【人工智能机器学习/深度学习】PaddlePaddle百度研发的深度学习平台

# Paddle HIgh reusability operator library (PHI) Design Paddle HIgh reusability operator library (PHI), or we also call it 'functional operator library', supports to implement new operator kernels based on existing operator kernel functions and 'Kernel Primitives API (KPS)', and supports plug-in access to new hardware or new acceleration library. In order to solve the problems of unclear operator interface in the original operator library of the Paddle Fluid Framework, high cost of operator reuse, and poor scheduling performance, we refactored the operator library of the Paddle Framework, designed flexible and efficient functional paradigm. The operator library PHI can implement new operators by combining calls to functional operator interfaces, which can greatly reduce the development cost of native operator and custom operator. ## 1. Background > Introduce the problems to be solved in designing and building the PHI operator library The PHI operator library project was initially launched to support the refactoring of the paddle dynamic graph architecture to reduce scheduling overhead and improve the reuse capability of OpKernel development. However, the subsequent decision to take this opportunity to establish an operator library that can be used in both training and inference scenarios (including server-side and mobile-side scenarios), reduce the cost of infrastructure development and operator maintenance in the paddle ecosystem in the long run, so we expanded the target scope of the project. Specifically, the PHI operator library project carries the expectation to solve the following problems of Paddle. ### 1.1 Poor reusability between Op&OpKernel Before version 2.3, the reusability between Operators (Op) in Paddle was relatively poor. Only in a few backward Ops, some simple Ops were reused by calling `SetType` in the `GradOpMaker` implementation. In most cases where the existing Op implementation can be reused, the code is rewritten by copy. The root cause of poor reusability is the inflexibility of the original Op architecture design: 1. When an Op reuses the `Opkernel::Compute` method of another Op, an `ExecutionContext` needs to be constructed first, and the reuse method is relatively cumbersome > It will be much more convenient if you can directly call the Kernel in the form of a function 2. Due to the overhead introduced by additional data structure construction and independent Op scheduling, from the perspective of computing performance, it is better to copy the calculation code directly when reusing Op, which leads us to gradually abandon the earlier principle of backward Op reusing forward Op, and began to implement Kernel separately for each backward Op, so that Paddle maintains a large number of backward OpKernel implementation codes internally. > Only when the overhead of reusing Ops is small enough, reusing existing Ops to implement new Ops can be widely promoted ### 1.2 Conciseness and fine-grained execution scheduling #### 1.2.1 Dynamic graph After the release of Paddle 2.0, it has received many feedbacks from internal and external users that the performance of the dynamic graph is several times lower than that of competing products in the execution scenario of small model on CPU. The main reason for this problem is: the execution path of the C++ side of the Padddle dynamic graph is relatively long and the scheduling overhead is relatively heavy, which is related to the early design of the dynamic graph which is compatible with the static graph and inherits many object construction processes of the static graph Op. Therefore, the dynamic graph needs to be upgraded to a function-based scheduling architecture, and this problem can be solved by abandoning the original complex Op architecture, which depends on the OpKernel being changed to a functional writing method. #### 1.2.2 Static image + IR Our current static graph mode are not "static" enough. At present, static graph mode still have a lot of logic for dynamic selection at runtime, for example, selecting OpKernel at runtime, judging whether to copy data across devices at runtime, etc.. However, these can actually be determined during the compilation of the static graph mode network, and the execution process is determined as a series of OpKernel executions, and no dynamic judgment selection is made, thereby further improving the execution efficiency. And these rely on the fine-grained OpKernel itself, decoupling the existing complex large OpKernel into small Kernels for specific scenarios and specific devices. ### 1.3 Ease of use improvement requirements for custom operators The new custom C++ external operator paradigm released in early 2021 has a relatively intuitive usage at the level of interface and function writing, but because we lack the C++ APIs for basic operations, in fact, when implementing specific custom Op operation logic, such as basic addition, subtraction, multiplication and division and matrix operations, still need to be reimplemented again, and Paddle's existing and optimized basic operations cannot be reused, development costs are still relatively high. In order to reuse the basic operations inside Paddle, the Op paradigm must be upgraded to functional paradigm, and build the corresponding C++ API system. ### 1.4 Build an integrated training and inference operator library to reduce the maintenance cost of inference operators For a long time, because the Paddle and Paddle-Lite operators are maintained separately, the new paddle operator, if Paddle-Lite needs it, must be manually reimplemented in Paddle-Lite, and when the Paddle operator is upgraded, Paddle-Lite does not perceive it in time, which will directly lead to bugs in the inference model when lite is executed, which introduces high maintenance costs. Only a unified operator library can solve this problem for a long time. Therefore, this functional operator library will be jointly constructed by training and inference team, and will serve as an independent compilation component and underlying infrastructure (not yet independently split), which can serve training, server-inference, and -inference execution systems at the same time. ### 1.5 The adaptation of the new inference Runtime design 'infrt' Inference team designed a new runtime `infrt`. It is expected to unify the execution system of Paddle-Inference and Paddle-Lite. It is necessary to directly call the operators in the PHI operator library jointly built this time. Therefore, the adaptation to `infrt` needs to be considered in the design. (Currently the `infrt` project is temporarily on hold). ### 1.6 Op and Kernel parameter normalization The Python 2.0 API project in 2020 standardized the argument list of the Paddle Python-side API, making it concise, easy to use, and standard. However, due to cost considerations, the argument list at the Op level was not standardized, so there will be many early developed operators that differ greatly in arguments from the Python API. For example, `conv` op, the Python API has only 8 arguments, but the corresponding C++ `Conv` Op has 29 arguments. API and Op are essentially the same layer of concepts, both are descriptions of an operation, and the arguments should be consistent. In order to solve this problem, 'the operator definition enhancement project' was launched, and the declarations of 'AsExtra' and 'AsQuant' were added to some unnecessary arguments, but the problem was not fundamentally solved, which is what the construction of the PHI operator library hopes to solve. We hope to be able to achieve the same three-layer arguments of Python API -> Op(C++ API) -> Kernel API, so that the overall structure is clear, and the reuse relationship of each layer is clear enough. Maintaining a set of official Python API documents can basically satisfy the common reference requirements of the three-tier API, no longer focus on maintaining additional document systems and reduce