Support read-only Numba types

aesara/link/numba/dispatch/basic.py

@@ -49,6 +49,7 @@
 
 
 if TYPE_CHECKING:
+    from aesara.graph.basic import Variable
     from aesara.graph.op import StorageMapType
 
 
@@ -79,13 +80,21 @@ def numba_vectorize(*args, **kwargs):
 
 
 @singledispatch
-def get_numba_type(aesara_type: Type, **kwargs) -> numba.types.Type:
-    r"""Create a Numba type object for a :class:`Type`."""
+def get_numba_type(aesara_type: Type, var: "Variable", **kwargs) -> numba.types.Type:
+    r"""Create a Numba type object for a :class:`Type`.
+
+    Parameters
+    ----------
+    aesara_type
+        The :class:`Type` to convert.
+    var
+        The :class:`Variable` corresponding to `aesara_type`.
+    """
     return numba.types.pyobject
 
 
 @get_numba_type.register(ScalarType)
-def get_numba_type_ScalarType(aesara_type, **kwargs):
+def get_numba_type_ScalarType(aesara_type, var, **kwargs):
     dtype = np.dtype(aesara_type.dtype)
     numba_dtype = numba.from_dtype(dtype)
     return numba_dtype
@@ -94,6 +103,7 @@ def get_numba_type_ScalarType(aesara_type, **kwargs):
 @get_numba_type.register(TensorType)
 def get_numba_type_TensorType(
     aesara_type,
+    var: "Variable",
     layout: str = "A",
     force_scalar: bool = False,
     reduce_to_scalar: bool = False,
@@ -103,6 +113,8 @@ def get_numba_type_TensorType(
     ----------
     aesara_type
         The :class:`Type` to convert.
+    var
+        The :class:`Variable` corresponding to `aesara_type`.
     layout
         The :class:`numpy.ndarray` layout to use.
     force_scalar
@@ -114,7 +126,10 @@ def get_numba_type_TensorType(
     numba_dtype = numba.from_dtype(dtype)
     if force_scalar or (reduce_to_scalar and getattr(aesara_type, "ndim", None) == 0):
         return numba_dtype
-    return numba.types.Array(numba_dtype, aesara_type.ndim, layout)
+
+    readonly = getattr(var.tag, "indestructible", False)
+
+    return numba.types.Array(numba_dtype, aesara_type.ndim, layout, readonly=readonly)
 
 
 def create_numba_signature(
@@ -123,11 +138,11 @@ def create_numba_signature(
     """Create a Numba type for the signature of an `Apply` node or `FunctionGraph`."""
     input_types = []
     for inp in node_or_fgraph.inputs:
-        input_types.append(get_numba_type(inp.type, **kwargs))
+        input_types.append(get_numba_type(inp.type, inp, **kwargs))
 
     output_types = []
     for out in node_or_fgraph.outputs:
-        output_types.append(get_numba_type(out.type, **kwargs))
+        output_types.append(get_numba_type(out.type, inp, **kwargs))
 
     if isinstance(node_or_fgraph, FunctionGraph):
         return numba.types.Tuple(output_types)(*input_types)
@@ -379,9 +394,9 @@ def numba_funcify_perform(op, node, storage_map=None, **kwargs) -> Callable:
     n_outputs = len(node.outputs)
 
     if n_outputs > 1:
-        ret_sig = numba.types.Tuple([get_numba_type(o.type) for o in node.outputs])
+        ret_sig = numba.types.Tuple([get_numba_type(o.type, o) for o in node.outputs])
     else:
-        ret_sig = get_numba_type(node.outputs[0].type)
+        ret_sig = get_numba_type(node.outputs[0].type, node.outputs[0])
 
     output_types = tuple(out.type for out in node.outputs)
     params = node.run_params()
@@ -821,7 +836,7 @@ def cholesky(a):
             UserWarning,
         )
 
-        ret_sig = get_numba_type(node.outputs[0].type)
+        ret_sig = get_numba_type(node.outputs[0].type, node.outputs[0])
 
         @numba_njit
         def cholesky(a):
@@ -850,7 +865,7 @@ def numba_funcify_Solve(op, node, **kwargs):
             UserWarning,
         )
 
-        ret_sig = get_numba_type(node.outputs[0].type)
+        ret_sig = get_numba_type(node.outputs[0].type, node.outputs[0])
 
         @numba_njit
         def solve(a, b):

aesara/link/numba/dispatch/extra_ops.py

@@ -184,7 +184,7 @@ def numba_funcify_Repeat(op, node, **kwargs):
             UserWarning,
         )
 
-        ret_sig = get_numba_type(node.outputs[0].type)
+        ret_sig = get_numba_type(node.outputs[0].type, node.outputs[0])
 
         @numba_basic.numba_njit
         def repeatop(x, repeats):
@@ -243,9 +243,11 @@ def unique(x):
         )
 
         if returns_multi:
-            ret_sig = numba.types.Tuple([get_numba_type(o.type) for o in node.outputs])
+            ret_sig = numba.types.Tuple(
+                [get_numba_type(o.type, o) for o in node.outputs]
+            )
         else:
-            ret_sig = get_numba_type(node.outputs[0].type)
+            ret_sig = get_numba_type(node.outputs[0].type, node.outputs[0])
 
         @numba_basic.numba_njit
         def unique(x):
@@ -308,7 +310,7 @@ def numba_funcify_Searchsorted(op, node, **kwargs):
             UserWarning,
         )
 
-        ret_sig = get_numba_type(node.outputs[0].type)
+        ret_sig = get_numba_type(node.outputs[0].type, node.outputs[0])
 
         @numba_basic.numba_njit
         def searchsorted(a, v, sorter):

aesara/link/numba/dispatch/nlinalg.py

@@ -36,7 +36,7 @@ def numba_funcify_SVD(op, node, **kwargs):
             UserWarning,
         )
 
-        ret_sig = get_numba_type(node.outputs[0].type)
+        ret_sig = get_numba_type(node.outputs[0].type, node.outputs[0])
 
         @numba_basic.numba_njit
         def svd(x):
@@ -101,7 +101,10 @@ def numba_funcify_Eigh(op, node, **kwargs):
 
         out_dtypes = tuple(o.type.numpy_dtype for o in node.outputs)
         ret_sig = numba.types.Tuple(
-            [get_numba_type(node.outputs[0].type), get_numba_type(node.outputs[1].type)]
+            [
+                get_numba_type(node.outputs[0].type, node.outputs[0]),
+                get_numba_type(node.outputs[1].type, node.outputs[1]),
+            ]
         )
 
         @numba_basic.numba_njit
@@ -173,9 +176,11 @@ def numba_funcify_QRFull(op, node, **kwargs):
         )
 
         if len(node.outputs) > 1:
-            ret_sig = numba.types.Tuple([get_numba_type(o.type) for o in node.outputs])
+            ret_sig = numba.types.Tuple(
+                [get_numba_type(o.type, o) for o in node.outputs]
+            )
         else:
-            ret_sig = get_numba_type(node.outputs[0].type)
+            ret_sig = get_numba_type(node.outputs[0].type, node.outputs[0])
 
         @numba_basic.numba_njit
         def qr_full(x):

aesara/link/numba/dispatch/sparse.py

@@ -214,7 +214,7 @@ def copy(inst):
 
 
 @get_numba_type.register(SparseTensorType)
-def get_numba_type_SparseType(aesara_type, **kwargs):
+def get_numba_type_SparseType(aesara_type, var, **kwargs):
     dtype = from_dtype(np.dtype(aesara_type.dtype))
 
     if aesara_type.format == "csr":

aesara/scan/op.py

@@ -3427,7 +3427,7 @@ def profile_printer(
             )
 
 
-@op_debug_information.register(Scan)
+@op_debug_information.register(Scan)  # type: ignore[has-type]
 def _op_debug_information_Scan(op, node):
     from typing import Sequence
 

setup.cfg

@@ -308,6 +308,9 @@ check_untyped_defs = False
 ignore_errors = True
 check_untyped_defs = False
 
+[mypy-aesara.scan.op]
+warn_unused_ignores = False
+
 [mypy-aesara.link.numba.dispatch.extra_ops]
 ignore_errors = True
 check_untyped_defs = False

tests/link/numba/test_basic.py

@@ -246,7 +246,7 @@ def assert_fn(x, y):
 
 
 @pytest.mark.parametrize(
-    "v, expected, force_scalar",
+    "typ, expected, force_scalar",
     [
         (MyType(), numba.types.pyobject, False),
         (
@@ -267,11 +267,19 @@ def assert_fn(x, y):
         (at.dmatrix, numba.types.float64, True),
     ],
 )
-def test_get_numba_type(v, expected, force_scalar):
-    res = numba_basic.get_numba_type(v, force_scalar=force_scalar)
+def test_get_numba_type(typ, expected, force_scalar):
+    res = numba_basic.get_numba_type(typ, typ(), force_scalar=force_scalar)
     assert res == expected
 
 
+def test_get_numba_type_readonly():
+    typ = at.dmatrix
+    var = typ()
+    var.tag.indestructible = True
+    res = numba_basic.get_numba_type(typ, var)
+    assert not res.mutable
+
+
 @pytest.mark.parametrize(
     "v, expected, force_scalar",
     [

tests/link/numba/test_elemwise.py

@@ -507,3 +507,13 @@ def test_MaxAndArgmax(x, axes, exc):
                 if not isinstance(i, (SharedVariable, Constant))
             ],
         )
+
+
+def test_sum_broadcast_to():
+    """Make sure that we handle the writability of `BroadcastTo` results correctly."""
+
+    x = at.vector("x")
+    out = at.broadcast_to(x, (2, 2)).sum()
+
+    x_val = np.array([1, 2], dtype=config.floatX)
+    compare_numba_and_py(((x,), (out,)), [x_val])