TensorRT Plugin gets incorrect input data when integrated into full model, but works fine in isolation

I am converting a custom PyTorch plugin (CorrSamplerPlugin) into a TensorRT plugin. When testing the plugin independently, the results are correct and consistent with PyTorch. However, after integrating the plugin into the full model and exporting it to a TensorRT engine, the plugin receives incorrect input data during inference, leading to wrong outputs.

Current status:
The plugin is verified independently with correct input/output compared to PyTorch;

After full model integration, the volume input received by the plugin differs significantly from the PyTorch version;

The calling order of plugin layers seems wrong (e.g., CorrSampler_0, CorrSampler_3, CorrSampler_2, CorrSampler_1, CorrSampler_4);

During inference, volume input values show large differences (even signs are flipped), while coord values only match in early iterations and then diverge;

Confirmed:

No memory errors inside the plugin;

Input shapes and binding names match one-by-one;

FP16 and INT8 are disabled — FP32 is used;

supportsFormatCombination enforces kFLOAT and kLINEAR;

I listed top 10 shapes and values of my test datas on the c++ tensorrt inference:

Here I alse listed top 10 shapes and values of my test datas on the pytorch:

This is my op: