Adding a distributed pipeline parallelism example for RPC (pytorch#749)

Co-authored-by: Shen Li <shenli@devfair017.maas>

distributed/rpc/pipeline/README.md

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+Distributed Pipeline Parallel Example
+
+This example shows how to distribute a ResNet50 model on two RPC workers and
+then implement distributed pipeline parallelism using RPC.
+
+```
+pip install -r requirements.txt
+python main.py
+```

distributed/rpc/pipeline/main.py

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+import os
+import threading
+import time
+from functools import wraps
+
+import torch
+import torch.nn as nn
+import torch.distributed.autograd as dist_autograd
+import torch.distributed.rpc as rpc
+import torch.multiprocessing as mp
+import torch.optim as optim
+from torch.distributed.optim import DistributedOptimizer
+from torch.distributed.rpc import RRef
+
+from torchvision.models.resnet import Bottleneck
+
+
+#########################################################
+#                   helper functions                    #
+#########################################################
+
+
+def _call_method(method, rref, *args, **kwargs):
+    r"""
+    a helper function to call a method on the given RRef
+    """
+    return method(rref.local_value(), *args, **kwargs)
+
+
+def _remote_on_rref(method, rref, *args, **kwargs):
+    r"""
+    a helper function to run method on the owner of rref and return an RRef
+    of the result.
+    """
+    return rpc.remote(
+        rref.owner(),
+        _call_method,
+        args=[method, rref] + list(args),
+        kwargs=kwargs
+    )
+
+
+def _async_on_rref(method, rref, *args, **kwargs):
+    r"""
+    a helper function to run method on the owner of rref and fetch back the
+    result using RPC
+    """
+    return rpc.rpc_async(
+        rref.owner(),
+        _call_method,
+        args=[method, rref] + list(args),
+        kwargs=kwargs
+    )
+
+
+def _parameter_rrefs(module):
+    r"""
+    Create one RRef for each parameter in the given local module, and return a
+    list of RRefs.
+    """
+    param_rrefs = []
+    for param in module.parameters():
+        param_rrefs.append(RRef(param))
+    return param_rrefs
+
+
+#########################################################
+#           Define Model Parallel ResNet50              #
+#########################################################
+
+
+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)
+
+
+class ResNetPart1(ResNetBase):
+    """
+    The first part of ResNet.
+    """
+    def __init__(self, device, *args, **kwargs):
+        super(ResNetPart1, 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 ResNetPart2(ResNetBase):
+    """
+    The second part of ResNet.
+    """
+    def __init__(self, device, *args, **kwargs):
+        super(ResNetPart2, 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],
+            ResNetPart1,
+            args = ("cuda:0",) + args,
+            kwargs = kwargs
+        )
+
+        # Put the second part of the ResNet50 on workers[1]
+        self.p2_rref = rpc.remote(
+            workers[1],
+            ResNetPart2,
+            args = ("cuda:1",) + 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 = _remote_on_rref(ResNetPart1.forward, self.p1_rref, x_rref)
+            z_fut = _async_on_rref(ResNetPart2.forward, self.p2_rref, y_rref)
+            out_futures.append(z_fut)
+
+        # wait for all RPC to finish
+        outs = [fut.wait() for fut in out_futures]
+        # cat all tensors into one tensor.
+        out = torch.cat(outs)
+        return out
+
+    def parameter_rrefs(self):
+        remote_params = []
+        remote_params.extend(_remote_on_rref(_parameter_rrefs, self.p1_rref).to_here())
+        remote_params.extend(_remote_on_rref(_parameter_rrefs, self.p2_rref).to_here())
+        return remote_params
+
+
+#########################################################
+#                   Run RPC Processes                   #
+#########################################################
+
+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(split_size, ["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)
+
+
+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=256)
+
+    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}")

distributed/rpc/pipeline/requirements.txt

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+torch==1.5.0
+torchvision==0.6.0