RECom is an ML compiler that aims to accelerate the expensive embedding column processing during the inference of deep recommendation models. It fuses massive embedding columns into a single kernel and maps each column into a separate threadblock on the GPU.
Currently, RECom is implemented as a TensorFlow add-on based on TensorFlow Addons using C++. We also utilize the SymEngine Library to perform symbolic expression computations to handle dynamic shapes.
We highly recommend building RECom in the docker nvidia/cuda:11.3.1-cudnn8-devel-ubuntu18.04, as the compatibility of RECom has not been checked yet.
You should also prepare the following environments before building RECom:
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Python 3.8
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TensorFlow 2.6.2
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Bazel 4.2.1 (bazelisk is recommended)
After preparing the environments, you should set some environment variables and run the configure.py to generate the .bazelrc:
export TF_NEED_CUDA="1"
# Set these if the below defaults are different on your system
export TF_CUDA_VERSION="11"
export TF_CUDNN_VERSION="8"
export CUDA_TOOLKIT_PATH="/usr/local/cuda"
export CUDNN_INSTALL_PATH="/usr/lib/x86_64-linux-gnu"
python ./configure.pyThen, you can start building the shared library of RECom:
bazel build //tensorflow_addons:librecom.soFinally, you will find the target librecom.so in bazel-bin/tensorflow_addons.
You can use tf.load_op_library/TF_LoadLibrary in your Python/C++ inference scripts to load the TensorFlow addon of RECom without modifying any source codes of the models.
Then, the models will be optimized automatically (warm-up required).
More details can be found in the examples folder.
[ASPLOS'24] RECom: A Compiler Approach to Accelerating Recommendation Model Inference with Massive Embedding Columns
Zaifeng Pan, Zhen Zheng, Feng Zhang, Ruofan Wu, Hao Liang, Dalin Wang, Xiafei Qiu, Junjie Bai, Wei Lin, Xiaoyong Du