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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
from torch.nn import TransformerEncoder, TransformerEncoderLayer
class Transformer(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
dropout = self.cfg.dropout
nhead = self.cfg.n_heads
nlayers = self.cfg.n_layers
input_dim = self.cfg.input_dim
output_dim = self.cfg.output_dim
d_model = input_dim
self.pos_encoder = PositionalEncoding(d_model, dropout)
encoder_layers = TransformerEncoderLayer(
d_model, nhead, dropout=dropout, batch_first=True
)
self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)
self.output_mlp = nn.Linear(d_model, output_dim)
def forward(self, x, mask=None):
"""
Args:
x: (N, seq_len, input_dim)
Returns:
output: (N, seq_len, output_dim)
"""
# (N, seq_len, d_model)
src = self.pos_encoder(x)
# model_stats["pos_embedding"] = x
# (N, seq_len, d_model)
output = self.transformer_encoder(src)
# (N, seq_len, output_dim)
output = self.output_mlp(output)
return output
class PositionalEncoding(nn.Module):
def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)
)
# Assume that x is (seq_len, N, d)
# pe = torch.zeros(max_len, 1, d_model)
# pe[:, 0, 0::2] = torch.sin(position * div_term)
# pe[:, 0, 1::2] = torch.cos(position * div_term)
# Assume that x in (N, seq_len, d)
pe = torch.zeros(1, max_len, d_model)
pe[0, :, 0::2] = torch.sin(position * div_term)
pe[0, :, 1::2] = torch.cos(position * div_term)
self.register_buffer("pe", pe)
def forward(self, x):
"""
Args:
x: Tensor, shape [N, seq_len, d]
"""
# Old: Assume that x is (seq_len, N, d), and self.pe is (max_len, 1, d_model)
# x = x + self.pe[: x.size(0)]
# Now: self.pe is (1, max_len, d)
x = x + self.pe[:, : x.size(1), :]
return self.dropout(x)
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