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import torch |
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import torch.nn as nn |
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import math |
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class PositionalEncoding(nn.Module): |
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def __init__(self, d_model, dropout=0.1, max_len=5000): |
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super().__init__() |
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self.dropout = nn.Dropout(p=dropout) |
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position = torch.arange(max_len).unsqueeze(1) |
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div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)) |
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pe = torch.zeros(max_len, d_model) |
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pe[:, 0::2] = torch.sin(position * div_term) |
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pe[:, 1::2] = torch.cos(position * div_term) |
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self.register_buffer("pe", pe) |
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def forward(self, x): |
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x = x + self.pe[:x.size(0)] |
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return self.dropout(x) |
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""" |
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Same scheduler as in "Attention Is All You Need" |
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""" |
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class NoamScheduler(): |
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def __init__(self, optimizer, warmup, model_size): |
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self.epoch = 0 |
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self.optimizer = optimizer |
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self.warmup = warmup |
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self.model_size = model_size |
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def step(self): |
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self.epoch += 1 |
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new_lr = self.model_size**(-0.5) * min(self.epoch**(-0.5), self.epoch * self.warmup**(-1.5)) |
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for param in self.optimizer.param_groups: |
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param["lr"] = new_lr |
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""" |
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Encoders to attend sentence level features. |
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""" |
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class TransformerInterEncoder(nn.Module): |
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def __init__(self, d_model, d_ff=2048, nheads=8, num_encoders=2, dropout=0.1, max_len=512): |
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super().__init__() |
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self.positional_enc = PositionalEncoding(d_model, dropout, max_len) |
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self.encoders = nn.TransformerEncoder( |
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nn.TransformerEncoderLayer(d_model=d_model, nhead=nheads, dim_feedforward=d_ff), |
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num_layers=num_encoders |
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) |
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self.layer_norm = nn.LayerNorm(d_model) |
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self.linear = nn.Linear(d_model, 1) |
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self.sigmoid = nn.Sigmoid() |
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def forward(self, x): |
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x = self.positional_enc(x) |
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x = self.encoders(x) |
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x = self.layer_norm(x) |
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logit = self.linear(x) |
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sentences_scores = self.sigmoid(logit) |
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return sentences_scores.squeeze(-1), logit.squeeze(-1) |
