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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
DIM = 512
DEVICE = "mps" if torch.backends.mps.is_available() else "cpu"
class MHA_SelfAttention(nn.Module):
def __init__(self, embed_dim=DIM, num_heads=8, *args, **kwargs):
super().__init__(*args, **kwargs)
self.mha = nn.MultiheadAttention(embed_dim, num_heads)
self.num_heads = num_heads
def forward(self, x, mask=None, triangle_mask=False):
attn_mask = None
seq_len = x.size(1)
if triangle_mask:
attn_mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=1) == 0
attn_mask = attn_mask.to(x.device)
if mask is not None:
if attn_mask is not None:
attn_mask = mask.unsqueeze(1) & attn_mask.unsqueeze(0)
else:
attn_mask = mask.unsqueeze(1).expand(-1, seq_len, -1)
if attn_mask is not None:
attn_mask = attn_mask.repeat(self.num_heads, 1, 1)
x = x.transpose(0, 1)
attn_output, _ = self.mha(x, x, x, attn_mask=attn_mask)
attn_output = attn_output.transpose(0, 1)
return attn_output
class MHA_EncoderDecoderAttention(nn.Module):
def __init__(self, embed_dim=DIM, num_heads=8, *args, **kwargs):
super().__init__(*args, **kwargs)
self.mha = nn.MultiheadAttention(embed_dim, num_heads)
self.num_heads = num_heads
def forward(self, x, encoded, mask=None):
attn_mask = None
seq_len_x = x.size(1)
seq_len_encoded = encoded.size(1)
if mask is not None:
attn_mask = mask.unsqueeze(1).expand(-1, seq_len_x, seq_len_encoded)
attn_mask = attn_mask.repeat(self.num_heads, 1, 1)
x = x.transpose(0, 1)
encoded = encoded.transpose(0, 1)
attn_output, _ = self.mha(x, encoded, encoded, attn_mask=attn_mask)
attn_output = attn_output.transpose(0, 1)
return attn_output
class FeedForward(nn.Module):
def __init__(self, dim=DIM, hidden_dim=None, *args, **kwargs):
super().__init__(*args, **kwargs)
self.dim = dim
self.hidden_dim = hidden_dim if hidden_dim is not None else dim
self.block = nn.Sequential(
nn.LayerNorm(self.dim),
nn.Linear(self.dim, self.hidden_dim),
nn.GELU(),
nn.Linear(self.hidden_dim, self.dim),
nn.GELU(),
)
def forward(self, x):
return self.block(x)
class EncoderBlock(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.sa = MHA_SelfAttention()
self.block = FeedForward()
def forward(self, x, padding_mask=None):
res_x = x
x = self.sa(x, padding_mask)
x = x + res_x
res_x_2 = x
x = self.block(x)
x = x + res_x_2
return x
class DecoderBlock(nn.Module):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.sa = MHA_SelfAttention()
self.eda = MHA_EncoderDecoderAttention()
self.block = FeedForward()
def forward(self, x, encoded, padding_mask=None):
res_x = x
x = self.sa(x, mask=padding_mask, triangle_mask=True)
x = x + res_x
res_x_2 = x
x = self.eda(x, encoded, mask=padding_mask)
x = x + res_x_2
res_x_3 = x
x = self.block(x)
x = x + res_x_3
return x
class PositionalEncoding(nn.Module):
def __init__(self, max_len=5000):
super().__init__()
position = torch.arange(0, max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, DIM, 2) * -(np.log(10000.0) / DIM))
pe = torch.zeros(max_len, DIM)
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
self.register_buffer("pe", pe.unsqueeze(0))
def forward(self, x):
seq_len = x.size(1)
return x + self.pe[:, :seq_len, :].to(x.device)
class Transformer(nn.Module):
def __init__(self, num_blocks=6, vocab_size=30522, seq_len=100, *args, **kwargs):
super().__init__(*args, **kwargs)
self.num_blocks = num_blocks
self.encoders = nn.ModuleList([EncoderBlock() for _ in range(num_blocks)])
self.decoders = nn.ModuleList([DecoderBlock() for _ in range(num_blocks)])
self.pos_encoding = PositionalEncoding()
self.enc_embedding = nn.Embedding(vocab_size, DIM)
self.oblock = nn.Sequential(
nn.Linear(DIM, vocab_size),
# nn.Softmax(dim=-1)
)
def forward(self, x, padding_mask=None):
if isinstance(x, tuple):
x, padding_mask = x
if padding_mask is not None:
padding_mask = padding_mask == 0
x = self.pos_encoding(self.enc_embedding(x))
for eidx, eblock in enumerate(self.encoders):
x = eblock(x, padding_mask=padding_mask)
encoded = x # No need to clone
x = self.pos_encoding(x)
for didx, dblock in enumerate(self.decoders):
x = dblock(x, encoded, padding_mask=padding_mask)
x = self.oblock(x)
return x
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