Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into feature/combine_open_files_and_double_buffer
yuyang18
7 years ago
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## Motivation
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There is a ```gap``` between the ```Program``` defined by
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user and the ```Executable``` that can be scheduled
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efficiently on heterogeneous hardware, either locally
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or distributedly.
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Usually, the ```gap``` is bridged by
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* A serious transformations with defined order.
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* These transformations usually involve
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```insert, delete, clustering, split, dependency analysis```.
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* Has a simple way to verify and debug each transformation.
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* Flexible to add, remove or customize transformations to fit
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the requirements of various algorithms (models) and hardware secenarios.
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Some other events also push us to a better unified pattern.
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* The deep learning framework is built around the concepts of graphs.
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To leverage tools such as compilation (e.g. TVM and nGraph) or
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cross-framework conversion (e.g. ONNX), we also need a intermediate
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representation that can be connected to the rest of the ecosystem.
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We need a unified pattern to naturally support the requirements
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described above. The pattern should fit both training, inference
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and other offline serielized model transformations.
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Learned from LLVM and other deep learning framework, we draft the
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design below.
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## Design
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### Major Concepts
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#### Node
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```Node``` represents an operation that performs some computation or
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a variable that is input or output of operation.
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```Node```s are connected to other ```Node```s via inputs and outputs.
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Other properties (maybe device placement information) can be added
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to ```Node``` in the future if it's a
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common requirement of many other ```Pass```es. Otherwise, it should live
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in a ```Node``` wrapper class that is private to some ```Pass``` or be
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a local member of a ```Pass```.
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#### Graph
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```Graph``` contains a list of ```Node```s, which are connected to
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each other via inputs and outputs.
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TODO: Better definitions for the graph.
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```Graph``` can also contain ```Attribute```s. ```Attribute```s
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can be ``any`` thing. For example, it can be a list of "wraper"
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nodes. The ```wrapper``` nodes compose ```Node```s and provide
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helper method for execution or transformation. ```Attribute```
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can also contain other things that describe some properties of
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the ```Graph``` or ```Graph``` nodes. ```Attribute``` can be passed
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across ```Pass```. However, it should be used with care.
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#### Pass
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```Pass``` represents a transformation of ```Graph```. Its input
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is a ```Graph``` and its output is also a ```Graph```. For example,
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a ```Pass``` can simply print out the ```Graph```. A ```Pass```
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can also fuse some ```Graph```'s ```Node```s.
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#### Optimize
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```Optimize``` contains a series of ```Pass``` with defined order.
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```Optimize``` transforms a ```Graph``` that only contains raw
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modeling logic to a ```Graph``` that can be run efficiently while
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maintaining the original modeling logic.
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### Optimize Process
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* Program is first converted to Graph.
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* Graph goes through a series of Pass
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* Graph is transformed from raw model logic to a
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form that is efficient to execute.
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Program->ProgramToGraph->Graph->Pass1->Graph->Pass2->Graph->Pass3->Graph->Executor
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