import math
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import torch
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import torch.nn.functional as F
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from torch import nn
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class MultiHeadSelfAttention(nn.Module):
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def __init__(self, n_units, h=8, dropout_rate=0.1):
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super().__init__()
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self.linearQ = nn.Linear(n_units, n_units)
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self.linearK = nn.Linear(n_units, n_units)
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self.linearV = nn.Linear(n_units, n_units)
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self.linearO = nn.Linear(n_units, n_units)
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self.d_k = n_units // h
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self.h = h
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self.dropout = nn.Dropout(dropout_rate)
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def __call__(self, x, batch_size, x_mask):
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q = self.linearQ(x).view(batch_size, -1, self.h, self.d_k)
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k = self.linearK(x).view(batch_size, -1, self.h, self.d_k)
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v = self.linearV(x).view(batch_size, -1, self.h, self.d_k)
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scores = torch.matmul(q.permute(0, 2, 1, 3), k.permute(0, 2, 3, 1)) / math.sqrt(self.d_k)
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if x_mask is not None:
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x_mask = x_mask.unsqueeze(1)
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scores = scores.masked_fill(x_mask == 0, -1e9)
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self.att = F.softmax(scores, dim=3)
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p_att = self.dropout(self.att)
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x = torch.matmul(p_att, v.permute(0, 2, 1, 3))
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x = x.permute(0, 2, 1, 3).contiguous().view(-1, self.h * self.d_k)
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return self.linearO(x)
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class PositionwiseFeedForward(nn.Module):
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def __init__(self, n_units, d_units, dropout_rate):
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super(PositionwiseFeedForward, self).__init__()
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self.linear1 = nn.Linear(n_units, d_units)
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self.linear2 = nn.Linear(d_units, n_units)
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self.dropout = nn.Dropout(dropout_rate)
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def __call__(self, x):
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return self.linear2(self.dropout(F.relu(self.linear1(x))))
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class PositionalEncoding(torch.nn.Module):
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def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
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super(PositionalEncoding, self).__init__()
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self.d_model = d_model
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self.reverse = reverse
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self.xscale = math.sqrt(self.d_model)
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self.dropout = torch.nn.Dropout(p=dropout_rate)
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self.pe = None
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self.extend_pe(torch.tensor(0.0).expand(1, max_len))
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def extend_pe(self, x):
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if self.pe is not None:
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if self.pe.size(1) >= x.size(1):
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if self.pe.dtype != x.dtype or self.pe.device != x.device:
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self.pe = self.pe.to(dtype=x.dtype, device=x.device)
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return
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pe = torch.zeros(x.size(1), self.d_model)
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if self.reverse:
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position = torch.arange(x.size(1) - 1, -1, -1.0, dtype=torch.float32).unsqueeze(1)
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else:
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position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
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div_term = torch.exp(
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torch.arange(0, self.d_model, 2, dtype=torch.float32)
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* -(math.log(10000.0) / self.d_model)
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)
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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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pe = pe.unsqueeze(0)
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self.pe = pe.to(device=x.device, dtype=x.dtype)
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def forward(self, x: torch.Tensor):
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self.extend_pe(x)
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x = x * self.xscale + self.pe[:, : x.size(1)]
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return self.dropout(x)
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class EENDOLATransformerEncoder(nn.Module):
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def __init__(
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self,
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idim: int,
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n_layers: int,
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n_units: int,
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e_units: int = 2048,
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h: int = 4,
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dropout_rate: float = 0.1,
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use_pos_emb: bool = False,
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):
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super(EENDOLATransformerEncoder, self).__init__()
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self.linear_in = nn.Linear(idim, n_units)
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self.lnorm_in = nn.LayerNorm(n_units)
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self.n_layers = n_layers
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self.dropout = nn.Dropout(dropout_rate)
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for i in range(n_layers):
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setattr(self, "{}{:d}".format("lnorm1_", i), nn.LayerNorm(n_units))
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setattr(self, "{}{:d}".format("self_att_", i), MultiHeadSelfAttention(n_units, h))
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setattr(self, "{}{:d}".format("lnorm2_", i), nn.LayerNorm(n_units))
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setattr(
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self,
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"{}{:d}".format("ff_", i),
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PositionwiseFeedForward(n_units, e_units, dropout_rate),
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)
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self.lnorm_out = nn.LayerNorm(n_units)
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def __call__(self, x, x_mask=None):
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BT_size = x.shape[0] * x.shape[1]
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e = self.linear_in(x.reshape(BT_size, -1))
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for i in range(self.n_layers):
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e = getattr(self, "{}{:d}".format("lnorm1_", i))(e)
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s = getattr(self, "{}{:d}".format("self_att_", i))(e, x.shape[0], x_mask)
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e = e + self.dropout(s)
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e = getattr(self, "{}{:d}".format("lnorm2_", i))(e)
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s = getattr(self, "{}{:d}".format("ff_", i))(e)
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e = e + self.dropout(s)
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return self.lnorm_out(e)
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