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118 lines
3.3 KiB
Python
118 lines
3.3 KiB
Python
# Copyright (c) 2026 LightSeek Foundation
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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from typing import List, Tuple
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import torch
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from tokenspeed_kernel.platform import current_platform
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_custom_allreduce = None
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if current_platform().is_nvidia:
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try:
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from flashinfer.comm import vllm_ar as _custom_allreduce
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except ImportError:
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pass
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def _check_available():
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if _custom_allreduce is None:
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raise ImportError(
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"FlashInfer custom allreduce extension is not available. "
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"Ensure FlashInfer is correctly installed."
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)
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def init_custom_ar(
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ipc_tensors: List[int],
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rank_data: torch.Tensor,
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rank: int,
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full_nvlink: bool,
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) -> int:
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_check_available()
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return _custom_allreduce.init_custom_ar(ipc_tensors, rank_data, rank, full_nvlink)
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def all_reduce(
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fa: int,
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inp: torch.Tensor,
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out: torch.Tensor,
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reg_buffer: int,
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reg_buffer_sz_bytes: int,
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num_ctas: int = 4,
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) -> None:
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_check_available()
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_custom_allreduce.all_reduce(
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fa, inp, out, reg_buffer, reg_buffer_sz_bytes, num_ctas
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)
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def dispose(fa: int) -> None:
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_check_available()
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_custom_allreduce.dispose(fa)
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def meta_size() -> int:
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_check_available()
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return _custom_allreduce.meta_size()
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def register_buffer(fa: int, ipc_tensors: List[int]) -> None:
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_check_available()
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return _custom_allreduce.register_buffer(fa, ipc_tensors)
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def get_graph_buffer_ipc_meta(fa: int) -> Tuple[List[int], List[int]]:
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_check_available()
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return _custom_allreduce.get_graph_buffer_ipc_meta(fa)
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def get_meta_buffer_ipc_handle(inp: torch.Tensor):
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_check_available()
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return _custom_allreduce.get_meta_buffer_ipc_handle(inp)
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def register_graph_buffers(
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fa: int, handles: List[List[int]], offsets: List[List[int]]
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) -> None:
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_check_available()
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_custom_allreduce.register_graph_buffers(fa, handles, offsets)
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def all_reduce_reg(
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fa: int,
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inp: torch.Tensor,
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out: torch.Tensor,
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) -> None:
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"""All-reduce for IPC-registered tensors."""
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_check_available()
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_custom_allreduce.all_reduce_reg(fa, inp, out)
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def all_reduce_unreg(
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fa: int,
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inp: torch.Tensor,
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buffer: torch.Tensor,
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out: torch.Tensor,
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) -> None:
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"""All-reduce for non-registered tensors."""
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_check_available()
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_custom_allreduce.all_reduce_unreg(fa, inp, buffer, out)
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