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[BE][5/6] fix typos in test/ (test/dynamo/) (#157639)

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Pull Request resolved: https://github.com/pytorch/pytorch/pull/157639
Approved by: https://github.com/yewentao256, https://github.com/jansel
ghstack dependencies: #157638
This commit is contained in:
Xuehai Pan
2025-07-06 10:59:02 +08:00
committed by PyTorch MergeBot
parent 17687eb792
commit 02715d0876
19 changed files with 40 additions and 41 deletions
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1
.lintrunner.toml
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@ -1169,7 +1169,6 @@ exclude_patterns = [
'test/**',
'test/test_*',
'test/[a-hA-h]*/**',
'test/dynamo/**',
'test/distributed/**',
'torch/**',
'torch/_*/**',

4
test/dynamo/test_aot_autograd.py
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@ -1235,7 +1235,7 @@ SeqNr|OrigAten|SrcFn|FwdSrcFn
def test_donated_buffer2(self):
logger_name = "torch._functorch._aot_autograd.jit_compile_runtime_wrappers"
# we will re-use the graph for g across f1 and f2
# we will reuse the graph for g across f1 and f2
@torch.compile()
def g(activation, param2):
return torch.matmul(activation, param2)
@ -1257,7 +1257,7 @@ SeqNr|OrigAten|SrcFn|FwdSrcFn
def test_donated_buffer3(self):
logger_name = "torch._functorch._aot_autograd.jit_compile_runtime_wrappers"
# we will re-use the graph for g across f1 and f2
# we will reuse the graph for g across f1 and f2
@torch.compile()
def g(activation, param2):
return torch.matmul(activation, param2)

2
test/dynamo/test_aot_autograd_cache.py
View File

@ -471,7 +471,7 @@ class AOTAutogradCacheTests(InductorTestCase):
)
def test_view_replay_bypass(self):
"""
Shoud bypass when view replay is turned on
Should bypass when view replay is turned on
"""
def fn(a):

2
test/dynamo/test_autograd_function.py
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@ -1429,7 +1429,7 @@ class GraphModule(torch.nn.Module):
result = grad_output * dx + grad_dx * 6 * x
# Intentionally return a wrong value to test if the backward is triggered twice.
# Since if the first MyCube.apply returns values w/o requires_grad=True,
# this backward would be only triggered once (the first MyCube.appy call),
# this backward would be only triggered once (the first MyCube.apply call),
# as the second MyCube.apply is inlined by Dynamo and the corresponding backward
# would be generated by autograd engine.
return result * 0.5

4
test/dynamo/test_ctx_manager.py
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@ -388,7 +388,7 @@ class CtxManagerTests(torch._dynamo.test_case.TestCase):
ref1 = fn(x, s1, s1)
res1 = opt_fn(x, s1, s1)
# We have a re-compilation because of chaning inputs
# We have a re-compilation because of changing inputs
self.assertEqual(cnts.frame_count, 2)
self.assertEqual(ref1, res1)
@ -403,7 +403,7 @@ class CtxManagerTests(torch._dynamo.test_case.TestCase):
ref0 = fn(x, s0, s1)
res0 = opt_fn(x, s0, s1)
# We have a re-compilation because of chaning inputs
# We have a re-compilation because of changing inputs
self.assertEqual(cnts.frame_count, 2)
self.assertEqual(ref0, res0)

2
test/dynamo/test_dicts.py
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@ -904,7 +904,7 @@ class DictTests(torch._dynamo.test_case.TestCase):
def fn(x):
# Dynamo should not cause a graph break here because it knows that
# the existing proxy cant point to this new dict
# the existing proxy can't point to this new dict
other_dict = {}
other_dict["d"] = 4
y = torch.sin(x * mp["c"])

2
test/dynamo/test_exceptions.py
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@ -292,7 +292,7 @@ class ExceptionTests(torch._dynamo.test_case.TestCase):
x = torch.randn(4)
fn(x)
# Cant use fullgraph=True because RERAISE is not supported
# Can't use fullgraph=True because RERAISE is not supported
opt_fn = torch.compile(fn, backend="eager")
opt_fn(x)

2
test/dynamo/test_export.py
View File

@ -3536,7 +3536,7 @@ class GraphModule(torch.nn.Module):
[3, 3, 4, 5],
[true_graph, true_graph, false_graph, false_graph],
[true_guard_code, true_guard_code, false_guard_code, false_guard_code],
# Outter shape env should have no guards in it because we never specialize on the outter symbool.
# Outer shape env should have no guards in it because we never specialize on the outer symbool.
[[], [], [], []],
)

2
test/dynamo/test_functions.py
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@ -1240,7 +1240,7 @@ class FunctionTests(torch._dynamo.test_case.TestCase):
@make_test
def test_inline_softmax(x, y):
# This is common in sme huggingface models
# This is common in some huggingface models
return torch.nn.Softmax(dim=-1)(x + y * 2)
@make_test

2
test/dynamo/test_higher_order_ops.py
View File

@ -2133,7 +2133,7 @@ def forward(self, child : torch.Tensor, const_unused : int):
and node.target == torch.ops.higher_order.cond
):
_, _, _, operands = node.args
# Since we compile wit dynamic, each branch takes 4 inputs (buffer, x, z, s1)
# Since we compile with dynamic, each branch takes 4 inputs (buffer, x, z, s1)
self.assertEqual(len(operands), 4)
if node.op == "get_attr":
if str(node.target) in ("cond_true_0, cond_false_0"):

2
test/dynamo/test_hooks.py
View File

@ -746,7 +746,7 @@ class HooksTests(torch._dynamo.test_case.TestCase):
if cnts:
self.assertEqual(cnts.frame_count, 1)
# These same exact assertions run on both eager and compiled
# X goes to x*2 becaue of mul_
# X goes to x*2 because of mul_
self.assertEqual(x, torch.tensor([0.5, 0.5, 0.5]) * 2)
# This test proves grad aliasing works -
self.assertEqual(x.grad, b * 5)

2
test/dynamo/test_inline_and_install.py
View File

@ -57,7 +57,7 @@ unittest.expectedFailure(
)
# These tests do string comparisson on the graphs, and since buffers are now inlined, they
# These tests do string comparison on the graphs, and since buffers are now inlined, they
# are named different, resulting in failure
unittest.expectedFailure(
InlineAndInstallExportTests.test_param_buffer_safe_from_mutation_simple_inline_and_install # noqa: F821

4
test/dynamo/test_metrics_context.py
View File

@ -64,7 +64,7 @@ class TestMetricsContext(TestCase):
def test_update_disallow_overwrite(self):
"""
Validate update won't overwite.
Validate update won't overwrite.
"""
with MetricsContext(self._on_exit) as context:
context.update({"m1": 1, "m2": 2})
@ -73,7 +73,7 @@ class TestMetricsContext(TestCase):
def test_update_allow_overwrite(self):
"""
Validate update will overwite when given param.
Validate update will overwrite when given param.
"""
with MetricsContext(self._on_exit) as context:
context.update({"m1": 1, "m2": 2})

32
test/dynamo/test_misc.py
View File

@ -4052,7 +4052,7 @@ utils_device.CURRENT_DEVICE == None""".split("\n"):
y = x
def make_x_get_set():
# NOTE: this `x` is a different cell object than the outter `x`.
# NOTE: this `x` is a different cell object than the outer `x`.
x = y
def set_x(v):
@ -4844,7 +4844,7 @@ utils_device.CURRENT_DEVICE == None""".split("\n"):
self.assertEqual(cnts.frame_count, 2)
def test_id_guarded_object(self):
class UDO:
class UserDefinedObject:
@torch.compile(backend="eager")
def call(self, x, ref_id):
self_id = id(self)
@ -4857,11 +4857,11 @@ utils_device.CURRENT_DEVICE == None""".split("\n"):
# Make sure we do recompile when id(self) is executed on
# different self objects.
x = torch.ones(2)
obj1 = UDO()
obj1 = UserDefinedObject()
obj1_id = id(obj1)
self.assertEqual(obj1.call(x, obj1_id), torch.ones(2))
obj2 = UDO()
obj2 = UserDefinedObject()
# if we do not install ID_MATCH: ___check_obj_id(L['self'], xxx) this fails.
self.assertEqual(obj2.call(x, obj1_id), torch.zeros(2))
@ -8698,7 +8698,7 @@ utils_device.CURRENT_DEVICE == None""".split("\n"):
),
testcase(expr="f(m.n[0], '1').x.y.z", expected="f(_var3, '1').x.y.z"),
testcase(expr="f(m.n[0], '2').x.y.z", expected="f(_var3, '2').x.y.z"),
# The whole expressiong gets CSE-d, as well as all of its sub-expressions.
# The whole expression gets CSE-d, as well as all of its sub-expressions.
testcase(
expr="self.g(a, b).k",
preface=["_var4 = self.g", "_var5 = _var4(a, b)", "_var6 = _var5.k"],
@ -10493,11 +10493,11 @@ def ___make_guard_fn():
self.assertEqual(actual, expected)
def test_pytree_tree_leaves(self):
implemtations = [("python", python_pytree)]
implementations = [("python", python_pytree)]
if cxx_pytree is not None:
implemtations.append(("cxx", cxx_pytree))
implementations.append(("cxx", cxx_pytree))
for name, module in implemtations:
for name, module in implementations:
with self.subTest(f"pytree implement: {name}"):
def fn(x):
@ -10527,11 +10527,11 @@ def ___make_guard_fn():
self.assertEqual(actual, expected)
def test_pytree_tree_flatten_unflatten(self):
implemtations = [("python", python_pytree)]
implementations = [("python", python_pytree)]
if cxx_pytree is not None:
implemtations.append(("cxx", cxx_pytree))
implementations.append(("cxx", cxx_pytree))
for name, module in implemtations:
for name, module in implementations:
with self.subTest(f"pytree implement: {name}"):
def fn(x, y):
@ -10578,11 +10578,11 @@ def ___make_guard_fn():
self.assertEqual(actual, expected)
def test_pytree_tree_map(self):
implemtations = [("python", python_pytree)]
implementations = [("python", python_pytree)]
if cxx_pytree is not None:
implemtations.append(("cxx", cxx_pytree))
implementations.append(("cxx", cxx_pytree))
for name, module in implemtations:
for name, module in implementations:
with self.subTest(f"pytree implement: {name}"):
def fn(x, y):
@ -11731,7 +11731,7 @@ fn
# Ensure that the generated graph returns only one output. We want the
# add_ on the grad to be part of the graph itself, so that inductor can
# theoretically move the add_ and resutling copy_ nodes at the right
# theoretically move the add_ and resulting copy_ nodes at the right
# place to free memory.
self.assertEqual(len(list(cnt.graphs[0].graph.nodes)[-1].all_input_nodes), 1)
self.assertEqual(z, ref_y)
@ -12293,7 +12293,7 @@ fn
self.ne_called = False
def __ne__(self, other):
# ne_called attr is later checked to ensure that overrideen
# ne_called attr is later checked to ensure that overridden
# `__ne__` is traced
self.ne_called = True
return not self.__eq__(other)

2
test/dynamo/test_modules.py
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@ -1987,7 +1987,7 @@ class OptimizedModuleTest(torch._dynamo.test_case.TestCase):
# Check order of _modules
def fn(x):
for idx, p in enumerate(mod.modules()):
# Something silly to force depedency on the order
# Something silly to force dependency on the order
x += coeffs_for_mod[p] * coeffs[idx]
for idx, p in enumerate(mod.named_modules()):
x += coeffs_for_mod[p[1]] * coeffs[idx]

4
test/dynamo/test_repros.py
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@ -6159,7 +6159,7 @@ def forward(self, s77 : torch.SymInt, s27 : torch.SymInt, L_x_ : torch.Tensor):
self.assertEqual(out_ref, out_test)
@requires_cuda
# This test will fail as flip in combination with particular input lenghts
# This test will fail as flip in combination with particular input lengths
# produces weird results.
# This is under investigations in
# https://github.com/pytorch/pytorch/issues/131805
@ -7449,7 +7449,7 @@ class ReproTestsDevice(torch._dynamo.test_case.TestCase):
# *are* saved for backward, and become back inputs.
# The easier-to-test thing I'm checking for here is that the recompute
# on primals_2 happens in the backward. With the recompute,
# there are 5 _to_copy ops in the backwrad. Without it, there are 4
# there are 5 _to_copy ops in the backward. Without it, there are 4
# (aka if you set torch._functorch.config.treat_parameters_as_free_to_save = False)
self.assertEqual(mode.ops_counter[torch.ops.aten._to_copy.default], 5)

8
test/dynamo/test_subclasses.py
View File

@ -1368,7 +1368,7 @@ class GraphModule(torch.nn.Module):
)
self.assertTrue(torch._is_functional_tensor(backend.example_inputs[1][0]))
# Cannot re-use the version from AOTAutograd, since that uses python functional tensors.
# Cannot reuse the version from AOTAutograd, since that uses python functional tensors.
def to_fun(x):
x_functional = torch._to_functional_tensor(x)
torch._mirror_autograd_meta_to(x, x_functional)
@ -2017,7 +2017,7 @@ class GraphModule(torch.nn.Module):
exp_frame_count=[1, 1, 2, 2],
exp_shape_env_guards=[
[],
# s0 is specialized and guarded in outter shape_env when dynamo checks the guards
# s0 is specialized and guarded in outer shape_env when dynamo checks the guards
["Eq(Piecewise((1, Eq(s0, 3)), (0, True)), 1)"],
[
"Eq(Piecewise((1, Eq(s0, 3)), (0, True)), 1)",
@ -2039,7 +2039,7 @@ class GraphModule(torch.nn.Module):
exp_frame_count=[1, 1, 2, 2],
exp_shape_env_guards=[
[],
# s0 is specialized and guarded in outter shape_env when dynamo checks the guards
# s0 is specialized and guarded in outer shape_env when dynamo checks the guards
["Ne(Piecewise((1, Eq(s0, 5)), (0, True)), 1)"],
[
"Ne(Piecewise((1, Eq(s0, 5)), (0, True)), 1)",
@ -3085,7 +3085,7 @@ class GraphModule(torch.nn.Module):
# triggers the eager logic to run, updating the counter and registry.
#
# Notably however, compile differs in two ways from eager:
# (1) The order in which the offsets are assigned ids is differnet
# (1) The order in which the offsets are assigned ids is different
# the registry would be set in the order the offsets are returned
# which is not necessarily the same order as they were constructed.
# (2) If a NestedTensor is not returned, then the AOTAutograd wrapping

2
test/dynamo/test_subgraphs.py
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@ -401,7 +401,7 @@ class SubGraphTests(torch._dynamo.test_case.TestCase):
y = torch.randn(3)
self.assertEqual(opt_fn(x, y), fn(x, y))
self.assertEqual(opt_fn(x, x), fn(x, x))
# NB: This COULD validly be 2, but we don't test disjointness in the
# NB: This COULD validly be 2, but we don't test disjointedness in the
# guards for when x and y didn't duck size together, so we end up
# with a generic graph that also works when x and y happen to duck
# size together.

2
test/dynamo/test_trace_rules.py
View File

@ -126,7 +126,7 @@ def gen_allowed_objs_and_ids(record=False, c_binding_only=True) -> AllowedObject
torch_name_rule_map = {}
# In some platforms, these functions were loaded as classes instead of functions.
# To mitigate these weired cases, we need this special check.
# To mitigate these weird cases, we need this special check.
def is_special_functions(obj):
return hashable(obj) and obj in {
torch._C._cuda_isCurrentStreamCapturing,