Illustration of how to Use Mutating Gradient Functions

When it comes to time critital operations, a main ingredient in Julia is given by mutating functions, i.e. those that compute in place without additional memory allocations. In the following, we illustrate how to do this with Manopt.jl.

Let's start with the same function as in Get Started: Optimize! and compute the mean of some points, only that here we use the sphere $\mathbb S^{30}$ and n=800 points.

From the aforementioned example, the implementation looks like

using Manopt, Manifolds, Random, BenchmarkTools

begin
    Random.seed!(42)
    m = 30
    M = Sphere(m)
    n = 800
    σ = π / 8
    x = zeros(Float64, m + 1)
    x[2] = 1.0
    data = [exp(M, x, random_tangent(M, x, Val(:Gaussian), σ)) for i in 1:n]
end;

Classical Definition

The variant from the previous tutorial defines a cost $F(x)$ and its gradient $gradF(x)$

F(M, x) = sum(1 / (2 * n) * distance.(Ref(M), Ref(x), data) .^ 2)

F (generic function with 1 method)

gradF(M, x) = sum(1 / n * grad_distance.(Ref(M), data, Ref(x)))

gradF (generic function with 1 method)

We further set the stopping criterion to be a little more strict. Then we obtain

begin
    sc = StopWhenGradientNormLess(1e-10)
    x0 = random_point(M)
    m1 = gradient_descent(M, F, gradF, x0; stopping_criterion=sc)
    @benchmark gradient_descent($M, $F, $gradF, $x0; stopping_criterion=$sc)
end

BenchmarkTools.Trial: 312 samples with 1 evaluation.
 Range (min … max):  11.824 ms … 28.610 ms  ┊ GC (min … max):  0.00% …  0.00%
 Time  (median):     15.273 ms              ┊ GC (median):     0.00%
 Time  (mean ± σ):   16.059 ms ±  3.840 ms  ┊ GC (mean ± σ):  11.50% ± 10.06%

  ▃▆█▁                                               ▂▂▃▄▅▁▃   
  █████▄▄▇▆▁▁▄▇▄▆▆▄▆▄▆▆▆▁▄▄▆▄▇▄▆▆▆▄▆▆▄▆▄▄▁▆▄▄▆▆▇▁▆▆▆▇███████▇ ▇
  11.8 ms      Histogram: log(frequency) by time        21 ms <

 Memory estimate: 24.52 MiB, allocs estimate: 84722.

In-place Computation of the Gradient

We can reduce the memory allocations by implementing the gradient as a functor. The motivation is twofold: on one hand, we want to avoid variables from the global scope, for example the manifold M or the data, being used within the function. Considering to do the same for more complicated cost functions might also be worth it.

Here, we store the data (as reference) and one temporary memory in order to avoid reallocation of memory per grad_distance computation. We have

begin
    struct grad!{TD,TTMP}
        data::TD
        tmp::TTMP
    end
    function (gradf!::grad!)(M, X, x)
        fill!(X, 0)
        for di in gradf!.data
            grad_distance!(M, gradf!.tmp, di, x)
            X .+= gradf!.tmp
        end
        X ./= length(gradf!.data)
        return X
    end
end

Then we just have to initialize the gradient and perform our final benchmark. Note that we also have to interpolate all variables passed to the benchmark with a $.

begin
    gradF2! = grad!(data, similar(data[1]))
    m2 = deepcopy(x0)
    gradient_descent!(
        M, F, gradF2!, m2; evaluation=MutatingEvaluation(), stopping_criterion=sc
    )
    @benchmark gradient_descent!(
        $M, $F, $gradF2!, m2; evaluation=$(MutatingEvaluation()), stopping_criterion=$sc
    ) setup = (m2 = deepcopy($x0))
end

BenchmarkTools.Trial: 904 samples with 1 evaluation.
 Range (min … max):  5.122 ms …   5.908 ms  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     5.528 ms               ┊ GC (median):    0.00%
 Time  (mean ± σ):   5.526 ms ± 127.220 μs  ┊ GC (mean ± σ):  0.00% ± 0.00%

                               ▁ ▃█▄                           
  ▂▁▂▂▂▂▂▂▁▁▂▂▃▃▂▄▄▄▃▃▄▄▆▅▄▆▆▆▅██████▇▅▄▄▃▄▄▃▄▃▃▃▃▃▃▄▃▃▄▃▃▂▃▂ ▃
  5.12 ms         Histogram: frequency by time        5.87 ms <

 Memory estimate: 57.77 KiB, allocs estimate: 940.

Note that the results m1and m2 are of course (approximately) the same.

distance(M, m1, m2)

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