From a quick browse of all above mentioned frameworks,

BoTorch (Recommended) - Built on top of Pytorch with autograd features. All basic Bayesian optimization tools are included. This should be preferred if you are using Pytorch
Pros - Modular, Simple and Scalable.
Cons - Not extensive
scikit-optimize - Integrated with scikit learn. Has extensive API and good example.
Pros - More extensive than BoTorch.
Cons - Not sure how easy it is to run in conjunction with Pytorch
Hyperopt - Provides parallelization capabilities with MongoDB and Apache Spark
Pros - Might be useful for if the database is huge and parallelization is required.
Cons- Might not be the right tool for prototyping.
bayesian-optimization - Runs on top of scipy and scikitlearn.
Pros - good documentation, Clear examples, should be good for basic prototyping
Cons - Not sure how it would go along with pytorch.