37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
import torch
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import torch.nn as nn
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class Brain(nn.Module):
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"""
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Minimal Transformer-based autoregressive model.
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"""
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def __init__(
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self,
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vocab_size: int,
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d_model: int = 256,
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nhead: int = 4,
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num_layers: int = 2,
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dim_feedforward: int = 512,
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max_seq_len: int = 128,
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):
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super().__init__()
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self.token_emb = nn.Embedding(vocab_size, d_model)
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self.pos_emb = nn.Parameter(torch.zeros(1, max_seq_len, d_model))
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encoder_layer = nn.TransformerEncoderLayer(
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d_model=d_model,
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nhead=nhead,
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dim_feedforward=dim_feedforward,
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batch_first=True,
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)
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self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
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self.fc_out = nn.Linear(d_model, vocab_size)
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self.max_seq_len = max_seq_len
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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seq_len = x.size(1)
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x = self.token_emb(x) + self.pos_emb[:, :seq_len, :]
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x = self.transformer(x)
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return self.fc_out(x)
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