Hello orong13,
In general, the CUDA engine produced by a given INetworkDefinition by a TensorRT builder may depend on various system factors (GPU, OS/kernel, CPU, system load, available memory, etc.) that affect layer implementation availability and timing during the process of building an engine.
The serialization of the engine is dependent on the layer implementations chosen for that engine and therefore is also dependent on these system factors. It is therefore not unexpected behavior if a given INetworkDefinition produces different engines which have different serializations over multiple runs. If the same engine in TensorRT produced different serializations over multiple runs, that would be unexpected behavior.