---

name: vpt_prompt_token_insertion description: Build shallow and deep Visual Prompt Tuning token sequences for Transformer-style image embeddings.

VPT Prompt Token Insertion

Use this skill when implementing or checking Visual Prompt Tuning (VPT) token formatting for a vision Transformer or a reduced VPT proxy. Do not use it to compute losses, choose hyperparameters, or emit classification metrics.

Inputs

• A batch of token embeddings shaped as [batch, 1 + patches, hidden] where the first token is [CLS].
• Prompt embeddings shaped as [num_prompt_tokens, hidden].
• Optional deep prompt embeddings shaped as [layers, num_prompt_tokens, hidden].
• Configuration metadata with location="prepend", prompt count, hidden size, and whether prompts are shallow or deep.

Outputs

• Prompt-augmented token batches shaped as [batch, 1 + prompts + patches, hidden].
• Metadata describing prompt count, hidden size, and placement.
• Validation errors for unsupported prompt locations or shape mismatches.

Workflow

1. Confirm the input sequence has [CLS] plus at least one image token.
2. Validate the prompt hidden size against the token hidden size.
3. Preserve [CLS] at position 0 and preserve image-token order.
4. Broadcast prompt embeddings across the batch and insert them immediately after [CLS].
5. For deep VPT, replace the existing prompt-token slots with the layer-specific prompt bank while keeping [CLS] and image tokens unchanged.
6. Return formatting metadata; leave training, pooling, and evaluation to downstream skills.

Validation

Run python tests/test_prompt_ops.py from this skill directory, or run the Distiller skill validator with --run-tests.

Limitations

This skill implements the paper's default prepend-token VPT mechanism. Pixel-padding prompt variants and pooling choices are intentionally outside this module's contract.