text: # -*- coding: utf-8 -*-
  """model_parallel_tutorial.ipynb
  
  Automatically generated by Colaboratory.
  
  Original file is located at
      https://colab.research.google.com/drive/1rqKZ_3nbTzDa7vuNoP8k73b99XQdx1l8
  """
  
  # Commented out IPython magic to ensure Python compatibility.
  # %matplotlib inline
  
  """Single-Machine Model Parallel Best Practices
  ================================
  **Author**: `Shen Li <https://mrshenli.github.io/>`_
  
  Model parallel is widely-used in distributed training
  techniques. Previous posts have explained how to use
  `DataParallel <https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html>`_
  to train a neural network on multiple GPUs; this feature replicates the
  same model to all GPUs, where each GPU consumes a different partition of the
  input data. Although it can significantly accelerate the training process, it
  does not work for some use cases where the model is too large to fit into a
  single GPU. This post shows how to solve that problem by using **model parallel**,
  which, in contrast to ``DataParallel``, splits a single model onto different GPUs,
  rather than replicating the entire model on each GPU (to be concrete, say a model
  ``m`` contains 10 layers: when using ``DataParallel``, each GPU will have a
  replica of each of these 10 layers, whereas when using model parallel on two GPUs,
  each GPU could host 5 layers).
  
  The high-level idea of model parallel is to place different sub-networks of a
  model onto different devices, and implement the ``forward`` method accordingly
  to move intermediate outputs across devices. As only part of a model operates
  on any individual device, a set of devices can collectively serve a larger
  model. In this post, we will not try to construct huge models and squeeze them
  into a limited number of GPUs. Instead, this post focuses on showing the idea
  of model parallel. It is up to the readers to apply the ideas to real-world
  applications.
  
  <div class="alert alert-info"><h4>Note</h4><p>For distributed model parallel training where a model spans multiple
      servers, please refer to
      `Getting Started With Distributed RPC Framework <rpc_tutorial.html>`__
      for examples and details.</p></div>
  
  Basic Usage
  -----------
  
  Let us start with a toy model that contains two linear layers. To run this
  model on two GPUs, simply put each linear layer on a different GPU, and move
  inputs and intermediate outputs to match the layer devices accordingly.
  """
  
  import torch
  import torch.nn as nn
  import torch.optim as optim
  
  
  class ToyModel(nn.Module):
      def __init__(self):
          super(ToyModel, self).__init__()
          self.net1 = torch.nn.Linear(10, 10).to('cuda:0')
          self.relu = torch.nn.ReLU()
          self.net2 = torch.nn.Linear(10, 5).to('cuda:1')
  
      def forward(self, x):
          x = self.relu(self.net1(x.to('cuda:0')))
          return self.net2(x.to('cuda:1'))
  
  """Note that, the above ``ToyModel`` looks very similar to how one would
  implement it on a single GPU, except the five ``to(device)`` calls which
  place linear layers and tensors on proper devices. That is the only place in
  the model that requires changes. The ``backward()`` and ``torch.optim`` will
  automatically take care of gradients as if the model is on one GPU. You only
  need to make sure that the labels are on the same device as the outputs when
  calling the loss function.
  """
  
  model = ToyModel()
  loss_fn = nn.MSELoss()
  optimizer = optim.SGD(model.parameters(), lr=0.001)
  
  optimizer.zero_grad()
  outputs = model(torch.randn(20, 10))
  labels = torch.randn(20, 5).to('cuda:1')
  loss_fn(outputs, labels).backward()
  optimizer.step()
  
  """Apply Model Parallel to Existing Modules
  ----------------------------------------
  
  It is also possible to run an existing single-GPU module on multiple GPUs
  with just a few lines of changes. The code below shows how to decompose
  ``torchvision.models.reset50()`` to two GPUs. The idea is to inherit from
  the existing ``ResNet`` module, and split the layers to two GPUs during
  construction. Then, override the ``forward`` method to stitch two
  sub-networks by moving the intermediate outputs accordingly.
  """
  
  from torchvision.models.resnet import ResNet, Bottleneck
  
  num_classes = 1000
  
  
  class ModelParallelResNet50(ResNet):
      def __init__(self, *args, **kwargs):
          super(ModelParallelResNet50, self).__init__(
              Bottleneck, [3, 4, 6, 3], num_classes=num_classes, *args, **kwargs)
  
          self.seq1 = nn.Sequential(
              self.conv1,
              self.bn1,
              self.relu,
              self.maxpool,
  
              self.layer1,
              self.layer2
          ).to('cuda:0')
  
          self.seq2 = nn.Sequential(
              self.layer3,
              self.layer4,
              self.avgpool,
          ).to('cuda:1')
  
          self.fc.to('cuda:1')
  
      def forward(self, x):
          x = self.seq2(self.seq1(x).to('cuda:1'))
          return self.fc(x.view(x.size(0), -1))
  
  """The above implementation solves the problem for cases where the model is too
  large to fit into a single GPU. However, you might have already noticed that
  it will be slower than running it on a single GPU if your model fits. It is
  because, at any point in time, only one of the two GPUs are working, while
  the other one is sitting there doing nothing. The performance further
  deteriorates as the intermediate outputs need to be copied from ``cuda:0`` to
  ``cuda:1`` between ``layer2`` and ``layer3``.
  
  Let us run an experiment to get a more quantitative view of the execution
  time. In this experiment, we train ``ModelParallelResNet50`` and the existing
  ``torchvision.models.reset50()`` by running random inputs and labels through
  them. After the training, the models will not produce any useful predictions,
  but we can get a reasonable understanding of the execution times.
  """
  
  import torchvision.models as models
  
  num_batches = 3
  batch_size = 120
  image_w = 128
  image_h = 128
  
  
  def train(model):
      model.train(True)
      loss_fn = nn.MSELoss()
      optimizer = optim.SGD(model.parameters(), lr=0.001)
  
      one_hot_indices = torch.LongTensor(batch_size) \
                             .random_(0, num_classes) \
                             .view(batch_size, 1)
  
      for _ in range(num_batches):
          # 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)
  
          # run forward pass
          optimizer.zero_grad()
          outputs = model(inputs.to('cuda:0'))
  
          # run backward pass
          labels = labels.to(outputs.device)
          loss_fn(outputs, labels).backward()
          optimizer.step()
  
  """The ``train(model)`` method above uses ``nn.MSELoss`` as the loss function,
  and ``optim.SGD`` as the optimizer. It mimics training on ``128 X 128``
  images which are organized into 3 batches where each batch contains 120
  images. Then, we use ``timeit`` to run the ``train(model)`` method 10 times
  and plot the execution times with standard deviations.
  """
  
  import matplotlib.pyplot as plt
  plt.switch_backend('Agg')
  import numpy as np
  import timeit
  
  num_repeat = 10
  
  stmt = "train(model)"
  
  setup = "model = ModelParallelResNet50()"
  # globals arg is only available in Python 3. In Python 2, use the following
  # import __builtin__
  # __builtin__.__dict__.update(locals())
  mp_run_times = timeit.repeat(
      stmt, setup, number=1, repeat=num_repeat, globals=globals())
  mp_mean, mp_std = np.mean(mp_run_times), np.std(mp_run_times)
  
  setup = "import torchvision.models as models;" + \
          "model = models.resnet50(num_classes=num_classes).to('cuda:0')"
  rn_run_times = timeit.repeat(
      stmt, setup, number=1, repeat=num_repeat, globals=globals())
  rn_mean, rn_std = np.mean(rn_run_times), np.std(rn_run_times)
  
  
  def plot(means, stds, labels, fig_name):
      fig, ax = plt.subplots()
      ax.bar(np.arange(len(means)), means, yerr=stds,
             align='center', alpha=0.5, ecolor='red', capsize=10, width=0.6)
      ax.set_ylabel('ResNet50 Execution Time (Second)')
      ax.set_xticks(np.arange(len(means)))
      ax.set_xticklabels(labels)
      ax.yaxis.grid(True)
      plt.tight_layout()
      plt.savefig(fig_name)
      plt.close(fig)
  
  
  
  """.. figure:: /_static/img/model-parallel-images/mp_vs_rn.png
     :alt:
  
  The result shows that the execution time of model parallel implementation is
  ``4.02/3.75-1=7%`` longer than the existing single-GPU implementation. So we
  can conclude there is roughly 7% overhead in copying tensors back and forth
  across the GPUs. There are rooms for improvements, as we know one of the two
  GPUs is sitting idle throughout the execution. One option is to further
  divide each batch into a pipeline of splits, such that when one split reaches
  the second sub-network, the following split can be fed into the first
  sub-network. In this way, two consecutive splits can run concurrently on two
  GPUs.
  
  Speed Up by Pipelining Inputs
  -----------------------------
  
  In the following experiments, we further divide each 120-image batch into
  20-image splits. As PyTorch launches CUDA operations asynchronizely, the
  implementation does not need to spawn multiple threads to achieve
  concurrency.
  """
  
  class PipelineParallelResNet50(ModelParallelResNet50):
      def __init__(self, split_size=20, *args, **kwargs):
          super(PipelineParallelResNet50, self).__init__(*args, **kwargs)
          self.split_size = split_size
  
      def forward(self, x):
          splits = iter(x.split(self.split_size, dim=0))
          s_next = next(splits)
          s_prev = self.seq1(s_next).to('cuda:1')
          ret = []
  
          for s_next in splits:
              # A. s_prev runs on cuda:1
              s_prev = self.seq2(s_prev)
              ret.append(self.fc(s_prev.view(s_prev.size(0), -1)))
  
              # B. s_next runs on cuda:0, which can run concurrently with A
              s_prev = self.seq1(s_next).to('cuda:1')
  
          s_prev = self.seq2(s_prev)
          ret.append(self.fc(s_prev.view(s_prev.size(0), -1)))
  
          return torch.cat(ret)
  
  
  setup = "model = PipelineParallelResNet50()"
  pp_run_times = timeit.repeat(
      stmt, setup, number=1, repeat=num_repeat, globals=globals())
  pp_mean, pp_std = np.mean(pp_run_times), np.std(pp_run_times)
  
  
  """Please note, device-to-device tensor copy operations are synchronized on
  current streams on the source and the destination devices. If you create
  multiple streams, you have to make sure that copy operations are properly
  synchronized. Writing the source tensor or reading/writing the destination
  tensor before finishing the copy operation can lead to undefined behavior.
  The above implementation only uses default streams on both source and
  destination devices, hence it is not necessary to enforce additional
  synchronizations.
  
  .. figure:: /_static/img/model-parallel-images/mp_vs_rn_vs_pp.png
     :alt:
  
  The experiment result shows that, pipelining inputs to model parallel
  ResNet50 speeds up the training process by roughly ``3.75/2.51-1=49%``. It is
  still quite far away from the ideal 100% speedup. As we have introduced a new
  parameter ``split_sizes`` in our pipeline parallel implementation, it is
  unclear how the new parameter affects the overall training time. Intuitively
  speaking, using small ``split_size`` leads to many tiny CUDA kernel launch,
  while using large ``split_size`` results to relatively long idle times during
  the first and last splits. Neither are optimal. There might be an optimal
  ``split_size`` configuration for this specific experiment. Let us try to find
  it by running experiments using several different ``split_size`` values.
  """
  
  pp_means = []
  pp_stds = []
  split_sizes = [1, 3, 5, 8, 10, 12, 20, 40, 60]
  
  for split_size in split_sizes:
      setup = "model = PipelineParallelResNet50(split_size=%d)" % split_size
      pp_run_times = timeit.repeat(
          stmt, setup, number=1, repeat=num_repeat, globals=globals())
      pp_means.append(np.mean(pp_run_times))
      pp_stds.append(np.std(pp_run_times))
  
  print("Single Node Time {}".format(rn_mean))
  print("Model Parallel Time {}".format(mp_mean))
  print("Pipeline 20 Mean {}".format(pp_mean))
  print("Pipeline Split Times {}".format(pp_means))
  
  """.. figure:: /_static/img/model-parallel-images/split_size_tradeoff.png
     :alt:
  
  The result shows that setting ``split_size`` to 12 achieves the fastest
  training speed, which leads to ``3.75/2.43-1=54%`` speedup. There are
  still opportunities to further accelerate the training process. For example,
  all operations on ``cuda:0`` is placed on its default stream. It means that
  computations on the next split cannot overlap with the copy operation of the
  prev split. However, as prev and next splits are different tensors, there is
  no problem to overlap one's computation with the other one's copy. The
  implementation need to use multiple streams on both GPUs, and different
  sub-network structures require different stream management strategies. As no
  general multi-stream solution works for all model parallel use cases, we will
  not discuss it in this tutorial.
  
  **Note:**
  
  This post shows several performance measurements. You might see different
  numbers when running the same code on your own machine, because the result
  depends on the underlying hardware and software. To get the best performance
  for your environment, a proper approach is to first generate the curve to
  figure out the best split size, and then use that split size to pipeline
  inputs.
  """
  