Fix B902 lint error: invalid first argument. (#18181)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18181
ghimport-source-id: 9c23551584a1a1b0b7ac246367f3a7ae1c50b315

Stack from [ghstack](https://github.com/ezyang/ghstack):
* #18184 Fix B903 lint: save memory for data classes with slots/namedtuple
* **#18181 Fix B902 lint error: invalid first argument.**
* #18178 Fix B006 lint errors: using mutable structure in default argument.
* #18177 Fix lstrip bug revealed by B005 lint

A variety of sins were committed:
- Some code was dead
- Some code was actually a staticmethod
- Some code just named it the wrong way
- Some code was purposely testing the omitted case

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Differential Revision: D14530876

fbshipit-source-id: 292a371d9a76ddc7bfcfd38b6f0da9165290a58e

.flake8

@@ -6,5 +6,5 @@ max-line-length = 120
 ignore =
     E203,E305,E402,E501,E721,E741,F401,F403,F405,F821,F841,F999,W503,W504,C408,
     # ignores below are temporary, fix them and remove please!
-    B007,B008,B902,B903
+    B007,B008,B903
 exclude = docs/src,venv,third_party,caffe2,scripts,docs/caffe2,tools/amd_build/pyHIPIFY,torch/lib/include,torch/lib/tmp_install,build,torch/include

test/onnx/verify.py

@@ -176,7 +176,7 @@ class Errors(object):
         if self.errors:
             self.fail()
 
-    def recover(parent_self):
+    def recover(self):
         """
         Returns a context manager which can be used to recover in case of
         an error.  Example usage:
@@ -184,6 +184,8 @@ class Errors(object):
         >>> with errs.recover():
         >>>     ...
         """
+        parent_self = self
+
         class Recover(object):
             def __enter__(self):
                 pass
@@ -193,7 +195,7 @@ class Errors(object):
                     return True
         return Recover()
 
-    def addErrCtxt(parent_self, msg):
+    def addErrCtxt(self, msg):
         """
         Returns a context manager which encloses a fragment of code with
         an extra contextual message, e.g., where an error occurred, or a hint
@@ -202,6 +204,8 @@ class Errors(object):
         >>> with errs.addErrCtx("Some text"):
         >>>     ...
         """
+        parent_self = self
+
         class AddContext(object):
             def __enter__(self):
                 parent_self.context.append(msg)

test/test_jit.py

@@ -8331,7 +8331,7 @@ a")
     def test_method_no_self(self):
         with self.assertRaisesRegex(RuntimeError, 'methods must have a self argument'):
             class MethodNoSelf(torch.jit.ScriptModule):
-                @torch.jit.script_method
+                @torch.jit.script_method  # noqa: B902
                 def forward():
                     return torch.zeros(3, 4)
 

test/test_multiprocessing.py

@@ -402,6 +402,7 @@ class TestMultiprocessing(TestCase):
             self.assertEqual(list(tensor), [4, 4, 4, 4])
         p.join()
 
+    @staticmethod
     def _test_event_multiprocess_child(event, p2c, c2p):
         c2p.put(0)  # notify parent child is ready
         p2c.get()  # wait for record in parent
@@ -457,6 +458,7 @@ class TestMultiprocessing(TestCase):
             # create handle on different device from recorded event
             e1.ipc_handle()
 
+    @staticmethod
     def _test_event_handle_importer_consumer(handle, p2c, c2p):
         e1 = torch.cuda.Event.from_ipc_handle(0, handle)
         c2p.put(0)  # notify parent child is ready
@@ -491,6 +493,7 @@ class TestMultiprocessing(TestCase):
         p2c.put(1)  # notify child that parent is done
         p.join()
 
+    @staticmethod
     def _test_event_handle_exporter_consumer(handle, p2c, c2p):
         stream = torch.cuda.Stream()
         with torch.cuda.stream(stream):

test/test_nn.py

@@ -587,7 +587,8 @@ class TestNN(NNTestCase):
                 self.par = nn.ParameterList()
                 self.par.append(nn.Parameter(torch.randn(10)))
 
-            def forward(inp):
+            def forward(self, inp):
+                # NB: dead code
                 return inp.clone()
 
         net = Net()

test/test_torch.py

@@ -100,16 +100,6 @@ class BytesIOContext(io.BytesIO):
 # This is intentionally prefixed by an underscore. Otherwise pytest will try to
 # run its methods as test cases.
 class _TestTorchMixin(object):
-    def _check_sum_dim(tensors, dim):
-        for tensor in tensors:
-            expected = tensor.numpy().sum(dim)
-            actual = tensor.sum(dim)
-            self.assertEqual(expected.shape, actual.shape)
-            if actual.dtype == torch.float:
-                self.assertTrue(np.allclose(expected, actual.numpy(), rtol=1e-03, atol=1e-05))
-            else:
-                self.assertTrue(np.allclose(expected, actual.numpy()))
-
     def _make_tensors(self, shape, val_range=(-100, 100), use_floating=True, use_integral=True):
         float_types = [torch.double,
                        torch.float]

torch/autograd/variable.py

@@ -3,7 +3,7 @@ from torch._six import with_metaclass
 
 
 class VariableMeta(type):
-    def __instancecheck__(self, other):
+    def __instancecheck__(cls, other):
         return isinstance(other, torch.Tensor)
 
 

torch/jit/__init__.py

@@ -979,21 +979,23 @@ class ScriptMeta(type(torch._C.ScriptModule)):
     # this has to inherit from pybind11's metaclass otherwise we get
     # issues because ScriptModule inherits from torch._C.ScriptModule,
     # a pybind11 type
-    def __init__(cls, name, bases, attrs):
+    def __init__(self, name, bases, attrs):
         # find all the script methods
-        cls._original_methods = {}
+        self._original_methods = {}
         methods = []
         for k, v in sorted(attrs.items()):
             if isinstance(v, ScriptMethodStub):
-                delattr(cls, k)
+                delattr(self, k)
                 methods.append(v)
-                cls._original_methods[v.original_method.__name__] = v.original_method
+                self._original_methods[v.original_method.__name__] = v.original_method
         # after the user's __init__ register all the script methods
         # with the module
-        original_init = getattr(cls, '__init__', lambda self: None)
-        super_constants = getattr(super(cls), '_constants_set', set())
-        cls._constants_set = set(getattr(cls, '__constants__', ())).union(super_constants)
-        cls._overloads = dict(getattr(cls, '__overloads__', {}))
+        original_init = getattr(self, '__init__', lambda self: None)
+        super_constants = getattr(super(self), '_constants_set', set())
+        self._constants_set = set(getattr(self, '__constants__', ())).union(super_constants)
+        self._overloads = dict(getattr(self, '__overloads__', {}))
+
+        cls = self
 
         @functools.wraps(original_init)
         def init_then_register(self, *args, **kwargs):
@@ -1005,8 +1007,8 @@ class ScriptMeta(type(torch._C.ScriptModule)):
             original_init(self, *args, **kwargs)
             _create_methods_from_stubs(self, methods)
 
-        cls.__init__ = init_then_register
-        return super(ScriptMeta, cls).__init__(name, bases, attrs)
+        self.__init__ = init_then_register
+        return super(ScriptMeta, self).__init__(name, bases, attrs)
 
 
 if _enabled: