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Seed logsumexp benchmark tests #185

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Merged
merged 1 commit into from
Jan 9, 2023
Merged

Seed logsumexp benchmark tests #185

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3 changes: 2 additions & 1 deletion tests/link/jax/test_elemwise.py
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Expand Up @@ -111,7 +111,8 @@ def test_logsumexp_benchmark(size, axis, benchmark):
X_max = at.switch(at.isinf(X_max), 0, X_max)
X_lse = at.log(at.sum(at.exp(X - X_max), axis=axis, keepdims=True)) + X_max

X_val = np.random.normal(size=size)
rng = np.random.default_rng(23920)
X_val = rng.normal(size=size)

X_lse_fn = pytensor.function([X], X_lse, mode="JAX")

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23 changes: 23 additions & 0 deletions tests/link/numba/test_elemwise.py
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Expand Up @@ -2,7 +2,9 @@

import numpy as np
import pytest
import scipy.special

import pytensor
import pytensor.tensor as at
import pytensor.tensor.inplace as ati
import pytensor.tensor.math as aem
Expand Down Expand Up @@ -532,3 +534,24 @@ def test_MaxAndArgmax(x, axes, exc):
if not isinstance(i, (SharedVariable, Constant))
],
)


@pytest.mark.parametrize("size", [(10, 10), (1000, 1000), (10000, 10000)])
@pytest.mark.parametrize("axis", [0, 1])
def test_logsumexp_benchmark(size, axis, benchmark):

X = at.matrix("X")
X_max = at.max(X, axis=axis, keepdims=True)
X_max = at.switch(at.isinf(X_max), 0, X_max)
X_lse = at.log(at.sum(at.exp(X - X_max), axis=axis, keepdims=True)) + X_max

rng = np.random.default_rng(23920)
X_val = rng.normal(size=size)

X_lse_fn = pytensor.function([X], X_lse, mode="JAX")

# JIT compile first
_ = X_lse_fn(X_val)
res = benchmark(X_lse_fn, X_val)
exp_res = scipy.special.logsumexp(X_val, axis=axis, keepdims=True)
np.testing.assert_array_almost_equal(res, exp_res)