improve PBT tuner (#2357)

src/sdk/pynni/nni/pbt_tuner/pbt_tuner.py

@@ -74,18 +74,16 @@ def exploit_and_explore(bot_trial_info, top_trial_info, factor, resample_probabi
     top_hyper_parameters = top_trial_info.hyper_parameters
     hyper_parameters = copy.deepcopy(top_hyper_parameters)
     random_state = np.random.RandomState()
+    hyper_parameters['load_checkpoint_dir'] = hyper_parameters['save_checkpoint_dir']
+    hyper_parameters['save_checkpoint_dir'] = os.path.join(bot_checkpoint_dir, str(epoch))
     for key in hyper_parameters.keys():
         hyper_parameter = hyper_parameters[key]
-        if key == 'load_checkpoint_dir':
-            hyper_parameters[key] = hyper_parameters['save_checkpoint_dir']
-            continue
-        elif key == 'save_checkpoint_dir':
-            hyper_parameters[key] = os.path.join(bot_checkpoint_dir, str(epoch))
+        if key == 'load_checkpoint_dir' or key == 'save_checkpoint_dir':
             continue
         elif search_space[key]["_type"] == "choice":
             choices = search_space[key]["_value"]
-            ub, uv = len(choices) - 1, choices.index(hyper_parameter["_value"]) + 1
-            lb, lv = 0, choices.index(hyper_parameter["_value"]) - 1
+            ub, uv = len(choices) - 1, choices.index(hyper_parameter) + 1
+            lb, lv = 0, choices.index(hyper_parameter) - 1
         elif search_space[key]["_type"] == "randint":
             lb, ub = search_space[key]["_value"][:2]
             ub -= 1
@@ -132,10 +130,11 @@ def exploit_and_explore(bot_trial_info, top_trial_info, factor, resample_probabi
         else:
             logger.warning("Illegal type to perturb: %s", search_space[key]["_type"])
             continue
+
         if search_space[key]["_type"] == "choice":
             idx = perturbation(search_space[key]["_type"], search_space[key]["_value"],
                                resample_probability, uv, ub, lv, lb, random_state)
-            hyper_parameters[key] = {'_index': idx, '_value': choices[idx]}
+            hyper_parameters[key] = choices[idx]
         else:
             hyper_parameters[key] = perturbation(search_space[key]["_type"], search_space[key]["_value"],
                                                  resample_probability, uv, ub, lv, lb, random_state)
@@ -231,6 +230,7 @@ class PBTTuner(Tuner):
         for i in range(self.population_size):
             hyper_parameters = json2parameter(
                 self.searchspace_json, is_rand, self.random_state)
+            hyper_parameters = split_index(hyper_parameters)
             checkpoint_dir = os.path.join(self.all_checkpoint_dir, str(i))
             hyper_parameters['load_checkpoint_dir'] = os.path.join(checkpoint_dir, str(self.epoch))
             hyper_parameters['save_checkpoint_dir'] = os.path.join(checkpoint_dir, str(self.epoch))
@@ -294,38 +294,19 @@ class PBTTuner(Tuner):
         trial_info.parameter_id = parameter_id
         self.running[parameter_id] = trial_info
         logger.info('Generate parameter : %s', trial_info.hyper_parameters)
-        return split_index(trial_info.hyper_parameters)
+        return trial_info.hyper_parameters
 
-    def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
+    def _proceed_next_epoch(self):
         """
-        Receive trial's result. if the number of finished trials equals ``self.population_size``, start the next epoch to
-        train the model.
-
-        Parameters
-        ----------
-        parameter_id : int
-            Unique identifier of used hyper-parameters, same with :meth:`generate_parameters`.
-        parameters : dict
-            Hyper-parameters generated by :meth:`generate_parameters`.
-        value : dict
-            Result from trial (the return value of :func:`nni.report_final_result`).
         """
-        logger.info('Get one trial result, id = %d, value = %s', parameter_id, value)
-        value = extract_scalar_reward(value)
-        if self.optimize_mode == OptimizeMode.Minimize:
-            value = -value
-        trial_info = self.running.pop(parameter_id, None)
-        trial_info.score = value
-        self.finished.append(trial_info)
-        self.finished_trials += 1
-        if self.finished_trials == self.population_size:
         logger.info('Proceeding to next epoch')
         self.epoch += 1
         self.population = []
         self.pos = -1
         self.running = {}
         #exploit and explore
-            self.finished = sorted(self.finished, key=lambda x: x.score, reverse=True)
+        reverse = True if self.optimize_mode == OptimizeMode.Maximize else False
+        self.finished = sorted(self.finished, key=lambda x: x.score, reverse=reverse)
         cutoff = int(np.ceil(self.fraction * len(self.finished)))
         tops = self.finished[:cutoff]
         bottoms = self.finished[self.finished_trials - cutoff:]
@@ -348,7 +329,117 @@ class PBTTuner(Tuner):
             trial_info = self.population[self.pos]
             trial_info.parameter_id = parameter_id
             self.running[parameter_id] = trial_info
-                self.send_trial_callback(parameter_id, split_index(trial_info.hyper_parameters))
+            self.send_trial_callback(parameter_id, trial_info.hyper_parameters)
+
+    def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
+        """
+        Receive trial's result. if the number of finished trials equals ``self.population_size``, start the next epoch to
+        train the model.
+
+        Parameters
+        ----------
+        parameter_id : int
+            Unique identifier of used hyper-parameters, same with :meth:`generate_parameters`.
+        parameters : dict
+            Hyper-parameters generated by :meth:`generate_parameters`.
+        value : dict
+            Result from trial (the return value of :func:`nni.report_final_result`).
+        """
+        logger.info('Get one trial result, id = %d, value = %s', parameter_id, value)
+        value = extract_scalar_reward(value)
+        trial_info = self.running.pop(parameter_id, None)
+        trial_info.score = value
+        self.finished.append(trial_info)
+        self.finished_trials += 1
+        if self.finished_trials == self.population_size:
+            self._proceed_next_epoch()
+
+    def trial_end(self, parameter_id, success, **kwargs):
+        """
+        Deal with trial failure
+
+        Parameters
+        ----------
+        parameter_id : int
+            Unique identifier for hyper-parameters used by this trial.
+        success : bool
+            True if the trial successfully completed; False if failed or terminated.
+        **kwargs
+            Unstable parameters which should be ignored by normal users.
+        """
+        if success:
+            return
+        if self.optimize_mode == OptimizeMode.Minimize:
+            value = float('inf')
+        else:
+            value = float('-inf')
+        trial_info = self.running.pop(parameter_id, None)
+        trial_info.score = value
+        self.finished.append(trial_info)
+        self.finished_trials += 1
+        if self.finished_trials == self.population_size:
+            self._proceed_next_epoch()
 
     def import_data(self, data):
-        pass
+        """
+        Parameters
+        ----------
+        data : json obj
+            imported data records
+
+        Returns
+        -------
+        int
+            the start epoch number after data imported, only used for unittest
+        """
+        if self.running:
+            logger.warning("Do not support importing data in the middle of experiment")
+            return
+        # the following is for experiment resume
+        _completed_num = 0
+        epoch_data_dict = {}
+        for trial_info in data:
+            logger.info("Process data record %s / %s", _completed_num, len(data))
+            _completed_num += 1
+            # simply validate data format
+            _params = trial_info["parameter"]
+            _value = trial_info['value']
+            # assign fake value for failed trials
+            if not _value:
+                logger.info("Useless trial data, value is %s, skip this trial data.", _value)
+                _value = float('inf') if self.optimize_mode == OptimizeMode.Minimize else float('-inf')
+            _value = extract_scalar_reward(_value)
+            if 'save_checkpoint_dir' not in _params:
+                logger.warning("Invalid data record: save_checkpoint_dir is missing, abandon data import.")
+                return
+            epoch_num = int(os.path.basename(_params['save_checkpoint_dir']))
+            if epoch_num not in epoch_data_dict:
+                epoch_data_dict[epoch_num] = []
+            epoch_data_dict[epoch_num].append((_params, _value))
+        if not epoch_data_dict:
+            logger.warning("No valid epochs, abandon data import.")
+            return
+        # figure out start epoch for resume
+        max_epoch_num = max(epoch_data_dict, key=int)
+        if len(epoch_data_dict[max_epoch_num]) < self.population_size:
+            max_epoch_num -= 1
+        # If there is no a single complete round, no data to import, start from scratch
+        if max_epoch_num < 0:
+            logger.warning("No completed epoch, abandon data import.")
+            return
+        assert len(epoch_data_dict[max_epoch_num]) == self.population_size
+        # check existence of trial save checkpoint dir
+        for params, _ in epoch_data_dict[max_epoch_num]:
+            if not os.path.isdir(params['save_checkpoint_dir']):
+                logger.warning("save_checkpoint_dir %s does not exist, data will not be resumed", params['save_checkpoint_dir'])
+                return
+        # resume data
+        self.epoch = max_epoch_num
+        self.finished_trials = self.population_size
+        for params, value in epoch_data_dict[max_epoch_num]:
+            checkpoint_dir = os.path.dirname(params['save_checkpoint_dir'])
+            self.finished.append(TrialInfo(checkpoint_dir=checkpoint_dir, hyper_parameters=params, score=value))
+        self._proceed_next_epoch()
+        logger.info("Successfully import data to PBT tuner, total data: %d, imported data: %d.", len(data), self.population_size)
+        logger.info("Start from epoch %d ...", self.epoch)
+        return self.epoch # return for test

src/sdk/pynni/tests/test_builtin_tuners.py

@@ -159,6 +159,62 @@ class BuiltinTunersTestCase(TestCase):
             logger.info("Full supported search space: %s", full_supported_search_space)
             self.search_space_test_one(tuner_factory, full_supported_search_space)
 
+    def import_data_test_for_pbt(self):
+        """
+        test1: import data with complete epoch
+        test2: import data with incomplete epoch
+        """
+        search_space = {
+            "choice_str": {
+                "_type": "choice",
+                "_value": ["cat", "dog", "elephant", "cow", "sheep", "panda"]
+            }
+        }
+        all_checkpoint_dir = os.path.expanduser("~/nni/checkpoint/test/")
+        population_size = 4
+        # ===import data at the beginning===
+        tuner = PBTTuner(
+            all_checkpoint_dir=all_checkpoint_dir,
+            population_size=population_size
+        )
+        self.assertIsInstance(tuner, Tuner)
+        tuner.update_search_space(search_space)
+        save_dirs = [os.path.join(all_checkpoint_dir, str(i), str(0)) for i in range(population_size)]
+        # create save checkpoint directory
+        for save_dir in save_dirs:
+            os.makedirs(save_dir, exist_ok=True)
+        # for simplicity, omit "load_checkpoint_dir"
+        data = [{"parameter": {"choice_str": "cat", "save_checkpoint_dir": save_dirs[0]}, "value": 1.1},
+                {"parameter": {"choice_str": "dog", "save_checkpoint_dir": save_dirs[1]}, "value": {"default": 1.2, "tmp": 2}},
+                {"parameter": {"choice_str": "cat", "save_checkpoint_dir": save_dirs[2]}, "value": 11},
+                {"parameter": {"choice_str": "cat", "save_checkpoint_dir": save_dirs[3]}, "value": 7}]
+        epoch = tuner.import_data(data)
+        self.assertEqual(epoch, 1)
+        logger.info("Imported data successfully at the beginning")
+        shutil.rmtree(all_checkpoint_dir)
+        # ===import another data at the beginning, test the case when there is an incompleted epoch===
+        tuner = PBTTuner(
+            all_checkpoint_dir=all_checkpoint_dir,
+            population_size=population_size
+        )
+        self.assertIsInstance(tuner, Tuner)
+        tuner.update_search_space(search_space)
+        for i in range(population_size - 1):
+            save_dirs.append(os.path.join(all_checkpoint_dir, str(i), str(1)))
+        for save_dir in save_dirs:
+            os.makedirs(save_dir, exist_ok=True)
+        data = [{"parameter": {"choice_str": "cat", "save_checkpoint_dir": save_dirs[0]}, "value": 1.1},
+                {"parameter": {"choice_str": "dog", "save_checkpoint_dir": save_dirs[1]}, "value": {"default": 1.2, "tmp": 2}},
+                {"parameter": {"choice_str": "cat", "save_checkpoint_dir": save_dirs[2]}, "value": 11},
+                {"parameter": {"choice_str": "cat", "save_checkpoint_dir": save_dirs[3]}, "value": 7},
+                {"parameter": {"choice_str": "cat", "save_checkpoint_dir": save_dirs[4]}, "value": 1.1},
+                {"parameter": {"choice_str": "dog", "save_checkpoint_dir": save_dirs[5]}, "value": {"default": 1.2, "tmp": 2}},
+                {"parameter": {"choice_str": "cat", "save_checkpoint_dir": save_dirs[6]}, "value": 11}]
+        epoch = tuner.import_data(data)
+        self.assertEqual(epoch, 1)
+        logger.info("Imported data successfully at the beginning with incomplete epoch")
+        shutil.rmtree(all_checkpoint_dir)
+
     def import_data_test(self, tuner_factory, stype="choice_str"):
         """
         import data at the beginning with number value and dict value
@@ -297,6 +353,7 @@ class BuiltinTunersTestCase(TestCase):
             all_checkpoint_dir=os.path.expanduser("~/nni/checkpoint/test/"),
             population_size=100
         ))
+        self.import_data_test_for_pbt()
 
     def tearDown(self):
         file_list = glob.glob("smac3*") + ["param_config_space.pcs", "scenario.txt", "model_path"]