[AutoParallel] recompute tuning

python/paddle/distributed/auto_parallel/constants.py

@@ -54,7 +54,7 @@ def set_field_default_config(category, field, default_value):
 #########################################
 RECOMPUTE = "recompute"
 set_field_default_config(RECOMPUTE, "enable", False)
-set_field_default_config(RECOMPUTE, "checkpoints", None)
+set_field_default_config(RECOMPUTE, "checkpoints", [])
 set_field_default_config(RECOMPUTE, "no_recompute_segments", [])
 set_field_default_config(RECOMPUTE, "enable_tuning", False)
 
@@ -113,12 +113,10 @@ def set_field_default_config(category, field, default_value):
 # #########################################
 TUNING = "tuning"
 set_field_default_config(TUNING, "enable", False)
-set_field_default_config(TUNING, "batch_size", 1)
-set_field_default_config(TUNING, "dataset", None)
 set_field_default_config(TUNING, "profile_start_step", 1)
 set_field_default_config(TUNING, "profile_end_step", 1)
 set_field_default_config(TUNING, "run_after_tuning", True)
-set_field_default_config(TUNING, "verbose", True)
+set_field_default_config(TUNING, "debug", False)
 
 #########################################
 # dataset configuration

python/paddle/distributed/auto_parallel/engine.py

@@ -609,7 +609,9 @@ def _build(self, mode):
         if mode != "train":
             serial_main_prog = serial_main_prog.clone(for_test=True)
 
-        auto_utils.set_recompute_ckpts(self._model, self._strategy)
+        auto_utils.set_recompute_segments(
+            self._model, self._losses, self._strategy, serial_main_prog
+        )
         self._dist_contexts[mode] = DistributedContext(
             serial_main_prog,
             serial_startup_prog,
@@ -649,7 +651,6 @@ def _optimization_tuning(self, mode, dataset, batch_size):
         from .tuner.optimization_tuner import OptimizationTuner
 
         self._optimization_tuner = OptimizationTuner(
-            self._tuning.to_dict(),
             self._dist_contexts[mode],
             dataset,
             self._inputs_spec,

python/paddle/distributed/auto_parallel/strategy.py

@@ -73,6 +73,10 @@ def __deepcopy__(self, memo):
             setattr(result, k, copy.deepcopy(v, memo))
         return result
 
+    def get(self, k, d=None):
+        result_dict = self.to_dict()
+        return result_dict.get(k, d)
+
 
 class RecomputeConfig(BaseConfig):
     def __init__(self, config_dict=None):

python/paddle/distributed/auto_parallel/tuner/algorithms.py

@@ -16,7 +16,7 @@
 import logging
 from abc import ABC, abstractmethod
 
-from ..utils import get_logger
+from ..utils import get_logger, is_recompute_op
 from .trial import OptimizationTunerTrial as Trial
 from .trial import TrialStatus
 
@@ -54,7 +54,7 @@ def changed_configs(self):
     def collect_model_info(self, main_prog, startup_prog):
         """
         Collect the model static info (from programs) that could be used to
-        pruning candidate trials and saving tuning time.For instance,
+        pruning candidate trials and saving tuning time. For instance,
         model info like number of model parameters and activation memory could be
         used to prune candidated trial and decide the next trial.
         """
@@ -116,7 +116,7 @@ def _init_spaces(self):
         self._max_stage = 3
         self._trial_idx = 0
 
-        stage_range = self._config.sharding.to_dict().get("tuning_range", None)
+        stage_range = self._config.sharding.get("tuning_range", None)
         if stage_range:
             assert set(stage_range).issubset(
                 set([0, 1, 2, 3])
@@ -157,3 +157,92 @@ def update(self, results):
             )
         else:
             self._trial_idx += 1
+
+
+@register_algor("recompute")
+class ReccomputeCheckpointAlgorithm(AlgorithmBase):
+    def __init__(self, config):
+        super().__init__(config)
+        self._changed_configs = ["recompute"]
+
+    def collect_model_info(self, main_prog, startup_prog):
+        segments = []
+        for op in main_prog.global_block().ops:
+            if not is_recompute_op(op):
+                continue
+
+            seg_name = op.attr('op_namescope')
+            if seg_name not in segments:
+                segments.append(seg_name)
+
+        self._total_num_trial = len(segments)
+        self._tuning_segments = list(range(len(segments)))
+        self._trail_left = 0
+        self._trail_right = len(segments) - 1
+        self._trial_idx = int(0 + (len(segments) - 1) / 2)
+
+    def _init_spaces(self):
+        self._recompute_mode = "all"
+
+    def next_trial(self):
+        if self._trial_idx < self._total_num_trial:
+            if self._recompute_mode == "all":
+                self._recompute_flag = False
+                new_strategy = copy.deepcopy(self._config.dist_strategy)
+                name = "trial-recompute-all-segments"
+                return Trial(new_strategy, name, self.changed_configs)
+            elif self._recompute_mode == "none":
+                self._recompute_flag = False
+                new_strategy = copy.deepcopy(self._config.dist_strategy)
+                recompute = new_strategy.recompute
+                recompute.enable = False
+                name = "trial-recompute-none-segments"
+                return Trial(new_strategy, name, self.changed_configs)
+            elif self._recompute_mode == "part":
+                new_no_recompute = self._tuning_segments[: self._trial_idx]
+                new_strategy = copy.deepcopy(self._config.dist_strategy)
+                recompute = new_strategy.recompute
+                recompute.no_recompute_segments.extend(new_no_recompute)
+                name = "trial-recompute-part-segments-idx{}".format(
+                    self._trial_idx
+                )
+                return Trial(new_strategy, name, self.changed_configs)
+        else:
+            return Trial(None, None, None, status=TrialStatus.STOPPED)
+
+    def update(self, results):
+
+        et = results.get("ErrorType", None)
+        if self._recompute_mode == "all":
+            if et and et == "ResourceExhaustedError":
+                self._trial_idx = self._total_num_trial
+                self._logger.info(
+                    "Recompute all candidate segments is failed with OOM, please reduce model size or batch size."
+                )
+            else:
+                self._recompute_mode = "none"
+        elif self._recompute_mode == "none":
+            if et and et == "ResourceExhaustedError":
+                self._recompute_mode = "part"
+            else:
+                self._trial_idx = self._total_num_trial
+                self._logger.info(
+                    "Recompute is unnecessary for this model size, which will reduce the Throughtput."
+                )
+        else:
+            if self._trail_left >= self._trail_right:
+                self._trial_idx = self._total_num_trial
+            elif et and et == "ResourceExhaustedError":
+                self._trail_left = self._trail_left
+                self._trail_right = self._trial_idx - 1
+                self._trial_idx = int(
+                    self._trail_left
+                    + (self._trail_right - self._trail_left) / 2
+                )
+            else:
+                self._trail_left = self._trial_idx + 1
+                self._trail_right = self._trail_right
+                self._trial_idx = int(
+                    self._trail_left
+                    + (self._trail_right - self._trail_left) / 2
+                )

python/paddle/distributed/auto_parallel/tuner/config.py

@@ -32,14 +32,11 @@ class TuningConfig:
     tuning config: configuration for the tuning process: mode (profile or cost model), log dir, extra tuning config for optimization like search range for specific
     """
 
-    def __init__(self, user_config, strategy):
+    def __init__(self, strategy):
 
         if not isinstance(strategy, Strategy):
             raise TypeError("'strategy' must be object of class `Strategy`.")
 
-        if not user_config:
-            user_config = {}
-
         self._tuning_passes_name = set()
         self._dist_strategy = copy.deepcopy(strategy)
         self._mode = None
@@ -48,9 +45,9 @@ def __init__(self, user_config, strategy):
         self._project_dir = None
         self._max_num_trial = None
         self._early_stop = None
-        self._verbose = None
+        self._debug = None
 
-        self._initialize(user_config)
+        self._initialize()
 
     @property
     def mode(self):
@@ -81,29 +78,25 @@ def early_stop(self):
         return self._early_stop
 
     @property
-    def verbose(self):
-        return self._verbose
+    def debug(self):
+        return self._debug
 
     @property
     def dist_strategy(self):
         return self._dist_strategy
 
     # initialize config with user define value or default value
-    def _initialize(self, user_config):
-
-        self._mode = user_config.get("mode", "PROFILE")
-
-        self._profile_start_step = user_config.get("profile_start_step", 10)
-
-        self._profile_end_step = user_config.get("profile_end_step", 30)
-
-        self._max_num_trial = user_config.get("max_num_trial", 50)
-
-        self._early_stop = user_config.get("early_stop", None)
+    def _initialize(self):
+        tuning_strategy = self._dist_strategy.tuning
 
-        self._verbose = user_config.get("verbose", False)
+        self._mode = tuning_strategy.get("mode", "PROFILE")
+        self._profile_start_step = tuning_strategy.get("profile_start_step", 10)
+        self._profile_end_step = tuning_strategy.get("profile_end_step", 30)
+        self._max_num_trial = tuning_strategy.get("max_num_trial", 50)
+        self._early_stop = tuning_strategy.get("early_stop", None)
+        self._debug = tuning_strategy.get("debug", False)
 
-        project_dir = user_config.get("project_dir", None)
+        project_dir = tuning_strategy.get("project_dir", None)
         if not project_dir:
             project_dir = os.path.join(os.getcwd(), "OptimizationTuning")
         self._project_dir = project_dir
@@ -116,15 +109,14 @@ def _initialize(self, user_config):
                 # TODO distinguish different args of each passes
                 self._tuning_passes_name.add(p)
 
-                config_name = p
-                p_dict = getattr(self._dist_strategy, config_name)
-                self.__dict__[config_name] = p_dict
+                p_strategy = getattr(self._dist_strategy, p)
+                self.__dict__[p] = p_strategy
 
-                # TODO verify the user defined configs
-                user_config_for_pass = user_config.get(p, None)
-                if user_config_for_pass:
-                    for k, v in user_config_for_pass.items():
-                        self.__dict__[config_name][k] = v
+                # # TODO verify the user defined configs
+                # tuning_config_for_pass = tuning_strategy.get(p, None)
+                # if tuning_config_for_pass:
+                #     for k, v in tuning_config_for_pass.items():
+                #         self.__dict__[p][k] = v
 
     # (NOTE)tuning config ONLY wraps dist strategy for pass config which is to be tuned
     def __getattr__(self, item):