minke.yu/sglang
1
0
Fork 0
Code Issues Pull Requests Actions 12 Packages Projects Releases Wiki Activity

[KDA] Optimize prefill kernels with diagonal and recompute fuse (#24271)

Browse Source 
Co-authored-by: luoyuan.luo <luoyuan.luo@antgroup.com>
This commit is contained in:
Yuan Luo
2026-05-09 08:52:51 +08:00
committed by GitHub
co-authored by luoyuan.luo
parent 9ee830346f
commit a61a14f416
4 changed files with 545 additions and 124 deletions
Download Patch File Download Diff File Expand all files Collapse all files
python/sglang/srt/layers/attention/fla/chunk_delta_h.py
+21 -14
View File
@@ -13,22 +13,30 @@ from sglang.srt.layers.attention.fla.index import (
prepare_chunk_offsets,
)
from sglang.srt.layers.attention.fla.op import exp, safe_exp
from sglang.srt.layers.attention.fla.utils import is_nvidia_hopper
from sglang.srt.layers.attention.fla.utils import (
autotune_cache_kwargs,
is_nvidia_hopper,
)
NUM_WARPS = [2, 4] if is_nvidia_hopper else [2, 4, 8, 16]
CHUNK_SIZE = 64
# @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 [2, 3, 4]
# for BV in [32, 64]
# ],
# key=["H", "K", "V", "BT", "USE_G"],
# use_cuda_graph=use_cuda_graph,
# )
@triton.autotune(
# Single hardcoded config. The kernel writes ht (final state) back into
# initial_state in-place; with multiple configs, triton's autotune benchmark
# phase invokes the kernel many times for timing and corrupts the cache pool,
# producing silently wrong output on the first user request. Restoring via
# `restore_value=["initial_state"]` works for unit tests but OOMs on
# production-scale models (e.g. Kimi-Linear-48B at default mem_fraction)
# because cloning the cache pool for each benchmark exceeds available memory.
# NT_BUCKET is kept in the autotune key for forward-compatibility (allows
# future per-bucket configs once the kernel is refactored to write final
# state to a separate output buffer).
configs=[triton.Config({"BV": 32}, num_warps=4, num_stages=2)],
key=["H", "K", "V", "BT", "USE_GK", "NT_BUCKET"],
**autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=["T"])
def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
k,
@@ -55,6 +63,7 @@ def chunk_gated_delta_rule_fwd_kernel_h_blockdim64(
INPLACE_UPDATE: tl.constexpr,
SAVE_NEW_VALUE: tl.constexpr,
IS_VARLEN: tl.constexpr,
NT_BUCKET: tl.constexpr,
):
i_v, i_nh = tl.program_id(0), tl.program_id(1)
i_n, i_h = i_nh // H, i_nh % H
@@ -325,14 +334,12 @@ def chunk_gated_delta_rule_fwd_h(
K=K,
V=V,
BT=BT,
BV=32,
USE_G=g is not None,
USE_GK=gk is not None,
USE_INITIAL_STATE=initial_state is not None,
INPLACE_UPDATE=True,
SAVE_NEW_VALUE=v_new is not None,
IS_VARLEN=cu_seqlens is not None,
num_warps=4,
num_stages=2,
NT_BUCKET=(0 if NT <= 32 else (1 if NT <= 128 else 2)),
)
return h, v_new
python/sglang/srt/layers/attention/fla/chunk_intra.py
+420 -29
View File
@@ -11,7 +11,7 @@ from sglang.srt.layers.attention.fla.chunk_intra_token_parallel import (
from sglang.srt.layers.attention.fla.index import (
prepare_chunk_indices,
)
from sglang.srt.layers.attention.fla.op import exp2, gather
from sglang.srt.layers.attention.fla.op import exp, exp2, gather
from sglang.srt.layers.attention.fla.utils import (
autotune_cache_kwargs,
is_gather_supported,
@@ -36,11 +36,11 @@ else:
)
@triton.autotune(
configs=[
triton.Config({"BK": BK}, num_warps=num_warps)
triton.Config({"BK": BK, "BV": 64}, num_warps=num_warps)
for BK in [32, 64]
for num_warps in [1, 2, 4]
],
key=["H", "K", "BC"],
key=["H", "K", "BC", "V", "FUSE_RECOMPUTE", "FUSE_DIAGONAL"],
**autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=["T"])
@@ -53,16 +53,24 @@ def chunk_kda_fwd_kernel_inter_solve_fused(
Akkd,
Akk,
scale,
v_in,
w_out,
u_out,
kg_out,
cu_seqlens,
chunk_indices,
T,
H: tl.constexpr,
K: tl.constexpr,
V: tl.constexpr,
BT: tl.constexpr,
BC: tl.constexpr,
BK: tl.constexpr,
BV: tl.constexpr,
IS_VARLEN: tl.constexpr,
USE_SAFE_GATE: tl.constexpr,
FUSE_RECOMPUTE: tl.constexpr,
FUSE_DIAGONAL: tl.constexpr,
):
"""
Fused kernel: compute inter-subchunk Akk + solve_tril in one pass.
@@ -125,8 +133,19 @@ def chunk_kda_fwd_kernel_inter_solve_fused(
b_Aqk32 = tl.zeros([BC, BC], dtype=tl.float32)
b_Akk32 = tl.zeros([BC, BC], dtype=tl.float32)
if FUSE_DIAGONAL:
b_Aqk_d0 = tl.zeros([BC, BC], dtype=tl.float32)
b_Akk_d0 = tl.zeros([BC, BC], dtype=tl.float32)
b_Aqk_d1 = tl.zeros([BC, BC], dtype=tl.float32)
b_Akk_d1 = tl.zeros([BC, BC], dtype=tl.float32)
b_Aqk_d2 = tl.zeros([BC, BC], dtype=tl.float32)
b_Akk_d2 = tl.zeros([BC, BC], dtype=tl.float32)
b_Aqk_d3 = tl.zeros([BC, BC], dtype=tl.float32)
b_Akk_d3 = tl.zeros([BC, BC], dtype=tl.float32)
m_tc0 = (i_tc0 + o_i) < T
################################################################################
# off-diagonal blocks
# off-diagonal blocks (+ optional diagonal blocks)
################################################################################
for i_k in range(tl.cdiv(K, BK)):
o_k = i_k * BK + tl.arange(0, BK)
@@ -141,6 +160,19 @@ def chunk_kda_fwd_kernel_inter_solve_fused(
b_k0 = tl.load(p_k0, boundary_check=(0, 1)).to(tl.float32)
b_g0 = tl.load(p_g0, boundary_check=(0, 1)).to(tl.float32)
if FUSE_DIAGONAL:
p_q0 = tl.make_block_ptr(
q, (T, K), (H * K, 1), (i_tc0, i_k * BK), (BC, BK), (1, 0)
)
b_q0 = tl.load(p_q0, boundary_check=(0, 1)).to(tl.float32)
b_gn0 = tl.load(g + i_tc0 * H * K + o_k, mask=m_k, other=0).to(tl.float32)
b_gm0 = tl.clamp(b_g0 - b_gn0[None, :], -126.0, 126.0)
b_gq0 = tl.where(m_tc0[:, None], exp2(b_gm0), 0.0)
b_gk0 = tl.where(m_tc0[:, None], exp2(-b_gm0), 0.0)
b_kgt_d0 = tl.trans(b_k0 * b_gk0)
b_Aqk_d0 += tl.dot(b_q0 * b_gq0, b_kgt_d0)
b_Akk_d0 += tl.dot(b_k0 * b_gq0, b_kgt_d0)
if i_tc1 < T:
p_q1 = tl.make_block_ptr(
q, (T, K), (H * K, 1), (i_tc1, i_k * BK), (BC, BK), (1, 0)
@@ -160,10 +192,19 @@ def chunk_kda_fwd_kernel_inter_solve_fused(
# [BC, BK]
b_gqn = tl.where(m_tc1[:, None], exp2(b_g1 - b_gn1[None, :]), 0)
# [BK, BC]
b_kgt = tl.trans(b_k0 * exp2(b_gn1[None, :] - b_g0))
b_kgt = tl.trans(b_k0 * exp2(b_gn1[None, :] - b_g0)).to(tl.bfloat16)
# [BC, BC]
b_Aqk10 += tl.dot(b_q1 * b_gqn, b_kgt)
b_Akk10 += tl.dot(b_k1 * b_gqn, b_kgt)
b_qg1 = (b_q1 * b_gqn).to(tl.bfloat16)
b_kg1 = (b_k1 * b_gqn).to(tl.bfloat16)
b_Aqk10 += tl.dot(b_qg1, b_kgt)
b_Akk10 += tl.dot(b_kg1, b_kgt)
if FUSE_DIAGONAL:
b_gm1_d = tl.clamp(b_gn1[None, :] - b_g1, -126.0, 126.0)
b_gk1_d = tl.where(m_tc1[:, None], exp2(b_gm1_d), 0.0)
b_kgt_d1 = tl.trans(b_k1 * b_gk1_d)
b_Aqk_d1 += tl.dot(b_q1 * b_gqn, b_kgt_d1)
b_Akk_d1 += tl.dot(b_k1 * b_gqn, b_kgt_d1)
if i_tc2 < T:
p_q2 = tl.make_block_ptr(
@@ -185,18 +226,25 @@ def chunk_kda_fwd_kernel_inter_solve_fused(
)
# [BC, BK]
b_gqn2 = tl.where(m_tc2[:, None], exp2(b_g2 - b_gn2[None, :]), 0)
b_qg2 = b_q2 * b_gqn2
b_kg2 = b_k2 * b_gqn2
b_qg2 = (b_q2 * b_gqn2).to(tl.bfloat16)
b_kg2 = (b_k2 * b_gqn2).to(tl.bfloat16)
# [BK, BC]
b_kgt = tl.trans(b_k0 * exp2(b_gn2[None, :] - b_g0))
b_kgt = tl.trans(b_k0 * exp2(b_gn2[None, :] - b_g0)).to(tl.bfloat16)
b_Aqk20 += tl.dot(b_qg2, b_kgt)
b_Akk20 += tl.dot(b_kg2, b_kgt)
# [BC, BC]
b_kgt = tl.trans(b_k1 * exp2(b_gn2[None, :] - b_g1))
b_kgt = tl.trans(b_k1 * exp2(b_gn2[None, :] - b_g1)).to(tl.bfloat16)
# [BC, BC]
b_Aqk21 += tl.dot(b_qg2, b_kgt)
b_Akk21 += tl.dot(b_kg2, b_kgt)
if FUSE_DIAGONAL:
b_gm2_d = tl.clamp(b_gn2[None, :] - b_g2, -126.0, 126.0)
b_gk2_d = tl.where(m_tc2[:, None], exp2(b_gm2_d), 0.0)
b_kgt_d2 = tl.trans(b_k2 * b_gk2_d)
b_Aqk_d2 += tl.dot(b_q2 * b_gqn2, b_kgt_d2)
b_Akk_d2 += tl.dot(b_k2 * b_gqn2, b_kgt_d2)
if i_tc3 < T:
p_q3 = tl.make_block_ptr(
q, (T, K), (H * K, 1), (i_tc3, i_k * BK), (BC, BK), (1, 0)
@@ -217,24 +265,31 @@ def chunk_kda_fwd_kernel_inter_solve_fused(
)
# [BC, BK]
b_gqn3 = tl.where(m_tc3[:, None], exp2(b_g3 - b_gn3[None, :]), 0)
b_qg3 = b_q3 * b_gqn3
b_kg3 = b_k3 * b_gqn3
b_qg3 = (b_q3 * b_gqn3).to(tl.bfloat16)
b_kg3 = (b_k3 * b_gqn3).to(tl.bfloat16)
# [BK, BC]
b_kgt = tl.trans(b_k0 * exp2(b_gn3[None, :] - b_g0))
b_kgt = tl.trans(b_k0 * exp2(b_gn3[None, :] - b_g0)).to(tl.bfloat16)
# [BC, BC]
b_Aqk30 += tl.dot(b_qg3, b_kgt)
b_Akk30 += tl.dot(b_kg3, b_kgt)
# [BK, BC]
b_kgt = tl.trans(b_k1 * exp2(b_gn3[None, :] - b_g1))
b_kgt = tl.trans(b_k1 * exp2(b_gn3[None, :] - b_g1)).to(tl.bfloat16)
# [BC, BC]
b_Aqk31 += tl.dot(b_qg3, b_kgt)
b_Akk31 += tl.dot(b_kg3, b_kgt)
# [BK, BC]
b_kgt = tl.trans(b_k2 * exp2(b_gn3[None, :] - b_g2))
b_kgt = tl.trans(b_k2 * exp2(b_gn3[None, :] - b_g2)).to(tl.bfloat16)
# [BC, BC]
b_Aqk32 += tl.dot(b_qg3, b_kgt)
b_Akk32 += tl.dot(b_kg3, b_kgt)
if FUSE_DIAGONAL:
b_gm3_d = tl.clamp(b_gn3[None, :] - b_g3, -126.0, 126.0)
b_gk3_d = tl.where(m_tc3[:, None], exp2(b_gm3_d), 0.0)
b_kgt_d3 = tl.trans(b_k3 * b_gk3_d)
b_Aqk_d3 += tl.dot(b_q3 * b_gqn3, b_kgt_d3)
b_Akk_d3 += tl.dot(b_k3 * b_gqn3, b_kgt_d3)
################################################################################
# save off-diagonal Aqk blocks and prepare Akk
################################################################################
@@ -299,6 +354,87 @@ def chunk_kda_fwd_kernel_inter_solve_fused(
b_Akk31 = b_Akk31 * b_b3[:, None]
b_Akk32 = b_Akk32 * b_b3[:, None]
if FUSE_DIAGONAL:
m_Aqk_diag = o_i[:, None] >= o_i[None, :]
m_Akk_diag = o_i[:, None] > o_i[None, :]
b_Aqk_d0 = tl.where(m_Aqk_diag, b_Aqk_d0, 0.0)
b_Akk_d0 = tl.where(m_Akk_diag, b_Akk_d0, 0.0)
b_Aqk_d1 = tl.where(m_Aqk_diag, b_Aqk_d1, 0.0)
b_Akk_d1 = tl.where(m_Akk_diag, b_Akk_d1, 0.0)
b_Aqk_d2 = tl.where(m_Aqk_diag, b_Aqk_d2, 0.0)
b_Akk_d2 = tl.where(m_Akk_diag, b_Akk_d2, 0.0)
b_Aqk_d3 = tl.where(m_Aqk_diag, b_Aqk_d3, 0.0)
b_Akk_d3 = tl.where(m_Akk_diag, b_Akk_d3, 0.0)
p_Aqk_d0 = tl.make_block_ptr(
Aqk, (T, BT), (H * BT, 1), (i_tc0, 0), (BC, BC), (1, 0)
)
p_Aqk_d1 = tl.make_block_ptr(
Aqk, (T, BT), (H * BT, 1), (i_tc1, BC), (BC, BC), (1, 0)
)
p_Aqk_d2 = tl.make_block_ptr(
Aqk, (T, BT), (H * BT, 1), (i_tc2, 2 * BC), (BC, BC), (1, 0)
)
p_Aqk_d3 = tl.make_block_ptr(
Aqk, (T, BT), (H * BT, 1), (i_tc3, 3 * BC), (BC, BC), (1, 0)
)
tl.store(
p_Aqk_d0, (b_Aqk_d0 * scale).to(Aqk.dtype.element_ty), boundary_check=(0, 1)
)
tl.store(
p_Aqk_d1, (b_Aqk_d1 * scale).to(Aqk.dtype.element_ty), boundary_check=(0, 1)
)
tl.store(
p_Aqk_d2, (b_Aqk_d2 * scale).to(Aqk.dtype.element_ty), boundary_check=(0, 1)
)
tl.store(
p_Aqk_d3, (b_Aqk_d3 * scale).to(Aqk.dtype.element_ty), boundary_check=(0, 1)
)
p_bd0 = tl.make_block_ptr(
beta + bos * H + i_h, (T,), (H,), (i_tc0,), (BC,), (0,)
)
p_bd1 = tl.make_block_ptr(
beta + bos * H + i_h, (T,), (H,), (i_tc1,), (BC,), (0,)
)
p_bd2 = tl.make_block_ptr(
beta + bos * H + i_h, (T,), (H,), (i_tc2,), (BC,), (0,)
)
p_bd3 = tl.make_block_ptr(
beta + bos * H + i_h, (T,), (H,), (i_tc3,), (BC,), (0,)
)
b_bd0 = tl.load(p_bd0, boundary_check=(0,)).to(tl.float32)
b_bd1 = tl.load(p_bd1, boundary_check=(0,)).to(tl.float32)
b_bd2 = tl.load(p_bd2, boundary_check=(0,)).to(tl.float32)
b_bd3 = tl.load(p_bd3, boundary_check=(0,)).to(tl.float32)
b_Akk_d0 = b_Akk_d0 * b_bd0[:, None]
b_Akk_d1 = b_Akk_d1 * b_bd1[:, None]
b_Akk_d2 = b_Akk_d2 * b_bd2[:, None]
b_Akk_d3 = b_Akk_d3 * b_bd3[:, None]
p_Akkd00 = tl.make_block_ptr(
Akkd, (T, BC), (H * BC, 1), (i_tc0, 0), (BC, BC), (1, 0)
)
p_Akkd11 = tl.make_block_ptr(
Akkd, (T, BC), (H * BC, 1), (i_tc1, 0), (BC, BC), (1, 0)
)
p_Akkd22 = tl.make_block_ptr(
Akkd, (T, BC), (H * BC, 1), (i_tc2, 0), (BC, BC), (1, 0)
)
p_Akkd33 = tl.make_block_ptr(
Akkd, (T, BC), (H * BC, 1), (i_tc3, 0), (BC, BC), (1, 0)
)
tl.store(p_Akkd00, b_Akk_d0.to(Akkd.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_Akkd11, b_Akk_d1.to(Akkd.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_Akkd22, b_Akk_d2.to(Akkd.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_Akkd33, b_Akk_d3.to(Akkd.dtype.element_ty), boundary_check=(0, 1))
b_Ai00 = b_Akk_d0
b_Ai11 = b_Akk_d1
b_Ai22 = b_Akk_d2
b_Ai33 = b_Akk_d3
else:
p_Akk00 = tl.make_block_ptr(
Akkd, (T, BC), (H * BC, 1), (i_tc0, 0), (BC, BC), (1, 0)
)
@@ -318,9 +454,14 @@ def chunk_kda_fwd_kernel_inter_solve_fused(
################################################################################
# forward substitution on diagonals
# Diagonal blocks are RAW (need substitution) when:
# - FUSE_DIAGONAL=True: blocks were computed fresh above as gated k·k.
# - FUSE_DIAGONAL=False with USE_SAFE_GATE=False: token_parallel wrote raw.
# They are pre-inverted only by the safe_gate diagonal kernel
# (USE_SAFE_GATE=True, FUSE_DIAGONAL=False).
################################################################################
if not USE_SAFE_GATE:
if FUSE_DIAGONAL or not USE_SAFE_GATE:
m_A = o_i[:, None] > o_i[None, :]
m_I = o_i[:, None] == o_i[None, :]
@@ -397,22 +538,225 @@ def chunk_kda_fwd_kernel_inter_solve_fused(
)
################################################################################
# store full Akk_inv to Akk
# Output: store Akk_inv OR compute w, u, kg from registers
################################################################################
p_Akk00 = tl.make_block_ptr(Akk, (T, BT), (H * BT, 1), (i_tc0, 0), (BC, BC), (1, 0))
p_Akk10 = tl.make_block_ptr(Akk, (T, BT), (H * BT, 1), (i_tc1, 0), (BC, BC), (1, 0))
if FUSE_RECOMPUTE:
# Cast A-inverse sub-blocks to input dtype for dot products
b_Ai00_h = b_Ai00.to(k.dtype.element_ty)
b_Ai10_h = b_Ai10.to(k.dtype.element_ty)
b_Ai11_h = b_Ai11.to(k.dtype.element_ty)
b_Ai20_h = b_Ai20.to(k.dtype.element_ty)
b_Ai21_h = b_Ai21.to(k.dtype.element_ty)
b_Ai22_h = b_Ai22.to(k.dtype.element_ty)
b_Ai30_h = b_Ai30.to(k.dtype.element_ty)
b_Ai31_h = b_Ai31.to(k.dtype.element_ty)
b_Ai32_h = b_Ai32.to(k.dtype.element_ty)
b_Ai33_h = b_Ai33.to(k.dtype.element_ty)
# Load beta for all 4 sub-chunks
p_b0 = tl.make_block_ptr(
beta + bos * H + i_h, (T,), (H,), (i_tc0,), (BC,), (0,)
)
b_b0 = tl.load(p_b0, boundary_check=(0,)).to(tl.float32)
p_b1r = tl.make_block_ptr(
beta + bos * H + i_h, (T,), (H,), (i_tc1,), (BC,), (0,)
)
b_b1r = tl.load(p_b1r, boundary_check=(0,)).to(tl.float32)
p_b2r = tl.make_block_ptr(
beta + bos * H + i_h, (T,), (H,), (i_tc2,), (BC,), (0,)
)
b_b2r = tl.load(p_b2r, boundary_check=(0,)).to(tl.float32)
p_b3r = tl.make_block_ptr(
beta + bos * H + i_h, (T,), (H,), (i_tc3,), (BC,), (0,)
)
b_b3r = tl.load(p_b3r, boundary_check=(0,)).to(tl.float32)
# ---- u = A_inv @ (v * beta) ----
v_base = v_in + (bos * H + i_h) * V
u_base = u_out + (bos * H + i_h) * V
for i_v in range(tl.cdiv(V, BV)):
p_v0 = tl.make_block_ptr(
v_base, (T, V), (H * V, 1), (i_tc0, i_v * BV), (BC, BV), (1, 0)
)
p_v1 = tl.make_block_ptr(
v_base, (T, V), (H * V, 1), (i_tc1, i_v * BV), (BC, BV), (1, 0)
)
p_v2 = tl.make_block_ptr(
v_base, (T, V), (H * V, 1), (i_tc2, i_v * BV), (BC, BV), (1, 0)
)
p_v3 = tl.make_block_ptr(
v_base, (T, V), (H * V, 1), (i_tc3, i_v * BV), (BC, BV), (1, 0)
)
b_v0 = tl.load(p_v0, boundary_check=(0, 1))
b_v1 = tl.load(p_v1, boundary_check=(0, 1))
b_v2 = tl.load(p_v2, boundary_check=(0, 1))
b_v3 = tl.load(p_v3, boundary_check=(0, 1))
b_vb0 = (b_v0 * b_b0[:, None]).to(b_v0.dtype)
b_vb1 = (b_v1 * b_b1r[:, None]).to(b_v1.dtype)
b_vb2 = (b_v2 * b_b2r[:, None]).to(b_v2.dtype)
b_vb3 = (b_v3 * b_b3r[:, None]).to(b_v3.dtype)
b_u0 = tl.dot(b_Ai00_h, b_vb0)
b_u1 = tl.dot(b_Ai10_h, b_vb0) + tl.dot(b_Ai11_h, b_vb1)
b_u2 = (
tl.dot(b_Ai20_h, b_vb0)
+ tl.dot(b_Ai21_h, b_vb1)
+ tl.dot(b_Ai22_h, b_vb2)
)
b_u3 = (
tl.dot(b_Ai30_h, b_vb0)
+ tl.dot(b_Ai31_h, b_vb1)
+ tl.dot(b_Ai32_h, b_vb2)
+ tl.dot(b_Ai33_h, b_vb3)
)
p_u0 = tl.make_block_ptr(
u_base, (T, V), (H * V, 1), (i_tc0, i_v * BV), (BC, BV), (1, 0)
)
p_u1 = tl.make_block_ptr(
u_base, (T, V), (H * V, 1), (i_tc1, i_v * BV), (BC, BV), (1, 0)
)
p_u2 = tl.make_block_ptr(
u_base, (T, V), (H * V, 1), (i_tc2, i_v * BV), (BC, BV), (1, 0)
)
p_u3 = tl.make_block_ptr(
u_base, (T, V), (H * V, 1), (i_tc3, i_v * BV), (BC, BV), (1, 0)
)
tl.store(p_u0, b_u0.to(p_u0.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_u1, b_u1.to(p_u1.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_u2, b_u2.to(p_u2.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_u3, b_u3.to(p_u3.dtype.element_ty), boundary_check=(0, 1))
# ---- w = A_inv @ (k * beta * exp(gk)), kg = k * exp(gn - gk) ----
w_base = w_out + (bos * H + i_h) * K
kg_base = kg_out + (bos * H + i_h) * K
last_idx = min(i_t * BT + BT, T) - 1
for i_k in range(tl.cdiv(K, BK)):
o_k = i_k * BK + tl.arange(0, BK)
m_k = o_k < K
b_gn = tl.load(g + last_idx * H * K + o_k, mask=m_k, other=0.0).to(
tl.float32
)
p_k0 = tl.make_block_ptr(
k, (T, K), (H * K, 1), (i_tc0, i_k * BK), (BC, BK), (1, 0)
)
p_k1 = tl.make_block_ptr(
k, (T, K), (H * K, 1), (i_tc1, i_k * BK), (BC, BK), (1, 0)
)
p_k2 = tl.make_block_ptr(
k, (T, K), (H * K, 1), (i_tc2, i_k * BK), (BC, BK), (1, 0)
)
p_k3 = tl.make_block_ptr(
k, (T, K), (H * K, 1), (i_tc3, i_k * BK), (BC, BK), (1, 0)
)
p_gk0 = tl.make_block_ptr(
g, (T, K), (H * K, 1), (i_tc0, i_k * BK), (BC, BK), (1, 0)
)
p_gk1 = tl.make_block_ptr(
g, (T, K), (H * K, 1), (i_tc1, i_k * BK), (BC, BK), (1, 0)
)
p_gk2 = tl.make_block_ptr(
g, (T, K), (H * K, 1), (i_tc2, i_k * BK), (BC, BK), (1, 0)
)
p_gk3 = tl.make_block_ptr(
g, (T, K), (H * K, 1), (i_tc3, i_k * BK), (BC, BK), (1, 0)
)
b_k0r = tl.load(p_k0, boundary_check=(0, 1))
b_k1r = tl.load(p_k1, boundary_check=(0, 1))
b_k2r = tl.load(p_k2, boundary_check=(0, 1))
b_k3r = tl.load(p_k3, boundary_check=(0, 1))
b_gk0r = tl.load(p_gk0, boundary_check=(0, 1)).to(tl.float32)
b_gk1r = tl.load(p_gk1, boundary_check=(0, 1)).to(tl.float32)
b_gk2r = tl.load(p_gk2, boundary_check=(0, 1)).to(tl.float32)
b_gk3r = tl.load(p_gk3, boundary_check=(0, 1)).to(tl.float32)
b_kb0 = (b_k0r * b_b0[:, None] * exp(b_gk0r)).to(b_k0r.dtype)
b_kb1 = (b_k1r * b_b1r[:, None] * exp(b_gk1r)).to(b_k1r.dtype)
b_kb2 = (b_k2r * b_b2r[:, None] * exp(b_gk2r)).to(b_k2r.dtype)
b_kb3 = (b_k3r * b_b3r[:, None] * exp(b_gk3r)).to(b_k3r.dtype)
b_w0 = tl.dot(b_Ai00_h, b_kb0)
b_w1 = tl.dot(b_Ai10_h, b_kb0) + tl.dot(b_Ai11_h, b_kb1)
b_w2 = (
tl.dot(b_Ai20_h, b_kb0)
+ tl.dot(b_Ai21_h, b_kb1)
+ tl.dot(b_Ai22_h, b_kb2)
)
b_w3 = (
tl.dot(b_Ai30_h, b_kb0)
+ tl.dot(b_Ai31_h, b_kb1)
+ tl.dot(b_Ai32_h, b_kb2)
+ tl.dot(b_Ai33_h, b_kb3)
)
p_w0 = tl.make_block_ptr(
w_base, (T, K), (H * K, 1), (i_tc0, i_k * BK), (BC, BK), (1, 0)
)
p_w1 = tl.make_block_ptr(
w_base, (T, K), (H * K, 1), (i_tc1, i_k * BK), (BC, BK), (1, 0)
)
p_w2 = tl.make_block_ptr(
w_base, (T, K), (H * K, 1), (i_tc2, i_k * BK), (BC, BK), (1, 0)
)
p_w3 = tl.make_block_ptr(
w_base, (T, K), (H * K, 1), (i_tc3, i_k * BK), (BC, BK), (1, 0)
)
tl.store(p_w0, b_w0.to(p_w0.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_w1, b_w1.to(p_w1.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_w2, b_w2.to(p_w2.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_w3, b_w3.to(p_w3.dtype.element_ty), boundary_check=(0, 1))
b_kg0 = b_k0r * exp(b_gn[None, :] - b_gk0r)
b_kg1 = b_k1r * exp(b_gn[None, :] - b_gk1r)
b_kg2 = b_k2r * exp(b_gn[None, :] - b_gk2r)
b_kg3 = b_k3r * exp(b_gn[None, :] - b_gk3r)
p_kg0 = tl.make_block_ptr(
kg_base, (T, K), (H * K, 1), (i_tc0, i_k * BK), (BC, BK), (1, 0)
)
p_kg1 = tl.make_block_ptr(
kg_base, (T, K), (H * K, 1), (i_tc1, i_k * BK), (BC, BK), (1, 0)
)
p_kg2 = tl.make_block_ptr(
kg_base, (T, K), (H * K, 1), (i_tc2, i_k * BK), (BC, BK), (1, 0)
)
p_kg3 = tl.make_block_ptr(
kg_base, (T, K), (H * K, 1), (i_tc3, i_k * BK), (BC, BK), (1, 0)
)
tl.store(p_kg0, b_kg0.to(p_kg0.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_kg1, b_kg1.to(p_kg1.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_kg2, b_kg2.to(p_kg2.dtype.element_ty), boundary_check=(0, 1))
tl.store(p_kg3, b_kg3.to(p_kg3.dtype.element_ty), boundary_check=(0, 1))
else:
p_Akk00 = tl.make_block_ptr(
Akk, (T, BT), (H * BT, 1), (i_tc0, 0), (BC, BC), (1, 0)
)
p_Akk10 = tl.make_block_ptr(
Akk, (T, BT), (H * BT, 1), (i_tc1, 0), (BC, BC), (1, 0)
)
p_Akk11 = tl.make_block_ptr(
Akk, (T, BT), (H * BT, 1), (i_tc1, BC), (BC, BC), (1, 0)
)
p_Akk20 = tl.make_block_ptr(Akk, (T, BT), (H * BT, 1), (i_tc2, 0), (BC, BC), (1, 0))
p_Akk20 = tl.make_block_ptr(
Akk, (T, BT), (H * BT, 1), (i_tc2, 0), (BC, BC), (1, 0)
)
p_Akk21 = tl.make_block_ptr(
Akk, (T, BT), (H * BT, 1), (i_tc2, BC), (BC, BC), (1, 0)
)
p_Akk22 = tl.make_block_ptr(
Akk, (T, BT), (H * BT, 1), (i_tc2, 2 * BC), (BC, BC), (1, 0)
)
p_Akk30 = tl.make_block_ptr(Akk, (T, BT), (H * BT, 1), (i_tc3, 0), (BC, BC), (1, 0))
p_Akk30 = tl.make_block_ptr(
Akk, (T, BT), (H * BT, 1), (i_tc3, 0), (BC, BC), (1, 0)
)
p_Akk31 = tl.make_block_ptr(
Akk, (T, BT), (H * BT, 1), (i_tc3, BC), (BC, BC), (1, 0)
)
@@ -573,8 +917,11 @@ def chunk_kda_fwd_intra(
chunk_indices: torch.LongTensor | None = None,
safe_gate: bool = False,
disable_recompute: bool = False,
fuse_recompute: bool = False,
fuse_diagonal: bool = False,
):
B, T, H, K = k.shape
V = v.shape[-1]
BT = chunk_size
BC = 16
if chunk_indices is None and cu_seqlens is not None:
@@ -582,14 +929,15 @@ def chunk_kda_fwd_intra(
NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices)
NC = triton.cdiv(BT, BC)
if fuse_diagonal:
Aqk = torch.zeros(B, T, H, BT, device=k.device, dtype=k.dtype)
# Akk must be zero-initialized - kernel only writes lower triangular
Akk = torch.zeros(B, T, H, BT, device=k.device, dtype=k.dtype)
# Separate fp32 buffer for diagonal 16x16 blocks (for precision in solve_tril)
Akkd = torch.zeros(B, T, H, BC, device=k.device, dtype=torch.float32)
else:
Aqk = torch.empty(B, T, H, BT, device=k.device, dtype=k.dtype)
Akkd = torch.empty(B, T, H, BC, device=k.device, dtype=torch.float32)
# Step 1: Run token_parallel first to compute diagonal blocks into Akkd (fp32)
# Step 1: compute diagonal blocks into Akk_diag (fp32)
# Step 1: compute diagonal blocks into Akkd (fp32)
# When fuse_diagonal=True, diagonal blocks are computed inside inter_solve
if not fuse_diagonal:
if safe_gate:
grid = (NT, NC, B * H)
BK = triton.next_power_of_2(K)
@@ -625,8 +973,42 @@ def chunk_kda_fwd_intra(
sub_chunk_size=BC,
)
# Step 2: Fused inter + solve_tril (works for both fixed-len and varlen)
# Step 2: inter_solve (+ optional fused recompute)
grid = (NT, B * H)
if fuse_recompute:
w = torch.empty_like(k)
u = torch.empty_like(v)
kg = torch.empty_like(k)
chunk_kda_fwd_kernel_inter_solve_fused[grid](
q=q,
k=k,
g=gk,
beta=beta,
Aqk=Aqk,
Akkd=Akkd,
Akk=k, # unused placeholder when FUSE_RECOMPUTE=True (dead branch)
scale=scale,
v_in=v,
w_out=w,
u_out=u,
kg_out=kg,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
T=T,
H=H,
K=K,
V=V,
BT=BT,
BC=BC,
USE_SAFE_GATE=safe_gate,
FUSE_RECOMPUTE=True,
FUSE_DIAGONAL=fuse_diagonal,
)
return w, u, None, kg, Aqk, None
# Non-fused path: inter_solve stores Akk, then separate recompute
Akk = torch.zeros(B, T, H, BT, device=k.device, dtype=k.dtype)
chunk_kda_fwd_kernel_inter_solve_fused[grid](
q=q,
k=k,
@@ -636,15 +1018,24 @@ def chunk_kda_fwd_intra(
Akkd=Akkd,
Akk=Akk,
scale=scale,
# v_in/w_out/u_out/kg_out unused when FUSE_RECOMPUTE=False (dead branch)
v_in=k,
w_out=k,
u_out=k,
kg_out=k,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
T=T,
H=H,
K=K,
V=0,
BT=BT,
BC=BC,
USE_SAFE_GATE=safe_gate,
FUSE_RECOMPUTE=False,
FUSE_DIAGONAL=fuse_diagonal,
)
from sglang.srt.layers.attention.fla.kda import (
recompute_w_u_fwd as kda_recompute_w_u_fwd,
)
python/sglang/srt/layers/attention/fla/cumsum.py
+2 -1
View File
@@ -70,9 +70,10 @@ def chunk_local_cumsum_scalar_kernel(
@triton.autotune(
configs=[
triton.Config({"BS": BS}, num_warps=num_warps)
triton.Config({"BS": BS}, num_warps=num_warps, num_stages=num_stages)
for BS in BS_LIST
for num_warps in [2, 4, 8]
for num_stages in [2, 3, 4]
],
key=["B", "H", "S", "BT", "IS_VARLEN", "REVERSE", "HAS_SCALE"],
)
python/sglang/srt/layers/attention/fla/kda.py
+36 -14
View File
@@ -17,10 +17,14 @@ from sglang.srt.layers.attention.fla.fused_norm_gate import layer_norm_gated_fwd
from sglang.srt.layers.attention.fla.fused_recurrent import (
fused_recurrent_gated_delta_rule_fwd_kernel,
)
from sglang.srt.layers.attention.fla.index import prepare_chunk_indices
from sglang.srt.layers.attention.fla.index import (
prepare_chunk_indices,
)
from sglang.srt.layers.attention.fla.l2norm import l2norm_fwd
from sglang.srt.layers.attention.fla.op import exp, log
from sglang.srt.layers.attention.fla.utils import check_shared_mem
from sglang.srt.layers.attention.fla.utils import (
check_shared_mem,
)
BS_LIST = [32, 64] if check_shared_mem() else [16, 32]
@@ -488,11 +492,13 @@ def chunk_kda_scaled_dot_kkt_fwd(
@triton.autotune(
configs=[
triton.Config({}, num_warps=num_warps, num_stages=num_stages)
triton.Config({"BK": BK, "BV": BV}, num_warps=num_warps, num_stages=num_stages)
for BK in [64, 128]
for BV in [64, 128]
for num_warps in [2, 4, 8]
for num_stages in [2, 3, 4]
],
key=["H", "K", "V", "BT", "BK", "BV", "IS_VARLEN"],
key=["H", "K", "V", "BT", "IS_VARLEN"],
)
@triton.jit(do_not_specialize=["T"])
def recompute_w_u_fwd_kernel(
@@ -650,8 +656,6 @@ def recompute_w_u_fwd(
) -> tuple[torch.Tensor, torch.Tensor]:
B, T, H, K, V = *k.shape, v.shape[-1]
BT = A.shape[-1]
BK = 64
BV = 64
if chunk_indices is None and cu_seqlens is not None:
chunk_indices = prepare_chunk_indices(cu_seqlens, BT)
@@ -678,12 +682,10 @@ def recompute_w_u_fwd(
K=K,
V=V,
BT=BT,
BK=BK,
BV=BV,
STORE_QG=False,
STORE_KG=kg is not None,
IS_VARLEN=cu_seqlens is not None,
DOT_PRECISION="ieee",
DOT_PRECISION="tf32",
)
return w, u, None, kg
@@ -691,8 +693,8 @@ def recompute_w_u_fwd(
@triton.autotune(
configs=[
triton.Config({"BK": BK, "BV": BV}, num_warps=num_warps, num_stages=num_stages)
for BK in [32, 64]
for BV in [64, 128]
for BK in [64]
for BV in [64]
for num_warps in [2, 4, 8]
for num_stages in [2, 3, 4]
],
@@ -803,7 +805,7 @@ def chunk_gla_fwd_kernel_o(
# [BT, BT]
b_A = tl.load(p_A, boundary_check=(0, 1))
b_A = tl.where(m_s, b_A, 0.0).to(b_v.dtype)
b_o += tl.dot(b_A, b_v, allow_tf32=False)
b_o += tl.dot(b_A, b_v)
tl.store(p_o, b_o.to(p_o.dtype.element_ty), boundary_check=(0, 1))
@@ -1065,7 +1067,23 @@ def chunk_kda_fwd(
chunk_indices=chunk_indices,
)
# Fused: scaled_dot_kkt + solve_tril + recompute_w_u
# FUSE_DIAGONAL (fold diagonal-block compute into inter+solve) and
# FUSE_RECOMPUTE (also fold w/u/kg recompute) save kernel launches and HBM
# round-trips, but cost register footprint per CTA. Wins at small grid
# where launch overhead dominates; loses at large grid where the extra
# register pressure spills. Gate both on the same grid heuristic.
# Total CTAs in inter_solve_fused = NT * B * H_per_rank. For varlen,
# chunks don't cross sequence boundaries, so per-sequence ceil-divs sum to
# more than cdiv(total_tokens, chunk_size); use chunk_indices.shape[0] which
# already enumerates all (seq, chunk) pairs.
_NT_pr = (
triton.cdiv(q.shape[1], chunk_size)
if cu_seqlens is None
else chunk_indices.shape[0]
)
_H_pr = q.shape[-2]
_B = q.shape[0]
_small_grid = _B * _NT_pr * _H_pr <= 256
w, u, _, kg, Aqk, _ = chunk_kda_fwd_intra(
q=q,
k=k,
@@ -1076,6 +1094,8 @@ def chunk_kda_fwd(
cu_seqlens=cu_seqlens,
chunk_size=chunk_size,
chunk_indices=chunk_indices,
fuse_diagonal=_small_grid,
fuse_recompute=_small_grid,
)
h, v_new = chunk_gated_delta_rule_fwd_h(
@@ -1089,6 +1109,7 @@ def chunk_kda_fwd(
chunk_indices=chunk_indices,
)
del w, u, kg
o = chunk_gla_fwd_o_gk(
q=q,
v=v_new,
@@ -1097,11 +1118,12 @@ def chunk_kda_fwd(
h=h,
o=v,
scale=scale,
cu_seqlens=cu_seqlens,
chunk_size=chunk_size,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
)
del Aqk, v_new, h
return o