nGraph Compiler stack architecture
The diagram below represents our current Beta release stack. In the diagram, nGraph components are colored in gray. Please note that the stack diagram is simplified to show how nGraph executes deep learning workloads with two hardware backends; however, many other deep learning frameworks and backends currently are functioning.
Starting from the top of the stack, nGraph receives a computational graph from a deep learning framework such as TensorFlow* or MXNet*. The computational graph is converted to an nGraph internal representation by a bridge created for the corresponding framework.
An nGraph bridge examines the whole graph to pattern match subgraphs which nGraph knows how to execute, and these subgraphs are encapsulated. Parts of the graph that are not encapsulated will default to framework implementation when executed.
nGraph uses a strongly-typed and platform-neutral
Intermediate Representation (IR) to construct a "stateless" computational graph. Each node, or op, in the graph corresponds to one
step in a computation, where each step produces zero or more tensor outputs from zero or more tensor inputs.
This allows nGraph to apply its state of the art optimizations instead of having to follow how a particular framework implements op execution, memory management, data layouts, etc.
In addition, using nGraph IR allows faster optimization delivery for many of the supported frameworks. For example, if nGraph optimizes ResNet* for TensorFlow*, the same optimization can be readily applied to MXNet* or ONNX* implementations of ResNet*.
Hybrid transformer takes the nGraph IR, and partitions it into subgraphs, which can then be assigned to the best-performing backend. There are two hardware backends shown in the stack diagram to demonstrate this graph partitioning. The Hybrid transformer assigns complex operations (subgraphs) to Intel® Nervana™ Neural Network Processor (NNP) to expedite the computation, and the remaining operations default to CPU. In the future, we will further expand the capabilities of Hybrid transformer by enabling more features, such as localized cost modeling and memory sharing.
Once the subgraphs are assigned, the corresponding backend will execute the IR.
Focusing our attention on the CPU backend, when the IR is passed to the Intel® Architecture (IA) transformer, it can be executed in two modes: Direct EXecution (DEX) and code generation (
codegen mode, nGraph generates and compiles code which can either call into highly optimized kernels like MKL-DNN or JITers like Halide. Although our team wrote kernels for nGraph for some operations, nGraph leverages existing kernel libraries such as MKL-DNN, Eigen, and MLSL.
MLSL library is called when nGraph executes distributed training. At the time of the nGraph Beta release, nGraph achieved state of the art results for ResNet50 with 16 nodes and 32 nodes for TensorFlow* and MXNet*. We are excited to continue our work in enabling distributed training, and we plan to expand to 256 nodes in Q4 ‘18. Additionally, we are testing model parallelism in addition to data parallelism.
The other mode of execution is Direct EXecution (DEX). In DEX mode, nGraph can execute the operations by directly calling associated kernels as it walks though the IR instead of compiling via
codegen. This mode reduces the compilation time, and it will be useful for training, deploying, and retraining a deep learning workload in production. In our tests, DEX mode reduced ResNet50 compilation time by 30X.
nGraph further tries to speed up the computation by leveraging multi-threading and graph scheduling libraries such as OpenMP and TBB Flow Graph.
nGraph Compiler full stack
In addition to IA and NNP transformers, nGraph Compiler stack has transformers for multiple GPU types and an upcoming Intel deep learning accelerator. To support the growing number of transformers, we plan to expand the capabilities of the hybrid transformer with a cost model and memory sharing. With these new features, even if nGraph has multiple backends targeting the same hardware, it will partition the graph into multiple subgraphs and determine the best way to execute each subgraph.