text: """
  Training Transformer models using Distributed Data Parallel and Pipeline Parallelism
  ====================================================================================
  
  **Author**: `Pritam Damania <https://github.com/pritamdamania87>`_
  
  This tutorial demonstrates how to train a large Transformer model across
  multiple GPUs using `Distributed Data Parallel <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`__ and
  `Pipeline Parallelism <https://pytorch.org/docs/stable/pipeline.html>`__. This tutorial is an extension of the
  `Sequence-to-Sequence Modeling with nn.Transformer and TorchText <https://tutorials.pytorch.kr/beginner/transformer_tutorial.html>`__ tutorial
  and scales up the same model to demonstrate how Distributed Data Parallel and
  Pipeline Parallelism can be used to train Transformer models.
  
  Prerequisites:
  
      * `Pipeline Parallelism <https://pytorch.org/docs/stable/pipeline.html>`__
      * `Sequence-to-Sequence Modeling with nn.Transformer and TorchText <https://tutorials.pytorch.kr/beginner/transformer_tutorial.html>`__
      * `Getting Started with Distributed Data Parallel <https://tutorials.pytorch.kr/intermediate/ddp_tutorial.html>`__
  """
  
  
  ######################################################################
  # Define the model
  # ----------------
  #
  
  ######################################################################
  # ``PositionalEncoding`` module injects some information about the
  # relative or absolute position of the tokens in the sequence. The
  # positional encodings have the same dimension as the embeddings so that
  # the two can be summed. Here, we use ``sine`` and ``cosine`` functions of
  # different frequencies.
  
  import sys
  import os
  import math
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import tempfile
  from torch.nn import TransformerEncoder, TransformerEncoderLayer
  
  class PositionalEncoding(nn.Module):
  
      def __init__(self, d_model, dropout=0.1, max_len=5000):
          super(PositionalEncoding, self).__init__()
          self.dropout = nn.Dropout(p=dropout)
  
          pe = torch.zeros(max_len, d_model)
          position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
          div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
          pe[:, 0::2] = torch.sin(position * div_term)
          pe[:, 1::2] = torch.cos(position * div_term)
          pe = pe.unsqueeze(0).transpose(0, 1)
          self.register_buffer('pe', pe)
  
      def forward(self, x):
          x = x + self.pe[:x.size(0), :]
          return self.dropout(x)
  
  
  ######################################################################
  # In this tutorial, we will split a Transformer model across two GPUs and use
  # pipeline parallelism to train the model. In addition to this, we use
  # `Distributed Data Parallel <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`__
  # to train two replicas of this pipeline. We have one process driving a pipe across
  # GPUs 0 and 1 and another process driving a pipe across GPUs 2 and 3. Both these
  # processes then use Distributed Data Parallel to train the two replicas. The
  # model is exactly the same model used in the `Sequence-to-Sequence Modeling with nn.Transformer and TorchText
  # <https://tutorials.pytorch.kr/beginner/transformer_tutorial.html>`__ tutorial,
  # but is split into two stages. The largest number of parameters belong to the
  # `nn.TransformerEncoder <https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoder.html>`__ layer.
  # The `nn.TransformerEncoder <https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoder.html>`__
  # itself consists of ``nlayers`` of `nn.TransformerEncoderLayer <https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoderLayer.html>`__.
  # As a result, our focus is on ``nn.TransformerEncoder`` and we split the model
  # such that half of the ``nn.TransformerEncoderLayer`` are on one GPU and the
  # other half are on another. To do this, we pull out the ``Encoder`` and
  # ``Decoder`` sections into seperate modules and then build an nn.Sequential
  # representing the original Transformer module.
  
  
  if sys.platform == 'win32':
      print('Windows platform is not supported for pipeline parallelism')
      sys.exit(0)
  if torch.cuda.device_count() < 4:
      print('Need at least four GPU devices for this tutorial')
      sys.exit(0)
  
  class Encoder(nn.Module):
      def __init__(self, ntoken, ninp, dropout=0.5):
          super(Encoder, self).__init__()
          self.pos_encoder = PositionalEncoding(ninp, dropout)
          self.encoder = nn.Embedding(ntoken, ninp)
          self.ninp = ninp
          self.init_weights()
  
      def init_weights(self):
          initrange = 0.1
          self.encoder.weight.data.uniform_(-initrange, initrange)
  
      def forward(self, src):
          # Need (S, N) format for encoder.
          src = src.t()
          src = self.encoder(src) * math.sqrt(self.ninp)
          return self.pos_encoder(src)
  
  class Decoder(nn.Module):
      def __init__(self, ntoken, ninp):
          super(Decoder, self).__init__()
          self.decoder = nn.Linear(ninp, ntoken)
          self.init_weights()
  
      def init_weights(self):
          initrange = 0.1
          self.decoder.bias.data.zero_()
          self.decoder.weight.data.uniform_(-initrange, initrange)
  
      def forward(self, inp):
          # Need batch dimension first for output of pipeline.
          return self.decoder(inp).permute(1, 0, 2)
  
  ######################################################################
  # Start multiple processes for training
  # -------------------------------------
  #
  
  
  ######################################################################
  # We start two processes where each process drives its own pipeline across two
  # GPUs. ``run_worker`` is executed for each process.
  
  def run_worker(rank, world_size):
  
  
  ######################################################################
  # Load and batch data
  # -------------------
  #
  
  
  ######################################################################
  # The training process uses Wikitext-2 dataset from ``torchtext``. The
  # vocab object is built based on the train dataset and is used to numericalize
  # tokens into tensors. Starting from sequential data, the ``batchify()``
  # function arranges the dataset into columns, trimming off any tokens remaining
  # after the data has been divided into batches of size ``batch_size``.
  # For instance, with the alphabet as the sequence (total length of 26)
  # and a batch size of 4, we would divide the alphabet into 4 sequences of
  # length 6:
  #
  # .. math::
  #   \begin{bmatrix}
  #   \text{A} & \text{B} & \text{C} & \ldots & \text{X} & \text{Y} & \text{Z}
  #   \end{bmatrix}
  #   \Rightarrow
  #   \begin{bmatrix}
  #   \begin{bmatrix}\text{A} \\ \text{B} \\ \text{C} \\ \text{D} \\ \text{E} \\ \text{F}\end{bmatrix} &
  #   \begin{bmatrix}\text{G} \\ \text{H} \\ \text{I} \\ \text{J} \\ \text{K} \\ \text{L}\end{bmatrix} &
  #   \begin{bmatrix}\text{M} \\ \text{N} \\ \text{O} \\ \text{P} \\ \text{Q} \\ \text{R}\end{bmatrix} &
  #   \begin{bmatrix}\text{S} \\ \text{T} \\ \text{U} \\ \text{V} \\ \text{W} \\ \text{X}\end{bmatrix}
  #   \end{bmatrix}
  #
  # These columns are treated as independent by the model, which means that
  # the dependence of ``G`` and ``F`` can not be learned, but allows more
  # efficient batch processing.
  #
  
  # In 'run_worker'
      def print_with_rank(msg):
          print('[RANK {}]: {}'.format(rank, msg))
  
      from torchtext.datasets import WikiText2
      from torchtext.data.utils import get_tokenizer
      from torchtext.vocab import build_vocab_from_iterator
  
      train_iter = WikiText2(split='train')
      tokenizer = get_tokenizer('basic_english')
      vocab = build_vocab_from_iterator(map(tokenizer, train_iter), specials=["<unk>"])
      vocab.set_default_index(vocab["<unk>"])
  
      def data_process(raw_text_iter):
        data = [torch.tensor(vocab(tokenizer(item)), dtype=torch.long) for item in raw_text_iter]
        return torch.cat(tuple(filter(lambda t: t.numel() > 0, data)))
  
      train_iter, val_iter, test_iter = WikiText2()
      train_data = data_process(train_iter)
      val_data = data_process(val_iter)
      test_data = data_process(test_iter)
  
      device = torch.device(2 * rank)
  
      def batchify(data, bsz, rank, world_size, is_train=False):
          # Divide the dataset into bsz parts.
          nbatch = data.size(0) // bsz
          # Trim off any extra elements that wouldn't cleanly fit (remainders).
          data = data.narrow(0, 0, nbatch * bsz)
          # Evenly divide the data across the bsz batches.
          data = data.view(bsz, -1).t().contiguous()
          # Divide the data across the ranks only for training data.
          if is_train:
              data_per_rank = data.size(0) // world_size
              data = data[rank * data_per_rank : (rank + 1) * data_per_rank]
          return data.to(device)
  
      batch_size = 20
      eval_batch_size = 10
      train_data = batchify(train_data, batch_size, rank, world_size, True)
      val_data = batchify(val_data, eval_batch_size, rank, world_size)
      test_data = batchify(test_data, eval_batch_size, rank, world_size)
  
  
  ######################################################################
  # Functions to generate input and target sequence
  # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
  #
  
  
  ######################################################################
  # ``get_batch()`` function generates the input and target sequence for
  # the transformer model. It subdivides the source data into chunks of
  # length ``bptt``. For the language modeling task, the model needs the
  # following words as ``Target``. For example, with a ``bptt`` value of 2,
  # we’d get the following two Variables for ``i`` = 0:
  #
  # .. image:: ../_static/img/transformer_input_target.png
  #
  # It should be noted that the chunks are along dimension 0, consistent
  # with the ``S`` dimension in the Transformer model. The batch dimension
  # ``N`` is along dimension 1.
  #
  
  # In 'run_worker'
      bptt = 35
      def get_batch(source, i):
          seq_len = min(bptt, len(source) - 1 - i)
          data = source[i:i+seq_len]
          target = source[i+1:i+1+seq_len].view(-1)
          # Need batch dimension first for pipeline parallelism.
          return data.t(), target
  
  ######################################################################
  # Model scale and Pipe initialization
  # -----------------------------------
  #
  
  
  ######################################################################
  # To demonstrate training large Transformer models using pipeline parallelism,
  # we scale up the Transformer layers appropriately. We use an embedding
  # dimension of 4096, hidden size of 4096, 16 attention heads and 8 total
  # transformer layers (``nn.TransformerEncoderLayer``). This creates a model with
  # **~1 billion** parameters.
  #
  # We need to initialize the `RPC Framework <https://pytorch.org/docs/stable/rpc.html>`__
  # since Pipe depends on the RPC framework via `RRef <https://pytorch.org/docs/stable/rpc.html#rref>`__
  # which allows for future expansion to cross host pipelining. We need to
  # initialize the RPC framework with only a single worker since we're using a
  # single process to drive multiple GPUs.
  #
  # The pipeline is then initialized with 8 transformer layers on one GPU and 8
  # transformer layers on the other GPU. One pipe is setup across GPUs 0 and 1 and
  # another across GPUs 2 and 3. Both pipes are then replicated using DistributedDataParallel.
  
  # In 'run_worker'
      ntokens = len(vocab) # the size of vocabulary
      emsize = 4096 # embedding dimension
      nhid = 4096 # the dimension of the feedforward network model in nn.TransformerEncoder
      nlayers = 8 # the number of nn.TransformerEncoderLayer in nn.TransformerEncoder
      nhead = 16 # the number of heads in the multiheadattention models
      dropout = 0.2 # the dropout value
  
      from torch.distributed import rpc
      tmpfile = tempfile.NamedTemporaryFile()
      rpc.init_rpc(
          name="worker",
          rank=0,
          world_size=1,
          rpc_backend_options=rpc.TensorPipeRpcBackendOptions(
              init_method="file://{}".format(tmpfile.name),
              # Specifying _transports and _channels is a workaround and we no longer
              # will have to specify _transports and _channels for PyTorch
              # versions >= 1.8.1
              _transports=["ibv", "uv"],
              _channels=["cuda_ipc", "cuda_basic"],
          )
      )
  
      # Num gpus for model parallelism.
      num_gpus = 2
      partition_len = ((nlayers - 1) // num_gpus) + 1
  
      # Add encoder in the beginning.
      tmp_list = [Encoder(ntokens, emsize, dropout).cuda(2 * rank)]
      module_list = []
  
      # Add all the necessary transformer blocks.
      for i in range(nlayers):
          transformer_block = TransformerEncoderLayer(emsize, nhead, nhid, dropout)
          if i != 0 and i % (partition_len) == 0:
              module_list.append(nn.Sequential(*tmp_list))
              tmp_list = []
          device = i // (partition_len)
          tmp_list.append(transformer_block.to(2 * rank + device))
  
      # Add decoder in the end.
      tmp_list.append(Decoder(ntokens, emsize).cuda(2 * rank + num_gpus - 1))
      module_list.append(nn.Sequential(*tmp_list))
  
      # Need to use 'checkpoint=never' since as of PyTorch 1.8, Pipe checkpointing
      # doesn't work with DDP.
      from torch.distributed.pipeline.sync import Pipe
      chunks = 8
      model = Pipe(torch.nn.Sequential(
          *module_list), chunks = chunks, checkpoint="never")
  
      # Initialize process group and wrap model in DDP.
      from torch.nn.parallel import DistributedDataParallel
      import torch.distributed as dist
      os.environ['MASTER_ADDR'] = 'localhost'
      os.environ['MASTER_PORT'] = '29500'
      dist.init_process_group(
                  backend="nccl", rank=rank, world_size=world_size)
      model = DistributedDataParallel(model)
  
      def get_total_params(module: torch.nn.Module):
          total_params = 0
          for param in module.parameters():
              total_params += param.numel()
          return total_params
  
      print_with_rank('Total parameters in model: {:,}'.format(get_total_params(model)))
  
  ######################################################################
  # Run the model
  # -------------
  #
  
  
  ######################################################################
  # `CrossEntropyLoss <https://pytorch.org/docs/master/nn.html?highlight=crossentropyloss#torch.nn.CrossEntropyLoss>`__
  # is applied to track the loss and
  # `SGD <https://pytorch.org/docs/master/optim.html?highlight=sgd#torch.optim.SGD>`__
  # implements stochastic gradient descent method as the optimizer. The initial
  # learning rate is set to 5.0. `StepLR <https://pytorch.org/docs/master/optim.html?highlight=steplr#torch.optim.lr_scheduler.StepLR>`__ is
  # applied to adjust the learn rate through epochs. During the
  # training, we use
  # `nn.utils.clip_grad_norm\_ <https://pytorch.org/docs/master/nn.html?highlight=nn%20utils%20clip_grad_norm#torch.nn.utils.clip_grad_norm_>`__
  # function to scale all the gradient together to prevent exploding.
  #
  
  # In 'run_worker'
      criterion = nn.CrossEntropyLoss()
      lr = 5.0 # learning rate
      optimizer = torch.optim.SGD(model.parameters(), lr=lr)
      scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)
  
      import time
      def train():
          model.train() # Turn on the train mode
          total_loss = 0.
          start_time = time.time()
          ntokens = len(vocab)
  
          # Train only for 50 batches to keep script execution time low.
          nbatches = min(50 * bptt, train_data.size(0) - 1)
  
          for batch, i in enumerate(range(0, nbatches, bptt)):
              data, targets = get_batch(train_data, i)
              optimizer.zero_grad()
              # Since the Pipe is only within a single host and process the ``RRef``
              # returned by forward method is local to this node and can simply
              # retrieved via ``RRef.local_value()``.
              output = model(data).local_value()
              # Need to move targets to the device where the output of the
              # pipeline resides.
              loss = criterion(output.view(-1, ntokens), targets.cuda(2 * rank + 1))
              loss.backward()
              torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
              optimizer.step()
  
              total_loss += loss.item()
              log_interval = 10
              if batch % log_interval == 0 and batch > 0:
                  cur_loss = total_loss / log_interval
                  elapsed = time.time() - start_time
                  print_with_rank('| epoch {:3d} | {:5d}/{:5d} batches | '
                        'lr {:02.2f} | ms/batch {:5.2f} | '
                        'loss {:5.2f} | ppl {:8.2f}'.format(
                          epoch, batch, nbatches // bptt, scheduler.get_last_lr()[0],
                          elapsed * 1000 / log_interval,
                          cur_loss, math.exp(cur_loss)))
                  total_loss = 0
                  start_time = time.time()
  
      def evaluate(eval_model, data_source):
          eval_model.eval() # Turn on the evaluation mode
          total_loss = 0.
          ntokens = len(vocab)
          # Evaluate only for 50 batches to keep script execution time low.
          nbatches = min(50 * bptt, data_source.size(0) - 1)
          with torch.no_grad():
              for i in range(0, nbatches, bptt):
                  data, targets = get_batch(data_source, i)
                  output = eval_model(data).local_value()
                  output_flat = output.view(-1, ntokens)
                  # Need to move targets to the device where the output of the
                  # pipeline resides.
                  total_loss += len(data) * criterion(output_flat, targets.cuda(2 * rank + 1)).item()
          return total_loss / (len(data_source) - 1)
  
  ######################################################################
  # Loop over epochs. Save the model if the validation loss is the best
  # we've seen so far. Adjust the learning rate after each epoch.
  
  # In 'run_worker'
      best_val_loss = float("inf")
      epochs = 3 # The number of epochs
      best_model = None
  
      for epoch in range(1, epochs + 1):
          epoch_start_time = time.time()
          train()
          val_loss = evaluate(model, val_data)
          print_with_rank('-' * 89)
          print_with_rank('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
                'valid ppl {:8.2f}'.format(epoch, (time.time() - epoch_start_time),
                                           val_loss, math.exp(val_loss)))
          print_with_rank('-' * 89)
  
          if val_loss < best_val_loss:
              best_val_loss = val_loss
              best_model = model
  
          scheduler.step()
  
  
  ######################################################################
  # Evaluate the model with the test dataset
  # -------------------------------------
  #
  # Apply the best model to check the result with the test dataset.
  
  # In 'run_worker'
      test_loss = evaluate(best_model, test_data)
      print_with_rank('=' * 89)
      print_with_rank('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format(
          test_loss, math.exp(test_loss)))
      print_with_rank('=' * 89)
  
  # Main execution
  import torch.multiprocessing as mp
  
  if __name__=="__main__":
      world_size = 2
      mp.spawn(run_worker, args=(world_size, ), nprocs=world_size, join=True)
  ######################################################################
  # Output
  # ------
  #
  
  
  ######################################################################
  #.. code-block:: py
  #
  #    [RANK 0]: | epoch   1 |    10/   50 batches | lr 5.00 | ms/batch 778.97 | loss 43.31 | ppl 6432469059895903232.00
  #    [RANK 1]: | epoch   1 |    10/   50 batches | lr 5.00 | ms/batch 778.90 | loss 44.50 | ppl 21245447128217366528.00
  #    [RANK 0]: | epoch   1 |    20/   50 batches | lr 5.00 | ms/batch 699.89 | loss 44.50 | ppl 21176949187407757312.00
  #    [RANK 1]: | epoch   1 |    20/   50 batches | lr 5.00 | ms/batch 699.87 | loss 44.62 | ppl 23975861229620961280.00
  #    [RANK 0]: | epoch   1 |    30/   50 batches | lr 5.00 | ms/batch 698.86 | loss 41.62 | ppl 1193312915629888256.00
  #    [RANK 1]: | epoch   1 |    30/   50 batches | lr 5.00 | ms/batch 698.87 | loss 40.69 | ppl 471605759847546240.00
  #    [RANK 0]: | epoch   1 |    40/   50 batches | lr 5.00 | ms/batch 698.34 | loss 45.20 | ppl 42812308420836458496.00
  #    [RANK 1]: | epoch   1 |    40/   50 batches | lr 5.00 | ms/batch 698.33 | loss 45.68 | ppl 68839569686012223488.00
  #    [RANK 1]: -----------------------------------------------------------------------------------------
  #    [RANK 1]: | end of epoch   1 | time: 40.08s | valid loss  0.80 | valid ppl     2.22
  #    [RANK 1]: -----------------------------------------------------------------------------------------
  #    [RANK 0]: -----------------------------------------------------------------------------------------
  #    [RANK 0]: | end of epoch   1 | time: 40.09s | valid loss  0.80 | valid ppl     2.22
  #    [RANK 0]: -----------------------------------------------------------------------------------------
  #    [RANK 0]: | epoch   2 |    10/   50 batches | lr 4.75 | ms/batch 768.51 | loss 36.34 | ppl 6063529544668166.00
  #    [RANK 1]: | epoch   2 |    10/   50 batches | lr 4.75 | ms/batch 769.23 | loss 37.41 | ppl 17651211266236086.00
  #    [RANK 0]: | epoch   2 |    20/   50 batches | lr 4.75 | ms/batch 699.57 | loss 28.97 | ppl 3798441739584.11
  #    [RANK 1]: | epoch   2 |    20/   50 batches | lr 4.75 | ms/batch 699.56 | loss 29.28 | ppl 5203636967575.47
  #    [RANK 0]: | epoch   2 |    30/   50 batches | lr 4.75 | ms/batch 699.04 | loss 28.43 | ppl 2212498693571.25
  #    [RANK 1]: | epoch   2 |    30/   50 batches | lr 4.75 | ms/batch 699.05 | loss 28.33 | ppl 2015144761281.48
  #    [RANK 0]: | epoch   2 |    40/   50 batches | lr 4.75 | ms/batch 699.10 | loss 23.30 | ppl 13121380184.92
  #    [RANK 1]: | epoch   2 |    40/   50 batches | lr 4.75 | ms/batch 699.09 | loss 23.41 | ppl 14653799192.87
  #    [RANK 0]: -----------------------------------------------------------------------------------------
  #    [RANK 0]: | end of epoch   2 | time: 39.97s | valid loss  0.24 | valid ppl     1.27
  #    [RANK 0]: -----------------------------------------------------------------------------------------
  #    [RANK 1]: -----------------------------------------------------------------------------------------
  #    [RANK 1]: | end of epoch   2 | time: 39.98s | valid loss  0.24 | valid ppl     1.27
  #    [RANK 1]: -----------------------------------------------------------------------------------------
  #    [RANK 0]: | epoch   3 |    10/   50 batches | lr 4.51 | ms/batch 769.36 | loss 12.80 | ppl 361681.11
  #    [RANK 1]: | epoch   3 |    10/   50 batches | lr 4.51 | ms/batch 768.97 | loss 12.57 | ppl 287876.61
  #    [RANK 0]: | epoch   3 |    20/   50 batches | lr 4.51 | ms/batch 698.27 | loss 12.01 | ppl 164364.60
  #    [RANK 1]: | epoch   3 |    20/   50 batches | lr 4.51 | ms/batch 698.30 | loss 11.98 | ppl 159095.89
  #    [RANK 0]: | epoch   3 |    30/   50 batches | lr 4.51 | ms/batch 697.75 | loss 10.90 | ppl 54261.91
  #    [RANK 1]: | epoch   3 |    30/   50 batches | lr 4.51 | ms/batch 697.72 | loss 10.89 | ppl 53372.39
  #    [RANK 0]: | epoch   3 |    40/   50 batches | lr 4.51 | ms/batch 699.49 | loss 10.78 | ppl 47948.35
  #    [RANK 1]: | epoch   3 |    40/   50 batches | lr 4.51 | ms/batch 699.50 | loss 10.79 | ppl 48664.42
  #    [RANK 0]: -----------------------------------------------------------------------------------------
  #    [RANK 0]: | end of epoch   3 | time: 39.96s | valid loss  0.38 | valid ppl     1.46
  #    [RANK 0]: -----------------------------------------------------------------------------------------
  #    [RANK 1]: -----------------------------------------------------------------------------------------
  #    [RANK 1]: | end of epoch   3 | time: 39.96s | valid loss  0.38 | valid ppl     1.46
  #    [RANK 1]: -----------------------------------------------------------------------------------------
  #    [RANK 0]: =========================================================================================
  #    [RANK 0]: | End of training | test loss  0.33 | test ppl     1.39
  #    [RANK 0]: =========================================================================================
  #    [RANK 1]: =========================================================================================
  #    [RANK 1]: | End of training | test loss  0.33 | test ppl     1.39
  #    [RANK 1]: =========================================================================================
  #
  