47 lines
1.7 KiB
Markdown
47 lines
1.7 KiB
Markdown
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About nGraph Compiler stack
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===========================
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## Bridge
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Starting from the top of the stack, nGraph receives a computational graph
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from a deep learning framework such as TensorFlow or MXNet. The
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computational graph is converted to an nGraph internal representation
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by a bridge created for the corresponding framework.
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An nGraph bridge examines the whole graph to pattern match subgraphs
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which nGraph knows how to execute, and these subgraphs are encapsulated.
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Parts of the graph that are not encapsulated will default to framework
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implementation when executed.
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## nGraph Core
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nGraph uses a strongly-typed and platform-neutral
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`Intermediate Representation (IR)` to construct a "stateless"
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computational graph. Each node, or op, in the graph corresponds to
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one `step` in a computation, where each step produces zero or
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more tensor outputs from zero or more tensor inputs.
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This allows nGraph to apply its state of the art optimizations instead
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of having to follow how a particular framework implements op execution,
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memory management, data layouts, etc.
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In addition, using nGraph IR allows faster optimization delivery
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for many of the supported frameworks. For example, if nGraph optimizes
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ResNet for TensorFlow, the same optimization can be readily applied
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to MXNet* or ONNX* implementations of ResNet.
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Features
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--------
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nGraph performs a combination of device-specific and
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non-device-specific optimizations:
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- **Fusion** -- Fuse multiple ops to to decrease memory usage.
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- **Data layout abstraction** -- Make abstraction easier and faster
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with nGraph translating element order to work best for a given or
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available device.
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- **Data reuse** -- Save results and reuse for subgraphs with the
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same input.
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