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Fix broadcast with scalar #63

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baa45e1
Fix broadcast with scalar
blegat May 14, 2026
70ee1d8
Add test
blegat May 14, 2026
715259f
Refactor
blegat May 14, 2026
1f0bc09
fix
blegat May 14, 2026
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180 changes: 154 additions & 26 deletions src/reverse_mode.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -391,46 +391,107 @@ function _forward_eval(
children_indices = SparseArrays.nzrange(f.adj, k)
N = length(children_indices)
if node.index == 1 # :+ (broadcasted)
for j in _eachindex(f.sizes, k)
tmp_sum = zero(T)
for c_idx in children_indices
ix = children_arr[c_idx]
@j f.partials_storage[ix] = one(T)
tmp_sum += @j f.forward_storage[ix]
# Broadcast-aware sum: scalar children contribute their
# single value to every output slot.
out = _view_linear(f.forward_storage, f.sizes, k)
fill!(out, zero(T))
for c_idx in children_indices
ix = children_arr[c_idx]
if f.sizes.ndims[ix] == 0
s = _getscalar(f.forward_storage, f.sizes, ix)
out .+= s
_setscalar!(f.partials_storage, one(T), f.sizes, ix)
else
v = _view_linear(f.forward_storage, f.sizes, ix)
out .+= v
fill!(
_view_linear(f.partials_storage, f.sizes, ix),
one(T),
)
end
@j f.forward_storage[k] = tmp_sum
end
elseif node.index == 2 # :- (broadcasted)
@assert N == 2
child1 = first(children_indices)
@inbounds ix1 = children_arr[child1]
@inbounds ix2 = children_arr[child1+1]
out = _view_linear(f.forward_storage, f.sizes, k)
v1 = _view_linear(f.forward_storage, f.sizes, ix1)
v2 = _view_linear(f.forward_storage, f.sizes, ix2)
out .= v1 .- v2
fill!(_view_linear(f.partials_storage, f.sizes, ix1), one(T))
fill!(_view_linear(f.partials_storage, f.sizes, ix2), -one(T))
ndims1 = f.sizes.ndims[ix1]
ndims2 = f.sizes.ndims[ix2]
if ndims1 == 0 && ndims2 != 0
s1 = _getscalar(f.forward_storage, f.sizes, ix1)
v2 = _view_linear(f.forward_storage, f.sizes, ix2)
out .= s1 .- v2
_setscalar!(f.partials_storage, one(T), f.sizes, ix1)
fill!(
_view_linear(f.partials_storage, f.sizes, ix2),
-one(T),
)
elseif ndims1 != 0 && ndims2 == 0
v1 = _view_linear(f.forward_storage, f.sizes, ix1)
s2 = _getscalar(f.forward_storage, f.sizes, ix2)
out .= v1 .- s2
fill!(
_view_linear(f.partials_storage, f.sizes, ix1),
one(T),
)
_setscalar!(f.partials_storage, -one(T), f.sizes, ix2)
else
v1 = _view_linear(f.forward_storage, f.sizes, ix1)
v2 = _view_linear(f.forward_storage, f.sizes, ix2)
out .= v1 .- v2
fill!(
_view_linear(f.partials_storage, f.sizes, ix1),
one(T),
)
fill!(
_view_linear(f.partials_storage, f.sizes, ix2),
-one(T),
)
end
elseif node.index == 3 # :* (broadcasted)
# Node `k` is not scalar, so we do matrix multiplication
# Node `k` is not scalar, so we do element-wise multiply
# (with scalar-broadcast support: when one operand is
# scalar, broadcast it across the matrix output).
if f.sizes.ndims[k] != 0
@assert N == 2
idx1 = first(children_indices)
idx2 = last(children_indices)
@inbounds ix1 = children_arr[idx1]
@inbounds ix2 = children_arr[idx2]
v1 = zeros(_size(f.sizes, ix1)...)
v2 = zeros(_size(f.sizes, ix2)...)
for j in _eachindex(f.sizes, ix1)
v1[j] = @j f.forward_storage[ix1]
@j f.partials_storage[ix2] = v1[j]
end
for j in _eachindex(f.sizes, ix2)
v2[j] = @j f.forward_storage[ix2]
@j f.partials_storage[ix1] = v2[j]
end
for j in _eachindex(f.sizes, k)
@j f.forward_storage[k] = v1[j] * v2[j]
out = _view_linear(f.forward_storage, f.sizes, k)
ndims1 = f.sizes.ndims[ix1]
ndims2 = f.sizes.ndims[ix2]
if ndims1 == 0 && ndims2 != 0
s = _getscalar(f.forward_storage, f.sizes, ix1)
v = _view_linear(f.forward_storage, f.sizes, ix2)
out .= s .* v
# Per-element partial w.r.t. the matrix child is
# the scalar; the scalar child's reverse is handled
# by the broadcasted-`:*` reverse branch below
# (sum of `rev_parent .* v`).
fill!(_view_linear(f.partials_storage, f.sizes, ix2), s)
elseif ndims1 != 0 && ndims2 == 0
v = _view_linear(f.forward_storage, f.sizes, ix1)
s = _getscalar(f.forward_storage, f.sizes, ix2)
out .= v .* s
fill!(_view_linear(f.partials_storage, f.sizes, ix1), s)
else
# Both children are arrays of the same shape —
# original element-wise path.
v1 = zeros(_size(f.sizes, ix1)...)
v2 = zeros(_size(f.sizes, ix2)...)
for j in _eachindex(f.sizes, ix1)
v1[j] = @j f.forward_storage[ix1]
@j f.partials_storage[ix2] = v1[j]
end
for j in _eachindex(f.sizes, ix2)
v2[j] = @j f.forward_storage[ix2]
@j f.partials_storage[ix1] = v2[j]
end
for j in _eachindex(f.sizes, k)
@j f.forward_storage[k] = v1[j] * v2[j]
end
end
# Node `k` is scalar
else
Expand Down Expand Up @@ -832,13 +893,80 @@ function _reverse_eval(
elseif node.type == NODE_CALL_MULTIVARIATE_BROADCASTED
if node.index in eachindex(DEFAULT_MULTIVARIATE_OPERATORS)
op = DEFAULT_MULTIVARIATE_OPERATORS[node.index]
# Broadcasted +/- with at least one scalar child: the
# scalar's reverse is the (signed) sum of the parent's
# adjoint over the broadcast positions. Handle both scalar
# and matrix children here so the generic
# diagonal-partial path below doesn't trip its
# `_size(k) == _size(ix)` assertion.
if (op == :+ || op == :-) &&
any(
c -> f.sizes.ndims[children_arr[c]] == 0,
children_indices,
) &&
f.sizes.ndims[k] != 0
Tr = eltype(f.reverse_storage)
rev_parent = _view_linear(f.reverse_storage, f.sizes, k)
for c_idx in children_indices
ix = children_arr[c_idx]
# `:-` flips the sign for the second operand, mirroring
# the partial we wrote in the forward pass.
partial_sign =
(op == :- && c_idx != first(children_indices)) ?
-one(Tr) : one(Tr)
if f.sizes.ndims[ix] == 0
_setscalar!(
f.reverse_storage,
partial_sign * sum(rev_parent),
f.sizes,
ix,
)
else
rev_child =
_view_linear(f.reverse_storage, f.sizes, ix)
rev_child .= partial_sign .* rev_parent
end
end
continue
end
if op == :*
if f.sizes.ndims[k] != 0
# Node `k` is not scalar, so we do matrix multiplication or broadcasted multiplication
idx1 = first(children_indices)
idx2 = last(children_indices)
ix1 = children_arr[idx1]
ix2 = children_arr[idx2]
rev_parent = _view_linear(f.reverse_storage, f.sizes, k)
ndims1 = f.sizes.ndims[ix1]
ndims2 = f.sizes.ndims[ix2]
if ndims1 == 0 && ndims2 != 0
v2 = _view_linear(f.forward_storage, f.sizes, ix2)
s1 = _getscalar(f.forward_storage, f.sizes, ix1)
rev_v2 =
_view_linear(f.reverse_storage, f.sizes, ix2)
rev_v2 .= rev_parent .* s1
_setscalar!(
f.reverse_storage,
LinearAlgebra.dot(rev_parent, v2),
f.sizes,
ix1,
)
continue
elseif ndims1 != 0 && ndims2 == 0
v1 = _view_linear(f.forward_storage, f.sizes, ix1)
s2 = _getscalar(f.forward_storage, f.sizes, ix2)
rev_v1 =
_view_linear(f.reverse_storage, f.sizes, ix1)
rev_v1 .= rev_parent .* s2
_setscalar!(
f.reverse_storage,
LinearAlgebra.dot(rev_parent, v1),
f.sizes,
ix2,
)
continue
end
# Both children are arrays of the same shape —
# original element-wise path.
v1 = zeros(_size(f.sizes, ix1)...)
v2 = zeros(_size(f.sizes, ix2)...)
for j in _eachindex(f.sizes, ix1)
Expand Down
55 changes: 30 additions & 25 deletions test/JuMP.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -457,35 +457,40 @@ function test_broadcast_nonsquare_matrix()
return
end

function test_broadcast_scalar_matrix_size_inference()
# Cover every `Number op MatrixVar` / `MatrixVar op Number` broadcast
# pattern that JuMP's `Base.broadcasted` produces — both the size inference
# (broadcast node inherits the matrix child's shape, not the old `(1, 1)`
# stub) and the eval/reverse paths (`out .= s op v`, `rev_s =
# ±sum(rev_parent)` or `dot(rev_parent, v)`). Loss is `norm(c op W)` so the
# analytic gradient is `dexpr_dW .* (c op W) ./ norm(c op W)`.
function test_broadcast_scalar_matrix_gradient()
c = 2.5
rows, cols = 2, 3
model = Model()
@variable(model, W[1:2, 1:3], container = ArrayDiff.ArrayOfVariables)
mode = ArrayDiff.Mode()
@testset "$(name)" for (name, expr) in [
("scalar .* M", LinearAlgebra.norm(2.5 .* W)),
("M .* scalar", LinearAlgebra.norm(W .* 2.5)),
("scalar .+ M", LinearAlgebra.norm(2.5 .+ W)),
("M .+ scalar", LinearAlgebra.norm(W .+ 2.5)),
("scalar .- M", LinearAlgebra.norm(2.5 .- W)),
("M .- scalar", LinearAlgebra.norm(W .- 2.5)),
@variable(model, W[1:rows, 1:cols], container = ArrayDiff.ArrayOfVariables)
x = Float64.(collect(1:(rows*cols)))
W_val = reshape(x, rows, cols)
@testset "$(name)" for (name, expr, ref_mat, dexpr_dW) in [
("scalar .+ M", c .+ W, c .+ W_val, fill(1.0, rows, cols)),
("M .+ scalar", W .+ c, W_val .+ c, fill(1.0, rows, cols)),
("scalar .- M", c .- W, c .- W_val, fill(-1.0, rows, cols)),
("M .- scalar", W .- c, W_val .- c, fill(1.0, rows, cols)),
("scalar .* M", c .* W, c .* W_val, fill(c, rows, cols)),
("M .* scalar", W .* c, W_val .* c, fill(c, rows, cols)),
]
ad = ArrayDiff.model(mode)
MOI.Nonlinear.set_objective(ad, JuMP.moi_function(expr))
evaluator = MOI.Nonlinear.Evaluator(
ad,
mode,
JuMP.index.(JuMP.all_variables(model)),
)
MOI.initialize(evaluator, [:Grad])
sizes = evaluator.backend.objective.expr.sizes
# Broadcast node is at index 2; it should inherit the matrix child's
# (2, 3) shape, not the old `(1, 1)` stub.
sizes, val, g = _eval(model, LinearAlgebra.norm(expr), x)
# Outer norm scalar (k=1), then the broadcast (k=2) which must
# inherit the matrix child's (rows, cols) shape — not the old
# `(1, 1)` stub — then the two children (one scalar leaf, one
# matrix leaf) in some order.
@test sizes.ndims[1] == 0
@test sizes.ndims[2] == 2
broadcast_size_off = sizes.size_offset[2]
@test sizes.size[broadcast_size_off+1] == 2
@test sizes.size[broadcast_size_off+2] == 3
# And the scalar leaf among the children stays ndims=0.
b_off = sizes.size_offset[2]
@test sizes.size[b_off+1] == rows
@test sizes.size[b_off+2] == cols
@test 0 in sizes.ndims[3:4]
@test val ≈ LinearAlgebra.norm(ref_mat)
@test g ≈ vec(dexpr_dW .* ref_mat) ./ LinearAlgebra.norm(ref_mat)
end
return
end
Expand Down
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