Transformer源碼分析

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math, copy, time
from torch.autograd import Variable
import matplotlib.pyplot as plt
import seaborn
seaborn.set_context(context="talk")
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 F.log_softmax(self.proj(x), dim=-1)
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)
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_2
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)))
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)
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)
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)
def subsequent_mask(size):

    "Mask out subsequent positions."

    attn_shape = (1, size, size)

    subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')

    return torch.from_numpy(subsequent_mask) == 0
plt.figure(figsize=(5,5))

plt.imshow(subsequent_mask(20)[0])
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 = F.softmax(scores, dim = -1)

    if dropout is not None:

        p_attn = dropout(p_attn)

    return torch.matmul(p_attn, value), p_attn
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) #複製4份linear層

        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 = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2)

             for l, 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)

        return self.linears[-1](x)
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(F.relu(self.w_1(x))))
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)
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) # [0,1,2] 展開成一列

        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 + Variable(self.pe[:, :x.size(1)], requires_grad=False)

        return self.dropout(x)
plt.figure(figsize=(15, 5))

pe = PositionalEncoding(20, 0)

y = pe.forward(Variable(torch.zeros(1, 100, 20)))

plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())

plt.legend(["dim %d"%p for p in [4,5,6,7]])
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 model
tmp_model = make_model(10, 10, 2)

 

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