[Model] Add Ling implementation

[ant-yy]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results

Purpose

Add [Model] Ling implementation

This PR adds support for the Ling Mixture-of-Experts (MoE) language model series open-sourced by InclusionAI (GitHub). The implementation includes:

• Ling-lite: 16.8B total parameters (2.75B activated)
• Ling-plus: 290B total parameters (28.8B activated)

Key features:

1. Scalable MoE architecture enabling flexible parameter allocation
2. State-of-the-art performance across NLP benchmarks
3. Task-adaptive structure for diverse applications
4. Apache 2.0 licensed open-source implementation

The implementation follows vLLM's model integration patterns and maintains compatibility with existing serving infrastructure. This addition will allow vLLM users to leverage Ling's efficient inference capabilities while benefiting from the framework's high-throughput serving optimizations.

Ling

Test Plan

We will conduct the following datasets tests in subsequent phases, and the results will be supplemented accordingly:
Datasets to be evaluated: MMLU(EM), GPQA(Pass@1), HumanEval(Pass@1), LiveCodeBench 2408-2502 (Pass@1), LCBench(pass@1), Math(EM), AIME2024(pass@1), OlympiadBench(pass@1), BBH(EM), IFEval(Prompt Strict), BFCL_live1.

Test Result

Here are the results based on vLLM 0.7.3.

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @ant-yy, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces comprehensive support for the Ling Mixture-of-Experts (MoE) language model series within the vLLM framework. It enables efficient inference for Ling-lite and Ling-plus models by integrating their specific architecture, including attention, MLP, and MoE layers, while leveraging vLLM's high-throughput serving optimizations. The changes involve adding new model definition files, registering the model, and defining its configuration parameters.

Highlights

• New Model Integration: Added full support for the Ling Mixture-of-Experts (MoE) language model series, including Ling-lite and Ling-plus variants, enabling their efficient inference within vLLM.
• MoE Architecture Implementation: Implemented the specific MoE architecture of Ling models, featuring BailingAttention, BailingMLP, and a BailingMoE layer with support for shared experts and FusedMoE.
• vLLM Framework Integration: Integrated the Ling model into vLLM's serving infrastructure, including support for LoRA, quantization, and pipeline parallelism, ensuring compatibility with existing vLLM features.
• Configuration Definition: Defined a new BailingMoeConfig class to specify the architectural parameters and default values for Ling MoE models, allowing vLLM to correctly load and configure them.

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[gemini-code-assist]

Code Review

This pull request adds support for the Ling (Bailing) Mixture-of-Experts model. The implementation looks solid and follows the existing patterns in vLLM. I've identified a few areas for improvement, mainly related to code cleanliness and consistency, such as removing unused imports, correcting LoRA configurations, and maintaining alphabetical order in registries and import statements. Addressing these points will enhance the maintainability of the new code.

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @ant-yy.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[ant-yy]

Due to an accidental Git operation, this pull request was mistakenly closed. A new PR will be resubmitted to add the Ling Model, and this existing PR is hereby marked as obsolete.

[ant-yy]

new pr: #20680