# Memory allocation limit for NUTS with custom logp function (but not with VI methods)

Hey @OriolAbril, thanks for taking a look at my issue. Yes, that was my initial suspicion as well. Since that model component (the user defined component comp1) is proportional to the product of the EMG cumulative distribution function and the Normal survival function (i.e. the _log_unscaled definition which = log(CDF*SF)) it must be numerically normalized in order to serve as a well behaved probability distribution (I can’t find a way to analytically solve that product to find a closed-form normalization factor). To numerically normalize it, I am numerically integrating it over a large range to approximate the integration over (-\infty, \infty). The _x array is that numerical integration range, the length of which will of course depend on the values of all the parameters at each step in the fitting. The _min and _max values of that integration range are also defined to be a large number of standard deviations (for a given choice of parameter values) from each end of the probability distribution, set by _n (in order to make sure the resulting probability value is of high precision and, thus, well-behaved). In this case _n = 10 standard deviations. So it seems like it would be a large numeric integration range, as you say.

However, I calculated an extreme case for this range and it doesn’t seem like it would be the culprit. Say I sampled 10\sigma out on each of the prior distributions (pretty far out into the tails) for the given values of the hyperparameters. In that case, the length of _x is still only on the order of 10e6. See below:

Nsig = 10

_n = 10
m0 = -Nsig*2000
s0 = Nsig*5000
t0 = Nsig*500
m2 = Nsig*10000 + 10000
s2 = Nsig*5000

_min = np.floor(min([m0-_n*s0, m2-_n*s2]))
_max = np.ceil(max([m0+_n*np.sqrt(s0**2+t0**2), m2+_n*s2]))
print(f"Length of _x (min:{_min}, max:{_max}): {abs(_min-_max):e}")
print(f"_x approx size in bytes: len*64/8 = {abs(_min-_max)*64/8:e}")


Output:

Length of _x (min:-520000.0, max:610000.0): 1.130000e+06
_x approx size in bytes: len*64/8 = 9.040000e+06


And 9e6 is still way smaller than the GiB scale of the memory error. This leads me to believe that the size of _x is not actually the memory bottleneck. Does that make sense? Am I missing something?

Yeah, the error always happens with the same shape of priors (indep of seed). However, the size of the array allocation leading to the memory error does vary. For example, I just ran it again and got the following:

MemoryError                               Traceback (most recent call last)
MemoryError: Unable to allocate 484. TiB for an array with shape (133056876572664,) and data type int64


This is much larger than the previously mentioned memory error. The stated memory value is random from run to run, but always in that same GiB to TiB range.

Also, along these same lines, I have decreased _n to 3 and changed dtype=int32 in order to decrease the length of _x and the memory footprint for each element to see if that solved the issue, but I’m still getting the same error.

As an aside: Is the best way of determining the length of a tensor object in aseara to use the .eval() method and then check the length? i.e.

x = at.arange(0, 100)
len(x.eval())


Return:

100


Or is there a more streamlined way that doesn’t require you to evaluate the actual tensor array?

I don’t know the aesara and pymc API’s/backend all that well yet, but am trying to learn.

You can do x.shape.eval() which for most cases will will not require computing the actual tensor outputs.

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I don’t know how ADVI works, but it seems to produce a much more compact graph.

You may need to dig a bit and profile the d/logp functions for your model: Profiling Aesara function — Aesara 2.8.7+37.geadc6e33e.dirty documentation

You can obtain them via model.compile_fn(model.logp(), point_fn=False), model.compile_fn(model.dlogp(), point_fn=False)) and the value and logp combined via model.compile_fn([model.logp(), model.dlogp()], point_fn=False)

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@ricardoV94 thanks for the guidance. I read through the tutorial at the link you provided, however I believe I’m missing some basics on how to perform the profiling. I think I understand the profiling example at the bottom of the page (here: aesara/profiling_example.py at main · aesara-devs/aesara · GitHub) but I don’t fully understand how that relates to the compiled model functions you mention.

Is the idea to use d/logp functions obtained using model.compile_fn() as the aesara function in that linked example, and then provide the function a bunch of random inputs across the input parameter space so the profiler can get estimates on where in the model time and memory are being used?

For example I can create the compiled logp function as follows:

comp_logp = mod.compile_fn(mod.logp(), point_fn=False)
comp_logp


Returns:

<aesara.compile.function.types.Function at 0x2abdabf3c790>


Which appears to have the following inputs:

list(comp_logp.inv_finder.values())

[In(m0),
In(s0_0_log__),
In(t0_0_log__),
In(m2_0_log__),
In(s2_0_log__),
In(w_simplex__)]


This aesara function I should be able to evaluate by providing it inputs for all model parameters, like so:

comp_logp(
m0=0,
s0_0_log__=np.log(100),
t0_0_log__=np.log(100),
m2_0_log__=np.log(500),
s2_0_log__=np.log(300),
w_simplex__=np.array([0.25, 0.25, 0.25])
)


Returns:

array(-110815.51280221)


So, in order to profile comp_logp I should just evaluate it some 10k times with random input values and with:

aesara.config.profile = True
aesara.config.profile_memory = True


in order to produce a profile report. Does that all seem correct? I’d just like to make sure I’m understanding your suggestion.

Assuming I’ve got the above right, I have a few other questions regarding the inputs to the compiled logp function:

• Are all parameters ending in _log__ simply representing the log of the underlying parameter, i.e. is s0_0_log__ equal to np.log(s0_0)?
• Why is m0 the only non-logged parameter in the inputs?
• What is the form of w_simplex__? Is it the weights of the first three components of the model and the last weight is inferred as the remained to sum to 1?

I appreciate any feedback, as I’ve still got a lot to learn about the inner workings of aesara. Thanks!

Yes your intuition is mostly correct, except you don’t necessarily need to test the function with random inputs (although sometimes the bottlenecks can be coordinate specific)

You can get a useable point via model.initial_point(). Evaluating the function a couple hundred times on this point should give you a stable profile.

The underscore names are for transformed parameters. Positive distributions will by default have a log transform and the variable name will be f"{name}_log__", other distributions have different default transforms (e.g, simplex for Dirichlet), and some will have none like the Normal, as any value has a nonzero density.

If you want to generate multiple points you can set initval="prior" for each variable and everytime you call model.initial_point you’ll get a new point. But again, a single initial point is usually a good enough for benchmarking.

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I still seem to be doing something incorrectly. Based on your last message, I ran the following code along with the full model build described above:

ae.config.profile = True
ae.config.profile_memory = True

for _ in range(100):
model.initial_point()


However, it returns nothing to stdout (no error either). What am I missing? Thanks!

@ricardoV94 also:

I assume I have to include this kwarg into the call to pm.Mixture? I did this by changing the last line of the model to:

full_model = pm.Mixture('full_model', w=w, comp_dists=components, observed=data, initval="prior")


However, when I call model.initial_point() multiple times, it returns the same dictionary:

{'m0': array(0.),
's0_0_log__': array(8.51719319),
't0_0_log__': array(6.2146081),
'm2_0_log__': array(9.21034037),
's2_0_log__': array(8.51719319),
'w_simplex__': array([0., 0., 0.])}


Am I including the initval="prior" kwarg in the wrong place?

@ricardoV94
Ah, it seems I can’t even get the basic profiling example (at the bottom of the page: Profiling Aesara function — Aesara 2.8.7+37.geadc6e33e.dirty documentation) to run correctly.

The file profiling_example.py containing the following:

import numpy as np

import aesara

x, y, z = aesara.tensor.vectors("xyz")
f = aesara.function([x, y, z], [(x + y + z) * 2])
xv = np.random.random((10,)).astype(aesara.config.floatX)
yv = np.random.random((10,)).astype(aesara.config.floatX)
zv = np.random.random((10,)).astype(aesara.config.floatX)
f(xv, yv, zv)


If I run it as follows (as stated in the example):

(pymc_env) \$ AESARA_FLAGS=optimizer_excluding=fusion:inplace,profile=True python profiling_example.py


I get the following error:

Exception ignored in atexit callback: <function _atexit_print_fn at 0x7f4d4f7ed120>
Traceback (most recent call last):
File "/Users/jast/miniconda3/envs/pymc_env/lib/python3.10/site-packages/aesara/compile/profiling.py", line 78, in _atexit_print_fn
ps.summary(
File "/Users/jast/miniconda3/envs/pymc_env/lib/python3.10/site-packages/aesara/compile/profiling.py", line 1452, in summary
self.summary_function(file)
File "/Users/jast/miniconda3/envs/pymc_env/lib/python3.10/site-packages/aesara/compile/profiling.py", line 786, in summary_function
print("Function profiling", file=file)
AttributeError: 'str' object has no attribute 'write'


Any thoughts?

Yes. The observed variable is the only one that is not part of the initial point :). You should set initval="prior" for every other unobserved variable in the model if you want their value to change everytime you call model.initial_point()

You don’t want to profile the initial point function, that’s just to get values you can use to profile the d/logp functions:

import pymc as pm

with pm.Model() as m:
x = pm.Normal("x", initval="prior")
y = pm.Normal("y", x, observed=[0])

f = m.compile_fn(m.logp(), profile=True)
for i in range(1000):
ip = m.initial_point()
f(ip)
print(f.f.profile.summary())


f.f accesses the actual aesara function

By the way, if after you figure out how to profile PyMC models you want to contribute that knowledge to our docs, that would be super valuable

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Also I forgot, there is a model.profile that you can use that does the calling with the initial point (just one) for you automatically :

model.profile(m.logp())

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We do have a notebook on profiling but it is still using 3.9: Profiling — PyMC example gallery

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I would certainly be happy to contribute to the documentation once I can make more sense of the profiling process!

I’m currently trying to make sense of the overarching design of the aesara library (mostly by reading the primary documentation) as I would eventually like to meaningfully contribute to pymc.

Though I’m finding it pretty difficult to get a handle on the design, so if you have any suggested resources I’d much appreciate it.

@ricardoV94 @aseyboldt @OriolAbril I had to set this aside for a couple weeks but am working on it again.

I’ve profiled the model using the mod.profile() method and as far as I can tell there’s nothing obviously wrong in the mod.logp() or mod.dlogp() functions. Though, to be honest, I’m still not sure what I’m looking at. The vast majority of the time is consumed by the Elemwise class which I assume is tasked with scaling all the intermediate tensors to the correct dimensions?

I’ve included the .summary() for both logp and dlogp below. Is there anything y’all see that could be the problem? How do I interpret the Class, Ops, and Apply summaries?

After running the “Mock Data” and “PyMC model build” code in my earlier post (from Nov. 5), I computed the logp summary as follows (those code blocks should be complete so that you can reproduce it locally):

mod_profile_logp = mod.profile(mod.logp())
mod_profile_logp.summary()


Return:

Function profiling
==================
Message: /Users/jast/miniconda3/envs/pymc_env/lib/python3.10/site-packages/pymc/aesaraf.py:970
Time in 1000 calls to Function.__call__: 6.009044e+01s
Time in Function.vm.__call__: 60.014588832855225s (99.874%)
Time in thunks: 59.87600064277649s (99.643%)
Total compilation time: 3.742687e+00s
Number of Apply nodes: 109
Aesara rewrite time: 2.879735e+00s
Aesara validate time: 3.279018e-02s
Aesara Linker time (includes C, CUDA code generation/compiling): 0.5069496631622314s
Import time 3.829298e-01s
Node make_thunk time 5.027940e-01s
Node Elemwise{Composite{(Switch(OR(i0, i1), i2, (i3 + i4)) + ((log1p(i5) + ((i6 + i5) * i7)) - ((i6 + i5) * (i8 + log(i9)))))}}[(0, 4)](Any{0}.0, Any{0}.0, TensorConstant{-inf}, TensorConstant{1.791759469228055}, Sum{acc_dtype=float64}.0, Shape_i{0}.0, TensorConstant{1}, Sum{acc_dtype=float64}.0, max, Sum{acc_dtype=float64}.0) time 2.737141e-02s
Node MakeVector{dtype='float64'}(m0_logprob, s0_0_log___logprob, t0_0_log___logprob, m2_0_log___logprob, s2_0_log___logprob, w_simplex___logprob, Sum{acc_dtype=float64}.0) time 2.539825e-02s
Node Elemwise{Log}[(0, 0)](InplaceDimShuffle{x,0}.0) time 2.488804e-02s
Node Elemwise{Composite{(Switch(GE(i0, i1), (i2 - (i3 * i0)), i4) + i5)}}[(0, 0)](m2_0_log___log, TensorConstant{0.0}, TensorConstant{-9.210340371976184}, TensorConstant{0.0001}, TensorConstant{-inf}, m2_0_log__) time 2.408910e-02s
Node Elemwise{Composite{Switch(i0, ((i1 + (i2 * sqr(i3))) - i4), i5)}}[(0, 3)](Elemwise{gt,no_inplace}.0, TensorConstant{(1, 1) of ..5332046727}, TensorConstant{(1, 1) of -0.5}, Elemwise{Composite{((i0 - i1) / i2)}}.0, Elemwise{Log}[(0, 0)].0, TensorConstant{(1, 1) of -inf}) time 2.279091e-02s

Time in all call to aesara.grad() 2.374974e+00s
Time since aesara import 219.557s
Class
---
<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name>
99.0%    99.0%      59.272s       1.04e-03s     C    57000      57   aesara.tensor.elemwise.Elemwise
0.3%    99.3%       0.185s       2.65e-05s     C     7000       7   aesara.tensor.math.Sum
0.2%    99.5%       0.114s       4.94e-06s     C    23000      23   aesara.tensor.elemwise.DimShuffle
0.2%    99.7%       0.112s       2.80e-05s     C     4000       4   aesara.tensor.math.Max
0.2%    99.8%       0.102s       3.39e-05s     C     3000       3   aesara.tensor.basic.Join
0.1%   100.0%       0.071s       7.05e-05s     Py    1000       1   aesara.tensor.basic.ARange
0.0%   100.0%       0.007s       1.45e-06s     C     5000       5   aesara.tensor.basic.MakeVector
0.0%   100.0%       0.005s       1.21e-06s     C     4000       4   aesara.tensor.math.All
0.0%   100.0%       0.004s       4.34e-06s     C     1000       1   aesara.tensor.nnet.basic.Softmax
0.0%   100.0%       0.002s       1.08e-06s     C     2000       2   aesara.tensor.math.Any
0.0%   100.0%       0.001s       1.28e-06s     C     1000       1   aesara.tensor.shape.Shape_i
0.0%   100.0%       0.001s       1.18e-06s     C     1000       1   aesara.tensor.math.Min
... (remaining 0 Classes account for   0.00%(0.00s) of the runtime)

Ops
---
<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Op name>
85.9%    85.9%      51.428s       5.14e-02s     C     1000        1   Elemwise{Composite{(exp(Switch(i0, Switch(i1, (Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}(((i2 - i3) / i4), i5, i6, i7, (i2 - i3), i4, i8, i9) + scalar_log1mexp(((((i3 - i2) / i10) + i11 + Switch(LT(((i2 - i12) / i4), i5), (log((i6 * erfcx(((i7 * (i2 - i12)) / i4)))) - (i6 * sqr(((i2 - i12) / i4)))), log1p((i8 * erfc(((i9 * (i2 - i12)) / i4)))))) - Composite{
7.0%    92.9%       4.186s       4.19e-03s     C     1000        1   Elemwise{Composite{((Switch(i0, Switch(i1, (Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}((i2 / i3), i4, i5, i6, i2, i3, i7, i8) + scalar_log1mexp(((i9 + i10 + i11) - Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}((i2 / i3), i4, i5, i6, i2, i3, i7, i8)))), Composite{Switch(LT(i0, i1), (
2.7%    95.6%       1.627s       1.63e-03s     C     1000        1   Elemwise{Composite{Switch(LT(((i0 - i1) / i2), i3), (log((i4 * erfcx(((i5 * (i0 - i1)) / i2)))) - (i4 * sqr(((i0 - i1) / i2)))), log1p((i6 * erfc(((i7 * (i0 - i1)) / i2)))))}}
1.7%    97.3%       1.025s       5.12e-04s     C     2000        2   Elemwise{Composite{Switch(i0, i1, exp((i2 - i3)))}}[(0, 2)]
0.7%    98.0%       0.421s       4.21e-04s     C     1000        1   Elemwise{Composite{Switch(i0, (i1 + log(i2)), i3)}}[(0, 1)]
0.4%    98.4%       0.235s       3.92e-05s     C     6000        6   Elemwise{exp,no_inplace}
0.3%    98.7%       0.151s       2.51e-05s     C     6000        6   Sum{acc_dtype=float64}
0.2%    98.8%       0.108s       1.08e-04s     C     1000        1   Max{maximum}{1}
0.2%    99.0%       0.108s       5.38e-05s     C     2000        2   Elemwise{Composite{((i0 - i1) / i2)}}
0.2%    99.2%       0.102s       3.39e-05s     C     3000        3   Join
0.1%    99.3%       0.071s       7.05e-05s     Py    1000        1   ARange{dtype='int32'}
0.1%    99.4%       0.062s       6.18e-05s     C     1000        1   Elemwise{Composite{Switch(i0, Switch(i1, (i2 + i3 + i4 + i5), (i6 - ((i7 * sqr(i8)) / i9))), i10)}}
0.1%    99.5%       0.058s       5.30e-06s     C     11000       11   InplaceDimShuffle{x}
0.1%    99.6%       0.046s       1.16e-05s     C     4000        4   Elemwise{Add}[(0, 1)]
0.1%    99.7%       0.040s       2.02e-05s     C     2000        2   Elemwise{isinf,no_inplace}
0.1%    99.7%       0.035s       3.45e-05s     C     1000        1   Sum{axis=[1], acc_dtype=float64}
0.0%    99.8%       0.029s       2.89e-05s     C     1000        1   Elemwise{Composite{Switch(i0, ((i1 + (i2 * sqr(i3))) - i4), i5)}}[(0, 3)]
0.0%    99.8%       0.027s       3.87e-06s     C     7000        7   InplaceDimShuffle{x,x}
0.0%    99.8%       0.013s       4.32e-06s     C     3000        3   InplaceDimShuffle{0,x}
0.0%    99.8%       0.011s       1.09e-05s     C     1000        1   Elemwise{sub,no_inplace}
... (remaining 37 Ops account for   0.16%(0.09s) of the runtime)

Apply
------
<% time> <sum %> <apply time> <time per call> <#call> <id> <Apply name>
85.9%    85.9%      51.428s       5.14e-02s   1000    77   Elemwise{Composite{(exp(Switch(i0, Switch(i1, (Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}(((i2 - i3) / i4), i5, i6, i7, (i2 - i3), i4, i8, i9) + scalar_log1mexp(((((i3 - i2) / i10) + i11 + Switch(LT(((i2 - i12) / i4), i5), (log((i6 * erfcx(((i7 * (i2 - i12)) / i4)))) - (i6 * sqr(((i2 - i12) / i4)))), log1p((i8 * erfc(((i9 * (i2 - i12)) / i4)))))) - Composite{Switch(LT(i
7.0%    92.9%       4.186s       4.19e-03s   1000    95   Elemwise{Composite{((Switch(i0, Switch(i1, (Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}((i2 / i3), i4, i5, i6, i2, i3, i7, i8) + scalar_log1mexp(((i9 + i10 + i11) - Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}((i2 / i3), i4, i5, i6, i2, i3, i7, i8)))), Composite{Switch(LT(i0, i1), (log((i2 * e
2.7%    95.6%       1.627s       1.63e-03s   1000    64   Elemwise{Composite{Switch(LT(((i0 - i1) / i2), i3), (log((i4 * erfcx(((i5 * (i0 - i1)) / i2)))) - (i4 * sqr(((i0 - i1) / i2)))), log1p((i6 * erfc(((i7 * (i0 - i1)) / i2)))))}}(TensorConstant{[[-2265.]
.. [ 7658.]]}, InplaceDimShuffle{0,x}.0, InplaceDimShuffle{x,x}.0, TensorConstant{(1, 1) of -1.0}, TensorConstant{(1, 1) of 0.5}, TensorConstant{(1, 1) of ..7932881648}, TensorConstant{(1, 1) of -0.5}, TensorConstant{(1, 1) of ..7932881648})
1.7%    97.3%       1.023s       1.02e-03s   1000   103   Elemwise{Composite{Switch(i0, i1, exp((i2 - i3)))}}[(0, 2)](Elemwise{isinf,no_inplace}.0, Elemwise{exp,no_inplace}.0, Elemwise{Add}[(0, 1)].0, InplaceDimShuffle{0,x}.0)
0.7%    98.0%       0.421s       4.21e-04s   1000   105   Elemwise{Composite{Switch(i0, (i1 + log(i2)), i3)}}[(0, 1)](InplaceDimShuffle{x}.0, max, Sum{axis=[1], acc_dtype=float64}.0, TensorConstant{(1,) of -inf})
0.4%    98.4%       0.229s       2.29e-04s   1000   101   Elemwise{exp,no_inplace}(InplaceDimShuffle{0,x}.0)
0.2%    98.6%       0.133s       1.33e-04s   1000    87   Sum{acc_dtype=float64}(Elemwise{Composite{(exp(Switch(i0, Switch(i1, (Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}(((i2 - i3) / i4), i5, i6, i7, (i2 - i3), i4, i8, i9) + scalar_log1mexp(((((i3 - i2) / i10) + i11 + Switch(LT(((i2 - i12) / i4), i5), (log((i6 * erfcx(((i7 * (i2 - i12)) / i4)))) - (i6 * sqr(((i2 - i12) / i4)))), log1p((i8 * erfc(((i9 * (i2 - i12)) / i4))))))
0.2%    98.8%       0.108s       1.08e-04s   1000    99   Max{maximum}{1}(Elemwise{Add}[(0, 1)].0)
0.1%    98.9%       0.086s       8.63e-05s   1000    97   Join(TensorConstant{1}, Elemwise{Composite{Switch(i0, Switch(i1, (i2 + i3 + i4 + i5), (i6 - ((i7 * sqr(i8)) / i9))), i10)}}.0, Elemwise{Composite{((Switch(i0, Switch(i1, (Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}((i2 / i3), i4, i5, i6, i2, i3, i7, i8) + scalar_log1mexp(((i9 + i10 + i11) - Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0)
0.1%    99.1%       0.071s       7.05e-05s   1000    69   ARange{dtype='int32'}(Elemwise{Floor}[(0, 0)].0, Elemwise{Ceil}[(0, 0)].0, TensorConstant{1})
0.1%    99.2%       0.062s       6.18e-05s   1000    90   Elemwise{Composite{Switch(i0, Switch(i1, (i2 + i3 + i4 + i5), (i6 - ((i7 * sqr(i8)) / i9))), i10)}}(InplaceDimShuffle{x,x}.0, InplaceDimShuffle{0,x}.0, Elemwise{Composite{(-log(i0))}}[(0, 0)].0, Elemwise{Composite{((i0 - i1) / i2)}}.0, InplaceDimShuffle{0,x}.0, Elemwise{Composite{Switch(LT(((i0 - i1) / i2), i3), (log((i4 * erfcx(((i5 * (i0 - i1)) / i2)))) - (i4 * sqr(((i0 - i1) / i2)))), log1p((i6 * erfc(((i7 * (i0 - i1)) / i2)))))}}.0, Elemwise{Com
0.1%    99.3%       0.054s       5.39e-05s   1000    40   Elemwise{Composite{((i0 - i1) / i2)}}(InplaceDimShuffle{x,x}.0, TensorConstant{[[-2265.]
.. [ 7658.]]}, InplaceDimShuffle{x,x}.0)
0.1%    99.3%       0.054s       5.38e-05s   1000    43   Elemwise{Composite{((i0 - i1) / i2)}}(TensorConstant{[[-12265.]..[ -2342.]]}, InplaceDimShuffle{x,x}.0, InplaceDimShuffle{x,x}.0)
0.1%    99.4%       0.044s       4.36e-05s   1000    98   Elemwise{Add}[(0, 1)](Elemwise{Log}[(0, 0)].0, Join.0)
0.1%    99.5%       0.039s       3.87e-05s   1000   102   Elemwise{isinf,no_inplace}(InplaceDimShuffle{0,x}.0)
0.1%    99.5%       0.035s       3.45e-05s   1000   104   Sum{axis=[1], acc_dtype=float64}(Elemwise{Composite{Switch(i0, i1, exp((i2 - i3)))}}[(0, 2)].0)
0.0%    99.6%       0.029s       2.89e-05s   1000    96   Elemwise{Composite{Switch(i0, ((i1 + (i2 * sqr(i3))) - i4), i5)}}[(0, 3)](Elemwise{gt,no_inplace}.0, TensorConstant{(1, 1) of ..5332046727}, TensorConstant{(1, 1) of -0.5}, Elemwise{Composite{((i0 - i1) / i2)}}.0, Elemwise{Log}[(0, 0)].0, TensorConstant{(1, 1) of -inf})
0.0%    99.6%       0.012s       1.25e-05s   1000   106   Sum{acc_dtype=float64}(0 <= weights <= 1, sum(weights) == 1)
0.0%    99.6%       0.011s       1.09e-05s   1000    10   Elemwise{sub,no_inplace}(TensorConstant{[[-2265.]
.. [ 7658.]]}, InplaceDimShuffle{x,x}.0)
0.0%    99.6%       0.011s       1.07e-05s   1000    32   Join(TensorConstant{0}, w_simplex__, Elemwise{neg,no_inplace}.0)
... (remaining 89 Apply instances account for 0.36%(0.21s) of the runtime)

Here are tips to potentially make your code run faster
(if you think of new ones, suggest them on the mailing list).
Test them first, as they are not guaranteed to always provide a speedup.
- Try the Aesara flag floatX=float32
- Try installing amdlibm and set the Aesara flag lib__amblibm=True. This speeds up only some Elemwise operation.


The dlogp profile was run as follows:

mod_profile_dlogp = mod.profile(mod.dlogp())
mod_profile_dlogp.summary()


Returns:

Function profiling
==================
Message: /Users/jast1849/miniconda3/envs/pymc_env/lib/python3.10/site-packages/pymc/aesaraf.py:970
Time in 1000 calls to Function.__call__: 1.049568e+02s
Time in Function.vm.__call__: 104.82313346862793s (99.873%)
Time in thunks: 103.50367903709412s (98.615%)
Total compilation time: 1.252786e+01s
Number of Apply nodes: 265
Aesara rewrite time: 1.154807e+01s
Aesara validate time: 2.012794e-01s
Aesara Linker time (includes C, CUDA code generation/compiling): 0.543797492980957s
Import time 2.388318e-01s
Node make_thunk time 5.282750e-01s
Node Elemwise{Composite{(((Switch(GE(i0, i1), i2, i3) + ((i4 * i5) / i6) + ((i7 * i8 * i9 * i10) / i6) + i11 + (((-i12) / i13) * sgn(i10)) + (i14 * i15 * i10) + i16 + ((i17 * i5) / i6) + ((i18 * i8 * i19 * i10) / i6) + i20 + i21 + i22 + ((i23 * i5) / i6) + ((i24 * i8 * i25 * i10) / i6) + i26 + i27) * i0) + i28)}}[(0, 0)](s0_0_log___log, TensorConstant{0.0}, TensorConstant{-0.0002}, TensorConstant{0}, Sum{acc_dtype=float64}.0, Elemwise{true_div,no_inplace}.0, Elemwise{add,no_inplace}.0, TensorConstant{-1.0}, TensorConstant{2.0}, Sum{acc_dtype=float64}.0, Elemwise{add,no_inplace}.0, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, Elemwise{abs,no_inplace}.0, TensorConstant{4.0}, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, TensorConstant{-1.0}, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, TensorConstant{-1.0}, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, (d__logp/dt0_0_log___logprob){1.0}) time 5.202770e-03s
Node Elemwise{exp,no_inplace}(s2_0_log__) time 5.120993e-03s
Node Elemwise{Composite{(((Switch(GE(i0, i1), i2, i3) + ((-i4) / i5) + i6 + ((-((i4 * i7 * i8) / i5)) / i5) + ((-(i9 * i10 * i11)) / sqr(i5)) + i12 + ((-((i13 * i7 * i8) / i5)) / i5) + ((-(i14 * i15 * i11)) / sqr(i5)) + i16 + ((-((i17 * i7 * i8) / i5)) / i5) + ((-(i18 * i19 * i11)) / sqr(i5))) * i0) + i20)}}[(0, 0)](t0_0_log___log, TensorConstant{0.0}, TensorConstant{-0.002}, TensorConstant{0}, Sum{acc_dtype=float64}.0, Elemwise{add,no_inplace}.0, Sum{acc_dtype=float64}.0, Elemwise{true_div,no_inplace}.0, Elemwise{add,no_inplace}.0, TensorConstant{-1.0}, Sum{acc_dtype=float64}.0, Elemwise{sqr,no_inplace}.0, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, TensorConstant{-1.0}, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, Sum{acc_dtype=float64}.0, TensorConstant{-1.0}, Sum{acc_dtype=float64}.0, (d__logp/dt0_0_log___logprob){1.0}) time 4.855871e-03s
Node Elemwise{Composite{((-Switch(i0, Switch(i1, ((((i2 * i3 * i4 * i4 * i5 * i6 * i7 * i8) / i9) / i10) - ((i11 * i12 * (((i13 * i4) / i14) + ((i15 * i4 * i4) / i10)) * i5 * i6 * i7 * i8) / i9)), i16), Switch(i1, ((i17 * i18 * i19 * i4 * i5 * i6 * i7 * i8) / (i9 * i20)), i16))) / i21)}}(Elemwise{lt,no_inplace}.0, Elemwise{Composite{GT(i0, (i1 * i2))}}.0, TensorConstant{(1,) of -1.0}, TensorConstant{(1,) of -0.5}, Elemwise{sub,no_inplace}.0, Elemwise{Composite{Switch(IsInf(Composite{(i0 / expm1((-i1)))}(i0, i1)), i2, Composite{(i0 / expm1((-i1)))}(i0, i1))}}[(0, 1)].0, InplaceDimShuffle{x}.0, Elemwise{Composite{erfc(((i0 * i1) / i2))}}.0, Elemwise{Composite{exp(Switch(i0, Switch(i1, (i2 + scalar_log1mexp(i3)), i2), i4))}}[(0, 2)].0, InplaceDimShuffle{x}.0, InplaceDimShuffle{x}.0, TensorConstant{(1,) of -1.0}, TensorConstant{(1,) of 0...3966440824}, TensorConstant{(1,) of -1..1670955126}, Elemwise{Composite{erfcx(((i0 * i1) / i2))}}.0, TensorConstant{(1,) of -1..5865763297}, TensorConstant{(1,) of 0}, TensorConstant{(1,) of -1.0}, TensorConstant{(1,) of 0...2872290391}, Elemwise{Composite{exp(((i0 * i1 * i1) / i2))}}.0, Elemwise{Composite{(i0 + (-i1))}}[(0, 1)].0, Elemwise{sqr,no_inplace}.0) time 4.809141e-03s
Node Elemwise{Composite{((-Switch(i0, Switch(i1, i2, ((((i3 * i4 * i5 * i5 * i6 * i7 * i8) / i9) / i10) - ((i11 * i12 * i13 * i6 * i7 * i8) / i9))), Switch(i1, i2, ((i14 * i15 * i16 * i5 * i6 * i7 * i8) / (i9 * i17))))) / i18)}}[(0, 5)](Elemwise{lt,no_inplace}.0, Elemwise{Composite{GT(i0, (i1 * i2))}}.0, TensorConstant{(1,) of 0}, TensorConstant{(1,) of -1.0}, TensorConstant{(1,) of -0.5}, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0, Elemwise{Composite{erfc(((i0 * i1) / i2))}}.0, Elemwise{Composite{exp(Switch(i0, Switch(i1, (i2 + scalar_log1mexp(i3)), i2), i4))}}[(0, 2)].0, InplaceDimShuffle{x}.0, InplaceDimShuffle{x}.0, TensorConstant{(1,) of -1.0}, TensorConstant{(1,) of 0...3966440824}, Elemwise{Composite{(((i0 * i1) / i2) + ((i3 * i1 * i1) / i4))}}.0, TensorConstant{(1,) of -1.0}, TensorConstant{(1,) of 0...2872290391}, Elemwise{Composite{exp(((i0 * i1 * i1) / i2))}}.0, Elemwise{Composite{(i0 + (-i1))}}[(0, 1)].0, Elemwise{sqr,no_inplace}.0) time 4.699945e-03s

Time in all call to aesara.grad() 2.374974e+00s
Time since aesara import 154.726s
Class
---
<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Class name>
96.5%    96.5%      99.895s       6.57e-04s     C   152000     152   aesara.tensor.elemwise.Elemwise
1.7%    98.3%       1.799s       4.73e-05s     C    38000      38   aesara.tensor.math.Sum
1.0%    99.3%       1.079s       2.70e-04s     C     4000       4   aesara.tensor.nnet.basic.Softmax
0.2%    99.5%       0.234s       6.16e-06s     C    38000      38   aesara.tensor.elemwise.DimShuffle
0.2%    99.7%       0.187s       1.87e-04s     Py    1000       1   aesara.tensor.basic.ARange
0.1%    99.8%       0.140s       3.49e-05s     C     4000       4   aesara.tensor.basic.Join
0.1%    99.9%       0.087s       2.90e-05s     C     3000       3   aesara.tensor.basic.Split
0.0%   100.0%       0.040s       8.07e-06s     C     5000       5   aesara.tensor.shape.Reshape
0.0%   100.0%       0.014s       1.43e-06s     C    10000      10   aesara.tensor.shape.SpecifyShape
0.0%   100.0%       0.010s       2.45e-06s     C     4000       4   aesara.tensor.basic.MakeVector
0.0%   100.0%       0.007s       7.32e-06s     C     1000       1   aesara.tensor.basic.Alloc
0.0%   100.0%       0.004s       2.13e-06s     C     2000       2   aesara.tensor.math.Max
0.0%   100.0%       0.003s       2.82e-06s     C     1000       1   aesara.tensor.shape.Shape_i
0.0%   100.0%       0.003s       2.66e-06s     C     1000       1   aesara.tensor.math.All
0.0%   100.0%       0.002s       2.09e-06s     C     1000       1   aesara.tensor.math.Min
... (remaining 0 Classes account for   0.00%(0.00s) of the runtime)

Ops
---
<% time> <sum %> <apply time> <time per call> <type> <#call> <#apply> <Op name>
18.2%    18.2%      18.848s       3.77e-03s     C     5000        5   Elemwise{Composite{erfc(((i0 * i1) / i2))}}
11.7%    29.9%      12.096s       2.02e-03s     C     6000        6   Elemwise{Composite{exp(((i0 * i1 * i1) / i2))}}
8.2%    38.1%       8.475s       8.47e-03s     C     1000        1   Elemwise{Composite{exp(Switch(i0, Switch(i1, (i2 + scalar_log1mexp(i3)), i2), i4))}}[(0, 2)]
8.0%    46.0%       8.235s       1.65e-03s     C     5000        5   Elemwise{Composite{erfcx(((i0 * i1) / i2))}}
6.8%    52.8%       7.034s       2.34e-03s     C     3000        3   Elemwise{Composite{Switch(i0, (log((i1 * i2)) - (i1 * sqr(i3))), log1p((i4 * i5)))}}[(0, 2)]
5.7%    58.5%       5.908s       5.91e-03s     C     1000        1   Elemwise{Composite{(((i0 / i1) + i2 + Switch(i3, (log((i4 * i5)) - (i4 * sqr(i6))), log1p((i7 * i8)))) - i9)}}[(0, 6)]
4.5%    63.1%       4.696s       4.70e-03s     C     1000        1   Elemwise{Composite{((-Switch(i0, Switch(i1, ((((i2 * i3 * i4 * i4 * i5 * i6 * i7 * i8) / i9) / i10) - ((i11 * i12 * (((i13 * i4) / i14) + ((i15 * i4 * i4) / i10)) * i5 * i6 * i7 * i8) / i9)), i16), Switch(i1, ((i17 * i18 * i19 * i4 * i5 * i6 * i7 * i8) / (i9 * i20)), i16))) / i21)}}
4.1%    67.2%       4.256s       2.13e-03s     C     2000        2   Elemwise{Composite{Switch(IsInf(Composite{(i0 / expm1((-i1)))}(i0, i1)), i2, Composite{(i0 / expm1((-i1)))}(i0, i1))}}[(0, 1)]
4.1%    71.3%       4.254s       4.25e-03s     C     1000        1   Elemwise{Composite{Switch(i0, Switch(i1, ((((i2 * i3 * i4 * i5 * i6 * i7 * i8) / i9) / i10) - ((i11 * i12 * ((i13 / (i14 * i15)) + ((i16 * i4) / i10)) * i5 * i6 * i7 * i8) / i9)), i17), Switch(i1, (((i18 * i19 * i20 * i5 * i6 * i7 * i8) / i9) / (i21 * i15)), i17))}}[(0, 4)]
2.9%    74.2%       3.039s       3.04e-03s     C     1000        1   Elemwise{Composite{Switch(i0, Switch(i1, i2, ((((i3 * i4 * i5 * i6 * i7 * i8) / i9) / i10) - ((i11 * i12 * i13 * i6 * i7 * i8) / i9))), Switch(i1, i2, (((i14 * i15 * i16 * i6 * i7 * i8) / i9) / i17)))}}[(0, 13)]
2.7%    76.9%       2.779s       2.78e-03s     C     1000        1   Elemwise{Composite{((-Switch(i0, Switch(i1, i2, ((((i3 * i4 * i5 * i5 * i6 * i7 * i8) / i9) / i10) - ((i11 * i12 * i13 * i6 * i7 * i8) / i9))), Switch(i1, i2, ((i14 * i15 * i16 * i5 * i6 * i7 * i8) / (i9 * i17))))) / i18)}}[(0, 5)]
2.2%    79.2%       2.317s       3.31e-04s     C     7000        7   Elemwise{true_div,no_inplace}
2.0%    81.2%       2.120s       2.65e-04s     C     8000        8   Elemwise{sub,no_inplace}
1.8%    83.1%       1.912s       9.56e-04s     C     2000        2   Elemwise{Composite{((-Switch(i0, Switch(i1, (((i2 * i3 * i3 * i4) / i5) - (i6 * i7 * i4)), i8), Switch(i1, (i9 * i10 * i3 * (i4 / i11)), i8))) / i12)}}[(0, 4)]
1.8%    84.9%       1.865s       6.22e-04s     C     3000        3   Elemwise{Composite{((i0 / (i1 * i2)) + ((i3 * i4) / i5))}}
1.8%    86.6%       1.847s       6.16e-04s     C     3000        3   Elemwise{Composite{(((i0 * i1) / i2) + ((i3 * i1 * i1) / i4))}}
1.7%    88.3%       1.731s       4.68e-05s     C     37000       37   Sum{acc_dtype=float64}
1.4%    89.7%       1.409s       4.70e-04s     C     3000        3   Elemwise{Composite{Switch(i0, Switch(i1, (((i2 * i3 * i4) / i5) - (i6 * i7 * i4)), i8), Switch(i1, ((i9 * i10 * i4) / i11), i8))}}
1.2%    90.9%       1.228s       1.23e-03s     C     1000        1   Elemwise{Composite{Switch(i0, (((i1 * i2 * i3 * i4 * i5 * i6 * i7) / i8) / i9), i10)}}[(0, 3)]
1.1%    91.9%       1.088s       1.09e-03s     C     1000        1   Elemwise{Composite{((Switch(i0, Switch(i1, (i2 + scalar_log1mexp(i3)), i2), i4) + Switch(i5, (log((i6 * i7)) - (i6 * sqr(i8))), log1p((i6 * i9)))) - i10)}}[(0, 2)]
... (remaining 83 Ops account for   8.08%(8.37s) of the runtime)

Apply
------
<% time> <sum %> <apply time> <time per call> <#call> <id> <Apply name>
8.2%     8.2%       8.475s       8.47e-03s   1000   146   Elemwise{Composite{exp(Switch(i0, Switch(i1, (i2 + scalar_log1mexp(i3)), i2), i4))}}[(0, 2)](InplaceDimShuffle{x}.0, Elemwise{Composite{GT(i0, (i1 * i2))}}.0, Elemwise{Composite{Switch(i0, (log((i1 * i2)) - (i1 * sqr(i3))), log1p((i4 * i5)))}}[(0, 2)].0, Elemwise{Composite{(((i0 / i1) + i2 + Switch(i3, (log((i4 * i5)) - (i4 * sqr(i6))), log1p((i7 * i8)))) - i9)}}[(0, 6)].0, TensorConstant{(1,) of -inf})
7.1%    15.2%       7.297s       7.30e-03s   1000   127   Elemwise{Composite{erfc(((i0 * i1) / i2))}}(TensorConstant{(1,) of 0...7932881648}, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0)
6.0%    21.2%       6.184s       6.18e-03s   1000   120   Elemwise{Composite{erfc(((i0 * i1) / i2))}}(TensorConstant{(1,) of 0...7932881648}, Elemwise{Composite{((i0 + i1) - i2)}}.0, InplaceDimShuffle{x}.0)
5.7%    26.9%       5.908s       5.91e-03s   1000   142   Elemwise{Composite{(((i0 / i1) + i2 + Switch(i3, (log((i4 * i5)) - (i4 * sqr(i6))), log1p((i7 * i8)))) - i9)}}[(0, 6)](Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0, Elemwise{Composite{(i0 * sqr(i1))}}.0, Elemwise{lt,no_inplace}.0, TensorConstant{(1,) of 0.5}, Elemwise{Composite{erfcx(((i0 * i1) / i2))}}.0, Elemwise{true_div,no_inplace}.0, TensorConstant{(1,) of -0.5}, Elemwise{Composite{erfc(((i0 * i1) / i2))}}.0, Elemwise{Composite{Switch(i0,
5.6%    32.5%       5.775s       5.77e-03s   1000   138   Elemwise{Composite{Switch(i0, (log((i1 * i2)) - (i1 * sqr(i3))), log1p((i4 * i5)))}}[(0, 2)](Elemwise{lt,no_inplace}.0, TensorConstant{(1,) of 0.5}, Elemwise{Composite{erfcx(((i0 * i1) / i2))}}.0, Elemwise{true_div,no_inplace}.0, TensorConstant{(1,) of -0.5}, Elemwise{Composite{erfc(((i0 * i1) / i2))}}.0)
4.5%    37.0%       4.696s       4.70e-03s   1000   202   Elemwise{Composite{((-Switch(i0, Switch(i1, ((((i2 * i3 * i4 * i4 * i5 * i6 * i7 * i8) / i9) / i10) - ((i11 * i12 * (((i13 * i4) / i14) + ((i15 * i4 * i4) / i10)) * i5 * i6 * i7 * i8) / i9)), i16), Switch(i1, ((i17 * i18 * i19 * i4 * i5 * i6 * i7 * i8) / (i9 * i20)), i16))) / i21)}}(Elemwise{lt,no_inplace}.0, Elemwise{Composite{GT(i0, (i1 * i2))}}.0, TensorConstant{(1,) of -1.0}, TensorConstant{(1,) of -0.5}, Elemwise{sub,no_inplace}.0, Elemwise{Com
4.3%    41.3%       4.425s       4.43e-03s   1000   123   Elemwise{Composite{erfc(((i0 * i1) / i2))}}(TensorConstant{(1,) of 0...7932881648}, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0)
4.1%    45.4%       4.254s       4.25e-03s   1000   206   Elemwise{Composite{Switch(i0, Switch(i1, ((((i2 * i3 * i4 * i5 * i6 * i7 * i8) / i9) / i10) - ((i11 * i12 * ((i13 / (i14 * i15)) + ((i16 * i4) / i10)) * i5 * i6 * i7 * i8) / i9)), i17), Switch(i1, (((i18 * i19 * i20 * i5 * i6 * i7 * i8) / i9) / (i21 * i15)), i17))}}[(0, 4)](Elemwise{lt,no_inplace}.0, Elemwise{Composite{GT(i0, (i1 * i2))}}.0, TensorConstant{(1,) of -1.0}, TensorConstant{(1,) of -0.5}, Elemwise{sub,no_inplace}.0, Elemwise{Composite{Sw
3.8%    49.2%       3.910s       3.91e-03s   1000   147   Elemwise{Composite{Switch(IsInf(Composite{(i0 / expm1((-i1)))}(i0, i1)), i2, Composite{(i0 / expm1((-i1)))}(i0, i1))}}[(0, 1)](TensorConstant{(1,) of -1.0}, Elemwise{Composite{(((i0 / i1) + i2 + Switch(i3, (log((i4 * i5)) - (i4 * sqr(i6))), log1p((i7 * i8)))) - i9)}}[(0, 6)].0, TensorConstant{(1,) of -inf})
3.8%    53.0%       3.892s       3.89e-03s   1000   126   Elemwise{Composite{erfcx(((i0 * i1) / i2))}}(TensorConstant{(1,) of -0..7932881648}, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0)
3.6%    56.6%       3.722s       3.72e-03s   1000   121   Elemwise{Composite{exp(((i0 * i1 * i1) / i2))}}(TensorConstant{(1,) of -0..0171142714}, Elemwise{Composite{((i0 + i1) - i2)}}.0, Elemwise{sqr,no_inplace}.0)
3.6%    60.1%       3.717s       3.72e-03s   1000   129   Elemwise{Composite{exp(((i0 * i1 * i1) / i2))}}(TensorConstant{(1,) of -0..0171142714}, Elemwise{sub,no_inplace}.0, Elemwise{sqr,no_inplace}.0)
3.6%    63.7%       3.714s       3.71e-03s   1000   125   Elemwise{Composite{exp(((i0 * i1 * i1) / i2))}}(TensorConstant{(1,) of -0..0171142714}, Elemwise{sub,no_inplace}.0, Elemwise{sqr,no_inplace}.0)
2.9%    66.7%       3.039s       3.04e-03s   1000   225   Elemwise{Composite{Switch(i0, Switch(i1, i2, ((((i3 * i4 * i5 * i6 * i7 * i8) / i9) / i10) - ((i11 * i12 * i13 * i6 * i7 * i8) / i9))), Switch(i1, i2, (((i14 * i15 * i16 * i6 * i7 * i8) / i9) / i17)))}}[(0, 13)](Elemwise{lt,no_inplace}.0, Elemwise{Composite{GT(i0, (i1 * i2))}}.0, TensorConstant{(1,) of 0}, TensorConstant{(1,) of -1.0}, TensorConstant{(1,) of -0.5}, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0, Elemwise{Composite{erfc(((i0 * i1
2.8%    69.4%       2.871s       2.87e-03s   1000   124   Elemwise{Composite{erfcx(((i0 * i1) / i2))}}(TensorConstant{(1,) of -0..7932881648}, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0)
2.7%    72.1%       2.779s       2.78e-03s   1000   230   Elemwise{Composite{((-Switch(i0, Switch(i1, i2, ((((i3 * i4 * i5 * i5 * i6 * i7 * i8) / i9) / i10) - ((i11 * i12 * i13 * i6 * i7 * i8) / i9))), Switch(i1, i2, ((i14 * i15 * i16 * i5 * i6 * i7 * i8) / (i9 * i17))))) / i18)}}[(0, 5)](Elemwise{lt,no_inplace}.0, Elemwise{Composite{GT(i0, (i1 * i2))}}.0, TensorConstant{(1,) of 0}, TensorConstant{(1,) of -1.0}, TensorConstant{(1,) of -0.5}, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0, Elemwise{Comp
1.7%    73.8%       1.752s       1.75e-03s   1000   226   Elemwise{Composite{((-Switch(i0, Switch(i1, (((i2 * i3 * i3 * i4) / i5) - (i6 * i7 * i4)), i8), Switch(i1, (i9 * i10 * i3 * (i4 / i11)), i8))) / i12)}}[(0, 4)](Elemwise{lt,no_inplace}.0, Elemwise{Composite{GT(i0, (i1 * i2))}}.0, TensorConstant{(1,) of -1.0}, Elemwise{sub,no_inplace}.0, Elemwise{Composite{((i0 * i1 * i2 * i3) + ((i4 * i5 * i6 * i7 * i2 * i3) / i8))}}.0, InplaceDimShuffle{x}.0, TensorConstant{(1,) of 0...7932881648}, Elemwise{Composit
1.6%    75.4%       1.655s       1.65e-03s   1000   134   Elemwise{Composite{((i0 / (i1 * i2)) + ((i3 * i4) / i5))}}(TensorConstant{(1,) of -1..1670955126}, Elemwise{Composite{erfcx(((i0 * i1) / i2))}}.0, InplaceDimShuffle{x}.0, TensorConstant{(1,) of -1..5865763297}, Elemwise{sub,no_inplace}.0, Elemwise{sqr,no_inplace}.0)
1.6%    77.0%       1.638s       1.64e-03s   1000   133   Elemwise{Composite{(((i0 * i1) / i2) + ((i3 * i1 * i1) / i4))}}(TensorConstant{(1,) of -1..1670955126}, Elemwise{sub,no_inplace}.0, Elemwise{Composite{erfcx(((i0 * i1) / i2))}}.0, TensorConstant{(1,) of -1..5865763297}, InplaceDimShuffle{x}.0)
1.2%    78.2%       1.228s       1.23e-03s   1000   203   Elemwise{Composite{Switch(i0, (((i1 * i2 * i3 * i4 * i5 * i6 * i7) / i8) / i9), i10)}}[(0, 3)](Elemwise{Composite{GT(i0, (i1 * i2))}}.0, TensorConstant{(1,) of -1.0}, TensorConstant{(1,) of -0.5}, Elemwise{sub,no_inplace}.0, Elemwise{Composite{Switch(IsInf(Composite{(i0 / expm1((-i1)))}(i0, i1)), i2, Composite{(i0 / expm1((-i1)))}(i0, i1))}}[(0, 1)].0, InplaceDimShuffle{x}.0, Elemwise{Composite{erfc(((i0 * i1) / i2))}}.0, Elemwise{Composite{exp(Swit
... (remaining 245 Apply instances account for 21.81%(22.57s) of the runtime)

Here are tips to potentially make your code run faster
(if you think of new ones, suggest them on the mailing list).
Test them first, as they are not guaranteed to always provide a speedup.
- Try the Aesara flag floatX=float32
- Try installing amdlibm and set the Aesara flag lib__amblibm=True. This speeds up only some Elemwise operation.


Any advice on where to go from here would be much appreciated. Thank you

The most useful here would be the profile with memory. You have to set some other flag for that, I think it’s mentioned on the doc page about profiling

Ah yes, thanks!

I reran it with the following flags:

aesara.config.profile = True
aesara.config.profile_memory = True


(it produced this warning)

/Users/jast/miniconda3/envs/pymc_env/lib/python3.10/site-packages/aesara/link/vm.py:1037: UserWarning: CVM does not support memory profiling, using Stack VM.
warnings.warn("CVM does not support memory profiling, using Stack VM.")


However, it did include more information in the summary. Here’s the Memory Profiling for dlogp:

Memory Profile
(Sparse variables are ignored)
(For values in brackets, it's for linker = c|py
---
Max peak memory with current setting
CPU: 17234KB (18008KB)
GPU: 0KB (0KB)
CPU + GPU: 17234KB (18008KB)
Max peak memory with current setting and Aesara flag optimizer_excluding=inplace
CPU: 18017KB (18712KB)
GPU: 0KB (0KB)
CPU + GPU: 18017KB (18712KB)
Max peak memory if allow_gc=False (linker don't make a difference)
CPU: 24988KB
GPU: 0KB
CPU + GPU: 24988KB
---

<Sum apply outputs (bytes)> <Apply outputs shape> <created/inplace/view> <Apply node>

{node_outputs_size:9d}B  [(120202,)] c Elemwise{Composite{((i0 + i1) - i2)}}(TensorConstant{(1,) of -10000.0}, ARange{dtype='int32'}.0, InplaceDimShuffle{x}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{sub,no_inplace}(ARange{dtype='int32'}.0, Elemwise{Composite{(i0 + (i1 / i2))}}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{sub,no_inplace}(ARange{dtype='int32'}.0, InplaceDimShuffle{x}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{sub,no_inplace}(InplaceDimShuffle{x}.0, ARange{dtype='int32'}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{Composite{erfc(((i0 * i1) / i2))}}(TensorConstant{(1,) of 0...7932881648}, Elemwise{Composite{((i0 + i1) - i2)}}.0, InplaceDimShuffle{x}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{Composite{exp(((i0 * i1 * i1) / i2))}}(TensorConstant{(1,) of -0..0171142714}, Elemwise{Composite{((i0 + i1) - i2)}}.0, Elemwise{sqr,no_inplace}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{true_div,no_inplace}(Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{Composite{erfc(((i0 * i1) / i2))}}(TensorConstant{(1,) of 0...7932881648}, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{Composite{erfcx(((i0 * i1) / i2))}}(TensorConstant{(1,) of -0..7932881648}, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{Composite{exp(((i0 * i1 * i1) / i2))}}(TensorConstant{(1,) of -0..0171142714}, Elemwise{sub,no_inplace}.0, Elemwise{sqr,no_inplace}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{Composite{erfcx(((i0 * i1) / i2))}}(TensorConstant{(1,) of -0..7932881648}, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{Composite{erfc(((i0 * i1) / i2))}}(TensorConstant{(1,) of 0...7932881648}, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{true_div,no_inplace}(Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{Composite{exp(((i0 * i1 * i1) / i2))}}(TensorConstant{(1,) of -0..0171142714}, Elemwise{sub,no_inplace}.0, Elemwise{sqr,no_inplace}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{Composite{(((i0 * i1) / i2) + ((i3 * i1 * i1) / i4))}}(TensorConstant{(1,) of -1..1670955126}, Elemwise{sub,no_inplace}.0, Elemwise{Composite{erfcx(((i0 * i1) / i2))}}.0, TensorConstant{(1,) of -1..5865763297}, InplaceDimShuffle{x}.0)
{node_outputs_size:9d}B  [(120202,)] c Elemwise{Composite{((i0 / (i1 * i2)) + ((i3 * i4) / i5))}}(TensorConstant{(1,) of -1..1670955126}, Elemwise{Composite{erfcx(((i0 * i1) / i2))}}.0, InplaceDimShuffle{x}.0, TensorConstant{(1,) of -1..5865763297}, Elemwise{sub,no_inplace}.0, Elemwise{sqr,no_inplace}.0)
{node_outputs_size:9d}B  [(120202,)] i Elemwise{Composite{Switch(i0, (log((i1 * i2)) - (i1 * sqr(i3))), log1p((i4 * i5)))}}[(0, 2)](Elemwise{lt,no_inplace}.0, TensorConstant{(1,) of 0.5}, Elemwise{Composite{erfcx(((i0 * i1) / i2))}}.0, Elemwise{true_div,no_inplace}.0, TensorConstant{(1,) of -0.5}, Elemwise{Composite{erfc(((i0 * i1) / i2))}}.0)
{node_outputs_size:9d}B  [(120202,)] i Elemwise{Composite{(i0 + (-i1))}}[(0, 1)](TensorConstant{(1,) of 2.0}, Elemwise{Composite{erfc(((i0 * i1) / i2))}}.0)
{node_outputs_size:9d}B  [(120202,)] i Elemwise{Composite{(((i0 / i1) + i2 + Switch(i3, (log((i4 * i5)) - (i4 * sqr(i6))), log1p((i7 * i8)))) - i9)}}[(0, 6)](Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x}.0, Elemwise{Composite{(i0 * sqr(i1))}}.0, Elemwise{lt,no_inplace}.0, TensorConstant{(1,) of 0.5}, Elemwise{Composite{erfcx(((i0 * i1) / i2))}}.0, Elemwise{true_div,no_inplace}.0, TensorConstant{(1,) of -0.5}, Elemwise{Composite{erfc(((i0 * i1) / i2))}}.0, Elemwise{Composite{Switch(i0, (log((i1 * i2)) - (i1 * sqr(i3))), log1p((i4 * i5)))}}[(0, 2)].0)
{node_outputs_size:9d}B  [(120202,)] i Elemwise{Composite{(i0 + (-i1))}}[(0, 1)](TensorConstant{(1,) of 2.0}, Elemwise{Composite{erfc(((i0 * i1) / i2))}}.0)
... (remaining 245 Apply account for 21987206B/41219526B ((53.34%)) of the Apply with dense outputs sizes)

<created/inplace/view> is taken from the Op's declaration.
Apply nodes marked 'inplace' or 'view' may actually allocate memory, this is not reported here. If you use DebugMode, warnings will be emitted in those cases.

Here are tips to potentially make your code run faster
(if you think of new ones, suggest them on the mailing list).
Test them first, as they are not guaranteed to always provide a speedup.
- Try the Aesara flag floatX=float32
- Try installing amdlibm and set the Aesara flag lib__amblibm=True. This speeds up only some Elemwise operation.


And for logp:

Memory Profile
(Sparse variables are ignored)
(For values in brackets, it's for linker = c|py
---
Max peak memory with current setting
CPU: 1643KB (1721KB)
GPU: 0KB (0KB)
CPU + GPU: 1643KB (1721KB)
Max peak memory with current setting and Aesara flag optimizer_excluding=inplace
CPU: 1643KB (1721KB)
GPU: 0KB (0KB)
CPU + GPU: 1643KB (1721KB)
Max peak memory if allow_gc=False (linker don't make a difference)
CPU: 2356KB
GPU: 0KB
CPU + GPU: 2356KB
---

<Sum apply outputs (bytes)> <Apply outputs shape> <created/inplace/view> <Apply node>

{node_outputs_size:9d}B  [(120202,)] c Elemwise{Composite{(exp(Switch(i0, Switch(i1, (Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}(((i2 - i3) / i4), i5, i6, i7, (i2 - i3), i4, i8, i9) + scalar_log1mexp(((((i3 - i2) / i10) + i11 + Switch(LT(((i2 - i12) / i4), i5), (log((i6 * erfcx(((i7 * (i2 - i12)) / i4)))) - (i6 * sqr(((i2 - i12) / i4)))), log1p((i8 * erfc(((i9 * (i2 - i12)) / i4)))))) - Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}(((i2 - i3) / i4), i5, i6, i7, (i2 - i3), i4, i8, i9)))), Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}(((i2 - i3) / i4), i5, i6, i7, (i2 - i3), i4, i8, i9)), i13)) * erfc(((i9 * ((i14 + i2) - i15)) / i16)))}}(InplaceDimShuffle{x}.0, Elemwise{Composite{GT(i0, (i1 * i2))}}.0, ARange{dtype='int32'}.0, InplaceDimShuffle{x}.0, InplaceDimShuffle{x}.0, TensorConstant{(1,) of -1.0}, TensorConstant{(1,) of 0.5}, TensorConstant{(1,) of -0..7932881648}, TensorConstant{(1,) of -0.5}, TensorConstant{(1,) of 0...7932881648}, InplaceDimShuffle{x}.0, Elemwise{Composite{(i0 * sqr((i1 / i2)))}}.0, Elemwise{Composite{(i0 + (i1 / i2))}}[(0, 1)].0, TensorConstant{(1,) of -inf}, TensorConstant{(1,) of -10000.0}, InplaceDimShuffle{x}.0, InplaceDimShuffle{x}.0)
{node_outputs_size:9d}B  [(120202,)] c ARange{dtype='int32'}(Elemwise{Floor}[(0, 0)].0, Elemwise{Ceil}[(0, 0)].0, TensorConstant{1})
{node_outputs_size:9d}B  [(10000, 4)] c Join(TensorConstant{1}, Elemwise{Composite{Switch(i0, Switch(i1, (i2 + i3 + i4 + i5), (i6 - ((i7 * sqr(i8)) / i9))), i10)}}.0, Elemwise{Composite{((Switch(i0, Switch(i1, (Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}((i2 / i3), i4, i5, i6, i2, i3, i7, i8) + scalar_log1mexp(((i9 + i10 + i11) - Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}((i2 / i3), i4, i5, i6, i2, i3, i7, i8)))), Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}((i2 / i3), i4, i5, i6, i2, i3, i7, i8)), i12) + Switch(GT(i13, i14), (log((i5 * erfcx((i8 * i13)))) - (i5 * sqr(i13))), log1p((i7 * erfc((i6 * i13)))))) - i15)}}[(0, 2)].0, Elemwise{Composite{Switch(i0, ((i1 + (i2 * sqr(i3))) - i4), i5)}}[(0, 3)].0, TensorConstant{(10000, 1)..0970631272})
{node_outputs_size:9d}B  [(10000, 4)] i Elemwise{Add}[(0, 1)](Elemwise{Log}[(0, 0)].0, Join.0)
{node_outputs_size:9d}B  [(10000, 4)] i Elemwise{Composite{Switch(i0, i1, exp((i2 - i3)))}}[(0, 2)](Elemwise{isinf,no_inplace}.0, Elemwise{exp,no_inplace}.0, Elemwise{Add}[(0, 1)].0, InplaceDimShuffle{0,x}.0)
{node_outputs_size:9d}B  [(10000, 1)] c Elemwise{sub,no_inplace}(TensorConstant{[[-2265.]
.. [ 7658.]]}, InplaceDimShuffle{x,x}.0)
{node_outputs_size:9d}B  [(10000, 1)] c Elemwise{Composite{((i0 - i1) / i2)}}(InplaceDimShuffle{x,x}.0, TensorConstant{[[-2265.]
.. [ 7658.]]}, InplaceDimShuffle{x,x}.0)
{node_outputs_size:9d}B  [(10000, 1)] c Elemwise{Composite{((i0 - i1) / i2)}}(TensorConstant{[[-12265.]..[ -2342.]]}, InplaceDimShuffle{x,x}.0, InplaceDimShuffle{x,x}.0)
{node_outputs_size:9d}B  [(10000, 1)] c Elemwise{Composite{Switch(LT(((i0 - i1) / i2), i3), (log((i4 * erfcx(((i5 * (i0 - i1)) / i2)))) - (i4 * sqr(((i0 - i1) / i2)))), log1p((i6 * erfc(((i7 * (i0 - i1)) / i2)))))}}(TensorConstant{[[-2265.]
.. [ 7658.]]}, InplaceDimShuffle{0,x}.0, InplaceDimShuffle{x,x}.0, TensorConstant{(1, 1) of -1.0}, TensorConstant{(1, 1) of 0.5}, TensorConstant{(1, 1) of ..7932881648}, TensorConstant{(1, 1) of -0.5}, TensorConstant{(1, 1) of ..7932881648})
{node_outputs_size:9d}B  [(10000, 1)] c Elemwise{Composite{Switch(i0, Switch(i1, (i2 + i3 + i4 + i5), (i6 - ((i7 * sqr(i8)) / i9))), i10)}}(InplaceDimShuffle{x,x}.0, InplaceDimShuffle{0,x}.0, Elemwise{Composite{(-log(i0))}}[(0, 0)].0, Elemwise{Composite{((i0 - i1) / i2)}}.0, InplaceDimShuffle{0,x}.0, Elemwise{Composite{Switch(LT(((i0 - i1) / i2), i3), (log((i4 * erfcx(((i5 * (i0 - i1)) / i2)))) - (i4 * sqr(((i0 - i1) / i2)))), log1p((i6 * erfc(((i7 * (i0 - i1)) / i2)))))}}.0, Elemwise{Composite{(i0 - log(Abs(i1)))}}.0, TensorConstant{(1, 1) of 0.5}, Elemwise{sub,no_inplace}.0, Elemwise{sqr,no_inplace}.0, TensorConstant{(1, 1) of -inf})
{node_outputs_size:9d}B  [(10000, 1)] i Elemwise{Composite{((Switch(i0, Switch(i1, (Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}((i2 / i3), i4, i5, i6, i2, i3, i7, i8) + scalar_log1mexp(((i9 + i10 + i11) - Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}((i2 / i3), i4, i5, i6, i2, i3, i7, i8)))), Composite{Switch(LT(i0, i1), (log((i2 * erfcx(((i3 * i4) / i5)))) - (i2 * sqr(i0))), log1p((i6 * erfc(((i7 * i4) / i5)))))}((i2 / i3), i4, i5, i6, i2, i3, i7, i8)), i12) + Switch(GT(i13, i14), (log((i5 * erfcx((i8 * i13)))) - (i5 * sqr(i13))), log1p((i7 * erfc((i6 * i13)))))) - i15)}}[(0, 2)](InplaceDimShuffle{x,x}.0, InplaceDimShuffle{0,x}.0, Elemwise{sub,no_inplace}.0, InplaceDimShuffle{x,x}.0, TensorConstant{(1, 1) of -1.0}, TensorConstant{(1, 1) of 0.5}, TensorConstant{(1, 1) of ..7932881648}, TensorConstant{(1, 1) of -0.5}, TensorConstant{(1, 1) of ..7932881648}, Elemwise{Composite{((i0 - i1) / i2)}}.0, InplaceDimShuffle{0,x}.0, Elemwise{Composite{Switch(LT(((i0 - i1) / i2), i3), (log((i4 * erfcx(((i5 * (i0 - i1)) / i2)))) - (i4 * sqr(((i0 - i1) / i2)))), log1p((i6 * erfc(((i7 * (i0 - i1)) / i2)))))}}.0, TensorConstant{(1, 1) of -inf}, Elemwise{Composite{((i0 - i1) / i2)}}.0, TensorConstant{(1, 1) of 1.0}, Elemwise{Composite{log((i0 * i1))}}[(0, 1)].0)
{node_outputs_size:9d}B  [(10000, 1)] i Elemwise{Composite{Switch(i0, ((i1 + (i2 * sqr(i3))) - i4), i5)}}[(0, 3)](Elemwise{gt,no_inplace}.0, TensorConstant{(1, 1) of ..5332046727}, TensorConstant{(1, 1) of -0.5}, Elemwise{Composite{((i0 - i1) / i2)}}.0, Elemwise{Log}[(0, 0)].0, TensorConstant{(1, 1) of -inf})
{node_outputs_size:9d}B  [(10000, 1)] v InplaceDimShuffle{0,x}(max)
{node_outputs_size:9d}B  [(10000, 1)] c Elemwise{exp,no_inplace}(InplaceDimShuffle{0,x}.0)
{node_outputs_size:9d}B  [(10000,)] c Sum{axis=[1], acc_dtype=float64}(Elemwise{Composite{Switch(i0, i1, exp((i2 - i3)))}}[(0, 2)].0)
{node_outputs_size:9d}B  [(10000,)] i Elemwise{Composite{Switch(i0, (i1 + log(i2)), i3)}}[(0, 1)](InplaceDimShuffle{x}.0, max, Sum{axis=[1], acc_dtype=float64}.0, TensorConstant{(1,) of -inf})
{node_outputs_size:9d}B  [(10000, 1)] c Elemwise{isinf,no_inplace}(InplaceDimShuffle{0,x}.0)
... (remaining 91 Apply account for  868B/3373292B ((0.03%)) of the Apply with dense outputs sizes)

<created/inplace/view> is taken from the Op's declaration.
Apply nodes marked 'inplace' or 'view' may actually allocate memory, this is not reported here. If you use DebugMode, warnings will be emitted in those cases.

Here are tips to potentially make your code run faster
(if you think of new ones, suggest them on the mailing list).
Test them first, as they are not guaranteed to always provide a speedup.
- Try the Aesara flag floatX=float32
- Try installing amdlibm and set the Aesara flag lib__amblibm=True. This speeds up only some Elemwise operation.


It looks like peak memory is around 20mb in dlogp

Doesn’t look too bad. Looking back at the original memory error was coming from the numpy.arange call of tensor.arange Op. You can add some print statements in the perform method here to check what the inputs of your arange are actually like?: aesara/basic.py at ae182f02f879741e409f927b27e874d8a1a4ef21 · aesara-devs/aesara · GitHub

If this Arange is indeed too large, you might need to implement your integration with a Scan?

1 Like

I’m going to try this locally now…

I monkey-patched the perform method like this

        try:
res = np.arange(start, stop, step, dtype=self.dtype)
except (MemoryError, ValueError):
print(f"Failed to allocate arange array with {start=}, {stop=}, {step=}, {dtype=}")
raise
out[0] = res


In my machine it raises a ValueError: Maximum value exceeded instead of a MemoryError:

Failed to allocate arange array with start=-196242792413697.0, stop=2.5870972395998505e+18, step=1, self.dtype='int64'

ValueError: array is too big; arr.size * arr.dtype.itemsize is larger than the maximum possible size.


This might be coming from very extreme integration bounds (or underflow/overflow somewhere, or a bug) that are not seen at the initial point (so your profile did not fail/ show anything extreme).

Ruling out those issues, my guess would be that the VI never tries such extreme bounds of integration, and that’s why it doesn’t fail.

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