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

Pull Request resolved: https://github.com/pytorch/pytorch/pull/157639
Approved by: https://github.com/yewentao256, https://github.com/jansel
ghstack dependencies: #157638

.lintrunner.toml

@@ -1169,7 +1169,6 @@ exclude_patterns = [
     'test/**',
     'test/test_*',
     'test/[a-hA-h]*/**',
-    'test/dynamo/**',
     'test/distributed/**',
     'torch/**',
     'torch/_*/**',

test/dynamo/test_aot_autograd.py

@@ -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)

test/dynamo/test_aot_autograd_cache.py

@@ -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):

test/dynamo/test_autograd_function.py

@@ -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

test/dynamo/test_ctx_manager.py

@@ -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)
 

test/dynamo/test_dicts.py

@@ -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"])

test/dynamo/test_exceptions.py

@@ -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)
 

test/dynamo/test_export.py

@@ -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.
             [[], [], [], []],
         )
 

test/dynamo/test_functions.py

@@ -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

test/dynamo/test_higher_order_ops.py

@@ -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"):

test/dynamo/test_hooks.py

@@ -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)

test/dynamo/test_inline_and_install.py

@@ -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

test/dynamo/test_metrics_context.py

@@ -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})

test/dynamo/test_misc.py

@@ -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)

test/dynamo/test_modules.py

@@ -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]

test/dynamo/test_repros.py

@@ -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)
 

test/dynamo/test_subclasses.py

@@ -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

test/dynamo/test_subgraphs.py

@@ -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.

test/dynamo/test_trace_rules.py

@@ -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,