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import torch
import torch.nn as nn
import torch.nn.functional as F
from attention import SelfAttention
class CLIPEmbedding(nn.Module):
def __init__(self, n_vocab: int, n_embd: int, n_token: int):
super().__init__()
self.token_embedding = nn.Embedding(n_vocab, n_embd)
# A learnable weight matrix encodes the position information for each token
self.position_embedding = nn.Parameter(torch.zeros((n_token, n_embd)))
def forward(self, tokens):
# (Batch_Size, Seq_Len) -> (Batch_Size, Seq_Len, Dim)
x = self.token_embedding(tokens)
# (Batch_Size, Seq_Len) -> (Batch_Size, Seq_Len, Dim)
x += self.position_embedding
return x
class CLIPLayer(nn.Module):
def __init__(self, n_head: int, n_embd: int):
super().__init__()
# Pre-attention norm
self.layernorm_1 = nn.LayerNorm(n_embd)
# Self attention
self.attention = SelfAttention(n_head, n_embd)
# Pre-FNN norm
self.layernorm_2 = nn.LayerNorm(n_embd)
# Feedforward layer
self.linear_1 = nn.Linear(n_embd, 4 * n_embd)
self.linear_2 = nn.Linear(4 * n_embd, n_embd)
def forward(self, x):
# (Batch_Size, Seq_Len, Dim)
residue = x
### SELF ATTENTION ###
# (Batch_Size, Seq_Len, Dim) -> (Batch_Size, Seq_Len, Dim)
x = self.layernorm_1(x)
# (Batch_Size, Seq_Len, Dim) -> (Batch_Size, Seq_Len, Dim)
x = self.attention(x, causal_mask=True)
# (Batch_Size, Seq_Len, Dim) + (Batch_Size, Seq_Len, Dim) -> (Batch_Size, Seq_Len, Dim)
x += residue
### FEEDFORWARD LAYER ###
# Apply a feedforward layer where the hidden dimension is 4 times the embedding dimension.
residue = x
# (Batch_Size, Seq_Len, Dim) -> (Batch_Size, Seq_Len, Dim)
x = self.layernorm_2(x)
# (Batch_Size, Seq_Len, Dim) -> (Batch_Size, Seq_Len, 4 * Dim)
x = self.linear_1(x)
# (Batch_Size, Seq_Len, 4 * Dim) -> (Batch_Size, Seq_Len, 4 * Dim)
x = x * torch.sigmoid(1.702 * x) # QuickGELU activation function
# (Batch_Size, Seq_Len, 4 * Dim) -> (Batch_Size, Seq_Len, Dim)
x = self.linear_2(x)
# (Batch_Size, Seq_Len, Dim) + (Batch_Size, Seq_Len, Dim) -> (Batch_Size, Seq_Len, Dim)
x += residue
return x
class CLIP(nn.Module):
def __init__(self):
super().__init__()
self.embedding = CLIPEmbedding(49408, 768, 77)
self.layers = nn.ModuleList([
CLIPLayer(12, 768) for i in range(12)
])
self.layernorm = nn.LayerNorm(768)
def forward(self, tokens: torch.LongTensor) -> torch.FloatTensor:
tokens = tokens.type(torch.long)
# (Batch_Size, Seq_Len) -> (Batch_Size, Seq_Len, Dim)
state = self.embedding(tokens)
# Apply encoder layers similar to the Transformer's encoder.
for layer in self.layers:
# (Batch_Size, Seq_Len, Dim) -> (Batch_Size, Seq_Len, Dim)
state = layer(state)
# (Batch_Size, Seq_Len, Dim) -> (Batch_Size, Seq_Len, Dim)
output = self.layernorm(state)
return output |