allreduce add ps filter

mindspore/nn/wrap/grad_reducer.py

@@ -50,8 +50,8 @@ def _init_allreduce_operators(length):
     return opt_list
 
 
-@reduce_opt.register("Number", "Bool", "Function", "Bool", "Tensor", "Function")
+@reduce_opt.register("Number", "Bool", "Function", "Bool", "Tensor", "Function", "Bool")
-def _tensors_allreduce(degree, mean, allgather, allreduce_filter, grad, allreduce):
+def _tensors_allreduce(degree, mean, allgather, allreduce_filter, grad, allreduce, ps_parameter):
     """
     Apply allreduce on gradient.
 
@@ -66,7 +66,7 @@ def _tensors_allreduce(degree, mean, allgather, allreduce_filter, grad, allreduc
     Returns:
         Tensor, the gradient tensor after operation.
     """
-    if allreduce_filter:
+    if not ps_parameter and allreduce_filter:
         grad = allreduce(grad)
         if mean:
             degree = F.scalar_cast(degree, F.dtype(grad))
@@ -257,6 +257,8 @@ class DistributedGradReducer(Cell):
         self.allreduce_filter = tuple(x.layerwise_parallel is False for x in parameters)
         self.opt_list = _init_allreduce_operators(len(parameters))
         self.allgather = AllGather(GlobalComm.WORLD_COMM_GROUP)
+        ps_filter = lambda x: x.is_param_ps
+        self.ps_parameters = tuple(ps_filter(x) for x in parameters)
 
     def construct(self, grads):
         """
@@ -273,7 +275,7 @@ class DistributedGradReducer(Cell):
         datatypes = self.map_(F.partial(_get_datatype), grads)
         grads = self.map_(F.partial(_cast_datatype, mstype.float32), grads)
         new_grad = self.map_(F.partial(reduce_opt, self.degree, self.mean, self.allgather),
-                             self.allreduce_filter, grads, self.opt_list)
+                             self.allreduce_filter, grads, self.opt_list, self.ps_parameters)
 
         new_grad = self.map_(F.partial(_cast_datatype), datatypes, new_grad)
         return new_grad