Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

More info for dupnoneed #1904

Merged
merged 3 commits into from
Sep 27, 2024
Merged

More info for dupnoneed #1904

Changes from 2 commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
99bf3b3
More info for dupnoneed
wsmoses Sep 27, 2024
6056cc4
Update lib/EnzymeCore/src/EnzymeCore.jl
wsmoses Sep 27, 2024
8f4ebc8
Update EnzymeCore.jl
wsmoses Sep 27, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
22 changes: 21 additions & 1 deletion lib/EnzymeCore/src/EnzymeCore.jl
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,27 @@ end
DuplicatedNoNeed(x, ∂f_∂x)

Like [`Duplicated`](@ref), except also specifies that Enzyme may avoid computing
the original result and only compute the derivative values.
the original result and only compute the derivative values. This creates opportunities
for improved performance.

```jldoctest dupnoneed

function square_byref(out, v)
out[] = v * v
nothing
end

out = Ref(0.0)
dout = Ref(1.0)
Enzyme.autodiff(Reverse, square_byref, DuplicatedNoNeed(out, dout), Active(1.0))
dout[]

# output
0.0
```

For example, marking the out variable as `DuplicatedNoNeed` instead of `Duplicated` allows
Enzyme to avoid computing `v * v` (while still computing its derivative).

This should only be used if `x` is a write-only variable. Otherwise, if the differentiated
function stores values in `x` and reads them back in subsequent computations, using
Expand Down
Loading