Implement Neg sparse Op in Numba backend

pytensor/link/numba/dispatch/sparse/basic.py

@@ -24,6 +24,7 @@ from pytensor.sparse import (
     GetItemListGrad,
     GetItemScalar,
     HStack,
+    Neg,
     RowScaleCSC,
     SparseFromDense,
     Transpose,
@@ -812,3 +813,15 @@ def numba_funcify_GetItemScalar(op, node, **kwargs):
         return np.asarray(out, dtype=out_dtype)
 
     return get_item_scalar_csc
+
+
+@register_funcify_default_op_cache_key(Neg)
+def numba_funcify_Neg(op, node, **kwargs):
+    @numba_basic.numba_njit
+    def neg(x):
+        z = x.copy()
+        z_data = z.data
+        z_data *= -1
+        return z
+
+    return neg

tests/link/numba/sparse/test_basic.py

@@ -654,3 +654,13 @@ def test_sparse_get_item_scalar_wrong_index(format):
 
     with pytest.raises(IndexError, match="column index out of bounds"):
         fn(x_test, 0, 5)
+
+
+@pytest.mark.parametrize("format", ("csr", "csc"))
+def test_sparse_neg(format):
+    x = ps.matrix(format, name="x", shape=(7, 6), dtype=config.floatX)
+    z = -x
+
+    x_test = sp.sparse.random(7, 6, density=0.4, format=format, dtype=config.floatX)
+
+    compare_numba_and_py_sparse([x], z, [x_test])