[bug] Fix qwen3 weight reloading

[hao-aaron]

Fixed a weight loading issue with qwen MoE: HF's Qwen3MoeExperts (transformers ≥ 4.x) packs all experts of an MoE layer into two 3-D tensors per layer (experts.gate_up_proj of shape (E, 2*I, H) and experts.down_proj of shape (E, H, I)), and transformers v5 will make this the universal MoE layout. vLLM's existing per-expert dispatch in Qwen3MoeModel.load_weights doesn't recognize these fused names.

• vllm/model_executor/layers/fused_moe/layer.py — (a) make_expert_params_mapping appends three HF-fused alias entries (experts.gate_up_proj → w13_weight for w1/w3 with expert_id repurposed as shard_idx, experts.down_proj → w2_weight for w2).
• vllm/model_executor/models/qwen3_moe.py — Qwen3MoeModel.load_weights plumbs the (augmented) mapping into every layer's FusedMoE and replaces the ~60-line inline expert-dispatch block with a delegation to FusedMoE.load_weights.

Test Plan

Make sure example passes with Qwen/Qwen1.5-MoE-A2.7B-Chat

---

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• 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
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

[claude]

Claude Code Review

This pull request is from a fork — automated review is disabled. A repository maintainer can comment @claude review to run a one-time review.

[mergify]

Documentation preview: https://vllm--42577.org.readthedocs.build/en/42577/

[gemini-code-assist]

Code Review

This pull request introduces a mechanism to unpack fused MoE weights in the RLHF NCCL example, specifically handling the conversion of fused 3D tensors into the per-expert layout expected by vLLM. Additionally, it updates the GPU worker to ensure the vLLM configuration is correctly set during layerwise weight reloading and updates, which is necessary for certain kernels like FlashInfer CUTLASS MoE. I have no feedback to provide as there were no review comments.

[SumanthRH]

LGTM

[hmellor]

Can we do this when FusedMoE is instantiated rather than patching it in later?

[mergify]

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

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

[hmellor]

The latest change should work with both 2D and 3D weights for Qwen3. It was tested using

# Wait for the vLLM server to be ready before running tests against it.
wait_for_server() {
  until curl -s http://localhost:8000/health > /dev/null; do
    sleep 1
  done
}

# Stop the vLLM server and wait for it to fully exit before continuing, so the
# next server isn't blocked by the old one still holding the port / GPU memory.
stop_server() {
  pkill -f "vllm serve"
  while pgrep -f "vllm serve" > /dev/null; do
    sleep 1
  done
}

# 2D expert weights
## vLLM impl
vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --enforce-eager --max-model-len 1024 > server_2d_vllm.log 2>&1 &
wait_for_server
vllm chat -q hello
stop_server
## Transformers impl
vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --enforce-eager --max-model-len 1024 --model-impl transformers > server_2d_transformers.log 2>&1 &
wait_for_server
vllm chat -q hello
stop_server
# 3D expert weights
## vLLM impl
vllm serve ../Qwen3-30B-A3B-Instruct-2507 --enforce-eager --max-model-len 1024 > server_3d_vllm.log 2>&1 &
wait_for_server
vllm chat -q hello
stop_server
## Transformers impl
vllm serve ../Qwen3-30B-A3B-Instruct-2507 --enforce-eager --max-model-len 1024 --model-impl transformers > server_3d_transformers.log 2>&1 &
wait_for_server
vllm chat -q hello
stop_server

Where ../Qwen3-30B-A3B-Instruct-2507 is a local version of the checkpoint saved with 3D expert weights using

from transformers import AutoModelForCausalLM, AutoTokenizer


MODEL_ID = "Qwen/Qwen3-30B-A3B-Instruct-2507"
OUTPUT_DIR = "Qwen3-30B-A3B-Instruct-2507"

model = AutoModelForCausalLM.from_pretrained(MODEL_ID)
model.save_pretrained(OUTPUT_DIR, save_original_format=False)

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
tokenizer.save_pretrained(OUTPUT_DIR)

The output of the test script should be:

# bash test.sh 

Using model: Qwen/Qwen3-30B-A3B-Instruct-2507
Hello! How can I assist you today? 😊
Using model: Qwen/Qwen3-30B-A3B-Instruct-2507
Hello! How can I assist you today? 😊
Using model: ../Qwen3-30B-A3B-Instruct-2507
Hello! How can I assist you today? 😊
Using model: ../Qwen3-30B-A3B-Instruct-2507
Hello! How can I assist you today? 😊

[aoshen02]

In terms of the flashinfer and trtllm assertion, I just make a pr about this #44613. I think it's more elegant, any comments?

[hmellor]

This is the culrit of the failures

[mergify]

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

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

[hmellor]

Weight loading superseded by #47058