Files
sglang/python/sglang/jit_kernel/fused_metadata_copy.py

317 lines
12 KiB
Python

"""
Fused metadata copy kernel for NSA backend CUDA graph replay.
This module provides JIT-compiled CUDA kernels for fusing multiple tensor
copy operations into single kernel launches, reducing kernel launch overhead
and improving CUDA graph replay performance.
The kernels are compiled on-demand using TVM FFI and cached for subsequent use.
"""
from __future__ import annotations
import logging
from typing import Optional
import torch
from sglang.jit_kernel.utils import cache_once, load_jit, make_cpp_args
logger = logging.getLogger(__name__)
# ============================================================================
# JIT Module Compilation
# ============================================================================
@cache_once
def _jit_fused_metadata_copy_module(
forward_mode: int, has_real_page_table: bool, has_flashmla: bool
):
"""Compile JIT module for single-backend fused metadata copy.
Args:
forward_mode: 0=DECODE, 1=TARGET_VERIFY, 2=DRAFT_EXTEND
has_real_page_table: Whether real_page_table tensors are used
has_flashmla: Whether FlashMLA metadata tensors are used
"""
args = make_cpp_args(forward_mode, has_real_page_table, has_flashmla)
try:
return load_jit(
"fused_metadata_copy",
*args,
cuda_files=["elementwise/fused_metadata_copy.cuh"],
cuda_wrappers=[
(
"fused_metadata_copy",
f"FusedMetadataCopyKernel<{args}>::run",
)
],
)
except Exception as e:
logger.error(
f"Failed to compile JIT fused metadata copy kernel "
f"(forward_mode={forward_mode}, has_real_page_table={has_real_page_table}, "
f"has_flashmla={has_flashmla}): {e}"
)
raise
@cache_once
def _jit_fused_metadata_copy_multi_module(
has_real_page_table: bool, has_flashmla: bool
):
"""Compile JIT module for multi-backend fused metadata copy (DECODE mode only).
Args:
has_real_page_table: Whether real_page_table tensors are used
has_flashmla: Whether FlashMLA metadata tensors are used
"""
args = make_cpp_args(has_real_page_table, has_flashmla)
try:
return load_jit(
"fused_metadata_copy_multi",
*args,
cuda_files=["elementwise/fused_metadata_copy.cuh"],
cuda_wrappers=[
(
"fused_metadata_copy_multi",
f"FusedMetadataCopyMultiKernel<{args}>::run",
)
],
)
except Exception as e:
logger.error(
f"Failed to compile JIT fused metadata copy multi kernel "
f"(has_real_page_table={has_real_page_table}, has_flashmla={has_flashmla}): {e}"
)
raise
# ============================================================================
# Public API
# ============================================================================
def fused_metadata_copy_cuda(
cache_seqlens_src: torch.Tensor,
cu_seqlens_k_src: torch.Tensor,
page_indices_src: torch.Tensor,
nsa_cache_seqlens_src: torch.Tensor,
seqlens_expanded_src: Optional[torch.Tensor],
nsa_cu_seqlens_k_src: torch.Tensor,
real_page_table_src: Optional[torch.Tensor],
flashmla_num_splits_src: Optional[torch.Tensor],
flashmla_metadata_src: Optional[torch.Tensor],
cache_seqlens_dst: torch.Tensor,
cu_seqlens_k_dst: torch.Tensor,
page_table_1_dst: torch.Tensor,
nsa_cache_seqlens_dst: torch.Tensor,
seqlens_expanded_dst: Optional[torch.Tensor],
nsa_cu_seqlens_k_dst: torch.Tensor,
real_page_table_dst: Optional[torch.Tensor],
flashmla_num_splits_dst: Optional[torch.Tensor],
flashmla_metadata_dst: Optional[torch.Tensor],
forward_mode: int,
bs: int,
max_len: int,
max_seqlen_k: int,
seqlens_expanded_size: int,
) -> None:
"""
Fused metadata copy kernel for NSA backend CUDA graph replay.
This function fuses multiple tensor copy operations into a single kernel launch,
reducing kernel launch overhead and improving performance.
Args:
cache_seqlens_src: Source cache sequence lengths [bs]
cu_seqlens_k_src: Source cumulative sequence lengths [bs+1]
page_indices_src: Source page indices [rows, max_len]
nsa_cache_seqlens_src: Source NSA cache sequence lengths [size]
seqlens_expanded_src: Optional source expanded sequence lengths [size] (required for TARGET_VERIFY/DRAFT_EXTEND)
nsa_cu_seqlens_k_src: Source NSA cumulative sequence lengths [size+1]
real_page_table_src: Optional source real page table [rows, cols]
flashmla_num_splits_src: Optional source FlashMLA num_splits [size+1]
flashmla_metadata_src: Optional source FlashMLA metadata tensor
cache_seqlens_dst: Destination cache sequence lengths [bs]
cu_seqlens_k_dst: Destination cumulative sequence lengths [bs+1]
page_table_1_dst: Destination page table [rows, stride]
nsa_cache_seqlens_dst: Destination NSA cache sequence lengths [size]
seqlens_expanded_dst: Optional destination expanded sequence lengths [size] (required for TARGET_VERIFY/DRAFT_EXTEND)
nsa_cu_seqlens_k_dst: Destination NSA cumulative sequence lengths [size+1]
real_page_table_dst: Optional destination real page table [rows, cols]
flashmla_num_splits_dst: Optional destination FlashMLA num_splits [size+1]
flashmla_metadata_dst: Optional destination FlashMLA metadata tensor
forward_mode: Forward mode (0=DECODE, 1=TARGET_VERIFY, 2=DRAFT_EXTEND)
bs: Batch size
max_len: Maximum length for decode/draft_extend mode
max_seqlen_k: Maximum sequence length for target_verify mode
seqlens_expanded_size: Size of expanded sequence lengths
"""
# Determine template parameters for kernel specialization
has_real_page_table = real_page_table_src is not None
has_flashmla = flashmla_num_splits_src is not None
# Get JIT-compiled module for this configuration (cached after first use)
module = _jit_fused_metadata_copy_module(
forward_mode, has_real_page_table, has_flashmla
)
# Ensure all required source tensors are contiguous (required for kernel's linear indexing)
# This matches the CHECK_INPUT checks in the verified sgl-kernel implementation
cache_seqlens_src = cache_seqlens_src.contiguous()
cu_seqlens_k_src = cu_seqlens_k_src.contiguous()
page_indices_src = page_indices_src.contiguous()
nsa_cache_seqlens_src = nsa_cache_seqlens_src.contiguous()
if seqlens_expanded_src is not None:
seqlens_expanded_src = seqlens_expanded_src.contiguous()
nsa_cu_seqlens_k_src = nsa_cu_seqlens_k_src.contiguous()
# Call JIT-compiled kernel (None values are passed as Optional with no value)
module.fused_metadata_copy(
cache_seqlens_src,
cu_seqlens_k_src,
page_indices_src,
nsa_cache_seqlens_src,
seqlens_expanded_src,
nsa_cu_seqlens_k_src,
real_page_table_src,
flashmla_num_splits_src,
flashmla_metadata_src,
cache_seqlens_dst,
cu_seqlens_k_dst,
page_table_1_dst,
nsa_cache_seqlens_dst,
seqlens_expanded_dst,
nsa_cu_seqlens_k_dst,
real_page_table_dst,
flashmla_num_splits_dst,
flashmla_metadata_dst,
bs,
max_len,
max_seqlen_k,
seqlens_expanded_size,
)
def fused_metadata_copy_multi_cuda(
cache_seqlens_src: torch.Tensor,
cu_seqlens_k_src: torch.Tensor,
page_indices_src: torch.Tensor,
nsa_cache_seqlens_src: torch.Tensor,
nsa_cu_seqlens_k_src: torch.Tensor,
real_page_table_src: Optional[torch.Tensor],
flashmla_num_splits_src: Optional[torch.Tensor],
flashmla_metadata_src: Optional[torch.Tensor],
cache_seqlens_dst0: torch.Tensor,
cu_seqlens_k_dst0: torch.Tensor,
page_table_1_dst0: torch.Tensor,
nsa_cache_seqlens_dst0: torch.Tensor,
nsa_cu_seqlens_k_dst0: torch.Tensor,
real_page_table_dst0: Optional[torch.Tensor],
flashmla_num_splits_dst0: Optional[torch.Tensor],
flashmla_metadata_dst0: Optional[torch.Tensor],
cache_seqlens_dst1: torch.Tensor,
cu_seqlens_k_dst1: torch.Tensor,
page_table_1_dst1: torch.Tensor,
nsa_cache_seqlens_dst1: torch.Tensor,
nsa_cu_seqlens_k_dst1: torch.Tensor,
real_page_table_dst1: Optional[torch.Tensor],
flashmla_num_splits_dst1: Optional[torch.Tensor],
flashmla_metadata_dst1: Optional[torch.Tensor],
cache_seqlens_dst2: torch.Tensor,
cu_seqlens_k_dst2: torch.Tensor,
page_table_1_dst2: torch.Tensor,
nsa_cache_seqlens_dst2: torch.Tensor,
nsa_cu_seqlens_k_dst2: torch.Tensor,
real_page_table_dst2: Optional[torch.Tensor],
flashmla_num_splits_dst2: Optional[torch.Tensor],
flashmla_metadata_dst2: Optional[torch.Tensor],
bs: int,
max_len: int,
seqlens_expanded_size: int,
) -> None:
"""
Multi-backend fused metadata copy kernel for NSA backend CUDA graph replay.
This function copies metadata from one source to THREE destinations in a single
kernel launch, eliminating the overhead of 3 separate kernel calls. Currently
only supports DECODE mode, which is the most common case.
Args:
cache_seqlens_src: Source cache sequence lengths [bs]
cu_seqlens_k_src: Source cumulative sequence lengths [bs+1]
page_indices_src: Source page indices [bs, max_len]
nsa_cache_seqlens_src: Source NSA cache sequence lengths [bs]
nsa_cu_seqlens_k_src: Source NSA cumulative sequence lengths [bs+1]
real_page_table_src: Optional source real page table [bs, cols]
flashmla_num_splits_src: Optional source FlashMLA num_splits [bs+1]
flashmla_metadata_src: Optional source FlashMLA metadata tensor
cache_seqlens_dst0-2: Destination cache sequence lengths for backends 0-2
cu_seqlens_k_dst0-2: Destination cumulative sequence lengths for backends 0-2
page_table_1_dst0-2: Destination page tables for backends 0-2
nsa_cache_seqlens_dst0-2: Destination NSA cache sequence lengths for backends 0-2
nsa_cu_seqlens_k_dst0-2: Destination NSA cumulative sequence lengths for backends 0-2
real_page_table_dst0-2: Optional destination real page tables for backends 0-2
flashmla_num_splits_dst0-2: Optional destination FlashMLA num_splits for backends 0-2
flashmla_metadata_dst0-2: Optional destination FlashMLA metadata tensors for backends 0-2
bs: Batch size
max_len: Maximum length for decode mode
seqlens_expanded_size: Size of expanded sequence lengths
"""
# Determine template parameters for kernel specialization
has_real_page_table = real_page_table_src is not None
has_flashmla = flashmla_num_splits_src is not None
# Get JIT-compiled module for this configuration (cached after first use)
module = _jit_fused_metadata_copy_multi_module(has_real_page_table, has_flashmla)
# Ensure all source tensors are contiguous (required for kernel's linear indexing)
# This matches the CHECK_INPUT checks in the verified sgl-kernel implementation
cache_seqlens_src = cache_seqlens_src.contiguous()
cu_seqlens_k_src = cu_seqlens_k_src.contiguous()
page_indices_src = page_indices_src.contiguous()
nsa_cache_seqlens_src = nsa_cache_seqlens_src.contiguous()
nsa_cu_seqlens_k_src = nsa_cu_seqlens_k_src.contiguous()
# Call JIT-compiled kernel (None values are passed as Optional with no value)
module.fused_metadata_copy_multi(
cache_seqlens_src,
cu_seqlens_k_src,
page_indices_src,
nsa_cache_seqlens_src,
nsa_cu_seqlens_k_src,
real_page_table_src,
flashmla_num_splits_src,
flashmla_metadata_src,
cache_seqlens_dst0,
cu_seqlens_k_dst0,
page_table_1_dst0,
nsa_cache_seqlens_dst0,
nsa_cu_seqlens_k_dst0,
real_page_table_dst0,
flashmla_num_splits_dst0,
flashmla_metadata_dst0,
cache_seqlens_dst1,
cu_seqlens_k_dst1,
page_table_1_dst1,
nsa_cache_seqlens_dst1,
nsa_cu_seqlens_k_dst1,
real_page_table_dst1,
flashmla_num_splits_dst1,
flashmla_metadata_dst1,
cache_seqlens_dst2,
cu_seqlens_k_dst2,
page_table_1_dst2,
nsa_cache_seqlens_dst2,
nsa_cu_seqlens_k_dst2,
real_page_table_dst2,
flashmla_num_splits_dst2,
flashmla_metadata_dst2,
bs,
max_len,
seqlens_expanded_size,
)