[CodeStyle][C416][C417] rewrite unnecessary comprehension with function call...

[CodeStyle][C416][C417] rewrite unnecessary comprehension with function call and use generator instead of map (#52140)

* codestyle c416 c417

* fix error

* fix inc

* unify all C4 rules into one

* fix inc

---------
Co-authored-by: NSigureMo <sigure.qaq@gmail.com>

paddle/phi/kernels/fusion/cutlass/memory_efficient_attention/generate_kernels.py

@@ -59,7 +59,7 @@ def find_arch_range(min_arch, max_arch):
 
 
 def find_max_arch(arch):
-    arch = list(sorted(arch))
+    arch = sorted(arch)
     idx = DEFAULT_ARCH.index(arch[-1])
     if idx == len(DEFAULT_ARCH) - 1:
         return MAX_ARCH

pyproject.toml

@@ -33,21 +33,7 @@ select = [
     "F401",
 
     # Comprehensions
-    "C400",
-    "C401",
-    "C402",
-    "C403",
-    "C404",
-    "C405",
-    "C408",
-    "C409",
-    "C410",
-    "C411",
-    # "C413",
-    # "C414",
-    # "C415",
-    # "C416",
-    # "C417",
+    "C4",
 
     # Pyupgrade
     "UP001",
@@ -174,5 +160,7 @@ unfixable = [
 "python/paddle/fluid/tests/unittests/dygraph_to_static/test_slice.py" = ["UP034"]
 # Ignore version check in setup.py
 "setup.py" = ["UP036"]
+# Ignore unnecessary comprehension in dy2st unittest test_loop
+"python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py" = ["C416"]
 # Ignore unnecessary lambda in dy2st unittest test_lambda
 "python/paddle/fluid/tests/unittests/dygraph_to_static/test_lambda.py" = ["PLC3002"]

python/paddle/distributed/auto_parallel/dist_saver.py

@@ -170,8 +170,8 @@ class DistributedSaver:
         global_block = dist_main_prog.global_block()
 
         ops = global_block.ops
-        feed_vars_names = list(map(lambda x: x.name, feed_vars))
-        fetch_vars_names = list(map(lambda x: x.name, fetch_vars))
+        feed_vars_names = [x.name for x in feed_vars]
+        fetch_vars_names = [x.name for x in fetch_vars]
 
         last_idx = -1
         for idx, op in enumerate(ops):

python/paddle/distributed/auto_parallel/dist_tensor.py

@@ -39,7 +39,7 @@ class DistributedTensor:
     ):
         if not (
             isinstance(sizes, (list, tuple))
-            and all(map(lambda x: isinstance(x, int) and x >= 0, sizes))
+            and all(isinstance(x, int) and x >= 0 for x in sizes)
         ):
             raise ValueError(
                 "The sizes must be list or tuple and item in sizes must be non-negative integer, but got {}".format(
@@ -48,7 +48,7 @@ class DistributedTensor:
             )
         if not (
             isinstance(dims_mapping, (list, tuple))
-            and all(map(lambda x: isinstance(x, int) and x >= -1, dims_mapping))
+            and all(isinstance(x, int) and x >= -1 for x in dims_mapping)
         ):
             raise ValueError(
                 "The dims_mapping must be list or tuple and item in dims_mapping must >= -1, but got {}".format(
@@ -57,7 +57,7 @@ class DistributedTensor:
             )
         if not (
             isinstance(processes, (list, tuple))
-            and all(map(lambda x: isinstance(x, int) and x >= 0, processes))
+            and all(isinstance(x, int) and x >= 0 for x in processes)
         ):
             raise ValueError(
                 "The processes must be list or tuple and item in processes must be integer, but got {}".format(
@@ -66,7 +66,7 @@ class DistributedTensor:
             )
         if not (
             isinstance(topology, (list, tuple))
-            and all(map(lambda x: isinstance(x, int) and x > 0, topology))
+            and all(isinstance(x, int) and x > 0 for x in topology)
         ):
             raise ValueError(
                 "The topology must be list or tuple and item in topology must be non-negative integer, but got {}".format(
@@ -162,9 +162,9 @@ class DistributedTensor:
             len(local_sizes), len(local_offsets)
         )
 
-        local_end_offsets = list(
-            map(lambda x: x[0] + x[1], zip(local_offsets, local_sizes))
-        )
+        local_end_offsets = [
+            x[0] + x[1] for x in zip(local_offsets, local_sizes)
+        ]
         local_shard = list(zip(local_offsets, local_end_offsets))
         return local_shard
 

python/paddle/distributed/auto_parallel/planner.py

@@ -337,7 +337,7 @@ class PlanSpace:
         vars = program.global_block().vars
 
         processes = reduce(lambda x, y: x * y, process_mesh_topology)
-        global_group = [i for i in range(processes)]
+        global_group = list(range(processes))
         global_process_mesh = None
         pipeline_process_meshes = None
 

python/paddle/distributed/auto_parallel/reshard.py

@@ -1340,15 +1340,13 @@ class Resharder:
         if op_input:
             op_input_dims_mapping = dist_attr[1]
             if all(
-                map(
-                    lambda x: x,
-                    [
+                x
+                for x in [
                     tensor_dims_mapping,
                     tensor_process_mesh,
                     op_input_dims_mapping,
                     op_process_mesh,
-                    ],
-                )
+                ]
             ):
                 # judge whether need reshard by dims_mapping
                 if tensor_dims_mapping != op_input_dims_mapping:
@@ -1379,15 +1377,13 @@ class Resharder:
         else:
             op_output_dims_mapping = dist_attr[1]
             if all(
-                map(
-                    lambda x: x,
-                    [
+                x
+                for x in [
                     tensor_dims_mapping,
                     tensor_process_mesh,
                     op_output_dims_mapping,
                     op_process_mesh,
-                    ],
-                )
+                ]
             ):
                 if tensor_dims_mapping != op_output_dims_mapping:
                     raise ValueError(
@@ -1554,7 +1550,7 @@ class Resharder:
                             i += 1
 
                         if i == len(has_used):
-                            has_used = list(map(lambda x: False, has_used))
+                            has_used = [False for x in has_used]
                             to_send_process = process_list[0]
                             has_used[0] = True
                         assert (
@@ -1744,11 +1740,9 @@ class Resharder:
             if isinstance(op_desc, AllGatherOpDesc):  # noqa: F401
                 if var_name not in self.has_allgather.keys():
                     self.has_allgather[var_name] = []
-                if not self.has_allgather[
-                    var_name
-                ] or op_desc.group not in list(
-                    map(lambda x: x[0], self.has_allgather[var_name])
-                ):
+                if not self.has_allgather[var_name] or op_desc.group not in [
+                    x[0] for x in self.has_allgather[var_name]
+                ]:
                     if op_desc.is_bool:
                         # for bool data allgather, cast to int64 -> allgather -> cast bool
                         out_cast = Inserter.insert_cast_op(

python/paddle/distributed/auto_parallel/tuner/parallel_tuner.py

@@ -290,7 +290,7 @@ class ParallelTuner:
             return self._cached_dims_mapping_candidates[key]
         candidates = []
         dims_mapping = [-1 for i in range(dims_mapping_len)]
-        dims_list = [i for i in range(process_mesh_len)]
+        dims_list = list(range(process_mesh_len))
         visited = [False for i in range(process_mesh_len)]
         self._generate_dims_mapping_candidates_helper(
             dims_mapping, dims_list, 0, visited, candidates

python/paddle/distributed/auto_parallel/tuner/rule_based_tuner.py

@@ -1631,13 +1631,12 @@ class RuleBasedTuner:
         Most of the logic is the same as the update completion in the completer.
         """
         world_ranks = ProcessMesh(
-            [
-                i
-                for i in range(
+            list(
+                range(
                     self._cluster.get_num_machines()
                     * self._cluster._num_devices_per_machine
                 )
-            ]
+            )
         )
         dist_tensors = sub_program_dist_context._dist_tensors_for_program
 
@@ -1958,10 +1957,9 @@ class RuleBasedTuner:
             self.device_meshes_list.append([])
             for device_mesh in device_meshes:
                 devices = reduce(lambda x, y: x * y, device_mesh)
-                processes = [
-                    i
-                    for i in range(has_used_devices, has_used_devices + devices)
-                ]
+                processes = list(
+                    range(has_used_devices, has_used_devices + devices)
+                )
                 device_mesh_shape = (
                     device_mesh
                     if device_mesh[0] != 1

python/paddle/distributed/auto_parallel/tuner/tunable_space.py

@@ -117,7 +117,7 @@ class TunableSpace:
                 {"class_name": v.__class__.__name__, "state": v.get_state()}
                 for v in self._variables.values()
             ],
-            "values": {k: v for (k, v) in self.values.items()},
+            "values": dict(self.values.items()),
         }
 
     @classmethod
@@ -126,7 +126,7 @@ class TunableSpace:
         for v in state["variables"]:
             v = _deserialize_tunable_variable(v)
             ts._variables[v.name] = v
-        ts._values = {k: v for (k, v) in state["values"].items()}
+        ts._values = dict(state["values"].items())
         return ts
 
 

python/paddle/distributed/auto_parallel/tuner/tunable_variable.py

@@ -115,7 +115,7 @@ class Choice(TunableVariable):
                 default = bool(default)
         else:
             self._is_unknown_type = True
-            self._indices = [i for i in range(len(values))]
+            self._indices = list(range(len(values)))
         self.values = values
 
         if default is not None and default not in values:

python/paddle/distributed/auto_parallel/utils.py

@@ -1684,7 +1684,7 @@ def get_standalone_cost_data(distributed_programs):
                 shape = info[
                     shape_left_boundary + 1 : shape_right_boundary
                 ].split(",")
-                shape = list(map(lambda x: int(x.strip()), shape))
+                shape = [int(x.strip()) for x in shape]
                 dtype_factor = 1
                 total_static_input_size += reduce(lambda x, y: x * y, shape)
                 if op.type == "c_embedding":

python/paddle/distributed/cloud_utils.py

@@ -87,9 +87,7 @@ paddlecloud environment.".format(
 
         if started_port is None:
             started_port = 6170
-        ports = [
-            x for x in range(started_port, started_port + len(selected_devices))
-        ]
+        ports = list(range(started_port, started_port + len(selected_devices)))
         trainer_endpoints = []
         for ip in node_ips:
             trainer_endpoints.append(["%s:%d" % (ip, port) for port in ports])

python/paddle/distributed/fleet/ascend_utils.py

@@ -129,9 +129,7 @@ def get_cloud_cluster(
         device_count = 1
 
     devices_per_proc = [str(x) for x in range(device_count)]
-    free_ports = [
-        x for x in range(start_port, start_port + len(devices_per_proc))
-    ]
+    free_ports = list(range(start_port, start_port + len(devices_per_proc)))
 
     trainer_endpoints = []
     for ip in node_ips:

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

@@ -382,7 +382,7 @@ class UtilBase:
             if paddle.static.io.is_persistable(v)
         ]
         pruned_vars = OrderedDict(pruned_vars)
-        pruned_vars_name = [name for name in pruned_vars]
+        pruned_vars_name = list(pruned_vars)
         print("persistable vars in pruned program: {}".format(pruned_vars_name))
 
         # feed and fetch op is added in pruned program when pruning, not need to be found in train program

python/paddle/distributed/fleet/cloud_utils.py

@@ -75,9 +75,7 @@ paddlecloud environment.".format(
 
         if started_port is None:
             started_port = 6170
-        ports = [
-            x for x in range(started_port, started_port + len(devices_per_proc))
-        ]
+        ports = list(range(started_port, started_port + len(devices_per_proc)))
         trainer_endpoints = []
         for ip in node_ips:
             trainer_endpoints.append(["%s:%d" % (ip, port) for port in ports])

python/paddle/distributed/fleet/elastic/manager.py

@@ -338,7 +338,7 @@ class ElasticManager:
                 ip = endpoints
                 port = start_port
 
-            ports = [x for x in range(port, port + len(devices_per_proc))]
+            ports = list(range(port, port + len(devices_per_proc)))
             endpoint_list.extend(["%s:%d" % (ip, port) for port in ports])
 
         dist_endpoints = ','.join(endpoint_list)
@@ -360,7 +360,7 @@ class ElasticManager:
             self.etcd.cancel_watch(watch)
         self.etcd.delete(self.host_path)
 
-        hosts = [i for i in self.etcd.get_prefix(self.node_prefix)]
+        hosts = list(self.etcd.get_prefix(self.node_prefix))
         if len(hosts) == 0:
             self.etcd.delete_prefix(self.prefix)
 

python/paddle/distributed/fleet/launch.py

@@ -314,9 +314,7 @@ def get_cluster_from_args(args, device_mode, devices_per_proc):
         if os.environ.get('FLAGS_START_PORT') is not None:
             start_port = int(os.environ.get('FLAGS_START_PORT'))
 
-        free_ports = [
-            x for x in range(start_port, start_port + len(devices_per_proc))
-        ]
+        free_ports = list(range(start_port, start_port + len(devices_per_proc)))
 
     trainer_endpoints = []
     for ip in node_ips:

python/paddle/distributed/fleet/launch_utils.py

@@ -951,7 +951,7 @@ def get_device_proc_info(args):
         if args.nproc_per_node is None:
             devices_per_proc = [0]
         else:
-            devices_per_proc = [x for x in range(0, args.nproc_per_node)]
+            devices_per_proc = list(range(0, args.nproc_per_node))
     else:
         raise AssertionError(
             "Can't support device_mode:{}, support only cpu|gpu|xpu now.".format(
@@ -1107,20 +1107,14 @@ def get_mapped_cluster_from_args_without_rank_mapping(args, device_mode):
         node_rank = node_ips.index(ip)
         if os.environ.get('PADDLE_PORT') is not None:
             start_port = int(os.getenv("PADDLE_PORT", ""))
-            free_ports = [
-                x
-                for x in range(
-                    start_port, start_port + len(node_ranks[node_rank])
+            free_ports = list(
+                range(start_port, start_port + len(node_ranks[node_rank]))
             )
-            ]
         elif os.environ.get('FLAGS_START_PORT') is not None:
             start_port = int(os.environ.get('FLAGS_START_PORT'))
-            free_ports = [
-                x
-                for x in range(
-                    start_port, start_port + len(node_ranks[node_rank])
+            free_ports = list(
+                range(start_port, start_port + len(node_ranks[node_rank]))
             )
-            ]
         else:
             free_ports = find_free_ports(len(node_ranks[node_rank]))
         trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports])
@@ -1250,20 +1244,14 @@ def get_mapped_cluster_from_args_with_rank_mapping(args, device_mode):
         node_rank = node_ips.index(ip)
         if os.environ.get('PADDLE_PORT') is not None:
             start_port = int(os.getenv("PADDLE_PORT", ""))
-            free_ports = [
-                x
-                for x in range(
-                    start_port, start_port + len(node_ranks[node_rank])
+            free_ports = list(
+                range(start_port, start_port + len(node_ranks[node_rank]))
             )
-            ]
         elif os.environ.get('FLAGS_START_PORT') is not None:
             start_port = int(os.environ.get('FLAGS_START_PORT'))
-            free_ports = [
-                x
-                for x in range(
-                    start_port, start_port + len(node_ranks[node_rank])
+            free_ports = list(
+                range(start_port, start_port + len(node_ranks[node_rank]))
             )
-            ]
         else:
             free_ports = find_free_ports(len(node_ranks[node_rank]))
         trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports])

python/paddle/distributed/fleet/meta_optimizers/ascend/ascend_optimizer.py

@@ -196,7 +196,7 @@ class AscendIRParser:
                 HcomGroupConfig(
                     name="hcom_group_0",
                     nranks=fleet.world_size(),
-                    rank_ids=[x for x in range(fleet.world_size())],
+                    rank_ids=list(range(fleet.world_size())),
                 )
             )
 

python/paddle/distributed/fleet/meta_optimizers/ascend/ascend_parser.py

@@ -2422,7 +2422,7 @@ class TransposeGradParser(AscendParserBase):
 
         x_shape = self.op.block.var(self.op.input_arg_names[1]).shape[1:]
         out_grad_shape = self.op.block.var(self.op.input_arg_names[0]).shape
-        assert list(map(lambda x: out_grad_shape[x], perm)) == list(x_shape)
+        assert [out_grad_shape[x] for x in perm] == list(x_shape)
 
         x_grad = (
             core.GEOperatorFactory.create_operator(

python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/heter_parallel_optimizer.py

@@ -27,7 +27,7 @@ def _obtain_optimizer_parameters_list(optimizer):
             for param in group['params']:
                 parameters_list.append(param)
     else:
-        parameters_list = [param for param in optimizer._parameter_list]
+        parameters_list = list(optimizer._parameter_list)
 
     return parameters_list
 

python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/hybrid_parallel_optimizer.py

@@ -37,7 +37,7 @@ def _obtain_optimizer_parameters_list(optimizer):
             for param in group['params']:
                 parameters_list.append(param)
     else:
-        parameters_list = [param for param in optimizer._parameter_list]
+        parameters_list = list(optimizer._parameter_list)
 
     return parameters_list
 

python/paddle/distributed/fleet/meta_parallel/pipeline_parallel.py

@@ -337,7 +337,7 @@ class PipelineParallel(MetaParallelBase):
                     assert len(outputs) == len(output_tensor_grad)
                     paddle.autograd.backward(
                         tensors=outputs,
-                        grad_tensors=[t for t in output_tensor_grad],
+                        grad_tensors=list(output_tensor_grad),
                     )
                 else:
                     paddle.autograd.backward(

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py

@@ -438,7 +438,7 @@ class GroupShardedOptimizerStage2(Optimizer):
 
         if self.offload:
             self._optim._master_weights = self._master_params
-            cpu_master_params = [p for p in self._master_params.values()]
+            cpu_master_params = list(self._master_params.values())
             for param in cpu_master_params:
                 size = param._numel() * align[Type.fp32.value]
                 remaining = size % alignment[self.offload_device]

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage2.py

@@ -79,12 +79,10 @@ class GroupShardedStage2(nn.Layer):
             else sharding_optimizer
         )
         assert all(
-            list(
-                map(
-                    lambda opt: isinstance(opt, GroupShardedOptimizerStage2),
-                    self._sharding_optimizers,
-                )
-            )
+            [
+                isinstance(opt, GroupShardedOptimizerStage2)
+                for opt in self._sharding_optimizers
+            ]
         ), "Please use GroupShardedOptimizerStage2 optimizer"
         self._sync_buffers = sync_buffers
         self._auto_refresh_trainable = auto_refresh_trainable

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage3.py

@@ -335,7 +335,7 @@ class GroupShardedStage3(nn.Layer):
             buffer_size[param.dtype] += param._numel() + p_align
 
         # Create unslice_params'grad
-        for param in sorted(list(self._unslice_params), key=lambda p: p.name):
+        for param in sorted(self._unslice_params, key=lambda p: p.name):
             if param.dtype not in self._grad_storages.keys():
                 self._grad_storages[param.dtype] = GradStorage(
                     buffer_size[param.dtype],

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

@@ -1445,7 +1445,7 @@ class TheOnePSRuntime(RuntimeBase):
         generate_vars = self.context[
             "user_defined_strategy"
         ].trainer_desc_configs["stat_var_names"]
-        generate_vars = [var for var in generate_vars]
+        generate_vars = list(generate_vars)
         remaining_vars = list(
             filter(
                 TheOnePSRuntime.__exclude_vars(sparse_names),

python/paddle/distributed/launch/controllers/master.py

@@ -207,7 +207,7 @@ class ETCDMaster(Master):
         while not self.ctx.status.is_done():
             self.client.put(path, value.encode('latin-1'))
 
-            result = [i for i in self.client.get_prefix(prefix)]
+            result = list(self.client.get_prefix(prefix))
             result = copy.deepcopy(result)
             self.ctx.logger.debug("sync peers {}".format(result))
 

python/paddle/distributed/launch/controllers/ps.py

@@ -47,8 +47,8 @@ class PSController(Controller):
         else:
             host = self.ctx.node.ip
 
-        server_endpoints = [s for s in self.ctx.args.servers.split(",")]
-        trainer_endpoints = [s for s in self.ctx.args.trainers.split(",")]
+        server_endpoints = list(self.ctx.args.servers.split(","))
+        trainer_endpoints = list(self.ctx.args.trainers.split(","))
         servers = [
             s for s in self.ctx.args.servers.split(",") if s.startswith(host)
         ]

python/paddle/distributed/passes/auto_parallel_data_parallel_optimization.py

@@ -643,7 +643,7 @@ class DataParallelOptimizationPass(PassBase):
                     )
 
         # insert dependency op
-        indice = sorted(list(dep_map.keys()), reverse=True)
+        indice = sorted(dep_map.keys(), reverse=True)
         for i in indice:
             for idx, prior_vars, post_vars, op_role in dep_map[i][::-1]:
                 depend_op = insert_dependencies_for_vars(

python/paddle/distributed/passes/auto_parallel_sharding.py

@@ -893,7 +893,7 @@ class ShardingPass(PassBase):
                     )
 
         # insert deps
-        indice = sorted(list(dep_map.keys()), reverse=True)
+        indice = sorted(dep_map.keys(), reverse=True)
         for i in indice:
             for idx, prior_vars, post_vars, comm_stream in dep_map[i][::-1]:
                 depend_op = insert_dependencies_for_vars(
@@ -1263,7 +1263,7 @@ class ShardingPass(PassBase):
             idx += 1
 
         # insert deps
-        indice = sorted(list(dep_map.keys()), reverse=True)
+        indice = sorted(dep_map.keys(), reverse=True)
         for i in indice:
             for idx, prior_vars, post_vars, comm_stream in dep_map[i][::-1]:
                 depend_op = insert_dependencies_for_vars(

python/paddle/distributed/passes/auto_parallel_supplement_explicit_dependencies.py

@@ -138,7 +138,7 @@ class AutoParalSupplementDepPass(PassBase):
                 prior_varname = op.output("ParamOut")[0]
 
         # insert deps
-        indice = sorted(list(deps_map.keys()), reverse=True)
+        indice = sorted(deps_map.keys(), reverse=True)
         for idx in indice:
             prior_var = main_block.var(deps_map[idx][0])
             post_var = main_block.var(deps_map[idx][1])

python/paddle/distributed/ps/the_one_ps.py

@@ -1579,7 +1579,7 @@ class TheOnePSRuntime(RuntimeBase):
         generate_vars = self.context[
             "user_defined_strategy"
         ].trainer_desc_configs["stat_var_names"]
-        generate_vars = [var for var in generate_vars]
+        generate_vars = list(generate_vars)
         remaining_vars = list(
             filter(
                 TheOnePSRuntime.__exclude_vars(sparse_names),

python/paddle/distributed/ps/utils/public.py

@@ -1698,7 +1698,7 @@ def add_send_op(program, block, _vars):
 
 def get_vars_name_in_block(block):
     vars_list = block.vars.keys()
-    vars_name_list = [var_name for var_name in vars_list]
+    vars_name_list = list(vars_list)
     return vars_name_list
 
 

python/paddle/distributed/spawn.py

@@ -331,7 +331,7 @@ def _get_subprocess_env_list(nprocs, options):
 
     # get cluster and pod config
     if options['backend'] == 'gloo':
-        devices_per_proc = [x for x in range(0, nprocs)]
+        devices_per_proc = list(range(0, nprocs))
         cluster, pod = get_cluster_from_args(
             args, DeviceMode.CPU, devices_per_proc
         )

python/paddle/distributed/utils/launch_utils.py

@@ -54,9 +54,9 @@ def get_cluster_from_args(args, selected_gpus):
         if args.started_port is not None:
             started_port = args.started_port
 
-        free_ports = [
-            x for x in range(started_port, started_port + len(selected_gpus))
-        ]
+        free_ports = list(
+            range(started_port, started_port + len(selected_gpus))
+        )
 
     trainer_endpoints = []
     for ip in node_ips:

python/paddle/fluid/tests/unittests/collective/collective_allreduce_api.py

@@ -16,9 +16,8 @@ from test_collective_api_base import TestCollectiveAPIRunnerBase, runtime_main
 
 import paddle
 import paddle.distributed as dist
-import paddle.fluid as fluid
-import paddle.fluid.data_feeder as data_feeder
-import paddle.framework as framework
+from paddle import fluid, framework
+from paddle.fluid import data_feeder
 
 paddle.enable_static()
 

python/paddle/fluid/tests/unittests/collective/collective_reduce_api.py

@@ -16,9 +16,8 @@ from test_collective_api_base import TestCollectiveAPIRunnerBase, runtime_main
 
 import paddle
 import paddle.distributed as dist
-import paddle.fluid as fluid
-import paddle.fluid.data_feeder as data_feeder
-import paddle.framework as framework
+from paddle import fluid, framework
+from paddle.fluid import data_feeder
 
 paddle.enable_static()
 

python/paddle/fluid/tests/unittests/collective/fleet/parallel_dygraph_control_flow_same.py

@@ -72,7 +72,7 @@ class TestSimpleNet(TestParallelDyGraphRunnerBase):
         return model, train_reader, optimizer
 
     def run_one_loop(self, model, optimizer, batch):
-        x_data = np.array([x for x in batch])
+        x_data = np.array(list(batch))
         x_data = x_data.reshape((-1, 10))
         x = to_variable(x_data)
         out = model(x)

python/paddle/fluid/tests/unittests/collective/fleet/parallel_dygraph_no_sync.py

@@ -60,7 +60,7 @@ class TestNoSync(TestParallelDyGraphRunnerBase):
         return model, train_reader, optimizer
 
     def run_one_loop(self, model, optimizer, batch):
-        x_data = np.array([x for x in batch])
+        x_data = np.array(list(batch))
         x_data = x_data.reshape((-1, 10))
         x = paddle.to_tensor(x_data)
         out = model(x)

python/paddle/fluid/tests/unittests/collective/fleet/parallel_dygraph_no_sync_control_flow.py

@@ -55,7 +55,7 @@ class TestNoSyncControlFlow(TestNoSync):
         return model, train_reader, optimizer
 
     def run_one_loop(self, model, optimizer, batch):
-        x_data = np.array([x for x in batch])
+        x_data = np.array(list(batch))
         x_data = x_data.reshape((-1, 10))
         x = paddle.to_tensor(x_data)
         out = model(x)

python/paddle/fluid/tests/unittests/collective/fleet/parallel_dygraph_no_sync_unused_params.py

@@ -54,7 +54,7 @@ class TestNoSyncUnusedParam(TestNoSync):
         return model, train_reader, optimizer
 
     def run_one_loop(self, model, optimizer, batch):
-        x_data = np.array([x for x in batch])
+        x_data = np.array(list(batch))
         x_data = x_data.reshape((-1, 10))
         x = paddle.to_tensor(x_data)
         out = model(x)

python/paddle/fluid/tests/unittests/dygraph_to_static/bert_utils.py

@@ -282,7 +282,7 @@ class DataReader:
             sent_ids = [0 for i in range(sent0_len)] + [
                 1 for i in range(sent1_len)
             ]
-            pos_ids = [i for i in range(sent0_len + sent1_len)]
+            pos_ids = list(range(sent0_len + sent1_len))
             label = 1
             yield token_ids, sent_ids, pos_ids, label
 

python/paddle/fluid/tests/unittests/dygraph_to_static/test_static_analysis.py

@@ -185,9 +185,7 @@ class TestStaticAnalysis(unittest.TestCase):
         if wrapper.parent is not None:
             self.assertTrue(wrapper in wrapper.parent.children)
 
-        children_ast_nodes = [
-            child for child in gast.iter_child_nodes(wrapper.node)
-        ]
+        children_ast_nodes = list(gast.iter_child_nodes(wrapper.node))
         self.assertEqual(len(wrapper.children), len(children_ast_nodes))
         for child in wrapper.children:
             self.assertTrue(child.node in children_ast_nodes)

python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py

@@ -55,8 +55,7 @@ class PrePostProcessLayer(Layer):
             elif cmd == "n":  # add layer normalization
                 self.functors.append(
                     self.add_sublayer(
-                        "layer_norm_%d"
-                        % len([layer for layer in self.children()]),
+                        "layer_norm_%d" % len(list(self.children())),
                         paddle.nn.LayerNorm(
                             normalized_shape=d_model,
                             weight_attr=fluid.ParamAttr(

python/paddle/fluid/tests/unittests/ir/inference/test_layer_norm_fuse_pass.py

@@ -90,7 +90,7 @@ class TestFcFusePass(PassAutoScanTest):
         if begin_norm_axis == x_shape_rank - 1 and draw(st.booleans()):
             reduce_mean_dim = [-1]
         else:
-            reduce_mean_dim = [i for i in range(x_shape_rank)]
+            reduce_mean_dim = list(range(x_shape_rank))
             reduce_mean_dim = reduce_mean_dim[begin_norm_axis:]
         error_test_ratio = draw(st.integers(min_value=1, max_value=10))
         if error_test_ratio > 9:

python/paddle/fluid/tests/unittests/ir/inference/test_onednn_reshape_transpose_matmul_fuse_pass.py

@@ -47,7 +47,7 @@ class TestOneDNNReshapeTransposeMatmulFusePass(PassAutoScanTest):
         def generate_input2(attrs):
             shape_x = [attrs[3]['batch_size'], attrs[3]['channel'], self.num]
             input_volume = reduce(lambda x, y: x * y, shape_x)
-            matmul_shape = [i for i in attrs[0]['shape']]
+            matmul_shape = list(attrs[0]['shape'])
             if 0 in matmul_shape:
                 for i in range(len(matmul_shape)):
                     if matmul_shape[i] == 0:

python/paddle/fluid/tests/unittests/ir/inference/test_transpose_flatten_concat_fuse_pass.py

@@ -89,7 +89,7 @@ class TestTransposeFlattenConcatFusePass(PassAutoScanTest):
         inputs = {}
         x_shape_rank = draw(st.integers(min_value=2, max_value=5))
         #  Generate axis of transpose
-        trans_axis = [j for j in range(x_shape_rank)]
+        trans_axis = list(range(x_shape_rank))
         for j in range(x_shape_rank - 1):
             if draw(st.booleans()):
                 trans_axis[j], trans_axis[-1] = trans_axis[-1], trans_axis[j]

python/paddle/fluid/tests/unittests/mkldnn/test_matmul_v2_mkldnn_op.py

@@ -48,14 +48,14 @@ def reference_matmul(X, Y, transpose_x=False, transpose_y=False):
         elif X.ndim == 2:
             X = X.T
         else:
-            dim = [i for i in range(len(X.shape))]
+            dim = list(range(len(X.shape)))
             dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
             X = np.transpose(X, tuple(dim))
     if transpose_y:
         if Y.ndim == 1:
             Y = Y.reshape((Y.size,))
         else:
-            dim = [i for i in range(len(Y.shape))]
+            dim = list(range(len(Y.shape)))
             dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
             Y = np.transpose(Y, tuple(dim))
 

python/paddle/fluid/tests/unittests/parallel_dygraph_none_var.py

@@ -64,7 +64,7 @@ class TestSimpleNet(TestParallelDyGraphRunnerBase):
         return model, train_reader, optimizer
 
     def run_one_loop(self, model, optimizer, batch):
-        x_data = np.array([x for x in batch])
+        x_data = np.array(list(batch))
         x_data = x_data.reshape((-1, 10))
         x = paddle.to_tensor(x_data)
         out = model(x)

python/paddle/fluid/tests/unittests/parallel_dygraph_shared_unused_var.py

@@ -60,7 +60,7 @@ class TestSimpleNet(TestParallelDyGraphRunnerBase):
         return model, train_reader, optimizer
 
     def run_one_loop(self, model, optimizer, batch):
-        x_data = np.array([x for x in batch])
+        x_data = np.array(list(batch))
         x_data = x_data.reshape((-1, 10))
         x = to_variable(x_data)
         out = model(x)

python/paddle/fluid/tests/unittests/test_coalesce_tensor_op.py

@@ -104,7 +104,7 @@ class TestAllocContinuousSpace(OpTest):
             out[0:length] = input[1].flatten()
             inputs.append(out)
 
-        coalesce_tensor_var = np.concatenate([input for input in inputs])
+        coalesce_tensor_var = np.concatenate(list(inputs))
         if set_constant:
             coalesce_tensor_var = np.ones(len(coalesce_tensor_var)) * constant
             outputs = [

python/paddle/fluid/tests/unittests/test_collective_api_base.py

@@ -65,7 +65,7 @@ def create_pyobject_test_data(shape=None, seed=None):
         np.random.seed(seed)
     list_shape = np.random.randint(0, high=100, size=(2)).tolist()
     list_data = np.random.random(shape).tolist()
-    dict_key = [i for i in range(0, shape[0])]
+    dict_key = list(range(0, shape[0]))
     dict_val = np.random.random(shape).tolist()
     dict_data = dict(zip(dict_key, dict_val))
     return [list_data, dict_data]

python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py

@@ -796,12 +796,10 @@ class TestCUDNN_FP16(TestConv2DTransposeOp):
                         ['Filter'],
                         'Output',
                         max_relative_error=0.02,
-                        no_grad_set=set(['Input']),
+                        no_grad_set={'Input'},
                     )
             else:
-                self.check_grad(
-                    ['Filter'], 'Output', no_grad_set=set(['Input'])
-                )
+                self.check_grad(['Filter'], 'Output', no_grad_set={'Input'})
 
     def test_check_grad_no_filter(self):
         if self.need_check_grad:
@@ -813,12 +811,10 @@ class TestCUDNN_FP16(TestConv2DTransposeOp):
                         ['Input'],
                         'Output',
                         max_relative_error=0.02,
-                        no_grad_set=set(['Filter']),
+                        no_grad_set={'Filter'},
                     )
             else:
-                self.check_grad(
-                    ['Input'], 'Output', no_grad_set=set(['Filter'])
-                )
+                self.check_grad(['Input'], 'Output', no_grad_set={'Filter'})
 
     def test_check_grad(self):
         if self.need_check_grad:
@@ -827,13 +823,13 @@ class TestCUDNN_FP16(TestConv2DTransposeOp):
                 if core.is_float16_supported(place):
                     self.check_grad_with_place(
                         place,
-                        set(['Input', 'Filter']),
+                        {'Input', 'Filter'},
                         'Output',
                         max_relative_error=0.02,
                     )
             else:
                 self.check_grad(
-                    set(['Input', 'Filter']), 'Output', max_relative_error=0.02
+                    {'Input', 'Filter'}, 'Output', max_relative_error=0.02
                 )
 
 
@@ -980,7 +976,7 @@ class TestCUDNN_BF16(TestConv2DTransposeOp):
             ['Filter'],
             'Output',
             max_relative_error=0.02,
-            no_grad_set=set(['Input']),
+            no_grad_set={'Input'},
             user_defined_grads=[numeric_grads],
         )
 
@@ -992,7 +988,7 @@ class TestCUDNN_BF16(TestConv2DTransposeOp):
             ['Input'],
             'Output',
             max_relative_error=0.02,
-            no_grad_set=set(['Filter']),
+            no_grad_set={'Filter'},
             user_defined_grads=[numeric_grads],
         )
 

python/paddle/fluid/tests/unittests/test_create_parameter.py

@@ -37,7 +37,7 @@ class TestCreateParameterError(unittest.TestCase):
 
             def test_attr():
                 paddle.create_parameter(
-                    [1, 2, 3], np.float32, attr=np.array([i for i in range(6)])
+                    [1, 2, 3], np.float32, attr=np.array(list(range(6)))
                 )
 
             self.assertRaises(TypeError, test_attr)
@@ -46,7 +46,7 @@ class TestCreateParameterError(unittest.TestCase):
                 paddle.create_parameter(
                     [1, 2, 3],
                     np.float32,
-                    default_initializer=np.array([i for i in range(6)]),
+                    default_initializer=np.array(list(range(6))),
                 )
 
             self.assertRaises(TypeError, test_default_initializer)

python/paddle/fluid/tests/unittests/test_crop_tensor_op.py

@@ -69,7 +69,7 @@ class TestCropTensorOp(OpTest):
         else:
             self.attrs['offsets'] = self.offsets
 
-        crop_shape = [val for val in self.crop_shape]
+        crop_shape = list(self.crop_shape)
         for i in range(len(self.crop_shape)):
             if self.crop_shape[i] == -1:
                 crop_shape[i] = self.x_shape[i] - self.offsets[i]
@@ -169,7 +169,7 @@ class TestCropTensorOpTensorAttr(OpTest):
 
         self.attrs['shape'] = self.crop_shape
         self.attrs['offsets'] = self.offsets
-        crop_shape = [val for val in self.crop_shape]
+        crop_shape = list(self.crop_shape)
         for i in range(len(self.crop_shape)):
             if self.crop_shape[i] == -1:
                 crop_shape[i] = self.x_shape[i] - self.offsets[i]

python/paddle/fluid/tests/unittests/test_desc_clone.py

@@ -121,8 +121,8 @@ def operator_equal(a, b):
             continue
 
         elif isinstance(v, collections.OrderedDict):
-            v0 = sorted(list(v.items()), key=lambda x: x[0])
-            v1 = sorted(list(b.__dict__[k].items()), key=lambda x: x[0])
+            v0 = sorted(v.items(), key=lambda x: x[0])
+            v1 = sorted(b.__dict__[k].items(), key=lambda x: x[0])
 
             if v0 != v1:
                 raise ValueError("In operator_equal not equal:{0}\n".format(k))

python/paddle/fluid/tests/unittests/test_dist_base.py

@@ -660,7 +660,7 @@ class TestParallelDyGraphRunnerBase:
                 if paddle.distributed.get_rank() == 0:
                     new_batch.append(batch[0])
                 elif paddle.distributed.get_rank() == 1:
-                    new_batch.extend([_ for _ in batch[1:]])
+                    new_batch.extend(list(batch[1:]))
                 else:
                     raise NotImplementedError(
                         "Current TestParallelDyGraphRunnerBase don't support world_size > 2"

python/paddle/fluid/tests/unittests/test_dist_lookup_sparse_table_fuse_ops.py

@@ -34,7 +34,7 @@ class TestLookupTableFuseOp(unittest.TestCase):
     def check_with_place(self, place):
         scope = fluid.global_scope()
         scope.var("LearningRate").get_tensor().set([0.01], place)
-        scope.var("Ids").get_tensor().set([i for i in range(100)], place)
+        scope.var("Ids").get_tensor().set(list(range(100)), place)
 
         init_program = fluid.Program()
 

python/paddle/fluid/tests/unittests/test_gru_op.py

@@ -40,7 +40,7 @@ def gru(
         for i in range(len(seq_lens)):
             seq_starts.append(seq_starts[-1] + seq_lens[i])
         sorted_seqs = sorted(
-            list(range(len(seq_lens))),
+            range(len(seq_lens)),
             key=functools.cmp_to_key(lambda x, y: seq_lens[y] - seq_lens[x]),
         )
         num_batch = seq_lens[sorted_seqs[0]]

python/paddle/fluid/tests/unittests/test_kldiv_loss_op.py

@@ -58,7 +58,7 @@ class TestKLDivLossOp(OpTest):
         self.check_output()
 
     def test_check_grad(self):
-        self.check_grad(['X'], 'Loss', no_grad_set=set(["Target"]))
+        self.check_grad(['X'], 'Loss', no_grad_set={"Target"})
 
     def initTestCase(self):
         self.x_shape = (4, 5, 5)

python/paddle/fluid/tests/unittests/test_kthvalue_op.py

@@ -58,7 +58,7 @@ class TestKthvalueOp(OpTest):
 
     def test_check_grad(self):
         paddle.enable_static()
-        self.check_grad(set(['X']), 'Out')
+        self.check_grad({'X'}, 'Out')
 
 
 class TestKthvalueOpWithKeepdim(OpTest):
@@ -85,7 +85,7 @@ class TestKthvalueOpWithKeepdim(OpTest):
 
     def test_check_grad(self):
         paddle.enable_static()
-        self.check_grad(set(['X']), 'Out')
+        self.check_grad({'X'}, 'Out')
 
 
 class TestKthvalueOpKernels(unittest.TestCase):

python/paddle/fluid/tests/unittests/test_lod_reset_op.py

@@ -115,7 +115,7 @@ class TestLodAppendOpByAttr(OpTest):
         lod = [[3, 2, 5]]
         # target_offset_lod and target_lod are the same lod info represented
         # in offset-based format and length-based format, respectively.
-        target_offset_lod = [i for i in range(11)]
+        target_offset_lod = list(range(11))
         self.inputs = {'X': (x, lod)}
         out_lod = [[3, 2, 5], [1] * 10]
         # The `target_lod` attribute is still based on offset

python/paddle/fluid/tests/unittests/test_lookup_table_bf16_op.py

@@ -128,7 +128,7 @@ class TestLookupTableBF16OpWIsSelectedRows(unittest.TestCase):
         self.place = core.CPUPlace()
 
     def prepare_w(self):
-        rows = [a for a in range(self.w_bf16.shape[0])]
+        rows = list(range(self.w_bf16.shape[0]))
         row_numel = self.w_bf16.shape[1]
 
         w_selected_rows = self.scope.var('W').get_selected_rows()

python/paddle/fluid/tests/unittests/test_matmul_op.py

@@ -65,14 +65,14 @@ def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
         elif X.ndim == 2:
             X = X.T
         else:
-            dim = [i for i in range(len(X.shape))]
+            dim = list(range(len(X.shape)))
             dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
             X = np.transpose(X, tuple(dim))
     if transpose_Y:
         if Y.ndim == 1:
             Y = Y.reshape((1, Y.size))
         else:
-            dim = [i for i in range(len(Y.shape))]
+            dim = list(range(len(Y.shape)))
             dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
             Y = np.transpose(Y, tuple(dim))
 

python/paddle/fluid/tests/unittests/test_matmul_v2_op.py

@@ -33,14 +33,14 @@ def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
         elif X.ndim == 2:
             X = X.T
         else:
-            dim = [i for i in range(len(X.shape))]
+            dim = list(range(len(X.shape)))
             dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
             X = np.transpose(X, tuple(dim))
     if transpose_Y:
         if Y.ndim == 1:
             Y = Y.reshape((Y.size,))
         else:
-            dim = [i for i in range(len(Y.shape))]
+            dim = list(range(len(Y.shape)))
             dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
             Y = np.transpose(Y, tuple(dim))
 

python/paddle/fluid/tests/unittests/test_mode_op.py

@@ -78,7 +78,7 @@ class TestModeOp(OpTest):
 
     def test_check_grad(self):
         paddle.enable_static()
-        self.check_grad(set(['X']), 'Out')
+        self.check_grad({'X'}, 'Out')
 
 
 class TestModeOpLastdim(OpTest):
@@ -103,7 +103,7 @@ class TestModeOpLastdim(OpTest):
 
     def test_check_grad(self):
         paddle.enable_static()
-        self.check_grad(set(['X']), 'Out')
+        self.check_grad({'X'}, 'Out')
 
 
 class TestModeOpKernels(unittest.TestCase):

python/paddle/fluid/tests/unittests/test_registered_phi_kernels.py

@@ -86,9 +86,7 @@ class TestRegisteredPhiKernels(unittest.TestCase):
 
     def test_registered_phi_kernels(self):
         phi_function_kernel_infos = core._get_registered_phi_kernels("function")
-        registered_kernel_list = [
-            name for name in phi_function_kernel_infos.keys()
-        ]
+        registered_kernel_list = list(phi_function_kernel_infos.keys())
         forward_kernels = get_all_kernels(
             self.forward_ops, registered_kernel_list
         )

python/paddle/fluid/tests/unittests/test_sgd_op_bf16.py

@@ -193,7 +193,7 @@ class TestSparseGradParamSGDOpBF16(TestSparseSGDOpBF16):
         self.grad_height = 10
         self.grad_rows = [0, 4, 7]
         self.grad_row_numel = 12
-        self.param_rows = [a for a in range(self.grad_height)]
+        self.param_rows = list(range(self.grad_height))
 
     def test_sparse_param_grad_sgd(self):
         scope = core.Scope()
@@ -228,7 +228,7 @@ class TestSparseGradParamSGDOpBF16Case2(TestSparseGradParamSGDOpBF16):
         self.grad_height = 14
         self.grad_rows = [1, 4, 12, 7, 8]
         self.grad_row_numel = 16
-        self.param_rows = [a for a in range(self.grad_height)]
+        self.param_rows = list(range(self.grad_height))
 
 
 @OpTestTool.skip_if_not_cpu_bf16()

python/paddle/fluid/tests/unittests/test_shard_index_op.py

@@ -23,7 +23,7 @@ import paddle
 def common_setup(self, index_num, nshards, shard_id, ignore_value):
     self.op_type = 'shard_index'
     self.python_api = paddle.tensor.shard_index
-    x_lod = [[i for i in range(10)]]
+    x_lod = [list(range(10))]
     N = sum(x_lod[0])
     x = [np.random.randint(0, index_num - 1) for i in range(N)]
     x = np.array(x).astype('int32').reshape([N, 1])

python/paddle/fluid/tests/unittests/test_smooth_l1_loss_op.py

@@ -102,7 +102,7 @@ class TestSmoothL1LossOp2(OpTest):
             ['Y'],
             'Out',
             max_relative_error=0.03,
-            no_grad_set=set(['X', 'InsideWeight', 'OutsideWeight']),
+            no_grad_set={'X', 'InsideWeight', 'OutsideWeight'},
         )
 
     def test_check_grad_ingore_y(self):
@@ -110,7 +110,7 @@ class TestSmoothL1LossOp2(OpTest):
             ['X'],
             'Out',
             max_relative_error=0.03,
-            no_grad_set=set(['Y', 'InsideWeight', 'OutsideWeight']),
+            no_grad_set={'Y', 'InsideWeight', 'OutsideWeight'},
         )
 
 

python/paddle/fluid/tests/unittests/test_sum_op.py

@@ -274,16 +274,14 @@ class TestLoDTensorAndSelectedRowsOp(TestSelectedRowsSumOp):
         self.assertEqual(out_t.shape[0], self.height)
         np.testing.assert_array_equal(
             out_t,
-            self._get_array([i for i in range(self.height)], self.row_numel)
+            self._get_array(list(range(self.height)), self.row_numel)
             * np.tile(np.array(result).reshape(self.height, 1), self.row_numel),
         )
 
     def create_lod_tensor(self, scope, place, var_name):
         var = scope.var(var_name)
         w_tensor = var.get_tensor()
-        w_array = self._get_array(
-            [i for i in range(self.height)], self.row_numel
-        )
+        w_array = self._get_array(list(range(self.height)), self.row_numel)
         w_tensor.set(w_array, place)
         return var
 

python/paddle/fluid/tests/unittests/test_target_assign_op.py

@@ -31,10 +31,10 @@ def gen_match_and_neg_indices(num_prior, gt_lod, neg_lod):
     offset = 0
     for n in range(batch_size):
         gt_num = gt_lod[n]
-        ids = random.sample([i for i in range(num_prior)], gt_num)
-        match_indices[n, ids] = [i for i in range(gt_num)]
+        ids = random.sample(list(range(num_prior)), gt_num)
+        match_indices[n, ids] = list(range(gt_num))
 
-        ret_ids = {i for i in range(num_prior)} - set(ids)
+        ret_ids = set(range(num_prior)) - set(ids)
         l = neg_lod[n]
         neg_ids = random.sample(ret_ids, l)
         neg_indices[offset : offset + neg_lod[n], :] = (

python/paddle/fluid/tests/unittests/test_tril_triu_op.py

@@ -122,14 +122,10 @@ cases = {
 for _op_type in ['tril', 'triu']:
     for _expected, _params in cases.items():
         for _Xshape, _diaglist in _params.items():
-            list(
-                map(
-                    lambda _diagonal: case_generator(
-                        _op_type, _Xshape, _diagonal, _expected
-                    ),
-                    _diaglist,
-                )
-            )
+            [
+                case_generator(_op_type, _Xshape, _diagonal, _expected)
+                for _diagonal in _diaglist
+            ]
 
 
 class TestTrilTriuOpAPI(unittest.TestCase):

python/paddle/fluid/tests/unittests/tokenizer/bert_tokenizer.py

@@ -483,14 +483,10 @@ class BertTokenizer(PretrainedTokenizer):
                     "You should not supply a second sequence if the provided sequence of "
                     "ids is already formatted with special tokens for the model."
                 )
-            return list(
-                map(
-                    lambda x: 1
-                    if x in [self.sep_token_id, self.cls_token_id]
-                    else 0,
-                    token_ids_0,
-                )
-            )
+            return [
+                1 if x in [self.sep_token_id, self.cls_token_id] else 0
+                for x in token_ids_0
+            ]
 
         if token_ids_1 is not None:
             return (

python/paddle/fluid/tests/unittests/xpu/test_coalesce_tensor_op_xpu.py

@@ -93,7 +93,7 @@ class XPUTestCoalesceTensorOp(XPUOpTestWrapper):
                 out[0:length] = input[1].flatten()
                 inputs.append(out)
 
-            coalesce_tensor_var = np.concatenate([input for input in inputs])
+            coalesce_tensor_var = np.concatenate(list(inputs))
             if set_constant:
                 coalesce_tensor_var = (
                     np.ones(len(coalesce_tensor_var)) * constant

python/paddle/fluid/tests/unittests/xpu/test_matmul_op_xpu.py

@@ -39,7 +39,7 @@ def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
         elif X.ndim == 2:
             X = X.T
         else:
-            dim = [i for i in range(len(X.shape))]
+            dim = list(range(len(X.shape)))
             dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
             X = np.transpose(X, tuple(dim))
     if transpose_Y:
@@ -48,7 +48,7 @@ def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
         elif Y.ndim == 2:
             Y = Y.T
         else:
-            dim = [i for i in range(len(Y.shape))]
+            dim = list(range(len(Y.shape)))
             dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
             Y = np.transpose(Y, tuple(dim))
 

python/paddle/fluid/tests/unittests/xpu/test_matmul_v2_op_xpu.py

@@ -38,14 +38,14 @@ def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
         elif X.ndim == 2:
             X = X.T
         else:
-            dim = [i for i in range(len(X.shape))]
+            dim = list(range(len(X.shape)))
             dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
             X = np.transpose(X, tuple(dim))
     if transpose_Y:
         if Y.ndim == 1:
             Y = Y.reshape((Y.size,))
         else:
-            dim = [i for i in range(len(Y.shape))]
+            dim = list(range(len(Y.shape)))
             dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
             Y = np.transpose(Y, tuple(dim))
     Out = np.matmul(X, Y)

python/paddle/fluid/tests/unittests/xpu/test_sum_op_xpu.py

@@ -250,7 +250,7 @@ class TestLoDTensorAndSelectedRowsOp(unittest.TestCase):
         self.assertEqual(out_t.shape[0], self.height)
         np.testing.assert_array_equal(
             out_t,
-            self._get_array([i for i in range(self.height)], self.row_numel)
+            self._get_array(list(range(self.height)), self.row_numel)
             * np.tile(np.array(result).reshape(self.height, 1), self.row_numel),
         )
 
@@ -281,9 +281,7 @@ class TestLoDTensorAndSelectedRowsOp(unittest.TestCase):
         return var
 
     def create_lod_tensor(self, place):
-        w_array = self._get_array(
-            [i for i in range(self.height)], self.row_numel
-        )
+        w_array = self._get_array(list(range(self.height)), self.row_numel)
         return paddle.to_tensor(w_array)
 
     def test_w_is_selected_rows(self):

python/paddle/hapi/callbacks.py

@@ -71,7 +71,7 @@ def config_callbacks(
 class CallbackList:
     def __init__(self, callbacks=None):
         # copy
-        self.callbacks = [c for c in callbacks]
+        self.callbacks = list(callbacks)
         self.params = {}
         self.model = None
 

python/paddle/incubate/autograd/primrules.py

@@ -315,7 +315,7 @@ def rsqrt_orig2prim(op, x):
 @REGISTER_ORIG2PRIM('matmul_v2')
 def matmul_v2_orig2prim(op, x, y):
     def trans(shape):
-        ret = [i for i in range(len(shape))]
+        ret = list(range(len(shape)))
         ret[-1], ret[-2] = ret[-2], ret[-1]
         return ret
 

python/paddle/incubate/distributed/fleet/parameter_server/ir/pserver_pass.py

@@ -178,9 +178,7 @@ def _append_pserver_ops(
         merged_vars = []
         merged_ordervars = []
 
-        param_vars = [
-            p for p in config.param_grad_ep_mapping[endpoint]["params"]
-        ]
+        param_vars = list(config.param_grad_ep_mapping[endpoint]["params"])
 
         for var in param_vars:
             name = var.name
@@ -1066,7 +1064,7 @@ def build_pserver_startup_program_pass(program, p_main_program, config):
 
 def add_geo_optimizer_pass(program, config):
     endpoint = config.get_ps_endpoint()
-    params = [p for p in config.param_grad_ep_mapping[endpoint]["params"]]
+    params = list(config.param_grad_ep_mapping[endpoint]["params"])
 
     sparse_tablenames = get_sparse_tablenames(
         config.get_origin_main_program(), False

python/paddle/incubate/distributed/fleet/parameter_server/ir/trainer_pass.py

@@ -2087,7 +2087,7 @@ def find_op_input_output(program, block, op):
 
 def get_vars_name_in_block(block):
     vars_list = block.vars.keys()
-    vars_name_list = [var_name for var_name in vars_list]
+    vars_name_list = list(vars_list)
     return vars_name_list
 
 

python/paddle/incubate/distributed/fleet/parameter_server/pslib/__init__.py

@@ -542,7 +542,7 @@ class PSLib(Fleet):
                 for i in tp.dense_table:
                     if table_id is not None and table_id != i.table_id:
                         continue
-                    var_list = [var for var in i.dense_variable_name]
+                    var_list = list(i.dense_variable_name)
                     skip = False
                     for var in var_list:
                         if scope.find_var(var) is None:
@@ -751,7 +751,7 @@ class PSLib(Fleet):
                 for i in tp.dense_table:
                     if table_id is not None and table_id != i.table_id:
                         continue
-                    table_var_names = [var for var in i.dense_variable_name]
+                    table_var_names = list(i.dense_variable_name)
                     skip = False
                     for var in table_var_names:
                         if scope.find_var(var) is None:

python/paddle/incubate/distributed/fleet/parameter_server/pslib/optimizer_factory.py

@@ -744,8 +744,8 @@ class DistributedAdam(DistributedOptimizerImplBase):
                 ]
 
                 program_configs[program_id] = {
-                    "pull_sparse": [t_index for t_index in sparse_table_index],
-                    "push_sparse": [t_index for t_index in sparse_table_index],
+                    "pull_sparse": list(sparse_table_index),
+                    "push_sparse": list(sparse_table_index),
                 }
 
                 params_grads = prog_id_to_param_grads[program_id]

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

@@ -941,7 +941,7 @@ class GeneralRoleMaker(RoleMakerBase):
         """
         if not self._role_is_generated:
             self.generate_role()
-        input_list = [i for i in input]
+        input_list = list(input)
         ans = self._node_type_comm.all_reduce(input_list, mode)
         for i in range(len(ans)):
             output[i] = ans[i]

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

@@ -96,7 +96,7 @@ def check_pruned_program_vars(train_prog, pruned_prog):
         if io_utils.is_persistable(v)
     ]
     pruned_vars = OrderedDict(pruned_vars)
-    pruned_vars_name = [name for name in pruned_vars]
+    pruned_vars_name = list(pruned_vars)
     logger.info(
         "persistable vars in pruned program: {}".format(pruned_vars_name)
     )
@@ -497,7 +497,7 @@ def parse_program(program, output_dir):
             f.write("\n")
 
     # all vars
-    all_vars = [v for v in program.list_vars()]
+    all_vars = list(program.list_vars())
     output["all_vars"] = [
         {
             'name': str(v.name),

python/paddle/jit/dy2static/convert_operators.py

@@ -55,7 +55,7 @@ def indexable(x, code=None):
     if isinstance(x, Variable):
         return x
     elif hasattr(x, '__iter__'):
-        return [i for i in x]
+        return list(x)
     elif hasattr(x, '__len__') and hasattr(
         x, '__getitem__'
     ):  # used for customed type and non-iterable type.
@@ -575,14 +575,14 @@ class VariableTuple:
 
 
 def convert_enumerate(*args):
-    has_variable = any(map(lambda x: isinstance(x, Variable), args))
+    has_variable = any(isinstance(x, Variable) for x in args)
     if has_variable:
         return VariableTuple(*args)
     return enumerate(*args)
 
 
 def convert_range(*args):
-    has_variable = any(map(lambda x: isinstance(x, Variable), args))
+    has_variable = any(isinstance(x, Variable) for x in args)
     if has_variable:
         if len(args) == 1:
             return paddle.arange(0, args[0], 1, paddle.int64)

python/paddle/jit/dy2static/utils.py

@@ -1279,7 +1279,7 @@ class FunctionNameLivenessAnalysis(gast.NodeVisitor):
         assert isinstance(
             node, gast.FunctionDef
         ), "Input node is not function define node"
-        names = [a for a in node.args.args]
+        names = list(node.args.args)
         names.append(node.args.vararg)
         names.append(node.args.kwarg)
         names = [i.id for i in names if i is not None]
@@ -1387,8 +1387,8 @@ class GetterSetterHelper:
     """
 
     def __init__(self, getter_func, setter_func, *name_lists):
-        name_lists = map(lambda x: [] if x is None else x, name_lists)
-        name_sets = map(lambda x: set(x), name_lists)
+        name_lists = ([] if x is None else x for x in name_lists)
+        name_sets = (set(x) for x in name_lists)
         self._union = list(
             functools.reduce(lambda x, y: x | y, name_sets, set())
         )
@@ -1412,7 +1412,7 @@ class GetterSetterHelper:
             ), "the name `{}` not in name union set`{}`.".format(
                 n, self.name2id.keys()
             )
-        return tuple(map(lambda n: vars[self.name2id[n]], names))
+        return tuple(vars[self.name2id[n]] for n in names)
 
     def set(self, names, values):
         if names is None:
@@ -1429,7 +1429,7 @@ class GetterSetterHelper:
                 n, self.name2id.keys()
             )
         vars = list(vars)
-        indices = list(map(lambda n: self.name2id[n], names))
+        indices = [self.name2id[n] for n in names]
         for i, v in zip(indices, values):
             vars[i] = v
         self.setter(vars)

python/paddle/static/nn/control_flow.py

@@ -329,7 +329,7 @@ class While:
             if inner_var:
                 out_vars.append(inner_var)
 
-        x_name_list |= set(map(lambda x: x.name, out_vars))
+        x_name_list |= {x.name for x in out_vars}
         # NOTE(dev): cond_var has been contained in Input('Condition'), so
         # we remove it from Input('X')
         x_name_list -= {self.cond_var.name}

python/paddle/static/quantization/post_training_quantization.py

@@ -75,12 +75,11 @@ def _remove_unused_var_nodes(graph):
             all_used_vars.add(output_node)
 
     all_used_vars = {n.node for n in all_used_vars}
-    all_unused_vars = {
-        n
-        for n in filter(
+    all_unused_vars = set(
+        filter(
             lambda node: node.node not in all_used_vars, graph.all_var_nodes()
         )
-    }
+    )
     graph.safe_remove_nodes(all_unused_vars)
     return graph
 

python/paddle/static/quantization/quant2_int8_mkldnn_pass.py

@@ -532,13 +532,12 @@ class Quant2Int8MkldnnPass:
                 all_used_vars.add(output_node)
 
         all_used_vars = {n.node for n in all_used_vars}
-        all_unused_vars = {
-            n
-            for n in filter(
+        all_unused_vars = set(
+            filter(
                 lambda node: node.node not in all_used_vars,
                 graph.all_var_nodes(),
             )
-        }
+        )
         graph.safe_remove_nodes(all_unused_vars)
         return graph
 

python/paddle/static/quantization/quant_int8_mkldnn_pass.py

@@ -280,11 +280,10 @@ class QuantInt8MkldnnPass:
                 all_used_vars.add(output_node)
 
         all_used_vars = {n.node for n in all_used_vars}
-        all_unused_vars = {
-            n
-            for n in filter(
+        all_unused_vars = set(
+            filter(
                 lambda node: node.node not in all_used_vars,
                 graph.all_var_nodes(),
             )
-        }
+        )
         graph.safe_remove_nodes(all_unused_vars)

python/paddle/static/quantization/quantization_pass.py

@@ -1407,13 +1407,12 @@ class QuantizationFreezePass:
                 all_used_vars.add(output_node)
 
         all_used_vars = {n.node for n in all_used_vars}
-        all_unused_vars = {
-            n
-            for n in filter(
+        all_unused_vars = set(
+            filter(
                 lambda node: node.node not in all_used_vars,
                 graph.all_var_nodes(),
             )
-        }
+        )
         graph.safe_remove_nodes(all_unused_vars)
 
     def _original_var_name(self, var_name):
@@ -1524,13 +1523,12 @@ class ConvertToInt8Pass:
                 all_used_vars.add(output_node)
 
         all_used_vars = {n.node for n in all_used_vars}
-        all_unused_vars = {
-            n
-            for n in filter(
+        all_unused_vars = set(
+            filter(
                 lambda node: node.node not in all_used_vars,
                 graph.all_var_nodes(),
             )
-        }
+        )
         graph.safe_remove_nodes(all_unused_vars)
 
 
@@ -3224,13 +3222,12 @@ class QuantWeightPass:
                 all_used_vars.add(output_node)
 
         all_used_vars = {n.node for n in all_used_vars}
-        all_unused_vars = {
-            n
-            for n in filter(
+        all_unused_vars = set(
+            filter(
                 lambda node: node.node not in all_used_vars,
                 graph.all_var_nodes(),
             )
-        }
+        )
         graph.safe_remove_nodes(all_unused_vars)
 
     def _load_var(self, name):

python/paddle/static/quantization/tests/quant2_int8_image_classification_comparison.py

@@ -246,7 +246,7 @@ class Quant2Int8ImageClassificationComparisonTest(unittest.TestCase):
                 if iters == skip_batch_num:
                     total_samples = 0
                     infer_start_time = time.time()
-                images = list(map(lambda x: x[0].reshape(dshape), data))
+                images = [x[0].reshape(dshape) for x in data]
                 images = np.array(images).astype('float32')
                 labels = np.array([x[1] for x in data]).astype('int64')
 

python/paddle/static/quantization/tests/quant_int8_image_classification_comparison.py

@@ -214,7 +214,7 @@ class QuantInt8ImageClassificationComparisonTest(unittest.TestCase):
                 if iters == skip_batch_num:
                     total_samples = 0
                     infer_start_time = time.time()
-                images = list(map(lambda x: x[0].reshape(dshape), data))
+                images = [x[0].reshape(dshape) for x in data]
                 images = np.array(images).astype('float32')
                 labels = np.array([x[1] for x in data]).astype('int64')
 

python/paddle/tensor/einsum.py

@@ -232,7 +232,7 @@ def build_global_view(nop_labels, rhs, n_bcast_dims):
 
     g_labels_sum = ''.join(labels)
     g_labels = g_labels_out + g_labels_sum
-    g_view = list(map(lambda i: build_view(i, g_labels), nop_labels))
+    g_view = [build_view(i, g_labels) for i in nop_labels]
     g_nout = len(g_labels_out)
     g_count = count
 
@@ -741,7 +741,7 @@ def parse_fake_shape(equation, operands, labels):
     list of shape
 
     """
-    origin_labels = map(lambda x: x.strip(), equation.split(','))
+    origin_labels = (x.strip() for x in equation.split(','))
     shaped = collections.namedtuple('shaped', ['shape'])
 
     def fake_shape(ori_label, label, op):
@@ -1047,7 +1047,7 @@ def einsum(equation, *operands):
     # To handle broadcasting, we should first know how many dimensions are there
     # We need to use that number to generate output labels
     # e.g. 1 for ['ij', 'i.', '.k']
-    n_bcast_dims = max(map(lambda s: s.count('.'), nop_labels))
+    n_bcast_dims = max(s.count('.') for s in nop_labels)
 
     # Build the data structures for planning. It's helpful to think of all the operands
     # broadcasting together from a global view. In this view, dimensions from multiple

python/paddle/tensor/manipulation.py

@@ -128,9 +128,7 @@ def tensor_array_to_tensor(input, axis=1, use_stack=False, name=None):
 
         op = stack if use_stack else concat
         res = op(input, axis=axis)
-        sizes = paddle.to_tensor(
-            np.array(list(map(lambda x: int(x.shape[axis]), input)))
-        )
+        sizes = paddle.to_tensor(np.array([int(x.shape[axis]) for x in input]))
         return res, sizes
     else:
         check_type(input, 'input', (list, Variable), 'tensor_array_to_tensor')
@@ -335,7 +333,7 @@ def slice(input, axes, starts, ends):
             ]
         elif isinstance(starts, tmp_tensor_type):
             tensor_t = starts.numpy(False)
-            starts = [ele for ele in tensor_t]
+            starts = list(tensor_t)
             infer_flags = [-1 for i in range(len(axes))]
 
         if isinstance(ends, (list, tuple)):
@@ -345,7 +343,7 @@ def slice(input, axes, starts, ends):
             ]
         elif isinstance(ends, tmp_tensor_type):
             tensor_t = ends.numpy(False)
-            ends = [ele for ele in tensor_t]
+            ends = list(tensor_t)
             infer_flags = [-1 for i in range(len(axes))]
 
         return _C_ops.slice(input, axes, starts, ends, infer_flags, [])
@@ -2048,12 +2046,10 @@ def split(x, num_or_sections, axis=0, name=None):
                     len(num_or_sections) <= input_shape[dim]
                 ), 'len(num_or_sections) must not be more than input.shape[dim].'
             num = len(num_or_sections)
-            attrs['sections'] = list(
-                map(
-                    lambda ele: -1 if isinstance(ele, Variable) else ele,
-                    num_or_sections,
-                )
-            )
+            attrs['sections'] = [
+                -1 if isinstance(ele, Variable) else ele
+                for ele in num_or_sections
+            ]
             if paddle.utils._contain_var(num_or_sections):
                 inputs['SectionsTensorList'] = _get_SectionsTensorList(
                     num_or_sections

python/paddle/tensor/to_string.py

@@ -115,8 +115,8 @@ def _to_summary(var):
     else:
         # recursively handle all dimensions
         if var.shape[0] > 2 * edgeitems:
-            begin = [x for x in var[:edgeitems]]
-            end = [x for x in var[(-1 * edgeitems) :]]
+            begin = list(var[:edgeitems])
+            end = list(var[(-1 * edgeitems) :])
             return np.stack([_to_summary(x) for x in (begin + end)])
         else:
             return np.stack([_to_summary(x) for x in var])

python/paddle/utils/layers_utils.py

@@ -313,8 +313,8 @@ def _recursive_assert_same_structure(nest1, nest2, check_types):
                         keys1, keys2
                     )
                 )
-    nest1_as_sequence = [n for n in _yield_value(nest1)]
-    nest2_as_sequence = [n for n in _yield_value(nest2)]
+    nest1_as_sequence = list(_yield_value(nest1))
+    nest2_as_sequence = list(_yield_value(nest2))
     for n1, n2 in zip(nest1_as_sequence, nest2_as_sequence):
         _recursive_assert_same_structure(n1, n2, check_types)
 
@@ -454,12 +454,7 @@ def convert_shape_to_list(shape):
     Convert shape(list, tuple, variable) to list in imperative mode
     """
     if isinstance(shape, (list, tuple)):
-        shape = list(
-            map(
-                lambda x: x.item(0) if isinstance(x, Variable) else x,
-                shape,
-            )
-        )
+        shape = [x.item(0) if isinstance(x, Variable) else x for x in shape]
     else:
         shape = shape.astype(int).tolist()
     return shape

test/autograd/test_autograd_functional_dynamic.py

@@ -636,7 +636,7 @@ class TestHessianBatchFirst(unittest.TestCase):
             np.array(expected),
             (xs_len, xs_len, self.nrow, self.nbatch, self.nrow),
         )
-        expected = [[n for n in row] for row in expected]
+        expected = [list(row) for row in expected]
         expected = utils._np_concat_matrix_sequence(expected)
 
         self.x.stop_gradient = False
@@ -662,7 +662,7 @@ class TestHessianBatchFirst(unittest.TestCase):
             np.array(expected),
             (xs_len, xs_len, self.nrow, self.nbatch, self.nrow),
         )
-        expected = [[n for n in row] for row in expected]
+        expected = [list(row) for row in expected]
         expected = utils._np_concat_matrix_sequence(expected)
         expected = utils._np_transpose_matrix_format(
             expected, utils.MatrixFormat.NBM, utils.MatrixFormat.BNM

test/autograd/utils.py

@@ -131,7 +131,7 @@ def concat_to_matrix(xs, is_batched=False):
     """Concats a tuple of tuple of Jacobian/Hessian matrix into one matrix"""
     rows = []
     for i in range(len(xs)):
-        rows.append(np.concatenate([x for x in xs[i]], -1))
+        rows.append(np.concatenate(list(xs[i]), -1))
     return np.concatenate(rows, 1) if is_batched else np.concatenate(rows, 0)
 
 

test/custom_kernel/custom_kernel_dot_setup.py

@@ -45,11 +45,9 @@ if core.is_compiled_with_npu():
 
 # include path
 site_packages_path = site.getsitepackages()
-paddle_custom_kernel_include = list(
-    map(
-        lambda path: os.path.join(path, 'paddle', 'include'), site_packages_path
-    )
-)
+paddle_custom_kernel_include = [
+    os.path.join(path, 'paddle', 'include') for path in site_packages_path
+]
 
 # include path third_party
 compile_third_party_path = os.path.join(
@@ -61,9 +59,9 @@ paddle_custom_kernel_include += [
 ]
 
 # libs path
-paddle_custom_kernel_library_dir = list(
-    map(lambda path: os.path.join(path, 'paddle', 'fluid'), site_packages_path)
-)
+paddle_custom_kernel_library_dir = [
+    os.path.join(path, 'paddle', 'fluid') for path in site_packages_path
+]
 
 # libs
 libs = [':libpaddle.so']

test/legacy_test/test_ops_roi_align.py

@@ -99,9 +99,7 @@ class TestRoIAlign(unittest.TestCase):
         self,
     ):
         data = (
-            np.array([i for i in range(1, 17)])
-            .reshape(1, 1, 4, 4)
-            .astype(np.float32)
+            np.array(list(range(1, 17))).reshape(1, 1, 4, 4).astype(np.float32)
         )
         boxes = np.array([[1.0, 1.0, 2.0, 2.0], [1.5, 1.5, 3.0, 3.0]]).astype(
             np.float32

test/legacy_test/test_ops_roi_pool.py

@@ -100,9 +100,7 @@ class TestRoIPool(unittest.TestCase):
         self,
     ):
         data = (
-            np.array([i for i in range(1, 17)])
-            .reshape(1, 1, 4, 4)
-            .astype(np.float32)
+            np.array(list(range(1, 17))).reshape(1, 1, 4, 4).astype(np.float32)
         )
         boxes = np.array([[1.0, 1.0, 2.0, 2.0], [1.5, 1.5, 3.0, 3.0]]).astype(
             np.float32

tools/gen_ut_cmakelists.py

@@ -624,7 +624,7 @@ if __name__ == "__main__":
             os.path.dirname(file) for file in args.files
         ]
     if len(args.dirpaths) >= 1:
-        current_work_dirs = current_work_dirs + [d for d in args.dirpaths]
+        current_work_dirs = current_work_dirs + list(args.dirpaths)
 
     cmake_generator = CMakeGenerator(current_work_dirs, args.ignore_cmake_dirs)
     cmake_generator.prepare_dist_ut_port()