Build CUDA and DML together (#22602)

### Description
Now, we need to build cuda and dml in one package.
But CUDA EP and DML EP can't run in one process.
It will throw the exception of `the GPU device instance has been
suspended`
So the issue is CUDA EP and DML EP coexist in compile time but can't
exist in run time.

This PR is to split cuda ep test and dml ep test in all unit tests.
The solution is to use 2 environment variable, NO_CUDA_TEST and
NO_DML_TEST, in CI.

For example, if NO_CUDA_TEST is set, the DefaultCudaExecutionProvider
will be nullptr, and the test will not run with CUDA EP.
In debugging, the CUDAExecutionProvider will not be called. 
I think, as long as cuda functions, like cudaSetDevice, are not called,
DML EP tests can pass.

Disabled java test of testDIrectML because it doesn't work now even
without CUDA EP.

java/src/test/java/ai/onnxruntime/InferenceTest.java

@@ -737,6 +737,7 @@ public class InferenceTest {
     runProvider(OrtProvider.CORE_ML);
   }
 
+  @Disabled("DirectML Java API hasn't been supported yet")
   @Test
   @EnabledIfSystemProperty(named = "USE_DML", matches = "1")
   public void testDirectML() throws OrtException {

onnxruntime/test/common/cuda_op_test_utils.h

@@ -5,6 +5,11 @@
 
 #include "test/util/include/default_providers.h"
 
+#define SKIP_CUDA_TEST_WITH_DML                                          \
+  if (DefaultCudaExecutionProvider() == nullptr) {                       \
+    GTEST_SKIP() << "CUDA Tests are not supported while DML is enabled"; \
+  }
+
 namespace onnxruntime {
 namespace test {
 
@@ -13,6 +18,10 @@ namespace test {
 int GetCudaArchitecture();
 
 inline bool HasCudaEnvironment(int min_cuda_architecture) {
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return false;
+  }
+
   if (DefaultCudaExecutionProvider().get() == nullptr) {
     return false;
   }

onnxruntime/test/contrib_ops/beam_search_test.cc

@@ -73,6 +73,9 @@ TEST(BeamSearchTest, GptBeamSearchFp32) {
   const char* const output_names[] = {"sequences"};
 
   Ort::SessionOptions session_options;
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
 #ifdef USE_CUDA
   OrtCUDAProviderOptionsV2 cuda_options;
   cuda_options.use_tf32 = false;
@@ -166,6 +169,9 @@ TEST(BeamSearchTest, GptBeamSearchFp16) {
   bool enable_rocm = (nullptr != DefaultRocmExecutionProvider().get());
   if (enable_cuda || enable_rocm) {
     Ort::SessionOptions session_options;
+#if defined(USE_CUDA) && defined(USE_DML)
+    SKIP_CUDA_TEST_WITH_DML;
+#endif
 #ifdef USE_CUDA
     OrtCUDAProviderOptionsV2 cuda_options;
     cuda_options.use_tf32 = false;

onnxruntime/test/contrib_ops/bias_dropout_op_test.cc

@@ -181,6 +181,9 @@ void RunBiasDropoutTest(const bool use_mask, const std::vector<int64_t>& input_s
   t.SetCustomOutputVerifier(output_verifier);
   std::vector<std::unique_ptr<IExecutionProvider>> t_eps;
 #ifdef USE_CUDA
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   t_eps.emplace_back(DefaultCudaExecutionProvider());
 #elif USE_ROCM
   t_eps.emplace_back(DefaultRocmExecutionProvider());

onnxruntime/test/contrib_ops/bitmask_dropout_op_test.cc

@@ -61,7 +61,9 @@ void RunTestForInference(const std::vector<int64_t>& input_dims, bool has_ratio
 
   std::vector<std::unique_ptr<IExecutionProvider>> test_eps;
 #ifdef USE_CUDA
-  test_eps.emplace_back(DefaultCudaExecutionProvider());
+  if (DefaultCudaExecutionProvider() != nullptr) {
+    test_eps.emplace_back(DefaultCudaExecutionProvider());
+  }
 #elif USE_ROCM
   test_eps.emplace_back(DefaultRocmExecutionProvider());
 #endif
@@ -122,6 +124,9 @@ void RunTestForTraining(const std::vector<int64_t>& input_dims) {
 
     std::vector<std::unique_ptr<IExecutionProvider>> dropout_eps;
 #ifdef USE_CUDA
+    if (DefaultCudaExecutionProvider() == nullptr) {
+      return;
+    }
     dropout_eps.emplace_back(DefaultCudaExecutionProvider());
 #elif USE_ROCM
     dropout_eps.emplace_back(DefaultRocmExecutionProvider());

onnxruntime/test/contrib_ops/layer_norm_test.cc

@@ -2,6 +2,7 @@
 // Licensed under the MIT License.
 
 #include "test/providers/compare_provider_test_utils.h"
+#include "test/util/include/default_providers.h"
 
 namespace onnxruntime {
 namespace test {
@@ -79,14 +80,20 @@ static void TestLayerNorm(const std::vector<int64_t>& x_dims,
 #endif
 
 #ifdef USE_CUDA
-  test.CompareWithCPU(kCudaExecutionProvider);
+  if (DefaultCudaExecutionProvider() != nullptr) {
+    test.CompareWithCPU(kCudaExecutionProvider);
+  }
 #elif USE_ROCM
   test.CompareWithCPU(kRocmExecutionProvider);
-#elif USE_DML
-  test.CompareWithCPU(kDmlExecutionProvider);
 #elif USE_WEBGPU
   test.CompareWithCPU(kWebGpuExecutionProvider);
 #endif
+
+#ifdef USE_DML
+  if (DefaultDmlExecutionProvider() != nullptr) {
+    test.CompareWithCPU(kDmlExecutionProvider);
+  }
+#endif
 }
 
 TEST(CudaKernelTest, LayerNorm_NullInput) {

onnxruntime/test/contrib_ops/matmul_4bits_test.cc

@@ -489,13 +489,17 @@ void RunTest(int64_t M, int64_t N, int64_t K, int64_t block_size, int64_t accura
   std::vector<std::unique_ptr<IExecutionProvider>> execution_providers;
   if (use_float16) {
 #ifdef USE_CUDA
-    execution_providers.push_back(DefaultCudaExecutionProvider());
+    if (DefaultCudaExecutionProvider() != nullptr) {
+      execution_providers.push_back(DefaultCudaExecutionProvider());
+    }
 #endif
 #ifdef USE_ROCM
     execution_providers.push_back(DefaultRocmExecutionProvider());
 #endif
 #ifdef USE_DML
-    execution_providers.push_back(DefaultDmlExecutionProvider());
+    if (DefaultDmlExecutionProvider() != nullptr) {
+      execution_providers.push_back(DefaultDmlExecutionProvider());
+    }
 #endif
 #ifdef USE_WEBGPU
     execution_providers.push_back(DefaultWebGpuExecutionProvider());
@@ -513,8 +517,11 @@ void RunTest(int64_t M, int64_t N, int64_t K, int64_t block_size, int64_t accura
 }  // namespace
 
 TEST(MatMulNBits, Float16Cuda) {
-#if defined(USE_CUDA) || defined(USE_ROCM)
-  auto has_gidx_options = {true, false};
+#if defined(USE_CUDA) || defined(USE_ROCM) || defined(USE_DML)
+  std::vector<bool> has_gidx_options = {true, false};
+  if (DefaultDmlExecutionProvider() != nullptr) {
+    has_gidx_options.assign(1, false);
+  }
 #else
   auto has_gidx_options = {false};
 #endif
@@ -525,7 +532,9 @@ TEST(MatMulNBits, Float16Cuda) {
         for (auto block_size : {16, 32, 64, 128}) {
           for (auto has_gidx : has_gidx_options) {
 #ifdef USE_DML
-            RunTest(M, N, K, block_size, 0, false, true, has_gidx, true, 0.04f);
+            if (DefaultDmlExecutionProvider() != nullptr) {
+              RunTest(M, N, K, block_size, 0, false, true, has_gidx, true, 0.04f);
+            }
 #else
             RunTest(M, N, K, block_size, 0, false, true, has_gidx);
             RunTest(M, N, K, block_size, 0, true, true, has_gidx, false);
@@ -538,12 +547,16 @@ TEST(MatMulNBits, Float16Cuda) {
 }
 
 TEST(MatMulNBits, Float16Large) {
-#ifdef USE_DML
+#if defined(USE_CUDA) || defined(USE_DML)
   // For some reason, the A10 machine that runs these tests during CI has a much bigger error than all retail
   // machines we tested on. All consumer-grade machines from Nvidia/AMD/Intel seem to pass these tests with an
   // absolute error of 0.08, but the A10 has errors going as high as 0.22. Ultimately, given the large number
   // of elements in this test, ULPs should probably be used instead of absolute/relative tolerances.
-  float abs_error = 0.3f;
+  float abs_error = 0.05f;
+  if (DefaultDmlExecutionProvider() != nullptr) {
+    // it means the ep is dml in runtime, the abs_error is changed to 0.3f
+    abs_error = 0.3f;
+  }
 #elif USE_WEBGPU
   // See Intel A770 to pass these tests with an absolute error of 0.08.
   float abs_error = 0.08f;
@@ -559,7 +572,6 @@ TEST(MatMulNBits, Float16Large) {
     }
   }
 }
-
 #endif  // defined(USE_CUDA) || defined(USE_ROCM) || defined(USE_DML)
 }  // namespace test
 }  // namespace onnxruntime

onnxruntime/test/contrib_ops/matmul_integer_to_float_test.cc

@@ -227,7 +227,7 @@ TEST(MatMulIntegerToFloat, HasZeroPoint_HasBias_test_U8S8) {
 }
 
 // DML EP supports Float16 output type and Signed A Matrix and Unsigned B Matric for Float32 output
-#if defined(USE_DML)
+#if defined(USE_DML) && !defined(USE_CUDA)
 
 TEST(MatMulIntegerToFloat, HasZeroPoint_NoBias_test_S8U8) {
   RunMatMulIntegerToFloatTest<int8_t, uint8_t, float, true, false>();

onnxruntime/test/contrib_ops/tensor_op_test.cc

@@ -121,7 +121,15 @@ void MeanVarianceNormalizationAcrossChannels(bool across_channels, bool normaliz
   test.AddAttribute("normalize_variance", normalize_variance ? one : zero);
   test.AddInput<float>("input", {N, C, H, W}, X);
   test.AddOutput<float>("output", {N, C, H, W}, result);
+#if defined(USE_CUDA) && defined(USE_DML)
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kOpenVINOExecutionProvider, kCudaExecutionProvider, kTensorrtExecutionProvider});
+  } else if (DefaultDmlExecutionProvider() == nullptr) {
+    test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kOpenVINOExecutionProvider, kDmlExecutionProvider, kTensorrtExecutionProvider});
+  }
+#else
   test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kOpenVINOExecutionProvider, kTensorrtExecutionProvider});  // OpenVINO doesn't support MVN operator below opset 9. TensorRT doesn't support opset 8 of MVN operator.
+#endif
 }
 
 void MeanVarianceNormalizationPerChannel(bool across_channels, bool normalize_variance) {
@@ -188,7 +196,15 @@ void MeanVarianceNormalizationPerChannel(bool across_channels, bool normalize_va
   test.AddAttribute("normalize_variance", normalize_variance ? one : zero);
   test.AddInput<float>("input", {N, C, H, W}, X);
   test.AddOutput<float>("output", {N, C, H, W}, result);
+#if defined(USE_CUDA) && defined(USE_DML)
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kOpenVINOExecutionProvider, kCudaExecutionProvider, kTensorrtExecutionProvider});
+  } else if (DefaultDmlExecutionProvider() == nullptr) {
+    test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kOpenVINOExecutionProvider, kDmlExecutionProvider, kTensorrtExecutionProvider});
+  }
+#else
   test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kOpenVINOExecutionProvider, kTensorrtExecutionProvider});  // OpenVINO doesn't support MVN operator below opset 9. TensorRT doesn't support opset 8 of MVN operator.
+#endif
 }
 
 TEST(MVNContribOpTest, MeanVarianceNormalizationCPUTest_Version1_TO_8) {
@@ -230,7 +246,9 @@ TEST(UnfoldTensorOpTest, LastDim) {
 
   std::vector<std::unique_ptr<IExecutionProvider>> execution_providers;
 #ifdef USE_CUDA
-  execution_providers.push_back(DefaultCudaExecutionProvider());
+  if (DefaultCudaExecutionProvider() != nullptr) {
+    execution_providers.push_back(DefaultCudaExecutionProvider());
+  }
 #endif
   execution_providers.push_back(DefaultCpuExecutionProvider());
   tester.Run(OpTester::ExpectResult::kExpectSuccess, "", {}, nullptr, &execution_providers);

onnxruntime/test/framework/allocation_planner_test.cc

@@ -28,6 +28,7 @@ using json = nlohmann::json;
 #ifdef USE_CUDA
 #include "core/providers/cuda/cuda_execution_provider.h"
 #include "core/providers/cuda/cuda_provider_factory.h"
+#include "test/common/cuda_op_test_utils.h"
 #endif  // USE_CUDA
 #include "core/session/onnxruntime_session_options_config_keys.h"
 using namespace ONNX_NAMESPACE;
@@ -894,6 +895,9 @@ TEST_F(PlannerTest, LocationPlanningForPassThroughExplicitAndImplicitSubgraphInp
   SessionOptions so;
   InferenceSession sess{so, GetEnvironment()};
 
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   auto status = sess.RegisterExecutionProvider(DefaultCudaExecutionProvider());
   ASSERT_TRUE(status.IsOK());
 
@@ -1036,6 +1040,9 @@ TEST_F(PlannerTest, LocationPlanningForInitializersOnlyUsedInANestedSubgraph) {
   SessionOptions so;
   InferenceSession sess{so, GetEnvironment()};
 
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   auto status = sess.RegisterExecutionProvider(DefaultCudaExecutionProvider());
   ASSERT_TRUE(status.IsOK());
 
@@ -1143,6 +1150,9 @@ TEST_F(PlannerTest, LocationPlanningForInitializersUsedOnDifferentDevicesInMainG
   SessionOptions so;
   InferenceSession sess{so, GetEnvironment()};
 
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   auto status = sess.RegisterExecutionProvider(DefaultCudaExecutionProvider());
   ASSERT_TRUE(status.IsOK());
 
@@ -1235,6 +1245,9 @@ TEST_F(PlannerTest, LocationPlanningForImplicitInputsWithoutExplicitConsumersInM
   SessionOptions so;
   InferenceSession sess{so, GetEnvironment()};
 
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   auto status = sess.RegisterExecutionProvider(DefaultCudaExecutionProvider());
   ASSERT_TRUE(status.IsOK());
 
@@ -1267,6 +1280,10 @@ TEST_F(PlannerTest, LocationPlanningForImplicitInputsWithoutExplicitConsumersInM
 // Test MultiStream scenario for the graph:
 // node1(CPU ep)->node2(CPU ep)->node3(CUDA ep)->node4(CPU ep)
 TEST_F(PlannerTest, MultiStream) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
+
   ONNX_NAMESPACE::TensorProto tensor;
   tensor.add_dims(1);
   tensor.add_float_data(1.0f);
@@ -1285,6 +1302,7 @@ TEST_F(PlannerTest, MultiStream) {
   onnxruntime::ProviderInfo_CUDA& ep = onnxruntime::GetProviderInfo_CUDA();
   auto epFactory = ep.CreateExecutionProviderFactory(epi);
   std::unique_ptr<IExecutionProvider> execution_provider = epFactory->CreateProvider();
+
   ORT_THROW_IF_ERROR(GetExecutionProviders().Add("CUDAExecutionProvider", std::move(execution_provider)));
 
   CreatePlan({}, false);
@@ -1312,6 +1330,9 @@ TEST_F(PlannerTest, MultiStream) {
 //      node3
 // All 3 nodes are CUDA EP, node1 is in stream0, node2 is in stream1, node3 is in stream2
 TEST_F(PlannerTest, MultiStream1StreamWaitFor2Streams) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
   std::unique_ptr<::onnxruntime::KernelDef> cudaKernel = KernelDefBuilder().SetName("Transpose").Provider(kCudaExecutionProvider).SinceVersion(1, 10).Build();
   std::unique_ptr<::onnxruntime::KernelDef> cudaKernelAdd = KernelDefBuilder().SetName("Add").Provider(kCudaExecutionProvider).SinceVersion(1, 10).Build();
   std::string Graph_input("Graph_input"), Arg1("Arg1"), Arg2("Arg2"), Arg3("Arg3"), node1("node1"), node2("node2"), node3("node3");
@@ -1353,6 +1374,9 @@ TEST_F(PlannerTest, MultiStream1StreamWaitFor2Streams) {
 // stream 1: node2 (CPU EP)
 // node1's output, which is consumed by both node2 and node3, is in CPU.
 TEST_F(PlannerTest, MultiStreamCudaEPNodeCPUOutput) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
   MemcpyToHostInCuda_TransposeInCudaAndCpu("./testdata/multi_stream_models/memcpyToHost_same_stream_with_transpose.json");
   EXPECT_EQ(GetState().GetExecutionPlan()->execution_plan.size(), 2) << "2 logic streams";
   EXPECT_EQ(GetState().GetExecutionPlan()->execution_plan[0]->steps_.size(), 5) << "stream 0 has 5 steps";
@@ -1374,6 +1398,11 @@ TEST_F(PlannerTest, MultiStreamCudaEPNodeCPUOutput) {
 // TODO(leca): there is a bug in the corresponding graph that node2 will be visited twice when traversing node1's output nodes
 // (see: for (auto it = node->OutputNodesBegin(); it != node->OutputNodesEnd(); ++it) in BuildExecutionPlan()). We can just break the loop and don't need the extra variables once it is fixed
 TEST_F(PlannerTest, MultiStreamMultiOutput) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
+#endif
   std::unique_ptr<::onnxruntime::KernelDef> cudaKernel = KernelDefBuilder().SetName("RNN").Provider(kCudaExecutionProvider).SinceVersion(7).Build();
   std::string Graph_input1("Graph_input1"), Graph_input2("Graph_input2"), Graph_input3("Graph_input3"), Arg1("Arg1"), Arg2("Arg2"), Arg3("Arg3"), node1("node1"), node2("node2");
   std::vector<onnxruntime::NodeArg*> input1{Arg(Graph_input1), Arg(Graph_input2), Arg(Graph_input3)}, output1{Arg(Arg1), Arg(Arg2)}, input2{Arg(Arg1), Arg(Arg2)}, output2{Arg(Arg3)};
@@ -1411,6 +1440,9 @@ TEST_F(PlannerTest, MultiStreamMultiOutput) {
 // TODO(leca): the ideal case is there is only 1 wait step before launching node3,
 // as there is a specific order between node1 and node2 if they are in the same stream, thus node3 will only need to wait the latter one
 TEST_F(PlannerTest, MultiStream2NodesSameStreamConsumedBy1NodeInDifferentStream) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
   std::unique_ptr<::onnxruntime::KernelDef> cudaKernel = KernelDefBuilder().SetName("Transpose").Provider(kCudaExecutionProvider).SinceVersion(1, 10).Build();
   std::string Graph_input1("Graph_input1"), Graph_input2("Graph_input2"), Graph_input3("Graph_input3"), Arg1("Arg1"), Arg2("Arg2"), Arg3("Arg3"), node1("node1"), node2("node2"), node3("node3");
   std::vector<onnxruntime::NodeArg*> input1{Arg(Graph_input1)}, input2{Arg(Graph_input2)}, output1{Arg(Arg1)}, output2{Arg(Arg2)}, input3{Arg(Arg1), Arg(Arg2)}, output3{Arg(Arg3)};
@@ -1448,6 +1480,9 @@ TEST_F(PlannerTest, MultiStream2NodesSameStreamConsumedBy1NodeInDifferentStream)
 
 #if !defined(__wasm__) && defined(ORT_ENABLE_STREAM)
 TEST_F(PlannerTest, ParaPlanCreation) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
   TypeProto graph_in_type;
   graph_in_type.mutable_tensor_type()->set_elem_type(TensorProto_DataType_FLOAT);
   auto* graph_in_shape = graph_in_type.mutable_tensor_type()->mutable_shape();
@@ -1889,6 +1924,10 @@ TEST_F(PlannerTest, ParaPlanCreation) {
 }
 
 TEST_F(PlannerTest, TestMultiStreamConfig) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
+
   const char* type = "DeviceBasedPartitioner";
   constexpr size_t type_len = 22;
 
@@ -1962,6 +2001,10 @@ TEST_F(PlannerTest, TestMultiStreamSaveConfig) {
 
 // Load with partition config where a node is missing, session load expected to fail.
 TEST_F(PlannerTest, TestMultiStreamMissingNodeConfig) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
+
   const char* config_file_path = "./testdata/multi_stream_models/conv_add_relu_single_stream_missing_node.json";
   SessionOptions sess_opt;
   sess_opt.graph_optimization_level = TransformerLevel::Default;
@@ -1982,6 +2025,9 @@ TEST_F(PlannerTest, TestMultiStreamMissingNodeConfig) {
 
 // Load with partition config where streams and devices has mismatch
 TEST_F(PlannerTest, TestMultiStreamMismatchDevice) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
   const char* config_file_path = "./testdata/multi_stream_models/conv_add_relu_single_stream_mismatch_device.json";
   SessionOptions sess_opt;
   sess_opt.graph_optimization_level = TransformerLevel::Default;
@@ -2007,6 +2053,9 @@ TEST_F(PlannerTest, TestCpuIf) {
   sess_opt.graph_optimization_level = TransformerLevel::Default;
 
   InferenceSession sess(sess_opt, GetEnvironment(), ORT_TSTR("./testdata/multi_stream_models/cpu_if.onnx"));
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   ASSERT_STATUS_OK(sess.RegisterExecutionProvider(DefaultCudaExecutionProvider()));
   ASSERT_STATUS_OK(sess.Load());
   ASSERT_STATUS_OK(sess.Initialize());
@@ -2067,10 +2116,17 @@ TEST_F(PlannerTest, TestCpuIf) {
 //    onnx.save(model, 'issue_19480.onnx')
 //
 TEST(AllocationPlannerTest, ReusedInputCrossDifferentStreams) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
+
   SessionOptions sess_opt;
   sess_opt.graph_optimization_level = TransformerLevel::Default;
 
   InferenceSession sess(sess_opt, GetEnvironment(), ORT_TSTR("./testdata/multi_stream_models/issue_19480.onnx"));
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   auto status = sess.RegisterExecutionProvider(DefaultCudaExecutionProvider());
   status = sess.Load();
   status = sess.Initialize();

onnxruntime/test/framework/cuda/fence_cuda_test.cc

@@ -115,6 +115,9 @@ TEST(CUDAFenceTests, DISABLED_PartOnCPU) {
   SessionOptions so;
   FenceCudaTestInferenceSession session(so, GetEnvironment());
   ASSERT_STATUS_OK(LoadInferenceSessionFromModel(session, *model));
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   ASSERT_STATUS_OK(session.RegisterExecutionProvider(DefaultCudaExecutionProvider()));
   ASSERT_TRUE(session.Initialize().IsOK());
   ASSERT_TRUE(1 == CountCopyNodes(graph));
@@ -164,6 +167,9 @@ TEST(CUDAFenceTests, TileWithInitializer) {
   SessionOptions so;
   FenceCudaTestInferenceSession session(so, GetEnvironment());
   ASSERT_STATUS_OK(LoadInferenceSessionFromModel(session, *model));
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   ASSERT_STATUS_OK(session.RegisterExecutionProvider(DefaultCudaExecutionProvider()));
   ASSERT_STATUS_OK(session.Initialize());
 
@@ -224,6 +230,9 @@ TEST(CUDAFenceTests, TileWithComputedInput) {
   SessionOptions so;
   FenceCudaTestInferenceSession session(so, GetEnvironment());
   ASSERT_STATUS_OK(LoadInferenceSessionFromModel(session, *model));
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   ASSERT_STATUS_OK(session.RegisterExecutionProvider(DefaultCudaExecutionProvider()));
   ASSERT_TRUE(session.Initialize().IsOK());
 

onnxruntime/test/framework/inference_session_test.cc

@@ -34,6 +34,7 @@
 #ifdef USE_CUDA
 #include "core/providers/cuda/cuda_provider_factory.h"
 #include "core/providers/cuda/gpu_data_transfer.h"
+#include "test/common/cuda_op_test_utils.h"
 #endif
 #ifdef USE_TENSORRT
 #include "core/providers/tensorrt/tensorrt_provider_options.h"
@@ -689,6 +690,9 @@ TEST(InferenceSessionTests, CheckRunProfilerWithSessionOptions) {
 
   InferenceSession session_object(so, GetEnvironment());
 #ifdef USE_CUDA
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   ASSERT_STATUS_OK(session_object.RegisterExecutionProvider(DefaultCudaExecutionProvider()));
 #endif
 #ifdef USE_ROCM
@@ -743,6 +747,9 @@ TEST(InferenceSessionTests, CheckRunProfilerWithSessionOptions2) {
 
   InferenceSession session_object(so, GetEnvironment());
 #ifdef USE_CUDA
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   ASSERT_STATUS_OK(session_object.RegisterExecutionProvider(DefaultCudaExecutionProvider()));
 #endif
 #ifdef USE_ROCM
@@ -1055,6 +1062,9 @@ static void TestBindHelper(const std::string& log_str,
   if (bind_provider_type == kCudaExecutionProvider || bind_provider_type == kRocmExecutionProvider) {
 #ifdef USE_CUDA
     auto provider = DefaultCudaExecutionProvider();
+    if (provider == nullptr) {
+      return;
+    }
     gpu_provider = provider.get();
     ASSERT_STATUS_OK(session_object.RegisterExecutionProvider(std::move(provider)));
 #endif
@@ -1650,6 +1660,9 @@ TEST(InferenceSessionTests, Test3LayerNestedSubgraph) {
 #if USE_TENSORRT
   ASSERT_STATUS_OK(session_object.RegisterExecutionProvider(DefaultTensorrtExecutionProvider()));
 #elif USE_CUDA
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   ASSERT_STATUS_OK(session_object.RegisterExecutionProvider(DefaultCudaExecutionProvider()));
 #elif USE_ROCM
   ASSERT_STATUS_OK(session_object.RegisterExecutionProvider(DefaultRocmExecutionProvider()));
@@ -1802,6 +1815,9 @@ TEST(InferenceSessionTests, Test2LayerNestedSubgraph) {
 #if USE_TENSORRT
   ASSERT_STATUS_OK(session_object.RegisterExecutionProvider(DefaultTensorrtExecutionProvider()));
 #elif USE_CUDA
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    return;
+  }
   ASSERT_STATUS_OK(session_object.RegisterExecutionProvider(DefaultCudaExecutionProvider()));
 #elif USE_ROCM
   ASSERT_STATUS_OK(session_object.RegisterExecutionProvider(DefaultRocmExecutionProvider()));
@@ -2157,6 +2173,9 @@ TEST(InferenceSessionTests, TestStrictShapeInference) {
 #ifdef USE_CUDA
 // disable it, since we are going to enable parallel execution with cuda ep
 TEST(InferenceSessionTests, DISABLED_TestParallelExecutionWithCudaProvider) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
   string model_uri = "testdata/transform/fusion/fuse-conv-bn-mul-add-unsqueeze.onnx";
 
   SessionOptions so;
@@ -2180,6 +2199,10 @@ TEST(InferenceSessionTests, DISABLED_TestParallelExecutionWithCudaProvider) {
 }
 
 TEST(InferenceSessionTests, TestArenaShrinkageAfterRun) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
+
   OrtArenaCfg arena_cfg;
   arena_cfg.arena_extend_strategy = 1;  // kSameAsRequested
 

onnxruntime/test/framework/memcpy_transformer_test.cc

@@ -9,6 +9,9 @@
 #include "default_providers.h"
 #include "gtest/gtest.h"
 #include "test_utils.h"
+#ifdef USE_CUDA
+#include "test/common/cuda_op_test_utils.h"
+#endif
 #include "test/test_environment.h"
 #include "asserts.h"
 
@@ -74,6 +77,9 @@ void ExpectCopy(const onnxruntime::Node& source, const std::string copy_op,
 #ifdef USE_CUDA
 
 TEST(TransformerTest, MemcpyTransformerTest) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
   std::unordered_map<std::string, int> domain_to_version;
   domain_to_version[kOnnxDomain] = 7;
   auto model = std::make_shared<onnxruntime::Model>("test", false, ModelMetaData(), PathString(),
@@ -106,7 +112,9 @@ TEST(TransformerTest, MemcpyTransformerTest) {
 
   KernelRegistryManager kernel_registry_manager;
   ExecutionProviders execution_providers;
+#if defined(USE_CUDA)
   ASSERT_STATUS_OK(execution_providers.Add(onnxruntime::kCudaExecutionProvider, DefaultCudaExecutionProvider()));
+#endif
   ASSERT_STATUS_OK(execution_providers.Add(onnxruntime::kCpuExecutionProvider,
                                            std::make_unique<CPUExecutionProvider>(CPUExecutionProviderInfo())));
   KernelRegistryManager test_registry_manager;
@@ -129,6 +137,9 @@ TEST(TransformerTest, MemcpyTransformerTest) {
 }
 
 TEST(TransformerTest, MemcpyTransformerTestCudaFirst) {
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
   std::unordered_map<std::string, int> domain_to_version;
   domain_to_version[kOnnxDomain] = 7;
   auto model = std::make_shared<onnxruntime::Model>("test", false, ModelMetaData(), PathString(),
@@ -161,7 +172,9 @@ TEST(TransformerTest, MemcpyTransformerTestCudaFirst) {
 
   KernelRegistryManager kernel_registry_manager;
   ExecutionProviders execution_providers;
+
   ASSERT_STATUS_OK(execution_providers.Add(onnxruntime::kCudaExecutionProvider, DefaultCudaExecutionProvider()));
+
   ASSERT_STATUS_OK(execution_providers.Add(onnxruntime::kCpuExecutionProvider,
                                            std::make_unique<CPUExecutionProvider>(CPUExecutionProviderInfo())));
   KernelRegistryManager test_registry_manager;
@@ -281,7 +294,11 @@ TEST(TransformerTest, TestInitializerDuplicationInSubgraph) {
 
   KernelRegistryManager kernel_registry_manager;
   ExecutionProviders execution_providers;
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
   ASSERT_STATUS_OK(execution_providers.Add(onnxruntime::kCudaExecutionProvider, DefaultCudaExecutionProvider()));
+
   ASSERT_STATUS_OK(execution_providers.Add(onnxruntime::kCpuExecutionProvider,
                                            std::make_unique<CPUExecutionProvider>(CPUExecutionProviderInfo())));
   KernelRegistryManager test_registry_manager;
@@ -323,7 +340,11 @@ TEST(TransformerTest, MemcpyTransformerTestGraphInputConsumedOnMultipleDevices)
 
   KernelRegistryManager kernel_registry_manager;
   ExecutionProviders execution_providers;
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
   ASSERT_STATUS_OK(execution_providers.Add(onnxruntime::kCudaExecutionProvider, DefaultCudaExecutionProvider()));
+
   ASSERT_STATUS_OK(execution_providers.Add(onnxruntime::kCpuExecutionProvider,
                                            std::make_unique<CPUExecutionProvider>(CPUExecutionProviderInfo())));
   KernelRegistryManager test_registry_manager;
@@ -425,7 +446,11 @@ TEST(TransformerTest, MemcpyTransformerTestImplicitInputConsumedOnMultipleDevice
 
   KernelRegistryManager kernel_registry_manager;
   ExecutionProviders execution_providers;
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
   ASSERT_STATUS_OK(execution_providers.Add(onnxruntime::kCudaExecutionProvider, DefaultCudaExecutionProvider()));
+
   ASSERT_STATUS_OK(execution_providers.Add(onnxruntime::kCpuExecutionProvider,
                                            std::make_unique<CPUExecutionProvider>(CPUExecutionProviderInfo())));
   KernelRegistryManager test_registry_manager;

onnxruntime/test/framework/sparse_kernels_test.cc

@@ -1457,6 +1457,9 @@ TEST(SparseTensorConversionTests, CsrConversion) {
 
 #ifdef USE_CUDA
   auto cuda_provider = DefaultCudaExecutionProvider();
+  if (cuda_provider == nullptr) {
+    return;
+  }
   auto cuda_allocator = cuda_provider->CreatePreferredAllocators()[0];
   {
     auto cuda_transfer = cuda_provider->GetDataTransfer();
@@ -1684,6 +1687,9 @@ TEST(SparseTensorConversionTests, CooConversion) {
 
 #ifdef USE_CUDA
   auto cuda_provider = DefaultCudaExecutionProvider();
+  if (cuda_provider == nullptr) {
+    return;
+  }
   auto cuda_allocator = cuda_provider->CreatePreferredAllocators()[0];
   {
     auto cuda_transfer = cuda_provider->GetDataTransfer();

onnxruntime/test/lora/lora_test.cc

@@ -201,6 +201,14 @@ TEST(LoraAdapterTest, Load) {
 
 #ifdef USE_CUDA
 TEST(LoraAdapterTest, VerifyCudaDeviceCopy) {
+  if (DefaultCudaExecutionProvider() == nullptr) {
+    GTEST_SKIP() << "Skip This Test Due to this EP is null";
+  }
+#ifdef USE_DML
+  if (DefaultDmlExecutionProvider() != nullptr) {
+    GTEST_FAIL() << "It should not run with DML EP";
+  }
+#endif
   auto cpu_ep = DefaultCpuExecutionProvider();
   auto cpu_allocator = cpu_ep->CreatePreferredAllocators()[0];
   auto cuda_allocator = DefaultCudaExecutionProvider()->CreatePreferredAllocators()[0];
@@ -234,6 +242,17 @@ TEST(LoraAdapterTest, VerifyCudaDeviceCopy) {
 
 #ifdef USE_DML
 TEST(LoraAdapterTest, VerifyDmlDeviceCopy) {
+  // NO_DML_TEST is set, DML test is skipped
+  if (DefaultDmlExecutionProvider() == nullptr) {
+    GTEST_SKIP() << "Skip This Test Due to this EP is null";
+  }
+
+#ifdef USE_CUDA
+  if (DefaultCudaExecutionProvider() != nullptr) {
+    GTEST_FAIL() << "It should not run with CUDA EP";
+  }
+#endif
+
   auto cpu_ep = DefaultCpuExecutionProvider();
   auto cpu_allocator = cpu_ep->CreatePreferredAllocators()[0];
 

onnxruntime/test/providers/base_tester.cc

@@ -529,6 +529,17 @@ void BaseTester::Run(ExpectResult expect_result, const std::string& expected_fai
   so.use_deterministic_compute = use_determinism_;
   so.graph_optimization_level = TransformerLevel::Default;  // 'Default' == off
 
+  // remove nullptr in execution_providers.
+  // it's a little ugly but we need to do this because DefaultXXXExecutionProvider() can return nullptr in Runtime.
+  // And there're many places adding DefaultXXXExecutionProvider() to execution_providers directly.
+  if (execution_providers != nullptr) {
+    execution_providers->erase(std::remove(execution_providers->begin(), execution_providers->end(), nullptr), execution_providers->end());
+    if (execution_providers->size() == 0) {
+      // In fact, no ep is needed to run
+      return;
+    }
+  }
+
   Run(so, expect_result, expected_failure_string, excluded_provider_types, run_options, execution_providers, options);
 }
 

onnxruntime/test/providers/compare_provider_test_utils.cc

@@ -53,6 +53,11 @@ void CompareOpTester::CompareWithCPU(const std::string& target_provider_type,
   SetTestFunctionCalled();
 
   std::unique_ptr<IExecutionProvider> target_execution_provider = GetExecutionProvider(target_provider_type);
+#if defined(USE_CUDA) && defined(USE_DML)
+  if (target_execution_provider == nullptr) {
+    return;
+  }
+#endif
   ASSERT_TRUE(target_execution_provider != nullptr) << "provider_type " << target_provider_type
                                                     << " is not supported.";
 

onnxruntime/test/providers/cpu/model_tests.cc

@@ -491,6 +491,18 @@ static constexpr ORT_STRING_VIEW provider_name_dml = ORT_TSTR("dml");
   // the number of times these are run to reduce the CI time.
   provider_names.erase(provider_name_cpu);
 #endif
+
+#if defined(USE_CUDA) && defined(USE_DML)
+  const std::string no_cuda_ep_test = Env::Default().GetEnvironmentVar("NO_CUDA_TEST");
+  if (no_cuda_ep_test == "1") {
+    provider_names.erase(provider_name_cuda);
+  }
+  const std::string no_dml_ep_test = Env::Default().GetEnvironmentVar("NO_DML_TEST");
+  if (no_dml_ep_test == "1") {
+    provider_names.erase(provider_name_dml);
+  }
+#endif
+
   std::vector<std::basic_string<ORTCHAR_T>> v;
   // Permanently exclude following tests because ORT support only opset starting from 7,
   // Please make no more changes to the list

onnxruntime/test/providers/cpu/tensor/gather_op_test.cc

@@ -3,6 +3,9 @@
 
 #include "core/session/onnxruntime_session_options_config_keys.h"
 #include "gtest/gtest.h"
+#if USE_CUDA
+#include "test/common/cuda_op_test_utils.h"
+#endif
 #include "test/providers/provider_test_utils.h"
 #include "test/util/include/default_providers.h"
 
@@ -122,6 +125,9 @@ TEST(GatherOpTest, Gather_invalid_index_gpu) {
                          4.0f, 5.0f, 6.0f, 7.0f,
                          0.0f, 0.0f, 0.0f, 0.0f});
 
+#if defined(USE_CUDA) && defined(USE_DML)
+  SKIP_CUDA_TEST_WITH_DML;
+#endif
   // On GPU, just set the value to 0 instead of report error. exclude all other providers
   test
 #if defined(USE_CUDA)

onnxruntime/test/providers/cpu/tensor/grid_sample_test.cc

@@ -15,11 +15,13 @@ std::vector<std::unique_ptr<IExecutionProvider>> GetExecutionProviders(int opset
   execution_providers.emplace_back(DefaultCpuExecutionProvider());
 
 #ifdef USE_CUDA
-  if (opset_version < 20) {
-    execution_providers.emplace_back(DefaultCudaExecutionProvider());
+  if (DefaultCudaExecutionProvider() != nullptr) {
+    if (opset_version < 20) {
+      execution_providers.emplace_back(DefaultCudaExecutionProvider());
 #ifdef ENABLE_CUDA_NHWC_OPS
-    execution_providers.push_back(DefaultCudaNHWCExecutionProvider());
+      execution_providers.push_back(DefaultCudaNHWCExecutionProvider());
 #endif
+    }
   }
 #endif
 

onnxruntime/test/util/default_providers.cc

@@ -122,6 +122,12 @@ std::unique_ptr<IExecutionProvider> DefaultOpenVINOExecutionProvider() {
 
 std::unique_ptr<IExecutionProvider> DefaultCudaExecutionProvider() {
 #ifdef USE_CUDA
+#ifdef USE_DML
+  const std::string no_cuda_ep_test = Env::Default().GetEnvironmentVar("NO_CUDA_TEST");
+  if (no_cuda_ep_test == "1") {
+    return nullptr;
+  }
+#endif
   OrtCUDAProviderOptionsV2 provider_options{};
   provider_options.do_copy_in_default_stream = true;
   provider_options.use_tf32 = false;
@@ -328,6 +334,12 @@ std::unique_ptr<IExecutionProvider> DefaultCannExecutionProvider() {
 
 std::unique_ptr<IExecutionProvider> DefaultDmlExecutionProvider() {
 #ifdef USE_DML
+#ifdef USE_CUDA
+  const std::string no_dml_ep_test = Env::Default().GetEnvironmentVar("NO_DML_TEST");
+  if (no_dml_ep_test == "1") {
+    return nullptr;
+  }
+#endif
   ConfigOptions config_options{};
   if (auto factory = DMLProviderFactoryCreator::CreateFromDeviceOptions(config_options, nullptr, false, false)) {
     return factory->CreateProvider();

tools/ci_build/github/azure-pipelines/stages/nuget-combine-cuda-stage.yml

@@ -50,6 +50,8 @@ stages:
     win_trt_home: ${{ parameters.win_trt_home }}
     win_cuda_home: ${{ parameters.win_cuda_home }}
     buildJava: ${{ parameters.buildJava }}
+    SpecificArtifact: ${{ parameters.SpecificArtifact }}
+    BuildId: ${{ parameters.BuildId }}
 
 - template: nuget-cuda-packaging-stage.yml
   parameters:

tools/ci_build/github/azure-pipelines/stages/nuget-win-cuda-packaging-stage.yml

@@ -34,7 +34,7 @@ parameters:
   displayName: Specific Artifact's BuildId
   type: string
   default: '0'
-  
+
 - name: buildJava
   type: boolean
 
@@ -50,13 +50,14 @@ stages:
     msbuildPlatform: x64
     packageName: x64-cuda
     CudaVersion: ${{ parameters.CudaVersion }}
-    buildparameter: --use_cuda --cuda_home=${{ parameters.win_cuda_home }} --enable_onnx_tests --enable_wcos --cmake_extra_defines "CMAKE_CUDA_ARCHITECTURES=60;61;70;75;80"
+    buildparameter: --use_cuda --cuda_home=${{ parameters.win_cuda_home }} --enable_onnx_tests --enable_wcos --cmake_extra_defines "CMAKE_CUDA_ARCHITECTURES=60;61;70;75;80" --use_dml --build_csharp --parallel
     runTests: ${{ parameters.RunOnnxRuntimeTests }}
     buildJava: ${{ parameters.buildJava }}
     java_artifact_id: onnxruntime_gpu
     UseIncreasedTimeoutForTests: ${{ parameters.UseIncreasedTimeoutForTests }}
     SpecificArtifact: ${{ parameters.SpecificArtifact }}
     BuildId: ${{ parameters.BuildId }}
+    ComboTests: true
 # Windows CUDA with TensorRT Packaging
 - template: ../templates/win-ci.yml
   parameters:
@@ -68,7 +69,7 @@ stages:
     msbuildPlatform: x64
     CudaVersion: ${{ parameters.CudaVersion }}
     packageName: x64-tensorrt
-    buildparameter: --use_tensorrt --tensorrt_home=${{ parameters.win_trt_home }} --cuda_home=${{ parameters.win_cuda_home }}  --enable_onnx_tests --enable_wcos --cmake_extra_defines "CMAKE_CUDA_ARCHITECTURES=60;61;70;75;80"
+    buildparameter: --use_tensorrt --tensorrt_home=${{ parameters.win_trt_home }} --cuda_home=${{ parameters.win_cuda_home }}  --enable_onnx_tests --enable_wcos --cmake_extra_defines "CMAKE_CUDA_ARCHITECTURES=60;61;70;75;80" --parallel
     runTests: ${{ parameters.RunOnnxRuntimeTests }}
     buildJava: ${{ parameters.buildJava }}
     java_artifact_id: onnxruntime_gpu

tools/ci_build/github/azure-pipelines/templates/win-ci.yml

@@ -25,7 +25,7 @@ parameters:
 
 - name: runTests
   type: boolean
-  default: true
+  default: false
 
 - name: buildJava
   type: boolean
@@ -71,6 +71,10 @@ parameters:
       - 11.8
       - 12.2
 
+- name: ComboTests
+  type: boolean
+  default: false
+
 - name: SpecificArtifact
   displayName: Use Specific Artifact
   type: boolean
@@ -220,7 +224,7 @@ stages:
           condition: and(succeeded(), eq('${{ parameters.runTests}}', true))
           inputs:
             scriptPath: '$(Build.SourcesDirectory)\tools\ci_build\build.py'
-            arguments: '--config RelWithDebInfo --use_binskim_compliant_compile_flags --enable_lto --disable_rtti --build_dir $(Build.BinariesDirectory) --skip_submodule_sync --build_shared_lib --test --cmake_generator "$(VSGenerator)" --enable_onnx_tests  $(TelemetryOption) ${{ parameters.buildparameter }}'
+            arguments: '--config RelWithDebInfo --use_binskim_compliant_compile_flags --enable_lto --disable_rtti --build_dir $(Build.BinariesDirectory) --test --skip_submodule_sync --build_shared_lib --cmake_generator "$(VSGenerator)" --enable_onnx_tests  $(TelemetryOption) ${{ parameters.buildparameter }}'
             workingDirectory: '$(Build.BinariesDirectory)'
       - ${{ else }}:
         - powershell: |
@@ -332,6 +336,10 @@ stages:
           displayName: 'Clean Agent Directories'
           condition: always()
 
+        - script:
+            echo ${{ parameters.SpecificArtifact }}
+          displayName: 'Print Specific Artifact'
+
         - checkout: self
           clean: true
           submodules: none
@@ -395,13 +403,34 @@ stages:
           displayName: 'Append dotnet x86  Directory to PATH'
           condition: and(succeeded(), eq('${{ parameters.buildArch}}', 'x86'))
 
-        - task: PythonScript@0
-          displayName: 'test'
-          condition: and(succeeded(), eq('${{ parameters.runTests}}', true))
-          inputs:
-            scriptPath: '$(Build.SourcesDirectory)\tools\ci_build\build.py'
-            arguments: '--config RelWithDebInfo --use_binskim_compliant_compile_flags --enable_lto --disable_rtti --build_dir $(Build.BinariesDirectory) --skip_submodule_sync --build_shared_lib --test --enable_onnx_tests  $(TelemetryOption) '
-            workingDirectory: '$(Build.BinariesDirectory)'
+        - ${{ if eq(parameters.ComboTests, 'true') }}:
+          - task: PythonScript@0
+            displayName: 'test excludes CUDA'
+            condition: and(succeeded(), eq('${{ parameters.runTests}}', true))
+            inputs:
+              scriptPath: '$(Build.SourcesDirectory)\tools\ci_build\build.py'
+              arguments: '--config RelWithDebInfo --use_binskim_compliant_compile_flags --enable_lto --disable_rtti --build_dir $(Build.BinariesDirectory) --skip_submodule_sync --build_shared_lib --test --enable_onnx_tests  $(TelemetryOption) '
+              workingDirectory: '$(Build.BinariesDirectory)'
+            env:
+              NO_CUDA_TEST: '1'
+          - task: PythonScript@0
+            displayName: 'test excludes DML'
+            condition: and(succeeded(), eq('${{ parameters.runTests}}', true))
+            inputs:
+              scriptPath: '$(Build.SourcesDirectory)\tools\ci_build\build.py'
+              arguments: '--config RelWithDebInfo --use_binskim_compliant_compile_flags --enable_lto --disable_rtti --build_dir $(Build.BinariesDirectory) --skip_submodule_sync --build_shared_lib --test --enable_onnx_tests  $(TelemetryOption) '
+              workingDirectory: '$(Build.BinariesDirectory)'
+            env:
+              NO_DML_TEST: '1'
+        - ${{ else }}:
+          - task: PythonScript@0
+            displayName: 'test'
+            condition: and(succeeded(), eq('${{ parameters.runTests}}', true))
+            inputs:
+              scriptPath: '$(Build.SourcesDirectory)\tools\ci_build\build.py'
+              arguments: '--config RelWithDebInfo --use_binskim_compliant_compile_flags --enable_lto --disable_rtti --build_dir $(Build.BinariesDirectory) --skip_submodule_sync --build_shared_lib --test --enable_onnx_tests  $(TelemetryOption) '
+              workingDirectory: '$(Build.BinariesDirectory)'
+
 # Previous stage only assembles the java binaries, testing will be done in this stage with GPU machine
         - ${{ if eq(parameters.buildJava, 'true') }}:
           - template: make_java_win_binaries.yml