[CodeStyle][E712] use `if cond`/`if cond is True` for comparison with `True` (#47464)

* [CodeStyle][E712] use `if cond`/`if cond is True` for comparison with `True`

* revert changes in fluid

* revert unrelated file

* revert changes in norm

* revert changes in auto_parallel_amp

* fix norm and auto_parallel_amp

* revert a typo fix due to fixed at #47477

paddle/fluid/eager/auto_code_generator/generator/python_c_gen.py

@@ -532,7 +532,7 @@ class PythonCGenerator(GeneratorBase):
             )
             status = f_generator.run()
 
-            if status == True:
+            if status:
                 self.python_c_functions_str += (
                     f_generator.python_c_function_str + "\n"
                 )

paddle/fluid/inference/tests/api/full_ILSVRC2012_val_preprocess.py

@@ -45,7 +45,7 @@ def resize_short(img, target_size):
 def crop_image(img, target_size, center):
     width, height = img.size
     size = target_size
-    if center == True:
+    if center:
         w_start = (width - size) // 2
         h_start = (height - size) // 2
     else:

python/paddle/audio/backends/init_backend.py

@@ -79,7 +79,7 @@ def list_available_backends() -> List[str]:
 
     if "paddleaudio" in sys.modules:
         version = paddleaudio.__version__
-        if _check_version(version) == False:
+        if not _check_version(version):
             err_msg = (
                 "the version of paddleaudio installed is {},\n"
                 "please ensure the paddleaudio >= 1.0.2."

python/paddle/dataset/conll05.py

@@ -109,9 +109,9 @@ def corpus_reader(data_path, words_name, props_name):
                             lbl_seq = []
                             verb_word = ''
                             for l in lbl:
-                                if l == '*' and is_in_bracket == False:
+                                if l == '*' and not is_in_bracket:
                                     lbl_seq.append('O')
-                                elif l == '*' and is_in_bracket == True:
+                                elif l == '*' and is_in_bracket:
                                     lbl_seq.append('I-' + cur_tag)
                                 elif l == '*)':
                                     lbl_seq.append('I-' + cur_tag)

python/paddle/distributed/auto_parallel/process_group.py

@@ -106,7 +106,7 @@ class ProcessGroup:
             return
         else:
             assert (
-                self.is_instantiate() == False
+                not self.is_instantiate()
             ), "Cannot add new ranks after instantiating the process group"
         self._ranks.extend(new_ranks)
         self._ranks = sorted(list(set(self.ranks)))

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

@@ -268,7 +268,7 @@ class ParallelTuner:
             return
 
         for idx, dim in enumerate(dims_list):
-            if visited[idx] == False:
+            if not visited[idx]:
                 dims_mapping[start] = dim
                 visited[idx] = True
                 self._generate_dims_mapping_candidates_helper(

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

@@ -514,7 +514,7 @@ class InMemoryDataset(DatasetBase):
                 self._set_fleet_send_batch_size(kwargs[key])
             elif key == "fleet_send_sleep_seconds":
                 self._set_fleet_send_sleep_seconds(kwargs[key])
-            elif key == "fea_eval" and kwargs[key] == True:
+            elif key == "fea_eval" and kwargs[key]:
                 candidate_size = kwargs.get("candidate_size", 10000)
                 self._set_fea_eval(candidate_size, True)
 

python/paddle/distributed/fleet/fleet.py

@@ -303,7 +303,7 @@ class Fleet(object):
                 paddle.distributed.init_parallel_env()
 
             # hybrid parallel not support for npu/xpu
-            if self._user_defined_strategy.heter_ccl_mode == False:
+            if not self._user_defined_strategy.heter_ccl_mode:
                 # init hybrid parallel environment in dygraph
                 if tp._HYBRID_PARALLEL_GROUP is None:
                     self._init_hybrid_parallel_env()

python/paddle/distributed/fleet/launch.py

@@ -369,7 +369,7 @@ def get_cluster_info(args):
     if os.environ.get('FLAGS_START_PORT') is not None:
         start_port = os.environ.get('FLAGS_START_PORT')
     # auto mapping between processes and devices for auto-parallel
-    if args.enable_auto_mapping == True:
+    if args.enable_auto_mapping:
         assert (
             args.cluster_topo_path is not None
         ), "The cluster topology must be provied when enabling auto mapping."

python/paddle/distributed/fleet/launch_utils.py

@@ -1582,7 +1582,7 @@ class ParameterServerLauncher(object):
             x.strip().split(":")[0] for x in self.worker_endpoints.split(",")
         ]
 
-        if self.with_coordinator == True:
+        if self.with_coordinator:
             self.coordinator_endpoints_ips = [
                 x.strip().split(":")[0]
                 for x in self.coordinator_endpoints.split(",")

python/paddle/distributed/fleet/layers/mpu/mp_ops.py

@@ -582,7 +582,7 @@ def _parallel_linear(
     # set is_distributed for splited bias
     # if a linear layer is splited by row, each rank would hold a complete bias and they should be the same in each rank.
     # if a linear layer is splited by col, the bias would also be split into each rank as its weight
-    if axis == 1 and linear._bias_attr != False:
+    if axis == 1 and linear._bias_attr is not False:
         _set_var_distributed(linear.bias)
 
     if not gather_out:

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

@@ -53,7 +53,7 @@ class GradientMergeOptimizer(MetaOptimizerBase):
             return False
 
         can_apply = (
-            self.user_defined_strategy.gradient_merge == True
+            self.user_defined_strategy.gradient_merge
         ) and self.user_defined_strategy.gradient_merge_configs["k_steps"] > 1
         return can_apply
 

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

@@ -177,7 +177,7 @@ class GraphExecutionOptimizer(MetaOptimizerBase):
             gradient_scale_configs['scale_strategy']
         ]
 
-        if self.user_defined_strategy.recompute == True:
+        if self.user_defined_strategy.recompute:
             logging.warn(
                 "set enable_sequential_execution=True since you have enable the recompute strategy"
             )

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

@@ -66,7 +66,7 @@ class PipelineOptimizer(MetaOptimizerBase):
         if self.use_sharding:
             return False
 
-        if self.user_defined_strategy.pipeline == True:
+        if self.user_defined_strategy.pipeline:
             return True
         return False
 

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

@@ -65,7 +65,7 @@ class RawProgramOptimizer(MetaOptimizerBase):
         if not self.role_maker._is_collective:
             return False
 
-        if self.without_graph_optimization == True:
+        if self.without_graph_optimization:
             return True
         return False
 

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

@@ -55,7 +55,7 @@ class RecomputeOptimizer(MetaOptimizerBase):
         if not self.role_maker._is_collective:
             return False
 
-        if self.user_defined_strategy.recompute == True:
+        if self.user_defined_strategy.recompute:
             if (
                 len(self.user_defined_strategy.recompute_configs["checkpoints"])
                 == 0

python/paddle/distributed/fleet/meta_optimizers/sharding/utils.py

@@ -38,7 +38,7 @@ def check_broadcast(block):
     broadcast_vars = {}
     for idx, op in enumerate(block.ops):
         if op.type == "c_broadcast":
-            if op.all_attrs()["use_calc_stream"] == False:
+            if not op.all_attrs()["use_calc_stream"]:
                 var_name = op.desc.input_arg_names()[0]
                 if "@BroadCast" in var_name:
                     if var_name in broadcast_vars:
@@ -72,7 +72,7 @@ def check_broadcast(block):
             last_sync_calc_op_idx = idx
             continue
         if op.type == "c_broadcast":
-            if op.all_attrs()["use_calc_stream"] == False:
+            if not op.all_attrs()["use_calc_stream"]:
                 var_name = op.desc.input_arg_names()[0]
                 if "@BroadCast" in var_name:
                     if broadcast_vars[var_name]["fill_constant_pos"] != -1:
@@ -117,7 +117,7 @@ def check_allreduce_sum(block, shard, sharding_ring_id, dp_ring_id=-1):
     for idx, op in enumerate(block.ops):
         # sharding use both allreduce and reduce to sync grad
         if op.type == "c_allreduce_sum" or op.type == "c_reduce_sum":
-            if op.all_attrs()["use_calc_stream"] == False:
+            if not op.all_attrs()["use_calc_stream"]:
                 ring_id = op.desc.attr("ring_id")
                 var_name = op.desc.input_arg_names()[0]
                 param = var_name.split("@")[0]
@@ -153,7 +153,7 @@ def check_allreduce_sum(block, shard, sharding_ring_id, dp_ring_id=-1):
                     dp_grads_status[var_name] = 1
         # check sharding allreduce and  reduce but skip megatron allreduce
         elif op.type == "c_allreduce_sum" or op.type == "c_reduce_sum":
-            if op.all_attrs()["use_calc_stream"] == False:
+            if not op.all_attrs()["use_calc_stream"]:
                 var_name = op.desc.input_arg_names()[0]
                 ring_id = op.desc.attr("ring_id")
                 if ring_id == sharding_ring_id:

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

@@ -57,7 +57,7 @@ class TensorParallelOptimizer(MetaOptimizerBase):
         if not self.role_maker._is_collective:
             return False
 
-        if self.user_defined_strategy.tensor_parallel == True:
+        if self.user_defined_strategy.tensor_parallel:
             return True
         return False
 

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

@@ -720,7 +720,7 @@ class PipelineLayer(Layer):
 
     def _need_recompute(self, funcs, inputs):
         if not any(
-            input_.stop_gradient == False
+            not input_.stop_gradient
             for input_ in inputs
             if isinstance(input_, paddle.Tensor)
         ):

python/paddle/distributed/fleet/model.py

@@ -90,7 +90,7 @@ def distributed_model(model):
 
     amp_enable = False
     strategy = fleet_env._user_defined_strategy
-    if strategy.amp == True:
+    if strategy.amp:
         amp_enable = True
         amp_level = "O2" if strategy.amp_configs['use_pure_fp16'] else "O1"
         if amp_level.upper() == "O2":
@@ -122,7 +122,7 @@ def distributed_model(model):
             use_dynamic_loss_scaling=use_dynamic_loss_scaling,
         )
 
-    if strategy.heter_ccl_mode == True:
+    if strategy.heter_ccl_mode:
         distributed_model = paddle.DataParallel(
             model,
             comm_buffer_size=strategy.fuse_grad_size_in_MB,

python/paddle/distributed/fleet/optimizer.py

@@ -59,7 +59,7 @@ def _dygraph_distributed_optimizer(optimizer, strategy=None):
     fleet_env._context = {}
 
     if fleet_env.worker_num() > 1:
-        if fleet_env._user_defined_strategy.heter_ccl_mode == False:
+        if not fleet_env._user_defined_strategy.heter_ccl_mode:
             return HybridParallelOptimizer(
                 optimizer, fleet_env._hcg, fleet_env._user_defined_strategy
             )

python/paddle/distributed/fleet/recompute/recompute.py

@@ -41,7 +41,7 @@ def detach_variable(inputs):
 
 def check_recompute_necessary(inputs):
     if not any(
-        input_.stop_gradient == False
+        not input_.stop_gradient
         for input_ in inputs
         if isinstance(input_, (core.eager.Tensor, paddle.Tensor))
     ):

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

@@ -337,7 +337,7 @@ class CommonAccessor:
         self.table_num = size
         self.table_dim = single_dim
 
-        if oop.type != 'adam' and adam_d2sum == True:
+        if oop.type != 'adam' and adam_d2sum:
             print('optimization algorithm is not adam, set adam_d2sum False')
             adam_d2sum = False
         print("adam_d2sum:", adam_d2sum)

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

@@ -231,7 +231,7 @@ class HybridParallelInferenceHelper(object):
             )
         else:
             if isinstance(role_maker, fleet.base.role_maker.RoleMakerBase):
-                assert role_maker._is_collective == True
+                assert role_maker._is_collective
                 self.role_maker = role_maker
 
         # communication_group info

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

@@ -210,7 +210,7 @@ class DistributedInfer:
                                         if found:
                                             break
                                     if found:
-                                        if output_indexes[j] == True:
+                                        if output_indexes[j]:
                                             warnings.warn(
                                                 "unable to re-arrange dags order to combine distributed embedding ops"
                                             )

python/paddle/distributed/passes/auto_parallel_amp.py

@@ -80,9 +80,9 @@ class AMPState(object):
                     fwd_op_id = dist_op_context.grad_op_id_to_op_id[
                         op.desc.original_id()
                     ]
-                    if self._is_fp16_op(fwd_op_id) == True:
+                    if self._is_fp16_op(fwd_op_id) is True:
                         self._op_fp16_dict[op.desc.original_id()] = True
-                    elif self._is_fp16_op(fwd_op_id) == False:
+                    elif self._is_fp16_op(fwd_op_id) is False:
                         self._op_fp16_dict[op.desc.original_id()] = False
             elif int(op.attr('op_role')) == int(OpRole.Optimize):
                 break
@@ -132,13 +132,13 @@ class AMPState(object):
                             # if it's one of inputs
                             if (
                                 self._is_fp16_op(prev_op.desc.original_id())
-                                == False
+                                is False
                                 or prev_op.type in amp_lists.black_list
                             ):
                                 is_black_op = True
                             elif (
                                 self._is_fp16_op(prev_op.desc.original_id())
-                                == True
+                                is True
                                 or prev_op.type in amp_lists.white_list
                             ):
                                 is_white_op = True
@@ -161,7 +161,7 @@ class AMPState(object):
             num_cast_ops = 0
             if int(op.attr('op_role')) == int(OpRole.Backward):
                 break
-            if self._is_fp16_op(op.desc.original_id()) == False:
+            if self._is_fp16_op(op.desc.original_id()) is False:
                 num_cast_ops = self._insert_cast_op_forward(
                     op,
                     idx,
@@ -169,7 +169,7 @@ class AMPState(object):
                     core.VarDesc.VarType.FP32,
                     dist_context,
                 )
-            elif self._is_fp16_op(op.desc.original_id()) == True:
+            elif self._is_fp16_op(op.desc.original_id()) is True:
                 num_cast_ops = self._insert_cast_op_forward(
                     op,
                     idx,
@@ -302,7 +302,7 @@ class AMPState(object):
             grad_op_orig_id = grad_op.desc.original_id()
             dist_op_context = dist_context.dist_op_context
             if grad_op_orig_id in dist_op_context.grad_op_id_to_op_id:
-                if self._is_fp16_op(grad_op_orig_id) == False:  # fp32
+                if self._is_fp16_op(grad_op_orig_id) is False:  # fp32
                     num_cast_ops = self._insert_cast_op_backward(
                         grad_op,
                         idx,
@@ -311,7 +311,7 @@ class AMPState(object):
                         dist_context,
                         appended_grad_times,
                     )
-                elif self._is_fp16_op(grad_op_orig_id) == True:  # fp16
+                elif self._is_fp16_op(grad_op_orig_id) is True:  # fp16
                     num_cast_ops = self._insert_cast_op_backward(
                         grad_op,
                         idx,

python/paddle/distributed/passes/auto_parallel_fp16.py

@@ -235,10 +235,7 @@ class FP16State(object):
         for op in block.ops:
             if is_forward_op(op):
                 # NOTE (JZ-LIANG) un-expected cast op when user call "+, -, *, /" in python
-                if (
-                    self._is_fp16_op(op.desc.original_id()) == True
-                    or op.type == "cast"
-                ):
+                if self._is_fp16_op(op.desc.original_id()) or op.type == "cast":
                     for in_name in op.input_names:
                         if _keep_fp32_input(op, in_name):
                             continue
@@ -255,7 +252,7 @@ class FP16State(object):
                             self.set_var_to_fp16(out_var_name, block)
                     set_op_dtype_to_fp16(op)
                 # NOTE (JZ-LIANG) un-expected cast op when user call "+, -, *, /" in python
-                elif self._is_fp16_op(op.desc.original_id()) == False:
+                elif not self._is_fp16_op(op.desc.original_id()):
                     for out_var_name in op.output_arg_names:
                         out_var = block.vars.get(out_var_name)
                         if out_var is None or out_var.type not in _valid_types:
@@ -263,7 +260,7 @@ class FP16State(object):
                         if out_var.dtype == core.VarDesc.VarType.FP16:
                             out_var.desc.set_dtype(core.VarDesc.VarType.FP32)
             elif is_backward_op(op):
-                if self._is_fp16_op(op.desc.original_id()) == True:
+                if self._is_fp16_op(op.desc.original_id()):
                     for out_name in op.output_names:
                         if _keep_fp32_output(op, out_name):
                             continue
@@ -271,7 +268,7 @@ class FP16State(object):
                             self.set_var_to_fp16(out_var_name, block)
                     set_op_dtype_to_fp16(op)
                 # NOTE (JZ-LIANG) un-expected cast op when user call "+, -, *, /" in python
-                elif self._is_fp16_op(op.desc.original_id()) == False:
+                elif not self._is_fp16_op(op.desc.original_id()):
                     for out_var_name in op.output_arg_names:
                         out_var = block.vars.get(out_var_name)
                         if out_var is None or out_var.type not in _valid_types:
@@ -290,7 +287,7 @@ class FP16State(object):
                 idx += 1
                 continue
             elif is_forward_op(op):
-                if self._is_fp16_op(op.desc.original_id()) == False:
+                if not self._is_fp16_op(op.desc.original_id()):
                     num_cast_ops = self._insert_forward_cast_ops(
                         op,
                         idx,
@@ -299,7 +296,7 @@ class FP16State(object):
                         core.VarDesc.VarType.FP32,
                         self.dist_context,
                     )
-                elif self._is_fp16_op(op.desc.original_id()) == True:
+                elif self._is_fp16_op(op.desc.original_id()):
                     num_cast_ops = self._insert_forward_cast_ops(
                         op,
                         idx,
@@ -310,7 +307,7 @@ class FP16State(object):
                     )
             elif is_backward_op(op):
                 if op.desc.original_id() in dist_op_context.grad_op_id_to_op_id:
-                    if self._is_fp16_op(op.desc.original_id()) == False:
+                    if not self._is_fp16_op(op.desc.original_id()):
                         num_cast_ops = self._insert_backward_cast_ops(
                             op,
                             idx,
@@ -319,7 +316,7 @@ class FP16State(object):
                             core.VarDesc.VarType.FP32,
                             self.dist_context,
                         )
-                    elif self._is_fp16_op(op.desc.original_id()) == True:
+                    elif self._is_fp16_op(op.desc.original_id()):
                         num_cast_ops = self._insert_backward_cast_ops(
                             op,
                             idx,

python/paddle/distributed/passes/ps_server_pass.py

@@ -140,7 +140,7 @@ class AddLrDecayTablePass(PassBase):
 
     def _apply_single_impl(self, main_program, startup_program, pass_ctx):
         attrs = pass_ctx._attrs
-        if hasattr(attrs['origin_main_program'], 'lr_sheduler') == False:
+        if not hasattr(attrs['origin_main_program'], 'lr_sheduler'):
             return
 
         assert isinstance(

python/paddle/distributed/passes/ps_trainer_pass.py

@@ -304,7 +304,7 @@ class DistributedOpsPass(PassBase):
                                 if found:
                                     break
                             if found:
-                                if output_indexes[j] == True:
+                                if output_indexes[j]:
                                     warnings.warn(
                                         "unable to re-arrange dags order to combine distributed embedding ops"
                                     )

python/paddle/distributed/ps/the_one_ps.py

@@ -443,7 +443,7 @@ class CommonAccessor(Accessor):
         self.table_num = size
         self.table_dim = single_dim
 
-        if oop.type != 'adam' and adam_d2sum == True:
+        if oop.type != 'adam' and adam_d2sum:
             print('optimization algorithm is not adam, set adam_d2sum False')
             adam_d2sum = False
         print("adam_d2sum:", adam_d2sum)
@@ -703,7 +703,7 @@ class SparseTable(Table):
         if (
             ctx.is_tensor_table()
             or len(ctx.origin_varnames()) < 1
-            or (ctx.is_sparse() == False)
+            or (not ctx.is_sparse())
         ):
             return
         table_proto.table_id = ctx.table_id()
@@ -810,7 +810,7 @@ class GeoSparseTable(SparseTable):
         if (
             ctx.is_tensor_table()
             or len(ctx.origin_varnames()) < 1
-            or (ctx.is_sparse() == False)
+            or (not ctx.is_sparse())
         ):
             return
         table_proto.table_id = ctx.table_id()
@@ -845,7 +845,7 @@ class DenseTable(Table):
         if (
             ctx.is_tensor_table()
             or len(ctx.origin_varnames()) < 1
-            or (ctx.is_sparse() == True)
+            or (ctx.is_sparse())
         ):
             return
 
@@ -1281,7 +1281,7 @@ class TheOnePSRuntime(RuntimeBase):
         if not is_test:
             if (
                 self.context['ps_mode'] == DistributedMode.GEO
-                or self.is_heter_ps_mode == True
+                or self.is_heter_ps_mode
             ):
                 self._communicator.init_params(dense_map)
             else:
@@ -1298,7 +1298,7 @@ class TheOnePSRuntime(RuntimeBase):
 
         if (
             self.context['ps_mode'] == DistributedMode.GEO
-            or self.is_heter_ps_mode == True
+            or self.is_heter_ps_mode
         ):
             if not self._communicator.is_running():
                 self._communicator.start()

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

@@ -1744,7 +1744,7 @@ def create_backward_block(
                 ):
                     is_skip = True
                     break
-            if is_skip == True:
+            if is_skip:
                 continue
         block_append_op(program, origin_program, heter_block, op)
 

python/paddle/fluid/tests/book/test_recognize_digits.py

@@ -237,7 +237,7 @@ def main(use_cuda, parallel, nn_type, combine):
     if not use_cuda and not parallel:
         save_dirname = "recognize_digits_" + nn_type + ".inference.model"
         save_full_dirname = "recognize_digits_" + nn_type + ".train.model"
-        if combine == True:
+        if combine:
             model_filename = "__model_combined__"
             params_filename = "__params_combined__"
 

python/paddle/fluid/tests/unittests/auto_parallel/test_to_static.py

@@ -144,7 +144,7 @@ class TestToStatic(unittest.TestCase):
         # inputs = InputSpec([batch_size, hidden_size], 'float32', 'x')
         # labels = InputSpec([batch_size], 'int64', 'label')
 
-        assert _non_static_mode() == True
+        assert _non_static_mode()
         engine = auto.Engine(
             model=mlp,
             loss=loss,
@@ -155,7 +155,7 @@ class TestToStatic(unittest.TestCase):
         engine.fit(dataset, batch_size=batch_size)
         engine.evaluate(dataset, batch_size=batch_size)
         engine.predict(dataset, batch_size=batch_size)
-        assert _non_static_mode() == False
+        assert not _non_static_mode()
 
 
 class TestLazyInit(unittest.TestCase):

python/paddle/fluid/tests/unittests/autograd/test_autograd_functional_dynamic.py

@@ -593,7 +593,7 @@ class TestHessianNoBatch(unittest.TestCase):
         numerical_hessian = utils._np_concat_matrix_sequence(numerical_hessian)
         self.x.stop_gradient = False
         hessian = paddle.incubate.autograd.Hessian(func, self.x)
-        assert hessian[:].stop_gradient == False
+        assert not hessian[:].stop_gradient
         np.testing.assert_allclose(
             hessian[:].numpy(), numerical_hessian, self.rtol, self.atol
         )

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

@@ -116,9 +116,7 @@ class TestFleetShardingMetaOptimizer(TestFleetMetaOptimizer):
         self.optimizer(avg_cost, strategy, train_prog, startup_prog)
         ops = [op.type for op in avg_cost.block.ops]
         vars = [x.name for x in train_prog.list_vars()]
-        parameters = [
-            x.name for x in train_prog.list_vars() if x.persistable == True
-        ]
+        parameters = [x.name for x in train_prog.list_vars() if x.persistable]
         self.assertIn('@BroadCast', ''.join(vars))
         self.assertIn('cast', ops)
         self.assertIn('check_finite_and_unscale', ops)
@@ -227,9 +225,7 @@ class TestFleetShardingMetaOptimizer(TestFleetMetaOptimizer):
 
         ops = [op.type for op in avg_cost.block.ops]
         vars = [x.name for x in train_prog.list_vars()]
-        parameters = [
-            x.name for x in train_prog.list_vars() if x.persistable == True
-        ]
+        parameters = [x.name for x in train_prog.list_vars() if x.persistable]
 
         self.assertIn('@BroadCast', ''.join(vars))
         self.assertIn('subprog', ''.join(vars))
@@ -316,9 +312,7 @@ class TestFleetShardingMetaOptimizer(TestFleetMetaOptimizer):
 
         ops = [op.type for op in avg_cost.block.ops]
         vars = [x.name for x in train_prog.list_vars()]
-        parameters = [
-            x.name for x in train_prog.list_vars() if x.persistable == True
-        ]
+        parameters = [x.name for x in train_prog.list_vars() if x.persistable]
 
         self.assertIn('@BroadCast', ''.join(vars))
         self.assertIn('subprog', ''.join(vars))
@@ -445,9 +439,7 @@ class TestFleetShardingMetaOptimizer(TestFleetMetaOptimizer):
 
         ops = [op.type for op in avg_cost.block.ops]
         vars = [x.name for x in train_prog.list_vars()]
-        parameters = [
-            x.name for x in train_prog.list_vars() if x.persistable == True
-        ]
+        parameters = [x.name for x in train_prog.list_vars() if x.persistable]
 
         self.assertIn('@BroadCast', ''.join(vars))
         self.assertIn('cast', ops)
@@ -564,9 +556,7 @@ class TestFleetShardingMetaOptimizer(TestFleetMetaOptimizer):
             startup_prog,
             regularization=regularization,
         )
-        parameters = [
-            x.name for x in train_prog.list_vars() if x.persistable == True
-        ]
+        parameters = [x.name for x in train_prog.list_vars() if x.persistable]
         ops = [op.type for op in avg_cost.block.ops]
         vars = [x.name for x in train_prog.list_vars()]
         self.assertIn('@BroadCast', ''.join(vars))
@@ -653,9 +643,7 @@ class TestFleetShardingMetaOptimizer(TestFleetMetaOptimizer):
         self.optimizer(
             avg_cost, strategy, train_prog, startup_prog, grad_clip=clip
         )
-        parameters = [
-            x.name for x in train_prog.list_vars() if x.persistable == True
-        ]
+        parameters = [x.name for x in train_prog.list_vars() if x.persistable]
         ops = [op.type for op in avg_cost.block.ops]
         vars = [x.name for x in train_prog.list_vars()]
         self.assertIn('@BroadCast', ''.join(vars))

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

@@ -420,13 +420,13 @@ class TestAmpScaler(unittest.TestCase):
                 decr_every_n_nan_or_inf=2,
                 use_dynamic_loss_scaling=True,
             )
-            self.assertEqual(scaler.is_enable() == True, True)
+            self.assertEqual(scaler.is_enable(), True)
             self.assertEqual(scaler.get_init_loss_scaling() == 1024, True)
             self.assertEqual(scaler.get_incr_ratio() == 2.0, True)
             self.assertEqual(scaler.get_decr_ratio() == 0.5, True)
             self.assertEqual(scaler.get_incr_every_n_steps() == 1000, True)
             self.assertEqual(scaler.get_decr_every_n_nan_or_inf() == 2, True)
-            self.assertEqual(scaler.is_use_dynamic_loss_scaling() == True, True)
+            self.assertEqual(scaler.is_use_dynamic_loss_scaling(), True)
             scaler.set_decr_every_n_nan_or_inf(4)
             self.assertEqual(scaler.get_decr_every_n_nan_or_inf() == 4, True)
             scaler.set_decr_ratio(0.1)
@@ -460,7 +460,7 @@ class TestAmpScaler(unittest.TestCase):
 
             scaler3 = paddle.amp.GradScaler(enable=False)
             scaler3.load_state_dict(scaler_state)
-            self.assertEqual(scaler3.is_enable() == False, True)
+            self.assertFalse(scaler3.is_enable())
 
     def test_state_dict_and_load_state_dict_error(self):
         def test_error():

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

@@ -419,13 +419,13 @@ class TestAmpScaler(unittest.TestCase):
                 decr_every_n_nan_or_inf=2,
                 use_dynamic_loss_scaling=True,
             )
-            self.assertEqual(scaler.is_enable() == True, True)
+            self.assertEqual(scaler.is_enable(), True)
             self.assertEqual(scaler.get_init_loss_scaling() == 1024, True)
             self.assertEqual(scaler.get_incr_ratio() == 2.0, True)
             self.assertEqual(scaler.get_decr_ratio() == 0.5, True)
             self.assertEqual(scaler.get_incr_every_n_steps() == 1000, True)
             self.assertEqual(scaler.get_decr_every_n_nan_or_inf() == 2, True)
-            self.assertEqual(scaler.is_use_dynamic_loss_scaling() == True, True)
+            self.assertEqual(scaler.is_use_dynamic_loss_scaling(), True)
             scaler.set_decr_every_n_nan_or_inf(4)
             self.assertEqual(scaler.get_decr_every_n_nan_or_inf() == 4, True)
             scaler.set_decr_ratio(0.1)
@@ -459,7 +459,7 @@ class TestAmpScaler(unittest.TestCase):
 
             scaler3 = paddle.amp.GradScaler(enable=False)
             scaler3.load_state_dict(scaler_state)
-            self.assertEqual(scaler3.is_enable() == False, True)
+            self.assertFalse(scaler3.is_enable())
 
     def test_state_dict_and_load_state_dict_error(self):
         def test_error():

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

@@ -356,7 +356,7 @@ class conv2d(fluid.dygraph.Layer):
     ):
         super(conv2d, self).__init__()
 
-        if use_bias == False:
+        if not use_bias:
             con_bias_attr = False
         else:
             con_bias_attr = fluid.ParamAttr(
@@ -426,7 +426,7 @@ class DeConv2D(fluid.dygraph.Layer):
     ):
         super(DeConv2D, self).__init__()
 
-        if use_bias == False:
+        if not use_bias:
             de_bias_attr = False
         else:
             de_bias_attr = fluid.ParamAttr(

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

@@ -93,7 +93,7 @@ class TestTensorSize(unittest.TestCase):
         prog_trans = paddle.jit.ProgramTranslator()
         prog_trans.enable(to_static)
         x = paddle.ones([1, 2, 3])
-        if to_static == False:
+        if not to_static:
             return tensor_size(x)
         return tensor_size(x).numpy()
 

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

@@ -134,7 +134,7 @@ class TestConvBnFusePass(PassAutoScanTest):
             data_layout=data_format,
             is_test=True,
         )
-        if has_bias == True:
+        if has_bias:
             conv2d_op.inputs["Bias"] = ["conv2d_bias"]
         ops = [conv2d_op, bn_op]
 
@@ -156,7 +156,7 @@ class TestConvBnFusePass(PassAutoScanTest):
             },
             outputs=["batch_norm_Y"],
         )
-        if has_bias == True:
+        if has_bias:
             program_config.weights["conv2d_bias"] = TensorConfig(
                 data_gen=partial(generate_conv2d_Bias)
             )
@@ -202,7 +202,7 @@ class TestConvBnFusePass(PassAutoScanTest):
         def teller2(program_config, predictor_config):
             return (
                 predictor_config.mkldnn_enabled()
-                and program_config.ops[0].attrs['has_bias'] == True
+                and program_config.ops[0].attrs['has_bias']
             )
 
         self.add_ignore_check_case(

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

@@ -43,11 +43,11 @@ class TestEmbeddingEltwiseLayerNormFusePass(PassAutoScanTest):
 
     def is_program_valid(self, program_config: ProgramConfig) -> bool:
         # is_sparse is only support False
-        if program_config.ops[0].attrs['is_sparse'] == True:
+        if program_config.ops[0].attrs['is_sparse']:
             return False
 
         # is_distributed only support False
-        if program_config.ops[0].attrs['is_distributed'] == True:
+        if program_config.ops[0].attrs['is_distributed']:
             return False
 
         # axis only support -1 and the last dim.

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

@@ -100,7 +100,7 @@ class TestConvAffineChannelFusePass(PassAutoScanTest):
             outputs={"Out": ["affine_channel_ouput"]},
             data_layout=data_format,
         )
-        if has_bias == True:
+        if has_bias:
             conv2d_op.inputs["Bias"] = ["conv2d_bias"]
         ops = [conv2d_op, ac_op]
 
@@ -123,7 +123,7 @@ class TestConvAffineChannelFusePass(PassAutoScanTest):
             },
             outputs=["affine_channel_ouput"],
         )
-        if has_bias == True:
+        if has_bias:
             program_config.weights["conv2d_bias"] = TensorConfig(
                 data_gen=partial(generate_bias)
             )
@@ -145,7 +145,7 @@ class TestConvAffineChannelFusePass(PassAutoScanTest):
         def teller2(program_config, predictor_config):
             return (
                 predictor_config.mkldnn_enabled()
-                and program_config.ops[0].attrs['has_bias'] == True
+                and program_config.ops[0].attrs['has_bias']
             )
 
         self.add_ignore_check_case(

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

@@ -304,7 +304,7 @@ class TrtConvertConcatTest(TrtLayerAutoScanTest):
             self.dynamic_shape.opt_input_shape = {}
 
         def generate_trt_nodes_num(attrs, dynamic_shape):
-            if dynamic_shape == True:
+            if dynamic_shape:
                 return 1, 4
             else:
                 if attrs[0]['axis'] != 0:

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

@@ -123,7 +123,7 @@ class TrtConvertDropoutTest(TrtLayerAutoScanTest):
         def generate_trt_nodes_num(attrs, dynamic_shape):
             if attrs[0]['dropout_implementation'] == "upscale_in_train":
                 return 0, 2
-            elif self.dims == 1 and dynamic_shape == False:
+            elif self.dims == 1 and not dynamic_shape:
                 return 0, 3
             else:
                 return 1, 2

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

@@ -85,7 +85,7 @@ class TrtConvertGatherTest(TrtLayerAutoScanTest):
                                         "index_data": TensorConfig(
                                             data_gen=partial(
                                                 generate_input2
-                                                if index_type_int32 == True
+                                                if index_type_int32
                                                 else generate_input4,
                                                 index,
                                             )
@@ -180,7 +180,7 @@ class TrtConvertGatherTest(TrtLayerAutoScanTest):
             if self.input_num == 3:
                 return 0, 5
             else:
-                if dynamic_shape and self.index_type_int32 == True:
+                if dynamic_shape and self.index_type_int32:
                     return 1, 3
                 else:
                     return 0, 4

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

@@ -107,7 +107,7 @@ class TrtConvertGeluTest(TrtLayerAutoScanTest):
             if compile_version >= valid_version:
                 return 1, 2
             else:
-                if attrs[0]['approximate'] == True:
+                if attrs[0]['approximate']:
                     return 0, 3
                 else:
                     return 1, 2

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

@@ -137,7 +137,7 @@ class TrtConvertNearestInterpTest(TrtLayerAutoScanTest):
                 and self.dynamic_shape.min_input_shape
             ):
                 return True
-            if program_config.ops[0].attrs['align_corners'] == True:
+            if program_config.ops[0].attrs['align_corners']:
                 return True
             return False
 

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pool2d.py

@@ -29,7 +29,7 @@ class TrtConvertPool2dTest(TrtLayerAutoScanTest):
         ksize = program_config.ops[0].attrs['ksize']
         pooling_type = program_config.ops[0].attrs['pooling_type']
         global_pooling = program_config.ops[0].attrs['global_pooling']
-        if global_pooling == False:
+        if not global_pooling:
             if pooling_type == 'avg':
                 for index in range(len(ksize)):
                     if ksize[index] <= paddings[index]:
@@ -174,10 +174,10 @@ class TrtConvertPool2dTest(TrtLayerAutoScanTest):
         def teller(program_config, predictor_config):
             if (
                 program_config.ops[0].attrs['pooling_type'] == 'avg'
-                and program_config.ops[0].attrs['global_pooling'] == False
-                and program_config.ops[0].attrs['exclusive'] == True
-                and program_config.ops[0].attrs['adaptive'] == False
-                and program_config.ops[0].attrs['ceil_mode'] == True
+                and not program_config.ops[0].attrs['global_pooling']
+                and program_config.ops[0].attrs['exclusive']
+                and not program_config.ops[0].attrs['adaptive']
+                and program_config.ops[0].attrs['ceil_mode']
             ):
                 return True
             return False

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

@@ -159,10 +159,10 @@ class TrtConvertRoiAlignTest(TrtLayerAutoScanTest):
 
         def generate_trt_nodes_num(attrs, dynamic_shape):
             if self.num_input == 0:
-                if dynamic_shape == True:
+                if dynamic_shape:
                     return 0, 5
             elif self.num_input == 1:
-                if dynamic_shape == True:
+                if dynamic_shape:
                     return 1, 3
                 else:
                     return 0, 4

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

@@ -77,7 +77,7 @@ class TrtConvertShuffleChannelTest(TrtLayerAutoScanTest):
             ver = paddle_infer.get_trt_compile_version()
             if (
                 ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8000
-                and dynamic_shape == True
+                and dynamic_shape
             ):
                 return 0, 3
             else:

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

@@ -192,7 +192,7 @@ class TrtConvertSkipLayernormTest(TrtLayerAutoScanTest):
             self.dynamic_shape.opt_input_shape = {}
 
         def generate_trt_nodes_num(attrs, dynamic_shape):
-            if dynamic_shape == True:
+            if dynamic_shape:
                 return 1, 3
             else:
                 return 0, 4

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

@@ -181,7 +181,7 @@ class TrtConvertStackTest(TrtLayerAutoScanTest):
             self.dynamic_shape.opt_input_shape = {}
 
         def generate_trt_nodes_num(attrs, dynamic_shape):
-            if dynamic_shape == True:
+            if dynamic_shape:
                 return 1, 4
             else:
                 return 0, 5

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

@@ -81,7 +81,7 @@ class TrtConvertTileTest(TrtLayerAutoScanTest):
         def generate_trt_nodes_num(attrs, dynamic_shape):
             ver = paddle_infer.get_trt_compile_version()
             if ver[0] * 1000 + ver[1] * 100 + ver[0] * 10 >= 7000:
-                if dynamic_shape == True:
+                if dynamic_shape:
                     return 0, 3
                 else:
                     return 1, 2

python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_top_k_v2.py

@@ -126,7 +126,7 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest):
         def generate_trt_nodes_num(attrs, dynamic_shape):
             if self.dims == 1:
                 return 0, 4
-            if self.sort == False:
+            if not self.sort:
                 return 0, 4
             return 1, 3
 

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

@@ -123,7 +123,7 @@ class TrtConvertTransposeTest(TrtLayerAutoScanTest):
             self.dynamic_shape.opt_input_shape = {}
 
         def generate_trt_nodes_num(attrs, dynamic_shape):
-            if dynamic_shape == True:
+            if dynamic_shape:
                 return 1, 2
             else:
                 if attrs[0]['axis'][0] == 0:

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

@@ -28,7 +28,7 @@ class TrtConvertYoloBoxTest(TrtLayerAutoScanTest):
 
     def sample_program_configs(self):
         def generate_input1(attrs: List[Dict[str, Any]], batch, channel):
-            if attrs[0]['iou_aware'] == True:
+            if attrs[0]['iou_aware']:
                 return np.ones([batch, 3 * (channel + 6), 13, 13]).astype(
                     np.float32
                 )
@@ -108,7 +108,7 @@ class TrtConvertYoloBoxTest(TrtLayerAutoScanTest):
         self, program_config
     ) -> (paddle_infer.Config, List[int], float):
         def generate_dynamic_shape(attrs):
-            if attrs[0]['iou_aware'] == True:
+            if attrs[0]['iou_aware']:
                 channel = 3 * (attrs[0]['class_num'] + 6)
                 self.dynamic_shape.min_input_shape = {
                     "yolo_box_input": [1, channel, 12, 12],

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

@@ -128,7 +128,7 @@ class TestInt8(TestElementwiseAddOp):
     def test_check_output(self):
         # TODO(wangzhongpu): support mkldnn op in dygraph mode
         self.init_scales()
-        self.check_output(check_dygraph=(self.use_mkldnn == False))
+        self.check_output(check_dygraph=(not self.use_mkldnn))
 
     def test_check_grad_normal(self):
         pass
@@ -165,9 +165,7 @@ class TestInt8Scales(TestInt8):
         # TODO(wangzhongpu): support mkldnn op in dygraph mode
         self.init_scales()
         int_atol = 1  # different quantization techniques
-        self.check_output(
-            check_dygraph=(self.use_mkldnn == False), atol=int_atol
-        )
+        self.check_output(check_dygraph=(not self.use_mkldnn), atol=int_atol)
 
 
 class TestUint8Scales(TestInt8Scales):

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

@@ -101,7 +101,7 @@ class TestInt8(ElementwiseMulOp):
     def test_check_output(self):
         # TODO(wangzhongpu): support mkldnn op in dygraph mode
         self.init_scales()
-        self.check_output(check_dygraph=(self.use_mkldnn == False))
+        self.check_output(check_dygraph=(not self.use_mkldnn))
 
     def test_check_grad_normal(self):
         pass
@@ -138,9 +138,7 @@ class TestInt8Scales(TestInt8):
         # TODO(wangzhongpu): support mkldnn op in dygraph mode
         self.init_scales()
         int_atol = 1  # different quantization techniques
-        self.check_output(
-            check_dygraph=(self.use_mkldnn == False), atol=int_atol
-        )
+        self.check_output(check_dygraph=(not self.use_mkldnn), atol=int_atol)
 
 
 class TestUint8Scales(TestInt8Scales):

python/paddle/fluid/tests/unittests/mkldnn/test_onnx_format_quantization_mobilenetv1.py

@@ -49,7 +49,7 @@ def resize_short(img, target_size):
 def crop_image(img, target_size, center):
     width, height = img.size
     size = target_size
-    if center == True:
+    if center:
         w_start = (width - size) / 2
         h_start = (height - size) / 2
     else:

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

@@ -371,25 +371,22 @@ class OpTest(unittest.TestCase):
             return True
 
         def is_xpu_op_test():
-            return hasattr(cls, "use_xpu") and cls.use_xpu == True
+            return hasattr(cls, "use_xpu") and cls.use_xpu
 
         def is_mkldnn_op_test():
-            return hasattr(cls, "use_mkldnn") and cls.use_mkldnn == True
+            return hasattr(cls, "use_mkldnn") and cls.use_mkldnn
 
         def is_rocm_op_test():
             return core.is_compiled_with_rocm()
 
         def is_npu_op_test():
-            return hasattr(cls, "use_npu") and cls.use_npu == True
+            return hasattr(cls, "use_npu") and cls.use_npu
 
         def is_mlu_op_test():
-            return hasattr(cls, "use_mlu") and cls.use_mlu == True
+            return hasattr(cls, "use_mlu") and cls.use_mlu
 
         def is_custom_device_op_test():
-            return (
-                hasattr(cls, "use_custom_device")
-                and cls.use_custom_device == True
-            )
+            return hasattr(cls, "use_custom_device") and cls.use_custom_device
 
         if not hasattr(cls, "op_type"):
             raise AssertionError(
@@ -465,17 +462,17 @@ class OpTest(unittest.TestCase):
         )
 
     def is_mkldnn_op(self):
-        return (hasattr(self, "use_mkldnn") and self.use_mkldnn == True) or (
+        return (hasattr(self, "use_mkldnn") and self.use_mkldnn) or (
             hasattr(self, "attrs")
             and "use_mkldnn" in self.attrs
-            and self.attrs["use_mkldnn"] == True
+            and self.attrs["use_mkldnn"]
         )
 
     def is_xpu_op(self):
-        return (hasattr(self, "use_xpu") and self.use_xpu == True) or (
+        return (hasattr(self, "use_xpu") and self.use_xpu) or (
             hasattr(self, "attrs")
             and "use_xpu" in self.attrs
-            and self.attrs["use_xpu"] == True
+            and self.attrs["use_xpu"]
         )
 
     # set the self.output_dtype .
@@ -1542,7 +1539,7 @@ class OpTest(unittest.TestCase):
     ):
 
         # disable legacy dygraph check when check_eager is True
-        if check_eager == True:
+        if check_eager:
             check_dygraph = False
 
         def find_imperative_actual(target_name, dygraph_outs, place):
@@ -1912,7 +1909,7 @@ class OpTest(unittest.TestCase):
             )
 
         if check_eager:
-            assert check_dygraph == False
+            assert not check_dygraph
             return outs, eager_dygraph_outs, fetch_list
         elif check_dygraph:
             return outs, dygraph_outs, fetch_list
@@ -2002,7 +1999,7 @@ class OpTest(unittest.TestCase):
     ):
 
         # disable legacy dygraph check when check_eager is True
-        if check_eager == True:
+        if check_eager:
             check_dygraph = False
 
         self.__class__.op_type = self.op_type
@@ -2024,7 +2021,7 @@ class OpTest(unittest.TestCase):
                 check_eager=check_eager,
             )
             if check_eager:
-                assert check_dygraph == False
+                assert not check_dygraph
                 outs, eager_dygraph_outs, fetch_list = res
             elif check_dygraph:
                 outs, dygraph_outs, fetch_list = res
@@ -2143,7 +2140,7 @@ class OpTest(unittest.TestCase):
     ):
 
         # disable legacy dygraph check when check_eager is True
-        if check_eager == True:
+        if check_eager:
             check_dygraph = False
 
         self._check_grad_helper()
@@ -2180,7 +2177,7 @@ class OpTest(unittest.TestCase):
     ):
 
         # disable legacy dygraph check when check_eager is True
-        if check_eager == True:
+        if check_eager:
             check_dygraph = False
 
         self.scope = core.Scope()
@@ -2207,7 +2204,7 @@ class OpTest(unittest.TestCase):
 
         # oneDNN numeric gradient should use CPU kernel
         use_onednn = False
-        if "use_mkldnn" in op_attrs and op_attrs["use_mkldnn"] == True:
+        if "use_mkldnn" in op_attrs and op_attrs["use_mkldnn"]:
             op_attrs["use_mkldnn"] = False
             use_onednn = True
 

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

@@ -51,7 +51,7 @@ class XPUOpTest(OpTest):
 
         if cls.dtype == np.float16:
             place = paddle.XPUPlace(0)
-            if core.is_float16_supported(place) == False:
+            if not core.is_float16_supported(place):
                 return
 
         if cls.dtype == np.float64:
@@ -98,7 +98,7 @@ class XPUOpTest(OpTest):
             return
 
         if self.dtype == np.float16:
-            if core.is_float16_supported(place) == False:
+            if not core.is_float16_supported(place):
                 return
 
         if self.dtype == np.float16:
@@ -172,7 +172,7 @@ class XPUOpTest(OpTest):
             return
 
         if self.dtype == np.float16:
-            if core.is_float16_supported(place) == False:
+            if not core.is_float16_supported(place):
                 return
 
         if self.dtype == np.float16:
@@ -254,7 +254,7 @@ class XPUOpTest(OpTest):
 
         # oneDNN numeric gradient should use CPU kernel
         use_onednn = False
-        if "use_mkldnn" in op_attrs and op_attrs["use_mkldnn"] == True:
+        if "use_mkldnn" in op_attrs and op_attrs["use_mkldnn"]:
             op_attrs["use_mkldnn"] = False
             use_onednn = True
 

python/paddle/fluid/tests/unittests/ps/ps_dnn_trainer.py

@@ -167,7 +167,7 @@ def get_user_defined_strategy(config):
         strategy.is_fl_ps_mode = (
             True if config.get("runner.is_fl_ps_mode") == 1 else False
         )
-        if strategy.is_fl_ps_mode == True:
+        if strategy.is_fl_ps_mode:
             strategy.pipeline = False
             micro_num = 1
             strategy.pipeline_configs = {

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

@@ -1126,11 +1126,11 @@ class TestMultiTensorAdam(unittest.TestCase):
             )
 
         for idx in range(2):
-            if place == 'gpu' and use_amp == True:
+            if place == 'gpu' and use_amp:
                 model = paddle.amp.decorate(models=model, level='O2')
                 scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
 
-            if place == 'gpu' and use_amp == True:
+            if place == 'gpu' and use_amp:
                 with paddle.amp.auto_cast(level='O2'):
                     output = model(input)
                     loss = paddle.mean(output)

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

@@ -302,11 +302,11 @@ class TestAdamWOpMultiPrecison(unittest.TestCase):
         )
 
         for idx in range(2):
-            if place == 'gpu' and use_amp == True:
+            if place == 'gpu' and use_amp:
                 model = paddle.amp.decorate(models=model, level='O2')
                 scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
 
-            if place == 'gpu' and use_amp == True:
+            if place == 'gpu' and use_amp:
                 with paddle.amp.auto_cast(level='O2'):
                     output = model(input)
                     loss = paddle.mean(output)

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

@@ -177,8 +177,8 @@ def train(use_cuda, thread_num, cpu_num):
                     fetch_list=[array, acc, prediction, avg_loss.name]
                 )
 
-                assert numpy.allclose(array_v[0], prediction_v) == True
-                assert numpy.allclose(array_v[1], acc_v) == True
+                assert numpy.allclose(array_v[0], prediction_v)
+                assert numpy.allclose(array_v[1], acc_v)
 
                 loss_val = numpy.mean(loss_val)
                 if step % 10 == 0:

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

@@ -313,7 +313,7 @@ class TestBatchNormOpInference(unittest.TestCase):
         # dims will be in NCHW order as it is MKL-DNN way
         # of memory descripting. So we need to convert NCHW
         # dims into NHWC.
-        if data_layout == "NHWC" and self.use_mkldnn == True:
+        if data_layout == "NHWC" and self.use_mkldnn:
             # Create executor to have MKL-DNN cache
             # cleared after NHWC unit test
             place = core.CPUPlace()

python/paddle/fluid/tests/unittests/test_batch_norm_op_v2.py

@@ -391,7 +391,7 @@ class TestBatchNormUseGlobalStats(unittest.TestCase):
                 )
                 net2.weight = net1.weight
                 net2.bias = net1.bias
-                if self.trainable_statistics == True:
+                if self.trainable_statistics:
                     net1.training = False
                     net2.training = False
                 y1 = net1(x)

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

@@ -20,8 +20,8 @@ import paddle.fluid.core as core
 
 
 def box_decoder(t_box, p_box, pb_v, output_box, norm, axis=0):
-    pb_w = p_box[:, 2] - p_box[:, 0] + (norm == False)
-    pb_h = p_box[:, 3] - p_box[:, 1] + (norm == False)
+    pb_w = p_box[:, 2] - p_box[:, 0] + (not norm)
+    pb_h = p_box[:, 3] - p_box[:, 1] + (not norm)
     pb_x = pb_w * 0.5 + p_box[:, 0]
     pb_y = pb_h * 0.5 + p_box[:, 1]
     shape = (1, p_box.shape[0]) if axis == 0 else (p_box.shape[0], 1)
@@ -55,8 +55,8 @@ def box_decoder(t_box, p_box, pb_v, output_box, norm, axis=0):
 
 
 def box_encoder(t_box, p_box, pb_v, output_box, norm):
-    pb_w = p_box[:, 2] - p_box[:, 0] + (norm == False)
-    pb_h = p_box[:, 3] - p_box[:, 1] + (norm == False)
+    pb_w = p_box[:, 2] - p_box[:, 0] + (not norm)
+    pb_h = p_box[:, 3] - p_box[:, 1] + (not norm)
     pb_x = pb_w * 0.5 + p_box[:, 0]
     pb_y = pb_h * 0.5 + p_box[:, 1]
     shape = (1, p_box.shape[0])

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

@@ -58,7 +58,7 @@ class TestCenterLossOp(OpTest):
             'CenterUpdateRate': rate,
         }
 
-        if self.need_update == True:
+        if self.need_update:
             self.outputs = {
                 'SampleCenterDiff': output,
                 'Loss': loss,

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

@@ -115,7 +115,7 @@ class TestEqualReduceAPI(unittest.TestCase):
         x = paddle.ones(shape=[10, 10], dtype="int32")
         y = paddle.ones(shape=[10, 10], dtype="int32")
         out = paddle.equal_all(x, y)
-        assert out.numpy()[0] == True
+        assert out.numpy()[0] is np.True_
         paddle.enable_static()
 
 

python/paddle/fluid/tests/unittests/test_conv2d_op.py

@@ -477,13 +477,12 @@ class TestConv2DOp(OpTest):
         place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
         # TODO(wangzhongpu): support mkldnn op in dygraph mode
         self.check_output_with_place(
-            place, atol=1e-5, check_dygraph=(self.use_mkldnn == False)
+            place, atol=1e-5, check_dygraph=(not self.use_mkldnn)
         )
 
     def test_check_grad(self):
         if self.dtype == np.float16 or (
-            hasattr(self, "no_need_check_grad")
-            and self.no_need_check_grad == True
+            hasattr(self, "no_need_check_grad") and self.no_need_check_grad
         ):
             return
         place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
@@ -493,13 +492,12 @@ class TestConv2DOp(OpTest):
             {'Input', 'Filter'},
             'Output',
             max_relative_error=0.02,
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
         )
 
     def test_check_grad_no_filter(self):
         if self.dtype == np.float16 or (
-            hasattr(self, "no_need_check_grad")
-            and self.no_need_check_grad == True
+            hasattr(self, "no_need_check_grad") and self.no_need_check_grad
         ):
             return
         place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
@@ -510,13 +508,12 @@ class TestConv2DOp(OpTest):
             'Output',
             max_relative_error=0.02,
             no_grad_set=set(['Filter']),
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
         )
 
     def test_check_grad_no_input(self):
         if self.dtype == np.float16 or (
-            hasattr(self, "no_need_check_grad")
-            and self.no_need_check_grad == True
+            hasattr(self, "no_need_check_grad") and self.no_need_check_grad
         ):
             return
         place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
@@ -526,7 +523,7 @@ class TestConv2DOp(OpTest):
             ['Filter'],
             'Output',
             no_grad_set=set(['Input']),
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
         )
 
     def init_test_case(self):
@@ -804,7 +801,7 @@ class TestConv2DOp_v2(OpTest):
         # TODO(wangzhongpu): support mkldnn op in dygraph mode
         place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
         self.check_output_with_place(
-            place, atol=1e-5, check_dygraph=(self.use_mkldnn == False)
+            place, atol=1e-5, check_dygraph=(not self.use_mkldnn)
         )
 
     def test_check_grad(self):
@@ -817,7 +814,7 @@ class TestConv2DOp_v2(OpTest):
             {'Input', 'Filter'},
             'Output',
             max_relative_error=0.02,
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
         )
 
     def test_check_grad_no_filter(self):
@@ -831,7 +828,7 @@ class TestConv2DOp_v2(OpTest):
             'Output',
             max_relative_error=0.02,
             no_grad_set=set(['Filter']),
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
         )
 
     def test_check_grad_no_input(self):
@@ -844,7 +841,7 @@ class TestConv2DOp_v2(OpTest):
             ['Filter'],
             'Output',
             no_grad_set=set(['Input']),
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
         )
 
     def init_test_case(self):

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

@@ -183,10 +183,10 @@ class TestConv2DTransposeOp(OpTest):
         if self.use_cudnn:
             place = core.CUDAPlace(0)
             self.check_output_with_place(
-                place, atol=1e-5, check_dygraph=(self.use_mkldnn == False)
+                place, atol=1e-5, check_dygraph=(not self.use_mkldnn)
             )
         else:
-            self.check_output(check_dygraph=(self.use_mkldnn == False))
+            self.check_output(check_dygraph=(not self.use_mkldnn))
 
     def test_check_grad_no_input(self):
         if self.need_check_grad:
@@ -724,10 +724,10 @@ class TestCUDNN_FP16(TestConv2DTransposeOp):
         if self.use_cudnn:
             place = core.CUDAPlace(0)
             self.check_output_with_place(
-                place, atol=0.02, check_dygraph=(self.use_mkldnn == False)
+                place, atol=0.02, check_dygraph=(not self.use_mkldnn)
             )
         else:
-            self.check_output(check_dygraph=(self.use_mkldnn == False))
+            self.check_output(check_dygraph=(not self.use_mkldnn))
 
 
 @unittest.skipIf(

python/paddle/fluid/tests/unittests/test_conv3d_op.py

@@ -327,7 +327,7 @@ class TestConv3DOp(OpTest):
         # TODO(wangzhongpu): support mkldnn op in dygraph mode
         place = core.CUDAPlace(0) if self.has_cudnn() else core.CPUPlace()
         self.check_output_with_place(
-            place, atol=1e-5, check_dygraph=(self.use_mkldnn == False)
+            place, atol=1e-5, check_dygraph=(not self.use_mkldnn)
         )
 
     def test_check_grad(self):
@@ -340,7 +340,7 @@ class TestConv3DOp(OpTest):
             {'Input', 'Filter'},
             'Output',
             max_relative_error=0.03,
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
         )
 
     def test_check_grad_no_filter(self):
@@ -354,7 +354,7 @@ class TestConv3DOp(OpTest):
             'Output',
             max_relative_error=0.03,
             no_grad_set=set(['Filter']),
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
         )
 
     def test_check_grad_no_input(self):
@@ -368,7 +368,7 @@ class TestConv3DOp(OpTest):
             'Output',
             max_relative_error=0.03,
             no_grad_set=set(['Input']),
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
         )
 
     def init_test_case(self):

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

@@ -34,7 +34,7 @@ class TestDataSetDownload(unittest.TestCase):
         except Exception as e:
             catch_exp = True
 
-        self.assertTrue(catch_exp == False)
+        self.assertTrue(not catch_exp)
 
         file_path = DATA_HOME + "/flowers/imagelabels.mat"
 

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

@@ -1330,8 +1330,8 @@ class TestDistBase(unittest.TestCase):
             tr_cmd += " --diff_batch"
         self.__use_cuda = False
         self.__use_xpu = False
-        assert self.__use_cuda == False, "gloo not support use cuda"
-        assert self.__use_xpu == False, "gloo not support use xpu"
+        assert not self.__use_cuda, "gloo not support use cuda"
+        assert not self.__use_xpu, "gloo not support use xpu"
         tr_cmd += " --use_cpu"
         env.update(
             {
@@ -1345,7 +1345,7 @@ class TestDistBase(unittest.TestCase):
             }
         )
 
-        assert self._use_dgc == False, "gloo not support use dgc"
+        assert not self._use_dgc, "gloo not support use dgc"
 
         if self._accumulate_gradient:
             tr_cmd += " --accumulate_gradient"
@@ -1353,7 +1353,7 @@ class TestDistBase(unittest.TestCase):
         if self._find_unused_parameters:
             tr_cmd += " --find_unused_parameters"
 
-        assert self._pipeline_mode == False, "gloo not support use pipeline"
+        assert not self._pipeline_mode, "gloo not support use pipeline"
 
         if self._enable_backward_deps:  # build strategy, save it
             tr_cmd += " --enable_backward_deps"
@@ -1361,8 +1361,8 @@ class TestDistBase(unittest.TestCase):
         if self._fuse_all_reduce is not None:
             tr_cmd += " --fuse_all_reduce {}".format(self._fuse_all_reduce)
 
-        assert self._use_fleet_api == False, "gloo not support use fleet api"
-        assert self._use_fleet_api_20 == False, "gloo not support use fleet api"
+        assert not self._use_fleet_api, "gloo not support use fleet api"
+        assert not self._use_fleet_api_20, "gloo not support use fleet api"
         return tr_cmd, env
 
     def _get_nccl2_trainer_cmd(

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

@@ -46,12 +46,12 @@ class TestElementwiseAddOp(OpTest):
         self.outputs = {'Out': self.out}
 
     def check_eager(self):
-        return self.use_mkldnn == False and self.axis == -1
+        return not self.use_mkldnn and self.axis == -1
 
     def test_check_output(self):
         # TODO(wangzhongpu): support mkldnn op in dygraph mode
         self.check_output(
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
             check_eager=self.check_eager(),
         )
 
@@ -62,7 +62,7 @@ class TestElementwiseAddOp(OpTest):
         self.check_grad(
             ['X', 'Y'],
             'Out',
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
             check_eager=self.check_eager(),
         )
 
@@ -74,7 +74,7 @@ class TestElementwiseAddOp(OpTest):
             ['Y'],
             'Out',
             no_grad_set=set("X"),
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
             check_eager=self.check_eager(),
         )
 
@@ -86,7 +86,7 @@ class TestElementwiseAddOp(OpTest):
             ['X'],
             'Out',
             no_grad_set=set('Y'),
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
             check_eager=self.check_eager(),
         )
 
@@ -115,7 +115,7 @@ class TestFP16ElementwiseAddOp(TestElementwiseAddOp):
             place = core.CUDAPlace(0)
             if core.is_float16_supported(place):
                 self.check_output_with_place(
-                    place, atol=1e-3, check_dygraph=(self.use_mkldnn == False)
+                    place, atol=1e-3, check_dygraph=(not self.use_mkldnn)
                 )
 
 

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

@@ -49,13 +49,11 @@ class ElementwiseMulOp(OpTest):
 
     def test_check_output(self):
         # TODO(wangzhongpu): support mkldnn op in dygraph mode
-        self.check_output(check_dygraph=(self.use_mkldnn == False))
+        self.check_output(check_dygraph=(not self.use_mkldnn))
 
     def test_check_grad_normal(self):
         # TODO(wangzhongpu): support mkldnn op in dygraph mode
-        self.check_grad(
-            ['X', 'Y'], 'Out', check_dygraph=(self.use_mkldnn == False)
-        )
+        self.check_grad(['X', 'Y'], 'Out', check_dygraph=(not self.use_mkldnn))
 
     def test_check_grad_ingore_x(self):
         # TODO(wangzhongpu): support mkldnn op in dygraph mode
@@ -63,7 +61,7 @@ class ElementwiseMulOp(OpTest):
             ['Y'],
             'Out',
             no_grad_set=set("X"),
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
         )
 
     def test_check_grad_ingore_y(self):
@@ -72,7 +70,7 @@ class ElementwiseMulOp(OpTest):
             ['X'],
             'Out',
             no_grad_set=set('Y'),
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
         )
 
     def init_input_output(self):

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

@@ -47,8 +47,8 @@ class TestEmptyLikeAPICommon(unittest.TestCase):
             )
         elif data_type in ['bool']:
             total_num = out.size
-            true_num = np.sum(out == True)
-            false_num = np.sum(out == False)
+            true_num = np.sum(out)
+            false_num = np.sum(~out)
             self.assertTrue(
                 total_num == true_num + false_num,
                 'The value should always be True or False.',

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

@@ -43,8 +43,8 @@ class TestEmptyOp(OpTest):
             )
         elif data_type in ['bool']:
             total_num = outs[0].size
-            true_num = np.sum(outs[0] == True)
-            false_num = np.sum(outs[0] == False)
+            true_num = np.sum(outs[0])
+            false_num = np.sum(~outs[0])
             self.assertTrue(
                 total_num == true_num + false_num,
                 'The value should always be True or False.',
@@ -132,8 +132,8 @@ class TestEmptyOp_ShapeTensor(OpTest):
             )
         elif data_type in ['bool']:
             total_num = outs[0].size
-            true_num = np.sum(outs[0] == True)
-            false_num = np.sum(outs[0] == False)
+            true_num = np.sum(outs[0])
+            false_num = np.sum(~outs[0])
             self.assertTrue(
                 total_num == true_num + false_num,
                 'The value should always be True or False.',
@@ -182,8 +182,8 @@ class TestEmptyOp_ShapeTensorList(OpTest):
             )
         elif data_type in ['bool']:
             total_num = outs[0].size
-            true_num = np.sum(outs[0] == True)
-            false_num = np.sum(outs[0] == False)
+            true_num = np.sum(outs[0])
+            false_num = np.sum(~outs[0])
             self.assertTrue(
                 total_num == true_num + false_num,
                 'The value should always be True or False.',

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

@@ -29,16 +29,16 @@ class TestImperativeLayerTrainable(unittest.TestCase):
 
             linear = dygraph.Linear(10, 10)
             y = linear(label)
-            self.assertTrue(y.stop_gradient == False)
+            self.assertFalse(y.stop_gradient)
 
             linear.weight.trainable = False
             linear.bias.trainable = False
 
-            self.assertTrue(linear.weight.trainable == False)
-            self.assertTrue(linear.weight.stop_gradient == True)
+            self.assertFalse(linear.weight.trainable)
+            self.assertTrue(linear.weight.stop_gradient)
 
             y = linear(label)
-            self.assertTrue(y.stop_gradient == True)
+            self.assertTrue(y.stop_gradient)
 
             with self.assertRaises(ValueError):
                 linear.weight.trainable = "1"

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

@@ -28,13 +28,13 @@ np.random.seed(10)
 
 
 def mean_wrapper(x, axis=None, keepdim=False, reduce_all=False):
-    if reduce_all == True:
+    if reduce_all:
         return paddle.mean(x, range(len(x.shape)), keepdim)
     return paddle.mean(x, axis, keepdim)
 
 
 def reduce_mean_wrapper(x, axis=0, keepdim=False, reduce_all=False):
-    if reduce_all == True:
+    if reduce_all:
         return paddle.mean(x, range(len(x.shape)), keepdim)
     return paddle.mean(x, axis, keepdim)
 

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

@@ -910,10 +910,10 @@ class TestMultiTensorMomentumDygraph(unittest.TestCase):
                 multi_precision=use_amp,
             )
         for idx in range(5):
-            if place == 'gpu' and use_amp == True:
+            if place == 'gpu' and use_amp:
                 model = paddle.amp.decorate(models=model, level='O2')
                 scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
-            if place == 'gpu' and use_amp == True:
+            if place == 'gpu' and use_amp:
                 with paddle.amp.auto_cast(level='O2'):
                     output = model(input)
                     loss = paddle.mean(output)

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

@@ -146,12 +146,8 @@ def iou(box_a, box_b, norm):
     xmax_b = max(box_b[0], box_b[2])
     ymax_b = max(box_b[1], box_b[3])
 
-    area_a = (ymax_a - ymin_a + (norm == False)) * (
-        xmax_a - xmin_a + (norm == False)
-    )
-    area_b = (ymax_b - ymin_b + (norm == False)) * (
-        xmax_b - xmin_b + (norm == False)
-    )
+    area_a = (ymax_a - ymin_a + (not norm)) * (xmax_a - xmin_a + (not norm))
+    area_b = (ymax_b - ymin_b + (not norm)) * (xmax_b - xmin_b + (not norm))
     if area_a <= 0 and area_b <= 0:
         return 0.0
 
@@ -160,9 +156,7 @@ def iou(box_a, box_b, norm):
     xb = min(xmax_a, xmax_b)
     yb = min(ymax_a, ymax_b)
 
-    inter_area = max(xb - xa + (norm == False), 0.0) * max(
-        yb - ya + (norm == False), 0.0
-    )
+    inter_area = max(xb - xa + (not norm), 0.0) * max(yb - ya + (not norm), 0.0)
 
     iou_ratio = inter_area / (area_a + area_b - inter_area)
 

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

@@ -55,7 +55,7 @@ def multiclass_nms(boxes, scores, category_idxs, iou_threshold, top_k):
 
         mask[cur_category_boxes_idxs[cur_category_keep_boxes_sub_idxs]] = True
 
-    keep_boxes_idxs = _find(mask == True)
+    keep_boxes_idxs = _find(mask)
     topK_sub_indices = np.argsort(-scores[keep_boxes_idxs])[:top_k]
     return keep_boxes_idxs[topK_sub_indices]
 

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

@@ -784,7 +784,7 @@ class TestRecomputeOptimizer(unittest.TestCase):
             type="mean", inputs={"X": b2_out}, outputs={"Out": mean_out}
         )
 
-        if return_input == True:
+        if return_input:
             return mul_x, mul_out, b1_out, b2_out, mean_out
         return mul_out, b1_out, b2_out, mean_out
 

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

@@ -58,8 +58,8 @@ class TestParallelExecutorDropExeScope(unittest.TestCase):
         train_exe.run(feed={"X": x}, fetch_list=[loss.name])
         test_exe.run(feed={"X": x}, fetch_list=[loss.name])
 
-        assert train_exe._need_create_local_exe_scopes() == False
-        assert test_exe._need_create_local_exe_scopes() == False
+        assert not train_exe._need_create_local_exe_scopes()
+        assert not test_exe._need_create_local_exe_scopes()
 
         # drop the local execution scope immediately
         train_exe.drop_local_exe_scopes()

python/paddle/fluid/tests/unittests/test_pool2d_op.py

@@ -181,7 +181,7 @@ def pool2D_forward_naive(
 
         if padding_algorithm == "VALID":
             paddings = [0, 0, 0, 0]
-            if ceil_mode != False:
+            if ceil_mode is not False:
                 raise ValueError(
                     "When Attr(pool_padding) is \"VALID\", Attr(ceil_mode)"
                     " must be False. "
@@ -346,10 +346,10 @@ class TestPool2D_Op_Mixin(object):
         if self.has_cudnn():
             place = core.CUDAPlace(0)
             self.check_output_with_place(
-                place, atol=1e-5, check_dygraph=(self.use_mkldnn == False)
+                place, atol=1e-5, check_dygraph=(not self.use_mkldnn)
             )
         else:
-            self.check_output(check_dygraph=(self.use_mkldnn == False))
+            self.check_output(check_dygraph=(not self.use_mkldnn))
 
     def test_check_grad(self):
         if self.dtype == np.float16:
@@ -362,14 +362,14 @@ class TestPool2D_Op_Mixin(object):
                 set(['X']),
                 'Out',
                 max_relative_error=0.07,
-                check_dygraph=(self.use_mkldnn == False),
+                check_dygraph=(not self.use_mkldnn),
             )
         elif self.pool_type != "max":
             self.check_grad(
                 set(['X']),
                 'Out',
                 max_relative_error=0.07,
-                check_dygraph=(self.use_mkldnn == False),
+                check_dygraph=(not self.use_mkldnn),
             )
 
     def init_data_format(self):
@@ -512,7 +512,7 @@ def create_test_cudnn_fp16_class(parent, check_grad=True):
                     self.check_output_with_place(
                         place,
                         atol=1e-3,
-                        check_dygraph=(self.use_mkldnn == False),
+                        check_dygraph=(not self.use_mkldnn),
                     )
 
         def test_check_grad(self):
@@ -528,7 +528,7 @@ def create_test_cudnn_fp16_class(parent, check_grad=True):
                     set(['X']),
                     'Out',
                     max_relative_error=0.07,
-                    check_dygraph=(self.use_mkldnn == False),
+                    check_dygraph=(not self.use_mkldnn),
                 )
 
     cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16Op")
@@ -553,7 +553,7 @@ def create_test_fp16_class(parent, check_grad=True):
                     self.check_output_with_place(
                         place,
                         atol=1e-3,
-                        check_dygraph=(self.use_mkldnn == False),
+                        check_dygraph=(not self.use_mkldnn),
                     )
 
         def test_check_grad(self):
@@ -569,7 +569,7 @@ def create_test_fp16_class(parent, check_grad=True):
                     set(['X']),
                     'Out',
                     max_relative_error=0.07,
-                    check_dygraph=(self.use_mkldnn == False),
+                    check_dygraph=(not self.use_mkldnn),
                 )
 
     cls_name = "{0}_{1}".format(parent.__name__, "Fp16Op")

python/paddle/fluid/tests/unittests/test_pool3d_op.py

@@ -68,7 +68,7 @@ def pool3D_forward_naive(
 
         if padding_algorithm == "VALID":
             paddings = [0, 0, 0, 0, 0, 0]
-            if ceil_mode != False:
+            if ceil_mode is not False:
                 raise ValueError(
                     "When Attr(pool_padding) is \"VALID\", Attr(ceil_mode)"
                     " must be False. "

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

@@ -321,12 +321,12 @@ class TestSGDMultiPrecision2_0(unittest.TestCase):
         optimizer = paddle.optimizer.SGD(
             parameters=model.parameters(), multi_precision=mp
         )
-        if mp == True:
+        if mp:
             model = paddle.amp.decorate(models=model, level='O2')
             scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
 
         for idx in range(5):
-            if mp == True:
+            if mp:
                 with paddle.amp.auto_cast(level='O2'):
                     output = model(input)
                     loss = paddle.mean(output)
@@ -429,12 +429,12 @@ class TestSGDMultiPrecision1_0(unittest.TestCase):
             parameter_list=model.parameters(),
             multi_precision=mp,
         )
-        if mp == True:
+        if mp:
             model = paddle.amp.decorate(models=model, level='O2')
             scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
 
         for idx in range(5):
-            if mp == True:
+            if mp:
                 with paddle.amp.auto_cast(level='O2'):
                     output = model(input)
                     loss = paddle.mean(output)

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

@@ -78,10 +78,10 @@ class TestSoftmaxOp(OpTest):
         if self.use_cudnn:
             place = core.CUDAPlace(0)
             self.check_output_with_place(
-                place, atol=1e-5, check_dygraph=(self.use_mkldnn == False)
+                place, atol=1e-5, check_dygraph=(not self.use_mkldnn)
             )
         else:
-            self.check_output(check_dygraph=(self.use_mkldnn == False))
+            self.check_output(check_dygraph=(not self.use_mkldnn))
 
     def test_check_grad(self):
         # TODO(wangzhongpu): support mkldnn op in dygraph mode
@@ -93,14 +93,14 @@ class TestSoftmaxOp(OpTest):
                     ["X"],
                     "Out",
                     max_relative_error=0.01,
-                    check_dygraph=(self.use_mkldnn == False),
+                    check_dygraph=(not self.use_mkldnn),
                 )
         else:
             self.check_grad(
                 ["X"],
                 "Out",
                 max_relative_error=0.01,
-                check_dygraph=(self.use_mkldnn == False),
+                check_dygraph=(not self.use_mkldnn),
             )
 
 
@@ -389,9 +389,7 @@ class TestSoftmaxBF16Op(OpTest):
 
     def test_check_output(self):
         place = core.CUDAPlace(0)
-        self.check_output_with_place(
-            place, check_dygraph=(self.use_mkldnn == False)
-        )
+        self.check_output_with_place(place, check_dygraph=(not self.use_mkldnn))
 
     def test_check_grad(self):
         place = core.CUDAPlace(0)
@@ -400,7 +398,7 @@ class TestSoftmaxBF16Op(OpTest):
             ["X"],
             "Out",
             numeric_grad_delta=0.05,
-            check_dygraph=(self.use_mkldnn == False),
+            check_dygraph=(not self.use_mkldnn),
         )
 
 

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

@@ -131,7 +131,7 @@ class TestSoftmaxWithCrossEntropyOp(OpTest):
             softmax, labels, self.soft_label, self.axis, self.ignore_index
         )
 
-        if self.use_softmax == False:
+        if not self.use_softmax:
             self.inputs = {"Logits": softmax, "Label": labels}
         else:
             self.inputs = {"Logits": logits, "Label": labels}

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

@@ -221,7 +221,7 @@ class TestSparseAttentionOp(OpTest):
 
         self.key_padding_mask = key_padding_mask.astype(self.dtype)
         self.attn_mask = attn_mask.astype(self.dtype)
-        if self.use_mask == True:
+        if self.use_mask:
             result, result_sdd, result_softmax = ref_batch_sparse_attention(
                 self.q,
                 self.k,
@@ -236,7 +236,7 @@ class TestSparseAttentionOp(OpTest):
                 self.q, self.k, self.v, self.offset, self.columns
             )
 
-        if self.use_mask == True:
+        if self.use_mask:
             self.inputs = {
                 'Q': self.q,
                 'K': self.k,
@@ -326,7 +326,7 @@ class TestSparseAttentionAPI(unittest.TestCase):
             )
             key_padding_mask_shape = (self.shape[0], self.shape[2])
             attn_mask_shape = (self.shape[2], self.shape[2])
-            if self.use_mask == True:
+            if self.use_mask:
                 key_padding_mask = paddle.static.data(
                     name="KeyPaddingMask",
                     shape=key_padding_mask_shape,
@@ -367,7 +367,7 @@ class TestSparseAttentionAPI(unittest.TestCase):
             attn_mask_np = attn_mask_np.astype(self.dtype)
 
             exe = fluid.Executor(self.place)
-            if self.use_mask == True:
+            if self.use_mask:
                 fetches_result = exe.run(
                     feed={
                         "Q": Q_np,
@@ -436,7 +436,7 @@ class TestSparseAttentionAPI(unittest.TestCase):
         paddle_kp_mask = paddle.to_tensor(key_padding_mask, place=self.place)
         paddle_attn_mask = paddle.to_tensor(attn_mask, place=self.place)
 
-        if self.use_mask == True:
+        if self.use_mask:
             paddle_result = F.sparse_attention(
                 paddle_query,
                 paddle_key,

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

@@ -1147,10 +1147,10 @@ class TestVarBase(unittest.TestCase):
             if var2:
                 var2_bool = True
 
-            assert var1_bool == False, "if var1 should be false"
-            assert var2_bool == True, "if var2 should be true"
-            assert bool(var1) == False, "bool(var1) is False"
-            assert bool(var2) == True, "bool(var2) is True"
+            assert not var1_bool, "if var1 should be false"
+            assert var2_bool, "if var2 should be true"
+            assert not bool(var1), "bool(var1) is False"
+            assert bool(var2), "bool(var2) is True"
 
     def test_if(self):
         with _test_eager_guard():

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

@@ -68,10 +68,10 @@ class TestWhereAPI(unittest.TestCase):
         self.out = np.where(self.cond, self.x, self.y)
 
     def ref_x_backward(self, dout):
-        return np.where((self.cond == True), dout, 0)
+        return np.where(self.cond, dout, 0)
 
     def ref_y_backward(self, dout):
-        return np.where((self.cond == False), dout, 0)
+        return np.where(~self.cond, dout, 0)
 
     def test_api(self, use_cuda=False):
         for x_stop_gradient in [False, True]:

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

@@ -377,7 +377,7 @@ class XPUTestBatchNormOp(XPUOpTestWrapper):
                     )
                     net2.weight = net1.weight
                     net2.bias = net1.bias
-                    if self.trainable_statistics == True:
+                    if self.trainable_statistics:
                         net1.training = False
                         net2.training = False
                     y1 = net1(x)

python/paddle/fluid/tests/unittests/xpu/test_conv2d_op_xpu.py

@@ -261,10 +261,7 @@ class XPUTestConv2DOp(XPUOpTestWrapper):
                 self.check_output_with_place(self.place)
 
         def test_check_grad(self):
-            if (
-                hasattr(self, "no_need_check_grad")
-                and self.no_need_check_grad == True
-            ):
+            if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
                 return
             if core.is_compiled_with_xpu():
                 paddle.enable_static()
@@ -273,10 +270,7 @@ class XPUTestConv2DOp(XPUOpTestWrapper):
                 )
 
         def test_check_grad_no_filter(self):
-            if (
-                hasattr(self, "no_need_check_grad")
-                and self.no_need_check_grad == True
-            ):
+            if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
                 return
             if core.is_compiled_with_xpu():
                 paddle.enable_static()
@@ -285,10 +279,7 @@ class XPUTestConv2DOp(XPUOpTestWrapper):
                 )
 
         def test_check_grad_no_input(self):
-            if (
-                hasattr(self, "no_need_check_grad")
-                and self.no_need_check_grad == True
-            ):
+            if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
                 return
             if core.is_compiled_with_xpu():
                 paddle.enable_static()
@@ -433,10 +424,7 @@ class XPUTestConv2DOp_v2(XPUOpTestWrapper):
 
         def test_check_grad(self):
             # TODO(wangzhongpu): support mkldnn op in dygraph mode
-            if (
-                hasattr(self, "no_need_check_grad")
-                and self.no_need_check_grad == True
-            ):
+            if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
                 return
             if core.is_compiled_with_xpu():
                 paddle.enable_static()
@@ -446,10 +434,7 @@ class XPUTestConv2DOp_v2(XPUOpTestWrapper):
 
         def test_check_grad_no_filter(self):
             # TODO(wangzhongpu): support mkldnn op in dygraph mode
-            if (
-                hasattr(self, "no_need_check_grad")
-                and self.no_need_check_grad == True
-            ):
+            if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
                 return
             if core.is_compiled_with_xpu():
                 paddle.enable_static()
@@ -459,10 +444,7 @@ class XPUTestConv2DOp_v2(XPUOpTestWrapper):
 
         def test_check_grad_no_input(self):
             # TODO(wangzhongpu): support mkldnn op in dygraph mode
-            if (
-                hasattr(self, "no_need_check_grad")
-                and self.no_need_check_grad == True
-            ):
+            if hasattr(self, "no_need_check_grad") and self.no_need_check_grad:
                 return
             if core.is_compiled_with_xpu():
                 paddle.enable_static()

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

@@ -52,7 +52,7 @@ class XPUTestDropoutOp(XPUOpTestWrapper):
             }
 
             out = self.inputs['X'] * (1.0 - self.dropout_prob)
-            if self.is_test == False:
+            if not self.is_test:
                 mask = None
                 if self.dropout_prob == 0.0:
                     mask = np.ones(self.shape).astype(self.dtype)
@@ -78,7 +78,7 @@ class XPUTestDropoutOp(XPUOpTestWrapper):
         def test_check_grad_normal(self):
             if (
                 hasattr(self.__class__, "no_need_check_grad")
-                and self.__class__.no_need_check_grad == True
+                and self.__class__.no_need_check_grad
             ):
                 return
 

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

@@ -61,7 +61,7 @@ class XPUTestElementwiseMulOp(XPUOpTestWrapper):
                     place,
                     ['X', 'Y'],
                     'Out',
-                    check_dygraph=(self.use_mkldnn == False),
+                    check_dygraph=(not self.use_mkldnn),
                 )
 
         def test_check_grad_ingore_x(self):
@@ -72,7 +72,7 @@ class XPUTestElementwiseMulOp(XPUOpTestWrapper):
                     ['Y'],
                     'Out',
                     no_grad_set=set("X"),
-                    check_dygraph=(self.use_mkldnn == False),
+                    check_dygraph=(not self.use_mkldnn),
                 )
 
         def test_check_grad_ingore_y(self):
@@ -83,7 +83,7 @@ class XPUTestElementwiseMulOp(XPUOpTestWrapper):
                     ['X'],
                     'Out',
                     no_grad_set=set('Y'),
-                    check_dygraph=(self.use_mkldnn == False),
+                    check_dygraph=(not self.use_mkldnn),
                 )
 
         def init_input_output(self):

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

@@ -72,8 +72,8 @@ class XPUTestEmptyOp(XPUOpTestWrapper):
                 )
             elif data_type in ['bool']:
                 total_num = outs[0].size
-                true_num = np.sum(outs[0] == True)
-                false_num = np.sum(outs[0] == False)
+                true_num = np.sum(outs[0])
+                false_num = np.sum(~outs[0])
                 self.assertTrue(
                     total_num == true_num + false_num,
                     'The value should always be True or False.',

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

@@ -106,14 +106,14 @@ class XPUTestFuseGemmOp(XPUOpTestWrapper):
                 - 0.5,
             }
 
-            if self.trans_x == True:
+            if self.trans_x:
                 numpy_input_x = (
                     self.inputs['X'].reshape((self.x_shape[0], -1)).T
                 )
             else:
                 numpy_input_x = self.inputs['X'].reshape((-1, self.x_shape[-1]))
 
-            if self.trans_y == True:
+            if self.trans_y:
                 numpy_input_y = self.inputs['Y'].T
             else:
                 numpy_input_y = self.inputs['Y']

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

@@ -106,7 +106,7 @@ def generate_compatible_shapes(
         shape_Y = [BATCH_SIZE] + shape_Y
 
     if dim_Y == 3 and dim_X == 2:
-        if transpose_X == False:
+        if not transpose_X:
             shape_X[1] = shape_X[1] * BATCH_SIZE
         else:
             shape_X[0] = shape_X[0] * BATCH_SIZE
@@ -326,7 +326,7 @@ class TestMatmulBaseGenerator(XPUOpTest):
     def test_check_grad_normal(self):
         if (
             hasattr(self.__class__, "no_need_check_grad")
-            and self.__class__.no_need_check_grad == True
+            and self.__class__.no_need_check_grad
         ):
             return
 
@@ -338,7 +338,7 @@ class TestMatmulBaseGenerator(XPUOpTest):
     def test_check_grad_ignore_x(self):
         if (
             hasattr(self.__class__, "no_need_check_grad")
-            and self.__class__.no_need_check_grad == True
+            and self.__class__.no_need_check_grad
         ):
             return
 
@@ -350,7 +350,7 @@ class TestMatmulBaseGenerator(XPUOpTest):
     def test_check_grad_ignore_y(self):
         if (
             hasattr(self.__class__, "no_need_check_grad")
-            and self.__class__.no_need_check_grad == True
+            and self.__class__.no_need_check_grad
         ):
             return
 

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

@@ -101,7 +101,7 @@ class XPUTestMatmulV2Op(XPUOpTestWrapper):
         def test_check_grad(self):
             if (
                 hasattr(self.__class__, "no_need_check_grad")
-                and self.__class__.no_need_check_grad == True
+                and self.__class__.no_need_check_grad
             ):
                 return
             place = paddle.XPUPlace(0)

python/paddle/fluid/tests/unittests/xpu/test_pool2d_op_xpu.py

@@ -178,7 +178,7 @@ def pool2D_forward_naive(
 
         if padding_algorithm == "VALID":
             paddings = [0, 0, 0, 0]
-            if ceil_mode != False:
+            if ceil_mode is not False:
                 raise ValueError(
                     "When Attr(pool_padding) is \"VALID\", Attr(ceil_mode)"
                     " must be False. "

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

@@ -91,10 +91,10 @@ class TestXPUWhereAPI(unittest.TestCase):
         self.out = np.where(self.cond, self.x, self.y)
 
     def ref_x_backward(self, dout):
-        return np.where(self.cond == True, dout, 0)
+        return np.where(self.cond, dout, 0)
 
     def ref_y_backward(self, dout):
-        return np.where(self.cond == False, dout, 0)
+        return np.where(~self.cond, dout, 0)
 
     def test_api(self):
         for x_stop_gradient in [False, True]:

python/paddle/hapi/model_summary.py

@@ -450,7 +450,7 @@ def summary_string(model, input_size=None, dtypes=None, input=None):
                 total_output += np.sum(np.prod(output_shape, axis=-1))
 
         if "trainable" in summary[layer]:
-            if summary[layer]["trainable"] == True:
+            if summary[layer]["trainable"]:
                 trainable_params += summary[layer]["trainable_params"]
         summary_str += line_new + "\n"
 

python/paddle/incubate/autograd/primrules.py

@@ -515,7 +515,7 @@ def dropout_orig2prim(op, seed_t, x):
     ), 'Can not lower dropout into prim ops with seedtensor.'
     mask = bernoulli(shape=x.shape, dtype=x.dtype, p=op.attr('dropout_prob'))
     if op.attr('dropout_implementation') == 'upscale_in_train':
-        if op.attr('is_test') == False:
+        if not op.attr('is_test'):
             out = div(
                 mul(x, mask),
                 fill_const(1.0 - op.attr('dropout_prob'), x.shape, x.dtype),
@@ -524,7 +524,7 @@ def dropout_orig2prim(op, seed_t, x):
         else:
             return primops.cast(mask, dtype=paddle.uint8), x
     elif op.attr('dropout_implementation') == 'downgrade_in_infer':
-        if op.attr('is_test') == False:
+        if not op.attr('is_test'):
             return primops.cast(mask, dtype=paddle.uint8), mul(x, mask)
         else:
             return primops.cast(mask, dtype=paddle.uint8), mul(

python/paddle/nn/functional/common.py

@@ -2109,7 +2109,7 @@ def class_center_sample(label, num_classes, num_samples, group=None):
         #Tensor(shape=[7], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
         #       [0, 1, 2, 3, 5, 7, 8])
     """
-    if not (group == False or group is None or hasattr(group, 'is_member')):
+    if not (group is False or group is None or hasattr(group, 'is_member')):
         raise ValueError(
             'Expected group is False, None or instance of paddle.distributed.collective.Group \
              (got group: {})'.format(
@@ -2124,7 +2124,7 @@ def class_center_sample(label, num_classes, num_samples, group=None):
     ring_id = 0
     rank = 0
     nranks = 1
-    if group != False:
+    if group is not False:
         if core.is_compiled_with_dist():
             parallel_env = paddle.distributed.ParallelEnv()
             global_rank = parallel_env.rank

python/paddle/nn/functional/loss.py

@@ -2033,7 +2033,7 @@ def margin_cross_entropy(
     """
 
     assert reduction in ['mean', 'sum', 'none', None]
-    if not (group == False or group is None or hasattr(group, 'is_member')):
+    if not (group is False or group is None or hasattr(group, 'is_member')):
         raise ValueError(
             'Expected group is False, None or instance of paddle.distributed.collective.Group \
              (got group: {})'.format(
@@ -2048,7 +2048,7 @@ def margin_cross_entropy(
     ring_id = 0
     rank = 0
     nranks = 1
-    if group != False:
+    if group is not False:
         ring_id = 0 if group is None else group.id
         if core.is_compiled_with_dist():
             parallel_env = paddle.distributed.ParallelEnv()
@@ -2537,7 +2537,7 @@ def cross_entropy(
             "should be 'sum', 'mean' or 'none', but received %s, which is not allowed."
             % reduction
         )
-    if ignore_index > 0 and soft_label == True:
+    if ignore_index > 0 and soft_label:
         raise ValueError(
             "When soft_label == True, the value of 'ignore_index' in softmax_cross_entropy"
             "should be '-100', but received %s, which is not allowed."
@@ -2560,12 +2560,12 @@ def cross_entropy(
         label = paddle.unsqueeze(label, axis=axis)
 
     if in_dygraph_mode():
-        if soft_label == False:
+        if not soft_label:
             valid_label = (
                 paddle.cast(label != ignore_index, dtype=label.dtype) * label
             )
         if core.is_compiled_with_npu() or core.is_compiled_with_mlu():
-            if soft_label == False:
+            if not soft_label:
                 _, _, out = _legacy_C_ops.softmax_with_cross_entropy(
                     input,
                     valid_label,
@@ -2603,7 +2603,7 @@ def cross_entropy(
         if weight is not None:
 
             # trans weight from class to sample, shape:N or [N,H,W] for 1d and 2d cases.
-            if soft_label == True:
+            if soft_label:
                 # chajchaj:
                 # weight's shape is C, where C is class num.
                 # for 1d case: label's shape is [N,C], weight_gather's shape is N.
@@ -2710,7 +2710,7 @@ def cross_entropy(
             return out
 
     elif _in_legacy_dygraph():
-        if soft_label == False:
+        if not soft_label:
             valid_label = (
                 paddle.cast(label != ignore_index, dtype=label.dtype) * label
             )
@@ -2725,7 +2725,7 @@ def cross_entropy(
                     "Target {} is out of upper bound.".format(label_max.item())
                 )
         if core.is_compiled_with_npu() or core.is_compiled_with_mlu():
-            if soft_label == False:
+            if not soft_label:
                 _, _, out = _legacy_C_ops.softmax_with_cross_entropy(
                     input,
                     valid_label,
@@ -2774,7 +2774,7 @@ def cross_entropy(
         if weight is not None:
 
             # trans weight from class to sample, shape:N or [N,H,W] for 1d and 2d cases.
-            if soft_label == True:
+            if soft_label:
                 # chajchaj:
                 # weight's shape is C, where C is class num.
                 # for 1d case: label's shape is [N,C], weight_gather's shape is N.
@@ -2921,7 +2921,7 @@ def cross_entropy(
             weight, 'weight', ['float32', 'float64'], 'softmax_cross_entropy'
         )
         weight_name = name if reduction == 'none' else None
-        if soft_label == True:
+        if soft_label:
             # chajchaj:
             # trans weight from class to sample, shape:N or [N,H,W] for 1d and 2d cases.
             # weight's shape is C, where C is class num.

python/paddle/nn/functional/pooling.py

@@ -110,7 +110,7 @@ def _update_padding_nd(padding, num_dims, channel_last=False, ceil_mode=False):
                 )
             )
         if padding == "VALID":
-            if ceil_mode != False:
+            if ceil_mode is not False:
                 raise ValueError(
                     "When Attr(padding) is \"VALID\", Attr(ceil_mode) must be False. "
                     "Received ceil_mode: True."

python/paddle/nn/layer/distance.py

@@ -76,7 +76,7 @@ class PairwiseDistance(Layer):
         main_str = 'p={p}'
         if self.epsilon != 1e-6:
             main_str += ', epsilon={epsilon}'
-        if self.keepdim != False:
+        if self.keepdim is not False:
             main_str += ', keepdim={keepdim}'
         if self.name != None:
             main_str += ', name={name}'

python/paddle/nn/layer/norm.py

@@ -71,7 +71,7 @@ class _InstanceNormBase(Layer):
     ):
         super(_InstanceNormBase, self).__init__()
 
-        if weight_attr == False or bias_attr == False:
+        if weight_attr is False or bias_attr is False:
             assert (
                 weight_attr == bias_attr
             ), "weight_attr and bias_attr must be set to False at the same time in InstanceNorm"
@@ -80,7 +80,7 @@ class _InstanceNormBase(Layer):
         self._bias_attr = bias_attr
         self._num_features = num_features
 
-        if weight_attr != False and bias_attr != False:
+        if weight_attr is not False and bias_attr is not False:
             self.scale = self.create_parameter(
                 attr=self._weight_attr,
                 shape=[num_features],
@@ -382,7 +382,7 @@ class GroupNorm(Layer):
 
         param_shape = [self._num_channels]
 
-        if weight_attr == False:
+        if weight_attr is False:
             self.weight = self.create_parameter(
                 attr=None, shape=param_shape, default_initializer=Constant(1.0)
             )
@@ -398,7 +398,7 @@ class GroupNorm(Layer):
                 and self._weight_attr.learning_rate == 0.0
             )
 
-        if bias_attr == False:
+        if bias_attr is False:
             self.bias = self.create_parameter(
                 attr=None,
                 shape=param_shape,
@@ -619,7 +619,7 @@ class _BatchNormBase(Layer):
         param_shape = [num_features]
 
         # create parameter
-        if weight_attr == False:
+        if weight_attr is False:
             self.weight = self.create_parameter(
                 attr=None,
                 shape=param_shape,
@@ -639,7 +639,7 @@ class _BatchNormBase(Layer):
                 and self._weight_attr.learning_rate == 0.0
             )
 
-        if bias_attr == False:
+        if bias_attr is False:
             self.bias = self.create_parameter(
                 attr=None,
                 shape=param_shape,
@@ -1315,7 +1315,10 @@ class SyncBatchNorm(_BatchNormBase):
                 layer._name,
             )
 
-            if layer._weight_attr != False and layer._bias_attr != False:
+            if (
+                layer._weight_attr is not False
+                and layer._bias_attr is not False
+            ):
                 with no_grad():
                     layer_output.weight = layer.weight
                     layer_output.bias = layer.bias

python/paddle/nn/layer/rnn.py

@@ -964,9 +964,9 @@ class RNNBase(LayerList):
             for direction in range(self.num_directions):
                 suffix = '_reverse' if direction == 1 else ''
                 param_names.extend(['weight_ih_l{}{}', 'weight_hh_l{}{}'])
-                if bias_ih_attr != False:
+                if bias_ih_attr is not False:
                     param_names.append('bias_ih_l{}{}')
-                if bias_hh_attr != False:
+                if bias_hh_attr is not False:
                     param_names.append('bias_hh_l{}{}')
                 param_names = [x.format(layer, suffix) for x in param_names]
         for name, param in zip(param_names, self.parameters()):
@@ -1187,7 +1187,7 @@ class RNNBase(LayerList):
         main_str = '{input_size}, {hidden_size}'
         if self.num_layers != 1:
             main_str += ', num_layers={num_layers}'
-        if self.time_major != False:
+        if self.time_major is not False:
             main_str += ', time_major={time_major}'
         if self.dropout != 0:
             main_str += ', dropout={dropout}'

python/paddle/nn/quant/quant_layers.py

@@ -298,7 +298,7 @@ class FakeQuantChannelWiseAbsMax(Layer):
         reduce_type=None,
     ):
         assert (
-            quant_on_weight == True
+            quant_on_weight
         ), "Channel_wise only can be used on weight quantization."
         super(FakeQuantChannelWiseAbsMax, self).__init__()
         self._quant_bits = quant_bits

python/paddle/profiler/profiler_statistic.py

@@ -1237,7 +1237,7 @@ def _build_table(
         if statistic_data.event_summary.items:
             all_row_values = []
             name_column_width = 52
-            if thread_sep == True:
+            if thread_sep:
                 thread_items = statistic_data.event_summary.thread_items
             else:
                 thread_items = {
@@ -1721,7 +1721,7 @@ def _build_table(
                     'ProfileStep'
                 ].general_gpu_time
             )
-            if thread_sep == True:
+            if thread_sep:
                 userdefined_thread_items = (
                     statistic_data.event_summary.userdefined_thread_items
                 )

python/paddle/profiler/utils.py

@@ -164,7 +164,7 @@ def load_profiler_result(filename: str):
 
 
 def in_profiler_mode():
-    return _is_profiler_used == True
+    return _is_profiler_used
 
 
 def wrap_optimizers():
@@ -182,7 +182,7 @@ def wrap_optimizers():
         return warpper
 
     global _has_optimizer_wrapped
-    if _has_optimizer_wrapped == True:
+    if _has_optimizer_wrapped:
         return
     import paddle.optimizer as optimizer
 

python/paddle/sparse/nn/layer/norm.py

@@ -398,7 +398,10 @@ class SyncBatchNorm(paddle.nn.SyncBatchNorm):
                     layer._name,
                 )
 
-            if layer._weight_attr != False and layer._bias_attr != False:
+            if (
+                layer._weight_attr is not False
+                and layer._bias_attr is not False
+            ):
                 with no_grad():
                     layer_output.weight = layer.weight
                     layer_output.bias = layer.bias

python/paddle/tensor/linalg.py

@@ -466,9 +466,7 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
         if in_dygraph_mode():
             out = _C_ops.abs(input)
             reduce_all = (
-                True
-                if axis == None or axis == [] or asvector == True
-                else False
+                True if axis == None or axis == [] or asvector else False
             )
             axis = axis if axis != None and axis != [] else [0]
             if reduce_all:
@@ -487,9 +485,7 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None):
             dtype=helper.input_dtype()
         )
 
-        reduce_all = (
-            True if axis == None or axis == [] or asvector == True else False
-        )
+        reduce_all = True if axis == None or axis == [] or asvector else False
         axis = axis if axis != None and axis != [] else [0]
 
         reduce_type = (
@@ -1322,7 +1318,7 @@ def cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):
         avg = nx.sum(axis=1) / w_sum
         nx_w = nx
 
-    if w is not None and aweights is not None and ddof == True:
+    if w is not None and aweights is not None and ddof:
         norm_factor = w_sum - (w * aweights).sum() / w_sum
     else:
         norm_factor = w_sum - ddof

python/paddle/tensor/manipulation.py

@@ -3206,7 +3206,7 @@ def tile(x, repeat_times, name=None):
     check_variable_and_dtype(
         x, 'x', ['bool', 'float32', 'float64', 'int32', 'int64'], 'tile'
     )
-    if convert_dtype(x.dtype) == 'bool' and x.stop_gradient == False:
+    if convert_dtype(x.dtype) == 'bool' and not x.stop_gradient:
         raise ValueError(
             "When the date type is bool for the input 'x' of tile op, you "
             "must set its stop_gradient to be True by "
@@ -3288,7 +3288,7 @@ def expand_as(x, y, name=None):
     )
     check_type(y, 'y', Variable, 'expand_as')
 
-    if convert_dtype(x.dtype) == 'bool' and x.stop_gradient == False:
+    if convert_dtype(x.dtype) == 'bool' and not x.stop_gradient:
         raise ValueError(
             "When the data type of input 'x' for expand_as is bool, "
             "you must set its stop_gradient to be False by "
@@ -3359,7 +3359,7 @@ def broadcast_to(x, shape, name=None):
         x, 'x', ['bool', 'float32', 'float64', 'int32', 'int64'], 'broadcast_to'
     )
     check_type(shape, 'shape', (list, tuple, Variable), 'broadcast_to')
-    if convert_dtype(x.dtype) == 'bool' and x.stop_gradient == False:
+    if convert_dtype(x.dtype) == 'bool' and not x.stop_gradient:
         raise ValueError(
             "When the data type of input 'x' for broadcast_to is bool, "
             "you must set its stop_gradient to be False by "
@@ -3457,7 +3457,7 @@ def expand(x, shape, name=None):
         'expand',
     )
     check_type(shape, 'shape', (list, tuple, Variable), 'expand')
-    if convert_dtype(x.dtype) == 'bool' and x.stop_gradient == False:
+    if convert_dtype(x.dtype) == 'bool' and not x.stop_gradient:
         raise ValueError(
             "When the data type of input 'x' for expand is bool, "
             "you must set its stop_gradient to be False by "

python/paddle/tensor/random.py

@@ -188,7 +188,7 @@ def multinomial(x, num_samples=1, replacement=False, name=None):
     """
 
     assert (
-        core.is_compiled_with_rocm() == False
+        not core.is_compiled_with_rocm()
     ), "multinomial op is not supported on ROCM yet."
 
     if in_dygraph_mode():

python/paddle/text/datasets/conll05.py

@@ -228,9 +228,9 @@ class Conll05st(Dataset):
                             lbl_seq = []
                             verb_word = ''
                             for l in lbl:
-                                if l == '*' and is_in_bracket == False:
+                                if l == '*' and not is_in_bracket:
                                     lbl_seq.append('O')
-                                elif l == '*' and is_in_bracket == True:
+                                elif l == '*' and is_in_bracket:
                                     lbl_seq.append('I-' + cur_tag)
                                 elif l == '*)':
                                     lbl_seq.append('I-' + cur_tag)

tools/analysisPyXml.py

@@ -46,28 +46,25 @@ def analysisPyXml(rootPath, ut):
                 command = 'sed -n %sp %s' % (line_number, clazz_filename)
                 _code, output = commands.getstatusoutput(command)
                 if _code == 0:
-                    if (
-                        output.strip().startswith(
-                            (
-                                'from',
-                                'import',
-                                '__all__',
-                                'def',
-                                'class',
-                                '"""',
-                                '@',
-                                '\'\'\'',
-                                'logger',
-                                '_logger',
-                                'logging',
-                                'r"""',
-                                'pass',
-                                'try',
-                                'except',
-                                'if __name__ == "__main__"',
-                            )
+                    if not output.strip().startswith(
+                        (
+                            'from',
+                            'import',
+                            '__all__',
+                            'def',
+                            'class',
+                            '"""',
+                            '@',
+                            '\'\'\'',
+                            'logger',
+                            '_logger',
+                            'logging',
+                            'r"""',
+                            'pass',
+                            'try',
+                            'except',
+                            'if __name__ == "__main__"',
                         )
-                        == False
                     ):
                         pattern = r"""(.*) = ('*')|(.*) = ("*")|(.*) = (\d)|(.*) = (-\d)|(.*) = (None)|(.*) = (True)|(.*) = (False)|(.*) = (URL_PREFIX*)|(.*) = (\[)|(.*) = (\{)|(.*) = (\()"""  # a='b'/a="b"/a=0
                         if re.match(pattern, output.strip()) == None:

tools/check_op_benchmark_result.py

@@ -40,7 +40,7 @@ def parse_log_file(log_file):
         for line in f.read().strip().split('\n')[::-1]:
             try:
                 result = json.loads(line)
-                if result.get("disabled", False) == True:
+                if result.get("disabled", False):
                     return None
                 return result
             except ValueError:

tools/get_pr_ut.py

@@ -349,7 +349,7 @@ class PRChecker(object):
                     file_list.append(filename)
                 else:
                     isWhiteFile = self.get_is_white_file(filename)
-                    if isWhiteFile == False:
+                    if not isWhiteFile:
                         file_list.append(filename)
                     else:
                         filterFiles.append(filename)
@@ -417,7 +417,7 @@ class PRChecker(object):
                                         == tempfilename.split(".")[0]
                                     ):
                                         f_judge_in_added_ut = True
-                            if f_judge_in_added_ut == True:
+                            if f_judge_in_added_ut:
                                 print(
                                     "Adding new unit tests not hit mapFiles: %s"
                                     % f_judge

tools/get_single_test_cov.py

@@ -91,7 +91,7 @@ def analysisFNDAFile(rootPath, test):
                 if matchObj == None:
                     OP_REGIST = False
                     break
-            if OP_REGIST == False:
+            if not OP_REGIST:
                 related_file_list.append(clazz_filename)
                 os.system(
                     'echo %s >> %s' % (clazz_filename, related_ut_map_file)

tools/infrt/generate_pd_op_dialect_from_paddle_op_maker.py

@@ -122,14 +122,14 @@ def generate_all_ops_inputs_outputs_map(op_descs):
         outpus = list()
         for input_ in op_proto[INPUTS]:
             if (
-                op_proto[INPUTS][input_][EXTRA] != True
-                and op_proto[INPUTS][input_][INTERMEDIATE] != True
+                not op_proto[INPUTS][input_][EXTRA]
+                and not op_proto[INPUTS][input_][INTERMEDIATE]
             ):
                 inputs.append(input_)
         for output_ in op_proto[OUTPUTS]:
             if (
-                op_proto[OUTPUTS][output_][EXTRA] != True
-                and op_proto[OUTPUTS][output_][INTERMEDIATE] != True
+                not op_proto[OUTPUTS][output_][EXTRA]
+                and not op_proto[OUTPUTS][output_][INTERMEDIATE]
             ):
                 outpus.append(output_)
         ops_inputs_map[op_type] = inputs
@@ -214,9 +214,9 @@ def get_constraint(op_type, op_proto):
     optional_input_num_ = 0
     for input_ in op_proto[INPUTS]:
         if (
-            op_proto[INPUTS][input_][EXTRA] != True
-            and op_proto[INPUTS][input_][INTERMEDIATE] != True
-            and op_proto[INPUTS][input_][DISPENSABLE] == True
+            not op_proto[INPUTS][input_][EXTRA]
+            and not op_proto[INPUTS][input_][INTERMEDIATE]
+            and op_proto[INPUTS][input_][DISPENSABLE]
         ):
             optional_input_num_ += 1
     if optional_input_num_ > 1:
@@ -306,11 +306,11 @@ def convert_op_proto_into_mlir(op_descs):
             # 2.3.1 inputs
             for input_ in op_proto[INPUTS]:
                 if (
-                    op_proto[INPUTS][input_][EXTRA] != True
-                    and op_proto[INPUTS][input_][INTERMEDIATE] != True
+                    not op_proto[INPUTS][input_][EXTRA]
+                    and not op_proto[INPUTS][input_][INTERMEDIATE]
                 ):
-                    if op_proto[INPUTS][input_][DISPENSABLE] != True:
-                        if op_proto[INPUTS][input_][DUPLICABLE] != True:
+                    if not op_proto[INPUTS][input_][DISPENSABLE]:
+                        if not op_proto[INPUTS][input_][DUPLICABLE]:
                             ARGUMENTS = (
                                 ARGUMENTS + " PD_Tensor:$" + input_ + ","
                             )
@@ -319,7 +319,7 @@ def convert_op_proto_into_mlir(op_descs):
                                 ARGUMENTS + " PD_Tensor_Array:$" + input_ + ","
                             )
                     else:
-                        if op_proto[INPUTS][input_][DUPLICABLE] != True:
+                        if not op_proto[INPUTS][input_][DUPLICABLE]:
                             ARGUMENTS = (
                                 ARGUMENTS
                                 + " Optional<PD_Tensor>:$"
@@ -350,7 +350,7 @@ def convert_op_proto_into_mlir(op_descs):
 
             # 2.3.2 attributes
             for attr in op_proto[ATTRS]:
-                if (op_proto[ATTRS][attr][EXTRA] == True) or (
+                if (op_proto[ATTRS][attr][EXTRA]) or (
                     attr in skipped_attr_list
                 ):
                     continue
@@ -434,10 +434,10 @@ def convert_op_proto_into_mlir(op_descs):
             outputs = ""
             for output_ in op_proto[OUTPUTS]:
                 if (
-                    op_proto[OUTPUTS][output_][EXTRA] != True
-                    and op_proto[OUTPUTS][output_][INTERMEDIATE] != True
+                    not op_proto[OUTPUTS][output_][EXTRA]
+                    and not op_proto[OUTPUTS][output_][INTERMEDIATE]
                 ):
-                    if op_proto[OUTPUTS][output_][DUPLICABLE] != True:
+                    if not op_proto[OUTPUTS][output_][DUPLICABLE]:
                         outputs = outputs + "PD_Tensor:${},".format(output_)
                     else:
                         outputs = outputs + "PD_Tensor_Array:${},".format(

tools/sampcd_processor.py

@@ -376,7 +376,7 @@ Please use '.. code-block:: python' to format the sample code."""
         # None - no sample code found;
         # False - it need other special equipment or environment.
         # so, the following conditional statements are intentionally arranged.
-        if matched == True:
+        if matched:
             tfname = os.path.join(
                 SAMPLECODE_TEMPDIR,
                 '{}_example{}'.format(
@@ -395,7 +395,7 @@ Please use '.. code-block:: python' to format the sample code."""
                 )
             )
             SUMMARY_INFO['skiptest'].append("{}-{}".format(name, cb['id']))
-        elif matched == False:
+        elif not matched:
             logger.info(
                 '{}\' code block (name:{}, id:{}) required({}) not match capacity({}).'.format(
                     name,