How does DeepSpeed implement multi-machine model parallelism? about deepspeed

[ShadenSmith]

There are several trade-offs to consider, so for a full answer let me first recommend this excellent survey on parallelism in deep learning: https://arxiv.org/abs/1802.09941

From a library perspective, it's difficult to provide general model parallelism because it is specific to the user model. Model parallelism certainly has uses such as being more memory scalable than data parallelism such as batch splitting.

ZeRO is a set of complementary optimizations that improve scalability without users having to implement model parallelism. The key idea is that users still provide a model without designing for parallelism and DeepSpeed can facilitate data parallelism and ZeRO to scale to large model and large degrees of parallelism. DeepSpeed has scaled to models with 6 billion parameters using only data parallelism and ZeRO on V100 GPUs. Adding model parallelism via Megatron-LM got DeepSpeed to 100B parameters.

I'd like to note that we are of course not anti-model parallelism. DeepSpeed is meant to work with model parallelism if the user has a model-parallel program. The Megatron tutorial touches on this in more depth.

[ShadenSmith]

Hi there! DeepSpeed does not implement model parallelism, but it does support models that use it. It's up to the user to implement model parallelism (e.g., a user might use some dist.XXX() communication routines to coordinate forward/backward passes). DeepSpeed just needs an mpu object at initialization to query things process ranks and groups (and world_size) during training.

The difficulty of model parallelism was one major motivation for ZeRO. If you enable ZeRO, you can avoid the need for model parallelism in many cases. For an example, the Megatron-LM tutorial combines Megatron's model parallelism with ZeRO.

[hhwode]

@ShadenSmith Got it. Is it because the model parallelism is not efficient or scalable that you study memory optimization, the ZeRO?
I'm new to this field, there is too little material about model parallelism. Can I ask why it is hard to do distributed model parallelism, in which way, inter-machine communication, network splitting, or algorithm level.
And thank you.

[stas00]

The Megatron tutorial touches on this in more depth.

The link to The Megatron tutorial is 404, here is a steady link:
https://github.com/microsoft/DeepSpeed/blob/46d2e2872b64ebccb8bf4eb5c8a3a55f9adaaa6c/docs/_tutorials/megatron.md