check if grad is none before calling all_reduce (#6428)

nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py

@@ -456,7 +456,11 @@ def _append_sequence_parallel_module_grads(self, module, grads):
 
         for param in module.parameters():
             sequence_parallel_param = getattr(param, 'sequence_parallel', False)
-            if sequence_parallel_param:
+            # (@adithyare) adapter training now extends MegatronGPTModel
+            # so we have to add this check here to ensure we do not
+            # perform all_reduce when grad is None.
+            # grad can be None when performing PeFT training.
+            if sequence_parallel_param and param.requires_grad:
                 if self.megatron_amp_o2:
                     grad = param.main_grad
                 else:
@@ -504,12 +508,15 @@ def allreduce_first_last_embeddings(self):
                     module = self.model
             if module.share_token_embeddings:
                 word_embeddings_weight = module.word_embeddings_weight()
-                if self.megatron_amp_o2:
-                    # O2 recipe stores a "main" copy of weights and grads
-                    grad = word_embeddings_weight.main_grad
-                else:
-                    grad = word_embeddings_weight.grad
-                torch.distributed.all_reduce(grad, group=parallel_state.get_embedding_group())
+                # (@adithyare) adapter training now extends MegatronGPTModel so we have to add this check here to ensure we do not perform all_reduce when grad is None.
+                # grad can be None when performing PeFT training.
+                if word_embeddings_weight.requires_grad:
+                    if self.megatron_amp_o2:
+                        # O2 recipe stores a "main" copy of weights and grads
+                        grad = word_embeddings_weight.main_grad
+                    else:
+                        grad = word_embeddings_weight.grad
+                    torch.distributed.all_reduce(grad, group=parallel_state.get_embedding_group())
 
     def get_forward_output_and_loss_func(self, validation_step=False):
         def fwd_output_and_loss_func(dataloader_iter, model, checkpoint_activations_all_layers=None):