diff --git a/examples/models/llama2/custom_ops/op_sdpa.cpp b/examples/models/llama2/custom_ops/op_sdpa.cpp
index e3b3eba5416..5bda2506460 100644
--- a/examples/models/llama2/custom_ops/op_sdpa.cpp
+++ b/examples/models/llama2/custom_ops/op_sdpa.cpp
@@ -664,7 +664,6 @@ void update_cache(
     const Tensor& cache,
     int64_t start_pos,
     int64_t seq_length) {
-  ET_CHECK_MSG(seq_length == 1, "seq_length must be 1");
   ET_CHECK_MSG(
       projected_value.size(0) == 1,
       "projected_value must have batch size of 1");
diff --git a/examples/models/llama2/custom_ops/test_sdpa_with_kv_cache.py b/examples/models/llama2/custom_ops/test_sdpa_with_kv_cache.py
index 5bd05e3db05..1b8f425b673 100644
--- a/examples/models/llama2/custom_ops/test_sdpa_with_kv_cache.py
+++ b/examples/models/llama2/custom_ops/test_sdpa_with_kv_cache.py
@@ -235,6 +235,69 @@ def setUp(self):
         self.is_causal = True
 
 
+class SDPAWithDynamicShape(unittest.TestCase):
+
+    def setUp(self):
+        torch.manual_seed(42)
+        self.k_cache = torch.zeros((1, 10, 8, 4))
+        self.v_cache = torch.zeros((1, 10, 8, 4))
+        self.mask = torch.full(
+            (10, 10),
+            float("-inf"),
+        )
+        self.mask = torch.triu(self.mask, diagonal=1)
+        self.use_mask_with_custom_op = False
+        self.is_causal = False
+
+    def test_sdpa_with_cache_dynamic_shape_0(self):
+        q = torch.rand((1, 4, 8, 4))
+        k = torch.rand((1, 4, 8, 4))
+        v = torch.rand((1, 4, 8, 4))
+        seq_len = q.size(1)
+        start_pos = 0
+        attn_mask = self.mask[start_pos : start_pos + seq_len, :]
+        attn_mask = attn_mask[:, : start_pos + seq_len]
+
+        ref_output = _sdpa_with_kv_cache_ref(
+            q, k, v, self.k_cache, self.v_cache, attn_mask, start_pos, seq_len
+        )
+
+        op_output = torch.ops.llama.sdpa_with_kv_cache(
+            q, k, v, self.k_cache, self.v_cache, start_pos, seq_len, None, 0, True
+        )
+        self.assertTrue(torch.allclose(ref_output, op_output))
+
+    def test_sdpa_with_cache_dynamic_shape_2(self):
+        q = torch.rand((1, 3, 8, 4))
+        k = torch.rand((1, 3, 8, 4))
+        v = torch.rand((1, 3, 8, 4))
+        seq_len = q.size(1)
+        start_pos = 2
+        attn_mask = self.mask[start_pos : start_pos + seq_len, :]
+        attn_mask = attn_mask[:, : start_pos + seq_len]
+
+        ref_output = _sdpa_with_kv_cache_ref(
+            q, k, v, self.k_cache, self.v_cache, attn_mask, start_pos, seq_len
+        )
+
+        op_output = torch.ops.llama.sdpa_with_kv_cache(
+            q, k, v, self.k_cache, self.v_cache, start_pos, seq_len, None, 0, True
+        )
+        self.assertTrue(torch.allclose(ref_output, op_output))
+
+    @unittest.skip("This test will expect failure but runtime is not bubbling it up.")
+    def test_sdpa_with_cache_dynamic_shape_4(self):
+        q = torch.rand((1, 11, 8, 4))
+        k = torch.rand((1, 11, 8, 4))
+        v = torch.rand((1, 11, 8, 4))
+        seq_len = q.size(1)
+        start_pos = 4
+
+        torch.ops.llama.sdpa_with_kv_cache(
+            q, k, v, self.k_cache, self.v_cache, start_pos, seq_len, None, 0, True
+        )
+
+
 class SDPATestWithMQA(unittest.TestCase):
 
     def setup_caches(self):