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onnxruntime/onnxruntime/python/onnxruntime_pybind_state.cc
Sheil Kumar af0001cdfd
1.9.1 Cherry-Picks (#9239) 
* Add full iOS job in package pipeline (#9036)

* Add full ios xcframework job

* create zip file of the xcframework

* Bump up TVM version to avoid conflict with existing one (#9159)

* Bump up tvm version

* Bump up onnxruntime-tvm version

There are some c++17 related fixes in TVM

Co-authored-by: KeDengMS <kedeng@microsoft.com>

* fix bug introduced by PR9130 (#9166)

* make uwp store apps link to statically-linked crt desktop builds (#9182)

Co-authored-by: Sheil Kumar <sheilk@microsoft.com>

* #9182 removed the `--is_store_build` option but one place where that was used was missed. (#9219)

This should fix the relevant packaging pipelines.

* DirectML.dll load fails when executable path contains Non-English characters (#9229)

* enable unicode dml

* add wide string L prefix

* Add Fail Fast back

Co-authored-by: Sheil Kumar <sheilk@microsoft.com>

* Fix Android build break after Virtual Environment update to 20210919  (#9163)

Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com>
Co-authored-by: ke1337 <22626095+ke1337@users.noreply.github.com>
Co-authored-by: KeDengMS <kedeng@microsoft.com>
Co-authored-by: George Wu <jywu@microsoft.com>
Co-authored-by: Sheil Kumar <sheilk@microsoft.com>
Co-authored-by: Scott McKay <skottmckay@gmail.com>
2021-10-01 07:35:48 -07:00

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// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#include "python/onnxruntime_pybind_exceptions.h"
#include "python/onnxruntime_pybind_mlvalue.h"
#include "python/onnxruntime_pybind_state_common.h"
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
#define PY_ARRAY_UNIQUE_SYMBOL onnxruntime_python_ARRAY_API
#include <numpy/arrayobject.h>
#include "core/common/logging/logging.h"
#include "core/common/logging/severity.h"
#include "core/common/optional.h"
#include "core/framework/arena_extend_strategy.h"
#include "core/framework/data_transfer_utils.h"
#include "core/framework/data_types_internal.h"
#include "core/framework/provider_options_utils.h"
#include "core/framework/random_seed.h"
#include "core/framework/sparse_tensor.h"
#include "core/framework/tensorprotoutils.h"
#include "core/framework/TensorSeq.h"
#include "core/graph/graph_viewer.h"
#include "core/platform/env.h"
#include "core/providers/get_execution_providers.h"
#include "core/session/IOBinding.h"
#include "core/session/abi_session_options_impl.h"
#include "core/session/onnxruntime_session_options_config_keys.h"
#include "core/session/provider_bridge_ort.h"
// Explicitly provide a definition for the static const var 'GPU' in the OrtDevice struct,
// GCC 4.x doesn't seem to define this and it breaks the pipelines based on CentOS as it uses
// GCC 4.x.
// (This static var is referenced in GetCudaToHostMemCpyFunction())
const OrtDevice::DeviceType OrtDevice::GPU;
namespace onnxruntime {
} // namespace onnxruntime
#if defined(_MSC_VER)
#pragma warning(disable : 4267 4996 4503 4003)
#endif // _MSC_VER
#include <iterator>
#if defined(_MSC_VER)
#pragma warning(disable : 4267 4996 4503 4003)
#endif // _MSC_VER
namespace onnxruntime {
namespace python {
namespace py = pybind11;
using namespace onnxruntime;
using namespace onnxruntime::logging;
static Env& platform_env = Env::Default();
#if !defined(ORT_MINIMAL_BUILD) || defined(ORT_MINIMAL_BUILD_CUSTOM_OPS)
// Custom op section starts
CustomOpLibrary::CustomOpLibrary(const char* library_path, OrtSessionOptions& ort_so) {
{
OrtPybindThrowIfError(platform_env.LoadDynamicLibrary(library_path, false, &library_handle_));
OrtStatus*(ORT_API_CALL * RegisterCustomOps)(OrtSessionOptions * options, const OrtApiBase* api);
OrtPybindThrowIfError(platform_env.GetSymbolFromLibrary(library_handle_, "RegisterCustomOps", (void**)&RegisterCustomOps));
auto* status_raw = RegisterCustomOps(&ort_so, OrtGetApiBase());
// Manage the raw Status pointer using a smart pointer
auto status = std::unique_ptr<OrtStatus>(status_raw);
// A non-nullptr indicates status indicates some error
if (status) {
// TODO: How to handle unload failure ?
// Currently we ignore the returned status assuming it is successful
platform_env.UnloadDynamicLibrary(library_handle_);
// Construct error message string
std::string error_string = status->msg;
// Throw
throw std::runtime_error(error_string);
}
library_path_ = std::string(library_path);
}
}
// Unload the library when the destructor is triggered
CustomOpLibrary::~CustomOpLibrary() {
UnloadLibrary();
}
// Logic to unload the library
void CustomOpLibrary::UnloadLibrary() {
auto status = platform_env.UnloadDynamicLibrary(library_handle_);
if (!status.IsOK()) {
const logging::Logger& default_logger = logging::LoggingManager::DefaultLogger();
LOGS(default_logger, WARNING) << "Unable to unload the custom op shared library: " << library_path_;
}
}
// Custom op section ends
#endif // !defined(ORT_MINIMAL_BUILD) || defined(ORT_MINIMAL_BUILD_CUSTOM_OPS)
template <typename T>
static py::object AddNonTensor(const OrtValue& val,
const DataTransferManager* /*data_transfer_manager*/,
const std::unordered_map<OrtDevice::DeviceType, MemCpyFunc>* /*mem_cpy_to_host_functions*/) {
return py::cast(val.Get<T>());
}
// In all cases, we may not have access to a DataTransferManager, hence the user may specify functions that
// pretty much does what a DataTransferManager does - copy data from device(s) to the host
void GetPyObjFromTensor(const Tensor& rtensor, py::object& obj,
const DataTransferManager* data_transfer_manager,
const std::unordered_map<OrtDevice::DeviceType, MemCpyFunc>* mem_cpy_to_host_functions) {
std::vector<npy_intp> npy_dims;
const TensorShape& shape = rtensor.Shape();
for (size_t n = 0; n < shape.NumDimensions(); ++n) {
npy_dims.push_back(shape[n]);
}
MLDataType dtype = rtensor.DataType();
const int numpy_type = OnnxRuntimeTensorToNumpyType(dtype);
obj = py::reinterpret_steal<py::object>(PyArray_SimpleNew(
shape.NumDimensions(), npy_dims.data(), numpy_type));
void* out_ptr = static_cast<void*>(
PyArray_DATA(reinterpret_cast<PyArrayObject*>(obj.ptr())));
if (numpy_type != NPY_OBJECT) {
//if it is not cpu tensor, need to copy to host
auto device_type = rtensor.Location().device.Type();
if (device_type != OrtDevice::CPU) {
if (!data_transfer_manager && !mem_cpy_to_host_functions)
throw std::runtime_error(
"GetPyObjFromTensor: Either data transfer manager or a "
"function to copy data to the host is needed to convert non-CPU tensor to numpy array");
static const OrtMemoryInfo cpu_alloc_info{onnxruntime::CPU, OrtDeviceAllocator};
// Prefer DataTransferManager if available
if (data_transfer_manager) {
auto span = gsl::make_span<char>(reinterpret_cast<char*>(out_ptr), dtype->Size() * shape.Size());
ORT_THROW_IF_ERROR(CopyTensorDataToByteSpan(
*data_transfer_manager, rtensor, cpu_alloc_info, span));
} else {
auto mem_cpy_to_host = mem_cpy_to_host_functions->find(device_type);
ORT_ENFORCE(mem_cpy_to_host != mem_cpy_to_host_functions->end(),
"Unable to locate a function that can copy data to the host from the device");
ORT_ENFORCE(mem_cpy_to_host->second != 0,
"No function that can copy data to the host from the device provided");
mem_cpy_to_host->second(out_ptr, rtensor.DataRaw(), dtype->Size() * shape.Size());
}
} else
memcpy(out_ptr, rtensor.DataRaw(dtype), dtype->Size() * shape.Size());
} else {
// Handle string type.
// Copying strings to cpu from device is currently not supported
ORT_ENFORCE(rtensor.Location().device.Type() == OrtDevice::CPU,
"Copying string tensors located on another device to the host is currently not supported");
py::object* outObj = static_cast<py::object*>(out_ptr);
const std::string* src = rtensor.template Data<std::string>();
for (int i = 0; i < rtensor.Shape().Size(); i++, src++) {
outObj[i] = py::cast(*src);
}
}
}
const char* GetDeviceName(const OrtDevice& device) {
switch (device.Type()) {
case OrtDevice::CPU:
return CPU;
case OrtDevice::GPU:
return CUDA;
case OrtDevice::FPGA:
return "FPGA";
default:
ORT_THROW("Unknown device type: ", device.Type());
}
}
py::object GetPyObjectFromSparseTensor(size_t pos, const OrtValue& ort_value, const DataTransferManager* data_transfer_manager) {
if (!ort_value.IsSparseTensor()) {
ORT_THROW("Must be a sparse tensor");
}
auto& logger = logging::LoggingManager::DefaultLogger();
const SparseTensor& src_sparse_tensor = ort_value.Get<SparseTensor>();
std::unique_ptr<PySparseTensor> py_sparse_tensor;
auto device_type = src_sparse_tensor.Location().device.Type();
if (device_type != OrtDevice::CPU) {
if (!data_transfer_manager) {
LOGS(logger, WARNING) << "Returned OrtValue with sparse tensor at position: " << pos << " is on GPU but no data_transfer_manager provided."
<< " Returned it will have its data on GPU, you can copy it using numpy_array_to_cpu()";
py_sparse_tensor.reset(new PySparseTensor(ort_value));
} else {
auto dst_sparse_tensor = std::make_unique<SparseTensor>(src_sparse_tensor.DataType(), src_sparse_tensor.DenseShape(), GetAllocator());
auto status = src_sparse_tensor.Copy(*data_transfer_manager, 0, *dst_sparse_tensor);
OrtPybindThrowIfError(status);
py_sparse_tensor.reset(new PySparseTensor(std::move(dst_sparse_tensor)));
}
}
py::object result = py::cast(py_sparse_tensor.get(), py::return_value_policy::take_ownership);
py_sparse_tensor.release();
return result;
}
template <>
py::object AddNonTensor<TensorSeq>(const OrtValue& val,
const DataTransferManager* data_transfer_manager,
const std::unordered_map<OrtDevice::DeviceType, MemCpyFunc>* mem_cpy_to_host_functions) {
const auto& seq_tensors = val.Get<TensorSeq>();
py::list py_list;
for (const auto& rtensor : seq_tensors) {
py::object obj;
GetPyObjFromTensor(rtensor, obj, data_transfer_manager, mem_cpy_to_host_functions);
py_list.append(obj);
}
// XToolChain kills the build
// local variable 'py_list' will be copied despite being returned by name [-Werror,-Wreturn-std-move]
// call 'std::move' explicitly to avoid copying
// We choose to cast it to object explicitly
return py::cast<py::object>(py_list);
}
py::object AddNonTensorAsPyObj(const OrtValue& val,
const DataTransferManager* data_transfer_manager,
const std::unordered_map<OrtDevice::DeviceType, MemCpyFunc>* mem_cpy_to_host_functions) {
// Should be in sync with core/framework/datatypes.h
auto val_type = val.Type();
if (val_type->IsTensorSequenceType()) {
return AddNonTensor<TensorSeq>(val, data_transfer_manager, mem_cpy_to_host_functions);
} else {
#if !defined(DISABLE_ML_OPS)
utils::ContainerChecker c_checker(val_type);
if (c_checker.IsMap()) {
if (c_checker.IsMapOf<std::string, std::string>()) {
return AddNonTensor<MapStringToString>(val, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<std::string, int64_t>()) {
return AddNonTensor<MapStringToInt64>(val, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<std::string, float>()) {
return AddNonTensor<MapStringToFloat>(val, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<std::string, double>()) {
return AddNonTensor<MapStringToDouble>(val, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<int64_t, std::string>()) {
return AddNonTensor<MapInt64ToString>(val, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<int64_t, int64_t>()) {
return AddNonTensor<MapInt64ToInt64>(val, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<int64_t, float>()) {
return AddNonTensor<MapInt64ToFloat>(val, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<int64_t, double>()) {
return AddNonTensor<MapInt64ToDouble>(val, data_transfer_manager, mem_cpy_to_host_functions);
}
} else {
if (c_checker.IsSequenceOf<std::map<std::string, float>>()) {
return AddNonTensor<VectorMapStringToFloat>(val, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsSequenceOf<std::map<int64_t, float>>()) {
return AddNonTensor<VectorMapInt64ToFloat>(val, data_transfer_manager, mem_cpy_to_host_functions);
}
}
#endif
}
ORT_THROW("Non-tensor type is not supported in this build: ", val_type);
}
py::object AddTensorAsPyObj(const OrtValue& val, const DataTransferManager* data_transfer_manager,
const std::unordered_map<OrtDevice::DeviceType, MemCpyFunc>* mem_cpy_to_host_functions) {
const Tensor& rtensor = val.Get<Tensor>();
py::object obj;
GetPyObjFromTensor(rtensor, obj, data_transfer_manager, mem_cpy_to_host_functions);
return obj;
}
static std::unique_ptr<onnxruntime::IExecutionProvider> LoadExecutionProvider(
const std::string& ep_shared_lib_path,
const ProviderOptions& provider_options = {},
const std::string& entry_symbol_name = "GetProvider") {
void* handle;
auto error = Env::Default().LoadDynamicLibrary(ep_shared_lib_path, false, &handle);
if (!error.IsOK()) {
throw std::runtime_error(error.ErrorMessage());
}
Provider* (*PGetProvider)();
OrtPybindThrowIfError(Env::Default().GetSymbolFromLibrary(handle, entry_symbol_name, (void**)&PGetProvider));
Provider* provider = PGetProvider();
std::shared_ptr<IExecutionProviderFactory> ep_factory = provider->CreateExecutionProviderFactory(&provider_options);
return ep_factory->CreateProvider();
}
#ifdef USE_CUDA
const CUDAExecutionProviderInfo GetCudaExecutionProviderInfo(ProviderInfo_CUDA* cuda_provider_info,
const ProviderOptionsMap& provider_options_map) {
ORT_ENFORCE(cuda_provider_info);
const auto it = provider_options_map.find(kCudaExecutionProvider);
CUDAExecutionProviderInfo info;
if (it != provider_options_map.end())
cuda_provider_info->CUDAExecutionProviderInfo__FromProviderOptions(it->second, info);
else {
info.device_id = cuda_device_id;
info.gpu_mem_limit = gpu_mem_limit;
info.arena_extend_strategy = arena_extend_strategy;
info.cudnn_conv_algo_search = cudnn_conv_algo_search;
info.do_copy_in_default_stream = do_copy_in_default_stream;
info.external_allocator_info = external_allocator_info;
}
return info;
}
#endif
#ifdef USE_ROCM
const ROCMExecutionProviderInfo GetROCMExecutionProviderInfo(const ProviderOptionsMap& provider_options_map) {
const auto it = provider_options_map.find(kRocmExecutionProvider);
return it != provider_options_map.end()
? ROCMExecutionProviderInfo::FromProviderOptions(it->second)
: [&]() {
ROCMExecutionProviderInfo info{};
info.device_id = cuda_device_id;
info.gpu_mem_limit = gpu_mem_limit;
info.arena_extend_strategy = arena_extend_strategy;
info.external_allocator_info = external_allocator_info;
return info;
}();
}
#endif
std::unique_ptr<IExecutionProvider> CreateExecutionProviderInstance(
const SessionOptions& session_options,
const std::string& type,
const ProviderOptionsMap& provider_options_map) {
if (type == kCpuExecutionProvider) {
return onnxruntime::CreateExecutionProviderFactory_CPU(
session_options.enable_cpu_mem_arena)
->CreateProvider();
} else if (type == kTensorrtExecutionProvider) {
#ifdef USE_TENSORRT
std::string calibration_table, cache_path, lib_path;
auto it = provider_options_map.find(type);
if (it != provider_options_map.end()) {
OrtTensorRTProviderOptions params{
0,
0,
nullptr,
1000,
1,
1 << 30,
0,
0,
nullptr,
0,
0,
0,
0,
0,
nullptr,
0,
nullptr,
0};
for (auto option : it->second) {
if (option.first == "device_id") {
if (!option.second.empty()) {
params.device_id = std::stoi(option.second);
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'device_id' should be a number i.e. '0'.\n");
}
} else if (option.first == "trt_max_partition_iterations") {
if (!option.second.empty()) {
params.trt_max_partition_iterations = std::stoi(option.second);
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_max_partition_iterations' should be a positive integer number i.e. '1000'.\n");
}
} else if (option.first == "trt_min_subgraph_size") {
if (!option.second.empty()) {
params.trt_min_subgraph_size = std::stoi(option.second);
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_min_subgraph_size' should be a positive integer number i.e. '1'.\n");
}
} else if (option.first == "trt_max_workspace_size") {
if (!option.second.empty()) {
params.trt_max_workspace_size = std::stoull(option.second);
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_max_workspace_size' should be a number in byte i.e. '1073741824'.\n");
}
} else if (option.first == "trt_fp16_enable") {
if (option.second == "True" || option.second == "true") {
params.trt_fp16_enable = true;
} else if (option.second == "False" || option.second == "false") {
params.trt_fp16_enable = false;
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_fp16_enable' should be a boolean i.e. 'True' or 'False'. Default value is False.\n");
}
} else if (option.first == "trt_int8_enable") {
if (option.second == "True" || option.second == "true") {
params.trt_int8_enable = true;
} else if (option.second == "False" || option.second == "false") {
params.trt_int8_enable = false;
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_int8_enable' should be a boolean i.e. 'True' or 'False'. Default value is False.\n");
}
} else if (option.first == "trt_int8_calibration_table_name") {
if (!option.second.empty()) {
calibration_table = option.second;
params.trt_int8_calibration_table_name = calibration_table.c_str();
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_int8_calibration_table_name' should be a file name i.e. 'cal_table'.\n");
}
} else if (option.first == "trt_int8_use_native_calibration_table") {
if (option.second == "True" || option.second == "true") {
params.trt_int8_use_native_calibration_table = true;
} else if (option.second == "False" || option.second == "false") {
params.trt_int8_use_native_calibration_table = false;
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_int8_use_native_calibration_table' should be a boolean i.e. 'True' or 'False'. Default value is False.\n");
}
} else if (option.first == "trt_dla_enable") {
if (option.second == "True" || option.second == "true") {
params.trt_dla_enable = true;
} else if (option.second == "False" || option.second == "false") {
params.trt_dla_enable = false;
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_dla_enable' should be a boolean i.e. 'True' or 'False'. Default value is False.\n");
}
} else if (option.first == "trt_dla_core") {
if (!option.second.empty()) {
params.trt_dla_core = std::stoi(option.second);
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_dla_core' should be a positive integer number i.e. '0'.\n");
}
} else if (option.first == "trt_dump_subgraphs") {
if (option.second == "True" || option.second == "true") {
params.trt_dump_subgraphs = true;
} else if (option.second == "False" || option.second == "false") {
params.trt_dump_subgraphs = false;
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_dump_subgraphs' should be a boolean i.e. 'True' or 'False'. Default value is False.\n");
}
} else if (option.first == "trt_engine_cache_enable") {
if (option.second == "True" || option.second == "true") {
params.trt_engine_cache_enable = true;
} else if (option.second == "False" || option.second == "false") {
params.trt_engine_cache_enable = false;
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_engine_cache_enable' should be a boolean i.e. 'True' or 'False'. Default value is False.\n");
}
} else if (option.first == "trt_engine_cache_path") {
if (!option.second.empty()) {
cache_path = option.second;
params.trt_engine_cache_path = cache_path.c_str();
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_engine_cache_path' should be a path string i.e. 'engine_cache'.\n");
}
} else if (option.first == "trt_engine_decryption_enable") {
if (option.second == "True" || option.second == "true") {
params.trt_engine_decryption_enable = true;
} else if (option.second == "False" || option.second == "false") {
params.trt_engine_decryption_enable = false;
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_engine_decryption_enable' should be a boolean i.e. 'True' or 'False'. Default value is False.\n");
}
} else if (option.first == "trt_engine_decryption_lib_path") {
if (!option.second.empty()) {
lib_path = option.second;
params.trt_engine_decryption_lib_path = lib_path.c_str();
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_engine_decryption_lib_path' should be a path string i.e. 'decryption_lib'.\n");
}
} else if (option.first == "trt_force_sequential_engine_build") {
if (option.second == "True" || option.second == "true") {
params.trt_force_sequential_engine_build = true;
} else if (option.second == "False" || option.second == "false") {
params.trt_force_sequential_engine_build = false;
} else {
ORT_THROW("[ERROR] [TensorRT] The value for the key 'trt_force_sequential_engine_build' should be a boolean i.e. 'True' or 'False'. Default value is False.\n");
}
} else {
ORT_THROW("Invalid TensorRT EP option: ", option.first);
}
}
return onnxruntime::CreateExecutionProviderFactory_Tensorrt(&params)->CreateProvider();
} else {
return onnxruntime::CreateExecutionProviderFactory_Tensorrt(cuda_device_id)->CreateProvider();
}
#endif
} else if (type == kMIGraphXExecutionProvider) {
#ifdef USE_MIGRAPHX
return onnxruntime::CreateExecutionProviderFactory_MIGraphX(0)->CreateProvider();
#endif
} else if (type == kCudaExecutionProvider) {
#ifdef USE_CUDA
// If the environment variable 'CUDA_UNAVAILABLE' exists, then we do not load cuda. This is set by _ld_preload for the manylinux case
// as in that case, trying to load the library itself will result in a crash due to the way that auditwheel strips dependencies.
if (Env::Default().GetEnvironmentVar("ORT_CUDA_UNAVAILABLE").empty()) {
if (auto* cuda_provider_info = TryGetProviderInfo_CUDA()) {
const CUDAExecutionProviderInfo info = GetCudaExecutionProviderInfo(cuda_provider_info,
provider_options_map);
// This variable is never initialized because the APIs by which it should be initialized are deprecated, however they still
// exist are are in-use. Neverthless, it is used to return CUDAAllocator, hence we must try to initialize it here if we can
// since FromProviderOptions might contain external CUDA allocator.
external_allocator_info = info.external_allocator_info;
return cuda_provider_info->CreateExecutionProviderFactory(info)->CreateProvider();
} else {
if (!Env::Default().GetEnvironmentVar("CUDA_PATH").empty()) {
ORT_THROW("CUDA_PATH is set but CUDA wasn't able to be loaded. Please install the correct version of CUDA and cuDNN as mentioned in the GPU requirements page (https://onnxruntime.ai/docs/reference/execution-providers/CUDA-ExecutionProvider.html#requirements), make sure they're in the PATH, and that your GPU is supported.");
}
}
}
#endif
} else if (type == kRocmExecutionProvider) {
#ifdef USE_ROCM
const ROCMExecutionProviderInfo info = GetROCMExecutionProviderInfo(provider_options_map);
// This variable is never initialized because the APIs by which is it should be initialized are deprecated, however they still
// exist are are in-use. Neverthless, it is used to return CUDAAllocator, hence we must try to initialize it here if we can
// since FromProviderOptions might contain external CUDA allocator.
external_allocator_info = info.external_allocator_info;
return onnxruntime::CreateExecutionProviderFactory_ROCM(info)->CreateProvider();
#endif
} else if (type == kDnnlExecutionProvider) {
#ifdef USE_DNNL
return onnxruntime::CreateExecutionProviderFactory_Dnnl(
session_options.enable_cpu_mem_arena)
->CreateProvider();
#endif
} else if (type == kOpenVINOExecutionProvider) {
#ifdef USE_OPENVINO
OrtOpenVINOProviderOptions params;
params.device_type = openvino_device_type.c_str();
std::string blob_dump_path;
auto it = provider_options_map.find(type);
if (it != provider_options_map.end()) {
for (auto option : it->second) {
if (option.first == "device_type") {
openvino_device_type = option.second;
params.device_type = openvino_device_type.c_str();
} else if (option.first == "enable_vpu_fast_compile") {
if (option.second == "True") {
params.enable_vpu_fast_compile = true;
} else if (option.second == "False") {
params.enable_vpu_fast_compile = false;
} else {
ORT_THROW("Invalid value passed for enable_vpu_fast_compile: ", option.second);
}
} else if (option.first == "use_compiled_network") {
if (option.second == "True") {
params.use_compiled_network = true;
} else if (option.second == "False") {
params.use_compiled_network = false;
} else {
ORT_THROW("Invalid value passed for use_compiled_network: ", option.second);
}
} else if (option.first == "device_id") {
params.device_id = option.second.c_str();
} else if (option.first == "num_of_threads") {
params.num_of_threads = std::stoi(option.second);
} else if (option.first == "blob_dump_path") {
blob_dump_path = option.second;
params.blob_dump_path = blob_dump_path.c_str();
} else {
ORT_THROW("Invalid OpenVINO EP option: ", option.first);
}
}
}
auto p = onnxruntime::CreateExecutionProviderFactory_OpenVINO(&params)->CreateProvider();
// Reset global variables config to avoid it being accidentally passed on to the next session
openvino_device_type.clear();
return p;
#endif
} else if (type == kNupharExecutionProvider) {
#if USE_NUPHAR
const auto it = provider_options_map.find(type);
if (it != provider_options_map.end()) {
ORT_THROW_IF_ERROR(
ProviderOptionsParser{}
.AddAssignmentToReference("nuphar_settings", nuphar_settings)
.Parse(it->second));
}
auto p = onnxruntime::CreateExecutionProviderFactory_Nuphar(true, nuphar_settings.c_str())->CreateProvider();
// clear nuphar_settings after use to avoid it being accidentally passed on to next session
nuphar_settings.clear();
return p;
#endif
} else if (type == kVitisAIExecutionProvider) {
#if USE_VITISAI
// Retrieve Vitis AI provider options
// `target`: The name of the DPU target (default is DPUCADX8G for backward compatibility).
// `export_runtime_module`: export a Vitis AI PyXIR runtime module to the specified file.
// This can be used for cross compilation or saving state.
// `load_runtime_module`: Load an exported runtime module from disk.
std::string target = "DPUCADX8G";
std::string export_runtime_module = "";
std::string load_runtime_module = "";
auto it = provider_options_map.find(type);
if (it != provider_options_map.end()) {
auto vitis_ai_provider_options = it->second;
auto vai_options_it = vitis_ai_provider_options.find("target");
if (vai_options_it != vitis_ai_provider_options.end()) {
target = vai_options_it->second;
}
vai_options_it = vitis_ai_provider_options.find("export_runtime_module");
if (vai_options_it != vitis_ai_provider_options.end()) {
export_runtime_module = vai_options_it->second;
}
vai_options_it = vitis_ai_provider_options.find("load_runtime_module");
if (vai_options_it != vitis_ai_provider_options.end()) {
load_runtime_module = vai_options_it->second;
}
}
return onnxruntime::CreateExecutionProviderFactory_VITISAI(target.c_str(), 0,
export_runtime_module.c_str(),
load_runtime_module.c_str())
->CreateProvider();
#endif
} else if (type == kAclExecutionProvider) {
#ifdef USE_ACL
return onnxruntime::CreateExecutionProviderFactory_ACL(
session_options.enable_cpu_mem_arena)
->CreateProvider();
#endif
} else if (type == kArmNNExecutionProvider) {
#ifdef USE_ARMNN
return onnxruntime::CreateExecutionProviderFactory_ArmNN(
session_options.enable_cpu_mem_arena)
->CreateProvider();
#endif
} else if (type == kDmlExecutionProvider) {
#ifdef USE_DML
int device_id = 0;
auto it = provider_options_map.find(type);
if (it != provider_options_map.end()) {
for (auto option : it->second) {
if (option.first == "device_id") {
if (!option.second.empty()) {
device_id = std::stoi(option.second);
}
}
}
}
return onnxruntime::CreateExecutionProviderFactory_DML(device_id)->CreateProvider();
#endif
} else if (type == kNnapiExecutionProvider) {
#if defined(USE_NNAPI)
#if !defined(__ANDROID__)
LOGS_DEFAULT(WARNING) << "NNAPI execution provider can only be used to generate ORT format model in this build.";
#endif
const auto partitioning_stop_ops_list = session_options.config_options.GetConfigEntry(
kOrtSessionOptionsConfigNnapiEpPartitioningStopOps);
return onnxruntime::CreateExecutionProviderFactory_Nnapi(0, partitioning_stop_ops_list)->CreateProvider();
#endif
} else if (type == kRknpuExecutionProvider) {
#ifdef USE_RKNPU
return onnxruntime::CreateExecutionProviderFactory_Rknpu()->CreateProvider();
#endif
} else if (type == kCoreMLExecutionProvider) {
#if defined(USE_COREML)
#if !defined(__APPLE__)
LOGS_DEFAULT(WARNING) << "CoreML execution provider can only be used to generate ORT format model in this build.";
#endif
return onnxruntime::CreateExecutionProviderFactory_CoreML(0)->CreateProvider();
#endif
} else {
// check whether it is a dynamic load EP:
const auto it = provider_options_map.find(type);
if (it != provider_options_map.end()) {
auto shared_lib_path_it = it->second.find(kExecutionProviderSharedLibraryPath);
if (shared_lib_path_it != it->second.end()) {
// this is an EP with dynamic loading
// construct the provider option
ProviderOptions provider_options;
std::string entry_symbol = kDefaultExecutionProviderEntry;
for (auto option : it->second) {
if (option.first == kExecutionProviderSharedLibraryEntry) {
entry_symbol = option.second;
} else if (option.first != kExecutionProviderSharedLibraryPath) {
provider_options.insert(option);
}
}
return LoadExecutionProvider(shared_lib_path_it->second, provider_options, entry_symbol);
}
}
// unknown provider
throw std::runtime_error("Unknown Provider Type: " + type);
}
return nullptr;
}
/*
* Register execution provider with options.
*/
static void RegisterExecutionProviders(InferenceSession* sess, const std::vector<std::string>& provider_types,
const ProviderOptionsMap& provider_options_map) {
ORT_UNUSED_PARAMETER(provider_options_map);
for (const std::string& type : provider_types) {
auto ep = CreateExecutionProviderInstance(sess->GetSessionOptions(), type, provider_options_map);
if (ep)
OrtPybindThrowIfError(sess->RegisterExecutionProvider(std::move(ep)));
}
}
/**
* Generate a map for mapping execution provider to excution provider options.
*
* @param providers vector of excution providers. [ep1, ep2, ...]
* @param provider_options_vector vector of excution provider options. [option1, option2 ...]
* @param provider_options_map an unordered map for mapping excution provider to excution provider options.
* {'ep1' -> option1, 'ep2' -> option2 ...}
*
*/
static void GenerateProviderOptionsMap(const std::vector<std::string>& providers,
const ProviderOptionsVector& provider_options_vector,
ProviderOptionsMap& provider_options_map) {
if (provider_options_vector.empty() || providers.empty()) {
return;
}
std::size_t j = 0; // index for provider_options_vector
for (const std::string& type : providers) {
if (j < provider_options_vector.size() && !provider_options_vector[j].empty()) {
provider_options_map[type] = provider_options_vector[j];
}
j += 1;
}
}
#if !defined(ORT_MINIMAL_BUILD) || defined(ORT_MINIMAL_BUILD_CUSTOM_OPS)
static void RegisterCustomOpDomainsAndLibraries(PyInferenceSession* sess, const PySessionOptions& so) {
if (!so.custom_op_domains_.empty()) {
// Register all custom op domains that will be needed for the session
std::vector<OrtCustomOpDomain*> custom_op_domains;
custom_op_domains.reserve(so.custom_op_domains_.size());
for (size_t i = 0; i < so.custom_op_domains_.size(); ++i) {
custom_op_domains.emplace_back(so.custom_op_domains_[i]);
}
OrtPybindThrowIfError(sess->GetSessionHandle()->AddCustomOpDomains(custom_op_domains));
// Register all custom op libraries that will be needed for the session
sess->AddCustomOpLibraries(so.custom_op_libraries_);
}
}
#endif
void InitializeSession(InferenceSession* sess,
ExecutionProviderRegistrationFn ep_registration_fn,
const std::vector<std::string>& provider_types,
const ProviderOptionsVector& provider_options,
const std::unordered_set<std::string>& disabled_optimizer_names) {
ProviderOptionsMap provider_options_map;
GenerateProviderOptionsMap(provider_types, provider_options, provider_options_map);
if (provider_types.empty()) {
// use default registration priority.
ep_registration_fn(sess, GetAllExecutionProviderNames(), provider_options_map);
} else {
ep_registration_fn(sess, provider_types, provider_options_map);
}
#if !defined(ORT_MINIMAL_BUILD)
if (!disabled_optimizer_names.empty()) {
OrtPybindThrowIfError(sess->FilterEnabledOptimizers(disabled_optimizer_names));
}
#else
ORT_UNUSED_PARAMETER(disabled_optimizer_names);
#endif
OrtPybindThrowIfError(sess->Initialize());
}
bool CheckIfTensor(const std::vector<const NodeArg*>& def_list,
const std::string& name,
/*out*/ onnx::TypeProto& type_proto) {
auto ret_it = std::find_if(std::begin(def_list), std::end(def_list),
[&name](const NodeArg* node_arg) { return name == node_arg->Name(); });
if (ret_it == std::end(def_list)) {
throw std::runtime_error("Failed to find NodeArg with name: " + name + " in the def list");
}
const auto* temp = (*ret_it)->TypeAsProto();
if (!temp) {
throw std::runtime_error("Corresponding type_proto is null");
} else {
type_proto = *temp;
}
return type_proto.has_tensor_type();
}
#if defined(USE_NUPHAR) || \
defined(USE_OPENVINO) || \
defined(USE_CUDA) || \
defined(USE_ROCM)
static void LogDeprecationWarning(
const std::string& deprecated, const optional<std::string>& alternative = nullopt) {
LOGS_DEFAULT(WARNING) << "This is DEPRECATED and will be removed in the future: " << deprecated;
LOGS_DEFAULT_IF(alternative.has_value(), WARNING) << "As an alternative, use: " << *alternative;
}
#endif
void addGlobalMethods(py::module& m, Environment& env) {
m.def("get_default_session_options", &GetDefaultCPUSessionOptions, "Return a default session_options instance.");
m.def("get_session_initializer", &SessionObjectInitializer::Get, "Return a default session object initializer.");
m.def(
"get_device", []() -> std::string { return BACKEND_DEVICE; },
"Return the device used to compute the prediction (CPU, MKL, ...)");
m.def(
"set_seed", [](const int64_t seed) { utils::SetRandomSeed(seed); },
"Sets the seed used for random number generation in Onnxruntime.");
m.def(
"set_default_logger_severity", [&env](int severity) {
ORT_ENFORCE(severity >= 0 && severity <= 4,
"Invalid logging severity. 0:Verbose, 1:Info, 2:Warning, 3:Error, 4:Fatal");
logging::LoggingManager* default_logging_manager = env.GetLoggingManager();
default_logging_manager->SetDefaultLoggerSeverity(static_cast<logging::Severity>(severity));
},
"Sets the default logging severity. 0:Verbose, 1:Info, 2:Warning, 3:Error, 4:Fatal");
m.def(
"get_all_providers", []() -> const std::vector<std::string>& { return GetAllExecutionProviderNames(); },
"Return list of Execution Providers that this version of Onnxruntime can support. "
"The order of elements represents the default priority order of Execution Providers "
"from highest to lowest.");
m.def(
"enable_telemetry_events", []() -> void { platform_env.GetTelemetryProvider().EnableTelemetryEvents(); },
"Enables platform-specific telemetry collection where applicable.");
m.def(
"disable_telemetry_events", []() -> void { platform_env.GetTelemetryProvider().DisableTelemetryEvents(); },
"Disables platform-specific telemetry collection.");
m.def(
"create_and_register_allocator", [&env](const OrtMemoryInfo& mem_info, const OrtArenaCfg* arena_cfg = nullptr) -> void {
auto st = env.CreateAndRegisterAllocator(mem_info, arena_cfg);
if (!st.IsOK()) {
throw std::runtime_error("Error when creating and registering allocator: " + st.ErrorMessage());
}
});
#ifdef USE_NUPHAR
// TODO remove deprecated global config
m.def("set_nuphar_settings", [](const std::string& str) {
LogDeprecationWarning("set_nuphar_settings", "Nuphar execution provider option \"nuphar_settings\"");
nuphar_settings = str;
});
// TODO remove deprecated global config
m.def("get_nuphar_settings", []() -> std::string {
LogDeprecationWarning("get_nuphar_settings");
return nuphar_settings;
});
#endif
#ifdef USE_OPENVINO
m.def(
"get_available_openvino_device_ids", []() -> std::vector<std::string> {
if (auto* info = GetProviderInfo_OpenVINO()) {
return info->GetAvailableDevices();
}
return {};
},
"Lists all OpenVINO device ids available.");
/*
* The following APIs to set config options are deprecated. Use Session.set_providers() instead.
*/
// TODO remove deprecated global config
m.def(
"set_openvino_device", [](const std::string& device_type) {
LogDeprecationWarning("set_openvino_device", "OpenVINO execution provider option \"device_type\"");
openvino_device_type = device_type;
},
"Set the prefered OpenVINO device type to be used. If left unset, the device type selected during build time will be used.");
// TODO remove deprecated global config
m.def(
"get_openvino_device", []() -> std::string {
LogDeprecationWarning("get_openvino_device");
return openvino_device_type;
},
"Gets the dynamically selected OpenVINO device type for inference.");
#endif
#if defined(USE_CUDA) || defined(USE_ROCM)
/*
* The following set_* methods are deprecated.
*
* To achieve same result, please use the following python api:
* InferenceSession.set_providers(list_of_providers, list_of_provider_option_dicts)
*
*/
// TODO remove deprecated global config
m.def("set_cuda_device_id", [](const int id) {
LogDeprecationWarning("set_cuda_device_id", "CUDA/ROCM execution provider option \"device_id\"");
cuda_device_id = static_cast<OrtDevice::DeviceId>(id);
});
// TODO remove deprecated global config
m.def("set_cudnn_conv_algo_search", [](const OrtCudnnConvAlgoSearch algo) {
LogDeprecationWarning("set_cudnn_conv_algo_search", "CUDA execution provider option \"cudnn_conv_algo_search\"");
#ifdef USE_ROCM
ORT_UNUSED_PARAMETER(algo);
ORT_THROW("set_cudnn_conv_algo_search is not supported in ROCM");
#else
cudnn_conv_algo_search = algo;
#endif
});
// TODO remove deprecated global config
m.def("set_do_copy_in_default_stream", [](const bool use_single_stream) {
LogDeprecationWarning(
"set_do_copy_in_default_stream", "CUDA execution provider option \"do_copy_in_default_stream\"");
#ifdef USE_ROCM
ORT_UNUSED_PARAMETER(use_single_stream);
ORT_THROW("set_do_copy_in_default_stream is not supported in ROCM");
#else
do_copy_in_default_stream = use_single_stream;
#endif
});
// TODO remove deprecated global config
m.def("set_gpu_mem_limit", [](const int64_t limit) {
LogDeprecationWarning(
"set_gpu_mem_limit",
"CUDA execution provider option \"gpu_mem_limit\", ROCM execution provider option \"gpu_mem_limit\"");
gpu_mem_limit = gsl::narrow<size_t>(limit);
});
// TODO remove deprecated global config
m.def("set_arena_extend_strategy", [](const onnxruntime::ArenaExtendStrategy strategy) {
LogDeprecationWarning("set_arena_extend_strategy", "CUDA/ROCM execution provider option \"arena_extend_strategy\"");
arena_extend_strategy = strategy;
});
#endif
}
void addObjectMethods(py::module& m, Environment& env, ExecutionProviderRegistrationFn ep_registration_fn) {
py::enum_<GraphOptimizationLevel>(m, "GraphOptimizationLevel")
.value("ORT_DISABLE_ALL", GraphOptimizationLevel::ORT_DISABLE_ALL)
.value("ORT_ENABLE_BASIC", GraphOptimizationLevel::ORT_ENABLE_BASIC)
.value("ORT_ENABLE_EXTENDED", GraphOptimizationLevel::ORT_ENABLE_EXTENDED)
.value("ORT_ENABLE_ALL", GraphOptimizationLevel::ORT_ENABLE_ALL);
py::enum_<ExecutionMode>(m, "ExecutionMode")
.value("ORT_SEQUENTIAL", ExecutionMode::ORT_SEQUENTIAL)
.value("ORT_PARALLEL", ExecutionMode::ORT_PARALLEL);
py::enum_<ExecutionOrder>(m, "ExecutionOrder")
.value("DEFAULT", ExecutionOrder::DEFAULT)
.value("PRIORITY_BASED", ExecutionOrder::PRIORITY_BASED);
py::enum_<OrtAllocatorType>(m, "OrtAllocatorType")
.value("INVALID", OrtAllocatorType::Invalid)
.value("ORT_DEVICE_ALLOCATOR", OrtAllocatorType::OrtDeviceAllocator)
.value("ORT_ARENA_ALLOCATOR", OrtAllocatorType::OrtArenaAllocator);
py::enum_<OrtMemType>(m, "OrtMemType")
.value("CPU_INPUT", OrtMemType::OrtMemTypeCPUInput)
.value("CPU_OUTPUT", OrtMemType::OrtMemTypeCPUOutput)
.value("CPU", OrtMemType::OrtMemTypeCPU)
.value("DEFAULT", OrtMemType::OrtMemTypeDefault);
py::class_<OrtDevice> device(m, "OrtDevice", R"pbdoc(ONNXRuntime device informaion.)pbdoc");
device.def(py::init<OrtDevice::DeviceType, OrtDevice::MemoryType, OrtDevice::DeviceId>())
.def("device_id", &OrtDevice::Id, R"pbdoc(Device Id.)pbdoc")
.def("device_type", &OrtDevice::Type, R"pbdoc(Device Type.)pbdoc")
.def_static("cpu", []() { return OrtDevice::CPU; })
.def_static("cuda", []() { return OrtDevice::GPU; })
.def_static("default_memory", []() { return OrtDevice::MemType::DEFAULT; });
py::class_<OrtArenaCfg> ort_arena_cfg_binding(m, "OrtArenaCfg");
// There is a global var: arena_extend_strategy, which means we can't use that var name here
// See docs/C_API.md for details on what the following parameters mean and how to choose these values
ort_arena_cfg_binding.def(py::init([](size_t max_mem, int arena_extend_strategy_local,
int initial_chunk_size_bytes, int max_dead_bytes_per_chunk) {
auto ort_arena_cfg = std::make_unique<OrtArenaCfg>();
ort_arena_cfg->max_mem = max_mem;
ort_arena_cfg->arena_extend_strategy = arena_extend_strategy_local;
ort_arena_cfg->initial_chunk_size_bytes = initial_chunk_size_bytes;
ort_arena_cfg->max_dead_bytes_per_chunk = max_dead_bytes_per_chunk;
return ort_arena_cfg;
}));
py::class_<OrtMemoryInfo> ort_memory_info_binding(m, "OrtMemoryInfo");
ort_memory_info_binding.def(py::init([](const char* name, OrtAllocatorType type, int id, OrtMemType mem_type) {
if (strcmp(name, onnxruntime::CPU) == 0) {
return std::make_unique<OrtMemoryInfo>(onnxruntime::CPU, type, OrtDevice(), id, mem_type);
} else if (strcmp(name, onnxruntime::CUDA) == 0) {
return std::make_unique<OrtMemoryInfo>(
onnxruntime::CUDA, type, OrtDevice(OrtDevice::GPU, OrtDevice::MemType::DEFAULT, static_cast<OrtDevice::DeviceId>(id)), id,
mem_type);
} else if (strcmp(name, onnxruntime::CUDA_PINNED) == 0) {
return std::make_unique<OrtMemoryInfo>(
onnxruntime::CUDA_PINNED, type, OrtDevice(OrtDevice::CPU, OrtDevice::MemType::CUDA_PINNED, static_cast<OrtDevice::DeviceId>(id)),
id, mem_type);
} else {
throw std::runtime_error("Specified device is not supported.");
}
}));
py::class_<PySessionOptions>
sess(m, "SessionOptions", R"pbdoc(Configuration information for a session.)pbdoc");
sess
.def(py::init())
.def_readwrite("enable_cpu_mem_arena", &PySessionOptions::enable_cpu_mem_arena,
R"pbdoc(Enables the memory arena on CPU. Arena may pre-allocate memory for future usage.
Set this option to false if you don't want it. Default is True.)pbdoc")
.def_readwrite("enable_profiling", &PySessionOptions::enable_profiling,
R"pbdoc(Enable profiling for this session. Default is false.)pbdoc")
.def_readwrite("profile_file_prefix", &PySessionOptions::profile_file_prefix,
R"pbdoc(The prefix of the profile file. The current time will be appended to the file name.)pbdoc")
.def_readwrite("optimized_model_filepath", &PySessionOptions::optimized_model_filepath,
R"pbdoc(
File path to serialize optimized model to.
Optimized model is not serialized unless optimized_model_filepath is set.
Serialized model format will default to ONNX unless:
- add_session_config_entry is used to set 'session.save_model_format' to 'ORT', or
- there is no 'session.save_model_format' config entry and optimized_model_filepath ends in '.ort' (case insensitive)
)pbdoc")
.def_readwrite("enable_mem_pattern", &PySessionOptions::enable_mem_pattern,
R"pbdoc(Enable the memory pattern optimization. Default is true.)pbdoc")
.def_readwrite("enable_mem_reuse", &PySessionOptions::enable_mem_reuse,
R"pbdoc(Enable the memory reuse optimization. Default is true.)pbdoc")
.def_readwrite("logid", &PySessionOptions::session_logid,
R"pbdoc(Logger id to use for session output.)pbdoc")
.def_readwrite("log_severity_level", &PySessionOptions::session_log_severity_level,
R"pbdoc(Log severity level. Applies to session load, initialization, etc.
0:Verbose, 1:Info, 2:Warning. 3:Error, 4:Fatal. Default is 2.)pbdoc")
.def_readwrite("log_verbosity_level", &PySessionOptions::session_log_verbosity_level,
R"pbdoc(VLOG level if DEBUG build and session_log_severity_level is 0.
Applies to session load, initialization, etc. Default is 0.)pbdoc")
.def_property(
"intra_op_num_threads",
[](const PySessionOptions* options) -> int { return options->intra_op_param.thread_pool_size; },
[](PySessionOptions* options, int value) -> void { options->intra_op_param.thread_pool_size = value; },
R"pbdoc(Sets the number of threads used to parallelize the execution within nodes. Default is 0 to let onnxruntime choose.)pbdoc")
.def_property(
"inter_op_num_threads",
[](const PySessionOptions* options) -> int { return options->inter_op_param.thread_pool_size; },
[](PySessionOptions* options, int value) -> void { options->inter_op_param.thread_pool_size = value; },
R"pbdoc(Sets the number of threads used to parallelize the execution of the graph (across nodes). Default is 0 to let onnxruntime choose.)pbdoc")
.def_readwrite("execution_mode", &PySessionOptions::execution_mode,
R"pbdoc(Sets the execution mode. Default is sequential.)pbdoc")
.def_readwrite("execution_order", &PySessionOptions::execution_order,
R"pbdoc(Sets the execution order. Default is basic topological order.)pbdoc")
.def_property(
"graph_optimization_level",
[](const PySessionOptions* options) -> GraphOptimizationLevel {
GraphOptimizationLevel retval = ORT_ENABLE_ALL;
switch (options->graph_optimization_level) {
case onnxruntime::TransformerLevel::Default:
retval = ORT_DISABLE_ALL;
break;
case onnxruntime::TransformerLevel::Level1:
retval = ORT_ENABLE_BASIC;
break;
case onnxruntime::TransformerLevel::Level2:
retval = ORT_ENABLE_EXTENDED;
break;
case onnxruntime::TransformerLevel::Level3:
retval = ORT_ENABLE_ALL;
break;
default:
retval = ORT_ENABLE_ALL;
LOGS_DEFAULT(WARNING) << "Got invalid graph optimization level; defaulting to ORT_ENABLE_ALL";
break;
}
return retval;
},
[](PySessionOptions* options, GraphOptimizationLevel level) -> void {
switch (level) {
case ORT_DISABLE_ALL:
options->graph_optimization_level = onnxruntime::TransformerLevel::Default;
break;
case ORT_ENABLE_BASIC:
options->graph_optimization_level = onnxruntime::TransformerLevel::Level1;
break;
case ORT_ENABLE_EXTENDED:
options->graph_optimization_level = onnxruntime::TransformerLevel::Level2;
break;
case ORT_ENABLE_ALL:
options->graph_optimization_level = onnxruntime::TransformerLevel::Level3;
break;
}
},
R"pbdoc(Graph optimization level for this session.)pbdoc")
.def_readwrite("use_deterministic_compute", &PySessionOptions::use_deterministic_compute,
R"pbdoc(Whether to use deterministic compute. Default is false.)pbdoc")
.def(
"add_free_dimension_override_by_denotation",
[](PySessionOptions* options, const char* dim_name, int64_t dim_value)
-> void { options->free_dimension_overrides.push_back(
onnxruntime::FreeDimensionOverride{
dim_name,
onnxruntime::FreeDimensionOverrideType::Denotation,
dim_value}); },
R"pbdoc(Specify the dimension size for each denotation associated with an input's free dimension.)pbdoc")
.def(
"add_free_dimension_override_by_name",
[](PySessionOptions* options, const char* dim_name, int64_t dim_value)
-> void { options->free_dimension_overrides.push_back(
onnxruntime::FreeDimensionOverride{
dim_name,
onnxruntime::FreeDimensionOverrideType::Name,
dim_value}); },
R"pbdoc(Specify values of named dimensions within model inputs.)pbdoc")
.def(
"add_session_config_entry",
[](PySessionOptions* options, const char* config_key, const char* config_value) -> void {
//config_key and config_value will be copied
const Status status = options->config_options.AddConfigEntry(config_key, config_value);
if (!status.IsOK())
throw std::runtime_error(status.ErrorMessage());
},
R"pbdoc(Set a single session configuration entry as a pair of strings.)pbdoc")
.def(
"get_session_config_entry",
[](const PySessionOptions* options, const char* config_key) -> std::string {
const std::string key(config_key);
std::string value;
if (!options->config_options.TryGetConfigEntry(key, value))
throw std::runtime_error("SessionOptions does not have configuration with key: " + key);
return value;
},
R"pbdoc(Get a single session configuration value using the given configuration key.)pbdoc")
.def(
"register_custom_ops_library",
[](PySessionOptions* options, const char* library_path) -> void {
#if !defined(ORT_MINIMAL_BUILD) || defined(ORT_MINIMAL_BUILD_CUSTOM_OPS)
// We need to pass in an `OrtSessionOptions` instance because the exported method in the shared library expects that
// Once we have access to the `OrtCustomOpDomains` within the passed in `OrtSessionOptions` instance, we place it
// into the container we are maintaining for that very purpose and the `ortSessionoptions` instance can go out of scope.
OrtSessionOptions s;
options->custom_op_libraries_.emplace_back(std::make_shared<CustomOpLibrary>(library_path, s));
// reserve enough memory to hold current contents and the new incoming contents
options->custom_op_domains_.reserve(options->custom_op_domains_.size() + s.custom_op_domains_.size());
for (size_t i = 0; i < s.custom_op_domains_.size(); ++i) {
options->custom_op_domains_.emplace_back(s.custom_op_domains_[i]);
}
#else
ORT_UNUSED_PARAMETER(options);
ORT_UNUSED_PARAMETER(library_path);
ORT_THROW("Custom Ops are not supported in this build.");
#endif
},
R"pbdoc(Specify the path to the shared library containing the custom op kernels required to run a model.)pbdoc")
.def(
"add_initializer", [](PySessionOptions* options, const char* name, py::object& ml_value_pyobject) -> void {
ORT_ENFORCE(strcmp(Py_TYPE(ml_value_pyobject.ptr())->tp_name, PYTHON_ORTVALUE_OBJECT_NAME) == 0, "The provided Python object must be an OrtValue");
// The user needs to ensure that the python OrtValue being provided as an overriding initializer
// is not destructed as long as any session that uses the provided OrtValue initializer is still in scope
// This is no different than the native APIs
const OrtValue* ml_value = ml_value_pyobject.attr(PYTHON_ORTVALUE_NATIVE_OBJECT_ATTR).cast<OrtValue*>();
options->AddInitializer(name, ml_value);
});
py::class_<RunOptions>(m, "RunOptions", R"pbdoc(Configuration information for a single Run.)pbdoc")
.def(py::init())
.def_readwrite("log_severity_level", &RunOptions::run_log_severity_level,
R"pbdoc(Log severity level for a particular Run() invocation. 0:Verbose, 1:Info, 2:Warning. 3:Error, 4:Fatal. Default is 2.)pbdoc")
.def_readwrite("log_verbosity_level", &RunOptions::run_log_verbosity_level,
R"pbdoc(VLOG level if DEBUG build and run_log_severity_level is 0.
Applies to a particular Run() invocation. Default is 0.)pbdoc")
.def_readwrite("logid", &RunOptions::run_tag,
"To identify logs generated by a particular Run() invocation.")
.def_readwrite("terminate", &RunOptions::terminate,
R"pbdoc(Set to True to terminate any currently executing calls that are using this
RunOptions instance. The individual calls will exit gracefully and return an error status.)pbdoc")
#ifdef ENABLE_TRAINING
.def_readwrite("training_mode", &RunOptions::training_mode,
R"pbdoc(Choose to run in training or inferencing mode)pbdoc")
#endif
.def_readwrite("only_execute_path_to_fetches", &RunOptions::only_execute_path_to_fetches,
R"pbdoc(Only execute the nodes needed by fetch list)pbdoc");
py::class_<ModelMetadata>(m, "ModelMetadata", R"pbdoc(Pre-defined and custom metadata about the model.
It is usually used to identify the model used to run the prediction and
facilitate the comparison.)pbdoc")
.def_readwrite("producer_name", &ModelMetadata::producer_name, "producer name")
.def_readwrite("graph_name", &ModelMetadata::graph_name, "graph name")
.def_readwrite("domain", &ModelMetadata::domain, "ONNX domain")
.def_readwrite("description", &ModelMetadata::description, "description of the model")
.def_readwrite("graph_description", &ModelMetadata::graph_description, "description of the graph hosted in the model")
.def_readwrite("version", &ModelMetadata::version, "version of the model")
.def_readwrite("custom_metadata_map", &ModelMetadata::custom_metadata_map, "additional metadata");
py::class_<onnxruntime::NodeArg>(m, "NodeArg", R"pbdoc(Node argument definition, for both input and output,
including arg name, arg type (contains both type and shape).)pbdoc")
.def_property_readonly("name", &onnxruntime::NodeArg::Name, "node name")
.def_property_readonly(
"type", [](const onnxruntime::NodeArg& na) -> std::string {
return *(na.Type());
},
"node type")
.def(
"__str__", [](const onnxruntime::NodeArg& na) -> std::string {
std::ostringstream res;
res << "NodeArg(name='" << na.Name() << "', type='" << *(na.Type()) << "', shape=";
auto shape = na.Shape();
std::vector<py::object> arr;
if (shape == nullptr || shape->dim_size() == 0) {
res << "[]";
} else {
res << "[";
for (int i = 0; i < shape->dim_size(); ++i) {
if (utils::HasDimValue(shape->dim(i))) {
res << shape->dim(i).dim_value();
} else if (utils::HasDimParam(shape->dim(i))) {
res << "'" << shape->dim(i).dim_param() << "'";
} else {
res << "None";
}
if (i < shape->dim_size() - 1) {
res << ", ";
}
}
res << "]";
}
res << ")";
return std::string(res.str());
},
"converts the node into a readable string")
.def_property_readonly(
"shape", [](const onnxruntime::NodeArg& na) -> std::vector<py::object> {
auto shape = na.Shape();
std::vector<py::object> arr;
if (shape == nullptr || shape->dim_size() == 0) {
return arr;
}
arr.resize(shape->dim_size());
for (int i = 0; i < shape->dim_size(); ++i) {
if (utils::HasDimValue(shape->dim(i))) {
arr[i] = py::cast(shape->dim(i).dim_value());
} else if (utils::HasDimParam(shape->dim(i))) {
arr[i] = py::cast(shape->dim(i).dim_param());
} else {
arr[i] = py::none();
}
}
return arr;
},
"node shape (assuming the node holds a tensor)");
py::class_<SessionObjectInitializer>(m, "SessionObjectInitializer");
py::class_<PyInferenceSession>(m, "InferenceSession", R"pbdoc(This is the main class used to run a model.)pbdoc")
// In Python3, a Python bytes object will be passed to C++ functions that accept std::string or char*
// without any conversion. So this init method can be used for model file path (string) and model content (bytes)
.def(py::init([&env](const PySessionOptions& so, const std::string arg, bool is_arg_file_name,
bool load_config_from_model = false) {
std::unique_ptr<PyInferenceSession> sess;
// separate creation of the session from model loading unless we have to read the config from the model.
// in a minimal build we only support load via Load(...) and not at session creation time
if (load_config_from_model) {
#if !defined(ORT_MINIMAL_BUILD)
sess = std::make_unique<PyInferenceSession>(env, so, arg, is_arg_file_name);
RegisterCustomOpDomainsAndLibraries(sess.get(), so);
OrtPybindThrowIfError(sess->GetSessionHandle()->Load());
#else
ORT_THROW("Loading configuration from an ONNX model is not supported in this build.");
#endif
} else {
sess = std::make_unique<PyInferenceSession>(env, so);
#if !defined(ORT_MINIMAL_BUILD) || defined(ORT_MINIMAL_BUILD_CUSTOM_OPS)
RegisterCustomOpDomainsAndLibraries(sess.get(), so);
#endif
if (is_arg_file_name) {
OrtPybindThrowIfError(sess->GetSessionHandle()->Load(arg));
} else {
OrtPybindThrowIfError(sess->GetSessionHandle()->Load(arg.data(), arg.size()));
}
}
return sess;
}))
.def(
"initialize_session",
[ep_registration_fn](PyInferenceSession* sess,
const std::vector<std::string>& provider_types = {},
const ProviderOptionsVector& provider_options = {},
const std::unordered_set<std::string>& disabled_optimizer_names = {}) {
InitializeSession(sess->GetSessionHandle(),
ep_registration_fn,
provider_types,
provider_options,
disabled_optimizer_names);
},
R"pbdoc(Load a model saved in ONNX or ORT format.)pbdoc")
.def("run",
[](PyInferenceSession* sess, std::vector<std::string> output_names,
std::map<std::string, py::object> pyfeeds, RunOptions* run_options = nullptr)
-> std::vector<py::object> {
NameMLValMap feeds;
for (auto feed : pyfeeds) {
OrtValue ml_value;
auto px = sess->GetSessionHandle()->GetModelInputs();
if (!px.first.IsOK() || !px.second) {
throw std::runtime_error("Either failed to get model inputs from the session object or the input def list was null");
}
CreateGenericMLValue(px.second, GetAllocator(), feed.first, feed.second, &ml_value);
ThrowIfPyErrOccured();
feeds.insert(std::make_pair(feed.first, ml_value));
}
std::vector<OrtValue> fetches;
common::Status status;
{
// release GIL to allow multiple python threads to invoke Run() in parallel.
py::gil_scoped_release release;
if (run_options != nullptr) {
OrtPybindThrowIfError(sess->GetSessionHandle()->Run(*run_options, feeds, output_names, &fetches));
} else {
OrtPybindThrowIfError(sess->GetSessionHandle()->Run(feeds, output_names, &fetches));
}
}
std::vector<py::object> rfetch;
rfetch.reserve(fetches.size());
size_t pos = 0;
for (auto fet : fetches) {
if (fet.IsTensor()) {
rfetch.push_back(AddTensorAsPyObj(fet, nullptr, nullptr));
} else if (fet.IsSparseTensor()) {
rfetch.push_back(GetPyObjectFromSparseTensor(pos, fet, nullptr));
} else {
rfetch.push_back(AddNonTensorAsPyObj(fet, nullptr, nullptr));
}
++pos;
}
return rfetch;
})
/// This method accepts a dictionary of feeds (name -> OrtValue) and the list of output_names
/// and returns a list of python objects representing OrtValues. Each name may represent either
/// a Tensor, SparseTensor or a TensorSequence.
.def("run_with_ort_values", [](PyInferenceSession* sess, const py::dict& feeds, const std::vector<std::string>& output_names, RunOptions* run_options = nullptr) -> std::vector<OrtValue> {
NameMLValMap ort_feeds;
// item is always a copy since dict returns a value and not a ref
// and Apple XToolChain barks
for (const auto item : feeds) {
auto name = item.first.cast<std::string>();
const OrtValue* ort_value = item.second.cast<const OrtValue*>();
ort_feeds.emplace(name, *ort_value);
}
std::vector<OrtValue> fetches;
{
// release GIL to allow multiple python threads to invoke Run() in parallel.
py::gil_scoped_release release;
if (run_options != nullptr) {
OrtPybindThrowIfError(sess->GetSessionHandle()->Run(*run_options, ort_feeds, output_names, &fetches));
} else {
OrtPybindThrowIfError(sess->GetSessionHandle()->Run(ort_feeds, output_names, &fetches));
}
}
return fetches;
})
.def("end_profiling", [](const PyInferenceSession* sess) -> std::string {
return sess->GetSessionHandle()->EndProfiling();
})
.def_property_readonly("get_profiling_start_time_ns", [](const PyInferenceSession* sess) -> uint64_t {
return sess->GetSessionHandle()->GetProfiling().GetStartTimeNs();
})
.def(
"get_providers", [](const PyInferenceSession* sess) -> const std::vector<std::string>& {
return sess->GetSessionHandle()->GetRegisteredProviderTypes();
},
py::return_value_policy::reference_internal)
.def(
"get_provider_options", [](const PyInferenceSession* sess) -> const ProviderOptionsMap& {
return sess->GetSessionHandle()->GetAllProviderOptions();
},
py::return_value_policy::reference_internal)
.def_property_readonly(
"session_options", [](const PyInferenceSession* sess) -> const PySessionOptions& {
const auto& session_options = sess->GetSessionHandle()->GetSessionOptions();
return static_cast<const PySessionOptions&>(session_options);
},
py::return_value_policy::reference_internal)
.def_property_readonly(
"inputs_meta", [](const PyInferenceSession* sess) -> const std::vector<const onnxruntime::NodeArg*>& {
auto res = sess->GetSessionHandle()->GetModelInputs();
OrtPybindThrowIfError(res.first);
return *(res.second);
},
py::return_value_policy::reference_internal)
.def_property_readonly(
"outputs_meta", [](const PyInferenceSession* sess) -> const std::vector<const onnxruntime::NodeArg*>& {
auto res = sess->GetSessionHandle()->GetModelOutputs();
OrtPybindThrowIfError(res.first);
return *(res.second);
},
py::return_value_policy::reference_internal)
.def_property_readonly(
"overridable_initializers", [](const PyInferenceSession* sess) -> const std::vector<const onnxruntime::NodeArg*>& {
auto res = sess->GetSessionHandle()->GetOverridableInitializers();
OrtPybindThrowIfError(res.first);
return *(res.second);
},
py::return_value_policy::reference_internal)
.def_property_readonly(
"model_meta", [](const PyInferenceSession* sess) -> const onnxruntime::ModelMetadata& {
auto res = sess->GetSessionHandle()->GetModelMetadata();
OrtPybindThrowIfError(res.first);
return *(res.second);
},
py::return_value_policy::reference_internal)
.def("run_with_iobinding", [](PyInferenceSession* sess, SessionIOBinding& io_binding, RunOptions* run_options = nullptr) -> void {
Status status;
if (!run_options)
status = sess->GetSessionHandle()->Run(*io_binding.Get());
else
status = sess->GetSessionHandle()->Run(*run_options, *io_binding.Get());
if (!status.IsOK())
throw std::runtime_error("Error in execution: " + status.ErrorMessage());
});
py::enum_<onnxruntime::ArenaExtendStrategy>(m, "ArenaExtendStrategy", py::arithmetic())
.value("kNextPowerOfTwo", onnxruntime::ArenaExtendStrategy::kNextPowerOfTwo)
.value("kSameAsRequested", onnxruntime::ArenaExtendStrategy::kSameAsRequested)
.export_values();
}
#if defined(USE_MIMALLOC_ARENA_ALLOCATOR)
static struct {
PyMemAllocatorEx mem;
PyMemAllocatorEx raw;
PyMemAllocatorEx obj;
} allocators;
#endif
void CreateInferencePybindStateModule(py::module& m) {
m.doc() = "pybind11 stateful interface to ONNX runtime";
RegisterExceptions(m);
#if defined(USE_MIMALLOC_ARENA_ALLOCATOR)
PyMemAllocatorEx alloc;
alloc.malloc = [](void* ctx, size_t size) {
ORT_UNUSED_PARAMETER(ctx);
return mi_malloc(size);
};
alloc.calloc = [](void* ctx, size_t nelem, size_t elsize) {
ORT_UNUSED_PARAMETER(ctx);
return mi_calloc(nelem, elsize);
};
alloc.realloc = [](void* ctx, void* ptr, size_t new_size) {
if (mi_is_in_heap_region(ptr)) {
return mi_realloc(ptr, new_size);
} else {
PyMemAllocatorEx* a = (PyMemAllocatorEx*)ctx;
return a->realloc(ctx, ptr, new_size);
}
};
alloc.free = [](void* ctx, void* ptr) {
if (mi_is_in_heap_region(ptr)) {
mi_free(ptr);
} else {
PyMemAllocatorEx* a = (PyMemAllocatorEx*)ctx;
a->free(ctx, ptr);
}
};
alloc.ctx = &allocators.raw;
PyMem_GetAllocator(PYMEM_DOMAIN_RAW, &allocators.raw);
PyMem_SetAllocator(PYMEM_DOMAIN_RAW, &alloc);
alloc.ctx = &allocators.mem;
PyMem_GetAllocator(PYMEM_DOMAIN_MEM, &allocators.mem);
PyMem_SetAllocator(PYMEM_DOMAIN_MEM, &alloc);
alloc.ctx = &allocators.obj;
PyMem_GetAllocator(PYMEM_DOMAIN_OBJ, &allocators.obj);
PyMem_SetAllocator(PYMEM_DOMAIN_OBJ, &alloc);
#endif
// Initialization of the module
([]() -> void {
// import_array1() forces a void return value.
import_array1();
})();
Environment& env = GetEnv();
addGlobalMethods(m, env);
addObjectMethods(m, env, RegisterExecutionProviders);
addOrtValueMethods(m);
addSparseTensorMethods(m);
addIoBindingMethods(m);
#if !defined(__APPLE__) && \
(!defined(ORT_MINIMAL_BUILD) || defined(ORT_EXTENDED_MINIMAL_BUILD) || defined(ORT_MINIMAL_BUILD_CUSTOM_OPS))
Ort::SessionOptions tmp_options;
if (!InitProvidersSharedLibrary()) {
const logging::Logger& default_logger = logging::LoggingManager::DefaultLogger();
LOGS(default_logger, WARNING) << "Init provider bridge failed.";
}
#endif
#ifdef onnxruntime_PYBIND_EXPORT_OPSCHEMA
addGlobalSchemaFunctions(m);
addOpSchemaSubmodule(m);
addOpKernelSubmodule(m);
#endif
}
void InitArray() {
([]() -> void {
// import_array1() forces a void return value.
import_array1();
})();
}
// static variable used to create inference session and training session.
static std::unique_ptr<Environment> session_env;
void InitializeEnv() {
auto initialize = [&]() {
// Initialization of the module
InitArray();
Env::Default().GetTelemetryProvider().SetLanguageProjection(OrtLanguageProjection::ORT_PROJECTION_PYTHON);
OrtPybindThrowIfError(Environment::Create(std::make_unique<LoggingManager>(
std::unique_ptr<ISink>{new CLogSink{}},
Severity::kWARNING, false, LoggingManager::InstanceType::Default,
&SessionObjectInitializer::default_logger_id),
session_env));
static bool initialized = false;
if (initialized) {
return;
}
initialized = true;
};
initialize();
}
onnxruntime::Environment& GetEnv() {
if (!session_env) {
InitializeEnv();
}
return *session_env;
}
} // namespace python
} // namespace onnxruntime
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