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wehub-resource-sync
2026-07-13 12:55:37 +08:00
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vllm/model_executor/layers/fla/__init__.py
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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
vllm/model_executor/layers/fla/ops/__init__.py
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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",
]
vllm/model_executor/layers/fla/ops/chunk.py
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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
vllm/model_executor/layers/fla/ops/chunk_delta_h.py
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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, prepare_chunk_offsets
from .op import exp, exp2
from .utils import FLA_CHUNK_SIZE, use_cuda_graph
NUM_WARPS = [2, 4, 8, 16]
# Triton's AMD backend fails to lower this kernel with num_stages=4.
_CHUNK_DELTA_H_NUM_STAGES = [2, 3] if torch.version.hip else [2, 3, 4]
@triton.heuristics(
{
"USE_G": lambda args: args["g"] is not None,
"USE_GK": lambda args: args["gk"] 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.autotune(
configs=[
triton.Config({"BV": BV}, num_warps=num_warps, num_stages=num_stages)
for num_warps in [2, 4]
for num_stages in _CHUNK_DELTA_H_NUM_STAGES
for BV in [32, 64]
],
key=["H", "K", "V", "BT"],
use_cuda_graph=use_cuda_graph,
)
@triton.jit(do_not_specialize=["T"])
def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
k,
v,
w,
v_new,
g,
gk,
h,
h0,
ht,
cu_seqlens,
chunk_offsets,
T,
H: tl.constexpr,
Hg: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BV: tl.constexpr,
USE_G: tl.constexpr,
USE_GK: tl.constexpr,
USE_INITIAL_STATE: tl.constexpr,
STORE_FINAL_STATE: tl.constexpr,
SAVE_NEW_VALUE: tl.constexpr,
IS_VARLEN: tl.constexpr,
USE_EXP2: tl.constexpr,
):
i_v, i_nh = tl.program_id(0), tl.program_id(1)
i_n, i_h = i_nh // H, i_nh % H
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
# [BV, BK]
b_h1 = tl.zeros([BV, 64], dtype=tl.float32)
if K > 64:
b_h2 = tl.zeros([BV, 64], dtype=tl.float32)
if K > 128:
b_h3 = tl.zeros([BV, 64], dtype=tl.float32)
if K > 192:
b_h4 = tl.zeros([BV, 64], dtype=tl.float32)
# calculate offset
h += ((boh * H + i_h) * V * K).to(tl.int64)
v += ((bos * H + i_h) * V).to(tl.int64)
k += ((bos * Hg + i_h // (H // Hg)) * K).to(tl.int64)
w += ((bos * H + i_h) * K).to(tl.int64)
if SAVE_NEW_VALUE:
v_new += ((bos * H + i_h) * V).to(tl.int64)
stride_v = H * V
stride_h = H * V * K
stride_k = Hg * K
stride_w = H * K
if USE_INITIAL_STATE:
h0 = h0 + i_nh * V * K
if STORE_FINAL_STATE:
ht = ht + i_nh * V * K
# load initial state
if USE_INITIAL_STATE:
p_h0_1 = tl.make_block_ptr(h0, (V, K), (K, 1), (i_v * BV, 0), (BV, 64), (1, 0))
b_h1 += tl.load(p_h0_1, boundary_check=(0, 1)).to(tl.float32)
if K > 64:
p_h0_2 = tl.make_block_ptr(
h0, (V, K), (K, 1), (i_v * BV, 64), (BV, 64), (1, 0)
)
b_h2 += tl.load(p_h0_2, boundary_check=(0, 1)).to(tl.float32)
if K > 128:
p_h0_3 = tl.make_block_ptr(
h0, (V, K), (K, 1), (i_v * BV, 128), (BV, 64), (1, 0)
)
b_h3 += tl.load(p_h0_3, boundary_check=(0, 1)).to(tl.float32)
if K > 192:
p_h0_4 = tl.make_block_ptr(
h0, (V, K), (K, 1), (i_v * BV, 192), (BV, 64), (1, 0)
)
b_h4 += tl.load(p_h0_4, boundary_check=(0, 1)).to(tl.float32)
# main recurrence
for i_t in range(NT):
p_h1 = tl.make_block_ptr(
h + i_t.to(tl.int64) * stride_h,
(V, K),
(K, 1),
(i_v * BV, 0),
(BV, 64),
(1, 0),
)
tl.store(p_h1, b_h1.to(p_h1.dtype.element_ty), boundary_check=(0, 1))
if K > 64:
p_h2 = tl.make_block_ptr(
h + i_t.to(tl.int64) * stride_h,
(V, K),
(K, 1),
(i_v * BV, 64),
(BV, 64),
(1, 0),
)
tl.store(p_h2, b_h2.to(p_h2.dtype.element_ty), boundary_check=(0, 1))
if K > 128:
p_h3 = tl.make_block_ptr(
h + i_t.to(tl.int64) * stride_h,
(V, K),
(K, 1),
(i_v * BV, 128),
(BV, 64),
(1, 0),
)
tl.store(p_h3, b_h3.to(p_h3.dtype.element_ty), boundary_check=(0, 1))
if K > 192:
p_h4 = tl.make_block_ptr(
h + i_t.to(tl.int64) * stride_h,
(V, K),
(K, 1),
(i_v * BV, 192),
(BV, 64),
(1, 0),
)
tl.store(p_h4, b_h4.to(p_h4.dtype.element_ty), boundary_check=(0, 1))
p_w = tl.make_block_ptr(
w, (T, K), (stride_w, 1), (i_t * BT, 0), (BT, 64), (1, 0)
)
b_w = tl.load(p_w, boundary_check=(0, 1))
b_v = tl.dot(b_w, tl.trans(b_h1).to(b_w.dtype))
if K > 64:
p_w = tl.make_block_ptr(
w, (T, K), (stride_w, 1), (i_t * BT, 64), (BT, 64), (1, 0)
)
b_w = tl.load(p_w, boundary_check=(0, 1))
b_v += tl.dot(b_w, tl.trans(b_h2).to(b_w.dtype))
if K > 128:
p_w = tl.make_block_ptr(
w, (T, K), (stride_w, 1), (i_t * BT, 128), (BT, 64), (1, 0)
)
b_w = tl.load(p_w, boundary_check=(0, 1))
b_v += tl.dot(b_w, tl.trans(b_h3).to(b_w.dtype))
if K > 192:
p_w = tl.make_block_ptr(
w, (T, K), (stride_w, 1), (i_t * BT, 192), (BT, 64), (1, 0)
)
b_w = tl.load(p_w, boundary_check=(0, 1))
b_v += tl.dot(b_w, tl.trans(b_h4).to(b_w.dtype))
p_v = tl.make_block_ptr(
v, (T, V), (stride_v, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)
)
b_v = tl.load(p_v, boundary_check=(0, 1)) - b_v
if SAVE_NEW_VALUE:
p_v = tl.make_block_ptr(
v_new, (T, V), (stride_v, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)
)
tl.store(p_v, b_v.to(p_v.dtype.element_ty), boundary_check=(0, 1))
last_idx = min((i_t.to(tl.int64) + 1) * BT, T) - 1
if USE_G:
m_t = (i_t.to(tl.int64) * BT + tl.arange(0, BT)) < T
b_g_last = tl.load(g + bos * H + last_idx * H + i_h)
p_g = tl.make_block_ptr(
g + bos * H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,)
)
b_g = tl.load(p_g, boundary_check=(0,))
if USE_EXP2:
b_v = b_v * tl.where(m_t, exp2(b_g_last - b_g), 0)[:, None]
b_g_last = exp2(b_g_last)
else:
b_v = b_v * tl.where(m_t, exp(b_g_last - b_g), 0)[:, None]
b_g_last = exp(b_g_last)
b_h1 *= b_g_last
if K > 64:
b_h2 *= b_g_last
if K > 128:
b_h3 *= b_g_last
if K > 192:
b_h4 *= b_g_last
if USE_GK:
o_k1 = tl.arange(0, 64)
b_gk_last1 = tl.load(
gk + (bos + last_idx) * H * K + i_h * K + o_k1,
mask=(o_k1 < K),
other=0.0,
)
if USE_EXP2:
b_h1 *= exp2(b_gk_last1)[None, :]
else:
b_h1 *= exp(b_gk_last1)[None, :]
if K > 64:
o_k2 = 64 + o_k1
b_gk_last2 = tl.load(
gk + (bos + last_idx) * H * K + i_h * K + o_k2,
mask=(o_k2 < K),
other=0.0,
)
if USE_EXP2:
b_h2 *= exp2(b_gk_last2)[None, :]
else:
b_h2 *= exp(b_gk_last2)[None, :]
if K > 128:
o_k3 = 128 + o_k1
b_gk_last3 = tl.load(
gk + (bos + last_idx) * H * K + i_h * K + o_k3,
mask=(o_k3 < K),
other=0.0,
)
if USE_EXP2:
b_h3 *= exp2(b_gk_last3)[None, :]
else:
b_h3 *= exp(b_gk_last3)[None, :]
if K > 192:
o_k4 = 192 + o_k1
b_gk_last4 = tl.load(
gk + (bos + last_idx) * H * K + i_h * K + o_k4,
mask=(o_k4 < K),
other=0.0,
)
if USE_EXP2:
b_h4 *= exp2(b_gk_last4)[None, :]
else:
b_h4 *= exp(b_gk_last4)[None, :]
b_v = b_v.to(k.dtype.element_ty)
p_k = tl.make_block_ptr(
k, (K, T), (1, stride_k), (0, i_t * BT), (64, BT), (0, 1)
)
b_k = tl.load(p_k, boundary_check=(0, 1))
b_h1 += tl.trans(tl.dot(b_k, b_v))
if K > 64:
p_k = tl.make_block_ptr(
k, (K, T), (1, stride_k), (64, i_t * BT), (64, BT), (0, 1)
)
b_k = tl.load(p_k, boundary_check=(0, 1))
b_h2 += tl.trans(tl.dot(b_k, b_v))
if K > 128:
p_k = tl.make_block_ptr(
k, (K, T), (1, stride_k), (128, i_t * BT), (64, BT), (0, 1)
)
b_k = tl.load(p_k, boundary_check=(0, 1))
b_h3 += tl.trans(tl.dot(b_k, b_v))
if K > 192:
p_k = tl.make_block_ptr(
k, (K, T), (1, stride_k), (192, i_t * BT), (64, BT), (0, 1)
)
b_k = tl.load(p_k, boundary_check=(0, 1))
b_h4 += tl.trans(tl.dot(b_k, b_v))
# epilogue
if STORE_FINAL_STATE:
p_ht = tl.make_block_ptr(ht, (V, K), (K, 1), (i_v * BV, 0), (BV, 64), (1, 0))
tl.store(p_ht, b_h1.to(p_ht.dtype.element_ty), boundary_check=(0, 1))
if K > 64:
p_ht = tl.make_block_ptr(
ht, (V, K), (K, 1), (i_v * BV, 64), (BV, 64), (1, 0)
)
tl.store(p_ht, b_h2.to(p_ht.dtype.element_ty), boundary_check=(0, 1))
if K > 128:
p_ht = tl.make_block_ptr(
ht, (V, K), (K, 1), (i_v * BV, 128), (BV, 64), (1, 0)
)
tl.store(p_ht, b_h3.to(p_ht.dtype.element_ty), boundary_check=(0, 1))
if K > 192:
p_ht = tl.make_block_ptr(
ht, (V, K), (K, 1), (i_v * BV, 192), (BV, 64), (1, 0)
)
tl.store(p_ht, b_h4.to(p_ht.dtype.element_ty), boundary_check=(0, 1))
def chunk_gated_delta_rule_fwd_h(
k: torch.Tensor,
w: torch.Tensor,
u: torch.Tensor,
g: torch.Tensor | None = None,
gk: torch.Tensor | None = None,
initial_state: torch.Tensor | None = None,
output_final_state: bool = False,
chunk_size: int = FLA_CHUNK_SIZE,
save_new_value: bool = True,
cu_seqlens: torch.Tensor | None = None,
chunk_indices: torch.Tensor | None = None,
chunk_offsets: torch.Tensor | None = None,
use_exp2: bool = False,
) -> 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
if chunk_indices is None and cu_seqlens is not None:
chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size)
# 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 = len(cu_seqlens) - 1, len(chunk_indices)
if chunk_offsets is None:
chunk_offsets = 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, V, K)
final_state = (
k.new_empty(N, H, V, K, dtype=torch.float32) if output_final_state else None
)
v_new = torch.empty_like(u) if save_new_value else None
def grid(meta):
return (triton.cdiv(V, meta["BV"]), N * H)
chunk_gated_delta_rule_fwd_kernel_h_blockdim64[grid](
k=k,
v=u,
w=w,
v_new=v_new,
g=g,
gk=gk,
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,
USE_EXP2=use_exp2,
)
return h, v_new, final_state
vllm/model_executor/layers/fla/ops/chunk_o.py
+190
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@@ -0,0 +1,190 @@
# 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 .op import exp
from .utils import FLA_CHUNK_SIZE, check_shared_mem, is_nvidia_hopper
BKV_LIST = [64, 128] if check_shared_mem() else [32, 64]
NUM_WARPS = [2, 4] if is_nvidia_hopper else [2, 4, 8]
@triton.heuristics(
{
"USE_G": lambda args: args["g"] is not None,
"IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
}
)
@triton.autotune(
configs=[
triton.Config({"BK": BK, "BV": BV}, num_warps=num_warps, num_stages=num_stages)
for BK in BKV_LIST
for BV in BKV_LIST
for num_warps in NUM_WARPS
for num_stages in [2, 3, 4]
],
key=["H", "K", "V", "BT"],
)
@triton.jit(do_not_specialize=["T"])
def chunk_fwd_kernel_o(
q,
k,
v,
h,
g,
o,
cu_seqlens,
chunk_indices,
scale,
T,
H: tl.constexpr,
Hg: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
USE_G: tl.constexpr,
IS_VARLEN: tl.constexpr,
):
i_v, 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_tg = i_t
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
NT = tl.cdiv(T, BT)
else:
NT = tl.cdiv(T, BT)
i_tg = i_b * NT + i_t
bos, eos = i_b * T, i_b * T + T
# offset calculation
q += (bos * Hg + i_h // (H // Hg)) * K
k += (bos * Hg + i_h // (H // Hg)) * K
v += (bos * H + i_h) * V
o += (bos * H + i_h) * V
h += (i_tg * H + i_h).to(tl.int64) * V * K
b_o = tl.zeros([BT, BV], dtype=tl.float32)
b_A = tl.zeros([BT, BT], dtype=tl.float32)
for i_k in range(tl.cdiv(K, BK)):
p_q = tl.make_block_ptr(
q, (T, K), (Hg * K, 1), (i_t * BT, i_k * BK), (BT, BK), (1, 0)
)
p_k = tl.make_block_ptr(
k, (K, T), (1, Hg * K), (i_k * BK, i_t * BT), (BK, BT), (0, 1)
)
p_h = tl.make_block_ptr(
h, (V, K), (K, 1), (i_v * BV, i_k * BK), (BV, BK), (1, 0)
)
# [BT, BK]
b_q = tl.load(p_q, boundary_check=(0, 1))
# [BK, BT]
b_k = tl.load(p_k, boundary_check=(0, 1))
# [BV, BK]
b_h = tl.load(p_h, boundary_check=(0, 1))
# [BT, BK] @ [BK, BV] -> [BT, BV]
b_o += tl.dot(b_q, tl.trans(b_h))
# [BT, BK] @ [BK, BT] -> [BT, BT]
b_A += tl.dot(b_q, b_k)
if USE_G:
g += bos * H + i_h
p_g = tl.make_block_ptr(g, (T,), (H,), (i_t * BT,), (BT,), (0,))
b_g = tl.load(p_g, boundary_check=(0,))
b_o = b_o * exp(b_g)[:, None]
b_A = b_A * exp(b_g[:, None] - b_g[None, :])
o_t = i_t * BT + tl.arange(0, BT)
m_t = o_t < T
m_A = (o_t[:, None] >= o_t[None, :]) & (m_t[:, None] & m_t)
b_A = tl.where(m_A, b_A, 0)
p_v = tl.make_block_ptr(
v, (T, V), (H * V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)
)
p_o = tl.make_block_ptr(
o, (T, V), (H * V, 1), (i_t * BT, i_v * BV), (BT, BV), (1, 0)
)
b_v = tl.load(p_v, boundary_check=(0, 1))
# to fix mma -> mma layout conversion
# already solved by triton v3.2 or higher
b_o = b_o * scale + tl.dot(b_A.to(b_v.dtype), b_v) * scale
tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))
def chunk_fwd_o(
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
h: torch.Tensor,
g: torch.Tensor | None = None, # cumsum of log decay
scale: float | None = None,
cu_seqlens: torch.Tensor | None = None,
chunk_indices: torch.Tensor | None = None,
chunk_size: int = FLA_CHUNK_SIZE,
core_attn_out: torch.Tensor | None = None,
) -> torch.Tensor:
B, T, Hg, K, V = *q.shape, v.shape[-1]
H = v.shape[-2]
BT = chunk_size
if chunk_indices is None and cu_seqlens is not None:
chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
if scale is None:
scale = k.shape[-1] ** -0.5
if core_attn_out is not None:
assert core_attn_out.numel() >= v.numel(), (
f"core_attn_out too small: {core_attn_out.numel()} < {v.numel()}"
)
o = core_attn_out[: v.numel()].view(*v.shape)
else:
o = torch.empty_like(v)
def grid(meta):
return (triton.cdiv(V, meta["BV"]), NT, B * H)
chunk_fwd_kernel_o[grid](
q,
k,
v,
h,
g,
o,
cu_seqlens,
chunk_indices,
scale,
T=T,
H=H,
Hg=Hg,
K=K,
V=V,
BT=BT,
)
return o
vllm/model_executor/layers/fla/ops/chunk_scaled_dot_kkt.py
+158
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@@ -0,0 +1,158 @@
# 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 .op import exp
from .utils import FLA_CHUNK_SIZE
@triton.heuristics(
{
"USE_G": lambda args: args["g"] is not None,
"IS_VARLEN": lambda args: args["cu_seqlens"] is not None,
}
)
@triton.autotune(
configs=[
triton.Config({"BK": BK}, num_warps=num_warps, num_stages=num_stages)
for BK in [32, 64, 128]
for num_warps in [2, 4, 8]
for num_stages in [2, 3, 4]
],
key=["H", "K", "BT", "IS_VARLEN"],
)
@triton.jit(do_not_specialize=["T"])
def chunk_scaled_dot_kkt_fwd_kernel(
k,
beta,
g,
A,
cu_seqlens,
chunk_indices,
T,
H: tl.constexpr,
Hg: tl.constexpr,
K: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
IS_VARLEN: tl.constexpr,
USE_G: 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
o_t = i_t * BT + tl.arange(0, BT)
m_t = o_t < T
p_beta = tl.make_block_ptr(
beta + bos * H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,)
)
b_beta = tl.load(p_beta, boundary_check=(0,))
b_A = tl.zeros([BT, BT], dtype=tl.float32)
for i_k in range(tl.cdiv(K, BK)):
p_k = tl.make_block_ptr(
k + (bos * Hg + i_h // (H // Hg)) * K,
(T, K),
(Hg * K, 1),
(i_t * BT, i_k * BK),
(BT, BK),
(1, 0),
)
b_k = tl.load(p_k, boundary_check=(0, 1))
b_kb = b_k * b_beta[:, None]
b_A += tl.dot(b_kb, tl.trans(b_k).to(b_kb.dtype))
if USE_G:
p_g = tl.make_block_ptr(g + bos * H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,))
b_g = tl.load(p_g, boundary_check=(0,))
b_g_diff = b_g[:, None] - b_g[None, :]
b_A = b_A * exp(b_g_diff)
m_A = (o_t[:, None] > o_t[None, :]) & (m_t[:, None] & m_t)
b_A = tl.where(m_A, b_A, 0)
p_A = tl.make_block_ptr(
A + (bos * H + i_h) * BT, (T, BT), (BT * H, 1), (i_t * BT, 0), (BT, BT), (1, 0)
)
tl.store(p_A, b_A.to(p_A.dtype.element_ty), boundary_check=(0, 1))
def chunk_scaled_dot_kkt_fwd(
k: torch.Tensor,
g: torch.Tensor | None = None,
beta: torch.Tensor | None = None,
cu_seqlens: torch.Tensor | None = None,
chunk_indices: torch.Tensor | None = None,
chunk_size: int = FLA_CHUNK_SIZE,
output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
r"""
Compute beta * K * K^T.
Args:
k (torch.Tensor):
The key tensor of shape `[B, T, H, K]`.
beta (torch.Tensor):
The beta tensor of shape `[B, T, H]`.
g (torch.Tensor):
The cumulative sum of the gate tensor of shape `[B, T, H]`. Default: `None`.
cu_seqlens (torch.Tensor):
The cumulative sequence lengths of the input tensor.
Default: None
chunk_indices (torch.Tensor):
Pre-computed chunk indices. If None and cu_seqlens is provided,
computed internally. Default: None
chunk_size (int):
The chunk size. Default: 64.
output_dtype (torch.dtype):
The dtype of the output tensor. Default: `torch.float32`
Returns:
beta * K * K^T of shape `[B, T, H, BT]` where `BT` is the chunk size.
"""
# 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 = k.shape
H = beta.shape[-1]
BT = chunk_size
if chunk_indices is None and cu_seqlens is not None:
chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
A = torch.empty(B, T, H, BT, device=k.device, dtype=output_dtype)
chunk_scaled_dot_kkt_fwd_kernel[(NT, B * H)](
k=k,
g=g,
beta=beta,
A=A,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
T=T,
H=H,
Hg=Hg,
K=K,
BT=BT,
)
return A
vllm/model_executor/layers/fla/ops/cumsum.py
+282
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@@ -0,0 +1,282 @@
# 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"
)
vllm/model_executor/layers/fla/ops/fused_gdn_prefill_post_conv.py
+248
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@@ -0,0 +1,248 @@
# 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
vllm/model_executor/layers/fla/ops/fused_recurrent.py
+619
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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
@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
vllm/model_executor/layers/fla/ops/fused_sigmoid_gating.py
+279
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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 torch
from vllm.triton_utils import tl, triton
@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_sigmoid_gating_delta_rule_update_kernel(
A_log,
a,
b,
dt_bias,
beta,
threshold,
q,
k,
v,
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
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
p_A_log = A_log + i_hv
if not IS_KDA:
p_a = a + bos * HV + i_hv
p_dt_bias = dt_bias + i_hv
else:
p_a = a + (bos * HV + i_hv) * K + o_k
p_dt_bias = dt_bias + i_hv * K + o_k
p_b = b + bos * HV + i_hv
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)
b_b = tl.load(p_b).to(tl.float32)
# If the model is loaded in fp16, without the .float() here, A might be -inf
x = tl.load(p_a).to(tl.float32) + tl.load(p_dt_bias).to(tl.float32)
softplus_x = tl.where(
beta * x <= threshold, (1 / beta) * tl.log(1 + tl.exp(beta * x)), x
)
b_g = -tl.exp(tl.load(p_A_log).to(tl.float32)) * softplus_x
# compute beta_output = sigmoid(b)
b_beta = tl.sigmoid(b_b.to(tl.float32))
if USE_QK_L2NORM_IN_KERNEL:
b_q = b_q * (tl.rsqrt(tl.sum(b_q * b_q) + 1e-6))
b_k = b_k * (tl.rsqrt(tl.sum(b_k * b_k) + 1e-6))
b_q = b_q * scale
# [BV, BK]
if not IS_KDA:
b_h *= tl.exp(b_g)
else:
b_h *= tl.exp(b_g[None, :])
# [BV]
b_v -= tl.sum(b_h * b_k[None, :], 1)
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)
# Update pointers for next timestep
p_q += H * K
p_k += H * K
p_o += HV * V
p_v += HV * V
p_b += HV
p_a += HV
def fused_sigmoid_gating_delta_rule_update(
A_log: torch.Tensor,
a: torch.Tensor,
b: torch.Tensor,
dt_bias: torch.Tensor,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
beta: float = 1.0,
threshold: float = 20.0,
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,
is_kda: bool = False,
):
"""
Fused triton implementation of sigmoid gating delta rule update.
This function uses a single fused kernel that combines both sigmoid gating
computation and the recurrent delta rule update for better performance.
"""
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 = 4
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]}"
f" when using `cu_seqlens`. Please flatten variable-length"
f" inputs before processing."
)
if scale is None:
scale = k.shape[-1] ** -0.5
else:
assert scale > 0, "scale must be positive"
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_sigmoid_gating_delta_rule_update_kernel[grid](
A_log=A_log,
a=a.contiguous(),
b=b.contiguous(),
dt_bias=dt_bias,
beta=beta,
threshold=threshold,
q=q.contiguous(),
k=k.contiguous(),
v=v.contiguous(),
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,
INPLACE_FINAL_STATE=inplace_final_state,
USE_QK_L2NORM_IN_KERNEL=use_qk_l2norm_in_kernel,
IS_KDA=is_kda,
num_warps=num_warps,
num_stages=num_stages,
)
o = o.squeeze(0)
return o, final_state
vllm/model_executor/layers/fla/ops/index.py
+37
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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 triton
from .utils import tensor_cache
@tensor_cache
def prepare_lens(cu_seqlens: torch.Tensor) -> torch.Tensor:
return cu_seqlens[1:] - cu_seqlens[:-1]
@tensor_cache
def prepare_chunk_indices(cu_seqlens: torch.Tensor, chunk_size: int) -> torch.Tensor:
indices = torch.cat(
[
torch.arange(n)
for n in triton.cdiv(prepare_lens(cu_seqlens), chunk_size).tolist()
]
)
return torch.stack([indices.eq(0).cumsum(0) - 1, indices], 1).to(cu_seqlens)
@tensor_cache
def prepare_chunk_offsets(cu_seqlens: torch.Tensor, chunk_size: int) -> torch.Tensor:
return torch.cat(
[cu_seqlens.new_tensor([0]), triton.cdiv(prepare_lens(cu_seqlens), chunk_size)]
).cumsum(-1)
vllm/model_executor/layers/fla/ops/kda.py
+1647
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vllm/model_executor/layers/fla/ops/l2norm.py
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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)
vllm/model_executor/layers/fla/ops/layernorm_guard.py
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# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# SPDX-FileCopyrightText: Tri Dao
#
# 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) 2024, Tri Dao.
# ruff: noqa: E501
# Based on the Triton LayerNorm tutorial: https://triton-lang.org/main/getting-started/tutorials/05-layer-norm.html
# For the backward pass, we keep weight_grad and bias_grad in registers and accumulate.
# This backward pass is faster for dimensions up to 8k, but after that it's much slower due to register spilling.
# The models we train have hidden dim up to 8k anyway (e.g. Llama 70B), so this is fine.
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from vllm.triton_utils import tl, triton
from vllm.utils.math_utils import cdiv, next_power_of_2
from vllm.utils.platform_utils import num_compute_units
from .utils import input_guard
def rms_norm_ref(
x,
weight,
bias,
z=None,
eps=1e-6,
group_size=None,
norm_before_gate=True,
upcast=True,
):
dtype = x.dtype
weight = weight.float()
bias = bias.float() if bias is not None else None
if upcast:
x = x.float()
z = z.float() if z is not None else z
if z is not None and not norm_before_gate:
x = x * F.silu(z)
if group_size is None:
rstd = 1 / torch.sqrt((x.square()).mean(dim=-1, keepdim=True) + eps)
out = (x * rstd * weight) + bias if bias is not None else (x * rstd * weight)
else:
x_group = rearrange(x, "... (g d) -> ... g d", d=group_size)
rstd = 1 / torch.sqrt((x_group.square()).mean(dim=-1, keepdim=True) + eps)
out = rearrange(x_group * rstd, "... g d -> ... (g d)") * weight
if bias is not None:
out = out + bias
if z is not None and norm_before_gate:
out *= F.silu(z)
return out.to(dtype)
@triton.heuristics(
{
"HAS_BIAS": lambda args: args["B"] is not None,
"HAS_Z": lambda args: args["Z"] is not None,
}
)
@triton.jit
def layer_norm_fwd_kernel(
X, # pointer to the input
Y, # pointer to the output
W, # pointer to the weights
B, # pointer to the biases
Z, # pointer to the other branch
Mean, # pointer to the mean
Rstd, # pointer to the 1/std
stride_x_row, # how much to increase the pointer when moving by 1 row
stride_y_row,
stride_z_row,
M, # number of rows in X
N: tl.constexpr, # number of columns in X
eps, # epsilon to avoid division by zero
BLOCK_N: tl.constexpr,
ROWS_PER_BLOCK: tl.constexpr,
HAS_BIAS: tl.constexpr,
HAS_Z: tl.constexpr,
NORM_BEFORE_GATE: tl.constexpr,
IS_RMS_NORM: tl.constexpr,
ACTIVATION: tl.constexpr,
):
# Map the program id to the starting row of X and Y it should compute.
row_start = tl.program_id(0) * ROWS_PER_BLOCK
group = tl.program_id(1)
# Create 2D tile: [ROWS_PER_BLOCK, BLOCK_N]
rows = row_start + tl.arange(0, ROWS_PER_BLOCK)
cols = tl.arange(0, BLOCK_N)
# Compute offsets for 2D tile
row_offsets = rows[:, None] * stride_x_row
col_offsets = cols[None, :] + group * N
# Base pointers
X_base = X + row_offsets + col_offsets
Y_base = Y + rows[:, None] * stride_y_row + col_offsets
# Create mask for valid rows and columns
row_mask = rows[:, None] < M
col_mask = cols[None, :] < N
mask = row_mask & col_mask
# Load input data with 2D tile
x = tl.load(X_base, mask=mask, other=0.0).to(tl.float32)
if HAS_Z and not NORM_BEFORE_GATE:
Z_base = Z + rows[:, None] * stride_z_row + col_offsets
z = tl.load(Z_base, mask=mask, other=0.0).to(tl.float32)
if ACTIVATION == "swish" or ACTIVATION == "silu":
x *= z * tl.sigmoid(z)
elif ACTIVATION == "sigmoid":
x *= tl.sigmoid(z)
# Compute mean and variance per row (reduce along axis 1)
if not IS_RMS_NORM:
mean = tl.sum(x, axis=1) / N # Shape: [ROWS_PER_BLOCK]
# Store mean for each row
mean_offsets = group * M + rows
mean_mask = rows < M
tl.store(Mean + mean_offsets, mean, mask=mean_mask)
# Broadcast mean back to 2D for subtraction
xbar = tl.where(mask, x - mean[:, None], 0.0)
var = tl.sum(xbar * xbar, axis=1) / N # Shape: [ROWS_PER_BLOCK]
else:
xbar = tl.where(mask, x, 0.0)
var = tl.sum(xbar * xbar, axis=1) / N # Shape: [ROWS_PER_BLOCK]
mean = 0.0 # Placeholder for RMS norm
rstd = tl.rsqrt(var + eps) # Shape: [ROWS_PER_BLOCK]
# Store rstd for each row
rstd_offsets = group * M + rows
rstd_mask = rows < M
tl.store(Rstd + rstd_offsets, rstd, mask=rstd_mask)
# Load weights and biases (broadcast across rows)
w_offsets = cols + group * N
w_mask = cols < N
w = tl.load(W + w_offsets, mask=w_mask, other=0.0).to(tl.float32)
if HAS_BIAS:
b = tl.load(B + w_offsets, mask=w_mask, other=0.0).to(tl.float32)
# Normalize and apply linear transformation
if not IS_RMS_NORM:
x_hat = (x - mean[:, None]) * rstd[:, None]
else:
x_hat = x * rstd[:, None]
y = x_hat * w[None, :] + b[None, :] if HAS_BIAS else x_hat * w[None, :]
if HAS_Z and NORM_BEFORE_GATE:
Z_base = Z + rows[:, None] * stride_z_row + col_offsets
z = tl.load(Z_base, mask=mask, other=0.0).to(tl.float32)
if ACTIVATION == "swish" or ACTIVATION == "silu":
y *= z * tl.sigmoid(z)
elif ACTIVATION == "sigmoid":
y *= tl.sigmoid(z)
# Write output
tl.store(Y_base, y, mask=mask)
def calc_rows_per_block(M: int, device: torch.device) -> int:
sm_count = num_compute_units(device.index)
rows_per_block = next_power_of_2(cdiv(M, 2 * sm_count))
rows_per_block = min(rows_per_block, 4)
return rows_per_block
def layer_norm_fwd(
x: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor,
eps: float,
z: torch.Tensor = None,
out: torch.Tensor = None,
group_size: int = None,
norm_before_gate: bool = True,
is_rms_norm: bool = False,
activation: str = "swish",
):
M, N = x.shape
if group_size is None:
group_size = N
assert N % group_size == 0
ngroups = N // group_size
assert x.stride(-1) == 1
if z is not None:
assert z.stride(-1) == 1
assert z.shape == (M, N)
assert weight.shape == (N,)
assert weight.stride(-1) == 1
if bias is not None:
assert bias.stride(-1) == 1
assert bias.shape == (N,)
# allocate output
if out is not None:
assert out.shape == x.shape
else:
out = torch.empty_like(x)
assert out.stride(-1) == 1
mean = (
torch.empty((ngroups * M,), dtype=torch.float32, device=x.device)
if not is_rms_norm
else None
)
rstd = torch.empty((ngroups * M,), dtype=torch.float32, device=x.device)
# Less than 64KB per feature: enqueue fused kernel
MAX_FUSED_SIZE = 65536 // x.element_size()
BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(group_size))
if group_size > BLOCK_N:
raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
# heuristics for number of warps
num_warps = min(max(BLOCK_N // 256, 1), 8)
# Calculate rows per block based on SM count
rows_per_block = calc_rows_per_block(M, x.device)
# Update grid to use rows_per_block
grid = (cdiv(M, rows_per_block), ngroups)
layer_norm_fwd_kernel[grid](
x,
out,
weight,
bias,
z,
mean,
rstd,
x.stride(0),
out.stride(0),
z.stride(0) if z is not None else 0,
M,
group_size,
eps,
BLOCK_N=BLOCK_N,
ROWS_PER_BLOCK=rows_per_block,
HAS_BIAS=bias is not None,
HAS_Z=z is not None,
NORM_BEFORE_GATE=norm_before_gate,
IS_RMS_NORM=is_rms_norm,
num_warps=num_warps,
ACTIVATION=activation,
)
return out, mean, rstd
def _layer_norm_fn_impl(
x,
weight,
bias,
z=None,
eps=1e-6,
group_size=None,
norm_before_gate=True,
is_rms_norm=False,
activation: str = "swish",
):
"""Triton layer/RMS norm with optional gating.
If z is not None, computes norm(x) * silu(z) when norm_before_gate,
else norm(x * silu(z)).
This calls the triton kernel directly. The original code wrapped this
in a torch.autograd.Function (LayerNormFn) to save tensors for a
backward pass, but vLLM is inference-only so there is no backward pass.
The autograd wrapper also prevented torch.compile/dynamo from tracing
through the function due to its @staticmethod forward.
"""
x_shape_og = x.shape
x = x.reshape(-1, x.shape[-1])
if x.stride(-1) != 1:
x = x.contiguous()
if z is not None:
assert z.shape == x_shape_og
z = z.reshape(-1, z.shape[-1])
if z.stride(-1) != 1:
z = z.contiguous()
weight = weight.contiguous()
if bias is not None:
bias = bias.contiguous()
y, _, _ = layer_norm_fwd(
x,
weight,
bias,
eps,
z=z,
group_size=group_size,
norm_before_gate=norm_before_gate,
is_rms_norm=is_rms_norm,
activation=activation,
)
return y.reshape(x_shape_og)
@input_guard
def layernorm_fn(
x,
weight,
bias,
z=None,
eps=1e-6,
group_size=None,
norm_before_gate=True,
is_rms_norm=False,
activation: str = "swish",
):
return _layer_norm_fn_impl(
x, weight, bias, z, eps, group_size, norm_before_gate, is_rms_norm, activation
)
@input_guard
def rmsnorm_fn(
x,
weight,
bias,
z=None,
eps=1e-6,
group_size=None,
norm_before_gate=True,
activation: str = "swish",
):
return _layer_norm_fn_impl(
x, weight, bias, z, eps, group_size, norm_before_gate, True, activation
)
class RMSNormGated(nn.Module):
def __init__(
self,
hidden_size,
eps: float = 1e-5,
group_size: int | None = None,
norm_before_gate: bool = False,
device: torch.device | None = None,
dtype: torch.dtype | None = None,
activation: str = "swish",
):
"""If group_size is not None, we do GroupNorm with each group having group_size elements.
group_size=None is equivalent to group_size=hidden_size (i.e. there's only 1 group).
"""
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
self.eps = eps
self.activation = activation
self.weight = nn.Parameter(torch.empty(hidden_size, **factory_kwargs))
self.register_parameter("bias", None)
self.group_size = group_size
self.norm_before_gate = norm_before_gate
self.reset_parameters()
def reset_parameters(self):
torch.nn.init.ones_(self.weight)
def forward(self, x, z=None):
"""If z is not None, we do norm(x) * silu(z) if norm_before_gate, else norm(x * silu(z))"""
return rmsnorm_fn(
x,
self.weight,
self.bias,
z=z,
eps=self.eps,
group_size=self.group_size,
norm_before_gate=self.norm_before_gate,
activation=self.activation,
)
vllm/model_executor/layers/fla/ops/op.py
+62
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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
from vllm.triton_utils import tl, tldevice, triton
from .utils import is_gather_supported
if os.environ.get("FLA_USE_FAST_OPS", "0") == "1":
exp = tldevice.fast_expf
exp2 = tl.exp2
log = tldevice.fast_logf
log2 = tldevice.fast_log2f
else:
exp = tl.exp
exp2 = tl.exp2
log = tl.log
log2 = tl.log2
if not is_gather_supported:
@triton.jit
def gather(src, index, axis, _builder=None):
"""
Gather operation that works when tl.gather is not supported.
This is a fallback implementation that returns None.
Just to make triton compiler happy.
"""
return None
else:
gather = tl.gather
if hasattr(triton.language, "_experimental_make_tensor_descriptor"):
# For Triton 3.3.x
make_tensor_descriptor = triton.language._experimental_make_tensor_descriptor
elif hasattr(triton.language, "make_tensor_descriptor"):
# For Triton 3.4.x and later
make_tensor_descriptor = triton.language.make_tensor_descriptor
else:
"""
Fallback implementation when TMA is not supported.
Returns None to indicate TMA descriptors are unavailable.
Just make triton compiler happy.
"""
@triton.jit
def make_tensor_descriptor(
base,
shape,
strides,
block_shape,
_builder=None,
):
return None
vllm/model_executor/layers/fla/ops/solve_tril.py
+558
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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 os
import torch
from vllm.triton_utils import tl, triton
from .index import prepare_chunk_indices
from .op import make_tensor_descriptor
from .utils import input_guard, is_amd, is_tma_supported
FLA_TRIL_PRECISION = os.environ.get("FLA_TRIL_PRECISION", "ieee")
ALLOWED_TRIL_PRECISIONS = ["ieee", "tf32"] if is_amd else ["ieee", "tf32", "tf32x3"]
assert FLA_TRIL_PRECISION in ALLOWED_TRIL_PRECISIONS, (
f"FLA_TRIL_PRECISION must be one of {ALLOWED_TRIL_PRECISIONS}, but got {FLA_TRIL_PRECISION}"
)
@triton.heuristics({"IS_VARLEN": lambda args: args["cu_seqlens"] is not None})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps, num_stages=num_stages)
for num_warps in [1, 2, 4, 8]
for num_stages in [2, 3, 4, 5]
],
key=["BT"],
)
@triton.jit(do_not_specialize=["T"])
def solve_tril_16x16_kernel(
A,
Ai,
cu_seqlens,
chunk_indices,
T,
H: tl.constexpr,
BT: tl.constexpr,
USE_TMA: tl.constexpr,
IS_VARLEN: tl.constexpr,
DOT_PRECISION: 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
o_i = tl.arange(0, 16)
m_A = o_i[:, None] > o_i[None, :]
m_I = o_i[:, None] == o_i[None, :]
A = A + (bos * H + i_h) * BT
Ai = Ai + (bos * H + i_h) * 16
offset = (i_t * 16) % BT
if not USE_TMA:
p_A = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * 16, offset), (16, 16), (1, 0)
)
# [16, 16]
b_A = tl.load(p_A, boundary_check=(0, 1)).to(tl.float32)
else:
desc = make_tensor_descriptor(A, [T, BT], [H * BT, 1], [16, 16])
desc_o = make_tensor_descriptor(Ai, [T, 16], [H * 16, 1], [16, 16])
b_A = desc.load([i_t * 16, offset]).to(tl.float32)
b_A = -tl.where(m_A, b_A, 0)
for i in range(2, min(16, T - i_t * 16)):
# [16]
b_a = -tl.load(A + (i_t * 16 + i) * H * BT + o_i + offset)
b_a = b_a + tl.sum(b_a[:, None] * b_A, 0)
b_A = tl.where((o_i == i)[:, None], b_a, b_A)
b_A += m_I
if not USE_TMA:
p_Ai = tl.make_block_ptr(
Ai, (T, 16), (H * 16, 1), (i_t * 16, 0), (16, 16), (1, 0)
)
tl.store(
p_Ai,
b_A.to(p_Ai.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
else:
desc_o.store([i_t * 16, 0], b_A.to(desc_o.dtype, fp_downcast_rounding="rtne"))
@triton.heuristics({"IS_VARLEN": lambda args: args["cu_seqlens"] is not None})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps, num_stages=num_stages)
for num_warps in [1, 2, 4, 8]
for num_stages in [2, 3, 4, 5]
],
key=["H", "BT", "IS_VARLEN"],
)
@triton.jit(do_not_specialize=["T"])
def merge_16x16_to_32x32_inverse_kernel(
A,
Ai,
cu_seqlens,
chunk_indices,
T,
H: tl.constexpr,
BT: tl.constexpr,
USE_TMA: tl.constexpr,
IS_VARLEN: tl.constexpr,
DOT_PRECISION: 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
o_i = tl.arange(0, 16)
m_A = o_i[:, None] > o_i[None, :]
m_I = o_i[:, None] == o_i[None, :]
A += (bos * H + i_h) * BT
Ai += (bos * H + i_h) * BT
if not USE_TMA:
p_A_11 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT, 0), (16, 16), (1, 0)
)
p_A_22 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT + 16, 16), (16, 16), (1, 0)
)
b_Ai_11 = tl.load(p_A_11, boundary_check=(0, 1)).to(tl.float32)
b_Ai_22 = tl.load(p_A_22, boundary_check=(0, 1)).to(tl.float32)
else:
desc = make_tensor_descriptor(A, [T, BT], [H * BT, 1], [16, 16])
desc_o = make_tensor_descriptor(Ai, [T, BT], [H * BT, 1], [16, 16])
b_Ai_11 = desc.load([i_t * BT + 0, 0]).to(tl.float32)
b_Ai_22 = desc.load([i_t * BT + 16, 16]).to(tl.float32)
# [16, 16]
b_Ai_11 = -tl.where(m_A, b_Ai_11, 0)
b_Ai_22 = -tl.where(m_A, b_Ai_22, 0)
for i in range(2, min(16, T - i_t * BT)):
b_a_11 = -tl.load(A + (i_t * BT + i) * H * BT + o_i)
b_a_11 += tl.sum(b_a_11[:, None] * b_Ai_11, 0)
b_Ai_11 = tl.where((o_i == i)[:, None], b_a_11, b_Ai_11)
for i in range(16 + 2, min(32, T - i_t * BT)):
b_a_22 = -tl.load(A + (i_t * BT + i) * H * BT + o_i + 16)
b_a_22 += tl.sum(b_a_22[:, None] * b_Ai_22, 0)
b_Ai_22 = tl.where((o_i == i - 16)[:, None], b_a_22, b_Ai_22)
b_Ai_11 += m_I
b_Ai_22 += m_I
if not USE_TMA:
p_A_21 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT + 16, 0), (16, 16), (1, 0)
)
b_A_21 = tl.load(p_A_21, boundary_check=(0, 1)).to(tl.float32)
else:
b_A_21 = desc.load([i_t * BT + 16, 0]).to(tl.float32)
b_Ai_21 = -tl.dot(
tl.dot(b_Ai_22, b_A_21, input_precision=DOT_PRECISION),
b_Ai_11,
input_precision=DOT_PRECISION,
)
if not USE_TMA:
p_Ai_11 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT, 0), (16, 16), (1, 0)
)
p_Ai_21 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT + 16, 0), (16, 16), (1, 0)
)
p_Ai_22 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT + 16, 16), (16, 16), (1, 0)
)
tl.store(
p_Ai_11,
b_Ai_11.to(p_Ai_11.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
tl.store(
p_Ai_22,
b_Ai_22.to(p_Ai_22.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
tl.store(
p_Ai_21,
b_Ai_21.to(p_Ai_21.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
else:
desc_o.store(
[i_t * BT + 0, 0], b_Ai_11.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
desc_o.store(
[i_t * BT + 16, 0], b_Ai_21.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
desc_o.store(
[i_t * BT + 16, 16], b_Ai_22.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
@triton.heuristics({"IS_VARLEN": lambda args: args["cu_seqlens"] is not None})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps, num_stages=num_stages)
for num_warps in [2, 4, 8]
for num_stages in [2, 3, 4, 5]
],
key=["H", "BT", "IS_VARLEN"],
)
@triton.jit(do_not_specialize=["T"])
def merge_16x16_to_64x64_inverse_kernel(
A,
Ai,
cu_seqlens,
chunk_indices,
T,
H: tl.constexpr,
BT: tl.constexpr,
USE_TMA: tl.constexpr,
IS_VARLEN: tl.constexpr,
DOT_PRECISION: 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
o_i = tl.arange(0, 16)
m_A = o_i[:, None] > o_i[None, :]
m_I = o_i[:, None] == o_i[None, :]
A += (bos * H + i_h) * BT
Ai += (bos * H + i_h) * BT
if not USE_TMA:
p_A_11 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT, 0), (16, 16), (1, 0)
)
p_A_22 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT + 16, 16), (16, 16), (1, 0)
)
p_A_33 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT + 32, 32), (16, 16), (1, 0)
)
p_A_44 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT + 48, 48), (16, 16), (1, 0)
)
b_Ai_11 = tl.load(p_A_11, boundary_check=(0, 1)).to(tl.float32)
b_Ai_22 = tl.load(p_A_22, boundary_check=(0, 1)).to(tl.float32)
b_Ai_33 = tl.load(p_A_33, boundary_check=(0, 1)).to(tl.float32)
b_Ai_44 = tl.load(p_A_44, boundary_check=(0, 1)).to(tl.float32)
else:
desc = make_tensor_descriptor(A, [T, BT], [H * BT, 1], [16, 16])
desc_o = make_tensor_descriptor(Ai, [T, BT], [H * BT, 1], [16, 16])
b_Ai_11 = desc.load([i_t * BT + 0, 0]).to(tl.float32)
b_Ai_22 = desc.load([i_t * BT + 16, 16]).to(tl.float32)
b_Ai_33 = desc.load([i_t * BT + 32, 32]).to(tl.float32)
b_Ai_44 = desc.load([i_t * BT + 48, 48]).to(tl.float32)
# [16, 16]
b_Ai_11 = -tl.where(m_A, b_Ai_11, 0)
b_Ai_22 = -tl.where(m_A, b_Ai_22, 0)
b_Ai_33 = -tl.where(m_A, b_Ai_33, 0)
b_Ai_44 = -tl.where(m_A, b_Ai_44, 0)
for i in range(2, min(16, T - i_t * BT)):
b_a_11 = -tl.load(A + (i_t * BT + i) * H * BT + o_i)
b_a_11 += tl.sum(b_a_11[:, None] * b_Ai_11, 0)
b_Ai_11 = tl.where((o_i == i)[:, None], b_a_11, b_Ai_11)
for i in range(16 + 2, min(32, T - i_t * BT)):
b_a_22 = -tl.load(A + (i_t * BT + i) * H * BT + o_i + 16)
b_a_22 += tl.sum(b_a_22[:, None] * b_Ai_22, 0)
b_Ai_22 = tl.where((o_i == i - 16)[:, None], b_a_22, b_Ai_22)
for i in range(32 + 2, min(48, T - i_t * BT)):
b_a_33 = -tl.load(A + (i_t * BT + i) * H * BT + o_i + 32)
b_a_33 += tl.sum(b_a_33[:, None] * b_Ai_33, 0)
b_Ai_33 = tl.where((o_i == i - 32)[:, None], b_a_33, b_Ai_33)
for i in range(48 + 2, min(64, T - i_t * BT)):
b_a_44 = -tl.load(A + (i_t * BT + i) * H * BT + o_i + 48)
b_a_44 += tl.sum(b_a_44[:, None] * b_Ai_44, 0)
b_Ai_44 = tl.where((o_i == i - 48)[:, None], b_a_44, b_Ai_44)
b_Ai_11 += m_I
b_Ai_22 += m_I
b_Ai_33 += m_I
b_Ai_44 += m_I
if not USE_TMA:
p_A_21 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT + 16, 0), (16, 16), (1, 0)
)
p_A_31 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT + 32, 0), (16, 16), (1, 0)
)
p_A_32 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT + 32, 16), (16, 16), (1, 0)
)
p_A_41 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT + 48, 0), (16, 16), (1, 0)
)
p_A_42 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT + 48, 16), (16, 16), (1, 0)
)
p_A_43 = tl.make_block_ptr(
A, (T, BT), (H * BT, 1), (i_t * BT + 48, 32), (16, 16), (1, 0)
)
b_A_21 = tl.load(p_A_21, boundary_check=(0, 1)).to(tl.float32)
b_A_31 = tl.load(p_A_31, boundary_check=(0, 1)).to(tl.float32)
b_A_32 = tl.load(p_A_32, boundary_check=(0, 1)).to(tl.float32)
b_A_41 = tl.load(p_A_41, boundary_check=(0, 1)).to(tl.float32)
b_A_42 = tl.load(p_A_42, boundary_check=(0, 1)).to(tl.float32)
b_A_43 = tl.load(p_A_43, boundary_check=(0, 1)).to(tl.float32)
else:
b_A_21 = desc.load([i_t * BT + 16, 0]).to(tl.float32)
b_A_31 = desc.load([i_t * BT + 32, 0]).to(tl.float32)
b_A_32 = desc.load([i_t * BT + 32, 16]).to(tl.float32)
b_A_41 = desc.load([i_t * BT + 48, 0]).to(tl.float32)
b_A_42 = desc.load([i_t * BT + 48, 16]).to(tl.float32)
b_A_43 = desc.load([i_t * BT + 48, 32]).to(tl.float32)
b_Ai_21 = -tl.dot(
tl.dot(b_Ai_22, b_A_21, input_precision=DOT_PRECISION),
b_Ai_11,
input_precision=DOT_PRECISION,
)
b_Ai_32 = -tl.dot(
tl.dot(b_Ai_33, b_A_32, input_precision=DOT_PRECISION),
b_Ai_22,
input_precision=DOT_PRECISION,
)
b_Ai_43 = -tl.dot(
tl.dot(b_Ai_44, b_A_43, input_precision=DOT_PRECISION),
b_Ai_33,
input_precision=DOT_PRECISION,
)
b_Ai_31 = -tl.dot(
b_Ai_33,
tl.dot(b_A_31, b_Ai_11, input_precision=DOT_PRECISION)
+ tl.dot(b_A_32, b_Ai_21, input_precision=DOT_PRECISION),
input_precision=DOT_PRECISION,
)
b_Ai_42 = -tl.dot(
b_Ai_44,
tl.dot(b_A_42, b_Ai_22, input_precision=DOT_PRECISION)
+ tl.dot(b_A_43, b_Ai_32, input_precision=DOT_PRECISION),
input_precision=DOT_PRECISION,
)
b_Ai_41 = -tl.dot(
b_Ai_44,
tl.dot(b_A_41, b_Ai_11, input_precision=DOT_PRECISION)
+ tl.dot(b_A_42, b_Ai_21, input_precision=DOT_PRECISION)
+ tl.dot(b_A_43, b_Ai_31, input_precision=DOT_PRECISION),
input_precision=DOT_PRECISION,
)
if not USE_TMA:
p_Ai_11 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT, 0), (16, 16), (1, 0)
)
p_Ai_22 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT + 16, 16), (16, 16), (1, 0)
)
p_Ai_33 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT + 32, 32), (16, 16), (1, 0)
)
p_Ai_44 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT + 48, 48), (16, 16), (1, 0)
)
p_Ai_21 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT + 16, 0), (16, 16), (1, 0)
)
p_Ai_31 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT + 32, 0), (16, 16), (1, 0)
)
p_Ai_32 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT + 32, 16), (16, 16), (1, 0)
)
p_Ai_41 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT + 48, 0), (16, 16), (1, 0)
)
p_Ai_42 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT + 48, 16), (16, 16), (1, 0)
)
p_Ai_43 = tl.make_block_ptr(
Ai, (T, BT), (H * BT, 1), (i_t * BT + 48, 32), (16, 16), (1, 0)
)
tl.store(
p_Ai_11,
b_Ai_11.to(p_Ai_11.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
tl.store(
p_Ai_22,
b_Ai_22.to(p_Ai_22.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
tl.store(
p_Ai_33,
b_Ai_33.to(p_Ai_33.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
tl.store(
p_Ai_44,
b_Ai_44.to(p_Ai_44.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
tl.store(
p_Ai_21,
b_Ai_21.to(p_Ai_21.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
tl.store(
p_Ai_31,
b_Ai_31.to(p_Ai_31.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
tl.store(
p_Ai_32,
b_Ai_32.to(p_Ai_32.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
tl.store(
p_Ai_41,
b_Ai_41.to(p_Ai_41.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
tl.store(
p_Ai_42,
b_Ai_42.to(p_Ai_42.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
tl.store(
p_Ai_43,
b_Ai_43.to(p_Ai_43.dtype.element_ty, fp_downcast_rounding="rtne"),
boundary_check=(0, 1),
)
else:
desc_o.store(
[i_t * BT + 0, 0], b_Ai_11.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
desc_o.store(
[i_t * BT + 16, 16], b_Ai_22.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
desc_o.store(
[i_t * BT + 32, 32], b_Ai_33.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
desc_o.store(
[i_t * BT + 48, 48], b_Ai_44.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
desc_o.store(
[i_t * BT + 16, 0], b_Ai_21.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
desc_o.store(
[i_t * BT + 32, 0], b_Ai_31.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
desc_o.store(
[i_t * BT + 32, 16], b_Ai_32.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
desc_o.store(
[i_t * BT + 48, 0], b_Ai_41.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
desc_o.store(
[i_t * BT + 48, 16], b_Ai_42.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
desc_o.store(
[i_t * BT + 48, 32], b_Ai_43.to(desc_o.dtype, fp_downcast_rounding="rtne")
)
@input_guard
def solve_tril(
A: torch.Tensor,
cu_seqlens: torch.Tensor | None = None,
chunk_indices: torch.Tensor | None = None,
output_dtype: torch.dtype = torch.float,
) -> torch.Tensor:
"""
Compute the inverse of the matrix I + A
A should be strictly lower triangular, i.e., A.triu() == 0.
Args:
A (torch.Tensor):
[B, T, H, BT], where BT should only be 16, 32, or 64.
cu_seqlens (torch.Tensor):
The cumulative sequence lengths of the input tensor. Default: `None`.
chunk_indices (torch.Tensor):
Pre-computed chunk indices. Default: `None`.
output_dtype (torch.dtype):
The dtype of the output tensor. Default: `torch.float`.
If `None`, the output dtype will be the same as the input dtype.
Returns:
(I + A)^-1 with the same shape as A
"""
assert A.shape[-1] in [16, 32, 64]
output_dtype = A.dtype if output_dtype is None else output_dtype
B, T, H, BT = A.shape
if chunk_indices is None and cu_seqlens is not None:
chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
NT = len(chunk_indices) if cu_seqlens is not None else triton.cdiv(T, BT)
Ai = torch.zeros_like(A, dtype=output_dtype)
if BT == 16:
merge_fn = solve_tril_16x16_kernel
elif BT == 32:
merge_fn = merge_16x16_to_32x32_inverse_kernel
elif BT == 64:
merge_fn = merge_16x16_to_64x64_inverse_kernel
merge_fn[NT, B * H](
A=A,
Ai=Ai,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
T=T,
H=H,
BT=BT,
USE_TMA=is_tma_supported,
DOT_PRECISION=FLA_TRIL_PRECISION,
)
return Ai
vllm/model_executor/layers/fla/ops/utils.py
+200
View File
@@ -0,0 +1,200 @@
# 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
vllm/model_executor/layers/fla/ops/wy_fast.py
+158
View File
@@ -0,0 +1,158 @@
# 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
@triton.heuristics({"IS_VARLEN": lambda args: args["cu_seqlens"] is not None})
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps, num_stages=num_stages)
for num_warps in [2, 4, 8]
for num_stages in [2, 3, 4]
],
key=["H", "K", "V", "BT", "BK", "BV", "IS_VARLEN"],
)
@triton.jit(do_not_specialize=["T"])
def recompute_w_u_fwd_kernel(
k,
v,
beta,
w,
u,
A,
g,
cu_seqlens,
chunk_indices,
T,
H: tl.constexpr,
Hg: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
IS_VARLEN: 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
p_beta = tl.make_block_ptr(
beta + bos * H + i_h, (T,), (H,), (i_t * BT,), (BT,), (0,)
)
p_g = tl.make_block_ptr(g + (bos * H + i_h), (T,), (H,), (i_t * BT,), (BT,), (0,))
p_A = tl.make_block_ptr(
A + (bos * H + i_h) * BT, (T, BT), (H * BT, 1), (i_t * BT, 0), (BT, BT), (1, 0)
)
b_beta = tl.load(p_beta, boundary_check=(0,))
b_A = tl.load(p_A, boundary_check=(0, 1))
b_g = tl.exp(tl.load(p_g, boundary_check=(0,)))
for i_v in range(tl.cdiv(V, BV)):
p_v = tl.make_block_ptr(
v + (bos * H + i_h) * V,
(T, V),
(H * V, 1),
(i_t * BT, i_v * BV),
(BT, BV),
(1, 0),
)
p_u = tl.make_block_ptr(
u + (bos * H + i_h) * V,
(T, V),
(H * V, 1),
(i_t * BT, i_v * BV),
(BT, BV),
(1, 0),
)
b_v = tl.load(p_v, boundary_check=(0, 1))
b_vb = (b_v * b_beta[:, None]).to(b_v.dtype)
b_u = tl.dot(b_A, b_vb, allow_tf32=False)
tl.store(p_u, b_u.to(p_u.dtype.element_ty), boundary_check=(0, 1))
for i_k in range(tl.cdiv(K, BK)):
p_k = tl.make_block_ptr(
k + (bos * Hg + i_h // (H // Hg)) * K,
(T, K),
(Hg * K, 1),
(i_t * BT, i_k * BK),
(BT, BK),
(1, 0),
)
p_w = tl.make_block_ptr(
w + (bos * H + i_h) * K,
(T, K),
(H * K, 1),
(i_t * BT, i_k * BK),
(BT, BK),
(1, 0),
)
b_k = tl.load(p_k, boundary_check=(0, 1))
b_kb = (b_k * b_beta[:, None] * b_g[:, None]).to(b_k.dtype)
b_w = tl.dot(b_A, b_kb)
tl.store(p_w, b_w.to(p_w.dtype.element_ty), boundary_check=(0, 1))
def recompute_w_u_fwd(
k: torch.Tensor,
v: torch.Tensor,
beta: torch.Tensor,
g_cumsum: torch.Tensor,
A: torch.Tensor,
cu_seqlens: torch.Tensor | None,
chunk_indices: torch.Tensor | None = None,
) -> tuple[torch.Tensor, torch.Tensor]:
B, T, Hg, K, V = *k.shape, v.shape[-1]
H = v.shape[-2]
BT = A.shape[-1]
if chunk_indices is None and cu_seqlens is not None:
chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
BK = 64
BV = 64
u = torch.empty_like(v)
w = k.new_empty(B, T, H, K)
recompute_w_u_fwd_kernel[(NT, B * H)](
k=k,
v=v,
beta=beta,
w=w,
u=u,
A=A,
g=g_cumsum,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
T=T,
H=H,
Hg=Hg,
K=K,
V=V,
BT=BT,
BK=BK,
BV=BV,
)
return w, u