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ref(upstream): 搬运 3 大 GDN 上游仓库 — FLA naive ops + vllm GDN 子树 + xllm C++ 参考

Browse Source 
来源:
  1. fla-org/flash-linear-attention (5538 stars)
     → upstream_ref/fla/ops/gated_delta_rule/naive.py (正确的纯 PyTorch GDN)
     → upstream_ref/fla/ops/gated_delta_rule/chunk.py (Triton chunk kernel)
     → upstream_ref/fla/layers/gated_deltanet.py (层集成)

  2. vllm-project/vllm main (88717 stars)
     → upstream_ref/vllm_gdn/gdn/qwen_gdn_linear_attn.py (1751行, Qwen3.5 原生 GDN)
     → upstream_ref/vllm_gdn/ops/causal_conv1d.py (1289行, 正确的 Conv1d)
     → upstream_ref/vllm_gdn/third_party/ops/ (FLA Triton ops vendored)
     → upstream_ref/vllm_gdn/models/qwen3_5.py (vllm 最新 Qwen3.5 模型)

  3. Deep-Spark/xllm (BI-V100 硬件厂商)
     → upstream_ref/xllm_latest/core/layers/npu_torch/qwen3_gated_delta_net_base.cpp (576行)
     → upstream_ref/xllm_latest/core/kernels/npu/npu_causal_conv1d.cpp
     → upstream_ref/xllm_latest/core/kernels/npu/npu_recurrent_gated_delta_rule.cpp

目的: 修复 corex_gdn.py Conv1d groups 接口不匹配问题
  错误: conv1d_weight shape (2560,1,4) 被当成 (num_k_heads,1,4) 索引
  conv_dim = key_dim*2 + value_dim = 10240, TP=4 后 2560
  FLA naive.py 和 vllm qwen_gdn_linear_attn.py 有正确的实现可直接对接
This commit is contained in:
Claude
2026-08-11 03:55:50 +00:00
parent 5862708b32
commit 6cdf2ec87b
46 changed files with 13857 additions and 811 deletions
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upstream_ref/vllm_gdn/third_party/__init__.py vendored 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

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upstream_ref/vllm_gdn/third_party/ops/__init__.py vendored 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
from .chunk import chunk_gated_delta_rule
from .fused_gdn_prefill_post_conv import fused_post_conv_prep
from .fused_recurrent import (
fused_recurrent_gated_delta_rule,
fused_recurrent_gated_delta_rule_packed_decode,
)
from .fused_sigmoid_gating import fused_sigmoid_gating_delta_rule_update
from .layernorm_guard import RMSNormGated
__all__ = [
"RMSNormGated",
"chunk_gated_delta_rule",
"fused_recurrent_gated_delta_rule",
"fused_recurrent_gated_delta_rule_packed_decode",
"fused_post_conv_prep",
"fused_sigmoid_gating_delta_rule_update",
]

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upstream_ref/vllm_gdn/third_party/ops/chunk.py vendored 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
import torch
from .chunk_delta_h import chunk_gated_delta_rule_fwd_h
from .chunk_o import chunk_fwd_o
from .chunk_scaled_dot_kkt import chunk_scaled_dot_kkt_fwd
from .cumsum import chunk_local_cumsum
from .l2norm import l2norm_fwd
from .solve_tril import solve_tril
from .utils import FLA_CHUNK_SIZE, SUPPRESS_LEVEL, input_guard
from .wy_fast import recompute_w_u_fwd
def chunk_gated_delta_rule_fwd(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
g: torch.Tensor,
beta: torch.Tensor,
scale: float,
initial_state: torch.Tensor,
output_final_state: bool,
cu_seqlens: torch.Tensor | None = None,
chunk_indices: torch.Tensor | None = None,
chunk_offsets: torch.Tensor | None = None,
core_attn_out: torch.Tensor | None = None,
):
g = chunk_local_cumsum(
g, chunk_size=FLA_CHUNK_SIZE, cu_seqlens=cu_seqlens, chunk_indices=chunk_indices
)
# obtain WY representation. u is actually the new v.
A = chunk_scaled_dot_kkt_fwd(
k=k,
beta=beta,
g=g,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
output_dtype=torch.float32,
)
A = solve_tril(
A=A, cu_seqlens=cu_seqlens, chunk_indices=chunk_indices, output_dtype=k.dtype
)
w, u = recompute_w_u_fwd(
k=k,
v=v,
beta=beta,
A=A,
g_cumsum=g,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
)
h, v_new, final_state = chunk_gated_delta_rule_fwd_h(
k=k,
w=w,
u=u,
g=g,
initial_state=initial_state,
output_final_state=output_final_state,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
chunk_offsets=chunk_offsets,
)
o = chunk_fwd_o(
q=q,
k=k,
v=v_new,
h=h,
g=g,
scale=scale,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
core_attn_out=core_attn_out,
)
if SUPPRESS_LEVEL < 3:
return g, o, A, final_state, None, None, None
elif SUPPRESS_LEVEL >= 3:
return g, o, A, final_state, w, h, v_new
class ChunkGatedDeltaRuleFunction(torch.autograd.Function):
@staticmethod
@input_guard
@torch.amp.custom_fwd(device_type="cuda")
def forward(
ctx,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
g: torch.Tensor,
beta: torch.Tensor,
scale: float,
initial_state: torch.Tensor,
output_final_state: bool,
cu_seqlens: torch.Tensor | None = None,
chunk_indices: torch.Tensor | None = None,
chunk_offsets: torch.Tensor | None = None,
use_qk_l2norm_in_kernel: bool = False,
core_attn_out: torch.Tensor | None = None,
):
if use_qk_l2norm_in_kernel:
q = l2norm_fwd(q)
k = l2norm_fwd(k)
g, o, A, final_state, w, h, v_new = chunk_gated_delta_rule_fwd(
q=q,
k=k,
v=v,
g=g,
beta=beta,
scale=scale,
initial_state=initial_state,
output_final_state=output_final_state,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
chunk_offsets=chunk_offsets,
core_attn_out=core_attn_out,
)
ctx.scale = scale
ctx.use_qk_l2norm_in_kernel = use_qk_l2norm_in_kernel
if core_attn_out is not None:
assert not torch.is_grad_enabled(), (
"core_attn_out buffer reuse is only supported for inference"
)
assert q.dtype == o.dtype, "Incompatible dtype for inplace computation"
return o.to(q.dtype), final_state
@torch.compiler.disable
def chunk_gated_delta_rule(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
g: torch.Tensor,
beta: torch.Tensor,
scale: float = None,
initial_state: torch.Tensor = None,
output_final_state: bool = False,
cu_seqlens: torch.Tensor | None = None,
chunk_indices: torch.Tensor | None = None,
chunk_offsets: torch.Tensor | None = None,
use_qk_l2norm_in_kernel: bool = False,
core_attn_out: torch.Tensor | None = None,
):
r"""
Args:
q (torch.Tensor):
Queries of shape `[B, T, H, K]`.
k (torch.Tensor):
Keys of shape `[B, T, H, K]`.
v (torch.Tensor):
Values of shape `[B, T, H, V]`.
g (torch.Tensor):
(forget) Gating tensor (in log space!) of shape `[B, T, H]`.
beta (torch.Tensor):
Betas of shape `[B, T, H]`.
scale (Optional[int]):
Scale factor for the RetNet attention scores.
If not provided, it will default to `1 / sqrt(K)`. Default: `None`.
initial_state (Optional[torch.Tensor]):
Initial state of shape `[N, H, V, K]` for `N` input sequences.
For equal-length input sequences, `N` equals the batch size `B`.
Default: `None`.
output_final_state (Optional[bool]):
Whether to output the final state of shape `[N, H, V, K]`. Default: `False`.
cu_seqlens (torch.Tensor):
Cumulative sequence lengths of shape `[N+1]` used for variable-length training,
consistent with the FlashAttention API.
Returns:
o (torch.Tensor):
Outputs of shape `[B, T, H, V]`.
final_state (torch.Tensor):
Final state of shape `[N, H, V, K]` if `output_final_state=True` else `None`.
Examples::
>>> import torch
>>> import torch.nn.functional as F
>>> from einops import rearrange
>>> from fla.ops.gated_delta_rule import chunk_gated_delta_rule
# inputs with equal lengths
>>> B, T, H, K, V = 4, 2048, 4, 512, 512
>>> q = torch.randn(B, T, H, K, dtype=torch.bfloat16, device='cuda')
>>> k = F.normalize(torch.randn(B, T, H, K, dtype=torch.bfloat16, device='cuda'), p=2, dim=-1)
>>> v = torch.randn(B, T, H, V, dtype=torch.bfloat16, device='cuda')
>>> beta = torch.rand(B, T, H, dtype=torch.bfloat16, device='cuda').sigmoid()
>>> g = F.logsigmoid(torch.rand(B, T, H, dtype=torch.bfloat16, device='cuda'))
>>> h0 = torch.randn(B, H, V, K, dtype=torch.bfloat16, device='cuda')
>>> o, ht = chunk_gated_delta_rule(
q, k, v, g, beta,
initial_state=h0,
output_final_state=True
)
# for variable-length inputs, the batch size `B` is expected to be 1 and `cu_seqlens` is required
>>> q, k, v, beta, g = map(lambda x: rearrange(x, 'b t ... -> 1 (b t) ...'), (q, k, v, beta, g))
# for a batch with 4 sequences, `cu_seqlens` with 5 start/end positions are expected
>>> cu_seqlens = q.new_tensor([0, 2048, 4096, 6144, 8192], dtype=torch.int32)
>>> o_var, ht_var = chunk_gated_delta_rule(
q, k, v, g, beta,
initial_state=h0,
output_final_state=True,
cu_seqlens=cu_seqlens
)
"""
assert q.dtype == k.dtype == v.dtype
assert q.dtype != torch.float32, (
"ChunkGatedDeltaRuleFunction does not support float32. Please use bfloat16."
)
assert len(beta.shape) == 3, "beta must be of shape [B, T, H]."
if cu_seqlens is not None:
if q.shape[0] != 1:
raise ValueError(
f"The batch size is expected to be 1 rather than {q.shape[0]} when using `cu_seqlens`."
f"Please flatten variable-length inputs before processing."
)
if initial_state is not None and initial_state.shape[0] != len(cu_seqlens) - 1:
raise ValueError(
f"The number of initial states is expected to be equal to the number of input sequences, "
f"i.e., {len(cu_seqlens) - 1} rather than {initial_state.shape[0]}."
)
if scale is None:
scale = k.shape[-1] ** -0.5
o, final_state = ChunkGatedDeltaRuleFunction.apply(
q,
k,
v,
g,
beta,
scale,
initial_state,
output_final_state,
cu_seqlens,
chunk_indices,
chunk_offsets,
use_qk_l2norm_in_kernel,
core_attn_out,
)
return o, final_state

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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
import torch
from vllm.triton_utils import tl, triton
from .index import prepare_chunk_indices
from .utils import check_shared_mem, input_guard
BS_LIST = [32, 64] if check_shared_mem() else [16, 32]
@triton.heuristics({"IS_VARLEN": lambda args: args["cu_seqlens"] is not None})
@triton.autotune(
configs=[triton.Config({}, num_warps=num_warps) for num_warps in [1, 2, 4, 8]],
key=["B", "H", "BT", "IS_VARLEN", "REVERSE"],
)
@triton.jit(do_not_specialize=["T"])
def chunk_local_cumsum_scalar_kernel(
s,
o,
cu_seqlens,
chunk_indices,
T,
B: tl.constexpr,
H: tl.constexpr,
BT: tl.constexpr,
REVERSE: tl.constexpr,
IS_VARLEN: tl.constexpr,
HEAD_FIRST: tl.constexpr,
):
i_t, i_bh = tl.program_id(0), tl.program_id(1)
i_b, i_h = i_bh // H, i_bh % H
if IS_VARLEN:
i_n, i_t = (
tl.load(chunk_indices + i_t * 2).to(tl.int32),
tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32),
)
bos, eos = (
tl.load(cu_seqlens + i_n).to(tl.int32),
tl.load(cu_seqlens + i_n + 1).to(tl.int32),
)
T = eos - bos
else:
bos, eos = i_b * T, i_b * T + T
if HEAD_FIRST:
p_s = tl.make_block_ptr(
s + bos * H + i_h * T, (T,), (1,), (i_t * BT,), (BT,), (0,)
)
p_o = tl.make_block_ptr(
o + bos * H + i_h * T, (T,), (1,), (i_t * BT,), (BT,), (0,)
)
else:
p_s = tl.make_block_ptr(s + bos * H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
p_o = tl.make_block_ptr(o + bos * H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
# [BT]
b_s = tl.load(p_s, boundary_check=(0,)).to(tl.float32)
b_o = tl.cumsum(b_s, axis=0)
if REVERSE:
b_z = tl.sum(b_s, axis=0)
b_o = -b_o + b_z[None] + b_s
tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0,))
@triton.heuristics({"IS_VARLEN": lambda args: args["cu_seqlens"] is not None})
@triton.autotune(
configs=[
triton.Config({"BS": BS}, num_warps=num_warps)
for BS in BS_LIST
for num_warps in [2, 4, 8]
],
key=["B", "H", "S", "BT", "IS_VARLEN", "REVERSE"],
)
@triton.jit(do_not_specialize=["T"])
def chunk_local_cumsum_vector_kernel(
s,
o,
cu_seqlens,
chunk_indices,
T,
B: tl.constexpr,
H: tl.constexpr,
S: tl.constexpr,
BT: tl.constexpr,
BS: tl.constexpr,
REVERSE: tl.constexpr,
IS_VARLEN: tl.constexpr,
HEAD_FIRST: tl.constexpr,
):
i_s, i_t, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
i_b, i_h = i_bh // H, i_bh % H
if IS_VARLEN:
i_n, i_t = (
tl.load(chunk_indices + i_t * 2).to(tl.int32),
tl.load(chunk_indices + i_t * 2 + 1).to(tl.int32),
)
bos, eos = (
tl.load(cu_seqlens + i_n).to(tl.int32),
tl.load(cu_seqlens + i_n + 1).to(tl.int32),
)
T = eos - bos
else:
bos, eos = i_b * T, i_b * T + T
o_i = tl.arange(0, BT)
if REVERSE:
m_s = tl.where(o_i[:, None] <= o_i[None, :], 1.0, 0.0)
else:
m_s = tl.where(o_i[:, None] >= o_i[None, :], 1.0, 0.0)
if HEAD_FIRST:
p_s = tl.make_block_ptr(
s + (bos * H + i_h * T) * S,
(T, S),
(S, 1),
(i_t * BT, i_s * BS),
(BT, BS),
(1, 0),
)
p_o = tl.make_block_ptr(
o + (bos * H + i_h * T) * S,
(T, S),
(S, 1),
(i_t * BT, i_s * BS),
(BT, BS),
(1, 0),
)
else:
p_s = tl.make_block_ptr(
s + (bos * H + i_h) * S,
(T, S),
(H * S, 1),
(i_t * BT, i_s * BS),
(BT, BS),
(1, 0),
)
p_o = tl.make_block_ptr(
o + (bos * H + i_h) * S,
(T, S),
(H * S, 1),
(i_t * BT, i_s * BS),
(BT, BS),
(1, 0),
)
# [BT, BS]
b_s = tl.load(p_s, boundary_check=(0, 1)).to(tl.float32)
b_o = tl.dot(m_s, b_s, allow_tf32=False)
tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))
def chunk_local_cumsum_scalar(
g: torch.Tensor,
chunk_size: int,
reverse: bool = False,
cu_seqlens: torch.Tensor | None = None,
chunk_indices: torch.Tensor | None = None,
head_first: bool = False,
output_dtype: torch.dtype | None = torch.float,
) -> torch.Tensor:
if head_first:
B, H, T = g.shape
else:
B, T, H = g.shape
assert chunk_size == 2 ** (chunk_size.bit_length() - 1), (
"chunk_size must be a power of 2"
)
if chunk_indices is None and cu_seqlens is not None:
chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size)
BT = chunk_size
NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype)
grid = (NT, B * H)
chunk_local_cumsum_scalar_kernel[grid](
g_org,
g,
cu_seqlens,
chunk_indices,
T=T,
B=B,
H=H,
BT=BT,
HEAD_FIRST=head_first,
REVERSE=reverse,
)
return g
def chunk_local_cumsum_vector(
g: torch.Tensor,
chunk_size: int,
reverse: bool = False,
cu_seqlens: torch.Tensor | None = None,
chunk_indices: torch.Tensor | None = None,
head_first: bool = False,
output_dtype: torch.dtype | None = torch.float,
) -> torch.Tensor:
if head_first:
B, H, T, S = g.shape
else:
B, T, H, S = g.shape
assert chunk_size == 2 ** (chunk_size.bit_length() - 1), (
"chunk_size must be a power of 2"
)
if chunk_indices is None and cu_seqlens is not None:
chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size)
BT = chunk_size
NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype)
def grid(meta):
return (triton.cdiv(meta["S"], meta["BS"]), NT, B * H)
# keep cumulative normalizer in fp32
# this kernel is equivalent to
# g = g.view(B, H, NT, BT, -1).cumsum(-2).view(B, H, T, -1)
chunk_local_cumsum_vector_kernel[grid](
g_org,
g,
cu_seqlens,
chunk_indices,
T=T,
B=B,
H=H,
S=S,
BT=BT,
HEAD_FIRST=head_first,
REVERSE=reverse,
)
return g
@input_guard
def chunk_local_cumsum(
g: torch.Tensor,
chunk_size: int,
reverse: bool = False,
cu_seqlens: torch.Tensor | None = None,
chunk_indices: torch.Tensor | None = None,
head_first: bool = False,
output_dtype: torch.dtype | None = torch.float,
**kwargs,
) -> torch.Tensor:
if cu_seqlens is not None:
assert g.shape[0] == 1, (
"Only batch size 1 is supported when cu_seqlens are provided"
)
if len(g.shape) == 3:
return chunk_local_cumsum_scalar(
g,
chunk_size,
reverse,
cu_seqlens,
chunk_indices,
head_first,
output_dtype,
)
elif len(g.shape) == 4:
return chunk_local_cumsum_vector(
g,
chunk_size,
reverse,
cu_seqlens,
chunk_indices,
head_first,
output_dtype,
)
else:
raise ValueError(
f"Unsupported input shape {g.shape}. "
f"which should be (B, T, H, D) if `head_first=False` "
f"or (B, H, T, D) otherwise"
)

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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Fused post-conv1d preparation for GDN prefill.
Replaces the chain:
split → rearrange → contiguous * 3 → l2norm * 2 → gating
with a **single Triton kernel** that reads the conv'd mixed_qkv output
and writes directly to q/k/v/g/beta in the target contiguous layout.
"""
from __future__ import annotations
import torch
from vllm.triton_utils import tl, triton
@triton.jit
def _fused_post_conv_kernel(
# ---- inputs ----
mixed_qkv_ptr, # [L, qkv_dim] conv'd output (contiguous)
a_ptr, # [L, HV]
b_ptr, # [L, HV]
# ---- params ----
A_log_ptr, # [HV]
dt_bias_ptr, # [HV]
# ---- outputs ----
q_ptr, # [L, H, K] contiguous
k_ptr, # [L, H, K] contiguous
v_ptr, # [L, HV, V] contiguous
g_ptr, # [L, HV] float32
beta_ptr, # [L, HV] float32
# ---- strides ----
stride_x_tok, # qkv_dim
stride_a_tok, # HV
stride_b_tok, # HV
stride_q_tok, # H * K
stride_k_tok, # H * K
stride_v_tok, # HV * V
# ---- dims ----
L,
H: tl.constexpr,
HV: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
APPLY_L2NORM: tl.constexpr,
L2NORM_EPS: tl.constexpr,
OUTPUT_G_EXP: tl.constexpr,
SOFTPLUS_THRESHOLD: tl.constexpr,
BLOCK_T: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
):
"""Single fused kernel for post-conv1d preparation.
Grid: (ceil(L, BLOCK_T), H + HV)
- program_id(1) in [0, H): Q/K head processing + l2norm
- program_id(1) in [H, H+HV): V head processing + gating
"""
i_tb = tl.program_id(0)
i_head = tl.program_id(1)
HK: tl.constexpr = H * K
offs_t = i_tb * BLOCK_T + tl.arange(0, BLOCK_T) # [BLOCK_T]
mask_t = offs_t < L
if i_head < H:
# ============ Q/K head processing ============
i_h = i_head
offs_k = tl.arange(0, BK) # [BK]
mask_k = offs_k < K
mask_2d = mask_t[:, None] & mask_k[None, :] # [BLOCK_T, BK]
# Load Q features: mixed_qkv[t, i_h*K + k]
q_offsets = offs_t[:, None] * stride_x_tok + i_h * K + offs_k[None, :]
q_f32 = tl.load(mixed_qkv_ptr + q_offsets, mask=mask_2d, other=0).to(tl.float32)
# Load K features: mixed_qkv[t, HK + i_h*K + k]
k_offsets = offs_t[:, None] * stride_x_tok + HK + i_h * K + offs_k[None, :]
k_f32 = tl.load(mixed_qkv_ptr + k_offsets, mask=mask_2d, other=0).to(tl.float32)
if APPLY_L2NORM:
q_sq_sum = tl.sum(q_f32 * q_f32, axis=1) # [BLOCK_T]
q_inv = 1.0 / tl.sqrt(q_sq_sum + L2NORM_EPS)
q_f32 = q_f32 * q_inv[:, None]
k_sq_sum = tl.sum(k_f32 * k_f32, axis=1)
k_inv = 1.0 / tl.sqrt(k_sq_sum + L2NORM_EPS)
k_f32 = k_f32 * k_inv[:, None]
# Store Q
q_out = offs_t[:, None] * stride_q_tok + i_h * K + offs_k[None, :]
tl.store(
q_ptr + q_out,
q_f32.to(q_ptr.dtype.element_ty),
mask=mask_2d,
)
# Store K
k_out = offs_t[:, None] * stride_k_tok + i_h * K + offs_k[None, :]
tl.store(
k_ptr + k_out,
k_f32.to(k_ptr.dtype.element_ty),
mask=mask_2d,
)
else:
# ============ V head + gating processing ============
i_hv = i_head - H
offs_v = tl.arange(0, BV) # [BV]
mask_v = offs_v < V
mask_2d = mask_t[:, None] & mask_v[None, :] # [BLOCK_T, BV]
V_OFFSET: tl.constexpr = 2 * H * K
# Load V features: mixed_qkv[t, 2*H*K + i_hv*V + v]
v_offsets = (
offs_t[:, None] * stride_x_tok + V_OFFSET + i_hv * V + offs_v[None, :]
)
v_vals = tl.load(mixed_qkv_ptr + v_offsets, mask=mask_2d, other=0)
# Store V
v_out = offs_t[:, None] * stride_v_tok + i_hv * V + offs_v[None, :]
tl.store(v_ptr + v_out, v_vals, mask=mask_2d)
# Gating: one scalar per (token, v-head)
A_log_val = tl.load(A_log_ptr + i_hv).to(tl.float32)
dt_bias_val = tl.load(dt_bias_ptr + i_hv).to(tl.float32)
a_offsets = offs_t * stride_a_tok + i_hv
b_offsets = offs_t * stride_b_tok + i_hv
a_vals = tl.load(a_ptr + a_offsets, mask=mask_t, other=0).to(tl.float32)
b_vals = tl.load(b_ptr + b_offsets, mask=mask_t, other=0).to(tl.float32)
# g = -exp(A_log) * softplus(a + dt_bias)
x = a_vals + dt_bias_val
sp = tl.where(x > 0, x + tl.log(1.0 + tl.exp(-x)), tl.log(1.0 + tl.exp(x)))
sp = tl.where(x <= SOFTPLUS_THRESHOLD, sp, x)
g_vals = -tl.exp(A_log_val) * sp
if OUTPUT_G_EXP:
g_vals = tl.exp(g_vals)
beta_vals = tl.sigmoid(b_vals)
gb_offsets = offs_t * HV + i_hv
tl.store(g_ptr + gb_offsets, g_vals, mask=mask_t)
tl.store(beta_ptr + gb_offsets, beta_vals, mask=mask_t)
def fused_post_conv_prep(
conv_output: torch.Tensor, # [L, qkv_dim] conv'd mixed_qkv
a: torch.Tensor, # [L, HV]
b: torch.Tensor, # [L, HV]
A_log: torch.Tensor, # [HV]
dt_bias: torch.Tensor, # [HV]
num_k_heads: int,
head_k_dim: int,
head_v_dim: int,
apply_l2norm: bool = True,
output_g_exp: bool = False,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Fused post-conv1d prep: split + l2norm + gating in one kernel.
Args:
conv_output: [L, qkv_dim] contiguous conv'd mixed_qkv
a: [L, HV] gating input
b: [L, HV] gating input
A_log: [HV] log decay parameter
dt_bias: [HV] dt bias parameter
num_k_heads: number of K heads (H)
head_k_dim: dimension per K head (K)
head_v_dim: dimension per V head (V)
apply_l2norm: whether to L2-normalize q and k
output_g_exp: if True, output exp(g) instead of g (for FlashInfer)
Returns:
q: [L, H, K] contiguous, optionally l2-normalized
k: [L, H, K] contiguous, optionally l2-normalized
v: [L, HV, V] contiguous
g: [L, HV] float32
beta: [L, HV] float32
"""
L = conv_output.shape[0]
qkv_dim = conv_output.shape[1]
H = num_k_heads
K = head_k_dim
V = head_v_dim
HV = A_log.shape[0]
dtype = conv_output.dtype
device = conv_output.device
assert qkv_dim == 2 * H * K + HV * V, (
f"qkv_dim={qkv_dim} != 2*H*K + HV*V = {2 * H * K + HV * V}"
)
# Allocate outputs in target contiguous layout
q = torch.empty(L, H, K, dtype=dtype, device=device)
k = torch.empty(L, H, K, dtype=dtype, device=device)
v = torch.empty(L, HV, V, dtype=dtype, device=device)
g = torch.empty(L, HV, dtype=torch.float32, device=device)
beta = torch.empty(L, HV, dtype=torch.float32, device=device)
if L == 0:
return q, k, v, g, beta
# ---- Kernel config ----
BK = triton.next_power_of_2(K)
BV = triton.next_power_of_2(V)
BLOCK_T = 16 # tokens per block
# Single kernel: blocks [0,H) do Q/K, blocks [H, H+HV) do V+gating
grid = (triton.cdiv(L, BLOCK_T), H + HV)
_fused_post_conv_kernel[grid](
mixed_qkv_ptr=conv_output,
a_ptr=a,
b_ptr=b,
A_log_ptr=A_log,
dt_bias_ptr=dt_bias,
q_ptr=q,
k_ptr=k,
v_ptr=v,
g_ptr=g,
beta_ptr=beta,
stride_x_tok=conv_output.stride(0),
stride_a_tok=a.stride(0),
stride_b_tok=b.stride(0),
stride_q_tok=q.stride(0),
stride_k_tok=k.stride(0),
stride_v_tok=v.stride(0),
L=L,
H=H,
HV=HV,
K=K,
V=V,
APPLY_L2NORM=apply_l2norm,
L2NORM_EPS=1e-6,
OUTPUT_G_EXP=output_g_exp,
SOFTPLUS_THRESHOLD=20.0,
BLOCK_T=BLOCK_T,
BK=BK,
BV=BV,
num_warps=4,
num_stages=2,
)
return q, k, v, g, beta

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upstream_ref/vllm_gdn/third_party/ops/fused_recurrent.py vendored Normal file
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@@ -0,0 +1,619 @@
# 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
import torch
from vllm.triton_utils import tl, triton
from .op import exp, log
@triton.heuristics(
{
"USE_INITIAL_STATE": lambda args: args["h0"] is not None,
"IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
"IS_CONTINUOUS_BATCHING": lambda args: args["ssm_state_indices"] is not None,
"IS_SPEC_DECODING": lambda args: args["num_accepted_tokens"] is not None,
}
)
@triton.jit(do_not_specialize=["N", "T"])
def fused_recurrent_gated_delta_rule_fwd_kernel(
q,
k,
v,
g,
beta,
o,
h0,
ht,
cu_seqlens,
ssm_state_indices,
num_accepted_tokens,
scale,
N: tl.int64, # num of sequences
T: tl.int64, # num of tokens
B: tl.constexpr,
H: tl.constexpr,
HV: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
stride_init_state_token: tl.constexpr,
stride_final_state_token: tl.constexpr,
stride_indices_seq: tl.constexpr,
stride_indices_tok: tl.constexpr,
USE_INITIAL_STATE: tl.constexpr, # whether to use initial state
INPLACE_FINAL_STATE: tl.constexpr, # whether to store final state inplace
IS_BETA_HEADWISE: tl.constexpr, # whether beta is headwise vector or scalar,
USE_QK_L2NORM_IN_KERNEL: tl.constexpr,
IS_VARLEN: tl.constexpr,
IS_CONTINUOUS_BATCHING: tl.constexpr,
IS_SPEC_DECODING: tl.constexpr,
IS_KDA: tl.constexpr,
):
i_k, i_v, i_nh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
i_n, i_hv = i_nh // HV, i_nh % HV
i_h = i_hv // (HV // H)
if IS_VARLEN:
bos, eos = (
tl.load(cu_seqlens + i_n).to(tl.int64),
tl.load(cu_seqlens + i_n + 1).to(tl.int64),
)
all = T
T = eos - bos
else:
bos, eos = i_n * T, i_n * T + T
all = B * T
if T == 0:
# no tokens to process for this sequence
return
o_k = i_k * BK + tl.arange(0, BK)
o_v = i_v * BV + tl.arange(0, BV)
p_q = q + (bos * H + i_h) * K + o_k
p_k = k + (bos * H + i_h) * K + o_k
p_v = v + (bos * HV + i_hv) * V + o_v
if IS_BETA_HEADWISE:
p_beta = beta + (bos * HV + i_hv) * V + o_v
else:
p_beta = beta + bos * HV + i_hv
if not IS_KDA:
p_g = g + bos * HV + i_hv
else:
p_gk = g + (bos * HV + i_hv) * K + o_k
p_o = o + ((i_k * all + bos) * HV + i_hv) * V + o_v
mask_k = o_k < K
mask_v = o_v < V
mask_h = mask_v[:, None] & mask_k[None, :]
b_h = tl.zeros([BV, BK], dtype=tl.float32)
if USE_INITIAL_STATE:
if IS_CONTINUOUS_BATCHING:
if IS_SPEC_DECODING:
i_t = tl.load(num_accepted_tokens + i_n).to(tl.int64) - 1
else:
i_t = 0
# Load state index and check for invalid entries
state_idx = tl.load(ssm_state_indices + i_n * stride_indices_seq + i_t).to(
tl.int64
)
# Skip if state index is invalid (NULL_BLOCK_ID=0)
if state_idx <= 0:
return
p_h0 = h0 + state_idx * stride_init_state_token
else:
p_h0 = h0 + bos * HV * V * K
p_h0 = p_h0 + i_hv * V * K + o_v[:, None] * K + o_k[None, :]
b_h += tl.load(p_h0, mask=mask_h, other=0).to(tl.float32)
for i_t in range(0, T):
b_q = tl.load(p_q, mask=mask_k, other=0).to(tl.float32)
b_k = tl.load(p_k, mask=mask_k, other=0).to(tl.float32)
b_v = tl.load(p_v, mask=mask_v, other=0).to(tl.float32)
if USE_QK_L2NORM_IN_KERNEL:
b_q = b_q / tl.sqrt(tl.sum(b_q * b_q) + 1e-6)
b_k = b_k / tl.sqrt(tl.sum(b_k * b_k) + 1e-6)
b_q = b_q * scale
# [BV, BK]
if not IS_KDA:
b_g = tl.load(p_g).to(tl.float32)
b_h *= exp(b_g)
else:
b_gk = tl.load(p_gk).to(tl.float32)
b_h *= exp(b_gk[None, :])
# [BV]
b_v -= tl.sum(b_h * b_k[None, :], 1)
if IS_BETA_HEADWISE:
b_beta = tl.load(p_beta, mask=mask_v, other=0).to(tl.float32)
else:
b_beta = tl.load(p_beta).to(tl.float32)
b_v *= b_beta
# [BV, BK]
b_h += b_v[:, None] * b_k[None, :]
# [BV]
b_o = tl.sum(b_h * b_q[None, :], 1)
tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=mask_v)
# keep the states for multi-query tokens
if INPLACE_FINAL_STATE:
# Load state index and check for invalid entries
final_state_idx = tl.load(
ssm_state_indices + i_n * stride_indices_seq + i_t
).to(tl.int64)
# Only store if state index is valid (not NULL_BLOCK_ID=0)
if final_state_idx > 0:
p_ht = ht + final_state_idx * stride_final_state_token
p_ht = p_ht + i_hv * V * K + o_v[:, None] * K + o_k[None, :]
tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h)
else:
p_ht = ht + (bos + i_t) * stride_final_state_token
p_ht = p_ht + i_hv * V * K + o_v[:, None] * K + o_k[None, :]
tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h)
p_q += H * K
p_k += H * K
p_o += HV * V
p_v += HV * V
if not IS_KDA:
p_g += HV
else:
p_gk += HV * K
p_beta += HV * (V if IS_BETA_HEADWISE else 1)
def fused_recurrent_gated_delta_rule_fwd(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
g: torch.Tensor,
beta: torch.Tensor,
scale: float,
initial_state: torch.Tensor,
inplace_final_state: bool = True,
cu_seqlens: torch.Tensor | None = None,
ssm_state_indices: torch.Tensor | None = None,
num_accepted_tokens: torch.Tensor | None = None,
use_qk_l2norm_in_kernel: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
B, T, H, K, V = *k.shape, v.shape[-1]
HV = v.shape[2]
N = B if cu_seqlens is None else len(cu_seqlens) - 1
BK, BV = triton.next_power_of_2(K), min(triton.next_power_of_2(V), 32)
NK, NV = triton.cdiv(K, BK), triton.cdiv(V, BV)
assert NK == 1, "NK > 1 is not supported yet"
num_stages = 3
num_warps = 1
o = q.new_empty(NK, *v.shape)
if inplace_final_state:
final_state = initial_state
else:
final_state = q.new_empty(T, HV, V, K, dtype=initial_state.dtype)
stride_init_state_token = initial_state.stride(0)
stride_final_state_token = final_state.stride(0)
if ssm_state_indices is None:
stride_indices_seq, stride_indices_tok = 1, 1
elif ssm_state_indices.ndim == 1:
stride_indices_seq, stride_indices_tok = ssm_state_indices.stride(0), 1
else:
stride_indices_seq, stride_indices_tok = ssm_state_indices.stride()
grid = (NK, NV, N * HV)
fused_recurrent_gated_delta_rule_fwd_kernel[grid](
q=q,
k=k,
v=v,
g=g,
beta=beta,
o=o,
h0=initial_state,
ht=final_state,
cu_seqlens=cu_seqlens,
ssm_state_indices=ssm_state_indices,
num_accepted_tokens=num_accepted_tokens,
scale=scale,
N=N,
T=T,
B=B,
H=H,
HV=HV,
K=K,
V=V,
BK=BK,
BV=BV,
stride_init_state_token=stride_init_state_token,
stride_final_state_token=stride_final_state_token,
stride_indices_seq=stride_indices_seq,
stride_indices_tok=stride_indices_tok,
IS_BETA_HEADWISE=beta.ndim == v.ndim,
USE_QK_L2NORM_IN_KERNEL=use_qk_l2norm_in_kernel,
INPLACE_FINAL_STATE=inplace_final_state,
IS_KDA=False,
num_warps=num_warps,
num_stages=num_stages,
)
o = o.squeeze(0)
return o, final_state
@triton.jit
def fused_recurrent_gated_delta_rule_packed_decode_kernel(
mixed_qkv,
a,
b,
A_log,
dt_bias,
o,
h0,
ht,
ssm_state_indices,
scale,
stride_mixed_qkv_tok: tl.constexpr,
stride_a_tok: tl.constexpr,
stride_b_tok: tl.constexpr,
stride_init_state_token: tl.constexpr,
stride_final_state_token: tl.constexpr,
stride_indices_seq: tl.constexpr,
H: tl.constexpr,
HV: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
SOFTPLUS_THRESHOLD: tl.constexpr,
USE_QK_L2NORM_IN_KERNEL: tl.constexpr,
):
i_v, i_nh = tl.program_id(0), tl.program_id(1)
i_n, i_hv = i_nh // HV, i_nh % HV
i_h = i_hv // (HV // H)
o_k = tl.arange(0, BK)
o_v = i_v * BV + tl.arange(0, BV)
mask_k = o_k < K
mask_v = o_v < V
mask_h = mask_v[:, None] & mask_k[None, :]
state_idx = tl.load(ssm_state_indices + i_n * stride_indices_seq).to(tl.int64)
p_o = o + (i_n * HV + i_hv) * V + o_v
# Skip if state index is invalid (NULL_BLOCK_ID=0)
if state_idx <= 0:
zero = tl.zeros([BV], dtype=tl.float32).to(p_o.dtype.element_ty)
tl.store(p_o, zero, mask=mask_v)
return
p_h0 = h0 + state_idx * stride_init_state_token
p_h0 = p_h0 + i_hv * V * K + o_v[:, None] * K + o_k[None, :]
b_h = tl.load(p_h0, mask=mask_h, other=0).to(tl.float32)
p_mixed = mixed_qkv + i_n * stride_mixed_qkv_tok
q_off = i_h * K + o_k
k_off = (H * K) + i_h * K + o_k
v_off = (2 * H * K) + i_hv * V + o_v
b_q = tl.load(p_mixed + q_off, mask=mask_k, other=0).to(tl.float32)
b_k = tl.load(p_mixed + k_off, mask=mask_k, other=0).to(tl.float32)
b_v = tl.load(p_mixed + v_off, mask=mask_v, other=0).to(tl.float32)
if USE_QK_L2NORM_IN_KERNEL:
b_q = b_q / tl.sqrt(tl.sum(b_q * b_q) + 1e-6)
b_k = b_k / tl.sqrt(tl.sum(b_k * b_k) + 1e-6)
b_q = b_q * scale
a_val = tl.load(a + i_n * stride_a_tok + i_hv).to(tl.float32)
b_val = tl.load(b + i_n * stride_b_tok + i_hv).to(tl.float32)
A_log_val = tl.load(A_log + i_hv).to(tl.float32)
dt_bias_val = tl.load(dt_bias + i_hv).to(tl.float32)
x = a_val + dt_bias_val
softplus_x = tl.where(x <= SOFTPLUS_THRESHOLD, tl.log(1.0 + tl.exp(x)), x)
g_val = -tl.exp(A_log_val) * softplus_x
beta_val = tl.sigmoid(b_val).to(b.dtype.element_ty).to(tl.float32)
b_h *= exp(g_val)
b_v -= tl.sum(b_h * b_k[None, :], 1)
b_v *= beta_val
b_h += b_v[:, None] * b_k[None, :]
b_o = tl.sum(b_h * b_q[None, :], 1)
tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=mask_v)
p_ht = ht + state_idx * stride_final_state_token
p_ht = p_ht + i_hv * V * K + o_v[:, None] * K + o_k[None, :]
tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h)
def fused_recurrent_gated_delta_rule_packed_decode(
mixed_qkv: torch.Tensor,
a: torch.Tensor,
b: torch.Tensor,
A_log: torch.Tensor,
dt_bias: torch.Tensor,
scale: float,
initial_state: torch.Tensor,
out: torch.Tensor,
ssm_state_indices: torch.Tensor,
use_qk_l2norm_in_kernel: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
if mixed_qkv.ndim != 2:
raise ValueError(
f"`mixed_qkv` must be a 2D tensor (got ndim={mixed_qkv.ndim})."
)
if mixed_qkv.stride(-1) != 1:
raise ValueError("`mixed_qkv` must be contiguous in the last dim.")
if a.ndim != 2 or b.ndim != 2:
raise ValueError(
f"`a` and `b` must be 2D tensors (got a.ndim={a.ndim}, b.ndim={b.ndim})."
)
if a.stride(-1) != 1 or b.stride(-1) != 1:
raise ValueError("`a`/`b` must be contiguous in the last dim.")
if A_log.ndim != 1 or dt_bias.ndim != 1:
raise ValueError("`A_log`/`dt_bias` must be 1D tensors.")
if A_log.stride(0) != 1 or dt_bias.stride(0) != 1:
raise ValueError("`A_log`/`dt_bias` must be contiguous.")
if ssm_state_indices.ndim != 1:
raise ValueError(
f"`ssm_state_indices` must be 1D for packed decode (got ndim={ssm_state_indices.ndim})."
)
if not out.is_contiguous():
raise ValueError("`out` must be contiguous.")
dev = mixed_qkv.device
if (
a.device != dev
or b.device != dev
or A_log.device != dev
or dt_bias.device != dev
or initial_state.device != dev
or out.device != dev
or ssm_state_indices.device != dev
):
raise ValueError("All inputs must be on the same device.")
B = mixed_qkv.shape[0]
if a.shape[0] != B or b.shape[0] != B:
raise ValueError(
"Mismatched batch sizes: "
f"mixed_qkv.shape[0]={B}, a.shape[0]={a.shape[0]}, b.shape[0]={b.shape[0]}."
)
if ssm_state_indices.shape[0] != B:
raise ValueError(
f"`ssm_state_indices` must have shape [B] (got {tuple(ssm_state_indices.shape)}; expected ({B},))."
)
if initial_state.ndim != 4:
raise ValueError(
f"`initial_state` must be a 4D tensor (got ndim={initial_state.ndim})."
)
if initial_state.stride(-1) != 1:
raise ValueError("`initial_state` must be contiguous in the last dim.")
HV, V, K = initial_state.shape[-3:]
if a.shape[1] != HV or b.shape[1] != HV:
raise ValueError(
f"`a`/`b` must have shape [B, HV] with HV={HV} (got a.shape={tuple(a.shape)}, b.shape={tuple(b.shape)})."
)
if A_log.numel() != HV or dt_bias.numel() != HV:
raise ValueError(
f"`A_log` and `dt_bias` must have {HV} elements (got A_log.numel()={A_log.numel()}, dt_bias.numel()={dt_bias.numel()})."
)
if out.shape != (B, 1, HV, V):
raise ValueError(
f"`out` must have shape {(B, 1, HV, V)} (got out.shape={tuple(out.shape)})."
)
qkv_dim = mixed_qkv.shape[1]
qk_dim = qkv_dim - HV * V
if qk_dim <= 0 or qk_dim % 2 != 0:
raise ValueError(
f"Invalid packed `mixed_qkv` last dim={qkv_dim} for HV={HV}, V={V}."
)
q_dim = qk_dim // 2
if q_dim % K != 0:
raise ValueError(f"Invalid packed Q size {q_dim}: must be divisible by K={K}.")
H = q_dim // K
if H <= 0 or HV % H != 0:
raise ValueError(
f"Invalid head config inferred from mixed_qkv: H={H}, HV={HV}."
)
BK = triton.next_power_of_2(K)
if triton.cdiv(K, BK) != 1:
raise ValueError(
f"Packed decode kernel only supports NK=1 (got K={K}, BK={BK})."
)
BV = min(triton.next_power_of_2(V), 32)
num_stages = 3
num_warps = 1
stride_mixed_qkv_tok = mixed_qkv.stride(0)
stride_a_tok = a.stride(0)
stride_b_tok = b.stride(0)
stride_init_state_token = initial_state.stride(0)
stride_final_state_token = initial_state.stride(0)
stride_indices_seq = ssm_state_indices.stride(0)
NV = triton.cdiv(V, BV)
grid = (NV, B * HV)
fused_recurrent_gated_delta_rule_packed_decode_kernel[grid](
mixed_qkv=mixed_qkv,
a=a,
b=b,
A_log=A_log,
dt_bias=dt_bias,
o=out,
h0=initial_state,
ht=initial_state,
ssm_state_indices=ssm_state_indices,
scale=scale,
stride_mixed_qkv_tok=stride_mixed_qkv_tok,
stride_a_tok=stride_a_tok,
stride_b_tok=stride_b_tok,
stride_init_state_token=stride_init_state_token,
stride_final_state_token=stride_final_state_token,
stride_indices_seq=stride_indices_seq,
H=H,
HV=HV,
K=K,
V=V,
BK=BK,
BV=BV,
SOFTPLUS_THRESHOLD=20.0,
USE_QK_L2NORM_IN_KERNEL=use_qk_l2norm_in_kernel,
num_warps=num_warps,
num_stages=num_stages,
)
return out, initial_state
class FusedRecurrentFunction(torch.autograd.Function):
@staticmethod
def forward(
ctx,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
g: torch.Tensor,
beta: torch.Tensor,
scale: float,
initial_state: torch.Tensor,
inplace_final_state: bool = True,
cu_seqlens: torch.Tensor | None = None,
ssm_state_indices: torch.Tensor | None = None,
num_accepted_tokens: torch.Tensor | None = None,
use_qk_l2norm_in_kernel: bool = False,
):
o, final_state = fused_recurrent_gated_delta_rule_fwd(
q=q.contiguous(),
k=k.contiguous(),
v=v.contiguous(),
g=g.contiguous(),
beta=beta.contiguous(),
scale=scale,
initial_state=initial_state,
inplace_final_state=inplace_final_state,
cu_seqlens=cu_seqlens,
ssm_state_indices=ssm_state_indices,
num_accepted_tokens=num_accepted_tokens,
use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
)
return o, final_state
def fused_recurrent_gated_delta_rule(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
g: torch.Tensor,
beta: torch.Tensor = None,
scale: float = None,
initial_state: torch.Tensor = None,
inplace_final_state: bool = True,
cu_seqlens: torch.Tensor | None = None,
ssm_state_indices: torch.Tensor | None = None,
num_accepted_tokens: torch.Tensor | None = None,
use_qk_l2norm_in_kernel: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
r"""
Args:
q (torch.Tensor):
queries of shape `[B, T, H, K]`.
k (torch.Tensor):
keys of shape `[B, T, H, K]`.
v (torch.Tensor):
values of shape `[B, T, HV, V]`.
GVA is applied if `HV > H`.
g (torch.Tensor):
g (decays) of shape `[B, T, HV]`.
beta (torch.Tensor):
betas of shape `[B, T, HV]`.
scale (Optional[int]):
Scale factor for the RetNet attention scores.
If not provided, it will default to `1 / sqrt(K)`. Default: `None`.
initial_state (Optional[torch.Tensor]):
Initial state of shape `[N, HV, V, K]` for `N` input sequences.
For equal-length input sequences, `N` equals the batch size `B`.
Default: `None`.
inplace_final_state: bool:
Whether to store the final state in-place to save memory.
Default: `True`.
cu_seqlens (torch.Tensor):
Cumulative sequence lengths of shape `[N+1]` used for variable-length training,
consistent with the FlashAttention API.
ssm_state_indices (Optional[torch.Tensor]):
Indices to map the input sequences to the initial/final states.
num_accepted_tokens (Optional[torch.Tensor]):
Number of accepted tokens for each sequence during decoding.
Returns:
o (torch.Tensor):
Outputs of shape `[B, T, HV, V]`.
final_state (torch.Tensor):
Final state of shape `[N, HV, V, K]`.
Examples::
>>> import torch
>>> import torch.nn.functional as F
>>> from einops import rearrange
>>> from fla.ops.gated_delta_rule import fused_recurrent_gated_delta_rule
# inputs with equal lengths
>>> B, T, H, HV, K, V = 4, 2048, 4, 8, 512, 512
>>> q = torch.randn(B, T, H, K, device='cuda')
>>> k = F.normalize(torch.randn(B, T, H, K, device='cuda'), p=2, dim=-1)
>>> v = torch.randn(B, T, HV, V, device='cuda')
>>> g = F.logsigmoid(torch.rand(B, T, HV, device='cuda'))
>>> beta = torch.rand(B, T, HV, device='cuda').sigmoid()
>>> h0 = torch.randn(B, HV, V, K, device='cuda')
>>> o, ht = fused_gated_recurrent_delta_rule(
q, k, v, g, beta,
initial_state=h0,
)
# for variable-length inputs, the batch size `B` is expected to be 1 and `cu_seqlens` is required
>>> q, k, v, g, beta = map(lambda x: rearrange(x, 'b t ... -> 1 (b t) ...'), (q, k, v, g, beta))
# for a batch with 4 sequences, `cu_seqlens` with 5 start/end positions are expected
>>> cu_seqlens = q.new_tensor([0, 2048, 4096, 6144, 8192], dtype=torch.int32)
>>> o_var, ht_var = fused_gated_recurrent_delta_rule(
q, k, v, g, beta,
initial_state=h0,
cu_seqlens=cu_seqlens
)
"""
if cu_seqlens is not None and q.shape[0] != 1:
raise ValueError(
f"The batch size is expected to be 1 rather than {q.shape[0]} when using `cu_seqlens`."
f"Please flatten variable-length inputs before processing."
)
if scale is None:
scale = k.shape[-1] ** -0.5
else:
assert scale > 0, "scale must be positive"
if beta is None:
beta = torch.ones_like(q[..., 0])
o, final_state = FusedRecurrentFunction.apply(
q,
k,
v,
g,
beta,
scale,
initial_state,
inplace_final_state,
cu_seqlens,
ssm_state_indices,
num_accepted_tokens,
use_qk_l2norm_in_kernel,
)
return o, final_state

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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
import os
import torch
from vllm.triton_utils import tl, triton
BT_LIST = [8, 16, 32, 64, 128]
USE_DEFAULT_FLA_NORM = int(os.getenv("USE_DEFAULT_FLA_NORM", "0"))
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps) for num_warps in [1, 2, 4, 8, 16, 32]
],
key=["D"],
)
@triton.jit
def l2norm_fwd_kernel1(
x,
y,
D,
BD: tl.constexpr,
eps,
):
i_t = tl.program_id(0)
x += i_t * D
y += i_t * D
# Compute mean and variance
cols = tl.arange(0, BD)
mask = cols < D
b_x = tl.load(x + cols, mask=mask, other=0.0).to(tl.float32)
b_var = tl.sum(b_x * b_x, axis=0)
b_rstd = 1 / tl.sqrt(b_var + eps)
# tl.store(Rstd + i_t, rstd)
# Normalize and apply linear transformation
b_y = b_x * b_rstd
tl.store(y + cols, b_y, mask=mask)
@triton.autotune(
configs=[
triton.Config({"BT": BT}, num_warps=num_warps)
for num_warps in [1, 2, 4, 8, 16]
for BT in BT_LIST
],
key=["D"],
)
@triton.jit(do_not_specialize=["NB"])
def l2norm_fwd_kernel(
x,
y,
eps,
NB,
T,
D: tl.constexpr,
BT: tl.constexpr,
BD: tl.constexpr,
):
i_t = tl.program_id(0)
p_x = tl.make_block_ptr(x, (T, D), (D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
b_x = tl.load(p_x, boundary_check=(0, 1)).to(tl.float32)
b_var = tl.sum(b_x * b_x, axis=1)
b_y = b_x / tl.sqrt(b_var + eps)[:, None]
p_y = tl.make_block_ptr(y, (T, D), (D, 1), (i_t * BT, 0), (BT, BD), (1, 0))
tl.store(p_y, b_y.to(p_y.dtype.element_ty), boundary_check=(0, 1))
@triton.jit
def l2norm_fwd_kernel2(
X, Y, eps, M, N: tl.constexpr, BD: tl.constexpr, MBLOCK: tl.constexpr
):
xoffset = tl.program_id(0) * MBLOCK
row_idx = xoffset + tl.arange(0, MBLOCK)[:, None]
xmask = row_idx < M
rindex = tl.arange(0, BD)[None, :]
cmask = rindex < N
mask = xmask & cmask
xs = tl.load(X + (rindex + N * row_idx), mask, other=0.0).to(tl.float32)
square = tl.broadcast_to(xs * xs, [MBLOCK, BD])
square_sum = tl.sum(tl.where(xmask, square, 0), 1)[:, None]
rsqrt = tl.rsqrt(square_sum + eps)
tl.store(Y + (rindex + N * row_idx), xs * rsqrt, mask)
def l2norm_fwd(
x: torch.Tensor, eps: float = 1e-6, output_dtype: torch.dtype | None = None
):
x_shape_og = x.shape
x = x.view(-1, x.shape[-1])
# allocate output
if output_dtype is None:
y = torch.empty_like(x)
else:
y = torch.empty_like(x, dtype=output_dtype)
assert y.stride(-1) == 1
T, D = x.shape[0], x.shape[-1]
# rstd = torch.empty((T,), dtype=torch.float32, device=x.device)
# Less than 64KB per feature: enqueue fused kernel
MAX_FUSED_SIZE = 65536 // x.element_size()
BD = min(MAX_FUSED_SIZE, triton.next_power_of_2(D))
if D > BD:
raise RuntimeError("This layer doesn't support feature dim >= 64KB.")
if not USE_DEFAULT_FLA_NORM:
MBLOCK = 32
# M, N = x.shape
l2norm_fwd_kernel2[(triton.cdiv(T, MBLOCK),)](
x,
y,
eps,
T,
D,
BD,
MBLOCK,
)
else:
if D <= 512:
NB = triton.cdiv(T, 2048)
def grid(meta):
return (triton.cdiv(T, meta["BT"]),)
l2norm_fwd_kernel[grid](
x,
y,
eps,
NB=NB,
T=T,
D=D,
BD=BD,
)
else:
l2norm_fwd_kernel1[(T,)](
x,
y,
eps=eps,
D=D,
BD=BD,
)
return y.view(x_shape_og)

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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
import contextlib
import functools
import logging
import os
from collections.abc import Callable
from enum import Enum
from typing import Any, Literal
import torch
from vllm.platforms import current_platform
from vllm.triton_utils import triton
logger = logging.getLogger(__name__)
COMPILER_MODE = os.getenv("FLA_COMPILER_MODE") == "1"
FLA_CI_ENV = os.getenv("FLA_CI_ENV") == "1"
SUPPRESS_LEVEL = int(os.getenv("GDN_RECOMPUTE_SUPPRESS_LEVEL", "0"))
# Default chunk size used across FLA triton kernels (kda, chunk, chunk_o, etc.)
FLA_CHUNK_SIZE = 64
def tensor_cache(fn: Callable[..., torch.Tensor]) -> Callable[..., torch.Tensor]:
"""
A decorator that caches the most recent results of a function with tensor inputs.
This decorator will store the output of the decorated function for the most recent set of input tensors.
The cache is limited to a fixed size (default is 4). When the cache is full, the oldest entry will be removed.
Args:
fn (Callable[..., torch.Tensor]):
The function to be decorated. It should take tensor inputs and return tensor outputs.
Returns:
Callable[..., torch.Tensor]:
A wrapped version of the input function with single-entry caching.
"""
cache_entries: tuple[tuple | None, dict | None, Any] = []
cache_size = 8
@functools.wraps(fn)
def wrapper(*args: Any, **kwargs: Any) -> Any:
nonlocal cache_entries, cache_size
for i, entry in enumerate(cache_entries):
last_args, last_kwargs, last_result = entry
if (
len(args) == len(last_args)
and len(kwargs) == len(last_kwargs)
and all(a is b for a, b in zip(args, last_args))
and all(
k in last_kwargs and v is last_kwargs[k] for k, v in kwargs.items()
)
):
cache_entries = (
cache_entries[:i]
+ cache_entries[i + 1 :]
+ [(args, kwargs, last_result)]
)
return last_result
result = fn(*args, **kwargs)
if len(cache_entries) >= cache_size:
cache_entries = cache_entries[1:]
cache_entries.append((args, kwargs, result))
return result
return wrapper
def input_guard(fn: Callable[..., torch.Tensor]) -> Callable[..., torch.Tensor]:
"""
A decorator to make sure all input tensors are contiguous and set the device based on input tensors.
"""
@functools.wraps(fn)
def wrapper(*args, **kwargs):
contiguous_args = (
i if not isinstance(i, torch.Tensor) else i.contiguous() for i in args
)
contiguous_kwargs = {
k: (v if not isinstance(v, torch.Tensor) else v.contiguous())
for k, v in kwargs.items()
}
tensor = None
for arg in args:
if isinstance(arg, torch.Tensor):
tensor = arg
break
if tensor is None:
for value in kwargs.values():
if isinstance(value, torch.Tensor):
tensor = value
break
if tensor is not None:
ctx = torch.accelerator.device_index(tensor.device.index)
else:
ctx = contextlib.nullcontext()
with ctx:
return fn(*contiguous_args, **contiguous_kwargs)
return wrapper
@functools.cache
def get_available_device() -> str:
try:
return triton.runtime.driver.active.get_current_target().backend
except (RuntimeError, AttributeError):
return "cpu"
@functools.cache
def _check_platform() -> Literal["nvidia", "amd", "intel", "musa"]:
device = get_available_device()
mapping = {
"cuda": "nvidia",
"hip": "amd",
"xpu": "intel",
}
# return the mapped value, or the original if not found
return mapping.get(device, device)
# For AMD GPUs, the triton backend is 'hip', while for Nvidia GPUs, the triton backend is 'cuda'.
# However, the torch backend is 'cuda' for both Nvidia and AMD GPUs.
# Therefore, we need to check the triton backend to determine the actual GPU vendor.
device = "cuda" if current_platform.is_cuda_alike() else get_available_device()
device_torch_lib = getattr(torch, device, None)
device_platform = _check_platform()
is_amd = device_platform == "amd"
is_intel = device_platform == "intel"
is_nvidia = device_platform == "nvidia"
is_intel_alchemist = is_intel and "Intel(R) Arc(TM) A" in torch.xpu.get_device_name(0)
is_nvidia_hopper = is_nvidia and (
"NVIDIA H" in torch.cuda.get_device_name(0)
or torch.cuda.get_device_capability()[0] >= 9
)
use_cuda_graph = is_nvidia and os.environ.get("FLA_USE_CUDA_GRAPH", "0") == "1"
is_gather_supported = hasattr(triton.language, "gather")
is_tma_supported = (
is_nvidia_hopper
and os.getenv("FLA_USE_TMA", "0") == "1"
and (
hasattr(triton.language, "_experimental_make_tensor_descriptor")
or hasattr(triton.language, "make_tensor_descriptor")
)
)
def get_all_max_shared_mem():
try:
return [
triton.runtime.driver.active.utils.get_device_properties(i)[
"max_shared_mem"
]
for i in range(device_torch_lib.device_count())
]
except BaseException:
return [-1]
class Backend(Enum):
ADA = 101376 # RTX 4090
AMPERE = 166912 # A100
HOPPER = 232448 # H100
DEFAULT = 102400 # Default
@classmethod
def get_shared_memory(cls, arch: str) -> int:
try:
return cls[arch.upper()].value
except KeyError:
return cls.DEFAULT.value
@functools.cache
def check_shared_mem(arch: str = "none", tensor_idx: int = 0) -> bool:
try:
device_shared_mem_list = get_all_max_shared_mem()
max_shared_memory = device_shared_mem_list[tensor_idx]
return max_shared_memory >= Backend.get_shared_memory(arch)
except Exception:
return False