[MetaSchedule] Fix Cyclic Dependency in PyClass Family

include/tvm/meta_schedule/runner.h

@@ -125,12 +125,18 @@ class RunnerFutureNode : public runtime::Object {
    * \brief Check whether the runner has finished.
    * \return A boolean indicating whether the runner has finished.
    */
-  bool Done() const { return f_done(); }
+  bool Done() const {
+    ICHECK(f_done != nullptr) << "PyRunnerFuture's Done method not implemented!";
+    return f_done();
+  }
   /*!
    * \brief Fetch the runner's output if it is ready.
    * \return The runner's output.
    */
-  RunnerResult Result() const { return f_result(); }
+  RunnerResult Result() const {
+    ICHECK(f_result != nullptr) << "PyRunnerFuture's Result method not implemented!";
+    return f_result();
+  }
 
   static constexpr const char* _type_key = "meta_schedule.RunnerFuture";
   TVM_DECLARE_FINAL_OBJECT_INFO(RunnerFutureNode, runtime::Object);

include/tvm/meta_schedule/space_generator.h

@@ -114,7 +114,7 @@ class PySpaceGeneratorNode : public SpaceGeneratorNode {
 
   void InitializeWithTuneContext(const TuneContext& context) final {
     ICHECK(f_initialize_with_tune_context != nullptr)
-        << "PySpaceGenerator's InitializeWithTuneContext !";
+        << "PySpaceGenerator's InitializeWithTuneContext method not implemented!";
     f_initialize_with_tune_context(context);
   }
 

python/tvm/meta_schedule/builder/builder.py

@@ -15,15 +15,14 @@
 # specific language governing permissions and limitations
 # under the License.
 """Meta Schedule builders that translate IRModule to runtime.Module, and then export"""
-from typing import Dict, List, Optional
+from typing import Callable, Dict, List, Optional
 
 from tvm._ffi import register_object
 from tvm.ir import IRModule
 from tvm.runtime import NDArray, Object
 from tvm.target import Target
 
 from .. import _ffi_api
-from ..utils import check_override
 
 
 @register_object("meta_schedule.BuilderInput")
@@ -125,17 +124,43 @@ def build(self, build_inputs: List[BuilderInput]) -> List[BuilderResult]:
 
 
 @register_object("meta_schedule.PyBuilder")
-class PyBuilder(Builder):
-    """An abstract builder with customized build method on the python-side."""
+class _PyBuilder(Builder):
+    """
+    A TVM object builder to support customization on the python side.
+    This is NOT the user facing class for function overloading inheritance.
 
-    def __init__(self):
-        """Constructor."""
+    See also: PyBuilder
+    """
 
-        @check_override(self.__class__, Builder)
-        def f_build(build_inputs: List[BuilderInput]) -> List[BuilderResult]:
-            return self.build(build_inputs)
+    def __init__(self, methods: List[Callable]):
+        """Constructor."""
 
         self.__init_handle_by_constructor__(
             _ffi_api.BuilderPyBuilder,  # type: ignore # pylint: disable=no-member
-            f_build,
+            *methods,
         )
+
+
+class PyBuilder:
+    """
+    An abstract builder with customized build method on the python-side.
+    This is the user facing class for function overloading inheritance.
+
+    Note: @derived_object is required for proper usage of any inherited class.
+    """
+
+    _tvm_metadata = {"cls": _PyBuilder, "methods": ["build"]}
+
+    def build(self, build_inputs: List[BuilderInput]) -> List[BuilderResult]:
+        """Build the given inputs.
+
+        Parameters
+        ----------
+        build_inputs : List[BuilderInput]
+            The inputs to be built.
+        Returns
+        -------
+        build_results : List[BuilderResult]
+            The results of building the given inputs.
+        """
+        raise NotImplementedError

python/tvm/meta_schedule/builder/local_builder.py

@@ -26,13 +26,19 @@
 from tvm.target import Target
 
 from ...contrib.popen_pool import MapResult, PopenPoolExecutor, StatusKind
-from ..utils import cpu_count, get_global_func_with_default_on_worker
+from ..utils import cpu_count, derived_object, get_global_func_with_default_on_worker
 from .builder import BuilderInput, BuilderResult, PyBuilder
 
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 
 
+T_BUILD = Callable[  # pylint: disable=invalid-name
+    [IRModule, Target, Optional[Dict[str, NDArray]]], Module
+]
+T_EXPORT = Callable[[Module], str]  # pylint: disable=invalid-name
+
+
 def _serialize_params(params: Optional[Dict[str, NDArray]]) -> Optional[bytearray]:
     if params is None:
         return None
@@ -45,6 +51,7 @@ def _deserialize_params(params: Optional[bytearray]) -> Optional[Dict[str, NDArr
     return load_param_dict(params)
 
 
+@derived_object
 class LocalBuilder(PyBuilder):
     """A builder that builds the given input on local host.
 
@@ -54,10 +61,10 @@ class LocalBuilder(PyBuilder):
         The process pool to run the build.
     timeout_sec : float
         The timeout in seconds for the build.
-    f_build : Union[None, str, LocalBuilder.T_BUILD]
+    f_build : Union[None, str, T_BUILD]
         Name of the build function to be used.
         Defaults to `meta_schedule.builder.default_build`.
-    f_export : Union[None, str, LocalBuilder.T_EXPORT]
+    f_export : Union[None, str, T_EXPORT]
         Name of the export function to be used.
         Defaults to `meta_schedule.builder.default_export`.
 
@@ -91,9 +98,6 @@ def default_export(mod: Module) -> str:
     please send the registration logic via initializer.
     """
 
-    T_BUILD = Callable[[IRModule, Target, Optional[Dict[str, NDArray]]], Module]
-    T_EXPORT = Callable[[Module], str]
-
     pool: PopenPoolExecutor
     timeout_sec: float
     f_build: Union[None, str, T_BUILD]
@@ -117,10 +121,10 @@ def __init__(
             Defaults to number of CPUs.
         timeout_sec : float
             The timeout in seconds for the build.
-        f_build : LocalBuilder.T_BUILD
+        f_build : T_BUILD
             Name of the build function to be used.
             Defaults to `meta_schedule.builder.default_build`.
-        f_export : LocalBuilder.T_EXPORT
+        f_export : T_EXPORT
             Name of the export function to be used.
             Defaults to `meta_schedule.builder.default_export`.
         initializer : Optional[Callable[[], None]]
@@ -148,7 +152,7 @@ def build(self, build_inputs: List[BuilderInput]) -> List[BuilderResult]:
 
         # Dispatch the build inputs to the worker processes.
         for map_result in self.pool.map_with_error_catching(
-            lambda x: LocalBuilder._worker_func(*x),
+            lambda x: _worker_func(*x),
             [
                 (
                     self.f_build,
@@ -188,28 +192,28 @@ def _check(f_build, f_export) -> None:
         value = self.pool.submit(_check, self.f_build, self.f_export)
         value.result()
 
-    @staticmethod
-    def _worker_func(
-        _f_build: Union[None, str, T_BUILD],
-        _f_export: Union[None, str, T_EXPORT],
-        mod: IRModule,
-        target: Target,
-        params: Optional[bytearray],
-    ) -> str:
-        # Step 0. Get the registered functions
-        f_build: LocalBuilder.T_BUILD = get_global_func_with_default_on_worker(
-            _f_build,
-            default_build,
-        )
-        f_export: LocalBuilder.T_EXPORT = get_global_func_with_default_on_worker(
-            _f_export,
-            default_export,
-        )
-        # Step 1. Build the IRModule
-        rt_mod: Module = f_build(mod, target, _deserialize_params(params))
-        # Step 2. Export the Module
-        artifact_path: str = f_export(rt_mod)
-        return artifact_path
+
+def _worker_func(
+    _f_build: Union[None, str, T_BUILD],
+    _f_export: Union[None, str, T_EXPORT],
+    mod: IRModule,
+    target: Target,
+    params: Optional[bytearray],
+) -> str:
+    # Step 0. Get the registered functions
+    f_build: T_BUILD = get_global_func_with_default_on_worker(
+        _f_build,
+        default_build,
+    )
+    f_export: T_EXPORT = get_global_func_with_default_on_worker(
+        _f_export,
+        default_export,
+    )
+    # Step 1. Build the IRModule
+    rt_mod: Module = f_build(mod, target, _deserialize_params(params))
+    # Step 2. Export the Module
+    artifact_path: str = f_export(rt_mod)
+    return artifact_path
 
 
 @register_func("meta_schedule.builder.default_build")

python/tvm/meta_schedule/cost_model/cost_model.py

@@ -16,17 +16,17 @@
 # under the License.
 """Meta Schedule CostModel."""
 import ctypes
-from typing import List
+from typing import Callable, List
 
 import numpy as np  # type: ignore
 from tvm._ffi import register_object
+from tvm.meta_schedule.utils import _get_default_str
 from tvm.runtime import Object
 
 from .. import _ffi_api
 from ..runner import RunnerResult
 from ..search_strategy import MeasureCandidate
 from ..tune_context import TuneContext
-from ..utils import _get_hex_address, check_override
 
 
 @register_object("meta_schedule.CostModel")
@@ -99,41 +99,28 @@ def predict(self, context: TuneContext, candidates: List[MeasureCandidate]) -> n
 
 
 @register_object("meta_schedule.PyCostModel")
-class PyCostModel(CostModel):
-    """An abstract CostModel with customized methods on the python-side."""
+class _PyCostModel(CostModel):
+    """
+    A TVM object cost model to support customization on the python side.
+    This is NOT the user facing class for function overloading inheritance.
 
-    def __init__(self):
-        """Constructor."""
-
-        @check_override(self.__class__, CostModel)
-        def f_load(path: str) -> None:
-            self.load(path)
-
-        @check_override(self.__class__, CostModel)
-        def f_save(path: str) -> None:
-            self.save(path)
+    See also: PyCostModel
+    """
 
-        @check_override(self.__class__, CostModel)
-        def f_update(
-            context: TuneContext,
-            candidates: List[MeasureCandidate],
-            results: List[RunnerResult],
-        ) -> None:
-            self.update(context, candidates, results)
+    def __init__(self, methods: List[Callable]):
+        """Constructor."""
 
-        @check_override(self.__class__, CostModel)
         def f_predict(context: TuneContext, candidates: List[MeasureCandidate], return_ptr) -> None:
             n = len(candidates)
             return_ptr = ctypes.cast(return_ptr, ctypes.POINTER(ctypes.c_double))
             array_wrapper = np.ctypeslib.as_array(return_ptr, shape=(n,))
-            array_wrapper[:] = self.predict(context, candidates)
+            res = predict_func(context, candidates)
+            array_wrapper[:] = res
             assert (
                 array_wrapper.dtype == "float64"
             ), "ValueError: Invalid data type returned from CostModel Predict!"
 
-        def f_as_string() -> str:
-            return str(self)
-
+        f_load, f_save, f_update, predict_func, f_as_string = methods
         self.__init_handle_by_constructor__(
             _ffi_api.CostModelPyCostModel,  # type: ignore # pylint: disable=no-member
             f_load,
@@ -143,5 +130,82 @@ def f_as_string() -> str:
             f_as_string,
         )
 
+
+class PyCostModel:
+    """
+    An abstract cost model with customized methods on the python-side.
+    This is the user facing class for function overloading inheritance.
+
+    Note: @derived_object is required for proper usage of any inherited class.
+    """
+
+    _tvm_metadata = {
+        "cls": _PyCostModel,
+        "methods": ["load", "save", "update", "predict", "__str__"],
+    }
+
+    def load(self, path: str) -> None:
+        """Load the cost model from given file location.
+
+        Parameters
+        ----------
+        path : str
+            The file path.
+        """
+        raise NotImplementedError
+
+    def save(self, path: str) -> None:
+        """Save the cost model to given file location.
+
+        Parameters
+        ----------
+        path : str
+            The file path.
+        """
+        raise NotImplementedError
+
+    def update(
+        self,
+        context: TuneContext,
+        candidates: List[MeasureCandidate],
+        results: List[RunnerResult],
+    ) -> None:
+        """Update the cost model given running results.
+
+        Parameters
+        ----------
+        context : TuneContext,
+            The tuning context.
+        candidates : List[MeasureCandidate]
+            The measure candidates.
+        results : List[RunnerResult]
+            The running results of the measure candidates.
+        """
+        raise NotImplementedError
+
+    def predict(self, context: TuneContext, candidates: List[MeasureCandidate]) -> np.ndarray:
+        """Update the cost model given running results.
+
+        Parameters
+        ----------
+        context : TuneContext,
+            The tuning context.
+        candidates : List[MeasureCandidate]
+            The measure candidates.
+
+        Return
+        ------
+        result : np.ndarray
+            The predicted normalized score.
+        """
+        raise NotImplementedError
+
     def __str__(self) -> str:
-        return f"{self.__class__.__name__}({_get_hex_address(self.handle)})"
+        """Get the cost model as string with name.
+
+        Return
+        ------
+        result : str
+            Get the cost model as string with name.
+        """
+        return _get_default_str(self)