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name: introspection-and-customization description: "Routes vit-pytorch attention recording, embedding extraction, custom transformer injection, flash-attention-style wrappers, and parallel transformer customization helpers." disable-model-invocation: true metadata: disco-role: operating license: MIT

Introspection and Customization

Use this sub-skill when the user wants to inspect a vit-pytorch backbone or swap in a transformer implementation, especially prompts mentioning Recorder, Extractor, attention maps, embeddings/latents, efficient.ViT, Nystromformer, x-transformers, simple_flash_attn_vit, simple_flash_attn_vit_3d, or parallel_vit.

Route by user intent

• Attention maps from a base ViT: use vit_pytorch.recorder.Recorder. Read API reference and run smoke_introspection.py when validating an install. The recorder wraps a backbone, registers hooks on first forward, returns (predictions, attention_maps), and must be ejected when hooks are no longer needed.
• Embedding or latent extraction: use vit_pytorch.extractor.Extractor. Read API reference before choosing layer_name, layer, layer_save_input, return_embeddings_only, or detach. Use this for transformer outputs, multi-scale outputs, and custom hook points.
• Custom efficient transformer injection: use vit_pytorch.efficient.ViT when the user already has or wants to provide a token-sequence transformer. Read API reference. External examples such as nystrom-attention and x-transformers are optional; the CPU-supported contract can be checked with the bundled smoke helper's tiny in-script transformer stub.
• Performance-oriented variants: route parallel_vit and simple_flash_attn_vit questions here. Treat flash/SDPA and CUDA behavior as optional performance paths, not requirements for the selected CPU scope. Read API reference and troubleshooting.

Boundaries

This sub-skill owns hook lifecycle, attention-map capture, latent extraction, custom transformer token contracts, optional acceleration caveats, and safe fallback advice for introspection/customization wrappers.

Route elsewhere instead of duplicating guidance:

• Plain image backbone selection, architecture zoo comparisons, patch-size choices, and normal ViT/SimpleViT classifier construction belong to the image-architecture route.
• Variable-resolution batches, 1D/3D/N-D routing, and video model selection belong to the variable-shapes/video route unless the user is specifically asking about simple_flash_attn_vit_3d as a flash/SDPA customization wrapper.
• Training, distillation, masked modeling, DINO/EsViT, decorrelation losses, and other loss-based wrappers belong to the pretraining/adaptation route.

Operating rules for future agents

1. Wrap raw backbones deliberately. Recorder and Extractor are wrappers around a model object; avoid stacking wrappers unless you explicitly pass the inner layer object to Extractor. If a wrapper is ejected, use the returned backbone or create a fresh wrapper for more introspection.

2. Validate hook targets before long runs. Hooks register on the first forward, so wrong layer_name values and unsupported attention-module types often fail only when the model is called. Prefer a tiny random tensor smoke check before training or large inference.

3. Keep custom transformer outputs token-shaped. efficient.ViT supplies tokens shaped (batch, token_count, dim) and then performs pooling and the classifier head itself. The injected transformer must return a compatible token tensor, not pooled logits, a tuple, or a changed embedding dimension.

4. Treat accelerations as optional. The flash-attention-style modules use PyTorch scaled dot-product attention when enabled; they do not require the external flash-attn package for CPU functional checks. If a backend or optional package is unavailable, fall back to standard vit-pytorch backbones or a stub/standard transformer rather than blocking the CPU workflow.

5. Use the bundled smoke helper for confidence. After installing vit-pytorch, run:

   python sub-skills/introspection-and-customization/scripts/smoke_introspection.py
   

   Run it from the generated vit-pytorch skill directory, or pass the script path directly from any working directory. It imports the installed package and does not read external source files.

References

• API reference and wrapper contracts
• Troubleshooting hook, optional dependency, and backend pitfalls
• CPU smoke helper