GitLab

* add zero init context

* add more flags for zero init context
fix bug of repeated converting param to ShardedParamV2

* polish code

Fixed bug for learning rate scheduler

* Added CPU Adam

* finished the cpu adam

* updated the license

* delete useless parameters, removed resnet

* modified the method off cpu adam unittest

* deleted some useless codes

* removed useless codes
Co-authored-by: ver217 <lhx0217@gmail.com>
Co-authored-by: Frank Lee <somerlee.9@gmail.com>
Co-authored-by: jiaruifang <fangjiarui123@gmail.com>

* init shard param from shape tuple

* add more unitest for shard param

* add set_payload method for ShardedParam

* [zero] add shareded tensor class

* polish code

* add shard stratgy

* move shard and gather logic to shard strategy from shard tensor.

* polish code

* init shard param from shape tuple

* add more unitest for shard param

* add set_payload method for ShardedParam

* [zero] add shareded tensor class

* polish code

* init shard param from shape tuple

* add more unitest for shard param

* add more unittests to shareded param

* added unit test for sharded optimizer

* refactor for elegance

* 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>

Decreased moe tests;

Added FFNExperts and ViTMoE model

fixed padding index issue for vocab parallel embedding layers; updated 3D linear to be compatible with examples in the tutorial

added branch context;
added vocab parallel layers;
moved split_batch from load_batch to tensor parallel embedding layers;
updated gpt model;
updated unit test cases;
fixed few collective communicator bugs

* add pytorch hooks
fix #175

* remove licenses in src code

* add gpu memory tracer

* replacing print with logger in ophooks.

* add pipeline shared module wrapper and update load batch

* added model parallel process group for amp and clip grad (#86)

* added model parallel process group for amp and clip grad

* update amp and clip with model parallel process group

* remove pipeline_prev/next group (#88)

* micro batch offload

* optimize pipeline gpu memory usage

* pipeline can receive tensor shape (#93)

* optimize pipeline gpu memory usage

* fix grad accumulation step counter

* rename classes and functions
Co-authored-by: Frank Lee <somerlee.9@gmail.com>

* optimized 1d layer apis; reorganized nn.layer modules; fixed tests

* fixed 2.5d runtime issue

* reworked split batch, now called in trainer.schedule.load_batch
Co-authored-by: BoxiangW <45734921+BoxiangW@users.noreply.github.com>