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Specialize norm(t, Inf) a bit so we can use it on GPU backed TensorMaps #586

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Merged
kshyatt merged 5 commits into from
Oct 22, 2026
Merged

Specialize norm(t, Inf) a bit so we can use it on GPU backed TensorMaps #586

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21744b9
Specialize norm(t, Inf) a bit so we can use it on GPU backed TensorMaps
kshyatt Oct 19, 2026
ae8f350
Formatter
kshyatt Oct 19, 2026
955ac91
Specialize for Adjoint and Diagonal instead
kshyatt Oct 20, 2026
879652e
Respond to comments
Oct 21, 2026
f9d0361
Help inference
Oct 21, 2026
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2 changes: 2 additions & 0 deletions src/tensors/adjoint.jl
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Expand Up @@ -53,6 +53,8 @@ end

Adapt.adapt_structure(to, x::AdjointTensorMap) = adjoint(Adapt.adapt(to, parent(x)))

LinearAlgebra.norm(t::AdjointTensorMap, p::Real = 2) = norm(parent(t), p)

# Show
#------
function Base.showarg(io::IO, t::AdjointTensorMap, toplevel::Bool)
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6 changes: 6 additions & 0 deletions src/tensors/diagonal.jl
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Expand Up @@ -375,6 +375,12 @@ end
function LinearAlgebra.isposdef(d::DiagonalTensorMap)
return all(isposdef, d.data)
end
function LinearAlgebra.norm(t::DiagonalTensorMap, p::Real = 2)
InnerProductStyle(t) === EuclideanInnerProduct() || throw_invalid_innerproduct(:norm)
init = float(zero(real(scalartype(t))))
p == Inf && return maximum(abs, t.data; init)
return _norm(blocks(t), p, init)
end

# matrix functions
for f in
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13 changes: 9 additions & 4 deletions src/tensors/linalg.jl
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Expand Up @@ -256,12 +256,13 @@ LinearAlgebra.dot(t1::AbstractTensorMap, t2::AbstractTensorMap) = inner(t1, t2)

function LinearAlgebra.norm(t::AbstractTensorMap, p::Real = 2)
InnerProductStyle(t) === EuclideanInnerProduct() || throw_invalid_innerproduct(:norm)
return _norm(blocks(t), p, float(zero(real(scalartype(t)))))
init = float(zero(real(scalartype(t))))
return _norm(blocks(t), p, init)
end
function _norm(blockiter, p::Real, init::Number)
if p == Inf
return mapreduce(max, blockiter; init = init) do (c, b)
return isempty(b) ? init : oftype(init, LinearAlgebra.normInf(b))
return oftype(init, maximum(abs, b; init))
end
elseif p > 0 # finite positive p
np = init
Expand All @@ -276,9 +277,13 @@ function _norm(blockiter, p::Real, init::Number)
end
function LinearAlgebra.norm(t::TensorMap, p::Real = 2)
InnerProductStyle(t) === EuclideanInnerProduct() || throw_invalid_innerproduct(:norm)
init = float(zero(real(scalartype(t))))
# performance specialization:
FusionStyle(sectortype(t)) isa UniqueFusion && return norm(t.data, p)
return _norm(blocks(t), p, float(zero(real(scalartype(t)))))
if FusionStyle(sectortype(t)) isa UniqueFusion
return p == Inf ? maximum(abs, t.data; init) : norm(t.data, p)
else
return _norm(blocks(t), p, init)
end
end

_default_rtol(t) = eps(real(float(scalartype(t)))) * min(dim(domain(t)), dim(codomain(t)))
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