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Distributed Pipeline Parallelism Using RPC

Author: Shen Li

Prerequisites:

This tutorial uses a Resnet50 model to demonstrate implementing distributed pipeline parallelism with torch.distributed.rpc APIs. This can be viewed as the distributed counterpart of the multi-GPU pipeline parallelism discussed in Single-Machine Model Parallel Best Practices.

Note

This tutorial requires PyTorch v1.6.0 or above.

Note

Full source code of this tutorial can be found at pytorch/examples.

Basics

The previous tutorial, Getting Started with Distributed RPC Framework shows how to use torch.distributed.rpc to implement distributed model parallelism for an RNN model. That tutorial uses one GPU to host the EmbeddingTable, and the provided code works fine. However, if a model lives on multiple GPUs, it would require some extra steps to increase the amortized utilization of all GPUs. Pipeline parallelism is one type of paradigm that can help in this case.

In this tutorial, we use ResNet50 as an example model which is also used by the Single-Machine Model Parallel Best Practices tutorial. Similarly, the ResNet50 model is divided into two shards and the input batch is partitioned into multiple splits and fed into the two model shards in a pipelined fashion. The difference is that, instead of parallelizing the execution using CUDA streams, this tutorial invokes asynchronous RPCs. So, the solution presented in this tutorial also works across machine boundaries. The remainder of this tutorial presents the implementation in four steps.

Step 1: Partition ResNet50 Model

This is the preparation step which implements ResNet50 in two model shards. The code below is borrowed from the ResNet implementation in torchvision. The ResNetBase module contains the common building blocks and attributes for the two ResNet shards.

import threading

import torch
import torch.nn as nn

from torchvision.models.resnet import Bottleneck

num_classes = 1000


def conv1x1(in_planes, out_planes, stride=1):
    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):
        return [RRef(p) for p in self.parameters()]

Now, we are ready to define the two model shards. For the constructor, we simply split all ResNet50 layers into two parts and move each part into the provided device. The forward functions of both shards take an RRef of the input data, fetch the data locally, and then move it to the expected device. After applying all layers to the input, it moves the output to CPU and returns. It is because the RPC API requires tensors to reside on CPU to avoid invalid device errors when the numbers of devices in the caller and the callee do not match.

class ResNetShard1(ResNetBase):
    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.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    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):
    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()

Step 2: Stitch ResNet50 Model Shards Into One Module

Then, we create a DistResNet50 module to assemble the two shards and implement the pipeline parallel logic. In the constructor, we use two rpc.remote calls to put the two shards on two different RPC workers respectively and hold on to the RRef to the two model parts so that they can be referenced in the forward pass. The forward function splits the input batch into multiple micro-batches, and feeds these micro-batches to the two model parts in a pipelined fashion. It first uses an rpc.remote call to apply the first shard to a micro-batch and then forwards the returned intermediate output RRef to the second model shard. After that, it collects the Future of all micro-outputs, and waits for all of them after the loop. Note that both remote() and rpc_async() return immediately and run asynchronously. Therefore, the entire loop is non-blocking, and will launch multiple RPCs concurrently. The execution order of one micro-batch on two model parts are preserved by intermediate output y_rref. The execution order across micro-batches does not matter. In the end, the forward function concatenates outputs of all micro-batches into one single output tensor and returns. The parameter_rrefs function is a helper to simplify distributed optimizer construction, which will be used later.

class DistResNet50(nn.Module):
    def __init__(self, num_split, workers, *args, **kwargs):
        super(DistResNet50, self).__init__()

        self.num_split = num_split

        # 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 = ("cuda:1",) + args,
            kwargs = kwargs
        )

    def forward(self, xs):
        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)
            z_fut = self.p2_rref.rpc_async().forward(y_rref)
            out_futures.append(z_fut)

        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

Step 3: Define The Training Loop

After defining the model, let us implement the training loop. We use a dedicated “master” worker to prepare random inputs and labels, and control the distributed backward pass and distributed optimizer step. It first creates an instance of the DistResNet50 module. It specifies the number of micro-batches for each batch, and also provides the name of the two RPC workers (i.e., “worker1”, and “worker2”). Then it defines the loss function and creates a DistributedOptimizer using the parameter_rrefs() helper to acquire a list of parameter RRefs. Then, the main training loop is very similar to regular local training, except that it uses dist_autograd to launch backward and provides the context_id for both backward and optimizer step().

import torch.distributed.autograd as dist_autograd
import torch.optim as optim
from torch.distributed.optim import DistributedOptimizer

num_batches = 3
batch_size = 120
image_w = 128
image_h = 128


def run_master(num_split):
    # put the two model parts on worker1 and worker2 respectively
    model = DistResNet50(num_split, ["worker1", "worker2"])
    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)

Step 4: Launch RPC Processes

Finally, the code below shows the target function for all processes. The main logic is defined in run_master. The workers passively waiting for commands from the master, and hence simply runs init_rpc and shutdown, where the shutdown by default will block until all RPC participants finish.

import os
import time

import torch.multiprocessing as mp


def run_worker(rank, world_size, num_split):
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '29500'
    options = rpc.ProcessGroupRpcBackendOptions(num_send_recv_threads=128)

    if rank == 0:
        rpc.init_rpc(
            "master",
            rank=rank,
            world_size=world_size,
            rpc_backend_options=options
        )
        run_master(num_split)
    else:
        rpc.init_rpc(
            f"worker{rank}",
            rank=rank,
            world_size=world_size,
            rpc_backend_options=options
        )
        pass

    # block until all rpcs finish
    rpc.shutdown()


if __name__=="__main__":
    world_size = 3
    for num_split in [1, 2, 4, 8]:
        tik = time.time()
        mp.spawn(run_worker, args=(world_size, num_split), nprocs=world_size, join=True)
        tok = time.time()
        print(f"number of splits = {num_split}, execution time = {tok - tik}")

The output below shows the speedup attained by increasing the number of splits in each batch.

$ python main.py
Processing batch 0
Processing batch 1
Processing batch 2
number of splits = 1, execution time = 16.45062756538391
Processing batch 0
Processing batch 1
Processing batch 2
number of splits = 2, execution time = 12.329529762268066
Processing batch 0
Processing batch 1
Processing batch 2
number of splits = 4, execution time = 10.164430618286133
Processing batch 0
Processing batch 1
Processing batch 2
number of splits = 8, execution time = 9.076049566268921

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