[RyanPCo]

On the lab's 8 x A40 nodes -- no NVLink, two NUMA islands joined by
cross-socket links -- the per-step gradient all-reduce, not the math, sets the
step time once a model is large enough. Measured 2026-09-16 on a ~1.2 B
-parameter policy: 6.4 s/step at 8 x batch 16 against 1.4 s/step on a single
GPU, and 3.6 s/step with the reduction in bf16. Only the reduction is bf16;
master weights and the optimizer stay fp32.

It is plain DDP otherwise -- no unused-parameter detection -- so a model that
leaves a trainable tensor without a gradient needs ddp_pi's
ddp_find_unused_parameters_true instead and will crash under this one. The
config says so, because nothing else would stop you selecting it for Pi.

The hook is a zero-argument factory rather than a Hydra _partial_ because
Lightning logs the hook's qualname, which a functools.partial does not
carry. make_bf16_compress_hook is named for what it is: torch's own hook is
bf16_compress_hook, and sharing that name made the factory look like the hook.

test_eval_determinism's trainer parametrization now enumerates the config group
instead of a hardcoded list, so a new trainer config cannot land without the
gradient-clip knob being checked.

🤖 Generated with Claude Code

https://claude.ai/code/session_01HjCQBqQCr2igFEMR7yUovw

[RyanPCo]

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• pi: initialize from PaliGemma with a scratch action expert #677 : 2 dependent PRs (#678 , #680 )
• perf: vectorized image augs and a torch.compile knob for HPT and Pi; Pi clips gradients like openpi #676
• loader: one zarr read per key, not one per keymap entry #674
• eval: build the pinned-episode video batches only where they are used #673
• checkpoints: last.ckpt as a symlink, not a second full copy #672
• trainHydra: metric_frames_per_episode: auto, and warn on empty prompts #671
• transforms: per-step wrist framing for proprio history #670
• viz: one keypoint renderer, the horizon trace #669
• configs: history and backbone-LR knobs on the keypoint wrist HPT model #668
• trainer: ddp_bf16_compress, DDP with a bf16 gradient all-reduce #667 👈 (View in Graphite)
• eval: overlay videos play at the source rate under DDP #666
• hpt: build the policy on CPU and let Lightning place it #664 : 1 other dependent PR (#665 )
• hpt: never re-initialise pretrained stems; hash-check them; mask text padding #645 : 1 other dependent PR (#646 )
• obs: proprio history (K proprio steps, one token each, learned time embedding) #650
• loss: mask the repeat-last action padding out of the HPT loss #649
• resnet: keep a frozen backbone in eval mode; give backbones their own LR group #652
• augs: per-sample image augmentation for HPT train-mode augs #651
• eval: deterministic validation (seeded prompts + flow sampling); log-only grad-norm MAD #644
• data: mecka_fold_flagship_top3_hpt_6d, the 3-operator flagship train split #643
• val: subsampled metric loaders + pinned contiguous video loaders #642
• infra: mecka 6D / keypoint recipes, 300M models, defaults, and the rollout revert #640
• eval: layout-aware frame-invariant metrics, and a train-split viz head #659
• data: stop letting one bad episode or a rotation channel take a run down #658
• norm: guard degenerate channels, horizon pooling, and no rot6d normalization #657
• keypoints: the 144-D wrist-first hand-keypoint action becomes the human default #656
• mecka: fix the double-mirrored LEFT wrist frame, and correct existing zarrs #655
• 6d: PI goes 6D-only; Eva and Human gain the continuous-rotation modes #654
• 6d: continuous rotation representation, bimanual layouts, shared norm helpers #653
• train: give each Slurm slurm job gets its own cache dir to avoid conflict #638 : 1 other dependent PR (#639 )
• write git version into the run dir for reproducibility #637
• train: don't load PI base weights on resume and verify that checkpoint path exists #636
• configs: declare proprio and actions dims once per HPT model config #634 : 1 other dependent PR (#635 )
• cache norm stats for data configs to minimize recomputation #633
• import cloud packages lazily to support local training #631 : 1 other dependent PR (#632 )
• cache dataset resolver for faster training initialization #630
• validate episode hashes in data config before training #628
• modify pi camera map to match data #629
• add tests to ensure training works between every model and data vendor #626
• run CI tests on every PR #625
• add env variables for user-specific paths #624
• build: set pyproject and uv as the dependency managers #623
• fix(configs): fix paths for data hydra yaml configs #622
• refactor(algo): delete ACT #627 : 1 other dependent PR (#621 )
• tests: create dedicated test folder with unit and integration tests #620
• add pyright type checker #619
• main

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[RyanPCo]

Automated adversarial review (read-only; findings were checked by a second skeptical pass before posting): 1 nit.

[RyanPCo]

nit (leftover-experiment): The dated 8xA40 benchmark (6.4 s/step vs 3.6 s/step, 2026-09-16) belongs in the PR description, not the config. 'bf16 gradient all-reduce for comm-bound DDP; master weights and optimizer stay fp32' is enough. On line 15, a one-line note that the parent's strategy: ddp string has to become a DDPStrategy object to accept ddp_comm_hook would explain the re-specified target.

[RyanPCo]

Review findings: status after the fixup pass. The fixes are squashed into this PR's commits, and the whole stack was force-pushed.

Fixed

• Dated cluster benchmark in config comment: Removed the 8xA40 numbers. Added a one-line comment explaining why the strategy is re-specified as a DDPStrategy object.