[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.

[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. This integration allows vLLM users to leverage Ling's efficient inference capabilities, including its scalable MoE architecture, while benefiting from vLLM's high-throughput serving optimizations.

Highlights

• New Model Implementation: I've added the full architectural implementation for the Ling Mixture-of-Experts (MoE) model, including its attention, MLP, and MoE layers, specifically designed for efficient inference within vLLM.
• MoE Specifics: The implementation incorporates the Ling MoE architecture, handling multiple experts, shared experts, and routing logic, leveraging vLLM's FusedMoE layer for optimized performance.
• Configuration Integration: A dedicated BailingMoeConfig class has been introduced to define the specific parameters and architecture of the Ling MoE models, ensuring proper model loading and configuration within the vLLM framework.
• Framework Compatibility: The new Ling model integrates seamlessly with vLLM's existing infrastructure, supporting tensor and pipeline parallelism, LoRA, and the standard weight loading mechanisms.
• Documentation & Testing: The Ling model has been added to the supported models documentation and included in the test registry for automated validation and future reference.

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[gemini-code-assist]

Code Review

This pull request adds support for the Ling MoE model series. The implementation is well-structured and follows vLLM's patterns for adding new models.

I've identified a critical issue regarding a potential architectural deviation in the BailingMoeBlock implementation compared to the original model, which could impact correctness. Please clarify if this architectural change is intentional and has been validated? If not, I strongly recommend refactoring this block to align with the original model's architecture to ensure correctness.

[github-actions]

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🚀

[jeejeelee]

Some nit comments

[jeejeelee]

After fixing the CI failure and deleting KVCache, overall LGTM. Thank you for your contributon, also cc @Isotr0py

[Isotr0py]

LGTM now!

[DarkLight1337]

Please merge from main to fix the Async test. Also you need to fix the models test