[Model] use AutoWeightsLoader for solar

[lengrongfu]

Issue: #15697

• olmo.py

python3 -m vllm.entrypoints.cli.main serve allenai/OLMo-1B-hf --trust-remote-code --gpu-memory-utilization 0.95 

• olmo2.py

python3 -m vllm.entrypoints.cli.main serve allenai/OLMo-2-0425-1B --trust-remote-code --gpu-memory-utilization 0.95 

• mixtral_quant.py

python3 -m vllm.entrypoints.cli.main serve TitanML/tiny-mixtral --trust-remote-code --gpu-memory-utilization 0.95

• solar.py

python3 -m vllm.entrypoints.cli.main serve upstage/solar-pro-preview-instruct --trust-remote-code --gpu-memory-utilization 0.95

• nemotron.py

python3 -m vllm.entrypoints.cli.main serve nvidia/Minitron-4B-Base --trust-remote-code --gpu-memory-utilization 0.95

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[mergify]

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

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

[Isotr0py]

Although this won't affect too much, I think rotary_emb.inv_freq, rotary_emb.cos_cached and rotary_emb.sin_cached are not prefixes.

[lengrongfu]

So what good suggestions do you have?

[Isotr0py]

We can filter out the weights to skip loading in advance before calling AutoWeightsLoader, just like what Phi-4-MM does:

vllm/vllm/model_executor/models/phi4mm.py

Lines 1241 to 1246 in 5418176

	def load_weights(self, weights: Iterable[tuple[str,
	torch.Tensor]]) -> None:
	weights = ((name, data) for name, data in weights
	if "lora" not in name)
	loader = AutoWeightsLoader(self)
	return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)

[lengrongfu]

Does the situation you are talking about occur? Currently, I have tested that it works properly with prefix

[Isotr0py]

Yea, this situation seldom occurred in most cases, because only some models checkpoint fine-tuned by ColossalAI may include these tensors, and the models you tested are likely not having these tensors.

['model.layers.1.self_attn.rotary_emb.inv_freq', 'model.layers.9.self_attn.rotary_emb.inv_freq', 'model.layers.22.self_attn.rotary_emb.inv_freq', 'model.layers.24.self_attn.rotary_emb.inv_freq', ... ]

But it's still reasonable to make the weights loading more robust to include this rare case.

[Isotr0py]

Anyway, since the RoPE buffer tensors in checkpoint is not a bug issue. Let's merge this PR and leave it to be handled with other models' modified loading logic together in a following PR.