9d09488b4a
### What this PR does / why we need it?
This PR implements Context Parallelism (CP) support for the Qwen3-Next
model, including PCP (Parallel Context Parallelism) and DCP
(Dynamic/Data Context Parallelism).
- vLLM version: v0.15.0
- vLLM main:
f176443446
---------
Signed-off-by: SunnyLee219 <3294305115@qq.com>
Signed-off-by: Jingchun Gao <gaojingchun1@huawei.com>
Signed-off-by: 白永斌 <baiyongbin3@h-partners.com>
Signed-off-by: Bai Yongbin <845473182@qq.com>
Co-authored-by: SunnyLee219 <3294305115@qq.com>
Co-authored-by: Jingchun Gao <gaojingchun1@huawei.com>
Co-authored-by: 白永斌 <baiyongbin3@h-partners.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
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#
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# This file contains code copied from the flash-linear-attention project.
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# The original source code was licensed under the MIT license and included
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# the following copyright notice:
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# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
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# ruff: noqa: E501
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import contextlib
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import functools
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from collections.abc import Callable
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import torch
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from vllm.triton_utils import tl, triton
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def prepare_lens(cu_seqlens: torch.LongTensor) -> torch.LongTensor:
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return cu_seqlens[1:] - cu_seqlens[:-1]
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def prepare_chunk_indices(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
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indices = torch.cat([torch.arange(n) for n in triton.cdiv(prepare_lens(cu_seqlens), chunk_size).tolist()])
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return torch.stack([indices.eq(0).cumsum(0) - 1, indices], 1).to(cu_seqlens)
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def prepare_final_chunk_indices(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
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indices = triton.cdiv(prepare_lens(cu_seqlens), chunk_size) + 1
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return torch.cumsum(indices, 0) - 1
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def prepare_chunk_offsets(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
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return torch.cat([cu_seqlens.new_tensor([0]), triton.cdiv(prepare_lens(cu_seqlens), chunk_size)]).cumsum(-1)
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def prepare_update_chunk_offsets(cu_seqlens: torch.LongTensor, chunk_size: int) -> torch.LongTensor:
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return torch.cat([cu_seqlens.new_tensor([0]), triton.cdiv(prepare_lens(cu_seqlens), chunk_size) + 1]).cumsum(-1)
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def input_guard(fn: Callable[..., torch.Tensor]) -> Callable[..., torch.Tensor]:
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"""
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A decorator to make sure all input tensors are contiguous and set the device based on input tensors.
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"""
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@functools.wraps(fn)
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def wrapper(*args, **kwargs):
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contiguous_args = (i if not isinstance(i, torch.Tensor) else i.contiguous() for i in args)
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contiguous_kwargs = {k: (v if not isinstance(v, torch.Tensor) else v.contiguous()) for k, v in kwargs.items()}
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tensor = None
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for arg in args:
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if isinstance(arg, torch.Tensor):
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tensor = arg
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break
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if tensor is None:
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for value in kwargs.values():
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if isinstance(value, torch.Tensor):
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tensor = value
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break
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if tensor is not None:
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ctx = torch.npu.device(tensor.device.index)
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else:
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ctx = contextlib.nullcontext()
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with ctx:
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return fn(*contiguous_args, **contiguous_kwargs)
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return wrapper
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@triton.jit
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def safe_exp(x):
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return tl.exp(tl.where(x <= 0, x, float("-inf")))
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