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

118 lines
3.3 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 above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# 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.
from typing import List, Tuple
import torch
from tokenspeed_kernel.platform import current_platform
_custom_allreduce = None
if current_platform().is_nvidia:
try:
from flashinfer.comm import vllm_ar as _custom_allreduce
except ImportError:
pass
def _check_available():
if _custom_allreduce is None:
raise ImportError(
"FlashInfer custom allreduce extension is not available. "
"Ensure FlashInfer is correctly installed."
)
def init_custom_ar(
ipc_tensors: List[int],
rank_data: torch.Tensor,
rank: int,
full_nvlink: bool,
) -> int:
_check_available()
return _custom_allreduce.init_custom_ar(ipc_tensors, rank_data, rank, full_nvlink)
def all_reduce(
fa: int,
inp: torch.Tensor,
out: torch.Tensor,
reg_buffer: int,
reg_buffer_sz_bytes: int,
num_ctas: int = 4,
) -> None:
_check_available()
_custom_allreduce.all_reduce(
fa, inp, out, reg_buffer, reg_buffer_sz_bytes, num_ctas
)
def dispose(fa: int) -> None:
_check_available()
_custom_allreduce.dispose(fa)
def meta_size() -> int:
_check_available()
return _custom_allreduce.meta_size()
def register_buffer(fa: int, ipc_tensors: List[int]) -> None:
_check_available()
return _custom_allreduce.register_buffer(fa, ipc_tensors)
def get_graph_buffer_ipc_meta(fa: int) -> Tuple[List[int], List[int]]:
_check_available()
return _custom_allreduce.get_graph_buffer_ipc_meta(fa)
def get_meta_buffer_ipc_handle(inp: torch.Tensor):
_check_available()
return _custom_allreduce.get_meta_buffer_ipc_handle(inp)
def register_graph_buffers(
fa: int, handles: List[List[int]], offsets: List[List[int]]
) -> None:
_check_available()
_custom_allreduce.register_graph_buffers(fa, handles, offsets)
def all_reduce_reg(
fa: int,
inp: torch.Tensor,
out: torch.Tensor,
) -> None:
"""All-reduce for IPC-registered tensors."""
_check_available()
_custom_allreduce.all_reduce_reg(fa, inp, out)
def all_reduce_unreg(
fa: int,
inp: torch.Tensor,
buffer: torch.Tensor,
out: torch.Tensor,
) -> None:
"""All-reduce for non-registered tensors."""
_check_available()
_custom_allreduce.all_reduce_unreg(fa, inp, buffer, out)