Fix flake8 warnings/errors in test_jit.py (#16409)

Summary:
These were really annoying to see in the phabricator UI when trying to land PRs that touched test_jit.py, so this fixes them.

One remaining item is the T484 error. Locally, flake8 still chokes on that line even though I put the noqa comment there (and tried varying whitespaces around it etc). Not sure why it still persists...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16409

Differential Revision: D13832658

Pulled By: jamesr66a

fbshipit-source-id: 46356ba6444ae5ee1a141c28489bdcc7c99e39c0

3 files changed, 69 insertions, 67 deletions

diff --git a/test/expect/TestScript.test_loop_unroll_unused_counter.expect b/test/expect/TestScript.test_loop_unroll_unused_counter.expect
index d0096c5daf..102405be6c 100644
--- a/test/expect/TestScript.test_loop_unroll_unused_counter.expect
+++ b/test/expect/TestScript.test_loop_unroll_unused_counter.expect
@@ -9,7 +9,7 @@ graph(%x : Tensor) {
   %8 : int = aten::mul(%6, %7)
   %9 : int = aten::sub(%4, %8)
   %y.3 : int = prim::Loop(%6, %1, %y.1)
-    block0(%i.1 : int, %12 : int) {
+    block0(%11 : int, %12 : int) {
       %y.12 : int = aten::add(%12, %3)
       %y.5 : int = aten::add(%y.12, %3)
       %y.6 : int = aten::add(%y.5, %3)
@@ -21,7 +21,7 @@ graph(%x : Tensor) {
       -> (%1, %y.11)
     }
   %y : int = prim::Loop(%9, %1, %y.3)
-    block0(%i : int, %23 : int) {
+    block0(%22 : int, %23 : int) {
       %y.4 : int = aten::add(%23, %3)
       -> (%1, %y.4)
     }
diff --git a/test/expect/TestScript.test_loop_unrolling_nested.expect b/test/expect/TestScript.test_loop_unrolling_nested.expect
index 6204965e93..abbc0b559b 100644
--- a/test/expect/TestScript.test_loop_unrolling_nested.expect
+++ b/test/expect/TestScript.test_loop_unrolling_nested.expect
@@ -3,7 +3,7 @@ graph(%x : Tensor) {
   %y.1 : int = prim::Constant[value=0]()
   %3 : int = prim::Constant[value=10]()
   %y : int = prim::Loop(%3, %1, %y.1)
-    block0(%i : int, %6 : int) {
+    block0(%5 : int, %6 : int) {
       %7 : int = prim::Int(%x)
       %8 : int = prim::Constant[value=0]()
       %9 : int = prim::Constant[value=8]()
diff --git a/test/test_jit.py b/test/test_jit.py
index b4de6fb70c..bdd31f40f7 100644
--- a/test/test_jit.py
+++ b/test/test_jit.py
@@ -448,7 +448,7 @@ class JitTestCase(TestCase):
                 vs = vs[:-drop]
             # we don't want all the grad for all the outputs to be the same
             # so we multiply each by a constant
-            return sum([math.log(i + 2) * v.sum() for i, v in enumerate(vs) if v is not None])
+            return sum(math.log(i + 2) * v.sum() for i, v in enumerate(vs) if v is not None)
         if input_tensors is None:
             input_tensors = reference_tensors
 
@@ -1153,7 +1153,7 @@ class TestJit(JitTestCase):
             float(z)  # Warning 4.
             z.tolist()  # Warning 5.
             z.numpy()  # Warning 6.
-            for elem in torch.ones(4, 4):  # Warning 7.
+            for _ in torch.ones(4, 4):  # Warning 7.
                 pass
             return z + 4
 
@@ -1752,7 +1752,7 @@ class TestJit(JitTestCase):
         def foo(bar, baz):
             baz = bar + 3
             quick_brown_fox = torch.neg(baz)
-            for i in range(20):
+            for _ in range(20):
                 yeet = quick_brown_fox - 3.14
             return yeet
 
@@ -2919,7 +2919,7 @@ class TestScript(JitTestCase):
 
         def bar():
             a = torch.jit.annotate(List[int], [])
-            for i in range(10):
+            for _ in range(10):
                 a.append(4)
             return a
 
@@ -3428,45 +3428,45 @@ a")
         inp = consec((4, 8, 5))
         to_check = [
             # [[0, 2], [1, 3]]
-            ['[i, j]', dict(i=[0, 2], j=[1, 3])],
+            ['[i, j]', {'i': [0, 2], 'j': [1, 3]}],
             # [[0, 2], [1, 3], [1, 1]]
-            ['[i, j, k]', dict(i=[0, 2], j=[1, 3], k=[1, 1])],
+            ['[i, j, k]', {'i': [0, 2], 'j': [1, 3], 'k': [1, 1]}],
             # [[0, 2], 1, [1, 1]]
-            ['[i, j, k]', dict(i=[0, 2], j=1, k=[1, 1])],
+            ['[i, j, k]', {'i': [0, 2], 'j': 1, 'k': [1, 1]}],
             # [:, :, [0, 3, 4]]
-            ['[:, :, i]', dict(i=[0, 3, 4])],
+            ['[:, :, i]', {'i': [0, 3, 4]}],
             # [:, [2, 4, 5, 7], 2:4]
-            ['[:, i, 2:4]', dict(i=[0, 2, 3])],
+            ['[:, i, 2:4]', {'i': [0, 2, 3]}],
             # [[2, 3], :, :]
-            ['[i, :, :]', dict(i=[2, 3])],
+            ['[i, :, :]', {'i': [2, 3]}],
             # [:, [0, 2, 3], [1, 3, 4]]
-            ['[:, i, j]', dict(i=[0, 2, 3], j=[1, 3, 4])],
+            ['[:, i, j]', {'i': [0, 2, 3], 'j': [1, 3, 4]}],
             # [:, [0], [1, 2, 4]]
-            ['[:, i, j]', dict(i=[0], j=[1, 2, 4])],
+            ['[:, i, j]', {'i': [0], 'j': [1, 2, 4]}],
             # [:, [0, 1, 3], [4]]
-            ['[:, i, j]', dict(i=[0, 1, 3], j=[4])],
+            ['[:, i, j]', {'i': [0, 1, 3], 'j': [4]}],
             # [:, [[0, 1], [1, 0]], [[2, 3]]]
-            ['[:, i, j]', dict(i=[[0, 1], [1, 0]], j=[[2, 3]])],
+            ['[:, i, j]', {'i': [[0, 1], [1, 0]], 'j': [[2, 3]]}],
             # [:, [[0, 1], [2, 3]], [[0]]]
-            ['[:, i, j]', dict(i=[[0, 1], [2, 3]], j=[[0]])],
+            ['[:, i, j]', {'i': [[0, 1], [2, 3]], 'j': [[0]]}],
             # [:, [[5, 6]], [[0, 3], [4, 4]]]
-            ['[:, i, j]', dict(i=[[5, 6]], j=[[0, 3], [4, 4]])],
+            ['[:, i, j]', {'i': [[5, 6]], 'j': [[0, 3], [4, 4]]}],
             # [[0, 2, 3], [1, 3, 4], :]
-            ['[i, j, :]', dict(i=[0, 2, 3], j=[1, 3, 4])],
+            ['[i, j, :]', {'i': [0, 2, 3], 'j': [1, 3, 4]}],
             # [0, [1, 2, 4], :]
-            ['[i, j, :]', dict(i=0, j=[1, 2, 4])],
+            ['[i, j, :]', {'i': 0, 'j': [1, 2, 4]}],
             # [[0, 1, 3], 4, :]
-            ['[i, j, :]', dict(i=[0, 1, 3], j=4)],
+            ['[i, j, :]', {'i': [0, 1, 3], 'j': 4}],
             # [[[0, 1], [1, 0]], [[2, 1], [3, 5]], :]
-            ['[i, j, :]', dict(i=[[0, 1], [1, 0]], j=[[2, 1], [3, 5]])],
+            ['[i, j, :]', {'i': [[0, 1], [1, 0]], 'j': [[2, 1], [3, 5]]}],
             # [[[0, 1], [1, 0]], [[2, 3]], :]
-            ['[i, j, :]', dict(i=[[0, 1], [1, 0]], j=[[2, 3]])],
+            ['[i, j, :]', {'i': [[0, 1], [1, 0]], 'j': [[2, 3]]}],
             # [[[0, 1], [2, 3]], [[0]], :]
-            ['[i, j, :]', dict(i=[[0, 1], [2, 3]], j=[[0]])],
+            ['[i, j, :]', {'i': [[0, 1], [2, 3]], 'j': [[0]]}],
             # [[[2, 1]], [[0, 3], [4, 4]], :]
-            ['[i, j, :]', dict(i=[[2, 1]], j=[[0, 3], [4, 4]])],
+            ['[i, j, :]', {'i': [[2, 1]], 'j': [[0, 3], [4, 4]]}],
             # [[[2]], [[0, 3], [4, 1]], 0:2]
-            ['[i, j, 0:2]', dict(i=[[2]], j=[[0, 3], [4, 1]])],
+            ['[i, j, 0:2]', {'i': [[2]], 'j': [[0, 3], [4, 1]]}],
         ]
 
         for expr, argdict in to_check:
@@ -3900,7 +3900,7 @@ a")
     def test_mutable_list_function_inline(self):
         @torch.jit.script
         def bar(y):
-            # type: (List[int])
+            # type: (List[int]) -> None
             y.append(4)
 
         @torch.jit.script
@@ -5180,7 +5180,7 @@ a")
 
                     @torch.jit.script_method
                     def forward(self, x, hiddens):
-                        # type: (torch.Tensor, Tuple[torch.Tensor, torch.Tensor])
+                        # type: (torch.Tensor, Tuple[torch.Tensor, torch.Tensor]) -> Tuple[torch.Tensor, torch.Tensor]
                         return self.cell(x, hiddens)
             else:
 
@@ -5191,7 +5191,7 @@ a")
 
                     @torch.jit.script_method
                     def forward(self, x, hiddens):
-                        # type: (torch.Tensor, torch.Tensor)
+                        # type: (torch.Tensor, torch.Tensor) -> torch.Tensor
                         return self.cell(x, hiddens)
 
             cell = ScriptWrapper(cell)
@@ -6116,10 +6116,12 @@ a")
                 # type: (BroadcastingListx[int]) -> List[int]
                 return x
 
+        # TODO: the type comment in this seems to trip up flake8 for some reason
+        # even though we have a noqa comment. Figure out why
         with self.assertRaisesRegex(RuntimeError, "Unknown type constructor"):
             @torch.jit.script
             def nested(x, y):
-                # type: (int, Tuple[int, int[2]]) -> List[int]
+                # type: (int, Tuple[int, int[2]]) -> List[int] # noqa: T484
                 return x
 
     def test_ntuple_builtins(self):
@@ -6143,10 +6145,10 @@ a")
 
         @torch.jit.script
         def embedding_norm_script(input, embedding_matrix, max_norm):
-            # type: (Tensor, Tensor, float)
+            # type: (Tensor, Tensor, float) -> None
             F.embedding(input, embedding_matrix, max_norm=0.01)
 
-        for fun in [embedding_norm, embedding_norm_script]:
+        for _ in [embedding_norm, embedding_norm_script]:
             input = torch.tensor([[1, 2, 4, 5], [4, 3, 2, 9]])
             embedding_matrix = torch.randn(10, 3)
 
@@ -7327,7 +7329,7 @@ a")
     def test_loop_unrolling_const(self):
         def fn():
             y = 0
-            for i in range(10):
+            for _ in range(10):
                 y += 1
             return y
 
@@ -7349,7 +7351,7 @@ a")
     def test_loop_unrolling_nested(self):
         def fn(x):
             y = 0
-            for i in range(10):
+            for _ in range(10):
                 for j in range(int(x)):
                     y += j
             return y
@@ -7362,7 +7364,7 @@ a")
     def test_loop_unroll_unused_counter(self):
         def fn(x):
             y = 0
-            for i in range(int(x)):
+            for _ in range(int(x)):
                 y += 1
             return y
 
@@ -7373,7 +7375,7 @@ a")
     def test_loop_unroll_negative(self):
         def fn(x):
             y = 0
-            for i in range(int(x)):
+            for _ in range(int(x)):
                 y += 1
             return y
 
@@ -7402,7 +7404,7 @@ a")
             class ReassignSelfLHS(torch.jit.ScriptModule):
                 @torch.jit.script_method
                 def forward(self, x):
-                    for i in range(20):
+                    for _ in range(20):
                         self = x
                     return self
 
@@ -7414,7 +7416,7 @@ a")
             class ReassignSelfRHS(torch.jit.ScriptModule):
                 @torch.jit.script_method
                 def forward(self, x):
-                    for i in range(20):
+                    for _ in range(20):
                         x = self
                     return self
 
@@ -7453,7 +7455,7 @@ a")
         with self.assertRaisesRegex(RuntimeError, r'range\(\) expects 1 argument but got 0'):
             @torch.jit.script
             def range_no_arg(x):
-                for i in range():
+                for _ in range():
                     x += 1
                 return x
 
@@ -9578,7 +9580,7 @@ a")
         with self.assertRaisesRegex(RuntimeError, "from a loop"):
             @torch.jit.script
             def nest_for_ret(x):
-                for i in range(3):
+                for _ in range(3):
                     if bool(x < 3):
                         return 4
                 return 5
@@ -10471,9 +10473,9 @@ def check_against_reference(self, func, reference_func, args, kwargs=None,
     def allSum(vs):
         if isinstance(vs, torch.Tensor):
             vs = (vs,)
-        return sum([(i + 1) * v.sum()
-                    for i, v in enumerate(vs)
-                    if v is not None and v.dtype.is_floating_point])
+        return sum((i + 1) * v.sum()
+                   for i, v in enumerate(vs)
+                   if v is not None and v.dtype.is_floating_point)
 
     def clone_inputs(requires_grad):
         inputs = [
@@ -11717,27 +11719,27 @@ nn_functional_single_grad = frozenset('test_nn_' + name for name in [
 # additional modules test
 # TODO: delete this list once we make all nn_tests work
 additional_module_tests = [
-    dict(
-        module_name='Bilinear',
-        constructor_args=(S, S, M),
-        input_size=(S, S),
-        extra_args=((S, S),)
-    ),
-    dict(
-        module_name='RNNCell',
-        constructor_args=(S, S),
-        input_size=(S, S),
-    ),
-    dict(
-        module_name='LSTMCell',
-        constructor_args=(S, S),
-        input_size=(S, S),
-    ),
-    dict(
-        module_name='GRUCell',
-        constructor_args=(S, S),
-        input_size=(S, S),
-    ),
+    {
+        'module_name': 'Bilinear',
+        'constructor_args': (S, S, M),
+        'input_size': (S, S),
+        'extra_args': ((S, S),)
+    },
+    {
+        'module_name': 'RNNCell',
+        'constructor_args': (S, S),
+        'input_size': (S, S),
+    },
+    {
+        'module_name': 'LSTMCell',
+        'constructor_args': (S, S),
+        'input_size': (S, S),
+    },
+    {
+        'module_name': 'GRUCell',
+        'constructor_args': (S, S),
+        'input_size': (S, S),
+    },
 ]
 
 
@@ -12189,7 +12191,7 @@ class TestAsync(JitTestCase):
                 super(Traced, self).__init__()
 
             def forward(self, x):
-                return tuple([torch.neg(x), x])
+                return (torch.neg(x), x)
 
         class Module(torch.jit.ScriptModule):
             def __init__(self):
@@ -12234,7 +12236,7 @@ class TestAsync(JitTestCase):
         self.assertGraphContainsExactly(module.graph, kind='aten::neg', num_kind_nodes=3, consider_subgraphs=True)
 
         y = torch.neg(x)
-        self.assertEqual(module(x), tuple([y, y, y, y, x, x]))
+        self.assertEqual(module(x), (y, y, y, y, x, x))
 
     def test_async_script_error(self):
         x = torch.rand(3, 4)