[BE]: Update Ruff to 0.0.280 (pytorch#105724)

Removes unusued loop values in python dictionary iteration. Automated fix from Ruff master

Pull Request resolved: pytorch#105724
Approved by: https://github.com/ezyang, https://github.com/janeyx99

.ci/pytorch/perf_test/compare_with_baseline.py

@@ -59,12 +59,12 @@
 print("z-value: ", z_value)
 
 if z_value >= 3:
-    raise Exception('''\n
+    raise Exception(f'''\n
 z-value >= 3, there is high chance of perf regression.\n
 To reproduce this regression, run
-`cd .ci/pytorch/perf_test/ && bash {}.sh` on your local machine
+`cd .ci/pytorch/perf_test/ && bash {test_name}.sh` on your local machine
 and compare the runtime before/after your code change.
-'''.format(test_name))
+''')
 else:
     print("z-value < 3, no perf regression detected.")
     if args.update:

.github/scripts/trymerge.py

@@ -620,7 +620,7 @@ def get_ghstack_prs(repo: GitRepo, pr: "GitHubPR") -> List[Tuple["GitHubPR", str
     Get the open PRs in the stack that are below this PR.  Throws error if any of the PRs are out of sync.
     """
     assert pr.is_ghstack_pr()
-    entire_stack: List[Tuple["GitHubPR", str]] = []
+    entire_stack: List[Tuple[GitHubPR, str]] = []
     # For ghstack, cherry-pick commits based from origin
     orig_ref = f"{repo.remote}/{re.sub(r'/head$', '/orig', pr.head_ref())}"
     rev_list = repo.revlist(f"{pr.default_branch()}..{orig_ref}")

.lintrunner.toml

@@ -3086,6 +3086,6 @@ init_command = [
     'python3',
     'tools/linter/adapters/pip_init.py',
     '--dry-run={{DRYRUN}}',
-    'ruff==0.0.277',
+    'ruff==0.0.280',
 ]
 is_formatter = true

pyproject.toml

@@ -31,7 +31,7 @@ ignore = [
     "B019", "B020",
     "B023", "B024", "B026",
     "B028", # No explicit `stacklevel` keyword argument found
-    "B904", "B905",
+    "B904",
     "E402",
     "C408", # C408 ignored because we like the dict keyword argument syntax
     "E501", # E501 is not flexible enough, we're using B950 instead
@@ -70,6 +70,7 @@ select = [
     # Not included in flake8
     "UP",
     "PERF",
+    "PGH004",
     "PLE",
     "TRY302",
 ]

test/cpp_api_parity/functional_impl_check.py

@@ -225,7 +225,7 @@ def build_cpp_tests(unit_test_class, print_cpp_source=False):
     assert len(unit_test_class.functional_test_params_map) > 0
     cpp_sources = TORCH_NN_COMMON_TEST_HARNESS + SAMPLE_FUNCTIONAL_CPP_SOURCE
     functions = []
-    for test_name, test_params in unit_test_class.functional_test_params_map.items():
+    for test_params in unit_test_class.functional_test_params_map.values():
         cpp_sources += generate_test_cpp_sources(test_params=test_params, template=TORCH_NN_FUNCTIONAL_TEST_FORWARD)
         functions.append(f'{test_params.functional_variant_name}_test_forward')
     if print_cpp_source:

test/cpp_api_parity/module_impl_check.py

@@ -292,7 +292,7 @@ def build_cpp_tests(unit_test_class, print_cpp_source=False):
     assert len(unit_test_class.module_test_params_map) > 0
     cpp_sources = TORCH_NN_COMMON_TEST_HARNESS + SAMPLE_MODULE_CPP_SOURCE
     functions = []
-    for test_name, test_params in unit_test_class.module_test_params_map.items():
+    for test_params in unit_test_class.module_test_params_map.values():
         cpp_sources += generate_test_cpp_sources(
             test_params=test_params, template=TORCH_NN_MODULE_TEST_FORWARD_BACKWARD)
         functions.append(f'{test_params.module_variant_name}_test_forward_backward')

test/distributed/fsdp/test_fsdp_optim_state.py

@@ -1870,14 +1870,14 @@ def step():
 
             step()
             original_osd = deepcopy(optim.state_dict())
-            for param_id, state in original_osd["state"].items():
+            for state in original_osd["state"].values():
                 # Add customized value
                 state["value1"] = 2.74
                 state["value2"] = None
 
             osd = FSDP.optim_state_dict(model, optim, optim_state_dict=original_osd)
             osd_to_load = FSDP.optim_state_dict_to_load(model, optim, osd)
-            for param_id, state in osd_to_load["state"].items():
+            for state in osd_to_load["state"].values():
                 self.assertEqual(state["value1"], 2.74)
                 self.assertEqual(state["value2"], None)
 

test/distributions/test_distributions.py

@@ -1898,8 +1898,8 @@ def rvs(self, n_sample):
         self._check_sampler_sampler(
             MixtureSameFamily(Categorical(probs=probs), Normal(loc, scale)),
             ScipyMixtureNormal(probs.numpy(), loc.numpy(), scale.numpy()),
-            '''MixtureSameFamily(Categorical(probs={}),
-            Normal(loc={}, scale={}))'''.format(probs, loc, scale))
+            f'''MixtureSameFamily(Categorical(probs={probs}),
+            Normal(loc={loc}, scale={scale}))''')
 
     def test_normal(self):
         loc = torch.randn(5, 5, requires_grad=True)

test/dynamo/test_modules.py

@@ -504,7 +504,7 @@ def __init__(
 
     def forward(self, init_features):
         features = [init_features]
-        for name, layer in self.items():
+        for layer in self.values():
             new_features = layer(features)
             features.append(new_features)
         return torch.cat(features, 1)

test/functorch/discover_coverage.py

@@ -321,7 +321,7 @@ def get_all_tested_ops():
     overridable_outplace_we_care_about = get_public_overridable_outplace_we_care_about()
     op_to_opinfo = get_ops_covered_by_opinfos()
     result = set({})
-    for name, op in get_covered_ops(overridable_outplace_we_care_about).items():
+    for op in get_covered_ops(overridable_outplace_we_care_about).values():
         opinfos = op_to_opinfo[op]
         for opinfo in opinfos:
             result.add(opinfo.name)
@@ -332,7 +332,7 @@ def get_skipped_or_xfailed_ops_for(test_name):
     overridable_outplace_we_care_about = get_public_overridable_outplace_we_care_about()
     op_to_opinfo = get_ops_covered_by_opinfos()
     result = set({})
-    for name, op in get_covered_ops(overridable_outplace_we_care_about).items():
+    for op in get_covered_ops(overridable_outplace_we_care_about).values():
         opinfos = op_to_opinfo[op]
         for opinfo in opinfos:
             for decorator in opinfo.decorators:

test/inductor/test_kernel_benchmark.py

@@ -27,7 +27,7 @@ def setUp(self):
 
     def get_compiled_module(self):
         compiled_module = None
-        for k, v in PyCodeCache.cache.items():
+        for v in PyCodeCache.cache.values():
             if hasattr(v, "benchmark_compiled_module"):
                 self.assertTrue(
                     compiled_module is None, "Found multiple compiled modules"

test/inductor/test_triton_wrapper.py

@@ -12,7 +12,7 @@
 class TestTritonWrapper(TestCase):
     def get_compiled_module(self):
         compiled_module = None
-        for k, v in PyCodeCache.cache.items():
+        for v in PyCodeCache.cache.values():
             if hasattr(v, "benchmark_compiled_module"):
                 self.assertTrue(
                     compiled_module is None, "Found multiple compiled modules"

test/jit/fixtures_srcs/test_upgrader_models_generation.py

@@ -7,7 +7,7 @@
 
 class TestUpgraderModelGeneration(TestCase):
     def test_all_modules(self):
-        for a_module, expect_operator in ALL_MODULES.items():
+        for a_module in ALL_MODULES.keys():
             module_name = type(a_module).__name__
             self.assertTrue(
                 isinstance(a_module, torch.nn.Module),

test/jit/test_list_dict.py

@@ -1964,7 +1964,7 @@ def __init__(self, configs):
                 self.configs = configs
 
             def forward(self, x):
-                for _id, config in self.configs.items():
+                for config in self.configs.values():
                     x += config.size
                 return x
 

test/jit/test_tracer.py

@@ -2383,7 +2383,7 @@ def __init__(self):
 
             def forward(self, feature_map: Dict[str, List[Tensor]]) -> Tensor:
                 output = []
-                for i, j in feature_map.items():
+                for j in feature_map.values():
                     output.append(self.linear(j[0]))
 
                 return torch.stack(output)

test/quantization/eager/test_numeric_suite_eager.py

@@ -104,7 +104,7 @@ def compare_and_validate_results(float_model, q_model):
                 float_model.state_dict(), q_model.state_dict()
             )
             self.assertEqual(len(weight_dict), 1)
-            for k, v in weight_dict.items():
+            for v in weight_dict.values():
                 self.assertTrue(v["float"].shape == v["quantized"].shape)
 
         model_list = [AnnotatedConvModel(qengine), AnnotatedConvBnReLUModel(qengine)]
@@ -126,7 +126,7 @@ def compare_and_validate_results(float_model, q_model):
                 float_model.state_dict(), q_model.state_dict()
             )
             self.assertEqual(len(weight_dict), 1)
-            for k, v in weight_dict.items():
+            for v in weight_dict.values():
                 self.assertTrue(v["float"].shape == v["quantized"].shape)
 
         model_list = [AnnotatedSingleLayerLinearModel(qengine)]
@@ -148,7 +148,7 @@ def compare_and_validate_results(float_model, q_model):
                 float_model.state_dict(), q_model.state_dict()
             )
             self.assertEqual(len(weight_dict), 1)
-            for k, v in weight_dict.items():
+            for v in weight_dict.values():
                 self.assertTrue(len(v["float"]) == len(v["quantized"]))
                 for i, val in enumerate(v["quantized"]):
                     self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
@@ -172,7 +172,7 @@ def compare_and_validate_results(float_model, q_model):
                 float_model.state_dict(), q_model.state_dict()
             )
             self.assertEqual(len(weight_dict), 1)
-            for k, v in weight_dict.items():
+            for v in weight_dict.values():
                 self.assertTrue(len(v["float"]) == len(v["quantized"]))
                 for i, val in enumerate(v["quantized"]):
                     self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
@@ -194,7 +194,7 @@ def test_compare_model_stub_conv_static(self):
         def compare_and_validate_results(float_model, q_model, module_swap_list, data):
             ob_dict = compare_model_stub(float_model, q_model, module_swap_list, data)
             self.assertEqual(len(ob_dict), 1)
-            for k, v in ob_dict.items():
+            for v in ob_dict.values():
                 self.assertTrue(len(v["float"]) == len(v["quantized"]))
                 for i, val in enumerate(v["quantized"]):
                     self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
@@ -221,7 +221,7 @@ def test_compare_model_stub_linear_static(self):
         def compare_and_validate_results(float_model, q_model, module_swap_list, data):
             ob_dict = compare_model_stub(float_model, q_model, module_swap_list, data)
             self.assertEqual(len(ob_dict), 1)
-            for k, v in ob_dict.items():
+            for v in ob_dict.values():
                 self.assertTrue(len(v["float"]) == len(v["quantized"]))
                 for i, val in enumerate(v["quantized"]):
                     self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
@@ -246,7 +246,7 @@ def test_compare_model_stub_partial(self):
         def compare_and_validate_results(float_model, q_model, module_swap_list, data):
             ob_dict = compare_model_stub(float_model, q_model, module_swap_list, data)
             self.assertEqual(len(ob_dict), 1)
-            for k, v in ob_dict.items():
+            for v in ob_dict.values():
                 self.assertTrue(len(v["float"]) == len(v["quantized"]))
                 for i, val in enumerate(v["quantized"]):
                     self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
@@ -301,7 +301,7 @@ def test_compare_model_stub_functional_static(self):
         self.assertTrue(isinstance(q_model.myadd_relu, Shadow))
         self.assertTrue(isinstance(q_model.my_scalar_add, Shadow))
         self.assertTrue(isinstance(q_model.my_scalar_mul, Shadow))
-        for k, v in ob_dict.items():
+        for v in ob_dict.values():
             self.assertTrue(len(v["float"]) == len(v["quantized"]))
             for i, val in enumerate(v["quantized"]):
                 self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
@@ -315,7 +315,7 @@ def test_compare_model_stub_linear_dynamic(self):
         def compare_and_validate_results(float_model, q_model, module_swap_list, data):
             ob_dict = compare_model_stub(float_model, q_model, module_swap_list, data)
             self.assertEqual(len(ob_dict), 1)
-            for k, v in ob_dict.items():
+            for v in ob_dict.values():
                 self.assertTrue(len(v["float"]) == len(v["quantized"]))
                 for i, val in enumerate(v["quantized"]):
                     self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
@@ -344,7 +344,7 @@ def compare_and_validate_results(
                 float_model, q_model, module_swap_list, input, hidden
             )
             self.assertEqual(len(ob_dict), 1)
-            for k, v in ob_dict.items():
+            for v in ob_dict.values():
                 self.assertTrue(len(v["float"]) == len(v["quantized"]))
                 for i, val in enumerate(v["quantized"]):
                     self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
@@ -375,7 +375,7 @@ def compare_and_validate_results(float_model, q_model, data):
             expected_act_compare_dict_keys = {"conv.stats", "quant.stats"}
 
             self.assertTrue(act_compare_dict.keys() == expected_act_compare_dict_keys)
-            for k, v in act_compare_dict.items():
+            for v in act_compare_dict.values():
                 self.assertTrue(v["float"][0].shape == v["quantized"][0].shape)
 
         model_list = [AnnotatedConvModel(qengine), AnnotatedConvBnReLUModel(qengine)]
@@ -398,7 +398,7 @@ def compare_and_validate_results(float_model, q_model, data):
             expected_act_compare_dict_keys = {"fc1.quant.stats", "fc1.module.stats"}
 
             self.assertTrue(act_compare_dict.keys() == expected_act_compare_dict_keys)
-            for k, v in act_compare_dict.items():
+            for v in act_compare_dict.values():
                 self.assertTrue(len(v["float"]) == len(v["quantized"]))
                 for i, val in enumerate(v["quantized"]):
                     self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
@@ -434,7 +434,7 @@ def test_compare_model_outputs_functional_static(self):
             "quant.stats",
         }
         self.assertTrue(act_compare_dict.keys() == expected_act_compare_dict_keys)
-        for k, v in act_compare_dict.items():
+        for v in act_compare_dict.values():
             self.assertTrue(len(v["float"]) == len(v["quantized"]))
             for i, val in enumerate(v["quantized"]):
                 self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
@@ -451,7 +451,7 @@ def compare_and_validate_results(float_model, q_model, data):
             expected_act_compare_dict_keys = {"fc1.stats"}
 
             self.assertTrue(act_compare_dict.keys() == expected_act_compare_dict_keys)
-            for k, v in act_compare_dict.items():
+            for v in act_compare_dict.values():
                 self.assertTrue(len(v["float"]) == len(v["quantized"]))
                 for i, val in enumerate(v["quantized"]):
                     self.assertTrue(v["float"][i].shape == v["quantized"][i].shape)
@@ -480,7 +480,7 @@ def compare_and_validate_results(float_model, q_model, input, hidden):
             expected_act_compare_dict_keys = {"lstm.stats"}
 
             self.assertTrue(act_compare_dict.keys() == expected_act_compare_dict_keys)
-            for k, v in act_compare_dict.items():
+            for v in act_compare_dict.values():
                 self.assertTrue(len(v["float"]) == len(v["quantized"]))
                 for i, val in enumerate(v["quantized"]):
                     self.assertTrue(len(v["float"][i]) == len(v["quantized"][i]))

test/quantization/fx/test_numeric_suite_fx.py

@@ -637,7 +637,7 @@ def test_op_relationship_mapping(self):
         # 4. go through the ops mapped to each QuantizeHandler type, and verify
         # correctness.
         def _op_in_base_sets_of_related_ops(op):
-            for name, ops in base_name_to_sets_of_related_ops.items():
+            for ops in base_name_to_sets_of_related_ops.values():
                 if op in ops:
                     return True
             return False
@@ -1829,7 +1829,7 @@ def test_extend_logger_results_with_comparison(self):
             results, 'fp32', 'int8', compute_cosine_similarity,
             'cosine_similarity_int8_vs_fp32')
 
-        for layer_name, layer_results in results.items():
+        for layer_results in results.values():
             assert 'sqnr_int8_vs_fp32' in \
                 layer_results['weight']['int8'][0].keys()
             assert 'l2_error_int8_vs_fp32' in \

test/run_test.py

@@ -846,7 +846,7 @@ def run_doctests(test_module, test_directory, options):
     if enabled["qengine"] == "auto":
         try:
             # Is there a better check if quantization is enabled?
-            import torch.ao.nn.quantized as nnq  # NOQA
+            import torch.ao.nn.quantized as nnq  # NOQA: F401
 
             torch.backends.quantized.engine = "qnnpack"
             torch.backends.quantized.engine = "fbgemm"
@@ -857,9 +857,9 @@ def run_doctests(test_module, test_directory, options):
 
     if enabled["onnx"] == "auto":
         try:
-            import onnx  # NOQA
-            import onnxruntime  # NOQA
-            import onnxscript  # NOQA
+            import onnx  # NOQA: F401
+            import onnxruntime  # NOQA: F401
+            import onnxscript  # NOQA: F401
         except ImportError:
             exclude_module_list.append("torch.onnx.*")
             enabled["onnx"] = False

test/test_dispatch.py

@@ -782,11 +782,11 @@ def test_find_dangling_impls_ext(self):
         impls = C._dispatch_find_dangling_impls()
         self.assertEqual(1, len(impls))
         self.assertEqual(
-            '''\
+            f'''\
 name: __test::foo
 schema: (none)
-CPU: registered at {}:5 :: () -> () [ boxed unboxed ]
-'''.format(extension_path),
+CPU: registered at {extension_path}:5 :: () -> () [ boxed unboxed ]
+''',
             impls[0])
 
     def test_dispatch_print_registrations_for_dispatch_key_invalid(self):

test/test_fx.py

@@ -3490,7 +3490,7 @@ def f_sum(x):
 
         def f_sum_dict(x):
             out = 0
-            for k, v in x.items():
+            for v in x.values():
                 out += v
             return out
 
@@ -4302,7 +4302,7 @@ def _get_functional(cls):
                 try:
                     sig = inspect.signature(fn)
                     has_tensor_arg = False
-                    for arg, param in sig.parameters.items():
+                    for param in sig.parameters.values():
                         if isinstance(param.annotation, type) and issubclass(param.annotation, torch.Tensor):
                             has_tensor_arg = True
                     if not has_tensor_arg: