The commentary net inspects the final state of the engine net, but not internal layers.
Deeper introspection is a really important goal, but by the time you make serious progress there, chess is the least of your worries.
I do really like the work people have put into introspection and visualization so far though: DeepDream comes to mind. There was also another great paper or page that I can't find.
Yes! I haven't done as much testing with GPU, but did validate running with 4x V100s. You just need to adjust the "search_threads" option to the number of GPUs, but set it to at least 2.
It loses some games to Stockfish 13 and 14, and Lc0 - rarely at slow time control, and more often at blitz and bullet (actually, it has losses all the way down to Stockfish 9 in blitz).
Partly because of the way it tries to search more widely to avoid tactical traps, it can also be a little sloppy in holding advantages or minimizing losses (this could use some more work and tuning). This ends up making it a little drawish, so it loses less than you'd expect to Stockfish 14, but also doesn't beat up weaker engines as well as Stockfish 14 does.
You can see some of this in the raw tournament results[1]. At 40 moves per 15 minutes, repeating, each engine draws with the ones above and below it, but starts to win and lose at a distance of 2 or 3.
At 5+3 time control, ChessCoach goes 1-0-29 vs. Stockfish 12, but Stockfish 12 is better at beating Stockfish 8-11 than ChessCoach is, so CC ends up between SF11 and SF12 in the end.
On Lichess, where there's no "free time" to get ready for searches, ChessCoach's naïve node allocation/deallocation makes it waste time, and means it can't ponder for very long on the opponent's time - a big opportunity for improvement (it needs a multi-threaded pool deallocator that can feed nodes back to local pools for the long-lived search threads). I think it's also hitting a bug with Syzygy memory mapping that Stockfish works around via reloading every "ucinewgame" (which I don't trigger on Lichess). So, overall, its performance on Lichess is worse.
Also, you can't read too much into this data - very few games, and no opening book.
It was partly because I was looking to improve self-play and training tractability on a home desktop with 1 GPU (complete failure), and partly to learn about everything from scratch. I would be interested to see how strong it is with the same search but with Leela's inference backend (for GPU at least) and network.
In terms of search-into-commentary, concatenating like that may be interesting, as long as it can learn to map across - definitely plausible without too much work. I was originally thinking of something more complicated, combining multiple raw network outputs across the tree through some kind of trained weighting, or additional model via recurrence, and punted it.
Ignore my BLEU comment, mixed those up between replies - that was the other potential use of search trees for commentary, an MCTS/PUCT-style alternative to traditional sequential top-k/top-p sampling, once you have logits and are deciding which paragraph to generate.
That's a good idea. A bigger problem than time-slicing is probably GPU/TPU device ownership issues and GPU/TPU memory usage with multiple games going in parallel. There may be some ways to multiplex it intelligently though.
Costs are difficult to work out - it depends on cloud vs. self-hosting, what kind of TPUs/GPUs, how long you're calculating over.
The advantage that classical/NNUE engines have is that they can more easily spread over distributed frameworks like Fishtest.
I think it always helps to have a project to apply things to as you're learning something, even if it means coming up with something small. While preparing, I found it helpful to read for at least an hour each morning, and then divided the rest of the day into learning vs. "diving in" as I felt like it.
Getting deep into RL specifically wasn't so necessary for me because I was just replicating AlphaZero there, although reading papers on other neural architectures, training methods, etc. helped with other experimentation.
You may be well past this, but my biggest general recommendation is the book, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" to quickly cover a broad range of statistics, APIs, etc., at the right level of practicality before going further into different areas (for PyTorch, I'm not sure what’s best).
Similarly, I was familiar with the calculus underpinnings but did appreciate Andrew Ng's courses for digging into backpropagation etc., especially when covering batching.
SentiMATE[1] looks at one of the reverse problems in a way - training an engine on commentary data - although it's not exactly what you're talking about.
I think this line of thinking could eventually lead to automated metrics for commentary evaluation, which could in turn lead to better methods than top-k/top-p for turning a bunch of sequential logits into a sentence or paragraph - basically treat it like MCTS/PUCT also.
The problem is that if you look at high-level commentary - maybe Radjabov-MVL on https://www.chess.com/news/view/2021-champions-chess-tour-fi... (I'm not the best judge, just a quick search) - it's not often possible to predict the move starting with the comment. And if you did, you might end up with very dry metrics and reverse commentary.
But this direction has a lot of potential I think, beyond just chess, into more of an algorithmic/generational support for pure NN-based language models.
The original hope was for this to be a third head on top of the AlphaZero model, but I couldn't think of a way to generate commentary during self-play (such that it would gradually improve), and trying to rotate supervised commentary training into the main schedule ended up hurting both sides because of the disjoint datasets.
So, now the commentary decoder is just trained separately on the final primary model. The previous and current game positions are fed into the primary model, and the outputs are taken from the final convolutional layer, just before the value and policy heads. Then, that data plus the side to play is positionally encoded and fed into a transformer decoder.
It would be better for a search tree/algorithm to be used for commentary too so that tactics could be better understood, but that would need some kind of subjective BLEU equivalent, and metrics like those don't work well for chess commentary.
It is using a full-sized transformer decoder, trained on about 1 million data samples, but with far fewer neural network parameters and training samples than GPT-2 or GPT-3.
Yes, in this case as long as they still agree when it actually names people, I don't think it would be too difficult. There may be factors I'm not considering though.
Harder would be more general models like GPT-2 and GPT-3.
I agree with what you're saying. On the flip side, there are multiple systems (Elo, Glicko), anchors, playing pools, etc. in use around the place, and FIDE and CCRL are offset by around 80 magnitude I heard, compared to about 600-700 difference between top humans and top engines.
So for a non-technical audience, I feel like it's easier to give a ballpark that they can understand without having to pull in too much context around Stockfish, CCRL, etc. It may have been better to clarify further in the docs though.
The "Data" document does give the relative Elo breakdown in the appendices.
Haha - I dislike how much of a black box it is, despite the statistical basis (for example, the back and forth on batch normalization rationale). But lots of interesting problems and tech to dig into.
Yes, each bot uses a v3-8 Cloud TPU VM, and tries to be constantly playing a game. The search tree is also very memory-hungry. And right now it's also using the Python API for TensorFlow, which is likely wasting a lot of potential.
It does tend to name-drop: often famous names, but also just "Jeff".
And if you spice up the commentary sampling parameters, it gets even more inventive, making up names, and saying that "the rook is pinning Fischer against the king".
Sometimes it seems really accurate (like the cherry-picked GIF in the overview docs) and sometimes really off.
I think for the most part, it knows more than it lets on, but finding the right sampling methods (or better yet, generalized search) to generate the best comments is a tough problem because it's difficult to evaluate quality.
Yeah, that's a massive problem with the natural language domain all across machine learning.
Unfortunately it's very difficult to track down training data for chess commentary in the first place, let alone trim down biases. For reference, I was able to gather about 1 million samples, but it really needs a billion.
Hopefully through data augmentation and better general intelligence models we can make better progress on bias issues soon, as that's a huge problem when we start trusting AI models too much in life.
Deeper introspection is a really important goal, but by the time you make serious progress there, chess is the least of your worries.
I do really like the work people have put into introspection and visualization so far though: DeepDream comes to mind. There was also another great paper or page that I can't find.