Disable PERF* rules in ruff to allow better readability (#16834)

### Description

Disable two PERF* rules in ruff to allow better readability. Rational
commented inline. This change also removes the unused noqa directives
because of the rule change.

### Motivation and Context

Readability

docs/python/_common/onnx_sphinx.py

@@ -683,7 +683,7 @@ def get_onnx_example(op_name):
         try:
             mod = importlib.import_module(m)
             module = m
-        except ImportError:  # noqa: PERF203
+        except ImportError:
             continue
     if module is None:
         # Unable to find an example for 'op_name'.

docs/python/examples/plot_common_errors.py

@@ -86,7 +86,7 @@ for x in [
     try:
         r = sess.run([output_name], {input_name: x})
         print(f"Shape={x.shape} and predicted labels={r}")
-    except (RuntimeError, InvalidArgument) as e:  # noqa: PERF203
+    except (RuntimeError, InvalidArgument) as e:
         print(f"ERROR with Shape={x.shape} - {e}")
 
 for x in [
@@ -99,7 +99,7 @@ for x in [
     try:
         r = sess.run(None, {input_name: x})
         print(f"Shape={x.shape} and predicted probabilities={r[1]}")
-    except (RuntimeError, InvalidArgument) as e:  # noqa: PERF203
+    except (RuntimeError, InvalidArgument) as e:
         print(f"ERROR with Shape={x.shape} - {e}")
 
 #########################
@@ -114,5 +114,5 @@ for x in [
     try:
         r = sess.run([output_name], {input_name: x})
         print(f"Shape={x.shape} and predicted labels={r}")
-    except (RuntimeError, InvalidArgument) as e:  # noqa: PERF203
+    except (RuntimeError, InvalidArgument) as e:
         print(f"ERROR with Shape={x.shape} - {e}")

onnxruntime/python/backend/backend.py

@@ -66,7 +66,7 @@ class OnnxRuntimeBackend(Backend):
                             " Got Domain '{}' version '{}'.".format(domain, opset.version)
                         )
                         return False, error_message
-                except AttributeError:  # noqa: PERF203
+                except AttributeError:
                     # for some CI pipelines accessing helper.OP_SET_ID_VERSION_MAP
                     # is generating attribute error. TODO investigate the pipelines to
                     # fix this error. Falling back to a simple version check when this error is encountered

onnxruntime/python/onnxruntime_inference_collection.py

@@ -191,7 +191,7 @@ class Session:
         missing_input_names = []
         for input in self._inputs_meta:
             if input.name not in feed_input_names and not input.type.startswith("optional"):
-                missing_input_names.append(input.name)  # noqa: PERF401
+                missing_input_names.append(input.name)
         if missing_input_names:
             raise ValueError(
                 f"Required inputs ({missing_input_names}) are missing from input feed ({feed_input_names})."

onnxruntime/python/tools/custom_op_wrapper/create_custom_op_wrapper.py

@@ -96,7 +96,7 @@ class ParseIOInfoAction(argparse.Action):
 
             try:
                 comp_strs = io_str.split(";")
-            except ValueError:  # noqa: PERF203
+            except ValueError:
                 parser.error(f"{opt_str}: {io_meta_name} info must be separated by ';'")
 
             if len(comp_strs) != 3:

onnxruntime/python/tools/kernel_explorer/kernels/batched_gemm_test.py

@@ -78,7 +78,7 @@ def _test_batched_gemm(
         for i in range(batch):
             try:
                 np.testing.assert_allclose(my_cs[i], ref_cs[i], rtol=bounds[i])
-            except Exception as err:  # noqa: PERF203
+            except Exception as err:
                 header = "*" * 30 + impl + "*" * 30
                 print(header, bounds[i])
                 print(err)

onnxruntime/python/tools/kernel_explorer/kernels/gemm_softmax_gemm_permute_test.py

@@ -182,7 +182,7 @@ def _test_gemm_softmax_gemm_permute(
                 is_zero_tol, atol, rtol = 1e-3, 2e-2, 1e-2
                 not_close_to_zeros = np.abs(ref) > is_zero_tol
                 np.testing.assert_allclose(out[not_close_to_zeros], ref[not_close_to_zeros], atol=atol, rtol=rtol)
-        except Exception as err:  # noqa: PERF203
+        except Exception as err:
             header = "*" * 30 + impl + "*" * 30
             print(header)
             print(err)

onnxruntime/python/tools/kernel_explorer/kernels/gemm_test.py

@@ -58,7 +58,7 @@ def _test_gemm(func, dtype: str, transa: bool, transb: bool, m: int, n: int, k:
 
         try:
             np.testing.assert_allclose(my_c, ref_c, rtol=bound)
-        except Exception as err:  # noqa: PERF203
+        except Exception as err:
             header = "*" * 30 + impl + "*" * 30
             print(header)
             print(err)

onnxruntime/python/tools/kernel_explorer/kernels/strided_batched_gemm_test.py

@@ -82,7 +82,7 @@ def _test_strided_batched_gemm(
         for i in range(batch):
             try:
                 np.testing.assert_allclose(my_c[i], ref_c[i], rtol=bounds[i])
-            except Exception as err:  # noqa: PERF203
+            except Exception as err:
                 header = "*" * 30 + impl + "*" * 30
                 print(header, bounds[i])
                 print(err)

onnxruntime/python/tools/pytorch_export_contrib_ops.py

@@ -96,7 +96,7 @@ def unregister():
     for name in _registered_ops:
         try:
             torch.onnx.unregister_custom_op_symbolic(name, _OPSET_VERSION)
-        except AttributeError:  # noqa: PERF203
+        except AttributeError:
             # The symbolic_registry module was removed in PyTorch 1.13.
             # We are importing it here for backwards compatibility
             # because unregister_custom_op_symbolic is not available before PyTorch 1.12

onnxruntime/python/tools/quantization/calibrate.py

@@ -370,7 +370,7 @@ class HistogramCalibrater(CalibraterBase):
         self.tensors_to_calibrate, value_infos = self.select_tensors_to_calibrate(self.model)
         for tensor in self.tensors_to_calibrate:
             if tensor not in self.model_original_outputs:
-                self.model.graph.output.append(value_infos[tensor])  # noqa: PERF401
+                self.model.graph.output.append(value_infos[tensor])
 
         onnx.save(
             self.model,

onnxruntime/python/tools/quantization/onnx_model.py

@@ -186,7 +186,7 @@ class ONNXModel:
         parents = []
         for input in node.input:
             if input in output_name_to_node:
-                parents.append(output_name_to_node[input])  # noqa: PERF401
+                parents.append(output_name_to_node[input])
         return parents
 
     def get_parent(self, node, idx, output_name_to_node=None):
@@ -222,7 +222,7 @@ class ONNXModel:
         for node in graph.node:
             for node_input in node.input:
                 if node_input == initializer.name:
-                    nodes.append(node)  # noqa: PERF401
+                    nodes.append(node)
         return nodes
 
     @staticmethod
@@ -379,7 +379,7 @@ class ONNXModel:
                 and not self.is_graph_output(node.output[0])
                 and node.output[0] not in input_name_to_nodes
             ):
-                unused_nodes.append(node)  # noqa: PERF401
+                unused_nodes.append(node)
 
         self.remove_nodes(unused_nodes)
 

onnxruntime/python/tools/quantization/qdq_loss_debug.py

@@ -145,7 +145,7 @@ def collect_activations(
 
     intermediate_outputs = []
     for input_d in input_reader:
-        intermediate_outputs.append(inference_session.run(None, input_d))  # noqa: PERF401
+        intermediate_outputs.append(inference_session.run(None, input_d))
     if not intermediate_outputs:
         raise RuntimeError("No data is collected while running augmented model!")
 

onnxruntime/python/tools/symbolic_shape_infer.py

@@ -1008,13 +1008,13 @@ class SymbolicShapeInference:
             right_ellipsis_index = right_equation.find(b"...")
             if right_ellipsis_index != -1:
                 for i in range(num_ellipsis_indices):
-                    new_sympy_shape.append(shape[i])  # noqa: PERF401
+                    new_sympy_shape.append(shape[i])
             for c in right_equation:
                 if c != 46:  # c != b'.'
-                    new_sympy_shape.append(letter_to_dim[c])  # noqa: PERF401
+                    new_sympy_shape.append(letter_to_dim[c])
         else:
             for i in range(num_ellipsis_indices):
-                new_sympy_shape.append(shape[i])  # noqa: PERF401
+                new_sympy_shape.append(shape[i])
             for c in left_equation:
                 if c != 44 and c != 46:  # c != b',' and c != b'.':
                     if c in num_letter_occurrences:

onnxruntime/python/tools/tensorrt/perf/benchmark.py

@@ -138,7 +138,7 @@ def run_trt_standalone(trtexec, model_name, model_path, test_data_dir, all_input
         logger.info(loaded_input)
         shape = []
         for j in all_inputs_shape[i]:
-            shape.append(str(j))  # noqa: PERF401
+            shape.append(str(j))
         shape = "x".join(shape)
         shape = name + ":" + shape
         input_shape.append(shape)
@@ -266,7 +266,7 @@ def get_ort_session_inputs_and_outputs(name, session, ort_input):
         for i in range(len(session.get_inputs())):
             sess_inputs[session.get_inputs()[i].name] = ort_input[i]
         for i in range(len(session.get_outputs())):
-            sess_outputs.append(session.get_outputs()[i].name)  # noqa: PERF401
+            sess_outputs.append(session.get_outputs()[i].name)
     return (sess_inputs, sess_outputs)
 
 
@@ -406,7 +406,7 @@ def inference_ort(
                 runtime = runtime[1:]  # remove warmup
             runtimes += runtime
 
-        except Exception as e:  # noqa: PERF203
+        except Exception as e:
             logger.error(e)
             if track_memory:
                 end_memory_tracking(p, success)
@@ -605,7 +605,7 @@ def validate(all_ref_outputs, all_outputs, rtol, atol, percent_mismatch):
                 # abs(desired-actual) < rtol * abs(desired) + atol
                 try:
                     np.testing.assert_allclose(ref_o, o, rtol, atol)
-                except Exception as e:  # noqa: PERF203
+                except Exception as e:
                     if percentage_in_allowed_threshold(e, percent_mismatch):
                         continue
                     logger.error(e)
@@ -2051,7 +2051,7 @@ class ParseDictArgAction(argparse.Action):
         for kv in values.split(","):
             try:
                 k, v = kv.split("=")
-            except ValueError:  # noqa: PERF203
+            except ValueError:
                 parser.error(f"argument {option_string}: Expected '=' between key and value")
 
             if k in dict_arg:

onnxruntime/python/tools/tensorrt/perf/post.py

@@ -146,7 +146,7 @@ def get_memory(memory, model_group):
     memory_columns = [model_title]
     for provider in provider_list:
         if cpu not in provider:
-            memory_columns.append(provider + memory_ending)  # noqa: PERF401
+            memory_columns.append(provider + memory_ending)
     memory_db_columns = [
         model_title,
         cuda,
@@ -273,7 +273,7 @@ def get_latency(latency, model_group):
 
     latency_columns = [model_title]
     for provider in provider_list:
-        latency_columns.append(provider + avg_ending)  # noqa: PERF401
+        latency_columns.append(provider + avg_ending)
     latency_db_columns = table_headers
     latency = adjust_columns(latency, latency_columns, latency_db_columns, model_group)
     return latency

onnxruntime/python/tools/tensorrt/perf/setup_scripts/setup_onnx_zoo.py

@@ -75,7 +75,7 @@ def main():
 
     model_list = []
     for link in links:
-        model_list.append(get_model_info(link))  # noqa: PERF401
+        model_list.append(get_model_info(link))
     write_json(model_list)
 
 

onnxruntime/python/tools/transformers/benchmark.py

@@ -417,7 +417,7 @@ def run_pytorch(
                     result.update(get_latency_result(runtimes, batch_size))
                     logger.info(result)
                     results.append(result)
-                except RuntimeError as e:  # noqa: PERF203
+                except RuntimeError as e:
                     logger.exception(e)
                     torch.cuda.empty_cache()
 
@@ -572,7 +572,7 @@ def run_tensorflow(
                     result.update(get_latency_result(runtimes, batch_size))
                     logger.info(result)
                     results.append(result)
-                except RuntimeError as e:  # noqa: PERF203
+                except RuntimeError as e:
                     logger.exception(e)
                     from numba import cuda
 

onnxruntime/python/tools/transformers/benchmark_helper.py

@@ -249,7 +249,7 @@ def output_summary(results, csv_filename, args):
                 data_names.append(f"b{batch_size}")
             else:
                 for sequence_length in args.sequence_lengths:
-                    data_names.append(f"b{batch_size}_s{sequence_length}")  # noqa: PERF401
+                    data_names.append(f"b{batch_size}_s{sequence_length}")
 
         csv_writer = csv.DictWriter(csv_file, fieldnames=header_names + data_names)
         csv_writer.writeheader()
@@ -386,7 +386,7 @@ def allocateOutputBuffers(output_buffers, output_buffer_max_sizes, device):  # n
     # for each test run.
 
     for i in output_buffer_max_sizes:
-        output_buffers.append(torch.empty(i, dtype=torch.float32, device=device))  # noqa: PERF401
+        output_buffers.append(torch.empty(i, dtype=torch.float32, device=device))
 
 
 def set_random_seed(seed=123):

onnxruntime/python/tools/transformers/convert_tf_models_to_pytorch.py

@@ -197,7 +197,7 @@ def tf2pt_pipeline_test():
         input = torch.randint(low=0, high=config.vocab_size - 1, size=(4, 128), dtype=torch.long)
         try:
             model(input)
-        except RuntimeError as e:  # noqa: PERF203
+        except RuntimeError as e:
             logger.exception(e)
 
 

onnxruntime/python/tools/transformers/convert_to_packing_mode.py

@@ -124,7 +124,7 @@ class PackingMode:
             attributes = []
             for attr in attention.attribute:
                 if attr.name in ["num_heads", "qkv_hidden_sizes", "scale"]:
-                    attributes.append(attr)  # noqa: PERF401
+                    attributes.append(attr)
 
             packed_attention.attribute.extend(attributes)
             packed_attention.domain = "com.microsoft"

onnxruntime/python/tools/transformers/models/bert/eval_squad.py

@@ -150,7 +150,7 @@ def output_summary(results: List[Dict[str, Any]], csv_filename: str, metric_name
         key_names = []
         for sequence_length in sequence_lengths:
             for batch_size in batch_sizes:
-                key_names.append(f"b{batch_size}_s{sequence_length}")  # noqa: PERF401
+                key_names.append(f"b{batch_size}_s{sequence_length}")
 
         csv_writer = csv.DictWriter(csv_file, fieldnames=header_names + key_names)
         csv_writer.writeheader()

onnxruntime/python/tools/transformers/models/gpt2/benchmark_gpt2.py

@@ -364,7 +364,7 @@ def main(args):
                                 # Results of IO binding might be in GPU. Copy outputs to CPU for comparison.
                                 copy_outputs = []
                                 for output in ort_outputs:
-                                    copy_outputs.append(output.cpu().numpy())  # noqa: PERF401
+                                    copy_outputs.append(output.cpu().numpy())
 
                             if gpt2helper.compare_outputs(
                                 outputs,
@@ -404,7 +404,7 @@ def main(args):
                             "onnxruntime_latency": f"{ort_latency:.2f}",
                         }
                         csv_writer.writerow(row)
-                    except Exception:  # noqa: PERF203
+                    except Exception:
                         logger.error("Exception", exc_info=True)
                         return None
 

onnxruntime/python/tools/transformers/models/gpt2/gpt2_helper.py

@@ -75,7 +75,7 @@ class MyGPT2Model(GPT2Model):
             for i in range(num_layer):
                 # Since transformers v4.*, past key and values are separated outputs.
                 # Here we concate them into one tensor to be compatible with Attention operator.
-                present.append(  # noqa: PERF401
+                present.append(
                     torch.cat(
                         (result[1][i][0].unsqueeze(0), result[1][i][1].unsqueeze(0)),
                         dim=0,

onnxruntime/python/tools/transformers/models/gpt2/gpt2_parity.py

@@ -256,7 +256,7 @@ def run_significance_test(rows, output_csv_path):
                     utest_statistic, utest_pvalue = scipy.stats.mannwhitneyu(
                         a, b, use_continuity=True, alternative="two-sided"
                     )  # TODO: shall we use one-sided: less or greater according to "top1_match_rate"
-                except ValueError:  # ValueError: All numbers are identical in mannwhitneyu  # noqa: PERF203
+                except ValueError:  # ValueError: All numbers are identical in mannwhitneyu
                     utest_statistic = None
                     utest_pvalue = None
                 ttest_statistic, ttest_pvalue = scipy.stats.ttest_ind(a, b, axis=None, equal_var=True)

onnxruntime/python/tools/transformers/models/longformer/benchmark_longformer.py

@@ -645,7 +645,7 @@ def run_tests(
 
                         args = parse_arguments(f"{arguments} -t {test_times}".split(" "))
                         latency_results = launch_test(args)
-                    except KeyboardInterrupt as exc:  # noqa: PERF203
+                    except KeyboardInterrupt as exc:
                         raise RuntimeError("Keyboard Interrupted") from exc
                     except Exception:
                         traceback.print_exc()
@@ -687,7 +687,7 @@ def output_summary(results, csv_filename, data_field="average_latency_ms"):
         data_names = []
         for sequence_length in sequence_lengths:
             for batch_size in batch_sizes:
-                data_names.append(f"b{batch_size}_s{sequence_length}")  # noqa: PERF401
+                data_names.append(f"b{batch_size}_s{sequence_length}")
 
         csv_writer = csv.DictWriter(csv_file, fieldnames=header_names + data_names)
         csv_writer.writeheader()

onnxruntime/python/tools/transformers/models/t5/t5_decoder.py

@@ -204,10 +204,10 @@ class T5DecoderInputs:
 
             past = []
             for _ in range(2 * num_layers):
-                past.append(torch.rand(self_attention_past_shape, dtype=float_type, device=device))  # noqa: PERF401
+                past.append(torch.rand(self_attention_past_shape, dtype=float_type, device=device))
 
             for _ in range(2 * num_layers):
-                past.append(torch.rand(cross_attention_past_shape, dtype=float_type, device=device))  # noqa: PERF401
+                past.append(torch.rand(cross_attention_past_shape, dtype=float_type, device=device))
         else:
             past = None
 

onnxruntime/python/tools/transformers/models/whisper/whisper_decoder.py

@@ -167,10 +167,10 @@ class WhisperDecoderInputs:
 
             past = []
             for _ in range(2 * num_layers):
-                past.append(torch.rand(self_attention_past_shape, dtype=float_type, device=device))  # noqa: PERF401
+                past.append(torch.rand(self_attention_past_shape, dtype=float_type, device=device))
 
             for _ in range(2 * num_layers):
-                past.append(torch.rand(cross_attention_past_shape, dtype=float_type, device=device))  # noqa: PERF401
+                past.append(torch.rand(cross_attention_past_shape, dtype=float_type, device=device))
         else:
             past = None
 

onnxruntime/python/tools/transformers/onnx_model.py

@@ -108,14 +108,14 @@ class OnnxModel:
         input_names = []
         for graph in self.graphs():
             for input in graph.input:
-                input_names.append(input.name)  # noqa: PERF401
+                input_names.append(input.name)
         return input_names
 
     def get_graphs_output_names(self):
         output_names = []
         for graph in self.graphs():
             for output in graph.output:
-                output_names.append(output.name)  # noqa: PERF401
+                output_names.append(output.name)
         return output_names
 
     def get_graph_by_node(self, node):
@@ -217,7 +217,7 @@ class OnnxModel:
         nodes = []
         for node in self.nodes():
             if node.op_type == op_type:
-                nodes.append(node)  # noqa: PERF401
+                nodes.append(node)
         return nodes
 
     def get_children(self, node, input_name_to_nodes=None):
@@ -238,7 +238,7 @@ class OnnxModel:
         parents = []
         for input in node.input:
             if input in output_name_to_node:
-                parents.append(output_name_to_node[input])  # noqa: PERF401
+                parents.append(output_name_to_node[input])
         return parents
 
     def get_parent(self, node, i, output_name_to_node=None):
@@ -792,7 +792,7 @@ class OnnxModel:
         nodes = self.nodes()
         for node in nodes:
             if node.op_type == "Constant" and node.output[0] not in input_name_to_nodes:
-                unused_nodes.append(node)  # noqa: PERF401
+                unused_nodes.append(node)
 
         self.remove_nodes(unused_nodes)
 
@@ -837,7 +837,7 @@ class OnnxModel:
         output_to_remove = []
         for output in self.model.graph.output:
             if output.name not in outputs:
-                output_to_remove.append(output)  # noqa: PERF401
+                output_to_remove.append(output)
         for output in output_to_remove:
             self.model.graph.output.remove(output)
 
@@ -882,7 +882,7 @@ class OnnxModel:
         if allow_remove_graph_inputs:
             for input in graph.input:
                 if input.name not in remaining_input_names:
-                    inputs_to_remove.append(input)  # noqa: PERF401
+                    inputs_to_remove.append(input)
             for input in inputs_to_remove:
                 graph.input.remove(input)
 
@@ -1058,7 +1058,7 @@ class OnnxModel:
         graph_inputs = []
         for input in self.model.graph.input:
             if self.get_initializer(input.name) is None:
-                graph_inputs.append(input)  # noqa: PERF401
+                graph_inputs.append(input)
         return graph_inputs
 
     def get_opset_version(self):

onnxruntime/python/tools/transformers/onnx_model_unet.py

@@ -47,7 +47,7 @@ class UnetOnnxModel(BertOnnxModel):
         nodes_to_remove = []
         for div in div_nodes:
             if self.find_constant_input(div, 1.0) == 1:
-                nodes_to_remove.append(div)  # noqa: PERF401
+                nodes_to_remove.append(div)
 
         for node in nodes_to_remove:
             self.replace_input_of_all_nodes(node.output[0], node.input[0])

onnxruntime/python/tools/transformers/shape_optimizer.py

@@ -78,7 +78,7 @@ class BertOnnxModelShapeOptimizer(OnnxModel):
         shape_inputs = []
         for node in self.model.graph.node:
             if node.op_type == "Reshape":
-                shape_inputs.append(node.input[1])  # noqa: PERF401
+                shape_inputs.append(node.input[1])
 
         return shape_inputs
 

onnxruntime/test/python/onnxruntime_test_python.py

@@ -406,7 +406,7 @@ class TestInferenceSession(unittest.TestCase):
                     run_base_test2()
                     run_advanced_test()
 
-                except OSError:  # noqa: PERF203
+                except OSError:
                     continue
                 else:
                     break

onnxruntime/test/python/quantization/test_calibration.py

@@ -35,7 +35,7 @@ class TestDataReader(CalibrationDataReader):
         self.count = 4
         self.input_data_list = []
         for _ in range(self.count):
-            self.input_data_list.append(np.random.normal(0, 0.33, [1, 3, 1, 3]).astype(np.float32))  # noqa: PERF401
+            self.input_data_list.append(np.random.normal(0, 0.33, [1, 3, 1, 3]).astype(np.float32))
 
     def get_next(self):
         if self.preprocess_flag:

onnxruntime/test/python/quantization/test_qdq_loss_debug.py

@@ -93,7 +93,7 @@ class TestDataReader(CalibrationDataReader):
         self.count = 2
         self.input_data_list = []
         for _ in range(self.count):
-            self.input_data_list.append(np.random.normal(0, 0.33, input_shape).astype(np.float32))  # noqa: PERF401
+            self.input_data_list.append(np.random.normal(0, 0.33, input_shape).astype(np.float32))
 
     def get_next(self):
         if self.preprocess_flag:
@@ -144,7 +144,7 @@ class TestSaveActivations(unittest.TestCase):
         data_reader.rewind()
         oracle_outputs = []
         for input_d in data_reader:
-            oracle_outputs.append(infer_session.run(None, input_d))  # noqa: PERF401
+            oracle_outputs.append(infer_session.run(None, input_d))
 
         output_dict = {}
         output_info = infer_session.get_outputs()

onnxruntime/test/python/transformers/test_data/bert_squad_tensorflow2.1_keras2onnx_opset11/generate_tiny_keras2onnx_bert_models.py

@@ -342,7 +342,7 @@ def generate_test_data(
         path = os.path.join(output_path, "test_data_set_" + str(test_case))
         try:
             os.mkdir(path)
-        except OSError:  # noqa: PERF203
+        except OSError:
             print("Creation of the directory %s failed" % path)
         else:
             print("Successfully created the directory %s " % path)

onnxruntime/test/python/transformers/test_data/gpt2_pytorch1.5_opset11/generate_tiny_gpt2_model.py

@@ -451,7 +451,7 @@ def generate_test_data(
         path = os.path.join(output_path, "test_data_set_" + str(test_case))
         try:
             os.mkdir(path)
-        except OSError:  # noqa: PERF203
+        except OSError:
             print("Creation of the directory %s failed" % path)
         else:
             print("Successfully created the directory %s " % path)

orttraining/orttraining/python/training/checkpoint.py

@@ -145,7 +145,7 @@ def _order_paths(paths, D_groups, H_groups):
     world_rank = _utils.state_dict_trainer_options_world_rank_key()
 
     for path in paths:
-        trainer_options_path_tuples.append(  # noqa: PERF401
+        trainer_options_path_tuples.append(
             (_checkpoint_storage.load(path, key=_utils.state_dict_trainer_options_key()), path)
         )
 
@@ -365,7 +365,7 @@ def _get_parallellism_groups(data_parallel_size, horizontal_parallel_size, world
     for data_group_id in range(num_data_groups):
         data_group_ranks = []
         for r in range(data_parallel_size):
-            data_group_ranks.append(data_group_id + horizontal_parallel_size * r)  # noqa: PERF401
+            data_group_ranks.append(data_group_id + horizontal_parallel_size * r)
         data_groups.append(data_group_ranks)
 
     num_horizontal_groups = world_size // horizontal_parallel_size
@@ -373,7 +373,7 @@ def _get_parallellism_groups(data_parallel_size, horizontal_parallel_size, world
     for hori_group_id in range(num_horizontal_groups):
         hori_group_ranks = []
         for r in range(horizontal_parallel_size):
-            hori_group_ranks.append(hori_group_id * horizontal_parallel_size + r)  # noqa: PERF401
+            hori_group_ranks.append(hori_group_id * horizontal_parallel_size + r)
         horizontal_groups.append(hori_group_ranks)
 
     return data_groups, horizontal_groups

orttraining/orttraining/python/training/onnxblock/optim/optim.py

@@ -187,7 +187,7 @@ class AdamW(onnxblock_module.ForwardBlock):
 
         # Prepare the tensor sequence inputs for params and moments
         for input_name in [params_name, gradients_name, first_order_moments_name, second_order_moments_name]:
-            onnx_model.graph.input.append(  # noqa: PERF401
+            onnx_model.graph.input.append(
                 onnx.helper.make_tensor_sequence_value_info(input_name, trainable_parameters[0].data_type, None)
             )
 

orttraining/orttraining/python/training/ort_triton/_lowering.py

@@ -294,7 +294,7 @@ class GraphLowering:
                 producers[output] = node
             for input in node.input:
                 if input in producers:
-                    precessors[node.name].append(producers[input])  # noqa: PERF401
+                    precessors[node.name].append(producers[input])
         for value in precessors.values():
             value.sort(key=sorted_nodes.index, reverse=True)
         for idx in range(len(sorted_nodes) - 1, -1, -1):
@@ -441,9 +441,7 @@ class GraphLowering:
                 assert isinstance(sub_nodes[nxt], ReduceForLoopEnd)
                 for reduce_node in sub_nodes[nxt].reduce_nodes:
                     if reduce_node.outputs[0].name in output_name_map:
-                        reduce_store_nodes.append(  # noqa: PERF401
-                            IONode(reduce_node.outputs[0], kernel_node.offset_calc, False)
-                        )
+                        reduce_store_nodes.append(IONode(reduce_node.outputs[0], kernel_node.offset_calc, False))
                 new_sub_nodes.append(sub_nodes[nxt])
                 nxt += 1
             cur = nxt

orttraining/orttraining/python/training/ort_triton/_sorted_graph.py

@@ -110,7 +110,7 @@ class SortedGraph:
         for node_idx, node in enumerate(self._sorted_nodes):
             inputs = []
             for input in node.input:
-                inputs.append(name_map.get(input, input))  # noqa: PERF401
+                inputs.append(name_map.get(input, input))
             inputs_str = ",".join(inputs)
             outputs = []
             for idx, output in enumerate(node.output):
@@ -180,7 +180,7 @@ class SortedGraph:
             else:
                 input_infos = []
                 for input in node.input:
-                    input_infos.append(self._node_arg_infos[input])  # noqa: PERF401
+                    input_infos.append(self._node_arg_infos[input])
                 output_infos = TypeAndShapeInfer.infer(node, input_infos, self._graph)
                 for idx, output in enumerate(node.output):
                     self._node_arg_infos[output] = output_infos[idx]

orttraining/orttraining/python/training/ort_triton/_utils.py

@@ -52,7 +52,7 @@ def topological_sort(inputs: List[str], nodes: List[NodeProto]) -> List[NodeProt
                 continue
             for consumer in non_const_nodes:
                 if output in consumer.input:
-                    output_consumers[node.name].append(consumer)  # noqa: PERF401
+                    output_consumers[node.name].append(consumer)
 
     # Topological sort.
     visited = set()

orttraining/orttraining/python/training/ort_triton/kernel/_slice_scel.py

@@ -357,7 +357,7 @@ def transform_slice_scel(graph):
     all_nodes = []
     for node in graph.node:
         if node not in remove_nodes:
-            all_nodes.append(node)  # noqa: PERF401
+            all_nodes.append(node)
 
     for node in triton_nodes:
         all_nodes.append(node)  # noqa: PERF402

orttraining/orttraining/python/training/ortmodule/experimental/hierarchical_ortmodule/_hierarchical_ortmodule.py

@@ -36,7 +36,7 @@ class _IteratedORTModule(torch.nn.Module):
         self._it = count - 1
         self._ortmodules = []
         for idx in range(count):
-            self._ortmodules.append(  # noqa: PERF401
+            self._ortmodules.append(
                 ORTModule(
                     module,
                     debug_options=DebugOptions(

orttraining/orttraining/python/training/ortmodule/torch_cpp_extensions/install.py

@@ -20,7 +20,7 @@ def _list_extensions(path):
     for root, _, files in os.walk(path):
         for name in files:
             if name.lower() == "setup.py":
-                extensions.append(os.path.join(root, name))  # noqa: PERF401
+                extensions.append(os.path.join(root, name))
     return extensions
 
 

orttraining/orttraining/python/training/orttrainer.py

@@ -933,7 +933,7 @@ class ORTTrainer:
             # so output will be on the same device as input.
             try:
                 torch.device(target_device)
-            except Exception:  # noqa: PERF203
+            except Exception:
                 # in this case, input/output must on CPU
                 assert input.device.type == "cpu"
                 target_device = "cpu"

orttraining/orttraining/python/training/postprocess.py

@@ -26,7 +26,7 @@ def find_input_node(model, arg):
     for node in model.graph.node:
         for output in node.output:
             if output == arg:
-                result.append(node)  # noqa: PERF401
+                result.append(node)
     return result[0] if len(result) == 1 else None
 
 
@@ -35,7 +35,7 @@ def find_output_node(model, arg):
     for node in model.graph.node:
         for input in node.input:
             if input == arg:
-                result.append(node)  # noqa: PERF401
+                result.append(node)
     return result[0] if len(result) == 1 else result
 
 
@@ -189,7 +189,7 @@ def find_nodes(graph, op_type):
     nodes = []
     for node in graph.node:
         if node.op_type == op_type:
-            nodes.append(node)  # noqa: PERF401
+            nodes.append(node)
     return nodes
 
 
@@ -382,7 +382,7 @@ def layer_norm_transform(model):
     all_nodes = []
     for node in graph.node:
         if node not in remove_nodes:
-            all_nodes.append(node)  # noqa: PERF401
+            all_nodes.append(node)
 
     for node in layer_norm_nodes:
         all_nodes.append(node)  # noqa: PERF402

orttraining/orttraining/test/python/onnxruntime_test_postprocess.py

@@ -64,7 +64,7 @@ class Test_PostPasses(unittest.TestCase):  # noqa: N801
         nodes = []
         for node in model.graph.node:
             if node.op_type == node_type:
-                nodes.append(node)  # noqa: PERF401
+                nodes.append(node)
         return nodes
 
     def get_name(self, name):

orttraining/orttraining/test/python/orttraining_test_data_loader.py

@@ -20,7 +20,7 @@ def ids_tensor(shape, vocab_size, rng=None, name=None):
 
     values = []
     for _ in range(total_dims):
-        values.append(rng.randint(0, vocab_size - 1))  # noqa: PERF401
+        values.append(rng.randint(0, vocab_size - 1))
 
     return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
 
@@ -36,7 +36,7 @@ def floats_tensor(shape, scale=1.0, rng=None, name=None):
 
     values = []
     for _ in range(total_dims):
-        values.append(rng.random() * scale)  # noqa: PERF401
+        values.append(rng.random() * scale)
 
     return torch.tensor(data=values, dtype=torch.float).view(shape).contiguous()
 

orttraining/orttraining/test/python/orttraining_test_hierarchical_ortmodule.py

@@ -213,7 +213,7 @@ def test_hierarchical_ortmodule():
         call_backward(y_ref)
         g_ref = []
         for param in m.parameters():
-            g_ref.append(param.grad.detach())  # noqa: PERF401
+            g_ref.append(param.grad.detach())
 
         m.zero_grad()
 
@@ -224,7 +224,7 @@ def test_hierarchical_ortmodule():
         call_backward(y)
         g = []
         for param in m.parameters():
-            g.append(param.grad.detach())  # noqa: PERF401
+            g.append(param.grad.detach())
 
         # Some sub-modules become ORTModule.
         assert expected_num_ortmodule == count_ortmodule(m)

orttraining/orttraining/test/python/orttraining_test_layer_norm_transform.py

@@ -173,7 +173,7 @@ def layer_norm_transform(model_proto):
     all_nodes = []
     for node in graph_proto.node:
         if node not in removed_nodes:
-            all_nodes.append(node)  # noqa: PERF401
+            all_nodes.append(node)
 
     for node in layer_norm_nodes:
         all_nodes.append(node)  # noqa: PERF402

orttraining/orttraining/test/python/orttraining_test_model_transform.py

@@ -13,7 +13,7 @@ def find_single_output_node(model, arg):
     for node in model.graph.node:
         for input in node.input:
             if input == arg:
-                result.append(node)  # noqa: PERF401
+                result.append(node)
     return result[0] if len(result) == 1 else None
 
 
@@ -63,7 +63,7 @@ def fix_transpose(model):
                 for n in model.graph.node:
                     for input in n.input:
                         if input == weight.name:
-                            result.append(n)  # noqa: PERF401
+                            result.append(n)
                 if len(result) > 1:
                     continue
                 perm = node.attribute[0]
@@ -93,7 +93,7 @@ def fix_transpose(model):
     old_ws = []
     for t in transpose:
         if find_single_output_node(model, t[1].name) is None:
-            old_ws.append(find_weight_index(model, t[1].name))  # noqa: PERF401
+            old_ws.append(find_weight_index(model, t[1].name))
     old_ws.sort(reverse=True)
     for w_i in old_ws:
         del model.graph.initializer[w_i]

orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py

@@ -1497,10 +1497,10 @@ def test_gradient_correctness_einsum(equation):
     rhs_op = equation[pos1 + 1 : pos2]
     lhs_shape = []
     for c in lhs_op:
-        lhs_shape.append(SIZE_MAP[c.upper()])  # noqa: PERF401
+        lhs_shape.append(SIZE_MAP[c.upper()])
     rhs_shape = []
     for c in rhs_op:
-        rhs_shape.append(SIZE_MAP[c.upper()])  # noqa: PERF401
+        rhs_shape.append(SIZE_MAP[c.upper()])
 
     pt_model = NeuralNetEinsum(lhs_shape[-1]).to(device)
     ort_model = ORTModule(copy.deepcopy(pt_model))
@@ -1577,7 +1577,7 @@ def test_gradient_correctness_einsum_2():
             random.shuffle(output_candidates)
             output_candidates = output_candidates[:8]
             for output_candidate in [list(candidate) for candidate in output_candidates]:
-                all_cases.append((lhs_candidate, rhs_candidate, output_candidate))  # noqa: PERF401
+                all_cases.append((lhs_candidate, rhs_candidate, output_candidate))
 
     for case in all_cases:
         equation = to_string(case[0]) + "," + to_string(case[1]) + "->" + to_string(case[2])
@@ -1587,10 +1587,10 @@ def test_gradient_correctness_einsum_2():
         rhs_op = equation[pos1 + 1 : pos2]
         lhs_shape = []
         for c in lhs_op:
-            lhs_shape.append(SIZE_MAP[c.upper()])  # noqa: PERF401
+            lhs_shape.append(SIZE_MAP[c.upper()])
         rhs_shape = []
         for c in rhs_op:
-            rhs_shape.append(SIZE_MAP[c.upper()])  # noqa: PERF401
+            rhs_shape.append(SIZE_MAP[c.upper()])
 
         pt_model = NeuralNetEinsum(lhs_shape[-1]).to(device)
         ort_model = ORTModule(copy.deepcopy(pt_model))
@@ -5895,7 +5895,7 @@ def test_ops_for_padding_elimination(test_cases):
         result = []
         for node in model.graph.node:
             if arg in node.output:
-                result.append(node)  # noqa: PERF401
+                result.append(node)
         return result[0].op_type if len(result) == 1 else None
 
     gathergrad_input_optypes = [find_input_node_type(training_model, arg) for arg in gathergrad_node.input]

orttraining/orttraining/test/python/orttraining_test_orttrainer_bert_toy_onnx.py

@@ -112,7 +112,7 @@ def optimizer_parameters(model):
     no_decay_param_group = []
     for initializer in model.graph.initializer:
         if any(key in initializer.name for key in no_decay_keys):
-            no_decay_param_group.append(initializer.name)  # noqa: PERF401
+            no_decay_param_group.append(initializer.name)
     params = [
         {
             "params": no_decay_param_group,

orttraining/tools/amdgpu/script/rocprof.py

@@ -15,7 +15,7 @@ def get_gpu_lines(path):
         reader = csv.reader(f, delimiter=",")
         for row in reader:
             if row[2].find("TotalDurationNs") < 0:
-                lines.append(row)  # noqa: PERF401
+                lines.append(row)
         return lines
 
 

orttraining/tools/scripts/gpt2_model_transform.py

@@ -28,7 +28,7 @@ def find_input_node(model, arg):
     for node in model.graph.node:
         for output in node.output:
             if output == arg:
-                result.append(node)  # noqa: PERF401
+                result.append(node)
     return result[0] if len(result) == 1 else None
 
 
@@ -37,7 +37,7 @@ def find_output_node(model, arg):
     for node in model.graph.node:
         for input in node.input:
             if input == arg:
-                result.append(node)  # noqa: PERF401
+                result.append(node)
     return result[0] if len(result) == 1 else None
 
 
@@ -136,7 +136,7 @@ def process_concat(model):
         assert reshape_node.op_type == "Reshape"
         new_nodes[get_node_index(model, reshape_node)] = shape
         for n in fuse_nodes:
-            delete_nodes.append(get_node_index(model, n))  # noqa: PERF401
+            delete_nodes.append(get_node_index(model, n))
 
     # insert new shape to reshape
     index = 0
@@ -189,7 +189,7 @@ def fix_transpose(model):
                 for n in model.graph.node:
                     for input in n.input:
                         if input == weight.name:
-                            result.append(n)  # noqa: PERF401
+                            result.append(n)
                 if len(result) > 1:
                     continue
                 perm = node.attribute[0]
@@ -280,7 +280,7 @@ def remove_input_ids_check_subgraph(model):
 
     remove_node_index = []
     for n in removed_nodes:
-        remove_node_index.append(get_node_index(model, n))  # noqa: PERF401
+        remove_node_index.append(get_node_index(model, n))
 
     remove_node_index = list(set(remove_node_index))
     remove_node_index.sort(reverse=True)

orttraining/tools/scripts/layer_norm_transform.py

@@ -141,7 +141,7 @@ def main():
     all_nodes = []
     for node in graph_proto.node:
         if node not in removed_nodes:
-            all_nodes.append(node)  # noqa: PERF401
+            all_nodes.append(node)
 
     for node in layer_norm_nodes:
         all_nodes.append(node)  # noqa: PERF402

orttraining/tools/scripts/model_transform.py

@@ -26,7 +26,7 @@ def find_input_node(model, arg):
     for node in model.graph.node:
         for output in node.output:
             if output == arg:
-                result.append(node)  # noqa: PERF401
+                result.append(node)
     return result[0] if len(result) == 1 else None
 
 
@@ -35,7 +35,7 @@ def find_output_node(model, arg):
     for node in model.graph.node:
         for input in node.input:
             if input == arg:
-                result.append(node)  # noqa: PERF401
+                result.append(node)
     return result[0] if len(result) == 1 else None
 
 
@@ -94,7 +94,7 @@ def process_concat(model):
         if node.op_type == "Concat":
             input_nodes = []
             for input in node.input:
-                input_nodes.append(find_input_node(model, input))  # noqa: PERF401
+                input_nodes.append(find_input_node(model, input))
             # figure out target shape
             shape = []
             for input_node in input_nodes:
@@ -116,7 +116,7 @@ def process_concat(model):
             assert reshape_node.op_type == "Reshape"
             new_nodes[get_node_index(model, reshape_node)] = shape
             for n in fuse_nodes:
-                delete_nodes.append(get_node_index(model, n))  # noqa: PERF401
+                delete_nodes.append(get_node_index(model, n))
     # insert new shape to reshape
     index = 0
     for reshape_node_index in new_nodes:
@@ -218,7 +218,7 @@ def fix_transpose(model):
                 for n in model.graph.node:
                     for input in n.input:
                         if input == weight.name:
-                            result.append(n)  # noqa: PERF401
+                            result.append(n)
                 if len(result) > 1:
                     continue
                 perm = node.attribute[0]
@@ -242,7 +242,7 @@ def fix_transpose(model):
     old_ws = []
     for t in transpose:
         if find_output_node(model, t[1].name) is None:
-            old_ws.append(find_weight_index(model, t[1].name))  # noqa: PERF401
+            old_ws.append(find_weight_index(model, t[1].name))
     old_ws.sort(reverse=True)
     for w_i in old_ws:
         del model.graph.initializer[w_i]

orttraining/tools/scripts/opset12_model_transform.py

@@ -34,7 +34,7 @@ def find_input_node(model, arg):
     for node in model.graph.node:
         for output in node.output:
             if output == arg:
-                result.append(node)  # noqa: PERF401
+                result.append(node)
     return result[0] if len(result) == 1 else None
 
 

orttraining/tools/scripts/performance_investigation.py

@@ -30,11 +30,11 @@ def process_file(onnx_file):
         if node.op_type == "ATen":
             for attr in node.attribute:
                 if attr.name == "operator":
-                    aten_ops.append(f"{node.name}: {attr.s.decode('utf-8')}")  # noqa: PERF401
+                    aten_ops.append(f"{node.name}: {attr.s.decode('utf-8')}")
         if node.op_type == "PythonOp":
             for attr in node.attribute:
                 if attr.name == "name":
-                    python_ops.append(f"{node.name}: {attr.s.decode('utf-8')}")  # noqa: PERF401
+                    python_ops.append(f"{node.name}: {attr.s.decode('utf-8')}")
 
         # Look for stand-alone Dropout node in *_execution_model_<mode>.onnx graph.
         # Examine whether it should be fused with surrounding Add ops into BiasDropout node.

orttraining/tools/scripts/pipeline_model_split.py

@@ -49,7 +49,7 @@ def split_graph(model, split_edge_groups):
                             element_types.append(1)
             for info in model.graph.value_info:
                 if info.name == id:
-                    output_shapes.append(info.type)  # noqa: PERF401
+                    output_shapes.append(info.type)
 
         send_input_signal_name = "send_input_signal" + str(cut_index)
         send_signal = model.graph.input.add()
@@ -279,14 +279,14 @@ def generate_subgraph(model, start_nodes, identity_node_list):
     # remove added identity node before copy to subgraph
     identity_node_index = []
     for n in identity_node_list:
-        identity_node_index.append(get_identity_index_for_deleting(main_graph.graph.node, n))  # noqa: PERF401
+        identity_node_index.append(get_identity_index_for_deleting(main_graph.graph.node, n))
     identity_node_index.sort(reverse=True)
 
     for i in reversed(range(len(main_graph.graph.node))):
         try:
             if i in identity_node_index:
                 del main_graph.graph.node[i]
-        except Exception:  # noqa: PERF203
+        except Exception:
             print("error deleting identity node", i)
 
     all_visited_nodes = []
@@ -316,19 +316,19 @@ def generate_subgraph(model, start_nodes, identity_node_list):
         # gather visited nodes
         visited_nodes = []
         for n in visited0:
-            visited_nodes.append(get_index(main_graph.graph.node, n))  # noqa: PERF401
+            visited_nodes.append(get_index(main_graph.graph.node, n))
         visited_nodes.sort(reverse=True)
 
         # gather visited inputs
         visited_inputs = []
         for n in inputs0:
-            visited_inputs.append(get_index(main_graph.graph.input, n))  # noqa: PERF401
+            visited_inputs.append(get_index(main_graph.graph.input, n))
         visited_inputs.sort(reverse=True)
 
         # gather visited outputs
         visited_outputs = []
         for n in outputs0:
-            visited_outputs.append(get_index(main_graph.graph.output, n))  # noqa: PERF401
+            visited_outputs.append(get_index(main_graph.graph.output, n))
         visited_outputs.sort(reverse=True)
 
         for i in reversed(range(len(main_graph.graph.node))):
@@ -337,7 +337,7 @@ def generate_subgraph(model, start_nodes, identity_node_list):
                     del subgraph.graph.node[i]
                 else:
                     del main_graph.graph.node[i]
-            except Exception:  # noqa: PERF203
+            except Exception:
                 print("error deleting node", i)
 
         for i in reversed(range(len(main_graph.graph.input))):
@@ -346,7 +346,7 @@ def generate_subgraph(model, start_nodes, identity_node_list):
                     del subgraph.graph.input[i]
                 else:
                     del main_graph.graph.input[i]
-            except Exception:  # noqa: PERF203
+            except Exception:
                 print("error deleting inputs", i)
 
         for i in reversed(range(len(main_graph.graph.output))):
@@ -355,7 +355,7 @@ def generate_subgraph(model, start_nodes, identity_node_list):
                     del subgraph.graph.output[i]
                 else:
                     del main_graph.graph.output[i]
-            except Exception:  # noqa: PERF203
+            except Exception:
                 print("error deleting outputs ", i)
 
         print("model", str(model_count), " length ", len(subgraph.graph.node))

pyproject.toml

@@ -71,6 +71,8 @@ ignore = [
     "N812", # Allow import torch.nn.functional as F
     "N999", # Module names
     "NPY002", # np.random.Generator may not always fit our use cases
+    "PERF203", # "try-except-in-loop" only affects Python <3.11, and the improvement is minor; can have false positives
+    "PERF401", # List comprehensions are not always readable
     "SIM102", # We don't perfer always combining if branches
     "SIM108", # We don't encourage ternary operators
     "SIM114", # Don't combine if branches for debugability

tools/ci_build/github/windows/post_binary_sizes_to_dashboard.py

@@ -69,7 +69,7 @@ def write_to_db(binary_size_data, args):
     branch_name = os.environ.get("BUILD_SOURCEBRANCHNAME", "main")
     rows = []
     for row in binary_size_data:
-        rows.append(  # noqa: PERF401
+        rows.append(
             [
                 now_str,
                 args.build_id,

tools/doc/rename_folders.py

@@ -16,7 +16,7 @@ def rename_folder(root):
     for r, dirs, _files in os.walk(root):
         for name in dirs:
             if name.startswith("_"):
-                found.append((r, name))  # noqa: PERF401
+                found.append((r, name))
     renamed = []
     for r, name in found:
         into = name.lstrip("_")

tools/nuget/generate_nuspec_for_native_nuget.py

@@ -87,9 +87,7 @@ def generate_file_list_for_ep(nuget_artifacts_dir, ep, files_list, include_pdbs,
         if child.name == "onnxruntime-android" or child.name == "onnxruntime-training-android":
             for child_file in child.iterdir():
                 if child_file.suffix in [".aar"]:
-                    files_list.append(  # noqa: PERF401
-                        '<file src="' + str(child_file) + '" target="runtimes/android/native"/>'
-                    )
+                    files_list.append('<file src="' + str(child_file) + '" target="runtimes/android/native"/>')
 
         if child.name == "onnxruntime-ios-xcframework":
             files_list.append('<file src="' + str(child) + "\\**" '" target="runtimes/ios/native"/>')  # noqa: ISC001
@@ -722,7 +720,7 @@ def generate_files(line_list, args):
                 ngraph_list_path = os.path.join(openvino_path, "deployment_tools\\ngraph\\lib\\")
                 for ngraph_element in os.listdir(ngraph_list_path):
                     if ngraph_element.endswith("dll"):
-                        files_list.append(  # noqa: PERF401
+                        files_list.append(
                             "<file src="
                             + '"'
                             + os.path.join(ngraph_list_path, ngraph_element)
@@ -732,7 +730,7 @@ def generate_files(line_list, args):
                         )
             for dll_element in os.listdir(dll_list_path):
                 if dll_element.endswith("dll"):
-                    files_list.append(  # noqa: PERF401
+                    files_list.append(
                         "<file src="
                         + '"'
                         + os.path.join(dll_list_path, dll_element)
@@ -762,7 +760,7 @@ def generate_files(line_list, args):
                 )
             for tbb_element in os.listdir(tbb_list_path):
                 if tbb_element.endswith("dll"):
-                    files_list.append(  # noqa: PERF401
+                    files_list.append(
                         "<file src="
                         + '"'
                         + os.path.join(tbb_list_path, tbb_element)

tools/python/dump_ort_model.py

@@ -33,7 +33,7 @@ class OrtFormatModelDumper:
             tensor = graph.Initializers(idx)
             dims = []
             for dim in range(0, tensor.DimsLength()):
-                dims.append(tensor.Dims(dim))  # noqa: PERF401
+                dims.append(tensor.Dims(dim))
 
             print(f"{tensor.Name().decode()} data_type={tensor.DataType()} dims={dims}")
         print("--------")

tools/python/onnx2tfevents.py

@@ -117,7 +117,7 @@ def parse(graph: GraphProto) -> GraphDef:
     for node in graph.node:
         _attr = []
         for s in node.attribute:
-            _attr.append(" = ".join([str(f[1]) for f in s.ListFields()]))  # noqa: PERF401
+            _attr.append(" = ".join([str(f[1]) for f in s.ListFields()]))
         attr = ", ".join(_attr).encode(encoding="utf_8")
         shape_proto = None
         elem_type = 0
@@ -331,7 +331,7 @@ class ListUnpackTransformer(TransformerBase):
                 new_output = f"{get_prefix(node.output[0])}{node.op_type}_{idx!s}_output"
                 for output in node.output:
                     if len(output) > 0:
-                        new_nodes.append(helper.make_node("ListUnpack", [new_output], [output]))  # noqa: PERF401
+                        new_nodes.append(helper.make_node("ListUnpack", [new_output], [output]))
                 node.ClearField("output")
                 node.output.extend([new_output])
         if len(new_nodes) > 0:

tools/python/util/convert_onnx_models_to_ort.py

@@ -165,7 +165,7 @@ def _convert(
             # new_size = os.path.getsize(ort_target_path)
             # print("Serialized {} to {}. Sizes: orig={} new={} diff={} new:old={:.4f}:1.0".format(
             #     onnx_target_path, ort_target_path, orig_size, new_size, new_size - orig_size, new_size / orig_size))
-        except Exception as e:  # noqa: PERF203
+        except Exception as e:
             print(f"Error converting {model}: {e}")
             if not allow_conversion_failures:
                 raise

tools/python/util/ort_format_model/operator_type_usage_processors.py

@@ -205,7 +205,7 @@ class DefaultTypeUsageProcessor(TypeUsageProcessor):
         domain = _ort_constant_for_domain(self.domain)
         for i in sorted(self._input_types.keys()):
             if self._input_types[i]:
-                entries.append(  # noqa: PERF401
+                entries.append(
                     "ORT_SPECIFY_OP_KERNEL_ARG_ALLOWED_TYPES({}, {}, Input, {}, {});".format(
                         domain, self.optype, i, ", ".join(sorted(self._input_types[i]))
                     )
@@ -213,7 +213,7 @@ class DefaultTypeUsageProcessor(TypeUsageProcessor):
 
         for o in sorted(self._output_types.keys()):
             if self._output_types[o]:
-                entries.append(  # noqa: PERF401
+                entries.append(
                     "ORT_SPECIFY_OP_KERNEL_ARG_ALLOWED_TYPES({}, {}, Output, {}, {});".format(
                         domain, self.optype, o, ", ".join(sorted(self._output_types[o]))
                     )