GitLab

- Accumulate encoder hidden state gradient to handle skip connection
- Correctly compute the number of layers in encoder / decoder for T5 model
- Ensure e weights are initialized the same way in embeddings
- Synchronize embedding gradients across encoder and decoder for T5 model
- Support for checkpoint loading and saving

- Split a model's computation into multiple virtual stages as needed,
and schedule communication correctly between these virtual stages
- Move schedule code into `schedules.py` and communication code into
`p2p_communication.py`
- Use hyphens instead of spaces in all time logging for consistency
- Factor out code in megatron/training.py into helper functions
- Refactor evaluate() function: make it use forward_backward_schedule
functions

Add the option for fp32 residual connection (fp32 residual connection machinery still needs to be added)

Also includes following changes for inter-layer model-parallel implementation:
- Refactoring of model implementations
- Training loop changes to support inter-layer communication using `ring_exchange`
- New groups for inter-layer communication
- Checkpoint changes
- Command line arguments

Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com>

Co-authored-by: shoeybi <shoeybim@gmail.com>
Co-authored-by: raulpuric <raulpuric@berkeley.edu>
Co-authored-by: jaredcasper <jaredcasper@gmail.com>
Co-authored-by: mpatwary <mostofa.patwary@gmail.com>
Co-authored-by: plegresl <plegresl@gmail.com>