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DDL Emulator

Prerequisite

First, please update the submodule for the project.

git submodule update --init --recursive

Second, we need mamba or conda installed. You can install mamba here

We also need to setup remote nodes for mpirun or torchrun, make sure that you have /etc/hosts and ~/.ssh/config properly configured. (i.e. you can ssh to the remote nodes using hostname without password)

Conda Environment

Suppose $ENV_PATH is the path to the conda environment, then we can use the following command to create a conda environment. We need two environments, we call it ori and emu. ori is the unmodified original code, emu is the code for emulator. We use environment variable ENV_PATH to specify the path to the conda environment. In this example, we set the ENV_PATH=~/neurona/ori or ENV_PATH=~/neurona/emu for ori and emu respectively.

mkdir -p $ENV_PATH
git submodule update --init --recursive
bash ./scripts/create_env.sh $ENV_PATH # takes about 30 minutes
nvcc --version
# expect:
# Cuda compilation tools, release 11.8, V11.8.89
# Build cuda_11.8.r11.8/compiler.31833905_0

We need to keep the $ENV_PATH set in config.sh.

touch config.sh
# in config.sh
export ENV_PATH=your_env_path

NCCL

Build

First, build the nccl and nccl make sure you have conda environment properly configurated.

We need to build nccl from source, for both emu and ori.

cd nccl
git switch [emu/ori] # switch to the branch you want to build 
cd ..
. ./config.sh
bash ./scripts/build_nccl.sh # should take less than 5 minutes

Add the following variables to config.sh that sets up the required environments: You can use query_gpu.py to get the compute capability of your GPU.

#config for debug
export NCCL_DEBUG=MOD
export NCCL_DEBUG_SUBSYS=ALL
export NCCL_DEBUG_FILE=your_debug_file.$(date "+%Y-%m-%d %H:%M:%S")_%h:%p%h:%p
export NCCL_PROTO=Simple
export NCCL_ALGO=Ring
export NCCL_BUILD_PATH=your_nccl_build_path # a local file system like /tmp is recommended
# export NVCC_GENCODE="-gencode=arch=compute_[your_compute],code=sm_[your_sm]"
# export ONLY_FUNCS="AllReduce Sum (f16|f32) RING SIMPLE"
# this two are used to reduce compile time

export DEBUG=1

# for current experiments, we only use 2 nodes, 1 gpu per node
export CUDA_VISIBLE_DEVICES=0
export OMPI_COMM_WORLD_SIZE=2
export OMPI_COMM_WORLD_LOCAL_RANK=0
#config for release
export NCCL_DEBUG=VERSION
export NCCL_DEBUG_SUBSYS=INIT
export NCCL_PROTO=Simple
export NCCL_ALGO=Ring
export NCCL_BUILD_PATH=your_nccl_build_path # a local file system like /tmp is recommended
unset ONLY_FUNCS
unset NVCC_GENCODE
export DEBUG=0
# for current experiments, we only use 2 nodes, 1 gpu per node
export CUDA_VISIBLE_DEVICES=0
export OMPI_COMM_WORLD_SIZE=2
export OMPI_COMM_WORLD_LOCAL_RANK=0

Pytorch

After building the nccl, we need to build pytorch using our nccl.

Build

cd pytorch
git switch [emu/ori] # switch to the branch you want to build
cd ..
bash ./scripts/build_pytorch.sh # takes about 30 mintues
conda activate $ENV_PATH
python
import torch
torch.__version__ # expect 2.2.0a0+git8ac9b20
torch.cuda.is_available()
torch.cuda.nccl.version() # expect 2.19.4

Eval

Please check the README.md in the eval folder for more details.