WebML WG Teleconference – 3 November 2022 - 14:00-15:00 UTC

Latest version: https://github.com/webmachinelearning/meetings/blob/main/telcons/2022-11-03-wg-agenda.md


WebML WG Teleconference – 3 November 2022 - 14:00-15:00 UTC
San Francisco (U.S.A. - California)     Thu, 3 November 2022    07:00   UTC-7 hours
Boston (U.S.A. - Massachusetts) Thu, 3 November 2022    10:00   UTC-4 hours
London (United Kingdom - England)       Thu, 3 November 2022    14:00   UTC+0 hours (adjusted for DST)
Berlin (Germany)        Thu, 3 November 2022    15:00   UTC+1 hours (adjusted for DST)
Helsinki (Finland)      Thu, 3 November 2022    16:00   UTC+2 hours (adjusted for DST)
Shanghai (China)        Thu, 3 November 2022    22:00   UTC+8 hours
Tokyo (Japan)   Thu, 3 November 2022    23:00   UTC+9 hours
Corresponding UTC (GMT) Thu, 3 November 2022    14:00 UTC

Other locations: https://www.timeanddate.com/worldclock/fixedtime.html?iso=20221103T14


<https://github.com/webmachinelearning/meetings/blob/main/telcons/2022-11-03-wg-agenda.md#logistics>Logistics

  *   Chair: Anssi
  *   Scribe: ? (see howto<https://github.com/webmachinelearning/meetings/blob/main/scribe-howto.md>, volunteers welcome!)
  *   IRC: irc://irc.w3.org:6667/#webmachinelearning

  *   IRC web client: https://irc.w3.org/?channels=#webmachinelearning

  *   Joining instructions: https://lists.w3.org/Archives/Member/internal-webmachinelearning/2020Apr/0000.html

  *   Minutes: https://www.w3.org/2022/11/03-webmachinelearning-minutes.html


<https://github.com/webmachinelearning/meetings/blob/main/telcons/2022-11-03-wg-agenda.md#%E2%84%B9%EF%B8%8F-webnn-api-candidate-recommendation-open-issues>ℹ️ WebNN API Candidate Recommendation open issues
Review and discuss the current CR issues, work out a plan to address the issues prior to the expected CR publication.

  *   Current CR issues https://github.com/webmachinelearning/webnn/labels/cr


  *   Support asynchronous context creation

     *    issue: webmachinelearning/webnn#272<https://github.com/webmachinelearning/webnn/issues/272>
     *    TAG issue: w3ctag/design-reviews#771<https://github.com/w3ctag/design-reviews/issues/771>
     *   ➡️ Status: TAG recommendation imminent.
  *   Web platform tests

     *    tracker issue: webmachinelearning/webnn#265<https://github.com/webmachinelearning/webnn/issues/265>
     *   webnn-baseline implementation plan https://github.com/webmachinelearning/webnn-baseline/issues

     *   ➡️ Status: Discuss & resolve any blockers for w-p-t & webnn-baseline reference impl.
  *   API review questions in prep for normative algorithm definition

     *    issue: webmachinelearning/webnn#298<https://github.com/webmachinelearning/webnn/issues/298>
     *   ➡️ Status: Process review questions to clear the path for algorithm updates.

<https://github.com/webmachinelearning/meetings/blob/main/telcons/2022-11-03-wg-agenda.md#%E2%84%B9%EF%B8%8F-webnn-webgpu-interop>ℹ️ WebNN-WebGPU interop
Review WebGPU interop mechanism and its normative WebGPU dependencies to assess whether WebGPU interop is a feasible CR target or a v2 feature. Consider implementation feedback from the WebNN DirectML backend.

WebNN API interface to record the ML workload onto a WebGPU-compatible GPUCommandBuffer:

  *   MLCommandEncoder https://www.w3.org/TR/webnn/#mlcommandencoder


Normative WebGPU API dependencies:

  *   GPUBuffer https://gpuweb.github.io/gpuweb/#buffer-interface

  *   GPUCommandBuffer https://gpuweb.github.io/gpuweb/#command-buffers

  *   GPUCommandBufferDescriptor https://gpuweb.github.io/gpuweb/#dictdef-gpucommandbufferdescriptor

  *   GPUDevice https://gpuweb.github.io/gpuweb/#gpu-device

  *   GPUQueue https://gpuweb.github.io/gpuweb/#gpuqueue

  *   GPUTexture https://gpuweb.github.io/gpuweb/#gputexture


See also:

  *    22 Sep 2022 discussion https://www.w3.org/2022/09/22-webmachinelearning-minutes.html#t06

  *    related issue: webmachinelearning/webnn#264<https://github.com/webmachinelearning/webnn/issues/264>

<https://github.com/webmachinelearning/meetings/blob/main/telcons/2022-11-03-wg-agenda.md#%E2%84%B9%EF%B8%8F-prs-in-review>ℹ️ PRs in review

  *   Use unsigned long for size related options of conv2d, convTranspose2d and pooling operations

     *    issue: webmachinelearning/webnn#269<https://github.com/webmachinelearning/webnn/issues/269>
     *    PR: webmachinelearning/webnn#294<https://github.com/webmachinelearning/webnn/pull/294>
  *   Change newShape of reshape to a sequence of nullable unsigned long

     *    issue: webmachinelearning/webnn#289<https://github.com/webmachinelearning/webnn/issues/289>
     *    PR: webmachinelearning/webnn#291<https://github.com/webmachinelearning/webnn/pull/291>

<https://github.com/webmachinelearning/meetings/blob/main/telcons/2022-11-03-wg-agenda.md#%E2%84%B9%EF%B8%8F-proposed-new-features>ℹ️ Proposed new features

  *   MLOperandDataType naming recommendation

     *    issue: webmachinelearning/webnn#299<https://github.com/webmachinelearning/webnn/issues/299>
  *   Unsqueeze operator is missing

     *    issue: webmachinelearning/webnn#296<https://github.com/webmachinelearning/webnn/issues/296>
  *   Need for type casting?

     *    issue: webmachinelearning/webnn#284<https://github.com/webmachinelearning/webnn/issues/284>
  *   Softmax should only support input of floating-point types

     *    issue: webmachinelearning/webnn#283<https://github.com/webmachinelearning/webnn/issues/283>

Revisit proposed new features discussed earlier:

  *   Should MLBufferView + MLOperandDescriptor be strongly typed

     *    proposed CR issue: webmachinelearning/webnn#275<https://github.com/webmachinelearning/webnn/issues/275>
  *   Support coordinate transformation modes for Resample2d

     *    proposed CR issue: webmachinelearning/webnn#270<https://github.com/webmachinelearning/webnn/issues/270>
  *   l2Pool2d algorithm clarifications

     *    issue: webmachinelearning/webnn#278<https://github.com/webmachinelearning/webnn/issues/278>