This document outlines the high level design of ONNX Runtime - a high performance, cross platform engine.
- Maximally and automatically leverage the custom accelerators and runtimes available on disparate platforms.
- Provide the right abstraction and runtime support for custom accelerators and runtimes. We call this abstraction an execution provider. It defines and exposes a set of its capabilities to ONNXRuntime: a set of single or fused nodes it can execute, its memory allocator, and more. Custom accelerators and runtimes are instances of execution providers.
- We don't expect that an execution provider can always run an ONNX model fully on its device. This means that ONNXRuntime must be able to execute a single model in a heterogeneous environment involving multiple execution providers.
- Provide support for high-level optimizations that can be expressed as model-to-model transformations via a graph-transformation API. Such transformations fall into two categories: global transformations, those that require analysis and transformation of the entire graph, and local transformations, which can be captured as simple (algebraic) rewriting rules.
The flow is quite simple. Starting from an ONNX model, ONNXRuntime first converts the model graph into its in-memory graph representation. It then applies a number of graph transformations that a) perform a set of provider independent optimizations such cast transformations between float16 and float32, and b) partition the graph into a set of subgraphs based on the available execution providers. Each subgraph is assigned to an execution provider. We ensure that a subgraph can be executed by an execution provider by querying the capability of the execution provider using the GetCapability() API.
ONNXRuntime partitions a model graph into subgraphs based on the available execution providers, one for each distinct provider. ONNXRuntime provides a default execution provider that is used as the fallback execution for the operators that cannot be pushed onto the more specialized but more efficient execution providers. Intuitively we want to push computation to more specialized execution providers whenever possible.
We use a simple graph partitioning technique. The available execution providers will be considered in a specific order, and each will be assigned the maximal subgraphs (possibly more than one) that it is able to handle. The ONNXRuntime-provided default execution provider will be the last one considered, and it ensures completeness. More sophisticated optimizations can be considered in the future (or can even be implemented as a composite execution provider).
Conceptually, each partition is reduced to a single fused operator. It is created by invoking the execution provider's Compile() method and wraps it as a custom operator. Currently we support only synchronous mode of execution. An execution provider exposes its memory allocator, which is used to allocate the input tensors for the execution provider. The rewriting and partitioning transform the initial model graph into a new graph composed of operators assigned to either the default execution provider or other registered execution providers. The ONNXRuntime execution engine is responsible for running this graph.
- Multiple threads can invoke the Run() method on the same inference session object. See API doc for more details.
- To facilitate this, the Compute() function of all kernels is const implying the kernels are stateless.
- Implementations of the operators by execution providers are called kernels. Each execution provider supports a subset of the (ONNX) operators/kernels.
- The ONNX Runtime guarantees that all operators are supported by the default execution provider.
- Tensor representation: ONNXRuntime will utilize a standard representation for the tensor runtime values. The execution providers can internally use a different representation if they choose to, but it is their responsibility to convert the values from/to the standard representation at the boundaries of their subgraph.