QAT support in core loop (#892)

Summary: Pull Request resolved: #892

Reviewed By: ywwwer, diego-urgell

Differential Revision: D61935530

fbshipit-source-id: 6c85ffdccf3a1014441e4bdfc1b527769a44fae9

Author: JKSenthil

tests/framework/test_loop_utils.py

@@ -12,6 +12,7 @@
 
 import torch
 from torch import distributed as dist, nn
+from torch.ao.quantization.pt2e.export_utils import model_is_exported
 from torch.distributed import launcher
 
 from torch.utils.data import DataLoader
@@ -88,6 +89,47 @@ def test_set_module_training_mode(self) -> None:
         self.assertFalse(prior_module_train_states["module"])
         self.assertFalse(prior_module_train_states["loss_fn"])
 
+    def test_set_module_training_mode_qat(self) -> None:
+        """
+        Test _set_module_training_mode
+        """
+
+        # define a floating point model
+        class M(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.fc = torch.nn.Linear(4, 4)
+
+            def forward(self, x):
+                x = self.fc(x)
+                return x
+
+        loss_fn = nn.CrossEntropyLoss()
+        module = torch.export.export(M(), (torch.rand(4, 4),)).module()
+
+        tracked_modules: Dict[str, torch.nn.Module] = {
+            "module": module,
+            "loss_fn": loss_fn,
+        }
+
+        self.assertTrue(model_is_exported(module))
+        prior_module_train_states = _set_module_training_mode(tracked_modules, False)
+
+        self.assertFalse(module.training)
+        self.assertFalse(loss_fn.training)
+
+        self.assertTrue(prior_module_train_states["module"])
+        self.assertTrue(prior_module_train_states["loss_fn"])
+
+        # set back to True
+        prior_module_train_states = _set_module_training_mode(tracked_modules, True)
+
+        self.assertTrue(module.training)
+        self.assertTrue(loss_fn.training)
+
+        self.assertFalse(prior_module_train_states["module"])
+        self.assertFalse(prior_module_train_states["loss_fn"])
+
     def test_reset_module_training_mode(self) -> None:
         """
         Test _reset_module_training_mode

torchtnt/framework/_loop_utils.py

@@ -96,14 +96,15 @@ def _set_module_training_mode(
         if _EXPORT_UTILS_AVAIL and model_is_exported(
             module.module if is_ddp else module
         ):
-            if mode:
-                module = torch.ao.quantization.move_exported_model_to_train(
-                    module.module if is_ddp else module
-                )
-            else:
-                module = torch.ao.quantization.move_exported_model_to_eval(
-                    module.module if is_ddp else module
-                )
+            move_fn = (
+                torch.ao.quantization.move_exported_model_to_train
+                if mode
+                else torch.ao.quantization.move_exported_model_to_eval
+            )
+            move_fn(module.module if is_ddp else module)
+            module.training = mode
+            if is_ddp:
+                module.module.training = mode
         else:
             module.train(mode)
 
@@ -118,7 +119,22 @@ def _reset_module_training_mode(
     # returning back to the user
     for name, module in modules.items():
         if name in prior_modes:
-            module.train(prior_modes[name])
+            is_ddp = isinstance(module, DistributedDataParallel)
+
+            if _EXPORT_UTILS_AVAIL and model_is_exported(
+                module.module if is_ddp else module
+            ):
+                move_fn = (
+                    torch.ao.quantization.move_exported_model_to_train
+                    if prior_modes[name]
+                    else torch.ao.quantization.move_exported_model_to_eval
+                )
+                move_fn(module.module if is_ddp else module)
+                module.training = prior_modes[name]
+                if is_ddp:
+                    module.module.training = prior_modes[name]
+            else:
+                module.train(prior_modes[name])
 
 
 def _log_api_usage(entry_point: str) -> None: