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>
148 lines
4.9 KiB
Python
148 lines
4.9 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
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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, safe_exp
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@triton.heuristics({
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'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
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'USE_G': lambda args: args['g_cumsum'] is not None,
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})
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@triton.jit(do_not_specialize=['T'])
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def chunk_scaled_dot_kkt_fwd_kernel(
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k,
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beta, # [H, B, T]
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g_cumsum, # [H, B, T]
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A,
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cu_seqlens,
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chunk_indices,
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T,
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B,
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H: tl.constexpr,
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Hg: tl.constexpr,
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K: tl.constexpr,
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BT: tl.constexpr,
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BK: tl.constexpr,
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IS_VARLEN: tl.constexpr,
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USE_G: tl.constexpr,
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):
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bt_stride = B * T
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i_t_i, _ = tl.program_id(0), tl.program_id(1)
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for i_bh in range(B * 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_i * 2).to(
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tl.int32), tl.load(chunk_indices + i_t_i * 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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i_t = i_t_i
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o_t = tl.arange(0, BT)
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o_t_fp32 = o_t.to(tl.float32)
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p_beta = tl.make_block_ptr(beta + i_h * bt_stride + bos, (T, ), (1, ),
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(i_t * BT, ), (BT, ), (0, ))
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b_beta = tl.load(p_beta, boundary_check=(0, ))
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b_A = tl.zeros([BT, BT], dtype=tl.float32)
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for i_k in range(tl.cdiv(K, BK)):
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p_k = tl.make_block_ptr(k + (bos * Hg + i_h // (H // Hg)) * K,
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(T, K), (Hg * K, 1), (i_t * BT, i_k * BK),
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(BT, BK), (1, 0))
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b_k = tl.load(p_k, boundary_check=(0, 1))
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b_A += tl.dot(b_k, tl.trans(b_k))
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if USE_G:
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p_g = tl.make_block_ptr(g_cumsum + i_h * bt_stride + bos, (T, ),
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(1, ), (i_t * BT, ), (BT, ), (0, ))
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b_g = tl.load(p_g, boundary_check=(0, ))
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b_g_diff = b_g[:, None] - b_g[None, :]
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b_A *= safe_exp(b_g_diff)
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b_A *= b_beta[:, None]
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b_A = tl.where(o_t_fp32[:, None] > o_t_fp32[None, :], b_A, 0)
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p_A = tl.make_block_ptr(A + (bos * H + i_h) * BT, (T, BT), (BT * H, 1),
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(i_t * BT, 0), (BT, BT), (1, 0))
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tl.store(p_A, b_A.to(p_A.dtype.element_ty), boundary_check=(0, 1))
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def chunk_scaled_dot_kkt_fwd(
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k: torch.Tensor,
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beta: torch.Tensor,
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g_cumsum: Optional[torch.Tensor] = None,
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cu_seqlens: Optional[torch.LongTensor] = None,
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chunk_size: int = 64,
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output_dtype: torch.dtype = torch.float32) -> torch.Tensor:
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r"""
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Compute beta * K * K^T.
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Args:
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k (torch.Tensor):
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The key tensor of shape `[B, T, H, K]`.
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beta (torch.Tensor):
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The beta tensor of shape `[B, T, H]`.
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g (torch.Tensor):
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The cumulative sum of the gate tensor of shape `[B, T, H]`. Default: `None`.
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gk (torch.Tensor):
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The cumulative sum of the gate tensor of shape `[B, T, H, K]` applied to the key tensor. Default: `None`.
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cu_seqlens (torch.LongTensor):
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The cumulative sequence lengths of the input tensor.
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Default: None
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chunk_size (int):
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The chunk size. Default: 64.
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output_dtype (torch.dtype):
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The dtype of the output tensor. Default: `torch.float32`
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Returns:
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beta * K * K^T of shape `[B, T, H, BT]` where `BT` is the chunk size.
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"""
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B, T, Hg, K = k.shape
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H = beta.shape[-1]
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BT = chunk_size
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if cu_seqlens is not None:
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cu_seqlens = cu_seqlens.cpu()
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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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chunk_indices = chunk_indices.npu()
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cu_seqlens = cu_seqlens.npu()
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else:
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chunk_indices = None
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NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
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A = torch.empty(B, T, H, BT, device=k.device, dtype=output_dtype)
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chunk_scaled_dot_kkt_fwd_kernel[(NT, 1)](
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k=k,
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beta=torch.permute(beta, (2, 0, 1)).contiguous(),
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g_cumsum=torch.permute(g_cumsum, (2, 0, 1)).contiguous(),
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A=A,
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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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B=B,
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H=H,
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Hg=Hg,
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K=K,
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BT=BT,
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BK=128,
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num_warps=8,
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num_stages=3,
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multibuffer=True,
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)
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return A
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