diff --git a/cgmanifests/generate_cgmanifest.py b/cgmanifests/generate_cgmanifest.py index 81181d3ccfb20..3cecbb0cc977f 100644 --- a/cgmanifests/generate_cgmanifest.py +++ b/cgmanifests/generate_cgmanifest.py @@ -115,8 +115,8 @@ def normalize_path_separators(path): submodule_lines = proc.stdout.splitlines() for submodule_line in submodule_lines: (absolute_path, url, commit) = submodule_line.split(" ") - git_deps[GitDep(commit, url)] = "git submodule at {}".format( - normalize_path_separators(os.path.relpath(absolute_path, REPO_DIR)) + git_deps[GitDep(commit, url)] = ( + f"git submodule at {normalize_path_separators(os.path.relpath(absolute_path, REPO_DIR))}" ) with open(os.path.join(SCRIPT_DIR, "..", "cmake", "deps.txt")) as f: diff --git a/docs/python/examples/plot_train_convert_predict.py b/docs/python/examples/plot_train_convert_predict.py index dcbc84b20767a..44b6bb74c29df 100644 --- a/docs/python/examples/plot_train_convert_predict.py +++ b/docs/python/examples/plot_train_convert_predict.py @@ -134,7 +134,7 @@ def loop(X_test, fct, n=None): nrow = X_test.shape[0] if n is None: n = nrow - for i in range(0, n): + for i in range(n): im = i % nrow fct(X_test[im : im + 1]) diff --git a/onnxruntime/python/tools/microbench/benchmark.py b/onnxruntime/python/tools/microbench/benchmark.py index a52740d45956c..a5936afcfe13e 100644 --- a/onnxruntime/python/tools/microbench/benchmark.py +++ b/onnxruntime/python/tools/microbench/benchmark.py @@ -147,20 +147,17 @@ def __init__(self, args): @classmethod @abstractmethod - def create_inputs_outputs(cls, op_param): - ... + def create_inputs_outputs(cls, op_param): ... def add_case(self, op_param, model): self.cases += [(op_param, model)] @abstractmethod - def create_cases(self): - ... + def create_cases(self): ... @classmethod @abstractmethod - def case_profile(cls, op_param, time): - ... + def case_profile(cls, op_param, time): ... def benchmark(self): self.create_cases() diff --git a/onnxruntime/python/tools/quantization/base_quantizer.py b/onnxruntime/python/tools/quantization/base_quantizer.py index 6fa88a9e44232..667d7047c1fbd 100644 --- a/onnxruntime/python/tools/quantization/base_quantizer.py +++ b/onnxruntime/python/tools/quantization/base_quantizer.py @@ -187,17 +187,13 @@ def check_opset_version(self): if opset_version == 10: logging.warning( - "The original model opset version is {}, which does not support node fusions. Please update the model to opset >= 11 for better performance.".format( - opset_version - ) + f"The original model opset version is {opset_version}, which does not support node fusions. Please update the model to opset >= 11 for better performance." ) return 10 if opset_version < 10: logging.warning( - "The original model opset version is {}, which does not support quantization. Please update the model to opset >= 11. Updating the model automatically to opset 11. Please verify the quantized model.".format( - opset_version - ) + f"The original model opset version is {opset_version}, which does not support quantization. Please update the model to opset >= 11. Updating the model automatically to opset 11. Please verify the quantized model." ) self.model.model.opset_import.remove(ai_onnx_domain[0]) self.model.model.opset_import.extend([onnx.helper.make_opsetid("", 11)]) @@ -205,9 +201,9 @@ def check_opset_version(self): if opset_version < 19 and self.weight_qType == onnx.TensorProto.FLOAT8E4M3FN: logging.warning( - "The original model opset version is {}, which does not support quantization to float 8. " + f"The original model opset version is {opset_version}, which does not support quantization to float 8. " "Please update the model to opset >= 19. Updating the model automatically to opset 19. " - "Please verify the quantized model.".format(opset_version) + "Please verify the quantized model." ) self.model.model.opset_import.remove(ai_onnx_domain[0]) self.model.model.opset_import.extend([onnx.helper.make_opsetid("", 19)]) diff --git a/onnxruntime/python/tools/quantization/calibrate.py b/onnxruntime/python/tools/quantization/calibrate.py index 624049b244580..971cc203f4d73 100644 --- a/onnxruntime/python/tools/quantization/calibrate.py +++ b/onnxruntime/python/tools/quantization/calibrate.py @@ -918,11 +918,7 @@ def compute_entropy(self): thresholds_dict = {} # per tensor thresholds print(f"Number of tensors : {len(histogram_dict)}") - print( - "Number of histogram bins : {} (The number may increase depends on the data it collects)".format( - self.num_bins - ) - ) + print(f"Number of histogram bins : {self.num_bins} (The number may increase depends on the data it collects)") print(f"Number of quantized bins : {self.num_quantized_bins}") for tensor, histogram in histogram_dict.items(): diff --git a/onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py b/onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py index a1916e806c5c0..f4bcd508960a1 100644 --- a/onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py +++ b/onnxruntime/python/tools/quantization/matmul_4bits_quantizer.py @@ -216,7 +216,7 @@ def pack_on_row_fast_248bit(pack_tensor, ori_int_tensor, bits): pack_tensor = pack_tensor.T if bits in [2, 4, 8]: compress_ratio = pack_tensor.element_size() * 8 // bits - for j in range(0, compress_ratio): + for j in range(compress_ratio): pack_tensor[0:] |= ori_int_tensor[j::compress_ratio] << (bits * (j)) else: raise NotImplementedError("Only 2,4,8 bits are supported.") diff --git a/onnxruntime/python/tools/quantization/onnx_model.py b/onnxruntime/python/tools/quantization/onnx_model.py index 46d245d353a07..716dd1eacec6a 100644 --- a/onnxruntime/python/tools/quantization/onnx_model.py +++ b/onnxruntime/python/tools/quantization/onnx_model.py @@ -79,11 +79,7 @@ def _clean_initializers_helper(graph, model): graph.input.remove(name_to_input[initializer.name]) except StopIteration: if model.ir_version < 4: - print( - "Warning: invalid weight name {} found in the graph (not a graph input)".format( - initializer.name - ) - ) + print(f"Warning: invalid weight name {initializer.name} found in the graph (not a graph input)") requesting_tensor_names.difference_update(input.name for input in graph.input) diff --git a/onnxruntime/python/tools/quantization/operators/concat.py b/onnxruntime/python/tools/quantization/operators/concat.py index a4f359cf56847..57fcec9cd380b 100644 --- a/onnxruntime/python/tools/quantization/operators/concat.py +++ b/onnxruntime/python/tools/quantization/operators/concat.py @@ -30,7 +30,7 @@ def quantize(self): zero_point_names, scale_names, nodes, - ) = self.quantizer.quantize_activation(node, [*range(0, len(node.input))]) + ) = self.quantizer.quantize_activation(node, [*range(len(node.input))]) if not data_found or q_input_names is None: return super().quantize() @@ -52,7 +52,7 @@ def quantize(self): qnode_name = node.name + "_quant" if node.name else "" qlconcat_inputs = [output_scale_name, output_zp_name] - for i in range(0, len(q_input_names)): + for i in range(len(q_input_names)): qlconcat_inputs.extend([q_input_names[i], scale_names[i], zero_point_names[i]]) qlconcat_node = onnx.helper.make_node( "QLinearConcat", qlconcat_inputs, [quantized_output_value.q_name], qnode_name, **kwargs diff --git a/onnxruntime/python/tools/quantization/operators/gemm.py b/onnxruntime/python/tools/quantization/operators/gemm.py index 32fdb729635a8..d269c8fb47bd1 100644 --- a/onnxruntime/python/tools/quantization/operators/gemm.py +++ b/onnxruntime/python/tools/quantization/operators/gemm.py @@ -157,7 +157,5 @@ def quantize(self): set_default_beta(self.node) else: logging.warning( - "Bias of Gemm node '{}' is not constant. Please exclude this node for better performance.".format( - self.node.name - ) + f"Bias of Gemm node '{self.node.name}' is not constant. Please exclude this node for better performance." ) diff --git a/onnxruntime/python/tools/quantization/qdq_quantizer.py b/onnxruntime/python/tools/quantization/qdq_quantizer.py index e221a2d57db8b..1875c552fab9c 100644 --- a/onnxruntime/python/tools/quantization/qdq_quantizer.py +++ b/onnxruntime/python/tools/quantization/qdq_quantizer.py @@ -153,9 +153,7 @@ def _is_tensor_quantizable(self, tensor_name): return True else: logging.warning( - "failed to infer the type of tensor: {}. Skip to quantize it. Please check if it is expected.".format( - tensor_name - ) + f"failed to infer the type of tensor: {tensor_name}. Skip to quantize it. Please check if it is expected." ) return False diff --git a/onnxruntime/python/tools/quantization/quant_utils.py b/onnxruntime/python/tools/quantization/quant_utils.py index 036f49b420734..131e55458fb86 100644 --- a/onnxruntime/python/tools/quantization/quant_utils.py +++ b/onnxruntime/python/tools/quantization/quant_utils.py @@ -276,7 +276,7 @@ def compute_scale_zp_float8(element_type, std): from onnx.reference.custom_element_types import float8e4m3fn zp_dtype = float8e4m3fn - all_values = [float8e4m3_to_float32(i) for i in range(0, 256)] + all_values = [float8e4m3_to_float32(i) for i in range(256)] values = numpy.array( [f for f in all_values if not numpy.isnan(f) and not numpy.isinf(f)], dtype=numpy.float32 ) @@ -530,7 +530,7 @@ def get_elem_index(elem_name, elem_list): Helper function to return index of an item in a node list """ elem_idx = -1 - for i in range(0, len(elem_list)): + for i in range(len(elem_list)): if elem_list[i] == elem_name: elem_idx = i return elem_idx diff --git a/onnxruntime/python/tools/symbolic_shape_infer.py b/onnxruntime/python/tools/symbolic_shape_infer.py index 4b029f9b172b0..8a911071864aa 100755 --- a/onnxruntime/python/tools/symbolic_shape_infer.py +++ b/onnxruntime/python/tools/symbolic_shape_infer.py @@ -282,7 +282,7 @@ def _add_suggested_merge(self, symbols, apply=False): # when nothing to map to, use the shorter one if map_to is None: if self.verbose_ > 0: - logger.warning("Potential unsafe merge between symbolic expressions: ({})".format(",".join(symbols))) + logger.warning("Potential unsafe merge between symbolic expressions: (%s)", ",".join(symbols)) symbols_list = list(symbols) lens = [len(s) for s in symbols_list] map_to = symbols_list[lens.index(min(lens))] @@ -335,10 +335,7 @@ def _merge_symbols(self, dims): int_dim = is_int.index(1) if self.verbose_ > 0: logger.debug( - "dim {} has been merged with value {}".format( - unique_dims[:int_dim] + unique_dims[int_dim + 1 :], - unique_dims[int_dim], - ) + f"dim {unique_dims[:int_dim] + unique_dims[int_dim + 1 :]} has been merged with value {unique_dims[int_dim]}" ) self._check_merged_dims(unique_dims, allow_broadcast=False) return unique_dims[int_dim] @@ -379,7 +376,7 @@ def _broadcast_shapes(self, shape1, shape2): if self.auto_merge_: self._add_suggested_merge([dim1, dim2], apply=True) else: - logger.warning("unsupported broadcast between " + str(dim1) + " " + str(dim2)) + logger.warning("unsupported broadcast between " + str(dim1) + " " + str(dim2)) # noqa: G003 new_shape = [new_dim, *new_shape] return new_shape @@ -663,12 +660,7 @@ def _new_symbolic_dim(self, prefix, dim): def _new_symbolic_dim_from_output(self, node, out_idx=0, dim=0): return self._new_symbolic_dim( - "{}{}_{}_o{}_".format( - node.op_type, - self.prefix_, - list(self.out_mp_.graph.node).index(node), - out_idx, - ), + f"{node.op_type}{self.prefix_}_{list(self.out_mp_.graph.node).index(node)}_o{out_idx}_", dim, ) @@ -1216,9 +1208,7 @@ def _infer_Loop(self, node): # noqa: N802 if need_second_infer: if self.verbose_ > 2: logger.debug( - "Rerun Loop: {}({}...), because of sequence in loop carried variables".format( - node.name, node.output[0] - ) + f"Rerun Loop: {node.name}({node.output[0]}...), because of sequence in loop carried variables" ) self._onnx_infer_subgraph(node, subgraph, inc_subgraph_id=False) @@ -1843,7 +1833,7 @@ def handle_negative_index(index, bound): axes = self._try_get_value(node, 3) steps = self._try_get_value(node, 4) if axes is None and not (starts is None and ends is None): - axes = list(range(0, len(starts if starts is not None else ends))) + axes = list(range(len(starts if starts is not None else ends))) if steps is None and not (starts is None and ends is None): steps = [1] * len(starts if starts is not None else ends) axes = as_list(axes, keep_none=True) @@ -2669,11 +2659,9 @@ def get_prereq(node): break if self.verbose_ > 2: - logger.debug(node.op_type + ": " + node.name) + logger.debug(node.op_type + ": " + node.name) # noqa: G003 for i, name in enumerate(node.input): - logger.debug( - " Input {}: {} {}".format(i, name, "initializer" if name in self.initializers_ else "") - ) + logger.debug(" Input %s: %s %s", i, name, "initializer" if name in self.initializers_ else "") # onnx automatically merge dims with value, i.e. Mul(['aaa', 'bbb'], [1000, 1]) -> [1000, 'bbb'] # symbolic shape inference needs to apply merge of 'aaa' -> 1000 in this case @@ -2722,7 +2710,7 @@ def get_prereq(node): seq_cls_type = out_type.sequence_type.elem_type.WhichOneof("value") if seq_cls_type == "tensor_type": logger.debug( - " {}: sequence of {} {}".format( + " {}: sequence of {} {}".format( # noqa: G001 node.output[i_o], str(get_shape_from_value_info(vi)), onnx.TensorProto.DataType.Name( @@ -2740,14 +2728,10 @@ def get_prereq(node): out_type_undefined = out_type.tensor_type.elem_type == onnx.TensorProto.UNDEFINED if self.verbose_ > 2: logger.debug( - " {}: {} {}".format( - node.output[i_o], - str(out_shape), - onnx.TensorProto.DataType.Name(vi.type.tensor_type.elem_type), - ) + f" {node.output[i_o]}: {out_shape!s} {onnx.TensorProto.DataType.Name(vi.type.tensor_type.elem_type)}" ) if node.output[i_o] in self.sympy_data_: - logger.debug(" Sympy Data: " + str(self.sympy_data_[node.output[i_o]])) + logger.debug(" Sympy Data: " + str(self.sympy_data_[node.output[i_o]])) # noqa: G003 # onnx >= 1.11.0, use unk__#index instead of None when the shape dim is uncertain if ( @@ -2848,24 +2832,16 @@ def get_prereq(node): if self.verbose_ > 0: if is_unknown_op: logger.debug( - "Possible unknown op: {} node: {}, guessing {} shape".format( - node.op_type, node.name, vi.name - ) + f"Possible unknown op: {node.op_type} node: {node.name}, guessing {vi.name} shape" ) if self.verbose_ > 2: - logger.debug( - " {}: {} {}".format( - node.output[i_o], - str(new_shape), - vi.type.tensor_type.elem_type, - ) - ) + logger.debug(f" {node.output[i_o]}: {new_shape!s} {vi.type.tensor_type.elem_type}") self.run_ = True continue # continue the inference after guess, no need to stop as no merge is needed if self.verbose_ > 0 or not self.auto_merge_ or out_type_undefined: - logger.debug("Stopping at incomplete shape inference at " + node.op_type + ": " + node.name) + logger.debug("Stopping at incomplete shape inference at %s: %s", node.op_type, node.name) logger.debug("node inputs:") for i in node.input: if i in self.known_vi_: @@ -2879,7 +2855,7 @@ def get_prereq(node): else: logger.debug(f"not in known_vi_ for {o}") if self.auto_merge_ and not out_type_undefined: - logger.debug("Merging: " + str(self.suggested_merge_)) + logger.debug("Merging: " + str(self.suggested_merge_)) # noqa: G003 return False self.run_ = False @@ -2964,9 +2940,9 @@ def parse_arguments(): if __name__ == "__main__": args = parse_arguments() - logger.info("input model: " + args.input) + logger.info("input model: " + args.input) # noqa: G003 if args.output: - logger.info("output model " + args.output) + logger.info("output model " + args.output) # noqa: G003 logger.info("Doing symbolic shape inference...") out_mp = SymbolicShapeInference.infer_shapes( onnx.load(args.input), diff --git a/onnxruntime/python/tools/tensorrt/perf/benchmark.py b/onnxruntime/python/tools/tensorrt/perf/benchmark.py index 20bb8a71dc35f..8af074f24acc9 100644 --- a/onnxruntime/python/tools/tensorrt/perf/benchmark.py +++ b/onnxruntime/python/tools/tensorrt/perf/benchmark.py @@ -790,7 +790,7 @@ def skip_ep(model_name, ep, model_to_fail_ep): # if ep in fail_ep_list and fail_ep_list[ep] == "runtime error": if ep in fail_ep_list: - logger.info("Skip testing " + model_name + " using " + ep + " since it has some issues.") + logger.info("Skip testing " + model_name + " using " + ep + " since it has some issues.") # noqa: G003 return True return False @@ -925,7 +925,7 @@ def find_model_path(path): logger.info(target_model_path) if len(target_model_path) > 1: - logger.error("We expect to find only one model in " + path) + logger.error("We expect to find only one model in " + path) # noqa: G003 raise return target_model_path[0] diff --git a/onnxruntime/python/tools/tensorrt/perf/benchmark_wrapper.py b/onnxruntime/python/tools/tensorrt/perf/benchmark_wrapper.py index 93d41551c7121..f12d4599817b7 100644 --- a/onnxruntime/python/tools/tensorrt/perf/benchmark_wrapper.py +++ b/onnxruntime/python/tools/tensorrt/perf/benchmark_wrapper.py @@ -80,9 +80,9 @@ def main(): benchmark = is_benchmark_mode(args.running_mode) # noqa: F405 for model, model_info in models.items(): - logger.info("\n" + "=" * 40 + "=" * len(model)) # noqa: F405 - logger.info("=" * 20 + model + "=" * 20) # noqa: F405 - logger.info("=" * 40 + "=" * len(model)) # noqa: F405 + logger.info("\n" + "=" * 40 + "=" * len(model)) # noqa: F405, G003 + logger.info("=" * 20 + model + "=" * 20) # noqa: F405, G003 + logger.info("=" * 40 + "=" * len(model)) # noqa: F405, G003 model_info["model_name"] = model diff --git a/onnxruntime/python/tools/transformers/benchmark.py b/onnxruntime/python/tools/transformers/benchmark.py index 89f9947688583..9baafbbfff0e3 100644 --- a/onnxruntime/python/tools/transformers/benchmark.py +++ b/onnxruntime/python/tools/transformers/benchmark.py @@ -802,7 +802,7 @@ def main(): try: os.mkdir(args.cache_dir) except OSError: - logger.error("Creation of the directory %s failed" % args.cache_dir) + logger.error("Creation of the directory %s failed" % args.cache_dir) # noqa: G002 enable_torch = "torch" in args.engines enable_torch2 = "torch2" in args.engines @@ -921,7 +921,7 @@ def main(): args, ) except Exception: - logger.error("Exception", exc_info=True) + logger.exception("Exception") time_stamp = datetime.now().strftime("%Y%m%d-%H%M%S") if model_fusion_statistics: diff --git a/onnxruntime/python/tools/transformers/benchmark_helper.py b/onnxruntime/python/tools/transformers/benchmark_helper.py index c9c815f01e053..66f7a63447764 100644 --- a/onnxruntime/python/tools/transformers/benchmark_helper.py +++ b/onnxruntime/python/tools/transformers/benchmark_helper.py @@ -142,7 +142,7 @@ def create_onnxruntime_session( session = onnxruntime.InferenceSession(onnx_model_path, sess_options, providers=providers) except Exception: - logger.error("Exception", exc_info=True) + logger.error("Exception", exc_info=True) # noqa: G201 return session diff --git a/onnxruntime/python/tools/transformers/bert_perf_test.py b/onnxruntime/python/tools/transformers/bert_perf_test.py index 9c743a83819c3..17c5d3602bb3b 100644 --- a/onnxruntime/python/tools/transformers/bert_perf_test.py +++ b/onnxruntime/python/tools/transformers/bert_perf_test.py @@ -232,9 +232,9 @@ def onnxruntime_inference(session, all_inputs, output_names): def to_string(model_path, session, test_setting): sess_options = session.get_session_options() option = f"model={os.path.basename(model_path)}," - option += "graph_optimization_level={},intra_op_num_threads={},".format( - sess_options.graph_optimization_level, sess_options.intra_op_num_threads - ).replace("GraphOptimizationLevel.ORT_", "") + option += f"graph_optimization_level={sess_options.graph_optimization_level},intra_op_num_threads={sess_options.intra_op_num_threads},".replace( + "GraphOptimizationLevel.ORT_", "" + ) option += f"batch_size={test_setting.batch_size},sequence_length={test_setting.sequence_length}," option += f"test_cases={test_setting.test_cases},test_times={test_setting.test_times}," diff --git a/onnxruntime/python/tools/transformers/compare_bert_results.py b/onnxruntime/python/tools/transformers/compare_bert_results.py index 61e4c97c75c8c..0c5125e74c8a4 100644 --- a/onnxruntime/python/tools/transformers/compare_bert_results.py +++ b/onnxruntime/python/tools/transformers/compare_bert_results.py @@ -59,16 +59,10 @@ def compare(baseline_results, treatment_results, verbose, rtol=1e-1, atol=1e-3): print(f"abs_diff={abs_diff}") if diff_count == 0: - print( - "100% passed for {} random inputs given thresholds (rtol={}, atol={}).".format( - len(baseline_results), rtol, atol - ) - ) + print(f"100% passed for {len(baseline_results)} random inputs given thresholds (rtol={rtol}, atol={atol}).") else: print( - "WARNING: {} out of {} results NOT passed for thresholds (rtol={}, atol={}).".format( - diff_count, len(baseline_results), rtol, atol - ) + f"WARNING: {diff_count} out of {len(baseline_results)} results NOT passed for thresholds (rtol={rtol}, atol={atol})." ) print(f"maximum absolute difference={max_abs_diff}") @@ -117,11 +111,7 @@ def run_test( baseline_model, all_inputs, use_gpu, disable_optimization=True ) if verbose: - print( - "baseline average latency (all optimizations disabled): {} ms".format( - statistics.mean(baseline_latency) * 1000 - ) - ) + print(f"baseline average latency (all optimizations disabled): {statistics.mean(baseline_latency) * 1000} ms") if output_dir is not None: for i, inputs in enumerate(all_inputs): diff --git a/onnxruntime/python/tools/transformers/float16.py b/onnxruntime/python/tools/transformers/float16.py index 48c79b1d5fa0f..2398bb9d6031b 100644 --- a/onnxruntime/python/tools/transformers/float16.py +++ b/onnxruntime/python/tools/transformers/float16.py @@ -411,9 +411,7 @@ def convert_float_to_float16( value_info_list.append(make_value_info_from_tensor(value.initializer)) if value.fp32_nodes and not force_fp16_initializers: logger.info( - "initializer is used by both fp32 and fp16 nodes. Consider add these nodes to block list:{}".format( - value.fp16_nodes - ) + f"initializer is used by both fp32 and fp16 nodes. Consider add these nodes to block list:{value.fp16_nodes}" ) # Some operators have data type fixed as float for some input. Add a float16 to float cast for those inputs. diff --git a/onnxruntime/python/tools/transformers/fusion_attention_unet.py b/onnxruntime/python/tools/transformers/fusion_attention_unet.py index 9a353e7e2d675..048c13cdb1e2c 100644 --- a/onnxruntime/python/tools/transformers/fusion_attention_unet.py +++ b/onnxruntime/python/tools/transformers/fusion_attention_unet.py @@ -373,9 +373,7 @@ def create_attention_node( else "MultiHeadAttention ({})".format( "self attention with packed qkv" if self.enable_packed_qkv - else "cross attention with packed kv" - if self.enable_packed_kv - else "cross attention" + else "cross attention with packed kv" if self.enable_packed_kv else "cross attention" ) ) self.increase_counter(counter_name) @@ -843,9 +841,7 @@ def create_attention_node_lora( else "MultiHeadAttention ({})".format( "self attention with packed qkv" if self.enable_packed_qkv - else "cross attention with packed kv" - if self.enable_packed_kv - else "cross attention" + else "cross attention with packed kv" if self.enable_packed_kv else "cross attention" ) ) self.increase_counter(counter_name) diff --git a/onnxruntime/python/tools/transformers/fusion_embedlayer.py b/onnxruntime/python/tools/transformers/fusion_embedlayer.py index 42156d9123383..70ff57f0626e1 100644 --- a/onnxruntime/python/tools/transformers/fusion_embedlayer.py +++ b/onnxruntime/python/tools/transformers/fusion_embedlayer.py @@ -345,18 +345,13 @@ def check_embedding(self, word_embedding_gather, segment_embedding_gather, posit and input_ids_shape[1] == position_ids_shape[1] ): logger.info( - "Cannot fuse EmbedLayerNormalization: input_ids and position_ids not matched in 2nd dimension: {} vs {}".format( - input_ids_shape, position_ids_shape - ) + f"Cannot fuse EmbedLayerNormalization: input_ids and position_ids not matched in 2nd dimension: {input_ids_shape} vs {position_ids_shape}" ) return False if segment_ids and not self.shape_infer.compare_shape(input_ids, segment_ids): logger.info( - "Cannot fuse EmbedLayerNormalization: input_ids and segment_ids does not have same shape: {} != {}".format( - input_ids_shape, - self.shape_infer.get_edge_shape(segment_ids), - ) + f"Cannot fuse EmbedLayerNormalization: input_ids and segment_ids does not have same shape: {input_ids_shape} != {self.shape_infer.get_edge_shape(segment_ids)}" ) return False diff --git a/onnxruntime/python/tools/transformers/fusion_qordered_gelu.py b/onnxruntime/python/tools/transformers/fusion_qordered_gelu.py index 6c44bb11e24dc..5f395b364eb6f 100644 --- a/onnxruntime/python/tools/transformers/fusion_qordered_gelu.py +++ b/onnxruntime/python/tools/transformers/fusion_qordered_gelu.py @@ -75,9 +75,11 @@ def fuse(self, node, input_name_to_nodes: Dict, output_name_to_node: Dict): if not self.model.is_safe_to_fuse_nodes( subgraph_nodes, - [node.output[0], downstream_quantize_node.output[0]] - if downstream_shape_node is not None - else downstream_quantize_node.output, + ( + [node.output[0], downstream_quantize_node.output[0]] + if downstream_shape_node is not None + else downstream_quantize_node.output + ), input_name_to_nodes, output_name_to_node, ): diff --git a/onnxruntime/python/tools/transformers/fusion_qordered_layernorm.py b/onnxruntime/python/tools/transformers/fusion_qordered_layernorm.py index cf2b357721757..5ec6dadc1e677 100644 --- a/onnxruntime/python/tools/transformers/fusion_qordered_layernorm.py +++ b/onnxruntime/python/tools/transformers/fusion_qordered_layernorm.py @@ -77,9 +77,11 @@ def fuse(self, node, input_name_to_nodes: Dict, output_name_to_node: Dict): if not self.model.is_safe_to_fuse_nodes( subgraph_nodes, - [node.output[0], downstream_quantize_node.output[0]] - if downstream_shape_node is not None - else downstream_quantize_node.output, + ( + [node.output[0], downstream_quantize_node.output[0]] + if downstream_shape_node is not None + else downstream_quantize_node.output + ), input_name_to_nodes, output_name_to_node, ): diff --git a/onnxruntime/python/tools/transformers/models/gpt2/benchmark_gpt2.py b/onnxruntime/python/tools/transformers/models/gpt2/benchmark_gpt2.py index e48f0adc832c5..6d6a057574a17 100644 --- a/onnxruntime/python/tools/transformers/models/gpt2/benchmark_gpt2.py +++ b/onnxruntime/python/tools/transformers/models/gpt2/benchmark_gpt2.py @@ -400,7 +400,7 @@ def main(args): } csv_writer.writerow(row) except Exception: - logger.error("Exception", exc_info=True) + logger.error("Exception", exc_info=True) # noqa: G201 return None logger.info(f"Results are saved to file {csv_filename}") diff --git a/onnxruntime/python/tools/transformers/models/gpt2/gpt2_helper.py b/onnxruntime/python/tools/transformers/models/gpt2/gpt2_helper.py index e01585ae84163..9153193a4974a 100644 --- a/onnxruntime/python/tools/transformers/models/gpt2/gpt2_helper.py +++ b/onnxruntime/python/tools/transformers/models/gpt2/gpt2_helper.py @@ -630,7 +630,7 @@ def pytorch_inference(model, inputs: Gpt2Inputs, total_runs: int = 0): latency.append(time.time() - start) average_latency = sum(latency) * 1000 / len(latency) - logger.debug("PyTorch inference time = {} ms".format(format(average_latency, ".2f"))) + logger.debug("PyTorch inference time = {} ms".format(format(average_latency, ".2f"))) # noqa: G001 return outputs, average_latency @@ -662,7 +662,7 @@ def onnxruntime_inference(ort_session, inputs: Gpt2Inputs, total_runs: int = 0): latency.append(time.time() - start) average_latency = sum(latency) * 1000 / len(latency) - logger.debug("OnnxRuntime Inference time = {} ms".format(format(average_latency, ".2f"))) + logger.debug("OnnxRuntime Inference time = {} ms".format(format(average_latency, ".2f"))) # noqa: G001 return ort_outputs, average_latency @@ -741,7 +741,7 @@ def onnxruntime_inference_with_binded_io( latency.append(time.time() - start) average_latency = sum(latency) * 1000 / len(latency) - logger.debug("OnnxRuntime with IO binding inference time = {} ms".format(format(average_latency, ".2f"))) + logger.debug("OnnxRuntime with IO binding inference time = %.2f ms", average_latency) return ort_outputs, average_latency diff --git a/onnxruntime/python/tools/transformers/models/gpt2/gpt2_parity.py b/onnxruntime/python/tools/transformers/models/gpt2/gpt2_parity.py index 4823f0d5874dd..b039f1351b1d0 100644 --- a/onnxruntime/python/tools/transformers/models/gpt2/gpt2_parity.py +++ b/onnxruntime/python/tools/transformers/models/gpt2/gpt2_parity.py @@ -179,7 +179,7 @@ def print_wins(wins, rows, test_name): for row in rows: if row["run_id"] == key: logger.info( - "{:02d}: WINs={:02d}, run_id={}, latency={:5.2f}, top1_match={:.4f}, size={}_MB, experiment={}, {}".format( + "{:02d}: WINs={:02d}, run_id={}, latency={:5.2f}, top1_match={:.4f}, size={}_MB, experiment={}, {}".format( # noqa: G001 rank, value, key, diff --git a/onnxruntime/python/tools/transformers/models/llama/benchmark.py b/onnxruntime/python/tools/transformers/models/llama/benchmark.py index f597cead40331..bfe108d21a595 100644 --- a/onnxruntime/python/tools/transformers/models/llama/benchmark.py +++ b/onnxruntime/python/tools/transformers/models/llama/benchmark.py @@ -55,11 +55,7 @@ def get_inputs(args: argparse.Namespace, ort_model_inputs_len: int): max_seq_len = ( 2048 if args.benchmark_type == "ort-msft" - else 16384 - if "codellama" in temp_name - else 4096 - if "llama2" in temp_name - else 2048 + else 16384 if "codellama" in temp_name else 4096 if "llama2" in temp_name else 2048 ) if args.benchmark_type in {"hf-pt-eager", "hf-pt-compile"}: @@ -278,21 +274,25 @@ def time_fn(args, fn, inputs): outputs = fn(inputs) logger.info(outputs) - input_sync = ( # noqa: E731 - lambda *kwargs: args.io_binding.synchronize_inputs() + input_sync = lambda *kwargs: ( # noqa: E731 + args.io_binding.synchronize_inputs() if args.device != "cpu" and args.benchmark_type in {"ort-msft", "ort-convert-to-onnx"} # ORT synchronize - else lambda *kwargs: torch.cuda.synchronize() - if args.device != "cpu" and torch.cuda.is_available() # PyTorch synchronize - else lambda *kwargs: None # no-op function - ) + else lambda *kwargs: ( + torch.cuda.synchronize() + if args.device != "cpu" and torch.cuda.is_available() # PyTorch synchronize + else lambda *kwargs: None + ) + ) # no-op function - output_sync = ( # noqa: E731 - lambda *kwargs: args.io_binding.synchronize_outputs() + output_sync = lambda *kwargs: ( # noqa: E731 + args.io_binding.synchronize_outputs() if args.device != "cpu" and args.benchmark_type in {"ort-msft", "ort-convert-to-onnx"} # ORT synchronize - else lambda *kwargs: torch.cuda.synchronize() - if args.device != "cpu" and torch.cuda.is_available() # PyTorch synchronize - else lambda *kwargs: None # no-op function - ) + else lambda *kwargs: ( + torch.cuda.synchronize() + if args.device != "cpu" and torch.cuda.is_available() # PyTorch synchronize + else lambda *kwargs: None + ) + ) # no-op function for _ in warmup_range: input_sync() diff --git a/onnxruntime/python/tools/transformers/models/llama/convert_to_onnx.py b/onnxruntime/python/tools/transformers/models/llama/convert_to_onnx.py index c9ff384a4c856..1ad58327b7fc2 100644 --- a/onnxruntime/python/tools/transformers/models/llama/convert_to_onnx.py +++ b/onnxruntime/python/tools/transformers/models/llama/convert_to_onnx.py @@ -944,9 +944,11 @@ def main(): ort_quantization.quantize_dynamic( fp32_path, int8_path, - op_types_to_quantize=["MatMul", "Gemm", "Gather"] - if args.quantize_embedding_layer - else ["MatMul", "Gemm"], + op_types_to_quantize=( + ["MatMul", "Gemm", "Gather"] + if args.quantize_embedding_layer + else ["MatMul", "Gemm"] + ), per_channel=args.quantize_per_channel, reduce_range=args.quantize_reduce_range, use_external_data_format=True, diff --git a/onnxruntime/python/tools/transformers/models/longformer/benchmark_longformer.py b/onnxruntime/python/tools/transformers/models/longformer/benchmark_longformer.py index 51a967cf22608..ab92a12343732 100644 --- a/onnxruntime/python/tools/transformers/models/longformer/benchmark_longformer.py +++ b/onnxruntime/python/tools/transformers/models/longformer/benchmark_longformer.py @@ -335,7 +335,7 @@ def test_ort(args, device) -> List[Dict[str, Any]]: onnx_model_path = find_onnx_model(model_name) if not args.onnx else args.onnx - optimized = onnx_model_path.endswith("_fp16.onnx") or onnx_model_path.endswith("_fp32.onnx") + optimized = onnx_model_path.endswith("_fp16.onnx") or onnx_model_path.endswith("_fp32.onnx") # noqa: PIE810 precision = "fp32" if not onnx_model_path.endswith("_fp16.onnx") else "fp16" model = load_torch_model(model_name, device) @@ -590,7 +590,7 @@ def run_tests( logger.info(f"ORT_LONGFORMER_COMPACT_MEMORY={compact_memory}") os.environ["ORT_LONGFORMER_USE_HALF4"] = "1" if use_half4 else "0" - logger.info("ORT_LONGFORMER_USE_HALF4={}".format("1" if use_half4 else "0")) + logger.info("ORT_LONGFORMER_USE_HALF4={}".format("1" if use_half4 else "0")) # noqa: G001 results = [] test_times = 1000 diff --git a/onnxruntime/python/tools/transformers/models/phi2/inference_example.py b/onnxruntime/python/tools/transformers/models/phi2/inference_example.py index 829334b46b469..eb66533f00834 100644 --- a/onnxruntime/python/tools/transformers/models/phi2/inference_example.py +++ b/onnxruntime/python/tools/transformers/models/phi2/inference_example.py @@ -121,9 +121,11 @@ def get_initial_inputs_and_outputs(self, encodings_dict): if not self.use_traced_inputs: for i in range(self.num_layers): past = torch.zeros(past_shape, device=self.device, dtype=self.torch_dtype) - inputs.update( - {f"past_key_{i}": past.contiguous(), f"past_value_{i}": past.clone().contiguous()} - ) if not self.packed_kv else inputs.update({f"past_{i}": past.contiguous()}) + ( + inputs.update({f"past_key_{i}": past.contiguous(), f"past_value_{i}": past.clone().contiguous()}) + if not self.packed_kv + else inputs.update({f"past_{i}": past.contiguous()}) + ) else: for i in range(self.num_layers): inputs.update( @@ -144,9 +146,13 @@ def get_initial_inputs_and_outputs(self, encodings_dict): ) for i in range(self.num_layers): present = torch.zeros(present_shape, device=self.device, dtype=self.torch_dtype) - outputs.update( - {f"present_key_{i}": present.contiguous(), f"present_value_{i}": present.contiguous()} - ) if not self.packed_kv else outputs.update({f"present_{i}": present.contiguous()}) + ( + outputs.update( + {f"present_key_{i}": present.contiguous(), f"present_value_{i}": present.contiguous()} + ) + if not self.packed_kv + else outputs.update({f"present_{i}": present.contiguous()}) + ) return inputs, outputs @@ -323,9 +329,16 @@ def generate_impl(self, encodings_dict, max_length, cuda_graph_annotation, bench ) for i in range(self.num_layers): present = torch.zeros(present_shape, device=self.device, dtype=self.torch_dtype) - outputs.update( - {f"present_key_{i}": present.contiguous(), f"present_value_{i}": present.clone().contiguous()} - ) if not self.packed_kv else outputs.update({f"present_{i}": present.contiguous()}) + ( + outputs.update( + { + f"present_key_{i}": present.contiguous(), + f"present_value_{i}": present.clone().contiguous(), + } + ) + if not self.packed_kv + else outputs.update({f"present_{i}": present.contiguous()}) + ) if benchmark: print( diff --git a/onnxruntime/python/tools/transformers/models/stable_diffusion/diffusion_models.py b/onnxruntime/python/tools/transformers/models/stable_diffusion/diffusion_models.py index 10af22e44d3a5..c2cfc165e32cf 100644 --- a/onnxruntime/python/tools/transformers/models/stable_diffusion/diffusion_models.py +++ b/onnxruntime/python/tools/transformers/models/stable_diffusion/diffusion_models.py @@ -414,7 +414,6 @@ def get_profile_id(self, batch_size, image_height, image_width, static_batch, st def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_image_shape): """For TensorRT""" - pass def get_shape_dict(self, batch_size, image_height, image_width): pass diff --git a/onnxruntime/python/tools/transformers/models/whisper/convert_to_onnx.py b/onnxruntime/python/tools/transformers/models/whisper/convert_to_onnx.py index 35211aab272e4..5921e4ed42936 100644 --- a/onnxruntime/python/tools/transformers/models/whisper/convert_to_onnx.py +++ b/onnxruntime/python/tools/transformers/models/whisper/convert_to_onnx.py @@ -414,9 +414,9 @@ def export_onnx_models( quantization.quantize_dynamic( onnx_path, output_path, - op_types_to_quantize=["MatMul", "Gemm", "Gather"] - if quantize_embedding_layer - else ["MatMul", "Gemm"], + op_types_to_quantize=( + ["MatMul", "Gemm", "Gather"] if quantize_embedding_layer else ["MatMul", "Gemm"] + ), use_external_data_format=use_external_data_format, per_channel=quantize_per_channel, reduce_range=quantize_reduce_range, diff --git a/onnxruntime/python/tools/transformers/models/whisper/whisper_chain.py b/onnxruntime/python/tools/transformers/models/whisper/whisper_chain.py index 14691da4ad643..0b128f122e0f4 100644 --- a/onnxruntime/python/tools/transformers/models/whisper/whisper_chain.py +++ b/onnxruntime/python/tools/transformers/models/whisper/whisper_chain.py @@ -149,9 +149,11 @@ def chain_model(args): helper.make_attribute("translate_token_id", tokenizer.convert_tokens_to_ids(["<|translate|>"])[0]), helper.make_attribute("transcribe_token_id", tokenizer.convert_tokens_to_ids(["<|transcribe|>"])[0]), helper.make_attribute("start_of_lm_token_id", tokenizer.convert_tokens_to_ids(["<|startoflm|>"])[0]), - helper.make_attribute("no_speech_token_id", tokenizer.convert_tokens_to_ids(["<|nospeech|>"])[0]) - if args.output_no_speech_probs - else "", + ( + helper.make_attribute("no_speech_token_id", tokenizer.convert_tokens_to_ids(["<|nospeech|>"])[0]) + if args.output_no_speech_probs + else "" + ), helper.make_attribute("no_timestamps_token_id", tokenizer.convert_tokens_to_ids(["<|notimestamps|>"])[0]), helper.make_attribute("beginning_timestamp_token_id", tokenizer.convert_tokens_to_ids(["<|0.00|>"])[0]), helper.make_attribute("no_repeat_ngram_size", args.no_repeat_ngram_size), diff --git a/onnxruntime/python/tools/transformers/models/whisper/whisper_helper.py b/onnxruntime/python/tools/transformers/models/whisper/whisper_helper.py index 1b47b9426d983..adf7f69470ae7 100644 --- a/onnxruntime/python/tools/transformers/models/whisper/whisper_helper.py +++ b/onnxruntime/python/tools/transformers/models/whisper/whisper_helper.py @@ -334,7 +334,7 @@ def verify_onnx( try: from datasets import load_dataset except Exception as e: - logger.error(f"An error occurred while importing `datasets`: {e}", exc_info=True) + logger.error(f"An error occurred while importing `datasets`: {e}", exc_info=True) # noqa: G201 install_cmd = "pip install datasets" logger.warning(f"Could not import `datasets`. Attempting to install `datasets` via `{install_cmd}`.") os.system(install_cmd) diff --git a/onnxruntime/python/tools/transformers/onnx_model_phi.py b/onnxruntime/python/tools/transformers/onnx_model_phi.py index 0fdce29ae0fa0..05a27ba487f4d 100644 --- a/onnxruntime/python/tools/transformers/onnx_model_phi.py +++ b/onnxruntime/python/tools/transformers/onnx_model_phi.py @@ -353,8 +353,10 @@ def process_graph_io(self, attn_op_type: AttentionOpType): elem_type=TensorProto.INT64, shape=[1], ) - new_inputs.extend([vi_iid, vi_step, vi_mask]) if not self.use_vllm else new_inputs.extend( - [vi_iid, vi_pid, vi_meta] + ( + new_inputs.extend([vi_iid, vi_step, vi_mask]) + if not self.use_vllm + else new_inputs.extend([vi_iid, vi_pid, vi_meta]) ) if self.use_attn: if "past_key" in vi.name: diff --git a/onnxruntime/python/tools/transformers/onnx_model_unet.py b/onnxruntime/python/tools/transformers/onnx_model_unet.py index 01298b3576eb1..77e24986f0fde 100644 --- a/onnxruntime/python/tools/transformers/onnx_model_unet.py +++ b/onnxruntime/python/tools/transformers/onnx_model_unet.py @@ -127,7 +127,7 @@ def optimize(self, options: Optional[FusionOptions] = None): with logging_redirect_tqdm(): steps = 18 - progress_bar = tqdm.tqdm(range(0, steps), initial=0, desc="fusion") + progress_bar = tqdm.tqdm(range(steps), initial=0, desc="fusion") self._optimize(options, progress_bar) else: logger.info("tqdm is not installed. Run optimization without progress bar") diff --git a/onnxruntime/python/tools/transformers/shape_optimizer.py b/onnxruntime/python/tools/transformers/shape_optimizer.py index ac62188662990..503930b23229f 100644 --- a/onnxruntime/python/tools/transformers/shape_optimizer.py +++ b/onnxruntime/python/tools/transformers/shape_optimizer.py @@ -133,9 +133,7 @@ def use_static_input(self, inputs, batch_size=1, max_seq_len=128): dim_proto.dim_value = max_seq_len elif dim_proto.HasField("dim_value") and dim_proto.dim_value != max_seq_len: raise ValueError( - "Unable to set dimension value to {} for axis {} of {}. Contradicts existing dimension value {}.".format( - max_seq_len, 1, input.name, dim_proto.dim_value - ) + f"Unable to set dimension value to {max_seq_len} for axis {1} of {input.name}. Contradicts existing dimension value {dim_proto.dim_value}." ) def create_dummy_inputs( diff --git a/onnxruntime/test/providers/cpu/reduction/reduction_test_cases_generator.py b/onnxruntime/test/providers/cpu/reduction/reduction_test_cases_generator.py index 727351cae84ac..568a4649f3977 100644 --- a/onnxruntime/test/providers/cpu/reduction/reduction_test_cases_generator.py +++ b/onnxruntime/test/providers/cpu/reduction/reduction_test_cases_generator.py @@ -59,7 +59,7 @@ def PrintResult(op, axes, keepdims, res): # noqa: N802 print(" // expected values") print("{", end="") - for i in range(0, res.size): + for i in range(res.size): print("%5.6ff," % res.item(i)) print("})},") @@ -128,7 +128,7 @@ def PrintReenableOptimizations(): # noqa: N802 print("ReductionTestCases testcases = {") print("// input_data") print("{") - for i in range(0, input_data.size): + for i in range(input_data.size): print( "%5.6ff," % input_data.item(i), ) diff --git a/onnxruntime/test/providers/cpu/rnn/GRU.py b/onnxruntime/test/providers/cpu/rnn/GRU.py index 144acaf14db61..f141710cf31ef 100644 --- a/onnxruntime/test/providers/cpu/rnn/GRU.py +++ b/onnxruntime/test/providers/cpu/rnn/GRU.py @@ -84,7 +84,7 @@ def run(self): hidden_size = f_output.shape[3] output = np.empty((0, 2, batch_size, hidden_size), np.float32) - for x in range(0, seq_length): + for x in range(seq_length): output = np.append(output, f_output[x]) output = np.append(output, r_output_orig_input_order[x]) diff --git a/onnxruntime/test/providers/cpu/rnn/LSTM.py b/onnxruntime/test/providers/cpu/rnn/LSTM.py index 116ec3671bf01..49e28a93385a4 100644 --- a/onnxruntime/test/providers/cpu/rnn/LSTM.py +++ b/onnxruntime/test/providers/cpu/rnn/LSTM.py @@ -124,7 +124,7 @@ def run(self): output = np.empty((0, 2, batch_size, hidden_size), np.float32) # Y_h = np.empty((0, 2, batch_size, hidden_size), np.float32) # Y_c = np.empty((0, 2, hidden_size, hidden_size), np.float32) - for x in range(0, seq_length): + for x in range(seq_length): output = np.append(output, f_output[x]) output = np.append(output, r_output_orig_input_order[x]) diff --git a/onnxruntime/test/python/quantization/test_op_pad.py b/onnxruntime/test/python/quantization/test_op_pad.py index 03e29dd64f8a7..291bf42405d58 100644 --- a/onnxruntime/test/python/quantization/test_op_pad.py +++ b/onnxruntime/test/python/quantization/test_op_pad.py @@ -222,12 +222,8 @@ def verify_quantize_with_pad_mode( activation_proto_qtype = TensorProto.UINT8 if activation_type == QuantType.QUInt8 else TensorProto.INT8 activation_type_str = "u8" if (activation_type == QuantType.QUInt8) else "s8" weight_type_str = "u8" if (weight_type == QuantType.QUInt8) else "s8" - model_i8_path = "qop_pad_{}_i8_{}{}_{}{}.onnx".format( - quantize_mode, - tag_pad_mode, - tag_constant_value, - activation_type_str, - weight_type_str, + model_i8_path = ( + f"qop_pad_{quantize_mode}_i8_{tag_pad_mode}{tag_constant_value}_{activation_type_str}{weight_type_str}.onnx" ) data_reader.rewind() self.quantize_model( diff --git a/onnxruntime/test/python/quantization/test_quantizeblockwise_4bits.py b/onnxruntime/test/python/quantization/test_quantizeblockwise_4bits.py index 765825d4b86e3..97931acf03f42 100644 --- a/onnxruntime/test/python/quantization/test_quantizeblockwise_4bits.py +++ b/onnxruntime/test/python/quantization/test_quantizeblockwise_4bits.py @@ -122,9 +122,11 @@ def test_quantize_blockwise_4bits(self): dequantize_blockwise_4bits( quant_value_ref[c, k], scales_ref[c, k], - (zero_point_ref[c, k // 2] >> 4) - if (k & 1) - else (zero_point_ref[c, k // 2] & 0x0F), + ( + (zero_point_ref[c, k // 2] >> 4) + if (k & 1) + else (zero_point_ref[c, k // 2] & 0x0F) + ), min(block_size, rows - k * block_size), ), dequantize_blockwise_4bits( diff --git a/onnxruntime/test/python/transformers/bert_model_generator.py b/onnxruntime/test/python/transformers/bert_model_generator.py index 9b9409545615b..a84137f092e64 100644 --- a/onnxruntime/test/python/transformers/bert_model_generator.py +++ b/onnxruntime/test/python/transformers/bert_model_generator.py @@ -94,12 +94,16 @@ def create_bert_attention( perm=[0, 2, 3, 1], ), # mask nodes - helper.make_node("Unsqueeze", ["input_mask", "axes_1"], ["unsqueeze0_out"], "unsqueeze0") - if has_unsqueeze_two_inputs - else helper.make_node("Unsqueeze", ["input_mask"], ["unsqueeze0_out"], "unsqueeze0", axes=[1]), - helper.make_node("Unsqueeze", ["unsqueeze0_out", "axes_2"], ["unsqueeze1_out"], "unsqueeze1") - if has_unsqueeze_two_inputs - else helper.make_node("Unsqueeze", ["unsqueeze0_out"], ["unsqueeze1_out"], "unsqueeze1", axes=[2]), + ( + helper.make_node("Unsqueeze", ["input_mask", "axes_1"], ["unsqueeze0_out"], "unsqueeze0") + if has_unsqueeze_two_inputs + else helper.make_node("Unsqueeze", ["input_mask"], ["unsqueeze0_out"], "unsqueeze0", axes=[1]) + ), + ( + helper.make_node("Unsqueeze", ["unsqueeze0_out", "axes_2"], ["unsqueeze1_out"], "unsqueeze1") + if has_unsqueeze_two_inputs + else helper.make_node("Unsqueeze", ["unsqueeze0_out"], ["unsqueeze1_out"], "unsqueeze1", axes=[2]) + ), # when attention_mask is float type, no need to cast helper.make_node("Cast", ["unsqueeze1_out"], ["cast_out"], "cast", to=1) if not use_float_mask else None, helper.make_node( @@ -291,9 +295,11 @@ def create_tf2onnx_attention_3d(input_hidden_size=16, num_heads=4, head_size=4, helper.make_node("Add", ["einsum_k_out", "add_k_weight"], ["add_k_out"], "add_k"), helper.make_node("Mul", ["add_k_out", "mul_weight_1"], ["mul_k_out"], "mul_k"), # mask nodes - helper.make_node("Unsqueeze", ["input_mask", "axes_1"], ["unsqueeze0_out"], "unsqueeze0") - if has_unsqueeze_two_inputs - else helper.make_node("Unsqueeze", ["input_mask"], ["unsqueeze0_out"], "unsqueeze0", axes=[1, 2]), + ( + helper.make_node("Unsqueeze", ["input_mask", "axes_1"], ["unsqueeze0_out"], "unsqueeze0") + if has_unsqueeze_two_inputs + else helper.make_node("Unsqueeze", ["input_mask"], ["unsqueeze0_out"], "unsqueeze0", axes=[1, 2]) + ), helper.make_node( "Slice", ["unsqueeze0_out", "slice_start", "slice_end", "slice_axes", "slice_steps"], diff --git a/onnxruntime/test/python/transformers/conformer_model_generator.py b/onnxruntime/test/python/transformers/conformer_model_generator.py index 71e4f2b63cf4f..5b27a46ea0fdc 100644 --- a/onnxruntime/test/python/transformers/conformer_model_generator.py +++ b/onnxruntime/test/python/transformers/conformer_model_generator.py @@ -22,9 +22,7 @@ def get_tensor_and_weight(name: str, shape: List[int], random=False, zeros=False weights = ( [np.random.uniform(low, high) for _ in range(total_elements)] if random - else [0.0] * total_elements - if zeros - else [1.0] * total_elements + else [0.0] * total_elements if zeros else [1.0] * total_elements ) return helper.make_tensor(name, TensorProto.FLOAT, shape, weights), weights diff --git a/onnxruntime/test/python/transformers/gpt2_model_generator.py b/onnxruntime/test/python/transformers/gpt2_model_generator.py index 4a1b48d4d1b48..0865c87b70da7 100644 --- a/onnxruntime/test/python/transformers/gpt2_model_generator.py +++ b/onnxruntime/test/python/transformers/gpt2_model_generator.py @@ -41,15 +41,17 @@ def create_gpt2_attention(hidden_size=64, num_heads=4, max_seq_len=32, switch_ad ["fc_out"], "add_fc", ), - helper.make_node("Split", ["fc_out", "split_q_k_v"], ["q", "k", "v"], "split_qkv", axis=2) - if is_opset_13_or_newer - else helper.make_node( - "Split", - ["fc_out"], - ["q", "k", "v"], - "split_qkv", - axis=2, - split=[hidden_size, hidden_size, hidden_size], + ( + helper.make_node("Split", ["fc_out", "split_q_k_v"], ["q", "k", "v"], "split_qkv", axis=2) + if is_opset_13_or_newer + else helper.make_node( + "Split", + ["fc_out"], + ["q", "k", "v"], + "split_qkv", + axis=2, + split=[hidden_size, hidden_size, hidden_size], + ) ), # q nodes helper.make_node("Reshape", ["q", "reshape_x_shape"], ["reshape_q_out"], "reshape_q"), @@ -79,19 +81,23 @@ def create_gpt2_attention(hidden_size=64, num_heads=4, max_seq_len=32, switch_ad perm=[0, 2, 1, 3], ), # past - helper.make_node("Split", ["past", "split_1_1"], ["split_k", "split_v"], "split_past", axis=0) - if is_opset_13_or_newer - else helper.make_node( - "Split", - ["past"], - ["split_k", "split_v"], - "split_past", - axis=0, - split=[1, 1], + ( + helper.make_node("Split", ["past", "split_1_1"], ["split_k", "split_v"], "split_past", axis=0) + if is_opset_13_or_newer + else helper.make_node( + "Split", + ["past"], + ["split_k", "split_v"], + "split_past", + axis=0, + split=[1, 1], + ) + ), + ( + helper.make_node("Squeeze", ["split_k", "axes_0"], ["past_k"], "squeeze_past_k") + if is_opset_13_or_newer + else helper.make_node("Squeeze", ["split_k"], ["past_k"], "squeeze_past_k", axes=[0]) ), - helper.make_node("Squeeze", ["split_k", "axes_0"], ["past_k"], "squeeze_past_k") - if is_opset_13_or_newer - else helper.make_node("Squeeze", ["split_k"], ["past_k"], "squeeze_past_k", axes=[0]), helper.make_node( "Concat", ["past_k", "transpose_k_out"], @@ -106,9 +112,11 @@ def create_gpt2_attention(hidden_size=64, num_heads=4, max_seq_len=32, switch_ad "transpose_concat_k", perm=[0, 1, 3, 2], ), - helper.make_node("Squeeze", ["split_v", "axes_0"], ["past_v"], "squeeze_past_v") - if is_opset_13_or_newer - else helper.make_node("Squeeze", ["split_v"], ["past_v"], "squeeze_past_v", axes=[0]), + ( + helper.make_node("Squeeze", ["split_v", "axes_0"], ["past_v"], "squeeze_past_v") + if is_opset_13_or_newer + else helper.make_node("Squeeze", ["split_v"], ["past_v"], "squeeze_past_v", axes=[0]) + ), helper.make_node( "Concat", ["past_v", "transpose_v_out"], @@ -117,33 +125,37 @@ def create_gpt2_attention(hidden_size=64, num_heads=4, max_seq_len=32, switch_ad axis=-2, ), # present - helper.make_node( - "Unsqueeze", - ["concat_k_out", "axes_0"], - ["concat_k_unsqueeze_out"], - "concat_k_unsqueeze", - ) - if is_opset_13_or_newer - else helper.make_node( - "Unsqueeze", - ["concat_k_out"], - ["concat_k_unsqueeze_out"], - "concat_k_unsqueeze", - axes=[0], + ( + helper.make_node( + "Unsqueeze", + ["concat_k_out", "axes_0"], + ["concat_k_unsqueeze_out"], + "concat_k_unsqueeze", + ) + if is_opset_13_or_newer + else helper.make_node( + "Unsqueeze", + ["concat_k_out"], + ["concat_k_unsqueeze_out"], + "concat_k_unsqueeze", + axes=[0], + ) ), - helper.make_node( - "Unsqueeze", - ["concat_v_out", "axes_0"], - ["concat_v_unsqueeze_out"], - "concat_v_unsqueeze", - ) - if is_opset_13_or_newer - else helper.make_node( - "Unsqueeze", - ["concat_v_out"], - ["concat_v_unsqueeze_out"], - "concat_v_unsqueeze", - axes=[0], + ( + helper.make_node( + "Unsqueeze", + ["concat_v_out", "axes_0"], + ["concat_v_unsqueeze_out"], + "concat_v_unsqueeze", + ) + if is_opset_13_or_newer + else helper.make_node( + "Unsqueeze", + ["concat_v_out"], + ["concat_v_unsqueeze_out"], + "concat_v_unsqueeze", + axes=[0], + ) ), helper.make_node( "Concat", @@ -159,19 +171,21 @@ def create_gpt2_attention(hidden_size=64, num_heads=4, max_seq_len=32, switch_ad ["transpose_q_shape_slice_out"], "transpose_q_shape_slice", ), - helper.make_node( - "Squeeze", - ["transpose_q_shape_slice_out", "axes_0"], - ["transpose_q_shape_slice_squeeze_out"], - "transpose_q_shape_slice_squeeze", - ) - if is_opset_13_or_newer - else helper.make_node( - "Squeeze", - ["transpose_q_shape_slice_out"], - ["transpose_q_shape_slice_squeeze_out"], - "transpose_q_shape_slice_squeeze", - axes=[0], + ( + helper.make_node( + "Squeeze", + ["transpose_q_shape_slice_out", "axes_0"], + ["transpose_q_shape_slice_squeeze_out"], + "transpose_q_shape_slice_squeeze", + ) + if is_opset_13_or_newer + else helper.make_node( + "Squeeze", + ["transpose_q_shape_slice_out"], + ["transpose_q_shape_slice_squeeze_out"], + "transpose_q_shape_slice_squeeze", + axes=[0], + ) ), helper.make_node("Shape", ["concat_k_out"], ["concat_k_shape_out"], "concat_k_shape"), helper.make_node( @@ -180,19 +194,21 @@ def create_gpt2_attention(hidden_size=64, num_heads=4, max_seq_len=32, switch_ad ["concat_k_shape_slice_out"], "concat_k_shape_slice", ), - helper.make_node( - "Squeeze", - ["concat_k_shape_slice_out", "axes_0"], - ["concat_k_shape_slice_squeeze_out"], - "concat_k_shape_slice_squeeze", - ) - if is_opset_13_or_newer - else helper.make_node( - "Squeeze", - ["concat_k_shape_slice_out"], - ["concat_k_shape_slice_squeeze_out"], - "concat_k_shape_slice_squeeze", - axes=[0], + ( + helper.make_node( + "Squeeze", + ["concat_k_shape_slice_out", "axes_0"], + ["concat_k_shape_slice_squeeze_out"], + "concat_k_shape_slice_squeeze", + ) + if is_opset_13_or_newer + else helper.make_node( + "Squeeze", + ["concat_k_shape_slice_out"], + ["concat_k_shape_slice_squeeze_out"], + "concat_k_shape_slice_squeeze", + axes=[0], + ) ), helper.make_node( "Sub", @@ -200,22 +216,26 @@ def create_gpt2_attention(hidden_size=64, num_heads=4, max_seq_len=32, switch_ad ["sub_out"], "sub", ), - helper.make_node("Unsqueeze", ["sub_out", "axes_0"], ["sub_unsqueeze_out"], "sub_unsqueeze") - if is_opset_13_or_newer - else helper.make_node("Unsqueeze", ["sub_out"], ["sub_unsqueeze_out"], "sub_unsqueeze", axes=[0]), - helper.make_node( - "Unsqueeze", - ["concat_k_shape_slice_squeeze_out", "axes_0"], - ["concat_k_shape_slice_squeeze_unsqueeze_out"], - "concat_k_shape_slice_squeeze_unsqueeze", - ) - if is_opset_13_or_newer - else helper.make_node( - "Unsqueeze", - ["concat_k_shape_slice_squeeze_out"], - ["concat_k_shape_slice_squeeze_unsqueeze_out"], - "concat_k_shape_slice_squeeze_unsqueeze", - axes=[0], + ( + helper.make_node("Unsqueeze", ["sub_out", "axes_0"], ["sub_unsqueeze_out"], "sub_unsqueeze") + if is_opset_13_or_newer + else helper.make_node("Unsqueeze", ["sub_out"], ["sub_unsqueeze_out"], "sub_unsqueeze", axes=[0]) + ), + ( + helper.make_node( + "Unsqueeze", + ["concat_k_shape_slice_squeeze_out", "axes_0"], + ["concat_k_shape_slice_squeeze_unsqueeze_out"], + "concat_k_shape_slice_squeeze_unsqueeze", + ) + if is_opset_13_or_newer + else helper.make_node( + "Unsqueeze", + ["concat_k_shape_slice_squeeze_out"], + ["concat_k_shape_slice_squeeze_unsqueeze_out"], + "concat_k_shape_slice_squeeze_unsqueeze", + axes=[0], + ) ), helper.make_node( "Slice", @@ -255,23 +275,27 @@ def create_gpt2_attention(hidden_size=64, num_heads=4, max_seq_len=32, switch_ad ["input_mask_reshape_out"], "input_mask_reshape", ), - helper.make_node( - "Unsqueeze", - ["input_mask_reshape_out", "axes_1"], - ["unsqueeze0_out"], - "unsqueeze0", - ) - if is_opset_13_or_newer - else helper.make_node( - "Unsqueeze", - ["input_mask_reshape_out"], - ["unsqueeze0_out"], - "unsqueeze0", - axes=[1], + ( + helper.make_node( + "Unsqueeze", + ["input_mask_reshape_out", "axes_1"], + ["unsqueeze0_out"], + "unsqueeze0", + ) + if is_opset_13_or_newer + else helper.make_node( + "Unsqueeze", + ["input_mask_reshape_out"], + ["unsqueeze0_out"], + "unsqueeze0", + axes=[1], + ) + ), + ( + helper.make_node("Unsqueeze", ["unsqueeze0_out", "axes_2"], ["unsqueeze1_out"], "unsqueeze1") + if is_opset_13_or_newer + else helper.make_node("Unsqueeze", ["unsqueeze0_out"], ["unsqueeze1_out"], "unsqueeze1", axes=[2]) ), - helper.make_node("Unsqueeze", ["unsqueeze0_out", "axes_2"], ["unsqueeze1_out"], "unsqueeze1") - if is_opset_13_or_newer - else helper.make_node("Unsqueeze", ["unsqueeze0_out"], ["unsqueeze1_out"], "unsqueeze1", axes=[2]), helper.make_node("Sub", ["sub_weight", "unsqueeze1_out"], ["mask_sub_out"], "sub_mask"), helper.make_node("Mul", ["mask_sub_out", "mul_weight"], ["mul_mask_out"], "mul_mask"), # qk nodes @@ -322,33 +346,37 @@ def create_gpt2_attention(hidden_size=64, num_heads=4, max_seq_len=32, switch_ad ["qkv_shape_slice_out"], "qkv_shape_slice", ), - helper.make_node( - "Squeeze", - ["qkv_shape_slice_out", "axes_0"], - ["qkv_shape_slice_squeeze_out"], - "qkv_shape_slice_squeeze", - ) - if is_opset_13_or_newer - else helper.make_node( - "Squeeze", - ["qkv_shape_slice_out"], - ["qkv_shape_slice_squeeze_out"], - "qkv_shape_slice_squeeze", - axes=[0], + ( + helper.make_node( + "Squeeze", + ["qkv_shape_slice_out", "axes_0"], + ["qkv_shape_slice_squeeze_out"], + "qkv_shape_slice_squeeze", + ) + if is_opset_13_or_newer + else helper.make_node( + "Squeeze", + ["qkv_shape_slice_out"], + ["qkv_shape_slice_squeeze_out"], + "qkv_shape_slice_squeeze", + axes=[0], + ) ), - helper.make_node( - "Unsqueeze", - ["qkv_shape_slice_squeeze_out", "axes_0"], - ["qkv_shape_slice_squeeze_unsqueeze_out"], - "qkv_shape_slice_squeeze_unsqueeze", - ) - if is_opset_13_or_newer - else helper.make_node( - "Unsqueeze", - ["qkv_shape_slice_squeeze_out"], - ["qkv_shape_slice_squeeze_unsqueeze_out"], - "qkv_shape_slice_squeeze_unsqueeze", - axes=[0], + ( + helper.make_node( + "Unsqueeze", + ["qkv_shape_slice_squeeze_out", "axes_0"], + ["qkv_shape_slice_squeeze_unsqueeze_out"], + "qkv_shape_slice_squeeze_unsqueeze", + ) + if is_opset_13_or_newer + else helper.make_node( + "Unsqueeze", + ["qkv_shape_slice_squeeze_out"], + ["qkv_shape_slice_squeeze_unsqueeze_out"], + "qkv_shape_slice_squeeze_unsqueeze", + axes=[0], + ) ), helper.make_node( "Concat", @@ -387,33 +415,37 @@ def create_gpt2_attention(hidden_size=64, num_heads=4, max_seq_len=32, switch_ad "shape_qkv_gather_0", axis=0, ), - helper.make_node( - "Unsqueeze", - ["qkv_shape_1", "axes_0"], - ["qkv_shape_1_unsqueeze_out"], - "qkv_shape_1_unsqueeze", - ) - if is_opset_13_or_newer - else helper.make_node( - "Unsqueeze", - ["qkv_shape_1"], - ["qkv_shape_1_unsqueeze_out"], - "qkv_shape_1_unsqueeze", - axes=[0], + ( + helper.make_node( + "Unsqueeze", + ["qkv_shape_1", "axes_0"], + ["qkv_shape_1_unsqueeze_out"], + "qkv_shape_1_unsqueeze", + ) + if is_opset_13_or_newer + else helper.make_node( + "Unsqueeze", + ["qkv_shape_1"], + ["qkv_shape_1_unsqueeze_out"], + "qkv_shape_1_unsqueeze", + axes=[0], + ) ), - helper.make_node( - "Unsqueeze", - ["qkv_shape_0", "axes_0"], - ["qkv_shape_0_unsqueeze_out"], - "qkv_shape_0_unsqueeze", - ) - if is_opset_13_or_newer - else helper.make_node( - "Unsqueeze", - ["qkv_shape_0"], - ["qkv_shape_0_unsqueeze_out"], - "qkv_shape_0_unsqueeze", - axes=[0], + ( + helper.make_node( + "Unsqueeze", + ["qkv_shape_0", "axes_0"], + ["qkv_shape_0_unsqueeze_out"], + "qkv_shape_0_unsqueeze", + ) + if is_opset_13_or_newer + else helper.make_node( + "Unsqueeze", + ["qkv_shape_0"], + ["qkv_shape_0_unsqueeze_out"], + "qkv_shape_0_unsqueeze", + axes=[0], + ) ), helper.make_node( "Concat", @@ -767,9 +799,11 @@ def create_gpt2_fused_embedlayer( "", "ids", ], - ["EmbedLayerNormalization_0_output", "EmbedLayerNormalization_0_dummy_mask_index", "embedding_sum"] - if output_embedding_sum - else ["EmbedLayerNormalization_0_output", "EmbedLayerNormalization_0_dummy_mask_index"], + ( + ["EmbedLayerNormalization_0_output", "EmbedLayerNormalization_0_dummy_mask_index", "embedding_sum"] + if output_embedding_sum + else ["EmbedLayerNormalization_0_output", "EmbedLayerNormalization_0_dummy_mask_index"] + ), "EmbedLayerNormalization_0", domain="com.microsoft", epsilon=epsilon, diff --git a/onnxruntime/test/python/transformers/sharded_moe/test_sharded_moe.py b/onnxruntime/test/python/transformers/sharded_moe/test_sharded_moe.py index af835d2906e87..fd1d58cd2a3b8 100644 --- a/onnxruntime/test/python/transformers/sharded_moe/test_sharded_moe.py +++ b/onnxruntime/test/python/transformers/sharded_moe/test_sharded_moe.py @@ -59,39 +59,41 @@ def create_moe_onnx_graph( ): use_sharded_moe = local_experts_start_index >= 0 nodes = [ - helper.make_node( - "MoE", - [ - "input", - "router_probs", - "fc1_experts_weights", - "fc2_experts_weights", - "fc1_experts_bias", - "fc2_experts_bias", - ], - ["output"], - "MoE_0", - k=1, - activation_type="gelu", - domain="com.microsoft", - ) - if not use_sharded_moe - else helper.make_node( - "ShardedMoE", - [ - "input", - "router_probs", - "fc1_experts_weights", - "fc2_experts_weights", - "fc1_experts_bias", - "fc2_experts_bias", - ], - ["output"], - "MoE_0", - k=1, - activation_type="gelu", - local_experts_start_index=local_experts_start_index, - domain="com.microsoft", + ( + helper.make_node( + "MoE", + [ + "input", + "router_probs", + "fc1_experts_weights", + "fc2_experts_weights", + "fc1_experts_bias", + "fc2_experts_bias", + ], + ["output"], + "MoE_0", + k=1, + activation_type="gelu", + domain="com.microsoft", + ) + if not use_sharded_moe + else helper.make_node( + "ShardedMoE", + [ + "input", + "router_probs", + "fc1_experts_weights", + "fc2_experts_weights", + "fc1_experts_bias", + "fc2_experts_bias", + ], + ["output"], + "MoE_0", + k=1, + activation_type="gelu", + local_experts_start_index=local_experts_start_index, + domain="com.microsoft", + ) ), ] diff --git a/onnxruntime/test/python/transformers/test_data/bert_squad_tensorflow2.1_keras2onnx_opset11/generate_tiny_keras2onnx_bert_models.py b/onnxruntime/test/python/transformers/test_data/bert_squad_tensorflow2.1_keras2onnx_opset11/generate_tiny_keras2onnx_bert_models.py index c42c42c3ca170..0086ce0d289c7 100644 --- a/onnxruntime/test/python/transformers/test_data/bert_squad_tensorflow2.1_keras2onnx_opset11/generate_tiny_keras2onnx_bert_models.py +++ b/onnxruntime/test/python/transformers/test_data/bert_squad_tensorflow2.1_keras2onnx_opset11/generate_tiny_keras2onnx_bert_models.py @@ -403,9 +403,7 @@ def generate_test_data( evalTime = timeit.default_timer() - start_time # noqa: N806 if outputs[0].tolist() != result[0].tolist(): print( - "Error: not same result after optimization. use_cpu={}, no_opt_output={}, opt_output={}".format( - use_cpu, result[0].tolist(), outputs[1].tolist() - ) + f"Error: not same result after optimization. use_cpu={use_cpu}, no_opt_output={result[0].tolist()}, opt_output={outputs[1].tolist()}" ) print(f"** Evaluation done in total {evalTime} secs") diff --git a/onnxruntime/test/python/transformers/test_flash_attn.py b/onnxruntime/test/python/transformers/test_flash_attn.py index 90d28872d3cc8..b784c83329c76 100644 --- a/onnxruntime/test/python/transformers/test_flash_attn.py +++ b/onnxruntime/test/python/transformers/test_flash_attn.py @@ -229,9 +229,11 @@ def create_group_query_attention_graph_prompt( [ config.batch_size, config.q_sequence_length, - (config.num_heads * config.head_size) - if not packed - else (config.num_heads * config.head_size + 2 * config.kv_num_heads * config.head_size), + ( + (config.num_heads * config.head_size) + if not packed + else (config.num_heads * config.head_size + 2 * config.kv_num_heads * config.head_size) + ), ], ), helper.make_tensor_value_info( @@ -415,9 +417,11 @@ def create_group_query_attention_graph_past( [ config.batch_size, config.sequence_length, - (config.num_heads * config.head_size) - if not packed - else (config.num_heads * config.head_size + 2 * config.kv_num_heads * config.head_size), + ( + (config.num_heads * config.head_size) + if not packed + else (config.num_heads * config.head_size + 2 * config.kv_num_heads * config.head_size) + ), ], ), helper.make_tensor_value_info( diff --git a/onnxruntime/test/python/transformers/whisper_model_generator.py b/onnxruntime/test/python/transformers/whisper_model_generator.py index 71d1a4cbdceeb..a57b45cbc5ea3 100644 --- a/onnxruntime/test/python/transformers/whisper_model_generator.py +++ b/onnxruntime/test/python/transformers/whisper_model_generator.py @@ -22,9 +22,7 @@ def get_tensor_and_weight(name: str, shape: List[int], random=False, zeros=False weights = ( [np.random.uniform(low, high) for _ in range(total_elements)] if random - else [0.0] * total_elements - if zeros - else [1.0] * total_elements + else [0.0] * total_elements if zeros else [1.0] * total_elements ) return helper.make_tensor(name, TensorProto.FLOAT, shape, weights), weights diff --git a/onnxruntime/test/testdata/custom_op_library/custom_op_test_float8.py b/onnxruntime/test/testdata/custom_op_library/custom_op_test_float8.py index 84cf71455f84a..6db8e8fe660f8 100644 --- a/onnxruntime/test/testdata/custom_op_library/custom_op_test_float8.py +++ b/onnxruntime/test/testdata/custom_op_library/custom_op_test_float8.py @@ -1,6 +1,7 @@ """ This file was used to generate model `custom_op_test_float8.py`. """ + from onnx import TensorProto from onnx.checker import check_model from onnx.helper import make_graph, make_model, make_node, make_opsetid, make_tensor_value_info diff --git a/onnxruntime/test/testdata/test_data_generation/adamw_test/adamw_test_data_generator.py b/onnxruntime/test/testdata/test_data_generation/adamw_test/adamw_test_data_generator.py index 4c1e3a70de1c7..443444044bb8d 100644 --- a/onnxruntime/test/testdata/test_data_generation/adamw_test/adamw_test_data_generator.py +++ b/onnxruntime/test/testdata/test_data_generation/adamw_test/adamw_test_data_generator.py @@ -190,7 +190,7 @@ def main(): device_candidates = ["cuda", "cpu"] test_data_step_count = 11 for device in device_candidates: - for adam_mode in range(0, 2): + for adam_mode in range(2): generate_adamw_single_weight_tests(adam_mode, test_data_step_count, device) generate_adamw_multiple_weights_tests(adam_mode, test_data_step_count, device) diff --git a/onnxruntime/test/testdata/transform/fusion/embed_layer_norm_gen.py b/onnxruntime/test/testdata/transform/fusion/embed_layer_norm_gen.py index ed06495b42beb..54fe7b808bf12 100644 --- a/onnxruntime/test/testdata/transform/fusion/embed_layer_norm_gen.py +++ b/onnxruntime/test/testdata/transform/fusion/embed_layer_norm_gen.py @@ -21,19 +21,21 @@ def GenerateNodes(model_name, has_cast, suffix=""): # noqa: N802 ["gather0_out" + suffix], "gather0" + suffix, ), - helper.make_node( - "Unsqueeze", - ["gather0_out" + suffix, "axes_0"], - ["unsqueeze0_out" + suffix], - "unsqueeze0" + suffix, - ) - if opset_version == 13 - else helper.make_node( - "Unsqueeze", - ["gather0_out" + suffix], - ["unsqueeze0_out" + suffix], - "unsqueeze0" + suffix, - axes=[0], + ( + helper.make_node( + "Unsqueeze", + ["gather0_out" + suffix, "axes_0"], + ["unsqueeze0_out" + suffix], + "unsqueeze0" + suffix, + ) + if opset_version == 13 + else helper.make_node( + "Unsqueeze", + ["gather0_out" + suffix], + ["unsqueeze0_out" + suffix], + "unsqueeze0" + suffix, + axes=[0], + ) ), helper.make_node("Shape", ["input_ids" + suffix], ["shape2_out" + suffix], "shape2" + suffix), helper.make_node( @@ -42,19 +44,21 @@ def GenerateNodes(model_name, has_cast, suffix=""): # noqa: N802 ["gather1_out" + suffix], "gather1" + suffix, ), - helper.make_node( - "Unsqueeze", - ["gather1_out" + suffix, "axes_0"], - ["unsqueeze1_out" + suffix], - "unsqueeze1" + suffix, - ) - if opset_version == 13 - else helper.make_node( - "Unsqueeze", - ["gather1_out" + suffix], - ["unsqueeze1_out" + suffix], - "unsqueeze1" + suffix, - axes=[0], + ( + helper.make_node( + "Unsqueeze", + ["gather1_out" + suffix, "axes_0"], + ["unsqueeze1_out" + suffix], + "unsqueeze1" + suffix, + ) + if opset_version == 13 + else helper.make_node( + "Unsqueeze", + ["gather1_out" + suffix], + ["unsqueeze1_out" + suffix], + "unsqueeze1" + suffix, + axes=[0], + ) ), helper.make_node( "Concat", @@ -80,19 +84,21 @@ def GenerateNodes(model_name, has_cast, suffix=""): # noqa: N802 ["range_out" + suffix], "range" + suffix, ), - helper.make_node( - "Unsqueeze", - ["range_out" + suffix, "axes_0"], - ["unsqueeze2_out" + suffix], - "unsqueeze2" + suffix, - ) - if opset_version == 13 - else helper.make_node( - "Unsqueeze", - ["range_out" + suffix], - ["unsqueeze2_out" + suffix], - "unsqueeze2" + suffix, - axes=[0], + ( + helper.make_node( + "Unsqueeze", + ["range_out" + suffix, "axes_0"], + ["unsqueeze2_out" + suffix], + "unsqueeze2" + suffix, + ) + if opset_version == 13 + else helper.make_node( + "Unsqueeze", + ["range_out" + suffix], + ["unsqueeze2_out" + suffix], + "unsqueeze2" + suffix, + axes=[0], + ) ), helper.make_node( "Expand", @@ -145,21 +151,23 @@ def GenerateNodes(model_name, has_cast, suffix=""): # noqa: N802 "mask_cast" + suffix, to=6, ), - helper.make_node( - "ReduceSum", - ["mask_cast_out" + suffix, "axes_1"], - ["mask_index_out" + suffix], - "mask_index" + suffix, - keepdims=0, - ) - if opset_version == 13 - else helper.make_node( - "ReduceSum", - ["mask_cast_out" + suffix], - ["mask_index_out" + suffix], - "mask_index" + suffix, - axes=[1], - keepdims=0, + ( + helper.make_node( + "ReduceSum", + ["mask_cast_out" + suffix, "axes_1"], + ["mask_index_out" + suffix], + "mask_index" + suffix, + keepdims=0, + ) + if opset_version == 13 + else helper.make_node( + "ReduceSum", + ["mask_cast_out" + suffix], + ["mask_index_out" + suffix], + "mask_index" + suffix, + axes=[1], + keepdims=0, + ) ), helper.make_node( "Attention", @@ -372,21 +380,23 @@ def GenerateModel5(model_name): # noqa: N802 epsion=0.000009999999747378752, ), helper.make_node("Cast", ["input_mask"], ["mask_cast_out"], "mask_cast", to=6), - helper.make_node( - "ReduceSum", - ["mask_cast_out", "axes_1"], - ["mask_index_out"], - "mask_index", - keepdims=0, - ) - if opset_version == 13 - else helper.make_node( - "ReduceSum", - ["mask_cast_out"], - ["mask_index_out"], - "mask_index", - axes=[1], - keepdims=0, + ( + helper.make_node( + "ReduceSum", + ["mask_cast_out", "axes_1"], + ["mask_index_out"], + "mask_index", + keepdims=0, + ) + if opset_version == 13 + else helper.make_node( + "ReduceSum", + ["mask_cast_out"], + ["mask_index_out"], + "mask_index", + axes=[1], + keepdims=0, + ) ), helper.make_node( "Attention", @@ -514,14 +524,18 @@ def GenerateModel6(model_name): # noqa: N802 nodes = [ # LayerNorm subgraph helper.make_node("Shape", ["input_ids"], ["shape1_out"], "shape1"), helper.make_node("Gather", ["shape1_out", "indices_0"], ["gather0_out"], "gather0"), - helper.make_node("Unsqueeze", ["gather0_out", "axes_0"], ["unsqueeze0_out"], "unsqueeze0") - if opset_version == 13 - else helper.make_node("Unsqueeze", ["gather0_out"], ["unsqueeze0_out"], "unsqueeze0", axes=[0]), + ( + helper.make_node("Unsqueeze", ["gather0_out", "axes_0"], ["unsqueeze0_out"], "unsqueeze0") + if opset_version == 13 + else helper.make_node("Unsqueeze", ["gather0_out"], ["unsqueeze0_out"], "unsqueeze0", axes=[0]) + ), helper.make_node("Shape", ["input_ids"], ["shape2_out"], "shape2"), helper.make_node("Gather", ["shape2_out", "indices_1"], ["gather1_out"], "gather1"), - helper.make_node("Unsqueeze", ["gather1_out", "axes_0"], ["unsqueeze1_out"], "unsqueeze1") - if opset_version == 13 - else helper.make_node("Unsqueeze", ["gather1_out"], ["unsqueeze1_out"], "unsqueeze1", axes=[0]), + ( + helper.make_node("Unsqueeze", ["gather1_out", "axes_0"], ["unsqueeze1_out"], "unsqueeze1") + if opset_version == 13 + else helper.make_node("Unsqueeze", ["gather1_out"], ["unsqueeze1_out"], "unsqueeze1", axes=[0]) + ), helper.make_node( "Concat", ["unsqueeze0_out", "unsqueeze1_out"], @@ -533,9 +547,11 @@ def GenerateModel6(model_name): # noqa: N802 helper.make_node("Equal", ["reshape_out", "equal_init"], ["equal_out"], "equal"), helper.make_node("Where", ["equal_out", "where_init", "reshape_out"], ["where_out"], "where"), helper.make_node("Range", ["start_0", "gather1_out", "delta_1"], ["range_out"], "range"), - helper.make_node("Unsqueeze", ["range_out", "axes_0"], ["unsqueeze2_out"], "unsqueeze2") - if opset_version == 13 - else helper.make_node("Unsqueeze", ["range_out"], ["unsqueeze2_out"], "unsqueeze2", axes=[0]), + ( + helper.make_node("Unsqueeze", ["range_out", "axes_0"], ["unsqueeze2_out"], "unsqueeze2") + if opset_version == 13 + else helper.make_node("Unsqueeze", ["range_out"], ["unsqueeze2_out"], "unsqueeze2", axes=[0]) + ), helper.make_node("Expand", ["unsqueeze2_out", "where_out"], ["expand_out"], "expand"), helper.make_node("Gather", ["pos_embed", "expand_out"], ["pos_gather_out"], "pos_gather"), helper.make_node("Gather", ["word_embed", "input_ids"], ["word_gather_out"], "word_gather"), @@ -556,21 +572,23 @@ def GenerateModel6(model_name): # noqa: N802 epsion=0.000009999999747378752, ), helper.make_node("Cast", ["input_mask"], ["mask_cast_out"], "mask_cast", to=6), - helper.make_node( - "ReduceSum", - ["mask_cast_out", "axes_1"], - ["mask_index_out"], - "mask_index", - keepdims=0, - ) - if opset_version == 13 - else helper.make_node( - "ReduceSum", - ["mask_cast_out"], - ["mask_index_out"], - "mask_index", - axes=[1], - keepdims=0, + ( + helper.make_node( + "ReduceSum", + ["mask_cast_out", "axes_1"], + ["mask_index_out"], + "mask_index", + keepdims=0, + ) + if opset_version == 13 + else helper.make_node( + "ReduceSum", + ["mask_cast_out"], + ["mask_index_out"], + "mask_index", + axes=[1], + keepdims=0, + ) ), helper.make_node( "Attention", @@ -756,9 +774,11 @@ def GenerateNodes2(attention_heads): # noqa: N802 helper.make_node("Shape", ["input_ids"], ["shape0_out"], "shape0"), helper.make_node("Gather", ["shape0_out", "indices_1"], ["gather0_out"], "gather0"), helper.make_node("Range", ["start", "gather0_out", "delta"], ["range0_out"], "range0"), - helper.make_node("Unsqueeze", ["range0_out", "axes_0"], ["unsqueeze0_out"], "unsqueeze0") - if opset_version == 13 - else helper.make_node("Unsqueeze", ["range0_out"], ["unsqueeze0_out"], "unsqueeze0", axes=[0]), + ( + helper.make_node("Unsqueeze", ["range0_out", "axes_0"], ["unsqueeze0_out"], "unsqueeze0") + if opset_version == 13 + else helper.make_node("Unsqueeze", ["range0_out"], ["unsqueeze0_out"], "unsqueeze0", axes=[0]) + ), helper.make_node("Shape", ["input_ids"], ["shape1_out"], "shape1"), helper.make_node("Expand", ["unsqueeze0_out", "shape1_out"], ["expand_out"], "expand"), helper.make_node( @@ -778,21 +798,23 @@ def GenerateNodes2(attention_heads): # noqa: N802 epsion=0.000009999999747378752, ), helper.make_node("Cast", ["input_mask"], ["mask_cast_out"], "mask_cast", to=6), - helper.make_node( - "ReduceSum", - ["mask_cast_out", "axes_1"], - ["mask_index_out"], - "mask_index", - keepdims=0, - ) - if opset_version == 13 - else helper.make_node( - "ReduceSum", - ["mask_cast_out"], - ["mask_index_out"], - "mask_index", - axes=[1], - keepdims=0, + ( + helper.make_node( + "ReduceSum", + ["mask_cast_out", "axes_1"], + ["mask_index_out"], + "mask_index", + keepdims=0, + ) + if opset_version == 13 + else helper.make_node( + "ReduceSum", + ["mask_cast_out"], + ["mask_index_out"], + "mask_index", + axes=[1], + keepdims=0, + ) ), helper.make_node( "Attention", @@ -898,12 +920,16 @@ def GenerateModel9(model_name): # noqa: N802 helper.make_node("Expand", ["unsqueeze0_out", "shape_out"], ["expand_out"], "expand"), helper.make_node("Gather", ["shape_out", "indices_0"], ["gather1_out"], "gather1"), helper.make_node("Gather", ["shape_out", "indices_1"], ["gather2_out"], "gather2"), - helper.make_node("Unsqueeze", ["gather1_out", "axes_0"], ["unsqueeze1_out"], "unsqueeze1") - if opset_version == 13 - else helper.make_node("Unsqueeze", ["gather1_out"], ["unsqueeze1_out"], "unsqueeze1", axes=[0]), - helper.make_node("Unsqueeze", ["gather2_out", "axes_0"], ["unsqueeze2_out"], "unsqueeze2") - if opset_version == 13 - else helper.make_node("Unsqueeze", ["gather2_out"], ["unsqueeze2_out"], "unsqueeze2", axes=[0]), + ( + helper.make_node("Unsqueeze", ["gather1_out", "axes_0"], ["unsqueeze1_out"], "unsqueeze1") + if opset_version == 13 + else helper.make_node("Unsqueeze", ["gather1_out"], ["unsqueeze1_out"], "unsqueeze1", axes=[0]) + ), + ( + helper.make_node("Unsqueeze", ["gather2_out", "axes_0"], ["unsqueeze2_out"], "unsqueeze2") + if opset_version == 13 + else helper.make_node("Unsqueeze", ["gather2_out"], ["unsqueeze2_out"], "unsqueeze2", axes=[0]) + ), helper.make_node( "Concat", ["unsqueeze1_out", "unsqueeze2_out"], diff --git a/orttraining/orttraining/python/training/__init__.py b/orttraining/orttraining/python/training/__init__.py index a3c22686a1039..1da95dff94f9f 100644 --- a/orttraining/orttraining/python/training/__init__.py +++ b/orttraining/orttraining/python/training/__init__.py @@ -23,9 +23,9 @@ try: if is_ortmodule_available(): - from .ortmodule import ORTModule # noqa: F401 + from .ortmodule import ORTModule - __all__.append("ORTModule") + __all__ += ["ORTModule"] except ImportError: # That is OK iff this is not a ORTModule training package pass diff --git a/orttraining/orttraining/python/training/optim/_apex_amp_modifier.py b/orttraining/orttraining/python/training/optim/_apex_amp_modifier.py index d7bbd249a000e..ff128c4da4259 100644 --- a/orttraining/orttraining/python/training/optim/_apex_amp_modifier.py +++ b/orttraining/orttraining/python/training/optim/_apex_amp_modifier.py @@ -15,7 +15,6 @@ class ApexAMPModifier(FP16OptimizerModifier): def __init__(self, optimizer, **kwargs) -> None: super().__init__(optimizer) - pass def can_be_modified(self): return self.check_requirements( diff --git a/orttraining/orttraining/python/training/ort_triton/_lowering.py b/orttraining/orttraining/python/training/ort_triton/_lowering.py index 5c848d2cecc58..4b580a0cc86de 100644 --- a/orttraining/orttraining/python/training/ort_triton/_lowering.py +++ b/orttraining/orttraining/python/training/ort_triton/_lowering.py @@ -312,7 +312,7 @@ def _group_nodes(self): for j in range(i + 1, len(groups)): if any(output in group_inputs for output in groups[j].nodes_groups[0].output): group_dependencies[i].add(j) - for k in range(0, i): + for k in range(i): if i in group_dependencies[k]: group_dependencies[k].add(j) diff --git a/orttraining/orttraining/python/training/ort_triton/kernel/_flash_attn.py b/orttraining/orttraining/python/training/ort_triton/kernel/_flash_attn.py index 03bb0f4373d8d..f7b7c1ff08300 100644 --- a/orttraining/orttraining/python/training/ort_triton/kernel/_flash_attn.py +++ b/orttraining/orttraining/python/training/ort_triton/kernel/_flash_attn.py @@ -694,7 +694,7 @@ def _bwd_kernel( LSE += off_hb * seqlen_q_rounded if not SEQUENCE_PARALLEL: num_block_n = tl.cdiv(seqlen_k, BLOCK_N) - for start_n in range(0, num_block_n): + for start_n in range(num_block_n): _bwd_kernel_one_col_block( start_n, Q, diff --git a/orttraining/orttraining/python/training/ortmodule/_fallback_exceptions.py b/orttraining/orttraining/python/training/ortmodule/_fallback_exceptions.py index 12780016a9ab1..871d3fff8ce3f 100644 --- a/orttraining/orttraining/python/training/ortmodule/_fallback_exceptions.py +++ b/orttraining/orttraining/python/training/ortmodule/_fallback_exceptions.py @@ -10,8 +10,6 @@ class ORTModuleFallbackException(Exception): # noqa: N818 it can also be used for generic exception that require fallback """ - pass - class ORTModuleInitException(ORTModuleFallbackException): """Trigger fallback for ORTModule initialization related exceptions @@ -20,8 +18,6 @@ class ORTModuleInitException(ORTModuleFallbackException): including PyTorch version, missing ORTModule's PyTorch C++ extension binaries, etc. """ - pass - class ORTModuleDeviceException(ORTModuleFallbackException): """Trigger fallback for device related exceptions @@ -31,8 +27,6 @@ class ORTModuleDeviceException(ORTModuleFallbackException): This exception does not capture these scenarios. """ - pass - class ORTModuleIOError(ORTModuleFallbackException): """Trigger fallback for I/O related exceptions @@ -42,8 +36,6 @@ class ORTModuleIOError(ORTModuleFallbackException): This exception does not capture these scenarios. """ - pass - class ORTModuleTorchModelException(ORTModuleFallbackException): """Trigger fallback for PyTorch modules related exceptions @@ -52,8 +44,6 @@ class ORTModuleTorchModelException(ORTModuleFallbackException): checking type(model) over a hardcoded list of incompatible models. """ - pass - class ORTModuleONNXModelException(ORTModuleFallbackException): """Trigger fallback for ONNX model related exceptions @@ -61,8 +51,6 @@ class ORTModuleONNXModelException(ORTModuleFallbackException): This exception is raised during model conversion to ONNX and post-processing validation within ORTModule frontend. """ - pass - def wrap_exception( new_exception: ORTModuleFallbackException, raised_exception: Exception diff --git a/orttraining/orttraining/python/training/ortmodule/_graph_execution_manager.py b/orttraining/orttraining/python/training/ortmodule/_graph_execution_manager.py index c67b05758c5aa..568c92b71277f 100755 --- a/orttraining/orttraining/python/training/ortmodule/_graph_execution_manager.py +++ b/orttraining/orttraining/python/training/ortmodule/_graph_execution_manager.py @@ -188,7 +188,6 @@ def forward(self): This is an abstract method and must be overridden by a concrete implementation. This is the only method that the user should call on a concrete instance of the ExecutionManager All other methods are internal""" - pass def _build_graph(self, config): if self._runtime_options.use_static_shape: @@ -412,9 +411,9 @@ def _get_exported_model(self, input_schema: ORTModelInputOutputSchemaType, *inpu # From some PyTorch version, autograd_inlining is a valid argument. # We allow it to be True if custom autograd function is disabled (where autograd.Function # anyway is not supported in ONNX until it can be inlined). - required_export_kwargs[ - "autograd_inlining" - ] = not self._runtime_options.enable_custom_autograd_function + required_export_kwargs["autograd_inlining"] = ( + not self._runtime_options.enable_custom_autograd_function + ) invalid_args = self._export_extra_kwargs.keys() & required_export_kwargs.keys() diff --git a/orttraining/orttraining/python/training/ortmodule/_logger.py b/orttraining/orttraining/python/training/ortmodule/_logger.py index a01db28374b8d..91b99d4323d6f 100644 --- a/orttraining/orttraining/python/training/ortmodule/_logger.py +++ b/orttraining/orttraining/python/training/ortmodule/_logger.py @@ -267,9 +267,11 @@ def wrapper(graph_execution_manager, *args, **kwargs): on_exit=partial( _log_with_filter, graph_execution_manager._logger, - graph_execution_manager._debug_options.onnxruntime_log_filter - if self.is_ort_filter - else graph_execution_manager._debug_options.torch_exporter_filter, + ( + graph_execution_manager._debug_options.onnxruntime_log_filter + if self.is_ort_filter + else graph_execution_manager._debug_options.torch_exporter_filter + ), self.phase.to_string(), ), ): diff --git a/orttraining/orttraining/python/training/ortmodule/_runtime_inspector.py b/orttraining/orttraining/python/training/ortmodule/_runtime_inspector.py index 22e31466887a6..d3fe132609a90 100644 --- a/orttraining/orttraining/python/training/ortmodule/_runtime_inspector.py +++ b/orttraining/orttraining/python/training/ortmodule/_runtime_inspector.py @@ -433,9 +433,7 @@ def _print_embed_label_stats(self): total_token, valid_token_per_batch, ) in self._stats: - stat += "\t| {:<10} | {:<10} | {:<15} | {:<10} | {:<9.2f}% | {:<15} | {:<15} | {:<15} |\n".format( - step, input_type, input_name, padding_idx, density, valid_token, total_token, valid_token_per_batch - ) + stat += f"\t| {step:<10} | {input_type:<10} | {input_name:<15} | {padding_idx:<10} | {density:<9.2f}% | {valid_token:<15} | {total_token:<15} | {valid_token_per_batch:<15} |\n" stat += "<<<\n" self._logger.info(stat) self._stats.clear() @@ -697,9 +695,11 @@ def _get_user_config_without_freq(configs: str): [ f" - Plan {index}", ":", - "ON" - if all(cluster_id in user_configs_with_out_freq for cluster_id in cluster_ids_without_freq) - else "OFF", + ( + "ON" + if all(cluster_id in user_configs_with_out_freq for cluster_id in cluster_ids_without_freq) + else "OFF" + ), ":", cluster_id, saving_symbolic.freq if details else "", diff --git a/orttraining/orttraining/python/training/ortmodule/_training_manager.py b/orttraining/orttraining/python/training/ortmodule/_training_manager.py index 73c32a2f51e41..5fa332d12f01c 100644 --- a/orttraining/orttraining/python/training/ortmodule/_training_manager.py +++ b/orttraining/orttraining/python/training/ortmodule/_training_manager.py @@ -171,10 +171,10 @@ def backward(ctx, *grad_outputs): for idx, grad_output in enumerate(grad_outputs): if idx in self._graph_info.output_grad_indices_non_differentiable: assert grad_output is None, ( - "ORT found the {}-th module output '{}' is " + f"ORT found the {idx}-th module output '{self._graph_info.user_output_names[idx]}' is " "non-differentiable according to the onnx graph. " "However, the gradient value is still provided by " - "PyTorch's autograd engine.".format(idx, self._graph_info.user_output_names[idx]) + "PyTorch's autograd engine." ) continue diff --git a/orttraining/orttraining/python/training/ortmodule/_utils.py b/orttraining/orttraining/python/training/ortmodule/_utils.py index 91825fc492208..5faa1c62bae4f 100644 --- a/orttraining/orttraining/python/training/ortmodule/_utils.py +++ b/orttraining/orttraining/python/training/ortmodule/_utils.py @@ -91,7 +91,7 @@ def _ortvalues_to_torch_tensor( # Second option makes it impossible to directly use `_from_dlpack` or # or `from_dlpack` from torch. # The best option would be to add boolean type in DLDataTypeCode. - for i in range(0, len(bool_indices)): + for i in range(len(bool_indices)): j = bool_indices[i] res[j] = res[j].to(torch.bool) diff --git a/orttraining/orttraining/python/training/ortmodule/options.py b/orttraining/orttraining/python/training/ortmodule/options.py index 7263a5719e262..1bde07dc29ba9 100644 --- a/orttraining/orttraining/python/training/ortmodule/options.py +++ b/orttraining/orttraining/python/training/ortmodule/options.py @@ -387,7 +387,6 @@ def _override_from_env_vars(self): try: import triton # noqa: F401 except ImportError: - pass self._logger.warning( "triton library missing. Please install triton with `pip install triton`. Triton feature will be off." ) diff --git a/orttraining/orttraining/test/external_custom_ops/setup.py b/orttraining/orttraining/test/external_custom_ops/setup.py index 435b83b818380..29383e3618346 100644 --- a/orttraining/orttraining/test/external_custom_ops/setup.py +++ b/orttraining/orttraining/test/external_custom_ops/setup.py @@ -28,9 +28,7 @@ def build_extension(self, ext): subprocess.check_call( [ "cmake", - "-DPYBIND11_PYTHON_VERSION={}.{}.{}".format( - sys.version_info.major, sys.version_info.minor, sys.version_info.micro - ), + f"-DPYBIND11_PYTHON_VERSION={sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}", f"-Dpybind11_DIR={pybind11.get_cmake_dir()}", f"-DONNX_INCLUDE={os.path.dirname(os.path.dirname(onnx.__file__))}", "-DONNXRUNTIME_EXTERNAL_INCLUDE={}".format( diff --git a/orttraining/orttraining/test/python/_test_commons.py b/orttraining/orttraining/test/python/_test_commons.py index fb7e62551de63..762c4c4d55f9f 100644 --- a/orttraining/orttraining/test/python/_test_commons.py +++ b/orttraining/orttraining/test/python/_test_commons.py @@ -25,5 +25,5 @@ def run_subprocess(args, cwd=None, capture=False, dll_path=None, shell=False, en completed_process = subprocess.run(args, cwd=cwd, check=True, stdout=stdout, stderr=stderr, env=my_env, shell=shell) if log: - log.debug("Subprocess completed. Return code=" + str(completed_process.returncode)) + log.debug("Subprocess completed. Return code=%s", completed_process.returncode) return completed_process diff --git a/orttraining/orttraining/test/python/_test_helpers.py b/orttraining/orttraining/test/python/_test_helpers.py index 8f2a18b5ec00b..65043c10d8a01 100644 --- a/orttraining/orttraining/test/python/_test_helpers.py +++ b/orttraining/orttraining/test/python/_test_helpers.py @@ -288,7 +288,6 @@ def cpu_barrier_func(): def cuda_barrier_func(): torch.cuda.synchronize() - pass cuda = torch.device("cuda:0") run_evaluate_test_on_device_and_compare( diff --git a/orttraining/orttraining/test/python/orttraining_test_gru.py b/orttraining/orttraining/test/python/orttraining_test_gru.py index fcb7e13b1694f..c9e22bf7384af 100644 --- a/orttraining/orttraining/test/python/orttraining_test_gru.py +++ b/orttraining/orttraining/test/python/orttraining_test_gru.py @@ -355,9 +355,7 @@ def backward_np( prev_h = ( all_hidden_states[t - 1, 0, idx, :] if t > 0 - else initial_hidden_state[0, idx, :] - if initial_hidden_state is not None - else 0 + else initial_hidden_state[0, idx, :] if initial_hidden_state is not None else 0 ) grad_update_gate = (prev_h - hidden_gate) * grad_h diff --git a/orttraining/orttraining/test/python/orttraining_test_lstm.py b/orttraining/orttraining/test/python/orttraining_test_lstm.py index 2b296cf70c2c1..4debe73951b2f 100644 --- a/orttraining/orttraining/test/python/orttraining_test_lstm.py +++ b/orttraining/orttraining/test/python/orttraining_test_lstm.py @@ -480,9 +480,7 @@ def backward_np( grad_forget_gate = grad_c * ( all_cell_states[t - 1, 0, idx, :] if t > 0 - else initial_cell_state[0, idx, :] - if initial_cell_state is not None - else 0 + else initial_cell_state[0, idx, :] if initial_cell_state is not None else 0 ) grad_control_gate = grad_c * input_gate @@ -522,9 +520,7 @@ def backward_np( prev_h = ( all_hidden_states[t - 1, 0, idx, :] if t > 0 - else initial_hidden_state[0, idx, :] - if initial_hidden_state is not None - else 0 + else initial_hidden_state[0, idx, :] if initial_hidden_state is not None else 0 ) grad_recurrence_weights[0, : self._hidden_size, :] += np.dot( np.expand_dims(grad_input_activation, axis=0).T, np.expand_dims(prev_h, axis=0) @@ -553,9 +549,7 @@ def backward_np( grad_peephole_weights[0, : self._hidden_size] += grad_input_activation * ( all_cell_states[t - 1, 0, idx, :] if t > 0 - else initial_cell_state[0, idx, :] - if initial_cell_state is not None - else 0 + else initial_cell_state[0, idx, :] if initial_cell_state is not None else 0 ) grad_peephole_weights[0, self._hidden_size : 2 * self._hidden_size] += ( grad_output_activation * all_cell_states[t, 0, idx, :] @@ -565,9 +559,7 @@ def backward_np( ] += grad_forget_activation * ( all_cell_states[t - 1, 0, idx, :] if t > 0 - else initial_cell_state[0, idx, :] - if initial_cell_state is not None - else 0 + else initial_cell_state[0, idx, :] if initial_cell_state is not None else 0 ) grad_c = grad_prev_c diff --git a/orttraining/orttraining/test/python/orttraining_test_ort_apis_onnxblock.py b/orttraining/orttraining/test/python/orttraining_test_ort_apis_onnxblock.py index 3d41c8678278c..11df3fa347ff8 100644 --- a/orttraining/orttraining/test/python/orttraining_test_ort_apis_onnxblock.py +++ b/orttraining/orttraining/test/python/orttraining_test_ort_apis_onnxblock.py @@ -190,9 +190,11 @@ def _get_training_ort_inputs(x, target, pt_model, onnx_model, target_type=None): ort_inputs = { onnx_model.graph.input[0].name: _to_numpy(copy.deepcopy(x)), - onnx_model.graph.input[1].name: _to_numpy(copy.deepcopy(target)) - if target_type is None - else _to_numpy(copy.deepcopy(target).type(target_type)), + onnx_model.graph.input[1].name: ( + _to_numpy(copy.deepcopy(target)) + if target_type is None + else _to_numpy(copy.deepcopy(target).type(target_type)) + ), } if target_type is not None: ort_inputs[onnx_model.graph.input[1].name] diff --git a/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py b/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py index f0261c776609e..7afad9145ed27 100644 --- a/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py +++ b/orttraining/orttraining/test/python/orttraining_test_ortmodule_api.py @@ -3797,7 +3797,7 @@ def forward(self, input1=None, input2=None): model.eval() # Must work because forward() and dict order match - y1, y2 = model(**{"input1": input1, "input2": input2}) + y1, y2 = model(input1=input1, input2=input2) assert y1 is not None assert y2 is not None if model._is_training(): @@ -3805,7 +3805,7 @@ def forward(self, input1=None, input2=None): loss.backward() # Must work even when forward() and dict order mismatch - y1, y2 = model(**{"input2": input2, "input1": input1}) + y1, y2 = model(input2=input2, input1=input1) assert y1 is not None assert y2 is not None if model._is_training(): @@ -3887,17 +3887,20 @@ def run_step(expected, a, b, c, d, e, f, y, z): None, None, ) - run_step( - a.item() + f.item(), **{"a": a, "b": None, "c": None, "d": None, "e": None, "f": f, "y": None, "z": None} - ) + run_step(a.item() + f.item(), a=a, b=None, c=None, d=None, e=None, f=f, y=None, z=None) run_step(a.item() + z.item(), a, None, None, None, None, None, None, z) - run_step( - a.item() + z.item(), **{"a": a, "b": None, "c": None, "d": None, "e": None, "f": None, "y": None, "z": z} - ) + run_step(a.item() + z.item(), a=a, b=None, c=None, d=None, e=None, f=None, y=None, z=z) run_step(a.item() + c.item() + y.item(), a, None, c, None, None, None, y, None) run_step( a.item() + c.item() + y.item(), - **{"a": a, "b": None, "c": c, "d": None, "e": None, "f": None, "y": y, "z": None}, + a=a, + b=None, + c=c, + d=None, + e=None, + f=None, + y=y, + z=None, ) run_step( a.item() + b.item() + c.item() + d.item() + e.item() + f.item() + y.item() + z.item(), @@ -3912,7 +3915,14 @@ def run_step(expected, a, b, c, d, e, f, y, z): ) run_step( a.item() + b.item() + c.item() + d.item() + e.item() + f.item() + y.item() + z.item(), - **{"a": a, "b": b, "c": c, "d": d, "e": e, "f": f, "y": y, "z": z}, + a=a, + b=b, + c=c, + d=d, + e=e, + f=f, + y=y, + z=z, ) del os.environ["ORTMODULE_SKIPCHECK_POLICY"] diff --git a/orttraining/orttraining/test/python/orttraining_test_ortmodule_bert_classifier.py b/orttraining/orttraining/test/python/orttraining_test_ortmodule_bert_classifier.py index 3d92e0b323c19..a1a7d4660f266 100644 --- a/orttraining/orttraining/test/python/orttraining_test_ortmodule_bert_classifier.py +++ b/orttraining/orttraining/test/python/orttraining_test_ortmodule_bert_classifier.py @@ -441,7 +441,7 @@ def main(): # 4. Train loop (fine-tune) total_training_time, total_test_time, epoch_0_training, validation_accuracy = 0, 0, 0, 0 - for epoch_i in range(0, args.epochs): + for epoch_i in range(args.epochs): total_training_time += train(model, optimizer, scheduler, train_dataloader, epoch_i, device, args) if not args.pytorch_only and epoch_i == 0: epoch_0_training = total_training_time diff --git a/orttraining/orttraining/test/python/orttraining_test_ortmodule_bert_classifier_autocast.py b/orttraining/orttraining/test/python/orttraining_test_ortmodule_bert_classifier_autocast.py index 87c8e66231a29..0d5aba1a1a5c4 100644 --- a/orttraining/orttraining/test/python/orttraining_test_ortmodule_bert_classifier_autocast.py +++ b/orttraining/orttraining/test/python/orttraining_test_ortmodule_bert_classifier_autocast.py @@ -446,7 +446,7 @@ def main(): # 4. Train loop (fine-tune) total_training_time, total_test_time, epoch_0_training, validation_accuracy = 0, 0, 0, 0 - for epoch_i in range(0, args.epochs): + for epoch_i in range(args.epochs): total_training_time += train(model, optimizer, scaler, scheduler, train_dataloader, epoch_i, device, args) if not args.pytorch_only and epoch_i == 0: epoch_0_training = total_training_time diff --git a/orttraining/orttraining/test/python/orttraining_test_ortmodule_deepspeed_zero_stage_1.py b/orttraining/orttraining/test/python/orttraining_test_ortmodule_deepspeed_zero_stage_1.py index 86e8d9aea1d37..5b28e9c52b480 100644 --- a/orttraining/orttraining/test/python/orttraining_test_ortmodule_deepspeed_zero_stage_1.py +++ b/orttraining/orttraining/test/python/orttraining_test_ortmodule_deepspeed_zero_stage_1.py @@ -8,6 +8,7 @@ --deepspeed_config=orttraining_test_ortmodule_deepspeed_zero_stage_1_config.json ``` """ + import argparse import time @@ -36,11 +37,7 @@ def forward(self, input1): def train(args, model, device, optimizer, loss_fn, train_loader, epoch): - print( - "\n======== Epoch {:} / {:} with batch size {:} ========".format( - epoch + 1, args.epochs, model.train_batch_size() - ) - ) + print(f"\n======== Epoch {epoch + 1} / {args.epochs} with batch size {model.train_batch_size()} ========") model.train() # Measure how long the training epoch takes. t0 = time.time() @@ -77,13 +74,7 @@ def train(args, model, device, optimizer, loss_fn, train_loader, epoch): curr_time = time.time() elapsed_time = curr_time - start_time print( - "[{:5}/{:5} ({:2.0f}%)]\tLoss: {:.6f}\tExecution time: {:.4f}".format( - iteration * len(data), - len(train_loader.dataset), - 100.0 * iteration / len(train_loader), - loss, - elapsed_time, - ) + f"[{iteration * len(data):5}/{len(train_loader.dataset):5} ({100.0 * iteration / len(train_loader):2.0f}%)]\tLoss: {loss:.6f}\tExecution time: {elapsed_time:.4f}" ) start_time = curr_time @@ -115,13 +106,7 @@ def test(args, model, device, loss_fn, test_loader): correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print( - "\nTest set: Batch size: {:}, Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format( - args.test_batch_size, - test_loss, - correct, - len(test_loader.dataset), - 100.0 * correct / len(test_loader.dataset), - ) + f"\nTest set: Batch size: {args.test_batch_size}, Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} ({100.0 * correct / len(test_loader.dataset):.0f}%)\n" ) # Report the final accuracy for this validation run. @@ -251,7 +236,7 @@ def main(): # Train loop total_training_time, total_test_time, epoch_0_training = 0, 0, 0 - for epoch in range(0, args.epochs): + for epoch in range(args.epochs): total_training_time += train(args, model, device, optimizer, my_loss, train_loader, epoch) if not args.pytorch_only and epoch == 0: epoch_0_training = total_training_time diff --git a/orttraining/orttraining/test/python/orttraining_test_ortmodule_fairscale_sharded_optimizer.py b/orttraining/orttraining/test/python/orttraining_test_ortmodule_fairscale_sharded_optimizer.py index 53e1928e2d2f3..4437611283122 100755 --- a/orttraining/orttraining/test/python/orttraining_test_ortmodule_fairscale_sharded_optimizer.py +++ b/orttraining/orttraining/test/python/orttraining_test_ortmodule_fairscale_sharded_optimizer.py @@ -123,13 +123,7 @@ def train_step(args, model, device, optimizer, loss_fn, train_loader, epoch): curr_time = time.time() elapsed_time = curr_time - start_time print( - "[{:5}/{:5} ({:2.0f}%)]\tLoss: {:.6f}\tExecution time: {:.4f}".format( - iteration * len(data), - len(train_loader.dataset), - 100.0 * iteration / len(train_loader), - loss, - elapsed_time, - ) + f"[{iteration * len(data):5}/{len(train_loader.dataset):5} ({100.0 * iteration / len(train_loader):2.0f}%)]\tLoss: {loss:.6f}\tExecution time: {elapsed_time:.4f}" ) start_time = curr_time @@ -160,13 +154,7 @@ def test(args, model, device, loss_fn, test_loader): correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print( - "\nTest set: Batch size: {:}, Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format( - args.test_batch_size, - test_loss, - correct, - len(test_loader.dataset), - 100.0 * correct / len(test_loader.dataset), - ) + f"\nTest set: Batch size: {args.test_batch_size}, Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} ({100.0 * correct / len(test_loader.dataset):.0f}%)\n" ) # Report the final accuracy for this validation run. diff --git a/orttraining/orttraining/test/python/orttraining_test_ortmodule_onnx_ops.py b/orttraining/orttraining/test/python/orttraining_test_ortmodule_onnx_ops.py index 2f240406b25b9..df0b5f195f0b9 100644 --- a/orttraining/orttraining/test/python/orttraining_test_ortmodule_onnx_ops.py +++ b/orttraining/orttraining/test/python/orttraining_test_ortmodule_onnx_ops.py @@ -1,6 +1,7 @@ """ @brief test log(time=3s) """ + import copy import unittest diff --git a/orttraining/orttraining/test/python/orttraining_test_ortmodule_poc.py b/orttraining/orttraining/test/python/orttraining_test_ortmodule_poc.py index 1cb0b3626e54e..d6f84d94c2838 100644 --- a/orttraining/orttraining/test/python/orttraining_test_ortmodule_poc.py +++ b/orttraining/orttraining/test/python/orttraining_test_ortmodule_poc.py @@ -64,13 +64,7 @@ def train(args, model, device, optimizer, loss_fn, train_loader, epoch): curr_time = time.time() elapsed_time = curr_time - start_time print( - "[{:5}/{:5} ({:2.0f}%)]\tLoss: {:.6f}\tExecution time: {:.4f}".format( - iteration * len(data), - len(train_loader.dataset), - 100.0 * iteration / len(train_loader), - loss, - elapsed_time, - ) + f"[{iteration * len(data):5}/{len(train_loader.dataset):5} ({100.0 * iteration / len(train_loader):2.0f}%)]\tLoss: {loss:.6f}\tExecution time: {elapsed_time:.4f}" ) start_time = curr_time @@ -102,13 +96,7 @@ def test(args, model, device, loss_fn, test_loader): correct += pred.eq(target.view_as(pred)).sum().item() test_loss /= len(test_loader.dataset) print( - "\nTest set: Batch size: {:}, Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format( - args.test_batch_size, - test_loss, - correct, - len(test_loader.dataset), - 100.0 * correct / len(test_loader.dataset), - ) + f"\nTest set: Batch size: {args.test_batch_size}, Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} ({100.0 * correct / len(test_loader.dataset):.0f}%)\n" ) # Report the final accuracy for this validation run. @@ -221,7 +209,7 @@ def main(): # Train loop total_training_time, total_test_time, epoch_0_training, validation_accuracy = 0, 0, 0, 0 - for epoch in range(0, args.epochs): + for epoch in range(args.epochs): total_training_time += train(args, model, device, optimizer, my_loss, train_loader, epoch) if not args.pytorch_only and epoch == 0: epoch_0_training = total_training_time diff --git a/orttraining/orttraining/test/python/qat_poc_example/quantize.py b/orttraining/orttraining/test/python/qat_poc_example/quantize.py index 6d9ea284fd3ef..225fb2f8e81b4 100644 --- a/orttraining/orttraining/test/python/qat_poc_example/quantize.py +++ b/orttraining/orttraining/test/python/qat_poc_example/quantize.py @@ -53,7 +53,7 @@ def quantize_static(input_model_dir, output_model_dir): logging.info( "Invoking onnxruntime.quantization.quantize_static with AddQDQPairToWeight=True and QuantizeBias=False.." ) - logging.info("Quantized model will be saved to %s." % output_model_dir) + logging.info("Quantized model will be saved to %s.", output_model_dir) quantization.quantize_static( input_model_dir, output_model_dir, diff --git a/orttraining/tools/amdgpu/script/rocprof.py b/orttraining/tools/amdgpu/script/rocprof.py index e5b107ba285bf..21dd8501f3f1d 100644 --- a/orttraining/tools/amdgpu/script/rocprof.py +++ b/orttraining/tools/amdgpu/script/rocprof.py @@ -68,18 +68,10 @@ def gpu_kernel_calls(activities): for name in groups: activities = groups[name] print( - "{}: N={}, calls={}, absolute={:.3f}s, percent={:.2f}%".format( - name, - len(activities), - gpu_kernel_calls(activities), - gpu_absolute_time(activities), - gpu_percent_time(activities), - ) + f"{name}: N={len(activities)}, calls={gpu_kernel_calls(activities)}, absolute={gpu_absolute_time(activities):.3f}s, percent={gpu_percent_time(activities):.2f}%" ) total = [item for name in groups for item in groups[name]] print( - "Total: N={}, calls={}, absolute={:.3f}s, percent={:.2f}%".format( - len(total), gpu_kernel_calls(total), gpu_absolute_time(total), gpu_percent_time(total) - ) + f"Total: N={len(total)}, calls={gpu_kernel_calls(total)}, absolute={gpu_absolute_time(total):.3f}s, percent={gpu_percent_time(total):.2f}%" ) diff --git a/orttraining/tools/ci_test/run_bert_perf_test.py b/orttraining/tools/ci_test/run_bert_perf_test.py index bb15d6f5965b6..13d5e9f140958 100644 --- a/orttraining/tools/ci_test/run_bert_perf_test.py +++ b/orttraining/tools/ci_test/run_bert_perf_test.py @@ -99,8 +99,8 @@ def main(): subprocess.run(cmds).check_returncode() # noqa: PLW1510 if c.expected_perf > 0.0: - json_filename = "onnxruntime_perf_metrics_{}.onnx_bert_{}_{}_Lamb.json".format( - model, precision_prefix, c.max_seq_length + json_filename = ( + f"onnxruntime_perf_metrics_{model}.onnx_bert_{precision_prefix}_{c.max_seq_length}_Lamb.json" ) with open(os.path.join(SCRIPT_DIR, "results", json_filename)) as json_file: results = json.load(json_file) diff --git a/orttraining/tools/scripts/nv_run_pretraining.py b/orttraining/tools/scripts/nv_run_pretraining.py index f64460f3ff0b9..8c57101f72ddb 100644 --- a/orttraining/tools/scripts/nv_run_pretraining.py +++ b/orttraining/tools/scripts/nv_run_pretraining.py @@ -81,9 +81,11 @@ def __len__(self): def __getitem__(self, index): [input_ids, input_mask, segment_ids, masked_lm_positions, masked_lm_ids, next_sentence_labels] = [ - torch.from_numpy(input[index].astype(np.int64)) - if indice < 5 - else torch.from_numpy(np.asarray(input[index].astype(np.int64))) + ( + torch.from_numpy(input[index].astype(np.int64)) + if indice < 5 + else torch.from_numpy(np.asarray(input[index].astype(np.int64))) + ) for indice, input in enumerate(self.inputs) ] @@ -231,9 +233,7 @@ def setup_training(args): ) if args.train_batch_size % args.gradient_accumulation_steps != 0: raise ValueError( - "Invalid gradient_accumulation_steps parameter: {}, batch size {} should be divisible".format( - args.gradient_accumulation_steps, args.train_batch_size - ) + f"Invalid gradient_accumulation_steps parameter: {args.gradient_accumulation_steps}, batch size {args.train_batch_size} should be divisible" ) args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps diff --git a/orttraining/tools/scripts/watch_experiment.py b/orttraining/tools/scripts/watch_experiment.py index aefa1f57cfc16..d2255b63c66b5 100644 --- a/orttraining/tools/scripts/watch_experiment.py +++ b/orttraining/tools/scripts/watch_experiment.py @@ -57,11 +57,7 @@ remote_root = args.remote_dir if run.get_status() in ["Completed", "Failed", "Canceled"]: - print( - "Downloading Experiment files from remote directory: '{}' to local directory: '{}'".format( - remote_root, local_root - ) - ) + print(f"Downloading Experiment files from remote directory: '{remote_root}' to local directory: '{local_root}'") files = [f for f in run.get_file_names() if f.startswith(remote_root)] for remote_path in files: local_path = os.path.join(local_root, os.path.basename(remote_path)) @@ -71,11 +67,7 @@ event = Event() session = Session() - print( - "Streaming Experiment files from remote directory: '{}' to local directory: '{}'".format( - remote_root, local_root - ) - ) + print(f"Streaming Experiment files from remote directory: '{remote_root}' to local directory: '{local_root}'") watcher = RunWatcher( run, local_root=local_root, remote_root=remote_root, executor=executor, event=event, session=session ) diff --git a/pyproject.toml b/pyproject.toml index 97515cb9fa62b..8fe114d4692c9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,19 +44,26 @@ reportMissingImports = false [tool.ruff] # NOTE: Do not create an exclude list. Edit .lintrunner.toml instead target-version = "py38" + +[tool.ruff.lint] select = [ "B", # flake8-bugbear "E", # pycodestyle "F", # Pyflakes + "FURB", # refurb + "G", # flake8-logging-format "ISC", # flake8-implicit-str-concat "N", # pep8-naming "NPY", # numpy "PERF", # Perflint + "PIE", # flake8-pie "PLC", # pylint conventions "PLE", # pylint errors "PLW", # pylint warnings + "PYI", # flake8-pyi "RUF", # Ruff-specific rules "SIM", # flake8-simplify + "SLOT", # flake8-slots "T10", # flake8-debugger "UP", # pyupgrade "W", # pycodestyle @@ -67,12 +74,15 @@ select = [ ignore = [ "B028", # FIXME: Add stacklevel to warnings "E501", # Line length controlled by black + "G004", # FIXME: Enable when the rule can be autofixed "N803", # Argument casing "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 + "PYI041", # May create confusion + "PYI024", # May create confusion "SIM102", # We don't perfer always combining if branches "SIM108", # We don't encourage ternary operators "SIM114", # Don't combine if branches for debugability @@ -84,7 +94,7 @@ unfixable = [ "SIM112", # Use upper case for env vars ] -[tool.ruff.per-file-ignores] +[tool.ruff.lint.per-file-ignores] # NOTE: Refrain from growing the ignore list unless for exceptional cases. # Prefer inline ignores with `noqa: xxx`. # Eventually this list should become empty. diff --git a/requirements-lintrunner.txt b/requirements-lintrunner.txt index 6836d5df69324..d19ebe379b50b 100644 --- a/requirements-lintrunner.txt +++ b/requirements-lintrunner.txt @@ -1,9 +1,9 @@ # This file is auto updated by dependabot lintrunner-adapters>=0.11.0 # RUFF -ruff==0.2.1 +ruff==0.3.2 # BLACK-ISORT -black==23.10.1 +black==24.2.0 isort==5.12.0 # CLANGFORMAT clang-format==17.0.4 diff --git a/setup.py b/setup.py index 9a5fc29dd5e02..ac7a70b991fbf 100644 --- a/setup.py +++ b/setup.py @@ -257,7 +257,7 @@ def run(self): auditwheel_cmd = ["auditwheel", "-v", "repair", "-w", self.dist_dir, file] for i in cuda_dependencies + rocm_dependencies + tensorrt_dependencies: auditwheel_cmd += ["--exclude", i] - logger.info("Running {}".format(" ".join([shlex.quote(arg) for arg in auditwheel_cmd]))) + logger.info("Running %s", " ".join([shlex.quote(arg) for arg in auditwheel_cmd])) try: subprocess.run(auditwheel_cmd, check=True, stdout=subprocess.PIPE) finally: @@ -614,9 +614,7 @@ def reformat_run_count(count_str): # TODO: this is the last time we have to do this!!! # We shall bump up release number right after release cut. if ort_version.major == 1 and ort_version.minor == 8 and ort_version.micro == 0: - version_number = "{major}.{minor}.{macro}".format( - major=ort_version.major, minor=ort_version.minor + 1, macro=ort_version.micro - ) + version_number = f"{ort_version.major}.{ort_version.minor + 1}.{ort_version.micro}" version_number = version_number + ".dev" + build_suffix @@ -667,9 +665,11 @@ def save_build_and_package_info(package_name, version_number, cuda_version, rocm else: print( "Error getting cudart version. ", - "did not find any cudart library" - if not cudart_versions or len(cudart_versions) == 0 - else "found multiple cudart libraries", + ( + "did not find any cudart library" + if not cudart_versions or len(cudart_versions) == 0 + else "found multiple cudart libraries" + ), ) elif rocm_version: f.write(f"rocm_version = '{rocm_version}'\n") diff --git a/tools/ci_build/build.py b/tools/ci_build/build.py index 1056c4ed84510..067f151844b1b 100644 --- a/tools/ci_build/build.py +++ b/tools/ci_build/build.py @@ -38,8 +38,6 @@ def version_to_tuple(version: str) -> tuple: class BaseError(Exception): """Base class for errors originating from build.py.""" - pass - class BuildError(BaseError): """Error from running build steps.""" @@ -89,7 +87,7 @@ def _openvino_verify_device_type(device_read): res = True elif device_read in choices1: res = True - elif device_read.startswith("HETERO:") or device_read.startswith("MULTI:") or device_read.startswith("AUTO:"): + elif device_read.startswith(("HETERO:", "MULTI:", "AUTO:")): res = True comma_separated_devices = device_read.split(":") comma_separated_devices = comma_separated_devices[1].split(",") @@ -118,7 +116,7 @@ def invalid_hetero_build(): print("pick the build type for specific Hardware Device from following options: ", choices) print("(or) from the following options with graph partitioning disabled: ", choices1) print("\n") - if not (device_read.startswith("HETERO") or device_read.startswith("MULTI") or device_read.startswith("AUTO")): + if not (device_read.startswith(("HETERO", "MULTI", "AUTO"))): invalid_hetero_build() sys.exit("Wrong Build Type selected") @@ -1721,9 +1719,7 @@ def setup_cuda_vars(args): if not cuda_home_valid or (not is_windows() and not cudnn_home_valid): raise BuildError( "cuda_home and cudnn_home paths must be specified and valid.", - "cuda_home='{}' valid={}. cudnn_home='{}' valid={}".format( - cuda_home, cuda_home_valid, cudnn_home, cudnn_home_valid - ), + f"cuda_home='{cuda_home}' valid={cuda_home_valid}. cudnn_home='{cudnn_home}' valid={cudnn_home_valid}", ) return cuda_home, cudnn_home @@ -2489,11 +2485,11 @@ def diff_file(path, regenerate_qualifiers=""): nonlocal have_diff have_diff = True log.warning( - "The updated document {} is different from the checked in version. " - "Please regenerate the file{}, or copy the updated version from the " - "CI build's published artifacts if applicable.".format(path, regenerate_qualifiers) + f"The updated document {path} is different from the checked in version. " + f"Please regenerate the file{regenerate_qualifiers}, or copy the updated version from the " + "CI build's published artifacts if applicable." ) - log.debug("diff:\n" + diff) + log.debug("diff:\n" + diff) # noqa: G003 diff_file(opkernel_doc_path, " with CPU, CUDA and DML execution providers enabled") diff_file(contrib_op_doc_path) @@ -2508,7 +2504,7 @@ def diff_file(path, regenerate_qualifiers=""): def main(): - log.debug("Command line arguments:\n {}".format(" ".join(shlex.quote(arg) for arg in sys.argv[1:]))) + log.debug("Command line arguments:\n {}".format(" ".join(shlex.quote(arg) for arg in sys.argv[1:]))) # noqa: G001 args = parse_arguments() diff --git a/tools/ci_build/clean_docker_image_cache.py b/tools/ci_build/clean_docker_image_cache.py index f9b41ce31f92a..8ec2b6b438176 100755 --- a/tools/ci_build/clean_docker_image_cache.py +++ b/tools/ci_build/clean_docker_image_cache.py @@ -237,13 +237,13 @@ def main(): def sorted_image_names(image_infos): return sorted([get_image_name(image_info) for image_info in image_infos]) - log.debug("All images:\n{}".format("\n".join(sorted_image_names(all_images)))) - log.debug("Valid images:\n{}".format("\n".join(sorted_image_names(valid_images)))) + log.debug("All images:\n{}".format("\n".join(sorted_image_names(all_images)))) # noqa: G001 + log.debug("Valid images:\n{}".format("\n".join(sorted_image_names(valid_images)))) # noqa: G001 images_to_clean = all_images - valid_images image_names_to_clean = sorted_image_names(images_to_clean) - log.info("Images to clean:\n{}".format("\n".join(image_names_to_clean))) + log.info("Images to clean:\n{}".format("\n".join(image_names_to_clean))) # noqa: G001 if args.dry_run: log.info("Dry run, no images will be cleaned.") diff --git a/tools/ci_build/get_docker_image.py b/tools/ci_build/get_docker_image.py index 2ce1764c96327..99ecaf677f339 100755 --- a/tools/ci_build/get_docker_image.py +++ b/tools/ci_build/get_docker_image.py @@ -56,11 +56,7 @@ def parse_args(): def main(): args = parse_args() - log.debug( - "Dockerfile: {}, context: {}, docker build args: '{}'".format( - args.dockerfile, args.context, args.docker_build_args - ) - ) + log.debug(f"Dockerfile: {args.dockerfile}, context: {args.context}, docker build args: '{args.docker_build_args}'") use_container_registry = args.container_registry is not None diff --git a/tools/ci_build/github/android/build_aar_package.py b/tools/ci_build/github/android/build_aar_package.py index f9688a1453e12..3aaced63dd410 100644 --- a/tools/ci_build/github/android/build_aar_package.py +++ b/tools/ci_build/github/android/build_aar_package.py @@ -149,9 +149,11 @@ def _build_aar(args): "-DminSdkVer=" + str(build_settings["android_min_sdk_version"]), "-DtargetSdkVer=" + str(build_settings["android_target_sdk_version"]), "-DbuildVariant=" + str(build_settings["build_variant"]), - "-DENABLE_TRAINING_APIS=1" - if "--enable_training_apis" in build_settings["build_params"] - else "-DENABLE_TRAINING_APIS=0", + ( + "-DENABLE_TRAINING_APIS=1" + if "--enable_training_apis" in build_settings["build_params"] + else "-DENABLE_TRAINING_APIS=0" + ), ] # clean, build, and publish to a local directory diff --git a/tools/ci_build/github/apple/build_and_assemble_apple_pods.py b/tools/ci_build/github/apple/build_and_assemble_apple_pods.py index 006dc4c33ffce..6188c7d7c0678 100755 --- a/tools/ci_build/github/apple/build_and_assemble_apple_pods.py +++ b/tools/ci_build/github/apple/build_and_assemble_apple_pods.py @@ -86,9 +86,7 @@ def run(arg_list, cwd=None): import shlex import subprocess - log.info( - "Running subprocess in '{}'\n {}".format(cwd or os.getcwd(), " ".join([shlex.quote(arg) for arg in arg_list])) - ) + log.info("Running subprocess in '%s'\n %s", cwd or os.getcwd(), " ".join([shlex.quote(arg) for arg in arg_list])) return subprocess.run(arg_list, check=True, cwd=cwd) diff --git a/tools/ci_build/github/apple/build_apple_framework.py b/tools/ci_build/github/apple/build_apple_framework.py index 5137a0644b2e7..7b8a87632f5c7 100644 --- a/tools/ci_build/github/apple/build_apple_framework.py +++ b/tools/ci_build/github/apple/build_apple_framework.py @@ -65,9 +65,11 @@ def _build_for_apple_sysroot( build_dir_current_arch, build_config, build_config + "-" + sysroot, - "onnxruntime.framework" - if build_dynamic_framework - else os.path.join("static_framework", "onnxruntime.framework"), + ( + "onnxruntime.framework" + if build_dynamic_framework + else os.path.join("static_framework", "onnxruntime.framework") + ), ) ort_libs.append(os.path.join(framework_dir, "onnxruntime")) diff --git a/tools/ci_build/github/linux/ort_minimal/check_build_binary_size.py b/tools/ci_build/github/linux/ort_minimal/check_build_binary_size.py index ea4a3fd32b18b..40debff3b2fef 100644 --- a/tools/ci_build/github/linux/ort_minimal/check_build_binary_size.py +++ b/tools/ci_build/github/linux/ort_minimal/check_build_binary_size.py @@ -31,9 +31,7 @@ def _check_binary_size(path, readelf, threshold, os_str, arch, build_config): if threshold is not None and sections_total > threshold: raise RuntimeError( - "Sections total size for {} of {} exceeds threshold of {} by {}. On-disk size={}".format( - path, sections_total, threshold, sections_total - threshold, ondisk_size - ) + f"Sections total size for {path} of {sections_total} exceeds threshold of {threshold} by {sections_total - threshold}. On-disk size={ondisk_size}" ) diff --git a/tools/ci_build/github/windows/post_binary_sizes_to_dashboard.py b/tools/ci_build/github/windows/post_binary_sizes_to_dashboard.py index acca4fb13c45a..a9667fe4d0654 100644 --- a/tools/ci_build/github/windows/post_binary_sizes_to_dashboard.py +++ b/tools/ci_build/github/windows/post_binary_sizes_to_dashboard.py @@ -49,7 +49,7 @@ def get_binary_sizes(size_data_file): break linedata = line.strip().split(",") tablerow = {} - for i in range(0, len(headers)): + for i in range(len(headers)): if headers[i] == "size": tablerow[headers[i]] = int(linedata[i]) else: diff --git a/tools/ci_build/op_registration_utils.py b/tools/ci_build/op_registration_utils.py index 3fd01253a3e37..811ce424eae10 100644 --- a/tools/ci_build/op_registration_utils.py +++ b/tools/ci_build/op_registration_utils.py @@ -104,14 +104,12 @@ def process_registration( :param end_version: End version or None if unversioned registration :param type: Type or types used in registration, if this is a typed registration """ - pass def process_other_line(self, line): """ Process a line that does not contain a kernel registration :param line: Original line """ - pass def ok(self): """ diff --git a/tools/ci_build/op_registration_validator.py b/tools/ci_build/op_registration_validator.py index 5c7edfa88a48b..d92050a31f967 100644 --- a/tools/ci_build/op_registration_validator.py +++ b/tools/ci_build/op_registration_validator.py @@ -45,7 +45,7 @@ def domain_and_op_str(self): def _log_registration_error(r: RegistrationInfo, message: str): - log.error("Invalid registration for {}. {}\n{}".format(r.domain_and_op_str(), message, "".join(r.lines))) + log.error("Invalid registration for %s. %s\n%s", r.domain_and_op_str(), message, "".join(r.lines)) class RegistrationValidator(op_registration_utils.RegistrationProcessor): diff --git a/tools/doc/rename_folders.py b/tools/doc/rename_folders.py index cc64775ae158d..90d800f2a4498 100644 --- a/tools/doc/rename_folders.py +++ b/tools/doc/rename_folders.py @@ -3,6 +3,7 @@ This extension does not publish any folder starting with `_`. These folders need to be renamed. """ + import os import re diff --git a/tools/nuget/generate_nuspec_for_native_nuget.py b/tools/nuget/generate_nuspec_for_native_nuget.py index 09fe99d36cc34..d5139f00e2f04 100644 --- a/tools/nuget/generate_nuspec_for_native_nuget.py +++ b/tools/nuget/generate_nuspec_for_native_nuget.py @@ -324,10 +324,12 @@ def generate_metadata(line_list, args): generate_owners(metadata_list, "Microsoft") generate_description(metadata_list, args.package_name) generate_copyright(metadata_list, "\xc2\xa9 " + "Microsoft Corporation. All rights reserved.") - generate_tags( - metadata_list, "ONNX ONNX Runtime Machine Learning" - ) if "Microsoft.ML.OnnxRuntime.Training." in args.package_name else generate_tags( - metadata_list, "native ONNX ONNXRuntime-Training Learning-on-The-Edge On-Device-Training MachineLearning" + ( + generate_tags(metadata_list, "ONNX ONNX Runtime Machine Learning") + if "Microsoft.ML.OnnxRuntime.Training." in args.package_name + else generate_tags( + metadata_list, "native ONNX ONNXRuntime-Training Learning-on-The-Edge On-Device-Training MachineLearning" + ) ) generate_icon(metadata_list, "ORT_icon_for_light_bg.png") generate_license(metadata_list) diff --git a/tools/python/dump_ort_model.py b/tools/python/dump_ort_model.py index 2177c42f5bc35..b9e3bfa0d3bcd 100644 --- a/tools/python/dump_ort_model.py +++ b/tools/python/dump_ort_model.py @@ -29,10 +29,10 @@ def __init__(self, model_path: str): def _dump_initializers(self, graph: fbs.Graph): print("Initializers:") - for idx in range(0, graph.InitializersLength()): + for idx in range(graph.InitializersLength()): tensor = graph.Initializers(idx) dims = [] - for dim in range(0, tensor.DimsLength()): + for dim in range(tensor.DimsLength()): dims.append(tensor.Dims(dim)) print(f"{tensor.Name().decode()} data_type={tensor.DataType()} dims={dims}") @@ -40,7 +40,7 @@ def _dump_initializers(self, graph: fbs.Graph): def _dump_nodeargs(self, graph: fbs.Graph): print("NodeArgs:") - for idx in range(0, graph.NodeArgsLength()): + for idx in range(graph.NodeArgsLength()): node_arg = graph.NodeArgs(idx) type = node_arg.Type() if not type: @@ -57,7 +57,7 @@ def _dump_nodeargs(self, graph: fbs.Graph): shape = tensor_type_and_shape.Shape() if shape: dims = [] - for dim in range(0, shape.DimLength()): + for dim in range(shape.DimLength()): d = shape.Dim(dim).Value() if d.DimType() == fbs.DimensionValueType.DimensionValueType.VALUE: dims.append(str(d.DimValue())) @@ -76,8 +76,8 @@ def _dump_node(self, node: fbs.Node): domain = node.Domain().decode() or "ai.onnx" # empty domain defaults to ai.onnx since_version = node.SinceVersion() - inputs = [node.Inputs(i).decode() for i in range(0, node.InputsLength())] - outputs = [node.Outputs(i).decode() for i in range(0, node.OutputsLength())] + inputs = [node.Inputs(i).decode() for i in range(node.InputsLength())] + outputs = [node.Outputs(i).decode() for i in range(node.OutputsLength())] print( f"{node.Index()}:{node.Name().decode()}({domain}:{optype}:{since_version}) " f'inputs=[{",".join(inputs)}] outputs=[{",".join(outputs)}]' @@ -91,12 +91,12 @@ def _dump_graph(self, graph: fbs.Graph): self._dump_initializers(graph) self._dump_nodeargs(graph) print("Nodes:") - for i in range(0, graph.NodesLength()): + for i in range(graph.NodesLength()): node = graph.Nodes(i) self._dump_node(node) # Read all the attributes - for j in range(0, node.AttributesLength()): + for j in range(node.AttributesLength()): attr = node.Attributes(j) attr_type = attr.Type() if attr_type == fbs.AttributeType.AttributeType.GRAPH: @@ -107,7 +107,7 @@ def _dump_graph(self, graph: fbs.Graph): # the ONNX spec doesn't currently define any operators that have multiple graphs in an attribute # so entering this 'elif' isn't currently possible print(f"## Subgraphs for {node.OpType().decode()}.{attr.Name().decode()} ##") - for k in range(0, attr.GraphsLength()): + for k in range(attr.GraphsLength()): print(f"## Subgraph {k} ##") self._dump_graph(attr.Graphs(k)) print(f"## End Subgraph {k} ##") diff --git a/tools/python/find_optimizer_opset_version_updates_required.py b/tools/python/find_optimizer_opset_version_updates_required.py index 8a5e57b51e38d..b46f7e4a54d9c 100644 --- a/tools/python/find_optimizer_opset_version_updates_required.py +++ b/tools/python/find_optimizer_opset_version_updates_required.py @@ -199,9 +199,7 @@ def find_potential_issues(root_dir, op_to_opset): latest = op_to_opset[op] if int(latest) != int(last_version): log.warning( - "Newer opset found for {}. Latest:{} Optimizer support ends at {}. File:{}".format( - op, latest, last_version, file - ) + f"Newer opset found for {op}. Latest:{latest} Optimizer support ends at {last_version}. File:{file}" ) else: log.error(f"Failed to find version information for {op}. File:{file}") diff --git a/tools/python/gen_contrib_doc.py b/tools/python/gen_contrib_doc.py index accab96bd3593..ab9421b395326 100644 --- a/tools/python/gen_contrib_doc.py +++ b/tools/python/gen_contrib_doc.py @@ -359,11 +359,7 @@ def main(output_path: str, domain_filter: [str]): for _, namemap in supportmap: for n, schema, versions in namemap: # noqa: B007 - s = ' * {}{}\n'.format( - support_level_str(schema.support_level), - format_name_with_domain(domain, n), - format_name_with_domain(domain, n), - ) + s = f' * {support_level_str(schema.support_level)}{format_name_with_domain(domain, n)}\n' fout.write(s) fout.write("\n") diff --git a/tools/python/util/convert_onnx_models_to_ort.py b/tools/python/util/convert_onnx_models_to_ort.py index 18bba78661796..08e840092bc22 100644 --- a/tools/python/util/convert_onnx_models_to_ort.py +++ b/tools/python/util/convert_onnx_models_to_ort.py @@ -302,9 +302,7 @@ def convert_onnx_models_to_ort( for optimization_style in optimization_styles: print( - "Converting models with optimization style '{}' and level '{}'".format( - optimization_style.name, optimization_level_str - ) + f"Converting models with optimization style '{optimization_style.name}' and level '{optimization_level_str}'" ) converted_models = _convert( @@ -330,9 +328,9 @@ def convert_onnx_models_to_ort( ) session_options_config_entries_for_second_conversion = session_options_config_entries.copy() # Limit the optimizations to those that can run in a model with runtime optimizations. - session_options_config_entries_for_second_conversion[ - "optimization.minimal_build_optimizations" - ] = "apply" + session_options_config_entries_for_second_conversion["optimization.minimal_build_optimizations"] = ( + "apply" + ) print( "Converting models again without runtime optimizations to generate a complete config file. " @@ -351,9 +349,7 @@ def convert_onnx_models_to_ort( ) print( - "Generating config file from ORT format models with optimization style '{}' and level '{}'".format( - optimization_style.name, optimization_level_str - ) + f"Generating config file from ORT format models with optimization style '{optimization_style.name}' and level '{optimization_level_str}'" ) config_file = _create_config_file_path( diff --git a/tools/python/util/mobile_helpers/check_model_can_use_ort_mobile_pkg.py b/tools/python/util/mobile_helpers/check_model_can_use_ort_mobile_pkg.py index f8cc34e04afa0..548d4a8ba6c45 100644 --- a/tools/python/util/mobile_helpers/check_model_can_use_ort_mobile_pkg.py +++ b/tools/python/util/mobile_helpers/check_model_can_use_ort_mobile_pkg.py @@ -230,7 +230,7 @@ def run_check_with_model( if unsupported_ops: logger.info("Unsupported operators:") for entry in sorted(unsupported_ops): - logger.info(" " + entry) + logger.info(" " + entry) # noqa: G003 if unsupported: logger.info("\nModel is not supported by the pre-built package due to unsupported types and/or operators.") diff --git a/tools/python/util/ort_format_model/operator_type_usage_processors.py b/tools/python/util/ort_format_model/operator_type_usage_processors.py index 22d7dff3e13b2..598549c42b60a 100644 --- a/tools/python/util/ort_format_model/operator_type_usage_processors.py +++ b/tools/python/util/ort_format_model/operator_type_usage_processors.py @@ -92,7 +92,6 @@ def to_config_entry(self): Generate a configuration file entry in JSON format with the required types for the operator. :return: JSON string with required type information. """ - pass @abstractmethod def from_config_entry(self, entry: str): @@ -101,7 +100,6 @@ def from_config_entry(self, entry: str): NOTE: Any existing type information should be cleared prior to re-creating from a config file entry. :param entry: Configuration file entry """ - pass class DefaultTypeUsageProcessor(TypeUsageProcessor): @@ -182,9 +180,7 @@ def process_node(self, node: fbs.Node, value_name_to_typeinfo: dict): # Don't know of any ops where the number of outputs changed across versions, so require a valid length if o >= node.OutputsLength(): raise RuntimeError( - "Node has {} outputs. Tracker for {} incorrectly configured as it requires {}.".format( - node.OutputsLength(), self.name, o - ) + f"Node has {node.OutputsLength()} outputs. Tracker for {self.name} incorrectly configured as it requires {o}." ) type_str = value_name_to_typestr(node.Outputs(o), value_name_to_typeinfo) @@ -514,7 +510,6 @@ def is_typed_registration_needed(self, domain: str, optype: str, type_registrati :param type_registration_str: Type string from kernel registration :return: True is required. False if not. """ - pass @abstractmethod def get_cpp_entries(self): @@ -522,7 +517,6 @@ def get_cpp_entries(self): Get the C++ code that specifies the operator types to enable. :return: List of strings. One line of C++ code per entry. """ - pass class OperatorTypeUsageManager: @@ -644,9 +638,7 @@ def __init__(self, globally_allowed_types: typing.Set[str]): if not globally_allowed_types.issubset(self._valid_allowed_types): raise ValueError( - "Globally allowed types must all be valid. Invalid types: {}".format( - sorted(globally_allowed_types - self._valid_allowed_types) - ) + f"Globally allowed types must all be valid. Invalid types: {sorted(globally_allowed_types - self._valid_allowed_types)}" ) self._globally_allowed_types = globally_allowed_types diff --git a/tools/python/util/ort_format_model/ort_model_processor.py b/tools/python/util/ort_format_model/ort_model_processor.py index d3a07efe92aa5..b20f3a0cfd97d 100644 --- a/tools/python/util/ort_format_model/ort_model_processor.py +++ b/tools/python/util/ort_format_model/ort_model_processor.py @@ -35,7 +35,7 @@ def _setup_type_info(graph: fbs.Graph, outer_scope_value_typeinfo={}): # noqa: :return: Dictionary of NodeArg name to TypeInfo """ value_name_to_typeinfo = outer_scope_value_typeinfo.copy() - for j in range(0, graph.NodeArgsLength()): + for j in range(graph.NodeArgsLength()): n = graph.NodeArgs(j) value_name_to_typeinfo[n.Name()] = n.Type() # TypeInfo for this NodeArg's name @@ -57,7 +57,7 @@ def _process_graph(self, graph: fbs.Graph, outer_scope_value_typeinfo: dict): # Merge the TypeInfo for all values in this level of the graph with the outer scope value TypeInfo. value_name_to_typeinfo = OrtFormatModelProcessor._setup_type_info(graph, outer_scope_value_typeinfo) - for i in range(0, graph.NodesLength()): + for i in range(graph.NodesLength()): node = graph.Nodes(i) optype = node.OpType().decode() @@ -69,7 +69,7 @@ def _process_graph(self, graph: fbs.Graph, outer_scope_value_typeinfo: dict): self._op_type_processors.process_node(node, value_name_to_typeinfo) # Read all the attributes - for j in range(0, node.AttributesLength()): + for j in range(node.AttributesLength()): attr = node.Attributes(j) attr_type = attr.Type() if attr_type == fbs.AttributeType.AttributeType.GRAPH: @@ -77,7 +77,7 @@ def _process_graph(self, graph: fbs.Graph, outer_scope_value_typeinfo: dict): elif attr_type == fbs.AttributeType.AttributeType.GRAPHS: # the ONNX spec doesn't currently define any operators that have multiple graphs in an attribute # so entering this 'elif' isn't currently possible - for k in range(0, attr.GraphsLength()): + for k in range(attr.GraphsLength()): self._process_graph(attr.Graphs(k), value_name_to_typeinfo) def process(self):