[GQA] Add varlen decoding kernel with logits saving (#1223)

* [Example] Add GQA varlen decoding kernel with logits return

* [Example] Support Sink for GQA varlen decoding

* [Example] Add for no-varlen support

* [Tune] Add high performance logits saving

* [Lint]

* [Lint]

* [Rename]

examples/flash_decoding/example_gqa_decode.py

@@ -40,9 +40,9 @@ def get_heuristic_config() -> Tuple[Dict, int]:
     sm_version = sm_major * 10 + sm_minor
     print(f"CUDA device capability: {sm_version}")
     if sm_version == 89:
-        cfg = dict(block_N=128, block_H=64, num_split=16, num_stages=0, threads=128)
+        cfg = dict(block_N=128, block_H=64, num_split=1, num_stages=0, threads=128)
     else:
-        cfg = dict(block_N=128, block_H=64, num_split=16, num_stages=2, threads=128)
+        cfg = dict(block_N=128, block_H=64, num_split=1, num_stages=2, threads=128)
     return cfg, sm_version
 
 
@@ -459,8 +459,9 @@ def main(batch: int = 1,
         k = torch.randn(batch, kv_seqlen, groups, dim, device="cuda", dtype=torch.float16)
         v = torch.randn(batch, kv_seqlen, groups, dim, device="cuda", dtype=torch.float16)
         mask = torch.randint(0, 2, (batch, kv_seqlen, groups), device="cuda", dtype=torch.uint8)
-        glse = torch.empty(batch, heads, 16, device="cuda", dtype=torch.float16)
-        Output_partial = torch.empty(batch, heads, 16, dim, device="cuda", dtype=torch.float16)
+        split = config["num_split"]
+        glse = torch.empty(batch, heads, split, device="cuda", dtype=torch.float16)
+        Output_partial = torch.empty(batch, heads, split, dim, device="cuda", dtype=torch.float16)
         o = kernel(q, k, v, mask, glse, Output_partial)
         o_ref = ref_program(q, k, v, mask, glse, Output_partial)
         o_ref_split = ref_split_program(q, k, v, mask, glse, Output_partial)

examples/flash_decoding/example_gqa_decode_varlen_logits.py

+import torch
+import triton
+import triton.language as tl
+import math
+import argparse
+import tilelang
+import tilelang.language as T
+from tilelang.autotuner import autotune
+
+torch.manual_seed(0)
+tilelang.disable_cache()
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen,
+                                                           head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+@triton.jit
+def _fwd_inner(
+    q,
+    k_ptrs,
+    v_ptrs,
+    s_ptrs,
+    m_i,
+    l_i,
+    acc,
+    offs_h,
+    mask_h,
+    offs_n,
+    seqlen,
+    softmax_scale,
+    lo,
+    hi,
+    stride_kt,
+    stride_vt,
+    stride_sh,
+    stride_sn,
+    BLOCK_N: tl.constexpr,
+):
+    """Inner loop computation for attention"""
+
+    for blk_idx in tl.range(lo, hi):
+        start_n = blk_idx * BLOCK_N
+        k = tl.load(k_ptrs + start_n * stride_kt, mask=offs_n[None, :] + start_n < seqlen)
+        v = tl.load(v_ptrs + start_n * stride_vt, mask=offs_n[:, None] + start_n < seqlen)
+
+        qk = tl.dot(q, k)
+        qk *= softmax_scale
+        qk += tl.where(offs_n[None, :] + start_n < seqlen, 0, -1.0e9)
+
+        row_max = tl.max(qk, 1)
+        tl.store(s_ptrs + offs_h * stride_sh + blk_idx * stride_sn, row_max, mask=mask_h)
+
+        m_ij = tl.maximum(m_i, row_max)
+        qk -= m_ij[:, None]
+        p = tl.math.exp(qk)
+        l_ij = tl.sum(p, 1)
+        alpha = tl.math.exp(m_i - m_ij)
+        l_i = l_i * alpha + l_ij
+        m_i = m_ij
+        acc *= alpha[:, None]
+        p = p.to(v.type.element_ty)
+        acc += tl.dot(p, v)
+
+    return m_i, l_i, acc
+
+
+
+@triton.autotune(
+    configs=[
+        triton.Config({}, num_warps=num_warps, num_stages=num_stages)
+        for num_warps in [4, 8]\
+        for num_stages in [2, 4]\
+    ],
+    key=['gqa_group_size', 'BLOCK_N', 'BLOCK_D', 'BLOCK_H'],
+)
+@triton.jit
+def _fwd_kernel_varlen(
+    Q,  # [token_q = b, h_q, dim]
+    K,  # [token_k, h_kv, dim]
+    V,
+    O,
+    S,
+    s_aux,
+    softmax_scale,
+    cu_seqlens_k,
+    stride_qt,
+    stride_qh,
+    stride_qd,
+    stride_kt,
+    stride_kh,
+    stride_kd,
+    stride_vt,
+    stride_vh,
+    stride_vd,
+    stride_ot,
+    stride_oh,
+    stride_od,
+    stride_sb,
+    stride_sh,
+    stride_sn,  #bmask shape [b, q_h, seq/BLOCK_N]
+    gqa_group_size: tl.constexpr,
+    BLOCK_H: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_D: tl.constexpr,
+):
+
+    off_z = tl.program_id(0)
+    off_h_for_kv = tl.program_id(1)
+    off_h_q = off_h_for_kv * gqa_group_size
+
+    cu_k_start = tl.load(cu_seqlens_k + off_z)
+    cu_k_end = tl.load(cu_seqlens_k + off_z + 1)
+
+    seqlen_k = cu_k_end - cu_k_start
+
+    offs_h = tl.arange(0, BLOCK_H)
+    offs_n = tl.arange(0, BLOCK_N)
+    offs_d = tl.arange(0, BLOCK_D)
+
+    Q_ptrs = Q + off_z * stride_qt + off_h_q * stride_qh
+    K_ptrs = K + (cu_k_start) * stride_kt + off_h_for_kv * stride_kh
+    V_ptrs = V + (cu_k_start) * stride_vt + off_h_for_kv * stride_vh
+    O_ptrs = O + off_z * stride_ot + off_h_q * stride_oh
+    S_ptrs = S + off_z * stride_sb + off_h_q * stride_sh
+
+    mask_h = offs_h < gqa_group_size
+    q = tl.load(
+        Q_ptrs + offs_d[None, :] * stride_qd + offs_h[:, None] * stride_qh, mask=mask_h[:, None])
+
+    if s_aux is not None:
+        sink = tl.load(s_aux + off_h_q + offs_h, mask=mask_h).to(tl.float32)
+        l_i = tl.zeros([BLOCK_H], dtype=tl.float32)
+        m_i = tl.zeros([BLOCK_H], dtype=tl.float32) + sink
+    else:
+        l_i = tl.full([BLOCK_H], 1.0, dtype=tl.float32)
+        m_i = tl.full([BLOCK_H], float("-inf"), dtype=tl.float32)
+
+    acc = tl.zeros([BLOCK_H, BLOCK_D], dtype=tl.float32)
+
+    k_ptrs = K_ptrs + offs_n[None, :] * stride_kt + offs_d[:, None] * stride_kd
+    v_ptrs = V_ptrs + offs_n[:, None] * stride_vt + offs_d[None, :] * stride_vd
+
+    lo, hi = 0, tl.cdiv(seqlen_k, BLOCK_N)
+    m_i, l_i, acc = _fwd_inner(
+        q,
+        k_ptrs,
+        v_ptrs,
+        S_ptrs,
+        m_i,
+        l_i,
+        acc,
+        offs_h,
+        mask_h,
+        offs_n,
+        seqlen_k,
+        softmax_scale,
+        lo,
+        hi,
+        stride_kt,
+        stride_vt,
+        stride_sh,
+        stride_sn,
+        BLOCK_N,
+    )
+
+    if s_aux is not None:
+        sink = tl.math.exp(sink - m_i)
+        l_i = l_i + sink
+        acc = acc / l_i[:, None]
+
+    else:
+        l_recip = 1 / l_i[:, None]
+        acc = acc * l_recip
+
+    for blk_idx in tl.range(lo, hi):
+        s = tl.load(S_ptrs + offs_h * stride_sh + blk_idx * stride_sn, mask=mask_h)
+        s = tl.exp(s - m_i) / l_i
+        tl.store(S_ptrs + offs_h * stride_sh + blk_idx * stride_sn, s, mask=mask_h)
+
+    acc = acc.to(O.dtype.element_ty)
+
+    tl.store(
+        O_ptrs + offs_h[:, None] * stride_oh + offs_d[None, :] * stride_od,
+        acc,
+        mask=mask_h[:, None])
+
+
+def get_configs():
+    import itertools
+    block_N = [64, 128]
+    block_H = [64]
+    num_split = [1]
+    num_stages = [1, 2, 3]
+    threads = [128]
+    _configs = list(itertools.product(block_N, block_H, num_split, num_stages, threads))
+
+    configs = [{
+        'block_N': c[0],
+        'block_H': c[1],
+        'num_split': c[2],
+        'num_stages': c[3],
+        'threads': c[4]
+    } for c in _configs]
+    return configs
+
+
+@autotune(configs=get_configs(), warmup=10, rep=10)
+@tilelang.jit(out_idx=[-2, -1], debug_root_path="./examples/flash_decoding")
+def flashattn(batch,
+              heads,
+              k_heads,
+              max_seqlen_kv,
+              total_seqlen_k,
+              dim,
+              has_sink,
+              block_N=128,
+              block_H=64,
+              num_split=1,
+              num_stages=1,
+              threads=128):
+    scale = (1.0 / dim)**0.5 * 1.44269504  # log2(e)
+    shape_q = [batch, heads, dim]
+    shape_k = [total_seqlen_k, k_heads, dim]
+    shape_v = [total_seqlen_k, k_heads, dim]
+    shape_o = [batch, heads, dim]
+    shape_s = [batch, heads, math.ceil(max_seqlen_kv / block_N)]
+    dtype = "float16"
+    accum_dtype = "float"
+    kv_group_num = heads // k_heads
+
+    valid_block_H = min(block_H, kv_group_num)
+    # TODO: check if max_seqlen_kv is correct for varlen case
+
+    @T.macro
+    def flash_attn(
+            Q: T.Tensor(shape_q, dtype),
+            K: T.Tensor(shape_k, dtype),
+            V: T.Tensor(shape_v, dtype),
+            cu_seqlens_k: T.Tensor([batch + 1], "int32"),
+            s_aux: T.Tensor([heads], "float32"),
+            Output: T.Tensor([batch, heads, dim], dtype),
+            S: T.Tensor(shape_s, dtype),
+    ):
+        with T.Kernel(batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz):
+            Q_shared = T.alloc_shared([block_H, dim], dtype)
+            K_shared = T.alloc_shared([block_N, dim], dtype)
+            V_shared = T.alloc_shared([block_N, dim], dtype)
+            O_shared = T.alloc_shared([valid_block_H, dim], dtype)
+            acc_s = T.alloc_fragment([block_H, block_N], accum_dtype)
+            acc_s_cast = T.alloc_fragment([block_H, block_N], dtype)
+            acc_o = T.alloc_fragment([block_H, dim], accum_dtype)
+            scores_max = T.alloc_fragment([block_H], accum_dtype)
+            scores_max_prev = T.alloc_fragment([block_H], accum_dtype)
+            scores_scale = T.alloc_fragment([block_H], accum_dtype)
+            scores_sum = T.alloc_fragment([block_H], accum_dtype)
+            logsum = T.alloc_fragment([block_H], accum_dtype)
+            S_shared = T.alloc_shared([block_H, math.ceil(max_seqlen_kv / block_N)], dtype)
+            # S_fragment = T.alloc_fragment([block_H, math.ceil(max_seqlen_kv / block_N)], accum_dtype)
+            s_aux_shared = T.alloc_shared([block_H], "float32")
+
+            T.annotate_layout({
+                # Q_shared: tilelang.layout.make_swizzled_layout(Q_shared),
+                # K_shared: tilelang.layout.make_swizzled_layout(K_shared),
+                # V_shared: tilelang.layout.make_swizzled_layout(V_shared),
+                # O_shared: tilelang.layout.make_swizzled_layout(O_shared),
+                # S_shared: tilelang.layout.make_swizzled_layout(S_shared),
+            })
+
+            bid = bx
+            hid = by
+            cur_kv_head = hid // (kv_group_num // valid_block_H)
+
+            cur_start_k = cu_seqlens_k[bid]
+            cur_end_k = cu_seqlens_k[bid + 1]
+            cur_seqlen_k = cur_end_k - cur_start_k
+
+            T.copy(Q[bid, hid * valid_block_H:hid * valid_block_H + block_H, :], Q_shared)
+            T.fill(acc_o, 0)
+            T.fill(logsum, 0)
+            T.fill(scores_max, -T.infinity(accum_dtype))
+
+            # loop_range = T.ceildiv((seqlen_kv // num_split), block_N)
+            loop_range = T.ceildiv((cur_seqlen_k // num_split), block_N)
+            for k in T.Pipelined(loop_range, num_stages=num_stages):
+                T.copy(K[cur_start_k + k * block_N:cur_start_k + (k + 1) * block_N, cur_kv_head, :],
+                       K_shared)
+                T.clear(acc_s)
+                T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
+                for i, j in T.Parallel(block_H, block_N):
+                    # acc_s[i, j] = T.if_then_else(mask_local[j] != 0 and k * block_N + j < cur_seqlen_k, acc_s[i, j],
+                    #                              -T.infinity(accum_dtype))
+                    acc_s[i, j] = T.if_then_else(k * block_N + j < cur_seqlen_k, acc_s[i, j],
+                                                 -T.infinity(accum_dtype))
+                T.copy(scores_max, scores_max_prev)
+                T.fill(scores_max, -T.infinity(accum_dtype))
+                T.reduce_max(acc_s, scores_max, dim=1, clear=False)
+                # scores_max_prev is m_i
+                # scores_max is row_max->m_ij in triton
+                T.copy(scores_max, S_shared[:, k])
+                # scores_scale is alpha in triton
+                for i in T.Parallel(block_H):
+                    scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale)
+                for i, j in T.Parallel(block_H, block_N):
+                    acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale)
+                T.reduce_sum(acc_s, scores_sum, dim=1)
+                # scores_sum is l_ij in triton
+                # logsum is l_i in triton
+                for i in T.Parallel(block_H):
+                    logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i]
+                T.copy(acc_s, acc_s_cast)
+                for i, j in T.Parallel(block_H, dim):
+                    acc_o[i, j] *= scores_scale[i]
+                T.copy(V[cur_start_k + k * block_N:cur_start_k + (k + 1) * block_N, cur_kv_head, :],
+                       V_shared)
+                T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
+
+            if has_sink:
+                T.copy(s_aux[hid * valid_block_H:hid * valid_block_H + block_H], s_aux_shared)
+                for i in T.Parallel(block_H):
+                    logsum[i] += s_aux_shared[i]
+            for i, j in T.Parallel(block_H, dim):
+                acc_o[i, j] /= logsum[i]
+            for h, k in T.Parallel(block_H, math.ceil(max_seqlen_kv / block_N)):
+                S_shared[h, k] = T.exp2((S_shared[h, k] - scores_max[h]) * scale) / logsum[h]
+            # T.copy(S_shared, S_fragment)
+            # for h, k in T.Parallel(block_H, math.ceil(max_seqlen_kv / block_N)):
+            #     S_fragment[h, k] = T.exp2((S_fragment[h, k] - scores_max[h]) * scale) / logsum[h]
+            for i in T.Parallel(block_H):
+                logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale
+            T.copy(acc_o[:valid_block_H, :], O_shared)
+            T.copy(O_shared, Output[bid, hid * valid_block_H:(hid + 1) * valid_block_H, :])
+            # T.copy(S_fragment, S_shared)
+            T.copy(S_shared[:valid_block_H, :], S[bid,
+                                                  hid * valid_block_H:(hid + 1) * valid_block_H, :])
+
+    @T.prim_func
+    def flashattn_gqa_decode_no_split(
+            Q: T.Tensor(shape_q, dtype),
+            K: T.Tensor(shape_k, dtype),
+            V: T.Tensor(shape_v, dtype),
+            cu_seqlens_k: T.Tensor([batch + 1], "int32"),
+            s_aux: T.Tensor([heads], "float32"),
+            Output: T.Tensor(shape_o, dtype),
+            S: T.Tensor(shape_s, dtype),
+    ):
+        flash_attn(Q, K, V, cu_seqlens_k, s_aux, Output, S)
+
+    # TODO: split version
+    return flashattn_gqa_decode_no_split
+
+
+def flash_attn_with_attn_pool_decode_tilelang(
+    Q: torch.Tensor,  ## [tq = b, q_h, q_dim]
+    K: torch.Tensor,  ## [tk, k_h, k_dim]
+    V: torch.Tensor,
+    cu_seqlens_k: torch.Tensor,
+    max_seqlen_k: int,
+    real_max_k_seqlen: int,
+    num_split: int,
+    softmax_scale: float,
+    s_aux: torch.Tensor = None,
+    block_size: int = 64,
+    use_per_kv_head_sparse_index: bool = False,
+    tl_kernel=None,
+):
+    num_tokens, q_h, head_size = Q.shape
+    batch = cu_seqlens_k.size(0) - 1
+    k_h = K.size(1)
+
+    assert Q.dim() == K.dim() == 3
+    assert Q.size(2) == K.size(2)
+    assert cu_seqlens_k.dim() == 1
+    assert head_size in {64, 128, 256}
+    assert Q.is_contiguous()
+    assert K.is_contiguous()
+    assert V.is_contiguous()
+
+    gqa_group_size = q_h // k_h
+
+    O_tl = torch.zeros_like(Q)
+    S_tl = torch.zeros((batch, q_h, math.ceil(real_max_k_seqlen / block_size)),
+                       dtype=Q.dtype,
+                       device=Q.device)
+    O_tl, S_tl = tl_kernel(Q, K, V, cu_seqlens_k, s_aux)
+
+    if use_per_kv_head_sparse_index:
+        S_tl = torch.max_pool2d(S_tl, kernel_size=(gqa_group_size, 1), stride=(gqa_group_size, 1))
+    else:
+        S_tl = torch.max_pool2d(S_tl, kernel_size=(q_h, 1), stride=(q_h, 1))
+
+    return O_tl, S_tl
+
+
+def flash_attn_with_attn_pool_decode(
+    Q: torch.Tensor,  ## [tq = b, q_h, q_dim]
+    K: torch.Tensor,  ## [tk, k_h, k_dim]
+    V: torch.Tensor,
+    cu_seqlens_k: torch.Tensor,
+    max_seqlen_k: int,
+    real_max_k_seqlen: int,
+    num_split: int,
+    softmax_scale: float,
+    s_aux: torch.Tensor = None,
+    block_size: int = 64,
+    use_per_kv_head_sparse_index: bool = False,
+):
+    num_tokens, q_h, head_size = Q.shape
+    batch = cu_seqlens_k.size(0) - 1
+    k_h = K.size(1)
+
+    assert Q.dim() == K.dim() == 3
+    assert Q.size(2) == K.size(2)
+    assert cu_seqlens_k.dim() == 1
+    assert head_size in {64, 128, 256}
+    assert Q.is_contiguous()
+    assert K.is_contiguous()
+    assert V.is_contiguous()
+
+    gqa_group_size = q_h // k_h
+
+    BLOCK_D = head_size
+    BLOCK_N = block_size
+    BLOCK_H = 64
+
+    O = torch.zeros_like(Q)
+    S = torch.zeros((batch, q_h, math.ceil(max_seqlen_k / block_size)),
+                    dtype=Q.dtype,
+                    device=Q.device)
+
+    def grid(META):
+        return (batch, k_h)
+
+    with torch.cuda.device(Q.device.index):
+        _fwd_kernel_varlen[grid](
+            Q,
+            K,
+            V,
+            O,
+            S,
+            s_aux,
+            softmax_scale,
+            cu_seqlens_k,
+            *Q.stride(),
+            *K.stride(),
+            *V.stride(),
+            *O.stride(),
+            *S.stride(),
+            gqa_group_size,
+            BLOCK_H=BLOCK_H,
+            BLOCK_N=BLOCK_N,
+            BLOCK_D=BLOCK_D,
+        )
+
+    if use_per_kv_head_sparse_index:
+        S = torch.max_pool2d(S, kernel_size=(gqa_group_size, 1), stride=(gqa_group_size, 1))
+    else:
+        S = torch.max_pool2d(S, kernel_size=(q_h, 1), stride=(q_h, 1))
+
+    return O, S
+
+
+def test_equal_seqlen_decode_main(args):
+    """Test decode kernel with equal sequence lengths"""
+    print("Testing decode kernel with equal sequence lengths")
+
+    batch_size = args.batch_size
+    q_heads = args.q_heads
+    kv_heads = args.kv_heads
+    k_seqlen = args.k_seqlen
+    real_max_k_seqlen = args.k_seqlen
+    head_size = args.head_size
+    block_size = args.block_size
+    dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
+
+    # For decode, query is just 1 token per batch
+    q = torch.randn(batch_size, q_heads, head_size, device='cuda', dtype=dtype)
+    k = torch.randn(batch_size, kv_heads, k_seqlen, head_size, device='cuda', dtype=dtype)
+    v = torch.randn(batch_size, kv_heads, k_seqlen, head_size, device='cuda', dtype=dtype)
+    softmax_scale = 1.0 / math.sqrt(head_size)
+
+    # Generate sink values if needed
+    sink = None
+    if args.test_sink:
+        sink = torch.randn(q_heads, device='cuda', dtype=torch.float32) * 0.1  # Small sink values
+        print(f"Using sink attention with sink values: {sink}")
+
+    # Convert to varlen format for K, V
+    k_varlen = k.transpose(1, 2).reshape(batch_size * k_seqlen, kv_heads, head_size)
+    v_varlen = v.transpose(1, 2).reshape(batch_size * k_seqlen, kv_heads, head_size)
+
+    # Generate cumulative sequence lengths
+    cu_seqlens_k = torch.arange(
+        0, (batch_size + 1) * k_seqlen, k_seqlen, device='cuda', dtype=torch.int32)
+    max_seqlen_k = k_seqlen
+
+    print(f"q shape: {q.shape}")
+    print(f"k_varlen shape: {k_varlen.shape}")
+    print(f"v_varlen shape: {v_varlen.shape}")
+
+    num_tokens, q_h, head_size = q.shape
+    batch = cu_seqlens_k.size(0) - 1
+    k_h = k_varlen.size(1)
+    tl_kernel = flashattn(batch, q_h, k_h, args.k_seqlen, cu_seqlens_k[-1].item(), head_size,
+                          args.test_sink)
+
+    # Test our decode kernel
+    O_triton, S_triton = flash_attn_with_attn_pool_decode(
+        q,
+        k_varlen,
+        v_varlen,
+        cu_seqlens_k,
+        max_seqlen_k,
+        real_max_k_seqlen,
+        args.num_split,
+        softmax_scale,
+        s_aux=sink,
+        block_size=block_size)
+    O_tilelang, S_tilelang = flash_attn_with_attn_pool_decode_tilelang(
+        q,
+        k_varlen,
+        v_varlen,
+        cu_seqlens_k,
+        max_seqlen_k,
+        real_max_k_seqlen,
+        args.num_split,
+        softmax_scale,
+        s_aux=sink,
+        block_size=block_size,
+        tl_kernel=tl_kernel,
+    )
+    for i in range(batch_size):
+        S_tilelang[i, :,
+                   math.ceil((cu_seqlens_k[i + 1].item() - cu_seqlens_k[i].item()) /
+                             block_size):] = 0
+
+    # Compute torch reference
+    q_expanded = q.unsqueeze(2)  # [b, q_heads, 1, head_size]
+    k_repeat = repeat_kv(k, q_heads // kv_heads)  # [b, q_heads, k_seqlen, head_size]
+    v_repeat = repeat_kv(v, q_heads // kv_heads)  # [b, q_heads, k_seqlen, head_size]
+
+    if sink is None:
+        # Standard scaled dot-product attention
+        logits = torch.matmul(q_expanded, k_repeat.transpose(
+            -2, -1)) * softmax_scale  # [batch, q_heads, 1, seqlen_k]
+        attn_weights = torch.softmax(logits, dim=-1)
+        O_torch = torch.matmul(attn_weights, v_repeat).squeeze(2)  # [batch, q_heads, head_size]
+    else:
+        # s_aux attention
+        logits = torch.matmul(q_expanded, k_repeat.transpose(
+            -2, -1)) * softmax_scale  # [batch, q_heads, 1, seqlen_k]
+
+        sink_expanded = sink.view(1, q_heads, 1, 1)  # [1, q_heads, 1, 1]
+        logits_max = torch.max(logits, dim=-1, keepdim=True).values
+        logits_or_sinks_max = torch.maximum(logits_max, sink_expanded)
+        sinks = torch.exp(sink_expanded - logits_or_sinks_max)
+        unnormalized_scores = torch.exp(logits - logits_or_sinks_max)
+        normalizer = unnormalized_scores.sum(dim=-1, keepdim=True) + sinks
+        attn_weights = unnormalized_scores / normalizer
+        O_torch = torch.matmul(attn_weights.to(v_repeat.dtype),
+                               v_repeat).squeeze(2)  # [batch, q_heads, head_size]
+
+    # Compute attention score pooling
+    attn_score_pooled = torch.max_pool2d(
+        attn_weights.squeeze(2),  # [b, q_heads, k_seqlen]
+        kernel_size=(q_heads, block_size),
+        stride=(q_heads, block_size),
+        ceil_mode=True).to(torch.float16)
+
+    print("S_tilelang", S_tilelang)
+    print("attn_score_pooled", attn_score_pooled)
+
+    max_diff_o = torch.max(torch.abs(O_triton - O_torch))
+    max_diff_s = torch.max(torch.abs(S_triton - attn_score_pooled))
+    max_diff_o_tilelang = torch.max(torch.abs(O_tilelang - O_torch))
+    max_diff_s_tilelang = torch.max(torch.abs(S_tilelang - attn_score_pooled))
+
+    print(f"Max difference in O: {max_diff_o.item()}")
+    print(f"Max difference in S: {max_diff_s.item()}")
+    print(f"Max difference in O_tilelang: {max_diff_o_tilelang.item()}")
+    print(f"Max difference in S_tilelang: {max_diff_s_tilelang.item()}")
+    assert torch.allclose(
+        O_triton, O_torch, atol=1e-2, rtol=1e-2), f"Output mismatch: {max_diff_o.item()}"
+    assert torch.allclose(
+        S_triton, attn_score_pooled, atol=1e-2, rtol=1e-2), f"Score mismatch: {max_diff_s.item()}"
+    assert torch.allclose(
+        O_tilelang, O_torch, atol=1e-2, rtol=1e-2), f"Output mismatch: {max_diff_o_tilelang.item()}"
+    assert torch.allclose(
+        S_tilelang, attn_score_pooled, atol=1e-2,
+        rtol=1e-2), f"Score mismatch: {max_diff_s_tilelang.item()}"
+    print("✅ All tests passed!")
+
+
+def test_varlen_decode_main(args):
+    """Test decode kernel with variable sequence lengths"""
+    batch_size = args.batch_size
+    q_heads = args.q_heads
+    kv_heads = args.kv_heads
+    max_k_seqlen = args.k_seqlen  # Use as max sequence length
+    real_max_k_seqlen = args.k_seqlen
+    head_size = args.head_size
+    block_size = args.block_size
+    dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
+
+    print(f"Testing decode kernel with variable sequence lengths (max_k_seqlen={max_k_seqlen})")
+
+    # Generate sink values if needed
+    sink = None
+    if args.test_sink:
+        sink = torch.randn(q_heads, device='cuda', dtype=torch.float32) * 0.1  # Small sink values
+        print(f"Using sink attention with sink values: {sink}")
+
+    # Generate variable length k sequences
+    k_seqlens = torch.randint(max_k_seqlen // 4, max_k_seqlen + 1, size=(batch_size,))
+    print(f"k_seqlens: {k_seqlens}")
+
+    # Generate cumulative sequence lengths for k
+    cu_seqlens_k = torch.zeros(batch_size + 1, device='cuda', dtype=torch.int32)
+    total_k_tokens = 0
+    for i in range(batch_size):
+        cu_seqlens_k[i] = total_k_tokens
+        total_k_tokens += k_seqlens[i]
+    cu_seqlens_k[batch_size] = total_k_tokens
+
+    print(f"cu_seqlens_k: {cu_seqlens_k}")
+
+    # Generate tensors - Q is [batch_size, q_heads, head_size] for decode
+    q_decode = torch.randn(batch_size, q_heads, head_size, device='cuda', dtype=dtype)
+    k_varlen = torch.randn(total_k_tokens, kv_heads, head_size, device='cuda', dtype=dtype)
+    v_varlen = torch.randn(total_k_tokens, kv_heads, head_size, device='cuda', dtype=dtype)
+
+    softmax_scale = 1.0 / math.sqrt(head_size)
+    max_seqlen_k = int(k_seqlens.max())
+
+    print(f"Actual max_seqlen_k: {max_seqlen_k}")
+    print(f"q_decode shape: {q_decode.shape}")
+    print(f"k_varlen shape: {k_varlen.shape}")
+    print(f"v_varlen shape: {v_varlen.shape}")
+
+    num_tokens, q_h, head_size = q_decode.shape
+    batch = cu_seqlens_k.size(0) - 1
+    k_h = k_varlen.size(1)
+    tl_kernel = flashattn(batch, q_h, k_h, args.k_seqlen, cu_seqlens_k[-1].item(), head_size,
+                          args.test_sink)
+
+    # Test our decode kernel
+    O_triton, S_triton = flash_attn_with_attn_pool_decode(
+        q_decode,
+        k_varlen,
+        v_varlen,
+        cu_seqlens_k,
+        max_seqlen_k,
+        real_max_k_seqlen,
+        args.num_split,
+        softmax_scale,
+        s_aux=sink,
+        block_size=block_size)
+    O_tilelang, S_tilelang = flash_attn_with_attn_pool_decode_tilelang(
+        q_decode,
+        k_varlen,
+        v_varlen,
+        cu_seqlens_k,
+        max_seqlen_k,
+        real_max_k_seqlen,
+        args.num_split,
+        softmax_scale,
+        s_aux=sink,
+        block_size=block_size,
+        tl_kernel=tl_kernel,
+    )
+    for i in range(batch_size):
+        S_tilelang[i, :,
+                   math.ceil((cu_seqlens_k[i + 1].item() - cu_seqlens_k[i].item()) /
+                             block_size):] = 0
+
+    # Create torch reference - pad tensors for comparison
+    k_padded_list = []
+    v_padded_list = []
+
+    for i in range(batch_size):
+        actual_k_len = k_seqlens[i]
+
+        # Extract and pad k, v for this batch
+        k_start = cu_seqlens_k[i]
+        k_end = cu_seqlens_k[i + 1]
+
+        # Pad to max_seqlen_k
+        k_padded = torch.zeros(max_seqlen_k, kv_heads, head_size, device='cuda', dtype=dtype)
+        v_padded = torch.zeros(max_seqlen_k, kv_heads, head_size, device='cuda', dtype=dtype)
+
+        k_padded[:actual_k_len] = k_varlen[k_start:k_end]
+        v_padded[:actual_k_len] = v_varlen[k_start:k_end]
+
+        k_padded_list.append(k_padded)
+        v_padded_list.append(v_padded)
+
+    # Stack to create batched tensors [b, max_seqlen, kv_heads, head_size]
+    k_padded_batched = torch.stack(
+        k_padded_list, dim=0).transpose(1, 2)  # [b, kv_heads, max_seqlen, head_size]
+    v_padded_batched = torch.stack(
+        v_padded_list, dim=0).transpose(1, 2)  # [b, kv_heads, max_seqlen, head_size]
+
+    # Expand q to match kv heads: [b, q_heads, 1, head_size]
+    q_expanded = q_decode.unsqueeze(2)  # [b, q_heads, 1, head_size]
+
+    print(f"q_expanded shape: {q_expanded.shape}")
+    print(f"k_padded_batched shape: {k_padded_batched.shape}")
+    print(f"v_padded_batched shape: {v_padded_batched.shape}")
+
+    # Compute torch reference
+    k_repeat = repeat_kv(k_padded_batched,
+                         q_heads // kv_heads)  # [b, q_heads, max_seqlen, head_size]
+    v_repeat = repeat_kv(v_padded_batched,
+                         q_heads // kv_heads)  # [b, q_heads, max_seqlen, head_size]
+
+    if sink is None:
+        # Standard attention computation: [b, q_heads, 1, head_size] @ [b, q_heads, head_size, max_seqlen]
+        attn_score = torch.matmul(q_expanded, k_repeat.transpose(
+            -2, -1)) * softmax_scale  # [b, q_heads, 1, max_seqlen]
+
+        # Apply sequence length masking
+        for i in range(batch_size):
+            actual_k_len = k_seqlens[i]
+            attn_score[i, :, :, actual_k_len:] = float('-inf')
+
+        attn_weights = attn_score.softmax(dim=-1)  # [b, q_heads, 1, max_seqlen]
+
+        # Mask out invalid positions
+        for i in range(batch_size):
+            actual_k_len = k_seqlens[i]
+            attn_weights[i, :, :, actual_k_len:] = 0.0
+
+        # Compute output: [b, q_heads, 1, max_seqlen] @ [b, q_heads, max_seqlen, head_size]
+        O_torch = torch.matmul(attn_weights, v_repeat)  # [b, q_heads, 1, head_size]
+    else:
+        # s_aux attention
+        logits = torch.matmul(q_expanded, k_repeat.transpose(
+            -2, -1)) * softmax_scale  # [b, q_heads, 1, max_seqlen]
+
+        # Apply sequence length masking
+        for i in range(batch_size):
+            actual_k_len = k_seqlens[i]
+            logits[i, :, :, actual_k_len:] = float('-inf')
+
+        sink_expanded = sink.view(1, q_heads, 1, 1)  # [1, q_heads, 1, 1]
+        logits_max = torch.max(logits, dim=-1, keepdim=True).values
+        logits_or_sinks_max = torch.maximum(logits_max, sink_expanded)
+        sinks = torch.exp(sink_expanded - logits_or_sinks_max)
+        unnormalized_scores = torch.exp(logits - logits_or_sinks_max)
+        normalizer = unnormalized_scores.sum(dim=-1, keepdim=True) + sinks
+        attn_weights = unnormalized_scores / normalizer
+
+        # Mask out invalid positions
+        for i in range(batch_size):
+            actual_k_len = k_seqlens[i]
+            attn_weights[i, :, :, actual_k_len:] = 0.0
+
+        # Compute output: [b, q_heads, 1, max_seqlen] @ [b, q_heads, max_seqlen, head_size]
+        O_torch = torch.matmul(attn_weights.to(v_repeat.dtype),
+                               v_repeat)  # [b, q_heads, 1, head_size]
+
+    O_torch = O_torch.squeeze(2)  # [b, q_heads, head_size]
+
+    # Compute attention score pooling for S
+    attn_score_pooled = torch.max_pool2d(
+        attn_weights.squeeze(2),  # [b, q_heads, max_seqlen]
+        kernel_size=(q_heads, block_size),
+        stride=(q_heads, block_size),
+        ceil_mode=True).to(dtype=torch.float16)  # [b, 1, ceil(max_seqlen/block_size)]
+
+    print(f"O_triton shape: {O_triton.shape}")
+    print(f"O_tilelang shape: {O_tilelang.shape}")
+    print(f"O_torch shape: {O_torch.shape}")
+    print(f"S_triton shape: {S_triton.shape}")
+    print(f"S_tilelang shape: {S_tilelang.shape}")
+    print(f"attn_score_pooled shape: {attn_score_pooled.shape}")
+
+    # Compare results
+    max_diff_o = torch.max(torch.abs(O_triton - O_torch))
+    max_diff_o_tl = torch.max(torch.abs(O_tilelang - O_torch))
+    print(f"Max difference in O: {max_diff_o.item()}")
+    print(f"Max difference in O_tilelang: {max_diff_o_tl.item()}")
+
+    max_diff_s = torch.max(torch.abs(S_triton - attn_score_pooled))
+    max_diff_s_tl = torch.max(
+        torch.abs(S_tilelang[:, :, :math.ceil(max_seqlen_k / block_size)] - attn_score_pooled))
+    print(f"Max difference in S: {max_diff_s.item()}")
+    print(f"Max difference in S_tilelang: {max_diff_s_tl.item()}")
+
+    assert torch.allclose(
+        O_triton, O_torch, atol=1e-2, rtol=1e-2), f"Output mismatch: {max_diff_o.item()}"
+    assert torch.allclose(
+        S_triton, attn_score_pooled, atol=1e-2, rtol=1e-2), f"Score mismatch: {max_diff_s.item()}"
+    assert torch.allclose(
+        O_tilelang, O_torch, atol=1e-2, rtol=1e-2), f"Output mismatch: {max_diff_o_tl.item()}"
+    assert torch.allclose(
+        S_tilelang[:, :, :math.ceil(max_seqlen_k / block_size)],
+        attn_score_pooled,
+        atol=1e-2,
+        rtol=1e-2), f"Score mismatch: {max_diff_s_tl.item()}"
+
+    print("✅ All tests passed!")
+
+
+def do_bench(fn, *args, warmup=10, rep=10, **kwargs):
+    """
+    Do benchmark for a function.
+    """
+    start_event = [torch.cuda.Event(enable_timing=True) for i in range(rep)]
+    end_event = [torch.cuda.Event(enable_timing=True) for i in range(rep)]
+    for _ in range(warmup):
+        fn(*args, **kwargs)
+
+    torch.cuda.synchronize()
+    for i in range(rep):
+        start_event[i].record()
+        fn(*args, **kwargs)
+        end_event[i].record()
+    torch.cuda.synchronize()
+
+    # Record clocks
+    times = torch.tensor(
+        [s.elapsed_time(e) for s, e in zip(start_event, end_event)],
+        dtype=torch.float,
+    )
+
+    return times.mean().item()
+
+
+def speed_benchmark_decode_comparison(args):
+    """Speed benchmark for decode kernel"""
+    batch_size = args.batch_size
+    q_heads = args.q_heads
+    kv_heads = args.kv_heads
+    max_k_seqlen = args.k_seqlen
+    head_size = args.head_size
+    block_size = args.block_size
+    dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
+
+    print("\n=== Decode Speed Benchmark Comparison ===")
+    print("Configuration:")
+    print(f"  Batch size: {batch_size}")
+    print(f"  Q heads: {q_heads}, KV heads: {kv_heads}")
+    print(f"  Max K sequence length: {max_k_seqlen}")
+    print(f"  Head size: {head_size}")
+    print(f"  Block size: {block_size}")
+    print(f"  Data type: {dtype}")
+    print(f"  Variable lengths: {args.test_varlen}")
+    print(f"  s_aux attention: {args.test_sink}")
+    print()
+
+    # Generate input data
+    if args.test_varlen:
+        k_seqlens = torch.randint(max_k_seqlen // 4, max_k_seqlen + 1, size=(batch_size,))
+    else:
+        k_seqlens = torch.full((batch_size,), max_k_seqlen, dtype=int)
+
+    # Generate cumulative sequence lengths for k
+    cu_seqlens_k = torch.zeros(batch_size + 1, device='cuda', dtype=torch.int32)
+    total_k_tokens = 0
+    for i in range(batch_size):
+        cu_seqlens_k[i] = total_k_tokens
+        total_k_tokens += k_seqlens[i]
+    cu_seqlens_k[batch_size] = total_k_tokens
+
+    # Generate tensors
+    q_decode = torch.randn(batch_size, q_heads, head_size, device='cuda', dtype=dtype)
+    k_varlen = torch.randn(total_k_tokens, kv_heads, head_size, device='cuda', dtype=dtype)
+    v_varlen = torch.randn(total_k_tokens, kv_heads, head_size, device='cuda', dtype=dtype)
+
+    softmax_scale = 1.0 / math.sqrt(head_size)
+    max_seqlen_k = int(k_seqlens.max())
+
+    # Generate sink values if needed
+    sink = None
+    if args.test_sink:
+        sink = torch.randn(q_heads, device='cuda', dtype=torch.float32) * 0.1  # Small sink values
+        print("  Using sink attention with sink values")
+
+    print("Setup complete:")
+    print(f"  Total K tokens: {total_k_tokens}")
+    print(f"  Actual max K seq len: {max_seqlen_k}")
+    if args.test_varlen:
+        print(f"  K sequence lengths: {k_seqlens.tolist()}")
+
+    # Warmup
+    num_tokens, q_h, head_size = q_decode.shape
+    batch = cu_seqlens_k.size(0) - 1
+    k_h = k_varlen.size(1)
+    tl_kernel = flashattn(batch, q_h, k_h, args.k_seqlen, cu_seqlens_k[-1].item(), head_size,
+                          args.test_sink)
+
+    # Benchmark
+    print("⚡ Benchmarking Tilelang kernel (100 iterations)...")
+    tilelang_time = do_bench(
+        flash_attn_with_attn_pool_decode_tilelang,
+        q_decode,
+        k_varlen,
+        v_varlen,
+        cu_seqlens_k,
+        max_seqlen_k,
+        args.k_seqlen,
+        1,
+        softmax_scale,
+        sink,
+        block_size,
+        False,
+        tl_kernel,
+    )
+    print(f"Average decode kernel time Tilelang: {tilelang_time:.3f} ms")
+
+    # Benchmark
+    print("⚡ Benchmarking Triton kernel (100 iterations)...")
+    triton_time = do_bench(flash_attn_with_attn_pool_decode, q_decode, k_varlen, v_varlen,
+                           cu_seqlens_k, max_seqlen_k, args.k_seqlen, 1, softmax_scale, sink,
+                           block_size)
+    print(f"Average decode kernel time Triton: {triton_time:.3f} ms")
+
+    print(f"Speedup: {(triton_time / tilelang_time):.3f}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Flash Attention Decode with Attention Pooling')
+    parser.add_argument('--batch_size', type=int, default=1, help='Batch size')
+    parser.add_argument('--q_heads', type=int, default=32, help='Number of query heads')
+    parser.add_argument('--kv_heads', type=int, default=8, help='Number of key-value heads')
+    parser.add_argument('--k_seqlen', type=int, default=8192, help='Key sequence length')
+    parser.add_argument(
+        '--head_size', type=int, default=128, choices=[64, 128, 256], help='Head dimension')
+    parser.add_argument('--block_size', type=int, default=64, help='Block size for computation')
+    parser.add_argument(
+        '--dtype', type=str, default='bfloat16', choices=['float16', 'bfloat16'], help='Data type')
+    parser.add_argument(
+        '--test_varlen', action='store_true', help='Test with truly variable sequence lengths')
+    parser.add_argument(
+        '--test_sink', action='store_true', help='Test with sink attention mechanism')
+    parser.add_argument('--benchmark', action='store_true', help='Run speed benchmark')
+    parser.add_argument(
+        '--num_split', type=int, default=1, choices=[1, 16], help='Number of splits')
+    args = parser.parse_args()
+    args.test_sink = True
+    args.test_varlen = False
+    args.dtype = 'float16'
+    args.num_split = 1
+
+    if args.benchmark:
+        speed_benchmark_decode_comparison(args)
+    elif args.test_varlen:
+        test_varlen_decode_main(args)
+    else:
+        test_equal_seqlen_decode_main(args)