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【OPS】qwen3-next support triton chunk_gated_delta_rule ops (#4070)

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### 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>
This commit is contained in:
shiyuan680
2025-11-28 20:55:43 +08:00
committed by GitHub
parent 5447a039b9
commit 1c4a0468ee
13 changed files with 1625 additions and 149 deletions
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vllm_ascend/ops/triton/fla/chunk_delta_h.py Normal file
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Songlin Yang, Yu Zhang
#
# This file contains code copied from the flash-linear-attention project.
# The original source code was licensed under the MIT license and included
# the following copyright notice:
# Copyright (c) 2023-2025, Songlin Yang, Yu Zhang
# ruff: noqa: E501
# mypy: ignore-errors
from typing import Optional
import torch
from vllm.triton_utils import tl, triton
from .utils import prepare_chunk_indices, prepare_chunk_offsets, safe_exp
_CONDITIONS = ("seq7168", )
@triton.heuristics({
"USE_G": lambda args: args["g"] is not None,
"USE_INITIAL_STATE": lambda args: args["h0"] is not None,
"STORE_FINAL_STATE": lambda args: args["ht"] is not None,
"SAVE_NEW_VALUE": lambda args: args["v_new"] is not None,
"IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
})
@triton.jit(do_not_specialize=["T"])
def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
k,
v,
w,
v_new,
g,
h,
h0,
ht,
cu_seqlens,
chunk_offsets,
T,
H: tl.constexpr,
Hg: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
USE_G: tl.constexpr,
USE_INITIAL_STATE: tl.constexpr,
STORE_FINAL_STATE: tl.constexpr,
SAVE_NEW_VALUE: tl.constexpr,
IS_VARLEN: tl.constexpr,
):
i_nh = tl.program_id(1)
i_n, i_h = i_nh // H, i_nh % H
T_max = 1 * T
if IS_VARLEN:
bos, eos = (
tl.load(cu_seqlens + i_n).to(tl.int32),
tl.load(cu_seqlens + i_n + 1).to(tl.int32),
)
T = eos - bos
NT = tl.cdiv(T, BT)
boh = tl.load(chunk_offsets + i_n).to(tl.int32)
else:
bos, eos = i_n * T, i_n * T + T
NT = tl.cdiv(T, BT)
boh = i_n * NT
stride_v = H * V
stride_k = Hg * K
stride_w = H * K
b_h1_bv1 = tl.zeros([128, 64], dtype=tl.float32)
b_h1_bv2 = tl.zeros([128, 64], dtype=tl.float32)
v_start1 = 0
v_start2 = 64
offs_k = tl.arange(0, 128)[:, None]
offs_v1 = v_start1 + tl.arange(0, 64)[None, :]
offs_v2 = v_start2 + tl.arange(0, 64)[None, :]
mask_kv1 = (offs_k < K) & (offs_v1 < V)
mask_kv2 = (offs_k < K) & (offs_v2 < V)
# load initial state
if USE_INITIAL_STATE:
h0_ptr = h0 + i_nh * K * V
ptr_h0_bv1 = h0_ptr + offs_k * V + offs_v1 * 1
b_h1_bv1 += tl.load(ptr_h0_bv1, mask=mask_kv1,
other=0.0).to(tl.float32)
ptr_h0_bv2 = h0_ptr + offs_k * V + offs_v2 * 1
b_h1_bv2 += tl.load(ptr_h0_bv2, mask=mask_kv2,
other=0.0).to(tl.float32)
# main recurrence
for i_t in range(NT):
h_base = h + (boh + i_t) * H * K * V + i_h * K * V
p_h1_bv1 = tl.make_block_ptr(h_base, (K, V), (V, 1), (0, v_start1),
(128, 64), (1, 0))
tl.store(p_h1_bv1,
b_h1_bv1.to(p_h1_bv1.dtype.element_ty),
boundary_check=(0, 1))
p_h1_bv2 = tl.make_block_ptr(h_base, (K, V), (V, 1), (0, v_start2),
(128, 64), (1, 0))
tl.store(p_h1_bv2,
b_h1_bv2.to(p_h1_bv2.dtype.element_ty),
boundary_check=(0, 1))
offs_t_wv = (i_t * BT + tl.arange(0, BT))[:, None]
offs_k_wv = tl.arange(0, 128)[None, :]
mask_w = (offs_t_wv < T) & (offs_k_wv < K)
w_base = w + bos * H * K + i_h * K
ptr_w = w_base + offs_t_wv * stride_w + offs_k_wv * 1
b_w = tl.load(ptr_w, mask=mask_w, other=0.0)
k_base = k + bos * Hg * K + (i_h // (H // Hg)) * K
p_k = tl.make_block_ptr(k_base, (K, T), (1, stride_k), (0, i_t * BT),
(128, BT), (0, 1))
b_k = tl.load(p_k, boundary_check=(0, 1))
v_new_base = v_new + bos * H * V + i_h * V
last_idx = min((i_t + 1) * BT, T) - 1
b_g_last = tl.load(g + bos + i_h * T_max + last_idx)
offs_t = i_t * BT + tl.arange(0, BT)
mask_t = offs_t < T
g_ptr = g + bos + i_h * T_max
b_g = tl.load(g_ptr + offs_t, mask=mask_t, other=0.0)
b_g = safe_exp(b_g_last - b_g)
b_g_last = tl.exp(b_g_last)
offs_t_v = (i_t * BT + tl.arange(0, BT))[:, None]
mask_v1 = (offs_t_v < T) & (offs_v1 < V)
v_base = v + bos * H * V + i_h * V
ptr_v1 = v_base + offs_t_v * stride_v + offs_v1 * 1
b_v1 = tl.load(ptr_v1, mask=mask_v1, other=0.0)
b_v_new1 = b_v1.to(tl.float32)
b_v_new1 -= tl.dot(b_w, b_h1_bv1.to(b_w.dtype))
if SAVE_NEW_VALUE:
p_v_new1 = tl.make_block_ptr(v_new_base, (T, V), (stride_v, 1),
(i_t * BT, v_start1), (BT, 64),
(1, 0))
tl.store(p_v_new1,
b_v_new1.to(p_v_new1.dtype.element_ty),
boundary_check=(0, 1))
if USE_G:
b_v_new1 = b_v_new1 * b_g[:, None]
b_h1_bv1 = b_h1_bv1 * b_g_last
b_v_new1 = b_v_new1.to(k.dtype.element_ty)
b_h1_bv1 += tl.dot(b_k, b_v_new1)
mask_v2 = (offs_t_v < T) & (offs_v2 < V)
ptr_v2 = v_base + offs_t_v * stride_v + offs_v2 * 1
b_v2 = tl.load(ptr_v2, mask=mask_v2, other=0.0)
b_v_new2 = b_v2.to(tl.float32)
b_v_new2 -= tl.dot(b_w, b_h1_bv2.to(b_w.dtype))
if SAVE_NEW_VALUE:
p_v_new2 = tl.make_block_ptr(v_new_base, (T, V), (stride_v, 1),
(i_t * BT, v_start2), (BT, 64),
(1, 0))
tl.store(p_v_new2,
b_v_new2.to(p_v_new2.dtype.element_ty),
boundary_check=(0, 1))
if USE_G:
b_v_new2 = b_v_new2 * b_g[:, None]
b_h1_bv2 = b_h1_bv2 * b_g_last
b_v_new2 = b_v_new2.to(k.dtype.element_ty)
b_h1_bv2 += tl.dot(b_k, b_v_new2)
# epilogue
if STORE_FINAL_STATE:
ht_ptr = ht + i_nh * K * V
p_ht1_bv1 = tl.make_block_ptr(ht_ptr, (K, V), (V, 1), (0, v_start1),
(128, 64), (1, 0))
tl.store(p_ht1_bv1,
b_h1_bv1.to(p_ht1_bv1.dtype.element_ty),
boundary_check=(0, 1))
p_ht1_bv2 = tl.make_block_ptr(ht_ptr, (K, V), (V, 1), (0, v_start2),
(128, 64), (1, 0))
tl.store(p_ht1_bv2,
b_h1_bv2.to(p_ht1_bv2.dtype.element_ty),
boundary_check=(0, 1))
def chunk_gated_delta_rule_fwd_h(
k: torch.Tensor,
w: torch.Tensor,
u: torch.Tensor,
g: Optional[torch.Tensor] = None,
initial_state: Optional[torch.Tensor] = None,
output_final_state: bool = False,
chunk_size: int = 64, # SY: remove this argument and force chunk size 64?
save_new_value: bool = True,
cu_seqlens: Optional[torch.LongTensor] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
# This kernel is slightly different from fla to support Q/K with different head numbers.
# In fla, Q/K always have the same head number, so Hg is always equal to H.
B, T, Hg, K, V = *k.shape, u.shape[-1]
H = u.shape[-2]
BT = chunk_size
chunk_indices = (prepare_chunk_indices(cu_seqlens, chunk_size)
if cu_seqlens is not None else None)
# N: the actual number of sequences in the batch with either equal or variable lengths
if cu_seqlens is None:
N, NT, chunk_offsets = B, triton.cdiv(T, BT), None
else:
N, NT, chunk_offsets = (
len(cu_seqlens) - 1,
len(chunk_indices),
prepare_chunk_offsets(cu_seqlens, BT),
)
assert K <= 256, "current kernel does not support head dimension larger than 256."
h = k.new_empty(B, NT, H, K, V)
final_state = (k.new_empty(N, H, K, V, dtype=torch.float32)
if output_final_state else None)
v_new = torch.empty_like(u) if save_new_value else None
g = g.transpose(1, 2).contiguous()
def grid(meta):
return (1, N * H)
chunk_gated_delta_rule_fwd_kernel_h_blockdim64[grid](
k=k,
v=u,
w=w,
v_new=v_new,
g=g,
h=h,
h0=initial_state,
ht=final_state,
cu_seqlens=cu_seqlens,
chunk_offsets=chunk_offsets,
T=T,
H=H,
Hg=Hg,
K=K,
V=V,
BT=BT,
num_warps=4,
num_stages=2,
)
return h, v_new, final_state