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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_seq_length):
super(PositionalEncoding, self).__init__()
pe = torch.zeros(max_seq_length, d_model)
position = torch.arange(0, max_seq_length, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * -(math.log(10000.0) / d_model))
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):
return x + self.pe[:, :x.size(1)]
class MultiHeadSelfAttention(nn.Module):
def __init__(self, d_model, num_heads, bias=False):
super(MultiHeadSelfAttention, self).__init__()
assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
self.d_model = d_model
self.num_heads = num_heads
self.depth = d_model // num_heads
self.wq = nn.Linear(d_model, d_model, bias)
self.wk = nn.Linear(d_model, d_model, bias)
self.wv = nn.Linear(d_model, d_model, bias)
self.fc = nn.Linear(d_model, d_model)
def split_heads(self, x, batch_size):
x = x.view(batch_size, -1, self.num_heads, self.depth)
return x.permute(0, 2, 1, 3)
def forward(self, q, k, v, mask=None):
batch_size = q.size(0)
q = self.split_heads(self.wq(q), batch_size)
k = self.split_heads(self.wk(k), batch_size)
v = self.split_heads(self.wv(v), batch_size)
scores = torch.matmul(q, k.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.depth, dtype=torch.float32))
if mask is not None:
scores = scores.masked_fill(mask == 0, -1e9)
attn = F.softmax(scores, dim=-1)
out = torch.matmul(attn, v)
out = out.permute(0, 2, 1, 3).contiguous()
out = out.view(batch_size, -1, self.d_model)
out = self.fc(out)
return out
class FeedForwardNetwork(nn.Module):
def __init__(self, d_model, d_ff, dropout=0.1):
super(FeedForwardNetwork, self).__init__()
self.fc1 = nn.Linear(d_model, d_ff)
self.fc2 = nn.Linear(d_ff, d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x = self.fc1(x)
x = F.relu(x)
x = self.dropout(x)
x = self.fc2(x)
return x
class EncoderLayer(nn.Module):
def __init__(self, d_model, num_heads, d_ff, dropout=0.1, bias=False):
super(EncoderLayer, self).__init__()
self.self_attn = MultiHeadSelfAttention(d_model, num_heads, bias)
self.ffn = FeedForwardNetwork(d_model, d_ff, dropout)
self.layernorm1 = nn.LayerNorm(d_model)
self.layernorm2 = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x, mask=None):
attn_output = self.self_attn(x, x, x, mask)
x = self.layernorm1(x + self.dropout(attn_output))
ffn_output = self.ffn(x)
x = self.layernorm2(x + self.dropout(ffn_output))
return x
class DecoderLayer(nn.Module):
def __init__(self, d_model, num_heads, d_ff, dropout=0.1, bias=False):
super(DecoderLayer, self).__init__()
self.self_attn = MultiHeadSelfAttention(d_model, num_heads, bias)
self.cross_attn = MultiHeadSelfAttention(d_model, num_heads, bias)
self.ffn = FeedForwardNetwork(d_model, d_ff, dropout)
self.layernorm1 = nn.LayerNorm(d_model)
self.layernorm2 = nn.LayerNorm(d_model)
self.layernorm3 = nn.LayerNorm(d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x, enc_output, src_mask=None, tgt_mask=None):
self_attn_output = self.self_attn(q=x, k=x, v=x, mask=tgt_mask)
x = self.layernorm1(x + self.dropout(self_attn_output))
cross_attn_output = self.cross_attn(q=x, k=enc_output, v=enc_output, mask=src_mask)
x = self.layernorm2(x + self.dropout(cross_attn_output))
ffn_output = self.ffn(x)
x = self.layernorm3(x + self.dropout(ffn_output))
return x
class TransformerBlock(nn.Module):
def __init__(self, tokenizer=None, config=None, stress=False):
super(TransformerBlock, self).__init__()
self.config = config
self.tokenizer = tokenizer
self.input_vocab_size = tokenizer.get_vocab_size()
self.target_vocab_size = tokenizer.get_vocab_size()
self.d_model = config.get('D_MODEL', 512)
self.num_heads = config.get('NUM_HEADS', 8)
self.num_encoder_layers = config.get('NUM', 6)
self.num_decoder_layers = config.get('NUM', 6)
self.d_ff = config.get('D_FF', 2048)
self.dropout = config.get('DROPOUT', 0.1)
self.bias = config.get('BIAS', False)
self.stress = stress
self.encoder_embedding = nn.Embedding(self.input_vocab_size, self.d_model)
self.decoder_embedding = nn.Embedding(self.target_vocab_size, self.d_model)
self.pos_embedding = PositionalEncoding(self.d_model, config.get('MAX_LEN', 32))
self.encoder_layers = nn.ModuleList(
[EncoderLayer(self.d_model, self.num_heads, self.d_ff, self.dropout, self.bias) for _ in
range(self.num_encoder_layers)])
self.decoder_layers = nn.ModuleList(
[DecoderLayer(self.d_model, self.num_heads, self.d_ff, self.dropout, self.bias) for _ in
range(self.num_decoder_layers)])
self.fc_out = nn.Linear(self.d_model, self.target_vocab_size)
def encode(self, src, src_mask):
src = self.pos_embedding(self.encoder_embedding(src))
for layer in self.encoder_layers:
src = layer(src, src_mask)
return src
def decode(self, memory, src_mask, tgt, tgt_mask):
tgt = self.pos_embedding(self.decoder_embedding(tgt))
for layer in self.decoder_layers:
tgt = layer(tgt, memory, src_mask, tgt_mask)
return tgt
def forward(self, src, tgt, src_mask, tgt_mask):
memory = self.encode(src, src_mask)
output = self.decode(memory, src_mask, tgt, tgt_mask)
output = self.fc_out(output)
return output
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