1c4a0468ee
### What this PR does / why we need it? qwen3-next suppot triton chunk_gated_delta_rule ops ### co-owners @OsirisDuan - vLLM version: v0.11.2 Signed-off-by: shiyuan680 <917935075@qq.com>
132 lines
4.3 KiB
Python
132 lines
4.3 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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# mypy: ignore-errors
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from typing import Optional, Tuple
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import torch
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from vllm.triton_utils import tl, triton
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from .utils import prepare_chunk_indices
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@triton.heuristics({'IS_VARLEN': lambda args: args['cu_seqlens'] is not None})
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@triton.jit(do_not_specialize=['T'])
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def recompute_w_u_fwd_kernel(k, v, beta, w, u, A, g, cu_seqlens, chunk_indices,
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T, H: tl.constexpr, Hg: tl.constexpr,
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K: tl.constexpr, V: tl.constexpr,
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BT: tl.constexpr, BK: tl.constexpr,
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BV: tl.constexpr, IS_VARLEN: tl.constexpr):
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T_max = T
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i_t_o = tl.program_id(0)
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for i_bh in range(H):
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i_b, i_h = i_bh // H, i_bh % H
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if IS_VARLEN:
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i_n, i_t = tl.load(chunk_indices + i_t_o * 2).to(
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tl.int32), tl.load(chunk_indices + i_t_o * 2 + 1).to(tl.int32)
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bos, eos = tl.load(cu_seqlens + i_n).to(
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tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32)
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T = eos - bos
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else:
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bos, eos = i_b * T, i_b * T + T
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offs_t = tl.arange(0, BT)
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global_offs_t = i_t * BT + offs_t
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mask_t = global_offs_t < T
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offs_t_2d = global_offs_t[:, None]
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offs_bt = tl.arange(0, BT)[None, :]
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ptr_A = (A + (bos * H + i_h) * BT + offs_t_2d * (H * BT) + offs_bt * 1)
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mask_A = mask_t[:, None]
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b_A = tl.load(ptr_A, mask=mask_A, other=0.0).to(tl.float32)
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ptr_g = g + bos + i_h * T_max + global_offs_t
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b_g = tl.exp(tl.load(ptr_g, mask=mask_t, other=0.0)).to(tl.float32)
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ptr_beta = beta + bos + i_h * T_max + global_offs_t
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b_beta = tl.load(ptr_beta, mask=mask_t, other=0.0).to(tl.float32)
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for i_v in range(tl.cdiv(V, BV)):
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offs_v = i_v * BV + tl.arange(0, BV)[None, :]
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mask_v = (mask_t[:, None]) & (offs_v < V)
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ptr_v = (v + (bos * H + i_h) * V + offs_t_2d * (H * V) +
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offs_v * 1)
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b_v = tl.load(ptr_v, mask=mask_v, other=0.0).to(tl.float32)
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b_vb = (b_v * b_beta[:, None])
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b_u = tl.dot(b_A, b_vb, allow_tf32=False)
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ptr_u = (u + (bos * H + i_h) * V + offs_t_2d * (H * V) +
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offs_v * 1)
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tl.store(ptr_u, b_u.to(ptr_u.dtype.element_ty), mask=mask_v)
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for i_k in range(tl.cdiv(K, BK)):
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offs_k = i_k * BK + tl.arange(0, BK)[None, :]
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mask_k = (mask_t[:, None]) & (offs_k < K)
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ptr_k = (k + (bos * Hg + i_h // (H // Hg)) * K + offs_t_2d *
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(Hg * K) + offs_k * 1)
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b_k = tl.load(ptr_k, mask=mask_k, other=0.0).to(tl.float32)
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b_kb = (b_k * b_beta[:, None] * b_g[:, None])
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b_w = tl.dot(b_A, b_kb)
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ptr_w = (w + (bos * H + i_h) * K + offs_t_2d * (H * K) +
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offs_k * 1)
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tl.store(ptr_w, b_w.to(ptr_w.dtype.element_ty), mask=mask_k)
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def recompute_w_u_fwd(
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k: torch.Tensor,
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v: torch.Tensor,
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beta: torch.Tensor,
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g_cumsum: torch.Tensor,
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A: torch.Tensor,
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cu_seqlens: Optional[torch.LongTensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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B, T, Hg, K, V = *k.shape, v.shape[-1]
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H = v.shape[-2]
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BT = A.shape[-1]
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chunk_indices = prepare_chunk_indices(cu_seqlens, BT) \
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if cu_seqlens is not None else None
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NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
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BK = 64
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BV = 64
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u = torch.empty_like(v)
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w = k.new_empty(B, T, H, K)
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beta = beta.transpose(1, 2).contiguous()
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g_cumsum = g_cumsum.transpose(1, 2).contiguous()
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recompute_w_u_fwd_kernel[(NT, B)](
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k=k,
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v=v,
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beta=beta,
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w=w,
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u=u,
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A=A,
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g=g_cumsum,
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cu_seqlens=cu_seqlens,
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chunk_indices=chunk_indices,
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T=T,
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H=H,
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Hg=Hg,
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K=K,
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V=V,
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BT=BT,
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BK=BK,
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BV=BV,
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num_warps=4,
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num_stages=3,
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)
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return w, u
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