Deep CFR multiprocessing benchmark 보강

src/coolrl_lost_cities/games/classic/deep_cfr/benchmark.py

@@ -32,3 +32,16 @@ def benchmark_traversal(
         "advantage_samples": metrics.advantage_samples,
         "strategy_samples": metrics.strategy_samples,
     }
+
+
+def benchmark_traversal_modes(
+    config: DeepCFRConfig | None = None,
+) -> dict[str, dict[str, float | int]]:
+    single = benchmark_traversal(config, num_workers=0)
+    multi = benchmark_traversal(config, num_workers=2)
+    speedup = float(multi["nodes_per_second"]) / max(float(single["nodes_per_second"]), 1.0e-12)
+    return {
+        "single": single,
+        "multi": multi,
+        "summary": {"speedup": speedup},
+    }

src/coolrl_lost_cities/games/classic/deep_cfr/cli.py

@@ -5,7 +5,10 @@ import json
 from dataclasses import replace
 from pathlib import Path
 
-from coolrl_lost_cities.games.classic.deep_cfr.benchmark import benchmark_traversal
+from coolrl_lost_cities.games.classic.deep_cfr.benchmark import (
+    benchmark_traversal,
+    benchmark_traversal_modes,
+)
 from coolrl_lost_cities.games.classic.deep_cfr.config import DeepCFRConfig, config_from_dict
 from coolrl_lost_cities.games.classic.deep_cfr.evaluate import (
     evaluate_strategy_network,
@@ -61,13 +64,17 @@ def eval_command(args: argparse.Namespace) -> None:
 
 
 def benchmark_command(args: argparse.Namespace) -> None:
+    config = DeepCFRConfig(
+        traversals_per_iteration=args.traversals,
+        max_traversal_depth=args.depth,
+        seed=args.seed,
+        save_every_iteration=False,
+    )
+    if args.compare:
+        print(json.dumps(benchmark_traversal_modes(config), indent=2, sort_keys=True))
+        return
     result = benchmark_traversal(
-        DeepCFRConfig(
-            traversals_per_iteration=args.traversals,
-            max_traversal_depth=args.depth,
-            seed=args.seed,
-            save_every_iteration=False,
-        ),
+        config,
         num_workers=args.workers,
     )
     print(json.dumps(result, indent=2, sort_keys=True))
@@ -104,6 +111,7 @@ def main(argv: list[str] | None = None) -> None:
     benchmark.add_argument("--traversals", type=int, default=8)
     benchmark.add_argument("--depth", type=int, default=4)
     benchmark.add_argument("--seed", type=int, default=1)
+    benchmark.add_argument("--compare", action="store_true")
     benchmark.set_defaults(func=benchmark_command)
 
     args = parser.parse_args(argv)

src/coolrl_lost_cities/games/classic/deep_cfr/config.py

@@ -45,7 +45,7 @@ class DeepCFRConfig:
     eval_games: int = 10
     eval_opponents: tuple[str, ...] = ("random",)
     eval_max_steps: int = 10_000
-    num_workers: int = 0
+    num_workers: int | str = 0
     traversal_worker_chunk_size: int = 4
 
     def to_dict(self) -> dict[str, Any]:
@@ -55,9 +55,24 @@ class DeepCFRConfig:
     def checkpoint_path(self) -> Path:
         return Path(self.checkpoint_dir)
 
+    def resolved_num_workers(self, batches: int | None = None) -> int:
+        if isinstance(self.num_workers, str):
+            token = self.num_workers.strip().lower()
+            if token == "auto":
+                guess = max(1, (os_cpu_count() or 2) // 2)
+                return min(guess, batches) if batches is not None and batches > 0 else guess
+            return max(0, int(token))
+        return max(0, int(self.num_workers))
+
 
 def config_from_dict(data: dict[str, Any]) -> DeepCFRConfig:
     values = dict(data)
     if "eval_opponents" in values:
         values["eval_opponents"] = tuple(values["eval_opponents"])
     return DeepCFRConfig(**values)
+
+
+def os_cpu_count() -> int | None:
+    import os
+
+    return os.cpu_count()

src/coolrl_lost_cities/games/classic/deep_cfr/trainer.py

@@ -138,7 +138,7 @@ class DeepCFRTrainer:
 
     def run_iteration(self, iteration: int) -> IterationMetrics:
         self.iteration = iteration
-        if self.config.num_workers > 1:
+        if self.config.resolved_num_workers() > 1:
             total_stats = self._run_traversals_parallel(iteration)
         else:
             total_stats = self._run_traversals_single_process(iteration)
@@ -206,7 +206,7 @@ class DeepCFRTrainer:
         total_stats = TraversalStats()
         if not batches:
             return total_stats
-        max_workers = min(self.config.num_workers, len(batches))
+        max_workers = self.config.resolved_num_workers(len(batches))
         with ProcessPoolExecutor(
             max_workers=max_workers,
             mp_context=mp.get_context("spawn"),

tests/games/classic/test_deep_cfr_trainer.py

@@ -3,7 +3,10 @@ from __future__ import annotations
 import numpy as np
 from coolrl_lost_cities.games.classic.game import GameState, LostCitiesConfig
 
-from coolrl_lost_cities.games.classic.deep_cfr.benchmark import benchmark_traversal
+from coolrl_lost_cities.games.classic.deep_cfr.benchmark import (
+    benchmark_traversal,
+    benchmark_traversal_modes,
+)
 from coolrl_lost_cities.games.classic.deep_cfr.config import DeepCFRConfig
 from coolrl_lost_cities.games.classic.deep_cfr.memory import ReservoirMemory, TrainingSample
 from coolrl_lost_cities.games.classic.deep_cfr.trainer import DeepCFRTrainer
@@ -234,6 +237,16 @@ def test_deep_cfr_traversal_benchmark_smoke() -> None:
 
     assert result["traversal_nodes"] > 0
     assert result["nodes_per_second"] > 0.0
+    comparison = benchmark_traversal_modes(
+        DeepCFRConfig(
+            traversals_per_iteration=1,
+            max_traversal_depth=2,
+            hidden_size=16,
+            save_every_iteration=False,
+            seed=48,
+        )
+    )
+    assert comparison["summary"]["speedup"] > 0.0
 
 
 def test_deep_cfr_self_play_league_records_snapshots(tmp_path) -> None: