GitHub

pytorch/executorch ciflow/cuda/20384 (opens in new tab)

#Python Use CapabilityBasedPartitioner in AotiPartitioner (Summary: AotiPartitioner (the base for the CUDA and Metal backends) groups the ops it delegates into one partition, by hand. Every other ExecuTorch backend (XNNPACK, Vulkan, CoreML) uses the shared CapabilityBasedPartitioner helper instead. This switches AotiPartitioner to that helper too. Why: Consistency -- same partitioning path as the other backends, and a real OperatorSupport hook instead of a hand-rolled tagging loop. It can bre...

Read the original article
Sign in to keep reading the full article.

Keyboard Shortcuts

Navigation

Next / previous post
j/k
Open post
oorEnter
Preview post
v

Post Actions

Love post
a
Like post
l
Dislike post
d
Undo reaction
u
Save / unsave
s

Recommendations

Add interest / feed
Enter
Not interested
x

Go to

Home
gh
Interests
gi
Feeds
gf
Likes
gl
History
gy
Changelog
gc
Settings
gs
Discover
gb
Search
/

General

Show this help
?
Submit feedback
!
Close modal / unfocus
Esc

Press ? anytime to show this help