Quantization of D-FINE in tensorrt 10.8 fails

Description

A clear and concise description of the bug or issue.

Environment

TensorRT Version: 10.8
GPU Type: L4
Nvidia Driver Version:
CUDA Version: 12.2
CUDNN Version:
Operating System + Version: Ubuntu 22.04
Python Version (if applicable):
TensorFlow Version (if applicable):
PyTorch Version (if applicable):
Baremetal or Container (if container which image + tag):

Steps To Reproduce

convert a D-FINE ONNX model to a quantized int8 engine

/usr/src/tensorrt/bin/trtexec --onnx=d_fine.onnx --saveEngine=dfine_int8_b1.engine --calib=calib.cache --int8

fails with:

 [TRT] [E] IBuilder::buildSerializedNetwork: Error Code 10: Internal Error (Could not find any implementation for node /model/encoder/fpn_blocks.1/cv4/conv_bn_fused/Conv + PWN(PWN(/model/encoder/fpn_blocks.1/cv4/act/Sigmoid), PWN(/model/encoder/fpn_blocks.1/cv4/act/Mul)).)

I can provide the calibration file & the onnx for this, if needed. I have successfully run this conversion with tensorrt 8.6, so I think there is nothing particular about the model.

anything on this? I tried downgrading to tensorrt 10.3, but this also doesnt work , causing the exact same error. So with DS 7.1 we cant use D-FINE it seems?

Hi @fdarvas ,
Considering the known issues and their fixes, can you pls confirm if you have already tried

  • Using FP32 scales for Q/DQ operations.
  • Enabling StronglyTyped mode.
  • Adjusting builder optimization levels.
    Please let me know if this helps, in case if the issue persist, pls share your model and repro scripts with us.

Thanks

can you point me to a description of the known issues?

when enabling “StronglyTyped” mode, then I no longer can set trt.BuilderFlag.INT8, which defeats the purpose? I want to create specifically an int8 engine. Essentially I just want recreate the same engine I could build with tensorrt 8.6.

How do I enable FP32 scales for Q/DQ operations?

I did adjust the builder optimization levels, basically I tried levels 4 and 5, but this did not help.
Where can I share the model & script? Or can I just attach to a post here?