[CodeStyle][UP004] remove useless object inheritance

paddle/fluid/imperative/README.md

@@ -14,7 +14,7 @@ https://www.tensorflow.org/guide/eager
 
 ## API
 ```python
-class Layer(object):
+class Layer:
 
   def __call__(inputs):
     # build some parameter once.
@@ -49,7 +49,7 @@ Longer term.
 ```python
 
 # Parent class.
-class PyVarBase(object):
+class PyVarBase:
   pass
 
 # Current python variable.

pyproject.toml

@@ -35,6 +35,7 @@ select = [
     # Pyupgrade
     "UP001",
     "UP003",
+    "UP004",
     "UP007",
     "UP010",
     "UP011",

python/paddle/device/__init__.py

@@ -523,7 +523,7 @@ def get_available_custom_device():
     return core.get_available_custom_device()
 
 
-class Event(object):
+class Event:
     '''
     A device event wrapper around StreamBase.
     Parameters:
@@ -668,7 +668,7 @@ def __repr__(self):
         return self.event_base
 
 
-class Stream(object):
+class Stream:
     '''
     A device stream wrapper around StreamBase.
     Parameters:
@@ -936,7 +936,7 @@ def set_stream(stream):
     return prev_stream
 
 
-class stream_guard(object):
+class stream_guard:
     '''
     Notes:
         This API only supports dynamic graph mode currently.

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

@@ -17,7 +17,7 @@
 __all__ = []
 
 
-class RuntimeFactory(object):
+class RuntimeFactory:
     def __init__(self):
         pass
 

python/paddle/distributed/passes/auto_parallel_bf16.py

@@ -45,7 +45,7 @@
 world_process_group = get_world_process_group()
 
 
-class BF16State(object):
+class BF16State:
     def __init__(self, block):
         self._block: Block = block
         self._op_bf16_dict = {}

python/paddle/distributed/passes/auto_parallel_sharding.py

@@ -1787,7 +1787,7 @@ def group_param(sharding_info, fuse_size):
     return group_to_param_map, param_to_group_map
 
 
-class ShardingInfo(object):
+class ShardingInfo:
     def __init__(self, group, rank, params_grads, partition_algor):
         self.group = group
         self.params_grads = dict([(p.name, (p, g)) for p, g in params_grads])
@@ -1869,7 +1869,7 @@ def get_param_grad(self, param_name):
         return self.params_grads.get(param_name, None)
 
 
-class VarGroup(object):
+class VarGroup:
     def __init__(self, max_size):
         self.max_siez = max_size
         self.dtype = None

python/paddle/fluid/tests/unittests/prim/model/bert.py

@@ -35,7 +35,7 @@
 VOCAB_SIZE = 30522
 
 
-class Stack(object):
+class Stack:
     def __init__(self, axis=0, dtype=None):
         self._axis = axis
         self._dtype = dtype

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

@@ -51,7 +51,7 @@ def get_dataset(inputs, config):
     return dataset
 
 
-class Main(object):
+class Main:
     def __init__(self):
         self.metrics = {}
         self.input_data = None

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

@@ -36,7 +36,7 @@
 CUDA_BLOCK_SIZE = 32
 
 
-class CTCForward(object):
+class CTCForward:
     def __init__(
         self,
         softmax,

python/paddle/jit/dy2static/program_translator.py

@@ -1081,7 +1081,7 @@ def _program_hash(self, program):
         return id(program)
 
 
-class FallbackProgramLayer(object):
+class FallbackProgramLayer:
     __slots__ = [
         '_instance',
         '_dy_func',

python/paddle/quantization/config.py

@@ -32,7 +32,7 @@
 DEFAULT_LEAVES = [nn.ReLU, nn.AvgPool2D]
 
 
-class SingleLayerConfig(object):
+class SingleLayerConfig:
     r"""
     Configure how to quantize the activations and weights of a single layer.
 
@@ -57,7 +57,7 @@ def __str__(self):
         return f"activation: {self._activation}\nweight: {self._weight}"
 
 
-class QuantConfig(object):
+class QuantConfig:
     r"""
     Configure how to quantize a model or a part of the model. It will map each layer to
     an instance of SingleLayerConfig by the settings. It provides diverse methods to set

python/paddle/quantization/quantize.py

@@ -25,7 +25,7 @@
 from .config import QuantConfig
 
 
-class Quantization(object, metaclass=abc.ABCMeta):
+class Quantization(metaclass=abc.ABCMeta):
     r"""
     Abstract class used to prepares a copy of the model for quantization calibration or quantization-aware training.
     Args:

tools/parse_kernel_info.py

@@ -15,7 +15,7 @@
 import paddle
 
 
-class KernelInfo(object):
+class KernelInfo:
     def __init__(self, op_type):
         self.op_type = op_type
         self.supported_dtypes = set()
@@ -53,7 +53,7 @@ def parse_fluid_dtypes(self, registered_info_list, device="gpu"):
                 self.supported_dtypes.add(dtype_str)
 
 
-class KernelRegistryStatistics(object):
+class KernelRegistryStatistics:
     def __init__(self):
         self.num_ops_for_dtypes = {
             "all": 0,