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[BE] Enable C419 rule for any all shortcircuiting (#99890)

Browse Source 
Apparently https://github.com/pytorch/pytorch/pull/78142 made torch.JIT allow for simple generator expressions which allows us to enable rules that replace unnecessary list comprehensions with generators in any/all. This was originally part of #99280 but I split it off into this PR so that it can be easily reverted should anything break.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/99890
Approved by: https://github.com/justinchuby, https://github.com/kit1980, https://github.com/malfet
This commit is contained in:
Aaron Gokaslan 2023-04-25 15:02:13 +00:00 committed by PyTorch MergeBot
parent e43918b93a
commit e2a3817dfd
83 changed files with 175 additions and 203 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
.ci/pytorch/print_sccache_log.py
View File

@ -13,5 +13,5 @@ for line in lines:
'CheckSymbolExists.c',
'test_compilation_error_formatting',
]
if all([keyword not in line for keyword in ignored_keywords]):
if all(keyword not in line for keyword in ignored_keywords):
print(line)

2
.flake8
View File

@ -16,7 +16,7 @@ ignore =
# these ignores are from flake8-bugbear; please fix!
B007,B008,B017,B019,B020,B023,B024,B026,B027,B028,B903,B904,B905,B906,B907
# these ignores are from flake8-comprehensions; please fix!
C407,C419,
C407,
# these ignores are from flake8-logging-format; please fix!
G100,G101,G200,G201,G202
# these ignores are from flake8-simplify. please fix or ignore with commented reason

2
.github/scripts/test_trymerge.py vendored
View File

@ -364,7 +364,7 @@ class TestTryMerge(TestCase):
lint_checks = [name for name in conclusions.keys() if "Lint" in name]
self.assertTrue(len(lint_checks) > 0)
self.assertTrue(
all([conclusions[name].status == "SUCCESS" for name in lint_checks])
all(conclusions[name].status == "SUCCESS" for name in lint_checks)
)
@mock.patch("trymerge.gh_get_pr_info", return_value=mock_gh_get_info())

11
.github/scripts/trymerge.py vendored
View File

@ -82,10 +82,7 @@ class FlakyRule:
and self.name in job.get("name", "")
and job.get("failure_captures") is not None
and all(
[
capture in job.get("failure_captures", [])
for capture in self.captures
]
capture in job.get("failure_captures", []) for capture in self.captures
)
)
@ -1575,7 +1572,7 @@ def get_classifications(
checks_with_classifications[name] = JobCheckState(
check.name, check.url, check.status, "BROKEN_TRUNK", check.job_id
)
elif any([rule.matches(head_sha_job) for rule in flaky_rules]):
elif any(rule.matches(head_sha_job) for rule in flaky_rules):
checks_with_classifications[name] = JobCheckState(
check.name, check.url, check.status, "FLAKY", check.job_id
)
@ -1710,11 +1707,11 @@ def categorize_checks(
relevant_checknames = [
name
for name in check_runs.keys()
if not required_checks or any([x in name for x in required_checks])
if not required_checks or any(x in name for x in required_checks)
]
for checkname in required_checks:
if all([checkname not in x for x in check_runs.keys()]):
if all(checkname not in x for x in check_runs.keys()):
pending_checks.append((checkname, None, None))
for checkname in relevant_checknames:

2
benchmarks/dynamo/runner.py
View File

@ -1004,7 +1004,7 @@ def find_last_2_with_filenames(lookup_file, dashboard_archive_path, dtype, filen
fullpaths = [
os.path.join(dashboard_archive_path, path, name) for name in filenames
]
if all([os.path.exists(fullpath) for fullpath in fullpaths]):
if all(os.path.exists(fullpath) for fullpath in fullpaths):
last2.append(output_dir)
if len(last2) >= 2:
return last2

2
benchmarks/operator_benchmark/pt_extension/cpp_extension_test.py
View File

@ -40,6 +40,6 @@ class TestConsumeOp(unittest.TestCase):
value = r(x)
self.assertTrue(
all([torch.allclose(t1, t2) for t1, t2 in zip(value, torch.chunk(x, 2))])
all(torch.allclose(t1, t2) for t1, t2 in zip(value, torch.chunk(x, 2)))
)
self.assertEqual(occurance, iters)

4
functorch/experimental/_cond.py
View File

@ -126,8 +126,8 @@ def cond_dense(pred, true_fn, false_fn, operands):
def cond_autograd(pred, true_fn, false_fn, *operands):
# TODO: support autograd
flat_operands, _ = tree_flatten([true_fn, false_fn] + [operands])
assert all([not f.requires_grad for f in flat_operands
if isinstance(f, torch.Tensor)])
assert all(not f.requires_grad for f in flat_operands
if isinstance(f, torch.Tensor))
guard = ExcludeDispatchKeyGuard(DispatchKeySet(DispatchKey.AutogradCPU))
return cond(pred, true_fn, false_fn, *operands)

4
functorch/experimental/_map.py
View File

@ -70,8 +70,8 @@ def map_cpu(f, xs, *args):
def map_autograd(f, xs, *args):
# TODO: support autograd
flat_operands, _ = tree_flatten([f, xs, args])
assert all([not f.requires_grad for f in flat_operands
if isinstance(f, torch.Tensor)])
assert all(not f.requires_grad for f in flat_operands
if isinstance(f, torch.Tensor))
_ = ExcludeDispatchKeyGuard(DispatchKeySet(DispatchKey.AutogradCPU))
return map(f, xs, *args)

1
pyproject.toml
View File

@ -36,7 +36,6 @@ ignore = [
"B027", "B904", "B905",
"E402",
"C408", # C408 ignored because we like the dict keyword argument syntax
"C419", # generators may not be supported by jit
"E501", # E501 is not flexible enough, we're using B950 instead
"E721",
"E731", # Assign lambda expression

2
scripts/release_notes/commitlist.py
View File

@ -265,7 +265,7 @@ class CommitList:
else:
# Below are some extra quick checks that aren't necessarily file-path related,
# but I found that to catch a decent number of extra commits.
if len(files_changed) > 0 and all([f_name.endswith(('.cu', '.cuh')) for f_name in files_changed]):
if len(files_changed) > 0 and all(f_name.endswith(('.cu', '.cuh')) for f_name in files_changed):
category = 'cuda'
elif '[PyTorch Edge]' in title:
category = 'mobile'

2
test/distributed/fsdp/test_fsdp_state_dict.py
View File

@ -134,7 +134,7 @@ class TestFSDPStateDict(FSDPTest):
self._state_compare(model, model_new, assert_fn)
if check_buffers:
has_buffers = any(
[len(list(m.buffers())) for m in (model, model_new)]
len(list(m.buffers())) for m in (model, model_new)
)
if has_buffers:
self._state_compare(

2
test/distributed/test_dynamo_distributed.py
View File

@ -406,7 +406,7 @@ class TestSingleProc(DynamoDistributedSingleProcTestCase):
explain_out = torch._dynamo.explain(ddp_m, inputs)
break_reasons = explain_out[4]
self.assertEqual(len(break_reasons), 3)
self.assertTrue(all(["DDPOptimizer" in r.reason for r in break_reasons]))
self.assertTrue(all("DDPOptimizer" in r.reason for r in break_reasons))
@patch.object(config, "optimize_ddp", True)
@unittest.skipIf(not has_triton(), "Inductor+gpu needs triton and recent GPU arch")

2
test/dynamo/test_modules.py
View File

@ -554,7 +554,7 @@ class LazyModuleWithListInput(torch.nn.Module):
def requires_grad1(module: torch.nn.Module, recurse: bool = False) -> bool:
requires_grad = any([p.requires_grad for p in module.parameters(recurse)])
requires_grad = any(p.requires_grad for p in module.parameters(recurse))
return requires_grad

2
test/dynamo/test_repros.py
View File

@ -138,7 +138,7 @@ def shapes_to_tensor(x, device=None):
return torch.as_tensor(x, device=device)
if torch.jit.is_tracing():
assert all(
[isinstance(t, torch.Tensor) for t in x]
isinstance(t, torch.Tensor) for t in x
), "Shape should be tensor during tracing!"
# as_tensor should not be used in tracing because it records a constant
ret = torch.stack(x)

4
test/functorch/discover_coverage.py
View File

@ -662,12 +662,12 @@ class Operator:
def any_opinfo_attr(self, attr):
if not self.has_opinfo():
raise RuntimeError()
return any([getattr(opinfo, attr) for opinfo in self.opinfos])
return any(getattr(opinfo, attr) for opinfo in self.opinfos)
def all_opinfo_attr(self, attr):
if not self.has_opinfo():
raise RuntimeError()
return all([getattr(opinfo, attr) for opinfo in self.opinfos])
return all(getattr(opinfo, attr) for opinfo in self.opinfos)
def supports_vjp(self):
if self.name in FACTORY_FNS:

6
test/functorch/test_aotdispatch.py
View File

@ -2030,7 +2030,7 @@ class TestPartitioning(AOTTestCase):
self.assertEqual(get_num_ins_outs(fw_graph), (3, 4))
self.assertEqual(get_num_ins_outs(bw_graph), (4, 3))
_, outs = get_ins_outs(fw_graph)
self.assertTrue(all([is_sym_node(n) for n in outs[1:]]))
self.assertTrue(all(is_sym_node(n) for n in outs[1:]))
def test_default_partitioner_output_tensor_shape_tensor(self):
@ -2648,7 +2648,7 @@ def _test_aot_autograd_forwards_backwards_helper(self, f, compiled_f, args):
reset_grads()
# See https://github.com/pytorch/pytorch/pull/98960#issuecomment-1505962215
if all([x is None for x in orig_grad]):
if all(x is None for x in orig_grad):
with self.assertRaisesRegex(RuntimeError, 'does not require grad and does not have a grad_fn'):
call_forwards_backwards(compiled_f)
else:
@ -2669,7 +2669,7 @@ def _test_aot_autograd_forwards_backwards_helper(self, f, compiled_f, args):
reset_grads()
# See https://github.com/pytorch/pytorch/pull/98960#issuecomment-1505962215
if all([x is None for x in orig_grad]):
if all(x is None for x in orig_grad):
with self.assertRaisesRegex(RuntimeError, 'does not require grad and does not have a grad_fn'):
call_forwards_backwards(compiled_f)
else:

4
test/functorch/test_control_flow.py
View File

@ -131,7 +131,7 @@ class TestControlFlowTraced(TestCase):
if node.op == "call_function":
all_ops_in_true_branch.append(node.target)
self.assertFalse(any([op._schema.is_mutable for op in all_ops_in_true_branch]))
self.assertFalse(any(op._schema.is_mutable for op in all_ops_in_true_branch))
graph_module = make_fx(torch.func.functionalize(f), tracing_mode="symbolic")(*example_inputs)
self.assertEqual(graph_module(*example_inputs), f(*example_inputs))
@ -188,7 +188,7 @@ class TestControlFlowTraced(TestCase):
if node.op == "call_function":
all_ops.append(node.target)
self.assertFalse(any([op._schema.is_mutable for op in all_ops]))
self.assertFalse(any(op._schema.is_mutable for op in all_ops))
def test_cond_functionalized_data_dependent_pred(self):
def true_fn(x):

4
test/functorch/test_ops.py
View File

@ -101,9 +101,9 @@ def diff_arg(arg, requires_grad=True):
else:
return arg.is_floating_point() or arg.is_complex()
if is_iterable_of_tensors(arg):
if all([is_differentiable_arg(a) for a in arg]):
if all(is_differentiable_arg(a) for a in arg):
return True
if all([not is_differentiable_arg(a) for a in arg]):
if all(not is_differentiable_arg(a) for a in arg):
return False
raise RuntimeError("NYI: The test runner can't handle this")
return isinstance(arg, Tensor) and is_differentiable_arg(arg)

2
test/inductor/test_torchinductor.py
View File

@ -339,7 +339,7 @@ def check_model(
correct_grad = compute_grads(ref_inputs, ref_kwargs, correct, grads)
flat_grads, _ = tree_flatten(correct_grad)
all_none_grads = all([x is None for x in flat_grads])
all_none_grads = all(x is None for x in flat_grads)
if all_none_grads:
# See Note [Detaching inputs that never need gradients]
# There are a handful of ops that can return None gradients, into of zero gradients.

10
test/profiler/test_profiler.py
View File

@ -106,7 +106,7 @@ class TestProfilerCUDA(TestCase):
# with CUDA events leaking the increase in memory was ~7 MB between
# profiler invocations above
is_increasing = all(
[last_rss[idx] > last_rss[idx - 1] for idx in range(1, len(last_rss))])
last_rss[idx] > last_rss[idx - 1] for idx in range(1, len(last_rss)))
max_diff = -1
for idx in range(1, len(last_rss)):
max_diff = max(max_diff, last_rss[idx] - last_rss[idx - 1])
@ -530,11 +530,11 @@ class TestProfiler(TestCase):
for e in p.function_events:
if "aten::add" in e.name or "AddBackward" in e.name:
self.assertTrue(any(["test_profiler" in entry for entry in e.stack]))
self.assertTrue(any([(
self.assertTrue(any("test_profiler" in entry for entry in e.stack))
self.assertTrue(any((
"test_source" in entry or
"ts_method_1" in entry or
"ts_method_2" in entry) for entry in e.stack]))
"ts_method_2" in entry) for entry in e.stack))
# TODO: https://github.com/pytorch/kineto/issues/617
if kineto_available() and not IS_WINDOWS:
@ -1429,7 +1429,7 @@ class TestProfiler(TestCase):
f_ts_1 = flow_f_to_ts[1]
s_ts_2 = flow_s_to_ts[2]
f_ts_2 = flow_f_to_ts[2]
self.assertTrue(all([ts in ts_to_name.keys() for ts in [s_ts_1, f_ts_1, s_ts_2, f_ts_2]]))
self.assertTrue(all(ts in ts_to_name.keys() for ts in [s_ts_1, f_ts_1, s_ts_2, f_ts_2]))
self.assertTrue(ts_to_name[s_ts_1] == "aten::binary_cross_entropy_with_logits")
self.assertTrue(ts_to_name[s_ts_2] == "aten::add")

4
test/quantization/core/test_quantized_op.py
View File

@ -2218,7 +2218,7 @@ class TestQuantizedOps(TestCase):
test_cases = itertools.product(scale_list, zero_point_list, shapes, dtypes, dims)
op = torch.mean
for scale, zp, shape, dtype, dim in test_cases:
if not all([d < len(shape) for d in dim]):
if not all(d < len(shape) for d in dim):
continue
X = torch.randn(*shape) * 10
qX = torch.quantize_per_tensor(X, scale, zp, dtype)
@ -2257,7 +2257,7 @@ class TestQuantizedOps(TestCase):
dtypes, dims, unbiased_list, keep_dim_list)
op = torch.std
for scale, zp, shape, dtype, dim, unbiased, keep_dim in test_cases:
if not all([d < len(shape) for d in dim]):
if not all(d < len(shape) for d in dim):
continue
X = torch.randn(*shape) * 10
qX = torch.quantize_per_tensor(X, scale, zp, dtype)

4
test/quantization/fx/test_quantize_fx.py
View File

@ -4994,7 +4994,7 @@ class TestQuantizeFx(QuantizationTestCase):
continue
matched_names.add(node.name)
# Match preceding dequantize
self.assertTrue(all([arg.target == "dequantize" for arg in node.args]))
self.assertTrue(all(arg.target == "dequantize" for arg in node.args))
# Match following quantize with the specific qparams and dtypes
expected_scale, expected_zp, expected_dtype = node_name_to_expected_quantize_args[node.name]
for user in node.users.keys():
@ -6513,7 +6513,7 @@ class TestQuantizeFx(QuantizationTestCase):
# Match preceding dequantize
self.assertTrue(len(node.args) == 1 or len(node.args) == 2)
self.assertTrue(all([arg.target == "dequantize" for arg in node.args]))
self.assertTrue(all(arg.target == "dequantize" for arg in node.args))
# Match following quantize with the specific dtypes
self.assertEqual(len(node.users), 1)

2
test/test_cuda.py
View File

@ -1625,7 +1625,7 @@ except RuntimeError as e:
'HSA_STATUS_ERROR_EXCEPTION', # ROCm
'Device-side assertion' # ROCm
]
self.assertTrue(any([msg in out or msg in err for msg in expected_messages]))
self.assertTrue(any(msg in out or msg in err for msg in expected_messages))
@slowTest
@unittest.skipIf(TEST_WITH_ROCM, "ROCm doesn't support device side asserts")

4
test/test_decomp.py
View File

@ -59,9 +59,9 @@ def diff_arg(arg, requires_grad=True):
return arg.is_floating_point() or arg.is_complex()
if is_iterable_of_tensors(arg):
if all([is_differentiable_arg(a) for a in arg]):
if all(is_differentiable_arg(a) for a in arg):
return True
if all([not is_differentiable_arg(a) for a in arg]):
if all(not is_differentiable_arg(a) for a in arg):
return False
raise RuntimeError("NYI: The test runner can't handle this")
return isinstance(arg, Tensor) and is_differentiable_arg(arg)

2
test/test_fx.py
View File

@ -2756,7 +2756,7 @@ class TestFX(JitTestCase):
mod_false = symbolic_trace(mod, concrete_args={'y': False})
self.assertEqual(mod_true(3, True), 6)
print(mod_true.code)
assert(any([i.target == torch._assert for i in mod_true.graph.nodes]))
assert(any(i.target == torch._assert for i in mod_true.graph.nodes))
with self.assertRaises(AssertionError):
mod_true(3, False)
self.assertEqual(mod_false(3, False), 3)

2
test/test_jit.py
View File

@ -449,7 +449,7 @@ class TestJit(JitTestCase):
buf.seek(0)
files = zipfile.ZipFile(buf).filelist
self.assertTrue(any(['archive/constants.pkl' == f.filename for f in files]))
self.assertTrue(any('archive/constants.pkl' == f.filename for f in files))
def test_script_fn_pkl(self):
with self.assertRaisesRegex(pickle.PickleError, "ScriptFunction cannot be pickled"):

4
test/test_jit_autocast.py
View File

@ -858,7 +858,7 @@ class TestJitTraceAutocast(JitTestCase):
with torch.cpu.amp.autocast(enabled=True):
self.assertEqual(fn_s(x), fn(x))
self.assertTrue(any(["is_autocast_cpu_enabled" in x.kind() for x in fn_s.graph.nodes()]))
self.assertTrue(any("is_autocast_cpu_enabled" in x.kind() for x in fn_s.graph.nodes()))
@unittest.skipIf(not TEST_CUDA, "No cuda")
def test_script_autocast_cuda(self):
@ -877,7 +877,7 @@ class TestJitTraceAutocast(JitTestCase):
with torch.cuda.amp.autocast(enabled=True):
self.assertEqual(fn_s(x), fn(x))
self.assertTrue(any(["is_autocast_enabled" in x.kind() for x in fn_s.graph.nodes()]))
self.assertTrue(any("is_autocast_enabled" in x.kind() for x in fn_s.graph.nodes()))
def test_scripted_aliasing(self):

2
test/test_ops_jit.py
View File

@ -158,7 +158,7 @@ class TestJit(JitCommonTestCase):
# right now, tuple of outputs and tensor output supported
# TODO: list of tensor outputs
tuple_of_tensors = isinstance(out, tuple) and all([isinstance(elem, torch.Tensor) for elem in out])
tuple_of_tensors = isinstance(out, tuple) and all(isinstance(elem, torch.Tensor) for elem in out)
if isinstance(out, torch.Tensor) or tuple_of_tensors:
if tuple_of_tensors:

4
test/test_proxy_tensor.py
View File

@ -428,8 +428,8 @@ def forward(self, x_1):
traced = make_fx(f, tracing_mode=self.tracing_mode)(torch.randn(3))
self.assertTrue(all([isinstance(node.target, torch._ops.OpOverload)
for node in traced.graph.nodes if node.op == 'call_function']))
self.assertTrue(all(isinstance(node.target, torch._ops.OpOverload)
for node in traced.graph.nodes if node.op == 'call_function'))
def test_tensor_constants(self):
def f():

4
test/test_tensorboard.py
View File

@ -764,11 +764,11 @@ class TestTensorBoardFigure(BaseTestCase):
figures.append(figure)
writer.add_figure("add_figure/figure_list", figures, 0, close=False)
self.assertTrue(all([plt.fignum_exists(figure.number) is True for figure in figures])) # noqa: F812
self.assertTrue(all(plt.fignum_exists(figure.number) is True for figure in figures)) # noqa: F812
writer.add_figure("add_figure/figure_list", figures, 1)
if matplotlib.__version__ != '3.3.0':
self.assertTrue(all([plt.fignum_exists(figure.number) is False for figure in figures])) # noqa: F812
self.assertTrue(all(plt.fignum_exists(figure.number) is False for figure in figures)) # noqa: F812
else:
print("Skipping fignum_exists, see https://github.com/matplotlib/matplotlib/issues/18163")

2
tools/autograd/load_derivatives.py
View File

@ -619,7 +619,7 @@ def create_differentiability_info(
output_differentiability = defn_dict.pop("output_differentiability", None)
output_differentiability_conditions = None
if output_differentiability and any(
[isinstance(diff, str) for diff in output_differentiability]
isinstance(diff, str) for diff in output_differentiability
):
if len(output_differentiability) != 1:
raise RuntimeError(

2
tools/code_coverage/package/tool/summarize_jsons.py
View File

@ -57,7 +57,7 @@ def is_intrested_file(
file_path: str, interested_folders: List[str], platform: TestPlatform
) -> bool:
ignored_patterns = ["cuda", "aten/gen_aten", "aten/aten_", "build/"]
if any([pattern in file_path for pattern in ignored_patterns]):
if any(pattern in file_path for pattern in ignored_patterns):
return False
# ignore files that are not belong to pytorch

2
tools/setup_helpers/env.py
View File

@ -13,7 +13,7 @@ IS_LINUX = platform.system() == "Linux"
IS_CONDA = (
"conda" in sys.version
or "Continuum" in sys.version
or any([x.startswith("CONDA") for x in os.environ])
or any(x.startswith("CONDA") for x in os.environ)
)
CONDA_DIR = os.path.join(os.path.dirname(sys.executable), "..")

4
torch/__init__.py
View File

@ -72,7 +72,7 @@ if sys.platform == 'win32':
dll_paths = list(filter(os.path.exists, [th_dll_path, py_dll_path, base_py_dll_path]))
if all([not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths]):
if all(not os.path.exists(os.path.join(p, 'nvToolsExt64_1.dll')) for p in dll_paths):
nvtoolsext_dll_path = os.path.join(
os.getenv('NVTOOLSEXT_PATH', os.path.join(pfiles_path, 'NVIDIA Corporation', 'NvToolsExt')), 'bin', 'x64')
else:
@ -80,7 +80,7 @@ if sys.platform == 'win32':
from .version import cuda as cuda_version
import glob
if cuda_version and all([not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths]):
if cuda_version and all(not glob.glob(os.path.join(p, 'cudart64*.dll')) for p in dll_paths):
cuda_version_1 = cuda_version.replace('.', '_')
cuda_path_var = 'CUDA_PATH_V' + cuda_version_1
default_path = os.path.join(pfiles_path, 'NVIDIA GPU Computing Toolkit', 'CUDA', 'v' + cuda_version)

6
torch/_dynamo/convert_frame.py
View File

@ -159,20 +159,20 @@ def has_tensor_in_frame(frame):
seen_ids[obj_id] = True
return seen_ids[obj_id]
elif istype(obj, (list, tuple)):
seen_ids[obj_id] = any([has_tensor(v) for v in obj])
seen_ids[obj_id] = any(has_tensor(v) for v in obj)
return seen_ids[obj_id]
elif istype(obj, dict):
# Some packages like pytest can be updated during runtime. So, make a
# copy of values to avoid issues like "RuntimeError: dictionary
# changed size during iteration"
values = list(obj.values())
seen_ids[obj_id] = any([has_tensor(v) for v in values])
seen_ids[obj_id] = any(has_tensor(v) for v in values)
return seen_ids[obj_id]
elif istype(obj, (str, int, float, type(None), bool)):
seen_ids[obj_id] = False
return seen_ids[obj_id]
elif is_namedtuple(obj):
seen_ids[obj_id] = any([has_tensor(getattr(obj, v)) for v in obj._fields])
seen_ids[obj_id] = any(has_tensor(getattr(obj, v)) for v in obj._fields)
return seen_ids[obj_id]
else:
# if config.debug:

2
torch/_dynamo/replay_record.py
View File

@ -99,7 +99,7 @@ class ExecutionRecorder:
@classmethod
def _is_excl(cls, mod):
return any([mod.__name__ == excl for excl in cls.MOD_EXCLUDES])
return any(mod.__name__ == excl for excl in cls.MOD_EXCLUDES)
# Convert ModuleRecords -> DummyModule tree
@classmethod

2
torch/_dynamo/repro/after_aot.py
View File

@ -411,7 +411,7 @@ def inductor_fails(fx_g, args, check_str=None):
try:
result = fx_g(*args)
assert isinstance(result, (tuple, list))
assert not any([isinstance(x, (tuple, list)) for x in result])
assert not any(isinstance(x, (tuple, list)) for x in result)
except Exception:
return False

2
torch/_dynamo/testing.py
View File

@ -102,7 +102,7 @@ def requires_bwd_pass(out):
if isinstance(out, torch.Tensor):
return out.requires_grad
elif isinstance(out, (list, tuple)):
return any([requires_bwd_pass(x) for x in out])
return any(requires_bwd_pass(x) for x in out)
elif out is None:
return False
elif isinstance(out, int):

4
torch/_dynamo/variables/builder.py
View File

@ -582,7 +582,7 @@ class VariableBuilder:
if (
istype(value, (tuple, list))
and all(
[isinstance(x, int) or is_numpy_int_type(x) or x is None for x in value]
isinstance(x, int) or is_numpy_int_type(x) or x is None for x in value
)
and not config.dynamic_shapes
):
@ -1058,7 +1058,7 @@ def wrap_fx_proxy_cls(
else:
return ConstantVariable(example_value, **options)
elif istype(example_value, torch.Size) and all(
[isinstance(x, int) for x in example_value]
isinstance(x, int) for x in example_value
):
sizes = [ConstantVariable(x) for x in example_value]
return SizeVariable(sizes, **options)

16
torch/_dynamo/variables/builtin.py
View File

@ -491,7 +491,7 @@ class BuiltinVariable(VariableTracker):
fn,
*proxy_args_kwargs(args, kwargs),
)
if any([isinstance(arg, FakeItemVariable) for arg in args]):
if any(isinstance(arg, FakeItemVariable) for arg in args):
return wrap_fx_proxy_cls(
FakeItemVariable,
tx,
@ -664,7 +664,7 @@ class BuiltinVariable(VariableTracker):
)
for i in [a, b]
):
if any([isinstance(val, FakeItemVariable) for val in [a, b]]):
if any(isinstance(val, FakeItemVariable) for val in [a, b]):
return variables.FakeItemVariable.from_tensor_variable(result)
if b.is_python_constant():
@ -724,8 +724,8 @@ class BuiltinVariable(VariableTracker):
return None
def _dynamic_args(self, *args, **kwargs):
return any([isinstance(x, SymNodeVariable) for x in args]) or any(
[isinstance(x, SymNodeVariable) for x in kwargs.values()]
return any(isinstance(x, SymNodeVariable) for x in args) or any(
isinstance(x, SymNodeVariable) for x in kwargs.values()
)
def call_slice(self, tx, *args):
@ -942,11 +942,9 @@ class BuiltinVariable(VariableTracker):
if (
isinstance(seq, (variables.ListVariable, variables.TupleVariable))
and all(
[
isinstance(x, variables.ConstantVariable)
and isinstance(x.value, (int, float))
for x in seq.items
]
isinstance(x, variables.ConstantVariable)
and isinstance(x.value, (int, float))
for x in seq.items
)
and not kwargs
):

2
torch/_dynamo/variables/constant.py
View File

@ -86,7 +86,7 @@ class ConstantVariable(VariableTracker):
items=self.unpack_var_sequence(tx), source=self.source, **options
).call_method(tx, name, args, kwargs)
if any([isinstance(x, SymNodeVariable) for x in args]):
if any(isinstance(x, SymNodeVariable) for x in args):
# Promote to SymNodeVariable for operations involving dynamic shapes.
return variables.SymNodeVariable(self.as_proxy(), self.value).call_method(
tx, name, args, kwargs

2
torch/_dynamo/variables/tensor.py
View File

@ -112,7 +112,7 @@ class TensorVariable(VariableTracker):
return self.dtype in dtypes
if type(tensor_type) is tuple:
return any([check_type(ty) for ty in tensor_type])
return any(check_type(ty) for ty in tensor_type)
else:
return check_type(tensor_type)

14
torch/_dynamo/variables/torch.py
View File

@ -472,16 +472,10 @@ class TorchVariable(VariableTracker):
tx, [args[0], result], {}
)
else:
any_symints_or_symfloats = any(
[isinstance(x, SymNodeVariable) for x in args]
)
any_symints_or_symfloats = any(isinstance(x, SymNodeVariable) for x in args)
all_ints_or_floats = all(
[
isinstance(
x, (variables.ConstantVariable, variables.SymNodeVariable)
)
for x in args
]
isinstance(x, (variables.ConstantVariable, variables.SymNodeVariable))
for x in args
)
bin_ops = {"add", "sub", "mul", "div", "sqrt"}
if (
@ -571,7 +565,7 @@ For now, dynamo will explicitly graph break when it encounters user code with th
# Ideally, we would be able to do this at ctor time, but alas we need a combination
# of value + args to determine this.
fn_ = self.value
if any([isinstance(x, SymNodeVariable) for x in args]):
if any(isinstance(x, SymNodeVariable) for x in args):
if self.value == math.sqrt:
from torch.fx.experimental.symbolic_shapes import sym_sqrt

10
torch/_functorch/aot_autograd.py
View File

@ -2587,16 +2587,14 @@ def aot_dispatch_autograd(flat_fn, flat_args: List[Any], aot_config: AOTConfig,
num_forward_returns:-num_symints_saved_for_bw
]
assert all(
[isinstance(x, torch.Tensor) for x in tensors_saved_for_backwards]
isinstance(x, torch.Tensor) for x in tensors_saved_for_backwards
)
# See Note [Detaching saved tensors in AOTAutograd]
ctx.save_for_backward(*(x.detach() if x._is_view() else x for x in tensors_saved_for_backwards))
symint_outs = fw_outs[-num_symints_saved_for_bw:]
assert all(
[
isinstance(x, (int, float, torch.SymInt, torch.SymFloat))
for x in symint_outs
]
isinstance(x, (int, float, torch.SymInt, torch.SymFloat))
for x in symint_outs
)
ctx.symints = symint_outs
else:
@ -2904,7 +2902,7 @@ def create_aot_dispatcher_function(
fake_flat_args = process_inputs(flat_args)
needs_autograd = (
any([x.requires_grad for x in fake_flat_args if isinstance(x, Tensor)])
any(x.requires_grad for x in fake_flat_args if isinstance(x, Tensor))
and torch.is_grad_enabled()
)
with enable_python_dispatcher():

2
torch/_inductor/cudagraph_trees.py
View File

@ -1414,7 +1414,7 @@ def get_block_addrs(pool_id, live_only=True):
def check_memory_pool(pool_id, live_storages_ptrs: List[StorageWeakRefWrapper]):
assert all([isinstance(elem, StorageWeakRefWrapper) for elem in live_storages_ptrs])
assert all(isinstance(elem, StorageWeakRefWrapper) for elem in live_storages_ptrs)
gc.collect()
unique_storages = {stor.data_ptr() for stor in live_storages_ptrs if stor()}

4
torch/_inductor/debug.py
View File

@ -127,8 +127,8 @@ def create_fx_from_snodes(snodes: List[BaseSchedulerNode]) -> fx.Graph:
def in_output(snode):
if isinstance(snode, FusedSchedulerNode):
return any([in_output(x) for x in snode.snodes])
return any([isinstance(user.node, OutputNode) for user in snode.users])
return any(in_output(x) for x in snode.snodes)
return any(isinstance(user.node, OutputNode) for user in snode.users)
if in_output(snode):
outputs.append(fx_node)

10
torch/_inductor/ir.py
View File

@ -625,7 +625,7 @@ class Reduction(Loops):
indices = []
changed = False
for md in sorted(read_writes.reads, key=lambda x: x.name):
if all([r in md.index.free_symbols for r in range_vars]):
if all(r in md.index.free_symbols for r in range_vars):
indices.append(md.index)
if md.name in V.graph.name_to_buffer:
buf = V.graph.name_to_buffer[md.name]
@ -651,7 +651,7 @@ class Reduction(Loops):
for i in indices:
i = V.graph.sizevars.simplify_with_ranges(i, ranges)
strides = V.graph.sizevars.stride_hints(i, reduction_vars, ranges.keys())
outer = all([s > 1 for s in strides])
outer = all(s > 1 for s in strides)
if outer:
num_outer += 1
else:
@ -837,8 +837,8 @@ class Reduction(Loops):
return isinstance(x, (int, sympy.Integer))
sr_qualified = (
all([_is_static(r) for r in ranges])
and all([_is_static(r) for r in reduction_ranges])
all(_is_static(r) for r in ranges)
and all(_is_static(r) for r in reduction_ranges)
and _is_static(reduction_numel)
)
@ -3796,7 +3796,7 @@ class StorageBox(MutableBox):
"""
heavy_ops = ["exp"] # a list of heavy ops
fn_str = loops.inner_fn_str()
return any([(op + "(") in fn_str for op in heavy_ops])
return any((op + "(") in fn_str for op in heavy_ops)
if (
users > 1

8
torch/_meta_registrations.py
View File

@ -1363,7 +1363,7 @@ def meta__foreach_binop_scalar(self, scalar=1):
)
def meta__foreach_addcop__scalar(self, tensor1, tensor2, scalar=1):
check(
all([isinstance(l, List) for l in [self, tensor1, tensor2]]),
all(isinstance(l, List) for l in [self, tensor1, tensor2]),
lambda: (
"All arguments of _foreach_addc*_ must be List[Tensor], "
f"but got {type(self)}, {type(tensor1)}, and {type(tensor2)}"
@ -1384,7 +1384,7 @@ def meta__foreach_addcop__scalar(self, tensor1, tensor2, scalar=1):
)
def meta__foreach_addcop_scalar(self, tensor1, tensor2, scalar=1):
check(
all([isinstance(l, List) for l in [self, tensor1, tensor2]]),
all(isinstance(l, List) for l in [self, tensor1, tensor2]),
lambda: (
"All arguments must be List[Tensor], "
f"but got {type(self)}, {type(tensor1)}, and {type(tensor2)}"
@ -2631,7 +2631,7 @@ def upsample_common_check(input_size, output_size, num_spatial_dims):
)
check(
all([s > 0 for s in input_size[2:]]) and all([s > 0 for s in output_size]),
all(s > 0 for s in input_size[2:]) and all(s > 0 for s in output_size),
lambda: f"Input and output sizes should be greater than 0, but got "
f"input size {input_size} and output size {output_size}",
)
@ -3062,7 +3062,7 @@ def meta_upsample_bilinear2d_aa(
input.size(), output_size, num_spatial_dims=2
)
check(
input.numel() != 0 or all([size > 0 for size in input.size()[1:]]),
input.numel() != 0 or all(size > 0 for size in input.size()[1:]),
lambda: f"Non-empty 4D data tensor expected but got a tensor with sizes {input.size()}",
)
return input.new_empty(full_output_size).to(

4
torch/_subclasses/fake_tensor.py
View File

@ -1140,7 +1140,7 @@ class FakeTensorMode(TorchDispatchMode):
flat_arg_fake_tensors = tree_flatten_only(FakeTensor, (args, kwargs))
flat_symints = tree_flatten_only(torch.SymInt, (args, kwargs))
has_symbolic_sizes = (
any([i._has_symbolic_sizes_strides for i in flat_arg_fake_tensors])
any(i._has_symbolic_sizes_strides for i in flat_arg_fake_tensors)
or len(flat_symints) > 0
)
@ -1331,7 +1331,7 @@ class FakeTensorMode(TorchDispatchMode):
and type(x) is not torch.nn.Parameter
)
return any([check(x) for x in tree_flatten_only(torch.Tensor, (args, kwargs))])
return any(check(x) for x in tree_flatten_only(torch.Tensor, (args, kwargs)))
def validate_and_convert_non_fake_tensors(self, func, converter, args, kwargs):
"""

4
torch/_vmap_internals.py
View File

@ -19,7 +19,7 @@ def _validate_and_get_batch_size(
for in_dim, arg in zip(flat_in_dims, flat_args)
if in_dim is not None
]
if batch_sizes and any([size != batch_sizes[0] for size in batch_sizes]):
if batch_sizes and any(size != batch_sizes[0] for size in batch_sizes):
raise ValueError(
f"vmap: Expected all tensors to have the same size in the mapped "
f"dimension, got sizes {batch_sizes} for the mapped dimension"
@ -168,7 +168,7 @@ def _check_out_dims_is_int_or_int_tuple(out_dims: out_dims_t, func: Callable) ->
if isinstance(out_dims, int):
return
if not isinstance(out_dims, tuple) or not all(
[isinstance(out_dim, int) for out_dim in out_dims]
isinstance(out_dim, int) for out_dim in out_dims
):
raise ValueError(
f"vmap({_get_name(func)}, ..., out_dims={out_dims}): `out_dims` must be "

6
torch/ao/pruning/_experimental/pruner/parametrization.py
View File

@ -7,10 +7,8 @@ def module_contains_param(module, parametrization):
if is_parametrized(module):
# see if any of the module tensors have a parametriztion attached that matches the one passed in
return any(
[
any(isinstance(param, parametrization) for param in param_list)
for key, param_list in module.parametrizations.items()
]
any(isinstance(param, parametrization) for param in param_list)
for key, param_list in module.parametrizations.items()
)
return False

6
torch/ao/pruning/sparsifier/utils.py
View File

@ -18,10 +18,8 @@ def module_contains_param(module: nn.Module, parametrization: Type[nn.Module]) -
if is_parametrized(module):
# see if any of the module tensors have a parametriztion attached that matches the one passed in
return any(
[
any(isinstance(param, parametrization) for param in param_list)
for key, param_list in module.parametrizations.items() # type: ignore[union-attr,operator]
]
any(isinstance(param, parametrization) for param in param_list)
for key, param_list in module.parametrizations.items() # type: ignore[union-attr,operator]
)
return False

10
torch/ao/quantization/fx/convert.py
View File

@ -110,9 +110,8 @@ def _replace_observer_with_quantize_dequantize_node_decomposed(
activation_post_process = modules[node.target]
# skip replacing observers to quant/dequant nodes if the qconfigs of all
# consumers and producers of this observer are None
skip_replacement = all([
_has_none_qconfig(n, node_name_to_qconfig) for n in
list(node.args) + list(node.users.keys())])
skip_replacement = all(_has_none_qconfig(n, node_name_to_qconfig) for n in
list(node.args) + list(node.users.keys()))
if skip_replacement or not _is_conversion_supported(activation_post_process):
# didn't find corresponding quantize op and info for the activation_post_process
# so we just remove the observer
@ -326,9 +325,8 @@ def _replace_observer_with_quantize_dequantize_node(
activation_post_process = modules[node.target]
# skip replacing observers to quant/dequant nodes if the qconfigs of all
# consumers and producers of this observer are None
skip_replacement = all([
_has_none_qconfig(n, node_name_to_qconfig) for n in
list(node.args) + list(node.users.keys())])
skip_replacement = all(_has_none_qconfig(n, node_name_to_qconfig) for n in
list(node.args) + list(node.users.keys()))
if skip_replacement or not _is_conversion_supported(activation_post_process):
# didn't find corresponding quantize op and info for the activation_post_process
# so we just remove the observer

10
torch/autograd/profiler_util.py
View File

@ -646,7 +646,7 @@ def _filter_stack_entry(entry):
("_internal/common_utils", "prof_func_call"),
("_internal/common_utils", "prof_meth_call"),
]
return all([not (f[0] in entry and f[1] in entry) for f in filtered_entries])
return all(not (f[0] in entry and f[1] in entry) for f in filtered_entries)
MEMORY_EVENT_NAME = "[memory]"
OUT_OF_MEMORY_EVENT_NAME = "[OutOfMemory]"
@ -692,10 +692,10 @@ def _build_table(
if len(events) == 0:
return ""
has_cuda_time = any([event.self_cuda_time_total > 0 for event in events])
has_cuda_mem = any([event.self_cuda_memory_usage > 0 for event in events])
has_cuda_time = any(event.self_cuda_time_total > 0 for event in events)
has_cuda_mem = any(event.self_cuda_memory_usage > 0 for event in events)
has_input_shapes = any(
[(event.input_shapes is not None and len(event.input_shapes) > 0) for event in events])
(event.input_shapes is not None and len(event.input_shapes) > 0) for event in events)
if sort_by is not None:
events = EventList(sorted(
@ -753,7 +753,7 @@ def _build_table(
'# of Calls'
)
# Only append Node ID if any event has a valid (>= 0) Node ID
append_node_id = any([evt.node_id != -1 for evt in events])
append_node_id = any(evt.node_id != -1 for evt in events)
if append_node_id:
headers.append('Node ID')

2
torch/cuda/__init__.py
View File

@ -183,7 +183,7 @@ If you want to use the {} GPU with PyTorch, please check the instructions at htt
for idx in range(device_count()):
cap_major, cap_minor = get_device_capability(idx)
# NVIDIA GPU compute architectures are backward compatible within major version
supported = any([sm // 10 == cap_major for sm in supported_sm])
supported = any(sm // 10 == cap_major for sm in supported_sm)
if not supported:
device_name = get_device_name(idx)
capability = cap_major * 10 + cap_minor

6
torch/distributed/_shard/sharding_spec/chunk_sharding_spec_ops/_common.py
View File

@ -200,10 +200,8 @@ def _result_distribute_with_col_rearrange(results, input, world_size, weight, pg
# Check if we need to rearrange columns appropriately for output.
rearrange_columns = any(
[
idx != placement.rank()
for idx, placement in enumerate(weight._sharding_spec.placements)
]
idx != placement.rank()
for idx, placement in enumerate(weight._sharding_spec.placements)
)
if not rearrange_columns:
return output

2
torch/distributed/_spmd/comm_tensor.py
View File

@ -122,7 +122,7 @@ class CommTensor(torch.Tensor):
@classmethod
def _is_supported(cls, op_name):
return any([comm in op_name for comm in cls._supported_comms])
return any(comm in op_name for comm in cls._supported_comms)
@classmethod
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):

12
torch/distributed/_spmd/distribute.py
View File

@ -648,13 +648,11 @@ def _rebuild_graph(
else:
value_remap[dtn] = gm.graph.node_copy(dtn, lambda n: value_remap[n])
if all(
[
isinstance(n.target, torch._ops.OpOverload)
and n.target._schema.name.startswith(
("aten::_foreach", "aten::_fused_adam")
)
for n in [dtn, node]
]
isinstance(n.target, torch._ops.OpOverload)
and n.target._schema.name.startswith(
("aten::_foreach", "aten::_fused_adam")
)
for n in [dtn, node]
):
# FIXME(@mrshenli): This is a temporary solution enable
# foreach ops. The problem is that foreach ops returns

28
torch/distributed/_spmd/experimental_ops.py
View File

@ -26,7 +26,7 @@ aten = torch.ops.aten # pyre-ignore
)
def _prop__foreach_unaop(op_schema: OpSchema) -> OutputSharding:
self = op_schema.args_schema[0]
assert isinstance(self, list) and all([isinstance(s, DTensorSpec) for s in self])
assert isinstance(self, list) and all(isinstance(s, DTensorSpec) for s in self)
# FIXME(@mrshenli): for sqrt, this is only mathematically correct for
# Replicate and Shard tensor.
return OutputSharding(output_spec=self)
@ -43,17 +43,17 @@ def _prop__foreach_binop_list(op_schema: OpSchema) -> OutputSharding:
self, other = op_schema.args_schema[:2]
scalar = None if len(op_schema.args_schema) < 3 else op_schema.args_schema[2]
assert isinstance(self, list) and all(
[isinstance(s, DTensorSpec) for s in self]
isinstance(s, DTensorSpec) for s in self
), f"Expect a List[DTensorSpec] but got {self}"
assert isinstance(other, list) and all(
[isinstance(o, DTensorSpec) for o in other]
isinstance(o, DTensorSpec) for o in other
), f"Expect a List[DTensorSpec] but got {other}"
assert len(self) == len(other), (
"Two tensor lists must match in length, "
f"but got {len(self)} and {len(other)}"
)
if any([s != o for s, o in zip(self, other)]):
if any(s != o for s, o in zip(self, other)):
# If DTensorSpec for the two operand do not match, suggest using
# self's DTensorSpec. This will trigger allreduce if other is partial
# and self is replicated.
@ -83,7 +83,7 @@ def _prop__foreach_binop_list(op_schema: OpSchema) -> OutputSharding:
)
def _prop__foreach_binop_scalar(op_schema: OpSchema) -> OutputSharding:
self, scalar = op_schema.args_schema
assert isinstance(self, list) and all([isinstance(s, DTensorSpec) for s in self])
assert isinstance(self, list) and all(isinstance(s, DTensorSpec) for s in self)
assert not isinstance(scalar, list)
return OutputSharding(output_spec=self)
@ -97,10 +97,10 @@ def _prop__foreach_binop_scalar(op_schema: OpSchema) -> OutputSharding:
def _prop__foreach_addcop_scalar(op_schema: OpSchema):
self, tensor1, tensor2 = op_schema.args_schema[:3]
scalar = None if len(op_schema.args_schema) < 4 else op_schema.args_schema[3]
assert isinstance(self, list) and all([isinstance(s, DTensorSpec) for s in self])
assert isinstance(tensor1, list) and all([isinstance(s, DTensorSpec) for s in self])
assert isinstance(tensor2, list) and all([isinstance(s, DTensorSpec) for s in self])
if any([s != t1 or s != t2 for s, t1, t2 in zip(self, tensor1, tensor2)]):
assert isinstance(self, list) and all(isinstance(s, DTensorSpec) for s in self)
assert isinstance(tensor1, list) and all(isinstance(s, DTensorSpec) for s in self)
assert isinstance(tensor2, list) and all(isinstance(s, DTensorSpec) for s in self)
if any(s != t1 or s != t2 for s, t1, t2 in zip(self, tensor1, tensor2)):
# If DTensorSpec for the two operand do not match, suggest using
# self's DTensorSpec. This will trigger allreduce if other is partial
# and self is replicated.
@ -126,7 +126,7 @@ def _prop__foreach_addcop_scalar(op_schema: OpSchema):
def _prop__foreach_pow_scalar_and_tensor(op_schema: OpSchema):
scala, exponent = op_schema.args_schema
assert isinstance(exponent, list) and all(
[isinstance(s, DTensorSpec) for s in exponent]
isinstance(s, DTensorSpec) for s in exponent
)
return OutputSharding(output_spec=exponent)
@ -136,21 +136,21 @@ def _prop__fused_adam(op_schema: OpSchema):
NT = 5
tesnor_list_args: Tuple[List[DTensorSpec]] = op_schema.args_schema[:NT] # type: ignore[assignment]
assert all([isinstance(schema, list) for schema in tesnor_list_args])
assert all(isinstance(schema, list) for schema in tesnor_list_args)
assert all(
[isinstance(s, DTensorSpec) for schema in tesnor_list_args for s in schema]
isinstance(s, DTensorSpec) for schema in tesnor_list_args for s in schema
)
tensor_schemas: Tuple[List[DTensorSpec]] = [ # type: ignore[assignment]
schema for schema in tesnor_list_args if len(schema)
]
assert all([len(s) == len(tensor_schemas[0]) for s in tensor_schemas]), (
assert all(len(s) == len(tensor_schemas[0]) for s in tensor_schemas), (
"expect the same number of gradients and states, but got "
f"{[len(s) for s in tensor_schemas]}."
)
if any([any([t != ts[0] for t in ts]) for ts in zip(*tensor_schemas)]):
if any(any(t != ts[0] for t in ts) for ts in zip(*tensor_schemas)):
new_schemas: Tuple[List[DTensorSpec]] = tuple( # type: ignore[assignment]
op_schema.args_schema[0] if len(s) else s for s in tesnor_list_args
)

2
torch/distributed/distributed_c10d.py
View File

@ -1922,7 +1922,7 @@ def all_reduce_coalesced(tensors, op=ReduceOp.SUM, group=None, async_op=False):
_warn_not_in_group("all_reduce_coalesced")
return
if any([t.is_complex() for t in tensors]) and not supports_complex(op):
if any(t.is_complex() for t in tensors) and not supports_complex(op):
raise RuntimeError(f"all_reduce does not support {op} on complex tensors")
tensors = [t if not t.is_complex() else torch.view_as_real(t) for t in tensors]

6
torch/fx/experimental/migrate_gradual_types/constraint_transformation.py
View File

@ -207,7 +207,7 @@ def generate_binconstraint_t(constraint, counter):
if constraint.lhs == Dyn:
return T(), counter
elif isinstance(constraint.lhs, TensorType):
is_fully_static = all([d != Dyn for d in constraint.lhs.__args__])
is_fully_static = all(d != Dyn for d in constraint.lhs.__args__)
if is_fully_static:
return BinConstraintT(constraint.lhs, constraint.rhs, op_eq), counter
else:
@ -403,7 +403,7 @@ def generate_calc_product(constraint, counter):
for p in all_possibilities:
p = list(p)
# this tells us there is a dynamic variable
contains_dyn = not(all([constraint.op == op_neq for constraint in p]))
contains_dyn = not(all(constraint.op == op_neq for constraint in p))
if contains_dyn:
mid_var = [Dyn]
total_constraints = lhs + mid_var + rhs
@ -438,7 +438,7 @@ def generate_reshape(constraint, counter):
target = constraint.target.__args__
is_fully_static = all([d != Dyn for d in target])
is_fully_static = all(d != Dyn for d in target)
# dynamic tensor
c1_dyn = BinConstraintT(constraint.src, Dyn, op_eq)

2
torch/fx/experimental/optimization.py
View File

@ -304,7 +304,7 @@ def optimize_for_inference(
if supports_mkldnn != MklSupport.NO:
if supports_mkldnn == MklSupport.UNKNOWN:
if not any([arg.target == 'to_dense' for arg in node.args]):
if not any(arg.target == 'to_dense' for arg in node.args):
continue
with fx_graph.inserting_before(node):
mkldnn_args = fx.map_arg(node.args, lambda n: fx_graph.call_method('to_mkldnn', (n, )))

6
torch/fx/experimental/partitioner_utils.py
View File

@ -50,7 +50,7 @@ class Partition:
# the remove this input node
for input_node in input_nodes:
if all(
[n not in self.nodes for n in input_node.users]
n not in self.nodes for n in input_node.users
) and input_node.op in {"placeholder", "get_attr"}:
self.nodes.remove(input_node)
self.recalculate_mem_size()
@ -144,10 +144,8 @@ def get_latency_of_one_partition(
# or its input nodes in this partition are placeholders and get_attrs
# this node is on the top bfs level in this partition
if not any(
[
n in partition.nodes and n.op not in {"placeholder", "get_attr"}
n in partition.nodes and n.op not in {"placeholder", "get_attr"}
for n in input_nodes
]
):
top_nodes.append(node)
return top_nodes

2
torch/fx/experimental/symbolic_shapes.py
View File

@ -2613,7 +2613,7 @@ class ShapeEnv:
if not expr.has(sympy.Mod):
try:
floor_div_atoms = lhs.atoms(FloorDiv).union(rhs.atoms(FloorDiv))
if len(floor_div_atoms) > 0 and any([a.divisor != 1 for a in floor_div_atoms]):
if len(floor_div_atoms) > 0 and any(a.divisor != 1 for a in floor_div_atoms):
raise NotImplementedError
solutions = sympy.solve(lhs - rhs, free[0], dict=True)
if len(solutions) != 1:

2
torch/fx/passes/splitter_base.py
View File

@ -747,7 +747,7 @@ class _SplitterBase:
# Determine which subgraph to start from based on which subgraph has
# 0-dep node
acc_subgraph: bool = not any([len(self.deps[n]) == 0 for n in current_cpu_nodes])
acc_subgraph: bool = not any(len(self.deps[n]) == 0 for n in current_cpu_nodes)
current_subgraph_nodes: NodeList = []

4
torch/fx/tensor_type.py
View File

@ -73,7 +73,7 @@ def is_consistent(t1, t2):
if isinstance(t1, TensorType) and isinstance(t2, TensorType):
return len(t1.__args__) == len(t2.__args__) and \
all([is_consistent(elem1, elem2) for elem1, elem2 in zip(t1.__args__, t2.__args__)])
all(is_consistent(elem1, elem2) for elem1, elem2 in zip(t1.__args__, t2.__args__))
else:
return False
@ -98,7 +98,7 @@ def is_more_precise(t1, t2):
if isinstance(t1, TensorType) and isinstance(t2, TensorType):
return len(t1.__args__) == len(t2.__args__) and \
all([is_more_precise(elem1, elem2) for elem1, elem2 in zip(t1.__args__, t2.__args__)])
all(is_more_precise(elem1, elem2) for elem1, elem2 in zip(t1.__args__, t2.__args__))
else:
return False

2
torch/nested/__init__.py
View File

@ -50,7 +50,7 @@ def as_nested_tensor(
tensor([0., 0., 0., 0., 0.])
"""
if not isinstance(tensor_list, list) or any(
[not isinstance(t, Tensor) for t in tensor_list]
not isinstance(t, Tensor) for t in tensor_list
):
raise TypeError(
"nested_tensor(): Expected first argument to be a list of tensors "

4
torch/nn/modules/activation.py
View File

@ -1172,9 +1172,9 @@ class MultiheadAttention(Module):
# generator expressions.
if torch.overrides.has_torch_function(tensor_args):
why_not_fast_path = "some Tensor argument has_torch_function"
elif not all([_arg_cuda_or_cpu(x) for x in tensor_args]):
elif not all(_arg_cuda_or_cpu(x) for x in tensor_args):
why_not_fast_path = "some Tensor argument is neither CUDA nor CPU"
elif torch.is_grad_enabled() and any([_arg_requires_grad(x) for x in tensor_args]):
elif torch.is_grad_enabled() and any(_arg_requires_grad(x) for x in tensor_args):
why_not_fast_path = ("grad is enabled and at least one of query or the "
"input/output projection weights or biases requires_grad")
if not why_not_fast_path:

2
torch/nn/modules/adaptive.py
View File

@ -129,7 +129,7 @@ class AdaptiveLogSoftmaxWithLoss(Module):
or (min(cutoffs) <= 0) \
or (max(cutoffs) > (n_classes - 1)) \
or (len(set(cutoffs)) != len(cutoffs)) \
or any([int(c) != c for c in cutoffs]):
or any(int(c) != c for c in cutoffs):
raise ValueError("cutoffs should be a sequence of unique, positive "
"integers sorted in an increasing order, where "

2
torch/nn/parallel/distributed.py
View File

@ -1407,7 +1407,7 @@ class DistributedDataParallel(Module, Joinable):
# We batch zero_grad for all params by resetting the whole grad
# buffer when the grad of all params is set to None.
all_param_grad_none = all(
[param.grad is None for param in self._delay_all_reduce_params]
param.grad is None for param in self._delay_all_reduce_params
)
for index, param in enumerate(self._delay_all_reduce_params):

2
torch/onnx/symbolic_helper.py
View File

@ -1513,7 +1513,7 @@ def _optional_input_placeholder_tensor(g):
def _handle_reduce_dim_none(g: jit_utils.GraphContext, self, op_name):
rank = _get_tensor_rank(self)
if rank is not None and any(
[_get_tensor_dim_size(self, i) == 0 for i in range(rank)]
_get_tensor_dim_size(self, i) == 0 for i in range(rank)
):
# If input tensor is empty, according to ONNX ReduceSum definition,
# set keepdims=1 so that the resulted tensor has the same rank as the input.

20
torch/onnx/symbolic_opset9.py
View File

@ -539,13 +539,11 @@ def cat(g: jit_utils.GraphContext, tensor_list, dim):
nonempty_tensors.append(t)
assert len(nonempty_tensors) > 0
assert all(
[
symbolic_helper._get_tensor_rank(nonempty_tensors[0]) is None
or symbolic_helper._get_tensor_rank(t) is None
or symbolic_helper._get_tensor_rank(t)
== symbolic_helper._get_tensor_rank(nonempty_tensors[0])
for t in nonempty_tensors
]
symbolic_helper._get_tensor_rank(nonempty_tensors[0]) is None
or symbolic_helper._get_tensor_rank(t) is None
or symbolic_helper._get_tensor_rank(t)
== symbolic_helper._get_tensor_rank(nonempty_tensors[0])
for t in nonempty_tensors
)
tensor_list.node().removeAllInputs()
for t in nonempty_tensors:
@ -1499,7 +1497,7 @@ def get_pool_ceil_padding(input, kernel_size, stride, padding):
# TODO(justinchuby): Looks like this op is deprecated in torch
sizes = symbolic_helper._get_tensor_sizes(input)
dim = sizes[-len(padding) :] if sizes is not None else None
if dim is None or any([i is None for i in dim]):
if dim is None or any(i is None for i in dim):
return symbolic_helper._unimplemented(
"get_pool_ceil_padding", "input size not accessible", input
)
@ -1824,7 +1822,7 @@ def _adaptive_pool(name, type, tuple_fn, fn=None):
# FIXME(justinchuby): Avoid catching Exception.
# Catch a more specific exception instead.
dim = None
if dim is None or any([i is None for i in dim]):
if dim is None or any(i is None for i in dim):
if output_size == [1] * len(output_size):
return g.op("GlobalMaxPool", input), None
return symbolic_helper._unimplemented(
@ -2453,7 +2451,7 @@ def _convolution(
# Catch a more specific exception instead.
kernel_shape = None
if kernel_shape is None or any([i is None for i in kernel_shape]):
if kernel_shape is None or any(i is None for i in kernel_shape):
raise errors.SymbolicValueError(
"Unsupported: ONNX export of convolution for kernel of unknown shape.",
input,
@ -6072,7 +6070,7 @@ def dim(g: jit_utils.GraphContext, self):
def __contains_(g: jit_utils.GraphContext, self, element):
unpacked_list = symbolic_helper._unpack_list(self)
if all(
[symbolic_helper._is_constant(x) for x in unpacked_list]
symbolic_helper._is_constant(x) for x in unpacked_list
) and symbolic_helper._is_constant(element):
return g.op(
"Constant",

2
torch/onnx/verification.py
View File

@ -917,7 +917,7 @@ def verify_aten_graph(
graph_inputs = list(graph.inputs())
jit_inputs = tuple([arg for arg in input_args if arg is not None])
weights = [params_dict[v.debugName()] for v in graph_inputs[len(jit_inputs) :]]
assert all([w is not None for w in weights])
assert all(w is not None for w in weights)
# TODO: Only copy the argument if mutation is detected in Graph.
jit_inputs = copy.deepcopy(jit_inputs)
jit_input_and_parameters = jit_inputs + tuple(weights)

2
torch/testing/_internal/common_distributed.py
View File

@ -737,7 +737,7 @@ class MultiProcessTestCase(TestCase):
if subprocess_error:
break
# All processes have joined cleanly if they all a valid exitcode
if all([p.exitcode is not None for p in self.processes]):
if all(p.exitcode is not None for p in self.processes):
break
# Check if we should time out the test. If so, we terminate each process.
elapsed = time.time() - start_time

6
torch/testing/_internal/common_utils.py
View File

@ -1789,7 +1789,7 @@ def check_if_enable(test: unittest.TestCase):
# Sanitize the platforms list so that we continue to disable the test for any valid platforms given
platforms = list(filter(lambda p: p in platform_to_conditional, platforms))
if platforms == [] or any([platform_to_conditional[platform] for platform in platforms]):
if platforms == [] or any(platform_to_conditional[platform] for platform in platforms):
should_skip = True
skip_msg = f"Test is disabled because an issue exists disabling it: {issue_url}" \
f" for {'all' if platforms == [] else ''}platform(s) {', '.join(platforms)}. " \
@ -3159,9 +3159,9 @@ class TestCase(expecttest.TestCase):
yield
if len(ws) == 0:
self.fail('no warning caught')
self.assertTrue(any([type(w.message) is category for w in ws]))
self.assertTrue(any(type(w.message) is category for w in ws))
self.assertTrue(
any([re.match(pattern, str(w.message)) for w in ws]),
any(re.match(pattern, str(w.message)) for w in ws),
f'{pattern}, {[w.message for w in ws if type(w.message) is category]}')
def assertExpected(self, s, subname=None):

6
torch/testing/_internal/composite_compliance.py
View File

@ -189,7 +189,7 @@ def generate_cct_and_mode(autograd_view_consistency=True):
# then the first argument is being written to. Introspection please save us!
mutated_argument = args[0]
if not isinstance(mutated_argument, CompositeCompliantTensor) and \
any([isinstance(a, CompositeCompliantTensor) for a in args[1:]]):
any(isinstance(a, CompositeCompliantTensor) for a in args[1:]):
raise RuntimeError(
'Not composite compliant: performing in-place operation '
f'{func.__name__} where the Tensor being written to is '
@ -249,10 +249,10 @@ def is_tensorlist(lst):
return False
if len(lst) == 0:
return False
all_tensors = all([isinstance(elt, torch.Tensor) for elt in lst])
all_tensors = all(isinstance(elt, torch.Tensor) for elt in lst)
if all_tensors:
return True
exists_one_tensor = all([isinstance(elt, torch.Tensor) for elt in lst])
exists_one_tensor = all(isinstance(elt, torch.Tensor) for elt in lst)
if exists_one_tensor:
raise RuntimeError('This test assumes that PyTorch APIs cannot take '
'mixed lists of Tensor and other things')

2
torch/testing/_internal/distributed/distributed_test.py
View File

@ -4666,7 +4666,7 @@ class DistributedTest:
ddp_model().backward(create_graph=True)
# grad tensors should require grad.
self.assertTrue(
all([param.requires_grad for param in ddp_model.parameters()])
all(param.requires_grad for param in ddp_model.parameters())
)
@skip_but_pass_in_sandcastle_if(

4
torch/testing/_internal/distributed/rpc/rpc_test.py
View File

@ -1839,7 +1839,7 @@ class RpcTest(RpcAgentTestFixture, RpcTestCommon):
trace = json.load(f)
event_names = [event['name'] for event in trace]
for expected_event_name in EXPECTED_REMOTE_EVENTS + [RPCExecMode.ASYNC.value]:
event_exists = any([expected_event_name in event_name for event_name in event_names])
event_exists = any(expected_event_name in event_name for event_name in event_names)
self.assertTrue(event_exists)
@dist_init
@ -2903,7 +2903,7 @@ class RpcTest(RpcAgentTestFixture, RpcTestCommon):
def launched_rpc(events):
expected_name = f"rpc_{RPCExecMode.ASYNC.value}#_rref_typeof_on_owner"
return any([e.name.startswith(expected_name) for e in events])
return any(e.name.startswith(expected_name) for e in events)
dst = worker_name((self.rank + 1) % self.world_size)
rref = rpc.remote(dst, torch.add, args=(torch.ones(2), 1))

4
torch/utils/_foreach_utils.py
View File

@ -21,7 +21,7 @@ from torch.autograd.grad_mode import no_grad
def _group_tensors_by_device_and_dtype(tensorlistlist: List[List[Tensor]],
with_indices: Optional[bool] = False) -> \
Dict[Tuple[torch.device, torch.dtype], List[List[Union[Tensor, int]]]]:
assert all([not x or len(x) == len(tensorlistlist[0]) for x in tensorlistlist]), (
assert all(not x or len(x) == len(tensorlistlist[0]) for x in tensorlistlist), (
"all specified tensorlists must match in length")
per_device_and_dtype_tensors: Dict[Tuple[torch.device, torch.dtype], List[List[Union[Tensor, int]]]] = defaultdict(
lambda: [[] for _ in range(len(tensorlistlist) + (1 if with_indices else 0))])
@ -39,4 +39,4 @@ def _group_tensors_by_device_and_dtype(tensorlistlist: List[List[Tensor]],
def _has_foreach_support(tensors: List[Tensor], device: torch.device) -> bool:
if device.type not in ['cpu', 'cuda'] or torch.jit.is_scripting():
return False
return all([t is None or type(t) == torch.Tensor for t in tensors])
return all(t is None or type(t) == torch.Tensor for t in tensors)

2
torch/utils/_freeze.py
View File

@ -176,7 +176,7 @@ class Freezer:
return
# Python packages are directories that have __init__.py in them.
is_package_dir = any([child.name == "__init__.py" for child in path.iterdir()])
is_package_dir = any(child.name == "__init__.py" for child in path.iterdir())
if not is_package_dir:
self.msg(path, "S")
return

4
torch/utils/cpp_extension.py
View File

@ -553,7 +553,7 @@ class BuildExtension(build_ext):
_ccbin = os.getenv("CC")
if (
_ccbin is not None
and not any([flag.startswith(('-ccbin', '--compiler-bindir')) for flag in cflags])
and not any(flag.startswith(('-ccbin', '--compiler-bindir')) for flag in cflags)
):
cflags.extend(['-ccbin', _ccbin])
@ -1462,7 +1462,7 @@ def _jit_compile(name,
if with_cuda is None:
with_cuda = any(map(_is_cuda_file, sources))
with_cudnn = any(['cudnn' in f for f in extra_ldflags or []])
with_cudnn = any('cudnn' in f for f in extra_ldflags or [])
old_version = JIT_EXTENSION_VERSIONER.get_version(name)
version = JIT_EXTENSION_VERSIONER.bump_version_if_changed(
name,

2
torch/utils/mobile_optimizer.py
View File

@ -45,7 +45,7 @@ def optimize_for_mobile(
preserved_methods_str: List[str] = [str(method) for method in preserved_methods]
bundled_inputs_attributes = _get_bundled_inputs_preserved_attributes(script_module, preserved_methods_str)
if all([hasattr(script_module, method) for method in bundled_inputs_attributes]):
if all(hasattr(script_module, method) for method in bundled_inputs_attributes):
preserved_methods_str = list(set(preserved_methods_str + bundled_inputs_attributes))
non_exist_methods = []

2
torchgen/model.py
View File

@ -1373,7 +1373,7 @@ class FunctionSchema:
len(mutable_returns) == 0 or len(immutable_returns) == 0
), f"NativeFunctions must have either only mutable returns, or only immutable returns. Found: {str(self)}"
for ret in mutable_returns:
assert any([ret.annotation == arg.annotation for arg in out_and_self]), (
assert any(ret.annotation == arg.annotation for arg in out_and_self), (
'All mutable returns must be aliased either to a keyword argument, or to "self". '
"Did you forget to mark an out argument as keyword-only?"
)