Throws an error when params in optimizer are not the same as that of …

…module's in `make_private` (pytorch#439)

Summary:
Pull Request resolved: pytorch#439

Compare nn.Module.parameters() with list of parameters from all param_groups of optimizer. If they are all not equal then raise error "Module parameters are different than optimizer Parameters"

Differential Revision: D37163873

fbshipit-source-id: 8e25fa1738f08c5aa52f856023f72948164d6f0e

opacus/privacy_engine.py

@@ -360,6 +360,10 @@ def make_private(
         if noise_generator and self.secure_mode:
             raise ValueError("Passing seed is prohibited in secure mode")
 
+        # compare module parameter with optimizer parameters
+        if not all(torch.eq(i,j).all() for i,j in zip(list(module.parameters()), sum([param_group['params'] for param_group in optimizer.param_groups], []))):
+            raise ValueError("Module parameters are different than optimizer Parameters")
+
         distributed = isinstance(module, (DPDDP, DDP))
 
         module = self._prepare_model(

opacus/tests/privacy_engine_test.py

@@ -27,6 +27,7 @@
 import torch.nn.functional as F
 from hypothesis import given, settings
 from opacus import PrivacyEngine
+from opacus.validators import ModuleValidator
 from opacus.layers.dp_multihead_attention import DPMultiheadAttention
 from opacus.optimizers.optimizer import _generate_noise
 from opacus.scheduler import StepNoise, _NoiseScheduler
@@ -464,6 +465,38 @@ def test_deterministic_run(self):
                 "Model parameters after deterministic run must match",
             )
 
+    def test_param_equal_module_optimizer(self):
+        """Test that the privacy engine raises error if nn.Module parameters are not equal to optimizer parameters """
+        model = models.densenet121(pretrained=True)
+        num_ftrs = model.classifier.in_features
+        model.classifier = nn.Sequential(nn.Linear(num_ftrs, 10), nn.Sigmoid())
+        optimizer = torch.optim.SGD(model.parameters(),lr=0.01,momentum=0,weight_decay=0)
+        dl = self._init_data()
+        model = ModuleValidator.fix(model)
+        privacy_engine = PrivacyEngine()
+        with self.assertRaisesRegex(ValueError, "Module parameters are different than optimizer Parameters"):
+            _, _, _ = privacy_engine.make_private(
+                module=model,
+                optimizer=optimizer,
+                data_loader=dl,
+                noise_multiplier=1.1,
+                max_grad_norm=1.0
+                )
+
+        # if optimizer is defined after ModuleValidator.fix() then raise no error
+        optimizer = torch.optim.SGD(model.parameters(),lr=0.01,momentum=0,weight_decay=0)
+        _, _, _ = privacy_engine.make_private(
+                module=model,
+                optimizer=optimizer,
+                data_loader=dl,
+                noise_multiplier=1.1,
+                max_grad_norm=1.0
+                )
+        self.assertTrue(1, 1)
+
+
+
+
     @given(noise_scheduler=st.sampled_from([None, StepNoise]))
     @settings(deadline=None)
     def test_checkpoints(self, noise_scheduler: Optional[_NoiseScheduler]):