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Add transformer #57

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c5c3504
Add transformer
blegat May 6, 2026
0a109ec
Add test
blegat May 6, 2026
ae7bf66
fix
blegat May 7, 2026
46b55ed
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blegat May 8, 2026
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283 changes: 234 additions & 49 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 @@ -391,46 +404,118 @@ 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 @@ -620,23 +705,54 @@ 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 Expand Up @@ -832,13 +948,82 @@ 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 All @@ -847,18 +1032,18 @@ function _reverse_eval(
for j in _eachindex(f.sizes, ix2)
v2[j] = @j f.forward_storage[ix2]
end
rev_parent = zeros(_size(f.sizes, k)...)
rev_parent_arr = zeros(_size(f.sizes, k)...)
for j in _eachindex(f.sizes, k)
rev_parent[j] = @j f.reverse_storage[k]
rev_parent_arr[j] = @j f.reverse_storage[k]
end
rev_v1 = zeros(_size(f.sizes, ix1)...)
rev_v2 = zeros(_size(f.sizes, ix2)...)
for j in _eachindex(f.sizes, ix1)
rev_v1[j] = rev_parent[j] * v2[j]
rev_v1[j] = rev_parent_arr[j] * v2[j]
@j f.reverse_storage[ix1] = rev_v1[j]
end
for j in _eachindex(f.sizes, ix2)
rev_v2[j] = rev_parent[j] * v1[j]
rev_v2[j] = rev_parent_arr[j] * v1[j]
@j f.reverse_storage[ix2] = rev_v2[j]
end
continue
Expand Down
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