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import torch
import torch.nn as nn
import math
class MultiHeadAttention(nn.Module):
def __init__(self, d_model = 256, num_heads = 8):
super().__init__()
self.d_model = d_model
self.num_heads = num_heads
assert d_model % num_heads == 0, "Number of dimensions should be divisible by heads"
self.d_k = d_model // num_heads
self.W_q = nn.Linear(d_model, d_model, bias=False)
self.W_k = nn.Linear(d_model, d_model, bias=False)
self.W_v = nn.Linear(d_model, d_model, bias=False)
self.projection = nn.Linear(d_model, d_model, bias=False)
self.dropout = nn.Dropout(0.1)
def forward(self, x, attention_mask=None):
batch_size, seq_length, d_model = x.shape
Q = self.W_q(x) #(batch_size, seq_len, d_model)
K = self.W_k(x)
V = self.W_v(x)
Q = Q.view(batch_size, seq_length, self.num_heads, self.d_k).transpose(1, 2) # (batch_size, num_heads, seq_length, d_k)
K = K.view(batch_size, seq_length, self.num_heads, self.d_k).transpose(1, 2)
V = V.view(batch_size, seq_length, self.num_heads, self.d_k).transpose(1, 2)
attention_scores = Q @ K.transpose(2, 3)
if attention_mask is not None:
mask = attention_mask.unsqueeze(1).unsqueeze(2) # (batch_dim, 1, 1, seq_length)
mask = mask.to(attention_scores.device) # making mask to prevent model attending to PAD tokens
attention_scores = attention_scores.masked_fill(mask == 0, float("-inf"))
attention_weights = torch.softmax(attention_scores / math.sqrt(self.d_k), dim=-1)
attention_weights = self.dropout(attention_weights)
final_weights = attention_weights @ V # (batch_size, num_heads, seq_length, d_k)
final_weights = final_weights.transpose(1,2).contiguous().view(batch_size, seq_length, d_model)
out_projection = self.projection(final_weights)
return out_projection
class FeedForward(nn.Module):
def __init__(self, d_model = 256):
super().__init__()
self.projection = nn.Sequential(
nn.Linear(d_model, d_model * 4),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(d_model * 4, d_model)
)
def forward(self, x):
return self.projection(x)
class TransformerBlock(nn.Module):
def __init__(self, d_model = 256):
super().__init__()
self.attn = MultiHeadAttention()
self.ffn = FeedForward()
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
def forward(self, x, attn_mask):
residual = x
x = self.norm1(x)
x = self.attn(x, attn_mask)
x += residual
residual = x
x = self.norm2(x)
x = self.ffn(x)
x += residual
return x |