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Remove improper src_mask from encoder tutorial #2423

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Jun 5, 2023
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Remove improper src_mask from encoder tutorial #2423

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45 changes: 14 additions & 31 deletions beginner_source/transformer_tutorial.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,12 +36,8 @@
# of the word (see the next paragraph for more details). The
# ``nn.TransformerEncoder`` consists of multiple layers of
# `nn.TransformerEncoderLayer <https://pytorch.org/docs/stable/generated/torch.nn.TransformerEncoderLayer.html>`__.
# Along with the input sequence, a square attention mask is required because the
# self-attention layers in ``nn.TransformerDecoder`` 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 produce a probability
# distribution over output words, the output of the ``nn.TransformerEncoder``
# model is passed through a linear layer followed by a log-softmax function.
# To produce a probability distribution over output words, the output of
# the ``nn.TransformerEncoder`` model is passed through a linear layer.
#

import math
Expand All @@ -51,7 +47,6 @@

import torch
from torch import nn, Tensor
import torch.nn.functional as F
from torch.nn import TransformerEncoder, TransformerEncoderLayer
from torch.utils.data import dataset

Expand All @@ -64,19 +59,19 @@ def __init__(self, ntoken: int, d_model: int, nhead: int, d_hid: int,
self.pos_encoder = PositionalEncoding(d_model, dropout)
encoder_layers = TransformerEncoderLayer(d_model, nhead, d_hid, dropout)
self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
self.encoder = nn.Embedding(ntoken, d_model)
self.embedding = nn.Embedding(ntoken, d_model)
self.d_model = d_model
self.decoder = nn.Linear(d_model, ntoken)
self.linear = nn.Linear(d_model, ntoken)

self.init_weights()

def init_weights(self) -> None:
initrange = 0.1
self.encoder.weight.data.uniform_(-initrange, initrange)
self.decoder.bias.data.zero_()
self.decoder.weight.data.uniform_(-initrange, initrange)
self.embedding.weight.data.uniform_(-initrange, initrange)
self.linear.bias.data.zero_()
self.linear.weight.data.uniform_(-initrange, initrange)

def forward(self, src: Tensor, src_mask: Tensor) -> Tensor:
def forward(self, src: Tensor, src_mask: Tensor = None) -> Tensor:
"""
Arguments:
src: Tensor, shape ``[seq_len, batch_size]``
Expand All @@ -85,18 +80,13 @@ def forward(self, src: Tensor, src_mask: Tensor) -> Tensor:
Returns:
output Tensor of shape ``[seq_len, batch_size, ntoken]``
"""
src = self.encoder(src) * math.sqrt(self.d_model)
src = self.embedding(src) * math.sqrt(self.d_model)
src = self.pos_encoder(src)
output = self.transformer_encoder(src, src_mask)
output = self.decoder(output)
output = self.linear(output)
return output


def generate_square_subsequent_mask(sz: int) -> Tensor:
"""Generates an upper-triangular matrix of ``-inf``, with zeros on ``diag``."""
return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)


######################################################################
# ``PositionalEncoding`` module injects some information about the
# relative or absolute position of the tokens in the sequence. The
Expand Down Expand Up @@ -286,7 +276,6 @@ def get_batch(source: Tensor, i: int) -> Tuple[Tensor, Tensor]:
# to prevent gradients from exploding.
#

import copy
import time

criterion = nn.CrossEntropyLoss()
Expand All @@ -299,16 +288,13 @@ def train(model: nn.Module) -> None:
total_loss = 0.
log_interval = 200
start_time = time.time()
src_mask = generate_square_subsequent_mask(bptt).to(device)

num_batches = len(train_data) // bptt
for batch, i in enumerate(range(0, train_data.size(0) - 1, bptt)):
data, targets = get_batch(train_data, i)
seq_len = data.size(0)
if seq_len != bptt: # only on last batch
src_mask = src_mask[:seq_len, :seq_len]
output = model(data, src_mask)
loss = criterion(output.view(-1, ntokens), targets)
output = model(data)
output_flat = output.view(-1, ntokens)
loss = criterion(output_flat, targets)

optimizer.zero_grad()
loss.backward()
Expand All @@ -330,14 +316,11 @@ def train(model: nn.Module) -> None:
def evaluate(model: nn.Module, eval_data: Tensor) -> float:
model.eval() # turn on evaluation mode
total_loss = 0.
src_mask = generate_square_subsequent_mask(bptt).to(device)
with torch.no_grad():
for i in range(0, eval_data.size(0) - 1, bptt):
data, targets = get_batch(eval_data, i)
seq_len = data.size(0)
if seq_len != bptt:
src_mask = src_mask[:seq_len, :seq_len]
output = model(data, src_mask)
output = model(data)
output_flat = output.view(-1, ntokens)
total_loss += seq_len * criterion(output_flat, targets).item()
return total_loss / (len(eval_data) - 1)
Expand Down