additional instruction for the grad_scale is too small error

egs/aishell/ASR/pruned_transducer_stateless7/do_not_use_it_directly.py

@@ -89,6 +89,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
@@ -881,9 +882,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error()
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]
             cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0

egs/aishell/ASR/pruned_transducer_stateless7/train.py

@@ -85,6 +85,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.lexicon import Lexicon
 from icefall.utils import (
@@ -878,9 +879,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]
             cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0

egs/aishell/ASR/pruned_transducer_stateless7_bbpe/train.py

@@ -78,6 +78,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
@@ -871,9 +872,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/aishell/ASR/pruned_transducer_stateless7_streaming/do_not_use_it_directly.py

@@ -78,6 +78,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
@@ -882,9 +883,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/aishell/ASR/pruned_transducer_stateless7_streaming/train.py

@@ -78,6 +78,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
@@ -881,9 +882,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/aishell/ASR/zipformer/train.py

@@ -86,6 +86,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.lexicon import Lexicon
 from icefall.utils import (
@@ -985,9 +986,7 @@ def save_bad_model(suffix: str = ""):
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
                 save_bad_model()
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = max(scheduler.get_last_lr())

egs/aishell/ASR/zipformer/train_bbpe.py

@@ -83,6 +83,7 @@
     update_averaged_model,
 )
 from icefall.dist import cleanup_dist, setup_dist
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
@@ -570,9 +571,7 @@ def save_bad_model(suffix: str = ""):
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
                 save_bad_model()
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = max(scheduler.get_last_lr())

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/train.py

@@ -70,6 +70,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
@@ -851,9 +852,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/ami/ASR/pruned_transducer_stateless7/train.py

@@ -69,6 +69,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
@@ -842,9 +843,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/ami/SURT/dprnn_zipformer/train.py

@@ -75,6 +75,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -1138,9 +1139,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/ami/SURT/dprnn_zipformer/train_adapt.py

@@ -75,6 +75,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -1129,9 +1130,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/commonvoice/ASR/pruned_transducer_stateless7/train.py

@@ -79,6 +79,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
@@ -871,9 +872,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/do_not_use_it_directly.py

@@ -889,9 +889,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise RuntimeError(f", exiting: {cur_grad_scale}")
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/finetune.py

@@ -81,6 +81,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
@@ -965,9 +966,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/train.py

@@ -78,6 +78,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
@@ -888,9 +889,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/csj/ASR/pruned_transducer_stateless7_streaming/do_not_use_it_directly.py

@@ -81,6 +81,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
@@ -909,9 +910,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/csj/ASR/pruned_transducer_stateless7_streaming/train.py

@@ -81,6 +81,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
@@ -908,9 +909,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]

egs/gigaspeech/ASR/zipformer/train.py

@@ -89,6 +89,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
@@ -1031,9 +1032,7 @@ def save_bad_model(suffix: str = ""):
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
                 save_bad_model()
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = max(scheduler.get_last_lr())

egs/gigaspeech/KWS/zipformer/finetune.py

@@ -100,6 +100,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
@@ -371,9 +372,7 @@ def save_bad_model(suffix: str = ""):
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
                 save_bad_model()
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = max(scheduler.get_last_lr())

egs/gigaspeech/KWS/zipformer/train.py

@@ -89,6 +89,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
@@ -1034,9 +1035,7 @@ def save_bad_model(suffix: str = ""):
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
                 save_bad_model()
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = max(scheduler.get_last_lr())

egs/libricss/SURT/dprnn_zipformer/train.py

@@ -85,6 +85,7 @@
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
+from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -1169,9 +1170,7 @@ def train_one_epoch(
             if cur_grad_scale < 0.01:
                 logging.warning(f"Grad scale is small: {cur_grad_scale}")
             if cur_grad_scale < 1.0e-05:
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
+                raise_grad_scale_is_too_small_error(cur_grad_scale)
 
         if batch_idx % params.log_interval == 0:
             cur_lr = scheduler.get_last_lr()[0]