116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
# Adapted from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/_custom_ops.py
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import logging
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from typing import List, Tuple
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import torch
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from sglang.srt.utils import get_bool_env_var, is_hip, is_hpu
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logger = logging.getLogger(__name__)
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use_vllm_custom_allreduce = get_bool_env_var(
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"USE_VLLM_CUSTOM_ALLREDUCE", default="false"
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)
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if not is_hpu():
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# ROCm does not use vllm custom allreduce
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if use_vllm_custom_allreduce and not is_hip():
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try:
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import vllm._C
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except ImportError as e:
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logger.warning("Failed to import from vllm._C with %r", e)
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else:
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try:
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import sgl_kernel
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except ImportError as e:
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logger.warning("Failed to import from custom_ar with %r", e)
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if not is_hip():
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if use_vllm_custom_allreduce:
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custom_op = torch.ops._C_custom_ar
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else:
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custom_op = sgl_kernel.allreduce
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# custom allreduce
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def init_custom_ar(
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ipc_tensors: List[torch.Tensor],
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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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return custom_op.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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) -> None:
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custom_op.all_reduce(fa, inp, out, reg_buffer, reg_buffer_sz_bytes)
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def dispose(fa: int) -> None:
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custom_op.dispose(fa)
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def meta_size() -> int:
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return custom_op.meta_size()
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def register_buffer(fa: int, ipc_tensors: List[int]) -> None:
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return custom_op.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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return custom_op.get_graph_buffer_ipc_meta(fa)
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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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custom_op.register_graph_buffers(fa, handles, offsets)
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else:
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# ROCM custom allreduce
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def init_custom_ar(
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meta: torch.Tensor,
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rank_data: torch.Tensor,
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handles: List[str],
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offsets: List[int],
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rank: int,
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full_nvlink: bool,
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) -> int:
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return sgl_kernel.allreduce.init_custom_ar(
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meta, rank_data, handles, offsets, rank, full_nvlink
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)
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def all_reduce_reg(fa: int, inp: torch.Tensor, out: torch.Tensor) -> None:
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sgl_kernel.allreduce.all_reduce_reg(fa, inp, out)
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def all_reduce_unreg(
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fa: int, inp: torch.Tensor, reg_buffer: torch.Tensor, out: torch.Tensor
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) -> None:
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sgl_kernel.allreduce.all_reduce_unreg(fa, inp, reg_buffer, out)
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def dispose(fa: int) -> None:
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sgl_kernel.allreduce.dispose(fa)
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def meta_size() -> int:
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return sgl_kernel.allreduce.meta_size()
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def register_buffer(
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fa: int, t: torch.Tensor, handles: List[str], offsets: List[int]
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) -> None:
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return sgl_kernel.allreduce.register_buffer(fa, t, handles, offsets)
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def get_graph_buffer_ipc_meta(fa: int) -> Tuple[torch.Tensor, List[int]]:
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return sgl_kernel.allreduce.get_graph_buffer_ipc_meta(fa)
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def register_graph_buffers(
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fa: int, handles: List[str], offsets: List[List[int]]
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) -> None:
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sgl_kernel.allreduce.register_graph_buffers(fa, handles, offsets)
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def allocate_meta_buffer(size: int) -> torch.Tensor:
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return sgl_kernel.allreduce.allocate_meta_buffer(size)
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def get_meta_buffer_ipc_handle(inp: torch.Tensor) -> torch.Tensor:
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return sgl_kernel.allreduce.get_meta_buffer_ipc_handle(inp)
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