Deep CFR evaluation 프로파일링 메트릭 추가

docs/deep-cfr-evaluation-profile-2026-05-07.md

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+# Deep CFR Evaluation Profile 2026-05-07
+
+Run directory:
+
+`/mnt/2tbhdd/coolrl-lost-cities-runs/2026-05-07_030634_deep_cfr_profile_eval_breakdown_10iter`
+
+Command:
+
+```bash
+uv run python -m coolrl_lost_cities.games.classic.deep_cfr.cli train \
+  --config configs/deep_cfr/deep_cfr_selfplay_full_depth_slot_playability.yaml \
+  --checkpoint-dir /mnt/2tbhdd/coolrl-lost-cities-runs/2026-05-07_030634_deep_cfr_profile_eval_breakdown_10iter \
+  --max-iterations 10 \
+  --save-latest-only
+```
+
+## Summary
+
+The run completed 10 iterations. Loss values stayed finite.
+
+Evaluation ran on iterations 5 and 10.
+
+| Iteration | `iteration_seconds` | `evaluation_seconds` |
+| ---: | ---: | ---: |
+| 5 | 16.473433 | 11.055458 |
+| 10 | 16.835262 | 10.855797 |
+
+## Opponent Averages
+
+Values below are averaged across iterations 5 and 10.
+
+| Opponent | elapsed | avg len | policy select | network | postprocess | encoding | legal mask | opponent act |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| `safe_heuristic_strict` | 2.184393 | 158.13 | 1.628546 | 1.279717 | 0.175923 | 0.043837 | 0.038600 | 0.518953 |
+| `random` | 2.072657 | 186.11 | 1.918007 | 1.515231 | 0.207319 | 0.051491 | 0.037316 | 0.112155 |
+| `safe_heuristic` | 1.942907 | 143.23 | 1.471839 | 1.155444 | 0.159592 | 0.039683 | 0.035180 | 0.435870 |
+| `noisy_safe` | 1.904932 | 143.71 | 1.493521 | 1.174909 | 0.161256 | 0.039845 | 0.034331 | 0.374162 |
+| `safe_heuristic_loose` | 1.829347 | 136.13 | 1.400569 | 1.099200 | 0.151752 | 0.037722 | 0.033497 | 0.394891 |
+| `passive_discard` | 1.014156 | 96.82 | 0.982162 | 0.774097 | 0.105579 | 0.026702 | 0.021929 | 0.006383 |
+
+Other averaged step costs were small:
+
+| Opponent | apply action | diagnostics | final scoring |
+| --- | ---: | ---: | ---: |
+| `safe_heuristic_strict` | 0.005185 | 0.008161 | 0.000569 |
+| `random` | 0.006229 | 0.009001 | 0.000632 |
+| `safe_heuristic` | 0.004748 | 0.007636 | 0.000581 |
+| `noisy_safe` | 0.004818 | 0.007761 | 0.000584 |
+| `safe_heuristic_loose` | 0.004473 | 0.007438 | 0.000592 |
+| `passive_discard` | 0.002911 | 0.005156 | 0.000477 |
+
+## Notes
+
+`policy_select_seconds` dominated every opponent.
+
+Inside policy selection, `policy_network_seconds` was the largest component.
+`policy_postprocess_seconds` was second. `policy_encoding_seconds` and
+`policy_legal_mask_seconds` were much smaller.
+
+`opponent_act_seconds` was meaningful for safe heuristic opponents, but was
+still smaller than policy network time.
+
+`apply_action_seconds`, `diagnostics_seconds`, and `final_scoring_seconds` were
+small in this run.

docs/deep-cfr-evaluation-profile-plan.md

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+# Deep CFR Evaluation Profile Plan
+
+Goal: split evaluation runtime into the main per-step costs so eval iteration
+spikes can be explained without guessing.
+
+The profiling run should keep the normal full config and `eval_every: 5`, then
+compare iterations 5 and 10.
+
+## Metrics
+
+Each metric is emitted with the existing opponent prefix:
+`eval_<opponent>_<metric_name>`.
+
+Top-level counters:
+
+- `policy_turns`
+- `opponent_turns`
+- `avg_game_length`
+- `elapsed_seconds`
+- `games_per_second`
+- `steps_per_second`
+
+Step-level runtime:
+
+- `policy_select_seconds`
+- `opponent_act_seconds`
+- `apply_action_seconds`
+- `diagnostics_seconds`
+- `final_scoring_seconds`
+
+Policy select breakdown:
+
+- `policy_legal_mask_seconds`
+- `policy_encoding_seconds`
+- `policy_network_seconds`
+- `policy_postprocess_seconds`
+
+## Interpretation
+
+If `policy_network_seconds` dominates, evaluation is mostly batch-size-1 model
+inference overhead.
+
+If `policy_encoding_seconds` or `policy_legal_mask_seconds` dominates, the eval
+policy path is paying per-turn state feature/mask construction costs.
+
+If `opponent_act_seconds` dominates, the opponent bot implementation is the
+main eval cost for that opponent.
+
+If `apply_action_seconds` dominates, the game step path itself is the eval
+bottleneck.