fix apex gradient clipping (#2829)

pytorch_lightning/trainer/training_loop.py

@@ -291,7 +291,7 @@ def transfer_batch_to_tpu(self, *args):
         """Warning: this is just empty shell for code implemented in other class."""
 
     @abstractmethod
-    def clip_gradients(self):
+    def clip_gradients(self, *args):
         """Warning: this is just empty shell for code implemented in other class."""
 
     @abstractmethod
@@ -817,7 +817,7 @@ def run_batch_backward_pass(self, split_batch, batch_idx, opt_idx, optimizer):
         # ------------------
         if self.use_amp and NATIVE_AMP_AVALAIBLE and not self.use_tpu:
             self.scaler.unscale_(optimizer)
-        self.clip_gradients()
+        self.clip_gradients(optimizer)
 
         # ------------------
         # .STEP + ZERO_GRAD

pytorch_lightning/trainer/training_tricks.py

@@ -27,9 +27,17 @@
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.callbacks import GradientAccumulationScheduler
 from pytorch_lightning.loggers.base import DummyLogger
+from pytorch_lightning.utilities import NATIVE_AMP_AVALAIBLE
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.memory import is_oom_error, garbage_collection_cuda
 
+try:
+    from apex import amp
+except ImportError:
+    APEX_AVAILABLE = False
+else:
+    APEX_AVAILABLE = True
+
 EPSILON = 1e-6
 EPSILON_FP16 = 1e-5
 
@@ -60,14 +68,17 @@ def restore(self, *args):
     def fit(self, *args):
         """Warning: this is just empty shell for code implemented in other class."""
 
-    def clip_gradients(self):
+    def clip_gradients(self, optimizer):
 
         # this code is a modification of torch.nn.utils.clip_grad_norm_
         # with TPU support based on https://github.com/pytorch/xla/blob/master/TROUBLESHOOTING.md
         if self.gradient_clip_val <= 0:
             return
         model = self.get_model()
-        parameters = model.parameters()
+        if self.use_amp and not NATIVE_AMP_AVALAIBLE:
+            parameters = amp.master_params(optimizer)
+        else:
+            parameters = model.parameters()
         max_norm = float(self.gradient_clip_val)
         norm_type = float(2.0)
         if isinstance(parameters, torch.Tensor):