working for single run

.gitignore

@@ -1,3 +1,6 @@
+# Ignoring onnxruntime libs
+/libs/onnxruntime/lib/*
+
 example-*/config.make
 example-*/*.sln
 example-*/*.vcxproj

README.md

@@ -1,4 +1,7 @@
 # ofxOnnxRuntime
+
+**Updated version, working with Windows 11, CUDA, and ONNXRuntime 1.20.1**
+
 [ONNX Runtime](https://github.com/microsoft/onnxruntime) tiny wrapper for openFrameworks
 
 !['test'](screenshot.png)
@@ -17,7 +20,7 @@
         - From `Browse` tab, search `Microsoft.ML.OnnxRuntime` (CPU) or `Microsoft.ML.OnnxRuntime.Gpu` (GPU) and install it.
     2. DLL direct download
         - You can download prebuilt DLLs from [here](https://github.com/microsoft/onnxruntime/releases).
-        - Unzip downloaded `onnxruntime-win-x64-(gpu-)1.10.0.zip` and locate files on `libs\onnxruntime\lib\vs\x64\` .
+        - Unzip downloaded `onnxruntime-win-x64-(gpu-)1.20.1.zip` and locate files on `libs\onnxruntime\lib\vs\x64\` .
         - Generate a project using ProjectGenerator, then all libs are linked correctly and all dlls are copied to `bin`.
 
 ## Tested environment

addon_config.mk

@@ -11,4 +11,6 @@ common:
 osx:
 	ADDON_LDFLAGS = -Xlinker -rpath -Xlinker @executable_path
 vs:
+	ADDON_INCLUDES = libs/onnxruntime/include
+	ADDON_INCLUDES += src
 

libs/onnxruntime/include/core/providers/cuda/cuda_context.h

@@ -0,0 +1,108 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+// This header is to expose a context for cuda custom ops.
+// By the context, a custom cuda operator could fetch existing resources,
+// such as cuda stream and cudnn handle, for reusing.
+
+// For concrete usage, pls find page here:
+// https://onnxruntime.ai/docs/reference/operators/add-custom-op.html#custom-ops-for-cuda-and-rocm
+
+#pragma once
+
+#define ORT_CUDA_CTX
+
+#include <cuda.h>
+#include <cuda_runtime.h>
+#ifndef USE_CUDA_MINIMAL
+#include <cublas_v2.h>
+#include <cudnn.h>
+#endif
+
+#include "core/providers/cuda/cuda_resource.h"
+#include "core/providers/custom_op_context.h"
+
+namespace Ort {
+
+namespace Custom {
+
+struct CudaContext : public CustomOpContext {
+  cudaStream_t cuda_stream = {};
+  cudnnHandle_t cudnn_handle = {};
+  cublasHandle_t cublas_handle = {};
+  OrtAllocator* deferred_cpu_allocator = {};
+  // below are cuda ep options
+  int16_t device_id = 0;
+  int32_t arena_extend_strategy = 0;
+  int32_t cudnn_conv_algo_search = 0;
+  bool cudnn_conv_use_max_workspace = true;
+  bool cudnn_conv1d_pad_to_nc1d = false;
+  bool enable_skip_layer_norm_strict_mode = false;
+  bool prefer_nhwc = false;
+  bool use_tf32 = true;
+  bool fuse_conv_bias = true;
+
+  void Init(const OrtKernelContext& kernel_ctx) {
+    cuda_stream = FetchResource<cudaStream_t>(kernel_ctx, CudaResource::cuda_stream_t);
+    cudnn_handle = FetchResource<cudnnHandle_t>(kernel_ctx, CudaResource::cudnn_handle_t);
+    cublas_handle = FetchResource<cublasHandle_t>(kernel_ctx, CudaResource::cublas_handle_t);
+    deferred_cpu_allocator = FetchResource<OrtAllocator*>(kernel_ctx, CudaResource::deferred_cpu_allocator_t);
+
+    device_id = FetchResource<int16_t>(kernel_ctx, CudaResource::device_id_t);
+    arena_extend_strategy = FetchResource<int32_t>(kernel_ctx, CudaResource::arena_extend_strategy_t);
+    cudnn_conv_algo_search = FetchResource<int32_t>(kernel_ctx, CudaResource::cudnn_conv_algo_search_t);
+    cudnn_conv_use_max_workspace = FetchResource<bool>(kernel_ctx, CudaResource::cudnn_conv_use_max_workspace_t);
+
+    cudnn_conv1d_pad_to_nc1d = FetchResource<bool>(kernel_ctx, CudaResource::cudnn_conv1d_pad_to_nc1d_t);
+    enable_skip_layer_norm_strict_mode = FetchResource<bool>(
+        kernel_ctx, CudaResource::enable_skip_layer_norm_strict_mode_t);
+    prefer_nhwc = FetchResource<bool>(kernel_ctx, CudaResource::prefer_nhwc_t);
+    use_tf32 = FetchResource<bool>(kernel_ctx, CudaResource::use_tf32_t);
+    fuse_conv_bias = FetchResource<bool>(kernel_ctx, CudaResource::fuse_conv_bias_t);
+  }
+
+  template <typename T>
+  T FetchResource(const OrtKernelContext& kernel_ctx, CudaResource resource_type) {
+    if constexpr (sizeof(T) > sizeof(void*)) {
+      ORT_CXX_API_THROW("void* is not large enough to hold resource type: " + std::to_string(resource_type),
+                        OrtErrorCode::ORT_INVALID_ARGUMENT);
+    }
+    const auto& ort_api = Ort::GetApi();
+    void* resource = {};
+    OrtStatus* status = ort_api.KernelContext_GetResource(
+        &kernel_ctx, ORT_CUDA_RESOURCE_VERSION, resource_type, &resource);
+    if (status) {
+      ORT_CXX_API_THROW("Failed to fetch cuda ep resource, resource type: " + std::to_string(resource_type),
+                        OrtErrorCode::ORT_RUNTIME_EXCEPTION);
+    }
+    T t = {};
+    memcpy(&t, &resource, sizeof(T));
+    return t;
+  }
+
+  void* AllocDeferredCpuMem(size_t size) const {
+    if (0 == size) {
+      return {};
+    }
+    const auto& ort_api = Ort::GetApi();
+    void* mem = {};
+    auto status = ort_api.AllocatorAlloc(deferred_cpu_allocator, size, &mem);
+    if (status) {
+      ORT_CXX_API_THROW("failed to allocate deferred cpu memory", OrtErrorCode::ORT_RUNTIME_EXCEPTION);
+    }
+    return mem;
+  }
+
+  void FreeDeferredCpuMem(void* mem) const {
+    if (mem) {
+      const auto& ort_api = Ort::GetApi();
+      auto status = ort_api.AllocatorFree(deferred_cpu_allocator, mem);
+      if (status) {
+        ORT_CXX_API_THROW("failed to free deferred cpu memory", OrtErrorCode::ORT_RUNTIME_EXCEPTION);
+      }
+    }
+  }
+};
+
+}  // namespace Custom
+}  // namespace Ort

libs/onnxruntime/include/core/providers/cuda/cuda_resource.h

@@ -0,0 +1,23 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#include "core/providers/resource.h"
+
+#define ORT_CUDA_RESOURCE_VERSION 3
+
+enum CudaResource : int {
+  cuda_stream_t = cuda_resource_offset,  // 10000
+  cudnn_handle_t,
+  cublas_handle_t,
+  deferred_cpu_allocator_t,
+  // below are cuda ep options
+  device_id_t,  // 10004
+  arena_extend_strategy_t,
+  cudnn_conv_algo_search_t,
+  cudnn_conv_use_max_workspace_t,
+  cudnn_conv1d_pad_to_nc1d_t,
+  enable_skip_layer_norm_strict_mode_t,
+  prefer_nhwc_t,
+  use_tf32_t,
+  fuse_conv_bias_t
+};

libs/onnxruntime/include/core/providers/custom_op_context.h

@@ -0,0 +1,10 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#pragma once
+
+// CustomOpContext defines an interface allowing a custom op to access ep-specific resources.
+struct CustomOpContext {
+  CustomOpContext() = default;
+  virtual ~CustomOpContext() {};
+};

libs/onnxruntime/include/core/providers/resource.h

@@ -0,0 +1,14 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#pragma once
+
+enum ResourceOffset {
+  cpu_resource_offset = 0,
+  cuda_resource_offset = 10000,
+  dml_resource_offset = 20000,
+  rocm_resource_offset = 30000,
+  // offsets for other ort eps
+  custom_ep_resource_offset = 10000000,
+  // offsets for customized eps
+};

libs/onnxruntime/include/onnxruntime_float16.h

@@ -0,0 +1,535 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#pragma once
+
+#include <stdint.h>
+#include <cmath>
+#include <cstring>
+#include <limits>
+
+namespace onnxruntime_float16 {
+
+namespace detail {
+
+enum class endian {
+#if defined(_WIN32)
+  little = 0,
+  big = 1,
+  native = little,
+#elif defined(__GNUC__) || defined(__clang__)
+  little = __ORDER_LITTLE_ENDIAN__,
+  big = __ORDER_BIG_ENDIAN__,
+  native = __BYTE_ORDER__,
+#else
+#error onnxruntime_float16::detail::endian is not implemented in this environment.
+#endif
+};
+
+static_assert(
+    endian::native == endian::little || endian::native == endian::big,
+    "Only little-endian or big-endian native byte orders are supported.");
+
+}  // namespace detail
+
+/// <summary>
+/// Shared implementation between public and internal classes. CRTP pattern.
+/// </summary>
+template <class Derived>
+struct Float16Impl {
+ protected:
+  /// <summary>
+  /// Converts from float to uint16_t float16 representation
+  /// </summary>
+  /// <param name="v"></param>
+  /// <returns></returns>
+  constexpr static uint16_t ToUint16Impl(float v) noexcept;
+
+  /// <summary>
+  /// Converts float16 to float
+  /// </summary>
+  /// <returns>float representation of float16 value</returns>
+  float ToFloatImpl() const noexcept;
+
+  /// <summary>
+  /// Creates an instance that represents absolute value.
+  /// </summary>
+  /// <returns>Absolute value</returns>
+  uint16_t AbsImpl() const noexcept {
+    return static_cast<uint16_t>(val & ~kSignMask);
+  }
+
+  /// <summary>
+  /// Creates a new instance with the sign flipped.
+  /// </summary>
+  /// <returns>Flipped sign instance</returns>
+  uint16_t NegateImpl() const noexcept {
+    return IsNaN() ? val : static_cast<uint16_t>(val ^ kSignMask);
+  }
+
+ public:
+  // uint16_t special values
+  static constexpr uint16_t kSignMask = 0x8000U;
+  static constexpr uint16_t kBiasedExponentMask = 0x7C00U;
+  static constexpr uint16_t kPositiveInfinityBits = 0x7C00U;
+  static constexpr uint16_t kNegativeInfinityBits = 0xFC00U;
+  static constexpr uint16_t kPositiveQNaNBits = 0x7E00U;
+  static constexpr uint16_t kNegativeQNaNBits = 0xFE00U;
+  static constexpr uint16_t kMaxValueBits = 0x7BFFU;  // Largest normal number
+  static constexpr uint16_t kOneBits = 0x3C00U;
+  static constexpr uint16_t kMinusOneBits = 0xBC00U;
+
+  uint16_t val{0};
+
+  Float16Impl() = default;
+
+  /// <summary>
+  /// Checks if the value is negative
+  /// </summary>
+  /// <returns>true if negative</returns>
+  bool IsNegative() const noexcept {
+    return static_cast<int16_t>(val) < 0;
+  }
+
+  /// <summary>
+  /// Tests if the value is NaN
+  /// </summary>
+  /// <returns>true if NaN</returns>
+  bool IsNaN() const noexcept {
+    return AbsImpl() > kPositiveInfinityBits;
+  }
+
+  /// <summary>
+  /// Tests if the value is finite
+  /// </summary>
+  /// <returns>true if finite</returns>
+  bool IsFinite() const noexcept {
+    return AbsImpl() < kPositiveInfinityBits;
+  }
+
+  /// <summary>
+  /// Tests if the value represents positive infinity.
+  /// </summary>
+  /// <returns>true if positive infinity</returns>
+  bool IsPositiveInfinity() const noexcept {
+    return val == kPositiveInfinityBits;
+  }
+
+  /// <summary>
+  /// Tests if the value represents negative infinity
+  /// </summary>
+  /// <returns>true if negative infinity</returns>
+  bool IsNegativeInfinity() const noexcept {
+    return val == kNegativeInfinityBits;
+  }
+
+  /// <summary>
+  /// Tests if the value is either positive or negative infinity.
+  /// </summary>
+  /// <returns>True if absolute value is infinity</returns>
+  bool IsInfinity() const noexcept {
+    return AbsImpl() == kPositiveInfinityBits;
+  }
+
+  /// <summary>
+  /// Tests if the value is NaN or zero. Useful for comparisons.
+  /// </summary>
+  /// <returns>True if NaN or zero.</returns>
+  bool IsNaNOrZero() const noexcept {
+    auto abs = AbsImpl();
+    return (abs == 0 || abs > kPositiveInfinityBits);
+  }
+
+  /// <summary>
+  /// Tests if the value is normal (not zero, subnormal, infinite, or NaN).
+  /// </summary>
+  /// <returns>True if so</returns>
+  bool IsNormal() const noexcept {
+    auto abs = AbsImpl();
+    return (abs < kPositiveInfinityBits)           // is finite
+           && (abs != 0)                           // is not zero
+           && ((abs & kBiasedExponentMask) != 0);  // is not subnormal (has a non-zero exponent)
+  }
+
+  /// <summary>
+  /// Tests if the value is subnormal (denormal).
+  /// </summary>
+  /// <returns>True if so</returns>
+  bool IsSubnormal() const noexcept {
+    auto abs = AbsImpl();
+    return (abs < kPositiveInfinityBits)           // is finite
+           && (abs != 0)                           // is not zero
+           && ((abs & kBiasedExponentMask) == 0);  // is subnormal (has a zero exponent)
+  }
+
+  /// <summary>
+  /// Creates an instance that represents absolute value.
+  /// </summary>
+  /// <returns>Absolute value</returns>
+  Derived Abs() const noexcept { return Derived::FromBits(AbsImpl()); }
+
+  /// <summary>
+  /// Creates a new instance with the sign flipped.
+  /// </summary>
+  /// <returns>Flipped sign instance</returns>
+  Derived Negate() const noexcept { return Derived::FromBits(NegateImpl()); }
+
+  /// <summary>
+  /// IEEE defines that positive and negative zero are equal, this gives us a quick equality check
+  /// for two values by or'ing the private bits together and stripping the sign. They are both zero,
+  /// and therefore equivalent, if the resulting value is still zero.
+  /// </summary>
+  /// <param name="lhs">first value</param>
+  /// <param name="rhs">second value</param>
+  /// <returns>True if both arguments represent zero</returns>
+  static bool AreZero(const Float16Impl& lhs, const Float16Impl& rhs) noexcept {
+    return static_cast<uint16_t>((lhs.val | rhs.val) & ~kSignMask) == 0;
+  }
+
+  bool operator==(const Float16Impl& rhs) const noexcept {
+    if (IsNaN() || rhs.IsNaN()) {
+      // IEEE defines that NaN is not equal to anything, including itself.
+      return false;
+    }
+    return val == rhs.val;
+  }
+
+  bool operator!=(const Float16Impl& rhs) const noexcept { return !(*this == rhs); }
+
+  bool operator<(const Float16Impl& rhs) const noexcept {
+    if (IsNaN() || rhs.IsNaN()) {
+      // IEEE defines that NaN is unordered with respect to everything, including itself.
+      return false;
+    }
+
+    const bool left_is_negative = IsNegative();
+    if (left_is_negative != rhs.IsNegative()) {
+      // When the signs of left and right differ, we know that left is less than right if it is
+      // the negative value. The exception to this is if both values are zero, in which case IEEE
+      // says they should be equal, even if the signs differ.
+      return left_is_negative && !AreZero(*this, rhs);
+    }
+    return (val != rhs.val) && ((val < rhs.val) ^ left_is_negative);
+  }
+};
+
+// The following Float16_t conversions are based on the code from
+// Eigen library.
+
+// The conversion routines are Copyright (c) Fabian Giesen, 2016.
+// The original license follows:
+//
+// Copyright (c) Fabian Giesen, 2016
+// All rights reserved.
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted.
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+namespace detail {
+union float32_bits {
+  unsigned int u;
+  float f;
+};
+}  // namespace detail
+
+template <class Derived>
+inline constexpr uint16_t Float16Impl<Derived>::ToUint16Impl(float v) noexcept {
+  detail::float32_bits f{};
+  f.f = v;
+
+  constexpr detail::float32_bits f32infty = {255 << 23};
+  constexpr detail::float32_bits f16max = {(127 + 16) << 23};
+  constexpr detail::float32_bits denorm_magic = {((127 - 15) + (23 - 10) + 1) << 23};
+  constexpr unsigned int sign_mask = 0x80000000u;
+  uint16_t val = static_cast<uint16_t>(0x0u);
+
+  unsigned int sign = f.u & sign_mask;
+  f.u ^= sign;
+
+  // NOTE all the integer compares in this function can be safely
+  // compiled into signed compares since all operands are below
+  // 0x80000000. Important if you want fast straight SSE2 code
+  // (since there's no unsigned PCMPGTD).
+
+  if (f.u >= f16max.u) {                         // result is Inf or NaN (all exponent bits set)
+    val = (f.u > f32infty.u) ? 0x7e00 : 0x7c00;  // NaN->qNaN and Inf->Inf
+  } else {                                       // (De)normalized number or zero
+    if (f.u < (113 << 23)) {                     // resulting FP16 is subnormal or zero
+      // use a magic value to align our 10 mantissa bits at the bottom of
+      // the float. as long as FP addition is round-to-nearest-even this
+      // just works.
+      f.f += denorm_magic.f;
+
+      // and one integer subtract of the bias later, we have our final float!
+      val = static_cast<uint16_t>(f.u - denorm_magic.u);
+    } else {
+      unsigned int mant_odd = (f.u >> 13) & 1;  // resulting mantissa is odd
+
+      // update exponent, rounding bias part 1
+      // Equivalent to `f.u += ((unsigned int)(15 - 127) << 23) + 0xfff`, but
+      // without arithmetic overflow.
+      f.u += 0xc8000fffU;
+      // rounding bias part 2
+      f.u += mant_odd;
+      // take the bits!
+      val = static_cast<uint16_t>(f.u >> 13);
+    }
+  }
+
+  val |= static_cast<uint16_t>(sign >> 16);
+  return val;
+}
+
+template <class Derived>
+inline float Float16Impl<Derived>::ToFloatImpl() const noexcept {
+  constexpr detail::float32_bits magic = {113 << 23};
+  constexpr unsigned int shifted_exp = 0x7c00 << 13;  // exponent mask after shift
+  detail::float32_bits o{};
+
+  o.u = (val & 0x7fff) << 13;            // exponent/mantissa bits
+  unsigned int exp = shifted_exp & o.u;  // just the exponent
+  o.u += (127 - 15) << 23;               // exponent adjust
+
+  // handle exponent special cases
+  if (exp == shifted_exp) {   // Inf/NaN?
+    o.u += (128 - 16) << 23;  // extra exp adjust
+  } else if (exp == 0) {      // Zero/Denormal?
+    o.u += 1 << 23;           // extra exp adjust
+    o.f -= magic.f;           // re-normalize
+  }
+
+  // Attempt to workaround the Internal Compiler Error on ARM64
+  // for bitwise | operator, including std::bitset
+#if (defined _MSC_VER) && (defined _M_ARM || defined _M_ARM64 || defined _M_ARM64EC)
+  if (IsNegative()) {
+    return -o.f;
+  }
+#else
+  // original code:
+  o.u |= (val & 0x8000U) << 16U;  // sign bit
+#endif
+  return o.f;
+}
+
+/// Shared implementation between public and internal classes. CRTP pattern.
+template <class Derived>
+struct BFloat16Impl {
+ protected:
+  /// <summary>
+  /// Converts from float to uint16_t float16 representation
+  /// </summary>
+  /// <param name="v"></param>
+  /// <returns></returns>
+  static uint16_t ToUint16Impl(float v) noexcept;
+
+  /// <summary>
+  /// Converts bfloat16 to float
+  /// </summary>
+  /// <returns>float representation of bfloat16 value</returns>
+  float ToFloatImpl() const noexcept;
+
+  /// <summary>
+  /// Creates an instance that represents absolute value.
+  /// </summary>
+  /// <returns>Absolute value</returns>
+  uint16_t AbsImpl() const noexcept {
+    return static_cast<uint16_t>(val & ~kSignMask);
+  }
+
+  /// <summary>
+  /// Creates a new instance with the sign flipped.
+  /// </summary>
+  /// <returns>Flipped sign instance</returns>
+  uint16_t NegateImpl() const noexcept {
+    return IsNaN() ? val : static_cast<uint16_t>(val ^ kSignMask);
+  }
+
+ public:
+  // uint16_t special values
+  static constexpr uint16_t kSignMask = 0x8000U;
+  static constexpr uint16_t kBiasedExponentMask = 0x7F80U;
+  static constexpr uint16_t kPositiveInfinityBits = 0x7F80U;
+  static constexpr uint16_t kNegativeInfinityBits = 0xFF80U;
+  static constexpr uint16_t kPositiveQNaNBits = 0x7FC1U;
+  static constexpr uint16_t kNegativeQNaNBits = 0xFFC1U;
+  static constexpr uint16_t kMaxValueBits = 0x7F7FU;
+  static constexpr uint16_t kRoundToNearest = 0x7FFFU;
+  static constexpr uint16_t kOneBits = 0x3F80U;
+  static constexpr uint16_t kMinusOneBits = 0xBF80U;
+
+  uint16_t val{0};
+
+  BFloat16Impl() = default;
+
+  /// <summary>
+  /// Checks if the value is negative
+  /// </summary>
+  /// <returns>true if negative</returns>
+  bool IsNegative() const noexcept {
+    return static_cast<int16_t>(val) < 0;
+  }
+
+  /// <summary>
+  /// Tests if the value is NaN
+  /// </summary>
+  /// <returns>true if NaN</returns>
+  bool IsNaN() const noexcept {
+    return AbsImpl() > kPositiveInfinityBits;
+  }
+
+  /// <summary>
+  /// Tests if the value is finite
+  /// </summary>
+  /// <returns>true if finite</returns>
+  bool IsFinite() const noexcept {
+    return AbsImpl() < kPositiveInfinityBits;
+  }
+
+  /// <summary>
+  /// Tests if the value represents positive infinity.
+  /// </summary>
+  /// <returns>true if positive infinity</returns>
+  bool IsPositiveInfinity() const noexcept {
+    return val == kPositiveInfinityBits;
+  }
+
+  /// <summary>
+  /// Tests if the value represents negative infinity
+  /// </summary>
+  /// <returns>true if negative infinity</returns>
+  bool IsNegativeInfinity() const noexcept {
+    return val == kNegativeInfinityBits;
+  }
+
+  /// <summary>
+  /// Tests if the value is either positive or negative infinity.
+  /// </summary>
+  /// <returns>True if absolute value is infinity</returns>
+  bool IsInfinity() const noexcept {
+    return AbsImpl() == kPositiveInfinityBits;
+  }
+
+  /// <summary>
+  /// Tests if the value is NaN or zero. Useful for comparisons.
+  /// </summary>
+  /// <returns>True if NaN or zero.</returns>
+  bool IsNaNOrZero() const noexcept {
+    auto abs = AbsImpl();
+    return (abs == 0 || abs > kPositiveInfinityBits);
+  }
+
+  /// <summary>
+  /// Tests if the value is normal (not zero, subnormal, infinite, or NaN).
+  /// </summary>
+  /// <returns>True if so</returns>
+  bool IsNormal() const noexcept {
+    auto abs = AbsImpl();
+    return (abs < kPositiveInfinityBits)           // is finite
+           && (abs != 0)                           // is not zero
+           && ((abs & kBiasedExponentMask) != 0);  // is not subnormal (has a non-zero exponent)
+  }
+
+  /// <summary>
+  /// Tests if the value is subnormal (denormal).
+  /// </summary>
+  /// <returns>True if so</returns>
+  bool IsSubnormal() const noexcept {
+    auto abs = AbsImpl();
+    return (abs < kPositiveInfinityBits)           // is finite
+           && (abs != 0)                           // is not zero
+           && ((abs & kBiasedExponentMask) == 0);  // is subnormal (has a zero exponent)
+  }
+
+  /// <summary>
+  /// Creates an instance that represents absolute value.
+  /// </summary>
+  /// <returns>Absolute value</returns>
+  Derived Abs() const noexcept { return Derived::FromBits(AbsImpl()); }
+
+  /// <summary>
+  /// Creates a new instance with the sign flipped.
+  /// </summary>
+  /// <returns>Flipped sign instance</returns>
+  Derived Negate() const noexcept { return Derived::FromBits(NegateImpl()); }
+
+  /// <summary>
+  /// IEEE defines that positive and negative zero are equal, this gives us a quick equality check
+  /// for two values by or'ing the private bits together and stripping the sign. They are both zero,
+  /// and therefore equivalent, if the resulting value is still zero.
+  /// </summary>
+  /// <param name="lhs">first value</param>
+  /// <param name="rhs">second value</param>
+  /// <returns>True if both arguments represent zero</returns>
+  static bool AreZero(const BFloat16Impl& lhs, const BFloat16Impl& rhs) noexcept {
+    // IEEE defines that positive and negative zero are equal, this gives us a quick equality check
+    // for two values by or'ing the private bits together and stripping the sign. They are both zero,
+    // and therefore equivalent, if the resulting value is still zero.
+    return static_cast<uint16_t>((lhs.val | rhs.val) & ~kSignMask) == 0;
+  }
+};
+
+template <class Derived>
+inline uint16_t BFloat16Impl<Derived>::ToUint16Impl(float v) noexcept {
+  uint16_t result;
+  if (std::isnan(v)) {
+    result = kPositiveQNaNBits;
+  } else {
+    auto get_msb_half = [](float fl) {
+      uint16_t result;
+#ifdef __cpp_if_constexpr
+      if constexpr (detail::endian::native == detail::endian::little) {
+#else
+      if (detail::endian::native == detail::endian::little) {
+#endif
+        std::memcpy(&result, reinterpret_cast<char*>(&fl) + sizeof(uint16_t), sizeof(uint16_t));
+      } else {
+        std::memcpy(&result, &fl, sizeof(uint16_t));
+      }
+      return result;
+    };
+
+    uint16_t upper_bits = get_msb_half(v);
+    union {
+      uint32_t U32;
+      float F32;
+    };
+    F32 = v;
+    U32 += (upper_bits & 1) + kRoundToNearest;
+    result = get_msb_half(F32);
+  }
+  return result;
+}
+
+template <class Derived>
+inline float BFloat16Impl<Derived>::ToFloatImpl() const noexcept {
+  if (IsNaN()) {
+    return std::numeric_limits<float>::quiet_NaN();
+  }
+  float result;
+  char* const first = reinterpret_cast<char*>(&result);
+  char* const second = first + sizeof(uint16_t);
+#ifdef __cpp_if_constexpr
+  if constexpr (detail::endian::native == detail::endian::little) {
+#else
+  if (detail::endian::native == detail::endian::little) {
+#endif
+    std::memset(first, 0, sizeof(uint16_t));
+    std::memcpy(second, &val, sizeof(uint16_t));
+  } else {
+    std::memcpy(first, &val, sizeof(uint16_t));
+    std::memset(second, 0, sizeof(uint16_t));
+  }
+  return result;
+}
+
+}  // namespace onnxruntime_float16

libs/onnxruntime/include/onnxruntime_run_options_config_keys.h

@@ -25,3 +25,27 @@
 // Example usage: "cpu:0;gpu:0" (or) "gpu:0"
 // By default, the value for this key is empty (i.e.) no memory arenas are shrunk
 static const char* const kOrtRunOptionsConfigEnableMemoryArenaShrinkage = "memory.enable_memory_arena_shrinkage";
+
+// Set to '1' to not synchronize execution providers with CPU at the end of session run.
+// Per default it will be set to '0'
+// Taking CUDA EP as an example, it omit triggering cudaStreamSynchronize on the compute stream.
+static const char* const kOrtRunOptionsConfigDisableSynchronizeExecutionProviders = "disable_synchronize_execution_providers";
+
+// Set HTP performance mode for QNN HTP backend before session run.
+// options for HTP performance mode: "burst", "balanced", "default", "high_performance",
+// "high_power_saver", "low_balanced", "extreme_power_saver", "low_power_saver", "power_saver",
+// "sustained_high_performance". Default to "default".
+static const char* const kOrtRunOptionsConfigQnnPerfMode = "qnn.htp_perf_mode";
+
+// Set HTP performance mode for QNN HTP backend post session run.
+static const char* const kOrtRunOptionsConfigQnnPerfModePostRun = "qnn.htp_perf_mode_post_run";
+
+// Set RPC control latency for QNN HTP backend
+static const char* const kOrtRunOptionsConfigQnnRpcControlLatency = "qnn.rpc_control_latency";
+
+// Set graph annotation id for CUDA EP. Use with enable_cuda_graph=true.
+// The value should be an integer. If the value is not set, the default value is 0 and
+// ORT session only captures one cuda graph before another capture is requested.
+// If the value is set to -1, cuda graph capture/replay is disabled in that run.
+// User are not expected to set the value to 0 as it is reserved for internal use.
+static const char* const kOrtRunOptionsConfigCudaGraphAnnotation = "gpu_graph_id";

libs/onnxruntime/include/onnxruntime_session_options_config_keys.h

@@ -44,13 +44,69 @@ static const char* const kOrtSessionOptionsConfigSetDenormalAsZero = "session.se
 // It controls to run quantization model in QDQ (QuantizelinearDeQuantizelinear) format or not.
 // "0": enable. ORT does fusion logic for QDQ format.
 // "1": disable. ORT doesn't do fusion logic for QDQ format.
-// Its default value is "0"
+// Its default value is "0" unless the DirectML execution provider is registered, in which case it defaults to "1".
 static const char* const kOrtSessionOptionsDisableQuantQDQ = "session.disable_quant_qdq";
 
+// It controls whether to enable Double QDQ remover and Identical Children Consolidation
+// "0": not to disable. ORT does remove the middle 2 Nodes from a Q->(QD->Q)->QD pairs
+// "1": disable. ORT doesn't remove the middle 2 Nodes from a Q->(QD->Q)->QD pairs
+// Its default value is "0"
+static const char* const kOrtSessionOptionsDisableDoubleQDQRemover = "session.disable_double_qdq_remover";
+
+// If set to "1", enables the removal of QuantizeLinear/DequantizeLinear node pairs once all QDQ handling has been
+// completed. e.g. If after all QDQ handling has completed and we have -> FloatOp -> Q -> DQ -> FloatOp -> the
+// Q -> DQ could potentially be removed. This will provide a performance benefit by avoiding going from float to
+// 8-bit and back to float, but could impact accuracy. The impact on accuracy will be model specific and depend on
+// other factors like whether the model was created using Quantization Aware Training or Post Training Quantization.
+// As such, it's best to test to determine if enabling this works well for your scenario.
+// The default value is "0"
+// Available since version 1.11.
+static const char* const kOrtSessionOptionsEnableQuantQDQCleanup = "session.enable_quant_qdq_cleanup";
+
 // Enable or disable gelu approximation in graph optimization. "0": disable; "1": enable. The default is "0".
 // GeluApproximation has side effects which may change the inference results. It is disabled by default due to this.
 static const char* const kOrtSessionOptionsEnableGeluApproximation = "optimization.enable_gelu_approximation";
 
+// This setting controls whether to enable AheadOfTime function inlining.
+// AOT function inlining examines the graph and attempts to inline as many locally defined functions in the model
+// as possible with the help of enabled execution providers.
+// This can reduce the number of function calls and improve performance because it is done before
+// Level1 optimizers and constant folding. However, under some circumstances, when the EPs are not available,
+// one can disable the AOT inlining, produce an optimized model and postpone AOT until run time.
+// "0": enable; "1": disable.
+// Its default value is "0".
+static const char* const kOrtSessionOptionsDisableAheadOfTimeFunctionInlining = "session.disable_aot_function_inlining";
+
+#ifdef ENABLE_TRAINING
+// Specifies a path of the file containing a list of memory optimization configurations.
+// The value should be a string indicating the file path of the config file.
+// The content of the config file is a JSON struct like this:
+// [
+//   "Gelu+Cast+:1:0",
+//   "Dropout+:1:1"
+// ]
+// Taking the example of "Gelu+Cast+:1:0",
+// > "Gelu+Cast+" is the subgraph string, a valid "subgraph string" should be one subgraph representation
+//    output by ORT graph transformations.
+// > "1" is "optimization strategy", valid values: 0 - disabled, 1 - recompute.
+// > "0" is "number of subgraph to apply" which is used to control how many subgraphs to apply optimization,
+//    to avoid "oversaving" the memory.
+static const char* const kOrtSessionOptionsMemoryOptimizerApplyConfig = "optimization.memory_optimizer_config";
+
+// Specifies the config for detecting subgraphs for memory footprint reduction.
+// The value should be a string contains int separated using commas. The default value is "0:0".
+static const char* const kOrtSessionOptionsMemoryOptimizerProbeConfig = "optimization.enable_memory_probe_recompute_config";
+#endif
+
+// This setting if set should contain a comma separated list of optimizers names that should be disabled.
+// Optimizers may take time to execute and affect model loading time. If you feel that a specific optimizer
+// does not provider runtime benefits, but affects your model loading time you may disable it using this config
+// entry. This option is not enabled in ORT_MINIMAL_BUILD build.
+// A list of optimizes is available in onnxruntime/core/optimizer/graph_transformer_utils.cc
+//
+// Default is an empty string which means no optimizers are disabled.
+static const char* const kOrtSessionOptionsDisableSpecifiedOptimizers = "optimization.disable_specified_optimizers";
+
 // Enable or disable using device allocator for allocating initialized tensor memory. "1": enable; "0": disable. The default is "0".
 // Using device allocators means the memory allocation is made using malloc/new.
 static const char* const kOrtSessionOptionsUseDeviceAllocatorForInitializers = "session.use_device_allocator_for_initializers";
@@ -69,23 +125,36 @@ static const char* const kOrtSessionOptionsConfigAllowIntraOpSpinning = "session
 // has to guarantee that the model bytes are valid until the ORT session using the model bytes is destroyed.
 static const char* const kOrtSessionOptionsConfigUseORTModelBytesDirectly = "session.use_ort_model_bytes_directly";
 
-// Save information for replaying graph optimizations later instead of applying them directly.
+/// <summary>
-//
+/// Key for using the ORT format model flatbuffer bytes directly for initializers.
-// When an ONNX model is loaded, ORT can perform various optimizations on the graph.
+/// This avoids copying the bytes and reduces peak memory usage during model loading and initialization.
-// However, when an ORT format model is loaded, these optimizations are typically not available - this scenario must
+/// Requires `session.use_ort_model_bytes_directly` to be true.
-// be supported by minimal builds.
+/// If set, the flatbuffer bytes provided when creating the InferenceSession MUST remain valid for the entire
-// When loading an ONNX model, ORT can optionally save the effects of some optimizations for later replay in an ORT
+/// duration of the InferenceSession.
-// format model. These are known as "runtime optimizations" - in an ORT format model, they happen at runtime.
+/// </summary>
-//
+static const char* const kOrtSessionOptionsConfigUseORTModelBytesForInitializers =
-// Note: This option is only applicable when loading an ONNX model and saving an ORT format model.
+    "session.use_ort_model_bytes_for_initializers";
-//
+
-// Note: Runtime optimizations are only supported for certain optimizations at the extended level or higher.
+// This should only be specified when exporting an ORT format model for use on a different platform.
-// Unsupported optimizations at those levels are not applied at all, while optimizations at other levels are applied
+// If the ORT format model will be used on ARM platforms set to "1". For other platforms set to "0"
-// directly.
+// Available since version 1.11.
-//
+static const char* const kOrtSessionOptionsQDQIsInt8Allowed = "session.qdqisint8allowed";
-// "0": disabled, "1": enabled
+
-// The default is "0".
+// x64 SSE4.1/AVX2/AVX512(with no VNNI) has overflow problem with quantizied matrix multiplication with U8S8.
-static const char* const kOrtSessionOptionsConfigSaveRuntimeOptimizations = "optimization.save_runtime_optimizations";
+// To avoid this we need to use slower U8U8 matrix multiplication instead. This option, if
+// turned on, use slower U8U8 matrix multiplications. Only effective with AVX2 or AVX512
+// platforms.
+static const char* const kOrtSessionOptionsAvx2PrecisionMode = "session.x64quantprecision";
+
+// Specifies how minimal build graph optimizations are handled in a full build.
+// These optimizations are at the extended level or higher.
+// Possible values and their effects are:
+// "save": Save runtime optimizations when saving an ORT format model.
+// "apply": Only apply optimizations available in a minimal build.
+// ""/<unspecified>: Apply optimizations available in a full build.
+// Available since version 1.11.
+static const char* const kOrtSessionOptionsConfigMinimalBuildOptimizations =
+    "optimization.minimal_build_optimizations";
 
 // Note: The options specific to an EP should be specified prior to appending that EP to the session options object in
 // order for them to take effect.
@@ -96,3 +165,127 @@ static const char* const kOrtSessionOptionsConfigSaveRuntimeOptimizations = "opt
 // If not specified, the default set of stop ops is used. To specify an empty stop ops types list and disable stop op
 // exclusion, set the value to "".
 static const char* const kOrtSessionOptionsConfigNnapiEpPartitioningStopOps = "ep.nnapi.partitioning_stop_ops";
+
+// Enabling dynamic block-sizing for multithreading.
+// With a positive value, thread pool will split a task of N iterations to blocks of size starting from:
+// N / (num_of_threads * dynamic_block_base)
+// As execution progresses, the size will decrease according to the diminishing residual of N,
+// meaning the task will be distributed in smaller granularity for better parallelism.
+// For some models, it helps to reduce the variance of E2E inference latency and boost performance.
+// The feature will not function by default, specify any positive integer, e.g. "4", to enable it.
+// Available since version 1.11.
+static const char* const kOrtSessionOptionsConfigDynamicBlockBase = "session.dynamic_block_base";
+
+// This option allows to decrease CPU usage between infrequent
+// requests and forces any TP threads spinning stop immediately when the last of
+// concurrent Run() call returns.
+// Spinning is restarted on the next Run() call.
+// Applies only to internal thread-pools
+static const char* const kOrtSessionOptionsConfigForceSpinningStop = "session.force_spinning_stop";
+
+// "1": all inconsistencies encountered during shape and type inference
+// will result in failures.
+// "0": in some cases warnings will be logged but processing will continue. The default.
+// May be useful to expose bugs in models.
+static const char* const kOrtSessionOptionsConfigStrictShapeTypeInference = "session.strict_shape_type_inference";
+
+// "1": every model using a more recent opset than the latest released one will fail
+// "0": the model may or may not work if onnxruntime cannot find an implementation, this option
+// is used for development purpose.
+static const char* const kOrtSessionOptionsConfigStrictAllowReleasedOpsetsOnly = "session.allow_released_opsets_only";
+
+// The file saves configuration for partitioning node among logic streams
+static const char* const kNodePartitionConfigFile = "session.node_partition_config_file";
+
+// This Option allows setting affinities for intra op threads.
+// Affinity string follows format:
+// logical_processor_id,logical_processor_id;logical_processor_id,logical_processor_id
+// Semicolon isolates configurations among threads, while comma split processors where ith thread expected to attach to.
+// e.g.1,2,3;4,5
+// specifies affinities for two threads, with the 1st thread attach to the 1st, 2nd, and 3rd processor, and 2nd thread to the 4th and 5th.
+// To ease the configuration, an "interval" is also allowed:
+// e.g. 1-8;8-16;17-24
+// orders that the 1st thread runs on first eight processors, 2nd thread runs on next eight processors, and so forth.
+// Note:
+// 1. Once set, the number of thread affinities must equal to intra_op_num_threads - 1, since ort does not set affinity on the main thread which
+//    is started and managed by the calling app;
+// 2. For windows, ort will infer the group id from a logical processor id, for example, assuming there are two groups with each has 64 logical processors,
+//    an id of 64 will be inferred as the last processor of the 1st group, while 65 will be interpreted as the 1st processor of the second group.
+//    Hence 64-65 is an invalid configuration, because a windows thread cannot be attached to processors across group boundary.
+static const char* const kOrtSessionOptionsConfigIntraOpThreadAffinities = "session.intra_op_thread_affinities";
+
+// This option will dump out the model to assist debugging any issues with layout transformation,
+// and is primarily intended for developer usage. It is only relevant if an execution provider that requests
+// NHWC layout is enabled such as NNAPI, XNNPACK or QNN.
+//
+// Default is off. Set to "1" to enable.
+//
+// If modified by layout transformation the model will be dumped after these steps:
+//   1) insertion of the layout transformation Transpose nodes
+//   2) after those are optimized using the transpose optimizer,
+//   3) after the L1 transformers are applied to the updated graph.
+// The model will be saved to filename post_layout_transform_step_<step_number>.onnx.
+static const char* const kDebugLayoutTransformation = "session.debug_layout_transformation";
+
+// Graph nodes that are not supported by the execution providers (EPs) explicitly added to the session are
+// assigned (i.e., "fallback") to the CPU EP by default.
+//
+// This option allows the user to disable the fallback of unsupported graph nodes to the CPU EP.
+// If this option is set to "1", session creation will fail if the execution providers other than the CPU EP cannot
+// fully support all of the nodes in the graph.
+//
+// It is invalid to set this option and explicitly add the CPU EP to the session. In this case, session creation
+// will also fail with an error.
+//
+// Option values:
+// - "0": CPU EP fallback is not disabled. [DEFAULT]
+// - "1": CPU EP fallback is disabled.
+static const char* const kOrtSessionOptionsDisableCPUEPFallback = "session.disable_cpu_ep_fallback";
+
+// Use this config when serializing a large model after optimization to specify an external initializers file
+static const char* const kOrtSessionOptionsOptimizedModelExternalInitializersFileName =
+    "session.optimized_model_external_initializers_file_name";
+
+// Use this config to control the minimum size of the initializer when externalizing it during serialization
+static const char* const kOrtSessionOptionsOptimizedModelExternalInitializersMinSizeInBytes =
+    "session.optimized_model_external_initializers_min_size_in_bytes";
+
+// Enable EP context feature to dump the partitioned graph which includes the EP context into Onnx file.
+// The dumped Onnx model with EP context can be used for future inference to avoid the EP graph partitioning/compile overhead.
+// "0": disable. (default)
+// "1": enable.
+static const char* const kOrtSessionOptionEpContextEnable = "ep.context_enable";
+
+// Specify the file path for the Onnx model which has EP context.
+// Default to original_file_name_ctx.onnx if not specified
+static const char* const kOrtSessionOptionEpContextFilePath = "ep.context_file_path";
+
+// Flag to specify whether to dump the EP context into the Onnx model.
+// "0": dump the EP context into separate file, keep the file name in the Onnx model.
+// "1": dump the EP context into the Onnx model. (default).
+static const char* const kOrtSessionOptionEpContextEmbedMode = "ep.context_embed_mode";
+
+// Specify the EPContext node name prefix to make it unique
+// in case user need to merge/connect multiple EPContext nodes in one model
+static const char* const kOrtSessionOptionEpContextNodeNamePrefix = "ep.context_node_name_prefix";
+
+// Share EP related resources across EPs
+static const char* const kOrtSessionOptionShareEpContexts = "ep.share_ep_contexts";
+
+// Gemm fastmath mode provides fp32 gemm acceleration with bfloat16 based matmul.
+// Option values:
+// - "0": Gemm FastMath mode is not enabled. [DEFAULT]
+// - "1": Gemm FastMath mode is enabled.
+static const char* const kOrtSessionOptionsMlasGemmFastMathArm64Bfloat16 = "mlas.enable_gemm_fastmath_arm64_bfloat16";
+
+// When converting DQ + MatMul -> MatMulNBits, the accuracy level of the MatMulNBits is controlled by this option.
+// Refer to MatMulNBits op schema for more details.
+// If not provided, default is 4.
+static const char* const kOrtSessionOptionsQDQMatMulNBitsAccuracyLevel = "session.qdq_matmulnbits_accuracy_level";
+
+// THIS OPTION IS NOT A REGULAR SESSION OPTION SINCE IT CAN BE MODIFIED AT ANY TIME
+// Meant to be used with SetEpDynamicOptions
+// Specify the type of workload for this session.
+// “Default”: OS determines the scheduling priority and processor performance to service this workload. [Default]
+// “Efficient”: OS treats this workload is efficiency oriented with low scheduling priority and efficient processor performance.
+static const char* const kOrtEpDynamicOptionsWorkloadType = "ep.dynamic.workload_type";

libs/onnxruntime/include/onnxruntime_training_c_api.h

@@ -0,0 +1,731 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+// This file contains the training c apis.
+
+#pragma once
+#include <stdbool.h>
+#include "onnxruntime_c_api.h"
+
+/** \page training_c_cpp_api Training C & C++ APIs
+ *
+ * Training C and C++ APIs are an extension of the \ref c_cpp_api "onnxruntime core C and C++ APIs" and should be used in conjunction with them.
+ *
+ * In order to train a model with onnxruntime, the following training artifacts must be generated:
+ * - The training onnx model
+ * - The checkpoint file
+ * - The optimizer onnx model
+ * - The eval onnx model model (optional)
+ *
+ * These training artifacts can be generated as part of an offline step using the python [utilities](https://github.com/microsoft/onnxruntime/blob/main/orttraining/orttraining/python/training/onnxblock/README.md) made available in the `onnxruntime-training` python package.
+ *
+ * After these artifacts have been generated, the C and C++ utilities listed in this documentation can be leveraged to perform training.
+ *
+ * If any problem is encountered, please create an [issue](https://github.com/microsoft/onnxruntime/issues/new) with your scenario and requirements, and we will be sure to respond and follow up on the request.
+ *
+ * <h1>Training C API</h1>
+ *
+ * ::OrtTrainingApi - Training C API functions.
+ *
+ * This C structure contains functions that enable users to perform training with onnxruntime.
+ *
+ * _Sample Code_:
+ *
+ * ```c
+ * #include <onnxruntime_training_api.h>
+ *
+ * OrtApi* g_ort_api = OrtGetApiBase()->GetApi(ORT_API_VERSION);
+ * OrtTrainingApi* g_ort_training_api = g_ort_api->GetTrainingApi(ORT_API_VERSION);
+ *
+ * OrtEnv* env = NULL;
+ * g_ort_api->CreateEnv(logging_level, logid, &env);
+ * OrtSessionOptions* session_options = NULL;
+ * g_ort_api->CreateSessionOptions(&session_options);
+ *
+ * OrtCheckpointState* state = NULL;
+ * g_ort_training_api->LoadCheckpoint(path_to_checkpoint, &state);
+ *
+ * OrtTrainingSession* training_session = NULL;
+ * g_ort_training_api->CreateTrainingSession(env, session_options, training_model_path,
+ *                                           state, eval_model_path, optimizer_model_path,
+ *                                           &training_session);
+ * // Training loop
+ * {
+ *     g_ort_training_api->TrainStep(...);
+ *     g_ort_training_api->OptimizerStep(...);
+ *     g_ort_training_api->LazyResetGrad(...);
+ * }
+ *
+ * g_ort_training_api->ExportModelForInferencing(training_session, inference_model_path, ...);
+ * g_ort_training_api->SaveCheckpoint(state, path_to_checkpoint, false);
+ *
+ * g_ort_training_api->ReleaseTrainingSession(training_session);
+ * g_ort_training_api->ReleaseCheckpointState(state);
+ * ```
+ *
+ * > **Note**
+ * > The ::OrtCheckpointState contains the entire training state that the ::OrtTrainingSession uses. As a result, the training session must always have access to the state. That is to say, the ::OrtCheckpointState instance must outlive the lifetime of the ::OrtTrainingSession instance.
+ *
+ * <h1>Training C++ API</h1>
+ *
+ * @ref TrainingCpp - Training C++ API classes and functions.
+ *
+ * These C++ classes and functions enable users to perform training with onnxruntime.
+ *
+ * _Sample Code_:
+ *
+ * ```cc
+ * #include <onnxruntime_training_cxx_api.h>
+ *
+ * Ort::Env env;
+ * Ort::SessionOptions session_options;
+ *
+ * auto state = Ort::CheckpointState::LoadCheckpoint(path_to_checkpoint);
+ * auto training_session = Ort::TrainingSession(env, session_options, state, training_model_path,
+ *                                              eval_model_path, optimizer_model_path);
+ *
+ * // Training Loop
+ * {
+ *     training_session.TrainStep(...);
+ *     training_session.OptimizerStep(...);
+ *     training_session.LazyResetGrad(...);
+ * }
+ *
+ * training_session->ExportModelForInferencing(inference_model_path, ...);
+ * Ort::CheckpointState::SaveCheckpoint(state, path_to_checkpoint, false);
+ * ```
+ * > **Note**
+ * > The ::Ort::CheckpointState contains the entire training state that the ::Ort::TrainingSession uses. As a result, the training session must always have access to the state. That is to say, the ::Ort::CheckpointState instance must outlive the lifetime of the ::Ort::TrainingSession instance.
+ */
+
+/** @defgroup TrainingC Ort Training C API
+ * @{
+ */
+ORT_RUNTIME_CLASS(TrainingSession);  // Type that enables performing training for the given user models.
+ORT_RUNTIME_CLASS(CheckpointState);  // Type that holds the training states for the training session.
+
+/** \brief Type of property to be added to or returned from the ::OrtCheckpointState.
+ */
+typedef enum OrtPropertyType {
+  OrtIntProperty = 0,
+  OrtFloatProperty = 1,
+  OrtStringProperty = 2,
+} OrtPropertyType;
+
+/** \brief The Training C API that holds onnxruntime training function pointers
+ *
+ * All the Training C API functions are defined inside this structure as pointers to functions.
+ * Call OrtApi::GetTrainingApi to get a pointer to this struct.
+ *
+ * \nosubgrouping
+ */
+struct OrtTrainingApi {
+  /// \name Accessing The Training Session State
+  /// @{
+
+  /** \brief Load a checkpoint state from a file on disk into checkpoint_state.
+   *
+   * This function will parse a checkpoint file, pull relevant data and load the training
+   * state into the checkpoint_state. This checkpoint state can then be used to create the
+   * training session by invoking OrtTrainingApi::CreateTrainingSession. By doing so, the training
+   * session will resume training from the given checkpoint state.
+   * \note Note that the training session created with a checkpoint state uses this state to store the entire
+   * training state (including model parameters, its gradients, the optimizer states and the properties).
+   * As a result, it is required that the checkpoint state outlive the lifetime of the training session.
+   * \note Note that the checkpoint file can be either the complete checkpoint or the nominal checkpoint.
+   *
+   * \param[in] checkpoint_path Path to the checkpoint file
+   * \param[out] checkpoint_state Checkpoint state that contains the states of the training session.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(LoadCheckpoint, _In_ const ORTCHAR_T* checkpoint_path,
+                  _Outptr_ OrtCheckpointState** checkpoint_state);
+
+  /** \brief Save the given state to a checkpoint file on disk.
+   *
+   * This function serializes the provided checkpoint state to a file on disk.
+   * This checkpoint can later be loaded by invoking OrtTrainingApi::LoadCheckpoint to resume
+   * training from this snapshot of the state.
+   *
+   * \param[in] checkpoint_state The checkpoint state to save.
+   * \param[in] checkpoint_path Path to the checkpoint file.
+   * \param[in] include_optimizer_state Flag to indicate whether to save the optimizer state or not.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(SaveCheckpoint, _In_ OrtCheckpointState* checkpoint_state, _In_ const ORTCHAR_T* checkpoint_path,
+                  const bool include_optimizer_state);
+
+  /// @}
+
+  /// \name Implementing The Training Loop
+  /// @{
+  /** \brief Create a training session that can be used to begin or resume training.
+   *
+   * This function creates a training session based on the env and session options provided that can
+   * begin or resume training from a given checkpoint state for the given onnx models.
+   * The checkpoint state represents the parameters of the training session which will be moved
+   * to the device specified by the user through the session options (if necessary).
+   * The training session requires four training artifacts
+   * - The training onnx model
+   * - The evaluation onnx model (optional)
+   * - The optimizer onnx model
+   * - The checkpoint file
+   *
+   * These artifacts can be generated using the `onnxruntime-training` python [utility](https://github.com/microsoft/onnxruntime/blob/main/orttraining/orttraining/python/training/onnxblock/README.md).
+   *
+   * \param[in] env Environment to be used for the training session.
+   * \param[in] options Session options that the user can customize for this training session.
+   * \param[in] checkpoint_state Training states that the training session uses as a starting point for training.
+   * \param[in] train_model_path Model to be used to perform training.
+   * \param[in] eval_model_path Model to be used to perform evaluation.
+   * \param[in] optimizer_model_path Model to be used to perform gradient descent.
+   * \param[out] out Created training session.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(CreateTrainingSession, _In_ const OrtEnv* env, _In_ const OrtSessionOptions* options,
+                  _Inout_ OrtCheckpointState* checkpoint_state, _In_ const ORTCHAR_T* train_model_path,
+                  _In_ const ORTCHAR_T* eval_model_path, _In_ const ORTCHAR_T* optimizer_model_path,
+                  _Outptr_result_maybenull_ OrtTrainingSession** out);
+
+  /** \brief Create a training session that can be used to begin or resume training.
+   * This api provides a way to load all the training artifacts from buffers instead of files.
+   *
+   * \param[in] env Environment to be used for the training session.
+   * \param[in] options Session options that the user can customize for this training session.
+   * \param[in] checkpoint_state Training states that the training session uses as a starting point for training.
+   * \param[in] train_model_data Buffer containing the model data to be used to perform training
+   * \param[in] train_data_length Length of the buffer containing train_model_data
+   * \param[in] eval_model_data Buffer containing the model data to be used to perform evaluation
+   * \param[in] eval_data_length Length of the buffer containing eval_model_data
+   * \param[in] optim_model_data Buffer containing the model data to be used to perform weight update
+   * \param[in] optim_data_length Length of the buffer containing optim_model_data
+   * \param[out] out Created training session.
+   *
+   */
+  ORT_API2_STATUS(CreateTrainingSessionFromBuffer, _In_ const OrtEnv* env,
+                  _In_ const OrtSessionOptions* options, _Inout_ OrtCheckpointState* checkpoint_state,
+                  _In_ const void* train_model_data, size_t train_data_length,
+                  _In_ const void* eval_model_data, size_t eval_data_length,
+                  _In_ const void* optim_model_data, size_t optim_data_length,
+                  _Outptr_result_maybenull_ OrtTrainingSession** out);
+
+  /// @}
+
+  /// \name Model IO Information
+  /// @{
+
+  /** \brief Retrieves the number of user outputs in the training model.
+   *
+   * This function returns the number of outputs of the training model so that the user can
+   * allocate space for the number of outputs when OrtTrainingApi::TrainStep is invoked.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[out] out Number of user outputs in the training model.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(TrainingSessionGetTrainingModelOutputCount, _In_ const OrtTrainingSession* sess, _Out_ size_t* out);
+
+  /** \brief Retrieves the number of user outputs in the eval model.
+   *
+   * This function returns the number of outputs of the eval model so that the user can
+   * allocate space for the number of outputs when OrtTrainingApi::EvalStep is invoked.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[out] out Number of user outputs in the eval model.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(TrainingSessionGetEvalModelOutputCount, _In_ const OrtTrainingSession* sess, _Out_ size_t* out);
+
+  /** \brief Retrieves the names of user outputs in the training model.
+   *
+   * This function returns the names of outputs of the training model that can be associated with the OrtValue(s)
+   * returned by the OrtTrainingApi::TrainStep function.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[in] index Index of the output name requested.
+   * \param[in] allocator Allocator to use to allocate the memory for the name.
+   * \param[out] output Name of the training model output at the given index.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(TrainingSessionGetTrainingModelOutputName, _In_ const OrtTrainingSession* sess, size_t index, _Inout_ OrtAllocator* allocator, _Outptr_ char** output);
+
+  /** \brief Retrieves the names of user outputs in the eval model.
+   *
+   * This function returns the names of outputs of the eval model that can be associated with the OrtValue(s) returned
+   * by the OrtTrainingApi::EvalStep function.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[in] index Index of the output name requested.
+   * \param[in] allocator Allocator to use to allocate the memory for the name.
+   * \param[out] output Name of the eval model output at the given index.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(TrainingSessionGetEvalModelOutputName, _In_ const OrtTrainingSession* sess, size_t index, _Inout_ OrtAllocator* allocator, _Outptr_ char** output);
+
+  /// @}
+
+  /// \name Implementing The Training Loop
+  /// @{
+
+  /** \brief Reset the gradients of all trainable parameters to zero lazily.
+   *
+   * This function sets the internal state of the training session such that the gradients of the trainable
+   * parameters in the OrtCheckpointState will be scheduled to be reset just before the new gradients are
+   * computed on the next invocation of the next OrtTrainingApi::TrainStep.
+   *
+   * \param[in] session The `this` pointer to the training session.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(LazyResetGrad, _Inout_ OrtTrainingSession* session);
+
+  /** \brief Computes the outputs of the training model and the gradients of the trainable parameters for the given inputs
+   *
+   * This function performs a training step that computes the outputs of the training model and the gradients
+   * of the trainable parameters for the given inputs. The train step is performed based on the training model
+   * that was provided to the training session.
+   * The OrtTrainingApi::TrainStep is equivalent of running forward propagation and backward propagation in a single
+   * step.
+   * The gradients computed are stored inside the training session state so they can be later consumed
+   * by the OrtTrainingApi::OptimizerStep function.
+   * The gradients can be lazily reset by invoking the OrtTrainingApi::LazyResetGrad function.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[in] run_options Run options for this training step.
+   * \param[in] inputs_len Number of user inputs to the training model.
+   * \param[in] inputs The user inputs to the training model.
+   * \param[in] outputs_len Number of user outputs expected from this training step.
+   * \param[out] outputs User outputs computed by train step.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(TrainStep, _Inout_ OrtTrainingSession* sess, _In_opt_ const OrtRunOptions* run_options,
+                  _In_ size_t inputs_len, _In_reads_(inputs_len) const OrtValue* const* inputs,
+                  _In_ size_t outputs_len, _Inout_updates_all_(outputs_len) OrtValue** outputs);
+
+  /** \brief Computes the outputs for the eval model for the given inputs
+   *
+   * This function performs an eval step that computes the outputs of the eval model for the given inputs.
+   * The eval step is performed based on the eval model that was provided to the training session.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[in] run_options Run options for this eval step.
+   * \param[in] inputs_len Number of user inputs to the eval model.
+   * \param[in] inputs The user inputs to the eval model.
+   * \param[in] outputs_len Number of user outputs expected from this eval step.
+   * \param[out] outputs User outputs computed by eval step.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(EvalStep, _In_ const OrtTrainingSession* sess, _In_opt_ const OrtRunOptions* run_options,
+                  _In_ size_t inputs_len, _In_reads_(inputs_len) const OrtValue* const* inputs,
+                  _In_ size_t outputs_len, _Inout_updates_all_(outputs_len) OrtValue** outputs);
+
+  /** \brief Sets the learning rate for this training session.
+   *
+   * This function allows users to set the learning rate for the training session. The current
+   * learning rate is maintained by the training session and can be overwritten by invoking
+   * this function with the desired learning rate. This function should not be used when a valid
+   * learning rate scheduler is registered. It should be used either to set the learning rate
+   * derived from a custom learning rate scheduler or to set a constant learning rate to be used
+   * throughout the training session.
+   * \note Please note that this function does not set the initial learning rate that may be needed
+   * by the predefined learning rate schedulers. To set the initial learning rate for learning
+   * rate schedulers, please look at the function OrtTrainingApi::RegisterLinearLRScheduler.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[in] learning_rate Desired learning rate to be set.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(SetLearningRate, _Inout_ OrtTrainingSession* sess, _In_ float learning_rate);
+
+  /** \brief Gets the current learning rate for this training session.
+   *
+   * This function allows users to get the learning rate for the training session. The current
+   * learning rate is maintained by the training session, and users can query it for the purpose
+   * of implementing their own learning rate schedulers.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[out] learning_rate Learning rate currently in use by the training session.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(GetLearningRate, _Inout_ OrtTrainingSession* sess, _Out_ float* learning_rate);
+
+  /** \brief Performs the weight updates for the trainable parameters using the optimizer model.
+   *
+   * This function performs the weight update step that updates the trainable parameters such that they
+   * take a step in the direction of their gradients (gradient descent). The optimizer step is performed
+   * based on the optimizer model that was provided to the training session.
+   * The updated parameters are stored inside the training state so that they can be used by the next
+   * OrtTrainingApi::TrainStep function call.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[in] run_options Run options for this optimizer step.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(OptimizerStep, _Inout_ OrtTrainingSession* sess,
+                  _In_opt_ const OrtRunOptions* run_options);
+
+  /** \brief Registers a linear learning rate scheduler for the training session.
+   *
+   * Register a linear learning rate scheduler that decays the learning rate by linearly updated
+   * multiplicative factor from the initial learning rate set on the training session to 0. The decay
+   * is performed after the initial warm up phase where the learning rate is linearly incremented
+   * from 0 to the initial learning rate provided.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[in] warmup_step_count Warmup steps for LR warmup.
+   * \param[in] total_step_count Total step count.
+   * \param[in] initial_lr The initial learning rate to be used by the training session.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(RegisterLinearLRScheduler, _Inout_ OrtTrainingSession* sess, _In_ const int64_t warmup_step_count,
+                  _In_ const int64_t total_step_count, _In_ const float initial_lr);
+
+  /** \brief Update the learning rate based on the registered learing rate scheduler.
+   *
+   * Takes a scheduler step that updates the learning rate that is being used by the training session.
+   * This function should typically be called before invoking the optimizer step for each round,
+   * or as determined necessary to update the learning rate being used by the training session.
+   * \note Please note that a valid predefined learning rate scheduler must be first registered to invoke this
+   * function.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(SchedulerStep, _Inout_ OrtTrainingSession* sess);
+
+  /// @}
+
+  /// \name Accessing The Training Session State
+  /// @{
+  /** \brief Retrieves the size of all the parameters.
+   *
+   * Calculates the total number of primitive (datatype of the parameters) elements of all the parameters in the
+   * training state.
+   * When trainable_only argument is true, the size is calculated for trainable params only.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[out] out Size of all parameter elements.
+   * \param[in] trainable_only Whether to skip non-trainable parameters
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(GetParametersSize, _Inout_ OrtTrainingSession* sess, _Out_ size_t* out, bool trainable_only);
+
+  /** \brief Copy all parameters to a contiguous buffer held by the argument parameters_buffer
+   *
+   * The parameters_buffer has to be of the size given by GetParametersSize api call,
+   * with matching setting for the argument trainable_only. All the target parameters must be of the same
+   * datatype. The OrtValue must be pre-allocated onto
+   * the desired device. This is a complementary function to OrtTrainingApi::CopyBufferToParameters.
+   * Parameter ordering is preserved.
+   * User is responsible for allocating and freeing the resources used by the parameters_buffer.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[in] trainable_only Whether to skip non-trainable parameters
+   * \param[out] parameters_buffer The pre-allocated OrtValue buffer to copy onto.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(CopyParametersToBuffer, _Inout_ OrtTrainingSession* sess,
+                  _Inout_ OrtValue* parameters_buffer, bool trainable_only);
+
+  /** \brief Copy parameter values from the given contiguous buffer held by parameters_buffer to the training state
+   *
+   * The parameters_buffer argument has to be of the size given by OrtTrainingApi::GetParametersSize api call,
+   * with matching setting for trainable_only argument. All the target parameters must be of the same
+   * datatype. This is a complementary function to OrtTrainingApi::CopyParametersToBuffer
+   * and can be used to load updated buffer values onto the training state.
+   * Parameter ordering is preserved.
+   * User is responsible for allocating and freeing the resources used by the parameters_buffer.
+   * In case the training session was created with a nominal checkpoint, invoking this function is required
+   * to load the updated parameters onto the checkpoint to complete it.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[in] trainable_only Whether to skip non-trainable parameters
+   * \param[out] parameters_buffer The pre-allocated OrtValue buffer to copy from.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(CopyBufferToParameters, _Inout_ OrtTrainingSession* sess,
+                  _Inout_ OrtValue* parameters_buffer, bool trainable_only);
+
+  /// @}
+
+  /// \name Release Training Resources
+  /// @{
+
+  /** \brief Frees up the memory used up by the training session.
+   *
+   * This function frees up any memory that was allocated in the training session. The training
+   * session can no longer be used after this call.
+   *
+   */
+  ORT_CLASS_RELEASE(TrainingSession);
+
+  /** \brief Frees up the memory used up by the checkpoint state.
+   *
+   * This function frees up any memory that was allocated in the checkpoint state. The checkpoint
+   * state can no longer be used after this call.
+   * \note Note that the checkpoint state must be released only after the training session has been released.
+   *
+   */
+  ORT_CLASS_RELEASE(CheckpointState);
+
+  /// @}
+
+  /// \name Prepare For Inferencing
+  /// @{
+  /** \brief Export a model that can be used for inferencing.
+   *
+   * If the training session was provided with an eval model, the training session can generate
+   * an inference model if it knows the inference graph outputs. The input inference graph outputs
+   * are used to prune the eval model so that the inference model's outputs align with the provided outputs.
+   * The exported model is saved at the path provided and can be used for inferencing with InferenceSession.
+   * \note Note that the function re-loads the eval model from the path provided to OrtTrainingApi::CreateTrainingSession
+   * and expects that this path still be valid.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[in] inference_model_path Path where the inference model should be serialized to.
+   * \param[in] graph_outputs_len Size of the graph output names array.
+   * \param[in] graph_output_names Names of the outputs that are needed in the inference model.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(ExportModelForInferencing, _Inout_ OrtTrainingSession* sess,
+                  _In_ const ORTCHAR_T* inference_model_path, size_t graph_outputs_len,
+                  _In_reads_(graph_outputs_len) const char* const* graph_output_names);
+
+  /// @}
+
+  /// \name Training Utilities
+  /// @{
+  /** \brief Sets the seed used for random number generation in Onnxruntime.
+   *
+   * Use this function to generate reproducible results. It should be noted that completely reproducible
+   * results are not guaranteed.
+   *
+   * \param[in] seed The seed to be set.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(SetSeed, _In_ const int64_t seed);
+
+  /// @}
+
+  /// \name Model IO Information
+  /// @{
+  /** \brief Retrieves the number of user inputs in the training model.
+   *
+   * This function returns the number of inputs of the training model so that the user can accordingly
+   * allocate the OrtValue(s) provided to the OrtTrainingApi::TrainStep function.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[out] out Number of user inputs in the training model.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(TrainingSessionGetTrainingModelInputCount, _In_ const OrtTrainingSession* sess, _Out_ size_t* out);
+
+  /** \brief Retrieves the number of user inputs in the eval model.
+   *
+   * This function returns the number of inputs of the eval model so that the user can accordingly
+   * allocate the OrtValue(s) provided to the OrtTrainingApi::EvalStep function.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[out] out Number of user inputs in the eval model.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(TrainingSessionGetEvalModelInputCount, _In_ const OrtTrainingSession* sess, _Out_ size_t* out);
+
+  /** \brief Retrieves the name of the user input at given index in the training model.
+   *
+   * This function returns the names of inputs of the training model that can be associated with the
+   * OrtValue(s) provided to the OrtTrainingApi::TrainStep function.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[in] index The index of the training model input name requested.
+   * \param[in] allocator The allocator to use to allocate the memory for the requested name.
+   * \param[out] output Name of the user input for the training model at the given index.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(TrainingSessionGetTrainingModelInputName, _In_ const OrtTrainingSession* sess, size_t index,
+                  _In_ OrtAllocator* allocator, _Outptr_ char** output);
+
+  /** \brief Retrieves the name of the user input at given index in the eval model.
+   *
+   * This function returns the names of inputs of the eval model that can be associated with the OrtValue(s) provided
+   * to the OrtTrainingApi::EvalStep function.
+   *
+   * \param[in] sess The `this` pointer to the training session.
+   * \param[in] index The index of the eval model input name requested.
+   * \param[in] allocator The allocator to use to allocate the memory for the requested name.
+   * \param[out] output Name of the user input for the eval model at the given index.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(TrainingSessionGetEvalModelInputName, _In_ const OrtTrainingSession* sess, size_t index,
+                  _In_ OrtAllocator* allocator, _Outptr_ char** output);
+
+  /// @}
+
+  /// \name Accessing The Training Session State
+  /// @{
+
+  /** \brief Adds or updates the given property to/in the checkpoint state.
+   *
+   * Runtime properties such as epoch, training step, best score, and others can be added to the checkpoint
+   * state by the user by calling this function with the corresponding property name and value.
+   * The given property name must be unique to be able to successfully add the property.
+   *
+   * \param[in] checkpoint_state The checkpoint state which should hold the property.
+   * \param[in] property_name Name of the property being added or updated.
+   * \param[in] property_type Type of the property associated with the given name.
+   * \param[in] property_value Property value associated with the given name.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(AddProperty, _Inout_ OrtCheckpointState* checkpoint_state,
+                  _In_ const char* property_name, _In_ enum OrtPropertyType property_type,
+                  _In_ void* property_value);
+
+  /** \brief Gets the property value associated with the given name from the checkpoint state.
+   *
+   * Gets the property value from an existing entry in the checkpoint state. The property must
+   * exist in the checkpoint state to be able to retrieve it successfully.
+   *
+   * \param[in] checkpoint_state The checkpoint state that is currently holding the property.
+   * \param[in] property_name Name of the property being retrieved.
+   * \param[in] allocator Allocator used to allocate the memory for the property_value.
+   * \param[out] property_type Type of the property associated with the given name.
+   * \param[out] property_value Property value associated with the given name.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(GetProperty, _In_ const OrtCheckpointState* checkpoint_state,
+                  _In_ const char* property_name, _Inout_ OrtAllocator* allocator,
+                  _Out_ enum OrtPropertyType* property_type, _Outptr_ void** property_value);
+
+  /// @}
+
+  /// \name Accessing The Training Session State
+  /// @{
+
+  /** \brief Load a checkpoint state from a buffer into checkpoint_state.
+   *
+   * This function will parse a checkpoint bytes buffer, pull relevant data and load the training
+   * state into the checkpoint_state. This checkpoint state can then be used to create the
+   * training session by invoking OrtTrainingApi::CreateTrainingSession. By doing so, the training
+   * session will resume training from the given checkpoint state.
+   * \note Note that the training session created with a checkpoint state uses this state to store the entire
+   * training state (including model parameters, its gradients, the optimizer states and the properties).
+   * As a result, it is required that the checkpoint state outlive the lifetime of the training session.
+   *
+   * \param[in] checkpoint_buffer Path to the checkpoint bytes buffer.
+   * \param[in] num_bytes Number of bytes in the checkpoint buffer.
+   * \param[out] checkpoint_state Checkpoint state that contains the states of the training session.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(LoadCheckpointFromBuffer, _In_ const void* checkpoint_buffer,
+                  _In_ const size_t num_bytes, _Outptr_ OrtCheckpointState** checkpoint_state);
+
+  /** \brief Retrieves the type and shape information of the parameter associated with the given parameter name.
+   *
+   * This function retrieves the type and shape of the parameter associated with the given parameter name.
+   * The parameter must exist in the checkpoint state to be able to retrieve its type and shape information successfully.
+   *
+   * \param[in] checkpoint_state The checkpoint state.
+   * \param[in] parameter_name Name of the parameter being retrieved.
+   * \param[out] parameter_type_and_shape The type and shape of the parameter being retrieved.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(GetParameterTypeAndShape, _In_ const OrtCheckpointState* checkpoint_state,
+                  _In_ const char* parameter_name, _Outptr_ OrtTensorTypeAndShapeInfo** parameter_type_and_shape);
+
+  /** \brief Updates the data associated with the model parameter in the checkpoint state for the given parameter name.
+   *
+   * This function updates a model parameter in the checkpoint state with the given parameter data.
+   * The training session must be already created with the checkpoint state that contains the parameter
+   * being updated. The given parameter is copied over to the registered device for the training session.
+   * The parameter must exist in the checkpoint state to be able to update it successfully.
+   *
+   * \param[in] checkpoint_state The checkpoint state.
+   * \param[in] parameter_name Name of the parameter being updated.
+   * \param[in] parameter The parameter data that should replace the existing parameter data.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(UpdateParameter, _Inout_ OrtCheckpointState* checkpoint_state,
+                  _In_ const char* parameter_name, _In_ OrtValue* parameter);
+
+  /** \brief Gets the data associated with the model parameter from the checkpoint state for the given parameter name.
+   *
+   * This function retrieves the model parameter data from the checkpoint state for the given parameter name.
+   * The parameter is copied over and returned as an OrtValue. The training session must be already created
+   * with the checkpoint state that contains the parameter being retrieved.
+   * The parameter must exist in the checkpoint state to be able to retrieve it successfully.
+   *
+   * \param[in] checkpoint_state The checkpoint state.
+   * \param[in] parameter_name Name of the parameter being retrieved.
+   * \param[in] allocator Allocator used to allocate the memory for the parameter.
+   * \param[out] parameter The parameter data that is retrieved from the checkpoint state.
+   *
+   * \snippet{doc} snippets.dox OrtStatus Return Value
+   *
+   */
+  ORT_API2_STATUS(GetParameter, _In_ const OrtCheckpointState* checkpoint_state,
+                  _In_ const char* parameter_name, _Inout_ OrtAllocator* allocator,
+                  _Outptr_ OrtValue** parameter);
+
+  /// @}
+};
+
+typedef struct OrtTrainingApi OrtTrainingApi;
+
+/// @}

libs/onnxruntime/include/onnxruntime_training_cxx_api.h

@@ -0,0 +1,418 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#pragma once
+#include "onnxruntime_training_c_api.h"
+#include <optional>
+#include <variant>
+
+namespace Ort::detail {
+
+#define ORT_DECLARE_TRAINING_RELEASE(NAME) \
+  void OrtRelease(Ort##NAME* ptr);
+
+// These release methods must be forward declared before including onnxruntime_cxx_api.h
+// otherwise class Base won't be aware of them
+ORT_DECLARE_TRAINING_RELEASE(CheckpointState);
+ORT_DECLARE_TRAINING_RELEASE(TrainingSession);
+
+}  // namespace Ort::detail
+
+#include "onnxruntime_cxx_api.h"
+
+namespace Ort {
+
+/// <summary>
+/// This function returns the C training api struct with the pointers to the ort training C functions.
+/// If using C++, please use the class instances instead of invoking the C functions directly.
+/// </summary>
+/// <returns>OrtTrainingApi struct with ort training C function pointers.</returns>
+inline const OrtTrainingApi& GetTrainingApi() { return *GetApi().GetTrainingApi(ORT_API_VERSION); }
+
+namespace detail {
+
+#define ORT_DEFINE_TRAINING_RELEASE(NAME) \
+  inline void OrtRelease(Ort##NAME* ptr) { GetTrainingApi().Release##NAME(ptr); }
+
+ORT_DEFINE_TRAINING_RELEASE(CheckpointState);
+ORT_DEFINE_TRAINING_RELEASE(TrainingSession);
+
+#undef ORT_DECLARE_TRAINING_RELEASE
+#undef ORT_DEFINE_TRAINING_RELEASE
+
+}  // namespace detail
+
+using Property = std::variant<int64_t, float, std::string>;
+
+/**
+ * \defgroup TrainingCpp Ort Training C++ API
+ * @{
+ */
+
+/** \brief Holds the state of the training session.
+ *
+ * This class holds the entire training session state that includes model parameters, their gradients,
+ * optimizer parameters, and user properties. The Ort::TrainingSession leverages the Ort::CheckpointState
+ * by accessing and updating the contained training state.
+ * \note Note that the training session created with a checkpoint state uses this state to store the entire
+ * training state (including model parameters, its gradients, the optimizer states and the properties).
+ * The Ort::TrainingSession does not hold a copy of the Ort::CheckpointState and as a result, it is required
+ * that the checkpoint state outlive the lifetime of the training session.
+ * \note Note that the checkpoint state can be either the complete checkpoint state or the nominal checkpoint
+ * state depending on the version provided while loading the checkpoint.
+ *
+ */
+class CheckpointState : public detail::Base<OrtCheckpointState> {
+ private:
+  CheckpointState(OrtCheckpointState* checkpoint_state) { p_ = checkpoint_state; }
+
+ public:
+  // Construct the checkpoint state by loading the checkpoint by calling LoadCheckpoint
+  CheckpointState() = delete;
+
+  /// \name Accessing The Training Session State
+  /// @{
+
+  /** \brief Load a checkpoint state from a file on disk into checkpoint_state.
+   *
+   * This function will parse a checkpoint file, pull relevant data and load the training
+   * state and return an instance of Ort::CheckpointState. This checkpoint state can then be used to create the
+   * training session by instantiating Ort::TrainingSession. By doing so, the training session will resume
+   * training from the given checkpoint state.
+   *
+   * \param[in] path_to_checkpoint Path to the checkpoint file
+   * \return Ort::CheckpointState object which holds the state of the training session parameters.
+   *
+   */
+  static CheckpointState LoadCheckpoint(const std::basic_string<ORTCHAR_T>& path_to_checkpoint);
+
+  /** \brief Load a checkpoint state from a buffer.
+   *
+   * This function will parse a checkpoint buffer, pull relevant data and load the training
+   * state and return an instance of Ort::CheckpointState. This checkpoint state can then be used to create the
+   * training session by instantiating Ort::TrainingSession. By doing so, the training session will resume
+   * training from the given checkpoint state.
+   *
+   * \param[in] buffer Buffer containing the checkpoint data.
+   * \return Ort::CheckpointState object which holds the state of the training session parameters.
+   *
+   */
+  static CheckpointState LoadCheckpointFromBuffer(const std::vector<uint8_t>& buffer);
+
+  /** \brief Save the given state to a checkpoint file on disk.
+   *
+   * This function serializes the provided checkpoint state to a file on disk.
+   * This checkpoint can later be loaded by invoking Ort::CheckpointState::LoadCheckpoint to resume
+   * training from this snapshot of the state.
+   *
+   * \param[in] checkpoint_state The checkpoint state to save.
+   * \param[in] path_to_checkpoint Path to the checkpoint file.
+   * \param[in] include_optimizer_state Flag to indicate whether to save the optimizer state or not.
+   *
+   */
+  static void SaveCheckpoint(const CheckpointState& checkpoint_state,
+                             const std::basic_string<ORTCHAR_T>& path_to_checkpoint,
+                             const bool include_optimizer_state = false);
+
+  /** \brief Adds or updates the given property to/in the checkpoint state.
+   *
+   * Runtime properties such as epoch, training step, best score, and others can be added to the checkpoint
+   * state by the user by calling this function with the corresponding property name and value.
+   * The given property name must be unique to be able to successfully add the property.
+   *
+   * \param[in] property_name Name of the property being added or updated.
+   * \param[in] property_value Property value associated with the given name.
+   *
+   */
+  void AddProperty(const std::string& property_name, const Property& property_value);
+
+  /** \brief Gets the property value associated with the given name from the checkpoint state.
+   *
+   * Gets the property value from an existing entry in the checkpoint state. The property must
+   * exist in the checkpoint state to be able to retrieve it successfully.
+   *
+   * \param[in] property_name Name of the property being retrieved.
+   * \return Property value associated with the given property name.
+   *
+   */
+  Property GetProperty(const std::string& property_name);
+
+  /** \brief Updates the data associated with the model parameter in the checkpoint state for the given parameter name.
+   *
+   * This function updates a model parameter in the checkpoint state with the given parameter data.
+   * The training session must be already created with the checkpoint state that contains the parameter
+   * being updated. The given parameter is copied over to the registered device for the training session.
+   * The parameter must exist in the checkpoint state to be able to update it successfully.
+   *
+   * \param[in] parameter_name Name of the parameter being updated.
+   * \param[in] parameter The parameter data that should replace the existing parameter data.
+   *
+   */
+  void UpdateParameter(const std::string& parameter_name, const Value& parameter);
+
+  /** \brief Gets the data associated with the model parameter from the checkpoint state for the given parameter name.
+   *
+   * This function retrieves the model parameter data from the checkpoint state for the given parameter name.
+   * The parameter is copied over to the provided OrtValue. The training session must be already created
+   * with the checkpoint state that contains the parameter being retrieved.
+   * The parameter must exist in the checkpoint state to be able to retrieve it successfully.
+   *
+   * \param[in] parameter_name Name of the parameter being retrieved.
+   * \return The parameter data that is retrieved from the checkpoint state.
+   *
+   */
+  Value GetParameter(const std::string& parameter_name);
+
+  /// @}
+};
+
+/** \brief Trainer class that provides training, evaluation and optimizer methods for training an ONNX models.
+ *
+ * The training session requires four training artifacts
+ * - The training onnx model
+ * - The evaluation onnx model (optional)
+ * - The optimizer onnx model
+ * - The checkpoint file
+ *
+ * These artifacts can be generated using the `onnxruntime-training` python [utility](https://github.com/microsoft/onnxruntime/blob/main/orttraining/orttraining/python/training/onnxblock/README.md).
+ *
+ */
+class TrainingSession : public detail::Base<OrtTrainingSession> {
+ private:
+  size_t training_model_output_count_, eval_model_output_count_;
+
+ public:
+  /// \name Constructing the Training Session
+  /// @{
+  /** \brief Create a training session that can be used to begin or resume training.
+   *
+   * This constructor instantiates the training session based on the env and session options provided that can
+   * begin or resume training from a given checkpoint state for the given onnx models.
+   * The checkpoint state represents the parameters of the training session which will be moved
+   * to the device specified by the user through the session options (if necessary).
+   *
+   * \param[in] env Env to be used for the training session.
+   * \param[in] session_options SessionOptions that the user can customize for this training session.
+   * \param[in] checkpoint_state Training states that the training session uses as a starting point for training.
+   * \param[in] train_model_path Model to be used to perform training.
+   * \param[in] eval_model_path Model to be used to perform evaluation.
+   * \param[in] optimizer_model_path Model to be used to perform gradient descent.
+   *
+   */
+  TrainingSession(const Env& env, const SessionOptions& session_options, CheckpointState& checkpoint_state,
+                  const std::basic_string<ORTCHAR_T>& train_model_path,
+                  const std::optional<std::basic_string<ORTCHAR_T>>& eval_model_path = std::nullopt,
+                  const std::optional<std::basic_string<ORTCHAR_T>>& optimizer_model_path = std::nullopt);
+
+  /** \brief Create a training session that can be used to begin or resume training.
+   * This constructor allows the users to load the models from buffers instead of files.
+   *
+   * \param[in] env Env to be used for the training session.
+   * \param[in] session_options SessionOptions that the user can customize for this training session.
+   * \param[in] checkpoint_state Training states that the training session uses as a starting point for training.
+   * \param[in] train_model_data Buffer containing training model data.
+   * \param[in] eval_model_data Buffer containing evaluation model data.
+   * \param[in] optim_model_data Buffer containing optimizer model (used for performing weight/parameter update).
+   *
+   */
+  TrainingSession(const Env& env, const SessionOptions& session_options, CheckpointState& checkpoint_state,
+                  const std::vector<uint8_t>& train_model_data, const std::vector<uint8_t>& eval_model_data = {},
+                  const std::vector<uint8_t>& optim_model_data = {});
+  /// @}
+
+  /// \name Implementing The Training Loop
+  /// @{
+  /** \brief Computes the outputs of the training model and the gradients of the trainable parameters for the given inputs
+   *
+   * This function performs a training step that computes the outputs of the training model and the gradients
+   * of the trainable parameters for the given inputs. The train step is performed based on the training model
+   * that was provided to the training session.
+   * The Ort::TrainingSession::TrainStep is equivalent of running forward propagation and backward propagation in a single
+   * step.
+   * The gradients computed are stored inside the training session state so they can be later consumed
+   * by the Ort::TrainingSession::OptimizerStep function.
+   * The gradients can be lazily reset by invoking the Ort::TrainingSession::LazyResetGrad function.
+   *
+   * \param[in] input_values The user inputs to the training model.
+   * \return A std::vector of Ort::Value objects that represents the output of the forward pass of the training model.
+   *
+   *
+   */
+  std::vector<Value> TrainStep(const std::vector<Value>& input_values);
+
+  /** \brief Reset the gradients of all trainable parameters to zero lazily.
+   *
+   * This function sets the internal state of the training session such that the gradients of the trainable
+   * parameters in the OrtCheckpointState will be scheduled to be reset just before the new gradients are
+   * computed on the next invocation of the next Ort::TrainingSession::TrainStep.
+   *
+   */
+  void LazyResetGrad();
+
+  /** \brief Computes the outputs for the eval model for the given inputs
+   *
+   * This function performs an eval step that computes the outputs of the eval model for the given inputs.
+   * The eval step is performed based on the eval model that was provided to the training session.
+   *
+   * \param[in] input_values The user inputs to the eval model.
+   * \return A std::vector of Ort::Value objects that represents the output of the eval pass.
+   *
+   */
+  std::vector<Value> EvalStep(const std::vector<Value>& input_values);
+
+  /** \brief Sets the learning rate for this training session.
+   *
+   * This function allows users to set the learning rate for the training session. The current
+   * learning rate is maintained by the training session and can be overwritten by invoking
+   * this function with the desired learning rate. This function should not be used when a valid
+   * learning rate scheduler is registered. It should be used either to set the learning rate
+   * derived from a custom learning rate scheduler or to set a constant learning rate to be used
+   * throughout the training session.
+   * \note Please note that this function does not set the initial learning rate that may be needed
+   * by the predefined learning rate schedulers. To set the initial learning rate for learning
+   * rate schedulers, please look at the function Ort::TrainingSession::RegisterLinearLRScheduler.
+   *
+   * \param[in] learning_rate Desired learning rate to be set.
+   *
+   */
+  void SetLearningRate(float learning_rate);
+
+  /** \brief Gets the current learning rate for this training session.
+   *
+   * This function allows users to get the learning rate for the training session. The current
+   * learning rate is maintained by the training session, and users can query it for the purpose
+   * of implementing their own learning rate schedulers.
+   *
+   * \return float representing the current learning rate.
+   *
+   */
+  float GetLearningRate() const;
+
+  /** \brief Registers a linear learning rate scheduler for the training session.
+   *
+   * Register a linear learning rate scheduler that decays the learning rate by linearly updated
+   * multiplicative factor from the initial learning rate set on the training session to 0. The decay
+   * is performed after the initial warm up phase where the learning rate is linearly incremented
+   * from 0 to the initial learning rate provided.
+   *
+   * \param[in] warmup_step_count Warmup steps for LR warmup.
+   * \param[in] total_step_count Total step count.
+   * \param[in] initial_lr The initial learning rate to be used by the training session.
+   *
+   */
+  void RegisterLinearLRScheduler(int64_t warmup_step_count, int64_t total_step_count,
+                                 float initial_lr);
+
+  /** \brief Update the learning rate based on the registered learing rate scheduler.
+   *
+   * Takes a scheduler step that updates the learning rate that is being used by the training session.
+   * This function should typically be called before invoking the optimizer step for each round,
+   * or as determined necessary to update the learning rate being used by the training session.
+   * \note Please note that a valid predefined learning rate scheduler must be first registered to invoke this
+   * function.
+   *
+   */
+  void SchedulerStep();
+
+  /** \brief Performs the weight updates for the trainable parameters using the optimizer model.
+   *
+   * This function performs the weight update step that updates the trainable parameters such that they
+   * take a step in the direction of their gradients (gradient descent). The optimizer step is performed
+   * based on the optimizer model that was provided to the training session.
+   * The updated parameters are stored inside the training state so that they can be used by the next
+   * Ort::TrainingSession::TrainStep function call.
+   *
+   */
+  void OptimizerStep();
+
+  /// @}
+
+  /// \name Prepare For Inferencing
+  /// @{
+
+  /** \brief Export a model that can be used for inferencing.
+   *
+   * If the training session was provided with an eval model, the training session can generate
+   * an inference model if it knows the inference graph outputs. The input inference graph outputs
+   * are used to prune the eval model so that the inference model's outputs align with the provided outputs.
+   * The exported model is saved at the path provided and can be used for inferencing with Ort::Session.
+   * \note Note that the function re-loads the eval model from the path provided to Ort::TrainingSession
+   * and expects that this path still be valid.
+   *
+   * \param[in] inference_model_path Path where the inference model should be serialized to.
+   * \param[in] graph_output_names Names of the outputs that are needed in the inference model.
+   *
+   */
+  void ExportModelForInferencing(const std::basic_string<ORTCHAR_T>& inference_model_path,
+                                 const std::vector<std::string>& graph_output_names);
+
+  /// @}
+
+  /// \name Model IO Information
+  /// @{
+  /** \brief Retrieves the names of the user inputs for the training and eval models.
+   *
+   * This function returns the names of inputs of the training or eval model that can be associated
+   * with the Ort::Value(s) provided to the Ort::TrainingSession::TrainStep or Ort::TrainingSession::EvalStep
+   * function.
+   *
+   * \param[in] training Whether the training model input names are requested or eval model input names.
+   * \return Graph input names for either the training model or the eval model.
+   *
+   */
+  std::vector<std::string> InputNames(const bool training);
+
+  /** \brief Retrieves the names of the user outputs for the training and eval models.
+   *
+   * This function returns the names of outputs of the training or eval model that can be associated
+   * with the Ort::Value(s) returned by the Ort::TrainingSession::TrainStep or Ort::TrainingSession::EvalStep
+   * function.
+   *
+   * \param[in] training Whether the training model output names are requested or eval model output names.
+   * \return Graph output names for either the training model or the eval model.
+   *
+   */
+  std::vector<std::string> OutputNames(const bool training);
+
+  /// @}
+
+  /// \name Accessing The Training Session State
+  /// @{
+
+  /** \brief Returns a contiguous buffer that holds a copy of all training state parameters
+   *
+   * \param[in] only_trainable Whether to only copy trainable parameters or to copy all parameters.
+   * \return Contiguous buffer to the model parameters.
+   *
+   */
+  Value ToBuffer(const bool only_trainable);
+
+  /** \brief Loads the training session model parameters from a contiguous buffer
+   *
+   * In case the training session was created with a nominal checkpoint, invoking this function is required
+   * to load the updated parameters onto the checkpoint to complete it.
+   *
+   * \param[in] buffer Contiguous buffer to load the parameters from.
+   */
+  void FromBuffer(Value& buffer);
+
+  /// @}
+};
+
+/// \name Training Utilities
+/// @{
+/** \brief This function sets the seed for generating random numbers.
+ *
+ * Use this function to generate reproducible results. It should be noted that completely
+ * reproducible results are not guaranteed.
+ *
+ * \param[in] seed Manual seed to use for random number generation.
+ */
+void SetSeed(const int64_t seed);
+/// @}
+
+/// @}
+
+}  // namespace Ort
+
+#include "onnxruntime_training_cxx_inline.h"

libs/onnxruntime/include/onnxruntime_training_cxx_inline.h

@@ -0,0 +1,295 @@
+// Copyright (c) Microsoft Corporation. All rights reserved.
+// Licensed under the MIT License.
+
+#pragma once
+#include "onnxruntime_training_c_api.h"
+#include "onnxruntime_cxx_api.h"
+
+namespace Ort {
+
+inline TrainingSession::TrainingSession(const Env& env, const SessionOptions& session_options,
+                                        CheckpointState& checkpoint_state,
+                                        const std::basic_string<ORTCHAR_T>& train_model_path,
+                                        const std::optional<std::basic_string<ORTCHAR_T>>& eval_model_path,
+                                        const std::optional<std::basic_string<ORTCHAR_T>>& optimizer_model_path) {
+  ThrowOnError(GetTrainingApi().CreateTrainingSession(
+      env, session_options, checkpoint_state,
+      train_model_path.c_str(),
+      eval_model_path.has_value() ? eval_model_path.value().c_str() : nullptr,
+      optimizer_model_path.has_value() ? optimizer_model_path.value().c_str() : nullptr,
+      &p_));
+
+  ThrowOnError(GetTrainingApi().TrainingSessionGetTrainingModelOutputCount(p_, &training_model_output_count_));
+
+  ThrowOnError(GetTrainingApi().TrainingSessionGetEvalModelOutputCount(p_, &eval_model_output_count_));
+}
+
+inline TrainingSession::TrainingSession(const Env& env, const SessionOptions& session_options,
+                                        CheckpointState& checkpoint_state,
+                                        const std::vector<uint8_t>& train_model_data,
+                                        const std::vector<uint8_t>& eval_model_data,
+                                        const std::vector<uint8_t>& optim_model_data) {
+  ThrowOnError(GetTrainingApi().CreateTrainingSessionFromBuffer(
+      env, session_options, checkpoint_state,
+      train_model_data.data(), train_model_data.size(),
+      eval_model_data.data(), eval_model_data.size(),
+      optim_model_data.data(), optim_model_data.size(),
+      &p_));
+
+  ThrowOnError(GetTrainingApi().TrainingSessionGetTrainingModelOutputCount(p_, &training_model_output_count_));
+
+  ThrowOnError(GetTrainingApi().TrainingSessionGetEvalModelOutputCount(p_, &eval_model_output_count_));
+}
+
+inline std::vector<Value> TrainingSession::TrainStep(const std::vector<Value>& input_values) {
+  std::vector<Value> output_values;
+  output_values.reserve(training_model_output_count_);
+  for (size_t i = 0; i < training_model_output_count_; i++) output_values.emplace_back(nullptr);
+  auto ort_input_values = reinterpret_cast<const OrtValue* const*>(input_values.data());
+  auto ort_output_values = reinterpret_cast<OrtValue**>(output_values.data());
+  RunOptions run_options;
+  ThrowOnError(GetTrainingApi().TrainStep(
+      p_, run_options, input_values.size(), ort_input_values,
+      training_model_output_count_, ort_output_values));
+
+  return output_values;
+}
+
+inline void TrainingSession::LazyResetGrad() {
+  ThrowOnError(GetTrainingApi().LazyResetGrad(p_));
+}
+
+inline std::vector<Value> TrainingSession::EvalStep(const std::vector<Value>& input_values) {
+  std::vector<Value> output_values;
+  output_values.reserve(eval_model_output_count_);
+  for (size_t i = 0; i < eval_model_output_count_; i++) output_values.emplace_back(nullptr);
+  auto ort_input_values = reinterpret_cast<const OrtValue* const*>(input_values.data());
+  auto ort_output_values = reinterpret_cast<OrtValue**>(output_values.data());
+  RunOptions run_options;
+  ThrowOnError(GetTrainingApi().EvalStep(
+      p_, run_options, input_values.size(), ort_input_values,
+      eval_model_output_count_, ort_output_values));
+
+  return output_values;
+}
+
+inline void TrainingSession::SetLearningRate(float learning_rate) {
+  ThrowOnError(GetTrainingApi().SetLearningRate(p_, learning_rate));
+}
+
+inline float TrainingSession::GetLearningRate() const {
+  float learning_rate = 0;
+  ThrowOnError(GetTrainingApi().GetLearningRate(p_, &learning_rate));
+  return learning_rate;
+}
+
+inline void TrainingSession::RegisterLinearLRScheduler(int64_t warmup_step_count, int64_t total_step_count,
+                                                       float initial_lr) {
+  ThrowOnError(GetTrainingApi().RegisterLinearLRScheduler(p_, warmup_step_count, total_step_count,
+                                                          initial_lr));
+}
+
+inline void TrainingSession::SchedulerStep() {
+  ThrowOnError(GetTrainingApi().SchedulerStep(p_));
+}
+
+inline void TrainingSession::OptimizerStep() {
+  RunOptions run_options;
+  ThrowOnError(GetTrainingApi().OptimizerStep(p_, run_options));
+}
+
+inline std::vector<std::string> TrainingSession::InputNames(const bool training) {
+  auto& input_count_function = training ? GetTrainingApi().TrainingSessionGetTrainingModelInputCount
+                                        : GetTrainingApi().TrainingSessionGetEvalModelInputCount;
+  auto& input_name_function = training ? GetTrainingApi().TrainingSessionGetTrainingModelInputName
+                                       : GetTrainingApi().TrainingSessionGetEvalModelInputName;
+
+  size_t input_count = 0;
+  ThrowOnError(input_count_function(p_, &input_count));
+  std::vector<std::string> input_names(input_count);
+  AllocatorWithDefaultOptions allocator;
+  for (size_t index = 0; index < input_count; ++index) {
+    char* input_name;
+    ThrowOnError(input_name_function(p_, index, allocator, &input_name));
+    input_names[index] = std::string(input_name);
+    allocator.Free(input_name);
+  }
+
+  return input_names;
+}
+
+inline std::vector<std::string> TrainingSession::OutputNames(const bool training) {
+  auto& output_count_function = training ? GetTrainingApi().TrainingSessionGetTrainingModelOutputCount
+                                         : GetTrainingApi().TrainingSessionGetEvalModelOutputCount;
+  auto& output_name_function = training ? GetTrainingApi().TrainingSessionGetTrainingModelOutputName
+                                        : GetTrainingApi().TrainingSessionGetEvalModelOutputName;
+
+  size_t output_count = 0;
+  ThrowOnError(output_count_function(p_, &output_count));
+  std::vector<std::string> output_names(output_count);
+  AllocatorWithDefaultOptions allocator;
+  for (size_t index = 0; index < output_count; ++index) {
+    char* output_name;
+    ThrowOnError(output_name_function(p_, index, allocator, &output_name));
+    output_names[index] = std::string(output_name);
+    allocator.Free(output_name);
+  }
+
+  return output_names;
+}
+
+inline Value TrainingSession::ToBuffer(const bool only_trainable) {
+  size_t buffer_size = 0U;
+  ThrowOnError(GetTrainingApi().GetParametersSize(p_, &buffer_size, only_trainable));
+
+  std::array<int64_t, 1> buffer_shape{static_cast<int64_t>(buffer_size)};
+
+  AllocatorWithDefaultOptions allocator;
+  Value buffer = Value::CreateTensor(allocator, buffer_shape.data(), 1U,
+                                     ONNXTensorElementDataType::ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT);
+
+  ThrowOnError(GetTrainingApi().CopyParametersToBuffer(p_, buffer, only_trainable));
+
+  return buffer;
+}
+
+inline void TrainingSession::FromBuffer(Value& buffer) {
+  if (!buffer.IsTensor()) {
+    ThrowStatus(Status("Incorrect buffer received. Expected a tensor buffer.", OrtErrorCode::ORT_INVALID_ARGUMENT));
+  }
+
+  auto tensor_info = buffer.GetTensorTypeAndShapeInfo();
+  auto buffer_shape = tensor_info.GetShape();
+
+  if (buffer_shape.size() != 1U) {
+    ThrowStatus(Status("Incorrect buffer received. Expected a contiguous tensor buffer.",
+                       OrtErrorCode::ORT_INVALID_ARGUMENT));
+  }
+
+  auto buffer_size = buffer_shape.front();
+
+  size_t session_buffer_size = 0U;
+  ThrowOnError(GetTrainingApi().GetParametersSize(p_, &session_buffer_size, false));
+
+  if (buffer_size == static_cast<int64_t>(session_buffer_size)) {
+    ThrowOnError(GetTrainingApi().CopyBufferToParameters(p_, buffer, false));
+    return;
+  }
+
+  size_t session_buffer_size_trainable_only = 0U;
+  ThrowOnError(GetTrainingApi().GetParametersSize(p_, &session_buffer_size_trainable_only, true));
+
+  if (buffer_size == static_cast<int64_t>(session_buffer_size_trainable_only)) {
+    ThrowOnError(GetTrainingApi().CopyBufferToParameters(p_, buffer, true));
+    return;
+  } else {
+    ThrowStatus(Status("Incorrect buffer size received.", OrtErrorCode::ORT_INVALID_ARGUMENT));
+  }
+}
+
+inline CheckpointState CheckpointState::LoadCheckpoint(const std::basic_string<ORTCHAR_T>& path_to_checkpoint) {
+  OrtCheckpointState* checkpoint_state;
+  ThrowOnError(GetTrainingApi().LoadCheckpoint(path_to_checkpoint.c_str(), &checkpoint_state));
+  return CheckpointState(checkpoint_state);
+}
+
+inline CheckpointState CheckpointState::LoadCheckpointFromBuffer(const std::vector<uint8_t>& buffer) {
+  OrtCheckpointState* checkpoint_state;
+  ThrowOnError(GetTrainingApi().LoadCheckpointFromBuffer(buffer.data(), buffer.size(), &checkpoint_state));
+  return CheckpointState(checkpoint_state);
+}
+
+inline void CheckpointState::SaveCheckpoint(const CheckpointState& checkpoint_states,
+                                            const std::basic_string<ORTCHAR_T>& path_to_checkpoint,
+                                            const bool include_optimizer_state) {
+  ThrowOnError(GetTrainingApi().SaveCheckpoint(checkpoint_states, path_to_checkpoint.c_str(),
+                                               include_optimizer_state));
+}
+
+inline void TrainingSession::ExportModelForInferencing(const std::basic_string<ORTCHAR_T>& inference_model_path,
+                                                       const std::vector<std::string>& graph_output_names) {
+  std::vector<const char*> output_names;
+  output_names.reserve(graph_output_names.size());
+  for (const auto& output_name : graph_output_names) {
+    output_names.push_back(output_name.c_str());
+  }
+  ThrowOnError(GetTrainingApi().ExportModelForInferencing(
+      p_, inference_model_path.c_str(), graph_output_names.size(), output_names.data()));
+}
+
+inline void SetSeed(const int64_t seed) {
+  ThrowOnError(GetTrainingApi().SetSeed(seed));
+}
+
+inline void CheckpointState::AddProperty(const std::string& property_name, const Property& property_value) {
+  if (std::holds_alternative<int64_t>(property_value)) {
+    int64_t value = std::get<int64_t>(property_value);
+    void* value_p = &value;
+    ThrowOnError(GetTrainingApi().AddProperty(p_, property_name.c_str(), OrtPropertyType::OrtIntProperty, value_p));
+  } else if (std::holds_alternative<float>(property_value)) {
+    float value = std::get<float>(property_value);
+    void* value_p = &value;
+    ThrowOnError(GetTrainingApi().AddProperty(p_, property_name.c_str(), OrtPropertyType::OrtFloatProperty, value_p));
+  } else if (std::holds_alternative<std::string>(property_value)) {
+    std::string value = std::get<std::string>(property_value);
+    auto buffer = std::make_unique<char[]>(value.length() + 1);
+    memcpy(buffer.get(), value.c_str(), value.length());
+    // AddProperty takes a char* and calls PropertyBag::AddProperty which takes a std::string. The data will be
+    // copied at that point so buffer can free the local allocation once the call is made.
+    ThrowOnError(GetTrainingApi().AddProperty(p_, property_name.c_str(), OrtPropertyType::OrtStringProperty,
+                                              buffer.get()));
+  } else {
+    ThrowStatus(Status("Unknown property type received.", OrtErrorCode::ORT_INVALID_ARGUMENT));
+  }
+}
+
+inline Property CheckpointState::GetProperty(const std::string& property_name) {
+  void* property_value = nullptr;
+  OrtPropertyType property_type;
+
+  AllocatorWithDefaultOptions allocator;
+  ThrowOnError(GetTrainingApi().GetProperty(p_, property_name.c_str(), allocator, &property_type, &property_value));
+
+  Property property;
+
+  switch (property_type) {
+    case OrtPropertyType::OrtIntProperty: {
+      auto value_p = reinterpret_cast<int64_t*>(property_value);
+      property = *value_p;
+      allocator.Free(property_value);
+      break;
+    }
+    case OrtPropertyType::OrtFloatProperty: {
+      auto value_p = reinterpret_cast<float*>(property_value);
+      property = *value_p;
+      allocator.Free(property_value);
+      break;
+    }
+    case OrtPropertyType::OrtStringProperty: {
+      auto value_p = reinterpret_cast<char*>(property_value);
+      property = std::string(value_p);
+      allocator.Free(property_value);
+      break;
+    }
+    default: {
+      ThrowStatus(Status("Unknown property type received.", OrtErrorCode::ORT_INVALID_ARGUMENT));
+      break;
+    }
+  }
+
+  return property;
+}
+
+inline void CheckpointState::UpdateParameter(const std::string& parameter_name, const Value& parameter) {
+  ThrowOnError(GetTrainingApi().UpdateParameter(p_, parameter_name.c_str(), parameter));
+}
+
+inline Value CheckpointState::GetParameter(const std::string& parameter_name) {
+  AllocatorWithDefaultOptions allocator;
+  OrtValue* parameter;
+  ThrowOnError(GetTrainingApi().GetParameter(p_, parameter_name.c_str(), allocator, &parameter));
+
+  return Value{parameter};
+}
+
+}  // namespace Ort

libs/onnxruntime/include/tensorrt_provider_factory.h

@@ -1,14 +0,0 @@
-// Copyright (c) Microsoft Corporation. All rights reserved.
-// Licensed under the MIT License.
-
-#include "onnxruntime_c_api.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-ORT_API_STATUS(OrtSessionOptionsAppendExecutionProvider_Tensorrt, _In_ OrtSessionOptions* options, int device_id);
-
-#ifdef __cplusplus
-}
-#endif

src/ofxOnnxRuntime.cpp

@@ -19,21 +19,17 @@ namespace ofxOnnxRuntime
 	void BaseHandler::setup(const std::string & onnx_path, const BaseSetting & base_setting)
 	{
 		Ort::SessionOptions session_options;
-		if (base_setting.infer_type == INFER_TENSORRT) {
+		session_options.SetIntraOpNumThreads(1);
-			OrtTensorRTProviderOptions op;
+		session_options.SetIntraOpNumThreads(1);
-			memset(&op, 0, sizeof(op));
+		session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
-			op.device_id = base_setting.device_id;
+
-			op.trt_fp16_enable = 1;
+		if (base_setting.infer_type == INFER_CUDA) {
-			op.trt_engine_cache_enable = 1;
+			OrtCUDAProviderOptions opts;
-			std::string path = ofToDataPath(onnx_path, true);
+			opts.device_id = 0;
-			ofStringReplace(path, ".onnx", "_trt_cache");
+			opts.cudnn_conv_algo_search = OrtCudnnConvAlgoSearchExhaustive;
-			op.trt_engine_cache_path = path.c_str();
+			opts.do_copy_in_default_stream = 0;
-			session_options.AppendExecutionProvider_TensorRT(op);
+			opts.arena_extend_strategy = 0;
-		}
+			session_options.AppendExecutionProvider_CUDA(opts);
-		if (base_setting.infer_type == INFER_CUDA || base_setting.infer_type == INFER_TENSORRT) {
-			OrtCUDAProviderOptions op;
-			op.device_id = base_setting.device_id;
-			session_options.AppendExecutionProvider_CUDA(op);
 		}
 		this->setup2(onnx_path, session_options);
 	}
@@ -49,49 +45,86 @@ namespace ofxOnnxRuntime
 
 		Ort::AllocatorWithDefaultOptions allocator;
 
-		// 2. input name & input dims
+		// 1. Gets Input Name/s & Shape ([1, 3, 28, 28]) -- In most cases this is usually just one
-		auto* input_name = ort_session->GetInputName(0, allocator);
+		for (std::size_t i = 0; i < ort_session->GetInputCount(); i++) {
-		input_node_names.resize(1);
+			input_node_names.emplace_back(ort_session->GetInputNameAllocated(i, allocator).get());
-		input_node_names[0] = input_name;
+			input_node_dims = ort_session->GetInputTypeInfo(i).GetTensorTypeAndShapeInfo().GetShape();
-
+
-		// 3. type info.
+			// Some models might have negative shape values to indicate dynamic shape, e.g., for variable batch size. (?, 3, 28, 28) -> (1, 3, 28, 28)
-		Ort::TypeInfo type_info = ort_session->GetInputTypeInfo(0);
+			for (auto& s : input_node_dims) if (s < 0) s = 1;
-		auto tensor_info = type_info.GetTensorTypeAndShapeInfo();
+
-		input_tensor_size = 1;
+			std::cout << input_node_names.at(i) << " : " << PrintShape(input_node_dims) << std::endl;
-		input_node_dims = tensor_info.GetShape();
+		}
-		for (unsigned int i = 0; i < input_node_dims.size(); ++i)
+
-			input_tensor_size *= input_node_dims.at(i);
+		// 2. Clear up output values
-		input_values_handler.resize(input_tensor_size);
-
-		// 4. output names & output dimms
-		num_outputs = ort_session->GetOutputCount();
-		output_node_names.resize(num_outputs);
 		output_node_dims.clear();
 		output_values.clear();
-		for (unsigned int i = 0; i < num_outputs; ++i)
+		
-		{
+		// 3. Gets Output name/s & Shapes
-			output_node_names[i] = ort_session->GetOutputName(i, allocator);
+		for (std::size_t i = 0; i < ort_session->GetOutputCount(); i++) {
-			Ort::TypeInfo output_type_info = ort_session->GetOutputTypeInfo(i);
+			output_node_names.emplace_back(ort_session->GetOutputNameAllocated(i, allocator).get());
-			auto output_tensor_info = output_type_info.GetTensorTypeAndShapeInfo();
+			auto output_shapes = ort_session->GetOutputTypeInfo(i).GetTensorTypeAndShapeInfo().GetShape();
-			auto output_dims = output_tensor_info.GetShape();
+
-			output_node_dims.emplace_back(output_dims);
+			for (auto& s : output_shapes) if (s < 0) s = 1; 
+			
 			output_values.emplace_back(nullptr);
+
+			std::cout << output_node_names.at(i) << " : " << PrintShape(output_shapes) << std::endl;
 		}
 	}
 
 	Ort::Value& BaseHandler::run()
 	{
-		auto input_tensor_ = Ort::Value::CreateTensor<float>(
+		std::vector<Ort::Value> input_tensors;
-			memory_info_handler, input_values_handler.data(), input_tensor_size,
+
-			input_node_dims.data(), input_node_dims.size());
+		input_tensors.emplace_back(GenerateTensor());
-		ort_session->Run(Ort::RunOptions{ nullptr }, input_node_names.data(), &input_tensor_, input_node_names.size(),
+
-			output_node_names.data(), output_values.data(), output_node_names.size());
+		// transform std::string -> const char*
+		std::vector<const char*> input_names_char(input_node_names.size(), nullptr);
+		std::transform(std::begin(input_node_names), std::end(input_node_names), std::begin(input_names_char),
+			[&](const std::string& str) { return str.c_str(); });
 
-		if (output_values.size() == 1) {
+		std::vector<const char*> output_names_char(output_node_names.size(), nullptr);
+		std::transform(std::begin(output_node_names), std::end(output_node_names), std::begin(output_names_char),
+			[&](const std::string& str) { return str.c_str(); });
+		
+
+		try {
+			output_values = ort_session->Run(Ort::RunOptions{ nullptr }, input_names_char.data(), input_tensors.data(),
+				input_names_char.size(), output_names_char.data(), output_names_char.size());
+			std::cout << "Success!" << std::endl;
 			return output_values.at(0);
 		}
-		else {
+		catch (const Ort::Exception& ex) {
-			return dummy_tensor;
+			std::cout << "ERROR running model inference: " << ex.what() << std::endl;
+			return dummy_output_tensor.at(0);
 		}
+		
+	}
+
+	// Prints the shape of the given tensor (ex. input: (1, 1, 512, 512))
+	std::string BaseHandler::PrintShape(const std::vector<std::int64_t>& v) {
+		std::stringstream ss;
+		for (std::size_t i = 0; i < v.size() - 1; i++) ss << v[i] << "x";
+		ss << v[v.size() - 1];
+		return ss.str();
+	}
+
+	Ort::Value BaseHandler::GenerateTensor() {
+		std::vector<float> random_input_tensor_values(CalculateProduct(input_node_dims));
+		std::generate(random_input_tensor_values.begin(), random_input_tensor_values.end(), [&] { return rand() % 255; });
+		return VectorToTensor(random_input_tensor_values, input_node_dims);
+	}
+
+	int BaseHandler::CalculateProduct(const std::vector<std::int64_t>& v) {
+		int total = 1;
+		for (auto& i : v) total *= i;
+		return total;
+	}
+
+	Ort::Value BaseHandler::VectorToTensor(std::vector<float>& data, const std::vector<std::int64_t>& shape) {
+		Ort::MemoryInfo mem_info = Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtArenaAllocator, OrtMemType::OrtMemTypeDefault);
+		auto tensor = Ort::Value::CreateTensor<float>(mem_info, data.data(), data.size(), shape.data(), shape.size());
+		return tensor;
 	}
 }

src/ofxOnnxRuntime.h

@@ -27,21 +27,29 @@ namespace ofxOnnxRuntime
 
 		Ort::Value& run();
 
-		float* getInputTensorData() {
+		// Utilities
-			return this->input_values_handler.data();
+		std::string PrintShape(const std::vector<std::int64_t>& v);
-		}
+		Ort::Value GenerateTensor();
+		int CalculateProduct(const std::vector<std::int64_t>& v);
+		Ort::Value VectorToTensor(std::vector<float>& data, const std::vector<std::int64_t>& shape);
+
 	protected:
 		Ort::Env ort_env;
 		std::shared_ptr<Ort::Session> ort_session;
-		std::vector<const char *> input_node_names;
+
+		std::vector<std::string> input_node_names;
 		std::vector<int64_t> input_node_dims; // 1 input only.
 		std::size_t input_tensor_size = 1;
-		std::vector<float> input_values_handler;
+
 		Ort::MemoryInfo memory_info_handler = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeDefault);
-		std::vector<const char *> output_node_names;
+
+		std::vector<std::string> output_node_names;
 		std::vector<std::vector<int64_t>> output_node_dims; // >=1 outputs
 		std::vector<Ort::Value> output_values;
+
 		Ort::Value dummy_tensor{ nullptr };
+		std::vector<Ort::Value> dummy_output_tensor;
+
 		int num_outputs = 1;
 	};
 }