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sgl-project--sglang/python/sglang/jit_kernel/diffusion/qknorm_rope.py
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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

98 lines
2.7 KiB
Python

from __future__ import annotations
import logging
from typing import TYPE_CHECKING
import torch
from sglang.jit_kernel.utils import (
cache_once,
is_arch_support_pdl,
load_jit,
make_cpp_args,
)
from sglang.srt.utils.custom_op import register_custom_op
if TYPE_CHECKING:
from tvm_ffi.module import Module
logger = logging.getLogger(__name__)
@cache_once
def _jit_qknorm_rope_module(
head_dim: int,
rope_dim: int,
is_neox: bool,
dtype: torch.dtype,
) -> Module:
args = make_cpp_args(head_dim, rope_dim, is_neox, is_arch_support_pdl(), dtype)
return load_jit(
"qknorm_rope",
*args,
cuda_files=["diffusion/qknorm_rope.cuh"],
cuda_wrappers=[("qknorm_rope", f"QKNormRopeKernel<{args}>::run")],
)
@torch.compiler.assume_constant_result
@cache_once
def can_use_fused_inplace_qknorm_rope(
head_dim: int,
rope_dim: int,
is_neox: bool,
dtype: torch.dtype,
) -> bool:
if head_dim not in (64, 128, 256):
logger.warning(f"Unsupported head_dim={head_dim} for JIT fused QKNorm+RoPE")
return False
if rope_dim <= 0 or rope_dim > head_dim:
logger.warning(
f"Unsupported rope_dim={rope_dim} for head_dim={head_dim} in fused QKNorm+RoPE"
)
return False
elems_per_thread = head_dim // 32
if rope_dim % elems_per_thread != 0:
logger.warning(
"rope_dim=%s must be divisible by per-thread width=%s for fused QKNorm+RoPE",
rope_dim,
elems_per_thread,
)
return False
if is_neox:
rotary_lanes = rope_dim // elems_per_thread
if rotary_lanes < 2 or rotary_lanes & (rotary_lanes - 1):
logger.warning(
"rope_dim=%s yields invalid rotary_lanes=%s for neox fused QKNorm+RoPE; rotary lane count must be a power of 2",
rope_dim,
rotary_lanes,
)
return False
try:
_jit_qknorm_rope_module(head_dim, rope_dim, is_neox, dtype)
return True
except Exception as e:
logger.warning(f"Failed to load JIT fused QKNorm+RoPE kernel: {e}")
return False
@register_custom_op(mutates_args=["q", "k"])
def fused_inplace_qknorm_rope(
q: torch.Tensor,
k: torch.Tensor,
q_weight: torch.Tensor,
k_weight: torch.Tensor,
cos_sin_cache: torch.Tensor,
positions: torch.Tensor,
*,
is_neox: bool,
eps: float = 1e-6,
head_dim: int = 0,
rope_dim: int = 0,
) -> None:
head_dim = head_dim or q.size(-1)
rope_dim = rope_dim or cos_sin_cache.size(-1)
module = _jit_qknorm_rope_module(head_dim, rope_dim, is_neox, q.dtype)
module.qknorm_rope(q, k, q_weight, k_weight, cos_sin_cache, positions, eps)