Numpy 2.x C implementation for AdvancedIncSubtensor1

[ricardoV94]

Description

In our bump to numpy 2.0 we had to stop supporting the C implementation of this Op #1194

This was because we were using this PyArrayMapIter functionality that numpy removed. The Op needs to be able to iterate over the first dimension of an array to increment the values provided in y. This increment may need to broadcast with the selected entries.

Example:

import numpy as np
import pytensor
import pytensor.tensor as pt

x = pt.tensor("x", shape=(6, 2, 3))
y = pt.vector("y", shape=(3,))
idxs = pt.vector("idxs", shape=(7,), dtype=int)
out = pt.subtensor.advanced_inc_subtensor1(x, y, idxs)

x_test = np.zeros(x.type.shape)
y_test = np.ones(y.type.shape)
idxs_test = [0, 0, 0, 1, 2, 3, 5]
res = out.eval({x: x_test, y: y_test, idxs: idxs_test})
print(res)
# [[[3. 3. 3.]
#   [3. 3. 3.]]
#  [[1. 1. 1.]
#   [1. 1. 1.]]
#  [[1. 1. 1.]
#   [1. 1. 1.]]
#  [[1. 1. 1.]
#   [1. 1. 1.]]
#  [[0. 0. 0.]
#   [0. 0. 0.]]
#  [[1. 1. 1.]
#   [1. 1. 1.]]]

It's a restricted version of numpy.add.at, where indices must be a single integer vector, whereas numpy.add.at supports arbitrary advanced indexing.

res_np = x_test.copy()
np.add.at(res_np, idxs_test, y_test)
np.testing.assert_allclose(res, res_np)

[ricardoV94]

One option is to reuse IncSubtensor which already allow doing this for x[0].inc(y), we could try to call the inner logic in a loop over the leading indexes