[Bug Report] `blocks.N.hook_out` never fires on OpenAI GPT because the HF block returns a list

Aperta
  • Qualcosa non va?
#1,969 2 commenti 0 reazioni 0 assegnatari Vedi su GitHub

I maintainer di solito rispondono entro 1 giorno

@ska2704 ci sta già lavorando.

Dal 3/11/2026.

Valutazione

Difficoltà
3/5
Tempo stimato
Mezza giornata
Idoneità per principianti
72/100
Tipo di issue
Bug
Chiarezza
Specificata chiaramente
Stato di attività
Attiva
Stack tecnologico
huggingface, python, pytorch
Ambito
machine-learning

Direzione di ricerca

Start in _apply_output_hook in transformer_lens/model_bridge/generalized_components/block.py, which handles only tuple and bare-tensor outputs. Add a list branch that hooks the first element and returns a list of the same length, then check _stop_on_block_exit for the special case that can be removed afterwards. Reproduce with the snippet in the issue, then add a test for the OpenAI GPT adapter. Done when blocks.0.hook_out is cached and a zeroing hook on the last block's hook_out changes the logits.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

bug complexity-simple TransformerBridge

Describe the bug

On openai-community/openai-gpt, every blocks.N.hook_out hook point is dead. It is absent from run_with_cache output and a hook added to it with run_with_hooks or add_hook is never called. hook_in works, the block is wired correctly, only the output side is skipped.

The cause is in BlockBridge._apply_output_hook (transformer_lens/model_bridge/generalized_components/block.py). It hooks the first element of a tuple output, or a bare tensor, and returns anything else untouched. HF's OpenAIGPT block returns outputs = [h] + attn_outputs[1:], a list (transformers/models/openai/modeling_openai.py), so it falls through the untouched branch and hook_out is never called.

Consequences: hook_resid_post is missing from the cache on this model, residual-stream patching at block outputs silently does nothing, and analysis tools that read blocks.N.hook_out get a KeyError. #1964 ran into this and handles the list in its exit hook so that stop_at_layer=n_layers still returns the residual, but that leaves an edit at blocks.11.hook_out reflected in the returned residual on GPT-2 and silently dropped on OpenAI GPT.

Code example

import torch
from transformer_lens.model_bridge import TransformerBridge

bridge = TransformerBridge.boot_transformers("openai-community/openai-gpt", device="cpu", dtype=torch.float32)
tokens = bridge.tokenizer("the capital of france is", return_tensors="pt").input_ids

_, cache = bridge.run_with_cache(tokens)
print("blocks.0.hook_in in cache: ", "blocks.0.hook_in" in cache)
print("blocks.0.hook_out in cache:", "blocks.0.hook_out" in cache)

fired = []
bridge.run_with_hooks(tokens, fwd_hooks=[("blocks.0.hook_out", lambda t, hook: fired.append(hook.name))])
print("hook_out calls:", len(fired))

Output:

blocks.0.hook_in in cache:  True
blocks.0.hook_out in cache: False
hook_out calls: 0

Expected: hook_out in the cache and the hook called once per forward, as on every other decoder adapter.

System Info

  • transformer_lens installed from source at dev (a7b3213ac)
  • macOS
  • Python 3.12, torch 2.11.0, transformers 5.13.0

Additional context

Suggested fix: give _apply_output_hook a list branch mirroring the tuple one, returning a list of the same length so the HF parent's outputs[0] indexing keeps working. Among the supported adapters, OpenAI GPT is the only one whose block returns a list in transformers 5.13.0 (the other list-returning HF blocks are vision/omni models without adapters), so the blast radius is one adapter. Once this lands, the list special-case in _stop_on_block_exit from #1964 can be dropped.

A regression test on the OpenAI GPT adapter should assert blocks.0.hook_out is cached and that a zeroing hook on the last block's hook_out changes the logits.

Checklist
  • I have checked that there is no similar issue in the repo (required)
Lingua principale
Python
Stelle
3.9k
Fork
708
Merge medio
1g 10h
PR unite (30g)
82

Preparare l'ambiente

Apri in Codespaces

Avvia il container di sviluppo del progetto nel browser, con il tuo account GitHub.

  • Nessun Dockerfile né file Docker Compose
  • Ha un modello di pull request
  • Nessuna guida per i contributori

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

Altre issue di TransformerLensOrg/TransformerLens

Tutte le issue di TransformerLensOrg/TransformerLens

Issue simili

Altre issue su Python

Ricevi le nuove issue nella tua casella

Un breve riepilogo di issue GitHub adatte ai principianti.