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* add tensor placement policies

* polish comments

* polish comments

* update moe unit tests

* adapt model weight initialization for methods in Pytorch nn.init

only process module's own parameters in Zero context

add zero hooks for all modules that contrain parameters

gather parameters only belonging to module itself

* Revert "polish code"

This reverts commit 8cf7ff08.

* Revert "rename variables"

This reverts commit e99af94a.

* Revert "remove surplus imports"

This reverts commit 46add4a5.

* Revert "update sharded optim and fix zero init ctx"

This reverts commit 57567ee7.

* place params on cpu after zero init context

* polish code

* add zero1 (#209)

* add zero1

* add test zero1

* update zero stage 1 develop (#212)

* Implement naive zero3 (#240)

* naive zero3 works well

* add zero3 param manager

* add TODOs in comments

* add gather full param ctx

* fix sub module streams

* add offload

* fix bugs of hook and add unit tests

* fix bugs of hook and add unit tests (#252)

* add gather full param ctx

* fix sub module streams

* add offload

* fix bugs of hook and add unit tests

* polish code and add state dict hook

* fix bug

* update unit test

* refactor reconstructed zero code

* clip_grad support zero3 and add unit test

* add unit test for Zero3ParameterManager

* [WIP] initialize the shard param class

* [WIP] Yet another sharded model implementation (#274)

* [WIP] initialize the shard param class

* [WIP] Yes another implementation of shardModel. Using a better hook method.

* torch.concat -> torch.cat

* fix test_zero_level_1.py::test_zero_level_1 unitest

* remove deepspeed implementation and refactor for the reconstructed zero module

* polish zero dp unittests
Co-authored-by: ver217 <lhx0217@gmail.com>
Co-authored-by: Frank Lee <somerlee.9@gmail.com>