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Implement gradient #11

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80 changes: 62 additions & 18 deletions src/reverse_mode.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -335,27 +335,71 @@ Reverse-mode evaluation of an expression tree given in `f`.
* This function assumes that `f.reverse_storage` has been initialized with 0.0.
"""
function _reverse_eval(f::_SubexpressionStorage)
@assert length(f.reverse_storage) >= length(f.nodes)
@assert length(f.partials_storage) >= length(f.nodes)
@assert length(f.reverse_storage) >= _length(f.sizes)
@assert length(f.partials_storage) >= _length(f.sizes)
# f.nodes is already in order such that parents always appear before
# children so a forward pass through nodes is a backwards pass through the
# tree.
f.reverse_storage[1] = one(Float64)
for k in 2:length(f.nodes)
children_arr = SparseArrays.rowvals(f.adj)
for i in _storage_range(f.sizes, 1)
f.reverse_storage[i] = one(Float64)
end
for k in 1:length(f.nodes)
@show f.reverse_storage
node = f.nodes[k]
if node.type == Nonlinear.NODE_VALUE ||
node.type == Nonlinear.NODE_LOGIC ||
node.type == Nonlinear.NODE_COMPARISON ||
node.type == Nonlinear.NODE_PARAMETER
children_indices = SparseArrays.nzrange(f.adj, k)
if node.type == MOI.Nonlinear.NODE_CALL_MULTIVARIATE
if node.index in
eachindex(MOI.Nonlinear.DEFAULT_MULTIVARIATE_OPERATORS)
op = MOI.Nonlinear.DEFAULT_MULTIVARIATE_OPERATORS[node.index]
if op == :vect
@assert _eachindex(f.sizes, k) == eachindex(children_indices)
for j in eachindex(children_indices)
rev_parent = @s f.reverse_storage[k]
ix = children_arr[children_indices[j]]
# partial is 1 so we can ignore it
@s f.reverse_storage[ix] = rev_parent
end
continue
elseif op == :dot
# Node `k` is scalar, the jacobian w.r.t. each vectorized input
# child is a row vector whose entries are stored in `f.partials_storage`
rev_parent = @s f.reverse_storage[k]
for j in _eachindex(f.sizes, k)
for child_idx in children_indices
ix = children_arr[child_idx]
partial = @j f.partials_storage[ix]
val = ifelse(
rev_parent == 0.0 && !isfinite(partial),
rev_parent,
rev_parent * partial,
)
@j f.reverse_storage[ix] = val
end
end
continue
end
end
elseif node.type != MOI.Nonlinear.NODE_CALL_UNIVARIATE
continue
end
rev_parent = f.reverse_storage[node.parent]
partial = f.partials_storage[k]
f.reverse_storage[k] = ifelse(
rev_parent == 0.0 && !isfinite(partial),
rev_parent,
rev_parent * partial,
)
# Node `k` has same size as its children.
# The Jacobian (between the vectorized versions) is diagonal and the diagonal entries
# are stored in `f.partials_storage`
for j in _eachindex(f.sizes, k)
rev_parent = @j f.reverse_storage[k]
for child_idx in children_indices
ix = children_arr[child_idx]
@assert _size(f.sizes, k) == _size(f.sizes, ix)
partial = @j f.partials_storage[ix]
val = ifelse(
rev_parent == 0.0 && !isfinite(partial),
rev_parent,
rev_parent * partial,
)
@j f.reverse_storage[ix] = val
end
end
end
return
end
Expand Down Expand Up @@ -406,12 +450,12 @@ function _extract_reverse_pass_inner(
subexpressions::AbstractVector{T},
scale::T,
) where {T}
@assert length(f.reverse_storage) >= length(f.nodes)
@assert length(f.reverse_storage) >= _length(f.sizes)
for (k, node) in enumerate(f.nodes)
if node.type == Nonlinear.NODE_VARIABLE
output[node.index] += scale * f.reverse_storage[k]
output[node.index] += scale * @s f.reverse_storage[k]
elseif node.type == Nonlinear.NODE_SUBEXPRESSION
subexpressions[node.index] += scale * f.reverse_storage[k]
subexpressions[node.index] += scale * @s f.reverse_storage[k]
end
end
return
Expand Down
17 changes: 12 additions & 5 deletions test/ArrayDiff.jl
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Expand Up @@ -32,28 +32,35 @@ function test_objective_dot_univariate()
@test sizes.size_offset == [0, 1, 0, 0, 0]
@test sizes.size == [1, 1]
@test sizes.storage_offset == [0, 1, 2, 3, 4, 5]
@test MOI.eval_objective(evaluator, [1.2]) == 1.2^2
x = [1.2]
@test MOI.eval_objective(evaluator, x) == x[1]^2
g = ones(1)
MOI.eval_objective_gradient(evaluator, g, x)
@test g[1] == 2x[1]
return
end

function test_objective_dot_bivariate()
model = Nonlinear.Model()
x = MOI.VariableIndex(1)
y = MOI.VariableIndex(1)
y = MOI.VariableIndex(2)
Nonlinear.set_objective(
model,
:(dot([$x, $y] - [1, 2], -[1, 2] + [$x, $y])),
)
evaluator = Nonlinear.Evaluator(model, ArrayDiff.Mode(), [x])
evaluator = Nonlinear.Evaluator(model, ArrayDiff.Mode(), [x, y])
MOI.initialize(evaluator, [:Grad])
sizes = evaluator.backend.objective.expr.sizes
@test sizes.ndims == [0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0]
@test sizes.size_offset == [0, 6, 5, 0, 0, 4, 0, 0, 3, 2, 1, 0, 0, 0, 0, 0]
@test sizes.size == [2, 2, 2, 2, 2, 2, 2]
@test sizes.storage_offset ==
[0, 1, 3, 5, 6, 7, 9, 10, 11, 13, 15, 17, 18, 19, 21, 22, 23]
g = [NaN]
@test MOI.eval_objective(evaluator, [5, -1]) ≈ 25
x = [5, -1]
@test MOI.eval_objective(evaluator, x) ≈ 25
g = ones(2)
MOI.eval_objective_gradient(evaluator, g, x)
@test g == 2(x - [1, 2])
return
end

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