75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
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, max_len=100):
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super().__init__()
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pe = torch.zeros(max_len, d_model)
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pos = torch.arange(0, max_len).unsqueeze(1)
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div = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
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pe[:, 0::2] = torch.sin(pos * div)
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pe[:, 1::2] = torch.cos(pos * div)
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self.register_buffer('pe', pe.unsqueeze(0))
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def forward(self, x):
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return x + self.pe[:, :x.size(1)].to(x.device)
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class ScaledDotProductAttention(nn.Module):
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def forward(self, Q, K, V, mask=None):
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d_k = Q.size(-1)
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scores = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(d_k)
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attn = torch.softmax(scores, dim=-1)
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return torch.matmul(attn, V), attn
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class MultiHeadAttention(nn.Module):
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def __init__(self, d_model, num_heads):
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super().__init__()
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assert d_model % num_heads == 0
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self.d_k = d_model // num_heads
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self.num_heads = num_heads
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self.Q = nn.Linear(d_model, d_model)
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self.K = nn.Linear(d_model, d_model)
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self.V = nn.Linear(d_model, d_model)
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self.out = nn.Linear(d_model, d_model)
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self.attn = ScaledDotProductAttention()
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self.last_attn_weights = None
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def forward(self, q, k, v, mask=None):
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B = q.size(0)
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Q = self.Q(q).view(B, -1, self.num_heads, self.d_k).transpose(1, 2)
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K = self.K(k).view(B, -1, self.num_heads, self.d_k).transpose(1, 2)
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V = self.V(v).view(B, -1, self.num_heads, self.d_k).transpose(1, 2)
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out, attn = self.attn(Q, K, V)
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self.last_attn_weights = attn.detach().cpu()
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out = out.transpose(1, 2).contiguous().view(B, -1, self.num_heads * self.d_k)
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return self.out(out)
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class FeedForward(nn.Module):
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def __init__(self, d_model, d_ff):
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super().__init__()
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self.ff = nn.Sequential(
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nn.Linear(d_model, d_ff),
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nn.ReLU(),
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nn.Linear(d_ff, d_model)
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)
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def forward(self, x):
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return self.ff(x)
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class EncoderLayer(nn.Module):
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def __init__(self, d_model, num_heads, d_ff):
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super().__init__()
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self.attn = MultiHeadAttention(d_model, num_heads)
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self.ff = FeedForward(d_model, d_ff)
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self.norm1 = nn.LayerNorm(d_model)
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self.norm2 = nn.LayerNorm(d_model)
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def forward(self, x):
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x2 = self.attn(x, x, x)
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x = self.norm1(x + x2)
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x2 = self.ff(x)
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x = self.norm2(x + x2)
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return x
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