Files
sglang/python/sglang/jit_kernel/hisparse.py
2026-03-22 23:09:31 -07:00

89 lines
2.2 KiB
Python

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