fix qwen3-30-a3b lcb-code score

[yao-fengchen]

No description provided.

[Copilot]

Pull request overview

This PR fixes accuracy issues in the dlinfer backend by improving numerical precision in rotary embeddings, updating tensor type conversions, and upgrading dependencies to stable releases.

• Refactored rotary embedding inverse frequency calculation to use native float types and eliminate unnecessary type conversions
• Updated Ascend NPU backend tensor operations to use explicit int32 conversions for improved compatibility
• Added support for grouped MoE routing with n_groups parameter
• Upgraded CANN and torch-npu dependencies from release candidates to stable versions

Reviewed changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
lmdeploy/pytorch/backends/dlinfer/rotary_embedding.py	Improved numerical precision by changing base parameter to float and refactoring inv_freq calculation to avoid intermediate int64 conversions
lmdeploy/pytorch/backends/dlinfer/moe.py	Added n_groups parameter support to align with base SoftmaxTopKBuilder interface
lmdeploy/pytorch/backends/dlinfer/ascend/op_backend.py	Updated tensor type conversions to explicitly use int32 for Ascend NPU compatibility
docker/Dockerfile_ascend_a3	Upgraded CANN from 8.3.rc1.alpha002 to 8.3.rc1 and torch-npu from 2.8.0rc1 to 2.8.0
docker/Dockerfile_ascend_a2_300i	Upgraded CANN from 8.3.rc1.alpha002 to 8.3.rc1 and torch-npu from 2.8.0rc1 to 2.8.0

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