feat: log slurm env vars to wandb config#1
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Fixes three distinct correctness issues in the Gemma 3 4B backbone +
Gemma-v1 action expert wiring that a careful second pass uncovered:
1. Vision projector crashes at 448×448. `Gemma3MultiModalProjector`
hard-codes `patches_per_image = image_size // patch_size`, so the
default config's `vision_config.image_size = 896` made the
projector reshape `(B, 1024, 64, 64)` when SigLIP actually emits
`B × 1024` patches at 448 input — a runtime crash on the first
forward pass. Set `image_size = 448` to match π0.6's stated
resolution (→ 32 patches/side, 256 mm tokens/image, which also
matches the model card).
2. RoPE θ asymmetry between backbone and expert for global layers.
Gemma 3 interleaves local layers (θ = 10 000) with global layers
(θ = 1 000 000). The expert's own `rope_theta = 10 000` was being
applied to its Q/K even on global layers, so the shared
cross-attention ran backbone-Q (rotated at 1M) against expert-K
(rotated at 10k) — the `q·R(Δpos)·k` invariant breaks when the two
rotations live in different RoPE bases. Fix: both streams now use
the backbone's per-layer θ; the expert's fallback θ is ignored at
runtime and documented as such.
3. Sliding-window mask used dense indices instead of absolute
positions. During expert cross-attention the Q tokens sit at
`prefix_offsets + chunk_idx`, but `_build_sliding_window_mask`
built its mask from `torch.arange(seq_len)` and the call site
sliced `[:, :T_suffix, :]`. Result: every prefix key farther than
`window` from the dense suffix row index was dropped — i.e. the
sliding layers saw essentially no prefix during cross-attention.
Fix: take `(query_positions, key_positions)` and compute
`|pos_q - pos_k| < window` over absolute positions; stash the
prefix positions in the KV cache so the expert can reconstruct
them on each denoising step.
Tests:
* `TestSlidingWindowMask.test_cross_attention_uses_absolute_positions`
— regression guard for #3.
* `TestGemma3WithExpertConfig.test_vision_image_size_matches_input_resolution`
and `.test_projector_accepts_448_inputs` — regression guards
for #1 (config invariant + end-to-end projector forward).
* `TestRopeThetaSymmetryDuringForward.test_expert_uses_backbone_per_layer_theta`
— regression guard for #2, uses monkeypatched `apply_rope` to
observe exactly which θ each layer asks for.
Local pytest: 29 passed / 1 deselected (up from 25). Full policies/
CPU suite: 75 passed / 2 skipped / 6 deselected. Pre-commit: all
hooks green (ruff, ruff-format, pyupgrade, typos, bandit, gitleaks).
https://claude.ai/code/session_01MibvjbcZo38nxrx6n9giLi
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shuheng-liu
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May 27, 2026
…ring Addresses inline review on PR #337: - #1 (test tautology): extract the strip predicate to a module-level `_is_normalize_buffer_key` helper in `modeling_pi07_low_level.py`. The production filter now calls it; the two new tests (`test_predicate_matches_all_eight_saved_buffer_keys`, `test_predicate_anchored_at_key_start`) call the same helper directly, so any future edit to the predicate trips them instead of passing vacuously. - #4 (config persistence): after the strip fires, reset `model.config.skip_normalization_weights = False` so that subsequent `save_pretrained` / resume / inference loads do not re-strip the now-correct finetuned buffers. - #2 (inf-buffer trap) + #3 (other policies share the trap): expanded the field docstring to call out the `dataset_stats` precondition, the one-shot semantic, and the current pi07_paligemma_low_level scope.
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Addresses the new findings on PR #340: - (#1, high priority) strict=True + skip_normalization_weights=True used to raise RuntimeError("Missing key(s) ...") in pi0/value because the strip removed Normalize buffer keys before load_state_dict, but the buffers were still registered on the model from __init__. The other eight policies hardcode strict=False on the load_state_dict call so they didn't hit this. Fix: pass effective_strict = strict and not stripped_keys so the strict semantics apply unchanged on the default-load path and only relax when the strip actually fired. - (#2) pi0/value's _load_as_safetensor now resolves is_main_process via get_proc_accelerator() and threads it to the strip helper, so the INFO/WARNING fires once per load (not once per rank under DDP/FSDP). - (#3, optional) Added a note in _assert_normalize_buffers_initialized explaining why "any normalize buffer is inf" is a safe proxy for "the stripped buffer is still inf" (create_stats_buffers rejects partial stats, so buffers are all-finite or all-inf — no reachable mixed state). Pre-commit clean on --all-files (CI parity); CPU suite 460 passed.
shuheng-liu
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May 27, 2026
Addresses the third-pass review on PR #340: - (#1) Defensive _materialize() before torch.isinf in the inline check inside _assert_normalize_buffers_initialized. Today from_pretrained runs before accelerator.prepare so params are plain Tensors, but exposing the helper without _materialize would break on a future caller invoking it on a fully_shard'd model. _materialize is a no-op on plain Tensors. Added a comment noting both the FSDP2 motivation and the named_parameters-vs-named_buffers convention (Normalize uses nn.Parameter(requires_grad=False), not register_buffer). - (#2) Clarified the one-shot reset docstring on both PreTrainedConfig.skip_normalization_weights and the helper itself: the reset is in-memory only. Persistence requires save_pretrained; re-running from_pretrained on the original source path reloads True and re-strips. The common in-process resume path (train.py → save_checkpoint → cfg.save_pretrained) is unaffected. - (#3) The named_parameters clarification is now part of the inline comment block in _assert_normalize_buffers_initialized. - (#4 logging vs print inconsistency) Pre-existing in pi05; not in scope for this PR. Pre-commit clean on --all-files; 30 targeted tests pass.
This was referenced May 28, 2026
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