Getting Started with Distributed RPC Framework — PyTorch Tutorials 2.7.0+cu126 documentation (original) (raw)

intermediate/rpc_tutorial

Run in Google Colab

Colab

Download Notebook

Notebook

View on GitHub

GitHub

Created On: Jan 01, 2020 | Last Updated: Jan 21, 2025 | Last Verified: Nov 05, 2024

Author: Shen Li

Note

View and edit this tutorial in github.

Prerequisites:

• PyTorch Distributed Overview
• RPC API documents

This tutorial uses two simple examples to demonstrate how to build distributed training with the torch.distributed.rpcpackage which was first introduced as an experimental feature in PyTorch v1.4. Source code of the two examples can be found inPyTorch examples.

Previous tutorials,Getting Started With Distributed Data Paralleland Writing Distributed Applications With PyTorch, described DistributedDataParallelwhich 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:

1. 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.
2. 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 servertraining framework, where model parameters and trainers live on different machines.

The torch.distributed.rpc package can help with the above scenarios. In case 1, RPCand RRef allow sending data from one worker to another while easily referencing remote data objects. In case 2, distributed autogradand distributed optimizermake executing backward pass and optimizer step as if it is local training. In the next two sections, we will demonstrate APIs oftorch.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 thetorch.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-threadexampleas 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 Agentconstructor 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-threadexample. 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 andfinish_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 byrpc.init_rpc andrpc.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 PyTorchexamplerepository, which contains three main components, an embedding table, anLSTM 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 theEmbedding 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 remoteAPI 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 localLSTM layer, and finally uses another RPC to send the output to theDecoder 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 callModule.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 stepfunction 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)