set the default to use set_to_none for clearing gradients in BF16 opt…

…imizer. (microsoft#5434)

as discussed in microsoft#5175, set the default to use set_to_none for clearing
gradients in BF16 optimizer.
Additionally, for the case of zero clearing, use foreach_zero.
Verified correctness with mega-ds llama 7B training.

FYI @loadams

---------

Co-authored-by: Logan Adams <114770087+loadams@users.noreply.github.com>

deepspeed/runtime/bf16_optimizer.py

@@ -341,7 +341,7 @@ def _update_hp_grad(self, lp, group_idx, param_idx, clear_lp_grads):
 
         # clear gradients
         if clear_lp_grads:
-            lp.grad._zero()
+            lp.grad.zero_()
 
     @torch.no_grad()
     def _update_hp_grads_func(self, clear_lp_grads=False):
@@ -441,11 +441,20 @@ def clear_hp_grads(self):
             self.fp32_groups_has_gradients[i] = [False] * len(group)
 
     def clear_lp_grads(self):
+
+        # using zero_() fixed memory address for graph replay
+        set_to_none = False if self.graph_harvesting else True
+        zero_grads_list = []
         for group in self.bf16_groups:
             for param in group:
-                if param.grad is not None:
-                    # Using zero_() fixed memory address for graph replay
-                    param.grad.zero_()
+                if set_to_none:
+                    param.grad = None
+                elif param.grad is not None:
+                    if param.grad.grad_fn is not None:
+                        param.grad.detach_()
+                    zero_grads_list.append(param.grad)
+        if not set_to_none and len(zero_grads_list) > 0:
+            torch._foreach_zero_(zero_grads_list)
 
     def state_dict(self):
         state_dict = {}