PlainTransformer.py

#!/usr/bin/env python
# coding: utf-8
# This code is mostly borrowed from the Annotated Transformer (https://github.com/harvardnlp/annotated-transformer)
# I just wrapped it. -- lukun199

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

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):  # x 是
        "Follow Figure 1 (right) for connections."
        m = memory
        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))  # self-atten
        x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))  # cross-atten

        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


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)
        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)
        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)

def make_encoder(N=6, d_model=512, d_ff=2048, h=8, dropout=0.1):
    attn = MultiHeadedAttention(h, d_model)
    ff = PositionwiseFeedForward(d_model, d_ff, dropout)
    model = Encoder(EncoderLayer(d_model, attn, ff, dropout), N)

    for p in model.parameters():
        if p.dim() > 1:
            nn.init.xavier_uniform(p)
    return model

def make_decoder(N=6, d_model=512, d_ff=2048, h=8, dropout=0.1):
    attn = MultiHeadedAttention(h, d_model)
    ff = PositionwiseFeedForward(d_model, d_ff, dropout)
    model = Decoder(DecoderLayer(d_model, attn, attn, ff, dropout), N)

    for p in model.parameters():
        if p.dim() > 1:
            nn.init.xavier_uniform(p)
    return model

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


if __name__ == '__main__':

    class Batch:
        "Object for holding a batch of data with mask during training."

        def __init__(self, src, trg=None, pad=0):
            self.src = src
            self.src_mask = (src != pad).unsqueeze(-2)
            if trg is not None:
                self.trg = trg[:, :-1] # teacher forcing.
                self.trg_y = trg[:, 1:]
                self.trg_mask = self.make_std_mask(self.trg, pad)
                self.ntokens = (self.trg_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 & Variable(
                subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data))
            return tgt_mask


    def run_epoch(data_iter, model, loss_compute):
        "Standard Training and Logging Function"
        start = time.time()
        total_tokens = 0
        total_loss = 0
        tokens = 0
        for i, batch in enumerate(data_iter):
            out = model.forward(batch.src, batch.trg,
                                batch.src_mask, batch.trg_mask)
            loss = loss_compute(out, batch.trg_y, batch.ntokens)
            total_loss += loss
            total_tokens += batch.ntokens
            tokens += batch.ntokens
            if i % 50 == 1:
                elapsed = time.time() - start
                print("Epoch Step: %d Loss: %f Tokens per Sec: %f" %
                      (i, loss / batch.ntokens, tokens / elapsed))
                start = time.time()
                tokens = 0
        return total_loss / total_tokens

    global max_src_in_batch, max_tgt_in_batch


    def batch_size_fn(new, count, sofar):
        "Keep augmenting batch and calculate total number of tokens + padding."
        global max_src_in_batch, max_tgt_in_batch
        if count == 1:
            max_src_in_batch = 0
            max_tgt_in_batch = 0
        max_src_in_batch = max(max_src_in_batch, len(new.src))
        max_tgt_in_batch = max(max_tgt_in_batch, len(new.trg) + 2)
        src_elements = count * max_src_in_batch
        tgt_elements = count * max_tgt_in_batch
        return max(src_elements, tgt_elements)


    class NoamOpt:
        "Optim wrapper that implements rate."

        def __init__(self, model_size, factor, warmup, optimizer):
            self.optimizer = optimizer
            self._step = 0
            self.warmup = warmup
            self.factor = factor
            self.model_size = model_size
            self._rate = 0

        def step(self):
            "Update parameters and rate"
            self._step += 1
            rate = self.rate()
            for p in self.optimizer.param_groups:
                p['lr'] = rate
            self._rate = rate
            self.optimizer.step()

        def rate(self, step=None):
            "Implement `lrate` above"
            if step is None:
                step = self._step
            return self.factor * (self.model_size ** (-0.5) *
                                  min(step ** (-0.5), step * self.warmup ** (-1.5)))


    def get_std_opt(model):
        return NoamOpt(model.src_embed[0].d_model, 2, 4000,
                       torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))


    class LabelSmoothing(nn.Module):
        "Implement label smoothing."

        def __init__(self, size, padding_idx, smoothing=0.0):
            super(LabelSmoothing, self).__init__()
            self.criterion = nn.KLDivLoss(size_average=False)
            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))
            idx = target.data.unsqueeze(1)
            idx = idx.type(torch.int64)
            true_dist.scatter_(1, idx, 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, Variable(true_dist, requires_grad=False))

    def loss(x):
        d = x + 3 * 1
        predict = torch.FloatTensor([[0, x / d, 1 / d, 1 / d, 1 / d],
                                     ])
        # print(predict)
        return crit(Variable(predict.log()),
                    Variable(torch.LongTensor([1]))).data[0]

    def data_gen(V, batch, nbatches):
        "Generate random data for a src-tgt copy task."
        for i in range(nbatches):
            data = torch.from_numpy(np.random.randint(1, V, size=(batch, 10)))
            data[:, 0] = 1
            src = Variable(data, requires_grad=False)
            tgt = Variable(data, requires_grad=False)
            yield Batch(src, tgt, 0)


    class SimpleLossCompute:
        "A simple loss compute and train function."

        def __init__(self, generator, criterion, opt=None):
            self.generator = generator
            self.criterion = criterion
            self.opt = opt

        def __call__(self, x, y, norm):
            x = self.generator(x)
            loss = self.criterion(x.contiguous().view(-1, x.size(-1)),
                                  y.contiguous().view(-1)) / norm
            loss.backward()
            if self.opt is not None:
                self.opt.step()
                self.opt.optimizer.zero_grad()
            return loss.item() * norm

    def greedy_decode(model, src, src_mask, max_len, start_symbol):
        memory = model.encode(src, src_mask)
        ys = torch.ones(1, 1).fill_(start_symbol).type_as(src.data)
        for i in range(max_len - 1):
            out = model.decode(memory, src_mask,
                               Variable(ys),
                               Variable(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.ones(1, 1).type_as(src.data).fill_(next_word)], dim=1)
        return ys


    tmp_model = make_model(10, 10, 2)
    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]])
    v = crit(Variable(predict.log()), Variable(torch.LongTensor([2, 1, 0])))
    crit = LabelSmoothing(5, 0, 0.1)


    V = 11
    criterion = LabelSmoothing(size=V, padding_idx=0, smoothing=0.0)
    model = make_model(V, V, N=2)
    model_opt = NoamOpt(model.src_embed[0].d_model, 1, 400,
                        torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))


    for epoch in range(10):
        model.train()
        run_epoch(data_gen(V, 30, 20), model,
                  SimpleLossCompute(model.generator, criterion, model_opt))
        model.eval()
        print(run_epoch(data_gen(V, 30, 5), model,
                        SimpleLossCompute(model.generator, criterion, None)))


    model.eval()
    src = Variable(torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]))
    src_mask = Variable(torch.ones(1, 1, 10))
    print(greedy_decode(model, src, src_mask, max_len=10, start_symbol=1))