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mnist_cpu_mp.py
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mnist_cpu_mp.py
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import argparse
from typing import Tuple
from tqdm import tqdm
import torch
from torch import nn, optim
from torch.distributed import Backend
from torch.nn.parallel.distributed import DistributedDataParallel
from torch.utils.data import DataLoader, DistributedSampler
from torchvision import datasets, transforms
from mpi4py import MPI
import torch.distributed as dist
import os
class distributed():
def get_size(self):
if dist.is_available() and dist.is_initialized():
size = dist.get_world_size()
else:
size = 1
return size
def get_rank(self):
if dist.is_available() and dist.is_initialized():
rank = dist.get_rank()
else:
rank = 0
return rank
def get_local_rank(self):
if not (dist.is_available() and dist.is_initialized()):
return 0
# Number of GPUs per node
if torch.cuda.is_available():
local_rank = dist.get_rank() % torch.cuda.device_count()
else:
# raise NotImplementedError()
# running on cpu device should not call this function
local_rank = -1
return local_rank
def __init__(self, method):
# MASTER_PORT - required; has to be a free port on machine with rank 0
# MASTER_ADDR - required (except for rank 0); address of rank 0 node
# WORLD_SIZE - required; can be set either here, or in a call to init function
# RANK - required; can be set either here, or in a call to init function
if method == "nccl-slurm":
# MASTER_ADDR can be set in the slurm batch script using command
# scontrol show hostnames $SLURM_JOB_NODELIST
if "MASTER_ADDR" not in os.environ:
# Try SLURM_LAUNCH_NODE_IPADDR but it is the IP address of the node
# from which the task launch was initiated (where the srun command
# ran from). It may not be the node of rank 0.
if "SLURM_LAUNCH_NODE_IPADDR" in os.environ:
os.environ["MASTER_ADDR"] = os.environ["SLURM_LAUNCH_NODE_IPADDR"]
else:
raise Exception("Error: nccl-slurm - SLURM_LAUNCH_NODE_IPADDR is not set")
# Use the default pytorch port
if "MASTER_PORT" not in os.environ:
if "SLURM_SRUN_COMM_PORT" in os.environ:
os.environ["MASTER_PORT"] = os.environ["SLURM_SRUN_COMM_PORT"]
else:
os.environ["MASTER_PORT"] = "29500"
# obtain WORLD_SIZE
if "WORLD_SIZE" not in os.environ:
if "SLURM_NTASKS" in os.environ:
world_size = os.environ["SLURM_NTASKS"]
else:
if "SLURM_JOB_NUM_NODES" in os.environ:
num_nodes = os.environ["SLURM_JOB_NUM_NODES"]
else:
raise Exception("Error: nccl-slurm - SLURM_JOB_NUM_NODES is not set")
if "SLURM_NTASKS_PER_NODE" in os.environ:
ntasks_per_node = os.environ["SLURM_NTASKS_PER_NODE"]
elif "SLURM_TASKS_PER_NODE" in os.environ:
ntasks_per_node = os.environ["SLURM_TASKS_PER_NODE"]
else:
raise Exception("Error: nccl-slurm - SLURM_(N)TASKS_PER_NODE is not set")
world_size = ntasks_per_node * num_nodes
os.environ["WORLD_SIZE"] = str(world_size)
# obtain RANK
if "RANK" not in os.environ:
if "SLURM_PROCID" in os.environ:
os.environ["RANK"] = os.environ["SLURM_PROCID"]
else:
raise Exception("Error: nccl-slurm - SLURM_PROCID is not set")
# Initialize DDP module
dist.init_process_group(backend = "nccl", init_method='env://')
elif method == "nccl-openmpi":
if "MASTER_ADDR" not in os.environ:
if "PMIX_SERVER_URI2" in os.environ:
os.environ["MASTER_ADDR"] = os.environ("PMIX_SERVER_URI2").split("//")[1]
else:
raise Exception("Error: nccl-openmpi - PMIX_SERVER_URI2 is not set")
# Use the default pytorch port
if "MASTER_PORT" not in os.environ:
os.environ["MASTER_PORT"] = "29500"
if "WORLD_SIZE" not in os.environ:
if "OMPI_COMM_WORLD_SIZE" not in os.environ:
raise Exception("Error: nccl-openmpi - OMPI_COMM_WORLD_SIZE is not set")
os.environ["WORLD_SIZE"] = os.environ["OMPI_COMM_WORLD_SIZE"]
if "RANK" not in os.environ:
if "OMPI_COMM_WORLD_RANK" not in os.environ:
raise Exception("Error: nccl-openmpi - OMPI_COMM_WORLD_RANK is not set")
os.environ["RANK"] = os.environ["OMPI_COMM_WORLD_RANK"]
# Initialize DDP module
dist.init_process_group(backend = "nccl", init_method='env://')
elif method == "nccl-mpich":
if "MASTER_ADDR" not in os.environ:
os.environ['MASTER_ADDR'] = "localhost"
# Use the default pytorch port
if "MASTER_PORT" not in os.environ:
os.environ["MASTER_PORT"] = "29500"
if "WORLD_SIZE" not in os.environ:
if "PMI_SIZE" in os.environ:
world_size = os.environ["PMI_SIZE"]
elif MPI.Is_initialized():
world_size = MPI.COMM_WORLD.Get_size()
else:
world_size = 1
os.environ["WORLD_SIZE"] = str(world_size)
if "RANK" not in os.environ:
if "PMI_RANK" in os.environ:
rank = os.environ["PMI_RANK"]
elif MPI.Is_initialized():
rank = MPI.COMM_WORLD.Get_rank()
else:
rank = 0
os.environ["RANK"] = str(rank)
# Initialize DDP module
dist.init_process_group(backend = "nccl", init_method='env://')
elif method == "gloo":
if "MASTER_ADDR" not in os.environ:
# check if OpenMPI is used
if "PMIX_SERVER_URI2" in os.environ:
addr = os.environ["PMIX_SERVER_URI2"]
addr = addr.split("//")[1].split(":")[0]
os.environ["MASTER_ADDR"] = addr
else:
os.environ['MASTER_ADDR'] = "localhost"
# Use the default pytorch port
if "MASTER_PORT" not in os.environ:
os.environ["MASTER_PORT"] = "29500"
# obtain WORLD_SIZE
if "WORLD_SIZE" not in os.environ:
# check if OpenMPI is used
if "OMPI_COMM_WORLD_SIZE" in os.environ:
world_size = os.environ["OMPI_COMM_WORLD_SIZE"]
elif "PMI_SIZE" in os.environ:
world_size = os.environ["PMI_SIZE"]
elif MPI.Is_initialized():
world_size = MPI.COMM_WORLD.Get_size()
else:
world_size = 1
os.environ["WORLD_SIZE"] = str(world_size)
# obtain RANK
if "RANK" not in os.environ:
# check if OpenMPI is used
if "OMPI_COMM_WORLD_RANK" in os.environ:
rank = os.environ["OMPI_COMM_WORLD_RANK"]
elif "PMI_RANK" in os.environ:
rank = os.environ["PMI_RANK"]
elif MPI.Is_initialized():
rank = MPI.COMM_WORLD.Get_rank()
else:
rank = 0
os.environ["RANK"] = str(rank)
# Initialize DDP module
dist.init_process_group(backend = "gloo", init_method='env://')
else:
raise NotImplementedError()
def reduceMAX(self, src, root):
# dist.reduce(src, root, dist.ReduceOp.MAX)
# return src.cpu().numpy()
import numpy
dst = numpy.empty(len(src))
MPI.COMM_WORLD.Reduce(src, dst, op=MPI.MAX, root=root)
return dst
def barrier(self):
MPI.COMM_WORLD.Barrier()
# dist.barrier()
def finalize(self):
dist.destroy_process_group()
def configure():
# Configuration options (overwrite default configuration with command-line arguments)
parser = argparse.ArgumentParser(description="Evaluate cost of reading input files")
add_arg = parser.add_argument
add_arg("--wireup_method", type=str, default="nccl-slurm",
choices=["nccl-slurm", "nccl-openmpi", "nccl-mpich", "gloo"],
help="Choose backend for distributed environment initialization")
add_arg("--data_path", type=str, default=None, help="File path to training samples")
add_arg("--data_limit", type=int, default=None, help="Max number of samples to be used")
add_arg("--batch_size", type=int, default=None, help="Batch size")
add_arg("--n_epochs", type=int, default=None, help="Number of epochs")
add_arg("--num_workers", type=int, default=None, help="Number of subprocesses to use for data loading")
add_arg("--parallel", action='store_true', help="To run in parallel")
add_arg("--hdf5", action='store_true', help="Read from HDF5 files")
args = parser.parse_args()
config = {}
config["trainer"] = {}
config["data"] = {}
config["trainer"]["batch_size"] = 128
config["trainer"]["wireup_method"] = args.wireup_method
config["trainer"]["parallel"] = args.parallel
config["trainer"]["device"] = 0
config["trainer"]["n_epochs"] = 1
config["trainer"]["num_workers"] = 0
config["data"]["limit"] = None
config["data"]["label_map"] = [ 0, 1, 0, 0, 2, 3, 1, 4 ]
config["data"]["hdf5"] = args.hdf5
if args.data_path != None: config["data"]["path"] = args.data_path
if args.data_limit != None: config["data"]["limit"] = args.data_limit
if args.batch_size != None: config["trainer"]["batch_size"] = args.batch_size
if args.n_epochs != None: config["trainer"]["n_epochs"] = args.n_epochs
if args.num_workers != None: config["trainer"]["num_workers"] = args.num_workers
return config
def init_parallel(config):
# check if cuda device is available
ngpu_per_node = torch.cuda.device_count()
if not torch.cuda.is_available():
config["trainer"]["wireup_method"] = "gloo"
config["trainer"]["device"] = "cpu"
rank = 0
world_size = 1
# initialize parallel/distributed environment
if config["trainer"]["parallel"]:
comm = distributed(config["trainer"]["wireup_method"])
rank = comm.get_rank()
world_size = comm.get_size()
# TODO: must check whether num_workers > 0 works in DDP
# config["trainer"]["num_workers"] = 0
# ignoring config["trainer"]["device"]
local_rank = comm.get_local_rank()
else:
comm = None
local_rank = config["trainer"]["device"]
# select training device: cpu or cuda
if config["trainer"]["device"] == "cpu":
device = torch.device("cpu")
else:
device = torch.device("cuda:"+str(local_rank))
config["trainer"]["device"] = device
config["trainer"]["rank"] = rank
config["trainer"]["world_size"] = world_size
# Print out the settings and init timings
if rank == 0:
import socket
print('------------------------------------------------------------------')
if config["trainer"]["parallel"]:
print('\n======== I/O evaluation in Parallel GNN Training ========')
else:
print('\n======== I/O evaluation in Serial GNN Training ========')
print("%-32s: %s" % ('Host name',socket.gethostname()))
print("%-32s: %d" % ('Number of processes', world_size))
print("%-32s: %d" % ('number of GPUs per node',ngpu_per_node))
if device.type == 'cuda':
print("%-32s: %s" % ('Rank 0 GPU device',device))
else:
print('Rank 0 is Using CPU device')
# print("%-32s: %s" % ('Input file path', config["data"]["path"]))
if config["data"]["hdf5"]:
print("%-32s: %s" % ('Input file format', 'A single HDF5 file'))
else:
print("%-32s: %s" % ('Input file format', 'Pytorch files, one per sample'))
print("%-32s: %d" % ('DataLoader num_workers',config["trainer"]["num_workers"]))
print("%-32s: %d" % ('Number of epochs', config["trainer"]["n_epochs"]))
print('------------------------------------------------------------------')
return comm
def create_data_loaders(config) -> Tuple[DataLoader, DataLoader]:
batch_size = config["trainer"]["batch_size"]
rank = config["trainer"]["rank"]
world_size = config["trainer"]["world_size"]
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
dataset_loc = './mnist_data'
train_dataset = datasets.MNIST(dataset_loc,
download=True,
train=True,
transform=transform)
sampler = DistributedSampler(train_dataset,
num_replicas=world_size, # Number of GPUs
rank=rank, # GPU where process is running
shuffle=True, # Shuffling is done by Sampler
seed=42)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=False, # This is mandatory to set this to False here, shuffling is done by Sampler
num_workers=4,
sampler=sampler,
pin_memory=True)
# This is not necessary to use distributed sampler for the test or validation sets.
test_dataset = datasets.MNIST(dataset_loc,
download=True,
train=False,
transform=transform)
test_loader = DataLoader(test_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=4,
pin_memory=True)
return train_loader, test_loader
def create_model():
# create model architecture
model = nn.Sequential(
nn.Linear(28*28, 128), # MNIST images are 28x28 pixels
nn.ReLU(),
nn.Dropout(0.2),
nn.Linear(128, 128),
nn.ReLU(),
nn.Linear(128, 10, bias=False) # 10 classes to predict
)
return model
def main(model: nn.Module,
train_loader: DataLoader,
test_loader: DataLoader,
config) -> nn.Module:
rank = config["trainer"]["rank"]
# if config["trainer"]["device"] == "cpu":
# device = torch.device("cpu")
# else:
# device = torch.device(f'cuda:{rank}')
device = torch.device("cpu")
epochs = config["trainer"]["n_epochs"]
model = model.to(device)
model = DistributedDataParallel(model)
# model = DistributedDataParallel(model, device_ids=[rank], output_device=rank)
# initialize optimizer and loss function
optimizer = optim.SGD(model.parameters(), lr=0.01)
loss = nn.CrossEntropyLoss()
# train the model
for i in range(epochs):
model.train()
train_loader.sampler.set_epoch(i)
epoch_loss = 0
# train the model for one epoch
pbar = tqdm(train_loader)
for x, y in pbar:
x = x.to(device, non_blocking=True)
y = y.to(device, non_blocking=True)
x = x.view(x.shape[0], -1)
optimizer.zero_grad()
y_hat = model(x)
batch_loss = loss(y_hat, y)
batch_loss.backward()
optimizer.step()
batch_loss_scalar = batch_loss.item()
epoch_loss += batch_loss_scalar / x.shape[0]
pbar.set_description(f'training batch_loss={batch_loss_scalar:.4f}')
# calculate validation loss
with torch.no_grad():
model.eval()
val_loss = 0
pbar = tqdm(test_loader)
for x, y in pbar:
x = x.to(device, non_blocking=True)
y = y.to(device, non_blocking=True)
x = x.view(x.shape[0], -1)
y_hat = model(x)
batch_loss = loss(y_hat, y)
batch_loss_scalar = batch_loss.item()
val_loss += batch_loss_scalar / x.shape[0]
pbar.set_description(f'validation batch_loss={batch_loss_scalar:.4f}')
print(f"Epoch={i}, train_loss={epoch_loss:.4f}, val_loss={val_loss:.4f}")
return model.module
if __name__ == '__main__':
config = configure()
# initialize parallel environment
comm = init_parallel(config)
rank = config["trainer"]["rank"]
# torch.cuda.set_device(rank)
# torch.distributed.init_process_group(backend=Backend.NCCL,
# init_method='env://')
train_loader, test_loader = create_data_loaders(config)
model = main(model=create_model(),
train_loader=train_loader,
test_loader=test_loader,
config = config)
if rank == 0:
torch.save(model.state_dict(), 'model.pt')