Optimize RoPEAttention implementation for onnx export#10
Merged
Aimol-l merged 1 commit intoAimol-l:mainfrom May 1, 2025
Merged
Optimize RoPEAttention implementation for onnx export#10Aimol-l merged 1 commit intoAimol-l:mainfrom
RoPEAttention implementation for onnx export#10Aimol-l merged 1 commit intoAimol-l:mainfrom
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矩阵乘法实现的
RoPEAttention在TensorRT框架下耗时较高，相比于pytorch下的复数实现，在nvidia-3080-laptop下，耗时30ms(pytorch) vs 100ms(tensorrt). 主要是由于大矩阵乘法耗时较高，所以对这部分做了优化。这个PR的内容是将复数实现的
RoPEAttention转换为实数运算，既避免了复数运算ONNX/TensorRT不支持的情况，又保留了复数运算的高效率。推理速度测试：
在sam2原工程上验证，输出结果与原版一致。