Fix gradient clipping (#1438)

* Fix gradient clipping

* Relax accuracy constraint

pytorch_lightning/trainer/training_tricks.py

@@ -41,7 +41,7 @@ def clip_gradients(self):
                 total_norm = torch.zeros([], device=device if parameters else None)
                 for p in parameters:
                     param_norm = p.grad.data.norm(norm_type) ** norm_type
-                total_norm.add_(param_norm)
+                    total_norm.add_(param_norm)
                 total_norm = (total_norm ** (1. / norm_type))
             eps = EPSILON_FP16 if self.precision == 16 else EPSILON
             clip_coef = torch.tensor(max_norm, device=device) / (total_norm + eps)

tests/trainer/test_trainer.py

@@ -658,3 +658,30 @@ def on_batch_start(self, trainer, pl_module):
     assert not trainer.interrupted
     trainer.fit(model)
     assert trainer.interrupted
+
+
+def test_gradient_clipping(tmpdir):
+    """
+    Test gradient clipping
+    """
+    tutils.reset_seed()
+
+    hparams = tutils.get_default_hparams()
+    model = LightningTestModel(hparams)
+
+    # test that gradient is clipped correctly
+    def _optimizer_step(*args, **kwargs):
+        parameters = model.parameters()
+        grad_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), 2) for p in parameters]), 2)
+        assert (grad_norm - 1.0).abs() < 0.01, "Gradient norm != 1.0: {grad_norm}".format(grad_norm=grad_norm)
+
+    trainer = Trainer(max_steps=1,
+                      max_epochs=1,
+                      gradient_clip_val=1.0,
+                      default_save_path=tmpdir)
+
+    # for the test
+    model.optimizer_step = _optimizer_step
+    model.prev_called_batch_idx = 0
+
+    trainer.fit(model)