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[3/N] Fix unused loop variables (#166509)

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This PR removes unused loop variables in tests.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/166509
Approved by: https://github.com/Lucaskabela, https://github.com/Skylion007
This commit is contained in:
Yuanyuan Chen 2025-10-30 20:13:47 +00:00 committed by PyTorch MergeBot
parent 99b05d1b78
commit 0d50e5d8d4
26 changed files with 107 additions and 113 deletions
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2
test/distributed/_composable/fsdp/test_fully_shard_training.py
View File

@ -827,7 +827,7 @@ class TestFullyShardShardPlacementFnMultiProcess(FSDPTest):
torch.manual_seed(42 + self.rank)
inp = torch.randint(0, model_args.vocab_size, (2, 16), device=device_type.type)
for iter_idx in range(5):
for _ in range(5):
ref_loss = ref_model(inp).sum()
loss = model(inp).sum()
self.assertEqual(ref_loss, loss)

20
test/dynamo/test_modules.py
View File

@ -262,7 +262,7 @@ class ConstLoop(torch.nn.Module):
self.count = 3
def forward(self, x):
for i in range(self.count):
for _ in range(self.count):
x = torch.sigmoid(self.linear1(x))
return x
@ -509,7 +509,7 @@ class CfgModule(torch.nn.Module):
self.layer = torch.nn.Linear(10, 10)
def forward(self, x):
for i in range(self.cfg.count):
for _ in range(self.cfg.count):
x = self.layer(x + self.cfg.val)
return x
@ -781,7 +781,7 @@ class ParametersModule5(torch.nn.Module):
def forward(self, x):
counter = 0
for param in self.parameters():
for _param in self.parameters():
counter += 1
return x * self.scale * counter
@ -841,7 +841,7 @@ class EnumValues(torch.nn.ModuleDict):
def forward(self, init_features):
features = [init_features]
for idx, layer in enumerate(self.values()):
for layer in self.values():
new_features = layer(features)
features.append(new_features)
return torch.cat(features, 1)
@ -2161,7 +2161,7 @@ class OptimizedModuleTest(torch._dynamo.test_case.TestCase):
cnts = torch._dynamo.testing.CompileCounterWithBackend("eager")
opt_mod = torch.compile(fn, backend=cnts)
for i in range(8):
for _ in range(8):
mod = Mod()
opt_mod(torch.randn(5, 5), mod)
@ -2516,7 +2516,7 @@ class OptimizedModuleTest(torch._dynamo.test_case.TestCase):
compiled_model = torch.compile(model, backend="aot_eager")
activations = compiled_activations
for i in range(2):
for _ in range(2):
# second iteration is key, hooks would have fired during aot trace
# on first iter
compiled_activations.clear()
@ -2526,7 +2526,7 @@ class OptimizedModuleTest(torch._dynamo.test_case.TestCase):
loss.backward()
activations = eager_activations
for i in range(2):
for _ in range(2):
# second iteration is key, hooks would have fired during aot trace
# on first iter
eager_activations.clear()
@ -2575,12 +2575,12 @@ class OptimizedModuleTest(torch._dynamo.test_case.TestCase):
def save_activations(mod, inp, out):
activations.append(inp)
for name, module in model.named_modules():
for module in model.modules():
module.register_forward_hook(save_activations)
cnt = torch._dynamo.testing.CompileCounter()
model = torch.compile(model, backend=cnt, fullgraph=True)
for i in range(2):
for _ in range(2):
# second iteration is key, hooks would have fired during aot trace
# on first iter
activations.clear()
@ -2703,7 +2703,7 @@ class OptimizedModuleTest(torch._dynamo.test_case.TestCase):
model = torch.compile(model, backend="aot_eager")
for i in range(2):
for _ in range(2):
# second iteration is key, hooks would have fired during aot trace
# on first iter
x = torch.randn((20, 10))

2
test/functorch/test_aotdispatch.py
View File

@ -7555,7 +7555,7 @@ metadata incorrectly.
(_inp, _tg3),
]
for i, (inp_fn, tg_fn) in enumerate(TEST_CASES):
for inp_fn, tg_fn in TEST_CASES:
ref_x = inp_fn()
x = ref_x.detach().clone().requires_grad_()

10
test/higher_order_ops/test_with_effects.py
View File

@ -491,9 +491,7 @@ def forward(self, arg0_1, arg1_1, arg2_1):
def ins_dense():
return torch.tensor([1.0, 2.0, 3.0]), torch.tensor([4.0, 5.0, 6.0])
for i, (ins_fn, expected_fw_count) in enumerate(
zip([ins_sc, ins_dense], [2, 1])
):
for ins_fn, expected_fw_count in zip([ins_sc, ins_dense], [2, 1]):
reset_counter()
ref_out = fn(*ins_fn())
assert_counter(expected_fw_count, 0)
@ -524,16 +522,14 @@ def forward(self, arg0_1, arg1_1, arg2_1):
),
)
for i, (
for (
ins_fn_req_grad,
(
expected_fw_count,
expected_fw_count_after_bw,
expected_bw_count_after_bw,
),
) in enumerate(
zip([ins_dense_req_grad, ins_sc_req_grad], [(1, 1, 1), (2, 2, 2)])
):
) in zip([ins_dense_req_grad, ins_sc_req_grad], [(1, 1, 1), (2, 2, 2)]):
ref_ins = ins_fn_req_grad()
reset_counter()
ref_out = fn(*ref_ins)

4
test/jit/test_async.py
View File

@ -35,7 +35,7 @@ class TestAsync(JitTestCase):
def test_async_future_type_python(self):
def foo(inp):
futures = torch.jit.annotate(List[torch.jit.Future[torch.Tensor]], [])
for i in range(5):
for _ in range(5):
futures.append(torch.jit.fork(lambda x: x, inp))
all_outputs = []
for future in futures:
@ -458,7 +458,7 @@ class TestAsync(JitTestCase):
class TestListFutureModule(nn.Module):
def forward(self, input):
input_list = []
for i in range(3):
for _ in range(3):
input_list.append(input)
fut_list: List[Future[torch.Tensor]] = []

8
test/jit/test_autodiff.py
View File

@ -68,7 +68,7 @@ class TestAutodiffJit(JitTestCase):
fn_s = torch.jit.script(fn)
for i in range(4):
for _ in range(4):
x, y = fn_s(a, b, c)
self.assertFalse(x.requires_grad)
self.assertTrue(y.requires_grad)
@ -90,7 +90,7 @@ class TestAutodiffJit(JitTestCase):
b = torch.rand((10, 10), requires_grad=False)
c = torch.rand((10, 10), requires_grad=True)
for i in range(4):
for _ in range(4):
x_s, y_s, z_s = fn_s(a, b, c)
x, y, z = fn(a, b, c)
@ -115,7 +115,7 @@ class TestAutodiffJit(JitTestCase):
b = torch.rand((10, 10), requires_grad=False)
c = torch.rand((10, 10), requires_grad=True)
for i in range(4):
for _ in range(4):
x_s, y_s, z_s = fn_s(a, b, c)
x, y, z = fn(a, b, c)
@ -141,7 +141,7 @@ class TestAutodiffJit(JitTestCase):
b = torch.rand((10, 10), requires_grad=True)
c = torch.rand((10, 10), requires_grad=True)
for i in range(4):
for _ in range(4):
x_s, y_s, z_s = fn_s(a, b, c)
x, y, z = fn(a, b, c)

2
test/jit/test_list_dict.py
View File

@ -2989,7 +2989,7 @@ class TestScriptList(JitTestCase):
test_script.segments_groupby_col
# Smoketest for flakiness. Takes around 2s.
for i in range(300):
for _ in range(300):
test = Test()
test_script = torch.jit.script(test)

10
test/jit/test_logging.py
View File

@ -19,7 +19,7 @@ class TestLogging(JitTestCase):
class ModuleThatLogs(torch.jit.ScriptModule):
@torch.jit.script_method
def forward(self, x):
for i in range(x.size(0)):
for _ in range(x.size(0)):
x += 1.0
torch.jit._logging.add_stat_value("foo", 1)
@ -33,7 +33,7 @@ class TestLogging(JitTestCase):
old_logger = torch.jit._logging.set_logger(logger)
try:
mtl = ModuleThatLogs()
for i in range(5):
for _ in range(5):
mtl(torch.rand(3, 4, 5))
self.assertEqual(logger.get_counter_val("foo"), 15)
@ -60,7 +60,7 @@ class TestLogging(JitTestCase):
class ModuleThatTimes(torch.jit.ScriptModule):
def forward(self, x):
tp_start = torch.jit._logging.time_point()
for i in range(30):
for _ in range(30):
x += 1.0
tp_end = torch.jit._logging.time_point()
torch.jit._logging.add_stat_value("mytimer", tp_end - tp_start)
@ -80,7 +80,7 @@ class TestLogging(JitTestCase):
@torch.jit.script_method
def forward(self, x):
tp_start = torch.jit._logging.time_point()
for i in range(30):
for _ in range(30):
x += 1.0
tp_end = torch.jit._logging.time_point()
torch.jit._logging.add_stat_value("mytimer", tp_end - tp_start)
@ -97,7 +97,7 @@ class TestLogging(JitTestCase):
def test_counter_aggregation(self):
def foo(x):
for i in range(3):
for _ in range(3):
torch.jit._logging.add_stat_value("foo", 1)
return x + 1.0

2
test/jit/test_misc.py
View File

@ -518,7 +518,7 @@ class TestMisc(JitTestCase):
ref = fn(x)
script_fn = torch.jit.script(fn)
for i in range(4):
for _ in range(4):
res = script_fn(x)
self.assertEqual(ref, res)

2
test/jit/test_tracer.py
View File

@ -2025,7 +2025,7 @@ class TestTracer(JitTestCase):
module = torch.jit.trace_module(n, inputs)
check_inputs = []
for i in range(2):
for _ in range(2):
check_weight = torch.rand(1, 1, 3, 3)
check_forward_input = torch.rand(1, 1, 3, 3)
check_inputs.append(

2
test/jit/test_types.py
View File

@ -341,7 +341,7 @@ class TestTypesAndAnnotation(JitTestCase):
self.x = x
def set(self, val: int):
for i in range(3):
for _ in range(3):
self.x: int = val
# Type annotation in __init__, should not fail

24
test/test_autograd.py
View File

@ -582,14 +582,14 @@ class TestAutograd(TestCase):
ctx_1 = torch.autograd.graph.saved_tensors_hooks(lambda x: x, unpack)
ctx_2 = torch.autograd.graph.saved_tensors_hooks(lambda x: x, lambda x: x)
for i in range(10):
for _ in range(10):
with ctx_2:
ctx_1.__enter__()
x = torch.randn(3, 3, requires_grad=True)
x.sin().sum().backward()
# Clean up
for i in range(10):
for _ in range(10):
ctx_1.__exit__()
# Validate there are no more hooks on the stack
@ -2989,7 +2989,7 @@ class TestAutograd(TestCase):
state = set()
with torch.enable_grad():
coro = coro_no_grad(state)
for i in range(5):
for _ in range(5):
next(coro)
coro.close()
@ -2998,7 +2998,7 @@ class TestAutograd(TestCase):
state = set()
with torch.no_grad():
coro = coro_enable_grad(state)
for i in range(5):
for _ in range(5):
next(coro)
coro.close()
@ -5293,7 +5293,7 @@ Done""",
rnn = torch.nn.LSTM(10, 20, 2)
total_time_s = 0
with profile(record_shapes=True, use_kineto=kineto_available()) as prof:
for i in range(20):
for _ in range(20):
input = torch.randn(5, 3, 10)
h = torch.randn(2, 3, 20)
c = torch.randn(2, 3, 20)
@ -5925,7 +5925,7 @@ Done""",
self.assertTrue(p_a == p_g or p_b == p_g)
# Run backwards multiple times to ensure accumulation works.
for i in range(10):
for _ in range(10):
loss.backward(retain_graph=True)
# non-contiguous indices and value, we should trigger a copy.
@ -5943,7 +5943,7 @@ Done""",
self.assertFalse(p_b == p_g)
# Run backwards multiple times to ensure accumulation works.
for i in range(10):
for _ in range(10):
loss.backward(retain_graph=True)
def test_gradcheck_single_input(self):
@ -7132,7 +7132,7 @@ for shape in [(1,), ()]:
)
feat_combined = []
for r in range(num_inp):
for _ in range(num_inp):
data_r = torch.empty(1, nz_inp)
data_r.uniform_()
data_r.requires_grad = True
@ -7202,7 +7202,7 @@ for shape in [(1,), ()]:
self.use_checkpoint = use_checkpoint
self.use_reentrant = use_reentrant
self.layers = nn.ModuleList()
for i in range(self.n):
for _ in range(self.n):
layer = nn.Sequential(
nn.Linear(256, 256), nn.Linear(256, 256), nn.Linear(256, 256)
)
@ -7513,7 +7513,7 @@ for shape in [(1,), ()]:
feat_combined = []
feat_combined_no_checkpoint = []
for r in range(num_inp):
for _ in range(num_inp):
data_r = torch.empty(1, nz_inp)
data_r.uniform_()
data_r.requires_grad = input_requires_grad
@ -11714,7 +11714,7 @@ class TestAutogradDeviceType(TestCase):
def test_parameter_resize(self, device):
asd = torch.nn.Parameter(torch.ones(16, dtype=torch.double, device=device))
for i in range(2):
for _ in range(2):
with torch.no_grad():
asd.set_(asd[1:])
asd.grad = None
@ -11942,7 +11942,7 @@ class TestAutogradDeviceType(TestCase):
# Child gpu graph (much longer than parent graph).
prev = t2 * t2
for i in range(10):
for _ in range(10):
prev = prev * t2
reentrant_root = prev

2
test/test_cuda.py
View File

@ -4784,7 +4784,7 @@ print(torch.cuda.get_allocator_backend())
total -= x.numel()
choices = [alloc, free, torch.cuda.memory.empty_cache]
for i in range(N):
for _ in range(N):
while total >= 1024 * 1024 * 1024 / (4 * 10):
free()
(action,) = random.choices(choices, weights=[1, 1 if mem else 0, 0.1])

2
test/test_fake_tensor.py
View File

@ -512,7 +512,7 @@ class FakeTensorTest(TestCase):
def test_upsample_bilinear_small_channels(self):
out = []
mode = FakeTensorMode()
for i, context in enumerate([contextlib.nullcontext, lambda: mode]):
for context in [contextlib.nullcontext, lambda: mode]:
with context():
arg0_1 = torch.empty_strided(
(3, 427, 640), (1, 1920, 3), dtype=torch.float32, device="cuda"

4
test/test_foreach.py
View File

@ -318,13 +318,11 @@ class TestForeach(TestCase):
return arg
scalar_self_arg_test_complete = False
for i, sample in enumerate(
op.sample_inputs(
for sample in op.sample_inputs(
device,
dtype,
noncontiguous=not is_fastpath,
allow_higher_dtype_scalars=True,
)
):
(rhs_arg,) = sample.args
kwargs = {} or sample.kwargs

2
test/test_indexing.py
View File

@ -156,7 +156,7 @@ class TestIndexing(TestCase):
torch.DoubleTensor if not device.startswith("mps") else torch.FloatTensor
)
tensor = _make_tensor(lst).to(device)
for _i in range(100):
for _ in range(100):
idx1_start = random.randrange(10)
idx1_end = idx1_start + random.randrange(1, 10 - idx1_start + 1)
idx1_step = random.randrange(1, 8)

64
test/test_jit.py
View File

@ -2337,9 +2337,9 @@ graph(%Ra, %Rb):
print("stays")
while False:
print("removed")
for _i in range(0):
for _ in range(0):
print("removed")
for _i in range(-4):
for _ in range(-4):
print("removed")
return b
@ -3138,7 +3138,7 @@ class TestScript(JitTestCase):
with enable_profiling_mode_for_profiling_tests():
def fct_loop(x):
for i in range(3):
for _ in range(3):
x = torch.cat((x, x), 0)
return x
@ -3245,7 +3245,7 @@ class TestScript(JitTestCase):
def test_nested_bailouts(self):
@torch.jit.script
def fct_loop(x):
for i in range(3):
for _ in range(3):
x = torch.cat((x, x), 0)
return x
@ -3907,7 +3907,7 @@ def foo(x):
else:
return f'v{idx - len(exprs)}'
for i in range(50):
for _ in range(50):
n = None
while n is None or n > len(exprs) + n_variables:
template = random.choice(templates)
@ -3922,7 +3922,7 @@ def foo(x):
src_lines.append(' return ({})\n'.format(''.join(f'v{i},' for i in range(n_variables))))
return '\n'.join(src_lines)
for i in range(100):
for _ in range(100):
g = {'torch': torch}
code = gen_code()
builtins.exec(code, g, None)
@ -4602,7 +4602,7 @@ def foo(xyz):
y = torch.randn(3, 3, requires_grad=True)
def grad_in_loop(x, y):
for i in range(100):
for _ in range(100):
x = y @ x
return x
@ -5559,7 +5559,7 @@ a")
@torch.jit.script
def test(x):
after_resize_alias = torch.zeros([2])
for _i in range(5):
for _ in range(5):
b = x + 1
f = [1]
before_resize_alias = b.sub_(1)
@ -5950,7 +5950,7 @@ a")
# type: (int) -> int
prev = 1
v = 1
for i in range(x):
for _ in range(x):
save = v
v = v + prev
prev = save
@ -7785,7 +7785,7 @@ dedent """
while int(tensor.add_(1)) < 4:
if y == 1:
continue
for i in range(y):
for _ in range(y):
continue
ret += 1
ret += 1
@ -7896,7 +7896,7 @@ dedent """
def assign_after_break_nested(y):
# type: (int)
x = 0
for i in range(y):
for _ in range(y):
if y == 1:
x = 5
break
@ -7916,7 +7916,7 @@ dedent """
def may_break(y):
# type: (int)
x = 0
for i in range(y):
for _ in range(y):
if y == 1:
x = 5
else:
@ -7988,7 +7988,7 @@ dedent """
def test_varexit(cond):
# type: (int)
m = 0
for i in range(3):
for _ in range(3):
if cond == 2:
if cond == 2:
m = 2
@ -8376,7 +8376,7 @@ dedent """
# find the last output, then all subsequent uses
fc.check(out_name[-1] + " : ")
# skip past node body
for i in range(contained_blocks(node)):
for _ in range(contained_blocks(node)):
fc.check("->")
if (node.kind() == "prim::If"):
fc.check("->").check("->").check("\n")
@ -8429,7 +8429,7 @@ dedent """
a = 1
b = 2
c = 3
for i in range(iter):
for _ in range(iter):
a = 4
b = 5
c = 6
@ -8445,7 +8445,7 @@ dedent """
a = 1
b = 2
c = 3
for i in range(iter):
for _ in range(iter):
c = c + 1
b = b + 1
a = a + 1
@ -10938,7 +10938,7 @@ dedent """
# Test symbolic differentiation
# Run Forward and Backward thrice to trigger autodiff graph
for i in range(3):
for _ in range(3):
y = jit_module(x)
y.backward(grad)
x.grad.zero_()
@ -11030,7 +11030,7 @@ dedent """
W.data /= 4
with enable_profiling_mode_for_profiling_tests():
for i in range(4):
for _ in range(4):
self.assertTrue((foo(x, y, W).grad_fn is None) == (jitted_foo(x, y, W).grad_fn is None))
@ -11822,7 +11822,7 @@ dedent """
def test_for_in_tensors(self):
def test_sizes(x):
sumz = 0
for s in x:
for _ in x:
sumz += 1
return sumz
self.checkScript(test_sizes, (torch.rand(5, 4, 3, 2, 1),))
@ -11834,7 +11834,7 @@ dedent """
@torch.jit.script
def test_sizes(x):
sumz = 0
for s in x:
for _ in x:
sumz += 1
return sumz
@ -11846,7 +11846,7 @@ dedent """
def test_sizes(x):
# type: (float) -> int
sumz = 0
for s in x:
for _ in x:
sumz += 1
return sumz
@ -11856,7 +11856,7 @@ dedent """
def test_sizes(x):
sumz = 0
for n in x:
for t in n:
for _ in n:
sumz += 1
return sumz
@ -12316,7 +12316,7 @@ dedent """
@torch.jit.script_method
def forward(self, x):
for _i in range(4):
for _ in range(4):
x += self.param
return x
@ -12840,7 +12840,7 @@ dedent """
# Load from filename
tracemalloc.start()
for i in range(num_iters):
for _ in range(num_iters):
torch._C.PyTorchFileReader(filename)
_, peak_from_string = tracemalloc.get_traced_memory()
tracemalloc.stop()
@ -12848,7 +12848,7 @@ dedent """
# Load from stream
tracemalloc.start()
with open(filename, 'rb') as f:
for i in range(num_iters):
for _ in range(num_iters):
f.seek(0)
torch._C.PyTorchFileReader(f)
_, peak_from_file = tracemalloc.get_traced_memory()
@ -13287,7 +13287,7 @@ dedent """
def test_pass(self):
def foo(x):
# type: (bool) -> int
for _i in range(3):
for _ in range(3):
pass
if x:
pass
@ -13903,7 +13903,7 @@ dedent """
def test_loop_no_escape(x):
# type: (int)
if x >= 0:
for i in range(x):
for _ in range(x):
raise RuntimeError("hi")
else:
return 5
@ -14116,7 +14116,7 @@ dedent """
def test_will_ret(y):
# type: (int) -> int
for i in range(y):
for _ in range(y):
return 2
return 1
@ -14125,8 +14125,8 @@ dedent """
def test_loop_nest_ret(y):
# type: (int) -> int
for i in range(y):
for i in range(y - 2):
for _ in range(y):
for _ in range(y - 2):
return 10
return 5
return 0
@ -15387,7 +15387,7 @@ dedent """
if isinstance(item, list):
return is_tensor_value(item[0])
return False
for name, value, the_type in self.get_pickle_values():
for name, value, _the_type in self.get_pickle_values():
if is_tensor_value(value):
continue
self.assertEqual(value, getattr(loaded, "_" + name))
@ -15768,7 +15768,7 @@ dedent """
def test_for_else(self):
def fn():
c = 0
for i in range(4):
for _ in range(4):
c += 10
else:
print("In else block of for...else")

2
test/test_jiterator.py
View File

@ -115,7 +115,7 @@ class TestPythonJiterator(TestCase):
@parametrize("num_inputs", [1, 5, 8])
def test_various_num_inputs(self, num_inputs):
inputs = []
for i in range(num_inputs):
for _ in range(num_inputs):
inputs.append(torch.rand(3, device='cuda').mul(10))
input_string = ",".join([f"T i{i}" for i in range(num_inputs)])

8
test/test_multiprocessing.py
View File

@ -58,7 +58,7 @@ class SubProcess(mp.Process):
def _test_cuda_ipc_deadlock_actor(queue, iterations):
for i in range(iterations):
for _ in range(iterations):
if not queue.empty():
queue.get()
time.sleep(0.01)
@ -66,7 +66,7 @@ def _test_cuda_ipc_deadlock_actor(queue, iterations):
def _test_cuda_ipc_deadlock_learner(queue, iterations):
net = torch.nn.LSTM(1, 1).cuda()
for i in range(iterations):
for _ in range(iterations):
if not queue.full():
queue.put(copy.deepcopy(net.state_dict()))
time.sleep(0.01)
@ -138,7 +138,7 @@ def send_tensor_with_untyped_storage(queue, event):
def receive_and_send_sum(queue, out_queue, event, device, dtype, count, size=5):
s = torch.full([size], 0, device=device, dtype=dtype)
for i in range(count):
for _ in range(count):
t = queue.get()
s += t
out_queue.put(s)
@ -146,7 +146,7 @@ def receive_and_send_sum(queue, out_queue, event, device, dtype, count, size=5):
def receive_and_send(queue, out_queue, event, count):
for i in range(count):
for _ in range(count):
t = queue.get()
out_queue.put(t.clone())
event.wait()

12
test/test_nn.py
View File

@ -1238,7 +1238,7 @@ tensor(..., device='meta', size=(1,), requires_grad=True)""")
def check():
self.assertEqual(len(parameter_dict), len(parameters))
for i, (k1, (k2, m2)) in enumerate(zip(parameters, parameter_dict.named_parameters())):
for (k1, (k2, m2)) in zip(parameters, parameter_dict.named_parameters()):
self.assertEqual(k1, k2)
self.assertIs(parameters[k1], m2)
for k1, k2 in zip(parameters, parameter_dict):
@ -2958,7 +2958,7 @@ tensor(..., device='meta', size=(1,), requires_grad=True)""")
batch_first=batch_first)
# set constant weights of the model
for idx, p in enumerate(model.parameters()):
for p in model.parameters():
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
@ -3108,7 +3108,7 @@ tensor(..., device='meta', size=(1,), requires_grad=True)""")
activation, batch_first=batch_first)
# set constant weights of the model
for idx, p in enumerate(model.parameters()):
for p in model.parameters():
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
@ -3185,7 +3185,7 @@ tensor(..., device='meta', size=(1,), requires_grad=True)""")
with torch.no_grad():
# set constant weights of the model
for idx, p in enumerate(layer.parameters()):
for p in layer.parameters():
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
@ -13129,7 +13129,7 @@ if __name__ == '__main__':
model = model.eval()
# set constant weights of the model
for idx, p in enumerate(model.parameters()):
for p in model.parameters():
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
@ -13349,7 +13349,7 @@ if __name__ == '__main__':
model = model.eval()
# set constant weights of the model
for idx, p in enumerate(model.parameters()):
for p in model.parameters():
x = p.data
sz = x.view(-1).size(0)
shape = x.shape

2
test/test_sparse.py
View File

@ -1483,7 +1483,7 @@ class TestSparse(TestSparseBase):
def test_shape(num_mats, dim_i, dim_j, dim_k, nnz):
a_list = []
b_list = []
for mat_idx in range(num_mats):
for _ in range(num_mats):
a_list.append(self._gen_sparse(2, nnz, [dim_i, dim_j], dtype, device, coalesced)[0])
b_list.append(torch.randn([dim_j, dim_k], dtype=dtype, device=device))

4
test/test_sparse_csr.py
View File

@ -4251,9 +4251,9 @@ class TestSparseCompressedTritonKernels(TestCase):
# Test warn_once when requesting non-existing tuned parameters multiple times
f = io.StringIO()
with redirect_stderr(f):
for i in range(5):
for _ in range(5):
get_meta(16, 16, 16)
for i in range(5):
for _ in range(5):
get_meta(16, 16, 32)
msg = f.getvalue()

2
test/test_static_runtime.py
View File

@ -139,7 +139,7 @@ def fork_wait_graph_exception(input1, input2):
def loop_graph(a, b, iters: int):
c = a + b * 2
for i in range(iters):
for _ in range(iters):
c = c + b
c *= 2
c -= a

12
test/test_tensor_creation_ops.py
View File

@ -81,7 +81,7 @@ def _generate_input(shape, dtype, device, with_extremal):
# TODO: replace with make_tensor
def _rand_shape(dim, min_size, max_size):
shape = []
for i in range(dim):
for _ in range(dim):
shape.append(random.randint(min_size, max_size))
return tuple(shape)
@ -942,7 +942,7 @@ class TestTensorCreation(TestCase):
num_tensors = random.randint(1, 5)
torch_input = []
# Create tensors with shape being different along one axis only
for param in range(num_tensors):
for _ in range(num_tensors):
shape[i] = random.randint(1, 5)
torch_input.append(_generate_input(tuple(shape), dtype, device, with_extremal=False))
@ -997,7 +997,7 @@ class TestTensorCreation(TestCase):
ops = ((torch.vstack, np.vstack), (torch.row_stack, np.vstack))
for torch_op, np_op in ops:
self._test_special_stacks(0, 2, torch_op, np_op, device, dtype)
for i in range(5):
for _ in range(5):
# Test dimension change for 1D tensor of size (N) and 2D tensor of size (1, N)
n = random.randint(1, 10)
input_a = _generate_input((n,), dtype, device, with_extremal=False)
@ -1012,7 +1012,7 @@ class TestTensorCreation(TestCase):
@dtypes(*all_types_and_complex_and(torch.half))
def test_dstack(self, device, dtype):
self._test_special_stacks(2, 3, torch.dstack, np.dstack, device, dtype)
for i in range(5):
for _ in range(5):
# Test dimension change for 1D tensor of size (N), 2D tensor of size (1, N), and 3D tensor of size (1, N, 1)
n = random.randint(1, 10)
input_a = _generate_input((n,), dtype, device, with_extremal=False)
@ -2885,7 +2885,7 @@ class TestTensorCreation(TestCase):
@dtypesIfCUDA(torch.float, torch.double, torch.bfloat16, torch.half, torch.long)
@dtypes(torch.float, torch.double, torch.long, torch.bfloat16, torch.float16)
def test_kaiser_window(self, device, dtype):
for num_test in range(50):
for _ in range(50):
self._test_signal_window_functions('kaiser', dtype, device, beta=random.random() * 30)
def _test_signal_windows_functions(self, name, dtype, device, **kwargs):
@ -2918,7 +2918,7 @@ class TestTensorCreation(TestCase):
@unittest.skipIf(not TEST_SCIPY, "Scipy not found")
@dtypes(torch.float, torch.double)
def test_kaiser(self, device, dtype):
for num_test in range(50):
for _ in range(50):
self._test_signal_windows_functions('kaiser', dtype, device, beta=random.random() * 30)
def test_tensor_factories_empty(self, device):

2
test/test_tensorexpr.py
View File

@ -1216,7 +1216,7 @@ class TestTensorExprFuser(BaseTestClass):
@torch.jit.script
def test(x: torch.Tensor, y: torch.Tensor, z: int) -> torch.Tensor:
b = y
for i in range(z):
for _ in range(z):
a = x + y
b = b + y
return b

6
test/test_transformers.py
View File

@ -303,7 +303,7 @@ class TestTransformers(NNTestCase):
encoder = nn.TransformerEncoder(layer, 2).to(device)
optimizer = optim.SGD(encoder.parameters(), lr=0.1, momentum=0.9)
encoder.train()
for i in range(iters):
for _ in range(iters):
encoder.train()
optimizer.zero_grad()
inputs = torch.cat([torch.randn(1, 2, 2), torch.zeros(1, 2, 2)], dim=1).to(device)
@ -537,7 +537,7 @@ class TestTransformers(NNTestCase):
with torch.no_grad():
# set constant weights of the model
for idx, p in enumerate(model.parameters()):
for p in model.parameters():
x = p.data
sz = x.view(-1).size(0)
shape = x.shape
@ -587,7 +587,7 @@ class TestTransformers(NNTestCase):
with torch.no_grad():
# set constant weights of the model
for idx, p in enumerate(layer.parameters()):
for p in layer.parameters():
x = p.data
sz = x.view(-1).size(0)
shape = x.shape