performance.md additions:
- Batched Traversal Inference design decision (A vs B vs C with
rationale).
- Option A bench result and structural ceiling (realized batch ~7.2,
IPC overhead exceeds GPU gain at small model size).
- Post-A optimization calculus: why compile/TensorRT remain
iter-neutral today and become meaningful only after model growth
and/or denser eval. Sequencing matters; do not retest these on the
current small model.
- Free-threaded Python (3.13t/3.14t) note: cleanest endpoint in
principle, but PyTorch maturity + Cython nogil audit cost block
near-term adoption.
docs/plans/ (4 plan documents for Codex execution):
- batched_traversal_inference_server.md (executed; deferred).
- amp_trainer.md.
- torch_compile.md.
- cython_safe_heuristic_bots.md (executed; first-pass landed).
docs/reports/ (3 cost reports):
- cost_pytorch_free_threaded_2026-05-07.md: WAIT 3-6 months;
PyTorch wheels exist but our Cython is the gating cost.
- cost_cython_nogil_audit_2026-05-07.md: medium effort, traversal.pyx
carries 90% of blockers; Steps 1-3 (cfr_math/encoding nogil
keywords, TraversalStats cdef class) are safe and cheap, Steps
4-6 wait for triggers.
- cost_pytorch_cuda_multithread_2026-05-07.md: risky;
optimizer.step / load_state_dict race silently with concurrent
forward; per-thread default streams unset means naive threading
serializes on default stream anyway.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Measured DeepCFRMLP forward at bs={1,4,16,64,256,1024} on RTX 3090.
Per-state cost drops 232× from bs=1 (80 µs) to bs=256 (0.34 µs) while
per-call latency stays near 90 µs through bs=256. Policy-call supply
from a real run is ~368 states per traversal and ~200k per iteration,
well above the bs=64–256 plateau, so batched inference is not
supply-limited. GPU forward is not the limiter once batching exists.
Verdict: Optimization Priorities #5 (batched traversal inference) is
worth pursuing. End-to-end gain will still be bounded by encoding and
worker-GPU coordination overhead.
- scripts/profile_gpu_forward.py: standalone profiling script
- docs/performance.md: new "GPU forward profiling for batched traversal"
experiment section with table, supply estimate, and verdict
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Make the link from the failed torch.compile experiment to Optimization
Priorities #5 explicit, so a future revisit happens at the right time
(once compile is on the dominant phase, not just trainer optimization
steps).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Wrapping trainer networks with torch.compile produced a 4.8% regression
in iteration time on default.yaml (17.93s → 18.79s). Two causes: (1)
the dominant phase is CPU traversal which bypasses the compiled
wrapper, (2) DeepCFRMLP is too small for compile dispatch overhead to
pay back. Implementation kept on experiments/torch-compile for future
revisits when the trainer model or inference path changes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
coolguyandClaude Opus 4.7