[Fix] Fix llama4 modelopt weight loading error

[jiahanc]

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

Purpose

modelopt Llama4-Maverick FP8 checkpoint was updated and vLLM has error when using the latest checkpoint:

(VllmWorker rank=5 pid=45037) ERROR 07-31 23:12:46 [multiproc_executor.py:557]   File "/scratch/vllm/vllm/model_executor/models/llama4.py", line 413, in load_moe_expert_weights
(VllmWorker rank=5 pid=45037) ERROR 07-31 23:12:46 [multiproc_executor.py:557]     param = params_dict[full_param_name]
(VllmWorker rank=5 pid=45037) ERROR 07-31 23:12:46 [multiproc_executor.py:557]             ~~~~~~~~~~~^^^^^^^^^^^^^^^^^
(VllmWorker rank=5 pid=45037) ERROR 07-31 23:12:46 [multiproc_executor.py:557] KeyError: 'layers.1.feed_forward.experts.w2_weight_input_scale'

Fix the logic in weight loading and processing to fit the new checkpoint

Test Plan

lm_eval --model vllm --model_args pretrained=nvidia/Llama-4-Maverick-17B-128E-Instruct-FP8,tensor_parallel_size=8,max_model_len=2048,gpu_memory_utilization=0.9,max_num_seqs=32,quantization=modelopt --trust_remote_code --tasks gsm8k --num_fewshot 5 --batch_size auto

vllm (pretrained=nvidia/Llama-4-Maverick-17B-128E-Instruct-FP8,tensor_parallel_size=8,max_model_len=2048,gpu_memory_utilization=0.9,max_num_seqs=32,quantization=modelopt,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9257|±  |0.0072|
|     |       |strict-match    |     5|exact_match|↑  |0.9272|±  |0.0072|

Test Result

(Optional) Documentation Update

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[gemini-code-assist]

Code Review

This pull request addresses a KeyError that occurs when loading weights from the updated modelopt Llama4-Maverick FP8 checkpoint. The fix involves updating the weight renaming logic in _rename_weight_for_modelopt_checkpoint to correctly handle weight names that may or may not already have the language_model.model. prefix.

The changes are well-targeted and effectively resolve the issue. The logic for creating the renamed variable is sound, and the subsequent refactoring to use this variable throughout the function simplifies the code and removes redundancy. The fix appears correct and robust for its intended purpose. I have no further comments.

[jiahanc]

Hi @mgoin , may you help review this fix PR?

[Edwardf0t1]

@jiahanc Could you run a test on https://huggingface.co/nvidia/Llama-4-Scout-17B-16E-Instruct-FP8 as well?

[jiahanc]

@jiahanc Could you run a test on https://huggingface.co/nvidia/Llama-4-Scout-17B-16E-Instruct-FP8 as well?

Sure.

2025-08-01:16:02:31,793 INFO     [lm_eval.loggers.evaluation_tracker:272] Output path not provided, skipping saving results aggregated
vllm (pretrained=nvidia/Llama-4-Scout-17B-16E-Instruct-FP8,tensor_parallel_size=8,max_model_len=2048,gpu_memory_utilization=0.9,max_num_seqs=32,quantization=modelopt,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9075|±  |0.0080|
|     |       |strict-match    |     5|exact_match|↑  |0.8863|±  |0.0087|

[Edwardf0t1]

@jiahanc Could you run a test on https://huggingface.co/nvidia/Llama-4-Scout-17B-16E-Instruct-FP8 as well?

Sure.

2025-08-01:16:02:31,793 INFO     [lm_eval.loggers.evaluation_tracker:272] Output path not provided, skipping saving results aggregated
vllm (pretrained=nvidia/Llama-4-Scout-17B-16E-Instruct-FP8,tensor_parallel_size=8,max_model_len=2048,gpu_memory_utilization=0.9,max_num_seqs=32,quantization=modelopt,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9075|±  |0.0080|
|     |       |strict-match    |     5|exact_match|↑  |0.8863|±  |0.0087|

Thanks - the scores are a bit lower than here:
#20419 (comment)
Might be due to different params.