Move NonZero Op dispatcher to tensor_basic

pytensor/link/numba/dispatch/basic.py

@@ -24,7 +24,6 @@ from pytensor.link.utils import (
 )
 from pytensor.scalar.basic import ScalarType
 from pytensor.sparse import SparseTensorType
-from pytensor.tensor.basic import Nonzero
 from pytensor.tensor.blas import BatchedDot
 from pytensor.tensor.math import Dot
 from pytensor.tensor.type import TensorType
@@ -457,15 +456,3 @@ def numba_funcify_IfElse(op, **kwargs):
             return res[0]
 
     return ifelse
-
-
-@numba_funcify.register(Nonzero)
-def numba_funcify_Nonzero(op, node, **kwargs):
-    @numba_njit
-    def nonzero(a):
-        result_tuple = np.nonzero(a)
-        if a.ndim == 1:
-            return result_tuple[0]
-        return list(result_tuple)
-
-    return nonzero

pytensor/link/numba/dispatch/tensor_basic.py

@@ -3,7 +3,11 @@ from textwrap import indent
 import numpy as np
 
 from pytensor.link.numba.dispatch import basic as numba_basic
-from pytensor.link.numba.dispatch.basic import create_tuple_string, numba_funcify
+from pytensor.link.numba.dispatch.basic import (
+    create_tuple_string,
+    numba_funcify,
+    numba_njit,
+)
 from pytensor.link.utils import compile_function_src, unique_name_generator
 from pytensor.tensor.basic import (
     Alloc,
@@ -13,6 +17,7 @@ from pytensor.tensor.basic import (
     Eye,
     Join,
     MakeVector,
+    Nonzero,
     ScalarFromTensor,
     Split,
     TensorFromScalar,
@@ -235,3 +240,15 @@ def numba_funcify_ScalarFromTensor(op, **kwargs):
         return numba_basic.to_scalar(x)
 
     return scalar_from_tensor
+
+
+@numba_funcify.register(Nonzero)
+def numba_funcify_Nonzero(op, node, **kwargs):
+    @numba_njit
+    def nonzero(a):
+        result_tuple = np.nonzero(a)
+        if a.ndim == 1:
+            return result_tuple[0]
+        return list(result_tuple)
+
+    return nonzero

tests/link/numba/test_basic.py

@@ -718,20 +718,6 @@ def test_function_overhead(mode, benchmark):
     benchmark(fn, test_x)
 
 
-@pytest.mark.parametrize(
-    "input_data",
-    [np.array([1, 0, 3]), np.array([[0, 1], [2, 0]]), np.array([[0, 0], [0, 0]])],
-)
-def test_Nonzero(input_data):
-    a = pt.tensor("a", shape=(None,) * input_data.ndim)
-
-    graph_outputs = pt.nonzero(a)
-
-    compare_numba_and_py(
-        graph_inputs=[a], graph_outputs=graph_outputs, test_inputs=[input_data]
-    )
-
-
 @pytest.mark.parametrize("dtype", ("float64", "float32", "mixed"))
 def test_mat_vec_dot_performance(dtype, benchmark):
     A = tensor("A", shape=(512, 512), dtype="float64" if dtype == "mixed" else dtype)

tests/link/numba/test_tensor_basic.py

@@ -326,3 +326,17 @@ def test_Eye(n, m, k, dtype):
         g,
         [n_test, m_test] if m is not None else [n_test],
     )
+
+
+@pytest.mark.parametrize(
+    "input_data",
+    [np.array([1, 0, 3]), np.array([[0, 1], [2, 0]]), np.array([[0, 0], [0, 0]])],
+)
+def test_Nonzero(input_data):
+    a = pt.tensor("a", shape=(None,) * input_data.ndim)
+
+    graph_outputs = pt.nonzero(a)
+
+    compare_numba_and_py(
+        graph_inputs=[a], graph_outputs=graph_outputs, test_inputs=[input_data]
+    )