f9842560cb
### What this PR does / why we need it? This PR repalce the vision tower in Qwen2.5-Omni-Thinker model, Qwen2_5_VisionTransformer, with AscendQwen2_5_VisionTransformer, which use QKV padding for padding performance. - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 Signed-off-by: Ting FU <futing10@huawei.com>
49 lines
1.9 KiB
Python
49 lines
1.9 KiB
Python
from vllm import ModelRegistry
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import vllm_ascend.envs as envs_ascend
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def register_model():
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ModelRegistry.register_model(
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"Qwen2VLForConditionalGeneration",
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"vllm_ascend.models.qwen2_vl:AscendQwen2VLForConditionalGeneration")
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ModelRegistry.register_model(
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"Qwen3VLMoeForConditionalGeneration",
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"vllm_ascend.models.qwen2_5_vl_without_padding:AscendQwen3VLMoeForConditionalGeneration"
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)
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ModelRegistry.register_model(
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"Qwen3VLForConditionalGeneration",
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"vllm_ascend.models.qwen2_5_vl_without_padding:AscendQwen3VLForConditionalGeneration"
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)
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if envs_ascend.USE_OPTIMIZED_MODEL:
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ModelRegistry.register_model(
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"Qwen2_5_VLForConditionalGeneration",
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"vllm_ascend.models.qwen2_5_vl:AscendQwen2_5_VLForConditionalGeneration"
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)
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ModelRegistry.register_model(
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"Qwen2_5OmniModel",
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"vllm_ascend.models.qwen2_5_omni_thinker:AscendQwen2_5OmniThinkerForConditionalGeneration"
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)
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else:
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ModelRegistry.register_model(
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"Qwen2_5_VLForConditionalGeneration",
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"vllm_ascend.models.qwen2_5_vl_without_padding:AscendQwen2_5_VLForConditionalGeneration_Without_Padding"
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)
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ModelRegistry.register_model(
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"DeepseekV32ForCausalLM",
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"vllm_ascend.models.deepseek_v3_2:CustomDeepseekV3ForCausalLM")
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# There is no PanguProMoEForCausalLM in vLLM, so we should register it before vLLM config initialization
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# to make sure the model can be loaded correctly. This register step can be removed once vLLM support PanguProMoEForCausalLM.
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ModelRegistry.register_model(
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"PanguProMoEForCausalLM",
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"vllm_ascend.torchair.models.torchair_pangu_moe:PanguProMoEForCausalLM"
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)
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ModelRegistry.register_model(
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"Qwen3NextForCausalLM",
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"vllm_ascend.models.qwen3_next:CustomQwen3NextForCausalLM")
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