Deepmd-kit

New feature:
- derivatives for deep tensor (805)

Performance improvement:
- speedup ROCm kernels which use atomicAdd (809 815 ) (from ByteDance)
- speedup CUDA kernels (use atomicAdd inside) by reducing the global memory write (811)

Enhancement:
- add type-embedding developer doc (762)
- add model compression support for models with exclude_types feature (754)
- improve the doc and user interface of model compression (772)
- allow c++ tests to run without internet (785)
- support converting models generated in v1.3 to 2.0 compatibility (725)
- give a default value to T and convert models from v1.2 to 2.0 compatibility (789)
- improved documents for conda (798)
- throw a message if tf runtime is incompatible (797)
- capture OOM and print debug message (801)

Bug fixings
- fix bug of custom op's multiple initialization (812)
- fix bug of empty input in gelu.cu (800)
- fix model compression bug of zero environment matrix (808)
- `box.npy` is not necessary for nopbc (810)
- fill rij with zero (818)
- `NOT_LOADABLE` should be `tuple` (825)

New features:
- Add subcommand and python interface to calculate model-deviation (715)

Enhancements
- Use fmod to wrap the coord of atoms. UT for force/virial ops (741)
- UT for model devi C++ interface (731)
- add CUDA/ROCM buidling documents (739)
- add op unittests for prod_force, prod_virial, prod_force_grad and prod_virial_grad (703)

Bug fixings:
- fix bug of adding int to a None random seed (705)
- reuse the zero layer rather than building a new one (714)
- fix CI (739)

New hardware support:
- ROCM support

Enhancement:
- Document and examples for the C++ interface
- Instructions for the i-pi
- Document for the network size and `sel`

Bug fixing:
- Illegal nlist 680
- Bug in `prod_virial_grad` that causes wrong results when training with virials 685
- Uniform random seed 691

The very first alpha release of deepmd-kit version 2.0.0. It includes the following new features
- Model compression
- New descriptor: three body embedding
- Hybridization of descriptors
- Long-range modification
- Type embedding (under development)
- Training and inference the dipole (vector) and polarizability (matrix). (under development)
- Split of training and validation dataset. (under development)
- ROCm device support (under development)

Enhancements
- More efficient training: all customized OPs are implemented with GPU.
- Parallel training with multiple GPU support (under development)

Improvement of the code for developers
- Supports version of the model. Easily check model compatability
- Clear and pythonic python interface
- C++ API that can be tested independently
- OP supports multi-device.

Bug fixings:
- remove `using namespace std`. Solves compiling compatability problem.
- added `deepmd` namespace for the C++ API