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View Code? Open in Web Editor NEWChainRules.jl integration for Unitful.jl
License: MIT License
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Add `frule`s
Currently only supports rrules, add frule support
`abs` rrule for Unitful.Quantity
abs's rrule returns a deconstructed Quantity as a NamedTuple, not a Quantity. We should add a manual rule to fix this and upstream to ChainRules.jl if it generalizes the existing behavior
julia> Zygote.gradient(abs, 1u"W")
((val = 1.0 W,),)Scalar-Array Arithmetic
It'd be useful to have really basic *(::AbstractArray, ::Unitful.Quantity) and \(...) methods rules. These don't take much to add, unlike the general case of #5, so I think it's reasonable to add them
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General Array Rules
Unitful.Quantitys are not compatible with most array-based math rules in ChainRules.jl right now. Relevant issues discussing why this is are here and here.
We could solve this problem by duplicating the array rules from ChainRules.jl for properly typed Quanititys. A better solution would be to solve the aforementioned issues upstream in such a way that we can adopt the rules automatically
Simplify Units In Pullback
It's possible to return gradients which are dimensionless, but still have units attached to them as shown below. It might be better to simplify units as we go to drop units that cancel.
Adding upreferred to the pullback after we project and introduce the units should probably fix this.
using Unitful, UnitfulChainRules, Zygote
f(x,y) = (x * u"W" + y * u"mW") / u"W"
f(3.0, 4.0)
# 3.004 kg m^2 s^-3 W^-1
# current behavior
gradient(f, 3.0, 4.0)
# (1.0, 1.0 mW W^-1)
# potential preferred behavior
upreferred.(gradient(f, 3.0, 4.0))
# (1.0, 0.001)Fractional powers not supported
Sometimes one ends up using Quantities with fractional powers, but they don't currently work with UnitfulChainRules.jl:
julia> Zygote.pullback(x -> x^(1//3), 3.0u"W^3")[2](1)
ERROR: MethodError: no method matching log(::Quantity{ComplexF64, ๐ ^6 ๐ ^3 ๐ ^-9, Unitful.FreeUnits{(W^3,), ๐ ^6 ๐ ^3 ๐^-9, nothing}})
Closest candidates are:
log(::T, ::T) where T<:Number at C:\Program Files\Julia-1.7.0\share\julia\base\math.jl:315
log(::Number, ::Number) at C:\Program Files\Julia-1.7.0\share\julia\base\math.jl:358
log(::RoundingMode, ::ForwardDiff.Dual{Ty}) where Ty at C:\Users\bks1\.julia\packages\ForwardDiff\wAaVJ\src\dual.jl:145
...
Stacktrace:
[1] _pow_grad_p(x::Quantity{Float64, ๐^6 ๐^3 ๐^-9, Unitful.FreeUnits{(W^3,), ๐^6 ๐^3 ๐^-9, nothing}} , p::Rational{Int64}, y::Quantity{Float64, ๐^2 ๐ ๐^-3, Unitful.FreeUnits{(W,), ๐^2 ๐ ๐^-3, nothing}} )
@ ChainRules C:\Users\bks1\.julia\packages\ChainRules\EyLkg\src\rulesets\Base\fastmath_able.jl:320
[2] (::ChainRules.var"#1244#1277"{Int64, Quantity{Float64, ๐^6 ๐^3 ๐^-9, Unitful.FreeUnits{(W^3,), ๐ ^6 ๐^3 ๐^-9,
nothing}}, Rational{Int64}, ProjectTo{Real, NamedTuple{(), Tuple{}}}, Quantity{Float64, ๐^2 ๐ ๐^ -3, Unitful.FreeUnits{(W,), ๐^2 ๐ ๐^-3, nothing}}}) ()
@ ChainRules C:\Users\bks1\.julia\packages\ChainRules\EyLkg\src\rulesets\Base\fastmath_able.jl:196
[3] unthunk
@ C:\Users\bks1\.julia\packages\ChainRulesCore\ctmSK\src\tangent_types\thunks.jl:199 [inlined]
[4] wrap_chainrules_output
@ C:\Users\bks1\.julia\packages\Zygote\IoW2g\src\compiler\chainrules.jl:104 [inlined]
[5] map
@ .\tuple.jl:223 [inlined]
[6] wrap_chainrules_output
@ C:\Users\bks1\.julia\packages\Zygote\IoW2g\src\compiler\chainrules.jl:105 [inlined]
[7] ZBack
@ C:\Users\bks1\.julia\packages\Zygote\IoW2g\src\compiler\chainrules.jl:205 [inlined]
[8] Pullback
@ .\REPL[51]:1 [inlined]
[9] (::typeof(โ(#111)))(ฮ::Int64)
@ Zygote C:\Users\bks1\.julia\packages\Zygote\IoW2g\src\compiler\interface2.jl:0
[10] (::Zygote.var"#60#61"{typeof(โ(#111))})(ฮ::Int64)
@ Zygote C:\Users\bks1\.julia\packages\Zygote\IoW2g\src\compiler\interface.jl:41
[11] top-level scope
@ REPL[51]:1Use @scalar_rule
Is it possible to handle these cases with @scalar_rule instead of manually writing both the frules and rrules?
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