Reorder functions in numba/dispatch/basic.py

pytensor/link/numba/dispatch/basic.py

@@ -166,6 +166,55 @@ def create_arg_string(x):
     return args
 
 
+@numba.extending.intrinsic
+def direct_cast(typingctx, val, typ):
+    if isinstance(typ, numba.types.TypeRef):
+        casted = typ.instance_type
+    elif isinstance(typ, numba.types.DTypeSpec):
+        casted = typ.dtype
+    else:
+        casted = typ
+
+    sig = casted(casted, typ)
+
+    def codegen(context, builder, signature, args):
+        val, _ = args
+        context.nrt.incref(builder, signature.return_type, val)
+        return val
+
+    return sig, codegen
+
+
+def int_to_float_fn(inputs, out_dtype):
+    """Create a Numba function that converts integer and boolean ``ndarray``s to floats."""
+
+    if (
+        all(inp.type.dtype == out_dtype for inp in inputs)
+        and np.dtype(out_dtype).kind == "f"
+    ):
+
+        @numba_njit(inline="always")
+        def inputs_cast(x):
+            return x
+
+    elif any(i.type.numpy_dtype.kind in "uib" for i in inputs):
+        args_dtype = np.dtype(f"f{out_dtype.itemsize}")
+
+        @numba_njit(inline="always")
+        def inputs_cast(x):
+            return x.astype(args_dtype)
+
+    else:
+        args_dtype_sz = max(_arg.type.numpy_dtype.itemsize for _arg in inputs)
+        args_dtype = np.dtype(f"f{args_dtype_sz}")
+
+        @numba_njit(inline="always")
+        def inputs_cast(x):
+            return x.astype(args_dtype)
+
+    return inputs_cast
+
+
 @singledispatch
 def numba_typify(data, dtype=None, **kwargs):
     return data
@@ -231,6 +280,22 @@ def numba_funcify(op, node=None, storage_map=None, **kwargs):
     return generate_fallback_impl(op, node, storage_map, **kwargs)
 
 
+@numba_funcify.register(FunctionGraph)
+def numba_funcify_FunctionGraph(
+    fgraph,
+    node=None,
+    fgraph_name="numba_funcified_fgraph",
+    **kwargs,
+):
+    return fgraph_to_python(
+        fgraph,
+        numba_funcify,
+        type_conversion_fn=numba_typify,
+        fgraph_name=fgraph_name,
+        **kwargs,
+    )
+
+
 @numba_funcify.register(OpFromGraph)
 def numba_funcify_OpFromGraph(op, node=None, **kwargs):
     _ = kwargs.pop("storage_map", None)
@@ -263,22 +328,6 @@ def numba_funcify_OpFromGraph(op, node=None, **kwargs):
     return opfromgraph
 
 
-@numba_funcify.register(FunctionGraph)
-def numba_funcify_FunctionGraph(
-    fgraph,
-    node=None,
-    fgraph_name="numba_funcified_fgraph",
-    **kwargs,
-):
-    return fgraph_to_python(
-        fgraph,
-        numba_funcify,
-        type_conversion_fn=numba_typify,
-        fgraph_name=fgraph_name,
-        **kwargs,
-    )
-
-
 @numba_funcify.register(DeepCopyOp)
 def numba_funcify_DeepCopyOp(op, node, **kwargs):
     if isinstance(node.inputs[0].type, TensorType):
@@ -296,55 +345,6 @@ def numba_funcify_DeepCopyOp(op, node, **kwargs):
     return deepcopy
 
 
-@numba.extending.intrinsic
-def direct_cast(typingctx, val, typ):
-    if isinstance(typ, numba.types.TypeRef):
-        casted = typ.instance_type
-    elif isinstance(typ, numba.types.DTypeSpec):
-        casted = typ.dtype
-    else:
-        casted = typ
-
-    sig = casted(casted, typ)
-
-    def codegen(context, builder, signature, args):
-        val, _ = args
-        context.nrt.incref(builder, signature.return_type, val)
-        return val
-
-    return sig, codegen
-
-
-def int_to_float_fn(inputs, out_dtype):
-    """Create a Numba function that converts integer and boolean ``ndarray``s to floats."""
-
-    if (
-        all(inp.type.dtype == out_dtype for inp in inputs)
-        and np.dtype(out_dtype).kind == "f"
-    ):
-
-        @numba_njit(inline="always")
-        def inputs_cast(x):
-            return x
-
-    elif any(i.type.numpy_dtype.kind in "uib" for i in inputs):
-        args_dtype = np.dtype(f"f{out_dtype.itemsize}")
-
-        @numba_njit(inline="always")
-        def inputs_cast(x):
-            return x.astype(args_dtype)
-
-    else:
-        args_dtype_sz = max(_arg.type.numpy_dtype.itemsize for _arg in inputs)
-        args_dtype = np.dtype(f"f{args_dtype_sz}")
-
-        @numba_njit(inline="always")
-        def inputs_cast(x):
-            return x.astype(args_dtype)
-
-    return inputs_cast
-
-
 @numba_funcify.register(IfElse)
 def numba_funcify_IfElse(op, **kwargs):
     n_outs = op.n_outs