Checkpoint.

Fix cost model comment.

Finish evolutionary seaarch.

Remove  extra code.

Fix compile.

Add comments.

Add python part.

Ad test.

Update other files & comments.

include/tvm/meta_schedule/cost_model.h

@@ -61,10 +61,10 @@ class CostModelNode : public runtime::Object {
                       const Array<RunnerResult>& results) = 0;
 
   /*!
-   * \brief Predict the running results of given measure candidates.
+   * \brief Predict the normalized score (the larger the better) of given measure candidates.
    * \param tune_context The tuning context.
    * \param candidates The measure candidates.
-   * \return The predicted running results.
+   * \return The predicted normalized score.
    */
   virtual std::vector<double> Predict(const TuneContext& tune_context,
                                       const Array<MeasureCandidate>& candidates) = 0;

include/tvm/meta_schedule/search_strategy.h

@@ -20,6 +20,8 @@
 #define TVM_META_SCHEDULE_SEARCH_STRATEGY_H_
 
 #include <tvm/meta_schedule/arg_info.h>
+#include <tvm/meta_schedule/database.h>
+#include <tvm/meta_schedule/mutator.h>
 #include <tvm/meta_schedule/runner.h>
 #include <tvm/meta_schedule/space_generator.h>
 #include <tvm/tir/schedule/schedule.h>
@@ -29,6 +31,7 @@ namespace meta_schedule {
 
 // Forward declaration
 class TuneContext;
+class CostModel;
 
 /*! \brief The schedule (with input shapes) to be measured. */
 class MeasureCandidateNode : public runtime::Object {
@@ -255,6 +258,17 @@ class SearchStrategy : public runtime::ObjectRef {
    */
   TVM_DLL static SearchStrategy ReplayFunc(int num_trials_per_iter, int num_trials_total);
 
+  TVM_DLL static SearchStrategy EvolutionarySearch(int num_trials_per_iter,               //
+                                                   int num_trials_total,                  //
+                                                   int population,                        //
+                                                   double init_measured_ratio,            //
+                                                   int genetic_algo_iters,                //
+                                                   double p_mutate,                       //
+                                                   double eps_greedy,                     //
+                                                   Map<Mutator, FloatImm> mutator_probs,  //
+                                                   Database database,                     //
+                                                   CostModel cost_model);
+
   TVM_DEFINE_MUTABLE_OBJECT_REF_METHODS(SearchStrategy, ObjectRef, SearchStrategyNode);
 };
 

python/tvm/meta_schedule/cost_model/cost_model.py

@@ -97,7 +97,7 @@ def predict(self, tune_context: TuneContext, candidates: List[MeasureCandidate])
         Return
         ------
         result : bool
-            The predicted running results.
+            The predicted normalized score.
         """
         n = len(candidates)
         results = np.zeros(shape=(n,), dtype="float64")
@@ -127,7 +127,6 @@ def f_save(file_location: str) -> bool:
 
         @check_override(self.__class__, CostModel)
         def f_update(
-            self,
             tune_context: TuneContext,
             candidates: List[MeasureCandidate],
             results: List[RunnerResult],

python/tvm/meta_schedule/search_strategy/__init__.py

@@ -23,3 +23,4 @@
 from .search_strategy import SearchStrategy, PySearchStrategy, MeasureCandidate
 from .replay_trace import ReplayTrace
 from .replay_func import ReplayFunc
+from .evolutionary_search import EvolutionarySearch

python/tvm/meta_schedule/search_strategy/evolutionary_search.py

@@ -0,0 +1,101 @@
+# 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.
+"""Evolutionary Search Strategy"""
+
+from typing import TYPE_CHECKING, Dict
+
+from tvm._ffi import register_object
+from ...tir import FloatImm
+
+from .search_strategy import SearchStrategy
+from ..mutator import Mutator
+from ..database import Database
+
+from .. import _ffi_api
+
+if TYPE_CHECKING:
+    from ..cost_model import CostModel
+
+
+@register_object("meta_schedule.EvolutionarySearch")
+class EvolutionarySearch(SearchStrategy):
+    """
+    Replay Trace Search Strategy is a search strategy that always replays the trace by removing its
+    decisions so that the decisions would be randomly re-generated.
+
+    Parameters
+    ----------
+    num_trials_per_iter : int
+        Number of trials per iteration.
+    num_trials_total : int
+        Total number of trials.
+    population : int
+        The initial population of traces from measured samples and randomly generated samples.
+    init_measured_ratio : int
+        The ratio of measured samples in the initial population.
+    genetic_algo_iters : int
+        The number of iterations for genetic algorithm.
+    p_mutate : float
+        The probability of mutation.
+    eps_greedy : float
+        The ratio of greedy selected samples in the final picks.
+    mutator_probs: Dict[Mutator, FloatImm]
+        The probability contribution of all mutators.
+    database : Database
+        The database used in the search.
+    cost_model : CostModel
+        The cost model used in the search.
+    """
+
+    num_trials_per_iter: int
+    num_trials_total: int
+    population: int
+    init_measured_ratio: int
+    genetic_algo_iters: int
+    p_mutate: float
+    eps_greedy: float
+    mutator_probs: Dict[Mutator, FloatImm]
+    database: Database
+    cost_model: "CostModel"
+
+    def __init__(
+        self,
+        num_trials_per_iter: int,
+        num_trials_total: int,
+        population: int,
+        init_measured_ratio: float,
+        genetic_algo_iters: int,
+        p_mutate: float,
+        eps_greedy: float,
+        mutator_probs: Dict[Mutator, FloatImm],
+        database: Database,
+        cost_model: "CostModel",
+    ):
+        """Constructor"""
+        self.__init_handle_by_constructor__(
+            _ffi_api.SearchStrategyEvolutionarySearch,  # pylint: disable=no-member
+            num_trials_per_iter,
+            num_trials_total,
+            population,
+            init_measured_ratio,
+            genetic_algo_iters,
+            p_mutate,
+            eps_greedy,
+            mutator_probs,
+            database,
+            cost_model,
+        )

python/tvm/meta_schedule/search_strategy/search_strategy.py

@@ -22,7 +22,7 @@
 
 from tvm._ffi import register_object
 from tvm.runtime import Object
-from tvm.tir.schedule import Schedule, Trace
+from tvm.tir.schedule import Schedule
 
 from .. import _ffi_api
 from ..arg_info import ArgInfo

python/tvm/tir/schedule/schedule.py

@@ -365,7 +365,7 @@ def sample_compute_location(
         decision: Optional[int] = None,
     ) -> LoopRV:
         """Sample a compute-at location on a BlockRV so that its producer can compute at that loop
-        
+
         Parameters
         ----------
         block : BlockRV