Fix (#72341)

Author: co63oc

python/paddle/base/reader.py

@@ -1638,7 +1638,7 @@ def __init__(self, dataset, places, drop_last):
         ), f"Filelist number of dataset {len(dataset.filelist)} must be not less than place number {thread_num}"
 
         if dataset.thread_num != 0 and dataset.thread_num != thread_num:
-            logging.warn(
+            logging.warning(
                 f'thread_num {dataset.thread_num} which is set in Dataset is ignored'
             )
 
@@ -1650,7 +1650,7 @@ def __init__(self, dataset, places, drop_last):
             )
             and dataset.queue_num > thread_num
         ):
-            logging.warn(
+            logging.warning(
                 f"queue_num {dataset.queue_num} which is set in Dataset is ignored"
             )
             dataset._set_queue_num(thread_num)

python/paddle/distributed/fleet/meta_optimizers/dgc_optimizer.py

@@ -543,10 +543,10 @@ def _can_apply(self):
 
         if self.user_defined_strategy.dgc:
             if not isinstance(self.inner_opt, Momentum):
-                logging.warn("dgc only works on Momentum optimizer")
+                logging.warning("dgc only works on Momentum optimizer")
                 return False
             if self.role_maker._worker_num() <= 1:
-                logging.warn("dgc only works on multi cards")
+                logging.warning("dgc only works on multi cards")
                 return False
 
             return True

python/paddle/distributed/fleet/meta_optimizers/lamb_optimizer.py

@@ -73,7 +73,7 @@ def _can_apply(self):
 
         if self.user_defined_strategy.lamb:
             if not isinstance(self.inner_opt, Adam):
-                logging.warn(
+                logging.warning(
                     f"lamb need the inner optimizer to be AdamOptimizer optimizer but got {self.inner_opt.type}."
                 )
                 return False

python/paddle/distributed/fleet/meta_optimizers/lars_optimizer.py

@@ -62,7 +62,7 @@ def _can_apply(self):
 
         if self.user_defined_strategy.lars:
             if not isinstance(self.inner_opt, Momentum):
-                logging.warn(
+                logging.warning(
                     f"lars need the inner optimizer to be Momentum optimizer but got {self.inner_opt.type}."
                 )
                 return False

python/paddle/distributed/fleet/runtime/collective_runtime.py

@@ -24,27 +24,27 @@ def __init__(self):
         super().__init__()
 
     def _init_worker(self):
-        logging.warn(
+        logging.warning(
             "You should not call 'init_worker' method for collective mode."
         )
 
     def _run_worker(self):
-        logging.warn(
+        logging.warning(
             "You should not call 'run_worker' method for collective mode."
         )
 
     def _init_server(self, *args, **kwargs):
-        logging.warn(
+        logging.warning(
             "You should not call 'init_server' method for collective mode."
         )
 
     def _run_server(self):
-        logging.warn(
+        logging.warning(
             "You should not call 'run_server' method for collective mode."
         )
 
     def _stop_worker(self):
-        logging.warn(
+        logging.warning(
             "You should not call 'stop_worker' method for collective mode."
         )
 

python/paddle/distributed/transpiler/distribute_transpiler.py

@@ -1529,7 +1529,7 @@ def __clone_lr_op_sub_block__(op, program, lr_block):
             )
 
         if len(optimize_blocks) == 0:
-            logging.warn(
+            logging.warning(
                 "pserver [" + str(endpoint) + "] has no optimize block!!"
             )
             pre_block_idx = pserver_program.num_blocks - 1
@@ -2216,7 +2216,7 @@ def _create_table_optimize_block(
         }
         outputs = {"ParamOut": [param_var]}
         # only support sgd now
-        logging.warn(
+        logging.warning(
             "distribute lookup table only support sgd optimizer, change it's optimizer to sgd instead of "
             + table_opt_op.type
         )

python/paddle/distributed/transpiler/memory_optimization_transpiler.py

@@ -24,7 +24,7 @@ def memory_optimize(
     This API is deprecated since 1.6. Please do not use it. The better
     memory optimization strategies are enabled by default.
     """
-    logging.warn(
+    logging.warning(
         'Caution! paddle.distributed.transpiler.memory_optimize() is deprecated '
         'and not maintained any more, since it is not stable!\n'
         'This API would not take any memory optimizations on your Program '
@@ -47,7 +47,7 @@ def release_memory(input_program, skip_opt_set=None):
     This API is deprecated since 1.6. Please do not use it. The better
     memory optimization strategies are enabled by default.
     """
-    logging.warn(
+    logging.warning(
         'paddle.distributed.transpiler.release_memory() is deprecated, it would not'
         ' take any memory release on your program'
     )

python/paddle/incubate/distributed/fleet/collective.py

@@ -46,27 +46,27 @@ def __init__(self):
         self._param_file_name = "_paddle_fleet_param__"
 
     def init_worker(self):
-        logging.warn(
+        logging.warning(
             "You should not call 'init_worker' method for collective mode."
         )
 
     def run_worker(self, main_programs=None, scopes=None):
-        logging.warn(
+        logging.warning(
             "You should not call 'run_worker' method for collective mode."
         )
 
     def init_server(self, model_dir=None):
-        logging.warn(
+        logging.warning(
             "You should not call 'init_server' method for collective mode."
         )
 
     def run_server(self):
-        logging.warn(
+        logging.warning(
             "You should not call 'run_server' method for collective mode."
         )
 
     def stop_worker(self):
-        logging.warn(
+        logging.warning(
             "You should not call 'stop_worker' method for collective mode."
         )
 
@@ -410,11 +410,13 @@ def _try_to_compile(self, startup_program, main_program):
 
         if node_num <= 1:
             if self._strategy.nccl_comm_num > 1:
-                logging.warn("set nccl_comm_num=1 since you only have 1 node.")
+                logging.warning(
+                    "set nccl_comm_num=1 since you only have 1 node."
+                )
             self._strategy.nccl_comm_num = 1
 
             if self._strategy.use_hierarchical_allreduce:
-                logging.warn(
+                logging.warning(
                     "set use_hierarchical_allreduce=False since you only have 1 node."
                 )
             self._strategy.use_hierarchical_allreduce = False
@@ -426,7 +428,7 @@ def _try_to_compile(self, startup_program, main_program):
         if sync_batch_norm is not None and sync_batch_norm is True:
             self._strategy.nccl_comm_num = 1
             self._strategy.use_hierarchical_allreduce = False
-            logging.warn(
+            logging.warning(
                 "use sync_batch_norm will hang when set num_threads > 1, so "
                 "set num_threads=1, nccl_comm_num=1, use_hierarchical_allreduce=False."
             )