|
| 1 | +using Test |
| 2 | +using DynamicExpressions |
| 3 | +using Random: MersenneTwister |
| 4 | +using ChainRulesCore: ChainRulesCore, ZeroTangent, NoTangent |
| 5 | +using ForwardDiff: gradient as fd_gradient |
| 6 | +using Zygote: gradient as zg_gradient |
| 7 | +using Suppressor: @suppress_err |
| 8 | +include("test_params.jl") |
| 9 | +include("tree_gen_utils.jl") |
| 10 | + |
| 11 | +let |
| 12 | + rng = MersenneTwister(0) |
| 13 | + n_features = 5 |
| 14 | + operators = OperatorEnum(; binary_operators=(+, *, -), unary_operators=(sin,)) |
| 15 | + tree = gen_random_tree_fixed_size(20, operators, n_features, Float64, Node, rng) |
| 16 | + X = rand(rng, Float64, n_features, 100) |
| 17 | + |
| 18 | + function f(X) |
| 19 | + y, _ = eval_tree_array(tree, X, operators) |
| 20 | + return sum(i -> y[i]^2, eachindex(y)) |
| 21 | + end |
| 22 | + |
| 23 | + @suppress_err begin |
| 24 | + # Check zg_gradient against fd_gradient; the latter of which is computed explicitly |
| 25 | + @test isapprox([only(zg_gradient(f, X))...], [fd_gradient(f, X)...]; atol=1e-6) |
| 26 | + end |
| 27 | +end |
| 28 | + |
| 29 | +mean(x) = sum(x) / length(x) |
| 30 | + |
| 31 | +let |
| 32 | + operators = OperatorEnum(; binary_operators=(+, *, -), unary_operators=(sin,)) |
| 33 | + x1, x2, x3 = [Node{Float64}(; feature=i) for i in 1:3] |
| 34 | + tree = sin(x1 * 3.2 - 0.9) + 0.2 * x2 - x3 |
| 35 | + X = [ |
| 36 | + 1.0 2.0 3.0 |
| 37 | + 4.0 5.0 6.0 |
| 38 | + 7.0 8.0 9.0 |
| 39 | + ] |
| 40 | + function eval_tree(X, tree) |
| 41 | + y, _ = eval_tree_array(tree, X, operators) |
| 42 | + return mean(y) |
| 43 | + end |
| 44 | + |
| 45 | + function true_eval_tree(X, c) |
| 46 | + y = @. sin(X[1, :] * c[1] - c[2]) + c[3] * X[2, :] - X[3, :] |
| 47 | + return mean(y) |
| 48 | + end |
| 49 | + |
| 50 | + evaluated_gradient = zg_gradient(tree -> eval_tree(X, tree), tree)[1] |
| 51 | + true_gradient = fd_gradient(c -> true_eval_tree(X, c), [3.2, 0.9, 0.2]) |
| 52 | + |
| 53 | + @test evaluated_gradient.tree == tree |
| 54 | + @test isapprox(evaluated_gradient.gradient, true_gradient) |
| 55 | + |
| 56 | + # Misc tests of uncovered portions |
| 57 | + let tree = tree, |
| 58 | + X = X, |
| 59 | + evaluated_gradient = evaluated_gradient, |
| 60 | + true_gradient = true_gradient |
| 61 | + |
| 62 | + evaluated_gradient_2 = zg_gradient(tree -> eval_tree(X, tree), tree)[1] |
| 63 | + true_gradient_2 = fd_gradient(c -> true_eval_tree(X, c), [3.2, 0.9, 0.2]) |
| 64 | + |
| 65 | + evaluated_aggregate = evaluated_gradient + evaluated_gradient_2 |
| 66 | + true_aggregate = true_gradient + true_gradient_2 |
| 67 | + @test evaluated_aggregate.tree == tree |
| 68 | + @test isapprox(evaluated_aggregate.gradient, true_aggregate) |
| 69 | + |
| 70 | + scalar_prod = evaluated_gradient * 2.0 |
| 71 | + scalar_prod2 = 2.0 * (1.0 * evaluated_gradient) |
| 72 | + true_scalar_prod = true_gradient * 2.0 |
| 73 | + @test scalar_prod.tree == tree |
| 74 | + @test isapprox(scalar_prod.gradient, true_scalar_prod) |
| 75 | + @test isapprox(scalar_prod2.gradient, true_scalar_prod) |
| 76 | + |
| 77 | + # Should be able to use with other types |
| 78 | + @test zero(evaluated_gradient) == ZeroTangent() |
| 79 | + |
| 80 | + @test evaluated_gradient + ZeroTangent() == evaluated_gradient |
| 81 | + @test evaluated_gradient + NoTangent() == evaluated_gradient |
| 82 | + end |
| 83 | +end |
| 84 | + |
| 85 | +# Operator that is NaN for forward pass |
| 86 | +bad_op(x) = x > 0.0 ? log(x) : convert(typeof(x), NaN) |
| 87 | +# And operator that is undefined for backward pass |
| 88 | +undefined_grad_op(x) = x >= 0.0 ? x : zero(x) |
| 89 | +# And operator that gives a NaN for backward pass |
| 90 | +bad_grad_op(x) = x |
| 91 | + |
| 92 | +function ChainRulesCore.rrule(::typeof(bad_grad_op), x) |
| 93 | + return bad_grad_op(x), (_) -> (NoTangent(), convert(typeof(x), NaN)) |
| 94 | +end |
| 95 | + |
| 96 | +# Also test NaN modes |
| 97 | +let |
| 98 | + operators = OperatorEnum(; |
| 99 | + binary_operators=(+, *, -), |
| 100 | + unary_operators=(sin, bad_op, bad_grad_op, undefined_grad_op), |
| 101 | + ) |
| 102 | + @extend_operators operators |
| 103 | + x1 = Node(Float64; feature=1) |
| 104 | + |
| 105 | + nan_forward = bad_op(x1 + 0.5) |
| 106 | + undefined_grad = undefined_grad_op(x1 + 0.5) |
| 107 | + nan_grad = bad_grad_op(x1) |
| 108 | + |
| 109 | + function eval_tree(X, tree) |
| 110 | + y, _ = eval_tree_array(tree, X, operators) |
| 111 | + return mean(y) |
| 112 | + end |
| 113 | + X = ones(1, 1) * -1.0 |
| 114 | + |
| 115 | + # Forward pass is NaN; Gradient will also be NaN |
| 116 | + @test isnan(only(eval_tree(X, nan_forward))) |
| 117 | + evaluated_gradient = zg_gradient(X -> eval_tree(X, nan_forward), X)[1] |
| 118 | + @test isnan(only(evaluated_gradient)) |
| 119 | + |
| 120 | + # Both forward and gradient are not NaN despite giving `nothing` back |
| 121 | + @test !isnan(only(eval_tree(X, undefined_grad))) |
| 122 | + evaluated_gradient = zg_gradient(X -> eval_tree(X, undefined_grad), X)[1] |
| 123 | + @test iszero(only(evaluated_gradient)) |
| 124 | + |
| 125 | + # Finally, the operator with a NaN gradient but non-NaN forward |
| 126 | + @test !isnan(only(eval_tree(X, nan_grad))) |
| 127 | + evaluated_gradient = zg_gradient(X -> eval_tree(X, nan_grad), X)[1] |
| 128 | + @test isnan(only(evaluated_gradient)) |
| 129 | + evaluated_gradient = zg_gradient(t -> eval_tree(X, t), nan_grad)[1] |
| 130 | + @show evaluated_gradient |
| 131 | + # @test isnan(only(evaluated_gradient.gradient)) |
| 132 | +end |
0 commit comments