Hacktoberfest 2026: le issue che i maintainer hanno segnato per ottobre, aperte e adatte ai principianti. Sfoglia le issue Hacktoberfest

[ET-VK] TagMemoryMetaPass skips a tensor that sits in a list alongside None

Aperta
  • Qualcosa non va?
#22,510 3 commenti 0 reazioni 1 assegnatario Vedi su GitHub

I maintainer di solito rispondono entro 1 giorno

@SS-JIA ci sta già lavorando.

Dal 10/9/2026.

  • #22664 di @Sanjays2402 — aperta

Valutazione

Questa issue non è ancora stata valutata.

Descrizione

bug module: vulkan triaged
🐛 Describe the bug

is_tensor_arg_node() and is_non_constant_tensor_node() in backends/vulkan/utils.py only recognise a list argument when every entry is a tensor node. An argument that mixes tensor nodes with None is therefore treated as a non-tensor and skipped entirely by TagMemoryMetaPass, so the tensors inside it never get a storage or memory-layout representation.

index.Tensor is the case that surfaces it: x[:, :, idx] reaches the edge dialect as index.Tensor(x, [None, None, idx]), and idx lives in a list whose other entries are None. Any op implementation that then asserts on the layout of that tensor fails, because nothing assigned it one.

This is easy to miss because the op that exposed it can be routed around. #22481 makes index.Tensor work on any axis by forcing CONTIGUOUS_BUFFER for higher-rank self, which sidesteps the tagging gap rather than closing it. The gap is still there for the next operator that takes an optional-tensor list.

A fix is a helper along the lines of

def tensor_nodes_in_arg(arg):
    """The tensor nodes of an argument, skipping Nones."""

used everywhere the tagging pass walks a list argument. Lists that are entirely tensors, which is every other operator today, would behave exactly as before.

Originally found while working on #22406, which is being closed in favour of #22481; filing separately so the finding is not lost with it.

Versions

Reproduces on main (a6b115bff6).

cc @SS-JIA @manuelcandales @digantdesai @cbilgin

Lingua principale
Python
Stelle
5.1k
Fork
1.2k
Merge medio
2g 9h
PR unite (30g)
632

Preparare l'ambiente

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 pytorch/executorch

Tutte le issue di pytorch/executorch

Issue simili

Altre issue su Python

Ricevi le nuove issue nella tua casella

Un breve riepilogo di issue GitHub adatte ai principianti.