Set layer precision still doesn't take effect in TensorRT 8.6.1.

Description

As I had reflected in this Skipping tactic 0x0000000000000000 due to Myelin error" degrade performance.，set layer precision may failed in TensorRT 8.4.3 due to the ConstShuffleFusion.

In these days, I try TensorRT 8.6.1, but it seems that setting layer precision may still fail due to the ConstShuffleFusion.
For example, as show in the graph, the Max op take a const input named "phase0_tf/predict_node/y:0", and the value seem to be fp16 subnormal, so I use set_precision api to set the layer ("phase0_tf/predict_node/y:0") to fp32 explicitly.

The verbose logs are as follows:

When the fp16 subnormal is not set to fp32, the log are as follows, the layer "phase0_tf/predict_node/y:0 + (Unnamed Layer* 522) [Shuffle]" is fp16 precision:

However, when the fp16 subnormal is set to fp32, the layer "phase0_tf/predict_node/y:0 + (Unnamed Layer* 522) [Shuffle]" is still fp16.

By the way, the ConstShuffleFusion produce two kind of layer, such as

I am confused the differences. Is that the reason set_precision fails of the layer "phase0_tf/predict_node/y:0"?

Looking forward to your reply. Thanks a lot!

Environment

TensorRT Version: 8.6.1

NVIDIA GPU: T4

NVIDIA Driver Version: 510

CUDA Version:12.0

CUDNN Version:

Operating System: Ubuntu20.04

Python Version (if applicable):

Tensorflow Version (if applicable): 1.4

PyTorch Version (if applicable):

Baremetal or Container (if so, version):

Relevant Files

Model link:

Steps To Reproduce

Commands or scripts:

Have you tried the latest release?:

Can this model run on other frameworks? For example run ONNX model with ONNXRuntime (polygraphy run <model.onnx> --onnxrt):