The Transformer has been on a lot of people’s minds over the last year five years. This post presents an annotated version of the paper in the form of a line-by-line implementation. It reorders and deletes some sections from the original paper and adds comments throughout. This document itself is a working notebook, and should be a completely usable implementation. Code is available here.
Transformer 在过去的五年中一直是许多人关注的焦点。本文以逐行实现的形式呈现了该论文的注释版本。它重新排列和删除了原始论文中的一些部分，并添加了注释。本文本身是一个工作笔记本，应该是一个完全可用的实现。代码在这里可用。
# !pip install -r requirements.txt# # Uncomment for colab
# #
# !pip install -q torchdata==0.3.0 torchtext==0.12 spacy==3.2 altair GPUtil
# !python -m spacy download de_core_news_sm
# !python -m spacy download en_core_web_smimport os
from os.path import exists
import torch
import torch.nn as nn
from torch.nn.functional import log_softmax, pad
import math
import copy
import time
from torch.optim.lr_scheduler import LambdaLR
import pandas as pd
import altair as alt
from torchtext.data.functional import to_map_style_dataset
from torch.utils.data import DataLoader
from torchtext.vocab import build_vocab_from_iterator
import torchtext.datasets as datasets
import spacy
import GPUtil
import warnings
from torch.utils.data.distributed import DistributedSampler
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
# Set to False to skip notebook execution (e.g. for debugging)
warnings.filterwarnings("ignore")
RUN_EXAMPLES = True# Some convenience helper functions used throughout the notebook
def is_interactive_notebook():
return __name__ == "__main__"
def show_example(fn, args=[]):
if __name__ == "__main__" and RUN_EXAMPLES:
return fn(*args)
def execute_example(fn, args=[]):
if __name__ == "__main__" and RUN_EXAMPLES:
fn(*args)
class DummyOptimizer(torch.optim.Optimizer):
def __init__(self):
self.param_groups = [{"lr": 0}]
None
def step(self):
None
def zero_grad(self, set_to_none=False):
None
class DummyScheduler:
def step(self):
NoneMy comments are blockquoted. The main text is all from the paper itself.
我的评论已被引用。正文全部来自论文本身。
The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU, ByteNet and ConvS2S, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention.
减少顺序计算的目标也构成了 Extended Neural GPU、ByteNet 和 ConvS2S 的基础，它们都使用卷积神经网络作为基本构建模块，在所有输入和输出位置并行计算隐藏表示。在这些模型中，从两个任意输入或输出位置关联信号所需的操作数量随着位置之间的距离增加而增长，对于 ConvS2S 是线性增长，对于 ByteNet 是对数增长。这使得学习远距离位置之间的依赖关系变得更加困难。在 Transformer 中，这被减少为一定数量的操作，尽管由于平均关注加权位置而导致有效分辨率降低，我们通过多头注意力(Multi-Head Attention)来抵消这种影响。
Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations. End-to-end memory networks are based on a recurrent attention mechanism instead of sequencealigned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks.
自注意力，有时称为内部注意力，是一种注意力机制，涉及不同位置的单个序列，以计算序列的表示。自注意力已成功应用于各种任务，包括阅读理解、抽象总结、文本蕴涵和学习任务独立的句子表示。端到端记忆网络基于循环注意机制，而不是序列对齐的循环，并且已被证明在简单语言问答和语言建模任务上表现良好。
To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence aligned RNNs or convolution.
据我们所知，然而，Transformer 是第一个完全依赖自注意力机制来计算其输入和输出表示的转导模型，而不使用序列对齐的循环神经网络或卷积。
Most competitive neural sequence transduction models have an encoder-decoder structure (cite). Here, the encoder maps an input sequence of symbol representations to a sequence of continuous representations . Given , the decoder then generates an output sequence of symbols one element at a time. At each step the model is auto-regressive (cite), consuming the previously generated symbols as additional input when generating the next.
大多数竞争性神经序列转导模型都具有编码器-解码器结构。在这里，编码器将符号表示的输入序列映射到连续表示的序列。给定解码器，解码器会逐步生成符号的输出序列。在每一步中，模型都是自回归的，生成下一个符号时会将先前生成的符号作为额外输入。
class EncoderDecoder(nn.Module):
"""
A standard Encoder-Decoder architecture. Base for this and many
other models.
"""
def __init__(self, encoder, decoder, src_embed, tgt_embed, generator):
super(EncoderDecoder, self).__init__()
self.encoder = encoder
self.decoder = decoder
self.src_embed = src_embed
self.tgt_embed = tgt_embed
self.generator = generator
def forward(self, src, tgt, src_mask, tgt_mask):
"Take in and process masked src and target sequences."
return self.decode(self.encode(src, src_mask), src_mask, tgt, tgt_mask)
def encode(self, src, src_mask):
return self.encoder(self.src_embed(src), src_mask)
def decode(self, memory, src_mask, tgt, tgt_mask):
return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)class Generator(nn.Module):
"Define standard linear + softmax generation step."
def __init__(self, d_model, vocab):
super(Generator, self).__init__()
self.proj = nn.Linear(d_model, vocab)
def forward(self, x):
return log_softmax(self.proj(x), dim=-1)The Transformer follows this overall architecture using stacked self-attention and point-wise, fully connected layers for both the encoder and decoder, shown in the left and right halves of Figure 1, respectively.
Transformer 遵循这种总体架构，使用堆叠的自注意力(self-attention)和逐点(point-wise)、全连接的层分别用于编码器和解码器，分别显示在图 1 的左半部分和右半部分。
The encoder is composed of a stack of identical layers.
编码器由一堆 个相同层组成。
def clones(module, N):
"Produce N identical layers."
return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])class Encoder(nn.Module):
"Core encoder is a stack of N layers"
def __init__(self, layer, N):
super(Encoder, self).__init__()
self.layers = clones(layer, N)
self.norm = LayerNorm(layer.size)
def forward(self, x, mask):
"Pass the input (and mask) through each layer in turn."
for layer in self.layers:
x = layer(x, mask)
return self.norm(x)We employ a residual connection (cite) around each of the two sub-layers, followed by layer normalization (cite).
我们在两个子层的周围采用了一个残差连接(引用)，然后是层归一化(引用)。
class LayerNorm(nn.Module):
"Construct a layernorm module (See citation for details)."
def __init__(self, features, eps=1e-6):
super(LayerNorm, self).__init__()
self.a_2 = nn.Parameter(torch.ones(features))
self.b_2 = nn.Parameter(torch.zeros(features))
self.eps = eps
def forward(self, x):
mean = x.mean(-1, keepdim=True)
std = x.std(-1, keepdim=True)
return self.a_2 * (x - mean) / (std + self.eps) + self.b_2That is, the output of each sub-layer is , where is the function implemented by the sub-layer itself. We apply dropout (cite) to the output of each sub-layer, before it is added to the sub-layer input and normalized.
换句话说，每个子层的输出是 ，其中 是子层本身实现的函数。我们在将每个子层的输出添加到子层输入并进行归一化之前，对其应用了辍学（引用）。
To facilitate these residual connections, all sub-layers in the model, as well as the embedding layers, produce outputs of dimension .
为了促进这些残差连接，模型中的所有子层以及嵌入层都产生维度为 的输出。
class SublayerConnection(nn.Module):
"""
A residual connection followed by a layer norm.
Note for code simplicity the norm is first as opposed to last.
"""
def __init__(self, size, dropout):
super(SublayerConnection, self).__init__()
self.norm = LayerNorm(size)
self.dropout = nn.Dropout(dropout)
def forward(self, x, sublayer):
"Apply residual connection to any sublayer with the same size."
return x + self.dropout(sublayer(self.norm(x)))Each layer has two sub-layers. The first is a multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward network.
每个层都有两个子层。第一个是多头自注意力机制，第二个是简单的、位置逐点全连接前馈网络。
class EncoderLayer(nn.Module):
"Encoder is made up of self-attn and feed forward (defined below)"
def __init__(self, size, self_attn, feed_forward, dropout):
super(EncoderLayer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.sublayer = clones(SublayerConnection(size, dropout), 2)
self.size = size
def forward(self, x, mask):
"Follow Figure 1 (left) for connections."
x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
return self.sublayer[1](x, self.feed_forward)The decoder is also composed of a stack of identical layers.
解码器也由一堆 个相同层组成。
class Decoder(nn.Module):
"Generic N layer decoder with masking."
def __init__(self, layer, N):
super(Decoder, self).__init__()
self.layers = clones(layer, N)
self.norm = LayerNorm(layer.size)
def forward(self, x, memory, src_mask, tgt_mask):
for layer in self.layers:
x = layer(x, memory, src_mask, tgt_mask)
return self.norm(x)In addition to the two sub-layers in each encoder layer, the decoder inserts a third sub-layer, which performs multi-head attention over the output of the encoder stack. Similar to the encoder, we employ residual connections around each of the sub-layers, followed by layer normalization.
除了每个编码器层中的两个子层外，解码器还插入第三个子层，该子层对编码器堆栈的输出执行多头注意力。与编码器类似，我们在每个子层周围采用残差连接，然后进行层归一化。
class DecoderLayer(nn.Module):
"Decoder is made of self-attn, src-attn, and feed forward (defined below)"
def __init__(self, size, self_attn, src_attn, feed_forward, dropout):
super(DecoderLayer, self).__init__()
self.size = size
self.self_attn = self_attn
self.src_attn = src_attn
self.feed_forward = feed_forward
self.sublayer = clones(SublayerConnection(size, dropout), 3)
def forward(self, x, memory, src_mask, tgt_mask):
"Follow Figure 1 (right) for connections."
m = memory
x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))
x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))
return self.sublayer[2](x, self.feed_forward)We also modify the self-attention sub-layer in the decoder stack to prevent positions from attending to subsequent positions. This masking, combined with fact that the output embeddings are offset by one position, ensures that the predictions for position can depend only on the known outputs at positions less than .
我们还修改了解码器堆栈中的自注意力子层，以防止位置关注后续位置。这种掩码，再加上输出嵌入偏移一个位置的事实，确保位置 的预测只能依赖于小于 位置的已知输出。
def subsequent_mask(size):
"Mask out subsequent positions."
attn_shape = (1, size, size)
subsequent_mask = torch.triu(torch.ones(attn_shape), diagonal=1).type(
torch.uint8
)
return subsequent_mask == 0Below the attention mask shows the position each tgt word (row) is allowed to look at (column). Words are blocked for attending to future words during training.
在下面的注意力掩码中显示了每个目标词（行）被允许查看的位置（列）。在训练过程中，单词被阻止关注未来的单词。
def example_mask():
LS_data = pd.concat(
[
pd.DataFrame(
{
"Subsequent Mask": subsequent_mask(20)[0][x, y].flatten(),
"Window": y,
"Masking": x,
}
)
for y in range(20)
for x in range(20)
]
)
return (
alt.Chart(LS_data)
.mark_rect()
.properties(height=250, width=250)
.encode(
alt.X("Window:O"),
alt.Y("Masking:O"),
alt.Color("Subsequent Mask:Q", scale=alt.Scale(scheme="viridis")),
)
.interactive()
)
show_example(example_mask)An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key.
注意力函数可以被描述为将一个查询和一组键-值对映射到一个输出，其中查询、键、值和输出都是向量。输出被计算为值的加权和，其中分配给每个值的权重由查询与相应键的兼容性函数计算得出。
We call our particular attention “Scaled Dot-Product Attention”. The input consists of queries and keys of dimension , and values of dimension . We compute the dot products of the query with all keys, divide each by , and apply a softmax function to obtain the weights on the values.
我们将我们的特别关注称为“缩放点积注意力”。输入由维度为 的查询和键以及维度为 的值组成。我们计算查询与所有键的点积，将每个除以 ，并应用 softmax 函数以获得值的权重。
In practice, we compute the attention function on a set of queries simultaneously, packed together into a matrix . The keys and values are also packed together into matrices and . We compute the matrix of outputs as:
在实践中，我们同时在一组查询上计算注意力函数，将这些查询打包成一个矩阵 。键和值也被打包到矩阵 和 中。我们计算输出矩阵如下：
def attention(query, key, value, mask=None, dropout=None):
"Compute 'Scaled Dot Product Attention'"
d_k = query.size(-1)
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
p_attn = scores.softmax(dim=-1)
if dropout is not None:
p_attn = dropout(p_attn)
return torch.matmul(p_attn, value), p_attnThe two most commonly used attention functions are additive attention (cite), and dot-product (multiplicative) attention. Dot-product attention is identical to our algorithm, except for the scaling factor of . Additive attention computes the compatibility function using a feed-forward network with a single hidden layer. While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice, since it can be implemented using highly optimized matrix multiplication code.
两种最常用的注意力函数是加性注意力(Additive Attention)，和点积(乘法)注意力。点积注意力与我们的算法相同，除了缩放因子为 。加性注意力使用具有单隐藏层的前馈网络计算兼容性函数。虽然在理论复杂性上两者相似，但在实践中，点积注意力更快速、更节省空间，因为它可以使用高度优化的矩阵乘法代码实现。
While for small values of the two mechanisms perform similarly, additive attention outperforms dot product attention without scaling for larger values of (cite). We suspect that for large values of , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients (To illustrate why the dot products get large, assume that the components of and are independent random variables with mean and variance . Then their dot product, , has mean and variance .). To counteract this effect, we scale the dot products by .
当 的值较小时，两种机制表现相似，但对于较大的 值，加性注意力优于无缩放的点积注意力(引用)。我们怀疑对于较大的 值，点积会变得很大，将 softmax 函数推入具有极小梯度的区域(为了说明为什么点积会变大，假设 和 的分量是独立的随机变量，均值为 ，方差为 。那么它们的点积 的均值为 ，方差为 )。为了抵消这种影响，我们通过 对点积进行缩放。
Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. With a single attention head, averaging inhibits this.
多头注意力机制允许模型同时关注不同表示子空间在不同位置的信息。使用单个注意力头，平均会抑制这一特性。
Where the projections are parameter matrices , , and .
投影是参数矩阵 ， ， 和 。
In this work we employ parallel attention layers, or heads. For each of these we use . Due to the reduced dimension of each head, the total computational cost is similar to that of single-head attention with full dimensionality.
在这项工作中，我们使用 个并行注意力层，或头部。对于每个注意力头部，我们使用 。由于每个头部的降维，总计算成本与具有完整维度的单头注意力相似。
class MultiHeadedAttention(nn.Module):
def __init__(self, h, d_model, dropout=0.1):
"Take in model size and number of heads."
super(MultiHeadedAttention, self).__init__()
assert d_model % h == 0
# We assume d_v always equals d_k
self.d_k = d_model // h
self.h = h
self.linears = clones(nn.Linear(d_model, d_model), 4)
self.attn = None
self.dropout = nn.Dropout(p=dropout)
def forward(self, query, key, value, mask=None):
"Implements Figure 2"
if mask is not None:
# Same mask applied to all h heads.
mask = mask.unsqueeze(1)
nbatches = query.size(0)
# 1) Do all the linear projections in batch from d_model => h x d_k
query, key, value = [
lin(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)
for lin, x in zip(self.linears, (query, key, value))
]
# 2) Apply attention on all the projected vectors in batch.
x, self.attn = attention(
query, key, value, mask=mask, dropout=self.dropout
)
# 3) "Concat" using a view and apply a final linear.
x = (
x.transpose(1, 2)
.contiguous()
.view(nbatches, -1, self.h * self.d_k)
)
del query
del key
del value
return self.linears[-1](x)The Transformer uses multi-head attention in three different ways: 1) In “encoder-decoder attention” layers, the queries come from the previous decoder layer, and the memory keys and values come from the output of the encoder. This allows every position in the decoder to attend over all positions in the input sequence. This mimics the typical encoder-decoder attention mechanisms in sequence-to-sequence models such as (cite).
Transformer 在三种不同方式中使用多头注意力：1) 在“编码器-解码器注意力”层中，查询来自前一个解码器层，而记忆键和值来自编码器的输出。这使得解码器中的每个位置都可以关注输入序列中的所有位置。这模仿了序列到序列模型中典型的编码器-解码器注意力机制。
The encoder contains self-attention layers. In a self-attention layer all of the keys, values and queries come from the same place, in this case, the output of the previous layer in the encoder. Each position in the encoder can attend to all positions in the previous layer of the encoder.
编码器包含自注意力层。在自注意力层中，所有的键、值和查询都来自同一个地方，即编码器中前一层的输出。编码器中的每个位置都可以关注编码器前一层的所有位置。
Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow in the decoder to preserve the auto-regressive property. We implement this inside of scaled dot-product attention by masking out (setting to ) all values in the input of the softmax which correspond to illegal connections.
类似地，解码器中的自注意力层允许解码器中的每个位置关注解码器中直到该位置的所有位置。我们需要阻止解码器中的左向信息流，以保持自回归属性。我们通过在缩放的点积注意力内部屏蔽（设置为 ）与非法连接对应的 softmax 输入中的所有值来实现这一点。
In addition to attention sub-layers, each of the layers in our encoder and decoder contains a fully connected feed-forward network, which is applied to each position separately and identically. This consists of two linear transformations with a ReLU activation in between.
除了注意力子层外，我们编码器和解码器中的每一层都包含一个全连接的前馈网络，该网络分别且相同地应用于每个位置。这包括两个线性变换，中间有一个 ReLU 激活。
While the linear transformations are the same across different positions, they use different parameters from layer to layer. Another way of describing this is as two convolutions with kernel size 1. The dimensionality of input and output is , and the inner-layer has dimensionality .
尽管线性变换在不同位置上是相同的，但它们在每一层中使用不同的参数。另一种描述这种情况的方式是使用核大小为 1 的两个卷积。输入和输出的维度为 ，内部层的维度为 。
class PositionwiseFeedForward(nn.Module):
"Implements FFN equation."
def __init__(self, d_model, d_ff, dropout=0.1):
super(PositionwiseFeedForward, self).__init__()
self.w_1 = nn.Linear(d_model, d_ff)
self.w_2 = nn.Linear(d_ff, d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
return self.w_2(self.dropout(self.w_1(x).relu()))Similarly to other sequence transduction models, we use learned embeddings to convert the input tokens and output tokens to vectors of dimension . We also use the usual learned linear transformation and softmax function to convert the decoder output to predicted next-token probabilities. In our model, we share the same weight matrix between the two embedding layers and the pre-softmax linear transformation, similar to (cite). In the embedding layers, we multiply those weights by .
与其他序列转导模型类似，我们使用学习到的嵌入将输入标记和输出标记转换为维度为 的向量。我们还使用通常的学习线性变换和 softmax 函数将解码器输出转换为预测的下一个标记概率。在我们的模型中，我们在两个嵌入层和预 softmax 线性变换之间共享相同的权重矩阵，类似于（引用）。在嵌入层中，我们将这些权重乘以 。
class Embeddings(nn.Module):
def __init__(self, d_model, vocab):
super(Embeddings, self).__init__()
self.lut = nn.Embedding(vocab, d_model)
self.d_model = d_model
def forward(self, x):
return self.lut(x) * math.sqrt(self.d_model)Since our model contains no recurrence and no convolution, in order for the model to make use of the order of the sequence, we must inject some information about the relative or absolute position of the tokens in the sequence. To this end, we add “positional encodings” to the input embeddings at the bottoms of the encoder and decoder stacks. The positional encodings have the same dimension as the embeddings, so that the two can be summed. There are many choices of positional encodings, learned and fixed (cite).
由于我们的模型不包含循环和卷积，为了使模型利用序列的顺序，我们必须注入一些关于序列中标记的相对或绝对位置的信息。为此，我们在编码器和解码器堆栈的底部向输入嵌入添加“位置编码”。位置编码与嵌入具有相同的维度 ，因此可以将两者相加。有许多位置编码的选择，可以是学习的，也可以是固定的（引用）。
In this work, we use sine and cosine functions of different frequencies:
在这项工作中，我们使用不同频率的正弦和余弦函数：
where is the position and is the dimension. That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from to . We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset , can be represented as a linear function of .
其中 是位置， 是维度。也就是说，位置编码的每个维度对应一个正弦波。波长从 到 形成一个几何级数。我们选择这个函数是因为我们假设它会让模型很容易学会通过相对位置进行注意力集中，因为对于任何固定的偏移 ， 可以表示为 的线性函数。
In addition, we apply dropout to the sums of the embeddings and the positional encodings in both the encoder and decoder stacks. For the base model, we use a rate of .
此外，我们在编码器和解码器堆栈中对嵌入和位置编码的总和应用了辍学(dropout)。对于基础模型，我们使用了一个速率为 。
class PositionalEncoding(nn.Module):
"Implement the PE function."
def __init__(self, d_model, dropout, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
# Compute the positional encodings once in log space.
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model, 2) * -(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)
self.register_buffer("pe", pe)
def forward(self, x):
x = x + self.pe[:, : x.size(1)].requires_grad_(False)
return self.dropout(x)Below the positional encoding will add in a sine wave based on position. The frequency and offset of the wave is different for each dimension.
在位置编码下方将根据位置添加基于正弦波的信号。每个维度的波的频率和偏移量都不同。
def example_positional():
pe = PositionalEncoding(20, 0)
y = pe.forward(torch.zeros(1, 100, 20))
data = pd.concat(
[
pd.DataFrame(
{
"embedding": y[0, :, dim],
"dimension": dim,
"position": list(range(100)),
}
)
for dim in [4, 5, 6, 7]
]
)
return (
alt.Chart(data)
.mark_line()
.properties(width=800)
.encode(x="position", y="embedding", color="dimension:N")
.interactive()
)
show_example(example_positional)We also experimented with using learned positional embeddings (cite) instead, and found that the two versions produced nearly identical results. We chose the sinusoidal version because it may allow the model to extrapolate to sequence lengths longer than the ones encountered during training.
我们还尝试使用学习到的位置嵌入(cite) ，发现两个版本产生的结果几乎相同。我们选择了正弦版本，因为它可能使模型能够推断到比训练过程中遇到的序列长度更长的序列。
Here we define a function from hyperparameters to a full model.
在这里，我们将一个从超参数到完整模型的函数定义为。
def make_model(
src_vocab, tgt_vocab, N=6, d_model=512, d_ff=2048, h=8, dropout=0.1
):
"Helper: Construct a model from hyperparameters."
c = copy.deepcopy
attn = MultiHeadedAttention(h, d_model)
ff = PositionwiseFeedForward(d_model, d_ff, dropout)
position = PositionalEncoding(d_model, dropout)
model = EncoderDecoder(
Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),
Decoder(DecoderLayer(d_model, c(attn), c(attn), c(ff), dropout), N),
nn.Sequential(Embeddings(d_model, src_vocab), c(position)),
nn.Sequential(Embeddings(d_model, tgt_vocab), c(position)),
Generator(d_model, tgt_vocab),
)
# This was important from their code.
# Initialize parameters with Glorot / fan_avg.
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
return modelHere we make a forward step to generate a prediction of the model. We try to use our transformer to memorize the input. As you will see the output is randomly generated due to the fact that the model is not trained yet. In the next tutorial we will build the training function and try to train our model to memorize the numbers from 1 to 10.
在这里，我们向前迈出一步，生成模型的预测。我们尝试使用我们的变压器来记忆输入。正如您将看到的，由于模型尚未经过训练，输出是随机生成的。在下一个教程中，我们将构建训练函数，并尝试训练我们的模型来记忆从 1 到 10 的数字。
def inference_test():
test_model = make_model(11, 11, 2)
test_model.eval()
src = torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
src_mask = torch.ones(1, 1, 10)
memory = test_model.encode(src, src_mask)
ys = torch.zeros(1, 1).type_as(src)
for i in range(9):
out = test_model.decode(
memory, src_mask, ys, subsequent_mask(ys.size(1)).type_as(src.data)
)
prob = test_model.generator(out[:, -1])
_, next_word = torch.max(prob, dim=1)
next_word = next_word.data[0]
ys = torch.cat(
[ys, torch.empty(1, 1).type_as(src.data).fill_(next_word)], dim=1
)
print("Example Untrained Model Prediction:", ys)
def run_tests():
for _ in range(10):
inference_test()
show_example(run_tests)Example Untrained Model Prediction: tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])
Example Untrained Model Prediction: tensor([[0, 3, 4, 4, 4, 4, 4, 4, 4, 4]])
Example Untrained Model Prediction: tensor([[ 0, 10, 10, 10, 3, 2, 5, 7, 9, 6]])
Example Untrained Model Prediction: tensor([[ 0, 4, 3, 6, 10, 10, 2, 6, 2, 2]])
Example Untrained Model Prediction: tensor([[ 0, 9, 0, 1, 5, 10, 1, 5, 10, 6]])
Example Untrained Model Prediction: tensor([[ 0, 1, 5, 1, 10, 1, 10, 10, 10, 10]])
Example Untrained Model Prediction: tensor([[ 0, 1, 10, 9, 9, 9, 9, 9, 1, 5]])
Example Untrained Model Prediction: tensor([[ 0, 3, 1, 5, 10, 10, 10, 10, 10, 10]])
Example Untrained Model Prediction: tensor([[ 0, 3, 5, 10, 5, 10, 4, 2, 4, 2]])
Example Untrained Model Prediction: tensor([[0, 5, 6, 2, 5, 6, 2, 6, 2, 2]])This section describes the training regime for our models.
本节描述了我们模型的训练制度。
We stop for a quick interlude to introduce some of the tools needed to train a standard encoder decoder model. First we define a batch object that holds the src and target sentences for training, as well as constructing the masks.
我们停下来进行一个快速插曲，介绍训练标准编码器解码器模型所需的一些工具。首先，我们定义一个批处理对象，用于保存训练的源句子和目标句子，同时构建掩码。
class Batch:
"""Object for holding a batch of data with mask during training."""
def __init__(self, src, tgt=None, pad=2): # 2 = <blank>
self.src = src
self.src_mask = (src != pad).unsqueeze(-2)
if tgt is not None:
self.tgt = tgt[:, :-1]
self.tgt_y = tgt[:, 1:]
self.tgt_mask = self.make_std_mask(self.tgt, pad)
self.ntokens = (self.tgt_y != pad).data.sum()
@staticmethod
def make_std_mask(tgt, pad):
"Create a mask to hide padding and future words."
tgt_mask = (tgt != pad).unsqueeze(-2)
tgt_mask = tgt_mask & subsequent_mask(tgt.size(-1)).type_as(
tgt_mask.data
)
return tgt_maskNext we create a generic training and scoring function to keep track of loss. We pass in a generic loss compute function that also handles parameter updates.
接下来，我们创建一个通用的训练和评分函数来跟踪损失。我们传入一个通用的损失计算函数，该函数还处理参数更新。
class TrainState:
"""Track number of steps, examples, and tokens processed"""
step: int = 0 # Steps in the current epoch
accum_step: int = 0 # Number of gradient accumulation steps
samples: int = 0 # total # of examples used
tokens: int = 0 # total # of tokens processeddef run_epoch(
data_iter,
model,
loss_compute,
optimizer,
scheduler,
mode="train",
accum_iter=1,
train_state=TrainState(),
):
"""Train a single epoch"""
start = time.time()
total_tokens = 0
total_loss = 0
tokens = 0
n_accum = 0
for i, batch in enumerate(data_iter):
out = model.forward(
batch.src, batch.tgt, batch.src_mask, batch.tgt_mask
)
loss, loss_node = loss_compute(out, batch.tgt_y, batch.ntokens)
# loss_node = loss_node / accum_iter
if mode == "train" or mode == "train+log":
loss_node.backward()
train_state.step += 1
train_state.samples += batch.src.shape[0]
train_state.tokens += batch.ntokens
if i % accum_iter == 0:
optimizer.step()
optimizer.zero_grad(set_to_none=True)
n_accum += 1
train_state.accum_step += 1
scheduler.step()
total_loss += loss
total_tokens += batch.ntokens
tokens += batch.ntokens
if i % 40 == 1 and (mode == "train" or mode == "train+log"):
lr = optimizer.param_groups[0]["lr"]
elapsed = time.time() - start
print(
(
"Epoch Step: %6d | Accumulation Step: %3d | Loss: %6.2f "
+ "| Tokens / Sec: %7.1f | Learning Rate: %6.1e"
)
% (i, n_accum, loss / batch.ntokens, tokens / elapsed, lr)
)
start = time.time()
tokens = 0
del loss
del loss_node
return total_loss / total_tokens, train_stateWe trained on the standard WMT 2014 English-German dataset consisting of about 4.5 million sentence pairs. Sentences were encoded using byte-pair encoding, which has a shared source-target vocabulary of about 37000 tokens. For English-French, we used the significantly larger WMT 2014 English-French dataset consisting of 36M sentences and split tokens into a 32000 word-piece vocabulary.
我们在标准的 WMT 2014 英德数据集上进行训练，该数据集包含约 450 万个句对。句子使用字节对编码进行编码，共享源-目标词汇约 37000 个标记。对于英法语言对，我们使用了规模显著更大的 WMT 2014 英法数据集，包含 3600 万个句子，并将标记分割为一个 32000 个词片词汇表。
Sentence pairs were batched together by approximate sequence length. Each training batch contained a set of sentence pairs containing approximately 25000 source tokens and 25000 target tokens.
句子对按照近似的序列长度分批处理。每个训练批次包含一组句子对，大约包含 25000 个源标记和 25000 个目标标记。
We trained our models on one machine with 8 NVIDIA P100 GPUs. For our base models using the hyperparameters described throughout the paper, each training step took about 0.4 seconds. We trained the base models for a total of 100,000 steps or 12 hours. For our big models, step time was 1.0 seconds. The big models were trained for 300,000 steps (3.5 days).
我们在一台配备 8 个 NVIDIA P100 GPU 的机器上训练了我们的模型。对于使用本文中描述的超参数的基础模型，每个训练步骤大约需要 0.4 秒。我们总共训练了基础模型 100,000 步或 12 小时。对于我们的大模型，步骤时间为 1.0 秒。大模型训练了 300,000 步（3.5 天）。
We used the Adam optimizer (cite) with , and . We varied the learning rate over the course of training, according to the formula:
我们使用了 Adam 优化器(cite)和 ， 以及 。我们根据以下公式随着训练过程改变学习率：
This corresponds to increasing the learning rate linearly for the first training steps, and decreasing it thereafter proportionally to the inverse square root of the step number. We used .
这相当于在前 个训练步骤中线性增加学习率，然后按照步数的倒数平方根成比例地减少学习率。我们使用了 。
Note: This part is very important. Need to train with this setup of the model.
注意：这部分非常重要。需要使用这个模型设置进行训练。
Example of the curves of this model for different model sizes and for optimization hyperparameters.
该模型在不同模型大小和优化超参数下的曲线示例。
def rate(step, model_size, factor, warmup):
"""
we have to default the step to 1 for LambdaLR function
to avoid zero raising to negative power.
"""
if step == 0:
step = 1
return factor * (
model_size ** (-0.5) * min(step ** (-0.5), step * warmup ** (-1.5))
)def example_learning_schedule():
opts = [
[512, 1, 4000], # example 1
[512, 1, 8000], # example 2
[256, 1, 4000], # example 3
]
dummy_model = torch.nn.Linear(1, 1)
learning_rates = []
# we have 3 examples in opts list.
for idx, example in enumerate(opts):
# run 20000 epoch for each example
optimizer = torch.optim.Adam(
dummy_model.parameters(), lr=1, betas=(0.9, 0.98), eps=1e-9
)
lr_scheduler = LambdaLR(
optimizer=optimizer, lr_lambda=lambda step: rate(step, *example)
)
tmp = []
# take 20K dummy training steps, save the learning rate at each step
for step in range(20000):
tmp.append(optimizer.param_groups[0]["lr"])
optimizer.step()
lr_scheduler.step()
learning_rates.append(tmp)
learning_rates = torch.tensor(learning_rates)
# Enable altair to handle more than 5000 rows
alt.data_transformers.disable_max_rows()
opts_data = pd.concat(
[
pd.DataFrame(
{
"Learning Rate": learning_rates[warmup_idx, :],
"model_size:warmup": ["512:4000", "512:8000", "256:4000"][
warmup_idx
],
"step": range(20000),
}
)
for warmup_idx in [0, 1, 2]
]
)
return (
alt.Chart(opts_data)
.mark_line()
.properties(width=600)
.encode(x="step", y="Learning Rate", color="model_size:warmup:N")
.interactive()
)
example_learning_schedule()During training, we employed label smoothing of value (cite). This hurts perplexity, as the model learns to be more unsure, but improves accuracy and BLEU score.
在训练过程中，我们采用了标签平滑的值 (引用)。这会降低困惑度，因为模型学会更加不确定，但会提高准确性和 BLEU 分数。
We implement label smoothing using the KL div loss. Instead of using a one-hot target distribution, we create a distribution that has
confidenceof the correct word and the rest of thesmoothingmass distributed throughout the vocabulary.
我们使用 KL 散度损失来实现标签平滑。我们不使用 one-hot 目标分布，而是创建一个分布，其中confidence是正确词的一部分，其余smoothing的质量分布在整个词汇表中。
class LabelSmoothing(nn.Module):
"Implement label smoothing."
def __init__(self, size, padding_idx, smoothing=0.0):
super(LabelSmoothing, self).__init__()
self.criterion = nn.KLDivLoss(reduction="sum")
self.padding_idx = padding_idx
self.confidence = 1.0 - smoothing
self.smoothing = smoothing
self.size = size
self.true_dist = None
def forward(self, x, target):
assert x.size(1) == self.size
true_dist = x.data.clone()
true_dist.fill_(self.smoothing / (self.size - 2))
true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
true_dist[:, self.padding_idx] = 0
mask = torch.nonzero(target.data == self.padding_idx)
if mask.dim() > 0:
true_dist.index_fill_(0, mask.squeeze(), 0.0)
self.true_dist = true_dist
return self.criterion(x, true_dist.clone().detach())Here we can see an example of how the mass is distributed to the words based on confidence.
这里我们可以看到一个示例，展示了基于置信度将质量分配给单词的方式。
# Example of label smoothing.
def example_label_smoothing():
crit = LabelSmoothing(5, 0, 0.4)
predict = torch.FloatTensor(
[
[0, 0.2, 0.7, 0.1, 0],
[0, 0.2, 0.7, 0.1, 0],
[0, 0.2, 0.7, 0.1, 0],
[0, 0.2, 0.7, 0.1, 0],
[0, 0.2, 0.7, 0.1, 0],
]
)
crit(x=predict.log(), target=torch.LongTensor([2, 1, 0, 3, 3]))
LS_data = pd.concat(
[
pd.DataFrame(
{
"target distribution": crit.true_dist[x, y].flatten(),
"columns": y,
"rows": x,
}
)
for y in range(5)
for x in range(5)
]
)
return (
alt.Chart(LS_data)
.mark_rect(color="Blue", opacity=1)
.properties(height=200, width=200)
.encode(
alt.X("columns:O", title=None),
alt.Y("rows:O", title=None),
alt.Color(
"target distribution:Q", scale=alt.Scale(scheme="viridis")
),
)
.interactive()
)
show_example(example_label_smoothing)Label smoothing actually starts to penalize the model if it gets very confident about a given choice.
标签平滑实际上开始对模型进行惩罚，如果它对给定选择非常自信。
def loss(x, crit):
d = x + 3 * 1
predict = torch.FloatTensor([[0, x / d, 1 / d, 1 / d, 1 / d]])
return crit(predict.log(), torch.LongTensor([1])).data
def penalization_visualization():
crit = LabelSmoothing(5, 0, 0.1)
loss_data = pd.DataFrame(
{
"Loss": [loss(x, crit) for x in range(1, 100)],
"Steps": list(range(99)),
}
).astype("float")
return (
alt.Chart(loss_data)
.mark_line()
.properties(width=350)
.encode(
x="Steps",
y="Loss",
)
.interactive()
)
show_example(penalization_visualization)We can begin by trying out a simple copy-task. Given a random set of input symbols from a small vocabulary, the goal is to generate back those same symbols.
我们可以从尝试一个简单的复制任务开始。给定一个来自小词汇表的随机输入符号集，目标是生成回那些相同的符号。
def data_gen(V, batch_size, nbatches):
"Generate random data for a src-tgt copy task."
for i in range(nbatches):
data = torch.randint(1, V, size=(batch_size, 10))
data[:, 0] = 1
src = data.requires_grad_(False).clone().detach()
tgt = data.requires_grad_(False).clone().detach()
yield Batch(src, tgt, 0)class SimpleLossCompute:
"A simple loss compute and train function."
def __init__(self, generator, criterion):
self.generator = generator
self.criterion = criterion
def __call__(self, x, y, norm):
x = self.generator(x)
sloss = (
self.criterion(
x.contiguous().view(-1, x.size(-1)), y.contiguous().view(-1)
)
/ norm
)
return sloss.data * norm, slossThis code predicts a translation using greedy decoding for simplicity.
该代码使用贪婪解码来预测翻译，以简化操作。
def greedy_decode(model, src, src_mask, max_len, start_symbol):
memory = model.encode(src, src_mask)
ys = torch.zeros(1, 1).fill_(start_symbol).type_as(src.data)
for i in range(max_len - 1):
out = model.decode(
memory, src_mask, ys, subsequent_mask(ys.size(1)).type_as(src.data)
)
prob = model.generator(out[:, -1])
_, next_word = torch.max(prob, dim=1)
next_word = next_word.data[0]
ys = torch.cat(
[ys, torch.zeros(1, 1).type_as(src.data).fill_(next_word)], dim=1
)
return ys# Train the simple copy task.
def example_simple_model():
V = 11
criterion = LabelSmoothing(size=V, padding_idx=0, smoothing=0.0)
model = make_model(V, V, N=2)
optimizer = torch.optim.Adam(
model.parameters(), lr=0.5, betas=(0.9, 0.98), eps=1e-9
)
lr_scheduler = LambdaLR(
optimizer=optimizer,
lr_lambda=lambda step: rate(
step, model_size=model.src_embed[0].d_model, factor=1.0, warmup=400
),
)
batch_size = 80
for epoch in range(20):
model.train()
run_epoch(
data_gen(V, batch_size, 20),
model,
SimpleLossCompute(model.generator, criterion),
optimizer,
lr_scheduler,
mode="train",
)
model.eval()
run_epoch(
data_gen(V, batch_size, 5),
model,
SimpleLossCompute(model.generator, criterion),
DummyOptimizer(),
DummyScheduler(),
mode="eval",
)[0]
model.eval()
src = torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])
max_len = src.shape[1]
src_mask = torch.ones(1, 1, max_len)
print(greedy_decode(model, src, src_mask, max_len=max_len, start_symbol=0))
# execute_example(example_simple_model)Now we consider a real-world example using the Multi30k German-English Translation task. This task is much smaller than the WMT task considered in the paper, but it illustrates the whole system. We also show how to use multi-gpu processing to make it really fast.
现在我们考虑一个使用 Multi30k 德英翻译任务的实际示例。这个任务比论文中考虑的 WMT 任务要小得多，但它展示了整个系统。我们还展示了如何使用多 GPU 处理使其运行速度非常快。
We will load the dataset using torchtext and spacy for tokenization.
我们将使用 torchtext 和 spacy 来加载数据集进行标记化。
# Load spacy tokenizer models, download them if they haven't been
# downloaded already
def load_tokenizers():
try:
spacy_de = spacy.load("de_core_news_sm")
except IOError:
os.system("python -m spacy download de_core_news_sm")
spacy_de = spacy.load("de_core_news_sm")
try:
spacy_en = spacy.load("en_core_web_sm")
except IOError:
os.system("python -m spacy download en_core_web_sm")
spacy_en = spacy.load("en_core_web_sm")
return spacy_de, spacy_endef tokenize(text, tokenizer):
return [tok.text for tok in tokenizer.tokenizer(text)]
def yield_tokens(data_iter, tokenizer, index):
for from_to_tuple in data_iter:
yield tokenizer(from_to_tuple[index])
def build_vocabulary(spacy_de, spacy_en):
def tokenize_de(text):
return tokenize(text, spacy_de)
def tokenize_en(text):
return tokenize(text, spacy_en)
print("Building German Vocabulary ...")
train, val, test = datasets.Multi30k(language_pair=("de", "en"))
vocab_src = build_vocab_from_iterator(
yield_tokens(train + val + test, tokenize_de, index=0),
min_freq=2,
specials=["<s>", "</s>", "<blank>", "<unk>"],
)
print("Building English Vocabulary ...")
train, val, test = datasets.Multi30k(language_pair=("de", "en"))
vocab_tgt = build_vocab_from_iterator(
yield_tokens(train + val + test, tokenize_en, index=1),
min_freq=2,
specials=["<s>", "</s>", "<blank>", "<unk>"],
)
vocab_src.set_default_index(vocab_src["<unk>"])
vocab_tgt.set_default_index(vocab_tgt["<unk>"])
return vocab_src, vocab_tgt
def load_vocab(spacy_de, spacy_en):
if not exists("vocab.pt"):
vocab_src, vocab_tgt = build_vocabulary(spacy_de, spacy_en)
torch.save((vocab_src, vocab_tgt), "vocab.pt")
else:
vocab_src, vocab_tgt = torch.load("vocab.pt")
print("Finished.\nVocabulary sizes:")
print(len(vocab_src))
print(len(vocab_tgt))
return vocab_src, vocab_tgt
if is_interactive_notebook():
# global variables used later in the script
spacy_de, spacy_en = show_example(load_tokenizers)
vocab_src, vocab_tgt = show_example(load_vocab, args=[spacy_de, spacy_en])Finished.
Vocabulary sizes:
59981
36745Batching matters a ton for speed. We want to have very evenly divided batches, with absolutely minimal padding. To do this we have to hack a bit around the default torchtext batching. This code patches their default batching to make sure we search over enough sentences to find tight batches.
批处理对速度影响很大。我们希望批次划分非常均匀，绝对最小化填充。为了做到这一点，我们必须在默认的 torchtext 批处理周围进行一些修改。这段代码修补了它们的默认批处理，以确保我们搜索足够的句子来找到紧凑的批次。
def collate_batch(
batch,
src_pipeline,
tgt_pipeline,
src_vocab,
tgt_vocab,
device,
max_padding=128,
pad_id=2,
):
bs_id = torch.tensor([0], device=device) # <s> token id
eos_id = torch.tensor([1], device=device) # </s> token id
src_list, tgt_list = [], []
for (_src, _tgt) in batch:
processed_src = torch.cat(
[
bs_id,
torch.tensor(
src_vocab(src_pipeline(_src)),
dtype=torch.int64,
device=device,
),
eos_id,
],
0,
)
processed_tgt = torch.cat(
[
bs_id,
torch.tensor(
tgt_vocab(tgt_pipeline(_tgt)),
dtype=torch.int64,
device=device,
),
eos_id,
],
0,
)
src_list.append(
# warning - overwrites values for negative values of padding - len
pad(
processed_src,
(
0,
max_padding - len(processed_src),
),
value=pad_id,
)
)
tgt_list.append(
pad(
processed_tgt,
(0, max_padding - len(processed_tgt)),
value=pad_id,
)
)
src = torch.stack(src_list)
tgt = torch.stack(tgt_list)
return (src, tgt)def create_dataloaders(
device,
vocab_src,
vocab_tgt,
spacy_de,
spacy_en,
batch_size=12000,
max_padding=128,
is_distributed=True,
):
# def create_dataloaders(batch_size=12000):
def tokenize_de(text):
return tokenize(text, spacy_de)
def tokenize_en(text):
return tokenize(text, spacy_en)
def collate_fn(batch):
return collate_batch(
batch,
tokenize_de,
tokenize_en,
vocab_src,
vocab_tgt,
device,
max_padding=max_padding,
pad_id=vocab_src.get_stoi()["<blank>"],
)
train_iter, valid_iter, test_iter = datasets.Multi30k(
language_pair=("de", "en")
)
train_iter_map = to_map_style_dataset(
train_iter
) # DistributedSampler needs a dataset len()
train_sampler = (
DistributedSampler(train_iter_map) if is_distributed else None
)
valid_iter_map = to_map_style_dataset(valid_iter)
valid_sampler = (
DistributedSampler(valid_iter_map) if is_distributed else None
)
train_dataloader = DataLoader(
train_iter_map,
batch_size=batch_size,
shuffle=(train_sampler is None),
sampler=train_sampler,
collate_fn=collate_fn,
)
valid_dataloader = DataLoader(
valid_iter_map,
batch_size=batch_size,
shuffle=(valid_sampler is None),
sampler=valid_sampler,
collate_fn=collate_fn,
)
return train_dataloader, valid_dataloaderdef train_worker(
gpu,
ngpus_per_node,
vocab_src,
vocab_tgt,
spacy_de,
spacy_en,
config,
is_distributed=False,
):
print(f"Train worker process using GPU: {gpu} for training", flush=True)
torch.cuda.set_device(gpu)
pad_idx = vocab_tgt["<blank>"]
d_model = 512
model = make_model(len(vocab_src), len(vocab_tgt), N=6)
model.cuda(gpu)
module = model
is_main_process = True
if is_distributed:
dist.init_process_group(
"nccl", init_method="env://", rank=gpu, world_size=ngpus_per_node
)
model = DDP(model, device_ids=[gpu])
module = model.module
is_main_process = gpu == 0
criterion = LabelSmoothing(
size=len(vocab_tgt), padding_idx=pad_idx, smoothing=0.1
)
criterion.cuda(gpu)
train_dataloader, valid_dataloader = create_dataloaders(
gpu,
vocab_src,
vocab_tgt,
spacy_de,
spacy_en,
batch_size=config["batch_size"] // ngpus_per_node,
max_padding=config["max_padding"],
is_distributed=is_distributed,
)
optimizer = torch.optim.Adam(
model.parameters(), lr=config["base_lr"], betas=(0.9, 0.98), eps=1e-9
)
lr_scheduler = LambdaLR(
optimizer=optimizer,
lr_lambda=lambda step: rate(
step, d_model, factor=1, warmup=config["warmup"]
),
)
train_state = TrainState()
for epoch in range(config["num_epochs"]):
if is_distributed:
train_dataloader.sampler.set_epoch(epoch)
valid_dataloader.sampler.set_epoch(epoch)
model.train()
print(f"[GPU{gpu}] Epoch {epoch} Training ====", flush=True)
_, train_state = run_epoch(
(Batch(b[0], b[1], pad_idx) for b in train_dataloader),
model,
SimpleLossCompute(module.generator, criterion),
optimizer,
lr_scheduler,
mode="train+log",
accum_iter=config["accum_iter"],
train_state=train_state,
)
GPUtil.showUtilization()
if is_main_process:
file_path = "%s%.2d.pt" % (config["file_prefix"], epoch)
torch.save(module.state_dict(), file_path)
torch.cuda.empty_cache()
print(f"[GPU{gpu}] Epoch {epoch} Validation ====", flush=True)
model.eval()
sloss = run_epoch(
(Batch(b[0], b[1], pad_idx) for b in valid_dataloader),
model,
SimpleLossCompute(module.generator, criterion),
DummyOptimizer(),
DummyScheduler(),
mode="eval",
)
print(sloss)
torch.cuda.empty_cache()
if is_main_process:
file_path = "%sfinal.pt" % config["file_prefix"]
torch.save(module.state_dict(), file_path)def train_distributed_model(vocab_src, vocab_tgt, spacy_de, spacy_en, config):
from the_annotated_transformer import train_worker
ngpus = torch.cuda.device_count()
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "12356"
print(f"Number of GPUs detected: {ngpus}")
print("Spawning training processes ...")
mp.spawn(
train_worker,
nprocs=ngpus,
args=(ngpus, vocab_src, vocab_tgt, spacy_de, spacy_en, config, True),
)
def train_model(vocab_src, vocab_tgt, spacy_de, spacy_en, config):
if config["distributed"]:
train_distributed_model(
vocab_src, vocab_tgt, spacy_de, spacy_en, config
)
else:
train_worker(
0, 1, vocab_src, vocab_tgt, spacy_de, spacy_en, config, False
)
def load_trained_model():
config = {
"batch_size": 32,
"distributed": False,
"num_epochs": 8,
"accum_iter": 10,
"base_lr": 1.0,
"max_padding": 72,
"warmup": 3000,
"file_prefix": "multi30k_model_",
}
model_path = "multi30k_model_final.pt"
if not exists(model_path):
train_model(vocab_src, vocab_tgt, spacy_de, spacy_en, config)
model = make_model(len(vocab_src), len(vocab_tgt), N=6)
model.load_state_dict(torch.load("multi30k_model_final.pt"))
return model
if is_interactive_notebook():
model = load_trained_model()Once trained we can decode the model to produce a set of translations. Here we simply translate the first sentence in the validation set. This dataset is pretty small so the translations with greedy search are reasonably accurate.
一旦训练完成，我们可以解码模型以生成一组翻译。在这里，我们只是翻译验证集中的第一句话。这个数据集相当小，因此使用贪婪搜索得到的翻译相当准确。
So this mostly covers the transformer model itself. There are four aspects that we didn’t cover explicitly. We also have all these additional features implemented in OpenNMT-py.
所以这主要涵盖了变压器模型本身。有四个方面我们没有明确涵盖。我们还在 OpenNMT-py 中实现了所有这些附加功能。
- BPE/ Word-piece: We can use a library to first preprocess the data into subword units. See Rico Sennrich’s subword-nmt implementation. These models will transform the training data to look like this:
BPE/ Word-piece: 我们可以使用一个库来首先将数据预处理为子词单元。请参阅 Rico Sennrich 的 subword-nmt 实现。这些模型将把训练数据转换成这样的形式：
▁Die ▁Protokoll datei ▁kann ▁ heimlich ▁per ▁E - Mail ▁oder ▁FTP ▁an ▁einen ▁bestimmte n ▁Empfänger ▁gesendet ▁werden .
▁协议文件 ▁可以 ▁秘密地 ▁通过 ▁电子邮件 ▁或 ▁FTP ▁发送给 ▁特定的 ▁接收者。
- Shared Embeddings: When using BPE with shared vocabulary we can share the same weight vectors between the source / target / generator. See the (cite) for details. To add this to the model simply do this:
共享嵌入：当使用具有共享词汇的 BPE 时，我们可以在源/目标/生成器之间共享相同的权重向量。有关详细信息，请参阅（引用）。要将此添加到模型中，只需执行以下操作：
if False:
model.src_embed[0].lut.weight = model.tgt_embeddings[0].lut.weight
model.generator.lut.weight = model.tgt_embed[0].lut.weight
- Beam Search: This is a bit too complicated to cover here. See the OpenNMT-py for a pytorch implementation.
束搜索(Beam Search): 这里涉及的内容有点复杂。请参考 OpenNMT-py 中的 pytorch 实现。
- Model Averaging: The paper averages the last k checkpoints to create an ensembling effect. We can do this after the fact if we have a bunch of models:
模型平均: 该论文对最后的 k 个检查点进行平均，以产生集成效果。如果我们有一堆模型，我们可以在事后这样做。
def average(model, models):
"Average models into model"
for ps in zip(*[m.params() for m in [model] + models]):
ps[0].copy_(torch.sum(*ps[1:]) / len(ps[1:]))On the WMT 2014 English-to-German translation task, the big transformer model (Transformer (big) in Table 2) outperforms the best previously reported models (including ensembles) by more than 2.0 BLEU, establishing a new state-of-the-art BLEU score of 28.4. The configuration of this model is listed in the bottom line of Table 3. Training took 3.5 days on 8 P100 GPUs. Even our base model surpasses all previously published models and ensembles, at a fraction of the training cost of any of the competitive models.
在 WMT 2014 英译德任务中，大型 Transformer 模型（表 2 中的 Transformer（big））的表现优于先前报告的最佳模型（包括集成模型）超过 2.0 BLEU，建立了新的最先进 BLEU 得分 28.4。该模型的配置列在表 3 的底部。在 8 个 P100 GPU 上训练耗时 3.5 天。即使是我们的基础模型也超过了先前发布的所有模型和集成模型，在训练成本的一小部分内。
On the WMT 2014 English-to-French translation task, our big model achieves a BLEU score of 41.0, outperforming all of the previously published single models, at less than 1/4 the training cost of the previous state-of-the-art model. The Transformer (big) model trained for English-to-French used dropout rate Pdrop = 0.1, instead of 0.3.
在 WMT 2014 英译法翻译任务中，我们的大模型取得了 41.0 的 BLEU 分数，在训练成本不到先前最先进模型的四分之一的情况下，胜过了所有先前发表的单一模型。用于英译法训练的 Transformer（大）模型使用了辍学率 Pdrop = 0.1，而不是 0.3。
With the addtional extensions in the last section, the OpenNMT-py replication gets to 26.9 on EN-DE WMT. Here I have loaded in those parameters to our reimplemenation.
通过上一节的额外扩展，OpenNMT-py 复制品在 EN-DE WMT 上达到了 26.9。在这里，我已经将这些参数加载到我们的重新实现中。
# Load data and model for output checksdef check_outputs(
valid_dataloader,
model,
vocab_src,
vocab_tgt,
n_examples=15,
pad_idx=2,
eos_string="</s>",
):
results = [()] * n_examples
for idx in range(n_examples):
print("\nExample %d ========\n" % idx)
b = next(iter(valid_dataloader))
rb = Batch(b[0], b[1], pad_idx)
greedy_decode(model, rb.src, rb.src_mask, 64, 0)[0]
src_tokens = [
vocab_src.get_itos()[x] for x in rb.src[0] if x != pad_idx
]
tgt_tokens = [
vocab_tgt.get_itos()[x] for x in rb.tgt[0] if x != pad_idx
]
print(
"Source Text (Input) : "
+ " ".join(src_tokens).replace("\n", "")
)
print(
"Target Text (Ground Truth) : "
+ " ".join(tgt_tokens).replace("\n", "")
)
model_out = greedy_decode(model, rb.src, rb.src_mask, 72, 0)[0]
model_txt = (
" ".join(
[vocab_tgt.get_itos()[x] for x in model_out if x != pad_idx]
).split(eos_string, 1)[0]
+ eos_string
)
print("Model Output : " + model_txt.replace("\n", ""))
results[idx] = (rb, src_tokens, tgt_tokens, model_out, model_txt)
return results
def run_model_example(n_examples=5):
global vocab_src, vocab_tgt, spacy_de, spacy_en
print("Preparing Data ...")
_, valid_dataloader = create_dataloaders(
torch.device("cpu"),
vocab_src,
vocab_tgt,
spacy_de,
spacy_en,
batch_size=1,
is_distributed=False,
)
print("Loading Trained Model ...")
model = make_model(len(vocab_src), len(vocab_tgt), N=6)
model.load_state_dict(
torch.load("multi30k_model_final.pt", map_location=torch.device("cpu"))
)
print("Checking Model Outputs:")
example_data = check_outputs(
valid_dataloader, model, vocab_src, vocab_tgt, n_examples=n_examples
)
return model, example_data
# execute_example(run_model_example)Even with a greedy decoder the translation looks pretty good. We can further visualize it to see what is happening at each layer of the attention
即使使用贪婪解码器，翻译看起来仍然相当不错。我们可以进一步将其可视化，以查看每个注意力层发生了什么。
def mtx2df(m, max_row, max_col, row_tokens, col_tokens):
"convert a dense matrix to a data frame with row and column indices"
return pd.DataFrame(
[
(
r,
c,
float(m[r, c]),
"%.3d %s"
% (r, row_tokens[r] if len(row_tokens) > r else "<blank>"),
"%.3d %s"
% (c, col_tokens[c] if len(col_tokens) > c else "<blank>"),
)
for r in range(m.shape[0])
for c in range(m.shape[1])
if r < max_row and c < max_col
],
# if float(m[r,c]) != 0 and r < max_row and c < max_col],
columns=["row", "column", "value", "row_token", "col_token"],
)
def attn_map(attn, layer, head, row_tokens, col_tokens, max_dim=30):
df = mtx2df(
attn[0, head].data,
max_dim,
max_dim,
row_tokens,
col_tokens,
)
return (
alt.Chart(data=df)
.mark_rect()
.encode(
x=alt.X("col_token", axis=alt.Axis(title="")),
y=alt.Y("row_token", axis=alt.Axis(title="")),
color="value",
tooltip=["row", "column", "value", "row_token", "col_token"],
)
.properties(height=400, width=400)
.interactive()
)def get_encoder(model, layer):
return model.encoder.layers[layer].self_attn.attn
def get_decoder_self(model, layer):
return model.decoder.layers[layer].self_attn.attn
def get_decoder_src(model, layer):
return model.decoder.layers[layer].src_attn.attn
def visualize_layer(model, layer, getter_fn, ntokens, row_tokens, col_tokens):
# ntokens = last_example[0].ntokens
attn = getter_fn(model, layer)
n_heads = attn.shape[1]
charts = [
attn_map(
attn,
0,
h,
row_tokens=row_tokens,
col_tokens=col_tokens,
max_dim=ntokens,
)
for h in range(n_heads)
]
assert n_heads == 8
return alt.vconcat(
charts[0]
# | charts[1]
| charts[2]
# | charts[3]
| charts[4]
# | charts[5]
| charts[6]
# | charts[7]
# layer + 1 due to 0-indexing
).properties(title="Layer %d" % (layer + 1))def viz_encoder_self():
model, example_data = run_model_example(n_examples=1)
example = example_data[
len(example_data) - 1
] # batch object for the final example
layer_viz = [
visualize_layer(
model, layer, get_encoder, len(example[1]), example[1], example[1]
)
for layer in range(6)
]
return alt.hconcat(
layer_viz[0]
# & layer_viz[1]
& layer_viz[2]
# & layer_viz[3]
& layer_viz[4]
# & layer_viz[5]
)
show_example(viz_encoder_self)Preparing Data ...
Loading Trained Model ...
Checking Model Outputs:
Example 0 ========
Source Text (Input) : <s> Zwei Frauen in pinkfarbenen T-Shirts und <unk> unterhalten sich vor einem <unk> . </s>
Target Text (Ground Truth) : <s> Two women wearing pink T - shirts and blue jeans converse outside clothing store . </s>
Model Output : <s> Two women in pink shirts and face are talking in front of a <unk> . </s>def viz_decoder_self():
model, example_data = run_model_example(n_examples=1)
example = example_data[len(example_data) - 1]
layer_viz = [
visualize_layer(
model,
layer,
get_decoder_self,
len(example[1]),
example[1],
example[1],
)
for layer in range(6)
]
return alt.hconcat(
layer_viz[0]
& layer_viz[1]
& layer_viz[2]
& layer_viz[3]
& layer_viz[4]
& layer_viz[5]
)
show_example(viz_decoder_self)Preparing Data ...
Loading Trained Model ...
Checking Model Outputs:
Example 0 ========
Source Text (Input) : <s> Eine Gruppe von Männern in Kostümen spielt Musik . </s>
Target Text (Ground Truth) : <s> A group of men in costume play music . </s>
Model Output : <s> A group of men in costumes playing music . </s>def viz_decoder_src():
model, example_data = run_model_example(n_examples=1)
example = example_data[len(example_data) - 1]
layer_viz = [
visualize_layer(
model,
layer,
get_decoder_src,
max(len(example[1]), len(example[2])),
example[1],
example[2],
)
for layer in range(6)
]
return alt.hconcat(
layer_viz[0]
& layer_viz[1]
& layer_viz[2]
& layer_viz[3]
& layer_viz[4]
& layer_viz[5]
)
show_example(viz_decoder_src)Preparing Data ...
Loading Trained Model ...
Checking Model Outputs:
Example 0 ========
Source Text (Input) : <s> Ein kleiner Junge verwendet einen Bohrer , um ein Loch in ein Holzstück zu machen . </s>
Target Text (Ground Truth) : <s> A little boy using a drill to make a hole in a piece of wood . </s>
Model Output : <s> A little boy uses a machine to be working in a hole in a log . </s>Hopefully this code is useful for future research. Please reach out if you have any issues.
希望这段代码对未来的研究有所帮助。如果您有任何问题，请随时联系。
Cheers, Sasha Rush, Austin Huang, Suraj Subramanian, Jonathan Sum, Khalid Almubarak, Stella Biderman
欢呼，Sasha Rush, Austin Huang, Suraj Subramanian, Jonathan Sum, Khalid Almubarak, Stella Biderman