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Training Transformer models using Distributed Data Parallel and Pipeline Parallelism

Author: Pritam Damania

This tutorial demonstrates how to train a large Transformer model across multiple GPUs using Distributed Data Parallel and Pipeline Parallelism. This tutorial is an extension of the Sequence-to-Sequence Modeling with nn.Transformer and TorchText tutorial and scales up the same model to demonstrate how Distributed Data Parallel and Pipeline Parallelism can be used to train Transformer models.

Prerequisites:

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 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 tutorial, but is split into two stages. The largest number of parameters belong to the nn.TransformerEncoder layer. The nn.TransformerEncoder itself consists of nlayers of nn.TransformerEncoderLayer. 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:

\[\begin{bmatrix} \text{A} & \text{B} & \text{C} & \ldots & \text{X} & \text{Y} & \text{Z} \end{bmatrix} \Rightarrow \begin{bmatrix} \begin{bmatrix}\text{A} \\ \text{B} \\ \text{C} \\ \text{D} \\ \text{E} \\ \text{F}\end{bmatrix} & \begin{bmatrix}\text{G} \\ \text{H} \\ \text{I} \\ \text{J} \\ \text{K} \\ \text{L}\end{bmatrix} & \begin{bmatrix}\text{M} \\ \text{N} \\ \text{O} \\ \text{P} \\ \text{Q} \\ \text{R}\end{bmatrix} & \begin{bmatrix}\text{S} \\ \text{T} \\ \text{U} \\ \text{V} \\ \text{W} \\ \text{X}\end{bmatrix} \end{bmatrix} \]

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:

../_images/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 since Pipe depends on the RPC framework via 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 is applied to track the loss and SGD implements stochastic gradient descent method as the optimizer. The initial learning rate is set to 5.0. StepLR is applied to adjust the learn rate through epochs. During the training, we use 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

[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]: =========================================================================================

Total running time of the script: ( 0 minutes 0.000 seconds)

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