Add dirty and possibly short lived tuner based on bench

stats/.gitignore

@@ -0,0 +1 @@
+quiet-labeled.v7.epd.gz

stats/nodebench/.gitignore

@@ -0,0 +1,2 @@
+.venv
+__pycache__

stats/nodebench/bench.json

@@ -0,0 +1,49 @@
+{
+  "depth": 6,
+  "uci_options": {
+    "Hash": "128",
+    "SyzygyPath": "../../syzygy",
+    "max_history_stats_impact": "20"
+  },
+  "cases": [
+    "rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1",
+    "r3k2r/p1ppqpb1/bn2pnp1/3PN3/1p2P3/2N2Q1p/PPPBBPPP/R3K2R w KQkq - 0 10",
+    "8/2p5/3p4/KP5r/1R3p1k/8/4P1P1/8 w - - 0 11",
+    "4rrk1/pp1n3p/3q2pQ/2p1pb2/2PP4/2P3N1/P2B2PP/4RRK1 b - - 7 19",
+    "rq3rk1/ppp2ppp/1bnpb3/3N2B1/3NP3/7P/PPPQ1PP1/2KR3R w - - 7 14 moves d4e6",
+    "r1bq1r1k/1pp1n1pp/1p1p4/4p2Q/4Pp2/1BNP4/PPP2PPP/3R1RK1 w - - 2 14 moves g2g4",
+    "r3r1k1/2p2ppp/p1p1bn2/8/1q2P3/2NPQN2/PPP3PP/R4RK1 b - - 2 15",
+    "r1bbk1nr/pp3p1p/2n5/1N4p1/2Np1B2/8/PPP2PPP/2KR1B1R w kq - 0 13",
+    "r1bq1rk1/ppp1nppp/4n3/3p3Q/3P4/1BP1B3/PP1N2PP/R4RK1 w - - 1 16",
+    "4r1k1/r1q2ppp/ppp2n2/4P3/5Rb1/1N1BQ3/PPP3PP/R5K1 w - - 1 17",
+    "2rqkb1r/ppp2p2/2npb1p1/1N1Nn2p/2P1PP2/8/PP2B1PP/R1BQK2R b KQ - 0 11",
+    "r1bq1r1k/b1p1npp1/p2p3p/1p6/3PP3/1B2NN2/PP3PPP/R2Q1RK1 w - - 1 16",
+    "3r1rk1/p5pp/bpp1pp2/8/q1PP1P2/b3P3/P2NQRPP/1R2B1K1 b - - 6 22",
+    "r1q2rk1/2p1bppp/2Pp4/p6b/Q1PNp3/4B3/PP1R1PPP/2K4R w - - 2 18",
+    "4k2r/1pb2ppp/1p2p3/1R1p4/3P4/2r1PN2/P4PPP/1R4K1 b - - 3 22",
+    "3q2k1/pb3p1p/4pbp1/2r5/PpN2N2/1P2P2P/5PP1/Q2R2K1 b - - 4 26",
+    "6k1/6p1/6Pp/ppp5/3pn2P/1P3K2/1PP2P2/3N4 b - - 0 1",
+    "3b4/5kp1/1p1p1p1p/pP1PpP1P/P1P1P3/3KN3/8/8 w - - 0 1",
+    "2K5/p7/7P/5pR1/8/5k2/r7/8 w - - 0 1 moves g5g6 f3e3 g6g5 e3f3",
+    "8/6pk/1p6/8/PP3p1p/5P2/4KP1q/3Q4 w - - 0 1",
+    "7k/3p2pp/4q3/8/4Q3/5Kp1/P6b/8 w - - 0 1",
+    "8/2p5/8/2kPKp1p/2p4P/2P5/3P4/8 w - - 0 1",
+    "8/1p3pp1/7p/5P1P/2k3P1/8/2K2P2/8 w - - 0 1",
+    "8/pp2r1k1/2p1p3/3pP2p/1P1P1P1P/P5KR/8/8 w - - 0 1",
+    "8/3p4/p1bk3p/Pp6/1Kp1PpPp/2P2P1P/2P5/5B2 b - - 0 1",
+    "5k2/7R/4P2p/5K2/p1r2P1p/8/8/8 b - - 0 1",
+    "6k1/6p1/P6p/r1N5/5p2/7P/1b3PP1/4R1K1 w - - 0 1",
+    "1r3k2/4q3/2Pp3b/3Bp3/2Q2p2/1p1P2P1/1P2KP2/3N4 w - - 0 1",
+    "6k1/4pp1p/3p2p1/P1pPb3/R7/1r2P1PP/3B1P2/6K1 w - - 0 1",
+    "8/3p3B/5p2/5P2/p7/PP5b/k7/6K1 w - - 0 1",
+    "8/8/8/8/5kp1/P7/8/1K1N4 w - - 0 1",
+    "8/8/8/5N2/8/p7/8/2NK3k w - - 0 1",
+    "8/3k4/8/8/8/4B3/4KB2/2B5 w - - 0 1",
+    "8/8/1P6/5pr1/8/4R3/7k/2K5 w - - 0 1",
+    "8/2p4P/8/kr6/6R1/8/8/1K6 w - - 0 1",
+    "8/8/3P3k/8/1p6/8/1P6/1K3n2 b - - 0 1",
+    "8/R7/2q5/8/6k1/8/1P5p/K6R w - - 0 124",
+    "6k1/3b3r/1p1p4/p1n2p2/1PPNpP1q/P3Q1p1/1R1RB1P1/5K2 b - - 0 1",
+    "r2r1n2/pp2bk2/2p1p2p/3q4/3PN1QP/2P3R1/P4PP1/5RK1 w - - 0 1"
+  ]
+}

stats/nodebench/bench.py

@@ -0,0 +1,73 @@
+import argparse
+import subprocess
+from pathlib import Path
+
+from pydantic import BaseModel
+from tqdm import tqdm
+
+
+class BenchSuite(BaseModel):
+    depth: int
+    uci_options: dict[str, str]
+    cases: list[str]
+
+
+class Case(BaseModel):
+    depth: int
+    uci_options: dict[str, str]
+    fen: str
+
+
+def run_bench(engine: Path, case: Case) -> int:
+    proc = subprocess.Popen([engine], stdout=subprocess.PIPE, stdin=subprocess.PIPE)
+    assert proc.stdout is not None
+    assert proc.stdin is not None
+
+    indata = "\n".join(
+        ["uci"]
+        + [f"setoption name {k} value {v}" for k, v in case.uci_options.items()]
+        + [f"position fen {case.fen}", f"go depth {case.depth}\n"]
+    ).encode()
+    proc.stdin.write(indata)
+    proc.stdin.flush()
+
+    nodes = None
+    for line in map(bytes.decode, iter(proc.stdout.readline, b"")):
+        if line.startswith("bestmove"):
+            break
+        if line.startswith("info"):
+            words = line.split()
+            for label, value in zip(words[1:], words[2:]):
+                if label == "nodes":
+                    nodes = int(value)
+
+    proc.stdin.write(b"quit\n")
+    proc.stdin.flush()
+    proc.wait()
+    assert nodes is not None, f"no nodes counted for {case.fen}"
+    return nodes
+
+
+def run_suite(engine: Path, suite: BenchSuite, progress: bool = True) -> int:
+    return sum(
+        run_bench(
+            engine, Case(depth=suite.depth, uci_options=suite.uci_options, fen=fen)
+        )
+        for fen in (suite.cases if not progress else tqdm(suite.cases))
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("engine", type=Path)
+    parser.add_argument("benchfile", type=Path)
+    parser.add_argument("--no-progress", action="store_true")
+    args = parser.parse_args()
+
+    assert args.engine.exists() and args.engine.is_file()
+    assert args.benchfile.exists() and args.benchfile.is_file()
+
+    with open(args.benchfile) as f:
+        suite = BenchSuite.model_validate_json(f.read())
+
+    print(f"{run_suite(args.engine, suite, progress=not args.no_progress):_} nodes")

stats/nodebench/tune.json

@@ -0,0 +1,11 @@
+{
+	"options": [
+		{
+			"name": "max_history_stats_impact",
+			"bounds": [
+				7,
+				13
+			]
+		}
+	]
+}

stats/nodebench/tune.py

@@ -0,0 +1,73 @@
+import argparse
+from functools import cache
+from pathlib import Path
+
+import numpy as np
+from pydantic import BaseModel
+from scipy.optimize import brute
+from tqdm import tqdm
+
+from bench import BenchSuite, run_suite
+
+
+class OptionConfig(BaseModel):
+    name: str
+    bounds: tuple[int, int]
+
+
+class TuneConfig(BaseModel):
+    options: list[OptionConfig]
+
+
+def tune(
+    engine: Path, config: TuneConfig, suite: BenchSuite
+) -> tuple[int, dict[str, int]]:
+
+    @cache
+    def inner(args: tuple[str]) -> int:
+        print("Running with args:", args)
+        for i, opt in enumerate(config.options):
+            suite.uci_options[opt.name] = args[i]
+        return run_suite(engine, suite, progress=True)
+
+    def objective(x: np.ndarray) -> int:
+        x = np.round(x).astype(int)
+        args = tuple(str(xi) for xi in x)
+        return inner(args)
+
+    def callback(x: np.ndarray, f: float, context: int):
+        print(f"\n\nnodes: {f} params: {x}, context: {context}\n")
+
+    bounds = [slice(*opt.bounds) for opt in config.options]
+
+    result = brute(objective, bounds, Ns=1, disp=True, full_output=True)
+
+    with open("/tmp/tune_result.pickl", "wb") as f:
+        import pickle
+
+        pickle.dump(result, f)
+
+    print(
+        "Global minimum:",
+        ", ".join(f"{opt.name}: {x} " for x, opt in zip(result[0], config.options)),
+    )
+    print("Nodes:", result[1])
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("engine", type=Path)
+    parser.add_argument("benchfile", type=Path)
+    parser.add_argument("configfile", type=Path)
+    args = parser.parse_args()
+
+    assert args.engine.exists() and args.engine.is_file()
+    assert args.benchfile.exists() and args.benchfile.is_file()
+    assert args.configfile.exists() and args.configfile.is_file()
+
+    with open(args.benchfile) as f:
+        suite = BenchSuite.model_validate_json(f.read())
+    with open(args.configfile) as f:
+        config = TuneConfig.model_validate_json(f.read())
+
+    tune(args.engine, config, suite)