TF Java 0.3.1 shows a performance degradation on GPU compared to v 0.2.0 when loading Hugging Face models

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System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Mint 20.1 (Ubuntu 20.04 LTS)
• Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
• TensorFlow installed from (source or binary): binary
• TensorFlow version (use command below): TF Java 0.3.1 (TF 2.4.1)
• Python version:
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version: 11.0 / 8.0.4
• GPU model and memory:
  GeForce GTX 1060 computeCapability: 6.1
  coreClock: 1.6705GHz coreCount: 10 deviceMemorySize: 5,93GiB deviceMemoryBandwidth: 178,99GiB/s

You can collect some of this information using our environment capture script
You can also obtain the TensorFlow version with
python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"

Describe the current behavior
The usage of version TF Java bindings 0.3.1 degradates performances of a 3x factor on GPU compared to version 0.2.0 .

Describe the expected behavior
Equal, hopefully better performances while migrating to newer versions.

Code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate the problem.
Performance tests are currently on going to validate the issue. We'll update with more info asap.
https://github.com/JohnSnowLabs/spark-nlp/tree/master/src/test/scala/com/johnsnowlabs

Other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

从 src/test/scala/com/johnsnowlabs 下的 Spark NLP 测试开始，建立一个最小的 GPU 基准测试，在加载 Hugging Face 模型时比较 TF Java 0.3.1 和 0.2.0。确认报告的三倍回归，然后记录可复现的案例，并验证较新版本是否达到预期性能。