diff --git a/tritonbench/operators/flex_attention/operator.py b/tritonbench/operators/flex_attention/operator.py
index 0a83266b..0f7e3a83 100644
--- a/tritonbench/operators/flex_attention/operator.py
+++ b/tritonbench/operators/flex_attention/operator.py
@@ -88,6 +88,9 @@ def parse_op_args(args: List[str]):
         "--sliding-window-size", type=int, default=128, help="sliding window size"
     )
     parser.add_argument("--prefix-length", type=int, default=128, help="prefix length")
+    parser.add_argument(
+        "--use-tma", action="store_true", help="Whether to enable TMA in kernel options"
+    )
     return parser.parse_args(args)
 
 
@@ -109,6 +112,7 @@ def __init__(
         self.mod_type = args.mod_type
         self.sliding_window_size = args.sliding_window_size
         self.prefix_length = args.prefix_length
+        self.use_tma = args.use_tma
 
     def get_input_iter(self) -> Generator:
         """Generate a single input configuration for benchmarking."""
@@ -327,12 +331,15 @@ def get_full_shape(
                 "Inputs must be either two torch.Tensor objects or two shape tuples"
             )
 
-    @staticmethod
-    def get_kernel_options(attn_type: str, shape: FullShape):
+    @classmethod
+    def get_kernel_options(cls, attn_type: str, shape: FullShape):
+        """
+        Get kernel options for the given attention type and shape.
+        This method can be called as either a class method or an instance method.
+        """
         B, Hq, M, Hkv, N, D = shape
         is_decoding = M == 1
-        # TODO add ways to specify TMA and warp spec
-        # "ENABLE_TMA": True,
+        # Get base kernel options
         kernel_opt_training_dict = {
             "noop": None,
             "causal": None,
@@ -379,12 +386,20 @@ def get_default_split_k(B: int, H: int, Mk: int) -> int:
             "softcap": {"SPLIT_KV": get_default_split_k(B, Hkv, N) * 2},
         }
 
-        return (
+        # Get base options
+        base_options = (
             kernel_opt_decoding_dict[attn_type]
             if is_decoding
             else kernel_opt_training_dict[attn_type]
         )
 
+        # Add USE_TMA if enabled and base_options exists
+        # Check if this is being called from an instance or the class
+        if hasattr(cls, "use_tma") and cls.use_tma and base_options is not None:
+            base_options = {**base_options, "USE_TMA": True}
+
+        return base_options
+
     @staticmethod
     def generate_block_mask(
         attn_type: str, shape: FullShape, sliding_window_size: int, prefix_length: int