add --aot-autograd

benchmark.py

@@ -51,6 +51,12 @@ except ImportError as e:
     FlopCountAnalysis = None
     has_fvcore_profiling = False
 
+try:
+    from functorch.compile import memory_efficient_fusion
+    has_functorch = True
+except ImportError as e:
+    has_functorch = False
+
 
 torch.backends.cudnn.benchmark = True
 _logger = logging.getLogger('validate')
@@ -95,10 +101,13 @@ parser.add_argument('--amp', action='store_true', default=False,
                     help='use PyTorch Native AMP for mixed precision training. Overrides --precision arg.')
 parser.add_argument('--precision', default='float32', type=str,
                     help='Numeric precision. One of (amp, float32, float16, bfloat16, tf32)')
-parser.add_argument('--torchscript', dest='torchscript', action='store_true',
-                    help='convert model torchscript for inference')
 parser.add_argument('--fuser', default='', type=str,
                     help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
+scripting_group = parser.add_mutually_exclusive_group()
+scripting_group.add_argument('--torchscript', dest='torchscript', action='store_true',
+                    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)")
 
 
 # train optimizer parameters
@@ -188,7 +197,7 @@ def profile_fvcore(model, input_size=(3, 224, 224), batch_size=1, detailed=False
 
 class BenchmarkRunner:
     def __init__(
-            self, model_name, detail=False, device='cuda', torchscript=False, precision='float32',
+            self, model_name, detail=False, device='cuda', torchscript=False, aot_autograd=False, precision='float32',
             fuser='', num_warm_iter=10, num_bench_iter=50, use_train_size=False, **kwargs):
         self.model_name = model_name
         self.detail = detail
@@ -220,6 +229,9 @@ class BenchmarkRunner:
         if torchscript:
             self.model = torch.jit.script(self.model)
             self.scripted = True
+        if aot_autograd:
+            assert has_functorch, "functorch is needed for --aot-autograd"
+            self.model = memory_efficient_fusion(self.model)
 
         data_config = resolve_data_config(kwargs, model=self.model, use_test_size=not use_train_size)
         self.input_size = data_config['input_size']

train.py

@@ -61,6 +61,13 @@ try:
 except ImportError:
     has_wandb = False
 
+try:
+    from functorch.compile import memory_efficient_fusion
+    has_functorch = True
+except ImportError as e:
+    has_functorch = False
+
+
 torch.backends.cudnn.benchmark = True
 _logger = logging.getLogger('train')
 
@@ -123,8 +130,11 @@ group.add_argument('-vb', '--validation-batch-size', type=int, default=None, met
                     help='Validation batch size override (default: None)')
 group.add_argument('--channels-last', action='store_true', default=False,
                     help='Use channels_last memory layout')
-group.add_argument('--torchscript', dest='torchscript', action='store_true',
+scripting_group = group.add_mutually_exclusive_group()
+scripting_group.add_argument('--torchscript', dest='torchscript', action='store_true',
                     help='torch.jit.script the full model')
+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)")
 group.add_argument('--fuser', default='', type=str,
                     help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')")
 group.add_argument('--grad-checkpointing', action='store_true', default=False,
@@ -445,6 +455,9 @@ def main():
         assert not use_amp == 'apex', 'Cannot use APEX AMP with torchscripted model'
         assert not args.sync_bn, 'Cannot use SyncBatchNorm with torchscripted model'
         model = torch.jit.script(model)
+    if args.aot_autograd:
+        assert has_functorch, "functorch is needed for --aot-autograd"
+        model = memory_efficient_fusion(model)
 
     optimizer = create_optimizer_v2(model, **optimizer_kwargs(cfg=args))