diff --git a/onnxruntime/core/optimizer/graph_transformer_utils.cc b/onnxruntime/core/optimizer/graph_transformer_utils.cc
index fce334f719239..3dc6e768e9262 100644
--- a/onnxruntime/core/optimizer/graph_transformer_utils.cc
+++ b/onnxruntime/core/optimizer/graph_transformer_utils.cc
@@ -272,12 +272,12 @@ InlinedVector<std::unique_ptr<GraphTransformer>> GenerateTransformers(
                                                                       onnxruntime::kCudaExecutionProvider,
                                                                       onnxruntime::kRocmExecutionProvider,
                                                                       onnxruntime::kDmlExecutionProvider};
-      const InlinedHashSet<std::string_view> cpu_cuda_rocm_acl_armnn_eps = {onnxruntime::kCpuExecutionProvider,
-                                                                            onnxruntime::kCudaExecutionProvider,
-                                                                            onnxruntime::kRocmExecutionProvider,
-                                                                            onnxruntime::kAclExecutionProvider,
-                                                                            onnxruntime::kArmNNExecutionProvider,
-                                                                            onnxruntime::kJsExecutionProvider };
+      const InlinedHashSet<std::string_view> cpu_cuda_rocm_acl_armnn_js_eps = {onnxruntime::kCpuExecutionProvider,
+                                                                               onnxruntime::kCudaExecutionProvider,
+                                                                               onnxruntime::kRocmExecutionProvider,
+                                                                               onnxruntime::kAclExecutionProvider,
+                                                                               onnxruntime::kArmNNExecutionProvider,
+                                                                               onnxruntime::kJsExecutionProvider};
       const InlinedHashSet<std::string_view> cpu_dml_eps = {onnxruntime::kCpuExecutionProvider,
                                                             onnxruntime::kDmlExecutionProvider};
 #ifdef MLAS_TARGET_AMD64_IX86
diff --git a/onnxruntime/core/providers/dml/DmlExecutionProvider/src/External/DirectMLHelpers/ApiTraits.h b/onnxruntime/core/providers/dml/DmlExecutionProvider/src/External/DirectMLHelpers/ApiTraits.h
index 1eb091b9b8ce1..3c0f49f3d2d49 100644
--- a/onnxruntime/core/providers/dml/DmlExecutionProvider/src/External/DirectMLHelpers/ApiTraits.h
+++ b/onnxruntime/core/providers/dml/DmlExecutionProvider/src/External/DirectMLHelpers/ApiTraits.h
@@ -2191,6 +2191,11 @@ struct OperatorTypeTraits<(DML_OPERATOR_TYPE)DML_OPERATOR_MULTIHEAD_ATTENTION>
 {
     using DescType = DML_MULTIHEAD_ATTENTION_OPERATOR_DESC;
 };
+template <>
+struct OperatorTypeTraits<(DML_OPERATOR_TYPE)DML_OPERATOR_MATRIX_MULTIPLY_INTEGER_TO_FLOAT>
+{
+    using DescType = DML_MATRIX_MULTIPLY_INTEGER_TO_FLOAT_OPERATOR_DESC;
+};
 
 // Calls a visitor functor, supplying an empty operator desc corresponding to the given DML_OPERATOR_TYPE as
 // the first argument.
@@ -2539,6 +2544,8 @@ auto OperatorTypeVisitor(DML_OPERATOR_TYPE type, Visitor&& visitor, Ts&&... args
         return std::invoke(std::forward<Visitor>(visitor), DML_ACTIVATION_SHRINK_OPERATOR_DESC{}, std::forward<Ts>(args)...);
     case DML_OPERATOR_ACTIVATION_GELU:
         return std::invoke(std::forward<Visitor>(visitor), DML_ACTIVATION_GELU_OPERATOR_DESC{}, std::forward<Ts>(args)...);
+    case DML_OPERATOR_MATRIX_MULTIPLY_INTEGER_TO_FLOAT:
+        return std::invoke(std::forward<Visitor>(visitor), DML_MATRIX_MULTIPLY_INTEGER_TO_FLOAT_OPERATOR_DESC{}, std::forward<Ts>(args)...);
 
 #pragma warning(push)
 #pragma warning(disable: 4063)
@@ -2700,7 +2707,7 @@ inline gsl::czstring ToString(DML_OPERATOR_TYPE value)
     case DML_OPERATOR_RESAMPLE2: return "DML_OPERATOR_RESAMPLE2";
     case DML_OPERATOR_RESAMPLE_GRAD1: return "DML_OPERATOR_RESAMPLE_GRAD1";
     case DML_OPERATOR_DIAGONAL_MATRIX1: return "DML_OPERATOR_DIAGONAL_MATRIX1";
-    case DML_OPERATOR_MULTIHEAD_ATTENTION: return "DML_OPERATOR_MULTIHEAD_ATTENTION";
+    case DML_OPERATOR_MATRIX_MULTIPLY_INTEGER_TO_FLOAT: return "DML_OPERATOR_MATRIX_MULTIPLY_INTEGER_TO_FLOAT";
     default:
         assert(false);
         return "<unknown>";
diff --git a/onnxruntime/core/providers/dml/OperatorAuthorHelper/OperatorHelper.h b/onnxruntime/core/providers/dml/OperatorAuthorHelper/OperatorHelper.h
index 71e808160163d..9080e8efedab9 100644
--- a/onnxruntime/core/providers/dml/OperatorAuthorHelper/OperatorHelper.h
+++ b/onnxruntime/core/providers/dml/OperatorAuthorHelper/OperatorHelper.h
@@ -1726,7 +1726,7 @@ using ShapeInferenceHelper_Identity14 = GetOutputShapeAsInputShapeHelper;
 using ShapeInferenceHelper_Identity16 = GetOutputShapeAsInputShapeHelper;
 using ShapeInferenceHelper_MatMul = MatMulHelper;
 using ShapeInferenceHelper_MatMulInteger = MatMulHelper;
-using ShapeInferenceHelper_MatMulIntegerToFloat = MatMulIntegerToFloatHelper;
+using ShapeInferenceHelper_MatMulIntegerToFloat = MatMulHelper;
 using ShapeInferenceHelper_QLinearMatMul = QLinearMatMulHelper;
 using ShapeInferenceHelper_QLinearAdd = GetBroadcastedOutputShapeHelper;
 using ShapeInferenceHelper_DynamicQuantizeLinear = GetOutputShapeAsInputShapeHelper;
diff --git a/onnxruntime/test/contrib_ops/matmul_integer_to_float_test.cc b/onnxruntime/test/contrib_ops/matmul_integer_to_float_test.cc
index 0d5dab35826c1..c7f2ec89fb817 100644
--- a/onnxruntime/test/contrib_ops/matmul_integer_to_float_test.cc
+++ b/onnxruntime/test/contrib_ops/matmul_integer_to_float_test.cc
@@ -39,12 +39,8 @@ static void CalculateMatMulIntegerToFloat(const int64_t M, const int64_t N, cons
     for (int64_t n = 0; n < N; n++) {
       float sum = 0.0f;
       for (int64_t k = 0; k < K; k++) {
-        float A_dequantized = has_zp ?
-                              (static_cast<int>(A_data[m * K + k]) - static_cast<int>(A_zero_point[0])) * A_scale[0] :
-                              A_data[m * K + k] * A_scale[0];
-        float B_dequantized = has_zp ?
-                              (static_cast<int>(B_data[k * N + n]) - static_cast<int>(B_zero_point[n])) * B_scale[n] :
-                              B_data[k * N + n] * B_scale[n];
+        float A_dequantized = has_zp ? (static_cast<int>(A_data[m * K + k]) - static_cast<int>(A_zero_point[0])) * A_scale[0] : A_data[m * K + k] * A_scale[0];
+        float B_dequantized = has_zp ? (static_cast<int>(B_data[k * N + n]) - static_cast<int>(B_zero_point[n])) * B_scale[n] : B_data[k * N + n] * B_scale[n];
 
         sum += A_dequantized * B_dequantized;
       }
@@ -81,8 +77,7 @@ void TestMatMulIntegerToFloat(bool is_matrix_b_constant,
 
   std::vector<WType> tmp_B_data;
   tmp_B_data = random.Uniform<WType>(B_dims,
-                                     (constexpr(std::is_same_v<WType, int8_t>)) ?
-                                     std::numeric_limits<int8_t>::lowest()/2 : std::numeric_limits<uint8_t>::lowest(),
+                                     (constexpr(std::is_same_v<WType, int8_t>)) ? std::numeric_limits<int8_t>::lowest() / 2 : std::numeric_limits<uint8_t>::lowest(),
                                      std::numeric_limits<WType>::max() / 2);
   std::transform(tmp_B_data.begin(), tmp_B_data.end(), std::back_inserter(B_data), [](int32_t v) -> WType {
     return static_cast<WType>(v);
@@ -148,7 +143,6 @@ void TestMatMulIntegerToFloat(bool is_matrix_b_constant,
   } else {
     test.Run();
   }
-
 }
 
 template <typename IType, typename WType, typename OType, bool HasZeroPoint, bool HasBias>
@@ -161,13 +155,6 @@ void RunMatMulIntegerToFloatTest() {
   );
 
   TestMatMulIntegerToFloat<IType, WType, OType>(
-                                         A_dims,
-                                         B_dims,
-                                         model_path,
-                                         true,         /*is_matrix_b_constant*/
-                                         false,        /*per_column*/
-                                         HasZeroPoint, /*has_zp*/
-                                         HasBias       /*has_bias*/
       true,         /*is_matrix_b_constant*/
       false,        /*per_column*/
       HasZeroPoint, /*has_zp*/