""" 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: * `Pipeline Parallelism `__ * `Sequence-to-Sequence Modeling with nn.Transformer and TorchText `__ * `Getting Started with Distributed Data Parallel `__ """ ###################################################################### # 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.src_mask = None 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 _generate_square_subsequent_mask(self, sz): mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)) return mask def forward(self, src): if self.src_mask is None or self.src_mask.size(0) != src.size(0): device = src.device mask = self._generate_square_subsequent_mask(src.size(0)).to(device) self.src_mask = mask 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): return self.decoder(inp) ###################################################################### # 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)) import io from torchtext.utils import download_from_url, extract_archive from torchtext.data.utils import get_tokenizer from torchtext.vocab import build_vocab_from_iterator url = 'https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip' test_filepath, valid_filepath, train_filepath = extract_archive(download_from_url(url, root=".data{}".format(rank))) tokenizer = get_tokenizer('basic_english') vocab = build_vocab_from_iterator(map(tokenizer, iter(io.open(train_filepath, encoding="utf8")))) def data_process(raw_text_iter): data = [torch.tensor([vocab[token] for token in tokenizer(item)], dtype=torch.long) for item in raw_text_iter] return torch.cat(tuple(filter(lambda t: t.numel() > 0, data))) train_data = data_process(iter(io.open(train_filepath, encoding="utf8"))) val_data = data_process(iter(io.open(valid_filepath, encoding="utf8"))) test_data = data_process(iter(io.open(test_filepath, encoding="utf8"))) 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) return data, 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.stoi) # 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 model = Pipe(torch.nn.Sequential( *module_list), chunks = 8, 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.stoi) # 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_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.stoi) # 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 1]: Total parameters in model: 1,041,453,167 # [RANK 0]: Total parameters in model: 1,041,453,167 # [RANK 0]: | epoch 1 | 10/ 50 batches | lr 5.00 | ms/batch 1414.18 | loss 48.70 | ppl 1406154472673147092992.00 # [RANK 1]: | epoch 1 | 10/ 50 batches | lr 5.00 | ms/batch 1414.42 | loss 48.49 | ppl 1146707511057334927360.00 # [RANK 0]: | epoch 1 | 20/ 50 batches | lr 5.00 | ms/batch 1260.76 | loss 42.74 | ppl 3648812398518492672.00 # [RANK 1]: | epoch 1 | 20/ 50 batches | lr 5.00 | ms/batch 1260.76 | loss 41.51 | ppl 1064844757565813248.00 # [RANK 0]: | epoch 1 | 30/ 50 batches | lr 5.00 | ms/batch 1246.80 | loss 41.85 | ppl 1497706388552644096.00 # [RANK 1]: | epoch 1 | 30/ 50 batches | lr 5.00 | ms/batch 1246.80 | loss 40.46 | ppl 373830103285747072.00 # [RANK 0]: | epoch 1 | 40/ 50 batches | lr 5.00 | ms/batch 1246.69 | loss 39.76 | ppl 185159839078666368.00 # [RANK 1]: | epoch 1 | 40/ 50 batches | lr 5.00 | ms/batch 1246.69 | loss 39.89 | ppl 211756997625874912.00 # [RANK 0]: ----------------------------------------------------------------------------------------- # [RANK 0]: | end of epoch 1 | time: 69.37s | valid loss 2.92 | valid ppl 18.46 # [RANK 0]: ----------------------------------------------------------------------------------------- # [RANK 1]: ----------------------------------------------------------------------------------------- # [RANK 1]: | end of epoch 1 | time: 69.39s | valid loss 2.92 | valid ppl 18.46 # [RANK 1]: ----------------------------------------------------------------------------------------- # [RANK 1]: | epoch 2 | 10/ 50 batches | lr 4.51 | ms/batch 1373.91 | loss 39.77 | ppl 187532281612905856.00 # [RANK 0]: | epoch 2 | 10/ 50 batches | lr 4.51 | ms/batch 1375.62 | loss 39.05 | ppl 91344349371016336.00 # [RANK 0]: | epoch 2 | 20/ 50 batches | lr 4.51 | ms/batch 1250.33 | loss 30.62 | ppl 19917977906884.78 # [RANK 1]: | epoch 2 | 20/ 50 batches | lr 4.51 | ms/batch 1250.33 | loss 30.48 | ppl 17250186491252.32 # [RANK 1]: | epoch 2 | 30/ 50 batches | lr 4.51 | ms/batch 1250.73 | loss 29.14 | ppl 4534527326854.47 # [RANK 0]: | epoch 2 | 30/ 50 batches | lr 4.51 | ms/batch 1250.73 | loss 29.43 | ppl 6035762659681.65 # [RANK 0]: | epoch 2 | 40/ 50 batches | lr 4.51 | ms/batch 1249.54 | loss 23.11 | ppl 10869828323.89 # [RANK 1]: | epoch 2 | 40/ 50 batches | lr 4.51 | ms/batch 1249.55 | loss 22.90 | ppl 8785318464.24 # [RANK 0]: ----------------------------------------------------------------------------------------- # [RANK 0]: | end of epoch 2 | time: 69.02s | valid loss 0.94 | valid ppl 2.55 # [RANK 0]: ----------------------------------------------------------------------------------------- # [RANK 1]: ----------------------------------------------------------------------------------------- # [RANK 1]: | end of epoch 2 | time: 69.05s | valid loss 0.94 | valid ppl 2.55 # [RANK 1]: ----------------------------------------------------------------------------------------- # [RANK 0]: | epoch 3 | 10/ 50 batches | lr 4.29 | ms/batch 1380.66 | loss 12.98 | ppl 434052.59 # [RANK 1]: | epoch 3 | 10/ 50 batches | lr 4.29 | ms/batch 1376.47 | loss 12.92 | ppl 410203.33 # [RANK 1]: | epoch 3 | 20/ 50 batches | lr 4.29 | ms/batch 1250.88 | loss 9.80 | ppl 18034.58 # [RANK 0]: | epoch 3 | 20/ 50 batches | lr 4.29 | ms/batch 1250.88 | loss 9.78 | ppl 17741.88 # [RANK 0]: | epoch 3 | 30/ 50 batches | lr 4.29 | ms/batch 1251.89 | loss 10.37 | ppl 32016.45 # [RANK 1]: | epoch 3 | 30/ 50 batches | lr 4.29 | ms/batch 1251.90 | loss 10.46 | ppl 34735.08 # [RANK 0]: | epoch 3 | 40/ 50 batches | lr 4.29 | ms/batch 1250.70 | loss 10.09 | ppl 24147.61 # [RANK 1]: | epoch 3 | 40/ 50 batches | lr 4.29 | ms/batch 1250.71 | loss 10.08 | ppl 23748.31 # [RANK 0]: ----------------------------------------------------------------------------------------- # [RANK 0]: | end of epoch 3 | time: 69.12s | valid loss 0.69 | valid ppl 2.00 # [RANK 0]: ----------------------------------------------------------------------------------------- # [RANK 1]: ----------------------------------------------------------------------------------------- # [RANK 1]: | end of epoch 3 | time: 69.12s | valid loss 0.69 | valid ppl 2.00 # [RANK 1]: ----------------------------------------------------------------------------------------- # [RANK 0]: ========================================================================================= # [RANK 0]: | End of training | test loss 0.60 | test ppl 1.83 # [RANK 0]: ========================================================================================= # [RANK 1]: ========================================================================================= # [RANK 1]: | End of training | test loss 0.60 | test ppl 1.83