Move C-specific content from aesara.graph.op to aesara.link.c.op

aesara/compile/debugmode.py

@@ -32,9 +32,10 @@ from aesara.configdefaults import config
 from aesara.graph.basic import Variable, io_toposort
 from aesara.graph.destroyhandler import DestroyHandler
 from aesara.graph.features import BadOptimization
-from aesara.graph.op import COp, HasInnerGraph, Op
+from aesara.graph.op import HasInnerGraph, Op
 from aesara.graph.utils import InconsistencyError, MethodNotDefined
 from aesara.link.basic import Container, LocalLinker
+from aesara.link.c.op import COp
 from aesara.link.utils import map_storage, raise_with_op
 from aesara.printing import _debugprint
 from aesara.utils import NoDuplicateOptWarningFilter, difference, get_unbound_function

aesara/compile/ops.py

@@ -11,8 +11,9 @@ import warnings
 from typing import Dict, Tuple
 
 from aesara.graph.basic import Apply
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
 from aesara.graph.type import CType
+from aesara.link.c.op import COp
 
 
 def register_view_op_c_code(type, code, version=()):

aesara/gpuarray/basic_ops.py

@@ -11,12 +11,13 @@ import aesara.tensor as at
 from aesara.configdefaults import config
 from aesara.gradient import grad_undefined
 from aesara.graph.basic import Apply, Variable
-from aesara.graph.op import COp, ExternalCOp, Op, _NoPythonOp
+from aesara.graph.op import Op, _NoPythonOp
 from aesara.graph.opt import copy_stack_trace
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import CType
 from aesara.graph.utils import MethodNotDefined
 from aesara.link.c.interface import HideC
+from aesara.link.c.op import COp, ExternalCOp
 from aesara.scalar import bool as bool_t
 from aesara.scalar import int32 as int32_t
 from aesara.tensor.basic import Alloc, AllocEmpty, Join, Split, infer_broadcastable

aesara/gpuarray/blas.py

@@ -10,9 +10,9 @@ from aesara.gpuarray.basic_ops import (
 )
 from aesara.gpuarray.opt_util import inplace_allocempty
 from aesara.graph.basic import Apply
-from aesara.graph.op import _NoPythonCOp
 from aesara.graph.opt import LocalOptGroup, in2out
 from aesara.graph.params_type import ParamsType
+from aesara.link.c.op import _NoPythonCOp
 from aesara.scalar import bool as bool_t
 from aesara.tensor.basic import as_tensor_variable
 

aesara/gpuarray/blocksparse.py

@@ -11,8 +11,8 @@ from aesara.gpuarray.basic_ops import (
 from aesara.gpuarray.type import gpu_context_type
 from aesara.gradient import grad_undefined
 from aesara.graph.basic import Apply
-from aesara.graph.op import _NoPythonExternalCOp
 from aesara.graph.params_type import ParamsType
+from aesara.link.c.op import _NoPythonExternalCOp
 from aesara.scalar import bool as bool_t
 from aesara.tensor import as_tensor_variable
 from aesara.tensor.type import discrete_dtypes

aesara/gpuarray/ctc.py

@@ -13,8 +13,8 @@ from aesara.gpuarray.elemwise import GpuDimShuffle
 from aesara.gpuarray.type import GpuArrayType, gpu_context_type
 from aesara.gradient import grad_undefined
 from aesara.graph.basic import Apply
-from aesara.graph.op import _NoPythonExternalCOp
 from aesara.graph.opt import local_optimizer
+from aesara.link.c.op import _NoPythonExternalCOp
 from aesara.tensor.basic import as_tensor_variable
 from aesara.tensor.basic_opt import register_canonicalize
 from aesara.tensor.blas import batched_dot

aesara/gpuarray/dnn.py

@@ -28,10 +28,10 @@ from aesara.gpuarray.basic_ops import (
 from aesara.gpuarray.type import GpuArraySharedVariable, get_context, gpu_context_type
 from aesara.gradient import DisconnectedType, grad_not_implemented
 from aesara.graph.basic import Apply, Variable
-from aesara.graph.op import ExternalCOp, _NoPythonCOp, _NoPythonExternalCOp
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import CDataType, EnumList, Generic
 from aesara.link.c.cmodule import GCC_compiler
+from aesara.link.c.op import ExternalCOp, _NoPythonCOp, _NoPythonExternalCOp
 from aesara.raise_op import Assert
 from aesara.scalar import as_scalar
 from aesara.scalar import bool as bool_t

aesara/gpuarray/linalg.py

@@ -14,8 +14,9 @@ from aesara.gpuarray.basic_ops import (
 )
 from aesara.gpuarray.type import GpuArrayType, gpu_context_type
 from aesara.graph.basic import Apply
-from aesara.graph.op import ExternalCOp, Op
+from aesara.graph.op import Op
 from aesara.graph.params_type import ParamsType
+from aesara.link.c.op import ExternalCOp
 from aesara.scalar import bool as bool_t
 from aesara.tensor import basic as at
 from aesara.tensor import math as tm

aesara/gpuarray/reduction.py

 from aesara.graph.basic import Apply
-from aesara.graph.op import COp
 from aesara.graph.type import Generic
+from aesara.link.c.op import COp
 
 from .basic_ops import as_gpuarray_variable, gpuarray_helper_inc_dir, infer_context_name
 from .type import GpuArrayType

aesara/gpuarray/subtensor.py

@@ -5,10 +5,11 @@ import numpy as np
 import aesara.tensor as at
 from aesara.gradient import grad_not_implemented
 from aesara.graph.basic import Apply
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import CType
 from aesara.link.c.interface import HideC
+from aesara.link.c.op import COp
 from aesara.scalar import bool as bool_t
 from aesara.scalar import int32 as int_t
 from aesara.scalar import uint32 as size_t

aesara/raise_op.py

@@ -7,9 +7,9 @@ import numpy as np
 
 from aesara.gradient import DisconnectedType
 from aesara.graph.basic import Apply, Variable
-from aesara.graph.op import COp
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import Generic
+from aesara.link.c.op import COp
 
 
 class ExceptionType(Generic):

aesara/sandbox/multinomial.py

@@ -7,7 +7,7 @@ import numpy as np
 import aesara.tensor as at
 from aesara.configdefaults import config
 from aesara.graph.basic import Apply
-from aesara.graph.op import COp
+from aesara.link.c.op import COp
 from aesara.scalar import Scalar, as_scalar
 from aesara.tensor.type import discrete_dtypes
 

aesara/sandbox/rng_mrg.py

@@ -25,9 +25,9 @@ from aesara.compile import optdb
 from aesara.configdefaults import config
 from aesara.gradient import undefined_grad
 from aesara.graph.basic import Apply, Constant, Variable
-from aesara.graph.op import COp, Op
 from aesara.graph.opt import in2out, local_optimizer
 from aesara.graph.params_type import ParamsType
+from aesara.link.c.op import COp, Op
 from aesara.sandbox import multinomial
 from aesara.scalar import bool as bool_t
 from aesara.scalar import int32 as int_t

aesara/scalar/basic.py

@@ -27,10 +27,10 @@ from aesara.configdefaults import config
 from aesara.gradient import DisconnectedType, grad_undefined
 from aesara.graph.basic import Apply, Constant, Variable, clone, list_of_nodes
 from aesara.graph.fg import FunctionGraph
-from aesara.graph.op import COp
 from aesara.graph.opt import MergeOptimizer
 from aesara.graph.type import CType
 from aesara.graph.utils import MetaObject, MethodNotDefined
+from aesara.link.c.op import COp
 from aesara.misc.safe_asarray import _asarray
 from aesara.printing import pprint
 from aesara.utils import (

aesara/sparse/basic.py

@@ -18,7 +18,8 @@ from aesara import scalar as aes
 from aesara.configdefaults import config
 from aesara.gradient import DisconnectedType, grad_not_implemented, grad_undefined
 from aesara.graph.basic import Apply, Constant, Variable
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
+from aesara.link.c.op import COp
 from aesara.misc.safe_asarray import _asarray
 from aesara.sparse.type import SparseType, _is_sparse
 from aesara.sparse.utils import hash_from_sparse

aesara/sparse/opt.py

@@ -5,8 +5,8 @@ import aesara
 import aesara.scalar as aes
 from aesara.configdefaults import config
 from aesara.graph.basic import Apply
-from aesara.graph.op import COp, _NoPythonCOp
 from aesara.graph.opt import PatternSub, TopoOptimizer, local_optimizer
+from aesara.link.c.op import COp, _NoPythonCOp
 from aesara.misc.safe_asarray import _asarray
 from aesara.sparse import basic as sparse
 from aesara.sparse.basic import (

aesara/tensor/basic.py

@@ -23,10 +23,11 @@ from aesara import scalar as aes
 from aesara.gradient import DisconnectedType, grad_not_implemented, grad_undefined
 from aesara.graph.basic import Apply, Constant, Variable
 from aesara.graph.fg import FunctionGraph
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
 from aesara.graph.opt_utils import optimize_graph
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import Type
+from aesara.link.c.op import COp
 from aesara.misc.safe_asarray import _asarray
 from aesara.printing import min_informative_str, pprint
 from aesara.raise_op import CheckAndRaise, assert_op

aesara/tensor/blas.py

@@ -147,7 +147,7 @@ from aesara.compile.mode import optdb
 from aesara.configdefaults import config
 from aesara.graph.basic import Apply, view_roots
 from aesara.graph.features import ReplacementDidNotRemoveError, ReplaceValidate
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
 from aesara.graph.opt import (
     EquilibriumOptimizer,
     GlobalOptimizer,
@@ -158,6 +158,7 @@ from aesara.graph.opt import (
 from aesara.graph.optdb import SequenceDB
 from aesara.graph.params_type import ParamsType
 from aesara.graph.utils import InconsistencyError, MethodNotDefined, TestValueError
+from aesara.link.c.op import COp
 from aesara.printing import FunctionPrinter, debugprint, pprint
 from aesara.scalar import bool as bool_t
 from aesara.tensor import basic as at

aesara/tensor/blas_c.py

 from aesara.configdefaults import config
-from aesara.graph.op import COp
 from aesara.graph.opt import in2out
 from aesara.graph.params_type import ParamsType
+from aesara.link.c.op import COp
 from aesara.scalar import bool as bool_t
 from aesara.tensor import basic as at
 from aesara.tensor.blas import (

aesara/tensor/elemwise.py

@@ -8,10 +8,10 @@ from aesara.configdefaults import config
 from aesara.gradient import DisconnectedType
 from aesara.graph.basic import Apply
 from aesara.graph.null_type import NullType
-from aesara.graph.op import COp, ExternalCOp, OpenMPOp
 from aesara.graph.params_type import ParamsType
 from aesara.graph.utils import MethodNotDefined
 from aesara.link.c.basic import failure_code
+from aesara.link.c.op import COp, ExternalCOp, OpenMPOp
 from aesara.misc.frozendict import frozendict
 from aesara.misc.safe_asarray import _asarray
 from aesara.printing import FunctionPrinter, Printer, pprint

aesara/tensor/extra_ops.py

@@ -11,9 +11,10 @@ from aesara.gradient import (
     grad_undefined,
 )
 from aesara.graph.basic import Apply, Variable, equal_computations
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import EnumList, Generic
+from aesara.link.c.op import COp
 from aesara.misc.safe_asarray import _asarray
 from aesara.raise_op import Assert
 from aesara.scalar import int32 as int_t

aesara/tensor/math.py

@@ -7,9 +7,10 @@ from aesara import config, printing
 from aesara import scalar as aes
 from aesara.gradient import DisconnectedType
 from aesara.graph.basic import Apply, Variable
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import Generic
+from aesara.link.c.op import COp
 from aesara.misc.safe_asarray import _asarray
 from aesara.printing import pprint
 from aesara.scalar.basic import BinaryScalarOp

aesara/tensor/nnet/basic.py

@@ -24,8 +24,9 @@ from aesara import scalar as aes
 from aesara.compile import optdb
 from aesara.gradient import DisconnectedType, grad_not_implemented
 from aesara.graph.basic import Apply
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
 from aesara.graph.opt import copy_stack_trace, local_optimizer, optimizer
+from aesara.link.c.op import COp
 from aesara.raise_op import Assert
 from aesara.scalar import UnaryScalarOp
 from aesara.tensor import basic as at

aesara/tensor/nnet/conv.py

@@ -24,7 +24,7 @@ except ImportError:
 
 import aesara
 from aesara.graph.basic import Apply
-from aesara.graph.op import OpenMPOp
+from aesara.link.c.op import OpenMPOp
 from aesara.tensor import blas
 from aesara.tensor.basic import (
     as_tensor_variable,

aesara/tensor/nnet/corr.py

@@ -5,9 +5,10 @@ from typing import Optional
 import aesara
 from aesara.configdefaults import config
 from aesara.graph.basic import Apply
-from aesara.graph.op import OpenMPOp, _NoPythonOp
+from aesara.graph.op import _NoPythonOp
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import EnumList
+from aesara.link.c.op import OpenMPOp
 from aesara.scalar import int8, int64
 from aesara.tensor import blas_headers
 from aesara.tensor.basic import as_tensor_variable

aesara/tensor/nnet/corr3d.py

@@ -5,9 +5,10 @@ from typing import Optional
 import aesara
 from aesara.configdefaults import config
 from aesara.graph.basic import Apply
-from aesara.graph.op import OpenMPOp, _NoPythonOp
+from aesara.graph.op import _NoPythonOp
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import EnumList
+from aesara.link.c.op import OpenMPOp
 from aesara.scalar import int64
 from aesara.tensor import blas_headers
 from aesara.tensor.basic import as_tensor_variable

aesara/tensor/nnet/ctc.py

@@ -5,9 +5,9 @@ import aesara.tensor as at
 from aesara.configdefaults import config
 from aesara.gradient import grad_undefined
 from aesara.graph.basic import Apply
-from aesara.graph.op import ExternalCOp, OpenMPOp
 from aesara.graph.opt import local_optimizer
 from aesara.link.c.cmodule import GCC_compiler
+from aesara.link.c.op import ExternalCOp, OpenMPOp
 from aesara.tensor.basic_opt import register_canonicalize
 from aesara.tensor.blas import batched_dot
 from aesara.tensor.extra_ops import cpu_contiguous

aesara/tensor/nnet/neighbours.py

@@ -7,8 +7,8 @@ import numpy as np
 import aesara
 from aesara.gradient import grad_not_implemented, grad_undefined
 from aesara.graph.basic import Apply
-from aesara.graph.op import COp
 from aesara.graph.type import EnumList
+from aesara.link.c.op import COp
 from aesara.tensor.basic import arange, as_tensor_variable, concatenate, stack, zeros
 from aesara.tensor.math import ceil_intdiv
 from aesara.tensor.subtensor import inc_subtensor, set_subtensor

aesara/tensor/shape.py

@@ -7,8 +7,8 @@ import numpy as np
 import aesara
 from aesara.gradient import DisconnectedType
 from aesara.graph.basic import Apply, Constant, Variable
-from aesara.graph.op import COp
 from aesara.graph.params_type import ParamsType
+from aesara.link.c.op import COp
 from aesara.misc.safe_asarray import _asarray
 from aesara.scalar import int32
 from aesara.tensor import _get_vector_length

aesara/tensor/signal/pool.py

@@ -12,10 +12,10 @@ import aesara.tensor.basic as at
 import aesara.tensor.math as tm
 from aesara.gradient import DisconnectedType
 from aesara.graph.basic import Apply, Constant, Variable
-from aesara.graph.op import OpenMPOp
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import EnumList
 from aesara.graph.utils import MethodNotDefined
+from aesara.link.c.op import OpenMPOp
 from aesara.scalar import bool as bool_t
 from aesara.tensor.type import TensorType, int_dtypes
 

aesara/tensor/subtensor.py

@@ -11,10 +11,11 @@ from aesara import scalar as aes
 from aesara.configdefaults import config
 from aesara.gradient import DisconnectedType
 from aesara.graph.basic import Apply, Constant, Variable
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
 from aesara.graph.params_type import ParamsType
 from aesara.graph.type import Type
 from aesara.graph.utils import MethodNotDefined
+from aesara.link.c.op import COp
 from aesara.misc.safe_asarray import _asarray
 from aesara.printing import Printer, pprint, set_precedence
 from aesara.scalar.basic import ScalarConstant

aesara/typed_list/basic.py

@@ -4,7 +4,8 @@ import aesara.tensor as at
 from aesara.compile.debugmode import _lessbroken_deepcopy
 from aesara.configdefaults import config
 from aesara.graph.basic import Apply, Constant, Variable
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
+from aesara.link.c.op import COp
 from aesara.tensor.type import scalar
 from aesara.tensor.type_other import SliceType
 from aesara.tensor.var import TensorVariable

doc/extending/creating_a_c_op.rst

@@ -475,7 +475,7 @@ storage with the right shape and number of dimensions.
     import numpy
     import aesara
 
-    from aesara.graph.op import COp
+    from aesara.link.c.op import COp
     from aesara.graph.basic import Apply
 
 
@@ -745,7 +745,7 @@ The new :class:`Op` is defined inside a Python file with the following code :
 .. testcode::
 
     import aesara
-    from aesara.graph.op import ExternalCOp
+    from aesara.link.c.op import ExternalCOp
 
     class VectorTimesVector(ExternalCOp):
         __props__ = ()

doc/extending/other_ops.rst

@@ -168,7 +168,7 @@ To allow consistent interface of Ops that support OpenMP, we have some
 helper code. Doing this also allows to enable/disable OpenMP globally
 or per op for fine-grained control.
 
-Your Op needs to inherit from ``aesara.graph.op.OpenMPOp``. If it overrides
+Your Op needs to inherit from ``aesara.link.c.op.OpenMPOp``. If it overrides
 the ``__init__()`` method, it must have an ``openmp=None`` parameter
 and must call ``super(MyOpClass, self).__init__(openmp=openmp)``.
 

doc/extending/using_params.rst

@@ -139,8 +139,8 @@ the params type.
 
 .. testcode::
 
-   from aesara.graph.op import COp
    from aesara.graph.type import Generic
+   from aesara.link.c.op import COp
    from aesara.scalar import as_scalar
 
    class MulOp(COp):

setup.cfg

@@ -151,6 +151,10 @@ check_untyped_defs = False
 ignore_errors = True
 check_untyped_defs = False
 
+[mypy-aesara.link.c.op]
+ignore_errors = True
+check_untyped_defs = False
+
 [mypy-aesara.link.utils]
 ignore_errors = True
 check_untyped_defs = False

tests/compile/test_debugmode.py

@@ -17,9 +17,10 @@ from aesara.compile.mode import predefined_modes
 from aesara.configdefaults import config
 from aesara.graph.basic import Apply, Variable
 from aesara.graph.features import BadOptimization
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
 from aesara.graph.opt import local_optimizer
 from aesara.graph.optdb import EquilibriumDB
+from aesara.link.c.op import COp
 from aesara.tensor.math import add, dot, log
 from aesara.tensor.type import TensorType, dvector, fmatrix, fvector, vector
 from tests import unittest_tools as utt

tests/graph/test_compute_test_value.py

@@ -9,8 +9,9 @@ from aesara import scalar as aes
 from aesara.configdefaults import config
 from aesara.graph import utils
 from aesara.graph.basic import Apply
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
 from aesara.graph.type import Type
+from aesara.link.c.op import COp
 from aesara.tensor.math import _allclose, dot
 from aesara.tensor.type import fmatrix, iscalar, matrix, vector
 

tests/graph/test_op.py

@@ -4,13 +4,12 @@ import pytest
 import aesara
 import aesara.graph.op as op
 import aesara.tensor as at
-from aesara import scalar as aes
 from aesara import shared
 from aesara.configdefaults import config
 from aesara.graph.basic import Apply, Variable
-from aesara.graph.op import COp, Op
+from aesara.graph.op import Op
 from aesara.graph.type import Generic, Type
-from aesara.graph.utils import MethodNotDefined, TestValueError
+from aesara.graph.utils import TestValueError
 from aesara.tensor.math import log
 from aesara.tensor.type import dmatrix, dscalar, dvector, vector
 
@@ -82,38 +81,6 @@ class NoInputOp(Op):
         output_storage[0][0] = "test Op no input"
 
 
-class StructOp(COp):
-    __props__ = ()
-
-    def do_constant_folding(self, fgraph, node):
-        # we are not constant
-        return False
-
-    # The input only serves to distinguish thunks
-    def make_node(self, i):
-        return Apply(self, [i], [aes.uint64()])
-
-    def c_support_code_struct(self, node, name):
-        return f"npy_uint64 counter{name};"
-
-    def c_init_code_struct(self, node, name, sub):
-        return f"counter{name} = 0;"
-
-    def c_code(self, node, name, input_names, outputs_names, sub):
-        return """
-%(out)s = counter%(name)s;
-counter%(name)s++;
-""" % dict(
-            out=outputs_names[0], name=name
-        )
-
-    def c_code_cache_version(self):
-        return (1,)
-
-    def perform(self, *args, **kwargs):
-        raise NotImplementedError("No Python implementation available.")
-
-
 class TestOp:
 
     # Sanity tests
@@ -141,106 +108,8 @@ class TestOp:
         rval = f()
         assert rval == "test Op no input"
 
-    @pytest.mark.skipif(
-        not config.cxx, reason="G++ not available, so we need to skip this test."
-    )
-    def test_op_struct(self):
-        sop = StructOp()
-        c = sop(aesara.tensor.constant(0))
-        mode = None
-        if config.mode == "FAST_COMPILE":
-            mode = "FAST_RUN"
-        f = aesara.function([], c, mode=mode)
-        rval = f()
-        assert rval == 0
-        rval = f()
-        assert rval == 1
-
-        c2 = sop(aesara.tensor.constant(1))
-        f2 = aesara.function([], [c, c2], mode=mode)
-        rval = f2()
-        assert rval == [0, 0]
-
 
 class TestMakeThunk:
-    def test_no_c_code(self):
-        class IncOnePython(COp):
-            """An Op with only a Python (perform) implementation"""
-
-            __props__ = ()
-
-            def make_node(self, input):
-                input = aes.as_scalar(input)
-                output = input.type()
-                return Apply(self, [input], [output])
-
-            def perform(self, node, inputs, outputs):
-                (input,) = inputs
-                (output,) = outputs
-                output[0] = input + 1
-
-        i = aes.int32("i")
-        o = IncOnePython()(i)
-
-        # Check that the c_code function is not implemented
-        with pytest.raises(NotImplementedError):
-            o.owner.op.c_code(o.owner, "o", ["x"], "z", {"fail": ""})
-
-        storage_map = {i: [np.int32(3)], o: [None]}
-        compute_map = {i: [True], o: [False]}
-
-        thunk = o.owner.op.make_thunk(
-            o.owner, storage_map, compute_map, no_recycling=[]
-        )
-
-        required = thunk()
-        # Check everything went OK
-        assert not required  # We provided all inputs
-        assert compute_map[o][0]
-        assert storage_map[o][0] == 4
-
-    def test_no_perform(self):
-        class IncOneC(COp):
-            """An Op with only a C (c_code) implementation"""
-
-            __props__ = ()
-
-            def make_node(self, input):
-                input = aes.as_scalar(input)
-                output = input.type()
-                return Apply(self, [input], [output])
-
-            def c_code(self, node, name, inputs, outputs, sub):
-                (x,) = inputs
-                (z,) = outputs
-                return f"{z} = {x} + 1;"
-
-            def perform(self, *args, **kwargs):
-                raise NotImplementedError("No Python implementation available.")
-
-        i = aes.int32("i")
-        o = IncOneC()(i)
-
-        # Check that the perform function is not implemented
-        with pytest.raises((NotImplementedError, MethodNotDefined)):
-            o.owner.op.perform(o.owner, 0, [None])
-
-        storage_map = {i: [np.int32(3)], o: [None]}
-        compute_map = {i: [True], o: [False]}
-
-        thunk = o.owner.op.make_thunk(
-            o.owner, storage_map, compute_map, no_recycling=[]
-        )
-        if config.cxx:
-            required = thunk()
-            # Check everything went OK
-            assert not required  # We provided all inputs
-            assert compute_map[o][0]
-            assert storage_map[o][0] == 4
-        else:
-            with pytest.raises((NotImplementedError, MethodNotDefined)):
-                thunk()
-
     def test_no_make_node(self):
         class DoubleOp(Op):
             """An Op without make_node"""

tests/graph/test_params_type.py

@@ -4,9 +4,9 @@ import pytest
 import aesara
 from aesara import tensor as at
 from aesara.graph.basic import Apply
-from aesara.graph.op import COp, ExternalCOp
 from aesara.graph.params_type import Params, ParamsType
 from aesara.graph.type import EnumList, Generic
+from aesara.link.c.op import COp, ExternalCOp
 from aesara.scalar import Scalar
 from aesara.tensor.type import TensorType, matrix
 from tests import unittest_tools as utt

tests/graph/test_types.py

@@ -6,8 +6,8 @@ import pytest
 import aesara
 from aesara import scalar as aes
 from aesara.graph.basic import Apply, Variable
-from aesara.graph.op import COp
 from aesara.graph.type import CDataType, CEnumType, EnumList, EnumType, Type
+from aesara.link.c.op import COp
 from aesara.tensor.type import TensorType, continuous_dtypes
 
 

tests/link/c/test_basic.py

@@ -7,10 +7,10 @@ from aesara.compile.mode import Mode
 from aesara.configdefaults import config
 from aesara.graph.basic import Apply, Constant, Variable
 from aesara.graph.fg import FunctionGraph
-from aesara.graph.op import COp
 from aesara.graph.type import CType
 from aesara.link.basic import PerformLinker
 from aesara.link.c.basic import CLinker, DualLinker, OpWiseCLinker
+from aesara.link.c.op import COp
 from aesara.tensor.type import iscalar, matrix, vector
 from tests.link.test_link import make_function
 

tests/link/c/test_op.py

+import numpy as np
+import pytest
+
+import aesara
+from aesara import scalar as aes
+from aesara.configdefaults import config
+from aesara.graph.basic import Apply
+from aesara.graph.utils import MethodNotDefined
+from aesara.link.c.op import COp
+
+
+class StructOp(COp):
+    __props__ = ()
+
+    def do_constant_folding(self, fgraph, node):
+        # we are not constant
+        return False
+
+    # The input only serves to distinguish thunks
+    def make_node(self, i):
+        return Apply(self, [i], [aes.uint64()])
+
+    def c_support_code_struct(self, node, name):
+        return f"npy_uint64 counter{name};"
+
+    def c_init_code_struct(self, node, name, sub):
+        return f"counter{name} = 0;"
+
+    def c_code(self, node, name, input_names, outputs_names, sub):
+        return """
+%(out)s = counter%(name)s;
+counter%(name)s++;
+""" % dict(
+            out=outputs_names[0], name=name
+        )
+
+    def c_code_cache_version(self):
+        return (1,)
+
+    def perform(self, *args, **kwargs):
+        raise NotImplementedError("No Python implementation available.")
+
+
+class TestCOp:
+    @pytest.mark.skipif(
+        not config.cxx, reason="G++ not available, so we need to skip this test."
+    )
+    def test_op_struct(self):
+        sop = StructOp()
+        c = sop(aesara.tensor.constant(0))
+        mode = None
+        if config.mode == "FAST_COMPILE":
+            mode = "FAST_RUN"
+        f = aesara.function([], c, mode=mode)
+        rval = f()
+        assert rval == 0
+        rval = f()
+        assert rval == 1
+
+        c2 = sop(aesara.tensor.constant(1))
+        f2 = aesara.function([], [c, c2], mode=mode)
+        rval = f2()
+        assert rval == [0, 0]
+
+
+class TestMakeThunk:
+    def test_no_c_code(self):
+        class IncOnePython(COp):
+            """An Op with only a Python (perform) implementation"""
+
+            __props__ = ()
+
+            def make_node(self, input):
+                input = aes.as_scalar(input)
+                output = input.type()
+                return Apply(self, [input], [output])
+
+            def perform(self, node, inputs, outputs):
+                (input,) = inputs
+                (output,) = outputs
+                output[0] = input + 1
+
+        i = aes.int32("i")
+        o = IncOnePython()(i)
+
+        # Check that the c_code function is not implemented
+        with pytest.raises(NotImplementedError):
+            o.owner.op.c_code(o.owner, "o", ["x"], "z", {"fail": ""})
+
+        storage_map = {i: [np.int32(3)], o: [None]}
+        compute_map = {i: [True], o: [False]}
+
+        thunk = o.owner.op.make_thunk(
+            o.owner, storage_map, compute_map, no_recycling=[]
+        )
+
+        required = thunk()
+        # Check everything went OK
+        assert not required  # We provided all inputs
+        assert compute_map[o][0]
+        assert storage_map[o][0] == 4
+
+    def test_no_perform(self):
+        class IncOneC(COp):
+            """An Op with only a C (c_code) implementation"""
+
+            __props__ = ()
+
+            def make_node(self, input):
+                input = aes.as_scalar(input)
+                output = input.type()
+                return Apply(self, [input], [output])
+
+            def c_code(self, node, name, inputs, outputs, sub):
+                (x,) = inputs
+                (z,) = outputs
+                return f"{z} = {x} + 1;"
+
+            def perform(self, *args, **kwargs):
+                raise NotImplementedError("No Python implementation available.")
+
+        i = aes.int32("i")
+        o = IncOneC()(i)
+
+        # Check that the perform function is not implemented
+        with pytest.raises((NotImplementedError, MethodNotDefined)):
+            o.owner.op.perform(o.owner, 0, [None])
+
+        storage_map = {i: [np.int32(3)], o: [None]}
+        compute_map = {i: [True], o: [False]}
+
+        thunk = o.owner.op.make_thunk(
+            o.owner, storage_map, compute_map, no_recycling=[]
+        )
+        if config.cxx:
+            required = thunk()
+            # Check everything went OK
+            assert not required  # We provided all inputs
+            assert compute_map[o][0]
+            assert storage_map[o][0] == 4
+        else:
+            with pytest.raises((NotImplementedError, MethodNotDefined)):
+                thunk()