""" Sequence-to-Sequence Modeling with nn.Transformer and TorchText =============================================================== This is a tutorial on how to train a sequence-to-sequence model that uses the `nn.Transformer `__ module. PyTorch 1.2 release includes a standard transformer module based on the paper `Attention is All You Need `__. The transformer model has been proved to be superior in quality for many sequence-to-sequence problems while being more parallelizable. The ``nn.Transformer`` module relies entirely on an attention mechanism (another module recently implemented as `nn.MultiheadAttention `__) to draw global dependencies between input and output. The ``nn.Transformer`` module is now highly modularized such that a single component (like `nn.TransformerEncoder `__ in this tutorial) can be easily adapted/composed. .. image:: ../_static/img/transformer_architecture.jpg """ ###################################################################### # Define the model # ---------------- # ###################################################################### # In this tutorial, we train ``nn.TransformerEncoder`` model on a # language modeling task. The language modeling task is to assign a # probability for the likelihood of a given word (or a sequence of words) # to follow a sequence of words. A sequence of tokens are passed to the embedding # layer first, followed by a positional encoding layer to account for the order # of the word (see the next paragraph for more details). The # ``nn.TransformerEncoder`` consists of multiple layers of # `nn.TransformerEncoderLayer `__. Along with the input sequence, a square # attention mask is required because the self-attention layers in # ``nn.TransformerEncoder`` are only allowed to attend the earlier positions in # the sequence. For the language modeling task, any tokens on the future # positions should be masked. To have the actual words, the output # of ``nn.TransformerEncoder`` model is sent to the final Linear # layer, which is followed by a log-Softmax function. # import math import torch import torch.nn as nn import torch.nn.functional as F class TransformerModel(nn.Module): def __init__(self, ntoken, ninp, nhead, nhid, nlayers, dropout=0.5): super(TransformerModel, self).__init__() from torch.nn import TransformerEncoder, TransformerEncoderLayer self.model_type = 'Transformer' self.pos_encoder = PositionalEncoding(ninp, dropout) encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout) self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) self.encoder = nn.Embedding(ntoken, ninp) self.ninp = ninp self.decoder = nn.Linear(ninp, ntoken) self.init_weights() 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 init_weights(self): initrange = 0.1 self.encoder.weight.data.uniform_(-initrange, initrange) self.decoder.bias.data.zero_() self.decoder.weight.data.uniform_(-initrange, initrange) def forward(self, src, src_mask): src = self.encoder(src) * math.sqrt(self.ninp) src = self.pos_encoder(src) output = self.transformer_encoder(src, src_mask) output = self.decoder(output) return output ###################################################################### # ``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``. 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 torchtext from torchtext.data.utils import get_tokenizer TEXT = torchtext.data.Field(tokenize=get_tokenizer("basic_english"), init_token='', eos_token='', lower=True) train_txt, val_txt, test_txt = torchtext.datasets.WikiText2.splits(TEXT) TEXT.build_vocab(train_txt) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") def batchify(data, bsz): data = TEXT.numericalize([data.examples[0].text]) # 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_txt, batch_size) val_data = batchify(val_txt, eval_batch_size) test_data = batchify(test_txt, 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 = 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].reshape(-1) return data, target ###################################################################### # Initiate an instance # -------------------- # ###################################################################### # The model is set up with the hyperparameter below. The vocab size is # equal to the length of the vocab object. # ntokens = len(TEXT.vocab.stoi) # the size of vocabulary emsize = 200 # embedding dimension nhid = 200 # the dimension of the feedforward network model in nn.TransformerEncoder nlayers = 2 # the number of nn.TransformerEncoderLayer in nn.TransformerEncoder nhead = 2 # the number of heads in the multiheadattention models dropout = 0.2 # the dropout value model = TransformerModel(ntokens, emsize, nhead, nhid, nlayers, dropout).to(device) ###################################################################### # 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(TEXT.vocab.stoi) src_mask = model.generate_square_subsequent_mask(bptt).to(device) for batch, i in enumerate(range(0, train_data.size(0) - 1, bptt)): data, targets = get_batch(train_data, i) optimizer.zero_grad() if data.size(0) != bptt: src_mask = model.generate_square_subsequent_mask(data.size(0)).to(device) output = model(data, src_mask) loss = criterion(output.view(-1, ntokens), targets) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5) optimizer.step() total_loss += loss.item() log_interval = 200 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, len(train_data) // 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(TEXT.vocab.stoi) src_mask = model.generate_square_subsequent_mask(bptt).to(device) with torch.no_grad(): for i in range(0, data_source.size(0) - 1, bptt): data, targets = get_batch(data_source, i) if data.size(0) != bptt: src_mask = model.generate_square_subsequent_mask(data.size(0)).to(device) output = eval_model(data, src_mask) output_flat = output.view(-1, ntokens) total_loss += len(data) * criterion(output_flat, targets).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)