[Bugfix] Cast bool dtype into int8 in blocksparse examples (#167)

* [Refactor] Update BitBLAS Benchmark with TileLang Carver Imports and Roller Hints Generation

- Replace BitBLAS imports with TileLang Carver imports in benchmark_matmul.py
- Modify roller hints generation using new TileLang Carver template and utility functions
- Update get_roller_hints_from_func to handle None cases and improve return logic
- Adjust DefaultPolicy to handle different codegen dictionary formats

* [Refactor] Update Thread Binding and Import Statements in TileLang Kernels

- Replace T.thread_binding() with T.get_thread_binding() across multiple kernel test files
- Update import statements for MMA layout and macro generator in dequantize GEMM and FP8 examples
- Move map_torch_type utility function to tilelang.utils.tensor
- Remove unnecessary imports and improve code organization

* Refactor Native Sparse Attention Example with Enhanced Triton Kernel

- Update parallel_nsa_fwd_kernel to support more flexible sparse attention computation
- Add support for block counts and offsets in the Triton kernel
- Modify kernel grid and computation logic for improved performance
- Update example script to use naive_nsa_simple reference implementation
- Improve type hints and kernel configuration

examples/native_sparse_attention/example_triton_nsa.py

@@ -2,7 +2,7 @@
 # Licensed under the MIT License.
 # ruff: noqa
 import torch
-from typing import Optional
+from typing import Optional, Union
 
 import torch
 import triton
@@ -12,34 +12,25 @@ from einops import rearrange
 from fla.ops.common.utils import (prepare_chunk_indices, prepare_lens, prepare_token_indices)
 from fla.utils import autocast_custom_bwd, autocast_custom_fwd, contiguous
 
-from reference import naive_nsa
+from reference import naive_nsa, naive_nsa_simple
 
 
+@triton.heuristics({
+    'USE_OFFSETS': lambda args: args['offsets'] is not None,
+    'USE_BLOCK_COUNTS': lambda args: isinstance(args['block_counts'], torch.Tensor),
+})
 @triton.autotune(
-    configs=[triton.Config({}, num_warps=num_warps) for num_warps in [1, 2, 4, 8, 16]],
+    configs=[triton.Config({}, num_warps=num_warps) for num_warps in [1]],
     key=['BS', 'BK', 'BV'],
 )
 @triton.jit
-def parallel_nsa_fwd_kernel(
-    q,
-    k,
-    v,
-    o,
-    lse,
-    scale,
-    block_indices,
-    T,
-    H: tl.constexpr,
-    HQ: tl.constexpr,
-    G: tl.constexpr,
-    K: tl.constexpr,
-    V: tl.constexpr,
-    S: tl.constexpr,
-    BS: tl.constexpr,
-    BK: tl.constexpr,
-    BV: tl.constexpr,
-):
-    i_v, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
+def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, block_indices,
+                            block_counts, offsets, token_indices, T, H: tl.constexpr,
+                            HQ: tl.constexpr, G: tl.constexpr, K: tl.constexpr, V: tl.constexpr,
+                            S: tl.constexpr, BS: tl.constexpr, WS: tl.constexpr, BK: tl.constexpr,
+                            BV: tl.constexpr, USE_OFFSETS: tl.constexpr,
+                            USE_BLOCK_COUNTS: tl.constexpr):
+    i_t, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
     i_b, i_h = i_bh // H, i_bh % H
 
     bos, eos = i_b * T, i_b * T + T
@@ -48,65 +39,99 @@ def parallel_nsa_fwd_kernel(
     v += (bos * H + i_h) * V
     block_indices += (bos + i_t) * H * S + i_h * S
 
+    # if USE_BLOCK_COUNTS:
+    #     NS = tl.load(block_counts + (bos + i_t) * H + i_h)
+    # else:
     NS = S
 
     p_q = tl.make_block_ptr(q + (bos + i_t) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK),
                             (1, 0))
-    p_o = tl.make_block_ptr(o + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), (G, BV),
-                            (1, 0))
-    p_lse = lse + (bos + i_t) * HQ + i_h * G + tl.arange(0, G)
-
     # the Q block is kept in the shared memory throughout the whole kernel
     # [G, BK]
     b_q = tl.load(p_q, boundary_check=(0, 1))
     b_q = (b_q * scale).to(b_q.dtype)
+
+    p_o_slc = tl.make_block_ptr(o_slc + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV),
+                                (G, BV), (1, 0))
+    p_lse_slc = lse_slc + (bos + i_t) * HQ + i_h * G + tl.arange(0, G)
     # [G, BV]
-    b_o = tl.zeros([G, BV], dtype=tl.float32)
+    b_o_slc = tl.zeros([G, BV], dtype=tl.float32)
 
-    b_m = tl.full([G], float('-inf'), dtype=tl.float32)
-    b_acc = tl.zeros([G], dtype=tl.float32)
+    b_m_slc = tl.full([G], float('-inf'), dtype=tl.float32)
+    b_acc_slc = tl.zeros([G], dtype=tl.float32)
     for i in range(NS):
         i_s = tl.load(block_indices + i).to(tl.int32) * BS
-        if i_s <= i_t:
-            p_k = tl.make_block_ptr(k, (K, T), (1, H * K), (0, i_s), (BK, BS), (0, 1))
-            p_v = tl.make_block_ptr(v, (T, V), (H * V, 1), (i_s, i_v * BV), (BS, BV), (1, 0))
+        if i_s <= i_t and i_s >= 0:
+            p_k_slc = tl.make_block_ptr(k, (K, T), (1, H * K), (0, i_s), (BK, BS), (0, 1))
+            p_v_slc = tl.make_block_ptr(v, (T, V), (H * V, 1), (i_s, i_v * BV), (BS, BV), (1, 0))
             # [BK, BS]
-            b_k = tl.load(p_k, boundary_check=(0, 1))
+            b_k_slc = tl.load(p_k_slc, boundary_check=(0, 1))
             # [BS, BV]
-            b_v = tl.load(p_v, boundary_check=(0, 1))
+            b_v_slc = tl.load(p_v_slc, boundary_check=(0, 1))
             # [G, BS]
-            b_s = tl.dot(b_q, b_k)
-            b_s = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s, float('-inf'))
+            b_s_slc = tl.dot(b_q, b_k_slc)
+            if i_t == 6:
+                print("b_s_slc", b_s_slc)
+            b_s_slc = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_slc, float('-inf'))
 
             # [G]
-            b_m, b_mp = tl.maximum(b_m, tl.max(b_s, 1)), b_m
-            b_r = tl.exp(b_mp - b_m)
+            b_m_slc, b_mp_slc = tl.maximum(b_m_slc, tl.max(b_s_slc, 1)), b_m_slc
+            b_r_slc = tl.exp(b_mp_slc - b_m_slc)
             # [G, BS]
-            b_p = tl.exp(b_s - b_m[:, None])
+            b_p_slc = tl.exp(b_s_slc - b_m_slc[:, None])
             # [G]
-            b_acc = b_acc * b_r + tl.sum(b_p, 1)
+            b_acc_slc = b_acc_slc * b_r_slc + tl.sum(b_p_slc, 1)
             # [G, BV]
-            b_o = b_o * b_r[:, None] + tl.dot(b_p.to(b_q.dtype), b_v)
+            b_o_slc = b_o_slc * b_r_slc[:, None] + tl.dot(b_p_slc.to(b_q.dtype), b_v_slc)
+
+            b_mp_slc = b_m_slc
+    b_o_slc = b_o_slc / b_acc_slc[:, None]
+    b_m_slc += tl.log(b_acc_slc)
+    tl.store(p_o_slc, b_o_slc.to(p_o_slc.dtype.element_ty), boundary_check=(0, 1))
+    tl.store(p_lse_slc, b_m_slc.to(p_lse_slc.dtype.element_ty))
+
+
+class ParallelNSAFunction(torch.autograd.Function):
+
+    @staticmethod
+    @contiguous
+    @autocast_custom_fwd
+    def forward(ctx, q, k, v, block_indices, block_size, scale, offsets):
+        ctx.dtype = q.dtype
+
+        # 2-d sequence indices denoting the offsets of tokens in each sequence
+        # for example, if the passed `offsets` is [0, 2, 6],
+        # then there are 2 and 4 tokens in the 1st and 2nd sequences respectively, and `token_indices` will be
+        # [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [1, 3]]
+        token_indices = prepare_token_indices(offsets) if offsets is not None else None
 
-            b_mp = b_m
-    b_o = b_o / b_acc[:, None]
-    b_m += tl.log(b_acc)
-    tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))
-    tl.store(p_lse, b_m.to(p_lse.dtype.element_ty))
+        o, lse = parallel_nsa_fwd(
+            q=q, k=k, v=v, block_indices=block_indices, block_size=block_size, scale=scale)
+        ctx.save_for_backward(q, k, v, o, lse)
+        ctx.block_indices = block_indices
+        ctx.block_size = block_size
+        ctx.scale = scale
+        return o.to(q.dtype)
 
 
 def parallel_nsa_fwd(
     q: torch.Tensor,
     k: torch.Tensor,
     v: torch.Tensor,
-    block_indices: torch.Tensor,
+    block_indices: torch.LongTensor,
+    block_counts: Union[torch.LongTensor, int],
     block_size: int,
-    scale: float,
+    window_size: int,
+    offsets: Optional[torch.LongTensor] = None,
+    token_indices: Optional[torch.LongTensor] = None,
 ):
+    import math
     B, T, H, K, V, S = *k.shape, v.shape[-1], block_indices.shape[-1]
     HQ = q.shape[2]
+    scale = 1.0 / math.sqrt(K)
     G = HQ // H
     BS = block_size
+    WS = window_size
     if torch.cuda.get_device_capability()[0] >= 9:
         BK = min(256, triton.next_power_of_2(K))
         BV = min(256, triton.next_power_of_2(V))
@@ -117,106 +142,43 @@ def parallel_nsa_fwd(
     NV = triton.cdiv(V, BV)
     assert NK == 1, "The key dimension can not be larger than 256"
 
-    grid = (NV, T, B * H)
-    o = torch.empty(B, T, HQ, V, dtype=v.dtype, device=q.device)
-    lse = torch.empty(B, T, HQ, dtype=torch.float32, device=q.device)
-    print("grid", grid)
+    grid = (T, NV, B * H)
+    o_slc = torch.empty(B, T, HQ, V, dtype=v.dtype, device=q.device)
+    o_swa = torch.empty(B, T, HQ, V, dtype=v.dtype, device=q.device) if window_size > 0 else None
+    lse_slc = torch.empty(B, T, HQ, dtype=torch.float, device=q.device)
+    lse_swa = torch.empty(B, T, HQ, dtype=torch.float, device=q.device) if window_size > 0 else None
+
     parallel_nsa_fwd_kernel[grid](
         q=q,
         k=k,
         v=v,
-        o=o,
-        lse=lse,
+        o_slc=o_slc,
+        o_swa=o_swa,
+        lse_slc=lse_slc,
+        lse_swa=lse_swa,
         scale=scale,
         block_indices=block_indices,
+        block_counts=block_counts,
+        offsets=offsets,
+        token_indices=token_indices,
+        T=T,
         H=H,
         HQ=HQ,
         G=G,
-        T=T,
         K=K,
         V=V,
         S=S,
         BS=BS,
+        WS=WS,
         BK=BK,
         BV=BV,
     )
-    return o, lse
-
-
-class ParallelNSAFunction(torch.autograd.Function):
-
-    @staticmethod
-    @contiguous
-    @autocast_custom_fwd
-    def forward(ctx, q, k, v, block_indices, block_size, scale, offsets):
-        ctx.dtype = q.dtype
-
-        # 2-d sequence indices denoting the offsets of tokens in each sequence
-        # for example, if the passed `offsets` is [0, 2, 6],
-        # then there are 2 and 4 tokens in the 1st and 2nd sequences respectively, and `token_indices` will be
-        # [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [1, 3]]
-        token_indices = prepare_token_indices(offsets) if offsets is not None else None
-
-        o, lse = parallel_nsa_fwd(
-            q=q, k=k, v=v, block_indices=block_indices, block_size=block_size, scale=scale)
-        ctx.save_for_backward(q, k, v, o, lse)
-        ctx.block_indices = block_indices
-        ctx.block_size = block_size
-        ctx.scale = scale
-        return o.to(q.dtype)
-
-
-def parallel_nsa(q: torch.Tensor,
-                 k: torch.Tensor,
-                 v: torch.Tensor,
-                 block_indices: torch.LongTensor,
-                 block_size: int = 64,
-                 scale: Optional[float] = None,
-                 cu_seqlens: Optional[torch.LongTensor] = None,
-                 head_first: bool = False) -> torch.Tensor:
-    r"""
-    Args:
-        q (torch.Tensor):
-            queries of shape `[B, T, HQ, K]` if `head_first=False` else `[B, HQ, T, K]`.
-        k (torch.Tensor):
-            keys of shape `[B, T, H, K]` if `head_first=False` else `[B, H, T, K]`.
-            GQA is enforced here. The ratio of query heads (HQ) to key/value heads (H) must be a power of 2 and >=16.
-        v (torch.Tensor):
-            values of shape `[B, T, H, V]` if `head_first=False` else `[B, H, T, V]`.
-        block_indices (torch.LongTensor):
-            Block indices of shape `[B, T, H, S]` if `head_first=False` else `[B, H, T, S]`.
-            `S` is the number of selected blocks for each query token, which is set to 16 in the paper.
-        block_size (int):
-            Selected block size. Default: 64.
-        scale (Optional[int]):
-            Scale factor for attention scores.
-            If not provided, it will default to `1 / sqrt(K)`. Default: `None`.
-        head_first (Optional[bool]):
-            Whether the inputs are in the head-first format. Default: `False`.
-        cu_seqlens (torch.LongTensor):
-            Cumulative sequence lengths of shape `[N+1]` used for variable-length training,
-            consistent with the FlashAttention API.
-
-    Returns:
-        o (torch.Tensor):
-            Outputs of shape `[B, T, HQ, V]` if `head_first=False` else `[B, HQ, T, V]`.
-    """
-    if scale is None:
-        scale = k.shape[-1]**-0.5
-    if cu_seqlens is not None:
-        assert q.shape[0] == 1, "batch size must be 1 when cu_seqlens are provided"
-    if head_first:
-        q, k, v, block_indices = map(lambda x: rearrange(x, 'b h t d -> b t h d'),
-                                     (q, k, v, block_indices))
-    o = ParallelNSAFunction.apply(q, k, v, block_indices, block_size, scale, cu_seqlens)
-    if head_first:
-        o = rearrange(o, 'b t h d -> b h t d')
-    return o
+    return o_slc, lse_slc, o_swa, lse_swa
 
 
 if __name__ == "__main__":
-    B, T, H, HQ, D, S, block_size, dtype, scale = 1, 64, 1, 16, 32, 1, 64, torch.float16, 0.1
-
+    B, T, H, HQ, D, S, block_size, dtype, scale = 2, 64, 1, 16, 32, 1, 32, torch.float16, 0.1
+    torch.random.manual_seed(0)
     q = torch.randn((B, T, HQ, D), dtype=dtype, device='cuda').requires_grad_(True)
     k = torch.randn((B, T, H, D), dtype=dtype, device='cuda').requires_grad_(True)
     v = torch.randn((B, T, H, D), dtype=dtype, device='cuda').requires_grad_(True)
@@ -232,32 +194,25 @@ if __name__ == "__main__":
 
     block_counts = torch.randint(1, S + 1, (B, T, H), device='cuda')
 
-    ref = naive_nsa(
+    ref = naive_nsa_simple(
         q=q,
         k=k,
         v=v,
         block_indices=block_indices,
         block_counts=block_counts,
         block_size=block_size,
-        scale=scale)
-
-    # print(ref)
+    )
 
-    tri = parallel_nsa(
-        q=q, k=k, v=v, block_indices=block_indices, block_size=block_size, scale=scale)
+    tri, _, _, _ = parallel_nsa_fwd(
+        q=q,
+        k=k,
+        v=v,
+        block_indices=block_indices,
+        block_size=block_size,
+        window_size=0,
+        block_counts=block_counts)
 
-    # print(tri)
+    print("tri", tri)
+    print("ref", ref)
 
     torch.testing.assert_close(ref, tri, atol=1e-2, rtol=1e-2)
-
-    # import flash_attn
-    # # gqa
-    # o_gqa = flash_attn.flash_attn_func(
-    #     q,
-    #     k,
-    #     v,
-    #     softmax_scale=scale,
-    # )
-    # print(o_gqa)
-
-    # torch.testing.assert_close(o_gqa, tri, atol=1e-2, rtol=1e-2)

examples/seer_attention/block_sparse_attn_tilelang.py

@@ -181,7 +181,7 @@ def test_topk_sparse_attention():
         BATCH, N_HEADS, SEQ_LEN, SEQ_LEN, D_HEAD, downsample_len, is_causal=True)
     kernel = tilelang.compile(program, out_idx=[4])
     print(kernel.get_kernel_source())
-    tilelang_output = kernel(q, k, v, block_mask)
+    tilelang_output = kernel(q, k, v, block_mask.to(torch.int8))
 
     # Compute reference
     # Expand block mask to full attention matrix
@@ -231,13 +231,7 @@ def test_topk_sparse_attention_qlen_lt_klen():
     print(program)
     kernel = tilelang.compile(program, out_idx=[4])
     print(kernel.get_kernel_source())
-    tilelang_output = kernel(q, k, v, block_mask)
-
-    # import flash_attn
-
-    # ref_out = flash_attn.flash_attn_func(q, k, v, causal=True)
-    # torch.testing.assert_close(tilelang_output, ref_out, atol=1e-2, rtol=1e-2)
-    # exit()
+    tilelang_output = kernel(q, k, v, block_mask.to(torch.int8))
 
     past_len = K_LEN - Q_LEN
 
@@ -268,5 +262,5 @@ def test_topk_sparse_attention_qlen_lt_klen():
 
 
 if __name__ == "__main__":
-    # test_topk_sparse_attention()
+    test_topk_sparse_attention()
     test_topk_sparse_attention_qlen_lt_klen()