[auto config] Resume from history csv file

python/paddle/distributed/auto_tuner/tuner.py

@@ -12,6 +12,8 @@
 # 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
 
@@ -54,6 +56,8 @@ def __init__(self, tuner_cfg):
             raise NotImplementedError()
 
         self.history_cfgs = []
+        self.resume_cfgs = []
+        self.tuner_cfg = tuner_cfg
 
     def search_once(self):
         """Return a new task config."""
@@ -67,3 +71,81 @@ def search_once(self):
     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
+                        # if value == "False":
+                        #    row[i] = bool(False)
+                        # if value == "True":
+                        #    row[i] = bool(True)
+                        # row[i] = value
+
+        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',
+        ]
+        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

python/paddle/distributed/launch/main.py

@@ -587,6 +587,10 @@ def launch():
         logger.info(
             f"Launch {len(auto_tuner.algo.all_tasks)} tasks by auto tuner: "
         )
+        resume_csv_file_path = tuner_cfg.get(
+            "resume_csv_file_path", history_file_path
+        )
+        auto_tuner.resume_form_history(resume_csv_file_path)
         cur_cfg = auto_tuner.search_once()
         auto_tuner.add_cfg(cur_cfg)
         assert cur_cfg is not None, "No config can run."
@@ -658,6 +662,73 @@ def launch():
             )
             logger.info(f"Launch task: job_id {task_job_id}, log_dir {log_dir}")
 
+            cur_resume_cfg = auto_tuner.get_cfg_from_resume(cur_cfg)
+            if cur_resume_cfg:
+                cur_cfg = cur_resume_cfg
+                cur_cfg['job_id'] = job_id
+                auto_tuner.history_cfgs.pop(-1)
+                auto_tuner.add_cfg(cur_cfg)
+                recorder.add_cfg(**cur_cfg)
+                cur_best_cfgs, err = recorder.get_best(
+                    metric=tuner_cfg['metric_cfg']['name'],
+                    direction=tuner_cfg['metric_cfg']['OptimizationDirection'],
+                )
+                if not err:
+                    ctx.logger.info(f"Current best config: {cur_best_cfgs}")
+                    logger.info(f"Current best config: {cur_best_cfgs}")
+                else:
+                    ctx.logger.info(
+                        "Get best config failed. Currently no config can be run."
+                    )
+                    logger.info(
+                        "Get best config failed. Currently no config can be run."
+                    )
+                if (
+                    "sharding_overlap" in cur_cfg
+                    and cur_cfg["sharding_overlap"]
+                ):
+                    add_overlap_performance(
+                        cur_cfg, tuner_cfg, recorder.history
+                    )
+
+                if cur_cfg["error_info"]:
+                    error_task_nums += 1
+                error_info = cur_cfg["error_info"]
+                task_nums = len(auto_tuner.algo.all_tasks)
+                cur_task_id = auto_tuner.algo.idx
+                ctx.logger.info(
+                    "Auto Tuner Schedule: [{}/{}], Pruned nums {}, Error nums {}, Error info {}, Remaining time {} min".format(
+                        cur_task_id,
+                        task_nums,
+                        cur_task_id - job_id,
+                        error_task_nums,
+                        error_info,
+                        round(
+                            (task_nums - cur_task_id) * max_time_per_task / 60,
+                            2,
+                        ),
+                    )
+                )
+                logger.info(
+                    "Auto Tuner Schedule: [{}/{}], Pruned nums {}, Error nums {}, Error info {}, Remaining time {} min".format(
+                        cur_task_id,
+                        task_nums,
+                        cur_task_id - job_id,
+                        error_task_nums,
+                        error_info,
+                        round(
+                            (task_nums - cur_task_id) * max_time_per_task / 60,
+                            2,
+                        ),
+                    )
+                )
+                recorder.store_history(history_file_path)
+                # generate a new config
+                new_cfg = auto_tuner.search_once()
+                cur_cfg = copy.deepcopy(new_cfg)
+                auto_tuner.add_cfg(cur_cfg)
+                continue
+
             # in single dp estimation scene, just some nodes not all nodes run
             ctx = gen_new_ctx(ctx, cur_cfg, tuner_cfg)
             actual_nnodes = int(ctx.args.nnodes.split(":")[0])