diff --git a/include/onnxruntime/core/providers/cann/cann_provider_options.h b/include/onnxruntime/core/providers/cann/cann_provider_options.h index 4b33ee77a892e..1ee418fa694b6 100644 --- a/include/onnxruntime/core/providers/cann/cann_provider_options.h +++ b/include/onnxruntime/core/providers/cann/cann_provider_options.h @@ -16,7 +16,7 @@ struct OrtCANNProviderOptions { int enable_cann_graph; // Flag indicating if prioritizing the use of // CANN's graph-running capabilities int enable_cann_subgraph; // Flag indicating whether to generate subgraph - // automaticly + // automatically int dump_graphs; // Flag indicating if dumping graphs int dump_om_model; // Flag indicating if dumping om model std::string precision_mode; // Operator Precision Mode diff --git a/include/onnxruntime/ep/adapter/kernel_registry.h b/include/onnxruntime/ep/adapter/kernel_registry.h index fe09cc241f065..d7ec076278f2d 100644 --- a/include/onnxruntime/ep/adapter/kernel_registry.h +++ b/include/onnxruntime/ep/adapter/kernel_registry.h @@ -69,7 +69,7 @@ struct KernelRegistry { // - `OrtEpApi::CreateLoopKernel` // - `OrtEpApi::CreateScanKernel` // - // If the kernel being created is one of the control flow kernels, `CreateControlFlowKernelImpl` should be overriden + // If the kernel being created is one of the control flow kernels, `CreateControlFlowKernelImpl` should be overridden // to write the value of `out` to the created `OrtKernelImpl`, and the returned status should be OK. status = kernel->CreateControlFlowKernelImpl(info, out); if (!status.IsOK()) { diff --git a/onnxruntime/contrib_ops/cuda/fused_conv.cc b/onnxruntime/contrib_ops/cuda/fused_conv.cc index 59161778612c3..2802eaecd40c7 100644 --- a/onnxruntime/contrib_ops/cuda/fused_conv.cc +++ b/onnxruntime/contrib_ops/cuda/fused_conv.cc @@ -193,7 +193,7 @@ class FusedConv : public onnxruntime::cuda::CudaKernel { // especially for EXHAUSTIVE algo search which may result in a better algo selection. // ORTModule uses different algo search options (HEURISTIC, and use max workspace size) compared to // inference build (EXHAUSTIVE, 32M workspace size). We observed better perf when we pad input shape - // [N,C,D] to [N,C,1,D], expecially on A100, and especially for ConvGrad. + // [N,C,D] to [N,C,1,D], especially on A100, and especially for ConvGrad. // PyTorch also pads to [N,C,1,D]. For inference build, we still pad it to [N, C, D, 1] as this seems // to be the sweet spot for all algo search options: EXHAUSTIVE, HEURISTIC, and DEFAULT. // See PR #7348 and #7702 for more context. diff --git a/onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/gemm/kernel/fused_moe_kernel.cuh b/onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/gemm/kernel/fused_moe_kernel.cuh index 0835f36213026..bb6366bee83de 100644 --- a/onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/gemm/kernel/fused_moe_kernel.cuh +++ b/onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/gemm/kernel/fused_moe_kernel.cuh @@ -66,8 +66,8 @@ struct Fused_Moe_Kernel_sm80 { : BaseKernelTraits::kSmemSize; static constexpr int kThreadCount = BaseKernelTraits::kThreadCount; - static constexpr bool can_implement(int const avaliable_smem_size) { - return BaseKernelTraits::can_implement(avaliable_smem_size); + static constexpr bool can_implement(int const available_smem_size) { + return BaseKernelTraits::can_implement(available_smem_size); } static Params to_underlying_arguments(Arguments const& args) { diff --git a/onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/gemm/kernel/fused_moe_kernel_traits.cuh b/onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/gemm/kernel/fused_moe_kernel_traits.cuh index cf01dfd597a79..53fa10cc560fd 100644 --- a/onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/gemm/kernel/fused_moe_kernel_traits.cuh +++ b/onnxruntime/contrib_ops/cuda/llm/cutlass_extensions/gemm/kernel/fused_moe_kernel_traits.cuh @@ -187,8 +187,8 @@ struct Fused_Moe_Kernel_traits_sm80 { static constexpr int kSmemSize = static_cast(sizeof(SharedStorage)); - static constexpr bool can_implement(int const avaliable_smem_size) { - return avaliable_smem_size > kSmemSize; + static constexpr bool can_implement(int const available_smem_size) { + return available_smem_size > kSmemSize; } // #endif diff --git a/onnxruntime/contrib_ops/webgpu/moe/final_mix.wgsl.template b/onnxruntime/contrib_ops/webgpu/moe/final_mix.wgsl.template index 23229fb57122e..c28b91111950d 100644 --- a/onnxruntime/contrib_ops/webgpu/moe/final_mix.wgsl.template +++ b/onnxruntime/contrib_ops/webgpu/moe/final_mix.wgsl.template @@ -12,7 +12,7 @@ $MAIN { let token_idx = expert_tokens.getByOffset(workgroup_idx); // token_idx is the offset into hidden state while fc2_outputs is for the chunk so - // we need to substract uniforms.token_offset + // we need to subtract uniforms.token_offset let router_value_offset = (token_idx - uniforms.token_offset) * uniforms.num_experts + uniforms.expert_idx; let router_value = router_values.getByOffset(router_value_offset); let fc2_outputs_offset = workgroup_idx * uniforms.hidden_size; diff --git a/onnxruntime/core/graph/graph_utils.h b/onnxruntime/core/graph/graph_utils.h index 96b2925833b55..9c6713e005450 100644 --- a/onnxruntime/core/graph/graph_utils.h +++ b/onnxruntime/core/graph/graph_utils.h @@ -82,7 +82,7 @@ NodeArg& AddInitializerWithOrtValue(Graph& graph, const ONNX_NAMESPACE::TensorPr /// destination /// initializers name /// if external data is in memory, copy data inline. -/// default is false. This is to accomodate EPs who load initializers on their own and do not understand +/// default is false. This is to accommodate EPs who load initializers on their own and do not understand /// our /*/_ORT_MEM_ADDR_/*/ external data reference void MakeInitializerCopyIfNotExist(const Graph& src_graph, Graph& dst_graph, const std::string& name, bool copy_in_memory_data = false); diff --git a/onnxruntime/core/mlas/lib/qlutgemm.cpp b/onnxruntime/core/mlas/lib/qlutgemm.cpp index 89dacf5bb1e29..b084c92401c11 100644 --- a/onnxruntime/core/mlas/lib/qlutgemm.cpp +++ b/onnxruntime/core/mlas/lib/qlutgemm.cpp @@ -454,7 +454,7 @@ MlasLutGemm( * * Currently, we only support FP32, add FP16 support later which requires conversion buffer * - * LUT Buffer for FP32 : K * M * 4 * sizeof(uint8_t) bytes + lut_scale_size * m * 2 * sizeof(float) bytes + allignment + * LUT Buffer for FP32 : K * M * 4 * sizeof(uint8_t) bytes + lut_scale_size * m * 2 * sizeof(float) bytes + alignment * */ diff --git a/onnxruntime/core/mlas/lib/s390x/qgemm_kernel_zvector.cpp b/onnxruntime/core/mlas/lib/s390x/qgemm_kernel_zvector.cpp index 92aa72b574f75..dd1c23a560b11 100644 --- a/onnxruntime/core/mlas/lib/s390x/qgemm_kernel_zvector.cpp +++ b/onnxruntime/core/mlas/lib/s390x/qgemm_kernel_zvector.cpp @@ -1027,7 +1027,7 @@ xvi8ger4pp_impl( const __vector unsigned char maskb = { 0, 4, 8, 12, 1, 5, 9, 13, 2, 6, 10, 14, 3, 7, 11, 15 }; __vector int va_interim[4]; - __vector unsigned char vb_interm = vec_perm(vb, vb, maskb); + __vector unsigned char vb_interim = vec_perm(vb, vb, maskb); __vector int va_prep; __vector int vb_prep[4]; @@ -1037,10 +1037,10 @@ xvi8ger4pp_impl( va_interim[2] = (__vector int) vec_unpackh(vec_unpackl((__vector signed char) va)); va_interim[3] = (__vector int) vec_unpackl(vec_unpackl((__vector signed char) va)); - vb_prep[0] = (__vector int) vec_unpackh(vec_unpackh(vb_interm)); - vb_prep[1] = (__vector int) vec_unpackl(vec_unpackh(vb_interm)); - vb_prep[2] = (__vector int) vec_unpackh(vec_unpackl(vb_interm)); - vb_prep[3] = (__vector int) vec_unpackl(vec_unpackl(vb_interm)); + vb_prep[0] = (__vector int) vec_unpackh(vec_unpackh(vb_interim)); + vb_prep[1] = (__vector int) vec_unpackl(vec_unpackh(vb_interim)); + vb_prep[2] = (__vector int) vec_unpackh(vec_unpackl(vb_interim)); + vb_prep[3] = (__vector int) vec_unpackl(vec_unpackl(vb_interim)); for (size_t i = 0; i < 4; ++i) { diff --git a/onnxruntime/core/providers/coreml/builders/impl/normalization_op_builder.cc b/onnxruntime/core/providers/coreml/builders/impl/normalization_op_builder.cc index c0db144602ee2..edb0724528564 100644 --- a/onnxruntime/core/providers/coreml/builders/impl/normalization_op_builder.cc +++ b/onnxruntime/core/providers/coreml/builders/impl/normalization_op_builder.cc @@ -203,7 +203,7 @@ bool NormalizationOpBuilder::IsOpSupportedImpl(const Node& node, const OpBuilder // Type of Mean and InvStdDev. This also specifies stage one’s computation precision. // if stash_type is 1, this operator casts all input variables to 32-bit float, // perform the computation, and finally cast Normalized back to the original type of X - // coreml didn't have a similiar attribute to stash_type, for now, we support the default value + // coreml didn't have a similar attribute to stash_type, for now, we support the default value if (node.OpType() != "InstanceNormalization") { NodeAttrHelper helper(node); const auto stash_type = helper.Get("stash_type", 1); diff --git a/onnxruntime/core/providers/cpu/ml/tree_ensemble_attribute.h b/onnxruntime/core/providers/cpu/ml/tree_ensemble_attribute.h index ba8a06e0d30e5..31545c57bc31d 100644 --- a/onnxruntime/core/providers/cpu/ml/tree_ensemble_attribute.h +++ b/onnxruntime/core/providers/cpu/ml/tree_ensemble_attribute.h @@ -234,7 +234,7 @@ struct TreeEnsembleAttributesV5 { std::vector tree_roots; private: - // `membership_values` are seperated by NAN for different nodes + // `membership_values` are separated by NAN for different nodes // It is more convenient to preserve the values for each node in a vector // The vector would be empty for nodes that are not `BRANCH_MEMBER` void getMembershipValuesById(std::vector>& membership_values_by_id) const { diff --git a/onnxruntime/core/providers/cuda/nn/conv.cc b/onnxruntime/core/providers/cuda/nn/conv.cc index 0d2692ca97d1a..d8abc6e9f88eb 100644 --- a/onnxruntime/core/providers/cuda/nn/conv.cc +++ b/onnxruntime/core/providers/cuda/nn/conv.cc @@ -475,7 +475,7 @@ Status Conv::ComputeInternal(OpKernelContext* context) const { if (s_.bias_fused && s_.z_data != nullptr) { s_.variant_pack.insert_or_assign(s_.cudnn_fe_Z, const_cast(s_.z_data)); if (Layout == LAYOUT_NCHW && s_.z_data == s_.y_data) { - // memset Z if it's required for a succesful fusion + // memset Z if it's required for a successful fusion CUDA_RETURN_IF_ERROR(cudaMemset(s_.y_data, 0, s_.Y->SizeInBytes())); } } diff --git a/onnxruntime/core/providers/cuda/nn/conv_transpose.cc b/onnxruntime/core/providers/cuda/nn/conv_transpose.cc index debc80cd323b5..4dfc507b6e349 100644 --- a/onnxruntime/core/providers/cuda/nn/conv_transpose.cc +++ b/onnxruntime/core/providers/cuda/nn/conv_transpose.cc @@ -447,7 +447,7 @@ Status ConvTranspose::UpdateState(OpKernelContext* context, bool dyna // especially for EXHAUSTIVE algo search which may result in a better algo selection. // ORTModule uses different algo search options (HEURISTIC, and use max workspace size) compared to // inference build (EXHAUSTIVE, 32M workspace size). We observed better perf when we pad input shape - // [N,C,D] to [N,C,1,D], expecially on A100, and especially for ConvGrad. + // [N,C,D] to [N,C,1,D], especially on A100, and especially for ConvGrad. // PyTorch also pads to [N,C,1,D]. For inference build, we still pad it to [N, C, D, 1] as this seems // to be the sweet spot for all algo search options: EXHAUSTIVE, HEURISTIC, and DEFAULT. // See PR #7348 and #7702 for more context. @@ -540,7 +540,7 @@ Status ConvTranspose::DoConvTranspose(OpKernelContext* context, bool if (s_.bias_fused && s_.z_data != nullptr) { s_.variant_pack.insert_or_assign(s_.cudnn_fe_Z, const_cast(s_.z_data)); if (Layout == LAYOUT_NCHW && s_.z_data == s_.y_data) { - // memset Z if it's required for a succesful fusion + // memset Z if it's required for a successful fusion CUDA_RETURN_IF_ERROR(cudaMemset(s_.y_data, 0, s_.Y->SizeInBytes())); } } diff --git a/onnxruntime/core/providers/migraphx/migraphx_provider_factory.cc b/onnxruntime/core/providers/migraphx/migraphx_provider_factory.cc index 9be6b9939404d..5d9176d17cc64 100644 --- a/onnxruntime/core/providers/migraphx/migraphx_provider_factory.cc +++ b/onnxruntime/core/providers/migraphx/migraphx_provider_factory.cc @@ -177,7 +177,7 @@ struct MIGraphX_Provider final : Provider { std::vector pathBuf; for (;;) { pathBuf.resize(pathBuf.size() + MAX_PATH); - if (const auto writen = GetModuleFileNameW(module, pathBuf.data(), static_cast(pathBuf.size())); writen < pathBuf.size()) { + if (const auto written = GetModuleFileNameW(module, pathBuf.data(), static_cast(pathBuf.size())); written < pathBuf.size()) { break; } } diff --git a/onnxruntime/core/providers/partitioning_utils.cc b/onnxruntime/core/providers/partitioning_utils.cc index be1492c2d5026..14c0c01b5f187 100644 --- a/onnxruntime/core/providers/partitioning_utils.cc +++ b/onnxruntime/core/providers/partitioning_utils.cc @@ -198,10 +198,10 @@ std::vector> CreateSupportedPartitionNodeGroups( } supported_group.push_back(&node); - const Node* redundent_clip_node = node_unit->GetRedundantClipNode(); - if (redundent_clip_node) { - supported_group.push_back(redundent_clip_node); - supported_group_border.erase(redundent_clip_node); + const Node* redundant_clip_node = node_unit->GetRedundantClipNode(); + if (redundant_clip_node) { + supported_group.push_back(redundant_clip_node); + supported_group_border.erase(redundant_clip_node); } for (const auto& q : node_unit->GetQNodes()) { diff --git a/onnxruntime/core/providers/qnn/builder/opbuilder/lstm_op_builder.cc b/onnxruntime/core/providers/qnn/builder/opbuilder/lstm_op_builder.cc index 7efe0f1279a4b..c9ed619c7ec2e 100644 --- a/onnxruntime/core/providers/qnn/builder/opbuilder/lstm_op_builder.cc +++ b/onnxruntime/core/providers/qnn/builder/opbuilder/lstm_op_builder.cc @@ -48,9 +48,9 @@ class LSTMOpBuilder : public BaseOpBuilder { initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [batch_size, num_directions, hidden_size]. ONNX LSTM optional outputs: - out[0]: Y [seq_length, num_directions, batch_size, hidden_size] = stack of out[0] from QNN_LSTM with varient directions - out[1]: Y_h [num_directions, batch_size, hidden_size] = stack of out[2] from QNN_LSTM with varient directions - out[2]: Y_c [num_directions, batch_size, hidden_size] = stack of out[1] from QNN_LSTM with varient directions + out[0]: Y [seq_length, num_directions, batch_size, hidden_size] = stack of out[0] from QNN_LSTM with variant directions + out[1]: Y_h [num_directions, batch_size, hidden_size] = stack of out[2] from QNN_LSTM with variant directions + out[2]: Y_c [num_directions, batch_size, hidden_size] = stack of out[1] from QNN_LSTM with variant directions QNN LSTM inputs: in[0]: x_t: 2D of shape [batch_size, input_size] or diff --git a/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc b/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc index 2d206a1251f84..055e2252b1dff 100644 --- a/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc +++ b/onnxruntime/core/providers/tensorrt/tensorrt_execution_provider.cc @@ -1323,7 +1323,7 @@ std::vector ParseTrtPreviewFeatures(const std::string& if (featureName == "ALIASED_PLUGIN_IO_10_03") { previewFeatures.push_back(nvinfer1::PreviewFeature::kALIASED_PLUGIN_IO_10_03); } else { - throw std::invalid_argument(std::string("Unkown or unsupported preview feature: ") + featureName); + throw std::invalid_argument(std::string("Unknown or unsupported preview feature: ") + featureName); } } diff --git a/onnxruntime/core/providers/vitisai/vitisai_execution_provider.h b/onnxruntime/core/providers/vitisai/vitisai_execution_provider.h index 70b43474777d8..efa373f5d4eab 100644 --- a/onnxruntime/core/providers/vitisai/vitisai_execution_provider.h +++ b/onnxruntime/core/providers/vitisai/vitisai_execution_provider.h @@ -41,7 +41,7 @@ class VitisAIExecutionProvider : public IExecutionProvider { std::unique_ptr GetProfiler() override; // This method is called after both `GetComputeCapabilityOps()` and `Compile()`. - // This timing is required to work with both compliation-based EPs and non-compilation-based EPs. + // This timing is required to work with both compilation-based EPs and non-compilation-based EPs. const InlinedVector GetEpContextNodes() const override; uint32_t GetEpContextDataCallbackRequirements(const GraphViewer& graph_viewer) const override; virtual common::Status SetEpDynamicOptions(gsl::span /*keys*/, diff --git a/onnxruntime/core/providers/webgpu/program_manager.cc b/onnxruntime/core/providers/webgpu/program_manager.cc index 5a50455002f58..a8e5df26977a5 100644 --- a/onnxruntime/core/providers/webgpu/program_manager.cc +++ b/onnxruntime/core/providers/webgpu/program_manager.cc @@ -383,7 +383,7 @@ Status ProgramManager::Build(const ProgramBase& program, // TODO: a new cache hierarchy for constants. // - // Explaination: + // Explanation: // Currently, we use Uniforms for dynamic data. This helps to reduce the number of program artifacts. // // "dynamic data" here means the data the determined at runtime, such as the shape of the input tensor. @@ -401,7 +401,7 @@ Status ProgramManager::Build(const ProgramBase& program, size_t constant_count = program.OverridableConstants().size(); // making a copy of the constant names is required because they are stored as std::string_view in the program - // metadata. A value of std::string_view is not guaranteed to be a C-stlye string (null-terminated) and hence + // metadata. A value of std::string_view is not guaranteed to be a C-style string (null-terminated) and hence // cannot be used directly in the WebGPU API (which expects a const char*). std::vector constant_names; constant_names.reserve(constant_count); diff --git a/onnxruntime/core/providers/webgpu/shader_helper.h b/onnxruntime/core/providers/webgpu/shader_helper.h index f58178435941b..4904e0cb9a74c 100644 --- a/onnxruntime/core/providers/webgpu/shader_helper.h +++ b/onnxruntime/core/providers/webgpu/shader_helper.h @@ -34,7 +34,7 @@ class ShaderHelper final { // - constants are defined as "const a:f32 = 1.0;". It's hard coded in the shader. // - overridable constants are defined as "override a:f32 = 1.0;" (may override or not) // or "override b:u32;" (must override) - // the value can be overriden by pipeline creation config. + // the value can be overridden by pipeline creation config. // // ** // ** section: inputs and outputs @@ -152,7 +152,7 @@ class ShaderHelper final { // Generate source code. // // This function: - // - performs validation if neccessary, + // - performs validation if necessary, // - appends the ranks for variables to the shape_uniform_ranks. // (The rank value is zero if no uniform is needed for the variable.) // - generates the final source code. diff --git a/onnxruntime/core/providers/webnn/builders/impl/expand_op_builder.cc b/onnxruntime/core/providers/webnn/builders/impl/expand_op_builder.cc index 202f1ee6db746..6293653703f9b 100644 --- a/onnxruntime/core/providers/webnn/builders/impl/expand_op_builder.cc +++ b/onnxruntime/core/providers/webnn/builders/impl/expand_op_builder.cc @@ -71,7 +71,7 @@ bool ExpandOpBuilder::IsOpSupportedImpl(const GraphViewer& graph_viewer, const auto& input_defs = node.InputDefs(); const auto& shape_name = input_defs[1]->Name(); - // We need a constant which can not be overriden by input + // We need a constant which can not be overridden by input const auto* shape_init = graph_viewer.GetConstantInitializer(shape_name); if (!shape_init) { LOGS(logger, VERBOSE) << "The shape must be a constant initializer."; diff --git a/onnxruntime/python/tools/qnn/preprocess.py b/onnxruntime/python/tools/qnn/preprocess.py index 14c62d2cd26e0..da5f642266937 100644 --- a/onnxruntime/python/tools/qnn/preprocess.py +++ b/onnxruntime/python/tools/qnn/preprocess.py @@ -106,7 +106,7 @@ def qnn_preprocess_model( """Preprocess ONNX model for QNN. Args: - model_input: A path or ONNX ModelProto specifiying the model to be preprocessed. + model_input: A path or ONNX ModelProto specifying the model to be preprocessed. model_output: A path specifying where the preprocessed model to be saved. fuse_layernorm: A bool specifying whether to fuse the matched sequence into a single LayerNormalization node. Defaults to False. diff --git a/onnxruntime/python/tools/transformers/models/stable_diffusion/README.md b/onnxruntime/python/tools/transformers/models/stable_diffusion/README.md index 766761614cc77..a18d618fb38ec 100644 --- a/onnxruntime/python/tools/transformers/models/stable_diffusion/README.md +++ b/onnxruntime/python/tools/transformers/models/stable_diffusion/README.md @@ -147,7 +147,7 @@ conda activate py310 First, we need install CUDA 12.x, [cuDNN](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html), and [TensorRT](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html) in the machine. -The verison of CuDNN can be found in https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements. +The version of CuDNN can be found in https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements. The version of TensorRT can be found in https://onnxruntime.ai/docs/execution-providers/TensorRT-ExecutionProvider.html#requirements. #### CUDA 12.*: diff --git a/onnxruntime/test/framework/allocation_planner_test.cc b/onnxruntime/test/framework/allocation_planner_test.cc index b8ea9e5ab0849..716a64306d216 100644 --- a/onnxruntime/test/framework/allocation_planner_test.cc +++ b/onnxruntime/test/framework/allocation_planner_test.cc @@ -103,7 +103,7 @@ class AllocationPlanTestUtility { EXPECT_EQ(plan.allocation_plan[i].alloc_kind, expected[i]) << "Error in allocation kind at position " << i; } } - // The free list has been re-implmented. + // The free list has been re-implemented. // remove those checkers first. // TODO: add the tests for new release plan. }; diff --git a/onnxruntime/test/python/onnxruntime_test_python_ort_parallel.py b/onnxruntime/test/python/onnxruntime_test_python_ort_parallel.py index c28bfb930e417..e1f68884de8c5 100644 --- a/onnxruntime/test/python/onnxruntime_test_python_ort_parallel.py +++ b/onnxruntime/test/python/onnxruntime_test_python_ort_parallel.py @@ -138,7 +138,7 @@ def run_inference_with_parallel_execution_mode(self, ep, num_device): if time.time() - t_time < hang_time: continue else: - print("Hang occured, ending test") + print("Hang occurred, ending test") exit(1) if threads_complete[0] == num_threads: break diff --git a/onnxruntime/test/shared_lib/test_model_builder_api.cc b/onnxruntime/test/shared_lib/test_model_builder_api.cc index 91fc61f19e0df..2fefa44d7055d 100644 --- a/onnxruntime/test/shared_lib/test_model_builder_api.cc +++ b/onnxruntime/test/shared_lib/test_model_builder_api.cc @@ -81,7 +81,7 @@ OrtNode* CreateNode(const OrtModelEditorApi& api, return node; } -// convenience func to convert initalizer lists to gsl::span +// convenience func to convert initializer lists to gsl::span OrtNode* CreateNode(const OrtModelEditorApi& api, const char* operator_name, const char* node_name, const std::initializer_list input_names, diff --git a/pyproject.toml b/pyproject.toml index 532e4bf0b8ccd..829b4a36d366d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -69,7 +69,7 @@ ignore = [ "SIM108", # We don't encourage ternary operators "SIM114", # Don't combine if branches for debugability "SIM116", # Don't use dict lookup to replace if-else - "UP038", # Using X | Y in isinstance checks is a little aggresive + "UP038", # Using X | Y in isinstance checks is a little aggressive ] [tool.ruff.lint.per-file-ignores]