Update gradient interface, support AbstractDifferentiation#90
Update gradient interface, support AbstractDifferentiation#90lostella merged 25 commits intoJuliaFirstOrder:masterfrom
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## master #90 +/- ##
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- Coverage 89.84% 89.03% -0.81%
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docs/src/guide/getting_started.jl
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| -ProximalAlgorithms.gradient(quadratic_cost, solution)[1] | ||
| v, pb = ProximalAlgorithms.value_and_pullback(quadratic_cost, solution) | ||
| -pb() |
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Maybe explain that the pullback outputs the gradient?
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| function value_and_pullback(f::AutoDifferentiable, x) | ||
| fx, pb = AbstractDifferentiation.value_and_pullback_function(f.backend, f.f, x) | ||
| return fx, () -> pb(one(fx))[1] |
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Striclty speaking this is not a pullback if it takes no input. The point of a pullback is to take a cotangent and pull it back into the input space.
In this case, the cotangent is a scalar, and taking this scalar to be 1 returns the gradient, so it's okay, but the terminology might confuse autodiff people
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Yeah right, I was unsure here, thanks for pointing that out. I guess I could call it simply “closure”
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value_and_gradient_closure other something like that
src/ProximalAlgorithms.jl
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| """ | ||
| AutoDifferentiable(f, backend) | ||
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| Wrap function `f` to be auto-differentiated using `backend`. |
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Specify that it's a callable struct?
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| (f::ZygoteFunction)(x) = f.f(x) | ||
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| function ProximalCore.gradient!(grad, f::ZygoteFunction, x) |
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The diff would be much smaller if you kept your functions gradient and gradient! but made them rely on value_and_pullback instead, right?
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I guess, but I’m thinking that these primitives don’t really fit the interface of ProximalCore maybe, see JuliaFirstOrder/ProximalCore.jl#3
Co-authored-by: Guillaume Dalle <22795598+gdalle@users.noreply.github.com>
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value_and_pullback(similar to AbstractDifferentiationvalue_and_pullback_function)Work in progress, opening for feedback and to take a look at CI workflows.
TODOs