[MSC][M5.3] Support torch.dynamo for dynamic models

gallery/how_to/work_with_msc/using_tools.py

@@ -57,11 +57,12 @@
 parser.add_argument("--test_iter", type=int, default=100, help="The iter for test")
 parser.add_argument("--calibrate_iter", type=int, default=100, help="The iter for calibration")
 parser.add_argument("--train_batch", type=int, default=32, help="The batch size for train")
-parser.add_argument("--train_iter", type=int, default=200, help="The iter for train")
-parser.add_argument("--train_epoch", type=int, default=100, help="The epoch for train")
+parser.add_argument("--train_iter", type=int, default=100, help="The iter for train")
+parser.add_argument("--train_epoch", type=int, default=5, help="The epoch for train")
 parser.add_argument(
     "--verbose", type=str, default="info", help="The verbose level, info|debug:1,2,3|critical"
 )
+parser.add_argument("--dynamic", action="store_true", help="Whether to use dynamic wrapper")
 args = parser.parse_args()
 
 
@@ -88,8 +89,8 @@ def get_config(calib_loader, train_loader):
         compile_type=args.compile_type,
         dataset=dataset,
         tools=tools,
-        skip_config={"all": "check"},
         verbose=args.verbose,
+        dynamic=args.dynamic,
     )
 
 
@@ -100,13 +101,13 @@ def _get_calib_datas():
         for i, (inputs, _) in enumerate(testloader, 0):
             if i >= args.calibrate_iter > 0:
                 break
-            yield {"input": inputs}
+            yield inputs if args.dynamic else {"input": inputs}
 
     def _get_train_datas():
         for i, (inputs, _) in enumerate(trainloader, 0):
             if i >= args.train_iter > 0:
                 break
-            yield {"input": inputs}
+            yield inputs if args.dynamic else {"input": inputs}
 
     model = resnet50(pretrained=args.checkpoint)
     if torch.cuda.is_available():

python/tvm/contrib/msc/core/gym/environment/method.py

@@ -105,7 +105,7 @@ def _get_loss(golden, result):
             outputs = runner.run(inputs)
             baseline = loader[idx]
             for name, data in outputs.items():
-                loss += _get_loss(baseline[name], data)
+                loss += _get_loss(baseline[name], msc_utils.cast_array(data))
         return {"loss": loss / len(loader)}
 
     @classmethod

python/tvm/contrib/msc/core/gym/environment/quantize_env.py

@@ -70,7 +70,7 @@ def _summary(self, actions: List[dict], rewards: List[dict]) -> Union[dict, str]
                 continue
             info.update(strategys[name].get_executor(msc_utils.MSCStage.QUANTIZE).config)
         summary_file = msc_utils.get_cache_dir().relpath("gym_summary.json")
-        return msc_utils.dump_dict(plan, summary_file)
+        return msc_utils.save_dict(plan, summary_file)
 
     @classmethod
     def role_type(cls):

python/tvm/contrib/msc/core/runtime/__init__.py

@@ -17,3 +17,4 @@
 """tvm.contrib.msc.core.runtime"""
 
 from .runner import *
+from .jit import *