Skip to content

Support per-op partitioning in XNNPACK delegate for NHWC ops #8265

Closed

Description

@GregoryComer

🚀 The feature, motivation and pitch

We currently support per-op partitioning in the XNNPACK delegate, which allows for all activation tensor memory to be owned by ExecuTorch and thus overlapped with other ExecuTorch-owned activation memory. However, this isn't currently practical for ops that run in channels-last (NHWC) dim order. This is because the delegate currently assumes that tensors that are inputs or outputs to the partition are always channels first (NCHW) and thus inserts dim order conversions around every op. This is perf issue, but more importantly, it means that XNNPACK ends up owning some of the activation memory.

Ideally, we can leverage the recent dim order support in the core runtime to let the framework manage the dim order conversion, at least in single-op mode. How this interacts with partitioning is not entirely clear, since this would have to happen after partitioning. For initial purposes, it's likely fine to let the dim order conversions not be delegated. This likely needs a bit more design discussion, but it is a high ROI feature and may be necessary for memory parity with LI in some cases, even with workspace sharing.

Alternatives

No response

Additional context

No response

RFC (Optional)

No response

cc @digantdesai @mcr229

Activity

  1. added
    module: xnnpackIssues related to xnnpack delegation and the code under backends/xnnpack/
    on Feb 6, 2025
  2. changed the title [-]Support per-op partitioning in XNNPACK delegate for NHWC oprt[/-] [+]Support per-op partitioning in XNNPACK delegate for NHWC ops[/+] on Feb 6, 2025
  3. added
    triagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate module
    on Feb 6, 2025
  4. mcr229 commented on Feb 13, 2025

    @mcr229
    Contributor

    let's convert this to a discussion

  5. locked and limited conversation to collaborators on Feb 13, 2025
  6. converted this issue into a discussion #8476 on Feb 13, 2025
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Metadata

Metadata

Assignees

No one assigned

    Labels

    module: xnnpackIssues related to xnnpack delegation and the code under backends/xnnpack/triagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate module

    Projects

    No projects

      Milestone

      No milestone

      Relationships

      None yet

      Development

      No branches or pull requests

      Issue actions