[Release] Bumpy version to v0.1.1 (#107)

* Remove Torch CPP backend and update execution backend options

- Remove TorchCPPKernelAdapter and related code from JIT modules
- Update execution backend options in jit/__init__.py, kernel.py, and adapter/__init__.py
- Remove "torch_cpp" from supported execution backend literals
- Simplify backend validation and remove unused torch_cpp-related code
。

* lint fix

* Add block sparse attention implementations for TileLang and Triton

- Implement block sparse attention kernels for TileLang and Triton
- Add example scripts for block sparse attention with top-k and threshold-based masking
- Include utility functions for generating sparse attention masks
- Demonstrate causal attention with block-level sparsity
- Add test cases to validate sparse attention implementations against PyTorch reference

* Bump version to 0.1.1

* Refactor block sparse attention examples for improved code quality

- Apply consistent code formatting and style in TileLang and Triton block sparse attention implementations
- Add ruff linter ignore comment for specific line in Triton implementation
- Improve readability by adjusting indentation and line breaks
- Standardize sparse mask generation and test function implementations
- Minor optimizations in test case configurations

* lint

MANIFEST.in

@@ -3,6 +3,7 @@ include CMakeLists.txt
 include requirements.txt
 include requirements-test.txt
 include requirements-dev.txt
+include tilelang/jit/adapter/cython/cython_wrapper.pyx
 recursive-include src *
 recursive-include 3rdparty *
 recursive-exclude 3rdparty/clang* * 

VERSION

@@ -1 +1 @@
-0.1.0
+0.1.1

examples/blocksparse_attention/block_sparse_attn_tilelang.py

@@ -7,24 +7,28 @@
 import tilelang.language as T
 import torch.nn.functional as F
 
+
 def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False):
     bsz, num_head, downsample_len, _ = x.shape
     # N_CTX = downsample_len * BLOCK
     sparse_index = torch.topk(x, topk, dim=-1).indices
-    dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device)
+    dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len],
+                            False,
+                            dtype=torch.bool,
+                            device=x.device)
     dense_mask.scatter_(-1, sparse_index, True)
     if use_dense_for_last_block:
-        dense_mask[:, :,-2:,:] = True 
+        dense_mask[:, :, -2:, :] = True
     dense_mask.tril_()
-    return  dense_mask
+    return dense_mask
 
 
 def get_sparse_attn_mask_from_threshold(x, threshold, use_dense_for_last_block=False):
-    dense_mask = x > threshold 
+    dense_mask = x > threshold
     if use_dense_for_last_block:
-        dense_mask[:, :,-2:,:] = True 
+        dense_mask[:, :, -2:, :] = True
     dense_mask.tril_()
-    return  dense_mask
+    return dense_mask
 
 
 def blocksparse_flashattn(batch, heads, seq_len, dim, downsample_len, is_causal):
@@ -136,7 +140,7 @@ def main(
                 scores_sum = T.alloc_fragment([block_M], accum_dtype)
                 logsum = T.alloc_fragment([block_M], accum_dtype)
                 block_mask = T.alloc_local([downsample_len], block_mask_dtype)
-                
+
                 T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared)
                 T.fill(acc_o, 0)
                 T.fill(logsum, 0)
@@ -165,6 +169,7 @@ def main(
 
     return kernel_func(block_M, block_N, num_stages, threads)
 
+
 def test_topk_sparse_attention():
     # Config
     BATCH, N_HEADS, SEQ_LEN, D_HEAD = 1, 1, 256, 64
@@ -177,13 +182,15 @@ def test_topk_sparse_attention():
     k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16)
     v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16)
 
-    sm_scale = 1.0 / (D_HEAD ** 0.5)
+    sm_scale = 1.0 / (D_HEAD**0.5)
 
     # Create sparse mask (downsampled to block level)
     downsample_factor = BLOCK
     downsample_len = math.ceil(SEQ_LEN / downsample_factor)
-    x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device='cuda', dtype=torch.bfloat16)
-    x_ds[:,:,:,0] = 100
+    x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len],
+                       device='cuda',
+                       dtype=torch.bfloat16)
+    x_ds[:, :, :, 0] = 100
     block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK)
 
     # Run Triton kernel
@@ -194,25 +201,24 @@ def test_topk_sparse_attention():
 
     # Compute reference
     # Expand block mask to full attention matrix
-    full_mask = torch.kron(block_mask.float(), 
-                         torch.ones(BLOCK, BLOCK, device='cuda'))
+    full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device='cuda'))
     full_mask = full_mask[..., :SEQ_LEN, :SEQ_LEN].bool()
     full_mask = full_mask & torch.tril(torch.ones_like(full_mask))  # Apply causal
-    
+
     # PyTorch reference implementation
     attn = torch.einsum('bhsd,bhtd->bhst', q, k) * sm_scale
     attn = attn.masked_fill(~full_mask, float('-inf'))
     attn = F.softmax(attn, dim=-1)
     ref_output = torch.einsum('bhst,bhtd->bhsd', attn, v)
-    
+
     print("ref_output", ref_output)
     print("tilelang_output", tilelang_output)
 
-
     # Verify accuracy
     assert torch.allclose(tilelang_output, ref_output, atol=1e-2, rtol=1e-2), \
         "TileLang output doesn't match reference"
     print("Pass topk sparse attention test with qlen == klen")
 
+
 if __name__ == "__main__":
     test_topk_sparse_attention()