因为业务需要，最近接到一项任务，是如何利用pytorch实现model parallel以及distributed training。搜罗了网上很多资料，以及阅读了pytorch官方的教程，都没有可参考的案例。讲的比较多的是data parallel，关于model parallel以及model pipeline的研究发现不多。
通过阅读pytorch官方主页，发现这个example是进行分布式的model pipeline的，
官方博客地址：DISTRIBUTED PIPELINE PARALLELISM USING RPC
官方的example地址：Distributed Pipeline Parallel Example
通过阅读代码发现，这个代码以Resnet 50 model为例，将model直接拆分成两部分，并指定两部分在不同的worker运行，代码实现了在同一台机器上，创建多进程来拆分模型运行。关于这个代码的详细介绍可搜索关键词：pytorch RPC 的分布式管道并行，这里不多介绍。
通过在本地运行代码发现，不满足多机器运行的需求。接下来是思考的心路里程。
- 首先通过代码发现，python main.py程序运行时，无法指定rank，那么在跨机器运行时如何知道哪台机器是worker1，worker2？这个地方，我们首先怀疑需要去修改worker，人为在代码中指定worker的IP地址，如修改main.py 代码中191行
修改前：
model = DistResNet50(split_size, ["worker1", "worker2"])
修改后：
model = DistResNet50(split_size, ["worker1@xxx.xxx.xxx.xxx", "worker2@xxx.xxx.xxx.xxx"])
然后，很自然的就报错了，这里无法识别这样的worker名，不支持直接指定，这条路也就走不通了。 - 接着只能重新阅读代码，到最后251行，我们发现
mp.spawn(run_worker, args=(world_size, num_split), nprocs=world_size, join=True)
尤其是这行代码中mp.spawn引起了我们的怀疑，这不是多进程么，这本质还是在多进程情况下来执行程序，无法跨机器啊，不符合我们的需求。 - 最后的最后，我们重新阅读pytorch rpc机制，并通过简单测试程序，让两台机器互相通信，其中一台机器发起运算请求并传输原始数据，另外一台机器负责接收数据并进行相关运算，这个程序当时在两台物理机器上测试成功了，那说明rpc实现通信这件事并不复杂。结合前面给的代码，我们决定将worke1和worker2分开写代码，分开执行，并且在代码中需要指定这些worker所属的rank，这样理论上就能够将原始代码修改成分机器的rpc通信运行了。
上面主要是我们的心理历程，话不多说，接下来show the code。
实验环境，两台机器，均是cpu环境，conda安装的环境也保证了一致。
master机器代码：
# https://github.com/pytorch/examples/blob/main/distributed/rpc/pipeline/main.py
import os
import threading
import time
import torch
import torch.nn as nn
import torch.distributed.autograd as dist_autograd
import torch.distributed.rpc as rpc
import torch.optim as optim
from torch.distributed.optim import DistributedOptimizer
from torch.distributed.rpc import RRef
from torchvision.models.resnet import Bottleneck
os.environ['MASTER_ADDR'] = 'XXX.XXX.XXX.XXX' # 指定master ip地址
os.environ['MASTER_PORT'] = '7856' # 指定master 端口号
#########################################################
# Define Model Parallel ResNet50 #
#########################################################
# In order to split the ResNet50 and place it on two different workers, we
# implement it in two model shards. The ResNetBase class defines common
# attributes and methods shared by two shards. ResNetShard1 and ResNetShard2
# contain two partitions of the model layers respectively.
num_classes = 1000
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class ResNetBase(nn.Module):
def __init__(self, block, inplanes, num_classes=1000,
groups=1, width_per_group=64, norm_layer=None):
super(ResNetBase, self).__init__()
self._lock = threading.Lock()
self._block = block
self._norm_layer = nn.BatchNorm2d
self.inplanes = inplanes
self.dilation = 1
self.groups = groups
self.base_width = width_per_group
def _make_layer(self, planes, blocks, stride=1):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if stride != 1 or self.inplanes != planes * self._block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * self._block.expansion, stride),
norm_layer(planes * self._block.expansion),
)
layers = []
layers.append(self._block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * self._block.expansion
for _ in range(1, blocks):
layers.append(self._block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def parameter_rrefs(self):
r"""
Create one RRef for each parameter in the given local module, and return a
list of RRefs.
"""
return [RRef(p) for p in self.parameters()]
class ResNetShard1(ResNetBase):
"""
The first part of ResNet.
"""
def __init__(self, device, *args, **kwargs):
super(ResNetShard1, self).__init__(
Bottleneck, 64, num_classes=num_classes, *args, **kwargs)
self.device = device
self.seq = nn.Sequential(
nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False),
self._norm_layer(self.inplanes),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
self._make_layer(64, 3),
self._make_layer(128, 4, stride=2)
).to(self.device)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
def forward(self, x_rref):
x = x_rref.to_here().to(self.device)
with self._lock:
out = self.seq(x)
return out.cpu()
class ResNetShard2(ResNetBase):
"""
The second part of ResNet.
"""
def __init__(self, device, *args, **kwargs):
super(ResNetShard2, self).__init__(
Bottleneck, 512, num_classes=num_classes, *args, **kwargs)
self.device = device
self.seq = nn.Sequential(
self._make_layer(256, 6, stride=2),
self._make_layer(512, 3, stride=2),
nn.AdaptiveAvgPool2d((1, 1)),
).to(self.device)
self.fc = nn.Linear(512 * self._block.expansion, num_classes).to(self.device)
def forward(self, x_rref):
x = x_rref.to_here().to(self.device)
with self._lock:
out = self.fc(torch.flatten(self.seq(x), 1))
return out.cpu()
class DistResNet50(nn.Module):
"""
Assemble two parts as an nn.Module and define pipelining logic
"""
def __init__(self, split_size, workers, *args, **kwargs):
super(DistResNet50, self).__init__()
self.split_size = split_size
# Put the first part of the ResNet50 on workers[0]
self.p1_rref = rpc.remote(
workers[0],
ResNetShard1,
args = ("cuda:0",) + args,
kwargs = kwargs
)
# Put the second part of the ResNet50 on workers[1]
self.p2_rref = rpc.remote(
workers[1],
ResNetShard2,
args = ("cpu",) + args,
kwargs = kwargs
)
def forward(self, xs):
# Split the input batch xs into micro-batches, and collect async RPC
# futures into a list
out_futures = []
for x in iter(xs.split(self.split_size, dim=0)):
x_rref = RRef(x)
y_rref = self.p1_rref.remote().forward(x_rref)
print(y_rref)
z_fut = self.p2_rref.rpc_async().forward(y_rref)
print(z_fut)
out_futures.append(z_fut)
# collect and cat all output tensors into one tensor.
return torch.cat(torch.futures.wait_all(out_futures))
def parameter_rrefs(self):
remote_params = []
remote_params.extend(self.p1_rref.remote().parameter_rrefs().to_here())
remote_params.extend(self.p2_rref.remote().parameter_rrefs().to_here())
return remote_params
#########################################################
# Run RPC Processes #
#########################################################
num_batches = 3
batch_size = 8
image_w = 128
image_h = 128
if __name__=="__main__":
options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=256, rpc_timeout=300)
# 初始化主节点的RPC连接
rpc.init_rpc("master", rank=0, world_size=2, rpc_backend_options=options)
for num_split in [1,2]:
tik = time.time()
model = DistResNet50(num_split, ["master", "worker"])
loss_fn = nn.MSELoss()
opt = DistributedOptimizer(
optim.SGD,
model.parameter_rrefs(),
lr=0.05,
)
one_hot_indices = torch.LongTensor(batch_size) \
.random_(0, num_classes) \
.view(batch_size, 1)
for i in range(num_batches):
print(f"Processing batch {i}")
# generate random inputs and labels
inputs = torch.randn(batch_size, 3, image_w, image_h)
labels = torch.zeros(batch_size, num_classes) \
.scatter_(1, one_hot_indices, 1)
with dist_autograd.context() as context_id:
outputs = model(inputs)
dist_autograd.backward(context_id, [loss_fn(outputs, labels)])
opt.step(context_id)
tok = time.time()
print(f"number of splits = {num_split}, execution time = {tok - tik}")
# 关闭RPC连接
rpc.shutdown()
worker端的代码
# https://github.com/pytorch/examples/blob/main/distributed/rpc/pipeline/main.py
import os
import threading
import time
from functools import wraps
import torch
import torch.nn as nn
import torch.distributed.rpc as rpc
from torch.distributed.rpc import RRef
from torchvision.models.resnet import Bottleneck
os.environ['MASTER_ADDR'] = 'XXX.XXX.XXX.XXX' # 指定master 端口号
os.environ['MASTER_PORT'] = '7856' # 指定master 端口号
#########################################################
# Define Model Parallel ResNet50 #
#########################################################
# In order to split the ResNet50 and place it on two different workers, we
# implement it in two model shards. The ResNetBase class defines common
# attributes and methods shared by two shards. ResNetShard1 and ResNetShard2
# contain two partitions of the model layers respectively.
num_classes = 1000
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class ResNetBase(nn.Module):
def __init__(self, block, inplanes, num_classes=1000,
groups=1, width_per_group=64, norm_layer=None):
super(ResNetBase, self).__init__()
self._lock = threading.Lock()
self._block = block
self._norm_layer = nn.BatchNorm2d
self.inplanes = inplanes
self.dilation = 1
self.groups = groups
self.base_width = width_per_group
def _make_layer(self, planes, blocks, stride=1):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if stride != 1 or self.inplanes != planes * self._block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * self._block.expansion, stride),
norm_layer(planes * self._block.expansion),
)
layers = []
layers.append(self._block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * self._block.expansion
for _ in range(1, blocks):
layers.append(self._block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def parameter_rrefs(self):
r"""
Create one RRef for each parameter in the given local module, and return a
list of RRefs.
"""
return [RRef(p) for p in self.parameters()]
class ResNetShard2(ResNetBase):
"""
The second part of ResNet.
"""
def __init__(self, device, *args, **kwargs):
super(ResNetShard2, self).__init__(
Bottleneck, 512, num_classes=num_classes, *args, **kwargs)
self.device = device
self.seq = nn.Sequential(
self._make_layer(256, 6, stride=2),
self._make_layer(512, 3, stride=2),
nn.AdaptiveAvgPool2d((1, 1)),
).to(self.device)
self.fc = nn.Linear(512 * self._block.expansion, num_classes).to(self.device)
def forward(self, x_rref):
x = x_rref.to_here().to(self.device)
print(x)
with self._lock:
out = self.fc(torch.flatten(self.seq(x), 1))
return out.cpu()
#########################################################
# Run RPC Processes #
#########################################################
if __name__=="__main__":
options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=256, rpc_timeout=300)
# 初始化工作节点的RPC连接
rpc.init_rpc("worker", rank=1, world_size=2, rpc_backend_options=options)
# 等待主节点的调用
rpc.shutdown()
代码中的MASTER_ADDR和port需要指定为一致，分别在master机器上运行master.py，worker机器上运行worker.py，这样就可以实现Resnet 50 model在两台物理机器上分布式model pipeline。
注意事项
- 确保物理机器能够互相ping通，同时关闭防火墙
- 两个物理机器最好都是linux环境，我们的实验发现pytorch的分布式不支持在Windows环境运行
- 两个物理机器的python运行环境要求保持一致
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