fixed clip_grads for when len(grads_for_norm) == 0.

megatron/optimizer/clip_grads.py

@@ -75,12 +75,15 @@ def clip_grad_norm_fp32(parameters, grads_for_norm,
             # Use apex's multi-tensor applier for efficiency reasons.
             # Multi-tensor applier takes a function and a list of list
             # and performs the operation on that list all in one kernel.
-            grad_norm, _ = multi_tensor_applier(
-                amp_C.multi_tensor_l2norm,
-                dummy_overflow_buf,
-                [grads_for_norm],
-                False # no per-parameter norm
-            )
+            if grads_for_norm:
+                grad_norm, _ = multi_tensor_applier(
+                    amp_C.multi_tensor_l2norm,
+                    dummy_overflow_buf,
+                    [grads_for_norm],
+                    False # no per-parameter norm
+                )
+            else:
+                grad_norm = torch.cuda.FloatTensor([0])
             # Since we will be summing across data parallel groups,
             # we need the pow(norm-type).
             total_norm = grad_norm ** norm_type