89 lines
2.6 KiB
Python
89 lines
2.6 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: int, dropout: float = 0.1, max_len: int = 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, 1, d_model)
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pe[:, 0, 0::2] = torch.sin(position * div_term)
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pe[:, 0, 1::2] = torch.cos(position * div_term)
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self.register_buffer('pe', pe)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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x: Tensor, shape [seq_len, batch_size, embedding_dim]
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"""
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x = x + self.pe[:x.size(0)]
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return self.dropout(x)
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class TransformerEncoder(nn.Module):
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def __init__(
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self,
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d_model,
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nhead,
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dim_feedforward,
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transformer_dropout,
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transformer_activation,
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num_encoder_layers,
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max_input_len,
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transformer_norm_input
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):
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super().__init__()
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self.d_model = d_model
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self.num_layer = num_encoder_layers
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self.max_input_len = max_input_len
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# Creating Transformer Encoder 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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dropout=transformer_dropout,
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activation=transformer_activation
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)
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encoder_norm = nn.LayerNorm(d_model)
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self.transformer = nn.TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
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self.norm_input = None
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if transformer_norm_input:
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self.norm_input = nn.LayerNorm(d_model)
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def forward(self, padded_h_node, src_padding_mask):
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"""
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padded_h_node: n_b x B x h_d # 63, 257, 128
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src_key_padding_mask: B x n_b # 257, 63
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"""
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# (S, B, h_d), (B, S)
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if self.norm_input is not None:
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padded_h_node = self.norm_input(padded_h_node)
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transformer_out = self.transformer(padded_h_node, src_key_padding_mask=src_padding_mask) # (S, B, h_d)
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return transformer_out, src_padding_mask
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if __name__ == '__main__':
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model = TransformerEncoder(
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d_model=12,
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nhead=4,
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dim_feedforward=32,
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transformer_dropout=0.0,
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transformer_activation='gelu',
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num_encoder_layers=4,
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max_input_len=34,
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transformer_norm_input=0
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)
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print(model.norm_input) |