Why does Int8 quantization occupy more GPU graphics memory than float16, TensorRT quantization ?

[nameli0722]

please descript your problem in English if possible. it will to helpful to more people
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[nameli0722]

我是通过Ttiny-tensorrt来做量化

[zerollzeng]

it's expected, the process of int8 quantization require FP32 inference to compute the scale.

[nameli0722]

I can't understand what you mean. I used int8 quantization and calibration set, and the inference result is also correct. It's large, but the GPU memory usage is larger than float16.
我不能理解您的意思，我使用了int8量化，用了校准集，推理结果也是对的，大，但是gpu显存占用比float16还大

[nameli0722]

@zerollzeng Thank you very much!

[QiangZhangCV]

Hello, could you please provide the gpu usage and inference speed， with int8 and FP16?

[nameli0722]

Hello, could you please provide the gpu usage and inference speed， with int8 and FP16?

thank you!

origin pt model: gpu usage 5099MB, inference time 1.7s;

tiny-tensorrt float16: gpu usage 3993 MB, inference time 0.4s;

tiny-tensorrt int 8 : gpu usage 4509 MB, inference time 0.4s;

all result is ok.

[zerollzeng]

How about building the engine first and then load the engine, I think it can save some memory.

Anyway I'll try to improve this.

[nameli0722]

How about building the engine first and then load the engine, I think it can save some memory.

Anyway I'll try to improve this.

./tinyexec --onnx /data/sdb/manager/RX0249_liming/coronary_model/onnx_model/unet.onnx --mode 2 --batch_size 1 --save_engine /data/sdb/manager/RX0249_liming/coronary_model/tiny_trt_model/float16_int8_calib/unet.trt --int8 --calibrate_data /data/sdb/manager/RX0249_liming/calib_data/tinyrt_data/

thank you!