Lc0 methods and input/output terms
A nice pictorial overview
Leela searches a tree of moves and game states. Each game state is a node in the tree, with an estimated value for that position, and a prioritized list of moves to consider (called the policy for that position). Traditional chess engines have a very-finely-crafted-by-humans value and policy generation system; unlike traditional engines, Leela uses its neural network trained without human knowledge for both value and policy generation. Then, Leela expands the tree to get a better understanding of the root node, the current position.
Leela uses PUCT (Predictor + Upper Confidence Bound tree search). We evaluate new nodes by doing a playout: start from the root node (the current position), pick a move to explore, and repeat down the tree until we reach a game position that has not been examined yet (or a position that ends the game, called a terminal node). We expand the tree with that new position (assuming non-terminal node) and use the neural network to create a first estimate of the value for the position as well as the policy for continuing moves. In Leela, a policy for a node is a list of moves and a probability for each move. The probability specifies the odds that an automatic player that executes the policy will make that move. After this node is added to the tree, backup that new value to all nodes visited during this playout. This slowly improves the value estimation of different paths through the game tree.
When a move is actually played on the board, the chosen move is made the new root of the tree. The old root and the other children of that root node are erased.
This is the same search specified by the AGZ paper, PUCT (Predictor + Upper Confidence Bound tree search). Many people call this MCTS (Monte-Carlo Tree Search), because it is very similar to the search algorithm the Go programs started using in 2006. But the PUCT used in AGZ and Lc0 replaces rollouts (sampling playouts to a terminal game state) with a neural network that estimates what a rollout would do. Other search algorithms are under consideration on the Github of Leela Go, but there isn’t yet any real consensus that something else is demonstrably better than PUCT. This is something of an active research topic in the overlap of the AI+Game Theory fields.
This section, which is currently under construction, aims at describing the actual implementation of the search. Before the search can start, threads are started, and while the main point of interest here is the information needed to successfully change how the actual search is done, a short summary of the threading framwork is useful.
The context of the search
Search::search() is called from
engine.cc), to construct a search object
search_. After some configuration of this object, the actual search for nodes to extend starts when
search_->StartThreads() is called.
Search::StartThreads() starts a watchdog thread and a number of threads running
SearchWorker::RunBlocking() which in turn calls
ExecuteOneIteration(), which is responsible for modifying and extending the search tree.
The actual search
In the source code, the search algorithm is described as follows:
// 1. Initialize internal structures. // 2. Gather minibatch. // 3. Prefetch into cache. // 4. Run NN computation. // 5. Retrieve NN computations (and terminal values) into nodes. // 6. Propagate the new nodes' information to all their parents in the tree. // 7. Update the Search's status and progress information.
This section aims to go into the details of each step.
- Initialize internal structures
Here, the global object
minibatch_is cleared. The
minibatch_is a vector of class
NodeToProcess, which in turn has the following fields (and others, but these are of particular interest if you want the change the search algorithm)
int multivisit = 0; // If greater than multivisit, and other parameters don't imply a lower // limit, multivist could be increased to this value without additional // change in outcome of next selection. int maxvisit = 0; uint16_t depth; bool nn_queried = false; bool is_cache_hit = false; bool is_collision = false; // Only populated for visits, std::vector<Move> moves_to_visit;
(NodeToProcess also has a field
node with the pointer to the actual node).
multivisit holds data about how many times a node was visited during this search, this measure is called
visits-in-flight. A normal visit corresponds to an evaluation of some kind: by the Neural network, or a check the resulted in the node is terminal (e.g. mate or draw by repetition). In contrast, a visit-in-flight does not guarantee any evaluation of any descendent node, however the visit-in-flight still gives useful information about how good the node is in comparision to its siblings which, if the node is a child of root, is relevant for move selection.
maxvisit is used by a particular optimization of PUCT, only recaculate PUCT-scores of the children if the best child has maxvisit lower than multivisit.
depth is used to determine if the node needs to be checked for draw by repetition, and it is used to calculate
seldepth (see SelDepth in the glossary below). reported via UCI. For a child of root,
depth == 2. Depth starts with 1 at root, so number of plies in PV is depth - 1.
nn_queried should be set to true for nodes that are not terminal and not cache-hits. Step 5. “Retrieve NN computations (and terminal values) into nodes.” will ignore nodes that do not have
nn_queried = true.
is_cache_hit Non-terminal nodes that are transpositions can have the NN-eval in the cache (see NNCache in the glossary below).
bool is_collision A collision happens whenever the same leaf is reached again during one search. A collisions count is used to determine when to cancel the search, since collisions imply time is wasted traversing the same line repeatedly.
moves_to_visit is a vector of class
Move and this vector should contain the moves leading up to the position that the node represents. If a node is not terminal
move_to_visit when calling
ExtendNode() to extend the node.
- 20x256: Shorthand for the size of the NN. 20 residual blocks and 256 filters.
- Backup: After a playout reaches a terminal node, and the NN is called, take V and average this into the Q of all nodes visited to reach that position.
- Batch Size: How many positions the GPU can train on simultaneously. Set as large as your GPU can handle.
- Batch normalization: Normalize channel outputs to have variance near 1 and outputs near 0.
- Batch Renormalization: Better but more complicated way for training to measure normalization statistics. Added during T40 to help fix Pawn promotion issues.
- Bestmove: Picks the move with the most visits.
tempdecayoptions allow picking lower moves for variety.
- Block: See Residual Block.
- CNN: Convolutional Neural Network. The largest part of Lc0’s NN uses this architecture, which convolves (slides) several 3x3xN filters across the entire board.
- Depth: Average depth. See also Seldepth.
- FC Layer: Fully Connected layer. Unlike filters which only look at a 3x3 area of the board, the FC layer looks at the entire board at once. This is used in the policy and value heads.
- Filters: A 3x3xN pattern. The N means it is looking at several input features. A “20x256” network has 20 3x3x256 filters in each layer (input layer is slightly different).
- KLD: Kullback-Leibler divergence is used during selfplay to measure how the distribution of visits to different root moves is changing. This allows selfplay to spend more time when the Policy prediction was bad, and less time on obvious moves. First used in T50.
- Learning Rate: How fast the neural net weights are adjusted. Too high and you don’t learn anything, or worse. Too low and your progress is too slow.
- MSE loss: Mean Squared Error of the value (V) NN output. One of the terms the NN training process tries to minimize. The NN improves in a feedback loop by trying to predict who wins each self-play game. The mean squared error of the predictions is the “loss”. See also policy loss and reg term.
- NNCache: Stores NN evaluations, which can be reused if the same positions is reached by transposition.
- Nodes: A potential game position in the tree of future gameplay. The root node of the tree is the current position.
- NPS: Nodes per second, including NNCache hits and terminal node hits, but excluding nodes carried over from tree reuse. In other words, total playouts generated per second.
- Overfitting: If the network trains on the same positions too much or with too low learning rate, it may memorize those positions and not generalize well to other similar positions.
- P: Neural network’s raw policy output (probability this is the best move)
- Playouts: As defined by the Monte Carlo Tree Search, starting from the root node: 1) pick a move to explore according to the policy and confidence of the choices (PUCT selection algorithm); 2) travel to the resulting game position node; 3) if this child node is already explored at least once, repeat steps 1) and 2), otherwise; 4) evaluate this node via the neural network, creating value and policy estimates for this position, and use this new value estimate to update all parent nodes’ values. After 4), the playout is complete.
- Policy loss: One of the terms the NN training process tries to minimize. The NN improves in a feedback loop by trying to predict what the self-play games say the policy should be. The amount the NN differs from the self-play game outputs is the “loss”. See also mse loss and reg term.
- PV: Principal Variation. The moves that would be taken if we on each level chose the node that has most visits (i.e. most playout traversals).
- Visits: Total number of playouts that have traversed this node. This is equal to the total size of the tree below this node (unless playouts have hit a terminal node, in which case visits = nodes + playouts_that_hit_terminal_node).
- Q: Average expected value of all playouts for this move, a value ranging from -1 to 1. See also V.
- Reg term: The L2-norm (roughly the size weights). The feedback training of the NN tries to minimize this along with policy loss and mse loss. This term tries to improve the generalization of the NN, and prevent overfiting.
- Residual Block: A residual block is a popular NN architecture that combines two CNN layers with a skip connection. A “20x256” network has 20 residual blocks. (Note that this is slightly different from the Deepmind paper where a “20x256” network has 19 residual blocks).
- Sampling Ratio: How many times each position from self-play games is used for training. Too high and your net may overfit. Too low and your progress is too slow.
- Score cp: Traditional centipawn value where 100 is a one pawn advantage. See [[FAQ#how-does-leela-chess-zero-calculate-the-cp-eval]].
- Squeeze Excite: SE is an extension to the Residual Block architecture that Deepmind used. It adds a global pooling layer (the squeeze part) and then transmits that global information back to all parts of the board (the excite part). First used in T35.
- Seldepth: Maximum depth.
- Terminal node: A node that has a game result (checkmate or draw).
- Train/Test Sets: Best practice is to split your data into two sets, train and test sets. You use the train set to improve the NN weights. You use the test set to validate the NN is able to generalize what it learned to positions it has never trained on. This way you can detect if your NN is overfitting.
- UCB: Upper Confidence Bound, aka U. This is the part of the PUCT formula that encourages exploring moves that have not been searched much yet. See also the other half, Q.
- V: Expected Value output of the NN, ranging from -1 to 1. See also Q.
- Z: Final result of a selfplay game (-1, 0, 1).
Example –verbose-move-stats output
Move Visits Policy Avg. Value UCB Raw NN Value info string g2g4 (378 ) N: 11 (+ 2) (P: 2.81%) (Q: -0.08786) (U: 0.19640) (Q+U: 0.10853) (V: -0.0652) info string f2f3 (346 ) N: 12 (+ 2) (P: 2.92%) (Q: -0.08100) (U: 0.19088) (Q+U: 0.10988) (V: -0.0759) info string b1a3 (34 ) N: 14 (+ 4) (P: 3.39%) (Q: -0.06031) (U: 0.17498) (Q+U: 0.11467) (V: -0.0385) info string g1h3 (161 ) N: 18 (+ 3) (P: 3.55%) (Q: -0.05069) (U: 0.15819) (Q+U: 0.10750) (V: -0.0315) info string a2a4 (207 ) N: 22 (+ 5) (P: 3.89%) (Q: -0.02759) (U: 0.13604) (Q+U: 0.10845) (V: -0.0084) info string h2h4 (403 ) N: 24 (+ 6) (P: 3.70%) (Q: -0.01613) (U: 0.11689) (Q+U: 0.10077) (V: -0.0109) info string b2b3 (230 ) N: 26 (+ 7) (P: 4.00%) (Q: -0.00999) (U: 0.11520) (Q+U: 0.10521) (V: -0.0041) info string f2f4 (351 ) N: 27 (+ 5) (P: 4.02%) (Q: -0.00992) (U: 0.11946) (Q+U: 0.10954) (V: -0.0014) info string b2b4 (234 ) N: 29 (+ 5) (P: 3.97%) (Q: -0.00536) (U: 0.11123) (Q+U: 0.10587) (V: -0.0090) info string a2a3 (204 ) N: 32 (+ 9) (P: 4.38%) (Q: 0.00459) (U: 0.10220) (Q+U: 0.10679) (V: 0.0026) info string h2h3 (400 ) N: 34 (+ 9) (P: 4.74%) (Q: 0.00160) (U: 0.10550) (Q+U: 0.10711) (V: -0.0021) info string d2d3 (288 ) N: 37 (+11) (P: 5.35%) (Q: 0.00055) (U: 0.10692) (Q+U: 0.10746) (V: -0.0001) info string c2c3 (259 ) N: 40 (+ 8) (P: 4.89%) (Q: 0.00996) (U: 0.09779) (Q+U: 0.10775) (V: 0.0089) info string b1c3 (36 ) N: 44 (+18) (P: 5.69%) (Q: 0.02348) (U: 0.08859) (Q+U: 0.11207) (V: 0.0180) info string g2g3 (374 ) N: 47 (+ 8) (P: 5.40%) (Q: 0.00971) (U: 0.09454) (Q+U: 0.10425) (V: 0.0132) info string e2e3 (317 ) N: 48 (+11) (P: 5.65%) (Q: 0.01524) (U: 0.09223) (Q+U: 0.10746) (V: 0.0231) info string g1f3 (159 ) N: 73 (+17) (P: 7.08%) (Q: 0.02949) (U: 0.07629) (Q+U: 0.10578) (V: 0.0343) info string d2d4 (293 ) N: 81 (+36) (P: 7.94%) (Q: 0.04077) (U: 0.06593) (Q+U: 0.10670) (V: 0.0471) info string c2c4 (264 ) N: 84 (+15) (P: 6.63%) (Q: 0.04203) (U: 0.06503) (Q+U: 0.10706) (V: 0.0542) info string e2e4 (322 ) N: 128 (+63) (P: 10.01%) (Q: 0.05778) (U: 0.05111) (Q+U: 0.10889) (V: 0.0556)
(Note the list is in reverse visits order since Arena GUI flips it again.)
Command line flags and UCI options
Most options have both a command line flag and a UCI option. In the list below both are listed. Most of the options should be left default. Here are some common ones you might change:
–weights, –threads, –backend, –backend-opts, –verbose-move-stats
TBD: If you have multiple GPUs, you can use these with e.g.
How does Lc0 calculate the cp eval?
Lc0 uses an average expected score Q in the range [-1,1]. This expected score is converted to a traditional centi-pawn (cp) eval using multiple formulas. For example:
cp = 111.714640912 * tan(1.5620688421 * Q).