This module contains the FlexNet simulator, which implements the co-optimization algorithm for DNN parallelization strategy search and topology construction. The source code was modified from the FlexFlow projcect. To check the original FlexFlow README file, please click here.
FlexNet follows the same build steps as the original FlexFlow project. Please check the installation document for how to compile and install the program.
This section describes the relavent directory structure for FlexNet.
| Directory | Description |
|---|---|
config |
Configuration files to generate the cmake build directory |
examples |
Example DNN implementations. examples/cpp/*sim are the DNNs used for the plots in the paper |
fbuf2 |
FlatBuffer directory used for generating and storing taskgraph from FlexNet |
include |
Header files for the project |
src |
Soruce file of FlexNet simulator |
Please read the original FlexFlow README first before proceeding.
To use the FlexNet simulator, there are a few essential steps. Here we will use the DLRM (examples/cpp/DLRMsim) as a running example.
In addition to the parameters from original FlexFlow, FlexNet requires the following additional parameters to be specified:
--interface-bandwidth <bw>: bandwidth of each interface in Gbps.
--network-latency <lat>: Network link latency in microseconds.
--degree <n>: number of interface for each server. For Fat-Tree topology this has to be 1.
--net-opt <0|1>: whether the program should enable the TotientPerm algorithm. 1 for ture, 0 for false.
--nsimnode <n>: number of nodes to be simulated.
--big-gpu <n>: to enable emulating multiple GPU in one machine.
--measure: specify the run to be a measurement run, which generate the profiled data in a json file.
--mfile <filename>: when the run is not a measurement run, this flag specifies the path the profiled json file.
--taskgraph <filename>: output taskgraph of the run. The taskgraph stores the entire DNN training task DAG and is used for the FlexNetPacket simulator.
--topology <topoopt|fattree|random>: specify which network topology to simulate and search in this run.
FlexNet stores the profiled data for each DNN in a file, so we don't have to re-profile each DNN each time we run a simulation. For the p Each measurement needs to specify the model, the cluster size, and the global batch size. For DLRM, for instance: cd build/examples/cpp/DLRM_sim
./dlrmsim -ll:gpu 1 -ll:cpu 1 -ll:zsize 20000 -ll:fsize 39000 -ll:util 4 -dm:memoize --embedding-bag-size 100 --arch-sparse-feature-size 256 --arch-embedding-size 10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000 --arch-mlp-bot 1024-1024-1024-1024 --arch-mlp-top 2048-2048-2048-2048-2048-2048-2048-2048-1 --search-budget 1 --interface-bandwidth 100 --inter-gpu-bandwidth 256 --gpu-dram-bandwidth 200 --network-latency 1 --net-opt 1 ---batch-size 4096 --nsimnode 16 --big-gpu 4 --simulator-workspace-size 38654705664 --measure
After this, we will get a measure_<>_<>.json file that profiles all different ways of chopping this DNN.
With the measurement file, we run the MCMC and topology search with
./dlrmsim -ll:gpu 1 -ll:cpu 1 -ll:zsize 20000 -ll:fsize 39000 -ll:util 4 -dm:memoize --embedding-bag-size 100 --arch-sparse-feature-size 256 --arch-embedding-size 10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000-10000000 --arch-mlp-bot 1024-1024-1024-1024 --arch-mlp-top 2048-2048-2048-2048-2048-2048-2048-2048-1 --search-budget 5000 --interface-bandwidth 100 --degree 4 --inter-gpu-bandwidth 256 --gpu-dram-bandwidth 200 --network-latency 1 --net-opt 1 --enable-propagation ---batch-size 4096 --nsimnode 16 --big-gpu 4 --simulator-workspace-size 65536 –mfile <measurement json from last step> --taskgraph <output taskgraph file name> --topology topoopt
Note that the DNN model parameters, batch size and number of nodes in the cluster has to be the same as the measurement file.
After the last commnad, we should get the taskgraph as a flatbuffer file. This is what we will then feed into the packet simulator. Please see the README file of FlexNetPacket for details on how to run the packet simulation.
The author would like to thank Zhihao Jia, the original author of FlexFlow, for providing invaluable help and guidence on making TopoOpt happening.