microsoft--unilm/ReSA/llm/kernel/tilelang_sparse_decoding.py

# Copyright (c) Tile-AI Corporation.
# Licensed under the MIT License.

import torch
import torch.nn.functional as F
import tilelang
from tilelang.autotuner import *
import tilelang.language as T
from einops import rearrange, einsum
import argparse
import time
import math

def num_splits_heuristic(total_mblocks, num_SMs, num_n_blocks, num_m_blocks, size_one_kv_head,
                         is_causal_or_local, max_splits):
    """
    Determines the optimal number of splits for maximizing GPU occupancy while balancing memory efficiency.

    Parameters:
    - total_mblocks (int): Total number of m_blocks.
    - num_SMs (int): Number of Streaming Multiprocessors (SMs) in the GPU.
    - num_n_blocks (int): Number of n_blocks.
    - num_m_blocks (int): Number of m_blocks.
    - size_one_kv_head (int): Size of one KV head in bytes.
    - is_causal_or_local (bool): Indicates whether the operation is causal or local.
    - max_splits (int): Maximum number of allowed splits.

    Returns:
    - int: The optimal number of splits.
    """
    # If we have enough m_blocks to almost fill the SMs, prefer 1 split unless memory constraints apply.
    if total_mblocks >= 0.8 * num_SMs:
        size_l2 = 50 * 1024 * 1024  # L2 cache size assumption (50MB)
        # Only split if each KV head is too large for L2 and there are enough m_blocks
        if size_one_kv_head > size_l2 and num_m_blocks >= num_SMs * 2 and not is_causal_or_local:
            return min((size_one_kv_head + size_l2 - 1) // size_l2, max_splits)
        else:
            return 1

    # If num_n_blocks is too small, we don't split
    if num_n_blocks <= 4:
        return 1

    # Limit max_splits to a reasonable range
    max_splits = min(max_splits, num_SMs, num_n_blocks)

    max_efficiency = 0.0
    efficiency = []

    # Compute efficiency for different splits
    for num_splits in range(1, max_splits + 1):
        n_waves = (total_mblocks * num_splits) / num_SMs
        eff = n_waves / math.ceil(n_waves)
        # Track max efficiency
        if eff > max_efficiency:
            max_efficiency = eff

        efficiency.append(eff)

    # Find the smallest number of splits that achieves at least 85% of max efficiency
    for num_splits in range(1, max_splits + 1):
        if efficiency[num_splits - 1] >= 0.95 * max_efficiency:
            return num_splits

    return 1

def flashattn(heads, heads_kv, dim, dim_v):
    scale = (1.0 / dim)**0.5 * 1.44269504  # log2(e)
    dtype = "float16"
    accum_dtype = "float"
    kv_group_num = heads // heads_kv


    def kernel_func(batch, block_N, block_H, num_split, num_stages, threads, max_cache_seqlen, max_selected_blocks):
        shape_q = [batch, heads, dim]
        shape_k = [batch, max_cache_seqlen, heads_kv, dim]
        shape_v = [batch, max_cache_seqlen, heads_kv, dim_v]
        shape_indices = [batch, heads_kv, max_selected_blocks]
        shape_o = [batch, heads, dim_v]
        part_shape = [batch, heads, num_split, dim_v]
        valid_block_H = min(block_H, kv_group_num)

        @T.macro
        def flash_attn_split(
                Q: T.Tensor(shape_q, dtype),
                K: T.Tensor(shape_k, dtype),
                V: T.Tensor(shape_v, dtype),
                block_indices: T.Tensor(shape_indices, "int32"),
                cache_seqlens: T.Tensor([batch], "int32"),
                # actual_num_blocks: T.Tensor([batch], "int32"),
                glse: T.Tensor([batch, heads, num_split], accum_dtype),
                Output_partial: T.Tensor(part_shape, accum_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_v], dtype)
                # O_shared = T.alloc_shared([valid_block_H, dim_v], 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_v], 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)
                has_valid_block=T.alloc_var("bool")
                # num_blocks = T.alloc_local([1], "int32")

                bid = bx
                hid = by
                sid = bz
                cur_kv_head = hid // (kv_group_num // valid_block_H)

                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))
                # num_blocks = actual_num_blocks[bid]
                num_blocks = max_selected_blocks
                blocks_per_split = T.floordiv(num_blocks, num_split)
                remaining_blocks = T.floormod(num_blocks, num_split)
                loop_range = (blocks_per_split + T.if_then_else(sid < remaining_blocks, 1, 0))
                start = blocks_per_split * sid + T.min(sid, remaining_blocks)
                has_valid_block=False
                # if (start < num_blocks):
                for k in T.Pipelined(loop_range, num_stages=num_stages):
                    i_s = block_indices[bid, cur_kv_head, start + k] 
                    if i_s >= 0:
                        has_valid_block = True
                        T.copy(
                            K[bid, i_s * block_N: (i_s + 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)
                        # if k == 0: # assume block_indices is sorted in reverse order, otherwise, remove this if condition
                        for i, j in T.Parallel(block_H, block_N):
                            acc_s[i, j] = T.if_then_else(i_s * block_N + j >= cache_seqlens[bid], -T.infinity(accum_dtype), acc_s[i, j])
                        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)
                        for i in T.Parallel(block_H):
                            scores_max[i] = T.if_then_else(scores_max[i] > scores_max_prev[i], scores_max[i], scores_max_prev[i])
                            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)
                        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_v):
                            acc_o[i, j] *= scores_scale[i]
                        T.copy(
                            V[bid, i_s * block_N: (i_s + 1) * block_N,
                            cur_kv_head, :], V_shared)
                        T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
                if has_valid_block:
                    for i, j in T.Parallel(block_H, dim_v):
                        acc_o[i, j] /= logsum[i]
                    
                    for i in T.Parallel(block_H):
                        logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale
                
                for i in T.Parallel(block_H):
                    if i < valid_block_H:
                        glse[bid, hid * valid_block_H + i, sid] = logsum[i]

                for i, j in T.Parallel(block_H, dim_v):
                    if i < valid_block_H:
                        Output_partial[bid, hid * valid_block_H + i, sid, j] = acc_o[i, j]
                              
        @T.macro
        def combine(
                glse: T.Tensor([batch, heads, num_split], accum_dtype),
                Output_partial: T.Tensor(part_shape, accum_dtype),
                Output: T.Tensor(shape_o, dtype),
        ):
            with T.Kernel(heads, batch, threads=128) as (by, bz):
                po_local = T.alloc_fragment([dim_v], accum_dtype)
                o_accum_local = T.alloc_fragment([dim_v], accum_dtype)
                lse_local_split = T.alloc_local([1], accum_dtype)
                lse_logsum_local = T.alloc_local([1], accum_dtype)
                lse_max_local = T.alloc_local([1], accum_dtype)
                scale_local = T.alloc_local([1], accum_dtype)
                max_split = T.alloc_local([1], "int32")

                T.annotate_layout({
                    lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i),
                })

                T.clear(lse_logsum_local)
                T.clear(o_accum_local)
                lse_max_local[0] = -T.infinity(accum_dtype)
                for k in T.serial(num_split):
                    lse_local_split[0] = glse[bz, by, k]
                    if (lse_local_split[0] != 0):
                        max_split[0] = k
                        lse_max_local[0] = T.max(lse_max_local[0], glse[bz, by, k])

                for k in T.Pipelined(num_split, num_stages=1):
                    if k <= max_split[0]:
                        lse_local_split[0] = glse[bz, by, k]
                        lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0])
                lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0]
                for k in T.serial(num_split):
                    if k <= max_split[0]:
                        for i in T.Parallel(dim_v):
                            po_local[i] = Output_partial[bz, by, k, i]
                        lse_local_split[0] = glse[bz, by, k]
                        scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0])
                        for i in T.Parallel(dim_v):
                            o_accum_local[i] += po_local[i] * scale_local[0]
                for i in T.Parallel(dim_v):
                    Output[bz, by, i] = o_accum_local[i]

        
        @T.prim_func
        def main(
                Q: T.Tensor(shape_q, dtype),
                K: T.Tensor(shape_k, dtype),
                V: T.Tensor(shape_v, dtype),
                block_indices: T.Tensor(shape_indices, "int32"),
                cache_seqlens: T.Tensor([batch], "int32"),
                # actual_num_blocks: T.Tensor([batch], "int32"),
                glse: T.Tensor([batch, heads, num_split], accum_dtype),
                Output_partial: T.Tensor(part_shape, accum_dtype),
                Output: T.Tensor(shape_o, dtype),
        ):
            # flash_attn_split(Q, K, V, block_indices, cache_seqlens, actual_num_blocks, glse, Output_partial)
            flash_attn_split(Q, K, V, block_indices, cache_seqlens, glse, Output_partial)
            combine(glse, Output_partial, Output)

        return main

    return kernel_func


class SparseFlashAttn(torch.nn.Module):
    def __init__(self, heads, heads_kv, dim, dim_v, block_size):
        super(SparseFlashAttn, self).__init__()
        self.heads = heads
        self.heads_kv = heads_kv
        self.dim = dim
        self.dim_v = dim_v
        self.block_size = block_size

        self.block_H = 64

        program = flashattn(heads, heads_kv, dim, dim_v)(
            batch=T.symbolic("batch"),
            block_N=block_size,
            block_H=self.block_H,
            num_split=T.symbolic("num_split"),
            num_stages=2,
            threads=128,
            max_cache_seqlen=T.symbolic("max_cache_seqlen"),
            max_selected_blocks=T.symbolic("max_selected_blocks")
        )

        self.kernel = tilelang.compile(
            program,
            out_idx=-1,
            target='cuda',
            execution_backend="cython"
        )

        props = torch.cuda.get_device_properties(torch.device("cuda:0"))
        self.num_sm = props.multi_processor_count

    def forward(self, query, key, value, block_indices, cache_seqlens):
        batch = query.shape[0]
        heads = self.heads
        heads_kv = self.heads_kv
        dim = self.dim
        dim_v = self.dim_v
        block_size = self.block_size
        max_selected_blocks = block_indices.shape[-1]

        # Compute static scheduling parameters
        num_m_blocks = 1 * (heads // heads_kv + self.block_H - 1) // self.block_H
        num_n_blocks = max_selected_blocks
        size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2
        total_mblocks = batch * heads_kv * num_m_blocks
        # num_sm = 132
        num_sm = self.num_sm

        num_split = num_splits_heuristic(
            total_mblocks, num_sm, num_n_blocks, num_m_blocks,
            size_one_kv_head, is_causal_or_local=True, max_splits=16
        )

        # Function to compile
        # def compute_actual_num_blocks(block_indices):
        #     actual_num_blocks = torch.sum(block_indices != -1, dim=-1).to(torch.int32)
        #     actual_num_blocks = actual_num_blocks[:, 0]  # [batch]
        #     return actual_num_blocks
        # compiled_fn = torch.compile(compute_actual_num_blocks)
        # actual_num_blocks = compiled_fn(block_indices)
        glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device='cuda')
        output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device='cuda')

        
        # output = self.kernel(
        #     query, key, value, block_indices, cache_seqlens,
        #     actual_num_blocks, glse, output_partial
        # )
        output = self.kernel(
            query, key, value, block_indices, cache_seqlens,
            glse, output_partial
        )
        return output

def sparse_gqa_decode_varlen_indice(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, block_size):
    """
    Args:
        query: [batch, heads, dim]
        key: [batch, max_cache_seqlen, heads_kv, dim]
        value: [batch, max_cache_seqlen, heads_kv, dim_v]
        block_indices: [batch, heads_kv, max_selected_blocks], indices of selected blocks, -1 for padding
        cache_seqlens: [batch], sequence lengths of the kvcache
        max_cache_seqlen: maximum sequence length of kvcache
        block_size: block size
    Returns:
        output: [batch, heads, dim_v]

    """
    
    batch, heads, dim = query.shape
    heads_kv = key.shape[2]
    dim_v = value.shape[-1]
    max_selected_blocks = block_indices.shape[-1]
    block_H = 64


    actual_num_blocks = torch.sum(block_indices != -1, dim=-1).to(torch.int32)
    actual_num_blocks = actual_num_blocks[:,0] #[batch],  number of valid blocks, assum all groups in the same batch have the same number of blocks
   
    # get num_split
    num_m_blocks = 1 * (heads // heads_kv + block_H - 1) // block_H
    num_n_blocks = max_selected_blocks#(kv_seqlen  + block_size - 1 ) // block_size
    # num_n_blocks = torch.sum(actual_num_blocks, dim=-1).item() * heads_kv # total number of blocks

    size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2 #kv_seqlen * (dim + dim_v) * 2
    total_mblocks = batch * heads_kv * num_m_blocks
    num_sm = 132
    num_split = num_splits_heuristic(total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128)
   
    program = flashattn(
        batch, heads, heads_kv, dim, dim_v)(
                block_N=block_size, block_H=block_H, num_split=T.symbolic("num_split"), num_stages=2, threads=128, 
                max_cache_seqlen=T.symbolic("max_cache_seqlen"), max_selected_blocks=T.symbolic("max_selected_blocks"))

    glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device='cuda')
    Output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device='cuda')
    kernel = tilelang.compile(program, out_idx=-1, target='cuda', execution_backend="cython", pass_configs={"tl.config_index_bitwidth": 64})
    # print(kernel.get_kernel_source())

    # output = kernel(query, key, value, block_indices, cache_seqlens, actual_num_blocks, glse, Output_partial)
    output = kernel(query, key, value, block_indices, cache_seqlens, glse, Output_partial)
    return output

def ref_program_torch(query, key, value,  block_indices, cache_seqlens, max_cache_seqlen, num_blocks, block_size):

    batch, heads, dim = query.shape
    heads_kv = key.shape[2]
    dim_v = value.shape[-1]
    num_head_groups = query.shape[1] // key.shape[2]
    scale = dim**0.5
    key = rearrange(key, 'b n h d -> b h n d')  # [batch_size, heads_kv, seqlen_kv, dim]
    value = rearrange(value, 'b n h d -> b h n d')  # [batch_size, heads_kv, seqlen_kv, dim]

    query = rearrange(
        query, 'b (h g) d -> b g h d',
        g=num_head_groups)  # [batch_size, num_head_groups, heads_kv, dim]

    scores = einsum(
        query, key,
        'b g h d, b h s d -> b g h s')  # [batch_size, num_head_groups, heads_kv, seqlen_kv]

    sparse_mask = torch.zeros_like(scores)
    # Assign mask values based on block_indices
    for b in range(batch):
        for h in range(heads_kv):
            valid_indices = block_indices[b, h]  # Extract indices for this batch and head
            for idx in valid_indices:
                if idx >= 0:
                    sparse_mask[b, :, h, idx * block_size: (idx + 1) * block_size] = 1
    scores = scores.masked_fill(sparse_mask == 0, float('-inf'))
    

    range_len = torch.arange(scores.shape[-1], device='cuda').unsqueeze(0)
    cache_seqlens_expanded = cache_seqlens.unsqueeze(1)
    pad_mask = range_len >= cache_seqlens_expanded     
    pad_mask = pad_mask[:, None, None, :]
    scores = scores.masked_fill(pad_mask, float('-inf'))
    attention = F.softmax(
        scores / scale, dim=-1)  # [batch_size, num_head_groups, heads_kv, seqlen_kv]

    out = einsum(attention, value,
                 'b g h s, b h s d -> b g h d')  # [batch_size, num_head_groups, heads_kv, dim]
    out = rearrange(out, 'b g h d -> b (h g) d')  # [batch_size, heads, dim]
    return out


def ref_program_fa(query, key, value,  block_indices, cache_seqlens, max_cache_seqlen, num_blocks, block_size):
    # latency reference
    # from flash_attn_interface import flash_attn_with_kvcache, flash_attn_func # fa3
    from flash_attn import flash_attn_with_kvcache, flash_attn_func #fa2
    query = query.unsqueeze(1)
    output = flash_attn_with_kvcache(query, key, value, cache_seqlens=cache_seqlens)
    output = output.squeeze(1)
    return output



def debug(name,expect, actual, atol=1e-3, rtol=1e-3):
    all_close = torch.allclose(expect, actual, atol=atol, rtol=rtol)
    print(name + "  all_close={}".format(all_close))
    if not all_close:
        # print(expect[3, 28])
        # print(actual[3, 28])
        diff = (expect - actual).abs()
        print("all_close={}, max={}, min={}, mean={}".format(all_close, diff.max().item(), diff.min().item(), diff.mean().item()))
        max_indices = torch.nonzero(diff == diff.max().item())
        first_index = tuple(max_indices[0].tolist())
        print(f"Index: {first_index}, expect: {expect[first_index]}, actual: {actual[first_index]}") 

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--batch', type=int, default=4, help='batch size')
    parser.add_argument('--heads', type=int, default=28, help='heads')
    parser.add_argument('--heads_kv', type=int, default=4, help='heads_kv')
    parser.add_argument('--max_cache_seqlen', type=int, default=1048576, help='kvcache sequence length')
    parser.add_argument('--dim', type=int, default=128, help='dim')
    parser.add_argument('--dim_v', type=int, default=128, help='dim_v')
    parser.add_argument('--sparse_ratio', type=float, default=0.9, help='sparse ratio')
    parser.add_argument('--block_size', type=int, default=32, help='block_size')
    parser.add_argument('--load_from_file', type=str, default=None, help='load from file')
    args = parser.parse_args()

    block_H = 64
    if args.load_from_file is None:
        batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v
        sparse_ratio = args.sparse_ratio
        block_size = args.block_size

        max_selected_blocks = int(math.ceil(max_cache_seqlen * (1-sparse_ratio)/ block_size))
        print("max_selected_blocks: ", max_selected_blocks)
        dtype = torch.float16

        Q = torch.randn((batch, heads, dim), dtype=dtype, device='cuda')
        K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device='cuda')
        V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device='cuda')
        cache_seqlens = torch.full((batch,), max_cache_seqlen, dtype=torch.int32, device='cuda')
        # cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device='cuda')
        # cache_seqlens = torch.full((batch,), max_cache_seqlen, dtype=torch.int32, device='cuda')
        # Ensure at least one element equals cache_seqlen
        random_index = torch.randint(0, batch, (1,), device='cuda').item()  # Select a random index
        cache_seqlens[random_index] = max_cache_seqlen  # Assign cache_seqlen to ensure at least one occurrence
    else:
        save_dict = torch.load(args.load_from_file)
        Q = save_dict["xq"]
        K = save_dict["key"]
        V = save_dict["value"]
        block_indices = save_dict["sparse_indices"]
        cache_seqlens = save_dict["seqlens_k"]
        batch, heads, dim = Q.shape
        heads_kv = K.shape[2]
        max_cache_seqlen = K.shape[1]
        dim_v = V.shape[-1]
        block_size = max_cache_seqlen // block_indices.shape[-1]
        max_selected_blocks = block_indices.shape[-1]
        print(f"Load debug data from file with batch={batch}, heads={heads}, heads_kv={heads_kv}, max_cache_seqlen={max_cache_seqlen}, dim={dim}, dim_v={dim_v}, block_size={block_size}")

    print("cache_seqlens: ", cache_seqlens)

    max_valid_num_blocks = torch.ceil(cache_seqlens / block_size).int()
    print("max_valid_num_blocks: ", max_valid_num_blocks)
    # Initialize block_indices with -1 (for padding blocks)
    block_indices = torch.full((batch, heads_kv, max_selected_blocks), -1, dtype=torch.int32, device='cuda')

    # Assign valid indices while ensuring no duplicates within each batch-group
    for b in range(batch):
        max_valid_block = max_valid_num_blocks[b].item()  # Max valid blocks for this batch
        if max_valid_block > 0:  # Ensure there's at least one valid block
            for h in range(heads_kv):
                valid_indices = torch.randperm(max_valid_block, device='cuda', dtype=torch.int32)[:max_selected_blocks]
                block_indices[b, h, :len(valid_indices)] = valid_indices 

    # Sort indices within each batch-group for consistency
    block_indices, _ = block_indices.sort(dim=-1, descending=True)
    # print("block_indices: ", block_indices)
    actual_num_blocks = torch.sum(block_indices != -1, dim=-1).to(torch.int32)[:,0]
    print("actual_num_blocks: ", actual_num_blocks)
    # print(block_indices.shape, actual_num_blocks.shape)
   
    max_num_blocks = torch.max(max_valid_num_blocks).item()
    print("max_num_blocks: ", max_num_blocks)

    # parity reference
    ref = ref_program_torch(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, block_size)
    # ref = ref_program_triton(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, block_size)
    # out = kernel(Q, K, V, block_indices, cache_seqlens, actual_num_blocks, glse, Output_partial)
    # out = sparse_gqa_decode_varlen_indice(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, block_size)
    sparse_kernel = SparseFlashAttn(heads, heads_kv, dim, dim_v, block_size)
    out = sparse_kernel(Q, K, V, block_indices, cache_seqlens)
    debug("output", ref, out, atol=1e-3, rtol=1e-3)

    ## latency reference
    for i in range(10):
        ref = ref_program_fa(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, block_size)
    torch.cuda.synchronize()
    start = time.time()
    for i in range(100):
        ref = ref_program_fa(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, block_size)
    torch.cuda.synchronize()
    print("dense time: ", (time.time() - start) / 100*1000)

    for i in range(10):
        # out = sparse_gqa_decode_varlen_indice(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, block_size)
        out = sparse_kernel(Q, K, V, block_indices, cache_seqlens)
    torch.cuda.synchronize()
    start = time.time()
    for i in range(100):
        # out = sparse_gqa_decode_varlen_indice(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, block_size)
        out = sparse_kernel(Q, K, V, block_indices, cache_seqlens)
    torch.cuda.synchronize()
    print("sparse time: ", (time.time() - start) / 100*1000)