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Migrate XTorch operations to Kunlun operations (accelerating iteration) (#177)

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Signed-off-by: dongxinyu03 <dongxinyu03@baidu.com>
This commit is contained in:
Xinyu Dong
2026-02-12 18:13:00 +08:00
committed by GitHub
parent 744719587e
commit bf9369f733
15 changed files with 125 additions and 119 deletions
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4
vllm_kunlun/ops/fla/fused_recurrent.py
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@@ -11,7 +11,7 @@ from typing import Optional
import torch
import xtorch_ops
import kunlun_ops
class FusedRecurrentFunction(torch.autograd.Function):
@@ -31,7 +31,7 @@ class FusedRecurrentFunction(torch.autograd.Function):
num_accepted_tokens: Optional[torch.Tensor] = None,
use_qk_l2norm_in_kernel: bool = False):
o, final_state = xtorch_ops.fused_recurrent_gated_delta_rule_fwdv2(
o, final_state = kunlun_ops.fused_recurrent_gated_delta_rule_fwdv2(
q.contiguous(),
k.contiguous(),
v.contiguous(),

4
vllm_kunlun/ops/fla/l2norm.py
View File

@@ -13,7 +13,7 @@ from typing import Optional
import torch
from vllm.triton_utils import tl, triton
import xtorch_ops
import kunlun_ops
BT_LIST = [8, 16, 32, 64, 128]
@@ -149,5 +149,5 @@ def l2norm_fwd(x: torch.Tensor,
eps: float = 1e-6,
output_dtype: Optional[torch.dtype] = None):
out = torch.empty_like(x)
xtorch_ops.l2norm(x, out, eps)
kunlun_ops.l2norm(x, out, eps)
return out