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feat: import CUDA kernels from xllm/CCCL/FLA upstream repos

Browse Source 
Sources cloned and tree'd (no --depth):
  - jd-opensource/xllm: ILU kernels, CUDA kernels, MoE kernels
  - NVIDIA/cccl: CUB tuning/dispatch headers (block-level primitives)
  - fla-org/flash-linear-attention: Triton GDN kernels
  - NVIDIA/cutlass: grouped GEMM reference (read, not copied)
  - Dao-AILab/flash-attention: attention kernel reference (SM80+, read only)

New CUDA kernels (from xllm, SM-agnostic, portable to BI-V100):
  ex_engine/xllm_kernels/cuda/activation.cu    (188 lines) — silu_and_mul, gelu
  ex_engine/xllm_kernels/cuda/norm.cu          (600 lines) — rms_norm, fused_add_rms_norm
  ex_engine/xllm_kernels/cuda/rope.cu          (258 lines) — rotary_embedding
  ex_engine/xllm_kernels/cuda/block_copy.cu    (209 lines) — copy_blocks, swap_blocks
  ex_engine/xllm_kernels/cuda/reshape_paged_cache.cu (101 lines) — KV cache ops
  ex_engine/xllm_kernels/cuda/headers/         (5 headers for compilation)

ILU bridge kernel sources (from xllm, verified SAME as upstream):
  ex_engine/xllm_kernels/ilu/    (10 files, 925 lines total)
  — activation.cpp, attention.cpp, fused_moe.cpp, group_gemm.cpp,
    matmul.cpp, norm.cpp, rope.cpp, ilu_ops_api.h, ixformer.h, utils.h

FLA Triton GDN kernels (for GatedDeltaNet without SM90+ FlashQLA):
  ex_engine/fla_kernels/gated_delta_rule/  (7 files, 2370 lines)
  — chunk_fwd.py (428), chunk.py (487), wy_fast.py (409),
    fused_recurrent.py (392), naive.py (161), gate.py (380)

CCCL sync (12 tuning + 14 dispatch headers updated from NVIDIA/cccl):
  cccl_upstream/cub/cub/device/dispatch/tuning/ — 12 changed files synced
  cccl_upstream/cub/cub/device/dispatch/ — 14 changed dispatch files synced

Compilation targets for real machine (ivcore10):
  1. CUDA kernels: --cuda-gpu-arch=ivcore10 via corex clang/16
  2. ILU bridges: torch.utils.cpp_extension linking ixformer .so
  3. FLA kernels: Triton JIT (if Triton works on BI-V100)
This commit is contained in:
claude
2026-08-14 07:48:52 +00:00
parent 051b02d3cd
commit 8d75652949
66 changed files with 12834 additions and 5458 deletions
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344
ex_engine/fla_kernels/gated_delta_rule/gate.py Normal file
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# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# For a list of all contributors, visit:
# https://github.com/fla-org/flash-linear-attention/graphs/contributors
import torch
import torch.nn.functional as F
import triton
import triton.language as tl
from fla.ops.backends import dispatch
from fla.ops.utils.cache import fla_cache_autotune
from fla.ops.utils.index import prepare_chunk_indices
from fla.ops.utils.op import exp
from fla.ops.utils.softplus import softplus
from fla.utils import autocast_custom_bwd, autocast_custom_fwd, autotune_cache_kwargs, input_guard
def naive_gdn_gate(
g: torch.Tensor,
A_log: torch.Tensor,
dt_bias: torch.Tensor | None = None,
output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
"""
Torch reference implementation for GDN gate computation.
Computes: ``g = -A_log.exp() * softplus(g + dt_bias)``
Args:
g (torch.Tensor):
Input tensor of shape `[..., HV]`.
A_log (torch.Tensor):
Decay parameter tensor with `HV` elements.
dt_bias (torch.Tensor | None):
Optional bias tensor added to `g` before activation, shape `[HV]`.
Returns:
Output tensor of shape `[..., HV]`.
"""
g = g.float()
if dt_bias is not None:
g = g + dt_bias.float()
return (-A_log.float().exp() * F.softplus(g)).to(output_dtype)
@triton.heuristics({
'HAS_BIAS': lambda args: args['dt_bias'] is not None,
'HAS_SCALE': lambda args: args['scale'] is not None,
'IS_VARLEN': lambda args: args['cu_seqlens'] is not None,
})
@fla_cache_autotune(
configs=[
triton.Config({}, num_warps=num_warps)
for num_warps in [1, 2, 4, 8]
],
key=['H', 'BT', 'IS_VARLEN', 'REVERSE'],
**autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def gdn_gate_chunk_cumsum_scalar_kernel(
g,
A_log,
dt_bias,
o,
scale,
cu_seqlens,
chunk_indices,
T,
H: tl.constexpr,
BT: tl.constexpr,
REVERSE: tl.constexpr,
HAS_BIAS: tl.constexpr,
HAS_SCALE: tl.constexpr,
IS_VARLEN: tl.constexpr,
):
i_t, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64)
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.int64)
bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64)
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_g = g + bos * H + i_h + o_t * H
p_o = o + bos * H + i_h + o_t * H
b_g = tl.load(p_g, mask=m_t, other=0.0).to(tl.float32)
if HAS_BIAS:
b_g = b_g + tl.load(dt_bias + i_h).to(tl.float32)
b_A = tl.load(A_log + i_h).to(tl.float32)
b_gate = -exp(b_A) * softplus(b_g)
b_o = tl.cumsum(b_gate, axis=0)
if REVERSE:
b_z = tl.sum(b_gate, axis=0)
b_o = -b_o + b_z[None] + b_gate
if HAS_SCALE:
b_o *= scale
tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=m_t)
@triton.heuristics({
'HAS_BIAS': lambda args: args['dt_bias'] is not None,
})
@fla_cache_autotune(
configs=[
triton.Config({}, num_warps=num_warps)
for num_warps in [1, 2, 4, 8]
],
key=['H', 'BT'],
**autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def gdn_gate_bwd_kernel(
g,
A_log,
dt_bias,
dyg,
dg,
dA,
T,
H: tl.constexpr,
BT: tl.constexpr,
HAS_BIAS: tl.constexpr,
):
i_t, i_h = tl.program_id(0).to(tl.int64), tl.program_id(1)
b_A = tl.load(A_log + i_h).to(tl.float32)
o_t = i_t * BT + tl.arange(0, BT)
m_t = o_t < T
p_g = g + i_h + o_t * H
p_dg = dg + i_h + o_t * H
p_dyg = dyg + i_h + o_t * H
b_g = tl.load(p_g, mask=m_t, other=0.0).to(tl.float32)
b_dyg = tl.load(p_dyg, mask=m_t, other=0.0).to(tl.float32)
if HAS_BIAS:
b_g = b_g + tl.load(dt_bias + i_h).to(tl.float32)
# gate = -exp(A_log) * softplus(g + bias)
# d(gate)/d(g) = -exp(A_log) * sigmoid(g + bias) (softplus' = sigmoid)
# d(gate)/d(A_log) = -exp(A_log) * softplus(g + bias) = gate
b_neg_expA = -exp(b_A)
b_yg = b_neg_expA * softplus(b_g)
b_dg = b_neg_expA * (b_dyg * tl.sigmoid(b_g))
b_dA = tl.sum(b_dyg * b_yg, 0)
tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), mask=m_t)
tl.store(dA + i_t * H + i_h, b_dA)
@input_guard
@dispatch('gated_delta_rule')
def gdn_gate_chunk_cumsum(
g: torch.Tensor,
A_log: torch.Tensor,
chunk_size: int,
scale: float = None,
dt_bias: torch.Tensor | None = None,
cu_seqlens: torch.LongTensor | None = None,
chunk_indices: torch.LongTensor | None = None,
output_dtype: torch.dtype | None = torch.float,
) -> torch.Tensor:
B, T, H = g.shape
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)
o = torch.empty_like(g, dtype=output_dtype or g.dtype)
gdn_gate_chunk_cumsum_scalar_kernel[(NT, B * H)](
g=g,
A_log=A_log,
dt_bias=dt_bias,
o=o,
scale=scale,
cu_seqlens=cu_seqlens,
chunk_indices=chunk_indices,
T=T,
H=H,
BT=BT,
REVERSE=False,
)
return o
@dispatch('gated_delta_rule')
def gdn_gate_bwd(
g: torch.Tensor,
A_log: torch.Tensor,
dt_bias: torch.Tensor | None,
dyg: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
H = g.shape[-1]
T = g.numel() // H
BT = 32
NT = triton.cdiv(T, BT)
dg = torch.empty_like(g, dtype=torch.float32)
dA = A_log.new_empty(NT, H, dtype=torch.float32)
gdn_gate_bwd_kernel[(NT, H)](
g=g,
A_log=A_log,
dt_bias=dt_bias,
dyg=dyg,
dg=dg,
dA=dA,
T=T,
H=H,
BT=BT,
)
dg = dg.view_as(g).type_as(g)
dA = dA.sum(0).view_as(A_log).type_as(A_log)
dbias = dg.view(-1, H).sum(0).to(dt_bias) if dt_bias is not None else None
return dg, dA, dbias
@triton.heuristics({
'HAS_BIAS': lambda args: args['dt_bias'] is not None,
})
@fla_cache_autotune(
configs=[
triton.Config({'BT': BT}, num_warps=num_warps, num_stages=num_stages)
for BT in [32, 64, 128]
for num_warps in [1, 2, 4, 8]
for num_stages in [2, 3]
],
key=['H'],
**autotune_cache_kwargs,
)
@triton.jit(do_not_specialize=['T'])
def gdn_gate_fwd_kernel(
g,
A_log,
dt_bias,
yg,
T,
H: tl.constexpr,
BT: tl.constexpr,
HAS_BIAS: tl.constexpr,
):
i_t, i_h = tl.program_id(0).to(tl.int64), tl.program_id(1)
b_A = tl.load(A_log + i_h).to(tl.float32)
o_t = i_t * BT + tl.arange(0, BT)
m_t = o_t < T
p_g = g + i_h + o_t * H
p_yg = yg + i_h + o_t * H
b_g = tl.load(p_g, mask=m_t, other=0.0).to(tl.float32)
if HAS_BIAS:
b_g = b_g + tl.load(dt_bias + i_h).to(tl.float32)
b_yg = -exp(b_A) * softplus(b_g)
tl.store(p_yg, b_yg.to(p_yg.dtype.element_ty), mask=m_t)
@dispatch('gated_delta_rule')
def gdn_gate_fwd(
g: torch.Tensor,
A_log: torch.Tensor,
dt_bias: torch.Tensor | None = None,
output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
H = g.shape[-1]
T = g.numel() // H
yg = torch.empty_like(g, dtype=output_dtype)
def grid(meta):
return (triton.cdiv(T, meta['BT']), H)
gdn_gate_fwd_kernel[grid](
g=g,
A_log=A_log,
dt_bias=dt_bias,
yg=yg,
T=T,
H=H,
)
return yg
class GDNGateFunction(torch.autograd.Function):
@staticmethod
@input_guard
@autocast_custom_fwd
def forward(
ctx,
g: torch.Tensor,
A_log: torch.Tensor,
dt_bias: torch.Tensor | None = None,
output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
yg = gdn_gate_fwd(g=g, A_log=A_log, dt_bias=dt_bias, output_dtype=output_dtype)
ctx.save_for_backward(g, A_log, dt_bias)
return yg
@staticmethod
@input_guard
@autocast_custom_bwd
def backward(ctx, dyg: torch.Tensor):
g, A_log, dt_bias = ctx.saved_tensors
dg, dA, dbias = gdn_gate_bwd(g=g, A_log=A_log, dt_bias=dt_bias, dyg=dyg)
return dg, dA, dbias, None
@torch.compiler.disable
def fused_gdn_gate(
g: torch.Tensor,
A_log: torch.Tensor,
dt_bias: torch.Tensor | None = None,
output_dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
r"""
Fused GDN gate computation with autograd support.
Computes: ``g = -A_log.exp() * softplus(g + dt_bias)``
Args:
g (torch.Tensor):
Input tensor of shape `[..., HV]`.
A_log (torch.Tensor):
Decay parameter tensor with `HV` elements.
dt_bias (torch.Tensor | None):
Optional bias tensor added to `g` before activation, shape `[HV]`.
output_dtype (torch.dtype):
The dtype of the output tensor. Default: `torch.float32`.
Returns:
Output tensor of shape `[..., HV]`.
"""
return GDNGateFunction.apply(g, A_log, dt_bias, output_dtype)