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Fix scalar * mat #62

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Fix scalar * mat #62

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6c69ac6
Fix scalar * mat
blegat May 14, 2026
4d4dba3
fix
blegat May 14, 2026
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83 changes: 64 additions & 19 deletions src/reverse_mode.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -171,19 +171,32 @@ function _forward_eval(
end
elseif node.index == 3 # :*
# Node `k` is not scalar, so we do matrix multiplication
# (or scalar `*` matrix scaling when one operand is scalar).
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 = _view_matrix(f.forward_storage, f.sizes, ix1)
v2 = _view_matrix(f.forward_storage, f.sizes, ix2)
out = _view_matrix(f.forward_storage, f.sizes, k)
LinearAlgebra.mul!(out, v1, v2)
out = _view_linear(f.forward_storage, f.sizes, k)
if f.sizes.ndims[ix1] == 0
s = _getscalar(f.forward_storage, f.sizes, ix1)
v = _view_linear(f.forward_storage, f.sizes, ix2)
out .= s .* v
elseif f.sizes.ndims[ix2] == 0
v = _view_linear(f.forward_storage, f.sizes, ix1)
s = _getscalar(f.forward_storage, f.sizes, ix2)
out .= v .* s
else
v1 = _view_matrix(f.forward_storage, f.sizes, ix1)
v2 = _view_matrix(f.forward_storage, f.sizes, ix2)
out_m = _view_matrix(f.forward_storage, f.sizes, k)
LinearAlgebra.mul!(out_m, v1, v2)
end
# We deliberately don't write v1/v2 into partials_storage
# here: the matmul reverse branch reads forward_storage
# directly, so those writes were dead.
# here: the matmul (or scalar-scaling) reverse branch
# reads forward_storage directly, so those writes were
# dead.
# Node `k` is scalar
else
tmp_prod = one(T)
Expand Down Expand Up @@ -620,23 +633,55 @@ function _reverse_eval(
op = DEFAULT_MULTIVARIATE_OPERATORS[node.index]
if op == :*
if f.sizes.ndims[k] != 0
# Matrix multiplication: rev_v1 = rev_parent * v2',
# rev_v2 = v1' * rev_parent. Both v1 and v2 are read
# straight from forward_storage (the matmul forward
# branch deliberately doesn't snapshot them into
# partials_storage), and the reverse views are written
# in place.
# Matmul (or `scalar * matrix` scaling): rev_v1 =
# rev_parent * v2', rev_v2 = v1' * rev_parent. With
# a scalar operand, the result is `s .* M`, so
# rev[s] = sum(rev_parent .* M) and rev[M] =
# rev_parent .* s. Both v1 and v2 are read straight
# from forward_storage.
idx1 = first(children_indices)
idx2 = last(children_indices)
ix1 = children_arr[idx1]
ix2 = children_arr[idx2]
v1 = _view_matrix(f.forward_storage, f.sizes, ix1)
v2 = _view_matrix(f.forward_storage, f.sizes, ix2)
rev_parent = _view_matrix(f.reverse_storage, f.sizes, k)
rev_v1 = _view_matrix(f.reverse_storage, f.sizes, ix1)
rev_v2 = _view_matrix(f.reverse_storage, f.sizes, ix2)
LinearAlgebra.mul!(rev_v1, rev_parent, v2')
LinearAlgebra.mul!(rev_v2, v1', rev_parent)
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,
)
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,
)
else
v1 = _view_matrix(f.forward_storage, f.sizes, ix1)
v2 = _view_matrix(f.forward_storage, f.sizes, ix2)
rev_parent_m =
_view_matrix(f.reverse_storage, f.sizes, k)
rev_v1 =
_view_matrix(f.reverse_storage, f.sizes, ix1)
rev_v2 =
_view_matrix(f.reverse_storage, f.sizes, ix2)
LinearAlgebra.mul!(rev_v1, rev_parent_m, v2')
LinearAlgebra.mul!(rev_v2, v1', rev_parent_m)
end
continue
end
elseif op == :vect
Expand Down
37 changes: 37 additions & 0 deletions test/JuMP.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -490,6 +490,43 @@ function test_broadcast_scalar_matrix_size_inference()
return
end

# Exercise the non-broadcasted `:*` scalar-times-matrix branch.
#
# JuMP's `Number * MatrixOfVariables` falls through to `Base.broadcasted`
# (no specialized method in `src/JuMP/operators.jl`), so the standard
# `c * W` syntax actually parses as broadcasted `:*`. To hit the
# non-broadcasted branch we build a `MatrixExpr` with `broadcasted = false`
# directly — the same shape `_matmul` produces, but with one scalar child
# instead of two matrix children. This is the path a hand-built
# `MOI.ScalarNonlinearFunction(:*, Any[c, W])` would land on.
function test_scalar_matrix_product_nonbroadcasted()
n, c = 2, 0.5
model = Model()
@variable(model, W[1:n, 1:n], container = ArrayDiff.ArrayOfVariables)
mat_expr = ArrayDiff.MatrixExpr(:*, Any[c, W], (n, n), false)
@test mat_expr.head == :*
@test !mat_expr.broadcasted
@test size(mat_expr) == (n, n)
loss = sum(mat_expr)
mode = ArrayDiff.Mode()
ad = ArrayDiff.model(mode)
MOI.Nonlinear.set_objective(ad, JuMP.moi_function(loss))
evaluator = MOI.Nonlinear.Evaluator(
ad,
mode,
JuMP.index.(JuMP.all_variables(model)),
)
MOI.initialize(evaluator, [:Grad])
x = Float64[1, 2, 3, 4]
# `sum(c * W) = c * sum(W)`
@test MOI.eval_objective(evaluator, x) ≈ c * sum(x)
g = zeros(n * n)
MOI.eval_objective_gradient(evaluator, g, x)
# `∂/∂W_ij sum(c * W) = c`, constant.
@test g ≈ fill(c, n * n)
return
end

end # module

TestJuMP.runtests()
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