[BE]: pyupgrade Python to 3.8 - imports and object inheritance only (p…

…ytorch#94308)

Apply parts of pyupgrade to torch (starting with the safest changes).
This PR only does two things: removes the need to inherit from object and removes unused future imports.

Pull Request resolved: pytorch#94308
Approved by: https://github.com/ezyang, https://github.com/albanD

benchmarks/distributed/ddp/benchmark.py

@@ -151,7 +151,7 @@ def print_measurements(prefix, nelem, measurements):
     return results
 
 
-class Benchmark(object):
+class Benchmark:
     def __init__(self, device, distributed_backend, bucket_size):
         self.device = device
         self.batch_size = 32

benchmarks/fastrnns/bench.py

@@ -44,7 +44,7 @@ def pretty_print(benchresult, colwidth=16, sep=' '):
     return sep.join(items)
 
 # shim for torch.cuda.Event when running on cpu
-class Event(object):
+class Event:
     def __init__(self, enable_timing):
         pass
 

benchmarks/framework_overhead_benchmark/C2Module.py

@@ -9,7 +9,7 @@ def add_blob(ws, blob_name, tensor_size):
     blob_tensor = np.random.randn(*tensor_size).astype(np.float32)
     ws.FeedBlob(blob_name, blob_tensor)
 
-class C2SimpleNet(object):
+class C2SimpleNet:
     """
     This module constructs a net with 'op_name' operator. The net consist
     a series of such operator.

benchmarks/framework_overhead_benchmark/pt_wrapper_module.py

@@ -1,6 +1,6 @@
 import torch
 
-class WrapperModule(object):
+class WrapperModule:
     """ Wraps the instance of wrapped_type.
     For graph_mode traces the instance of wrapped_type.
     Randomaly initializes num_params tensors with single float element.

benchmarks/operator_benchmark/benchmark_caffe2.py

@@ -12,7 +12,7 @@
 """
 
 
-class Caffe2BenchmarkBase(object):
+class Caffe2BenchmarkBase:
     """ This is a base class used to create Caffe2 operator benchmark
     """
     tensor_index = 0
@@ -103,7 +103,7 @@ def extract_inputs_tuple(self):
         pass
 
 
-class Caffe2OperatorTestCase(object):
+class Caffe2OperatorTestCase:
     """ This class includes all the information needed to benchmark an operator.
         op_bench: it's a user-defined class (child of Caffe2BenchmarkBase)
         which includes input and operator, .etc

benchmarks/operator_benchmark/benchmark_core.py

@@ -150,7 +150,7 @@ def _build_test(configs, bench_op, OperatorTestCase, run_backward, op_name_funct
             yield _create_test(new_op, test_attrs, tags, OperatorTestCase, run_backward, input_name)
 
 
-class BenchmarkRunner(object):
+class BenchmarkRunner:
     """BenchmarkRunner is responsible for benchmarking all the registered
     benchmark test groups.
 

benchmarks/operator_benchmark/benchmark_pytorch.py

@@ -100,7 +100,7 @@ def test_name(self, **kargs):
         return name
 
 
-class PyTorchOperatorTestCase(object):
+class PyTorchOperatorTestCase:
     """ This class includes all the information needed to benchmark an operator.
         op_bench: it's a user-defined class (child of TorchBenchmarkBase)
         which includes input and operator, .etc

benchmarks/operator_benchmark/benchmark_utils.py

@@ -185,7 +185,7 @@ def attr_probs(**probs):
     return probs
 
 
-class RandomSample(object):
+class RandomSample:
 
     def __init__(self, configs):
         self.saved_cum_distribution = {}

benchmarks/sparse/utils.py

@@ -6,7 +6,7 @@
 import time
 
 # shim for torch.cuda.Event when running on cpu
-class Event(object):
+class Event:
     def __init__(self, enable_timing):
         pass
 

benchmarks/tensorexpr/benchmark.py

@@ -7,7 +7,7 @@
 import json
 
 
-class Benchmark(object):
+class Benchmark:
     def __init__(self, mode, device, dtype):
         self.mode = mode
         self.deterministic = False
@@ -238,7 +238,7 @@ def cuda_pointwise_context(loop_levels, block_count, block_size):
         torch._C._jit_set_te_cuda_pointwise_block_size(old_block_size)
 
 # Auxiliary class to facilitate dynamic input shape
-class DynamicShape(object):
+class DynamicShape:
     r'''
     An Auxiliary class for dynamic shape benchmarks
 

benchmarks/tensorexpr/microbenchmarks.py

@@ -7,7 +7,7 @@
 import seaborn as sns
 import argparse
 
-class kernel_arena_scope(object):
+class kernel_arena_scope:
     def __enter__(self):
         self.scope = te.KernelScope()
 

benchmarks/tensorexpr/pt_engine.py

@@ -1,7 +1,7 @@
 import torch
 
 
-class TorchTensorEngine(object):
+class TorchTensorEngine:
     def rand(self, shape, device=None, dtype=None, requires_grad=False):
         return torch.rand(shape, device=device, dtype=dtype, requires_grad=requires_grad)
 

caffe2/contrib/playground/AnyExp.py

@@ -76,7 +76,7 @@ def initialize_params_from_file(*args, **kwargs):
     return checkpoint.initialize_params_from_file(*args, **kwargs)
 
 
-class AnyExpTrainer(object):
+class AnyExpTrainer:
 
     def __init__(self, opts):
         import logging

caffe2/contrib/playground/meter.py

@@ -6,7 +6,7 @@
 from abc import abstractmethod
 
 
-class Meter(object):
+class Meter:
 
     @abstractmethod
     def __init__(self, **kwargs):

caffe2/contrib/tensorboard/tensorboard.py

@@ -28,7 +28,7 @@
         # tensorflow<=0.12.1
         from tensorflow.train import SummaryWriter as FileWriter
 
-class Config(object):
+class Config:
     HEIGHT = 600
     ASPECT_RATIO = 1.6
 

caffe2/distributed/store_ops_test_util.py

@@ -12,7 +12,7 @@
 from caffe2.python import core, workspace
 
 
-class StoreOpsTests(object):
+class StoreOpsTests:
     @classmethod
     def _test_set_get(cls, queue, create_store_handler_fn, index, num_procs):
         store_handler = create_store_handler_fn()

caffe2/experiments/python/device_reduce_sum_bench.py

@@ -47,7 +47,7 @@ def __new__(metacls, name, bases, class_dict):
 
 
 @add_metaclass(BenchmarkMeta)
-class Benchmark(object):
+class Benchmark:
 
     def __init__(self):
         self.results = []

caffe2/python/binarysize.py

@@ -24,7 +24,7 @@
 import sys
 
 
-class Trie(object):
+class Trie:
     """A simple class that represents a Trie."""
 
     def __init__(self, name):

caffe2/python/caffe_translator.py

@@ -192,7 +192,7 @@ def _GetInputDims(caffe_net):
     return input_dims
 
 
-class TranslatorRegistry(object):
+class TranslatorRegistry:
     registry_ = {}
 
     @classmethod

caffe2/python/checkpoint.py

@@ -146,7 +146,7 @@ def db_name(epoch, node_name, db_prefix, path_prefix=None):
     return db_name
 
 
-class CheckpointManager(object):
+class CheckpointManager:
     """
     Controls saving and loading of workspaces on every epoch boundary of a job.
     If a CheckpointManager instance is passed to JobRunner, then JobRunner will
@@ -429,7 +429,7 @@ def cp_accessible(self, epoch=None):
             return True
 
 
-class MultiNodeCheckpointManager(object):
+class MultiNodeCheckpointManager:
     """
     Coordinates checkpointing and checkpointing across multiple nodes.
     Each of `init`, `load` and `save` will build TaskGroups which will
@@ -634,7 +634,7 @@ def cp_accessible(self, epoch=None):
             return True
 
 
-class UploadTaskGroupBuilder(object):
+class UploadTaskGroupBuilder:
     """A simple class to upload checkpoints."""
     def build(self, epoch, checkpoint_manager):
         """Builds the task group to upload checkpoints.
@@ -652,7 +652,7 @@ def build(self, epoch, checkpoint_manager):
         raise NotImplementedError()
 
 
-class JobRunner(object):
+class JobRunner:
     """
     Implement the runtime logic for jobs with checkpointing at the level of
     epoch. Can be used to run either single-host or distributed jobs. Job

caffe2/python/context.py

@@ -6,7 +6,7 @@
 import functools
 
 
-class _ContextInfo(object):
+class _ContextInfo:
     def __init__(self, cls, allow_default):
         self.cls = cls
         self.allow_default = allow_default
@@ -35,7 +35,7 @@ def get_active(self, required=True):
         return self._stack[-1]
 
 
-class _ContextRegistry(object):
+class _ContextRegistry:
     def __init__(self):
         self._ctxs = {}
 
@@ -62,7 +62,7 @@ def _get_managed_classes(obj):
 
 
 
-class Managed(object):
+class Managed:
     """
     Managed makes the inheritted class a context managed class.
 

caffe2/python/core.py

@@ -200,7 +200,7 @@ def InferOpDeviceAsBlobDevices(op):
 GradientSlice = namedtuple('GradientSlice', ['indices', 'values'])
 
 
-class BlobReference(object):
+class BlobReference:
     """A wrapper around a blob in a net.
 
     BlobReference gives us a way to refer to the network that the blob is
@@ -485,7 +485,7 @@ def GetIndexFromGradientList(g_list, name):
 ])
 
 
-class IR(object):
+class IR:
     """A simple IR class to keep track of all intermediate representations used
     in the gradient computation.
     """
@@ -1103,7 +1103,7 @@ def GetBackwardPass(self, ys):
         return all_gradient_ops, all_input_to_grad_out
 
 
-class GradientRegistry(object):
+class GradientRegistry:
     """GradientRegistry holds the mapping from operators to their gradients."""
     gradient_registry_ = {}
 
@@ -1444,7 +1444,7 @@ def _recover_record_by_prefix(names, prefix=''):
         col_blobs=[_get_blob_ref(prefix + name) for name in column_names])
 
 
-class Net(object):
+class Net:
     _net_names_used = set()
     operator_registry_ = {}
 
@@ -2666,7 +2666,7 @@ def _add_net_to_dict(net_dict, net):
         return True
 
 
-class ExecutionStep(object):
+class ExecutionStep:
     _step_names_used = set()
 
     @staticmethod
@@ -2872,7 +2872,7 @@ def add_nets_in_order(step, net_list):
         net_list.append(proto.report_net)
 
 
-class Plan(object):
+class Plan:
 
     def __init__(self, name_or_step):
         self._plan = caffe2_pb2.PlanDef()

caffe2/python/crf.py

@@ -13,7 +13,7 @@
 """
 
 
-class CRFWithLoss(object):
+class CRFWithLoss:
     def __init__(self, model, num_classes, transitions_blob=None):
         self.model = model
         self.num_classes = num_classes

caffe2/python/data_parallel_model.py

@@ -1304,7 +1304,7 @@ def modify_ops(net):
     modify_ops(model.net)
 
 
-class CollectivesConcurrencyControl(object):
+class CollectivesConcurrencyControl:
     """
     Creates common worlds (up to max_concurrent_context) and manage the
     sequential execution of collectives that shares the same context with

caffe2/python/dataio.py

@@ -26,7 +26,7 @@
 import time
 
 
-class Reader(object):
+class Reader:
     """
     Reader is an abstract class to be implemented in order to provide
     operations capable of iterating through a dataset or stream of data.
@@ -143,7 +143,7 @@ def execution_step(self, reader_net_name=None, external_should_stop=None):
         return (read_step, fields)
 
 
-class Writer(object):
+class Writer:
     """
     Writer is an abstract class to be implemented in order to provide
     operations capable of feeding a data stream or a dataset.
@@ -207,7 +207,7 @@ def commit(self, finish_net):
         pass
 
 
-class ReaderBuilder(object):
+class ReaderBuilder:
     """ Allow usage of a reader in distributed fashion. """
     def schema(self):
         raise NotImplementedError()
@@ -256,7 +256,7 @@ def new_reader(self, **kwargs):
         return output if isinstance(output, Reader) else output.reader()
 
 
-class Pipe(object):
+class Pipe:
     def __init__(self, schema=None, obj_key=None):
         self._num_writers = 0
         self._num_readers = 0

caffe2/python/dataset.py

@@ -182,7 +182,7 @@ def execution_step_with_progress(name, init_net, substeps, rows_read):
         report_interval=5)
 
 
-class Dataset(object):
+class Dataset:
     """Represents an in-memory dataset with fixed schema.
 
     Use this to store and iterate through datasets with complex schema that

caffe2/python/device_checker.py

@@ -6,7 +6,7 @@
 from caffe2.python.core import InferOpBlobDevicesAsDict
 
 
-class DeviceChecker(object):
+class DeviceChecker:
     """A device checker in Python to check consistency across multiple devices.
 
     This is not the most efficient way to check devices, as the Python interface

caffe2/python/docs/formatter.py

@@ -7,7 +7,7 @@
 from caffe2.python.docs.parser import Parser
 
 
-class Formatter(object):
+class Formatter:
     def __init__(self):
         self.content = ""