dev-4.x contribution follow ups

[jlarson4]

Description

Works through the follow-up backlog from our PR reviews for the dev-4.x changes, verified against current dev-4.x. The review caught and fixed several additional bugs, including two long-standing ones nobody had seen because the affected notebooks had never executed coherently end to end.

Main changes:

• MLP weight orientation: W_in/W_out/W_gate were raw aliases to the wrapped module's weight with no layout conversion, so every nn.Linear-backed model (all of boot_native) returned transposed weights; Conv1D models were right by luck. Now layout-aware properties on MLPBridge, tested in both aspect ratios (the bottleneck shape defeats shape heuristics).
• boot_native init parity: gpt2-mode default std was 0.02 instead of the legacy 0.8/√d_model (toy-model training dynamics depend on this — the grokking demo memorizes vs. stalls); seeded init now generates on CPU/fp32 and copies in, so the same seed reproduces identical weights before or after .to(device)/.to(dtype); initializer_range acts as the xavier/kaiming gain like the legacy init did.
• get_bridge_params GQA expansion: grouped K/V and biases expand to n_heads (repeat_interleave, matching HF repeat_kv pairing) so SVDInterpreter's per-head OV pairing is correct on GQA models; non-divisible head counts raise instead of silently floor-dividing.
• Hook alias fallback chains: hook_aliases targets can be a priority-ordered list; OLMo-Hybrid's hook_mlp_out now resolves to ln2_post.hook_out on post-norm layers and falls back to mlp.hook_out on linear-attention layers.
• Smaller fixes: audio benchmarks take feature-extractor-driven input (AST-style spectrogram models can verify); enable_compatibility_mode() on audio models raises a clear NotImplementedError instead of a KeyError; bias-free checkpoint stacking raises an actionable error that now survives __getattr__ (CPython discards the original diagnostic otherwise); print(bridge) no longer crashes (__str__ called a method that never existed); train() works on a config copy instead of mutating the caller's; dead _set_processed_weight_attributes removed.
• Doctests: executable examples ported to TransformerBridge (merged with docs: migrate executable doctests to TransformerBridge #1576's version of the same migration; conflicts resolved in its favor), a decorative comment-only doctest in ActivationCache made live, and a repo guard test that fails if any surviving module's doctest references HookedTransformer.
• Grokking + No_Position notebooks ported to boot_native and actually green (96/96 and 30/30 under nbval). Three root causes found along the way:
  1. The "causes CI to hang" mystery was tqdm's 25k updates tripping Jupyter's IOPub rate limiter into >5s silent gaps, which nbval's hard-coded output-wait reads as a hang.
  2. doc_sanitize.cfg's device-string stripper has never loaded — nbval's parser requires a space after replace:, so regex4 silently didn't exist. One-character fix.
  3. Grokking's cell 70 hardcoded key_freqs = [17, 25, 32, 47] from a past run — which matched neither observable model (HEAD's own stored norms plot shows different frequencies). The notebook now detects key frequencies from the embedding norms, and the restricted/excluded-loss cells finally demonstrate the canonical result against their own model (restricted 1.19e-07, excluded 5.07).
     Notebook diffs are stripped to necessary changes only (metadata/execution counts/env-specific outputs reverted to HEAD wherever nbval provably never compares them).
• CI: nightly slow-tier workflow (slow-tests.yml) — scheduled + manual dispatch, HF cache chained to the existing key, swap + disk reclaim, concurrency-guarded, with the >16GB-RAM adapters explicitly ignored until we have a large runner. Note the cron only fires once this reaches main; dispatch works before that.
• Docs: the NSP section of the migration guide now states what the code actually does (the high-level NSP API requires the legacy encoder surface and is not yet ported).

Deferred with reasons (tracked in the plan doc): GIDD's ScaledLinear scale needs the real hub class to verify before touching; copy-split staleness, storage-identity load grouping, muP native init, and SSM primitive generalization are issue-shaped product work.

Type of change

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)

Checklist:

• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes (unit 5163 post-merge with dev-4.x; mypy clean on 422 files; doctests incl. the gpt2-booting evals example; Grokking 96/96 and No_Position 30/30 under nbval)
• I have not rewritten tests relating to key interfaces which would affect backward compatibility