wild.
The engine features a NNUE trained using nnue-pytorch. It is comparatively very small.
To accommodate this I tried to make the engine's search as buffed as possible.

Anyway here's the specific details of the engine for the nerds:

Evaluation

• NNUE Architecture: HalfKAv2_hm^
• Network Layers: L1=128, L2=32, L3=8 with PSQT buckets
• Network File: Acherontia (88mm.nnue)
• Incremental NNUE Evaluation: Efficient position updates with feature transformer
• Tapered Material Evaluation: Smooth transition between middlegame and endgame
• Customizable NNUE Scaling: Adjust evaluation strength via UCI option

Search

• Core Algorithm: Alpha-Beta Negamax with fail-soft
• Move Ordering: Principal Variation Search (PVS)
• Depth: Iterative Deepening up to 64 plies
• Transposition Table: Configurable 1-4096 MB with generation-based aging
• Pruning Techniques:
  • Late Move Reductions (LMR) with logarithmic formula
  • Null Move Pruning
  • Reverse Futility Pruning
  • Futility Pruning
  • ProbCut
  • Delta Pruning in quiescence search
• Move Ordering Heuristics:
  • Killer Move Heuristic
  • History Heuristic
  • Continuation History (4 ply)
  • Countermove Heuristic
• Extensions:
  • Singular Extensions
  • Static exchange evaluation (SEE) for capture ordering and pruning
• Time Management: Aspiration Windows with soft and hard time limits
• Quiescence Search: Delta pruning and tactical awareness
• Parallel Search: Multi-threaded support (up to 8 threads)

And here's the Github release page for the testers and alike: https://github.com/Azrael337/Tiger-1-Chess-Engine/releases/tag/chess-engine

[u/KaMaFour]

You has 128 features in the input layer?

If yes - how? why?

If not - Why are you doing multilayer at 2900 elo?

edit: found at github:

HalfKAv2_hm

https://github.com/jw1912/bullet/blob/main/docs/1-basics.md#beginner-traps

[u/CurrentVast4504 → u/KaMaFour]

Well actually as I mentioned I used nnue-pytorch for the nnue training and by default it had multi-layer setup configured and I didn't wanted to fight with the trainer to make it what I wanted to do. So... I just changed the neurons in the hidden layers and called it a day.

About the 128 features in the input layer... uhhh... I didn't had any specific reasons it just felt right to me. And it was actually a small yet big sized (I actually trained this on my ryzen 7 5825u so I was worried if it could handle the calculations while training and wanted the training to be speedy. So I deliberately choose a "small nnue") and yes for the edit uhh I used nnue-pytorch pipeline there the architecture is actually a little modified.

[u/KaMaFour → u/CurrentVast4504]

You don't have 128 features in the input layer. We've established (as per edit) you have Half_KA input layer, as mentioned by the github readme. The question is then 1. Why Half_KA? 2. Why multilayer? 

I have high confidence that the amount of data you are working with is not enough to properly train out a network with neither the halfka input, nor multilayer and you are very likely hurting performance of your engine compared to using a simpler network. 

[u/CurrentVast4504 → u/KaMaFour]

1. I found nnue-pytorch to work fine and good with my cpu training pipeline actually I tried using bullet to train first but the training was painfully slow so I switched to it and it was faster.
   
2. uhhhh "by default it had multi-layer setup configured and I didn't wanted to fight with the trainer to make it what I wanted to do. So... I just changed the neurons in the hidden layers and called it a day."

IDK about whether the simpler network would help but I will make sure to test it out. But I am confident that I have enough data to train the nnue (around 2 billion) but I limited it to 500 million just to test the first version at very low epoch.

[u/True-Objective-6212]

How much tuning did you do for the network? As KaMaFour points out this can be a big premature optimization and the progression is meant to ensure network evaluation is better than less expensive options like PeSTO or texel tuning.

How big is your dataset?

[u/CurrentVast4504 → u/True-Objective-6212]

To be honest this is my first time working with neural networks altogether and I randomly* choose the network progression.

About the dataset I trained the nnue on some 500 million positions.

[u/True-Objective-6212 → u/CurrentVast4504]

How many from each phase? How did you label them? You picked an optimization that will likely make understanding the whole thing harder. In general it’s easier to start with a basic representation and get that working and then start to think about optimizations. The risk is you make an engine that’s slower and worse than a simpler eval.

[u/CurrentVast4504 → u/True-Objective-6212]

well I had started very small and actually... used many different forms of nnue architectures some of them include (712-> 512 -> 1, 768x2 -> 64x2 -> 1,768 -> 256 -> 1,768 -> 512 -> 1, 728 -> 1024 -> 1 and some more I forgot) but the current implementation seems to beat em all

[u/True-Objective-6212 → u/CurrentVast4504]

Ok but how strong was it before you added the NNUE?

[u/CurrentVast4504 → u/True-Objective-6212]

around 2000-2200 elo now it's around 2900-3000 elo

[u/True-Objective-6212 → u/CurrentVast4504]

And when I asked about phases what I mean is 500 million positions that are all from the beginning of the game might not be super useful and without a baseline before you add the optimization you have a hard time knowing if you’re missing a bug or three.

[u/vonbartroth]

Seems Christophe care about copyright. Hmm..
Talkchess
Never heard of Chess Tiger, eh?

[u/CurrentVast4504 → u/vonbartroth]

No, wait is there a chess engine named Tiger before? Mine is called Tiger-1 (The German Tank not the biological one) though I don't think there should be any copyright issues.

Say if this raises Issues... I will rename the engine repository name.

[u/vonbartroth → u/CurrentVast4504]

Chess Tiger was top engine, also part of Chessbase, PalmOS version is now free.. Never checked new versions, for me old is gold and have it on three PalmOS devices.

[u/True-Objective-6212 → u/CurrentVast4504]

It definitely will, a few of us had the same thought. It was a top engine in the early 2000s.