[BE]: Update ruff to 0.285 (pytorch#107519)

This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.

I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)

Pull Request resolved: pytorch#107519
Approved by: https://github.com/ezyang

.lintrunner.toml

@@ -2666,6 +2666,6 @@ init_command = [
     'python3',
     'tools/linter/adapters/pip_init.py',
     '--dry-run={{DRYRUN}}',
-    'ruff==0.0.280',
+    'ruff==0.0.285',
 ]
 is_formatter = true

benchmarks/functional_autograd_benchmark/torchvision_models.py

@@ -168,7 +168,7 @@ def __init__(
         if len(replace_stride_with_dilation) != 3:
             raise ValueError(
                 "replace_stride_with_dilation should be None "
-                "or a 3-element tuple, got {}".format(replace_stride_with_dilation)
+                f"or a 3-element tuple, got {replace_stride_with_dilation}"
             )
         self.groups = groups
         self.base_width = width_per_group

benchmarks/operator_benchmark/benchmark_core.py

@@ -200,10 +200,10 @@ def __init__(self, args):
     def _print_header(self):
         DASH_LINE = "-" * 40
         print(
-            "# {}\n"
+            f"# {DASH_LINE}\n"
             "# PyTorch/Caffe2 Operator Micro-benchmarks\n"
-            "# {}\n"
-            "# Tag : {}\n".format(DASH_LINE, DASH_LINE, self.args.tag_filter)
+            f"# {DASH_LINE}\n"
+            f"# Tag : {self.args.tag_filter}\n"
         )
         if self.args.list_tests:
             print("# List of tests:")

benchmarks/overrides_benchmark/bench.py

@@ -57,10 +57,8 @@ def main():
 
         bench_min, bench_std = bench(tensor_1, tensor_2)
         print(
-            "Type {} had a minimum time of {} us"
-            " and a standard deviation of {} us.".format(
-                t.__name__, (10**6 * bench_min), (10**6) * bench_std
-            )
+            f"Type {t.__name__} had a minimum time of {10**6 * bench_min} us"
+            f" and a standard deviation of {(10**6) * bench_std} us."
         )
 
 

pyproject.toml

@@ -74,6 +74,7 @@ select = [
     "PIE807",
     "PIE810",
     "PLE",
+    "RUF017",
     "TRY302",
 ]
 

test/cpp_api_parity/functional_impl_check.py

@@ -173,7 +173,7 @@ def write_test_to_test_class(
 
     assert not ('cpp_options_args' in test_params_dict and 'cpp_function_call' in test_params_dict), (
         "Only one of `cpp_options_args` and `cpp_function_call` entries "
-        "should be present in test params dict:\n{}").format(pprint.pformat(test_params_dict))
+        f"should be present in test params dict:\n{pprint.pformat(test_params_dict)}")
 
     functional_name = compute_functional_name(test_params_dict)
 

test/cpp_api_parity/module_impl_check.py

@@ -209,11 +209,11 @@ def process_test_params_for_module(test_params_dict, device, test_instance_class
     if 'constructor_args' in test_params_dict:
         assert 'cpp_constructor_args' in test_params_dict, (
             "If `constructor_args` is present in test params dict, to enable C++ API parity test, "
-            "`cpp_constructor_args` must be present in:\n{}"
+            f"`cpp_constructor_args` must be present in:\n{pprint.pformat(test_params_dict)}"
             "If you are interested in adding the C++ API parity test, please see:\n"
             "NOTE [How to check NN module / functional API parity between Python and C++ frontends]. \n"
             "If not, please add `test_cpp_api_parity=False` to the test params dict and file an issue about this."
-        ).format(pprint.pformat(test_params_dict))
+        )
 
     return TorchNNModuleTestParams(
         module_name=module_name,
@@ -233,16 +233,16 @@ def write_test_to_test_class(
     module_name = compute_module_name(test_params_dict)
 
     assert hasattr(torch.nn, module_name), (
-        "`torch.nn` doesn't have module `{}`. "
+        f"`torch.nn` doesn't have module `{module_name}`. "
         "If you are adding a new test, please set `fullname` using format `ModuleName_desc` "
-        "or set `module_name` using format `ModuleName` in the module test dict:\n{}"
-    ).format(module_name, pprint.pformat(test_params_dict))
+        f"or set `module_name` using format `ModuleName` in the module test dict:\n{pprint.pformat(test_params_dict)}"
+    )
 
     module_full_name = 'torch::nn::' + module_name
 
     assert module_full_name in parity_table['torch::nn'], (
-        "Please add `{}` entry to `torch::nn` section of `test/cpp_api_parity/parity-tracker.md`. "
-        "(Discovered while processing\n{}.)").format(module_full_name, pprint.pformat(test_params_dict))
+        f"Please add `{module_full_name}` entry to `torch::nn` section of `test/cpp_api_parity/parity-tracker.md`. "
+        f"(Discovered while processing\n{pprint.pformat(test_params_dict)}.)")
 
     for device in devices:
         test_params = process_test_params_for_module(

test/nn/test_multihead_attention.py

@@ -329,7 +329,7 @@ def test_multihead_attn_3d_attn_mask(self):
         key = torch.rand(batch_size, src_len, embed_dim)  # [N, S, D]
         value = key  # [N, S, D]
         attn_mask = torch.randint(0, 2, (batch_size, tgt_len, src_len)).float()  # [N, T, S]
-        attn_mask = attn_mask.masked_fill(attn_mask == 0, float('-inf')).masked_fill(attn_mask == 1, float(0.0))
+        attn_mask = attn_mask.masked_fill(attn_mask == 0, float('-inf')).masked_fill(attn_mask == 1, 0.0)
 
         mta_model = torch.nn.MultiheadAttention(embed_dim, num_heads)
 

test/onnx/model_defs/squeezenet.py

@@ -32,8 +32,7 @@ def __init__(self, version=1.0, num_classes=1000, ceil_mode=False):
         super().__init__()
         if version not in [1.0, 1.1]:
             raise ValueError(
-                "Unsupported SqueezeNet version {version}:"
-                "1.0 or 1.1 expected".format(version=version)
+                f"Unsupported SqueezeNet version {version}:" "1.0 or 1.1 expected"
             )
         self.num_classes = num_classes
         if version == 1.0:

test/quantization/core/test_docs.py

@@ -82,10 +82,8 @@ def get_correct_path(path_from_pytorch):
 
             # want to make sure we are actually getting some code,
             assert last_line_num - line_num_start > 3 or short_snippet, (
-                "The code in {} identified by {} seems suspiciously short:"
-                "\n\n###code-start####\n{}###code-end####".format(
-                    path_to_file, unique_identifier, code
-                )
+                f"The code in {path_to_file} identified by {unique_identifier} seems suspiciously short:"
+                f"\n\n###code-start####\n{code}###code-end####"
             )
             return code
 

test/quantization/core/test_quantized_op.py

@@ -806,11 +806,11 @@ def _test_binary_op_scalar_relu(self, A, b, binary_op_name, binary_op, quantized
             C_relu, C_relu_hat.q_scale(), C_relu_hat.q_zero_point(), dtype)
 
         self.assertEqual(C_ref.dequantize(), C_hat.dequantize(),
-                         msg="{}_scalar results don't match: "
-                         "{} vs {}".format(binary_op_name, C_ref.dequantize(), C_hat.dequantize()))
+                         msg=f"{binary_op_name}_scalar results don't match: "
+                         f"{C_ref.dequantize()} vs {C_hat.dequantize()}")
         self.assertEqual(C_relu_ref.dequantize(), C_relu_hat.dequantize(),
-                         msg="{}_scalar_relu results don't match: "
-                         "{} vs {}".format(binary_op_name, C_relu_ref.dequantize(), C_relu_hat.dequantize()))
+                         msg=f"{binary_op_name}_scalar_relu results don't match: "
+                         f"{C_relu_ref.dequantize()} vs {C_relu_hat.dequantize()}")
 
     @unittest.skipIf(IS_MACOS, "skipping macos test")
     @given(A=hu.tensor(shapes=hu.array_shapes(1, 4, 1, 5),

test/test_autocast.py

@@ -67,7 +67,7 @@ def compare(first, second):
             if (output is not None) and (output_method is not None):
                 self.assertTrue(type(output) == type(output_method))
                 comparison = compare(output, output_method)
-                self.assertTrue(comparison, "torch.{0} result did not match Tensor.{0} result".format(op))
+                self.assertTrue(comparison, f"torch.{op} result did not match Tensor.{op} result")
 
             # Compare numerics to Python-side "autocasting" that (we expect) does the same thing
             # as the C++-side autocasting, and should be bitwise accurate.

test/test_binary_ufuncs.py

@@ -168,9 +168,9 @@ def _numel(x):
             if _numel(l) <= 100 and _numel(r) <= 100:
                 msg = (
                     "Failed to produce expected results! Input lhs tensor was"
-                    " {}, rhs tensor was {}, torch result is {}, and reference result is"
-                    " {}."
-                ).format(l, r, actual, expected)
+                    f" {l}, rhs tensor was {r}, torch result is {actual}, and reference result is"
+                    f" {expected}."
+                )
             else:
                 msg = None
 

test/test_cpp_extensions_jit.py

@@ -150,17 +150,14 @@ def _check_cuobjdump_output(expected_values, is_ptx=False):
             err = err.decode("ascii")
 
             if not p.returncode == 0 or not err == '':
-                raise AssertionError("Flags: {}\nReturncode: {}\nStderr: {}\n"
-                                     "Output: {} ".format(flags, p.returncode,
-                                                          err, output))
+                raise AssertionError(f"Flags: {flags}\nReturncode: {p.returncode}\nStderr: {err}\n"
+                                     f"Output: {output} ")
 
             actual_arches = sorted(re.findall(r'sm_\d\d', output))
             expected_arches = sorted(['sm_' + xx for xx in expected_values])
             self.assertEqual(actual_arches, expected_arches,
-                             msg="Flags: {},  Actual: {},  Expected: {}\n"
-                                 "Stderr: {}\nOutput: {}".format(
-                                     flags, actual_arches, expected_arches,
-                                     err, output))
+                             msg=f"Flags: {flags},  Actual: {actual_arches},  Expected: {expected_arches}\n"
+                                 f"Stderr: {err}\nOutput: {output}")
 
         temp_dir = tempfile.mkdtemp()
         old_envvar = os.environ.get('TORCH_CUDA_ARCH_LIST', None)

test/test_cuda.py

@@ -168,7 +168,7 @@ def test_out_of_memory_retry(self):
     def test_set_per_process_memory_fraction(self):
         # test invalid fraction value.
         with self.assertRaisesRegex(TypeError, "Invalid type"):
-            torch.cuda.set_per_process_memory_fraction(int(1))
+            torch.cuda.set_per_process_memory_fraction(1)
         with self.assertRaisesRegex(ValueError, "Invalid fraction value"):
             torch.cuda.set_per_process_memory_fraction(-0.1)
         with self.assertRaisesRegex(ValueError, "Invalid fraction value"):
@@ -1765,7 +1765,7 @@ def compare(first, second):
             if (output is not None) and (output_method is not None):
                 self.assertTrue(type(output) == type(output_method))
                 comparison = compare(output, output_method)
-                self.assertTrue(comparison, "torch.{0} result did not match Tensor.{0} result".format(op))
+                self.assertTrue(comparison, f"torch.{op} result did not match Tensor.{op} result")
 
             # Compare numerics to Python-side "autocasting" that (we expect) does the same thing
             # as the C++-side autocasting, and should be bitwise accurate.

test/test_dispatch.py

@@ -152,10 +152,10 @@ def check_invariants(actual_provenance):
                 # NB: this finally test asserts that if a registrations fails,
                 # the dispatcher is left in the same state *that it was before*!
                 check_invariants(
-                    "running ctors {} and then failing to run ctor {} "
+                    f"running ctors {ctor_order[:i]} and then failing to run ctor {op_ix} "
                     "(did this failure leave the dispatcher in a wedged state? "
                     "it shouldn't!)"
-                    .format(ctor_order[:i], op_ix))
+                )
                 break
         last_ctor = i
         if expect_raises and len(active_ops) == len(ops):
@@ -165,7 +165,7 @@ def check_invariants(actual_provenance):
             self.assertTrue(
                 False,
                 "expected exception to be raised, but nothing was raised "
-                "(after running ctors {})".format(ctor_order))
+                f"(after running ctors {ctor_order})")
         # In the order specified by dtor_order, run deregistrations
         for i, op_ix in enumerate(dtor_order):
             # Trigger a destruction

test/test_jit.py

@@ -5810,19 +5810,19 @@ def test_dispatch(op, expects, dtype, binary=False):
                 raise RuntimeError('Unknown dtype')
 
             if binary:
-                code = '''
+                code = f'''
                     graph(%3 : Tensor, %4 : Tensor):
-                        %2 : {dtype}(*, *) = aten::{op}(%3, %4)
-                        %1 : {dtype}(*, *) = aten::relu(%2)
+                        %2 : {dtype_str}(*, *) = aten::{op}(%3, %4)
+                        %1 : {dtype_str}(*, *) = aten::relu(%2)
                         return (%1)
-                '''.format(op=op, dtype=dtype_str)
+                '''
             else:
-                code = '''
+                code = f'''
                     graph(%3 : Tensor):
-                        %2 : {dtype}(*, *) = aten::{op}(%3)
-                        %1 : {dtype}(*, *) = aten::relu(%2)
+                        %2 : {dtype_str}(*, *) = aten::{op}(%3)
+                        %1 : {dtype_str}(*, *) = aten::relu(%2)
                         return (%1)
-                '''.format(op=op, dtype=dtype_str)
+                '''
 
             graph = parse_ir(code)
             inputs = (2 if binary else 1) * [torch.rand(26, 2048, dtype=dtype)]
@@ -14936,7 +14936,7 @@ def jit_multihead_attn_forward(query,                   # type: Tensor
         value = torch.rand((src_l, bsz, embed_size))
 
         mask = (torch.triu(torch.ones(src_l, src_l)) == 1).transpose(0, 1)
-        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)).to(torch.get_default_dtype())
+        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, 0.0).to(torch.get_default_dtype())
 
         jit_out = jit_multihead_attn_forward(query, key, value,
                                              embed_size, nhead,

test/test_mobile_optimizer.py

@@ -582,7 +582,7 @@ def dummy_method_ref_attr_pqr(self):
         self.assertTrue(
             cloned.qualified_name.startswith('__torch__.'),
             ("Expected the cloned module's name to start with the string "
-             "'__torch__.', but got: {}").format(cloned.qualified_name),
+             f"'__torch__.', but got: {cloned.qualified_name}"),
         )
 
 

test/test_mps.py

@@ -9436,8 +9436,8 @@ def get_grid(device='cpu', data=None):
                     output = F.grid_sample(input, grid, mode=mode, padding_mode=padding_mode,
                                            align_corners=align_corners)
                     self.assertEqual(output, groundtruth, atol=1e-5, rtol=0,
-                                     msg="groundtruth comparison failed for mode={}, "
-                                     "padding_mode={}".format(mode, padding_mode))
+                                     msg=f"groundtruth comparison failed for mode={mode}, "
+                                     f"padding_mode={padding_mode}")
 
 class TestAdvancedIndexing(TestCaseMPS):
     supported_dtypes = [torch.float32, torch.float16, torch.int64, torch.int32, torch.int16, torch.uint8]

test/test_nn.py

@@ -5959,8 +5959,8 @@ def get_grid(device='cpu', data=None):
                     output = F.grid_sample(input, grid, mode=mode, padding_mode=padding_mode,
                                            align_corners=align_corners)
                     self.assertEqual(output, groundtruth, atol=1e-5, rtol=0,
-                                     msg="groundtruth comparison failed for mode={}, "
-                                     "padding_mode={}".format(mode, padding_mode))
+                                     msg=f"groundtruth comparison failed for mode={mode}, "
+                                     f"padding_mode={padding_mode}")
 
                     # See NOTE [ grid_sample CPU fallback ]
                     output = torch._grid_sampler_2d_cpu_fallback(
@@ -6047,8 +6047,8 @@ def get_grid(device='cpu', data=None):
                         F.grid_sample(input, grid, mode=mode, padding_mode=padding_mode,
                                       align_corners=align_corners).sum().backward()
                         self.assertEqual(grid.grad, groundtruth, atol=1e-5, rtol=0,
-                                         msg="gradient groundtruth comparison failed for mode={}, "
-                                         "padding_mode={}, input_requires_grad={}".format(mode, padding_mode, input_requires_grad))
+                                         msg=f"gradient groundtruth comparison failed for mode={mode}, "
+                                         f"padding_mode={padding_mode}, input_requires_grad={input_requires_grad}")
                         grid.grad.zero_()
 
                     # See NOTE [ grid_sample CPU fallback ]

test/test_ops.py

@@ -1098,10 +1098,8 @@ def _test_consistency_helper(samples, variants):
                             RuntimeError,
                             msg=(
                                 "inplace variant either incorrectly allowed "
-                                "resizing or you have marked the sample {}"
-                                " incorrectly with `broadcasts_self=True".format(
-                                    sample.summary()
-                                )
+                                f"resizing or you have marked the sample {sample.summary()}"
+                                " incorrectly with `broadcasts_self=True"
                             ),
                         ):
                             variant_forward = variant(

test/test_reductions.py

@@ -3503,8 +3503,8 @@ def to_numpy(input):
             expected = np.asarray(expected)  # transform numpy scalars to numpy.ndarray instances
 
             msg = ("Failed to produce expected results! Input tensor was"
-                   " {}, torch result is {}, and reference result is"
-                   " {}.").format(t, actual, expected) if t.numel() < 10 else None
+                   f" {t}, torch result is {actual}, and reference result is"
+                   f" {expected}.") if t.numel() < 10 else None
 
             self.assertEqual(actual, expected, msg, exact_dtype=exact_dtype)
 

test/test_tensor_creation_ops.py

@@ -451,9 +451,8 @@ def dtype_name(dtype):
             other_dtype = torch.float64 if dtype == torch.float32 else torch.float32
             a = torch.tensor([1, 2], device=device, dtype=dtype)
             b = torch.tensor([3, 4], device=device, dtype=other_dtype)
-            error = "Expected object of scalar type {} but got scalar type " \
-                    "{} for second argument".format(dtype_name(dtype),
-                                                    dtype_name(other_dtype))
+            error = f"Expected object of scalar type {dtype_name(dtype)} but got scalar type " \
+                    f"{dtype_name(other_dtype)} for second argument"
             with self.assertRaisesRegex(RuntimeError, error):
                 op(a, b)
 
@@ -472,9 +471,8 @@ def complex_dtype_name(dtype):
             b = torch.tensor([3, 4], device=device, dtype=dtype)
             out = torch.zeros(2, device=device, dtype=dtype)
             expected_dtype = torch.complex64 if dtype == torch.float32 else torch.complex128
-            error = "Expected object of scalar type {} but got scalar type " \
-                    "{} for argument 'out'".format(
-                        complex_dtype_name(expected_dtype), dtype_name(dtype))
+            error = f"Expected object of scalar type {complex_dtype_name(expected_dtype)} but got scalar type " \
+                    f"{dtype_name(dtype)} for argument 'out'"
             with self.assertRaisesRegex(RuntimeError, error):
                 op(a, b, out=out)