Merge branch 'main' into yifanl/ep_perf_sort_fix

microsoft · Nov 1, 2023 · 20e9401 · 20e9401
2 parents a7cef9a + a2e9ba7
commit 20e9401
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diff --git a/cgmanifests/generated/cgmanifest.json b/cgmanifests/generated/cgmanifest.json
@@ -26,7 +26,7 @@
       "component": {
         "type": "git",
         "git": {
-          "commitHash": "0c296085f9f65f0f8ef7aec7b9eed55faf37dc40",
+          "commitHash": "b86cc54efce19530fb953e4b21f57e6b3888534c",
           "repositoryUrl": "https://github.com/onnx/onnx.git"
         },
         "comments": "git submodule at cmake/external/onnx"
@@ -192,16 +192,6 @@
         "comments": "mp11"
       }
     },
-    {
-      "component": {
-        "type": "git",
-        "git": {
-          "commitHash": "6a20ba82b439ea1fd650da4d389e96b60a1dd828",
-          "repositoryUrl": "https://github.com/onnx/onnx.git"
-        },
-        "comments": "onnx"
-      }
-    },
     {
       "component": {
         "type": "git",

diff --git a/cmake/CMakeLists.txt b/cmake/CMakeLists.txt
@@ -1282,14 +1282,6 @@ if (onnxruntime_USE_OPENVINO)
     add_definitions(-DOPENVINO_CONFIG_CPU_FP16=1)
   endif()
 
-  if (onnxruntime_USE_OPENVINO_VPUX_FP16)
-    add_definitions(-DOPENVINO_CONFIG_VPUX_FP16=1)
-  endif()
-
-  if (onnxruntime_USE_OPENVINO_VPUX_U8)
-    add_definitions(-DOPENVINO_CONFIG_VPUX_U8=1)
-  endif()
-
   if (onnxruntime_USE_OPENVINO_GPU_FP32_NP)
     add_definitions(-DOPENVINO_CONFIG_GPU_FP32=1)
     add_definitions(-DOPENVINO_DISABLE_GRAPH_PARTITION=1)
@@ -1310,16 +1302,6 @@ if (onnxruntime_USE_OPENVINO)
     add_definitions(-DOPENVINO_DISABLE_GRAPH_PARTITION=1)
   endif()
 
-  if (onnxruntime_USE_OPENVINO_VPUX_FP32_NP)
-    add_definitions(-DOPENVINO_CONFIG_VPUX_FP32=1)
-    add_definitions(-DOPENVINO_DISABLE_GRAPH_PARTITION=1)
-  endif()
-
-  if (onnxruntime_USE_OPENVINO_VPUX_FP16_NP)
-    add_definitions(-DOPENVINO_CONFIG_VPUX_FP16=1)
-    add_definitions(-DOPENVINO_DISABLE_GRAPH_PARTITION=1)
-  endif()
-
   if (onnxruntime_USE_OPENVINO_HETERO)
     add_definitions(-DOPENVINO_CONFIG_HETERO=1)
     add_definitions(-DDEVICE_NAME="${onnxruntime_USE_OPENVINO_DEVICE}")

diff --git a/cmake/deps.txt b/cmake/deps.txt
@@ -24,7 +24,7 @@ microsoft_gsl;https://github.com/microsoft/GSL/archive/refs/tags/v4.0.0.zip;cf36
 microsoft_wil;https://github.com/microsoft/wil/archive/refs/tags/v1.0.230629.1.zip;e4a542a323c070376f7c2d1973d0f7ddbc1d2fa5
 mimalloc;https://github.com/microsoft/mimalloc/archive/refs/tags/v2.1.1.zip;d5ee7d34223d0567892db5179849939c8769dc41
 mp11;https://github.com/boostorg/mp11/archive/refs/tags/boost-1.82.0.zip;9bc9e01dffb64d9e0773b2e44d2f22c51aace063
-onnx;https://github.com/onnx/onnx/archive/6a20ba82b439ea1fd650da4d389e96b60a1dd828.zip;179a22ad4cd67109c60031ae4b6cf2f434d8bd7e
+onnx;https://github.com/onnx/onnx/archive/refs/tags/v1.15.0.zip;54c3f960a0541c5d8d3e60c2933e11f5d3688a11
 #use the commit of supporting all the plugins and TRT 8.6-GA (https://github.com/onnx/onnx-tensorrt/commit/0462dc31ae78f48744b6141ae376df1f96d3f459)
 onnx_tensorrt;https://github.com/onnx/onnx-tensorrt/archive/a43ce67187bab219520fd80f21af8bbd4354bc8c.zip;572535aefef477050f86744dfab1fef840198035
 protobuf;https://github.com/protocolbuffers/protobuf/archive/refs/tags/v21.12.zip;7cf2733949036c7d52fda017badcab093fe73bfa

diff --git a/cmake/external/onnx b/cmake/external/onnx
diff --git a/cmake/onnxruntime_providers_cuda.cmake b/cmake/onnxruntime_providers_cuda.cmake
@@ -40,6 +40,7 @@
         "${ONNXRUNTIME_ROOT}/contrib_ops/cuda/collective/distributed_slice.cc"
         "${ONNXRUNTIME_ROOT}/contrib_ops/cuda/collective/distributed_reshape.cc"
         "${ONNXRUNTIME_ROOT}/contrib_ops/cuda/collective/distributed_expand.cc"
+        "${ONNXRUNTIME_ROOT}/contrib_ops/cuda/collective/distributed_reduce.cc"
       )
     endif()
     # add using ONNXRUNTIME_ROOT so they show up under the 'contrib_ops' folder in Visual Studio

diff --git a/cmake/onnxruntime_rocm_hipify.cmake b/cmake/onnxruntime_rocm_hipify.cmake
@@ -109,6 +109,7 @@ if (NOT onnxruntime_USE_NCCL)
   list(APPEND contrib_ops_excluded_files "collective/distributed_slice.cc")
   list(APPEND contrib_ops_excluded_files "collective/distributed_reshape.cc")
   list(APPEND contrib_ops_excluded_files "collective/distributed_expand.cc")
+  list(APPEND contrib_ops_excluded_files "collective/distributed_reduce.cc")
 endif()
 
 set(provider_excluded_files

diff --git a/docs/ContribOperators.md b/docs/ContribOperators.md
@@ -95,6 +95,7 @@ Do not modify directly.*
   * <a href="#com.microsoft.RotaryEmbedding">com.microsoft.RotaryEmbedding</a>
   * <a href="#com.microsoft.SampleOp">com.microsoft.SampleOp</a>
   * <a href="#com.microsoft.Sampling">com.microsoft.Sampling</a>
+  * <a href="#com.microsoft.SkipGroupNorm">com.microsoft.SkipGroupNorm</a>
   * <a href="#com.microsoft.SkipLayerNormalization">com.microsoft.SkipLayerNormalization</a>
   * <a href="#com.microsoft.SkipSimplifiedLayerNormalization">com.microsoft.SkipSimplifiedLayerNormalization</a>
   * <a href="#com.microsoft.Snpe">com.microsoft.Snpe</a>
@@ -2342,7 +2343,7 @@ This version of the operator has been available since version 1 of the 'com.micr
 
 <dl>
 <dt><tt>activation</tt> : int (required)</dt>
-<dd>Activation after group normalization: 0 for None, 1 for Swish</dd>
+<dd>Activation after group normalization: 0 for None, 1 for SiLU</dd>
 <dt><tt>channels_last</tt> : int</dt>
 <dd>1 if the input and output are in the NHWC layout, 0 if it is in the NCHW layout. Defaults to 1.</dd>
 <dt><tt>epsilon</tt> : float</dt>
@@ -2582,6 +2583,7 @@ This version of the operator has been available since version 1 of the 'com.micr
   Input B is stored as uint8_t with shape: [(N * K + 1) / 2].
   Input absmax is stored in same type as original type of B(float32, float16) with shape like: [(N * K + block_size - 1) / block_size].
 
+
 #### Version
 
 This version of the operator has been available since version 1 of the 'com.microsoft' operator set.
@@ -5083,6 +5085,72 @@ This version of the operator has been available since version 1 of the 'com.micr
 </dl>
 
 
+### <a name="com.microsoft.SkipGroupNorm"></a><a name="com.microsoft.skipgroupnorm">**com.microsoft.SkipGroupNorm**</a>
+
+  This operator element-wise adds x, skip and bias, then apply group normalization and optional activation.
+
+  This operator transforms input according to
+    s = x + skip + bias
+    y = gamma * (s - mean) / sqrt(variance + epsilon) + beta
+
+  The input channels are separated into num_groups groups, each containing num_channels / num_groups channels.
+  The num_channels must be divisible by num_groups.
+  The mean and standard-deviation of s are calculated separately over the each group.
+  The weight and bias are per-channel affine transform parameter vectors of size num_channels.
+
+  The activation attribute can be used to enable activation after group normalization.
+
+#### Version
+
+This version of the operator has been available since version 1 of the 'com.microsoft' operator set.
+
+#### Attributes
+
+<dl>
+<dt><tt>activation</tt> : int (required)</dt>
+<dd>Activation after group normalization: 0 for None, 1 for SiLU</dd>
+<dt><tt>channels_last</tt> : int</dt>
+<dd>1 if the input and output are in the NHWC layout, 0 if it is in the NCHW layout. Defaults to 1.</dd>
+<dt><tt>epsilon</tt> : float</dt>
+<dd>The epsilon value to use to avoid division by zero</dd>
+<dt><tt>groups</tt> : int (required)</dt>
+<dd>The number of groups of channels. It should be a divisor of the number of channels C</dd>
+</dl>
+
+#### Inputs (4 - 5)
+
+<dl>
+<dt><tt>X</tt> : T</dt>
+<dd>Input data tensor. Dimensions are (N x H x W x C) when channels_last is 1  or (N x C x H x W) otherwise, where N is the batch size, C is the number of channels, and H and W are the height and width of the data</dd>
+<dt><tt>gamma</tt> : M</dt>
+<dd>1D gamma tensor for normalization with shape (C), where C is number of channels</dd>
+<dt><tt>beta</tt> : M</dt>
+<dd>1D beta tensor for normalization with shape (C), where C is number of channels</dd>
+<dt><tt>skip</tt> : T</dt>
+<dd>4D or 2D skip tensor. The shape can be (N x H x W x C) or (N x 1 x 1 x C) or (N x C)</dd>
+<dt><tt>bias</tt> (optional) : T</dt>
+<dd>1D bias tensor. Dimensions are (C), where C is number of channels</dd>
+</dl>
+
+#### Outputs (1 - 2)
+
+<dl>
+<dt><tt>Y</tt> : T</dt>
+<dd>The output tensor of the same shape as X</dd>
+<dt><tt>S</tt> (optional) : T</dt>
+<dd>The element-wise sum of input x, skip and bias tensors. It has the same shape as X</dd>
+</dl>
+
+#### Type Constraints
+
+<dl>
+<dt><tt>T</tt> : tensor(float16), tensor(float)</dt>
+<dd>Constrain input X, skip, bias and output Y, S types to float tensors.</dd>
+<dt><tt>M</tt> : tensor(float16), tensor(float)</dt>
+<dd>Constrain gamma and beta to float tensors.</dd>
+</dl>
+
+
 ### <a name="com.microsoft.SkipLayerNormalization"></a><a name="com.microsoft.skiplayernormalization">**com.microsoft.SkipLayerNormalization**</a>
 
   Skip and Layer Normalization Fusion

diff --git a/docs/OperatorKernels.md b/docs/OperatorKernels.md
@@ -861,6 +861,7 @@ Do not modify directly.*
 |Rfft|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
 |RotaryEmbedding|*in* input:**T**<br> *in* position_ids:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *out* output:**T**|1+|**M** = tensor(int64)<br/> **T** = tensor(float), tensor(float16)|
 |Sampling|*in* input_ids:**I**<br> *in* max_length:**I**<br> *in* min_length:**I**<br> *in* repetition_penalty:**T**<br> *in* vocab_mask:**I**<br> *in* prefix_vocab_mask:**I**<br> *in* attention_mask:**I**<br> *in* presence_mask:**I**<br> *in* seed:**I**<br> *out* sequences:**I**<br> *out* filtered_logits:**T**|1+|**T** = tensor(float), tensor(float16)|
+|SkipGroupNorm|*in* X:**T**<br> *in* gamma:**M**<br> *in* beta:**M**<br> *in* skip:**T**<br> *in* bias:**T**<br> *out* Y:**T**<br> *out* S:**T**|1+|**T** = tensor(float), tensor(float16)|
 |SkipLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* beta:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**<br> *out* input_skip_bias_sum:**T**|1+|**T** = tensor(float), tensor(float16)|
 |SkipSimplifiedLayerNormalization|*in* input:**T**<br> *in* skip:**T**<br> *in* gamma:**T**<br> *in* bias:**T**<br> *out* output:**T**<br> *out* mean:**U**<br> *out* inv_std_var:**U**<br> *out* input_skip_bias_sum:**T**|1+|**T** = tensor(float), tensor(float16)|
 |TransposeMatMul|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|

diff --git a/docs/python/ReadMeOV.rst b/docs/python/ReadMeOV.rst
@@ -7,7 +7,6 @@ OpenVINO™ Execution Provider for ONNX Runtime accelerates inference across man
  - Intel® CPUs
  - Intel® integrated GPUs
  - Intel® discrete GPUs
- - Intel® integrated VPUs
 
 Installation
 ------------
@@ -22,7 +21,6 @@ This package supports:
  - Intel® CPUs
  - Intel® integrated GPUs
  - Intel® discrete GPUs
- - Intel® integrated VPUs
 
 ``pip3 install onnxruntime-openvino``
 

diff --git a/include/onnxruntime/core/session/onnxruntime_c_api.h b/include/onnxruntime/core/session/onnxruntime_c_api.h
@@ -611,7 +611,7 @@ typedef struct OrtMIGraphXProviderOptions {
 typedef struct OrtOpenVINOProviderOptions {
 #ifdef __cplusplus
   OrtOpenVINOProviderOptions() : device_type{},
-                                 enable_vpu_fast_compile{},
+                                 enable_npu_fast_compile{},
                                  device_id{},
                                  num_of_threads{},
                                  cache_dir{},
@@ -624,7 +624,7 @@ typedef struct OrtOpenVINOProviderOptions {
    * Valid settings are one of: "CPU_FP32", "CPU_FP16", "GPU_FP32", "GPU_FP16"
    */
   const char* device_type;
-  unsigned char enable_vpu_fast_compile;  ///< 0 = disabled, nonzero = enabled
+  unsigned char enable_npu_fast_compile;  ///< 0 = disabled, nonzero = enabled
   const char* device_id;
   size_t num_of_threads;  ///< 0 = Use default number of threads
   const char* cache_dir;  // path is set to empty by default
@@ -4605,6 +4605,10 @@ struct OrtCustomOp {
   OrtStatusPtr(ORT_API_CALL* KernelComputeV2)(_In_ void* op_kernel, _In_ OrtKernelContext* context);
 
   OrtStatusPtr(ORT_API_CALL* InferOutputShapeFn)(_In_ const struct OrtCustomOp* op, _In_ OrtShapeInferContext*);
+
+  // Get start range
+  int(ORT_API_CALL* GetStartVersion)(_In_ const struct OrtCustomOp* op);
+  int(ORT_API_CALL* GetEndVersion)(_In_ const struct OrtCustomOp* op);
 };
 
 /*

diff --git a/include/onnxruntime/core/session/onnxruntime_cxx_api.h b/include/onnxruntime/core/session/onnxruntime_cxx_api.h
@@ -2228,6 +2228,8 @@ struct ShapeInferContext {
 
 using ShapeInferFn = Ort::Status (*)(Ort::ShapeInferContext&);
 
+#define MAX_CUSTOM_OP_END_VER (1UL << 31) - 1
+
 template <typename TOp, typename TKernel, bool WithStatus = false>
 struct CustomOpBase : OrtCustomOp {
   CustomOpBase() {
@@ -2280,6 +2282,14 @@ struct CustomOpBase : OrtCustomOp {
     }
 
     SetShapeInferFn<TOp>(0);
+
+    OrtCustomOp::GetStartVersion = [](const OrtCustomOp* this_) {
+      return static_cast<const TOp*>(this_)->start_ver_;
+    };
+
+    OrtCustomOp::GetEndVersion = [](const OrtCustomOp* this_) {
+      return static_cast<const TOp*>(this_)->end_ver_;
+    };
   }
 
   // Default implementation of GetExecutionProviderType that returns nullptr to default to the CPU provider
@@ -2348,6 +2358,9 @@ struct CustomOpBase : OrtCustomOp {
  protected:
   // Helper function that returns a map of session config entries specified by CustomOpBase::GetSessionConfigKeys.
   void GetSessionConfigs(std::unordered_map<std::string, std::string>& out, ConstSessionOptions options) const;
+
+  int start_ver_ = 1;
+  int end_ver_ = MAX_CUSTOM_OP_END_VER;
 };
 
 }  // namespace Ort

diff --git a/include/onnxruntime/core/session/onnxruntime_lite_custom_op.h b/include/onnxruntime/core/session/onnxruntime_lite_custom_op.h
@@ -773,8 +773,11 @@ struct OrtLiteCustomOp : public OrtCustomOp {
   PARSE_ARGS(Ort::Float8E5M2FNUZ_t, ONNX_TENSOR_ELEMENT_DATA_TYPE_FLOAT8E5M2FNUZ)
 
   OrtLiteCustomOp(const char* op_name,
-                  const char* execution_provider) : op_name_(op_name),
-                                                    execution_provider_(execution_provider) {
+                  const char* execution_provider,
+                  int start_ver = 1, int end_ver = MAX_CUSTOM_OP_END_VER) : op_name_(op_name),
+                                                                            execution_provider_(execution_provider),
+                                                                            start_ver_(start_ver),
+                                                                            end_ver_(end_ver) {
     OrtCustomOp::version = ORT_API_VERSION;
 
     OrtCustomOp::GetName = [](const OrtCustomOp* op) { return static_cast<const OrtLiteCustomOp*>(op)->op_name_.c_str(); };
@@ -837,13 +840,26 @@ struct OrtLiteCustomOp : public OrtCustomOp {
     OrtCustomOp::KernelCompute = {};
 
     OrtCustomOp::InferOutputShapeFn = {};
+
+    OrtCustomOp::GetStartVersion = [](const OrtCustomOp* op) {
+      auto self = reinterpret_cast<const OrtLiteCustomOp*>(op);
+      return self->start_ver_;
+    };
+
+    OrtCustomOp::GetEndVersion = [](const OrtCustomOp* op) {
+      auto self = reinterpret_cast<const OrtLiteCustomOp*>(op);
+      return self->end_ver_;
+    };
   }
 
   const std::string op_name_;
   const std::string execution_provider_;
 
   std::vector<ONNXTensorElementDataType> input_types_;
   std::vector<ONNXTensorElementDataType> output_types_;
+
+  int start_ver_ = 1;
+  int end_ver_ = MAX_CUSTOM_OP_END_VER;
 };
 
 //////////////////////////// OrtLiteCustomFunc ////////////////////////////////
@@ -873,9 +889,11 @@ struct OrtLiteCustomFunc : public OrtLiteCustomOp {
   OrtLiteCustomFunc(const char* op_name,
                     const char* execution_provider,
                     ComputeFn compute_fn,
-                    ShapeInferFn shape_infer_fn = {}) : OrtLiteCustomOp(op_name, execution_provider),
-                                                        compute_fn_(compute_fn),
-                                                        shape_infer_fn_(shape_infer_fn) {
+                    ShapeInferFn shape_infer_fn = {},
+                    int start_ver = 1,
+                    int end_ver = MAX_CUSTOM_OP_END_VER) : OrtLiteCustomOp(op_name, execution_provider, start_ver, end_ver),
+                                                           compute_fn_(compute_fn),
+                                                           shape_infer_fn_(shape_infer_fn) {
     ParseArgs<Args...>(input_types_, output_types_);
 
     OrtCustomOp::KernelCompute = [](void* op_kernel, OrtKernelContext* context) {
@@ -911,9 +929,11 @@ struct OrtLiteCustomFunc : public OrtLiteCustomOp {
   OrtLiteCustomFunc(const char* op_name,
                     const char* execution_provider,
                     ComputeFnReturnStatus compute_fn_return_status,
-                    ShapeInferFn shape_infer_fn = {}) : OrtLiteCustomOp(op_name, execution_provider),
-                                                        compute_fn_return_status_(compute_fn_return_status),
-                                                        shape_infer_fn_(shape_infer_fn) {
+                    ShapeInferFn shape_infer_fn = {},
+                    int start_ver = 1,
+                    int end_ver = MAX_CUSTOM_OP_END_VER) : OrtLiteCustomOp(op_name, execution_provider, start_ver, end_ver),
+                                                           compute_fn_return_status_(compute_fn_return_status),
+                                                           shape_infer_fn_(shape_infer_fn) {
     ParseArgs<Args...>(input_types_, output_types_);
 
     OrtCustomOp::KernelComputeV2 = [](void* op_kernel, OrtKernelContext* context) -> OrtStatusPtr {
@@ -985,8 +1005,9 @@ struct OrtLiteCustomStruct : public OrtLiteCustomOp {
   };
 
   OrtLiteCustomStruct(const char* op_name,
-                      const char* execution_provider) : OrtLiteCustomOp(op_name,
-                                                                        execution_provider) {
+                      const char* execution_provider,
+                      int start_ver = 1,
+                      int end_ver = MAX_CUSTOM_OP_END_VER) : OrtLiteCustomOp(op_name, execution_provider, start_ver, end_ver) {
     SetCompute(&CustomOp::Compute);
 
     OrtCustomOp::CreateKernel = [](const OrtCustomOp* this_, const OrtApi* ort_api, const OrtKernelInfo* info) {
@@ -1049,25 +1070,31 @@ template <typename... Args>
 OrtLiteCustomOp* CreateLiteCustomOp(const char* op_name,
                                     const char* execution_provider,
                                     void (*custom_compute_fn)(Args...),
-                                    Status (*shape_infer_fn)(ShapeInferContext&) = {}) {
+                                    Status (*shape_infer_fn)(ShapeInferContext&) = {},
+                                    int start_ver = 1,
+                                    int end_ver = MAX_CUSTOM_OP_END_VER) {
   using LiteOp = OrtLiteCustomFunc<Args...>;
-  return std::make_unique<LiteOp>(op_name, execution_provider, custom_compute_fn, shape_infer_fn).release();
+  return std::make_unique<LiteOp>(op_name, execution_provider, custom_compute_fn, shape_infer_fn, start_ver, end_ver).release();
 }
 
 template <typename... Args>
 OrtLiteCustomOp* CreateLiteCustomOp(const char* op_name,
                                     const char* execution_provider,
                                     Status (*custom_compute_fn_v2)(Args...),
-                                    Status (*shape_infer_fn)(ShapeInferContext&) = {}) {
+                                    Status (*shape_infer_fn)(ShapeInferContext&) = {},
+                                    int start_ver = 1,
+                                    int end_ver = MAX_CUSTOM_OP_END_VER) {
   using LiteOp = OrtLiteCustomFunc<Args...>;
-  return std::make_unique<LiteOp>(op_name, execution_provider, custom_compute_fn_v2, shape_infer_fn).release();
+  return std::make_unique<LiteOp>(op_name, execution_provider, custom_compute_fn_v2, shape_infer_fn, start_ver, end_ver).release();
 }
 
 template <typename CustomOp>
 OrtLiteCustomOp* CreateLiteCustomOp(const char* op_name,
-                                    const char* execution_provider) {
+                                    const char* execution_provider,
+                                    int start_ver = 1,
+                                    int end_ver = MAX_CUSTOM_OP_END_VER) {
   using LiteOp = OrtLiteCustomStruct<CustomOp>;
-  return std::make_unique<LiteOp>(op_name, execution_provider).release();
+  return std::make_unique<LiteOp>(op_name, execution_provider, start_ver, end_ver).release();
 }
 
 }  // namespace Custom

diff --git a/js/web/docs/webgpu-operators.md b/js/web/docs/webgpu-operators.md
@@ -40,6 +40,7 @@ Do not modify directly.*
 | Expand | ai.onnx(8-12,13+) |  |
 | Flatten | ai.onnx(1-8,9-10,11-12,13+) |  |
 | Floor | ai.onnx(6-12,13+) |  |
+| FusedConv | com.microsoft(1+) |  |
 | Gather | ai.onnx(1-10,11-12,13+) |  |
 | GatherElements | ai.onnx(11-12,13+) |  |
 | Gelu | com.microsoft(1+) |  |
+1 −1		VERSION_NUMBER
+2 −0		cmake/summary.cmake
+1 −1		workflow_scripts/protobuf/build_protobuf_win.ps1