Regarding debugging I don't know what's the usual way to go about it. For me I just put a break point in the compile_function_src. Which gives me source string for required function.
That function is located in link/utils.py I think
The problem is some of the functions aren't generated with strings while some of them are. So it's pretty hard to get a uniform debug functionality on them.
1 reply
and both are technically taking the exact same approach
the difference is the interface
at least in the case of
Scan
that has more to do with JAX's limitations (or scope/purpose), though
it's a tensor library
Numba is much more general
attempting to cover nearly all of Python
but, yeah, I see what you're saying
it's a limitation in terms of mapping simplicity and the like
or something of that nature
but Numba's generality is what will ultimately make it considerably easier to write high performance
Ops
e.g. like writing standard Python
Ops
I'm saying that
Op.perform implementations will ultimately be able to serve as the only necessary Op implementation code
we might need/want to refactor the interface a bit, and the Python needs to be Numba compatible
but that's the idea
like
Op.perform
what it takes as arguments and what it returns/does
the current design is built around Theano's old ideas
e.g. that
lists serve as "pointers" to memory that Theano manages manually
it's neat, but not particularly good
brandonwillard: I tried to follow your blogpost on DLMs, but I didn't succeed in getting auto_updates to work with this minimal graph:
rng = aesara.shared(np.random.default_rng(), borrow=True)
rng.tag.is_rng = True
rng.default_update = rng
def step(v_tm1, rng):
v_t = at.random.normal(v_tm1, 1.0, name='v', rng=rng)
return v_t
v, updates = aesara.scan(
fn=step,
outputs_info=[np.array(0.0)],
non_sequences=[rng],
n_steps=5,
strict=True,
)
v_draws = aesara.function([], v, updates=updates)()
assert len(np.unique(np.diff(v_draws))) > 1 # All values have the same offsetAny thing obvious I am missing?
I don't think the old Theano was necessarily using the auto updates in that case
I believe it was in-placing the shared RNG objects
if you do something like the following, it should work, though:
for key, value in updates.items():
key.default_update = value:point_up: Edit: brandonwillard: I tried to follow your blogpost on DLMs, but I didn't succeed in getting auto_updates to work with this minimal graph:
rng = aesara.shared(np.random.default_rng(), borrow=True)
rng.tag.is_rng = True
rng.default_update = rng
def step(v_tm1, rng):
v_t = at.random.normal(v_tm1, 1.0, name='v', rng=rng)
return v_t
v, updates = aesara.scan(
fn=step,
outputs_info=[np.array(0.0)],
non_sequences=[rng],
n_steps=5,
strict=True,
)
for key, value in updates.items():
key.default_update = value
v_draws = aesara.function([], v, updates=updates)()
print(np.diff(v_draws))Any thing obvious I am missing?
:point_up: Edit: brandonwillard: I tried to follow your blogpost on DLMs, but I didn't succeed in getting auto_updates to work with this minimal graph:
rng = aesara.shared(np.random.default_rng(), borrow=True)
rng.tag.is_rng = True
rng.default_update = rng
def step(v_tm1, rng):
v_t = at.random.normal(v_tm1, 1.0, name='v', rng=rng)
return v_t
v, updates = aesara.scan(
fn=step,
outputs_info=[np.array(0.0)],
non_sequences=[rng],
n_steps=5,
strict=True,
)
for key, value in updates.items():
key.default_update = value
v_draws = aesara.function([], v, updates=updates)()
print(np.diff(v_draws)) # [0.57665853 0.57665853 0.57665853 0.57665853]Any thing obvious I am missing?
Still not working. I updated the example
using
non_sequences with the RNG, right?
:point_up: Edit: brandonwillard: I tried to follow your blogpost on DLMs, but I didn't succeed in getting auto_updates to work with this minimal graph:
import numpy as np
import aesara
import aesara.tensor as at
rng = aesara.shared(np.random.default_rng(), borrow=True)
rng.tag.is_rng = True
rng.default_update = rng
def step(v_tm1, rng):
v_t = at.random.normal(v_tm1, 1.0, name='v', rng=rng)
return v_t
v, updates = aesara.scan(
fn=step,
outputs_info=[np.array(0.0)],
non_sequences=[rng],
n_steps=5,
strict=True,
)
for key, value in updates.items():
key.default_update = value
v_draws = aesara.function
print(np.diff(v_draws)) # [0.57665853 0.57665853 0.57665853 0.57665853]Any thing obvious I am missing?
Not supposed to do that?
it worked in the old Theano because of some odd in-place settings
first off, you shouldn't need the
rng attributes added in the beginning
Is there a canonical way of doing it with Aesara's RandomVariables?