Merge pull request #680 from PaddlePaddle/checkpoint

checkpoint refactor to save disk space

deepspeech/training/trainer.py

@@ -18,8 +18,8 @@ import paddle
 from paddle import distributed as dist
 from tensorboardX import SummaryWriter
 
-from deepspeech.utils import checkpoint
 from deepspeech.utils import mp_tools
+from deepspeech.utils.checkpoint import Checkpoint
 from deepspeech.utils.log import Log
 
 __all__ = ["Trainer"]
@@ -139,7 +139,7 @@ class Trainer():
             "epoch": self.epoch,
             "lr": self.optimizer.get_lr()
         })
-        checkpoint.save_parameters(self.checkpoint_dir, self.iteration
+        self.checkpoint.add_checkpoint(self.checkpoint_dir, self.iteration
                                        if tag is None else tag, self.model,
                                        self.optimizer, infos)
 
@@ -151,7 +151,7 @@ class Trainer():
         resume training.
         """
         scratch = None
-        infos = checkpoint.load_parameters(
+        infos = self.checkpoint.load_latest_parameters(
             self.model,
             self.optimizer,
             checkpoint_dir=self.checkpoint_dir,
@@ -180,7 +180,7 @@ class Trainer():
         from_scratch = self.resume_or_scratch()
         if from_scratch:
             # save init model, i.e. 0 epoch
-            self.save(tag='init')
+            self.save(tag='init', infos=None)
 
         self.lr_scheduler.step(self.iteration)
         if self.parallel:
@@ -263,6 +263,10 @@ class Trainer():
 
         self.checkpoint_dir = checkpoint_dir
 
+        self.checkpoint = Checkpoint(
+            kbest_n=self.config.training.checkpoint.kbest_n,
+            latest_n=self.config.training.checkpoint.latest_n)
+
     @mp_tools.rank_zero_only
     def destory(self):
         """Close visualizer to avoid hanging after training"""

deepspeech/utils/checkpoint.py

@@ -11,9 +11,11 @@
 # 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 glob
 import json
 import os
 import re
+from pathlib import Path
 from typing import Union
 
 import paddle
@@ -25,17 +27,143 @@ from deepspeech.utils.log import Log
 
 logger = Log(__name__).getlog()
 
-__all__ = ["load_parameters", "save_parameters"]
+__all__ = ["Checkpoint"]
 
 
-def _load_latest_checkpoint(checkpoint_dir: str) -> int:
+class Checkpoint(object):
+    def __init__(self, kbest_n: int=5, latest_n: int=1):
+        self.best_records: Mapping[Path, float] = {}
+        self.latest_records = []
+        self.kbest_n = kbest_n
+        self.latest_n = latest_n
+        self._save_all = (kbest_n == -1)
+
+    def add_checkpoint(self,
+                       checkpoint_dir,
+                       tag_or_iteration,
+                       model,
+                       optimizer,
+                       infos,
+                       metric_type="val_loss"):
+        if (metric_type not in infos.keys()):
+            self._save_parameters(checkpoint_dir, tag_or_iteration, model,
+                                  optimizer, infos)
+            return
+
+        #save best
+        if self._should_save_best(infos[metric_type]):
+            self._save_best_checkpoint_and_update(
+                infos[metric_type], checkpoint_dir, tag_or_iteration, model,
+                optimizer, infos)
+        #save latest
+        self._save_latest_checkpoint_and_update(
+            checkpoint_dir, tag_or_iteration, model, optimizer, infos)
+
+        if isinstance(tag_or_iteration, int):
+            self._save_checkpoint_record(checkpoint_dir, tag_or_iteration)
+
+    def load_latest_parameters(self,
+                               model,
+                               optimizer=None,
+                               checkpoint_dir=None,
+                               checkpoint_path=None):
+        """Load a last model checkpoint from disk. 
+        Args:
+            model (Layer): model to load parameters.
+            optimizer (Optimizer, optional): optimizer to load states if needed.
+                Defaults to None.
+            checkpoint_dir (str, optional): the directory where checkpoint is saved.
+            checkpoint_path (str, optional): if specified, load the checkpoint
+                stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will 
+                be ignored. Defaults to None. 
+        Returns:
+            configs (dict): epoch or step, lr and other meta info should be saved.
+        """
+        return self._load_parameters(model, optimizer, checkpoint_dir,
+                                     checkpoint_path, "checkpoint_latest")
+
+    def load_best_parameters(self,
+                             model,
+                             optimizer=None,
+                             checkpoint_dir=None,
+                             checkpoint_path=None):
+        """Load a last model checkpoint from disk. 
+        Args:
+            model (Layer): model to load parameters.
+            optimizer (Optimizer, optional): optimizer to load states if needed.
+                Defaults to None.
+            checkpoint_dir (str, optional): the directory where checkpoint is saved.
+            checkpoint_path (str, optional): if specified, load the checkpoint
+                stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will 
+                be ignored. Defaults to None. 
+        Returns:
+            configs (dict): epoch or step, lr and other meta info should be saved.
+        """
+        return self._load_parameters(model, optimizer, checkpoint_dir,
+                                     checkpoint_path, "checkpoint_best")
+
+    def _should_save_best(self, metric: float) -> bool:
+        if not self._best_full():
+            return True
+
+        # already full
+        worst_record_path = max(self.best_records, key=self.best_records.get)
+        # worst_record_path = max(self.best_records.iteritems(), key=operator.itemgetter(1))[0]
+        worst_metric = self.best_records[worst_record_path]
+        return metric < worst_metric
+
+    def _best_full(self):
+        return (not self._save_all) and len(self.best_records) == self.kbest_n
+
+    def _latest_full(self):
+        return len(self.latest_records) == self.latest_n
+
+    def _save_best_checkpoint_and_update(self, metric, checkpoint_dir,
+                                         tag_or_iteration, model, optimizer,
+                                         infos):
+        # remove the worst
+        if self._best_full():
+            worst_record_path = max(self.best_records,
+                                    key=self.best_records.get)
+            self.best_records.pop(worst_record_path)
+            if (worst_record_path not in self.latest_records):
+                logger.info(
+                    "remove the worst checkpoint: {}".format(worst_record_path))
+                self._del_checkpoint(checkpoint_dir, worst_record_path)
+
+        # add the new one
+        self._save_parameters(checkpoint_dir, tag_or_iteration, model,
+                              optimizer, infos)
+        self.best_records[tag_or_iteration] = metric
+
+    def _save_latest_checkpoint_and_update(
+            self, checkpoint_dir, tag_or_iteration, model, optimizer, infos):
+        # remove the old
+        if self._latest_full():
+            to_del_fn = self.latest_records.pop(0)
+            if (to_del_fn not in self.best_records.keys()):
+                logger.info(
+                    "remove the latest checkpoint: {}".format(to_del_fn))
+                self._del_checkpoint(checkpoint_dir, to_del_fn)
+        self.latest_records.append(tag_or_iteration)
+
+        self._save_parameters(checkpoint_dir, tag_or_iteration, model,
+                              optimizer, infos)
+
+    def _del_checkpoint(self, checkpoint_dir, tag_or_iteration):
+        checkpoint_path = os.path.join(checkpoint_dir,
+                                       "{}".format(tag_or_iteration))
+        for filename in glob.glob(checkpoint_path + ".*"):
+            os.remove(filename)
+            logger.info("delete file: {}".format(filename))
+
+    def _load_checkpoint_idx(self, checkpoint_record: str) -> int:
         """Get the iteration number corresponding to the latest saved checkpoint.
         Args:
-        checkpoint_dir (str): the directory where checkpoint is saved.
+            checkpoint_path (str): the saved path of checkpoint.
         Returns:
             int: the latest iteration number. -1 for no checkpoint to load.
         """
-    checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
         if not os.path.isfile(checkpoint_record):
             return -1
 
@@ -45,8 +173,7 @@ def _load_latest_checkpoint(checkpoint_dir: str) -> int:
             iteration = int(latest_checkpoint.split(":")[-1])
         return iteration
 
-
-def _save_record(checkpoint_dir: str, iteration: int):
+    def _save_checkpoint_record(self, checkpoint_dir: str, iteration: int):
         """Save the iteration number of the latest model to be checkpoint record.
         Args:
             checkpoint_dir (str): the directory where checkpoint is saved.
@@ -54,17 +181,24 @@ def _save_record(checkpoint_dir: str, iteration: int):
         Returns:
             None
         """
-    checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
-    # Update the latest checkpoint index.
-    with open(checkpoint_record, "a+") as handle:
-        handle.write("model_checkpoint_path:{}\n".format(iteration))
+        checkpoint_record_latest = os.path.join(checkpoint_dir,
+                                                "checkpoint_latest")
+        checkpoint_record_best = os.path.join(checkpoint_dir, "checkpoint_best")
 
+        with open(checkpoint_record_best, "w") as handle:
+            for i in self.best_records.keys():
+                handle.write("model_checkpoint_path:{}\n".format(i))
+        with open(checkpoint_record_latest, "w") as handle:
+            for i in self.latest_records:
+                handle.write("model_checkpoint_path:{}\n".format(i))
 
-def load_parameters(model,
+    def _load_parameters(self,
+                         model,
                          optimizer=None,
                          checkpoint_dir=None,
-                    checkpoint_path=None):
-    """Load a specific model checkpoint from disk. 
+                         checkpoint_path=None,
+                         checkpoint_file=None):
+        """Load a last model checkpoint from disk. 
         Args:
             model (Layer): model to load parameters.
             optimizer (Optimizer, optional): optimizer to load states if needed.
@@ -73,6 +207,7 @@ def load_parameters(model,
             checkpoint_path (str, optional): if specified, load the checkpoint
                 stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will 
                 be ignored. Defaults to None. 
+            checkpoint_file "checkpoint_latest" or "checkpoint_best"
         Returns:
             configs (dict): epoch or step, lr and other meta info should be saved.
         """
@@ -80,14 +215,16 @@ def load_parameters(model,
 
         if checkpoint_path is not None:
             tag = os.path.basename(checkpoint_path).split(":")[-1]
-    elif checkpoint_dir is not None:
-        iteration = _load_latest_checkpoint(checkpoint_dir)
+        elif checkpoint_dir is not None and checkpoint_file is not None:
+            checkpoint_record = os.path.join(checkpoint_dir, checkpoint_file)
+            iteration = self._load_checkpoint_idx(checkpoint_record)
             if iteration == -1:
                 return configs
-        checkpoint_path = os.path.join(checkpoint_dir, "{}".format(iteration))
+            checkpoint_path = os.path.join(checkpoint_dir,
+                                           "{}".format(iteration))
         else:
             raise ValueError(
-            "At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
+                "At least one of 'checkpoint_dir' and 'checkpoint_file' and 'checkpoint_path' should be specified!"
             )
 
         rank = dist.get_rank()
@@ -110,9 +247,9 @@ def load_parameters(model,
                 configs = json.load(fin)
         return configs
 
-
-@mp_tools.rank_zero_only
-def save_parameters(checkpoint_dir: str,
+    @mp_tools.rank_zero_only
+    def _save_parameters(self,
+                         checkpoint_dir: str,
                          tag_or_iteration: Union[int, str],
                          model: paddle.nn.Layer,
                          optimizer: Optimizer=None,
@@ -147,6 +284,3 @@ def save_parameters(checkpoint_dir: str,
         with open(info_path, 'w') as fout:
             data = json.dumps(infos)
             fout.write(data)
-
-    if isinstance(tag_or_iteration, int):
-        _save_record(checkpoint_dir, tag_or_iteration)

examples/aishell/s0/conf/deepspeech2.yaml

@@ -48,6 +48,9 @@ training:
   weight_decay: 1e-06
   global_grad_clip: 3.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
   
 decoding:
   batch_size: 128

examples/aishell/s1/conf/chunk_conformer.yaml

@@ -93,6 +93,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 
 decoding:

examples/aishell/s1/conf/conformer.yaml

@@ -88,6 +88,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 
 decoding:

examples/librispeech/s0/conf/deepspeech2.yaml

@@ -48,6 +48,9 @@ training:
   weight_decay: 1e-06
   global_grad_clip: 5.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 decoding:
   batch_size: 128

examples/librispeech/s1/conf/chunk_confermer.yaml

@@ -93,6 +93,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 
 decoding:

examples/librispeech/s1/conf/chunk_transformer.yaml

@@ -86,6 +86,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 
 decoding:

examples/librispeech/s1/conf/conformer.yaml

@@ -89,6 +89,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 
 decoding:

examples/librispeech/s1/conf/transformer.yaml

@@ -84,6 +84,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 100
+  checkpoint:
+    kbest_n: 50
+    latest_n: 5
 
 
 decoding:

examples/tiny/s0/conf/deepspeech2.yaml

@@ -43,12 +43,16 @@ model:
   share_rnn_weights: True 
 
 training:
-  n_epoch: 24
+  n_epoch: 10
   lr: 1e-5 
   lr_decay: 1.0 
   weight_decay: 1e-06
   global_grad_clip: 5.0
   log_interval: 1
+  checkpoint:
+    kbest_n: 3
+    latest_n: 2
+
 
 decoding:
   batch_size: 128

examples/tiny/s1/conf/chunk_confermer.yaml

@@ -91,6 +91,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 1
+  checkpoint:
+    kbest_n: 10
+    latest_n: 1
 
 
 decoding:

examples/tiny/s1/conf/chunk_transformer.yaml

@@ -84,6 +84,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 1
+  checkpoint:
+    kbest_n: 10
+    latest_n: 1
 
 
 decoding:

examples/tiny/s1/conf/conformer.yaml

@@ -87,6 +87,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 1
+  checkpoint:
+    kbest_n: 10
+    latest_n: 1
 
 
 decoding:

examples/tiny/s1/conf/transformer.yaml

@@ -84,6 +84,9 @@ training:
     warmup_steps: 25000
     lr_decay: 1.0
   log_interval: 1
+  checkpoint:
+    kbest_n: 10
+    latest_n: 1
 
 
 decoding: