Remove the MLContext.compute() method - #795
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@huningxin @fdwr PTAL? |
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LGTM
This does make me realize that as part of #529 we'll likely need to define queuing for operations on the timeline that share tensors, to avoid racy behavior. With ArrayBuffers transferred away synchronously during compute(), there wasn't the possibility for multiple compute()s to compete for access to the buffer or script to read/write the contents.
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LGTM with a comment to example, thanks!
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Thanks for the reviews, @huningxin and @inexorabletash @fdwr PTAL? |
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Not in the spec anymore: webmachinelearning/webnn#795
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Fixes #791. Made possible by #787 which specified
dispatch()The changes proposed in #786 have also been rolled into this PR, since they can't really be decoupled from
compute()removal at this pointI've tried to keep this PR minimal and focused on removing algorithms and IDL definitions which are no longer relevant. The spec still contains lots of normative text which was written with
compute()in mind which doesn't map as well todispatch()- specifically text which contains assumptions about how graph execution relates to compute devices. I've lightly updated some of this text where necessary to match thedispatch()paradigm, but my intention is to avoid opening the can of worms that is #749 in this PR :)Preview | Diff