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onnxruntime/onnxruntime/python/onnxruntime_pybind_state.cc
Chun-Wei Chen 2b6b3a2ee6
Add GetProfilingStartTimeNs() to Python/C# APIs (#5280) 
* add Python API for getProfilingStartTime

* debug for using Python API

* add in C# api

* use uint intead of uint64_t to prevent warning

* typo for GetProfilingStartTimeNs

* remove const

* Update onnxruntime/python/session.py

Co-authored-by: Pranav Sharma <emailpranav@gmail.com>

* remove unnecessary return

* Add Python unit test

* Add C# unit test and refactor Python test

* use ulong in C# for uint64_t in C++

* remove time.monotonic_ns

* syntax: remove public for inner function

* correct the API's order

* getprofilingstarttime after run

* Correct the right order in NativeMethod.cs

* update order

* nit: remove spaces

* Update csharp/src/Microsoft.ML.OnnxRuntime/InferenceSession.cs

Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com>

* use the updated function

* add comment about the precision

* add more comments

* add session.py back

* fix flake8

* remove session.py

* Add comments in C, C#, Python APIs about precision

Co-authored-by: Pranav Sharma <emailpranav@gmail.com>
Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com>
2020-10-14 05:32:43 -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/framework/bfc_arena.h"
#include "core/framework/data_transfer_utils.h"
#include "core/framework/data_types_internal.h"
#include "core/framework/kernel_registry.h"
#include "core/framework/random_seed.h"
#include "core/framework/tensorprotoutils.h"
#include "core/framework/TensorSeq.h"
#include "core/graph/graph_viewer.h"
#include "core/session/IOBinding.h"
#include "core/session/abi_session_options_impl.h"
#include "core/platform/env.h"
#if USE_OPENVINO
#include <inference_engine.hpp>
#endif
struct OrtStatus {
OrtErrorCode code;
char msg[1]; // a null-terminated string
};
#if USE_CUDA
#define BACKEND_PROC "GPU"
#else
#define BACKEND_PROC "CPU"
#endif
#if _OPENMP
#define BACKEND_OPENMP "-OPENMP"
#else
#define BACKEND_OPENMP ""
#endif
#if USE_DNNL
#define BACKEND_DNNL "-DNNL"
#else
#define BACKEND_DNNL ""
#endif
#if USE_MKLML
#define BACKEND_MKLML "-MKL-ML"
#else
#define BACKEND_MKLML ""
#endif
#if USE_NGRAPH
#define BACKEND_NGRAPH "-NGRAPH"
#include "core/providers/ngraph/ngraph_execution_provider.h"
#else
#define BACKEND_NGRAPH ""
#endif
#if USE_MIGRAPHX
#define BACKEND_MIGRAPHX "-MIGRAPHX"
#else
#define BACKEND_MIGRAPHX ""
#endif
#ifdef USE_OPENVINO
#if OPENVINO_CONFIG_CPU_FP32
#define BACKEND_OPENVINO "-OPENVINO_CPU_FP32"
#elif OPENVINO_CONFIG_GPU_FP32
#define BACKEND_OPENVINO "-OPENVINO_GPU_FP32"
#elif OPENVINO_CONFIG_GPU_FP16
#define BACKEND_OPENVINO "-OPENVINO_GPU_FP16"
#elif OPENVINO_CONFIG_MYRIAD
#define BACKEND_OPENVINO "-OPENVINO_MYRIAD"
#elif OPENVINO_CONFIG_VAD_M
#define BACKEND_OPENVINO "-OPENVINO_VAD_M"
#elif OPENVINO_CONFIG_VAD_F
#define BACKEND_OPENVINO "-OPENVINO_VAD_F"
#endif
#else
#define BACKEND_OPENVINO ""
#endif
#ifdef USE_NUPHAR
#define BACKEND_NUPHAR "-NUPHAR"
#else
#define BACKEND_NUPHAR ""
#endif
#if USE_VITISAI
#define BACKEND_VITISAI "-VITISAI"
#include "core/providers/vitisai/vitisai_execution_provider.h"
#else
#define BACKEND_VITISAI ""
#endif
#if USE_OPENBLAS
#define BACKEND_OPENBLAS "-OPENBLAS"
#else
#define BACKEND_OPENBLAS ""
#endif
#if USE_ACL
#define BACKEND_ACL "-ACL"
#else
#define BACKEND_ACL ""
#endif
#if USE_ARMNN
#define BACKEND_ARMNN "-ARMNN"
#else
#define BACKEND_ARMNN ""
#endif
#if USE_DML
#define BACKEND_DML "-DML"
#else
#define BACKEND_DML ""
#endif
#define BACKEND_DEVICE BACKEND_PROC BACKEND_DNNL BACKEND_MKLML BACKEND_NGRAPH BACKEND_OPENVINO BACKEND_NUPHAR BACKEND_OPENBLAS BACKEND_MIGRAPHX BACKEND_ACL BACKEND_ARMNN BACKEND_DML
#include "core/session/onnxruntime_cxx_api.h"
#include "core/providers/providers.h"
#include "core/providers/cpu/cpu_execution_provider.h"
#include "core/providers/cpu/cpu_provider_factory.h"
#ifdef USE_CUDA
#include "core/providers/cuda/cuda_provider_factory.h"
#include "core/providers/cuda/shared_inc/cuda_call.h"
#include "core/providers/cuda/cuda_execution_provider.h"
#include "core/providers/cuda/cuda_allocator.h"
OrtDevice::DeviceId cuda_device_id = 0;
OrtCudnnConvAlgoSearch cudnn_conv_algo_search = OrtCudnnConvAlgoSearch::EXHAUSTIVE;
size_t cuda_mem_limit = std::numeric_limits<size_t>::max();
onnxruntime::ArenaExtendStrategy arena_extend_strategy = onnxruntime::ArenaExtendStrategy::kNextPowerOfTwo;
bool do_copy_in_default_stream = true;
#endif
#ifdef USE_TENSORRT
#include "core/providers/tensorrt/tensorrt_provider_factory.h"
#endif
#ifdef USE_MIGRAPHX
#include "core/providers/migraphx/migraphx_provider_factory.h"
#endif
#ifdef USE_NGRAPH
#include "core/providers/ngraph/ngraph_provider_factory.h"
#endif
#ifdef USE_OPENVINO
#include "core/providers/openvino/openvino_provider_factory.h"
std::string openvino_device_type;
#endif
#ifdef USE_NUPHAR
#include "core/providers/nuphar/nuphar_provider_factory.h"
std::string nuphar_settings;
#endif
#ifdef USE_VITISAI
#include "core/providers/vitisai/vitisai_provider_factory.h"
#endif
#ifdef USE_ACL
#include "core/providers/acl/acl_provider_factory.h"
#endif
#ifdef USE_ARMNN
#include "core/providers/armnn/armnn_provider_factory.h"
#endif
#ifdef USE_DML
#include "core/providers/dml/dml_provider_factory.h"
#endif
#define PYBIND_UNREFERENCED_PARAMETER(parameter) ((void)(parameter))
// 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 {
std::shared_ptr<IExecutionProviderFactory> CreateExecutionProviderFactory_CPU(int use_arena);
std::shared_ptr<IExecutionProviderFactory> CreateExecutionProviderFactory_CUDA(OrtDevice::DeviceId device_id,
OrtCudnnConvAlgoSearch cudnn_conv_algo_search,
size_t cuda_mem_limit,
onnxruntime::ArenaExtendStrategy arena_extend_strategy,
bool do_copy_in_default_stream);
std::shared_ptr<IExecutionProviderFactory> CreateExecutionProviderFactory_Tensorrt(int device_id);
std::shared_ptr<IExecutionProviderFactory> CreateExecutionProviderFactory_MIGraphX(int device_id);
std::shared_ptr<IExecutionProviderFactory> CreateExecutionProviderFactory_Dnnl(int use_arena);
std::shared_ptr<IExecutionProviderFactory> CreateExecutionProviderFactory_NGraph(const char* ng_backend_type);
std::shared_ptr<IExecutionProviderFactory> CreateExecutionProviderFactory_OpenVINO(const char* device_type,
bool enable_vpu_fast_compile,
const char* device_id);
std::shared_ptr<IExecutionProviderFactory> CreateExecutionProviderFactory_Nuphar(bool, const char*);
std::shared_ptr<IExecutionProviderFactory> CreateExecutionProviderFactory_VITISAI(const char* backend_type, int device_id);
std::shared_ptr<IExecutionProviderFactory> CreateExecutionProviderFactory_ACL(int use_arena);
std::shared_ptr<IExecutionProviderFactory> CreateExecutionProviderFactory_ArmNN(int use_arena);
std::shared_ptr<IExecutionProviderFactory> CreateExecutionProviderFactory_DML(int device_id);
} // 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;
#if !defined(ORT_MINIMAL_BUILD)
// Custom op section starts
static Env& platform_env = Env::Default();
CustomOpLibrary::CustomOpLibrary(const char* library_path, OrtSessionOptions& ort_so) {
{
OrtPybindThrowIfError(platform_env.LoadDynamicLibrary(library_path, &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)
template <typename T>
static void AddNonTensor(const OrtValue& val, std::vector<py::object>& pyobjs,
const DataTransferManager* /*data_transfer_manager*/,
const std::unordered_map<OrtDevice::DeviceType, MemCpyFunc>* /*mem_cpy_to_host_functions*/) {
pyobjs.push_back(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);
}
}
}
static 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());
}
}
template <>
void AddNonTensor<TensorSeq>(const OrtValue& val, std::vector<py::object>& pyobjs,
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);
}
pyobjs.push_back(py_list);
}
static void AddNonTensorAsPyObj(const OrtValue& val, std::vector<py::object>& pyobjs,
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()) {
AddNonTensor<TensorSeq>(val, pyobjs, 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>()) {
AddNonTensor<MapStringToString>(val, pyobjs, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<std::string, int64_t>()) {
AddNonTensor<MapStringToInt64>(val, pyobjs, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<std::string, float>()) {
AddNonTensor<MapStringToFloat>(val, pyobjs, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<std::string, double>()) {
AddNonTensor<MapStringToDouble>(val, pyobjs, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<int64_t, std::string>()) {
AddNonTensor<MapInt64ToString>(val, pyobjs, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<int64_t, int64_t>()) {
AddNonTensor<MapInt64ToInt64>(val, pyobjs, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<int64_t, float>()) {
AddNonTensor<MapInt64ToFloat>(val, pyobjs, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsMapOf<int64_t, double>()) {
AddNonTensor<MapInt64ToDouble>(val, pyobjs, data_transfer_manager, mem_cpy_to_host_functions);
}
} else {
if (c_checker.IsSequenceOf<std::map<std::string, float>>()) {
AddNonTensor<VectorMapStringToFloat>(val, pyobjs, data_transfer_manager, mem_cpy_to_host_functions);
} else if (c_checker.IsSequenceOf<std::map<int64_t, float>>()) {
AddNonTensor<VectorMapInt64ToFloat>(val, pyobjs, data_transfer_manager, mem_cpy_to_host_functions);
} else {
throw std::runtime_error("Output is a non-tensor type which is not supported.");
}
}
#else
throw std::runtime_error("Map type is not supported in this build.");
#endif
}
}
static void AddTensorAsPyObj(const OrtValue& val, std::vector<py::object>& pyobjs,
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);
pyobjs.push_back(obj);
}
static inline void RegisterExecutionProvider(InferenceSession* sess, onnxruntime::IExecutionProviderFactory& f) {
auto p = f.CreateProvider();
OrtPybindThrowIfError(sess->RegisterExecutionProvider(std::move(p)));
}
// ordered by default priority from highest to lowest. kCpuExecutionProvider should always be last.
static const std::vector<std::string>& GetAllProviders() {
static std::vector<std::string> all_providers = {kTensorrtExecutionProvider, kCudaExecutionProvider, kMIGraphXExecutionProvider,
kNGraphExecutionProvider, kOpenVINOExecutionProvider, kDnnlExecutionProvider,
kNupharExecutionProvider, kVitisAIExecutionProvider, kArmNNExecutionProvider,
kAclExecutionProvider, kDmlExecutionProvider, kCpuExecutionProvider};
return all_providers;
}
static const std::vector<std::string>& GetAvailableProviders() {
auto InitializeProviders = []() {
std::vector<std::string> available_providers(std::begin(providers_available), std::end(providers_available));
return available_providers;
};
static std::vector<std::string> available_providers = InitializeProviders();
return available_providers;
}
#ifdef USE_CUDA
/*
* Validate a string that is positive integer or zero.
*
* (-1234, 43.21, +43.21 ... are not valid,
* 1234, +4321, 12 ... are valid)
*/
static bool IsPositiveInteger(const std::string& s) {
if (s.length() == 0) {
return false;
}
for (size_t i = 0; i < s.length(); i++) {
if (i == 0 && s[i] == '+' && s.length() > 1) {
continue;
}
if (!isdigit(s[i])) {
return false;
}
}
return true;
}
static bool IsCudaDeviceIdValid(const onnxruntime::logging::Logger& logger, int id) {
int num_devices = 0;
CUDA_CALL_THROW(cudaGetDeviceCount(&num_devices));
if (0 == num_devices) {
LOGS(logger, WARNING) << "your system does not have a CUDA capable device.";
return false;
}
if (id < 0 || id >= num_devices) {
LOGS(logger, WARNING) << "cuda_device=" << id << " is invalid, must choose device ID between 0 and " << num_devices - 1;
return false;
}
return true;
}
static void UpdateCudaProviderOptions(InferenceSession* sess, onnxruntime::CUDAExecutionProviderInfo& options,
std::unordered_map<std::string, std::string> options_map) {
std::unordered_map<std::string, std::string>::iterator it;
it = options_map.find("device_id");
if (it != options_map.end()) {
OrtDevice::DeviceId device_id;
try {
int id = std::stoi(it->second);
device_id = static_cast<int8_t>(id);
} catch (...) {
throw std::runtime_error("Please provide device id with integer.");
}
if (!IsCudaDeviceIdValid(*sess->GetLogger(), device_id)) {
throw std::runtime_error("Please provide available device id.");
}
options.device_id = device_id;
LOGS(*(sess->GetLogger()), INFO) << "cuda device id is set to " << device_id;
}
it = options_map.find("cuda_mem_limit");
if (it != options_map.end()) {
// The reason to check whether the string is positive integer upfront is that
// when calling stoull(), if the minus sign was part of the input sequence,
// the numeric value calculated from the sequence of digits is negated.
// In other words, it will cause wraparound.
// So, we rule out negative integer string beforehand.
if (!IsPositiveInteger(it->second)) {
throw std::runtime_error("Please provide cuda memory limitation size with positive integer.");
}
size_t size;
try {
#if (defined(__amd64__) || defined(_M_AMD64) || defined(__aarch64__))
size = std::stoull(it->second, nullptr, 0);
#else
size = std::stoul(it->second, nullptr, 0);
#endif
} catch (...) {
throw std::runtime_error("Please provide cuda memory limitation size with positive integer and within range.");
}
options.cuda_mem_limit = size;
LOGS(*(sess->GetLogger()), INFO) << "cuda memory limitation is set to " << size;
}
it = options_map.find("arena_extend_strategy");
if (it != options_map.end()) {
onnxruntime::ArenaExtendStrategy strategy;
if (it->second.compare("kNextPowerOfTwo") == 0) {
strategy = onnxruntime::ArenaExtendStrategy::kNextPowerOfTwo;
} else if (it->second.compare("kSameAsRequested") == 0) {
strategy = onnxruntime::ArenaExtendStrategy::kSameAsRequested;
} else {
throw std::runtime_error("Please provide proper cuda arena extend strategy.");
}
options.arena_extend_strategy = strategy;
LOGS(*(sess->GetLogger()), INFO) << "cuda arean extend strategy is set to " << it->second;
}
}
static AllocatorPtr GetCudaAllocator(OrtDevice::DeviceId id) {
// Current approach is not thread-safe, but there are some bigger infra pieces to put together in order to make
// multi-threaded CUDA allocation work we need to maintain a per-thread CUDA allocator
static std::unordered_map<OrtDevice::DeviceId, AllocatorPtr> id_to_allocator_map;
if (id_to_allocator_map.find(id) == id_to_allocator_map.end()) {
// Use arena-based allocator
AllocatorCreationInfo default_memory_info(
[](OrtDevice::DeviceId id) {
return onnxruntime::make_unique<CUDAAllocator>(id, CUDA);
},
id,
true,
{cuda_mem_limit,
static_cast<int>(arena_extend_strategy),
-1, -1});
auto allocator = CreateAllocator(default_memory_info);
id_to_allocator_map.insert({id, allocator});
}
return id_to_allocator_map[id];
}
static void CpuToCudaMemCpy(void* dst, const void* src, size_t num_bytes) {
CUDA_CALL_THROW(cudaMemcpy(dst, src, num_bytes, cudaMemcpyHostToDevice));
}
static void CudaToCpuMemCpy(void* dst, const void* src, size_t num_bytes) {
CUDA_CALL_THROW(cudaMemcpy(dst, src, num_bytes, cudaMemcpyDeviceToHost));
}
static const std::unordered_map<OrtDevice::DeviceType, MemCpyFunc>* GetCudaToHostMemCpyFunction() {
static std::unordered_map<OrtDevice::DeviceType, MemCpyFunc> map{
{OrtDevice::GPU, CudaToCpuMemCpy}};
return &map;
}
#endif
/*
* Register execution provider with options.
*
* (note: currently only cuda EP supports this feature and rest of EPs use default options)
*/
static void RegisterExecutionProviders(InferenceSession* sess, const std::vector<std::string>& provider_types,
ProviderOptionsMap& provider_options_map) {
PYBIND_UNREFERENCED_PARAMETER(provider_options_map);
for (const std::string& type : provider_types) {
if (type == kCpuExecutionProvider) {
RegisterExecutionProvider(sess, *onnxruntime::CreateExecutionProviderFactory_CPU(
sess->GetSessionOptions().enable_cpu_mem_arena));
} else if (type == kTensorrtExecutionProvider) {
#ifdef USE_TENSORRT
RegisterExecutionProvider(sess, *onnxruntime::CreateExecutionProviderFactory_Tensorrt(0));
#endif
} else if (type == kMIGraphXExecutionProvider) {
#ifdef USE_MIGRAPHX
RegisterExecutionProvider(sess, *onnxruntime::CreateExecutionProviderFactory_MIGraphX(0));
#endif
} else if (type == kCudaExecutionProvider) {
#ifdef USE_CUDA
auto it = provider_options_map.find(type);
if (it != provider_options_map.end()) {
onnxruntime::CUDAExecutionProviderInfo cuda_provider_options;
UpdateCudaProviderOptions(sess, cuda_provider_options, it->second);
RegisterExecutionProvider(
sess, *onnxruntime::CreateExecutionProviderFactory_CUDA(cuda_provider_options.device_id,
cuda_provider_options.cudnn_conv_algo,
cuda_provider_options.cuda_mem_limit,
cuda_provider_options.arena_extend_strategy,
cuda_provider_options.do_copy_in_default_stream));
} else {
RegisterExecutionProvider(
sess, *onnxruntime::CreateExecutionProviderFactory_CUDA(cuda_device_id,
cudnn_conv_algo_search,
cuda_mem_limit,
arena_extend_strategy,
do_copy_in_default_stream));
}
#endif
} else if (type == kDnnlExecutionProvider) {
#ifdef USE_DNNL
RegisterExecutionProvider(
sess, *onnxruntime::CreateExecutionProviderFactory_Dnnl(sess->GetSessionOptions().enable_cpu_mem_arena));
#endif
} else if (type == kNGraphExecutionProvider) {
#if USE_NGRAPH
RegisterExecutionProvider(sess, *onnxruntime::CreateExecutionProviderFactory_NGraph("CPU"));
#endif
} else if (type == kOpenVINOExecutionProvider) {
#ifdef USE_OPENVINO
bool enable_vpu_fast_compile = false;
std::string openvino_device_id;
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;
else if (option.first == "enable_vpu_fast_compile") {
if (option.second == "True") {
enable_vpu_fast_compile = true;
} else if (option.second == "False") {
enable_vpu_fast_compile = false;
} else {
ORT_THROW("Invalid value passed for enable_vpu_fast_compile: ", option.second);
}
} else if (option.first == "device_id")
openvino_device_id = option.second;
else {
ORT_THROW("Invalid OpenVINO EP option: ", option.first);
}
}
}
RegisterExecutionProvider(sess, *onnxruntime::CreateExecutionProviderFactory_OpenVINO(openvino_device_type.c_str(),
enable_vpu_fast_compile,
openvino_device_id.c_str()));
// Reset global variables config to avoid it being accidentally passed on to the next session
openvino_device_type.clear();
#endif
} else if (type == kNupharExecutionProvider) {
#if USE_NUPHAR
RegisterExecutionProvider(
sess, *onnxruntime::CreateExecutionProviderFactory_Nuphar(true, nuphar_settings.c_str()));
// clear nuphar_settings after use to avoid it being accidentally passed on to next session
nuphar_settings.clear();
#endif
} else if (type == kVitisAIExecutionProvider) {
#if USE_VITISAI
RegisterExecutionProvider(sess, *onnxruntime::CreateExecutionProviderFactory_VITISAI("dpuv1", 0));
#endif
} else if (type == kAclExecutionProvider) {
#ifdef USE_ACL
RegisterExecutionProvider(
sess, *onnxruntime::CreateExecutionProviderFactory_ACL(sess->GetSessionOptions().enable_cpu_mem_arena));
#endif
} else if (type == kArmNNExecutionProvider) {
#ifdef USE_ARMNN
RegisterExecutionProvider(
sess, *onnxruntime::CreateExecutionProviderFactory_ArmNN(sess->GetSessionOptions().enable_cpu_mem_arena));
#endif
} else if (type == kDmlExecutionProvider) {
#ifdef USE_DML
RegisterExecutionProvider(sess, *onnxruntime::CreateExecutionProviderFactory_DML(0));
#endif
} else {
// unknown provider
throw std::runtime_error("Unknown Provider Type: " + type);
}
}
}
/**
* 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)
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, const std::vector<std::string>& provider_types,
const ProviderOptionsVector& provider_options) {
ProviderOptionsMap provider_options_map;
GenerateProviderOptionsMap(provider_types, provider_options, provider_options_map);
if (provider_types.empty()) {
// use default registration priority.
RegisterExecutionProviders(sess, GetAllProviders(), provider_options_map);
} else {
RegisterExecutionProviders(sess, provider_types, provider_options_map);
}
OrtPybindThrowIfError(sess->Initialize());
}
static 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();
}
void addGlobalMethods(py::module& m, const 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 GetAllProviders(); },
"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(
"get_available_providers", []() -> const std::vector<std::string>& { return GetAvailableProviders(); },
"Return list of available Execution Providers available in this installed version of Onnxruntime.");
#ifdef USE_NUPHAR
m.def("set_nuphar_settings", [](const std::string& str) {
nuphar_settings = str;
});
m.def("get_nuphar_settings", []() -> std::string {
return nuphar_settings;
});
#endif
#ifdef USE_OPENVINO
m.def(
"get_available_openvino_device_ids", []() -> std::vector<std::string> {
InferenceEngine::Core ie_core;
return ie_core.GetAvailableDevices();
},
"Lists all OpenVINO device ids available.");
/*
* The following APIs to set config options are deprecated. Use Session.set_providers() instead.
*/
m.def(
"set_openvino_device", [](const std::string& 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.");
m.def(
"get_openvino_device", []() -> std::string {
return openvino_device_type;
},
"Gets the dynamically selected OpenVINO device type for inference.");
#endif
#ifdef onnxruntime_PYBIND_EXPORT_OPSCHEMA
m.def(
"get_all_operator_schema", []() -> const std::vector<ONNX_NAMESPACE::OpSchema> {
return ONNX_NAMESPACE::OpSchemaRegistry::get_all_schemas_with_history();
},
"Return a vector of OpSchema all registed operators");
m.def(
"get_all_opkernel_def", []() -> const std::vector<onnxruntime::KernelDef> {
std::vector<onnxruntime::KernelDef> result;
std::vector<std::shared_ptr<onnxruntime::IExecutionProviderFactory>> factories = {
onnxruntime::CreateExecutionProviderFactory_CPU(0),
#ifdef USE_CUDA
onnxruntime::CreateExecutionProviderFactory_CUDA(cuda_device_id, cudnn_conv_algo_search, cuda_mem_limit, arena_extend_strategy, do_copy_in_default_stream),
#endif
#ifdef USE_DNNL
onnxruntime::CreateExecutionProviderFactory_Dnnl(1),
#endif
#ifdef USE_NGRAPH
onnxruntime::CreateExecutionProviderFactory_NGraph("CPU"),
#endif
#ifdef USE_OPENVINO
onnxruntime::CreateExecutionProviderFactory_OpenVINO(openvino_device_type, false, ""),
#endif
#ifdef USE_TENSORRT
onnxruntime::CreateExecutionProviderFactory_Tensorrt(0),
#endif
#ifdef USE_MIGRAPHX
onnxruntime::CreateExecutionProviderFactory_MIGraphX(0),
#endif
#ifdef USE_VITISAI
onnxruntime::CreateExecutionProviderFactory_VitisAI("DPU", 0),
#endif
#ifdef USE_ACL
onnxruntime::CreateExecutionProviderFactory_ACL(0),
#endif
#ifdef USE_ARMNN
onnxruntime::CreateExecutionProviderFactory_ArmNN(0),
#endif
#ifdef USE_DML
onnxruntime::CreateExecutionProviderFactory_DML(0)
#endif
};
for (const auto& f : factories) {
for (const auto& m : f->CreateProvider()
->GetKernelRegistry()
->GetKernelCreateMap()) {
result.emplace_back(*(m.second.kernel_def));
}
}
return result;
},
"Return a vector of KernelDef for all registered OpKernels");
#endif //onnxruntime_PYBIND_EXPORT_OPSCHEMA
#ifdef USE_CUDA
/*
* 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)
*
*/
m.def("set_cuda_device_id", [](const int id) { cuda_device_id = static_cast<OrtDevice::DeviceId>(id); });
m.def("set_cudnn_conv_algo_search", [](const OrtCudnnConvAlgoSearch algo) { cudnn_conv_algo_search = algo; });
m.def("set_cuda_mem_limit", [](const int64_t limit) { cuda_mem_limit = static_cast<size_t>(limit); });
m.def("set_arena_extend_strategy", [](const onnxruntime::ArenaExtendStrategy strategy) { arena_extend_strategy = strategy; });
m.def("set_do_copy_in_default_stream", [](const bool use_single_stream) { do_copy_in_default_stream = use_single_stream; });
#endif
}
#ifdef onnxruntime_PYBIND_EXPORT_OPSCHEMA
void addOpKernelSubmodule(py::module& m) {
auto opkernel = m.def_submodule("opkernel");
opkernel.doc() = "OpKernel submodule";
py::class_<onnxruntime::KernelDef> kernel_def(opkernel, "KernelDef");
kernel_def.def_property_readonly("op_name", &onnxruntime::KernelDef::OpName)
.def_property_readonly("domain", &onnxruntime::KernelDef::Domain)
.def_property_readonly("provider", &onnxruntime::KernelDef::Provider)
.def_property_readonly("version_range",
[](const onnxruntime::KernelDef& kernelDef) -> std::pair<int, int> {
return kernelDef.onnxruntime::KernelDef::SinceVersion();
})
.def_property_readonly("type_constraints",
[](const onnxruntime::KernelDef& kernelDef) -> std::unordered_map<std::string, std::vector<std::string>> {
std::unordered_map<std::string, std::vector<std::string>> result;
const auto& tempResult = kernelDef.TypeConstraints();
for (const auto& tc : tempResult) {
result[tc.first] = std::vector<std::string>();
for (const auto& dt : tc.second) {
result[tc.first].emplace_back(onnxruntime::DataTypeImpl::ToString(dt));
}
}
return result;
});
}
void addOpSchemaSubmodule(py::module& m) {
auto schemadef = m.def_submodule("schemadef");
schemadef.doc() = "Schema submodule";
// Keep this binding local to this module
py::class_<ONNX_NAMESPACE::OpSchema> op_schema(schemadef, "OpSchema", py::module_local());
op_schema.def_property_readonly("file", &ONNX_NAMESPACE::OpSchema::file)
.def_property_readonly("line", &ONNX_NAMESPACE::OpSchema::line)
.def_property_readonly("support_level", &ONNX_NAMESPACE::OpSchema::support_level)
.def_property_readonly(
"doc", &ONNX_NAMESPACE::OpSchema::doc, py::return_value_policy::reference)
.def_property_readonly("since_version", &ONNX_NAMESPACE::OpSchema::since_version)
.def_property_readonly("deprecated", &ONNX_NAMESPACE::OpSchema::deprecated)
.def_property_readonly("domain", &ONNX_NAMESPACE::OpSchema::domain)
.def_property_readonly("name", &ONNX_NAMESPACE::OpSchema::Name)
.def_property_readonly("min_input", &ONNX_NAMESPACE::OpSchema::min_input)
.def_property_readonly("max_input", &ONNX_NAMESPACE::OpSchema::max_input)
.def_property_readonly("min_output", &ONNX_NAMESPACE::OpSchema::min_output)
.def_property_readonly("max_output", &ONNX_NAMESPACE::OpSchema::max_output)
.def_property_readonly("attributes", &ONNX_NAMESPACE::OpSchema::attributes)
.def_property_readonly("inputs", &ONNX_NAMESPACE::OpSchema::inputs)
.def_property_readonly("outputs", &ONNX_NAMESPACE::OpSchema::outputs)
.def_property_readonly(
"has_type_and_shape_inference_function",
&ONNX_NAMESPACE::OpSchema::has_type_and_shape_inference_function)
.def_property_readonly(
"type_constraints", &ONNX_NAMESPACE::OpSchema::typeConstraintParams)
.def_static("is_infinite", [](int v) {
return v == std::numeric_limits<int>::max();
});
// Keep this binding local to this module
py::class_<ONNX_NAMESPACE::OpSchema::Attribute>(op_schema, "Attribute", py::module_local())
.def_readonly("name", &ONNX_NAMESPACE::OpSchema::Attribute::name)
.def_readonly("description", &ONNX_NAMESPACE::OpSchema::Attribute::description)
.def_readonly("type", &ONNX_NAMESPACE::OpSchema::Attribute::type)
.def_property_readonly(
"_default_value",
[](ONNX_NAMESPACE::OpSchema::Attribute* attr) -> py::bytes {
std::string out;
attr->default_value.SerializeToString(&out);
return out;
})
.def_readonly("required", &ONNX_NAMESPACE::OpSchema::Attribute::required);
// Keep this binding local to this module
py::class_<ONNX_NAMESPACE::OpSchema::TypeConstraintParam>(op_schema, "TypeConstraintParam", py::module_local())
.def_readonly(
"type_param_str", &ONNX_NAMESPACE::OpSchema::TypeConstraintParam::type_param_str)
.def_readonly("description", &ONNX_NAMESPACE::OpSchema::TypeConstraintParam::description)
.def_readonly(
"allowed_type_strs",
&ONNX_NAMESPACE::OpSchema::TypeConstraintParam::allowed_type_strs);
// Keep this binding local to this module
py::enum_<ONNX_NAMESPACE::OpSchema::FormalParameterOption>(op_schema, "FormalParameterOption", py::module_local())
.value("Single", ONNX_NAMESPACE::OpSchema::Single)
.value("Optional", ONNX_NAMESPACE::OpSchema::Optional)
.value("Variadic", ONNX_NAMESPACE::OpSchema::Variadic);
// Keep this binding local to this module
py::class_<ONNX_NAMESPACE::OpSchema::FormalParameter>(op_schema, "FormalParameter", py::module_local())
.def_property_readonly("name", &ONNX_NAMESPACE::OpSchema::FormalParameter::GetName)
.def_property_readonly("types", &ONNX_NAMESPACE::OpSchema::FormalParameter::GetTypes)
.def_property_readonly("typeStr", &ONNX_NAMESPACE::OpSchema::FormalParameter::GetTypeStr)
.def_property_readonly(
"description", &ONNX_NAMESPACE::OpSchema::FormalParameter::GetDescription)
.def_property_readonly("option", &ONNX_NAMESPACE::OpSchema::FormalParameter::GetOption)
.def_property_readonly(
"isHomogeneous", &ONNX_NAMESPACE::OpSchema::FormalParameter::GetIsHomogeneous);
// Keep this binding local to this module
py::enum_<ONNX_NAMESPACE::AttributeProto::AttributeType>(op_schema, "AttrType", py::module_local())
.value("FLOAT", ONNX_NAMESPACE::AttributeProto::FLOAT)
.value("INT", ONNX_NAMESPACE::AttributeProto::INT)
.value("STRING", ONNX_NAMESPACE::AttributeProto::STRING)
.value("TENSOR", ONNX_NAMESPACE::AttributeProto::TENSOR)
.value("GRAPH", ONNX_NAMESPACE::AttributeProto::GRAPH)
.value("FLOATS", ONNX_NAMESPACE::AttributeProto::FLOATS)
.value("INTS", ONNX_NAMESPACE::AttributeProto::INTS)
.value("STRINGS", ONNX_NAMESPACE::AttributeProto::STRINGS)
.value("TENSORS", ONNX_NAMESPACE::AttributeProto::TENSORS)
.value("GRAPHS", ONNX_NAMESPACE::AttributeProto::GRAPHS);
// Keep this binding local to this module
py::enum_<ONNX_NAMESPACE::OpSchema::SupportType>(op_schema, "SupportType", py::module_local())
.value("COMMON", ONNX_NAMESPACE::OpSchema::SupportType::COMMON)
.value("EXPERIMENTAL", ONNX_NAMESPACE::OpSchema::SupportType::EXPERIMENTAL);
}
#endif //onnxruntime_PYBIND_EXPORT_OPSCHEMA
void addObjectMethods(py::module& m, Environment& env) {
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::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_<OrtValue> ortvalue_binding(m, "OrtValue");
ortvalue_binding
// Factory method to create an OrtValue (Tensor) from the given Numpy object
// The Tensor allocates and manages its own memory (on the specified device) and copies data from the Numpy data buffer
.def_static("ortvalue_from_numpy", [](py::object& array_on_cpu, OrtDevice& device) {
if (!IsNumericNumpyArray(array_on_cpu)) {
throw std::runtime_error("Creation of OrtValues is currently only supported from non-string numpy arrays");
}
auto ml_value = onnxruntime::make_unique<OrtValue>();
// The tensor's memory is allocated on the CPU
if (GetDeviceName(device) == CPU) {
// InputDeflist is null because OrtValue creation is not tied to a specific model
// Likewise, there is no need to specify the name (as the name was previously used to lookup the def list)
CreateGenericMLValue(nullptr, GetAllocator(), "", array_on_cpu, ml_value.get(), true);
} else if (GetDeviceName(device) == CUDA) {
// The tensor's memory is allocated on CUDA
#ifdef USE_CUDA
if (!IsCudaDeviceIdValid(logging::LoggingManager::DefaultLogger(), device.Id())) {
throw std::runtime_error("The provided device id doesn't match any available GPUs on the machine.");
}
// InputDeflist is null because OrtValue creation is not tied to a specific model
// Likewise, there is no need to specify the name (as the name was previously used to lookup the def list)
// TODO: Add check to ensure that string arrays are not passed - we currently don't support string tensors in CUDA
CreateGenericMLValue(nullptr, GetCudaAllocator(device.Id()), "", array_on_cpu, ml_value.get(), true, false, CpuToCudaMemCpy);
#else
throw std::runtime_error(
"Can't allocate memory on the CUDA device using this package of OnnxRuntime. "
"Please use the CUDA package of OnnxRuntime to use this feature.");
#endif
} else {
throw std::runtime_error("Unsupported device: Cannot place the OrtValue on this device");
}
return ml_value;
})
// Factory method to create an OrtValue (Tensor) from the given shape and element type with memory on the specified device
// The memory is left uninitialized
.def_static("ortvalue_from_shape_and_type", [](std::vector<int64_t>& shape, py::object& element_type, OrtDevice& device) {
PyArray_Descr* dtype;
if (!PyArray_DescrConverter(element_type.ptr(), &dtype)) {
throw std::runtime_error("Not a valid numpy type");
}
int type_num = dtype->type_num;
Py_DECREF(dtype);
if (!IsNumericNumpyType(type_num)) {
throw std::runtime_error("Creation of OrtValues is currently only supported from non-string numpy arrays");
}
auto ml_value = onnxruntime::make_unique<OrtValue>();
std::unique_ptr<Tensor> tensor;
// The tensor's memory is allocated on the CPU
if (GetDeviceName(device) == CPU) {
tensor = onnxruntime::make_unique<Tensor>(NumpyTypeToOnnxRuntimeType(type_num), shape, GetAllocator());
} else if (GetDeviceName(device) == CUDA) {
// The tensor's memory is allocated on CUDA
#ifdef USE_CUDA
if (!IsCudaDeviceIdValid(logging::LoggingManager::DefaultLogger(), device.Id())) {
throw std::runtime_error("The provided device id doesn't match any available GPUs on the machine.");
}
tensor = onnxruntime::make_unique<Tensor>(NumpyTypeToOnnxRuntimeType(type_num), shape, GetCudaAllocator(device.Id()));
#else
throw std::runtime_error(
"Can't allocate memory on the CUDA device using this package of OnnxRuntime. "
"Please use the CUDA package of OnnxRuntime to use this feature.");
#endif
} else {
throw std::runtime_error("Unsupported device: Cannot place the OrtValue on this device");
}
auto ml_tensor = DataTypeImpl::GetType<Tensor>();
ml_value->Init(tensor.release(),
ml_tensor,
ml_tensor->GetDeleteFunc());
return ml_value;
})
.def("data_ptr", [](OrtValue* ml_value) -> int64_t {
// TODO: Assumes that the OrtValue is a Tensor, make this generic to handle non-Tensors
ORT_ENFORCE(ml_value->IsTensor(), "Only OrtValues that are Tensors are currently supported");
auto* tensor = ml_value->GetMutable<Tensor>();
if (tensor->Shape().Size() == 0) {
return 0;
}
// Should cover x86 and x64 platforms
return reinterpret_cast<int64_t>(tensor->MutableDataRaw());
})
.def("device_name", [](OrtValue* ml_value) -> std::string {
// TODO: Assumes that the OrtValue is a Tensor, make this generic to handle non-Tensors
ORT_ENFORCE(ml_value->IsTensor(), "Only OrtValues that are Tensors are currently supported");
return std::string(GetDeviceName(ml_value->Get<Tensor>().Location().device));
})
.def("shape", [](OrtValue* ml_value) -> py::list {
// TODO: Assumes that the OrtValue is a Tensor, make this generic to handle non-Tensors
ORT_ENFORCE(ml_value->IsTensor(), "Only OrtValues that are Tensors are currently supported");
py::list shape_arr;
const auto& dims = ml_value->Get<Tensor>().Shape().GetDims();
for (auto dim : dims) {
// For sequence tensors - we would append a list of dims to the outermost list
// For now only tensors are supported in OrtValue
shape_arr.append(dim);
}
return shape_arr;
})
.def("data_type", [](OrtValue* ml_value) -> std::string {
// TODO: Assumes that the OrtValue is a Tensor, make this generic to handle non-Tensors
ORT_ENFORCE(ml_value->IsTensor(), "Only OrtValues that are Tensors are currently supported");
return DataTypeImpl::ToString(ml_value->Get<Tensor>().DataType());
})
.def("is_tensor", [](OrtValue* ml_value) -> bool {
return ml_value->IsTensor();
})
.def("numpy", [](OrtValue* ml_value) -> py::object {
ORT_ENFORCE(ml_value->IsTensor(), "Only OrtValues that are Tensors are convertible to Numpy objects");
py::object obj;
#ifdef USE_CUDA
GetPyObjFromTensor(ml_value->Get<Tensor>(), obj, nullptr, GetCudaToHostMemCpyFunction());
#else
GetPyObjFromTensor(ml_value->Get<Tensor>(), obj, nullptr, nullptr);
#endif
return obj;
});
py::class_<SessionIOBinding> session_io_binding(m, "SessionIOBinding");
session_io_binding
.def(py::init([](PyInferenceSession* sess) {
auto sess_io_binding = onnxruntime::make_unique<SessionIOBinding>(sess->GetSessionHandle());
return sess_io_binding;
}))
.def("bind_input", [](SessionIOBinding* io_binding, const std::string& name, py::object& arr_on_cpu) -> void {
InferenceSession* sess = io_binding->GetInferenceSession();
auto px = sess->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");
}
// For now, limit binding support to only non-string Tensors
// TODO: Support non-tensors
const auto& def_list = *px.second;
onnx::TypeProto type_proto;
if (!CheckIfTensor(def_list, name, type_proto)) {
throw std::runtime_error("Only binding Tensors is currently supported");
}
ORT_ENFORCE(type_proto.tensor_type().has_elem_type());
if (type_proto.tensor_type().elem_type() == onnx::TensorProto::STRING) {
throw std::runtime_error("Only binding non-string Tensors is currently supported");
}
OrtValue ml_value;
// Set the parameter `accept_only_numpy_array` to `true` (we only support binding Tensors)
CreateGenericMLValue(px.second, GetAllocator(), name, arr_on_cpu, &ml_value, true);
auto status = io_binding->Get()->BindInput(name, ml_value);
if (!status.IsOK()) {
throw std::runtime_error("Error when bind input: " + status.ErrorMessage());
}
})
.def("bind_input", [](SessionIOBinding* io_binding, const std::string& name, const OrtDevice& device, py::object& element_type, std::vector<int64_t>& shape, int64_t data_ptr) -> void {
ORT_ENFORCE(data_ptr != 0, "Pointer to data memory is not valid");
PyArray_Descr* dtype;
if (!PyArray_DescrConverter(element_type.ptr(), &dtype)) {
throw std::runtime_error("Not a valid numpy type");
}
int type_num = dtype->type_num;
Py_DECREF(dtype);
OrtMemoryInfo info(GetDeviceName(device), OrtDeviceAllocator, device);
std::unique_ptr<Tensor> p_tensor =
onnxruntime::make_unique<Tensor>(NumpyTypeToOnnxRuntimeType(type_num), shape, reinterpret_cast<void*>(data_ptr), info);
OrtValue ml_value;
ml_value.Init(p_tensor.release(),
DataTypeImpl::GetType<Tensor>(),
DataTypeImpl::GetType<Tensor>()->GetDeleteFunc());
auto status = io_binding->Get()->BindInput(name, ml_value);
if (!status.IsOK()) {
throw std::runtime_error("Error when binding input: " + status.ErrorMessage());
}
})
.def("bind_ortvalue_input", [](SessionIOBinding* io_binding, const std::string& name, OrtValue& ml_value) -> void {
auto status = io_binding->Get()->BindInput(name, ml_value);
if (!status.IsOK()) {
throw std::runtime_error("Error when binding input: " + status.ErrorMessage());
}
})
.def("bind_output", [](SessionIOBinding* io_binding, const std::string& name, const OrtDevice& device, py::object& element_type, std::vector<int64_t>& shape, int64_t data_ptr) -> void {
ORT_ENFORCE(data_ptr != 0, "Pointer to data memory is not valid");
InferenceSession* sess = io_binding->GetInferenceSession();
auto px = sess->GetModelOutputs();
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");
}
// For now, limit binding support to only non-string Tensors
// TODO: Support non-tensors
const auto& def_list = *px.second;
onnx::TypeProto type_proto;
if (!CheckIfTensor(def_list, name, type_proto)) {
throw std::runtime_error("Only binding Tensors is currently supported");
}
ORT_ENFORCE(type_proto.tensor_type().has_elem_type());
if (type_proto.tensor_type().elem_type() == onnx::TensorProto::STRING) {
throw std::runtime_error("Only binding non-string Tensors is currently supported");
}
PyArray_Descr* dtype;
if (!PyArray_DescrConverter(element_type.ptr(), &dtype)) {
throw std::runtime_error("Not a valid numpy type");
}
int type_num = dtype->type_num;
Py_DECREF(dtype);
OrtMemoryInfo info(GetDeviceName(device), OrtDeviceAllocator, device);
std::unique_ptr<Tensor> p_tensor = onnxruntime::make_unique<Tensor>(NumpyTypeToOnnxRuntimeType(type_num), shape, reinterpret_cast<void*>(data_ptr), info);
OrtValue ml_value;
ml_value.Init(p_tensor.release(),
DataTypeImpl::GetType<Tensor>(),
DataTypeImpl::GetType<Tensor>()->GetDeleteFunc());
auto status = io_binding->Get()->BindOutput(name, ml_value);
if (!status.IsOK()) {
throw std::runtime_error("Error when binding output: " + status.ErrorMessage());
}
})
.def("bind_output", [](SessionIOBinding* io_binding, const std::string& name, const OrtDevice& device) -> void {
auto status = io_binding->Get()->BindOutput(name, device);
if (!status.IsOK()) {
throw std::runtime_error("Error when binding output: " + status.ErrorMessage());
}
})
.def("bind_ortvalue_output", [](SessionIOBinding* io_binding, const std::string& name, OrtValue& ml_value) -> void {
auto status = io_binding->Get()->BindOutput(name, ml_value);
if (!status.IsOK()) {
throw std::runtime_error("Error when binding output: " + status.ErrorMessage());
}
})
.def("clear_binding_inputs", [](SessionIOBinding* io_binding) -> void {
io_binding->Get()->ClearInputs();
})
.def("clear_binding_outputs", [](SessionIOBinding* io_binding) -> void {
io_binding->Get()->ClearOutputs();
})
.def("get_outputs", [](SessionIOBinding* io_binding) -> std::vector<OrtValue>& {
return io_binding->Get()->GetOutputs();
})
.def("copy_outputs_to_cpu", [](SessionIOBinding* io_binding) -> std::vector<py::object> {
const std::vector<OrtValue>& outputs = io_binding->Get()->GetOutputs();
std::vector<py::object> rfetch;
rfetch.reserve(outputs.size());
for (const auto& _ : outputs) {
if (_.IsTensor()) {
AddTensorAsPyObj(_, rfetch, &io_binding->GetInferenceSession()->GetDataTransferManager(), nullptr);
} else {
AddNonTensorAsPyObj(_, rfetch, &io_binding->GetInferenceSession()->GetDataTransferManager(), nullptr);
}
}
return rfetch;
});
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("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("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_verbosity_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}); },
"Rpbdoc(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}); },
"Rpbdoc(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 {
const Status status = options->AddConfigEntry(config_key, config_value);
if (!status.IsOK())
throw std::runtime_error(status.ErrorMessage());
},
"Rpbdoc(Set a single session configuration entry as a pair of strings.)pbdoc")
.def(
"get_session_config_entry",
[](PySessionOptions* options, const char* config_key) -> std::string {
const std::string key(config_key);
std::string value;
if (!options->TryGetConfigEntry(key, value))
throw std::runtime_error("SessionOptions does not have configuration with key: " + key);
return value;
},
"Rpbdoc(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)
// 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
},
"Rpbdoc(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
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("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 = onnxruntime::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 = onnxruntime::make_unique<PyInferenceSession>(env, so);
#if !defined(ORT_MINIMAL_BUILD)
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",
[](PyInferenceSession* sess,
const std::vector<std::string>& provider_types = {},
const ProviderOptionsVector& provider_options = {}) {
InitializeSession(sess->GetSessionHandle(), provider_types, provider_options);
},
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 _ : 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(), _.first, _.second, &ml_value);
if (PyErr_Occurred()) {
PyObject *ptype, *pvalue, *ptraceback;
PyErr_Fetch(&ptype, &pvalue, &ptraceback);
PyObject* pStr = PyObject_Str(ptype);
std::string sType = py::reinterpret_borrow<py::str>(pStr);
Py_XDECREF(pStr);
pStr = PyObject_Str(pvalue);
sType += ": ";
sType += py::reinterpret_borrow<py::str>(pStr);
Py_XDECREF(pStr);
throw std::runtime_error(sType);
}
feeds.insert(std::make_pair(_.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());
for (auto _ : fetches) {
if (_.IsTensor()) {
AddTensorAsPyObj(_, rfetch, nullptr, nullptr);
} else {
AddNonTensorAsPyObj(_, rfetch, nullptr, nullptr);
}
}
return rfetch;
})
.def("end_profiling", [](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", [](PyInferenceSession* sess) -> const std::vector<std::string>& {
return sess->GetSessionHandle()->GetRegisteredProviderTypes();
})
.def("get_provider_options", [](const PyInferenceSession* sess) -> const ProviderOptionsMap& {
return sess->GetSessionHandle()->GetAllProviderOptions();
})
.def_property_readonly("session_options", [](PyInferenceSession* sess) -> const PySessionOptions& {
const auto& session_options = sess->GetSessionHandle()->GetSessionOptions();
return static_cast<const PySessionOptions&>(session_options);
})
.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);
})
.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);
})
.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);
})
.def_property_readonly("model_meta", [](const PyInferenceSession* sess) -> const onnxruntime::ModelMetadata& {
auto res = sess->GetSessionHandle()->GetModelMetadata();
OrtPybindThrowIfError(res.first);
return *(res.second);
})
.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
#ifdef ENABLE_TRAINING
void addObjectMethodsForTraining(py::module& m);
#endif
PYBIND11_MODULE(onnxruntime_pybind11_state, 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);
#ifdef ENABLE_TRAINING
addObjectMethodsForTraining(m);
#endif // ENABLE_TRAINING
#ifdef onnxruntime_PYBIND_EXPORT_OPSCHEMA
addOpSchemaSubmodule(m);
addOpKernelSubmodule(m);
#endif
}
// 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
([]() -> void {
// import_array1() forces a void return value.
import_array1();
})();
Env::Default().GetTelemetryProvider().SetLanguageProjection(OrtLanguageProjection::ORT_PROJECTION_PYTHON);
OrtPybindThrowIfError(Environment::Create(onnxruntime::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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