diff --git a/test/testextra/test_tk.py b/test/testextra/test_tk.py
index e71c8bf947..a932a62c9c 100644
--- a/test/testextra/test_tk.py
+++ b/test/testextra/test_tk.py
@@ -807,7 +807,7 @@ class TestTK(unittest.TestCase):
 
     Tensor.manual_seed(42)
 
-    B, N, H, H_KV, D = 1, 32, 2, 1, 32
+    B, N, H, H_KV, D = 1, 1024, 32, 32, 128
 
     with Context(DEBUG=0):
       q = Tensor.randn(B, N, H, D, dtype=dtypes.bfloat16, requires_grad=True).contiguous()
@@ -840,5 +840,57 @@ class TestTK(unittest.TestCase):
     np.testing.assert_allclose(v.grad.numpy(), v_ref.grad.numpy(), atol=2e-2, rtol=2e-2)
     np.testing.assert_allclose(k.grad.numpy(), k_ref.grad.numpy(), atol=5e-2, rtol=2e-2)
 
+  @unittest.expectedFailure
+  def test_fast_fa_bwd_causal_jitted(self):
+    from extra.thunder.tiny.fa import flash_attention
+
+    Tensor.manual_seed(42)
+
+    B, N, H, H_KV, D = 1, 1024, 32, 32, 128
+
+    with Context(DEBUG=0):
+      q = Tensor.randn(B, N, H, D, dtype=dtypes.bfloat16, requires_grad=True).contiguous()
+      k = Tensor.randn(B, N, H_KV, D, dtype=dtypes.bfloat16, requires_grad=True).contiguous()
+      v = Tensor.randn(B, N, H_KV, D, dtype=dtypes.bfloat16, requires_grad=True).contiguous()
+      Tensor.realize(q, k, v)
+
+      do = Tensor.ones(B, N, H, D, dtype=dtypes.float32).contiguous()
+      Tensor.realize(do)
+
+    def fn(q, k, v, do):
+      q_, k_, v_ = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
+      out = flash_attention(q_, k_, v_, is_causal=True)
+      out = out.float().transpose(1, 2)
+      out.backward(do)
+      Tensor.realize(out, q.grad, k.grad, v.grad)
+      return q.grad, k.grad, v.grad
+
+    fn_jitted = TinyJit(fn)
+
+    for _ in range(10):
+      q = Tensor.randn(B, N, H, D, dtype=dtypes.bfloat16, requires_grad=True).contiguous()
+      k = Tensor.randn(B, N, H_KV, D, dtype=dtypes.bfloat16, requires_grad=True).contiguous()
+      v = Tensor.randn(B, N, H_KV, D, dtype=dtypes.bfloat16, requires_grad=True).contiguous()
+      Tensor.realize(q, k, v)
+      do = Tensor.ones(B, N, H, D, dtype=dtypes.float32).contiguous()
+      Tensor.realize(do)
+      q.grad, k.grad, v.grad = fn_jitted(q, k, v, do)
+
+    with Context(DEBUG=0):
+      q_ref = q.detach().clone().requires_grad_(True)
+      k_ref = k.detach().clone().requires_grad_(True)
+      v_ref = v.detach().clone().requires_grad_(True)
+      Tensor.realize(q_ref, k_ref, v_ref)
+
+    q_ref_, k_ref_, v_ref_ = q_ref.transpose(1, 2), k_ref.transpose(1, 2), v_ref.transpose(1, 2)
+    ref = q_ref_.scaled_dot_product_attention(k_ref_, v_ref_, is_causal=True)
+    ref = ref.float().transpose(1, 2)
+    ref.backward(do)
+    Tensor.realize(q_ref.grad, k_ref.grad, v_ref.grad)
+
+    np.testing.assert_allclose(q.grad.numpy(), q_ref.grad.numpy(), atol=5e-2, rtol=2e-2)
+    np.testing.assert_allclose(v.grad.numpy(), v_ref.grad.numpy(), atol=2e-2, rtol=2e-2)
+    np.testing.assert_allclose(k.grad.numpy(), k_ref.grad.numpy(), atol=5e-2, rtol=2e-2)
+
 if __name__ == "__main__":
   unittest.main()