Memcopy (Host->Device) very slow on TX2 with Jetpack 4.5

Nobody has claimed this yet.

**Describe the bug**
A memcopy from CPU to GPU that apparently has to be done takes 1.44 seconds, where the total inference time for the computation is 1.60 seconds. This issue occurs on a Jetson TX2 with Jetpack 4.5 (Cuda 10.2, cuDNN 8.0), but does not take place on a Jetson TX2 with Jetpack 4.2.1 (Cuda 10.0, cuDNN 7.5.0): there the same memcopy only takes 50ms. This duration grows proportional to the batch size of the input.

This seems to be a bug in ONNXRuntime with newer CUDA/cuDNN versions.

I'm not sure what is being copied to GPU btw, or how to figure this out. I converted the model from Tensorflow 2.3 using tf2onnx.convert, and first copied the two inputs to the model to GPU using io_binding(), but this makes no difference in inference time, so I guess it's not the input that's being copied from CPU to GPU. If someone could explain how I do figure this out (and prevent it from happening if possible), that would also be much appreciated.

**Urgency**
None

**System information**
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04.5 LTS on Jetpack 4.5
- ONNX Runtime installed from (source or binary): from source
- ONNX Runtime version: 1.7.0 (without TensorRT bindings)
- Python version: 3.6.9
- GCC/Compiler version (if compiling from source): 7.5.0
- CUDA/cuDNN version: CUDA 10.2.89 cuDNN 8.0.0.180
- GPU model and memory: Jetson TX2 8GB

**To Reproduce**
Run attached onnx.py on Jetpack 4.5 to reproduce issue.

onnxruntime_perf_test -e cuda -I ~/onnx/model.onnx -r 10 -t 10 also works, but because that batch size is set to 1, the issue is less clear.

**Expected behavior**
You would expect the memcopy to be not slower than on older Jetpack (CUDA/cuDNN) versions

**Additional attachments**
Attached a trace from both Jetpack 4.2.1 and Jetpack 4.5, the verbose log from Jetpack 4.5, the tf2onnx conversion log and the files to reproduce those traces.
[github issue.zip](https://github.com/microsoft/onnxruntime/files/6029316/github.issue.zip)

**Additional context**
The Tensorflow model is from https://github.com/alleveenstra/attentionocr where I removed the positional embedding generation using tf.eye because tf2onnx does not support this.