I'm finding the switch from Theano to Aesara straightforward, our
simulation code produced the same output, and one program runs 19% faster
once there's an ~/.aesara cache!
This is fantastic. I was getting worried about depending on Theano.
It did print two kinds of warnings:
RuntimeWarning: divide by zero encountered in true_divide
.../lib/python3.8/site-packages/aesara/graph/fg.py:525: UserWarning:
Variable Elemwise{mul}.0 cannot be replaced; it isn't in the FunctionGraph
The first warning is from our code calling NumPy. It turns out that `import
theano` suppresses that warning globally!
It's great that Aesara doesn't suppress that warning, but it could've saved
debugging time if the release notes mentioned that. :)
The second kind of warning comes from deep in Aesara, in code that used to
"just
return silently because it makes it easier to implement some optimizations
for multiple-output ops".
Here's the stack trace (OCR'd from a screen shot of the PyCharm debugger's
"frames" pane):
replace, fg.py:525
replace_all_validate, toolbox.py:574
replace_all_validate_remove, toolbox.py:642
process_node, opt.py:2058
apply, opt.py:2535
optimize, opt.py:84
apply, opt.py:246
optimize, opt.py:84
__call__, opt.py:93
__init__, types.py:1616
orig_function, types.py:1971
pfunc, pfunc.py:524
function, __init__.py:337
make_thunk, op.py:970
make_all, vm.py:1133
make_thunk, basic.py:293
create, types.py:1837
orig_function, types.py:1982
pfunc, pfunc.py:524
function, __init__.py:337
km_loss_function, rna_decay.py:179
...
km_loss_function is in our code:
import aesara.tensor as T
from aesara import function, gradient
km = T.dvector()
...
J = gradient.jacobian(LossFunction, km)
J_aux = gradient.jacobian(LossFunction_aux, km)
Jacob = function([km], J)
Should our code do this differently?
Do you need a small test case?
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