[Meta Schedule] Add XGBoost Model & Random Model

include/tvm/meta_schedule/cost_model.h

@@ -39,17 +39,15 @@ class CostModelNode : public runtime::Object {
 
   /*!
    * \brief Load the cost model from given file location.
-   * \param file_location The file location.
-   * \return Whether cost model was loaded successfully.
+   * \param path The file path.
    */
-  virtual bool Load(const String& file_location) = 0;
+  virtual void Load(const String& path) = 0;
 
   /*!
    * \brief Save the cost model to given file location.
-   * \param file_location The file location.
-   * \return Whether cost model was saved successfully.
+   * \param path The file path.
    */
-  virtual bool Save(const String& file_location) = 0;
+  virtual void Save(const String& path) = 0;
 
   /*!
    * \brief Update the cost model given running results.
@@ -78,16 +76,14 @@ class PyCostModelNode : public CostModelNode {
  public:
   /*!
    * \brief Load the cost model from given file location.
-   * \param file_location The file location.
-   * \return Whether cost model was loaded successfully.
+   * \param path The file path.
    */
-  using FLoad = runtime::TypedPackedFunc<bool(String)>;
+  using FLoad = runtime::TypedPackedFunc<void(String)>;
   /*!
    * \brief Save the cost model to given file location.
-   * \param file_location The file location.
-   * \return Whether cost model was saved successfully.
+   * \param path The file path.
    */
-  using FSave = runtime::TypedPackedFunc<bool(String)>;
+  using FSave = runtime::TypedPackedFunc<void(String)>;
   /*!
    * \brief Update the cost model given running results.
    * \param tune_context The tuning context.
@@ -130,16 +126,15 @@ class PyCostModelNode : public CostModelNode {
     // `f_as_string` is not visited
   }
 
-  bool Load(const String& file_location) {
+  void Load(const String& path) {
     ICHECK(f_load != nullptr) << "PyCostModel's Load method not implemented!";
-    return f_load(file_location);
+    f_load(path);
   }
 
-  bool Save(const String& file_location) {
+  void Save(const String& path) {
     ICHECK(f_save != nullptr) << "PyCostModel's Save method not implemented!";
-    return f_save(file_location);
+    f_save(path);
   }
-
   void Update(const TuneContext& tune_context, const Array<MeasureCandidate>& candidates,
               const Array<RunnerResult>& results) {
     ICHECK(f_update != nullptr) << "PyCostModel's Update method not implemented!";

python/tvm/meta_schedule/cost_model/__init__.py

@@ -18,3 +18,5 @@
 The tvm.meta_schedule.cost_model package.
 """
 from .cost_model import CostModel, PyCostModel
+from .random_model import RandomModel
+from .xgb_model import XGBModel

python/tvm/meta_schedule/cost_model/cost_model.py

@@ -35,35 +35,25 @@
 class CostModel(Object):
     """Cost model."""
 
-    def load(self, file_location: str) -> bool:
+    def load(self, path: str) -> None:
         """Load the cost model from given file location.
 
         Parameters
         ----------
-        file_location : str
-            The file location.
-
-        Return
-        ------
-        result : bool
-            Whether cost model was loaded successfully.
+        path : str
+            The file path.
         """
-        return bool(_ffi_api.CostModelLoad(self, file_location))  # type: ignore # pylint: disable=no-member
+        _ffi_api.CostModelLoad(self, path)  # type: ignore # pylint: disable=no-member
 
-    def save(self, file_location: str) -> bool:
+    def save(self, path: str) -> None:
         """Save the cost model to given file location.
 
         Parameters
         ----------
-        file_location : str
-            The file location.
-
-        Return
-        ------
-        result : bool
-            Whether cost model was saved successfully.
+        path : str
+            The file path.
         """
-        return bool(_ffi_api.CostModelSave(self, file_location))  # type: ignore # pylint: disable=no-member
+        _ffi_api.CostModelSave(self, path)  # type: ignore # pylint: disable=no-member
 
     def update(
         self,
@@ -96,7 +86,7 @@ def predict(self, tune_context: TuneContext, candidates: List[MeasureCandidate])
 
         Return
         ------
-        result : bool
+        result : np.ndarray
             The predicted running results.
         """
         n = len(candidates)
@@ -118,20 +108,20 @@ def __init__(self):
         """Constructor."""
 
         @check_override(self.__class__, CostModel)
-        def f_load(file_location: str) -> bool:
-            return self.load(file_location)
+        def f_load(path: str) -> None:
+            self.load(path)
 
         @check_override(self.__class__, CostModel)
-        def f_save(file_location: str) -> bool:
-            return self.save(file_location)
+        def f_save(path: str) -> None:
+            self.save(path)
 
         @check_override(self.__class__, CostModel)
         def f_update(
             self,
             tune_context: TuneContext,
             candidates: List[MeasureCandidate],
             results: List[RunnerResult],
-        ) -> bool:
+        ) -> None:
             self.update(tune_context, candidates, results)
 
         @check_override(self.__class__, CostModel)

python/tvm/meta_schedule/cost_model/metric.py

@@ -0,0 +1,39 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Cost model metrics for meta schedule"""
+import numpy as np
+
+
+def max_curve(trial_scores: np.ndarray):
+    """f(n) = max([s[i] fo i < n])
+
+    Parameters
+    ----------
+    trial_scores: Array of float
+        the score of i th trial
+
+    Returns
+    -------
+    curve: Array of float
+        function values
+    """
+    ret = np.empty(len(trial_scores))
+    keep = -1e9
+    for i, score in enumerate(trial_scores):
+        keep = max(keep, score)
+        ret[i] = keep
+    return ret

python/tvm/meta_schedule/cost_model/random_model.py

@@ -0,0 +1,121 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Random cost model
+"""
+from typing import List, Union, Tuple, Optional
+
+import numpy as np
+
+from ..runner import RunnerResult
+from ..tune_context import TuneContext
+from ..search_strategy import MeasureCandidate
+from ..cost_model import PyCostModel
+
+
+class RandomModel(PyCostModel):
+    """Random cost model
+
+    Parameters
+    ----------
+    random_state : Union[Tuple[str, np.ndarray, int, int, float], dict]
+        The random state of the random number generator.
+    path : Optional[str]
+        The path of the random cost model.
+    max_range : Optional[int]
+        The maximum range of random results, [0, max_range].
+
+    Reference
+    ---------
+    https://numpy.org/doc/stable/reference/random/generated/numpy.random.get_state.html
+    """
+
+    random_state: Union[Tuple[str, np.ndarray, int, int, float], dict]
+    path: Optional[str]
+
+    def __init__(
+        self,
+        *,
+        seed: Optional[int] = None,
+        path: Optional[str] = None,
+        max_range: Optional[int] = 100,
+    ):
+        super().__init__()
+        if path is not None:
+            self.load(path)
+        else:
+            np.random.seed(seed)
+            self.random_state = np.random.get_state()
+        self.max_range = max_range
+
+    def load(self, path: str) -> None:
+        """Load the cost model from given file location.
+
+        Parameters
+        ----------
+        path : str
+            The file path.
+        """
+        self.random_state = tuple(np.load(path, allow_pickle=True))
+
+    def save(self, path: str) -> None:
+        """Save the cost model to given file location.
+
+        Parameters
+        ----------
+        path : str
+            The file path.
+        """
+        np.save(path, np.array(self.random_state, dtype=object), allow_pickle=True)
+
+    def update(
+        self,
+        tune_context: TuneContext,
+        candidates: List[MeasureCandidate],
+        results: List[RunnerResult],
+    ) -> None:
+        """Update the cost model given running results.
+
+        Parameters
+        ----------
+        tune_context : TuneContext,
+            The tuning context.
+        candidates : List[MeasureCandidate]
+            The measure candidates.
+        results : List[RunnerResult]
+            The running results of the measure candidates.
+        """
+
+    def predict(self, tune_context: TuneContext, candidates: List[MeasureCandidate]) -> np.ndarray:
+        """Update the cost model given running results.
+
+        Parameters
+        ----------
+        tune_context : TuneContext,
+            The tuning context.
+        candidates : List[MeasureCandidate]
+            The measure candidates.
+
+        Return
+        ------
+        result : np.ndarray
+            The predicted running results.
+        """
+        np.random.set_state(self.random_state)
+        result = np.random.rand(len(candidates)) * self.max_range
+        self.random_state = np.random.get_state()
+        return result