Getting Started with Distributed RPC Framework¶
Author: Shen Li
참고
View and edit this tutorial in github.
Prerequisites:
This tutorial uses two simple examples to demonstrate how to build distributed training with the torch.distributed.rpc package which was first introduced as an experimental feature in PyTorch v1.4. Source code of the two examples can be found in PyTorch examples.
Previous tutorials, Getting Started With Distributed Data Parallel and Writing Distributed Applications With PyTorch, described DistributedDataParallel which supports a specific training paradigm where the model is replicated across multiple processes and each process handles a split of the input data. Sometimes, you might run into scenarios that require different training paradigms. For example:
In reinforcement learning, it might be relatively expensive to acquire training data from environments while the model itself can be quite small. In this case, it might be useful to spawn multiple observers running in parallel and share a single agent. In this case, the agent takes care of the training locally, but the application would still need libraries to send and receive data between observers and the trainer.
Your model might be too large to fit in GPUs on a single machine, and hence would need a library to help split the model onto multiple machines. Or you might be implementing a parameter server training framework, where model parameters and trainers live on different machines.
The torch.distributed.rpc package can help with the above scenarios. In case 1, RPC and RRef allow sending data from one worker to another while easily referencing remote data objects. In case 2, distributed autograd and distributed optimizer make executing backward pass and optimizer step as if it is local training. In the next two sections, we will demonstrate APIs of torch.distributed.rpc using a reinforcement learning example and a language model example. Please note, this tutorial does not aim at building the most accurate or efficient models to solve given problems, instead, the main goal here is to show how to use the torch.distributed.rpc package to build distributed training applications.
Distributed Reinforcement Learning using RPC and RRef¶
This section describes steps to build a toy distributed reinforcement learning
model using RPC to solve CartPole-v1 from OpenAI Gym.
The policy code is mostly borrowed from the existing single-thread
example
as shown below. We will skip details of the Policy design, and focus on RPC
usages.
import torch.nn as nn
import torch.nn.functional as F
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.affine1 = nn.Linear(4, 128)
self.dropout = nn.Dropout(p=0.6)
self.affine2 = nn.Linear(128, 2)
def forward(self, x):
x = self.affine1(x)
x = self.dropout(x)
x = F.relu(x)
action_scores = self.affine2(x)
return F.softmax(action_scores, dim=1)
We are ready to present the observer. In this example, each observer creates its
own environment, and waits for the agent’s command to run an episode. In each
episode, one observer loops at most n_steps iterations, and in each
iteration, it uses RPC to pass its environment state to the agent and gets an
action back. Then it applies that action to its environment, and gets the reward
and the next state from the environment. After that, the observer uses another
RPC to report the reward to the agent. Again, please note that, this is
obviously not the most efficient observer implementation. For example, one
simple optimization could be packing current state and last reward in one RPC to
reduce the communication overhead. However, the goal is to demonstrate RPC API
instead of building the best solver for CartPole. So, let’s keep the logic
simple and the two steps explicit in this example.
import argparse
import gym
import torch.distributed.rpc as rpc
parser = argparse.ArgumentParser(
description="RPC Reinforcement Learning Example",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument('--world_size', default=2, type=int, metavar='W',
help='number of workers')
parser.add_argument('--log_interval', type=int, default=10, metavar='N',
help='interval between training status logs')
parser.add_argument('--gamma', type=float, default=0.99, metavar='G',
help='how much to value future rewards')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed for reproducibility')
args = parser.parse_args()
class Observer:
def __init__(self):
self.id = rpc.get_worker_info().id
self.env = gym.make('CartPole-v1')
self.env.seed(args.seed)
def run_episode(self, agent_rref):
state, ep_reward = self.env.reset(), 0
for _ in range(10000):
# send the state to the agent to get an action
action = agent_rref.rpc_sync().select_action(self.id, state)
# apply the action to the environment, and get the reward
state, reward, done, _ = self.env.step(action)
# report the reward to the agent for training purpose
agent_rref.rpc_sync().report_reward(self.id, reward)
# finishes after the number of self.env._max_episode_steps
if done:
break
The code for agent is a little more complex, and we will break it into multiple
pieces. In this example, the agent serves as both the trainer and the master,
such that it sends command to multiple distributed observers to run episodes,
and it also records all actions and rewards locally which will be used during
the training phase after each episode. The code below shows Agent
constructor where most lines are initializing various components. The loop at
the end initializes observers remotely on other workers, and holds RRefs to
those observers locally. The agent will use those observer RRefs later to
send commands. Applications don’t need to worry about the lifetime of RRefs.
The owner of each RRef maintains a reference counting map to track its
lifetime, and guarantees the remote data object will not be deleted as long as
there is any live user of that RRef. Please refer to the RRef
design doc for details.
import gym
import numpy as np
import torch
import torch.distributed.rpc as rpc
import torch.optim as optim
from torch.distributed.rpc import RRef, rpc_async, remote
from torch.distributions import Categorical
class Agent:
def __init__(self, world_size):
self.ob_rrefs = []
self.agent_rref = RRef(self)
self.rewards = {}
self.saved_log_probs = {}
self.policy = Policy()
self.optimizer = optim.Adam(self.policy.parameters(), lr=1e-2)
self.eps = np.finfo(np.float32).eps.item()
self.running_reward = 0
self.reward_threshold = gym.make('CartPole-v1').spec.reward_threshold
for ob_rank in range(1, world_size):
ob_info = rpc.get_worker_info(OBSERVER_NAME.format(ob_rank))
self.ob_rrefs.append(remote(ob_info, Observer))
self.rewards[ob_info.id] = []
self.saved_log_probs[ob_info.id] = []
Next, the agent exposes two APIs to observers for selecting actions and reporting rewards. Those functions only run locally on the agent, but will be triggered by observers through RPC.
class Agent:
...
def select_action(self, ob_id, state):
state = torch.from_numpy(state).float().unsqueeze(0)
probs = self.policy(state)
m = Categorical(probs)
action = m.sample()
self.saved_log_probs[ob_id].append(m.log_prob(action))
return action.item()
def report_reward(self, ob_id, reward):
self.rewards[ob_id].append(reward)
Let’s add a run_episode function on agent which tells all observers
to execute an episode. In this function, it first creates a list to collect
futures from asynchronous RPCs, and then loop over all observer RRefs to
make asynchronous RPCs. In these RPCs, the agent also passes an RRef of
itself to the observer, so that the observer can call functions on the agent as
well. As shown above, each observer will make RPCs back to the agent, which are
nested RPCs. After each episode, the saved_log_probs and rewards will
contain the recorded action probs and rewards.
class Agent:
...
def run_episode(self):
futs = []
for ob_rref in self.ob_rrefs:
# make async RPC to kick off an episode on all observers
futs.append(
rpc_async(
ob_rref.owner(),
ob_rref.rpc_sync().run_episode,
args=(self.agent_rref,)
)
)
# wait until all obervers have finished this episode
for fut in futs:
fut.wait()
Finally, after one episode, the agent needs to train the model, which
is implemented in the finish_episode function below. There is no RPCs in
this function and it is mostly borrowed from the single-thread
example.
Hence, we skip describing its contents.
class Agent:
...
def finish_episode(self):
# joins probs and rewards from different observers into lists
R, probs, rewards = 0, [], []
for ob_id in self.rewards:
probs.extend(self.saved_log_probs[ob_id])
rewards.extend(self.rewards[ob_id])
# use the minimum observer reward to calculate the running reward
min_reward = min([sum(self.rewards[ob_id]) for ob_id in self.rewards])
self.running_reward = 0.05 * min_reward + (1 - 0.05) * self.running_reward
# clear saved probs and rewards
for ob_id in self.rewards:
self.rewards[ob_id] = []
self.saved_log_probs[ob_id] = []
policy_loss, returns = [], []
for r in rewards[::-1]:
R = r + args.gamma * R
returns.insert(0, R)
returns = torch.tensor(returns)
returns = (returns - returns.mean()) / (returns.std() + self.eps)
for log_prob, R in zip(probs, returns):
policy_loss.append(-log_prob * R)
self.optimizer.zero_grad()
policy_loss = torch.cat(policy_loss).sum()
policy_loss.backward()
self.optimizer.step()
return min_reward
With Policy, Observer, and Agent classes, we are ready to launch
multiple processes to perform the distributed training. In this example, all
processes run the same run_worker function, and they use the rank to
distinguish their role. Rank 0 is always the agent, and all other ranks are
observers. The agent serves as master by repeatedly calling run_episode and
finish_episode until the running reward surpasses the reward threshold
specified by the environment. All observers passively waiting for commands
from the agent. The code is wrapped by
rpc.init_rpc and
rpc.shutdown,
which initializes and terminates RPC instances respectively. More details are
available in the API page.
import os
from itertools import count
import torch.multiprocessing as mp
AGENT_NAME = "agent"
OBSERVER_NAME="obs{}"
def run_worker(rank, world_size):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29500'
if rank == 0:
# rank0 is the agent
rpc.init_rpc(AGENT_NAME, rank=rank, world_size=world_size)
agent = Agent(world_size)
print(f"This will run until reward threshold of {agent.reward_threshold}"
" is reached. Ctrl+C to exit.")
for i_episode in count(1):
agent.run_episode()
last_reward = agent.finish_episode()
if i_episode % args.log_interval == 0:
print(f"Episode {i_episode}\tLast reward: {last_reward:.2f}\tAverage reward: "
f"{agent.running_reward:.2f}")
if agent.running_reward > agent.reward_threshold:
print(f"Solved! Running reward is now {agent.running_reward}!")
break
else:
# other ranks are the observer
rpc.init_rpc(OBSERVER_NAME.format(rank), rank=rank, world_size=world_size)
# observers passively waiting for instructions from the agent
# block until all rpcs finish, and shutdown the RPC instance
rpc.shutdown()
mp.spawn(
run_worker,
args=(args.world_size, ),
nprocs=args.world_size,
join=True
)
Below are some sample outputs when training with world_size=2.
This will run until reward threshold of 475.0 is reached. Ctrl+C to exit.
Episode 10 Last reward: 26.00 Average reward: 10.01
Episode 20 Last reward: 16.00 Average reward: 11.27
Episode 30 Last reward: 49.00 Average reward: 18.62
Episode 40 Last reward: 45.00 Average reward: 26.09
Episode 50 Last reward: 44.00 Average reward: 30.03
Episode 60 Last reward: 111.00 Average reward: 42.23
Episode 70 Last reward: 131.00 Average reward: 70.11
Episode 80 Last reward: 87.00 Average reward: 76.51
Episode 90 Last reward: 86.00 Average reward: 95.93
Episode 100 Last reward: 13.00 Average reward: 123.93
Episode 110 Last reward: 33.00 Average reward: 91.39
Episode 120 Last reward: 73.00 Average reward: 76.38
Episode 130 Last reward: 137.00 Average reward: 88.08
Episode 140 Last reward: 89.00 Average reward: 104.96
Episode 150 Last reward: 97.00 Average reward: 98.74
Episode 160 Last reward: 150.00 Average reward: 100.87
Episode 170 Last reward: 126.00 Average reward: 104.38
Episode 180 Last reward: 500.00 Average reward: 213.74
Episode 190 Last reward: 322.00 Average reward: 300.22
Episode 200 Last reward: 165.00 Average reward: 272.71
Episode 210 Last reward: 168.00 Average reward: 233.11
Episode 220 Last reward: 184.00 Average reward: 195.02
Episode 230 Last reward: 284.00 Average reward: 208.32
Episode 240 Last reward: 395.00 Average reward: 247.37
Episode 250 Last reward: 500.00 Average reward: 335.42
Episode 260 Last reward: 500.00 Average reward: 386.30
Episode 270 Last reward: 500.00 Average reward: 405.29
Episode 280 Last reward: 500.00 Average reward: 443.29
Episode 290 Last reward: 500.00 Average reward: 464.65
Solved! Running reward is now 475.3163778435275!
In this example, we show how to use RPC as the communication vehicle to pass
data across workers, and how to use RRef to reference remote objects. It is true
that you could build the entire structure directly on top of ProcessGroup
send and recv APIs or use other communication/RPC libraries. However,
by using torch.distributed.rpc, you can get the native support and
continuously optimized performance under the hood.
Next, we will show how to combine RPC and RRef with distributed autograd and distributed optimizer to perform distributed model parallel training.
Distributed RNN using Distributed Autograd and Distributed Optimizer¶
In this section, we use an RNN model to show how to build distributed model parallel training with the RPC API. The example RNN model is very small and can easily fit into a single GPU, but we still divide its layers onto two different workers to demonstrate the idea. Developer can apply the similar techniques to distribute much larger models across multiple devices and machines.
The RNN model design is borrowed from the word language model in PyTorch
example
repository, which contains three main components, an embedding table, an
LSTM layer, and a decoder. The code below wraps the embedding table and the
decoder into sub-modules, so that their constructors can be passed to the RPC
API. In the EmbeddingTable sub-module, we intentionally put the
Embedding layer on GPU to cover the use case. In v1.4, RPC always creates
CPU tensor arguments or return values on the destination worker. If the function
takes a GPU tensor, you need to move it to the proper device explicitly.
class EmbeddingTable(nn.Module):
r"""
Encoding layers of the RNNModel
"""
def __init__(self, ntoken, ninp, dropout):
super(EmbeddingTable, self).__init__()
self.drop = nn.Dropout(dropout)
self.encoder = nn.Embedding(ntoken, ninp).cuda()
self.encoder.weight.data.uniform_(-0.1, 0.1)
def forward(self, input):
return self.drop(self.encoder(input.cuda()).cpu()
class Decoder(nn.Module):
def __init__(self, ntoken, nhid, dropout):
super(Decoder, self).__init__()
self.drop = nn.Dropout(dropout)
self.decoder = nn.Linear(nhid, ntoken)
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-0.1, 0.1)
def forward(self, output):
return self.decoder(self.drop(output))
With the above sub-modules, we can now piece them together using RPC to
create an RNN model. In the code below ps represents a parameter server,
which hosts parameters of the embedding table and the decoder. The constructor
uses the remote
API to create an EmbeddingTable object and a Decoder object on the
parameter server, and locally creates the LSTM sub-module. During the
forward pass, the trainer uses the EmbeddingTable RRef to find the
remote sub-module and passes the input data to the EmbeddingTable using RPC
and fetches the lookup results. Then, it runs the embedding through the local
LSTM layer, and finally uses another RPC to send the output to the
Decoder sub-module. In general, to implement distributed model parallel
training, developers can divide the model into sub-modules, invoke RPC to create
sub-module instances remotely, and use on RRef to find them when necessary.
As you can see in the code below, it looks very similar to single-machine model
parallel training. The main difference is replacing Tensor.to(device) with
RPC functions.
class RNNModel(nn.Module):
def __init__(self, ps, ntoken, ninp, nhid, nlayers, dropout=0.5):
super(RNNModel, self).__init__()
# setup embedding table remotely
self.emb_table_rref = rpc.remote(ps, EmbeddingTable, args=(ntoken, ninp, dropout))
# setup LSTM locally
self.rnn = nn.LSTM(ninp, nhid, nlayers, dropout=dropout)
# setup decoder remotely
self.decoder_rref = rpc.remote(ps, Decoder, args=(ntoken, nhid, dropout))
def forward(self, input, hidden):
# pass input to the remote embedding table and fetch emb tensor back
emb = _remote_method(EmbeddingTable.forward, self.emb_table_rref, input)
output, hidden = self.rnn(emb, hidden)
# pass output to the rremote decoder and get the decoded output back
decoded = _remote_method(Decoder.forward, self.decoder_rref, output)
return decoded, hidden
Before introducing the distributed optimizer, let’s add a helper function to
generate a list of RRefs of model parameters, which will be consumed by the
distributed optimizer. In local training, applications could call
Module.parameters() to grab references to all parameter tensors, and pass it
to the local optimizer for subsequent updates. However, the same API does not
work in distributed training scenarios as some parameters live on remote
machines. Therefore, instead of taking a list of parameter Tensors, the
distributed optimizer takes a list of RRefs, one RRef per model
parameter for both local and remote model parameters. The helper function is
pretty simple, just call Module.parameters() and creates a local RRef on
each of the parameters.
def _parameter_rrefs(module):
param_rrefs = []
for param in module.parameters():
param_rrefs.append(RRef(param))
return param_rrefs
Then, as the RNNModel contains three sub-modules, we need to call
_parameter_rrefs three times, and wrap that into another helper function.
class RNNModel(nn.Module):
...
def parameter_rrefs(self):
remote_params = []
# get RRefs of embedding table
remote_params.extend(_remote_method(_parameter_rrefs, self.emb_table_rref))
# create RRefs for local parameters
remote_params.extend(_parameter_rrefs(self.rnn))
# get RRefs of decoder
remote_params.extend(_remote_method(_parameter_rrefs, self.decoder_rref))
return remote_params
Now, we are ready to implement the training loop. After initializing model
arguments, we create the RNNModel and the DistributedOptimizer. The
distributed optimizer will take a list of parameter RRefs, find all distinct
owner workers, and create the given local optimizer (i.e., SGD in this case,
you can use other local optimizers as well) on each of the owner worker using
the given arguments (i.e., lr=0.05).
In the training loop, it first creates a distributed autograd context, which
will help the distributed autograd engine to find gradients and involved RPC
send/recv functions. The design details of the distributed autograd engine can
be found in its design note.
Then, it kicks off the forward pass as if it is a local
model, and run the distributed backward pass. For the distributed backward, you
only need to specify a list of roots, in this case, it is the loss Tensor.
The distributed autograd engine will traverse the distributed graph
automatically and write gradients properly. Next, it runs the step
function on the distributed optimizer, which will reach out to all involved
local optimizers to update model parameters. Compared to local training, one
minor difference is that you don’t need to run zero_grad() because each
autograd context has dedicated space to store gradients, and as we create a
context per iteration, those gradients from different iterations will not
accumulate to the same set of Tensors.
def run_trainer():
batch = 5
ntoken = 10
ninp = 2
nhid = 3
nindices = 3
nlayers = 4
hidden = (
torch.randn(nlayers, nindices, nhid),
torch.randn(nlayers, nindices, nhid)
)
model = rnn.RNNModel('ps', ntoken, ninp, nhid, nlayers)
# setup distributed optimizer
opt = DistributedOptimizer(
optim.SGD,
model.parameter_rrefs(),
lr=0.05,
)
criterion = torch.nn.CrossEntropyLoss()
def get_next_batch():
for _ in range(5):
data = torch.LongTensor(batch, nindices) % ntoken
target = torch.LongTensor(batch, ntoken) % nindices
yield data, target
# train for 10 iterations
for epoch in range(10):
for data, target in get_next_batch():
# create distributed autograd context
with dist_autograd.context() as context_id:
hidden[0].detach_()
hidden[1].detach_()
output, hidden = model(data, hidden)
loss = criterion(output, target)
# run distributed backward pass
dist_autograd.backward(context_id, [loss])
# run distributed optimizer
opt.step(context_id)
# not necessary to zero grads since they are
# accumulated into the distributed autograd context
# which is reset every iteration.
print("Training epoch {}".format(epoch))
Finally, let’s add some glue code to launch the parameter server and the trainer processes.
def run_worker(rank, world_size):
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29500'
if rank == 1:
rpc.init_rpc("trainer", rank=rank, world_size=world_size)
_run_trainer()
else:
rpc.init_rpc("ps", rank=rank, world_size=world_size)
# parameter server do nothing
pass
# block until all rpcs finish
rpc.shutdown()
if __name__=="__main__":
world_size = 2
mp.spawn(run_worker, args=(world_size, ), nprocs=world_size, join=True)