diff --git a/scripts/test_block_sparse_attn.py b/scripts/test_block_sparse_attn.py
deleted file mode 100644
index c5fe0d4..0000000
--- a/scripts/test_block_sparse_attn.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import torch
-import block_sparse_attn
-from block_sparse_attn import block_sparse_attn_func, token_streaming_attn_func, block_streaming_attn_func
-
-print("block_sparse_attn version:", block_sparse_attn.__version__)
-print("Available funcs:", [k for k in dir(block_sparse_attn) if not k.startswith("_")])
-
-device = "cuda:0"
-batch_size = 1
-nheads = 4
-head_dim = 64
-seqlen = 256
-
-total_len = batch_size * seqlen
-q = torch.randn(total_len, nheads, head_dim, device=device, dtype=torch.float16)
-k = torch.randn(total_len, nheads, head_dim, device=device, dtype=torch.float16)
-v = torch.randn(total_len, nheads, head_dim, device=device, dtype=torch.float16)
-
-cu_seqlens_q = torch.arange(0, total_len + 1, seqlen, device=device, dtype=torch.int32)
-cu_seqlens_k = cu_seqlens_q.clone()
-
-# 1. dense attention (all heads mask_type=0)
-head_mask_type_dense = torch.zeros(nheads, device=device, dtype=torch.int32)
-streaming_info = torch.tensor([1, 3] * nheads, device=device, dtype=torch.int32)
-out_dense = block_sparse_attn_func(
-    q, k, v,
-    cu_seqlens_q, cu_seqlens_k,
-    head_mask_type_dense,
-    streaming_info,
-    None,
-    seqlen, seqlen,
-    0.0,
-    is_causal=False,
-)
-print("dense output shape:", out_dense.shape)
-assert out_dense.shape == q.shape
-
-# 2. block-sparse attention (2 dense heads + 2 blocksparse heads)
-num_blocksparse_heads = 2
-nrow = seqlen // 128
-ncol = seqlen // 128
-base_blockmask = torch.zeros(batch_size, num_blocksparse_heads, nrow, ncol, device=device, dtype=torch.bool)
-base_blockmask[:, :, :, :ncol//2] = True  # attend to first half
-
-head_mask_type_sparse = torch.tensor([0, 0, 1, 1], device=device, dtype=torch.int32)
-out_sparse = block_sparse_attn_func(
-    q, k, v,
-    cu_seqlens_q, cu_seqlens_k,
-    head_mask_type_sparse,
-    streaming_info,
-    base_blockmask,
-    seqlen, seqlen,
-    0.0,
-    is_causal=False,
-)
-print("block-sparse output shape:", out_sparse.shape)
-assert out_sparse.shape == q.shape
-
-# 3. token-level streaming attention
-head_mask_type_stream = torch.full((nheads,), -1, device=device, dtype=torch.int32)
-out_stream = token_streaming_attn_func(
-    q, k, v,
-    cu_seqlens_q, cu_seqlens_k,
-    head_mask_type_stream,
-    streaming_info,
-    seqlen, seqlen,
-)
-print("token-streaming output shape:", out_stream.shape)
-assert out_stream.shape == q.shape
-
-# 4. block-level streaming attention
-out_block_stream = block_streaming_attn_func(
-    q, k, v,
-    cu_seqlens_q, cu_seqlens_k,
-    head_mask_type_stream,
-    streaming_info,
-    seqlen, seqlen,
-    0.0,
-    is_causal=True,
-)
-print("block-streaming output shape:", out_block_stream.shape)
-assert out_block_stream.shape == q.shape
-
-print("All smoke tests passed!")