# Deep CFR Performance Notes This document tracks current runtime bottlenecks for the active Deep CFR training path. The numbers below are observational, not a benchmark contract. ## Current Default Runtime Source run: ```text runs/tmp/2026-05-07_171535_deep-cfr-default/metrics.jsonl ``` The run used `configs/deep_cfr/default.yaml` with CUDA enabled. At the time of inspection, completed metrics covered iterations 70 through 95. The training process was still running, so later rows may differ. Non-evaluation iterations averaged: | Metric | Mean | Share | | --- | ---: | ---: | | `iteration_seconds` | 17.85s | 100% | | `traversal_seconds` | 10.71s | 60% | | `advantage_train_seconds` | 4.72s | 26% | | `strategy_train_seconds` | 2.39s | 13% | | `memory_add_seconds` | 1.25s | 7% | | `checkpoint_seconds` | 0.02s | <1% | `batch_tensor_seconds` averaged 4.60s. This is not an additional phase; it is included inside the advantage and strategy training timers. It measures the cost of building per-step tensors from sampled replay entries, including `np.stack` and transfer to the trainer device. One evaluation iteration was present in the inspected window: | Iteration | `iteration_seconds` | `evaluation_seconds` | | ---: | ---: | ---: | | 75 | 40.30s | 18.29s | With the default `evaluation.eval_every: 25`, an 18.29s evaluation amortizes to about 0.73s per training iteration. This makes evaluation noticeable in logs but not the primary long-run wall-clock bottleneck. If the same 100-game evaluation is run more often, the amortized cost becomes: | `evaluation.eval_every` | Amortized eval cost | | ---: | ---: | | 25 | 0.73s/iteration | | 10 | 1.83s/iteration | | 5 | 3.66s/iteration | If `evaluation.games` increases from 100 to 1000, evaluation cost should be expected to grow roughly linearly unless fixed overhead or batching effects dominate. Using the observed 18.29s eval as a rough base, a 1000-game eval would be about 183s: | `evaluation.games` | `evaluation.eval_every` | Rough amortized eval cost | | ---: | ---: | ---: | | 1000 | 25 | 7.3s/iteration | | 1000 | 10 | 18.3s/iteration | | 1000 | 5 | 36.6s/iteration | At that point evaluation becomes a first-order wall-clock concern. ## Current Bottleneck Shape Default training is split between CPU-heavy traversal and CUDA-backed network training: - Traversal is the largest measured phase at roughly 60% of non-eval iteration time. - Advantage and strategy training together are roughly 40% of non-eval iteration time. - Tensor materialization is a major part of training time, so reducing pure GPU compute alone cannot remove all of the training cost. The default traversal settings are: ```yaml traversal: traversals_per_player: 280 num_workers: 8 worker_chunk_size: 8 ``` This gives 560 traversals per iteration, split into 70 worker batches. ## Device Use The trainer constructs the advantage and strategy networks on `run.device`. `configs/deep_cfr/default.yaml` sets: ```yaml run: device: cuda use_amp: false ``` Traversal workers currently reconstruct networks on CPU: ```python device = torch.device("cpu") ``` So, in the default multiprocessing traversal path, setting `run.device: cuda` accelerates the trainer's optimization steps and trainer-device evaluation, but does not move traversal worker inference to GPU. On ROCm systems, AMD GPUs may still appear through PyTorch's `cuda` device API if a compatible ROCm build is installed. The project does not have a separate AMD-specific device path. ## AMP Status `run.use_amp` exists in configuration, but automatic mixed precision is not currently wired into the training loop. There are no active `autocast` or `GradScaler` calls in the Deep CFR trainer. Practical implication: setting ```bash --set run.use_amp=true ``` should be treated as a no-op until the trainer implements AMP explicitly. If implemented, AMP would mainly target the network optimization phases: - `advantage_train_seconds` - `strategy_train_seconds` It would not directly reduce traversal CPU time or replay tensor materialization overhead. ## Batching Status There are two separate meanings of batching in the current codebase. Implemented: - Worker batching via `traversal.worker_chunk_size`. - Evaluation policy batching via `evaluation.batch_size`. - Optimization batching via `optimization.advantage_batch_size` and `optimization.strategy_batch_size`. Not implemented: - Batched network inference inside traversal. Traversal policy evaluation currently encodes one state and runs a single-row network call, effectively `batch_size == 1`, then converts the result back to CPU/Numpy for Cython-side policy logic. This means traversal is not structured to feed many states to the GPU in a single inference call. Evaluation batching is already implemented. During evaluation, active games that need a policy-network action are grouped into chunks of `evaluation.batch_size`, then evaluated together on the evaluation device. Default config uses: ```yaml evaluation: batch_size: 64 ``` This makes evaluation much more suitable for GPU inference optimization than traversal is today. The remaining question is whether policy-network forward time is actually the dominant part of evaluation. ## Evaluation Breakdown The current source emits evaluation metrics as: ```text eval// ``` The inspected run uses an older flattened scheme: ```text eval__ ``` For that run, iteration 75 had `evaluation_seconds = 18.29s`. Evaluation was parallelized by opponent, so per-opponent `elapsed_seconds` values overlap and must not be summed as wall-clock time. The slow safe-heuristic opponents dominated the eval wall-clock. Representative per-opponent breakdown: | Opponent | Elapsed | Network | Postprocess | Opponent act | | --- | ---: | ---: | ---: | ---: | | `random` | 0.57s | 0.18s | 0.25s | 0.06s | | `passive_discard` | 0.36s | 0.13s | 0.17s | 0.00s | | `safe_heuristic` | 14.54s | 2.65s | 3.34s | 7.57s | | `safe_heuristic_loose` | 11.20s | 2.55s | 3.30s | 4.63s | | `safe_heuristic_strict` | 15.94s | 2.51s | 3.14s | 9.22s | | `noisy_safe` | 1.68s | 0.40s | 0.58s | 0.57s | The important read is that safe-heuristic evaluation is not primarily GPU network forward time. `opponent_act_seconds` and policy post-processing are larger than `policy_network_seconds` for the slowest opponents. Useful eval runtime keys to inspect: ```text eval__elapsed_seconds eval__policy_network_seconds eval__policy_encoding_seconds eval__policy_postprocess_seconds eval__policy_legal_mask_seconds eval__opponent_act_seconds eval__apply_action_seconds eval__diagnostics_seconds eval__final_scoring_seconds ``` For newer runs, replace `eval__` with `eval//`. ## Evaluation Optimization Options The practical eval tuning levers are: 1. Tune `evaluation.batch_size`. Try 128 or 256 if GPU memory allows. This helps most when `policy_network_seconds` is a large fraction of opponent elapsed time. 2. Tune `evaluation.num_workers`. Multiple workers parallelize opponents, but they can also split GPU work across processes and duplicate model copies. Compare 1, 2, and 4 workers for CUDA eval instead of assuming the largest value is fastest. 3. Split light and full evaluation. A useful schedule would run a small opponent/games set frequently and the full opponent suite less often. The current config has one eval schedule, so this would require a feature change. 4. Reduce frequent opponents. The safe-heuristic opponents dominate wall-clock in the inspected run. For frequent checks, evaluate against one or two representative opponents and run the full suite less often. 5. Add a diagnostics-light mode. Current eval records many action-quality and expedition diagnostics. The measured `diagnostics_seconds` is small in the inspected run, but a basic win-rate/score-only mode would still make frequent large evals simpler and cheaper. 6. Consider asynchronous evaluation. A separate process can evaluate checkpoints while training continues. This does not reduce total compute, and it can contend for GPU if run on the same device, but it removes eval pauses from the trainer wall-clock. 7. Consider eval-only AMP or compiled inference. This is simpler than training AMP because evaluation has no backward pass. It should be measured against `policy_network_seconds`; it will not reduce opponent policy time or game-state transition time. ## TensorRT Assessment TensorRT is not an obvious high-priority optimization for the current default training loop. Reasons: - The largest phase is traversal, and default multiprocessing traversal runs on CPU workers. - Traversal network inference is single-state, control-flow-heavy, and crosses between encoded state arrays, PyTorch tensors, and CPU/Numpy outputs. - TensorRT mainly helps inference, while the CUDA-backed trainer phases are training steps with backward passes and optimizer updates. - Evaluation can benefit from inference optimization in principle, but default evaluation is only every 25 iterations. Even making evaluation much faster has limited effect on long-run average iteration time. For evaluation specifically, TensorRT is more plausible because GPU batching is already implemented. It would replace or wrap the strategy-network forward pass with a precompiled inference engine. Its maximum impact is bounded by `policy_network_seconds`, not by total eval time. In the inspected eval row, the slow safe-heuristic opponents spent about 2.5-2.6s in policy-network forward but 4.6-9.2s in opponent action selection and about 3.1-3.3s in policy post-processing. That means TensorRT could help eval, especially for larger `evaluation.games`, but it is not expected to collapse the 18.29s eval to a tiny number by itself. TensorRT becomes more attractive if: - `evaluation.games` is raised substantially, such as 1000 games. - `evaluation.eval_every` is reduced to 5 or 10. - `policy_network_seconds / elapsed_seconds` rises after batch-size and worker tuning. TensorRT may also become relevant for traversal after a larger traversal redesign that batches many policy-needed states into GPU inference requests. ## Optimization Priorities Based on the current metrics, the more plausible performance work is: 1. Improve traversal throughput. Tune worker count and chunk size, then profile the Cython traversal hot path. 2. Reduce training tensor materialization cost. `batch_tensor_seconds` is a large part of train time. More contiguous replay storage or tensor-ready sampled batches may help more than model-kernel tuning alone. 3. Implement and test AMP. This should be gated by `run.use_amp` and measured against loss stability and wall-clock, since it only targets the optimization phases. 4. Consider `torch.compile` for the trainer networks. This should be measured separately from traversal because the default training loop has substantial non-kernel overhead. 5. Consider batched traversal inference only as a structural project. This is the path that could make GPU inference accelerators more meaningful, but it requires changing traversal scheduling, not just swapping the network backend. 6. For eval-heavy runs, optimize the safe-heuristic opponents and policy post-processing before assuming TensorRT is the main lever. The inspected eval row shows those costs dominate the slowest opponents. ## Experiments ### `torch.compile` on trainer networks (2026-05-07, regression) Wrapped both advantage networks and the strategy network with `torch.compile()` at trainer construction time. Implementation also required a `_clean_state_dict()` helper to strip the `_orig_mod.` prefix that compiled modules add to `state_dict()`, plus a `_orig_mod`-routed path for `load_state_dict()` so multiprocessing traversal workers and checkpoint restoration could keep using the uncompiled `DeepCFRMLP` class. Measurement (8 iterations on `default.yaml`, eval and checkpoint disabled, iteration 1 dropped as compile warm-up): | | iter mean | 1000-iter projection | | --- | ---: | ---: | | Baseline (no compile) | 17.93s | 4.98h | | `torch.compile` on trainer nets | 18.79s | 5.22h | | Effect | +0.86s (+4.8%) | +14 min | Net result: regression. Two reasons: - Traversal is ~60% of iteration time and runs in CPU multiprocessing workers that reconstruct networks from cleaned `state_dict`s, so they bypass the compiled wrapper entirely. - `DeepCFRMLP` (512-hidden, 3-layer) is small enough that the compiled call dispatch overhead exceeds the kernel-fusion benefit. Implementation preserved on branch `experiments/torch-compile` for revisiting if the trainer model grows substantially or after the batched-traversal-inference work in Optimization Priorities #5 lands — that is the change that would put compile on the dominant phase, not just on the trainer's optimization steps. Not enabled on `main`. ### GPU forward profiling for batched traversal (2026-05-07, decision support) To decide whether Optimization Priorities #5 (batched traversal inference) is worth implementing, profiled `DeepCFRMLP` from `default.yaml` (input_dim=365, output_dim=22, hidden=512, 3 layers, ReLU) on an RTX 3090 in `eval()` + `inference_mode`, with 10-iter warm-up and 1000-iter measurement per batch size. Script: `scripts/profile_gpu_forward.py`. | Batch size | μs/call | μs/state | Speedup vs bs=1 | | ---: | ---: | ---: | ---: | | 1 | 80.07 | 80.074 | 1.00× | | 4 | 81.20 | 20.299 | 3.94× | | 16 | 91.30 | 5.706 | 14.03× | | 64 | 93.61 | 1.463 | 54.75× | | 256 | 88.34 | 0.345 | 232.03× | | 1024 | 161.95 | 0.158 | 506.30× | Policy-call supply from `runs/tmp/2026-05-07_181155_deep-cfr-default/metrics.jsonl`: mean `traversal/nodes` ≈ 205,810 over 280 traversals/player → ~368 policy calls per traversal (rough upper bound on batchable states), ~200k per iteration across 560 traversals. Verdict: **Priority #5 is worth pursuing.** Per-state cost drops from 80 μs at bs=1 to 0.34 μs at bs=256 (>230×). The available supply of ~368 states per traversal sits comfortably in the bs=64–256 range where μs/call plateaus near 90 μs. End-to-end gain will be bounded by encoding and worker-GPU coordination overhead, but the GPU forward is not the limiter once batching is in place.