RaNNC is an automatic parallelization middleware used to train very large-scale neural networks. Since modern networks for deep learning often have billions of parameters, they do not fit the memory of GPUs. RaNNC automatically partitions such a huge network with model parallelism and computes it using multiple GPUs.
- Documentation
- Examples (Training codes for enlarged BERT and ResNet)
Compared to existing frameworks, including Megatron-LM and Mesh-TensorFlow, which require users to implement partitioning of the given network, RaNNC automatically partitions a network for PyTorch without any modification to its description. In addition, RaNNC basically has no limitation on its network architecture while the existing frameworks are only applicable to transformer-based networks.
The code below shows a simple usage of RaNNC. Notice that you only need to insert one line to a typical training code for PyTorch.
model = Net() # Define a network
model.to(torch.device("cuda")) # Move parameters to a cuda device
optimizer = optim.Adam(model.parameters(), lr=0.01) # Define an optimizer
model = pyrannc.RaNNCModule(model, optimizer) ##### Wrap by RaNNCModule #####
loss = model(input) # Run a forward pass
loss.backward() # Run a backward pass
optimizer.step() # Update parametersModels used with RaNNC (Net in the above example) do not need special operators for distributed computation or
annotations for partitioning
(See our examples:
model for the tutorial, enlarged versions
of BERT and
ResNet). RaNNC automatically partitions
a model to subcomponents so that each subcomponent fits to the GPU memory and a high training throughput is achieved.
In contrast, for example, Megatron-LM needs special operators like ColumnParallelLinear and RowParallelLinear
(See
an example
in Transformer). Implementing a model using such operators is very hard even for experts because the user needs to
consider computational loads of the model, memory usages, and communication overheads. In addition, some existing
frameworks including Megatron-LM can be applicable only to Transformer family networks.
We confirmed that RaNNC can train a BERT model with approximately 100 billion parameters without a manual modification/optimization of the definition of the network for model partitioning.
The initial ideas of RaNNC were published at IPDPS 2021. See our paper for RaNNC's partitioning algorithm and performance comparisons with other frameworks (preprint).
Automatic Graph Partitioning for Very Large-scale Deep Learning, Masahiro Tanaka, Kenjiro Taura, Toshihiro Hanawa and Kentaro Torisawa, In the Proceedings of 35th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2021), pp. 1004-1013, May, 2021.