Attention is all you need 2025-03-28 15:09 0 条回复 1、 https://github.com/zhoutengteng/learn-nlp-with-transformers/blob/main/docs/%E7%AF%87%E7%AB%A02-Transformer%E7%9B%B8%E5%85%B3%E5%8E%9F%E7%90%86/2.2.1-Pytorch%E7%BC%96%E5%86%99Transformer.md  2、pip install -i https://pypi.tuna.tsinghua.edu.cn/simple torch numpy matplotlib spacy torchtext seaborn 3、可直接运行 ```python 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 matplotlib.use('TkAgg') import matplotlib.pyplot as plt import seaborn seaborn.set_context(context="talk") class EncoderDecoder(nn.Module): """ 基础的Encoder-Decoder结构。 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): "定义生成器，由linear和softmax组成" "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): "产生N个完全相同的网络层" "Produce N identical layers." return nn.ModuleList([copy.deepcopy(module) for _ in range(N)]) class Encoder(nn.Module): "完整的Encoder包含N层" def __init__(self, layer, N): super(Encoder, self).__init__() self.layers = clones(layer, N) self.norm = LayerNorm(layer.size) def forward(self, x, mask): "每一层的输入是x和mask" 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]) # plt.colorbar() # 可选：添加颜色条 # plt.show() # 显示图像 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) # 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]]) # plt.show() 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) 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] 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)) # opts = [NoamOpt(512, 1, 4000, None), # NoamOpt(512, 1, 8000, None), # NoamOpt(256, 1, 4000, None)] # plt.plot(np.arange(1, 20000), [[opt.rate(i) for opt in opts] for i in range(1, 20000)]) # plt.legend(["512:4000", "512:8000", "256:4000"]) # plt.show() 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)) true_dist.scatter_(1, target.data.unsqueeze(1).long(), self.confidence) # 增加.long()转换 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)) # 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]))) # # # Show the target distributions expected by the system. # plt.imshow(crit.true_dist) # plt.show() # print(crit.true_dist) # crit = LabelSmoothing(5, 0, 0.1) # 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 + 1e-9).log()), Variable(torch.LongTensor([1]))).item() # # y = [loss(x) for x in range(1, 100)] # x = np.arange(1, 100) # plt.plot(x, y) # plt.show() 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 # Train the simple copy task. 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))) 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 model.eval() src = Variable(torch.LongTensor([[1,2,3,4,5,6,7,8,9,10,11]]) ) src_mask = Variable(torch.ones(1, 1, 10) ) print(greedy_decode(model, src, src_mask, max_len=10, start_symbol=1)) ```
