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chore: import upstream snapshot with attribution
2026-07-13 12:32:31 +08:00

84 lines
3.0 KiB
Python

# Copyright (c) 2026 LightSeek Foundation
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
"""FlashInfer rotary embedding kernels."""
import torch
from tokenspeed_kernel.platform import CapabilityRequirement, current_platform
from tokenspeed_kernel.registry import Priority, register_kernel
from tokenspeed_kernel.signature import format_signatures
platform = current_platform()
if platform.is_nvidia:
from flashinfer.rope import mla_rope_quantize_fp8
@register_kernel(
"embedding",
"rope_mla",
name="flashinfer_embedding_rope_mla",
solution="flashinfer",
capability=CapabilityRequirement(vendors=frozenset({"nvidia"})),
signatures=format_signatures(
("q_rope", "k_rope", "q_nope", "k_nope"),
"dense",
{torch.float16, torch.bfloat16},
),
priority=Priority.SPECIALIZED,
traits={
"is_neox": frozenset({True, False}),
"quantize_dtype": frozenset({torch.float8_e4m3fn}),
"has_scale_q_tensor": frozenset({False}),
"has_scale_kv_tensor": frozenset({False}),
},
)
def flashinfer_embedding_rope_mla(
*,
positions: torch.Tensor,
q_rope: torch.Tensor,
k_rope: torch.Tensor,
q_nope: torch.Tensor,
k_nope: torch.Tensor,
cos_sin_cache: torch.Tensor,
q_rope_out: torch.Tensor,
k_rope_out: torch.Tensor,
q_nope_out: torch.Tensor,
k_nope_out: torch.Tensor,
is_neox: bool = True,
quant_scale_q: float = 1.0,
quant_scale_kv: float = 1.0,
enable_pdl: bool = False,
) -> None:
mla_rope_quantize_fp8(
q_rope=q_rope,
k_rope=k_rope,
q_nope=q_nope,
k_nope=k_nope,
cos_sin_cache=cos_sin_cache,
pos_ids=positions,
is_neox=is_neox,
quantize_dtype=torch.float8_e4m3fn,
q_rope_out=q_rope_out,
k_rope_out=k_rope_out,
q_nope_out=q_nope_out,
k_nope_out=k_nope_out,
quant_scale_q=quant_scale_q,
quant_scale_kv=quant_scale_kv,
enable_pdl=enable_pdl,
)