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dfdx
XGrad.jl
Julia2017–2020
dfdx / XGrad.jl

XGrad.jl

eXpression gradients in Julia

Explore on GitHubRead Hacker News discussions
2Distinct people
3Mentions
0Explicit recommenders
3GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

eXpression gradients in Julia

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month3 dated mentions

Observed mentions: 2017-11-18 21:07:20 UTC to 2020-04-08 19:58:57 UTC.

0122017-11-01 00:00:00 UTC to 2017-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsNov 20172017-12-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-01-01 00:00:00 UTC to 2018-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-02-01 00:00:00 UTC to 2018-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-03-01 00:00:00 UTC to 2018-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-04-01 00:00:00 UTC to 2018-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-05-01 00:00:00 UTC to 2018-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-06-01 00:00:00 UTC to 2018-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-07-01 00:00:00 UTC to 2018-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-08-01 00:00:00 UTC to 2018-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-09-01 00:00:00 UTC to 2018-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-10-01 00:00:00 UTC to 2018-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-11-01 00:00:00 UTC to 2018-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2018-12-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-01-01 00:00:00 UTC to 2019-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJan 20192019-02-01 00:00:00 UTC to 2019-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-03-01 00:00:00 UTC to 2019-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-04-01 00:00:00 UTC to 2019-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-05-01 00:00:00 UTC to 2019-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-06-01 00:00:00 UTC to 2019-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-07-01 00:00:00 UTC to 2019-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-08-01 00:00:00 UTC to 2019-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-09-01 00:00:00 UTC to 2019-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-10-01 00:00:00 UTC to 2019-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-11-01 00:00:00 UTC to 2019-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2019-12-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-01-01 00:00:00 UTC to 2020-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-02-01 00:00:00 UTC to 2020-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-03-01 00:00:00 UTC to 2020-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-04-01 00:00:00 UTC to 2020-05-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsApr 2020

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Nov 20172017-11-01 00:00:00 UTC2017-12-01 00:00:00 UTC101
Dec 20172017-12-01 00:00:00 UTC2018-01-01 00:00:00 UTC000
Jan 20182018-01-01 00:00:00 UTC2018-02-01 00:00:00 UTC000
Feb 20182018-02-01 00:00:00 UTC2018-03-01 00:00:00 UTC000
Mar 20182018-03-01 00:00:00 UTC2018-04-01 00:00:00 UTC000
Apr 20182018-04-01 00:00:00 UTC2018-05-01 00:00:00 UTC000
May 20182018-05-01 00:00:00 UTC2018-06-01 00:00:00 UTC000
Jun 20182018-06-01 00:00:00 UTC2018-07-01 00:00:00 UTC000
Jul 20182018-07-01 00:00:00 UTC2018-08-01 00:00:00 UTC000
Aug 20182018-08-01 00:00:00 UTC2018-09-01 00:00:00 UTC000
Sep 20182018-09-01 00:00:00 UTC2018-10-01 00:00:00 UTC000
Oct 20182018-10-01 00:00:00 UTC2018-11-01 00:00:00 UTC000
Nov 20182018-11-01 00:00:00 UTC2018-12-01 00:00:00 UTC000
Dec 20182018-12-01 00:00:00 UTC2019-01-01 00:00:00 UTC000
Jan 20192019-01-01 00:00:00 UTC2019-02-01 00:00:00 UTC000
Feb 20192019-02-01 00:00:00 UTC2019-03-01 00:00:00 UTC000
Mar 20192019-03-01 00:00:00 UTC2019-04-01 00:00:00 UTC000
Apr 20192019-04-01 00:00:00 UTC2019-05-01 00:00:00 UTC000
May 20192019-05-01 00:00:00 UTC2019-06-01 00:00:00 UTC000
Jun 20192019-06-01 00:00:00 UTC2019-07-01 00:00:00 UTC000
Jul 20192019-07-01 00:00:00 UTC2019-08-01 00:00:00 UTC000
Aug 20192019-08-01 00:00:00 UTC2019-09-01 00:00:00 UTC000
Sep 20192019-09-01 00:00:00 UTC2019-10-01 00:00:00 UTC000
Oct 20192019-10-01 00:00:00 UTC2019-11-01 00:00:00 UTC000
Nov 20192019-11-01 00:00:00 UTC2019-12-01 00:00:00 UTC000
Dec 20192019-12-01 00:00:00 UTC2020-01-01 00:00:00 UTC000
Jan 20202020-01-01 00:00:00 UTC2020-02-01 00:00:00 UTC000
Feb 20202020-02-01 00:00:00 UTC2020-03-01 00:00:00 UTC000
Mar 20202020-03-01 00:00:00 UTC2020-04-01 00:00:00 UTC000
Apr 20202020-04-01 00:00:00 UTC2020-05-01 00:00:00 UTC202

What Hacker News says

3 sources
oxinabox·Apr 8, 2020
We have so many AD packages than that in julia. Julia suffers the lisp problem for AD. http://winestockwebdesign.com/Essays/Lisp_Curse.html It is a real problem. One of my on going projects is ChainRules (http://www.juliadiff.org/ChainRulesCore.jl/dev/) which will unite them under one set of custom senstitivities and more generally make it easier to mix and match them I need to update the JuliaDiff website, I want to list all of them in a table with some some key points. Forward mode: - [ForwardDiff](https://github.com/JuliaDiff/ForwardDiff.jl) - [ForwardDiff2](https://github.com/YingboMa//ForwardDiff2.jl) Reverse Mode: - [Nabla](https://github.com/invenia/Nabla.jl/) - [Tracker](https://github.com/FluxML/Tracker.jl) - [Yota](https://github.com/dfdx/Yota.jl) - [Zygote](https://github.com/FluxML/Zygote.jl) - [ReverseDiff](https://github.com
Read the rest
/JuliaDiff/ReverseDiff.jl) - [AutoGrad.jl](https://github.com/denizyuret/AutoGrad.jl) - [NiLang](https://github.com/GiggleLiu/NiLang.jl) (arguably not reverse mode) Symbolic: - [ModelingToolKit](https://github.com/JuliaDiffEq/ModelingToolkit.jl) - [XGrad.jl](https://github.com/dfdx/XGrad.jl) Finite Differencing: - [Calculus](https://github.com/JuliaMath/Calculus.jl) (please stop) - [FiniteDifferences](https://github.com/JuliaDiff/FiniteDifferences.jl) - [FiniteDiff](https://github.com/JuliaDiff/FiniteDiff.jl)
In Jax – Composable transformations of Python and NumPy programs
oxinabox·Apr 8, 2020
AST manipulation does not do it for AD in general (though XGrad.jl is doing it as a symbolic diff approach https://github.com/dfdx/XGrad.jl/), you want to work with the code reduced down to Single assignment form, and work with code blocks and the control flow graph. for a number of reasons: 1. all control flow looks the same now (no 2 different kinds of loops + GOTOs) 2. one expression per line, no need to untangle larger experessions 3. Host of techniques from compiler world can come out to be used. This is how Zgyote works, it used code that has been lowered into this form
In Jax – Composable transformations of Python and NumPy programs
ffriend·Nov 18, 2017
Some time ago I implemented a library [1] similar to this tool. The tricky part is that derivatives quickly exceed 2 dimensions, e.g. derivative of a vector output w.r.t. input matrix is a 3D tensor (e.g. if `y = f(X)`, you need to find derivative of each `y[i]` w.r.t. each `X[m,n]`), and we don't have a notation for it. Also, often such tensors are very sparse (e.g. for element-wise `log()` derivative is a matrix where only the main diagonal has non-zero values corresponding to derivatives `dy[i]/dx[i]` where `y = log(x)`). The way I dealt with it is to first translate vectorized expression to so-called Einstein notation [2] - indexed expression with implicit sums over repeated indices. E.g. matrix product `Z = X * Y` may be written in it as: Z[i,j] = X[i,k] * Y[k,j] # implicitly sum over k It worked pretty well and I was able to
Read the rest
get results in Einstein notation for element-wise functions, matrix multiplication and even convolutions. Unfortunately, the only way to calculate such expressions efficiently is to convert them back to vectorized notation, and it's not always possible (e.g. because of sparse structure) and very error-prone. The good news is that if the result of the whole expression is a scalar, all the derivatives will have the same number of dimensions as corresponding inputs. E.g. in: y = sum(W * X + b) if `W` is a matrix, then `dy/dW` is also a matrix (without sum it would be a 3D tensor). This is the reason why backpropogation algorithm (and symbolic/automatic differentiation in general) in machine learning works. So finally I ended up with a another library [3], which can only deal with scalar outputs, but is much more stable. Theoretical description of the method for the first library can be found in [4] (page 1338-1343, caution - 76M) while the set of rule I've derived is in [5]. [1]: https://github.com/dfdx/XDiff.jl [2]: https://en.wikipedia.org/wiki/Einstein_notation [3]: https://github.com/dfdx/XGrad.jl [4]: http://docs.mipro-proceedings.com/proceedings/mipro_2017_pro... [5]: https://github.com/dfdx/XDiff.jl/blob/master/src/trules.jl
In Matrix Calculus

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