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feat: support vector mode AD #519

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feat: support vector mode AD #519

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56d1701
feat: support vector mode AD
avik-pal Jan 12, 2025
229add4
Update test/autodiff.jl
avik-pal Jan 12, 2025
761ed0d
Update Interpreter.jl
wsmoses Jan 12, 2025
fde80a3
Merge branch 'main' into ap/chunked_ad
avik-pal Feb 8, 2025
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19 changes: 10 additions & 9 deletions src/Interpreter.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -192,7 +192,11 @@ function push_acts!(ad_inputs, x::BatchDuplicated, path, reverse)
predims = size(x.val)
cval = MLIR.IR.result(
MLIR.Dialects.stablehlo.concatenate(
[Ops.reshape(v, Int64[1, predims...]) for v in x.dval]; dimension=Int64(0)
[
TracedUtils.get_mlir_data(Ops.reshape(v, Int64[1, predims...])) for
v in x.dval
];
dimension=Int64(0),
),
)
tval = TracedRArray{ET,length(predims) + 1}((), cval, (length(x.dval), predims...))
Expand Down Expand Up @@ -244,12 +248,6 @@ function overload_autodiff(
width = Enzyme.same_or_one(1, args...)
if width == 0
throw(ErrorException("Cannot differentiate with a batch size of 0"))
elseif width != 1
throw(
ErrorException(
"EnzymeMLIR does not presently support width=$width, please rewrite your code to not use BatchDuplicated and/or call gradient(; chunk=1)",
),
)
end

primf = f.val
Expand Down Expand Up @@ -381,6 +379,7 @@ function overload_autodiff(
[TracedUtils.transpose_val(v) for v in ad_inputs];
outputs=outtys,
fn=fname,
width,
activity=MLIR.IR.Attribute([act_attr(a) for a in activity]),
ret_activity=MLIR.IR.Attribute([act_attr(a) for a in ret_activity]),
)
Expand Down Expand Up @@ -423,8 +422,10 @@ function overload_autodiff(
push!(starts, 0)
push!(limits, v)
end
sval = Ops.slice(sval, starts, limits)
TracedUtils.set!(dresult[i], path[2:end], sval)
sval = Ops.slice(TracedRArray(tval), starts, limits)
TracedUtils.set!(
dresult[i], path[2:end], TracedUtils.get_mlir_data(sval)
)
end
end
residx += 1
Expand Down
14 changes: 14 additions & 0 deletions test/autodiff.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -135,6 +135,20 @@ end
@test res2 ≈ 4 * 3 * 3.1^2
end

fn(x) = sum(abs2, x)

function vector_forward_ad(x, dx1, dx2)
return Enzyme.autodiff(Forward, fn, BatchDuplicated(x, (dx1, dx2)))
end

@testset "Vector Mode AD" begin
x = Reactant.to_rarray([1.0, 3.0])
dx1 = Reactant.to_rarray([1.0, 0.0])
dx2 = Reactant.to_rarray([0.0, 1.0])

res = @jit vector_forward_ad(x, dx1, dx2)
end

@testset "Seed initialization of Complex arrays on matmul: Issue #593" begin
a = ones(ComplexF64, 2, 2)
b = 2.0 * ones(ComplexF64, 2, 2)
Expand Down
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