在torch.distributed中，如何正确平均不同GPU上的梯度？
修改自 https://github.com/seba-1511/dist_tuto.pth/blob/gh-pages/train_dist.py ，下面的代码可以成功使用两个 GPU(可以使用 nvidia-smi 检查)。
但难以理解的一件事是，下面的“average_gradients”是否确实是在两个 GPU 上对两个模型进行梯度平均的正确方法。与下面的代码一样，使用两个进程运行的两个“model = Net()”是两个不同 GPU 上的两个模型，但是“average_gradients(model)”行只是在一个 GPU 上“平均”模型的梯度，而不是两个 GPU两个 GPU 上的模型。
问题是下面的代码确实是在两个 GPU 上平均梯度的正确方法吗？如果是真的，如何阅读、如何理解代码？如果不是，在下面的两个模型上平均梯度的正确方法是什么？
import os
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from math import ceil
from random import Random
from torch.multiprocessing import Process
from torchvision import datasets, transforms
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"
class Partition(object):
""" Dataset-like object, but only access a subset of it. """
def __init__(self, data, index):
self.data = data
self.index = index
def __len__(self):
return len(self.index)
def __getitem__(self, index):
data_idx = self.index[index]
return self.data[data_idx]
class DataPartitioner(object):
""" Partitions a dataset into different chuncks. """
def __init__(self, data, sizes=[0.7, 0.2, 0.1], seed=1234):
self.data = data
self.partitions = []
rng = Random()
rng.seed(seed)
data_len = len(data)
indexes = [x for x in range(0, data_len)]
rng.shuffle(indexes)
for frac in sizes:
part_len = int(frac * data_len)
self.partitions.append(indexes[0:part_len])
indexes = indexes[part_len:]
def use(self, partition):
return Partition(self.data, self.partitions[partition])
class Net(nn.Module):
""" Network architecture. """
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = self.fc2(x)
return F.log_softmax(x)
def partition_dataset():
""" Partitioning MNIST """
dataset = datasets.MNIST(
'./data',
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]))
size = dist.get_world_size()
bsz = int(256 / float(size))
partition_sizes = [1.0 / size for _ in range(size)]
partition = DataPartitioner(dataset, partition_sizes)
partition = partition.use(dist.get_rank())
train_set = torch.utils.data.DataLoader(
partition, batch_size=bsz, shuffle=True)
return train_set, bsz
def average_gradients(model):
""" Gradient averaging. """
size = float(dist.get_world_size())
for param in model.parameters():
dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM)
param.grad.data /= size
def run(rank, size):
""" Distributed Synchronous SGD Example """
# print("107 size = ", size)
# print("dist.get_world_size() = ", dist.get_world_size()) ## 2
torch.manual_seed(1234)
train_set, bsz = partition_dataset()
device = torch.device("cuda:{}".format(rank))
model = Net()
model = model.to(device)
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
num_batches = ceil(len(train_set.dataset) / float(bsz))
for epoch in range(10):
epoch_loss = 0.0
for data, target in train_set:
# data, target = Variable(data), Variable(target)
# data, target = Variable(data.cuda(rank)), Variable(target.cuda(rank))
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
epoch_loss += loss.item()
loss.backward()
average_gradients(model)
optimizer.step()
print('Rank ',
dist.get_rank(), ', epoch ', epoch, ': ',
epoch_loss / num_batches)
# if epoch == 4:
# from utils import module_utils
# module_utils.save_model()
def init_processes(rank, size, fn, backend='gloo'):
""" Initialize the distributed environment. """
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '29500'
dist.init_process_group(backend, rank=rank, world_size=size)
fn(rank, size)
if __name__ == "__main__":
size = 2
processes = []
for rank in range(size):
p = Process(target=init_processes, args=(rank, size, run))
p.start()
processes.append(p)
for p in processes:
p.join()
最佳答案
我的解决方案是使用 DistributedDataParallel 而不是如下所示的 DataParallel。
代码
for param in self.model.parameters():
torch.distributed.all_reduce(param.grad.data)
可以成功工作。
class DDPOptimizer:
def __init__(self, model, torch_optim=None, learning_rate=None):
"""
:param parameters:
:param torch_optim: like torch.optim.Adam(parameters, lr=learning_rate, eps=1e-9)
or optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
:param is_ddp:
"""
if torch_optim is None:
torch_optim = torch.optim.Adam(model.parameters(), lr=3e-4, eps=1e-9)
if learning_rate is not None:
torch_optim.defaults["lr"] = learning_rate
self.model = model
self.optimizer = torch_optim
def optimize(self, loss):
self.optimizer.zero_grad()
loss.backward()
for param in self.model.parameters():
torch.distributed.all_reduce(param.grad.data)
self.optimizer.step()
pass
def run():
""" Distributed Synchronous SGD Example """
module_utils.initialize_torch_distributed()
start = time.time()
train_set, bsz = partition_dataset()
model = Net()
local_rank = torch.distributed.get_rank()
device = torch.device("cuda", local_rank)
model = model.to(device)
sgd = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
optimizer = DDPOptimizer(model, torch_optim=sgd)
# optimizer = NoamOptimizerDistributed(100, 1, 10, model)
num_batches = math.ceil(len(train_set.dataset) / float(bsz))
epoch, end_epoch = 1, 10
while epoch <= end_epoch:
epoch_loss = 0.0
for data, target in train_set:
data, target = data.to(device), target.to(device)
output = model(data)
loss = F.nll_loss(output, target)
epoch_loss += loss.item()
optimizer.optimize(loss)
print('Rank ', dist.get_rank(), ', epoch ', epoch, ': ', epoch_loss / num_batches)
# if epoch % 6 == 0:
# if local_rank == 0:
# module_utils.save_model(model, "a.pt")
epoch += 1
print("Time take to train: ", time.time() - start)
关于python - 在torch.distributed中，如何正确平均不同GPU上的梯度？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/58671916/