Audit of the Deep CFR information-state encoding identified two tiers of
non-pure features and removed both:
- Tier 3 (judgment): is_bad_open_candidate, open_risk_score,
is_safe_continuation. Same heuristic family used to label bad_open in
evaluation, embedded as model input.
- Tier 2 (projection): recoverable_score_no_bonus,
recoverable_margin_no_bonus, min_needed_to_break_even,
cards_needed_for_bonus, has_bonus_path. Mechanical but assumption-laden
("commit and play all currently-playable cards"). The no_bonus form is
asymmetric: it amplifies the immediate -20 penalty while truncating the
+20 bonus upside, biasing the model toward the same "don't open" basin
the diagnostics already flagged.
Input dim 365 -> 297. DERIVED_PLAYABILITY_PER_COLOR 19 -> 15;
SLOT_AWARE_PLAYABILITY_PER_SLOT 12 -> 6. Test shape assertions updated.
Also adds selectivity diagnostic infrastructure used to reach this point:
- traversal.outcome_unsampled_first_open_prior_alpha config field with
signed-prior overlay on unsampled first-open advantage targets (A1).
- analyze_first_open_counterfactual.py --post-policy to swap the
policy_player rollout policy and isolate selection bias (D1).
- analyze_first_open_followup.py to inspect post-forced-open behavior
(E2): same-color play vs discard counts, other-open rate, terminal
hand composition.
Findings recorded in docs/plans/deep-cfr-selectivity.md sections 3-6.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds torch.autocast(fp16) + GradScaler around _train_advantage and
_train_strategy when run.use_amp=true and device=cuda. CPU/non-CUDA
falls back to fp32 no-op. Mitigations:
- scaler.unscale_(optimizer) before grad_clip.
- nonfinite-loss guard skips overflowing batches and counts them.
- diff.float().square() in advantage loss to avoid fp16 overflow.
- strategy mask/log_softmax kept in fp32.
New metrics: amp/grad_scale, amp/nonfinite_loss_count.
Tests: AMP CUDA smoke + CPU fallback in test_deep_cfr_trainer.py.
Bench: scripts/bench_amp_trainer.py micro-benches train phases under
synthetic replay memory. smoke.yaml result is fp32 3.22ms / AMP 3.92ms
(0.82×, regression). 100-iter A/B on default.yaml deliberately
skipped: smoke regression mirrors the 2026-05-07 torch.compile
regression dynamic (dispatch overhead > kernel benefit at this model
size) and re-confirming on the same size adds no information.
Default stays run.use_amp: false. Re-enable trigger documented in
docs/performance.md: hidden_size >= 1024 or num_layers >= 6, then run
the bench script + 100-iter A/B before flipping default.
Implements the central inference server pattern: a dedicated GPU
process owns advantage/strategy/league networks, batches policy
requests across traversal workers via shared-memory tensor pool, and
returns logits. Workers route forward calls through InferenceClient /
NetworkProxy when traversal.inference_backend == "server".
Default remains traversal.inference_backend: local. The server
backend regresses iter time ~3.8× on the inspected default config
(small-model dispatch + sync-blocking traversal capping realized
batch at ~num_workers=8 instead of the bs=64-256 needed to amortize
IPC overhead). Keeping the implementation behind the flag lets us
re-enable when (a) model size grows, (b) per-worker interleaved
traversal lands, or (c) eval becomes dominant — see
docs/performance.md "Option A Bench Result and Structural Ceiling"
for the full diagnosis.
Plumbing included:
- inference_buffers.py: shared-memory tensor pool with slot
management.
- inference_client.py: per-worker client + NetworkProxy adapter for
the existing traversal.pyx call sites.
- inference_server.py: spawn-context server process with
batch-window aggregation, weight sync, shutdown sentinel.
- bench_inference_backend.py: A/B between local and server backends
with eval/checkpoint disabled.
- test_inference_server.py: round-trip and integration tests.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Cython implementation in heuristic_cy.pyx achieves ~2.55× speedup on
opponent_act_seconds (200-game eval: 59.20s → 23.24s). Original Python
implementation preserved verbatim in heuristic_py.py as the equivalence
reference. Action-sequence equivalence is verified by
test_safe_heuristic_equivalence.py against seeded game corpora.
Key implementation notes:
- File-local wraparound=True override required for negative discard
indexing; Cython global wraparound=False would segfault.
- annotation_typing=False preserves verbatim Python semantics.
- _CachedState materializes hands/expeditions/discards/deck once per
act() call — this is the dominant performance win.
Further C-array optimization of _card_value_for_me /
_card_value_for_opponent / _color_commitment / _bonus_potential is
deferred. The current 2.55× delivers most of the dense-eval future
benefit; further work is gated on actually adopting denser eval
schedules (eval_every=5, games=1000).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adopt a 5-namespace scheme so wandb groups related metrics in the
sidebar and capture-group regex (eval/(random|safe_heuristic)/win_rate0
vs eval/(?:random|safe_heuristic)/win_rate0) controls panel splitting:
- loss/{advantage,strategy}
- samples/{advantage,strategy,advantage_player_N}
- memory/{advantage,strategy,advantage_player_N}
- time/{iteration_seconds,traversal_seconds,advantage_train_seconds,
strategy_train_seconds,evaluation_seconds,memory_add_seconds,
checkpoint_seconds,batch_tensor_seconds,nodes_per_second,
advantage_player_N_sample_seconds,strategy_sample_seconds}
- traversal/{nodes,terminals,depth_cutoffs,node_limit_cutoffs,
max_depth_reached,endpoints,avg_endpoint_depth,
endpoint_depth_bucket_*,regret_fallback_*,sampled_actions}
- eval/<opponent>/<metric> (3-level so opponent can be the capture group)
`iteration` keeps no namespace (it's the wandb step axis). Internal
TraversalStats.to_dict() and benchmark.py's standalone result dict
keep their flat names — only the trainer's emitted metrics are
remapped, with the traversal_*→traversal/* translation done at
insertion into runtime_metrics.
analyze.py updated to read the new keys (PlotSpec metrics, color map,
opponent_names parser, _first_existing_eval lookup). Tests updated for
the new eval_metrics dict keys.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Make traversal progress and iteration-complete summary lines easier to
visually scan during long runs by leading with [i=N]. Drop the redundant
"iteration=N" kv from the body to keep lines short.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The avg-strategy 1000iter file (with the recent +traversals/+LR/+LCFR
changes) is the canonical "best-known" config. Renamed it to
default.yaml so users start from a single, obvious entry point and
override one field per ablation via --set. Other 12 configs moved to
configs/archive/ — kept for historical reproduction, not for active use.
- configs/deep_cfr/{default.yaml, smoke.yaml} are the only active configs
- experiment_name shortened to "deep-cfr-default" (was a long mouthful)
- AGENTS.md examples and Project Layout section rewritten around
default.yaml; ablation example shows the override-one-field pattern
- Tests pointed at the archived slot-playability config for the legacy
reproduction assertions
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
If eval_every is positive but max_iterations falls before the next scheduled
eval iteration (including resume cases where current_iteration is already
past the last eval boundary), log a one-time warning at run start so the
user notices the misconfiguration. We deliberately do not force an
end-of-run eval, which would distort time budgets and reproducibility.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Drop checkpoint.directory from config — config defines what an experiment
is, not where its outputs go. The CLI now computes the run directory from
run.experiment_name plus a timestamp, defaulting to runs/tmp/ for
throwaway runs and runs/ when --keep is passed.
- Remove CheckpointConfig.directory and DeepCFRConfig.checkpoint_path
- DeepCFRTrainer takes run_dir: Path explicitly
- CLI: add --keep boolean; --resume requires an explicit path (no shortcut)
- Auto path: runs/[tmp/]<YYYY-MM-DD_HHMMSS>_<experiment_name-kebab>/
- Rename 13 configs to kebab-case; strip directory: lines; kebab their
experiment_name values
- Rewrite AGENTS.md training/run sections; document
archive/tmp/<flat> layout, --keep, kebab-case scope
- Update tests for new run_dir flow and dropped --resume shortcut
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Remove legacy aliases, rename max_hours to max_minutes, collapse the
four checkpoint save flags into save_every + save_latest, and change
defaults to safer values (opponent_policy=self_play_league,
device=auto, eval_every=50, max_depth=null). Migrate all archived
yaml configs and tests to the new schema.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Mirror Deep CFR training metrics to W&B via a new WandbRunTracker
wired through CompositeRunTracker; wandb is an optional extra so
default installs and runs stay unchanged. Train CLI gains
--wandb/--wandb-project/--wandb-mode/--wandb-name/--wandb-tag, and
train() now closes the tracker in a finally block so runs finalize
even on early exit.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Add network.kind field to config (mlp/color_shared)
- Implement ColorSharedNetwork that:
- Splits input into 5 equal color blocks
- Encodes each block with shared weights
- Pools with mean/max aggregation
- Concatenates pooled embeddings with remainder
- Outputs same action logits as MLP
- Add ColorAttention for optional self-attention over color embeddings
- Add network.color_attention_layers and color_attention_heads config
- Maintain full backward compatibility (default kind=mlp)
- Add 28 comprehensive tests covering all architectures
Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
coolguyandClaude Opus 4.7