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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

66 lines
2.0 KiB
Python

from __future__ import annotations
from typing import TYPE_CHECKING
import torch
from sglang.jit_kernel.utils import cache_once, load_jit
if TYPE_CHECKING:
from tvm_ffi.module import Module
@cache_once
def _jit_concat_mla_k_module() -> Module:
return load_jit(
"concat_mla_k",
cuda_files=["elementwise/concat_mla.cuh"],
cuda_wrappers=[("concat_mla_k", "ConcatMlaKKernel::run")],
)
@cache_once
def _jit_concat_mla_absorb_q_module() -> Module:
return load_jit(
"concat_mla_absorb_q",
cuda_files=["elementwise/concat_mla.cuh"],
cuda_wrappers=[("concat_mla_absorb_q", "ConcatMlaAbsorbQKernel::run")],
)
def concat_mla_k(k: torch.Tensor, k_nope: torch.Tensor, k_rope: torch.Tensor) -> None:
"""
Concatenate k_nope and k_rope into k for MLA (Multi-head Latent Attention).
This kernel efficiently broadcasts k_rope across all heads while copying
k_nope values directly.
Args:
k: Output tensor of shape [num_tokens, num_heads=128, k_head_dim=192], dtype=bfloat16
k_nope: Input tensor of shape [num_tokens, num_heads=128, nope_head_dim=128], dtype=bfloat16
k_rope: Input tensor of shape [num_tokens, 1, rope_head_dim=64], dtype=bfloat16
"""
module = _jit_concat_mla_k_module()
module.concat_mla_k(k, k_nope, k_rope)
def concat_mla_absorb_q(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
"""
Concatenate tensors a and b for MLA absorbed Q computation.
Args:
a: Input tensor of shape [dim_0, dim_1, a_last_dim], dtype=bfloat16
b: Input tensor of shape [dim_0, dim_1, b_last_dim], dtype=bfloat16
Returns:
Output tensor of shape [dim_0, dim_1, a_last_dim + b_last_dim], dtype=bfloat16
"""
out = torch.empty(
(*a.shape[:-1], a.shape[-1] + b.shape[-1]),
dtype=a.dtype,
device=a.device,
)
module = _jit_concat_mla_absorb_q_module()
module.concat_mla_absorb_q(a, b, out)
return out