Needs help on performance optimization

[Void Main]

Hi community, I'm building a triton kernel which first loads some discontinuous indexes from one tensor, and loads actual data with these indexes from another tensor.
I'm trying to implement PagedAttention with triton if it helps.

I'm loading K blocks from KV Cache tensor. But since the K blocks are divided into pages, I need to load offsets in KV cache page by page.
I should also take the block offsets into consideration during computing the K indexes.

For example, assume the block table content be:

Block Table	Content
0	40
1	30

And the block size be 8, the expected offsets should be:

Token Index	Offset
0	40
1	41
2	42
3	43
4	44
5	45
6	46
7	47
8	30
9	31
10	32
11	33
12	34
13	35
14	36
15	37

I need to update the index tensor page by page.

I tried tl.view, but the result seems to be wrong. And to update only part of the Index, all I could come up with is use a global memory tensor, and tl.store with mask to update only part of the index, and tl.load immediately after all store.

Here's my kernel code:

        # the block_idx tensor is setup to BLOCK_N before kernel launch
        # key_block_idx = torch.zeros([BLOCK_N], dtype=torch.int64, device=key_cache.device)
        # use global memory to build key block offs
        for page_idx in range(0, PAGES_PER_BLOCK_N):
            block_idx = tl.load(block_tables + (start_page_idx + page_idx) * stride_btb,
                                mask=start_page_idx + page_idx < max_block_pages, other=0)
            current_block_idx = tl.full([BLOCK_SIZE], block_idx, tl.int64) * stride_kb + offs_kv_blocks * stride_kbs
            tl.store(key_block_index + page_idx * BLOCK_SIZE + offs_kv_blocks, current_block_idx)
        # trigger memory fence before direct load
        tl.debug_barrier()
        key_block_offs = tl.load(key_block_index + offs_page)

        k_offs = cur_kv_head * stride_kh + key_head_offs[:, None] + key_block_offs[None, :]
        k = tl.load(key_cache + k_offs, mask=(start_n + offs_page[None, :]) < cur_batch_seq_len, other=0.0)
        tl.debug_barrier()

I know this is really slow, but how could I optimize this kernel?

[Keren Zhou]

I know this is really slow

How slow it is comparing to the original implementation?

[Void Main]

I know this is really slow

How slow it is comparing to the original implementation?

Hi @Jokeren , I commented out this mask, and the performance boosts by approx. 30%+.

So seems this part of code is the bottleneck.