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
that's why you can specify a
signature with explicit dimensions for each input
yeah,
jax.vmap looks like an clone of numpy.vectorize
with a somewhat different interface
e.g. specified in terms of numbers of axes
or labeled axes?
yeah, it looks like their explicit version of
numpy.vectorize just constructs vmap calls
anyway, we could provide a
numpy.vectorize helper function
TIL about the
signature keyword:import numpy as np
def dirichlet_logp(a, x):
return st.dirichlet(a).logpdf(x)
vfunc = np.vectorize(dirichlet_logp, signature='(n),(n)->()')
vfunc(np.arange(1, 10).reshape(3, 3), np.ones(3)/3)
this kind of logic is essentially what we're doing in the
RandomVariable class interface