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https://github.com/pytorch/glow

Compiler for Neural Network hardware accelerators
https://github.com/pytorch/glow

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Compiler for Neural Network hardware accelerators

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README 

        
![Glow Logo](./docs/logo.svg)



[![pytorch](https://circleci.com/gh/pytorch/glow.svg?style=shield)](https://circleci.com/gh/pytorch/glow)



Glow is a machine learning compiler and execution engine for hardware

accelerators.  It is designed to be used as a backend for high-level machine

learning frameworks.  The compiler is designed to allow state of the art

compiler optimizations and code generation of neural network graphs. This

library is in active development. The project plan is described in the Github

issues section and in the

[Roadmap](https://github.com/pytorch/glow/wiki/Glow-Roadmap) wiki page.



## Partners



Contributions to Glow are welcomed and encouraged! Glow is developed in

collaboration with the following partners:



| ![Bitmain Logo](./docs/partners/bitmain.png) | ![Habana Logo](./docs/partners/habana.png) | ![ST Logo](./docs/partners/st.png)  |

:-------------------------:|:-------------------------:|:-------------------------:

| ![Cadence Logo](./docs/partners/cadence.png) | ![Intel Logo](./docs/partners/intel.png) | ![Synopsys Logo](./docs/partners/synopsys.png) |

| ![CEVA Logo](./docs/partners/ceva.png)   |  ![Marvell Logo](./docs/partners/marvell.png) |  |

| ![Esperanto Logo](./docs/partners/esperanto.png)  | ![NXP Logo](./docs/partners/nxp.png) |  |



## How does it work?



Glow lowers a traditional neural network dataflow graph into a two-phase

strongly-typed [intermediate representation (IR)](./docs/IR.md). The high-level

IR allows the optimizer to perform domain-specific optimizations. The

lower-level instruction-based address-only IR allows the compiler to perform

memory-related optimizations, such as instruction scheduling, static memory

allocation and copy elimination. At the lowest level, the optimizer performs

machine-specific code generation to take advantage of specialized hardware

features. Glow features a lowering phase which enables the compiler to support a

high number of input operators as well as a large number of hardware targets by

eliminating the need to implement all operators on all targets. The lowering

phase is designed to reduce the input space and allow new hardware backends to

focus on a small number of linear algebra primitives.

The design philosophy is described in an [arXiv paper](https://arxiv.org/abs/1805.00907).



![](./docs/3LevelIR.png)



## Getting Started



### System Requirements



Glow builds and runs on macOS and Linux. The software depends on a modern C++

compiler that supports C++11, on CMake, LLVM (>=7.0), glog, protocol buffers, and

libpng.



#### Get Glow!



  ```bash

  git clone [email protected]:pytorch/glow.git  # or: git clone https://github.com/pytorch/glow.git

  cd glow

  ```



#### Submodules



Glow depends on a few submodules: googletest, onnx, and a library

for FP16 conversions.



To get them, from the glow directory, run:



  ```bash

  git submodule update --init --recursive

  ```



#### Source dependencies



Glow depends on `fmt`, which must be built from source:

```bash

git clone https://github.com/fmtlib/fmt

mkdir fmt/build

cd fmt/build

cmake ..

make

sudo make install

```



#### macOS



Install the required dependencies using either [Homebrew](https://brew.sh/) or

[MacPorts](https://www.macports.org/). If using Homebrew, run:



  ```bash

  brew install cmake graphviz libpng ninja protobuf wget glog autopep8 llvm   \

      boost double-conversion gflags jemalloc libevent lz4 openssl pkg-config \

      snappy xz

  ```



If using MacPorts, run:



  ```bash

  port install cmake graphviz libpng ninja protobuf-cpp wget google-glog \

      boost double-conversion gflags jemalloc libevent lz4 openssl snappy xz

  # Choose version >= 7

  export LLVM_VERSION=7

  port install llvm-$LLVM_VERSION.0 

  ```



Note that LLVM is installed in a non-default location to avoid conflicts with

the system's LLVM --Homebrew usually installs LLVM in `/usr/local/opt/llvm/`,

whereas MacPorts installs it in `/opt/local/libexec/llvm-$LLVM_VERSION.0/`. This means that

CMake will need to be told where to find LLVM when building; instructions on

that can be found [here](#building-with-dependencies-llvm).



Finally, create a symbolic link to the Homebrew- or MacPorts-installed

`clang-*` tools so that the `utils/format.sh` script is able to find them later

on. For a Homebrew-managed installation, run:

  ```

  ln -s "/usr/local/opt/llvm/bin/clang-format" "/usr/local/bin/clang-format"

  ln -s "/usr/local/opt/llvm/bin/clang-tidy" "/usr/local/bin/clang-tidy"

  ```

For MacPorts, run:

  ```

  ln -s "/opt/local/libexec/llvm-$LLVM_VERSION.0/bin/clang-format" "/usr/local/bin/clang-format"

  ln -s "/opt/local/libexec/llvm-$LLVM_VERSION.0/bin/clang-tidy" "/usr/local/bin/clang-tidy"

```



> **Note:** Starting with macOS Mojave, Xcode's command line tools changed header layout. 

> In order for Glow to build on Mojave, you might need to install

> `macOS_SDK_headers_for_macOS_10.14.pkg`, located in 

> `/Library/Developer/CommandLineTools/Packages/`.

> For macOS Catalina you might need to explicitly specify SDKROOT: 

> `export SDKROOT="/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk"`



#### Ubuntu



[The following instructions have been tested on Ubuntu 16.04 and 18.04]



In order to build Glow on Ubuntu it is necessary to install a few packages. The

following command should install the required dependencies:



  ```bash

  sudo apt-get install clang clang-8 cmake graphviz libpng-dev \

      libprotobuf-dev llvm-8 llvm-8-dev ninja-build protobuf-compiler wget \

      opencl-headers libgoogle-glog-dev libboost-all-dev \

      libdouble-conversion-dev libevent-dev libssl-dev libgflags-dev \

      libjemalloc-dev libpthread-stubs0-dev liblz4-dev libzstd-dev libbz2-dev \

      libsodium-dev libfmt-dev

  ```



[Note: Ubuntu 16.04 and 18.04 ship with llvm-6 and need to be upgraded before building Glow. Building Glow on Ubuntu 16.04 with llvm-7 fails because llvm-7 xenial distribution uses an older c++ ABI, however building Glow on Ubuntu 18.04 with llvm-7 has been tested and is successful]



It may be desirable to use `update-alternatives` to manage the version of

clang/clang++:



  ```bash

  sudo update-alternatives --install /usr/bin/clang clang \

      /usr/lib/llvm-8/bin/clang 50

  sudo update-alternatives --install /usr/bin/clang++ clang++ \

      /usr/lib/llvm-8/bin/clang++ 50

  ```



Glow uses the system default C/C++ compiler (/usr/bin/c++), and so you may also

want to switch your default C/C++ compiler to clang:



  ```bash

  sudo update-alternatives --config cc

      # Select the option corresponding to /usr/bin/clang ...

  sudo update-alternatives --config c++

      # Select the option corresponding to /usr/bin/clang++ ...

  ```



Glow *should* build just fine with gcc (e.g. gcc 5.4), but we mostly use clang

and are more attentive to compatibility with clang.



Finally, in order to support the ONNX net serialization format, Glow requires

`protobuf >= 2.6.1`, but the above command may install older

version on older Ubuntu (e.g. 14.04). If this is the case, we suggest to look

at `utils/install_protobuf.sh` to install a newer version from source.



For details on installing OpenCL on Ubuntu please see

[these instructions](docs/Building.md#opencl-on-ubuntu).



### Configure and Build



To build the compiler, create a build directory and run cmake on the source

directory. It's a good idea to build two configurations (Release and Debug)

because some programs take a really long time to run in Debug mode. It's also a

good idea to build the project outside of the source directory.



  ```bash

  mkdir build_Debug

  cd build_Debug

  cmake -G Ninja -DCMAKE_BUILD_TYPE=Debug ../glow

  ninja all

  ```



It's possible to configure and build the compiler with any CMake generator,

like GNU Makefiles, Ninja and Xcode build.



For platform-specific build instructions and advanced options, such as

building with Address-Sanitizers refer to this guide:

[Building the Compiler](docs/Building.md).



If you're running macOS v10.14 (Mojave) and `ninja all` fails because it can't

find headers (e.g. `string.h`), run this command to fix it, and try again.

More information is available [here](https://developer.apple.com/documentation/xcode_release_notes/xcode_10_release_notes)

under "Command Line Tools".



  ```bash

  open /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg

  ```



For macOS v10.15 (Catalina) you might need to explicitly specify SDKROOT:



   ```bash

   export SDKROOT="/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk"

   ```



#### Building with dependencies (LLVM)



By default, Glow will use a system provided LLVM.  Note that Glow requires LLVM

7.0 or later. If you have LLVM installed in a non-default location (for

example, if you installed it using Homebrew on macOS), you need to tell CMake

where to find llvm using `-DLLVM_DIR`. For example, if LLVM were

installed in `/usr/local/opt`:



  ```bash

  cmake -G Ninja ../glow \

      -DCMAKE_BUILD_TYPE=Debug \

      -DLLVM_DIR=/usr/local/opt/llvm/lib/cmake/llvm

  ```



If LLVM is not available on your system you'll need to build it manually.  Run

the script '`/utils/build_llvm.sh` to clone, build and install LLVM in a local

directory. You will need to configure Glow with the flag `-DLLVM_DIR` to tell

the build system where to find LLVM given the local directory you installed it

in (e.g. `-DLLVM_DIR=/path/to/llvm_install/lib/cmake/llvm` if using

`build_llvm.sh`).



## Testing and Running



### Unit tests



The project has a few unit tests in the tests/unittests subdirectory. To run all

of them, simply run `ninja test`.



### C++ API examples



A few test programs that use Glow's C++ API are found under the `examples/`

subdirectory. The `mnist`, `cifar10`, `fr2en` and `ptb` programs train and run digit

recognition, image classification and language modeling benchmarks,

respectively.



To run these programs, build Glow in Release mode, then run the following commands

to download the cifar10, mnist and ptb databases.



  ```bash

  python ../glow/utils/download_datasets_and_models.py --all-datasets

  ```



Now run the examples. Note that the databases should be in the current working

directory.



  ```bash

  ./bin/mnist

  ./bin/cifar10

  ./bin/fr2en

  ./bin/ptb

  ./bin/char-rnn

  ```



If everything goes well you should see:

  * `mnist`: pictures from the mnist digits database

  * `cifar10`: image classifications that steadily improve

  * `fr2en`: an interactive French-to-English translator

  * `ptb`: decreasing perplexity on the dataset as the network trains

  * `char-rnn`: generates random text based on some document



Note that the default build mode is `Debug`, which means that the compiler

itself is easy to debug because the binary contains debug info, lots of

assertions, and the optimizations are disabled. It also means that the compiler

and runtime are very slow, and the execution time can be hundreds of times

slower than that of release builds. If you wish to benchmark the compiler, run

long benchmarks, or release the product then you should compile the compiler in

Release mode. Check the main CMake file for more details.



More details on testing and running Glow can be found in:

[Testing the Glow Compiler](docs/Testing.md).



### Ahead-of-time Compilation



Glow can be used to compile neural networks into object files containing native

code.  We provide resnet50 (both quantized and non-quantized versions) as an

example of this capability in `examples/bundles/resnet50`.  See [Creating

Standalone Executable Bundles](docs/AOT.md) for more detail.



## Contributing



To get started contributing, please refer to the following guides:

* [Contributing](CONTRIBUTING.md)

* [Coding Standards](docs/CodingStandards.md)

* [Code of Conduct](CODE_OF_CONDUCT.md)



### Communication



* Forums: discuss implementations, research, etc: https://discuss.pytorch.org/c/glow.

  Make sure to label topic with the ["glow"](https://discuss.pytorch.org/c/glow) category.

* GitHub issues: bug reports, feature requests, install issues, RFCs, thoughts, etc.



## License



Glow is licensed under the [Apache 2.0 License](LICENSE).