CopyPackedKernel is taking too long, and how to optimize it

Description

I have a model that uses a slice operator for feature crossing, but it turns out that the slice operator calls the CopyPackedKernel API, and it consumes a lot of time. I also re-implemented the slice operator myself, but the same result was achieved,I don't know when CopyPackedKernel is running, how to optimize it.

nsys profile -o test --stats=true  python infer.py -e test.plan

output:

Time(%)      Total Time   Instances         Average         Minimum         Maximum  Name
-------  --------------  ----------  --------------  --------------  --------------  --------------------------------------------------------------------------------------------------------------------
   99.9         9863089        2835          3479.0            3423            3872  void genericReformat::copyPackedKernel<float, float, true, true, genericReformat::IdentityCoordMapper<4>, 4>(unsigned int, unsigned int, void const*, genericReformat::ArrayN<4>, genericReformat::ArrayNWithReducedDivisors<4>, genericReformat::ArrayN<4>, int, int, int, float const*, void*, genericReformat::ArrayN<4>, genericReformat:
    0.1            7264           3          2421.3            2304            2656  slice(float const*, float*, int, int, int, int)

Environment

TensorRT Version: 7.2.2.1
NVIDIA GPU: T4
NVIDIA Driver Version: 450.51.06
CUDA Version: 11.1
CUDNN Version:
Operating System:
Python Version (if applicable): 3.8
Tensorflow Version (if applicable):
PyTorch Version (if applicable):
Baremetal or Container (if so, version): Container 20.12

I need help, thank you very much.

Start with the reported nsys command, infer.py, and test.plan, then trace where the slice operator invokes CopyPackedKernel in the TensorRT 7.2.2.1 environment. Compare the kernel's profiling cost with the slice call and determine what optimization is appropriate; the issue does not define a specific success criterion.