HiSparse for Sparse Attention (#20343)
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from __future__ import annotations
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import functools
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from typing import TYPE_CHECKING
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import torch
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from sglang.jit_kernel.utils import load_jit, make_cpp_args
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if TYPE_CHECKING:
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from tvm_ffi.module import Module
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@functools.cache
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def _jit_sparse_module(
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item_size_bytes: int,
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block_size: int,
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num_top_k: int,
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hot_buffer_size: int,
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is_mla: bool = False,
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) -> Module:
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template_args = make_cpp_args(block_size, num_top_k, hot_buffer_size, is_mla)
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cache_args = make_cpp_args(
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item_size_bytes, block_size, num_top_k, hot_buffer_size, is_mla
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)
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return load_jit(
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"sparse_cache",
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*cache_args,
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cuda_files=["hisparse.cuh"],
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cuda_wrappers=[
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(
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"load_cache_to_device_buffer",
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f"load_cache_to_device_buffer<{template_args}>",
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)
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],
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)
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def load_cache_to_device_buffer_mla(
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top_k_tokens: torch.Tensor,
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device_buffer_tokens: torch.Tensor,
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host_cache_locs: torch.Tensor,
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device_buffer_locs: torch.Tensor,
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host_cache: torch.Tensor,
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device_buffer: torch.Tensor,
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top_k_device_locs: torch.Tensor,
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req_pool_indices: torch.Tensor,
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seq_lens: torch.Tensor,
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lru_slots: torch.Tensor,
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item_size_bytes: int,
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num_top_k: int,
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hot_buffer_size: int,
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page_size: int = 1,
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block_size: int = 256,
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num_real_reqs: torch.Tensor | None = None,
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) -> None:
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assert (
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hot_buffer_size >= num_top_k
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), f"hot_buffer_size ({hot_buffer_size}) must be >= num_top_k ({num_top_k})"
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module = _jit_sparse_module(
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item_size_bytes, block_size, num_top_k, hot_buffer_size, is_mla=True
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)
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empty = torch.empty(0)
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if num_real_reqs is None:
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num_real_reqs = torch.tensor(
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[top_k_tokens.size(0)], dtype=torch.int32, device=top_k_tokens.device
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)
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module.load_cache_to_device_buffer(
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top_k_tokens,
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device_buffer_tokens,
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host_cache_locs,
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device_buffer_locs,
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host_cache,
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empty,
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device_buffer,
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empty,
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top_k_device_locs,
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req_pool_indices,
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seq_lens,
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lru_slots,
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num_real_reqs,
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page_size,
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item_size_bytes,
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)
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