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fix qwen3vl mrope op (#4484)

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### What this PR does / why we need it?
Qwen2.5-VL mrope precision problem would been solved once this pr is
merged
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Test on G8600 with textVQA dataset

- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2

---------

Signed-off-by: 李少鹏 <lishaopeng21@huawei.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
shaopeng-666
2025-12-08 19:19:17 +08:00
committed by GitHub
parent 3c3c9a5386
commit 9766cf9128
1 changed files with 2 additions and 6 deletions
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8
vllm_ascend/ops/rotary_embedding.py
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@@ -24,7 +24,6 @@ from vllm.forward_context import get_forward_context
from vllm.model_executor.layers.rotary_embedding import ( from vllm.model_executor.layers.rotary_embedding import (
DeepseekScalingRotaryEmbedding, MRotaryEmbedding, RotaryEmbedding, DeepseekScalingRotaryEmbedding, MRotaryEmbedding, RotaryEmbedding,
YaRNScalingRotaryEmbedding) YaRNScalingRotaryEmbedding)
from vllm.platforms import CpuArchEnum
from vllm_ascend.platform import NPUPlatform from vllm_ascend.platform import NPUPlatform
from vllm_ascend.utils import (AscendDeviceType, enable_custom_op, from vllm_ascend.utils import (AscendDeviceType, enable_custom_op,
@@ -411,10 +410,7 @@ class AscendMRotaryEmbedding(MRotaryEmbedding):
query: torch.Tensor, query: torch.Tensor,
key: torch.Tensor, key: torch.Tensor,
): ):
# TODO: This judgment will be removed once the mrope precision issue is fixed if self.mrope_section != [16, 24, 24]:
if self.mrope_section != [
16, 24, 24
] or NPUPlatform.get_cpu_architecture() == CpuArchEnum.X86:
return super().forward_oot(positions, query, key) return super().forward_oot(positions, query, key)
import torch_npu import torch_npu
@@ -429,7 +425,7 @@ class AscendMRotaryEmbedding(MRotaryEmbedding):
self.cos_sin_cache = self.cos_sin_cache.to( # type: ignore self.cos_sin_cache = self.cos_sin_cache.to( # type: ignore
query.dtype) # type: ignore query.dtype) # type: ignore
query, key = torch_npu.npu_mrope(positions, query, key = torch_npu.npu_mrope(positions.contiguous(),
query.contiguous(), query.contiguous(),
key.contiguous(), key.contiguous(),
self.cos_sin_cache.contiguous(), self.cos_sin_cache.contiguous(),