""" Training Transformer models using Pipeline Parallelism ====================================================== **Author**: `Pritam Damania `_ This tutorial demonstrates how to train a large Transformer model across multiple GPUs using 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 pipeline parallelism can be used to train Transformer models. Prerequisites: * `Pipeline Parallelism `__ * `Sequence-to-Sequence Modeling with nn.Transformer and TorchText `__ """ ###################################################################### # Define the model # ---------------- # ###################################################################### # In this tutorial, we will split a Transformer model across two GPUs and use # pipeline parallelism to train the model. 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 separate modules and then build an ``nn.Sequential`` # representing the original Transformer module. import sys import math import torch import torch.nn as nn import torch.nn.functional as F import tempfile from torch.nn import TransformerEncoder, TransformerEncoderLayer if sys.platform == 'win32': print('Windows platform is not supported for pipeline parallelism') sys.exit(0) if torch.cuda.device_count() < 2: print('Need at least two 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) ###################################################################### # ``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. 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) ###################################################################### # Load and batch data # ------------------- # ###################################################################### # The training process uses Wikitext-2 dataset from ``torchtext``. # To access torchtext datasets, please install torchdata following instructions at https://github.com/pytorch/data. # # 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. # import torch 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=[""]) vocab.set_default_index(vocab[""]) 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("cuda") def batchify(data, bsz): # 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() return data.to(device) batch_size = 20 eval_batch_size = 10 train_data = batchify(train_data, batch_size) val_data = batchify(val_data, eval_batch_size) test_data = batchify(test_data, eval_batch_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. # bptt = 25 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 12 total # transformer layers (``nn.TransformerEncoderLayer``). This creates a model with # **~1.4 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. # # .. note:: # For efficiency purposes we ensure that the ``nn.Sequential`` passed to # ``Pipe`` only consists of two elements (corresponding to two GPUs), this # allows the Pipe to work with only two partitions and avoid any # cross-partition overheads. ntokens = len(vocab) # the size of vocabulary emsize = 4096 # embedding dimension nhid = 4096 # the dimension of the feedforward network model in ``nn.TransformerEncoder`` nlayers = 12 # the number of ``nn.TransformerEncoderLayer`` in ``nn.TransformerEncoder`` nhead = 16 # the number of heads in the Multihead Attention 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 = 2 partition_len = ((nlayers - 1) // num_gpus) + 1 # Add encoder in the beginning. tmp_list = [Encoder(ntokens, emsize, dropout).cuda(0)] 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(device)) # Add decoder in the end. tmp_list.append(Decoder(ntokens, emsize).cuda(num_gpus - 1)) module_list.append(nn.Sequential(*tmp_list)) from torch.distributed.pipeline.sync import Pipe # Build the pipeline. chunks = 8 model = Pipe(torch.nn.Sequential(*module_list), chunks = chunks) def get_total_params(module: torch.nn.Module): total_params = 0 for param in module.parameters(): total_params += param.numel() return total_params print ('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. # 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(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('| 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_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(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. 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('-' * 89) print('| 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('-' * 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. test_loss = evaluate(best_model, test_data) print('=' * 89) print('| End of training | test loss {:5.2f} | test ppl {:8.2f}'.format( test_loss, math.exp(test_loss))) print('=' * 89)