diff --git a/examples/models/llama/source_transformation/sdpa.py b/examples/models/llama/source_transformation/sdpa.py
index 59bfbe6f95..eff6ee5aec 100644
--- a/examples/models/llama/source_transformation/sdpa.py
+++ b/examples/models/llama/source_transformation/sdpa.py
@@ -9,7 +9,7 @@
 # Example script for exporting Llama2 to flatbuffer
 
 import math
-from typing import Tuple, Union
+from typing import Optional, Tuple, Union
 
 import torch
 
@@ -22,20 +22,24 @@
 class SDPACustom(torch.nn.Module):
     def __init__(
         self,
-        kv_cache: Union[KVCache, QuantizedKVCache],
-        dim: int,
+        kv_cache: Optional[Union[KVCache, QuantizedKVCache]] = None,
+        dim: int = -1,
+        is_causal=True,
     ):
         super().__init__()
         # Custom op only supports float32 currently. Converting to/from float32 is
         # faster than not having the op.
         self.kv_cache = kv_cache
-        if not isinstance(kv_cache, QuantizedKVCache):
+        if kv_cache is None:
+            pass
+        elif not isinstance(kv_cache, QuantizedKVCache):
             self.kv_cache = kv_cache.to(torch.float)
         else:
             assert (
                 kv_cache.cache_fp_type == torch.float32
             ), "Only float32 is supported for custom SDPA"
         self.dim = dim
+        self.is_causal = is_causal
 
     def forward(
         self,
@@ -44,8 +48,8 @@ def forward(
         k: torch.Tensor,
         v: torch.Tensor,
         bsz,
-        seqlen,
-        mask,
+        seqlen=None,
+        mask=None,
     ):
         # Custom op only supports float32 currently. Converting to/from float32 is
         # faster than not having the op.
@@ -54,9 +58,20 @@ def forward(
         k = k.to(dtype=torch.float)
         v = v.to(dtype=torch.float)
 
-        k_cache = self.kv_cache.k_cache
-        v_cache = self.kv_cache.v_cache
-        if hasattr(self.kv_cache, "quantized_cache_dtype"):
+        k_cache = self.kv_cache.k_cache if self.kv_cache is not None else None
+        v_cache = self.kv_cache.v_cache if self.kv_cache is not None else None
+
+        if self.kv_cache is None:
+            output = torch.ops.llama.custom_sdpa(
+                q,
+                k,
+                v,
+                input_pos,
+                None,  # Attention mask
+                0,  # dropout probability. Ignored by the code
+                self.is_causal,  # is_causal
+            )
+        elif isinstance(self.kv_cache, QuantizedKVCache):
             # updated quantize cache, scale and zero points
             # returns dequantized kv cache
             # Not most optimal. Optimizations to follow next
@@ -68,7 +83,7 @@ def forward(
                 input_pos[0].item(),
                 None,  # Attention mask
                 0,  # dropout probability. Ignored by the code
-                True,  # is_causal
+                self.is_causal,  # is_causal
             )
         else:
             output = torch.ops.llama.sdpa_with_kv_cache(
@@ -81,7 +96,7 @@ def forward(
                 seqlen,
                 None,  # Attention mask
                 0,  # dropout probability. Ignored by the code
-                True,  # is_causal
+                self.is_causal,  # is_causal
             )
         return output.view(bsz, seqlen, self.dim).to(dtype=input_dtype)
 
@@ -99,7 +114,7 @@ def _replace_sdpa_with_custom_op(module: torch.nn.Module):
 
 
 def replace_sdpa_with_custom_op(module: torch.nn.Module) -> torch.nn.Module:
-    from executorch.extension.llm.custom_ops import custom_ops  # noqa
+    from executorch.extension.llm.custom_ops import custom_ops
 
     _replace_sdpa_with_custom_op(module)
     return module