you can see this all over the
scan codebase
one of those clone functions comes from that
and started to be used all throughout the library
I believe it created one of the bad dependency chains we had to fix early on
that issue is very related to all of this
especially the excessive need to clone entire graphs and manually remap the cloned values
we could/should separate the non-in-place replacement logic from
FunctionGraph
when that's implemented
that would be an answer to a lot of clone-like use cases
Trying to see what you mean by non-inplace replacement.
a = at.scalar('a')
b = a + 1; b.name = 'b'
c = b + 1; c.name = 'c'
fg = FunctionGraph([a], [c], clone=False)
fg.replace(b, at.sin(a))
Would anything change in that toy example under the hood?
if only to set expectations
Totally unrelated this is interesting. They are suggesting there is no need for R_Ops at all: Theano/Theano#6400
try porting that PR to Aesara
I think they were blocked with special behavior of OpFromGraphs but I can try to open a draft and see what we get
Would it be too insane to add aliases for numpy constans (np.pi, np.inf, np.e...) in
aesara.tensor?
or to reduce the need to explicitly import
numpy in some cases?
either one is probably a reason enough
The second one. So that we can just write
at.sqrt(x + at.pi) or at.switch(..., ..., at.inf)
They only have a handful of constants, ignoring their own aliases: https://numpy.org/doc/stable/reference/constants.html
Vectorization of "arbitrary" subgraphs. I have tried this in the past to vectorize a model logp, and the best I could come up with was OpFromGraph + Scan
Which for a small model was no better than a Python list comprehension
this all depends on exactly what vectorization means in this context
for instance, if it's more directly aligned with NumPy-like
ufuncs, then that's already covered by Aesara's Composite Op
That would be more in line with numpy.vectorize (although that one does nothing clever), I think?
That's what Composite is more like right? Builds on top of scalar operators
same with
Elemwise
but, yes, there is no
numpy.vectorize-like helper function in Aesara
and I don't recall
jax.vmap doing anything particularly special
it seemed quite literally like a
numpy.vectorize clone
the effective difference being that the end result is a JITed function
I think one big difference is that numpy.vectorize requires a base function that works only with scalars, whereas vmap can be built on top of tensor functions?
I think
numpy.vectorize can handle non-scalar functions