Enable CoreML EP for minimal extended mode

ThirdPartyNotices.txt

@@ -3868,7 +3868,7 @@ _____
    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.	
+   OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 _____
 huggingface/transformers
 
@@ -4628,7 +4628,7 @@ https://github.com/dmlc/dlpack
    limitations under the License.
 
 _____
-   
+
 emsdk
 
 MIT/Expat license
@@ -4663,4 +4663,25 @@ This is the MIT/Expat Licence. For more information see:
 
 2. http://en.wikipedia.org/wiki/MIT_License
 
-_____
+_____
+
+coremltools
+
+BSD-3-Clause License
+
+https://github.com/apple/coremltools
+
+Copyright (c) 2020, Apple Inc. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+1.  Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+2.  Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+3.  Neither the name of the copyright holder(s) nor the names of any contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+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 OWNER 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.
+© 2021 GitHub, Inc.
+
+_____

cmake/onnxruntime_providers.cmake

@@ -279,7 +279,7 @@ if (onnxruntime_USE_CUDA)
   endif()
 
   add_library(onnxruntime_providers_cuda ${onnxruntime_providers_cuda_src})
-  
+
   #target_compile_options(onnxruntime_providers_cuda PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:SHELL:-Xcompiler \"/analyze:stacksize 131072\">")
   if (HAS_GUARD_CF)
     target_compile_options(onnxruntime_providers_cuda PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:SHELL:-Xcompiler /guard:cf>")
@@ -289,7 +289,7 @@ if (onnxruntime_USE_CUDA)
   endif()
   foreach(ORT_FLAG ${ORT_WARNING_FLAGS})
       target_compile_options(onnxruntime_providers_cuda PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:SHELL:-Xcompiler \"${ORT_FLAG}\">")
-  endforeach()  
+  endforeach()
   if (UNIX)
     target_compile_options(onnxruntime_providers_cuda PRIVATE "$<$<COMPILE_LANGUAGE:CUDA>:SHELL:-Xcompiler -Wno-reorder>"
             "$<$<NOT:$<COMPILE_LANGUAGE:CUDA>>:-Wno-reorder>")
@@ -617,6 +617,10 @@ if (onnxruntime_USE_OPENVINO)
 endif()
 
 if (onnxruntime_USE_COREML)
+  if (onnxruntime_MINIMAL_BUILD AND NOT onnxruntime_EXTENDED_MINIMAL_BUILD)
+    message(FATAL_ERROR "CoreML EP can not be used in a basic minimal build. Please build with '--minimal_build extended'")
+  endif()
+
   add_compile_definitions(USE_COREML=1)
 
   # Compile CoreML proto definition to ${CMAKE_CURRENT_BINARY_DIR}/coreml
@@ -1014,10 +1018,10 @@ if (onnxruntime_USE_ROCM)
       #list(APPEND HIP_CXX_FLAGS -O0)
   endif(CMAKE_BUILD_TYPE MATCHES Debug)
 
-  list(APPEND HIP_CLANG_FLAGS ${HIP_CXX_FLAGS})  
+  list(APPEND HIP_CLANG_FLAGS ${HIP_CXX_FLAGS})
 
   # Generate GPU code during compilation
-  list(APPEND HIP_CLANG_FLAGS -fno-gpu-rdc)  
+  list(APPEND HIP_CLANG_FLAGS -fno-gpu-rdc)
 
   # Generate GPU code for GFX9 Generation
   list(APPEND HIP_CLANG_FLAGS --amdgpu-target=gfx906 --amdgpu-target=gfx908)

onnxruntime/test/providers/coreml/coreml_basic_test.cc

@@ -13,7 +13,10 @@
 #include "test/util/include/test/test_environment.h"
 #include "test/util/include/test_utils.h"
 
+#if !defined(ORT_MINIMAL_BUILD)
+// if this is a full build we need the provider test utils
 #include "test/providers/provider_test_utils.h"
+#endif  // !(ORT_MINIMAL_BUILD)
 
 #include "gtest/gtest.h"
 #include "gmock/gmock.h"
@@ -25,6 +28,11 @@ using namespace ::onnxruntime::logging;
 namespace onnxruntime {
 namespace test {
 
+// We want to run UT on CPU only to get output value without losing precision to pass the verification
+static constexpr uint32_t s_coreml_flags = COREML_FLAG_USE_CPU_ONLY;
+
+#if !defined(ORT_MINIMAL_BUILD)
+
 TEST(CoreMLExecutionProviderTest, FunctionTest) {
   const ORTCHAR_T* model_file_name = ORT_TSTR("coreml_execution_provider_test_graph.onnx");
 
@@ -67,25 +75,43 @@ TEST(CoreMLExecutionProviderTest, FunctionTest) {
   std::vector<float> values_mul_x = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f};
   OrtValue ml_value_x;
 
-  // We want to run UT on CPU only to get output value without losing precision
-  uint32_t coreml_flags = 0;
-  coreml_flags |= COREML_FLAG_USE_CPU_ONLY;
-
-  CreateMLValue<float>(TestCoreMLExecutionProvider(coreml_flags)->GetAllocator(0, OrtMemTypeDefault), dims_mul_x, values_mul_x,
-                       &ml_value_x);
+  CreateMLValue<float>(TestCoreMLExecutionProvider(s_coreml_flags)->GetAllocator(0, OrtMemTypeDefault),
+                       dims_mul_x, values_mul_x, &ml_value_x);
   OrtValue ml_value_y;
-  CreateMLValue<float>(TestCoreMLExecutionProvider(coreml_flags)->GetAllocator(0, OrtMemTypeDefault), dims_mul_x, values_mul_x,
-                       &ml_value_y);
+  CreateMLValue<float>(TestCoreMLExecutionProvider(s_coreml_flags)->GetAllocator(0, OrtMemTypeDefault),
+                       dims_mul_x, values_mul_x, &ml_value_y);
   OrtValue ml_value_z;
-  CreateMLValue<float>(TestCoreMLExecutionProvider(coreml_flags)->GetAllocator(0, OrtMemTypeDefault), dims_mul_x, values_mul_x,
-                       &ml_value_z);
+  CreateMLValue<float>(TestCoreMLExecutionProvider(s_coreml_flags)->GetAllocator(0, OrtMemTypeDefault),
+                       dims_mul_x, values_mul_x, &ml_value_z);
+
   NameMLValMap feeds;
   feeds.insert(std::make_pair("X", ml_value_x));
   feeds.insert(std::make_pair("Y", ml_value_y));
   feeds.insert(std::make_pair("Z", ml_value_z));
 
   RunAndVerifyOutputsWithEP(model_file_name, "CoreMLExecutionProviderTest.FunctionTest",
-                            onnxruntime::make_unique<CoreMLExecutionProvider>(coreml_flags),
+                            onnxruntime::make_unique<CoreMLExecutionProvider>(s_coreml_flags),
+                            feeds);
+}
+
+#endif  // !(ORT_MINIMAL_BUILD)
+
+TEST(CoreMLExecutionProviderTest, TestOrtFormatModel) {
+  // mnist model that has only had basic optimizations applied. nnapi should be able to take at least some of the nodes
+  const ORTCHAR_T* model_file_name = ORT_TSTR("testdata/mnist.level1_opt.ort");
+
+  RandomValueGenerator random{};
+  const std::vector<int64_t> dims = {1, 1, 28, 28};
+  std::vector<float> data = random.Gaussian<float>(dims, 0.0f, 1.f);
+
+  OrtValue ml_value;
+  CreateMLValue<float>(TestCPUExecutionProvider()->GetAllocator(0, OrtMemTypeDefault), dims, data, &ml_value);
+
+  NameMLValMap feeds;
+  feeds.insert(std::make_pair("Input3", ml_value));
+
+  RunAndVerifyOutputsWithEP(model_file_name, "CoreMLExecutionProviderTest.TestOrtFormatModel",
+                            onnxruntime::make_unique<CoreMLExecutionProvider>(s_coreml_flags),
                             feeds);
 }