Update validate.py to use updated amp args for impl/dtype

validate.py

@@ -103,11 +103,11 @@ parser.add_argument('--channels-last', action='store_true', default=False,
 parser.add_argument('--device', default='cuda', type=str,
                     help="Device (accelerator) to use.")
 parser.add_argument('--amp', action='store_true', default=False,
-                    help='Use AMP mixed precision. Defaults to Apex, fallback to native Torch AMP.')
+                    help='use NVIDIA Apex AMP or Native AMP for mixed precision training')
-parser.add_argument('--apex-amp', action='store_true', default=False,
+parser.add_argument('--amp-dtype', default='float16', type=str,
-                    help='Use NVIDIA Apex AMP mixed precision')
+                    help='lower precision AMP dtype (default: float16)')
-parser.add_argument('--native-amp', action='store_true', default=False,
+parser.add_argument('--amp-impl', default='native', type=str,
-                    help='Use Native Torch AMP mixed precision')
+                    help='AMP impl to use, "native" or "apex" (default: native)')
 parser.add_argument('--tf-preprocessing', action='store_true', default=False,
                     help='Use Tensorflow preprocessing pipeline (require CPU TF installed')
 parser.add_argument('--use-ema', dest='use_ema', action='store_true',
@@ -142,21 +142,22 @@ def validate(args):
 
     device = torch.device(args.device)
 
-    amp_autocast = suppress  # do nothing
+    # resolve AMP arguments based on PyTorch / Apex availability
+    use_amp = None
+    amp_autocast = suppress
     if args.amp:
-        if has_native_amp:
+        if args.amp_impl == 'apex':
-            args.native_amp = True
+            assert has_apex, 'AMP impl specified as APEX but APEX is not installed.'
-        elif has_apex:
+            assert args.amp_dtype == 'float16'
-            args.apex_amp = True
+            use_amp = 'apex'
+            _logger.info('Validating in mixed precision with NVIDIA APEX AMP.')
         else:
-            _logger.warning("Neither APEX or Native Torch AMP is available.")
+            assert has_native_amp, 'Please update PyTorch to a version with native AMP (or use APEX).'
-    assert not args.apex_amp or not args.native_amp, "Only one AMP mode should be set."
+            assert args.amp_dtype in ('float16', 'bfloat16')
-    if args.native_amp:
+            use_amp = 'native'
-        amp_autocast = partial(torch.autocast, device_type=device.type)
+            amp_dtype = torch.bfloat16 if args.amp_dtype == 'bfloat16' else torch.float16
-        _logger.info('Validating in mixed precision with native PyTorch AMP.')
+            amp_autocast = partial(torch.autocast, device_type=device.type, dtype=amp_dtype)
-    elif args.apex_amp:
+            _logger.info('Validating in mixed precision with native PyTorch AMP.')
-        assert device.type == 'cuda'
-        _logger.info('Validating in mixed precision with NVIDIA APEX AMP.')
     else:
         _logger.info('Validating in float32. AMP not enabled.')
 
@@ -204,7 +205,7 @@ def validate(args):
         model = memory_efficient_fusion(model)
 
     model = model.to(device)
-    if args.apex_amp:
+    if use_amp == 'apex':
         model = amp.initialize(model, opt_level='O1')
 
     if args.channels_last: