[Core]ModelConfig use architecture rather than archiectures

[charlotte12l]

Purpose

Currently, hf_config/PretrainedConfig has s field architectures which is of type None| List[str]. However, when it's a List, it always of length 1 according to @hmellor in #28454 (comment)

Therefore, this PR follow the discussion #28454 (comment) and consolidate to always use architecture in ModelConfig. We will raise error if config architecture is different from inspected model cls architecture

Test Plan

python -m pytest tests/config/test_model_arch_config.py

Test Result

passed

---

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• [x ] The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• [ x] The test plan, such as providing test command.
• [x ] 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.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

cc @heheda12345 @hmellor @DarkLight1337

[gemini-code-assist]

Code Review

This pull request refactors the handling of model architectures by transitioning from a list of architectures (architectures: list[str]) to a single architecture string (architecture: str) across the codebase. This change impacts configuration files (base_model_arch_groundtruth.json, draft_model_arch_groundtruth.json), test assertions, and core logic within vllm/config/model.py, vllm/config/model_arch.py, vllm/lora/layers/fused_moe.py, vllm/model_executor/models/registry.py, vllm/model_executor/models/transformers/moe.py, and vllm/transformers_utils/model_arch_config_convertor.py. The ModelArchitectureConfig class is updated to reflect this, and methods that previously accepted or returned a list of architectures now handle a single string. A review comment highlighted a potential AttributeError in vllm/model_executor/models/transformers/moe.py where self.config.architecture was used instead of the correct self.config.architectures[0] for transformers.PretrainedConfig objects, which was subsequently addressed by the author.

[charlotte12l]

Just fyi @heheda12345 @hmellor I found some risky codes when writing this PR:

EAGLE models uses EAGLEConfig to update hf_config architectures in draft_model_config. However, draft_model_config 's model_info and resolved architecture are not updated. Therefore, for eagle draft models, hf_config.architectures is EagleLlamaForCausalLM but draft_model_config.architecture is LlamaForCausalLM. I didn't find a good way to resolve this, unless we re-inspect model_cls.

vllm/vllm/config/speculative.py

Lines 398 to 406 in 0d4044e

	eagle_config = EAGLEConfig(
	self.draft_model_config.hf_config,
	method=self.method,
	model_type="eagle",
	)
	self.draft_model_config.hf_config = eagle_config
	self.draft_model_config.model_arch_config = (
	self.draft_model_config.get_model_arch_config()
	)

[heheda12345]

Just fyi @heheda12345 @hmellor I found some risky codes when writing this PR:

EAGLE models uses EAGLEConfig to update hf_config architectures in draft_model_config. However, draft_model_config 's model_info and resolved architecture are not updated. Therefore, for eagle draft models, hf_config.architectures is EagleLlamaForCausalLM but draft_model_config.architecture is LlamaForCausalLM. I didn't find a good way to resolve this, unless we re-inspect model_cls.

vllm/vllm/config/speculative.py

Lines 398 to 406 in 0d4044e

	eagle_config = EAGLEConfig(
	self.draft_model_config.hf_config,
	method=self.method,
	model_type="eagle",
	)
	self.draft_model_config.hf_config = eagle_config
	self.draft_model_config.model_arch_config = (
	self.draft_model_config.get_model_arch_config()
	)

Is this a bug on main branch?

[charlotte12l]

Just fyi @heheda12345 @hmellor I found some risky codes when writing this PR:
EAGLE models uses EAGLEConfig to update hf_config architectures in draft_model_config. However, draft_model_config 's model_info and resolved architecture are not updated. Therefore, for eagle draft models, hf_config.architectures is EagleLlamaForCausalLM but draft_model_config.architecture is LlamaForCausalLM. I didn't find a good way to resolve this, unless we re-inspect model_cls.

vllm/vllm/config/speculative.py

Lines 398 to 406 in 0d4044e

	eagle_config = EAGLEConfig(
	self.draft_model_config.hf_config,
	method=self.method,
	model_type="eagle",
	)
	self.draft_model_config.hf_config = eagle_config
	self.draft_model_config.model_arch_config = (
	self.draft_model_config.get_model_arch_config()
	)

Is this a bug on main branch?

Yes, it's on main. Let me try to fix it first in a separate PR

[cursor]

Comment @cursor review or bugbot run to trigger another review on this PR

[charlotte12l]

Yes, it's on main. Let me try to fix it first in a separate PR

fixed in #31753

[22quinn]

Test failures look legit

AttributeError: 'ModelConfig' object has no attribute '_architecture'. Did you mean: 'architecture'?

[charlotte12l]

Currently, hf_config/PretrainedConfig has s field architectures which is of type None| List[str]. However, when it's a List, it always of length 1 according to @hmellor in #28454 (comment)

@hmellor Just found some models have multiple architectures :(

• CofeAI/FLM-2-52B-Instruct-2407

• vllm/vllm/model_executor/models/hunyuan_vision.py

  Lines 882 to 883 in 1337657

  	"HunYuanDenseV1ForCausalLM",
  	"HunYuanMoEV1ForCausalLM",

Would appreciate any suggestions on how to proceed! cc @heheda12345

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @charlotte12l.

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