# Reviving Named Tensors: Strategy for addressing the "Experimental" backlog

**URL:** <https://dev-discuss.pytorch.org/t/reviving-named-tensors-strategy-for-addressing-the-experimental-backlog/3289>\
**Category:** Tensor subclass\
**Created:** [January 14, 2026, 11:15pm UTC](https://dev-discuss.pytorch.org/t/reviving-named-tensors-strategy-for-addressing-the-experimental-backlog/3289 "2026-01-14T23:15:22Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![adendek](https://yyz2.discourse-cdn.com/flex036/user_avatar/dev-discuss.pytorch.org/adendek/32/2649_2.png) [@adendek](https://dev-discuss.pytorch.org/u/adendek)\
**Post date:** [January 14, 2026, 11:15pm UTC](https://dev-discuss.pytorch.org/t/reviving-named-tensors-strategy-for-addressing-the-experimental-backlog/3289/1 "2026-01-14T23:15:22Z")

</div>

Hi everyone,

I’ve been looking into the current state of **Named Tensors**. Despite the clear UX benefits for model readability and error prevention, the feature has remained in an experimental state with a growing list of [open issues](https://github.com/pytorch/pytorch/issues?q=is%3Aopen+is%3Aissue+label%3A%22module%3A+named+tensor%22).

I am interested in contributing to this module to help move it toward a more stable release. Specifically, I see a gap in how named tensors integrate with high-level ops like `einsum` or modern functional transformations.

**The Vision:** Named tensors should allow for a more “self-documenting” interface. For example, instead of tracking dimensions by index, we could leverage names directly in contractions:

Python

```auto
# Proposed interaction style
As = torch.randn(3, 2, 5, names=('Batch', 'Width', 'Height'))
Bs = torch.randn(3, 5, 4, names=('Batch', 'Height', 'Depth'))

# Instead of: torch.einsum('bwh, bhd -> bwd', As, Bs)
# We leverage the existing names for safety:
output = torch.einsum_named(As, Bs, out_names=('Batch', 'Width', 'Depth'))

```

**My Questions for the Maintainers:**

1. **Priority:** Is there still an appetite for first-class Named Tensor support, or is the consensus to let community libraries (like Einops) handle dimension naming?

2. **Roadblock:** Are there specific architectural blockers (e.g., interaction with `torch.compile` or Autograd) that led to the current stagnation?

3. **Onboarding:** I am prepared to start by triaging and fixing the “module: named tensor” labeled issues. Would the team prefer a series of small bug-fix PRs, or a broader RFC on the future of the API?

I’d love to hear from anyone who has worked on the core implementation to understand where I can be most helpful.