Distributed Training in nGraph¶
Why distributed training?¶
A tremendous amount of data is required to train DNNs in diverse areas – from computer vision to natural language processing. Meanwhile, computation used in AI training has been increasing exponentially. And even though significant improvements have been made in algorithms and hardware, using one machine to train a very large NN is usually not optimal. The use of multiple nodes, then, becomes important for making deep learning training feasible with large datasets.
Data parallelism is the most popular parallel architecture to accelerate deep learning with large datasets. The first algorithm we support is based on the synchronous SGD method, and partitions the dataset among workers where each worker executes the same neural network model. For every iteration, nGraph backend computes the gradients in back-propagation, aggregates the gradients across all workers, and then update the weights.
How? (Generic frameworks)¶
To synchronize gradients across all workers, the essential operation for data parallel training, due to its simplicity and scalability over parameter servers, is “allreduce”. The AllReduce op is one of the nGraph Library’s core ops. To enable gradient synchronization for a network, we simply inject the AllReduce op into the computation graph, connecting the graph for the autodiff computation and optimizer update (which then becomes part of the nGraph graph). The nGraph Backend will handle the rest.
Data scientists with locally-scalable rack or cloud-based resources will likely find it worthwhile to experiment with different modes or variations of distributed training. Deployments using nGraph Library with supported backends can be configured to train with data parallelism and will soon work with model parallelism. Distributing workloads is increasingly important, as more data and bigger models mean the ability to Train using multiple nGraph CPU backends with data parallel work with larger and larger datasets, or to work with models having many layers that aren’t designed to fit to a single device.
Distributed training with data parallelism splits the data and each worker node has the same model; during each iteration, the gradients are aggregated across all workers with an op that performs “allreduce”, and applied to update the weights.
Using multiple machines helps to scale and speed up deep learning. With large mini-batch training, one could train ResNet-50 with Imagenet-1k data to the Top 5 classifier in minutes using thousands of CPU nodes. See also: arxiv.org/pdf/1709.05011.pdf.
We implemented a KVStore in MXNet* (KVStore is unique to MXNet) to modify the SGD update op so the nGraph graph will contain the allreduce op and generate corresponding collective communication kernels for different backends. We are using OpenMPI for CPU backends and plan to integrate Intel MLSL in future.
The figure below shows a bar chart with preliminary results from a Resnet-50 I1K training in MXNet 1, 2, 4, (and 8 if available) nodes, x-axis is the number of nodes while y-axis is the throughput (images/sec).
We plan to support the same in nGraph-TensorFlow. It is still work in progress. Meanwhile, users could still use Horovod and the current nGraph TensorFlow, where allreduce op is placed on CPU instead of on nGraph device. Figure: a bar chart shows preliminary results Resnet-50 I1K training in TF 1, 2, 4, (and 8 if available) nodes, x-axis is the number of nodes while y-axis is the throughput (images/sec).
Model parallelism with more communication ops support is in the works. For more general parallelism, such as model parallel, we plan to add more communication collective ops such as allgather, scatter, gather, etc. in the future.