Disable torch.compile for dynamic rope models in Transformers backend
#23738
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… backend Signed-off-by: Harry Mellor <[email protected]>
… rope Signed-off-by: Harry Mellor <[email protected]>
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This pull request adds support for the InternVLForConditionalGeneration architecture and disables torch.compile for models with dynamic rope scaling. The changes for the new model support are correct. However, I've identified a critical bug in the implementation for disabling torch.compile where a potential AttributeError can occur. Please see my comment for the suggested fix.
Signed-off-by: Harry Mellor <[email protected]>
tests/models/registry.py
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| "3.5-qwen3moe": "OpenGVLab/InternVL3_5-30B-A3B", # noqa: E501 | ||
| "3.5-gptoss": "OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview"}, # noqa: E501 | ||
| trust_remote_code=True), | ||
| "InternVLForConditionalGeneration": _HfExamplesInfo("OpenGVLab/InternVL3-1B-hf"), # noqa: E501 |
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I plan to enable native support through existing InternS1ForConditionalGeneration with video input support in #23742, because their implementations are exactly same.
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Could we add video support to the Transformers backend instead 👀
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Long term, if it's not necessary to reimplement a model in vLLM we should avoid it if possible
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Could we add video support to the Transformers backend instead 👀
I think this needs extra refactoring at Transformers side first, because we only added helper function in processor for images previously IIRC.
Long term, if it's not necessary to reimplement a model in vLLM we should avoid it if possible
Yes, but since InternS1ForConditionalGeneration is already here, we can reuse it simply with video support and no need to disable torch.compile:
"InternVLForConditionalGeneration": ("interns1", "InternS1ForConditionalGeneration")
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Yes, but since InternS1ForConditionalGeneration is already here, we can reuse it simply with video support
For now I will remove the InternVL stuff from my PR
no need to disable torch.compile
InternVLChatModel and InternS1ForConditionalGeneration do not support torch.compile in vLLM. Neither of them are decorated with @support_torch_compile
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InternVLChatModel and InternS1ForConditionalGeneration do not support torch.compile in vLLM. Neither of them are decorated with @support_torch_compile
No, we only compile text backbone for VLM exactly (no ViT compilation yet), @support_torch_compile will only take effect at language model level. And Qwen2 and Qwen3MoE (without MRoPE) they used have supported @support_torch_compile.
(There are no VLM implementations wrapped by @support_torch_compile, because we only added it to backbone)
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Oh, I wasn't aware that's how it worked.
TransformersForMultimodalLM is wrapped with @support_torch_compile and it seems to work fine. In TransformersForMultimodalLM there is no separate backbone to decorate as we use inheritance to add MM support to TransformersForCausalLM. If we removed @support_torch_compile from TransformersForMultimodalLM, would the @support_torch_compile on TransformersForCausalLM do anything?
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Yes, the torch compile will be limited to the modules that have the decorator (and their submodules)
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TransformersForMultimodalLM is a subclass of TransformersForCausalLM, not a submodule.
Do you know how @support_torch_compile interacts with inheritance?
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I think torch.compile on the base class is also applied to subclasses. Maybe @youkaichao can confirm this
hmellor
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InternVLForConditionalGeneration and disable torch.compile for dynamic rope modelstorch.compile for dynamic rope models in Transformers backend
Signed-off-by: Harry Mellor <[email protected]>
Signed-off-by: Harry Mellor <[email protected]>
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Signed-off-by: Harry Mellor <[email protected]>
…nd (vllm-project#23738) Signed-off-by: Harry Mellor <[email protected]>
…nd (vllm-project#23738) Signed-off-by: Harry Mellor <[email protected]>
…nd (vllm-project#23738) Signed-off-by: Harry Mellor <[email protected]>
…nd (vllm-project#23738) Signed-off-by: Harry Mellor <[email protected]>
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Fix the Transformers backend
torch.compilesupport by disabling it when using models with dynamic rope are loaded.