modify clip_grad with no to_global

python/oneflow/nn/utils/clip_grad.py

@@ -100,31 +100,23 @@ def clip_grad_norm_(
         param0_placement = parameters[0].placement
         if norm_type == float("inf"):
             norms = [
-                p.grad.detach()
-                .to_global(sbp=sbp_broadcast)
-                .abs()
-                .max()
-                .to_global(placement=param0_placement)
+                p.grad.detach().abs().max().to_global(placement=param0_placement)
                 for p in parameters
             ]
             total_norm = norms[0] if len(norms) == 1 else flow.max(flow.stack(norms))
         elif norm_type == float("-inf"):
             norms = [
-                p.grad.detach()
-                .to_global(sbp=sbp_broadcast)
-                .abs()
-                .min()
-                .to_global(placement=param0_placement)
+                p.grad.detach().abs().min().to_global(placement=param0_placement)
                 for p in parameters
             ]
             total_norm = norms[0] if len(norms) == 1 else flow.min(flow.stack(norms))
         else:
             total_norm = flow.linalg.vector_norm(
                 flow.stack(
                     [
-                        flow.linalg.vector_norm(
-                            p.grad.detach().to_global(sbp=sbp_broadcast), norm_type
-                        ).to_global(placement=param0_placement)
+                        flow.linalg.vector_norm(p.grad.detach(), norm_type).to_global(
+                            placement=param0_placement
+                        )
                         for p in parameters
                     ]
                 ),

python/oneflow/test/modules/test_clip_grad.py

@@ -117,6 +117,7 @@ def _test_graph_clip_grad_value_impl(test_case, shape, device, clip_value):
     )
 
 
+# TODO(lml): find why fail on ci machine
 def _test_clip_grad_norm_global_impl(
     test_case, shape, sbp, placement, max_norm, norm_type
 ):