Could not find any implementation for node Error while converting the instructor-large model from ONNX to TensorRT engine

Description

We tried to convert the instructor-large onnx model to support TensorRT but we got errors:

[07/24/2023-03:37:25] [V] [TRT] --------------- Timing Runner: {ForeignNode[0.auto_model.shared.weight.../3/Div]} (Myelin[0x80000023])
[07/24/2023-03:38:05] [V] [TRT] Skipping tactic 0x0000000000000000 due to exception Internal bug. Please report with reproduction steps.
[07/24/2023-03:38:05] [V] [TRT] {ForeignNode[0.auto_model.shared.weight.../3/Div]} (Myelin[0x80000023]) profiling completed in 40.0429 seconds. Fastest Tactic: 0xd15ea5edd15ea5ed Time: inf
[07/24/2023-03:38:05] [E] Error[10]: Could not find any implementation for node {ForeignNode[0.auto_model.shared.weight.../3/Div]}.
[07/24/2023-03:38:05] [E] Error[10]: [optimizer.cpp::computeCosts::3869] Error Code 10: Internal Error (Could not find any implementation for node {ForeignNode[0.auto_model.shared.weight.../3/Div]}.)
[07/24/2023-03:38:05] [E] Engine could not be created from network
[07/24/2023-03:38:05] [E] Building engine failed
[07/24/2023-03:38:05] [E] Failed to create engine from model or file.
[07/24/2023-03:38:05] [E] Engine set up failed

Environment

TensorRT Version: 8.6.1.6

NVIDIA GPU:T4

NVIDIA Driver Version: 535.54.03

CUDA Version: 12.1

CUDNN Version:8.9.2

Operating System:Unbuntu 22.04

Python Version (if applicable): Python 3.10

Baremetal or Container (if so, version):Container Version of TensoRT (23.06) docker image

Relevant Files

Model link: https://github.com/bingo-ctrl/onnx.git

Steps To Reproduce

Commands or scripts:

docker run -it --gpus all -v /path/to/instructor-large.onnx:/trt_optimize nvcr.io/nvidia/tensorrt:23.06-py3

trtexec --onnx=/trt_optimize/instructor-large.onnx --saveEngine=model.plan --explicitBatch --verbose
--minShapes='input_ids.1':1x32,'attention_mask.1':1x32,'context_masks.1':1
--optShapes='input_ids.1':1x64,'attention_mask.1':1x64,'context_masks.1':1
--maxShapes='input_ids.1':256x1024,'attention_mask.1':256x1024,'context_masks.1':256