Comments (9)
I don't think it would break anything. If jitted there would be no performance change.
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I expect that the performance change would be small in any event, but are you really confident that the jit analysis would be able to identify a multiplication by a variable that happens to have been assigned as a unit matrix?
Independent of this, is it possible to re-arrange the representation a bit so that we can set W = 1.0 (i.e. a scalar) in the case of no weighting?
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iirc the default isn’t an IdentityMatrix, but rather the Identity LinOp— it’s eval is a no-op.
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cf
Lines 214 to 215 in 4684909
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Only concern would be that the diagonal is explicitly set to snp.ones
:
Line 211 in 4684909
But can this be scalar 1.0
?
Then it could be that the shape of W
are different than the shape of W.diagonal
. Not sure whether that is a problem.
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The shape of W
and W.diagonal
should be the same; we can't stop a user from doing something like W.diagonal = x
where x.shape != W.shape
, but that's just shooting themselves in the foot. The Diagonal class really treats the .diagonal
attribute as a private thing, but Python doesn't have private attributes, so there you go.
As for identity: yes, it is true that Identity(shape).diagonal
is a matrix of ones, but look at the eval:
Lines 213 to 217 in 4684909
the
.diagonal
never gets used.
In earlier times, Identity
did not inherit from Diagonal
and this wasn't a problem
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In earlier times, Identity did not inherit from Diagonal and this wasn't a problem
What then is the reasoning to inherit from diagonal? Seems like in other contexts (other than the issue at hand) it may also be wasteful to store ones
.
In other words, if SquaredL2Loss
inherits from WeightedSquaredL2Loss
and it is slightly inefficient, that is not exactly because of the inheritance but because the identity operator has some inefficiency?
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Also, this means that I(x)
is very different than I.diagonal * x
, if I
is the Identity
operator.
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I suppose you could relax W.diagonal
to be something that broadcasts up to input_shape
.
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