FlexFlow is a deep learning framework that accelerates distributed DNN training by automatically discovering fast parallelization strategies.
- CUDNN is used to perform low-level operations. Download CUDNN and install it locally or at the system level.
export CUDNN_HOME=/path/to/cudnn
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Legion is the underlying runtime FlexFlow built on.
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Protocol Buffer is used for representing parallelization strategies in FlexFlow.
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(Optional) NCCL is used for parameter synchronization. When NCCL is not available, FlexFlow uses the Legion DMA subsystem for transferring parameters.
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(Optional) GASNet is used for multi-node executions. (see GASNet installation instructions)
See instructions to install FlexFlow from source.
See the examples folders for existing FlexFlow applications. Use the following command line to build a DNN model (e.g., InceptionV3).
./ffcompile.sh examples/InceptionV3
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDNN_HOME/lib64:$FF_HOME/protobuf/src/.libs
To train a DNN model, run the complied application with the path to the training dataset, the path to the parallelization strategy, and some additional configuration flags. For example:
./alexnet -e 10 -b 256 --lr 0.1 --wd 1e-4 -p 10 -d path_to_dataset -s path_to_strategy -ll:gpu 4 -ll:fsize 90000 -ll:zsize 5000 -ll:cpu 4
-eor--epochs: number of total epochs to run (default: 90)-bor--batch-size: global batch size in each iteration (default: 64)--lror--learning-rate: initial learning rate (default: 0.1)--wdor--weight-decay: weight decay (default: 1e-4)-por--print-freq: print frequency (default 10)-dor--dataset: path to the training dataset. If not set, synthetic data is used to conduct training.-sor--strategy: path to the strategy to parallelize training. If not set, data parallelism is used as the default strategy.-ll:gpu: number of GPU processors to use on each node-ll:fsize: size of device memory on each GPU (in MB)-ll:zsize: size of zero-copy memory (pinned DRAM with direct GPU access) on each node (in MB). This is used for prefecthing training images from disk.-ll:cpu: number of data loading workers (default: 4)
FlexFlow supports training a DNN model using parallelization strategies in the SOAP search space (see paper). A parallelization strategy should describe how to parallelize each operator in a DNN. An example parallelization strategy for AlexNet is as follows.
| Operator | Type | Configuration | Devices |
|---|---|---|---|
| conv1 | conv2d | n=4 c=1 h=1 w=1 | 0 1 2 3 |
| pool1 | pool2d | n=4 c=1 h=1 w=1 | 0 1 2 3 |
| conv2 | conv2d | n=1 c=1 h=2 w=2 | 0 2 1 3 |
| pool2 | pool2d | n=1 c=1 h=2 w=2 | 0 2 1 3 |
| flat1 | flat | n=2 c=1 | 0 2 |
| linear1 | linear | n=1 c=3 | 0 2 3 |
| linear2 | linear | n=1 c=3 | 0 1 2 |
| linear3 | linear | n=1 c=1 | 0 |
Some example parallelization strategies are available in the strategies subfolder. |
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Zhihao Jia, Matei Zaharia, and Alex Aiken. Beyond Data and Model Parallelism for Deep Neural Networks. In Proceedings of the 2nd Conference on Machine Learning and Systems (MLSys), Palo Alto, CA, April 2019.
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Zhihao Jia, Sina Lin, Charles R. Qi, and Alex Aiken. Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks. In Proceedings of the International Conference on Machine Learning (ICML), Stockholm, Sweden, July 2018.