Sync ORT main 16 07 25

.github/workflows/windows_webgpu.yml

@@ -22,6 +22,7 @@ jobs:
     strategy:
       matrix:
         vcpkg_option: [novcpkg, vcpkg]
+        wgsl_template: [static, dynamic]
     env:
       OrtPackageId: Microsoft.ML.OnnxRuntime
       OnnxRuntimeBuildDirectory: ${{ github.workspace }}
@@ -123,6 +124,7 @@ jobs:
             --build_nodejs `
             --build_java `
             --use_webgpu `
+            --wgsl_template ${{ matrix.wgsl_template }} `
             ${{ matrix.vcpkg_option == 'vcpkg' && '--use_vcpkg' || '' }} `
             --cmake_extra_defines `
             onnxruntime_BUILD_UNIT_TESTS=ON `

cmake/CMakeLists.txt

@@ -151,6 +151,7 @@ option(onnxruntime_DISABLE_SPARSE_TENSORS "Disable sparse tensors data types" OF
 option(onnxruntime_DISABLE_OPTIONAL_TYPE "Disable optional type" OFF)
 option(onnxruntime_DISABLE_FLOAT8_TYPES "Disable float 8 types" OFF)
 option(onnxruntime_MINIMAL_BUILD "Exclude as much as possible from the build. Support ORT format models. No support for ONNX format models." OFF)
+option(onnxruntime_CLIENT_PACKAGE_BUILD "Enables default settings that are more appropriate for client/on-device workloads." OFF)
 cmake_dependent_option(onnxruntime_DISABLE_RTTI "Disable RTTI" ON "NOT onnxruntime_ENABLE_PYTHON;NOT onnxruntime_USE_CUDA" OFF)
 # For now onnxruntime_DISABLE_EXCEPTIONS will only work with onnxruntime_MINIMAL_BUILD, more changes (ONNX, non-CPU EP, ...) are required to run this standalone
 cmake_dependent_option(onnxruntime_DISABLE_EXCEPTIONS "Disable exception handling. Requires onnxruntime_MINIMAL_BUILD currently." ON "onnxruntime_MINIMAL_BUILD;NOT onnxruntime_ENABLE_PYTHON" OFF)

cmake/adjust_global_compile_flags.cmake

@@ -95,6 +95,11 @@ if (onnxruntime_MINIMAL_BUILD)
   endif()
 endif()
 
+# ORT build with default settings more appropriate for client/on-device workloads.
+if (onnxruntime_CLIENT_PACKAGE_BUILD)
+  add_compile_definitions(ORT_CLIENT_PACKAGE_BUILD)
+endif()
+
 if (onnxruntime_ENABLE_LTO)
     include(CheckIPOSupported)
     check_ipo_supported(RESULT ipo_enabled OUTPUT ipo_output)

cmake/external/onnxruntime_external_deps.cmake

@@ -774,13 +774,24 @@ if (onnxruntime_USE_WEBGPU)
   endif()
 
   if (NOT CMAKE_SYSTEM_NAME STREQUAL "Emscripten" AND onnxruntime_WGSL_TEMPLATE STREQUAL "dynamic")
-    onnxruntime_fetchcontent_declare(
-      duktape
-      URL ${DEP_URL_duktape}
-      URL_HASH SHA1=${DEP_SHA1_duktape}
-      EXCLUDE_FROM_ALL
-    )
-    onnxruntime_fetchcontent_makeavailable(duktape)
+    if(onnxruntime_USE_VCPKG)
+      find_package(unofficial-duktape CONFIG REQUIRED)
+      add_library(duktape_static ALIAS unofficial::duktape::duktape)
+    else()
+      onnxruntime_fetchcontent_declare(
+        duktape
+        URL ${DEP_URL_duktape}
+        URL_HASH SHA1=${DEP_SHA1_duktape}
+        EXCLUDE_FROM_ALL
+      )
+      onnxruntime_fetchcontent_makeavailable(duktape)
+
+      if(NOT TARGET duktape_static)
+        add_library(duktape_static STATIC "${duktape_SOURCE_DIR}/src/duktape.c")
+        target_compile_features(duktape_static PRIVATE c_std_99)
+        target_include_directories(duktape_static INTERFACE $<BUILD_INTERFACE:${duktape_SOURCE_DIR}/src>)
+      endif()
+    endif()
   endif()
 endif()
 

cmake/onnxruntime_mlas.cmake

@@ -31,6 +31,7 @@ onnxruntime_add_static_library(onnxruntime_mlas
   ${MLAS_SRC_DIR}/eltwise.cpp
   ${MLAS_SRC_DIR}/erf.cpp
   ${MLAS_SRC_DIR}/compute.cpp
+  ${MLAS_SRC_DIR}/dequantize.cpp
   ${MLAS_SRC_DIR}/quantize.cpp
   ${MLAS_SRC_DIR}/qgemm_kernel_default.cpp
   ${MLAS_SRC_DIR}/qladd.cpp

cmake/onnxruntime_providers_tensorrt.cmake

@@ -72,26 +72,21 @@
   endif()
 
   # TensorRT 10 GA onwards, the TensorRT libraries will have major version appended to the end on Windows,
-  # for example, nvinfer_10.dll, nvinfer_plugin_10.dll, nvonnxparser_10.dll ...
+  # for example, nvinfer_10.dll, nvonnxparser_10.dll ...
   if (WIN32 AND TRT_GREATER_OR_EQUAL_TRT_10_GA)
     set(NVINFER_LIB "nvinfer_${NV_TENSORRT_MAJOR}")
-    set(NVINFER_PLUGIN_LIB "nvinfer_plugin_${NV_TENSORRT_MAJOR}")
     set(PARSER_LIB "nvonnxparser_${NV_TENSORRT_MAJOR}")
   endif()
 
   if (NOT NVINFER_LIB)
      set(NVINFER_LIB "nvinfer")
   endif()
 
-  if (NOT NVINFER_PLUGIN_LIB)
-     set(NVINFER_PLUGIN_LIB "nvinfer_plugin")
-  endif()
-
   if (NOT PARSER_LIB)
      set(PARSER_LIB "nvonnxparser")
   endif()
 
-  MESSAGE(STATUS "Looking for ${NVINFER_LIB} and ${NVINFER_PLUGIN_LIB}")
+  MESSAGE(STATUS "Looking for ${NVINFER_LIB}")
 
   find_library(TENSORRT_LIBRARY_INFER ${NVINFER_LIB}
     HINTS ${TENSORRT_ROOT}
@@ -101,14 +96,6 @@
     MESSAGE(STATUS "Can't find ${NVINFER_LIB}")
   endif()
 
-  find_library(TENSORRT_LIBRARY_INFER_PLUGIN ${NVINFER_PLUGIN_LIB}
-    HINTS  ${TENSORRT_ROOT}
-    PATH_SUFFIXES lib lib64 lib/x64)
-
-  if (NOT TENSORRT_LIBRARY_INFER_PLUGIN)
-    MESSAGE(STATUS "Can't find ${NVINFER_PLUGIN_LIB}")
-  endif()
-
   if (onnxruntime_USE_TENSORRT_BUILTIN_PARSER)
     MESSAGE(STATUS "Looking for ${PARSER_LIB}")
 
@@ -120,7 +107,7 @@
       MESSAGE(STATUS "Can't find ${PARSER_LIB}")
     endif()
 
-    set(TENSORRT_LIBRARY ${TENSORRT_LIBRARY_INFER} ${TENSORRT_LIBRARY_INFER_PLUGIN} ${TENSORRT_LIBRARY_NVONNXPARSER})
+    set(TENSORRT_LIBRARY ${TENSORRT_LIBRARY_INFER} ${TENSORRT_LIBRARY_NVONNXPARSER})
     MESSAGE(STATUS "Find TensorRT libs at ${TENSORRT_LIBRARY}")
   else()
     if (TRT_GREATER_OR_EQUAL_TRT_10_GA)
@@ -153,15 +140,15 @@
     endif()
     # Static libraries are just nvonnxparser_static on all platforms
     set(onnxparser_link_libs nvonnxparser_static)
-    set(TENSORRT_LIBRARY ${TENSORRT_LIBRARY_INFER} ${TENSORRT_LIBRARY_INFER_PLUGIN})
+    set(TENSORRT_LIBRARY ${TENSORRT_LIBRARY_INFER})
     MESSAGE(STATUS "Find TensorRT libs at ${TENSORRT_LIBRARY}")
   endif()
 
   # ${TENSORRT_LIBRARY} is empty if we link nvonnxparser_static.
   # nvonnxparser_static is linked against tensorrt libraries in onnx-tensorrt
   # See https://github.com/onnx/onnx-tensorrt/blob/8af13d1b106f58df1e98945a5e7c851ddb5f0791/CMakeLists.txt#L121
   # However, starting from TRT 10 GA, nvonnxparser_static doesn't link against tensorrt libraries.
-  # Therefore, the above code finds ${TENSORRT_LIBRARY_INFER} and ${TENSORRT_LIBRARY_INFER_PLUGIN}.
+  # Therefore, the above code finds ${TENSORRT_LIBRARY_INFER}.
   if(onnxruntime_CUDA_MINIMAL)
     set(trt_link_libs ${CMAKE_DL_LIBS} ${TENSORRT_LIBRARY})
   else()

cmake/onnxruntime_providers_webgpu.cmake

@@ -172,10 +172,12 @@
     file(MAKE_DIRECTORY ${WGSL_GENERATED_DIR})
 
     # Find all WGSL template input files
-    file(GLOB_RECURSE WGSL_TEMPLATE_FILES "${ONNXRUNTIME_ROOT}/core/providers/webgpu/*.wgsl.template")
+    file(GLOB_RECURSE WGSL_TEMPLATE_FILES
+      "${ONNXRUNTIME_ROOT}/core/providers/webgpu/*.wgsl.template"
+      "${ONNXRUNTIME_ROOT}/contrib_ops/webgpu/*.wgsl.template")
 
     # Set wgsl-gen command line options as a list
-    set(WGSL_GEN_OPTIONS "-i" "../" "--output" "${WGSL_GENERATED_DIR}" "-I" "wgsl_template_gen/" "--preserve-code-ref" "--verbose")
+    set(WGSL_GEN_OPTIONS "-i" "${ONNXRUNTIME_ROOT}/core/providers/webgpu/" "-i" "${ONNXRUNTIME_ROOT}/contrib_ops/webgpu/" "--output" "${WGSL_GENERATED_DIR}" "-I" "wgsl_template_gen/" "--preserve-code-ref" "--verbose")
     if (onnxruntime_WGSL_TEMPLATE STREQUAL "static")
       if (CMAKE_BUILD_TYPE STREQUAL "Debug")
         list(APPEND WGSL_GEN_OPTIONS "--generator" "static-cpp-literal")
@@ -207,10 +209,9 @@
       # Add the generated directory to include paths
       target_include_directories(onnxruntime_providers_webgpu PRIVATE ${WGSL_GENERATED_ROOT})
     elseif(onnxruntime_WGSL_TEMPLATE STREQUAL "dynamic")
-      add_library(duktape_static STATIC "${duktape_SOURCE_DIR}/src/duktape.c")
-      target_compile_features(duktape_static PRIVATE c_std_99)
       target_link_libraries(onnxruntime_providers_webgpu duktape_static)
-      target_include_directories(onnxruntime_providers_webgpu PRIVATE ${duktape_SOURCE_DIR}/src)
+      onnxruntime_add_include_to_target(onnxruntime_providers_webgpu duktape_static)
+
       # Define the path to the generated templates.js file
       target_compile_definitions(onnxruntime_providers_webgpu PRIVATE
         "ORT_WGSL_TEMPLATES_JS_PATH=\"${WGSL_GENERATED_TEMPLATES_JS}\"")

cmake/vcpkg.json

@@ -43,7 +43,6 @@
     "ms-gsl",
     "nlohmann-json",
     "onnx",
-    "optional-lite",
     {
       "name": "protobuf",
       "version>=": "3.21.12"
@@ -94,6 +93,10 @@
     "webgpu-ep": {
       "description": "Build with WebGPU EP",
       "dependencies": []
+    },
+    "webgpu-ep-wgsl-template-dynamic": {
+      "description": "Build with WebGPU EP with dynamic WGSL template code generator",
+      "dependencies": ["duktape"]
     }
   },
   "overrides": [
@@ -104,6 +107,10 @@
     {
       "name": "flatbuffers",
       "version": "23.5.26"
+    },
+    {
+      "name": "duktape",
+      "version": "2.7.0#2"
     }
   ]
 }

csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Mobile/EndToEndTests.Mobile.Automation/Tests.cs

@@ -40,10 +40,12 @@ public void RunPlatformUnitTest()
             var serializedResultSummary = _app.Invoke(_getResultsBackdoorMethodName)?.ToString();
             Assert.IsNotEmpty(serializedResultSummary, "Test results were not returned");
 
+            // Fix security issue (overflow with too much nesting): GHSA-5crp-9r3c-p9vr
+            JsonConvert.DefaultSettings = () => new JsonSerializerSettings { MaxDepth = 128 };
             var testSummary = JsonConvert.DeserializeObject<TestResultSummary>(serializedResultSummary);
             Assert.AreEqual(testSummary.Failed, 0, $"{testSummary.Failed} tests failed");
 
             _app.Screenshot("Post-testing");
         }
     }
-}
+}

csharp/test/Microsoft.ML.OnnxRuntime.Tests.Devices/TestResultProcessor.cs

@@ -45,8 +45,9 @@ public TestResultSummary GetResults()
         public string GetSerializedResults()
         {
             var resultSummary = GetResults();
+	    JsonConvert.DefaultSettings = () => new JsonSerializerSettings { MaxDepth = 128 };
             var serializedResultSummary = JsonConvert.SerializeObject(resultSummary, Formatting.Indented);
             return serializedResultSummary;
         }
     }
-}
+}

docs/ContribOperators.md

@@ -2545,6 +2545,8 @@ This version of the operator has been available since version 1 of the 'com.micr
 <dd>left_window_size for local attention (like Mistral). Default value is -1 meaning unused.</dd>
 <dt><tt>num_heads</tt> : int (required)</dt>
 <dd>Number of attention heads for q</dd>
+<dt><tt>qk_output</tt> : int</dt>
+<dd>Output values of QK matrix multiplication before (1) or after (2) softmax normalization. Default value is 0 (don't output).</dd>
 <dt><tt>rotary_interleaved</tt> : int</dt>
 <dd>Rotate using interleaved pattern. Default value is 0 (False).</dd>
 <dt><tt>scale</tt> : float</dt>
@@ -2555,7 +2557,7 @@ This version of the operator has been available since version 1 of the 'com.micr
 <dd>Softcap value for attention weights. Default value is 0.</dd>
 </dl>
 
-#### Inputs (7 - 11)
+#### Inputs (7 - 12)
 
 <dl>
 <dt><tt>query</tt> : T</dt>
@@ -2580,9 +2582,11 @@ This version of the operator has been available since version 1 of the 'com.micr
 <dd>2D tensor with shape (batch_size, sequence_length). When processing the first prompt the kernel uses only the first element</dd>
 <dt><tt>attention_bias</tt> (optional) : T</dt>
 <dd>additional add to QxK' with shape (batch_size or 1, num_heads or 1, sequence_length, total_sequence_length)</dd>
+<dt><tt>head_sink</tt> (optional) : T</dt>
+<dd>1D tensor with shape (num_heads). Each head has a smooth factor adding to the denominator of softmax.</dd>
 </dl>
 
-#### Outputs
+#### Outputs (3 - 4)
 
 <dl>
 <dt><tt>output</tt> : T</dt>
@@ -2591,6 +2595,8 @@ This version of the operator has been available since version 1 of the 'com.micr
 <dd>present state key with support for format BNSH. When past_key uses same tensor as present_key(k-v buffer), it is of length max_sequence_length... otherwise of length past_sequence_length +kv_sequence_length.</dd>
 <dt><tt>present_value</tt> : T</dt>
 <dd>present state value with support for format BNSH. When past_value uses same tensor as present_value(k-v buffer), it is of length max_sequence_length... otherwise of length past_sequence_length +kv_sequence_length.</dd>
+<dt><tt>output_qk</tt> (optional) : T</dt>
+<dd>Values of QK matrix multiplication, either before or after softmax normalization</dd>
 </dl>
 
 #### Type Constraints

docs/OperatorKernels.md

@@ -538,7 +538,7 @@ Do not modify directly.*
 |Gelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float)|
 |GreedySearch|*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> *out* sequences:**I**|1+|**T** = tensor(float)|
 |GridSample|*in* X:**T1**<br> *in* Grid:**T1**<br> *out* Y:**T2**|1+|**T1** = tensor(float)<br/> **T2** = tensor(float)|
-|GroupQueryAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* seqlens_k:**M**<br> *in* total_sequence_length:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *in* position_ids:**tensor(int64)**<br> *in* attention_bias:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(float), tensor(float16)|
+|GroupQueryAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* seqlens_k:**M**<br> *in* total_sequence_length:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *in* position_ids:**tensor(int64)**<br> *in* attention_bias:**T**<br> *in* head_sink:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**<br> *out* output_qk:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(float), tensor(float16)|
 |Inverse|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
 |MatMulBnb4|*in* A:**T1**<br> *in* B:**T2**<br> *in* absmax:**T1**<br> *out* Y:**T1**|1+|**T1** = tensor(float)<br/> **T2** = tensor(uint8)|
 |MatMulFpQ4|*in* A:**T1**<br> *in* B:**T2**<br> *in* B_shape:**T3**<br> *out* Y:**T1**|1+|**T1** = tensor(float)<br/> **T2** = tensor(uint8)<br/> **T3** = tensor(int64)|
@@ -942,7 +942,7 @@ Do not modify directly.*
 |GreedySearch|*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> *out* sequences:**I**|1+|**T** = tensor(float), tensor(float16)|
 |GridSample|*in* X:**T1**<br> *in* Grid:**T1**<br> *out* Y:**T2**|1+|**T1** = tensor(float)<br/> **T2** = tensor(float)|
 |GroupNorm|*in* X:**T**<br> *in* gamma:**M**<br> *in* beta:**M**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
-|GroupQueryAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* seqlens_k:**M**<br> *in* total_sequence_length:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *in* position_ids:**tensor(int64)**<br> *in* attention_bias:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(bfloat16), tensor(float16)|
+|GroupQueryAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* seqlens_k:**M**<br> *in* total_sequence_length:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *in* position_ids:**tensor(int64)**<br> *in* attention_bias:**T**<br> *in* head_sink:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**<br> *out* output_qk:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(bfloat16), tensor(float16)|
 |Inverse|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
 |Irfft|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
 |LongformerAttention|*in* input:**T**<br> *in* weight:**T**<br> *in* bias:**T**<br> *in* mask:**T**<br> *in* global_weight:**T**<br> *in* global_bias:**T**<br> *in* global:**G**<br> *out* output:**T**|1+|**T** = tensor(float), tensor(float16)|
@@ -1420,7 +1420,7 @@ Do not modify directly.*
 |FusedMatMulActivation|*in* A:**T**<br> *in* B:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
 |Gelu|*in* X:**T**<br> *out* Y:**T**|1+|**T** = tensor(float), tensor(float16)|
 |GroupNorm|*in* X:**T**<br> *in* gamma:**M**<br> *in* beta:**M**<br> *out* Y:**T**|1+|**M** = tensor(float), tensor(float16)<br/> **T** = tensor(float), tensor(float16)|
-|GroupQueryAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* seqlens_k:**M**<br> *in* total_sequence_length:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *in* position_ids:**tensor(int64)**<br> *in* attention_bias:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(float), tensor(float16)|
+|GroupQueryAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* seqlens_k:**M**<br> *in* total_sequence_length:**M**<br> *in* cos_cache:**T**<br> *in* sin_cache:**T**<br> *in* position_ids:**tensor(int64)**<br> *in* attention_bias:**T**<br> *in* head_sink:**T**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**<br> *out* output_qk:**T**|1+|**M** = tensor(int32)<br/> **T** = tensor(float), tensor(float16)|
 |MatMulIntegerToFloat|*in* A:**T1**<br> *in* B:**T2**<br> *in* a_scale:**T3**<br> *in* b_scale:**T3**<br> *in* a_zero_point:**T1**<br> *in* b_zero_point:**T2**<br> *in* bias:**T3**<br> *out* Y:**T3**|1+|**T1** = tensor(int8), tensor(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(float), tensor(float16)|
 |MatMulNBits|*in* A:**T1**<br> *in* B:**T2**<br> *in* scales:**T1**<br> *in* zero_points:**T3**<br> *in* g_idx:**T4**<br> *in* bias:**T1**<br> *out* Y:**T1**|1+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(uint8)|
 |MultiHeadAttention|*in* query:**T**<br> *in* key:**T**<br> *in* value:**T**<br> *in* bias:**T**<br> *in* key_padding_mask:**M**<br> *in* attention_bias:**T**<br> *in* past_key:**T**<br> *in* past_value:**T**<br> *in* past_sequence_length:**M**<br> *in* cache_indirection:**M**<br> *out* output:**T**<br> *out* present_key:**T**<br> *out* present_value:**T**<br> *out* qk:**QK**|1+|**M** = tensor(int32)<br/> **T** = tensor(float), tensor(float16)|

include/onnxruntime/core/graph/graph.h

@@ -952,9 +952,12 @@ class Graph {  // NOLINT(clang-analyzer-optin.performance.Padding): preserve exi
     return const_cast<Graph*>(this)->GetNodeArg(name);
   }
 
-  // search this and up through any parent_graph_ instance for a NodeArg
+  // Searches for a NodeArg in the current graph and its parent graphs, and returns the corresponding mutable NodeArg
   NodeArg* GetNodeArgIncludingParentGraphs(const std::string& node_arg_name);
 
+  // Searches for a NodeArg in the current graph and its parent graphs, and returns the corresponding const NodeArg
+  const NodeArg* GetNodeArgIncludingParentGraphs(const std::string& node_arg_name) const;
+
   /** Gets a mutable NodeArg by name. Creates a new NodeArg that is owned by this Graph if not found.
   @param name The NodeArg name.
   @param[in] p_arg_type Optional TypeProto to use if the NodeArg needs to be created.