diff --git a/onnxruntime/contrib_ops/cuda/bert/group_query_attention.cc b/onnxruntime/contrib_ops/cuda/bert/group_query_attention.cc
index 93892169f6c79..9b0f7c8f06e42 100644
--- a/onnxruntime/contrib_ops/cuda/bert/group_query_attention.cc
+++ b/onnxruntime/contrib_ops/cuda/bert/group_query_attention.cc
@@ -72,6 +72,8 @@ Status GroupQueryAttention<T>::ComputeInternal(OpKernelContext* context) const {
   const Tensor* past_value = context->Input<Tensor>(4);
   const Tensor* seqlens_k = context->Input<Tensor>(5);
   const Tensor* total_seqlen = context->Input<Tensor>(6);
+  const Tensor* cos_cache = context->Input<Tensor>(7);
+  const Tensor* sin_cache = context->Input<Tensor>(8);
 
   auto& device_prop = GetDeviceProp();
   GroupQueryAttentionParameters parameters;
diff --git a/onnxruntime/core/graph/contrib_ops/bert_defs.cc b/onnxruntime/core/graph/contrib_ops/bert_defs.cc
index a99bb36984538..8302a7561b868 100644
--- a/onnxruntime/core/graph/contrib_ops/bert_defs.cc
+++ b/onnxruntime/core/graph/contrib_ops/bert_defs.cc
@@ -1008,6 +1008,14 @@ ONNX_MS_OPERATOR_SET_SCHEMA(
               "left_window_size for local attention (like Mistral). Default value is -1 meaning unused.",
               AttributeProto::INT,
               static_cast<int64_t>(-1))
+        .Attr("do_rotary",
+              "Whether to use rotary position embedding. Default value is 0.",
+              AttributeProto::INT,
+              OPTIONAL_VALUE)
+        .Attr("rotary_interleaved",
+              "Rotate using interleaved pattern. Default value is 0 (False).",
+              AttributeProto::INT,
+              OPTIONAL_VALUE)
         .Input(0,
                "query",
                "Query with shape (batch_size, sequence_length, hidden_size)",
@@ -1040,6 +1048,14 @@ ONNX_MS_OPERATOR_SET_SCHEMA(
                "total_sequence_length",
                "Scalar tensor of total sequence length (past + new).",
                "M")
+        .Input(7,
+               "cos_cache",
+               "2D tensor with shape (max_sequence_length, head_size / 2).",
+               "T")
+        .Input(8,
+               "sin_cache",
+               "2D tensor with shape (max_sequence_length, head_size / 2).",
+               "T")
         .Output(0,
                 "output",
                 "3D output tensor with shape (batch_size, sequence_length, hidden_size)",