[CodeStyle][UP031] fix /python/paddle/distributed/fleet/* - part 12 (…

…PaddlePaddle#65572)

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

@@ -776,14 +776,13 @@ def sparse_optimizer_config(sgd, strategy, prefix):
         def set_sparse_table_config(table_data, config):
             for key in config:
                 if key not in support_sparse_key_list:
-                    raise ValueError("strategy key '%s' not support" % (key))
+                    raise ValueError(f"strategy key '{key}' not support")
             table_class = config.get(
                 "sparse_table_class", "DownpourSparseTable"
             )
             if table_class not in support_sparse_table_class:
                 raise ValueError(
-                    "support sparse_table_class: ['DownpourSparseTable, DownpourSparseSSDTable'], but actual %s"
-                    % (table_class)
+                    f"support sparse_table_class: ['DownpourSparseTable, DownpourSparseSSDTable'], but actual {table_class}"
                 )
             if table_class == "DownpourSparseSSDTable":
                 table_data.table_class = 'SSDSparseTable'
@@ -806,8 +805,7 @@ def set_sparse_table_config(table_data, config):
             )
             if accessor_class not in support_sparse_accessor_class:
                 raise ValueError(
-                    "support sparse_accessor_class: ['DownpourSparseValueAccessor', 'DownpourCtrAccessor', 'DownpourCtrDoubleAccessor', 'DownpourUnitAccessor', 'DownpourDoubleUnitAccessor', 'DownpourCtrDymfAccessor'], but actual %s"
-                    % (accessor_class)
+                    f"support sparse_accessor_class: ['DownpourSparseValueAccessor', 'DownpourCtrAccessor', 'DownpourCtrDoubleAccessor', 'DownpourUnitAccessor', 'DownpourDoubleUnitAccessor', 'DownpourCtrDymfAccessor'], but actual {accessor_class}"
                 )
 
             if accessor_class.find("Double") >= 0:

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

@@ -21,7 +21,7 @@
 
 def crepr(v):
     if isinstance(v, str):
-        return '"%s"' % v
+        return f'"{v}"'
     return str(v)
 
 
@@ -255,7 +255,7 @@ def add_op(self, opType, **kwargs):
             highlight = kwargs['highlight']
             del kwargs['highlight']
         return self.graph.node(
-            "<<B>%s</B>>" % opType,
+            f"<<B>{opType}</B>>",
             prefix="op",
             description=opType,
             shape="box",

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

@@ -115,7 +115,7 @@ def __repr__(self):
         if not self.list_of_group:
             return debug_str + "No group."
         for i in range(len(self.list_of_group)):
-            debug_str += f"Group[{i}]: {str(self.list_of_group[i])}; "
+            debug_str += f"Group[{i}]: {self.list_of_group[i]}; "
         return debug_str
 
 

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

@@ -404,8 +404,7 @@ def _proto_check(self, config):
                 train_prog_var = train_prog.global_block().var(var_name)
             except ValueError as e:
                 print(
-                    "Not find variable '%s' in train program. please check pruning."
-                    % var_name
+                    f"Not find variable '{var_name}' in train program. please check pruning."
                 )
                 is_match = False
                 continue
@@ -669,7 +668,7 @@ def check_not_expected_ops(prog, not_expected_op_types):
                     return_numpy=return_numpy,
                 )
             for i, v in enumerate(fetch_list):
-                print("fetch_targets name: %s" % v.name)
+                print(f"fetch_targets name: {v.name}")
                 print(f"fetch_targets: {results[i]}")
             return results
 

python/paddle/distributed/fleet/data_generator/data_generator.py

@@ -318,10 +318,10 @@ def _gen_str(self, line):
             for item in line:
                 name, elements = item
                 if not isinstance(name, str):
-                    raise ValueError("name%s must be in str type" % type(name))
+                    raise ValueError(f"name{type(name)} must be in str type")
                 if not isinstance(elements, list):
                     raise ValueError(
-                        "elements%s must be in list type" % type(elements)
+                        f"elements{type(elements)} must be in list type"
                     )
                 if not elements:
                     raise ValueError(
@@ -336,8 +336,7 @@ def _gen_str(self, line):
                         self._proto_info[-1] = (name, "float")
                     elif not isinstance(elem, int):
                         raise ValueError(
-                            "the type of element%s must be in int or float"
-                            % type(elem)
+                            f"the type of element{type(elem)} must be in int or float"
                         )
                     output += " " + str(elem)
         else:
@@ -348,10 +347,10 @@ def _gen_str(self, line):
             for index, item in enumerate(line):
                 name, elements = item
                 if not isinstance(name, str):
-                    raise ValueError("name%s must be in str type" % type(name))
+                    raise ValueError(f"name{type(name)} must be in str type")
                 if not isinstance(elements, list):
                     raise ValueError(
-                        "elements%s must be in list type" % type(elements)
+                        f"elements{type(elements)} must be in list type"
                     )
                 if not elements:
                     raise ValueError(
@@ -370,8 +369,7 @@ def _gen_str(self, line):
                             self._proto_info[index] = (name, "float")
                         elif not isinstance(elem, int):
                             raise ValueError(
-                                "the type of element%s must be in int or float"
-                                % type(elem)
+                                f"the type of element{type(elem)} must be in int or float"
                             )
                     output += " " + str(elem)
         return output + "\n"

python/paddle/distributed/fleet/dataset/dataset.py

@@ -315,8 +315,7 @@ def _check_use_var_with_data_generator(
                     for i, ele in enumerate(user_parsed_line):
                         if len(ele[1]) == 0:
                             raise ValueError(
-                                "var length error: var %s's length in data_generator is 0"
-                                % ele[0]
+                                f"var length error: var {ele[0]}'s length in data_generator is 0"
                             )
 
                         if var_list[i].dtype == paddle.float32 and not all(

python/paddle/distributed/fleet/fleet.py

@@ -83,8 +83,7 @@ def __impl__(*args, **kwargs):
             and cls._role_maker._is_non_distributed() is True
         ):
             logger.warning(
-                "%s() function doesn't work when use non_distributed fleet."
-                % (func.__name__)
+                f"{func.__name__}() function doesn't work when use non_distributed fleet."
             )
             return
 

python/paddle/distributed/fleet/launch_utils.py

@@ -291,7 +291,7 @@ def get_cluster(
                     trainer.accelerators.extend(devices_per_proc[i])
                 else:
                     trainer.accelerators.append(devices_per_proc[i])
-            trainer.endpoint = "%s" % (cur_node_endpoints[i])
+            trainer.endpoint = f"{cur_node_endpoints[i]}"
             trainer.rank = trainer_rank
             trainer_rank += 1
 
@@ -498,7 +498,7 @@ def start_local_trainers(
     for idx, t in enumerate(pod.trainers):
         proc_env = {
             "PADDLE_TRAINER_ID": "%d" % t.rank,
-            "PADDLE_CURRENT_ENDPOINT": "%s" % t.endpoint,
+            "PADDLE_CURRENT_ENDPOINT": f"{t.endpoint}",
             "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(),
             "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints()),
             "PADDLE_RANK_IN_NODE": str(idx),
@@ -523,18 +523,18 @@ def start_local_trainers(
             ]
 
         if len(t.accelerators) > 0 and pod.device_mode == DeviceMode.GPU:
-            proc_env["FLAGS_selected_gpus"] = "%s" % ",".join(
-                [str(g) for g in t.accelerators]
+            proc_env["FLAGS_selected_gpus"] = "{}".format(
+                ",".join([str(g) for g in t.accelerators])
             )
 
         if len(t.accelerators) > 0:
-            proc_env["FLAGS_selected_accelerators"] = "%s" % ",".join(
-                [str(g) for g in t.accelerators]
+            proc_env["FLAGS_selected_accelerators"] = "{}".format(
+                ",".join([str(g) for g in t.accelerators])
             )
         # to do: same code style in future
         if framework.core.is_compiled_with_xpu() and len(t.accelerators) > 0:
-            proc_env["FLAGS_selected_xpus"] = "%s" % ",".join(
-                [str(g) for g in t.accelerators]
+            proc_env["FLAGS_selected_xpus"] = "{}".format(
+                ",".join([str(g) for g in t.accelerators])
             )
 
         current_env.update(proc_env)
@@ -571,9 +571,9 @@ def start_local_trainers(
         pre_fn = None if os.name == 'nt' else os.setsid
         if log_dir is not None:
             os.makedirs(log_dir, exist_ok=True)
-            if os.path.exists("%s/endpoints.log" % log_dir):
+            if os.path.exists(f"{log_dir}/endpoints.log"):
                 os.remove(f"{log_dir}/endpoints.log")
-            with open("%s/endpoints.log" % log_dir, "w") as f:
+            with open(f"{log_dir}/endpoints.log", "w") as f:
                 f.write("PADDLE_TRAINER_ENDPOINTS: \n")
                 f.write("\n".join(cluster.trainers_endpoints()))
             if (
@@ -613,8 +613,7 @@ def pull_worker_log(tp):
                 except UnicodeEncodeError:
                     sys.stdout.write(
                         'UnicodeEncodeError occurs at this line. '
-                        'Please refer to the original log file "%s"\n'
-                        % tp.log_fn.name
+                        f'Please refer to the original log file "{tp.log_fn.name}"\n'
                     )
             tp.log_offset = fin.tell()
 
@@ -883,7 +882,7 @@ def get_mapped_cluster_without_rank_mapping(
         assert len(ranks_per_node) == 1
         for i in range(len(ranks_per_node)):
             trainer = Trainer()
-            trainer.endpoint = "%s" % (cur_node_endpoints[i])
+            trainer.endpoint = f"{cur_node_endpoints[i]}"
             trainer.rank = ranks_per_node[i]
             pod.trainers.append(trainer)
         cluster.pods.append(pod)
@@ -1002,7 +1001,7 @@ def get_relative_gpu_id(gpu_id):
             trainer.accelerators.append(
                 get_relative_gpu_id(local_device_ids[0])
             )
-            trainer.endpoint = "%s" % (cur_node_endpoints[i])
+            trainer.endpoint = f"{cur_node_endpoints[i]}"
             trainer.rank = ranks_per_node[i]
             pod.trainers.append(trainer)
         cluster.pods.append(pod)
@@ -1936,7 +1935,7 @@ def check_backend(backend):
             "paddle.distributed initialize error, "
             "backend argument can only be one of "
             "'nccl', 'gloo', 'bkcl', 'auto', 'heter', 'xccl' "
-            "but got %s" % backend
+            f"but got {backend}"
         )
 
     if backend == 'nccl' and not framework.core.is_compiled_with_cuda():

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

@@ -82,10 +82,9 @@ def __init__(
                 raise TypeError(
                     "The type of grad_clip should be 'ClipGradByNorm', because DGCMomentumOptimizer only support ClipGradByNorm"
                 )
-            assert isinstance(num_trainers, int), (
-                "The type of num_trainers should be 'int', but received %s"
-                % type(num_trainers)
-            )
+            assert isinstance(
+                num_trainers, int
+            ), f"The type of num_trainers should be 'int', but received {type(num_trainers)}"
             assert (
                 num_trainers > 0
             ), "The value of num_trainers should be greater than 0!"

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

@@ -288,8 +288,7 @@ def get_sys_free_mem():
                 return free
             else:
                 raise ValueError(
-                    "%s platform is unsupported is parameter server optimizer"
-                    % (platform.system())
+                    f"{platform.system()} platform is unsupported is parameter server optimizer"
                 )
 
         if not isinstance(self.inner_opt, paddle.optimizer.SGD):
@@ -334,8 +333,7 @@ def get_sys_free_mem():
                     if x < 0:
                         if neg_dim_count >= 1:
                             raise ValueError(
-                                "Var %s has more than one negative dim."
-                                % (var_name)
+                                f"Var {var_name} has more than one negative dim."
                             )
                         neg_dim_count += 1
                         data_count *= -x

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

@@ -208,8 +208,7 @@ def get_sys_free_mem():
                 return free
             else:
                 raise ValueError(
-                    "%s platform is unsupported is parameter server optimizer"
-                    % (platform.system())
+                    f"{platform.system()} platform is unsupported is parameter server optimizer"
                 )
 
         if not isinstance(self.inner_opt, paddle.optimizer.SGD):
@@ -248,8 +247,7 @@ def get_sys_free_mem():
                     if x < 0:
                         if neg_dim_count >= 1:
                             raise ValueError(
-                                "Var %s has more than one negative dim."
-                                % (var_name)
+                                f"Var {var_name} has more than one negative dim."
                             )
                         neg_dim_count += 1
                         data_count *= -x

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

@@ -132,7 +132,7 @@ def _get_sharding_segment_strategy(self):
             self._forward_remain_anchors = []
         else:
             raise NotImplementedError(
-                f"the sharding segment strategy [{str(segment_strategy)}] is not implemented"
+                f"the sharding segment strategy [{segment_strategy}] is not implemented"
             )
         self._sharding_segment_strategy = segment_strategy
 

python/paddle/distributed/fleet/meta_parallel/parallel_layers/pp_layers.py

@@ -631,7 +631,7 @@ def _print_segmentation_for_debug(self):
             )
 
             for index, layer in enumerate(self._layers_desc[start:end]):
-                logger.info(f"{index + start}: {str(layer)}")
+                logger.info(f"{index + start}: {layer}")
 
         if self._num_virtual_pipeline_stages > 1:
             for stage in range(self._num_stages):

python/paddle/distributed/fleet/meta_parallel/pp_utils/four_directions_p2p_communication.py

@@ -67,8 +67,8 @@ def initialize_p2p_groups(
     ) = _hcg.get_p2p_groups()
 
     debug_str = (
-        f"P2pInfo: send_next_group: {repr(send_next_group)}, send_prev_group: {repr(send_prev_group)}, "
-        f"recv_next_group: {repr(recv_next_group)}, recv_prev_group: {repr(recv_prev_group)}"
+        f"P2pInfo: send_next_group: {send_next_group!r}, send_prev_group: {send_prev_group!r}, "
+        f"recv_next_group: {recv_next_group!r}, recv_prev_group: {recv_prev_group!r}"
     )
     logger.info(debug_str)
 

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

@@ -472,11 +472,11 @@ def __init__(self):
     def to_string(self, indent):
         program_str = "{}tensor {{{}\n{}}}"
         attrs = ""
-        attrs += f"feed_var_name: \"{str(self.feed_var_name)}\" "
-        attrs += f"fetch_var_name: \"{str(self.fetch_var_name)}\" "
-        attrs += f"startup_program_id: {str(self.startup_program_id)} "
-        attrs += f"main_program_id: {str(self.main_program_id)} "
-        attrs += f"tensor_table_class: \"{str(self.tensor_table_class)}\" "
+        attrs += f"feed_var_name: \"{self.feed_var_name}\" "
+        attrs += f"fetch_var_name: \"{self.fetch_var_name}\" "
+        attrs += f"startup_program_id: {self.startup_program_id} "
+        attrs += f"main_program_id: {self.main_program_id} "
+        attrs += f"tensor_table_class: \"{self.tensor_table_class}\" "
         attrs += "\n"
         return program_str.format(
             conv_indent(indent), attrs, conv_indent(indent)

python/paddle/distributed/fleet/utils/fs.py

@@ -481,7 +481,7 @@ def __init__(
         sleep_inter=1000,
     ):  # ms
         self.pre_commands = []
-        hadoop_bin = '%s/bin/hadoop' % hadoop_home
+        hadoop_bin = f'{hadoop_home}/bin/hadoop'
         self.pre_commands.append(hadoop_bin)
         dfs = 'fs'
         self.pre_commands.append(dfs)
@@ -1216,7 +1216,7 @@ def list_files_info(self, path_list):
         )
         ret, lines = self._run_cmd(cmd)
         if len(lines) == 0:
-            logger.warning("list_files empty, path[%s]" % path_list)
+            logger.warning(f"list_files empty, path[{path_list}]")
             return []
         for line in lines:
             arr = line.split(' ')

python/paddle/distributed/fleet/utils/log_util.py

@@ -67,8 +67,6 @@ def layer_to_str(base, *args, **kwargs):
         if kwargs:
             name += ", "
     if kwargs:
-        name += ", ".join(
-            f"{key}={str(value)}" for key, value in kwargs.items()
-        )
+        name += ", ".join(f"{key}={value}" for key, value in kwargs.items())
     name += ")"
     return name

python/paddle/distributed/fleet/utils/pp_parallel_adaptor.py

@@ -109,7 +109,7 @@ def apply(self, src_model_path: str, dst_model_path: str):
                 # first rank extract shared layer
                 with_shared = True
                 for dir in src_dirs:
-                    print("extract layer params in dir %s" % dir)
+                    print(f"extract layer params in dir {dir}")
                     layers.extend(self.extract_layers(dir, with_shared))
                     with_shared = False
                 # 2、sort and unique layers
@@ -240,15 +240,15 @@ def priority(elem):
             return float(match.group(1).lstrip("."))
 
         # strictly sort layers
-        print("before sort %s" % ("|".join([e[0] for e in layers])))
+        print("before sort {}".format("|".join([e[0] for e in layers])))
         layers.sort(key=priority)
         # unique
         unique_layers = []
         for e in layers:
             if unique_layers and e[0] == unique_layers[-1][0]:
                 continue
             unique_layers.append(e)
-        print("after sort %s " % ("|".join([e[0] for e in unique_layers])))
+        print("after sort {} ".format("|".join([e[0] for e in unique_layers])))
         return unique_layers
 
     def segment_layers(
@@ -358,7 +358,7 @@ def merge(src, dst, map_k=None):
 
         lr_scheduler = None
         for layer_names, file_path in layers_segment:
-            print("load %s" % file_path)
+            print(f"load {file_path}")
             layer = paddle.load(file_path)
 
             def get_param_name_mapper(layer_name):