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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+dict.p filter=lfs diff=lfs merge=lfs -text

ch09util.py

@@ -0,0 +1,340 @@
+
+import torch
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+import numpy as np
+def subsequent_mask(size):
+    attn_shape = (1, size, size)
+    subsequent_mask = np.triu(np.ones(attn_shape),
+                              k=1).astype('uint8')
+    output = torch.from_numpy(subsequent_mask) == 0
+    return output
+
+
+def make_std_mask(tgt, pad):
+    tgt_mask=(tgt != pad).unsqueeze(-2)
+    output=tgt_mask & subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data)
+    return output 
+
+# define the Batch class
+class Batch:
+    def __init__(self, src, trg=None, pad=0):
+        src = torch.from_numpy(src).to(DEVICE).long()
+        self.src = src
+        self.src_mask = (src != pad).unsqueeze(-2)
+        if trg is not None:
+            trg = torch.from_numpy(trg).to(DEVICE).long()
+            self.trg = trg[:, :-1]
+            self.trg_y = trg[:, 1:]
+            self.trg_mask = make_std_mask(self.trg, pad)
+            self.ntokens = (self.trg_y != pad).data.sum()
+
+from torch import nn
+# An encoder-decoder transformer
+class Transformer(nn.Module):
+    def __init__(self, encoder, decoder,
+                 src_embed, tgt_embed, generator):
+        super().__init__()
+        self.encoder = encoder
+        self.decoder = decoder
+        self.src_embed = src_embed
+        self.tgt_embed = tgt_embed
+        self.generator = generator
+
+    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)
+
+    def forward(self, src, tgt, src_mask, tgt_mask):
+        memory = self.encode(src, src_mask)
+        output = self.decode(memory, src_mask, tgt, tgt_mask)
+        return output
+
+# Create an encoder
+from copy import deepcopy
+class Encoder(nn.Module):
+    def __init__(self, layer, N):
+        super().__init__()
+        self.layers = nn.ModuleList(
+            [deepcopy(layer) for i in range(N)])
+        self.norm = LayerNorm(layer.size)
+
+    def forward(self, x, mask):
+        for layer in self.layers:
+            x = layer(x, mask)
+            output = self.norm(x)
+        return output
+
+class EncoderLayer(nn.Module):
+    def __init__(self, size, self_attn, feed_forward, dropout):
+        super().__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.sublayer = nn.ModuleList([deepcopy(
+        SublayerConnection(size, dropout)) for i in range(2)])
+        self.size = size  
+
+    def forward(self, x, mask):
+        x = self.sublayer[0](
+            x, lambda x: self.self_attn(x, x, x, mask))
+        output = self.sublayer[1](x, self.feed_forward)
+        return output 
+    
+class SublayerConnection(nn.Module):
+    def __init__(self, size, dropout):
+        super().__init__()
+        self.norm = LayerNorm(size)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x, sublayer):
+        output = x + self.dropout(sublayer(self.norm(x)))
+        return output  
+
+class LayerNorm(nn.Module):
+    def __init__(self, features, eps=1e-6):
+        super().__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)
+        x_zscore = (x - mean) / torch.sqrt(std ** 2 + self.eps)
+        output = self.a_2*x_zscore+self.b_2
+        return output
+
+# Create a decoder
+class Decoder(nn.Module):
+    def __init__(self, layer, N):
+        super().__init__()
+        self.layers = nn.ModuleList(
+            [deepcopy(layer) for i in range(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)
+        output = self.norm(x)
+        return output
+
+class DecoderLayer(nn.Module):
+    def __init__(self, size, self_attn, src_attn,
+                 feed_forward, dropout):
+        super().__init__()
+        self.size = size
+        self.self_attn = self_attn
+        self.src_attn = src_attn
+        self.feed_forward = feed_forward
+        self.sublayer = nn.ModuleList([deepcopy(
+        SublayerConnection(size, dropout)) for i in range(3)])
+
+    def forward(self, x, memory, src_mask, tgt_mask):
+        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, memory, memory, src_mask))
+        output = self.sublayer[2](x, self.feed_forward)
+        return output 
+
+
+
+# create the model
+def create_model(src_vocab, tgt_vocab, N, d_model,
+                 d_ff, h, dropout=0.1):
+    attn=MultiHeadedAttention(h, d_model).to(DEVICE)
+    ff=PositionwiseFeedForward(d_model, d_ff, dropout).to(DEVICE)
+    pos=PositionalEncoding(d_model, dropout).to(DEVICE)
+    model = Transformer(
+        Encoder(EncoderLayer(d_model,deepcopy(attn),deepcopy(ff),
+                             dropout).to(DEVICE),N).to(DEVICE),
+        Decoder(DecoderLayer(d_model,deepcopy(attn),
+             deepcopy(attn),deepcopy(ff), dropout).to(DEVICE),
+                N).to(DEVICE),
+        nn.Sequential(Embeddings(d_model, src_vocab).to(DEVICE),
+                      deepcopy(pos)),
+        nn.Sequential(Embeddings(d_model, tgt_vocab).to(DEVICE),
+                      deepcopy(pos)),
+        Generator(d_model, tgt_vocab)).to(DEVICE)
+    for p in model.parameters():
+        if p.dim() > 1:
+            nn.init.xavier_uniform_(p)
+    return model.to(DEVICE)
+
+import math
+class Embeddings(nn.Module):
+    def __init__(self, d_model, vocab):
+        super().__init__()
+        self.lut = nn.Embedding(vocab, d_model)
+        self.d_model = d_model
+
+    def forward(self, x):
+        out = self.lut(x) * math.sqrt(self.d_model)
+        return out
+
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, dropout, max_len=5000):
+        super().__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        pe = torch.zeros(max_len, d_model, device=DEVICE)
+        position = torch.arange(0., max_len, 
+                                device=DEVICE).unsqueeze(1)
+        div_term = torch.exp(torch.arange(
+            0., d_model, 2, device=DEVICE)
+            * -(math.log(10000.0) / d_model))
+        pe_pos = torch.mul(position, div_term)
+        pe[:, 0::2] = torch.sin(pe_pos)
+        pe[:, 1::2] = torch.cos(pe_pos)
+        pe = pe.unsqueeze(0)
+        self.register_buffer('pe', pe)  
+
+    def forward(self, x):
+        x = x + self.pe[:, :x.size(1)].requires_grad_(False)
+        out = self.dropout(x)
+        return out
+
+def attention(query, key, value, mask=None, dropout=None):
+    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 = nn.functional.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):
+        super().__init__()
+        assert d_model % h == 0
+        self.d_k = d_model // h
+        self.h = h
+        self.linears = nn.ModuleList([deepcopy(
+            nn.Linear(d_model, d_model)) for i in range(4)])
+        self.attn = None
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, query, key, value, mask=None):
+        if mask is not None:
+            mask = mask.unsqueeze(1)
+        nbatches = query.size(0)  
+        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))]
+        x, self.attn = attention(
+            query, key, value, mask=mask, dropout=self.dropout)
+        x = x.transpose(1, 2).contiguous().view(
+            nbatches, -1, self.h * self.d_k)
+        output = self.linears[-1](x)
+        return output 
+    
+class Generator(nn.Module):
+    def __init__(self, d_model, vocab):
+        super().__init__()
+        self.proj = nn.Linear(d_model, vocab)
+
+    def forward(self, x):
+        out = self.proj(x)
+        probs = nn.functional.log_softmax(out, dim=-1)
+        return probs   
+    
+    
+class PositionwiseFeedForward(nn.Module):
+    def __init__(self, d_model, d_ff, dropout=0.1):
+        super().__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):
+        h1 = self.w_1(x)
+        h2 = self.dropout(h1)
+        return self.w_2(h2)   
+    
+    
+
+
+class LabelSmoothing(nn.Module):
+    def __init__(self, size, padding_idx, smoothing=0.1):
+        super().__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
+        output = self.criterion(x, true_dist.clone().detach())
+        return output
+
+class SimpleLossCompute:
+    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.data.item() * norm.float()
+
+class NoamOpt:
+    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):
+        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):
+        if step is None:
+            step = self._step
+        output = self.factor * (self.model_size ** (-0.5) *
+        min(step ** (-0.5), step * self.warmup ** (-1.5)))
+        return output
+
+
+
+
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+
+   
+    
+    

dict.p

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c749ac01743123bd4a4683e075dbd828745e122146f160835149737f8bf23f2
+size 492544