wehub-resource-sync
116 lines
4.0 KiB
Markdown
116 lines
4.0 KiB
Markdown
```
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\::/ / /:/ / \:\__\ \:\__\
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\/__/ \/__/ \/__/ \/__/
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```
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# CINN : Compiler Infrastructure for Neural Networks
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The project CINN is a machine learning compiler and executor for multiple hardware backends.
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It is designed to provide multiple layers of APIs to make tensor computation easier to define, faster to execute, and more convenient to extend with hardware backends.
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Currently, it targets x86 CPUs and Nvidia GPUs.
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This project is under active development.
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## How it works
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The CINN lowers a traditional DNN model into a two-level intermediate representation(IR), the high-level IR(HLIR) and CINN IR.
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The HLIR helps to define some domain-specific computation and perform some overall optimization on the IR-graph;
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the CINN IR helps to represent some computation semantic and finally lower to a hardware backend.
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Both levels of IR have the similar SSA graph, analysis and optimization facilities.
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The schedule transform is applied on the CINN IR to do optimizations.
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For more details, you can refer to:
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https://github.com/PaddlePaddle/docs/tree/develop/docs/guides/cinn
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## Getting Started
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### Compile
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Clone PaddlePaddle first.
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```
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git clone https://github.com/PaddlePaddle/Paddle.git
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cd Paddle
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mkdir build
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cd build
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```
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Build paddle with cinn:
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```
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cmake .. -DWITH_CINN=ON -DWITH_GPU=ON
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```
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And then
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```
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make -j
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```
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### Install
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Install paddle with cinn:
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```
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pip install python/dist/paddlepaddle_gpu-xxx.whl
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```
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Install cinn only:
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```
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pip install python/dist/cinn_gpu-xxx.whl
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```
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Then you can import paddle in the python environment and check if a paddle version with CINN is installed.
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```
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import paddle
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paddle.is_compiled_with_cinn()
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```
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### Concepts
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There are two levels of APIs in CINN, the higher level is HLIR and the lower level is CINN IR, both contain some concepts.
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In HLIR
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- `frontend::Program`, the program helps to define a machine learning computation,
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- `hlir::framework::Tensor`, multi-dimensional arrays helps to manage a memory buffer.
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- `hlir::framework::Program`, the final executable program in runtime. It holds many basic executable elements.
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- `hlir::framework::Graph`, the graph that represents the structure of a model. Each node in the graph represents an operator (conv2d, relu, mul, etc.).
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- `hlir::framework::GraphCompiler`, the compiler that transforms the graph representation(hlir::framework::Graph) of a model into an executable program(hlir::framework::Program).
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In CINN IR
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- `Compute`, the method to define a computation,
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- `Lower`, the method to lower a computation to the corresponding IR,
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- `LoweredFunc`, the function defined in CINN IR,
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- `Var`, a scalar variable,
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- `Expr`, an expression represents any CINN IR node(no specified Statement node),
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## License
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CINN is licensed under the [Apache 2.0 license](LICENSE).
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## Acknowledgement
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CINN learned a lot from the following projects:
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- [Halide](https://github.com/halide/Halide): Referenced the design of most IR nodes,
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- [TVM](https://github.com/apache/tvm): We learned many ideas including the semantics of some schedule primitives, TOPI, NNVM, and so on,
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- [tiramisu](https://github.com/Tiramisu-Compiler): The isl usage, polyhedral compilation, schedule primitive implementation, and so on,
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- [tensorflow/xla](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/xla): Referenced the semantics of the primitive operations.
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