chore: import upstream snapshot with attribution

python/paddle/distributed/auto_tuner/tuner.py

@@ -0,0 +1,155 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+import csv
+import os
+
+from .utils import default_candidates, gbs_default_candidates
+
+
+class AutoTuner:
+    """
+    The AutoTuner can automatically provide running task based on user-defined settings
+    and the task will be launched for execution.
+
+    Args:
+        tuner_cfg (dict): The configuration of auto tuner user defined.
+    """
+
+    def __init__(self, tuner_cfg):
+        self.cur_task_id = 1
+        self.task_limit = tuner_cfg.get("task_limit", 100)
+
+        search_algo = tuner_cfg.get("search_algo", {"name": "grid"})["name"]
+
+        if search_algo == "grid":
+            from .search import GridSearch
+
+            tuner_cfg["candidates"] = default_candidates(tuner_cfg)
+            self.algo = GridSearch(tuner_cfg)
+        elif search_algo == "dp_estimation":
+            from .search import DpEstimationSearch
+
+            tuner_cfg["candidates"] = default_candidates(tuner_cfg)
+            self.algo = DpEstimationSearch(tuner_cfg)
+        elif search_algo == "gbs":
+            from .search import GBSSearch
+
+            tuner_cfg["candidates"] = gbs_default_candidates(tuner_cfg)
+            self.algo = GBSSearch(tuner_cfg)
+        elif search_algo == "customize":
+            from .search import CustomizeSearch
+
+            self.algo = CustomizeSearch(tuner_cfg)
+        else:
+            raise NotImplementedError
+
+        self.history_cfgs = []
+        self.resume_cfgs = []
+        self.tuner_cfg = tuner_cfg
+
+    def search_once(self):
+        """Return a new task config."""
+        if self.cur_task_id > self.task_limit:
+            return None
+        new_cfg = self.algo.search_once(self.history_cfgs)
+        self.cur_task_id += 1
+
+        return new_cfg
+
+    def add_cfg(self, cfg):
+        """Add cfg into history cfgs"""
+        self.history_cfgs.append(cfg)
+
+    def resume_form_history(self, history_csv_path="./history.csv"):
+        """Resume form history csv file"""
+        # The breakpoint resume function does not start when the resume csv file does not exist.
+        if not os.path.exists(history_csv_path):
+            return
+        resume_csv_path = os.path.join(
+            os.path.dirname(history_csv_path),
+            f'{os.path.basename(history_csv_path).split(".")[0]}_copy.csv',
+        )
+        with open(history_csv_path, "r") as fread:
+            reader = csv.reader(fread)
+            data_list = list(reader)
+            with open(resume_csv_path, "w") as fwrite:
+                writer = csv.writer(fwrite)
+                for row in data_list:
+                    writer.writerow(row)
+        # chang str type to real type
+        for row in data_list:
+            for i, value in enumerate(row):
+                try:
+                    row[i] = int(value)
+                except ValueError:
+                    try:
+                        row[i] = float(value)
+                    except ValueError:
+                        pass
+
+        data_dict = []
+        keys = data_list[0]
+        values = data_list[1:]
+        for val in values:
+            val = [x if x != '' else None for x in val]
+            val = [True if x == 'True' else x for x in val]
+            val = [False if x == 'False' else x for x in val]
+            dictionary = dict(zip(keys, val))
+            time_val = -1
+            target_key = self.tuner_cfg["metric_cfg"]["name"]
+            if dictionary[target_key]:
+                time_val = dictionary[target_key]
+            dictionary["time"] = time_val
+            data_dict.append(dictionary)
+        self.resume_cfgs = data_dict
+
+    def get_cfg_from_resume(self, cur_cfg):
+        """Get cfg from resume cfgs"""
+        keys_to_compare = [
+            'mp_degree',
+            'sharding_degree',
+            'pp_degree',
+            'dp_degree',
+            'sharding_stage',
+            'micro_batch_size',
+            'vpp_degree',
+            'use_recompute',
+            'recompute_granularity',
+            'num_gpus',
+            'nodes',
+            'global_batch_size',
+            'sharding_overlap',
+            'acc_steps',
+        ]
+
+        if self.tuner_cfg.get("refined_recompute", None):
+            for rr in self.tuner_cfg["refined_recompute"]:
+                keys_to_compare.append(rr)
+
+        if self.tuner_cfg.get("custom_search_dim", None):
+            for key in self.tuner_cfg["custom_search_dim"]:
+                keys_to_compare.append(key)
+
+        for cfg in self.resume_cfgs:
+            ret_is_same = True
+            for key in keys_to_compare:
+                if not cfg.get(key) and not cur_cfg.get(key):
+                    continue
+                else:
+                    is_same = str(cfg.get(key)) == str(cur_cfg.get(key))
+                ret_is_same = ret_is_same and is_same
+            if ret_is_same:
+                return cfg
+        return None