Sync before and after deleting

recipes/AISHELL-1/ASR/transformer/train.py

@@ -171,7 +171,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
                 stage_stats["CER"] = self.cer_metric.summarize("error_rate")
 
         # log stats and save checkpoint at end-of-epoch
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
 
             # report different epoch stages according current stage
             current_epoch = self.hparams.epoch_counter.current

recipes/AISHELL-1/ASR/transformer/train_with_wav2vect.py

@@ -173,7 +173,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
                 stage_stats["CER"] = self.cer_metric.summarize("error_rate")
 
         # log stats and save checkpoint at end-of-epoch
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
 
             # report different epoch stages according current stage
             current_epoch = self.hparams.epoch_counter.current

recipes/CommonVoice/ASR/transformer/train.py

@@ -183,7 +183,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
                 stage_stats["CER"] = self.cer_metric.summarize("error_rate")
 
         # log stats and save checkpoint at end-of-epoch
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
 
             # report different epoch stages according current stage
             current_epoch = self.hparams.epoch_counter.current

recipes/Fisher-Callhome-Spanish/ST/transformer/train.py

@@ -251,7 +251,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
                 stage_stats["BLEU"] = self.bleu_metric.summarize("BLEU")
 
         # log stats and save checkpoint at end-of-epoch
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
             current_epoch = self.hparams.epoch_counter.current
 
             # report different epoch stages according current stage

recipes/IWSLT22_lowresource/train.py

@@ -148,7 +148,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
             current_epoch = self.hparams.epoch_counter.current
 
         # log stats and save checkpoint at end-of-epoch
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
             current_epoch = self.hparams.epoch_counter.current
             old_lr_adam, new_lr_adam = self.hparams.lr_annealing_adam(
                 stage_stats["BLEU"]

recipes/KsponSpeech/ASR/transformer/train.py

@@ -221,7 +221,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
                 stage_stats["CER"] = self.cer_metric.summarize("error_rate")
 
         # log stats and save checkpoint at end-of-epoch
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
 
             lr = self.hparams.noam_annealing.current_lr
             steps = self.optimizer_step

recipes/KsponSpeech/LM/train.py

@@ -81,7 +81,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
         if stage == sb.Stage.TRAIN:
             self.train_stats = stage_stats
 
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
             if not (
                 isinstance(
                     self.hparams.lr_annealing, sb.nnet.schedulers.NoamScheduler

recipes/LibriSpeech/ASR/transducer/train.py

@@ -256,7 +256,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
             stage_stats["WER"] = self.wer_metric.summarize("error_rate")
 
         # Perform end-of-iteration things, like annealing, logging, etc.
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
 
             lr = self.hparams.noam_annealing.current_lr
             steps = self.optimizer_step

recipes/LibriSpeech/ASR/transformer/train.py

@@ -194,7 +194,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
                 stage_stats["WER"] = self.wer_metric.summarize("error_rate")
 
         # log stats and save checkpoint at end-of-epoch
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
 
             lr = self.hparams.noam_annealing.current_lr
             steps = self.optimizer_step

recipes/LibriSpeech/LM/train.py

@@ -72,7 +72,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
         if stage == sb.Stage.TRAIN:
             self.train_stats = stage_stats
 
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
             if not (
                 isinstance(
                     self.hparams.lr_annealing, sb.nnet.schedulers.NoamScheduler

recipes/LibriSpeech/self-supervised-learning/wav2vec2/train_sb_wav2vec2.py

@@ -84,7 +84,7 @@ def compute_objectives(self, forward_outputs, batch, stage):
         loss, accuracy = self.hparams.loss(embeddings, targets, negs)
 
         # This is only used for logging purpose
-        if stage != sb.Stage.TRAIN and sb.utils.distributed.if_main_process():
+        if stage != sb.Stage.TRAIN:
             self.acc_metric.append(accuracy)
 
         objectives = {

recipes/Switchboard/ASR/transformer/train.py

@@ -247,7 +247,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
                 stage_stats["WER"] = self.wer_metric.summarize("error_rate")
 
         # log stats and save checkpoint at end-of-epoch
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
 
             lr = self.hparams.noam_annealing.current_lr
             steps = self.optimizer_step

recipes/Switchboard/LM/train.py

@@ -69,7 +69,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
         if stage == sb.Stage.TRAIN:
             self.train_stats = stage_stats
 
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
             if not (
                 isinstance(
                     self.hparams.lr_annealing, sb.nnet.schedulers.NoamScheduler

recipes/TIMIT/ASR/transducer/train.py

@@ -21,7 +21,7 @@
 import logging
 import speechbrain as sb
 from hyperpyyaml import load_hyperpyyaml
-from speechbrain.utils.distributed import run_on_main, if_main_process
+from speechbrain.utils.distributed import run_on_main
 
 logger = logging.getLogger(__name__)
 
@@ -136,16 +136,25 @@ def on_stage_end(self, stage, stage_loss, epoch):
                 stats_meta={"Epoch loaded": self.hparams.epoch_counter.current},
                 test_stats={"loss": stage_loss, "PER": per},
             )
-            if if_main_process():
-                with open(self.hparams.test_wer_file, "w") as w:
-                    w.write("Transducer loss stats:\n")
-                    self.transducer_metrics.write_stats(w)
-                    w.write("\nPER stats:\n")
-                    self.per_metrics.write_stats(w)
-                    print(
-                        "Transducer and PER stats written to file",
-                        self.hparams.test_wer_file,
-                    )
+            run_on_main(
+                save_metrics_to_file,
+                args=[
+                    self.hparams.test_wer_file,
+                    self.transducer_metrics,
+                    self.per_metrics,
+                ],
+            )
+
+
+def save_metrics_to_file(wer_file, transducer_metrics, per_metrics):
+    with open(wer_file, "w") as w:
+        w.write("Transducer loss stats:\n")
+        transducer_metrics.write_stats(w)
+        w.write("\nPER stats:\n")
+        per_metrics.write_stats(w)
+        print(
+            "Transducer and PER stats written to file", hparams.test_wer_file,
+        )
 
 
 def dataio_prep(hparams):

recipes/Tedlium2/ASR/transformer/train.py

@@ -184,7 +184,7 @@ def on_stage_end(self, stage, stage_loss, epoch):
                 stage_stats["WER"] = self.wer_metric.summarize("error_rate")
 
         # log stats and save checkpoint at end-of-epoch
-        if stage == sb.Stage.VALID and sb.utils.distributed.if_main_process():
+        if stage == sb.Stage.VALID:
 
             lr = self.hparams.noam_annealing.current_lr
             steps = self.optimizer_step

speechbrain/utils/checkpoints.py

@@ -60,7 +60,11 @@
 import warnings
 from packaging import version
 import speechbrain.utils._workarounds as __wa
-from speechbrain.utils.distributed import main_process_only, if_main_process
+from speechbrain.utils.distributed import (
+    main_process_only,
+    if_main_process,
+    ddp_barrier,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -990,11 +994,21 @@ def delete_checkpoints(
                 )
             )
 
+        # Sync before deleting to avoid another process saving at the same time.
+        # This has led to errors as documented here:
+        # https://github.com/speechbrain/speechbrain/issues/2250
+        ddp_barrier()
+
         # Delete unprotected checkpoints
         for ckpt in potential_deletions:
             if ckpt not in protected_checkpoints:
                 Checkpointer._delete_checkpoint(ckpt, verbosity=verbosity)
 
+        # Sync after deleting to avoid another process saving at the same time.
+        # This has led to errors as documented here:
+        # https://github.com/speechbrain/speechbrain/issues/2250
+        ddp_barrier()
+
     @staticmethod
     @main_process_only
     def _delete_checkpoint(checkpoint, verbosity=logging.INFO):

speechbrain/utils/distributed.py

@@ -3,12 +3,15 @@
 Authors:
  * Abdel Heba 2020
  * Aku Rouhe 2020
+ * Peter Plantinga 2023
 """
 import datetime
 import os
 import torch
 from functools import wraps
 
+MAIN_PROC_ONLY = 0
+
 
 def run_on_main(
     func,
@@ -54,27 +57,17 @@ def run_on_main(
     if post_kwargs is None:
         post_kwargs = {}
 
-    if if_main_process():
-        # Main comes here
-        try:
-            func(*args, **kwargs)
-        finally:
-            ddp_barrier()
-    else:
-        # Others go here
-        ddp_barrier()
+    main_process_only(func)(*args, **kwargs)
+    ddp_barrier()
+
     if post_func is not None:
         if run_post_on_main:
             # Just run on every process without any barrier.
             post_func(*post_args, **post_kwargs)
-        elif not if_main_process():
-            # Others go here
-            try:
-                post_func(*post_args, **post_kwargs)
-            finally:
-                ddp_barrier()
         else:
-            # But main comes here
+            # Do the opposite of `run_on_main`
+            if not if_main_process():
+                post_func(*post_args, **post_kwargs)
             ddp_barrier()
 
 
@@ -103,8 +96,14 @@ def main_process_only(function):
     @wraps(function)
     def main_proc_wrapped_func(*args, **kwargs):
         """This decorated function runs only if this is the main process."""
+        global MAIN_PROC_ONLY
+        MAIN_PROC_ONLY += 1
         if if_main_process():
-            return function(*args, **kwargs)
+            result = function(*args, **kwargs)
+        else:
+            result = None
+        MAIN_PROC_ONLY -= 1
+        return result
 
     return main_proc_wrapped_func
 
@@ -114,7 +113,10 @@ def ddp_barrier():
     torch.distributed.barrier() will block processes until the whole
     group enters this function.
     """
-    if torch.distributed.is_initialized():
+    # Check if we're in a single-threaded section, skip barrier
+    if MAIN_PROC_ONLY >= 1:
+        return
+    elif torch.distributed.is_initialized():
         torch.distributed.barrier()