Files
sglang/python/sglang/jit_kernel/norm.py
DarkSharpness ba9f6d8f26 [Refactor] Clean up JIT kernel utilites (#16884)
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: Xiaoyu Zhang <35585791+BBuf@users.noreply.github.com>
2026-01-13 17:54:16 +08:00

79 lines
2.0 KiB
Python

from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Optional
import torch
from sglang.jit_kernel.utils import (
cache_once,
is_arch_support_pdl,
load_jit,
make_cpp_args,
)
if TYPE_CHECKING:
from tvm_ffi.module import Module
@cache_once
def _jit_qknorm_module(head_dim: int, dtype: torch.dtype) -> Module:
args = make_cpp_args(head_dim, is_arch_support_pdl(), dtype)
return load_jit(
"qknorm",
*args,
cuda_files=["elementwise/qknorm.cuh"],
cuda_wrappers=[("qknorm", f"QKNormKernel<{args}>::run")],
)
@cache_once
def _jit_rmsnorm_module(hidden_size: int, dtype: torch.dtype) -> Module:
args = make_cpp_args(hidden_size, is_arch_support_pdl(), dtype)
return load_jit(
"rmsnorm",
*args,
cuda_files=["elementwise/rmsnorm.cuh"],
cuda_wrappers=[("rmsnorm", f"RMSNormKernel<{args}>::run")],
)
@cache_once
def can_use_fused_inplace_qknorm(head_dim: int, dtype: torch.dtype) -> bool:
logger = logging.getLogger(__name__)
if head_dim not in [64, 128, 256, 512, 1024]:
logger.warning(f"Unsupported head_dim={head_dim} for JIT QK-Norm kernel")
return False
try:
_jit_qknorm_module(head_dim, dtype)
return True
except Exception as e:
logger.warning(f"Failed to load JIT QK-Norm kernel: {e}")
return False
def fused_inplace_qknorm(
q: torch.Tensor,
k: torch.Tensor,
q_weight: torch.Tensor,
k_weight: torch.Tensor,
eps: float = 1e-6,
*,
head_dim: int = 0,
) -> None:
head_dim = head_dim or q.size(-1)
module = _jit_qknorm_module(head_dim, q.dtype)
module.qknorm(q, k, q_weight, k_weight, eps)
def rmsnorm(
input: torch.Tensor,
weight: torch.Tensor,
output: Optional[torch.Tensor] = None,
eps: float = 1e-6,
) -> None:
output = output if output is not None else input
hidden_size = input.size(-1)
module = _jit_rmsnorm_module(hidden_size, input.dtype)
module.rmsnorm(input, weight, output, eps)