Add --fast-norm arg to benchmark.py, train.py, validate.py

Author: rwightman

benchmark.py

@@ -19,7 +19,7 @@
 import torch.nn.parallel
 
 from timm.data import resolve_data_config
-from timm.models import create_model, is_model, list_models
+from timm.models import create_model, is_model, list_models, set_fast_norm
 from timm.optim import create_optimizer_v2
 from timm.utils import setup_default_logging, set_jit_fuser, decay_batch_step, check_batch_size_retry
 
@@ -109,7 +109,8 @@
                     help='convert model torchscript for inference')
 scripting_group.add_argument('--aot-autograd', default=False, action='store_true',
                     help="Enable AOT Autograd support. (It's recommended to use this option with `--fuser nvfuser` together)")
-
+scripting_group.add_argument('--fast-norm', default=False, action='store_true',
+                    help='enable experimental fast-norm')
 
 # train optimizer parameters
 parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
@@ -598,6 +599,9 @@ def main():
     model_cfgs = []
     model_names = []
 
+    if args.fast_norm:
+        set_fast_norm()
+
     if args.model_list:
         args.model = ''
         with open(args.model_list) as f:

timm/models/__init__.py

@@ -69,5 +69,6 @@
 from .layers import TestTimePoolHead, apply_test_time_pool
 from .layers import convert_splitbn_model, convert_sync_batchnorm
 from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable, is_no_jit, set_no_jit
+from .layers import set_fast_norm
 from .registry import register_model, model_entrypoint, list_models, is_model, list_modules, is_model_in_modules,\
     is_model_pretrained, get_pretrained_cfg, has_pretrained_cfg_key, is_pretrained_cfg_key, get_pretrained_cfg_value

train.py

@@ -33,7 +33,7 @@
 from timm.loss import JsdCrossEntropy, SoftTargetCrossEntropy, BinaryCrossEntropy, \
     LabelSmoothingCrossEntropy
 from timm.models import create_model, safe_model_name, resume_checkpoint, load_checkpoint, \
-    convert_splitbn_model, convert_sync_batchnorm, model_parameters
+    convert_splitbn_model, convert_sync_batchnorm, model_parameters, set_fast_norm
 from timm.optim import create_optimizer_v2, optimizer_kwargs
 from timm.scheduler import create_scheduler
 from timm.utils import ApexScaler, NativeScaler
@@ -135,6 +135,8 @@
                     help="Enable AOT Autograd support. (It's recommended to use this option with `--fuser nvfuser` together)")
 group.add_argument('--fuser', default='', type=str,
                     help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
+group.add_argument('--fast-norm', default=False, action='store_true',
+                    help='enable experimental fast-norm')
 group.add_argument('--grad-checkpointing', action='store_true', default=False,
                     help='Enable gradient checkpointing through model blocks/stages')
 
@@ -395,6 +397,8 @@ def main():
 
     if args.fuser:
         utils.set_jit_fuser(args.fuser)
+    if args.fast_norm:
+        set_fast_norm()
 
     model = create_model(
         args.model,

validate.py

@@ -20,7 +20,7 @@
 from collections import OrderedDict
 from contextlib import suppress
 
-from timm.models import create_model, apply_test_time_pool, load_checkpoint, is_model, list_models
+from timm.models import create_model, apply_test_time_pool, load_checkpoint, is_model, list_models, set_fast_norm
 from timm.data import create_dataset, create_loader, resolve_data_config, RealLabelsImagenet
 from timm.utils import accuracy, AverageMeter, natural_key, setup_default_logging, set_jit_fuser,\
     decay_batch_step, check_batch_size_retry
@@ -117,6 +117,8 @@
                     help="Enable AOT Autograd support. (It's recommended to use this option with `--fuser nvfuser` together)")
 parser.add_argument('--fuser', default='', type=str,
                     help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
+parser.add_argument('--fast-norm', default=False, action='store_true',
+                    help='enable experimental fast-norm')
 parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
                     help='Output csv file for validation results (summary)')
 parser.add_argument('--real-labels', default='', type=str, metavar='FILENAME',
@@ -150,6 +152,8 @@ def validate(args):
 
     if args.fuser:
         set_jit_fuser(args.fuser)
+    if args.fast_norm:
+        set_fast_norm()
 
     # create model
     model = create_model(