[BE][Ez]: FURB148 - remove useless enumerate calls (#145619)

Remove useless enumerate calls

Pull Request resolved: https://github.com/pytorch/pytorch/pull/145619
Approved by: https://github.com/drisspg

test/distributed/test_c10d_common.py

@@ -182,7 +182,7 @@ class TimeoutTest(TestCase):
                 threads.append(t)
                 t.start()
 
-            for _, thread in enumerate(threads):
+            for thread in threads:
                 thread.join()
 
             # we expect the world_size-1 threads to have failed

test/distributed/test_fake_pg.py

@@ -48,7 +48,7 @@ class TestFakePG(TestCase):
         input_tensor = torch.ones(3, 3) * dist.get_rank()
         output_tensors = [torch.empty_like(input_tensor) for _ in range(2)]
         dist.all_gather(output_tensors, input_tensor)
-        for _, out_tensor in enumerate(output_tensors):
+        for out_tensor in output_tensors:
             self.assertEqual(tuple(out_tensor.shape), (3, 3))
 
     def test_reduce_scatter(self):

test/jit/test_alias_analysis.py

@@ -145,7 +145,7 @@ class TestAliasAnalysis(JitTestCase):
                 def forward(self, x):
                     return x + 2
 
-            for _, fname in enumerate(fnames):
+            for fname in fnames:
                 mod = torch.jit.script(MyModuleCUTest())
                 torch.jit.save(mod, fname)
                 loaded_mod = torch.jit.load(fname)

test/profiler/test_profiler.py

@@ -845,7 +845,7 @@ class TestProfiler(TestCase):
                 super().__init__(*args, **kwargs)
 
         def train():
-            for _, data in enumerate(dataloader):
+            for data in dataloader:
                 x, y = data[0], data[1]
                 y_pred = model(x)
                 loss = criterion(y_pred, y)

test/test_jit_fuser_te.py

@@ -2499,7 +2499,7 @@ class TestTEFuser(JitTestCase):
 
                     for i, func in enumerate(funcs):
                         num_args = i + 1
-                        for j, gen in enumerate(gen_tensor):
+                        for gen in gen_tensor:
                             inps = (gen(n), gen(n), gen(n))
                             func_s = torch.jit.trace(func, inps, check_trace=False)
                             torch._C._jit_pass_erase_shape_information(func_s.graph)

test/test_ops.py

@@ -1686,7 +1686,7 @@ class TestCommon(TestCase):
     def test_meta_consistency_out_dtype_mismatch(self, device, dtype, op):
         samples = op.sample_inputs(device, dtype)
 
-        for i, sample in enumerate(samples):
+        for sample in samples:
             input, args, kwargs = (sample.input, sample.args, sample.kwargs)
 
             try:
@@ -2763,7 +2763,7 @@ class TestFakeTensor(TestCase):
     def _test_fake_crossref_helper(self, device, dtype, op, context):
         samples = op.sample_inputs(device, dtype, requires_grad=True)
 
-        for iter, sample in enumerate(samples):
+        for sample in samples:
             args = [sample.input] + list(sample.args)
             kwargs = sample.kwargs
 

test/test_spectral_ops.py

@@ -1358,7 +1358,7 @@ class TestFFT(TestCase):
                 'onesided': True,
             },
         ]
-        for i, pattern in enumerate(patterns):
+        for pattern in patterns:
             _test_istft_is_inverse_of_stft(pattern)
 
     @onlyNativeDeviceTypes
@@ -1425,7 +1425,7 @@ class TestFFT(TestCase):
                 'onesided': True,
             },
         ]
-        for i, pattern in enumerate(patterns):
+        for pattern in patterns:
             _test_istft_is_inverse_of_stft_with_padding(pattern)
 
     @onlyNativeDeviceTypes

test/torch_np/numpy_tests/core/test_scalarmath.py

@@ -116,7 +116,7 @@ class TestTypes(TestCase):
                 )
 
     def test_type_create(self):
-        for _, atype in enumerate(types):
+        for atype in types:
             a = np.array([1, 2, 3], atype)
             b = atype([1, 2, 3])
             assert_equal(a, b)

tools/gen_vulkan_spv.py

@@ -190,7 +190,7 @@ def preprocess(
     # Indicates whether this is the first line inside Python
     # code block (i.e. for, while, if, elif, else)
     python_block_start = True
-    for i, input_line in enumerate(input_lines):
+    for input_line in input_lines:
         if input_line == "":
             blank_lines += 1
             continue

tools/test/test_set_linter.py

@@ -63,7 +63,7 @@ class TestSetLinter(LinterTestCase):
                 {0: 25, 2: 24, 3: 23, 6: 8, 12: 14, 18: 22},
             ),
         )
-        for i, (s, expected) in enumerate(TESTS):
+        for s, expected in TESTS:
             pl = python_lines(s)
             if s:
                 actual = pl.token_lines[0].bracket_pairs
@@ -83,7 +83,7 @@ class TestSetLinter(LinterTestCase):
             ("{1, 2}", 1),
             ("{One({'a': 1}), Two([{}, {2}, {1, 2}])}", 3),
         )
-        for i, (s, expected) in enumerate(TESTS):
+        for s, expected in TESTS:
             pl = python_lines(s)
             actual = pl.token_lines and pl.token_lines[0].braced_sets
             self.assertEqual(len(actual), expected)

torch/_higher_order_ops/utils.py

@@ -457,7 +457,7 @@ def save_tensors_and_symints_for_backward(ctx, args):
     ), args
     partitioned_args: list[Any] = [[], []]
     pos = []
-    for i, arg in enumerate(args):
+    for arg in args:
         idx = 0 if isinstance(arg, torch.Tensor) else 1
         partitioned_args[idx].append(arg)
         pos.append(idx)

torch/_inductor/codegen/simd.py

@@ -1416,7 +1416,7 @@ class SIMDScheduling(BaseScheduling):
             kernel.finalize_indexing(all_indexing.keys())
 
             # Second pass to do codegen
-            for i, node in enumerate(node_schedule):
+            for node in node_schedule:
                 if node is DisableReduction:
                     stack.enter_context(kernel.disable_reduction())
                 elif node is EnableReduction:

torch/_inductor/fuzzer.py

@@ -893,15 +893,15 @@ def visualize_results(
     """
 
     html_content += "<tr><th>\\</th>"
-    for i, col_name in enumerate(input_list):
+    for col_name in input_list:
         col = "<br>".join(col_name)
         html_content += f"<th>{col}</th>"
     html_content += "</tr></thead><tbody>"
 
     # Add table rows
-    for i, row_name in enumerate(input_list):
+    for row_name in input_list:
         html_content += f"<tr><th>{row_name}</th>"
-        for j, col_name in enumerate(input_list):
+        for col_name in input_list:
             # Determine the status class for the cell
             status_enum = results.lookup((row_name, col_name))
             status_class = ""

torch/_inductor/memory.py

@@ -411,7 +411,7 @@ def topological_sort_lpmf(
 
     # compute the amount of memory that is allocated when a node is scheduled
     # and the amount of memory that can be freed when a node is scheduled
-    for i, node in enumerate(nodes):
+    for node in nodes:
         # 1. if a buffer read by this node is last used by this node
         for buf in node.mpi_node.pred_buffers:
             if buf_info[buf]["outdegree"] == 1:

torch/export/unflatten.py

@@ -634,7 +634,7 @@ class UnflattenedModule(torch.nn.Module):
         for orig_fqn, indexed_call_modules in called_modules.items():
             call_modules = [mod for _, mod in sorted(indexed_call_modules)]
             if len(call_modules) > 1:
-                for i, call_module in enumerate(call_modules):
+                for i in range(len(call_modules)):
                     fqn = _call_name(orig_fqn, i + 1)
                     if fqn not in redirected_call_indices:
                         *prefix, name = fqn.split(".")