Add evolutionary search.

Co-authored-by: Junru Shao <junrushao1994@gmail.com>
Co-authored-by: Bohan Hou <32121147+spectrometerHBH@users.noreply.github.com>
Co-authored-by: Ruihang Lai <lairuihangdongdong@qq.com>
Co-authored-by: Hongyi Jin <3231950289@qq.com>
Co-authored-by: Wuwei Lin <wuwei@apache.org>
Co-authored-by: Siyuan Feng <Hzfengsy@sjtu.edu.cn>

Author: 7 people

python/tvm/meta_schedule/search_strategy/__init__.py

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

python/tvm/meta_schedule/search_strategy/evolutionary_search.py

@@ -0,0 +1,117 @@
+# 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 NamedTuple
+
+from tvm._ffi import register_object
+
+from .. import _ffi_api
+from .search_strategy import SearchStrategy
+
+
+@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_size : 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.
+    init_max_fail_count : int
+        The maximum number to fail trace replaying.
+    genetic_num_iters : int
+        The number of iterations for genetic algorithm.
+    genetic_mutate_prob : float
+        The probability of mutation.
+    genetic_max_fail_count : int
+        The maximum number to retry mutation.
+    eps_greedy : float
+        The ratio of greedy selected samples in the final picks.
+    """
+
+    num_trials_per_iter: int
+    num_trials_total: int
+    population_size: int
+    init_measured_ratio: int
+    init_max_fail_count: int
+    genetic_num_iters: int
+    genetic_mutate_prob: float
+    genetic_max_fail_count: int
+    eps_greedy: float
+
+    def __init__(
+        self,
+        *,
+        num_trials_per_iter: int,
+        num_trials_total: int,
+        population_size: int,
+        init_measured_ratio: float,
+        init_max_fail_count: int,
+        genetic_num_iters: int,
+        genetic_mutate_prob: float,
+        genetic_max_fail_count: int,
+        eps_greedy: float,
+    ) -> None:
+        """Constructor"""
+        self.__init_handle_by_constructor__(
+            _ffi_api.SearchStrategyEvolutionarySearch,  # type: ignore # pylint: disable=no-member
+            num_trials_per_iter,
+            num_trials_total,
+            population_size,
+            init_measured_ratio,
+            init_max_fail_count,
+            genetic_num_iters,
+            genetic_mutate_prob,
+            genetic_max_fail_count,
+            eps_greedy,
+        )
+
+
+class EvolutionarySearchConfig(NamedTuple):
+    """Configuration for EvolutionarySearch"""
+
+    num_trials_per_iter: int
+    num_trials_total: int
+    population_size: int = 2048
+    init_measured_ratio: float = 0.2
+    init_max_fail_count: int = 64
+    genetic_num_iters: int = 4
+    genetic_mutate_prob: float = 0.85
+    genetic_max_fail_count: int = 10
+    eps_greedy: float = 0.05
+
+    def create_strategy(self) -> EvolutionarySearch:
+        return EvolutionarySearch(
+            num_trials_per_iter=self.num_trials_per_iter,
+            num_trials_total=self.num_trials_total,
+            population_size=self.population_size,
+            init_measured_ratio=self.init_measured_ratio,
+            init_max_fail_count=self.init_max_fail_count,
+            genetic_num_iters=self.genetic_num_iters,
+            genetic_mutate_prob=self.genetic_mutate_prob,
+            genetic_max_fail_count=self.genetic_max_fail_count,
+            eps_greedy=self.eps_greedy,
+        )