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[Attention] add gpt-oss support (#5901)

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### What this PR does / why we need it?
Please refer to the following link for the historical conversation
https://github.com/vllm-project/vllm-ascend/pull/4467. We have made
updates in light of the comments from the prior PR review. Given the
refactoring of the attention_v1 component, we have carried out necessary
adjustments to fit the newly revised code.

### Does this PR introduce _any_ user-facing change?

1. Modified the code in the Attention section to adapt to the SWA and
Sink features required by gpt-oss.
2. Modified the code in the MoE section to add support for bias and
swigluoai.

### How was this patch tested?
Please refer to the
https://github.com/vllm-project/vllm-ascend/pull/4467 for performance
tests, on the basis of which the accuracy tests from AIME2024 have been
newly added.

![img_v3_02tu_501e88e3-2217-4565-8edf-b9acf4f43f2g](https://github.com/user-attachments/assets/024f8283-18ab-4d4d-ab12-27917b5d7d06)


- vLLM version: v0.13.0
- vLLM main:
bde38c11df

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: mikequan0425 <mikequan0425@foxmail.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
Signed-off-by: pu-zhe <zpuaa@outlook.com>
Signed-off-by: liziyu <liziyu16@huawei.com>
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Signed-off-by: luomin2005 <luomin2005@huawei.com>
Signed-off-by: whx-sjtu <2952154980@qq.com>
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
Signed-off-by: wxsIcey <1790571317@qq.com>
Signed-off-by: MrZ20 <2609716663@qq.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: leon_tao <taoyao2@huawei.com>
Co-authored-by: nurxat <738457498@qq.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: mikequan <199741451@qq.com>
Co-authored-by: LI SHENGYONG <49200266+shenchuxiaofugui@users.noreply.github.com>
Co-authored-by: jiangyunfan1 <jiangyunfan1@h-partners.com>
Co-authored-by: pu-zhe <zpuaa@outlook.com>
Co-authored-by: luomin2005 <luomin2005@huawei.com>
Co-authored-by: liziyu <56102866+liziyu179@users.noreply.github.com>
Co-authored-by: wangxiaoteng <wangxiaoteng@huawei.com>
Co-authored-by: whx <56632993+whx-sjtu@users.noreply.github.com>
Co-authored-by: Cao Yi <slightwindsec@gmail.com>
Co-authored-by: Icey <1790571317@qq.com>
Co-authored-by: SILONG ZENG <2609716663@qq.com>
This commit is contained in:
jiahao.quan
2026-02-12 10:55:34 +08:00
committed by GitHub
parent f71812011d
commit 7221045777
8 changed files with 111 additions and 19 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
.github/workflows/scripts/config.yaml vendored
View File

@@ -68,6 +68,8 @@ e2e-2card-light:
estimated_time: 220
- name: tests/e2e/multicard/2-cards/test_offline_inference_distributed.py::test_deepseek3_2_w8a8_pruning_mtp_tp2_ep
estimated_time: 90
- name: tests/e2e/multicard/2-cards/test_offline_inference_distributed.py::test_gpt_oss_distributed_tp2
estimated_time: 180
e2e-multicard-2-cards:
# TODO: recover skipped tests

18
tests/e2e/multicard/2-cards/test_offline_inference_distributed.py
View File

@@ -22,7 +22,6 @@ Run `pytest tests/test_offline_inference.py`.
"""
import os
from unittest.mock import patch
import pytest
from vllm import SamplingParams
@@ -48,6 +47,9 @@ DEEPSEEK_W4A8_MODELS = [
"vllm-ascend/DeepSeek-V3.1-W4A8-puring",
]
GPT_OSS_MODELS = [
"unsloth/gpt-oss-20b-BF16",
]
def test_deepseek_multistream_moe_tp2():
example_prompts = [
@@ -289,3 +291,17 @@ def test_qwen3_w4a4_distributed_tp2(model):
quantization="ascend",
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)
@pytest.mark.parametrize("model", GPT_OSS_MODELS)
def test_gpt_oss_distributed_tp2(model):
example_prompts = [
"Hello, my name is",
]
max_tokens = 5
with VllmRunner(
model,
tensor_parallel_size=2,
enforce_eager=True,
) as vllm_model:
vllm_model.generate_greedy(example_prompts, max_tokens)

32
vllm_ascend/attention/attention_v1.py
View File

@@ -350,6 +350,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
logits_soft_cap: float | None,
attn_type: str,
kv_sharing_target_layer_name: str | None,
sinks: torch.Tensor = None,
**kwargs,
) -> None:
self.vllm_config = get_current_vllm_config()
@@ -372,6 +373,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
self.is_kv_producer = (
self.vllm_config.kv_transfer_config is not None and self.vllm_config.kv_transfer_config.is_kv_producer
)
self.sinks = sinks
@staticmethod
def update_graph_params(
@@ -766,6 +768,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
attn_metadata.attn_state == AscendAttentionState.DecodeOnly
and self.sliding_window is not None
and attn_metadata.seq_lens.shape[0] == query.size(0)
and self.sinks is None
):
return self._forward_fia_slidingwindow(query, attn_metadata, output)
key, value, block_size, block_table, actual_seq_lengths_kv = self._get_fia_params(key, value, attn_metadata)
@@ -778,6 +781,35 @@ class AscendAttentionBackendImpl(AttentionImpl):
key = key[:num_tokens]
value = value[:num_tokens]
# Get workspace from cache or calculate it if not present.
if self.sinks is not None:
actual_seq_qlen = attn_metadata.actual_seq_lengths_q
if attn_metadata.attn_state == AscendAttentionState.DecodeOnly:
actual_seq_qlen = torch.tensor([1] * len(attn_metadata.seq_lens_list), dtype=torch.int32).cumsum(dim=0)
if self.sliding_window is not None:
atten_mask = attn_metadata.swa_mask
sparse_mode = 4
else:
atten_mask = attn_metadata.attn_mask
sparse_mode = 3
attn_output, _ = torch_npu.npu_fused_infer_attention_score_v2(
query,
key,
value,
num_query_heads=self.num_heads,
num_key_value_heads=self.num_kv_heads,
input_layout="TND",
pre_tokens=self.sliding_window if self.sliding_window is not None else SWA_INT_MAX,
next_tokens=0,
atten_mask=atten_mask,
sparse_mode=sparse_mode,
softmax_scale=self.scale,
block_table=block_table,
block_size=block_size,
actual_seq_qlen=actual_seq_qlen,
actual_seq_kvlen=actual_seq_lengths_kv,
learnable_sink=self.sinks,
)
else:
attn_output, _ = torch_npu.npu_fused_infer_attention_score(
query=query,
key=key,

13
vllm_ascend/ops/activation.py
View File

@@ -16,7 +16,7 @@
#
import torch
from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul, SwigluOAIAndMul
from vllm_ascend.utils import get_weight_prefetch_method
@@ -38,3 +38,14 @@ class AscendSiluAndMul(SiluAndMul):
out = torch_npu.npu_swiglu(x)
weight_prefetch_method.maybe_prefetch_mlp_weight_postprocess(out)
return out
class AscendSwigluOAIAndMul:
def swiglu_oai_forward(x: torch.Tensor, alpha: float = 1.702, limit: float = 7.0) -> torch.Tensor:
class MinimalSwigluOAIAndMul:
def __init__(self):
self.alpha = alpha
self.limit = limit
layer = MinimalSwigluOAIAndMul()
return SwigluOAIAndMul.forward_native(layer, x)

4
vllm_ascend/ops/fused_moe/fused_moe.py
View File

@@ -94,6 +94,7 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
global_num_experts: int = -1,
expert_map: torch.Tensor | None = None,
apply_router_weight_on_input: bool = False,
activation: str = "silu",
enable_force_load_balance: bool = False,
log2phy: torch.Tensor = None,
**kwargs,
@@ -137,6 +138,9 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
w1_bias=layer.w13_bias if self.moe.has_bias else None,
w2_bias=layer.w2_bias if self.moe.has_bias else None,
activation=activation,
topk_weights=topk_weights,
topk_ids=topk_ids,
expert_map=expert_map,

7
vllm_ascend/ops/fused_moe/moe_comm_method.py
View File

@@ -110,6 +110,8 @@ class MoECommMethod(ABC):
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
activation: str = "silu",
w1_bias: torch.Tensor = None,
w2_bias: torch.Tensor = None,
apply_router_weight_on_input: bool = False,
use_int8_w8a8: bool = False,
use_int4_w4a8: bool = False,
@@ -158,6 +160,9 @@ class MoECommMethod(ABC):
w1_scale=w1_scale,
w2=w2,
w2_scale=w2_scale,
w1_bias=w1_bias,
w2_bias=w2_bias,
activation=activation,
group_list=dispatch_results.group_list,
dynamic_scale=dispatch_results.dynamic_scale,
group_list_type=dispatch_results.group_list_type,
@@ -286,6 +291,8 @@ class FusedMC2CommImpl(MoECommMethod):
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
activation: str = "silu",
w1_bias: torch.Tensor = None,
w2_bias: torch.Tensor = None,
apply_router_weight_on_input: bool = False,
use_int8_w8a8: bool = False,
use_int4_w4a8: bool = False,

17
vllm_ascend/ops/fused_moe/moe_mlp.py
View File

@@ -22,6 +22,7 @@ from vllm.forward_context import get_forward_context
from vllm.triton_utils import HAS_TRITON
from vllm_ascend.ascend_forward_context import MoECommType
from vllm_ascend.ops.activation import AscendSwigluOAIAndMul
from vllm_ascend.utils import (
dispose_tensor,
enable_custom_op,
@@ -270,6 +271,9 @@ def unquant_apply_mlp(
w1: torch.Tensor,
w2: torch.Tensor,
group_list: torch.Tensor,
w1_bias: torch.Tensor = None,
w2_bias: torch.Tensor = None,
activation: str | None = None,
group_list_type: int = 1,
topk_scales: torch.Tensor | None = None,
need_trans: bool = True,
@@ -281,11 +285,17 @@ def unquant_apply_mlp(
gate_up_out = torch_npu.npu_grouped_matmul(
x=[hidden_states],
weight=[w1],
bias=[w1_bias.to(dtype=torch.float32)] if w1_bias is not None else None,
split_item=2,
group_list_type=group_list_type,
group_type=0,
group_list=group_list,
)[0]
if activation == "swigluoai":
num_experts, _, hidden_size = w1.shape
gate_up_out = AscendSwigluOAIAndMul.swiglu_oai_forward(gate_up_out.view(-1, hidden_size))
else:
gate_up_out = torch_npu.npu_swiglu(gate_up_out)
if topk_scales is not None:
@@ -294,6 +304,7 @@ def unquant_apply_mlp(
hidden_states = torch_npu.npu_grouped_matmul(
x=[gate_up_out],
weight=[w2],
bias=[w2_bias.to(dtype=torch.float32)] if w2_bias is not None else None,
split_item=2,
group_list_type=group_list_type,
group_type=0,
@@ -309,6 +320,9 @@ def unified_apply_mlp(
group_list: torch.Tensor,
w1_scale: list[torch.Tensor] | None = None,
w2_scale: list[torch.Tensor] | None = None,
activation: str | None = None,
w1_bias: torch.Tensor = None,
w2_bias: torch.Tensor = None,
dynamic_scale: torch.Tensor = None,
group_list_type: int = 1,
w1_scale_bias: torch.Tensor = None,
@@ -344,6 +358,9 @@ def unified_apply_mlp(
hidden_states=hidden_states,
w1=w1,
w2=w2,
w1_bias=w1_bias,
w2_bias=w2_bias,
activation=activation,
group_list=group_list,
group_list_type=group_list_type,
topk_scales=topk_scales,

3
vllm_ascend/ops/rotary_embedding.py
View File

@@ -256,12 +256,15 @@ class AscendYaRNRotaryEmbedding(YaRNScalingRotaryEmbedding):
attn_factor: float = 1,
beta_fast: int = 32,
beta_slow: int = 1,
truncate: bool = False,
) -> None:
extra_kwargs = {
"extrapolation_factor": extrapolation_factor,
"attn_factor": attn_factor,
"beta_fast": beta_fast,
"beta_slow": beta_slow,
# TODO: current not support actual truncate，adaptation for extra parameters to be compatible with vllm
"truncate": truncate,
}
super().__init__(
head_size, rotary_dim, max_position_embeddings, base, is_neox_style, scaling_factor, dtype, **extra_kwargs