[BE] Fix all B022 useless-contextlib-suppress (#100335)

No arguments passed to contextlib.suppress. No exceptions will be suppressed and therefore this context manager is redundant

Pull Request resolved: https://github.com/pytorch/pytorch/pull/100335
Approved by: https://github.com/Skylion007

pyproject.toml

@@ -30,7 +30,6 @@ ignore = [
     "B007", "B008", "B017",
     "B018", # Useless expression
     "B019", "B020",
-    "B022", # Allow empty context manager
     "B023", "B024", "B026",
     "B028", # No explicit `stacklevel` keyword argument found
     "B027", "B904", "B905",

test/distributed/algorithms/test_join.py

@@ -250,7 +250,7 @@ class TestJoin(MultiProcessTestCase):
         with self.assertRaisesRegex(
             RuntimeError,
             expected_msg
-        ) if throw_on_early_termination else contextlib.suppress():
+        ) if throw_on_early_termination else contextlib.nullcontext():
             with Join(
                 allreducers,
                 enable=enable,

test/distributed/fsdp/test_fsdp_checkpoint.py

@@ -257,7 +257,7 @@ class TestFSDPCheckpoint(FSDPTest):
                         offload_ctx = (
                             get_patched_save_on_cpu()(pin_memory=True)
                             if offload_activations
-                            else contextlib.suppress()
+                            else contextlib.nullcontext()
                         )
                         with offload_ctx:
                             out = checkpoint(m, inp, use_reentrant=True)

test/distributed/fsdp/test_fsdp_comm.py

@@ -1,7 +1,7 @@
 # Owner(s): ["oncall: distributed"]
 
 import sys
-from contextlib import suppress
+from contextlib import nullcontext
 from enum import auto, Enum
 from typing import Optional
 from unittest.mock import patch
@@ -76,7 +76,7 @@ class TestCommunication(FSDPTest):
 
     def _run_iter(self, fsdp_model, batch, use_no_sync: bool):
         """Runs an iteration inside or outside the ``no_sync()`` context."""
-        context = fsdp_model.no_sync() if use_no_sync else suppress()
+        context = fsdp_model.no_sync() if use_no_sync else nullcontext()
         with context:
             output = fsdp_model(*batch)
             loss = fsdp_model.module.get_loss(batch, output)

test/distributed/fsdp/test_fsdp_exec_order.py

@@ -2,7 +2,7 @@
 
 import sys
 import warnings
-from contextlib import suppress
+from contextlib import nullcontext
 
 import torch
 from torch import distributed as dist
@@ -159,7 +159,7 @@ class TestFSDPExecOrder(FSDPTest):
                 expected_regex=regex,
             )
             if self.rank != 0
-            else suppress()
+            else nullcontext()
         )
         if self.rank != 0:
             fsdp_model.flip_path()

test/distributed/fsdp/test_fsdp_grad_acc.py

@@ -183,7 +183,7 @@ class TestGradAcc(FSDPTest):
         batch_idx = 0
         for config in configs:
             sync_context = (
-                fsdp_model.no_sync() if config.use_no_sync else contextlib.suppress()
+                fsdp_model.no_sync() if config.use_no_sync else contextlib.nullcontext()
             )
             with sync_context:
                 for _ in range(config.num_iters):

test/distributed/fsdp/test_fsdp_misc.py

@@ -5,7 +5,7 @@ import os
 import sys
 import warnings
 from collections import namedtuple
-from contextlib import suppress
+from contextlib import nullcontext
 from copy import deepcopy
 from typing import Any, Tuple
 
@@ -371,7 +371,7 @@ class TestFSDPMisc(FSDPTest):
         context = (
             self.assertRaisesRegex(ValueError, f"cuda:{self.rank} vs cuda:0")
             if self.rank != 0
-            else suppress()
+            else nullcontext()
         )
         with context:
             NestedWrappedModule.init(
@@ -427,7 +427,7 @@ class TestFSDPMisc(FSDPTest):
                 )
             )
             if self.rank != 0
-            else suppress()
+            else nullcontext()
         )
         with context:
             module = FSDP(no_params, device_id=0)

test/distributed/fsdp/test_fsdp_state_dict.py

@@ -3,7 +3,7 @@
 import io
 import itertools
 import sys
-from contextlib import suppress
+from contextlib import nullcontext
 from copy import deepcopy
 from functools import partial
 from typing import Any, Dict
@@ -899,7 +899,7 @@ class TestFSDPStateDict(FSDPTest):
 
         def _create_module(wrap_fsdp=True):
             LINEAR_SKIP = "linear_skip"
-            ctx = enable_wrap(wrapper_cls=FSDP) if wrap_fsdp else suppress()
+            ctx = enable_wrap(wrapper_cls=FSDP) if wrap_fsdp else nullcontext()
             with ctx:
                 module = SkipModel(double_nest=double_nest)
                 # Full name of linear_skip param tensors in SkipModel, as would be

test/distributed/fsdp/test_fsdp_unshard_params.py

@@ -522,7 +522,7 @@ class TestUnshardParams(TestUnshardParamsBase):
 
         def _get_error_context(is_supported: bool):
             return (
-                contextlib.suppress()
+                contextlib.nullcontext()
                 if is_supported
                 else self.assertRaises(NotImplementedError)
             )  # some configs are not implemented yet

test/distributed/optim/test_zero_redundancy_optimizer.py

@@ -9,7 +9,7 @@ import copy
 import os
 import sys
 import unittest
-from contextlib import suppress
+from contextlib import nullcontext
 from typing import Any, cast, List
 
 import numpy as np
@@ -301,7 +301,7 @@ class TestZeroRedundancyOptimizerSingleRank(TestZeroRedundancyOptimizer):
             (list(m.parameters()), None),  # `params` as a list
         ]
         for ctor_input, error in ctor_inputs:
-            context = self.assertRaises(error) if error else suppress()
+            context = self.assertRaises(error) if error else nullcontext()
             with context:
                 ZeroRedundancyOptimizer(
                     ctor_input,
@@ -371,7 +371,7 @@ class TestZeroRedundancyOptimizerDistributed(TestZeroRedundancyOptimizer):
     @property
     def context(self):
         return (
-            suppress()
+            nullcontext()
             if not torch.cuda.is_available()
             else torch.cuda.device(self.rank)
         )

test/distributed/test_c10d_common.py

@@ -7,7 +7,7 @@ import sys
 import tempfile
 import threading
 import time
-from contextlib import suppress
+from contextlib import nullcontext
 from dataclasses import dataclass
 from datetime import timedelta
 from itertools import product
@@ -545,7 +545,7 @@ class CommonDistributedDataParallelTest:
         process_group = self._get_process_group()
         for use_bucket_view in (True, False):
             err_ctx = (
-                suppress() if not use_reentrant else
+                nullcontext() if not use_reentrant else
                 self.assertRaisesRegex(
                     RuntimeError,
                     "Expected to mark a variable ready only once."
@@ -577,7 +577,7 @@ class CommonDistributedDataParallelTest:
         process_group = self._get_process_group()
         for use_bucket_view in (True, False):
             err_ctx = (
-                suppress() if not use_reentrant else
+                nullcontext() if not use_reentrant else
                 self.assertRaisesRegex(
                     RuntimeError,
                     "Expected to mark a variable ready only once."
@@ -678,7 +678,7 @@ class CommonDistributedDataParallelTest:
                 self.assertRaisesRegex(
                     RuntimeError,
                     "Your training graph has changed in this iteration"
-                ) if static_graph and not use_reentrant else suppress()
+                ) if static_graph and not use_reentrant else nullcontext()
             )
             with err_ctx:
                 self._test_ddp_checkpointing(

test/test_autograd.py

@@ -5739,7 +5739,7 @@ for shape in [(1,), ()]:
                 "none of output has requires_grad=True"
             )
             if use_reentrant
-            else contextlib.suppress()
+            else contextlib.nullcontext()
         )
 
         a = torch.randn(2, 2, requires_grad=True)

torch/distributed/fsdp/fully_sharded_data_parallel.py

@@ -899,7 +899,7 @@ class FullyShardedDataParallel(nn.Module, _FSDPState):
         context = (
             self._deregister_orig_params_ctx()
             if self._use_orig_params
-            else contextlib.suppress()
+            else contextlib.nullcontext()
         )
         with context:
             return super()._apply(*args, **kwargs)

torch/distributed/rpc/api.py

@@ -916,7 +916,7 @@ def _get_should_profile():
 
 
 def _enable_rpc_profiler(should_profile, qualified_name, func, rpc_type, dst_worker_info):
-    ctx_manager = contextlib.suppress()
+    ctx_manager = contextlib.nullcontext()
 
     if should_profile:
         # Create appropriate string representation based on type of func

torch/testing/_internal/common_fsdp.py

@@ -5,7 +5,7 @@ import os
 import re
 import sys
 from abc import ABC, abstractmethod
-from contextlib import suppress
+from contextlib import nullcontext
 from copy import deepcopy
 from enum import auto, Enum
 from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
@@ -117,7 +117,7 @@ def _zero_model(
     summon_full=True,
 ):
     """Zeros the parameters and optionally buffers of ``model`` in place."""
-    ctx = FSDP.summon_full_params(model) if summon_full else suppress()
+    ctx = FSDP.summon_full_params(model) if summon_full else nullcontext()
     with ctx:
         for param in model.parameters():
             with torch.no_grad():
@@ -1121,7 +1121,7 @@ class FSDPTest(MultiProcessTestCase):
                 "has parameters on cuda",
             )
             if expects_device_error
-            else suppress()
+            else nullcontext()
         )
         with context:
             fsdp_loss = self._train_for_several_steps(

torch/testing/_internal/distributed/ddp_under_dist_autograd_test.py

@@ -251,7 +251,7 @@ class Trainer:
             else:
                 input_batches = batches
 
-        with self.hybrid_module.join() if simulate_uneven_inputs else contextlib.suppress():
+        with self.hybrid_module.join() if simulate_uneven_inputs else contextlib.nullcontext():
             for b in input_batches:
                 with dist_autograd.context() as context_id:
                     output = self.hybrid_module.forward(b)

torch/testing/_internal/distributed/distributed_test.py

@@ -7,7 +7,7 @@ import sys
 import tempfile
 import time
 from collections import namedtuple, OrderedDict
-from contextlib import contextmanager, suppress
+from contextlib import contextmanager, nullcontext
 from datetime import timedelta
 from functools import reduce
 from typing import Union, NamedTuple, Callable, Any
@@ -1559,7 +1559,7 @@ class DistributedTest:
             torch.cuda.set_device(device_id)
 
             tensor = _build_tensor(rank + 1, device_id=device_id)
-            profiler_cls = profiler_ctx if profiler_ctx is not None else suppress()
+            profiler_cls = profiler_ctx if profiler_ctx is not None else nullcontext()
             with profiler_cls as prof:
                 for src in range(0, world_size):
                     if src == rank:
@@ -1629,7 +1629,7 @@ class DistributedTest:
             rank = dist.get_rank()
             send_size = rank + 1
             tensor = _build_tensor(send_size)
-            ctx = profiler_ctx if profiler_ctx is not None else suppress()
+            ctx = profiler_ctx if profiler_ctx is not None else nullcontext()
             with ctx as prof:
                 for src in range(0, dist.get_world_size()):
                     if src == rank:
@@ -1697,7 +1697,7 @@ class DistributedTest:
             recv_ranks = list()
             irecv_ranks = list()
 
-            ctx = profiler_ctx if profiler_ctx is not None else suppress()
+            ctx = profiler_ctx if profiler_ctx is not None else nullcontext()
             with ctx as prof:
                 for dst in range(0, dist.get_world_size()):
                     if dst == rank:
@@ -1801,7 +1801,7 @@ class DistributedTest:
             world_size = dist.get_world_size()
             send_recv_size = 10
             tensor = _build_tensor(send_recv_size, value=rank)
-            ctx = profiler_ctx if profiler_ctx is not None else suppress()
+            ctx = profiler_ctx if profiler_ctx is not None else nullcontext()
             with ctx as prof:
                 for dst in range(0, world_size):
                     if dst == rank:
@@ -1859,7 +1859,7 @@ class DistributedTest:
         def _test_isend(self, profiler_ctx):
             rank = dist.get_rank()
             world_size = dist.get_world_size()
-            ctx = profiler_ctx if profiler_ctx is not None else suppress()
+            ctx = profiler_ctx if profiler_ctx is not None else nullcontext()
             with ctx as prof:
                 if rank == 0:
                     requests = [
@@ -7262,7 +7262,7 @@ class DistributedTest:
                         "Detected at least one rank that exhausted inputs.",
                     )
             else:
-                exception_ctx = suppress()
+                exception_ctx = nullcontext()
             with exception_ctx:
                 with net.join(
                     throw_on_early_termination=test_case.throw_on_early_termination
@@ -7273,7 +7273,7 @@ class DistributedTest:
                         if i % sync_interval != 0:
                             context = net.no_sync()
                         else:
-                            context = suppress()
+                            context = nullcontext()
                         with context:
                             if isinstance(inp, tuple):
                                 loss = net(*inp).sum()

torch/testing/_internal/distributed/rpc/rpc_test.py

@@ -59,11 +59,11 @@ def foo_add():
     return torch.add(torch.ones(1), torch.ones(1))
 
 def udf_with_torch_ops(device=-1, use_record_function=False):
-    device_ctx = contextlib.suppress() if device == -1 else torch.cuda.device(device)
+    device_ctx = contextlib.nullcontext() if device == -1 else torch.cuda.device(device)
     record_function_ctx = (
         torch.autograd.profiler.record_function("##forward##")
         if use_record_function
-        else contextlib.suppress()
+        else contextlib.nullcontext()
     )
     with device_ctx, record_function_ctx:
         t1, t2 = torch.ones(1), torch.ones(1)
@@ -2164,7 +2164,7 @@ class RpcTest(RpcAgentTestFixture, RpcTestCommon):
         if self.rank == 1:
             with p() as prof:
                 record_function_ctx_mgr = (
-                    contextlib.suppress()
+                    contextlib.nullcontext()
                     if not use_record_function
                     else torch.autograd.profiler.record_function(
                         "foo"