[Model] Decouple glm4v

[jeejeelee]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, FIX [Bug]: GLM-4.1V lora trained model reports target_module mismatch error #22077
• 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

• Decouple Glm4vForConditionalGeneration and Glm4vMoeForConditionalGeneration. On one hand, their packed_modules_mapping have some differences(the root cause of [Bug]: GLM-4.1V lora trained model reports target_module mismatch error #22077), and on the other hand, it also facilitates the maintainability of the model code.
• Currently, Glm4vMoeForConditionalGeneration does not support LoRA and will raise the following error. I have removed the LoRA label for now and will investigate the issue ASAP

(EngineCore_0 pid=1393314) (VllmWorker TP0 pid=1393320) (VllmWorker TP1 pid=1393322) (EngineCore_0 pid=1393314) (VllmWorker TP2 pid=1393324) ERROR 08-12 16:22:17 [multiproc_executor.py:596]   File "/root/anaconda3/envs/py310_vllm_dev/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
ERROR 08-12 16:22:17 [multiproc_executor.py:596]   File "/root/Code/vllm_dev/vllm/vllm/lora/ops/triton_ops/lora_shrink_op.py", line 139, in _lora_shrink
(VllmWorker TP3 pid=1393326) ERROR 08-12 16:22:17 [multiproc_executor.py:596]   File "/root/Code/vllm_dev/vllm/vllm/lora/ops/triton_ops/lora_shrink_op.py", line 139, in _lora_shrink
(EngineCore_0 pid=1393314) (EngineCore_0 pid=1393314) (VllmWorker TP0 pid=1393320) ERROR 08-12 16:22:17 [multiproc_executor.py:596]     assert inputs.dtype == lora_a_weights[0].dtype
(EngineCore_0 pid=1393314) (VllmWorker TP1 pid=1393322) ERROR 08-12 16:22:17 [multiproc_executor.py:596]     return func(*args, **kwargs)
(EngineCore_0 pid=1393314) (VllmWorker TP2 pid=1393324) ERROR 08-12 16:22:17 [multiproc_executor.py:596]     assert inputs.dtype == lora_a_weights[0].dtype
(EngineCore_0 pid=1393314) (VllmWorker TP3 pid=1393326) ERROR 08-12 16:22:17 [multiproc_executor.py:596]     assert inputs.dtype == lora_a_weights[0].dtype
(EngineCore_0 pid=1393314) (VllmWorker TP0 pid=1393320) ERROR 08-12 16:22:17 [multiproc_executor.py:596] AssertionError

cc @zRzRzRzRzRzRzR

Test Plan

Test Result

(Optional) Documentation Update

[github-actions]

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[gemini-code-assist]

Code Review

This pull request effectively decouples Glm4vForConditionalGeneration and Glm4vMoeForConditionalGeneration to address differences in their configurations and improve maintainability. The changes to the model registry, documentation, and packed_modules_mapping are appropriate. I have one suggestion to make the code more robust by explicitly disabling LoRA support for Glm4vMoeForConditionalGeneration to prevent potential runtime errors, aligning the code's behavior with the stated intention in the documentation and pull request description.

[gemini-code-assist]

The pull request description and documentation state that LoRA is not supported for Glm4vMoeForConditionalGeneration due to a bug. However, the class currently inherits SupportsLoRA from its parent, which will cause vLLM to attempt to apply LoRA adapters if provided, leading to a runtime error. To prevent this, I suggest adding an __init__ method that explicitly checks for and disallows LoRA configuration for this model. This will provide a clear error message to users and make the code's behavior consistent with the documentation.

Suggested change

	packed_modules_mapping = {
	"qkv_proj": [
	"q_proj",
	"k_proj",
	"v_proj",
	],
	"gate_up_proj": [
	"gate_proj",
	"up_proj",
	],
	}
	packed_modules_mapping = {
	"qkv_proj": [
	"q_proj",
	"k_proj",
	"v_proj",
	],
	"gate_up_proj": [
	"gate_proj",
	"up_proj",
	],
	}
	def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
	# LoRA is not supported for this model yet.
	if vllm_config.lora_config:
	raise NotImplementedError(
	"LoRA is not currently supported for "
	"Glm4vMoeForConditionalGeneration."
	)
	super().__init__(vllm_config=vllm_config, prefix=prefix)