[CP] align fleet param (#31220)

* update, test=develop (#30692)

* align the default value of some configuration for fleet to that of single cards (#30740)

* update, test=develop

Author: lilong12

paddle/fluid/framework/distributed_strategy.proto

@@ -141,9 +141,9 @@ message DistributedStrategy {
   optional bool fuse_all_reduce_ops = 18 [ default = true ];
   optional int32 fuse_grad_size_in_MB = 19 [ default = 32 ];
   optional float fuse_grad_size_in_TFLOPS = 20 [ default = 50 ];
-  optional bool cudnn_exhaustive_search = 21 [ default = true ];
-  optional int32 conv_workspace_size_limit = 22 [ default = 4000 ];
-  optional bool cudnn_batchnorm_spatial_persistent = 23 [ default = true ];
+  optional bool cudnn_exhaustive_search = 21 [ default = false ];
+  optional int32 conv_workspace_size_limit = 22 [ default = 512 ];
+  optional bool cudnn_batchnorm_spatial_persistent = 23 [ default = false ];
   optional bool adaptive_localsgd = 24 [ default = false ];
   optional bool fp16_allreduce = 25 [ default = false ];
   optional bool sharding = 26 [ default = false ];

python/paddle/distributed/fleet/base/distributed_strategy.py

@@ -118,6 +118,22 @@ def __init__(self):
 
         """
         self.strategy = distributed_strategy_pb2.DistributedStrategy()
+
+        # Set the default values of the following flags to the ones set by users
+        key = 'FLAGS_cudnn_batchnorm_spatial_persistent'
+        if core.globals().is_public(key):
+            self.strategy.cudnn_batchnorm_spatial_persistent = bool(
+                core.globals()[key])
+        key = 'FLAGS_conv_workspace_size_limit'
+        if core.globals().is_public(key):
+            self.strategy.conv_workspace_size_limit = int(core.globals()[key])
+        key = 'FLAGS_cudnn_exhaustive_search'
+        if core.globals().is_public(key):
+            self.strategy.cudnn_exhaustive_search = bool(core.globals()[key])
+        key = 'FLAGS_sync_nccl_allreduce'
+        if core.globals().is_public(key):
+            self.strategy.sync_nccl_allreduce = bool(core.globals()[key])
+
         self.__lock_attr = True
 
     def __setattr__(self, key, value):