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"""
transformer based model, but with few minimal tweaks
trained a 2.5billion parameters model with current set configurations
"""
import torch
import json
import os
current_directory = os.path.dirname(os.path.abspath(__file__))
os.chdir(current_directory)
import torch.nn as nn
from torch.nn import functional as F
with open('config_enigma.json', 'r', encoding='utf-8') as file:
params = json.load(file)
batch_size = params['batch_size']
block_size = params['block_size']
n_head = params['n_head']
d_model = params['d_model']
n_layers = params['n_layer']
dropout = params['dropout']
norm_eps = params['norm_eps']
device = 'cuda' if torch.cuda.is_available() else 'cpu'
class AttentionHead(nn.Module):
"""
initialize a single head of self attention.
Args:
- d_model (int): dimensionality of the model's hidden layers
- head_size (int): dimensionality of each attention head
- dropout (float): dropout probability
- block_size (int): the maximum sequence length for positional encoding
"""
def __init__(self, d_model, head_size, dropout, block_size):
super().__init__()
self.key = nn.Linear(d_model, head_size, bias=True)
self.query = nn.Linear(d_model, head_size, bias=True)
self.value = nn.Linear(d_model, head_size, bias=False)
self.dropout = nn.Dropout(dropout)
self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
self.rel_pos_emb = nn.Parameter(torch.randn(block_size, block_size, head_size))
def forward(self, x, mask=False):
"""
forward pass of a single attention head.
Args:
- x (Tensor): input tensor.
- mask (bool): flag indicating whether to apply masking
Returns:
- out (Tensor): output tensor after self attention
"""
B, T, C = x.shape
key = self.key(x)
query = self.query(x)
scores = torch.matmul(query, key.transpose(-2, -1)) / (key.shape[-1] ** -0.5)
rel_pos_scores = torch.einsum('btc,tvc->btv', query, self.rel_pos_emb[:T, :T])
scores += rel_pos_scores
if mask:
scores = scores.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
weights = F.softmax(scores, dim=-1)
weights = self.dropout(weights)
value = self.value(x)
out = torch.matmul(weights, value)
return out
class MultiHeadAttention(nn.Module):
"""
initialize a multi-head attention module.
Args:
- d_model (int): dimensionality of the model's hidden layers
- n_head (int): no of attention heads
- dropout (float): dropout probability
- block_size (int): context length
"""
def __init__(self, d_model, n_head, dropout, block_size):
head_size = d_model // n_head
super().__init__()
self.heads = nn.ModuleList([AttentionHead(d_model=d_model, dropout=dropout, head_size=head_size, block_size=block_size) for _ in range(n_head)])
self.proj = nn.Linear(n_head * head_size, d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x, mask):
"""
forward pass of the multi-head attention module
Args:
- x (Tensor): input tensor
- mask (bool): flag indicating whether to apply masking
Returns:
- out (Tensor): output tensor after multi-head attention
"""
out = torch.cat([h(x, mask=mask) for h in self.heads], dim=-1)
out = self.dropout(self.proj(out))
return out
class FeedForward(nn.Module):
"""
initialize a feedforward network module
Args:
- d_model (int): the dimensionality of the model's hidden layers
- dropout (float): dropout probability
"""
def __init__(self, d_model, dropout):
super().__init__()
self.net = nn.Sequential(
nn.Linear(d_model, 10*d_model),
nn.GELU(),
nn.Linear(10*d_model, d_model),
nn.Dropout(dropout)
)
def forward(self, x):
"""
forward pass of the feedforward network module
Args:
- x (Tensor): input tensor
Returns:
- out (Tensor): output tensor after passing through the feedforward network
"""
return self.net(x)
class EncoderNetwork(nn.Module):
"""
initialize an encoder network module
Args:
- d_model (int): dimensionality of the model's hidden layers
- n_head (int): no of attention heads in multi-head attention layers
- norm_eps (float): epsilon value for layer normalization
- dropout (float): dropout probability
- block_size (int): the maximum sequence length for positional encoding
"""
def __init__(self, d_model, n_head, norm_eps, dropout, block_size):
super().__init__()
self.s_att = MultiHeadAttention(n_head=n_head, d_model=d_model, dropout=dropout, block_size=block_size)
self.ffwd = FeedForward(d_model, dropout)
self.dropout = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(d_model, eps=norm_eps)
self.norm2 = nn.LayerNorm(d_model, eps=norm_eps)
def forward(self, src):
"""
forward pass of the encoder network module.
Args:
- src (Tensor): input tensor representing source data
Returns:
- src (Tensor): output tensor after passing through the encoder network
"""
src2 = self.s_att(src, mask=False)
src = src + self.dropout(src2)
src = self.norm1(src)
src2 = self.ffwd(src)
src = src + self.dropout(src2)
src = self.norm2(src)
return src
class DecoderNetwork(nn.Module):
"""
initialize a decoder network module
Args:
- d_model (int): dimensionality of the model's hidden layers
- n_head (int): no of attention heads in multi-head attention layers
- norm_eps (float): epsilon value for layer normalization
- dropout (float): dropout probability
- block_size (int): the maximum sequence length for positional encoding
"""
def __init__(self, d_model, n_head, norm_eps, dropout, block_size):
super().__init__()
self.s_att = MultiHeadAttention(n_head=n_head, d_model=d_model, dropout=dropout, block_size=block_size)
self.ffwd = FeedForward(d_model, dropout)
self.dropout = nn.Dropout(dropout)
self.norm1 = nn.LayerNorm(d_model, eps=norm_eps)
self.norm2 = nn.LayerNorm(d_model, eps=norm_eps)
def forward(self, src, att):
"""
forward pass of the decoder network module.
Args:
- src (Tensor): input tensor, same as the encoder's inputs
- trg (Tensor): encoder's attention matrix
Returns:
- src_f (Tensor): final output tensor
"""
src2 = self.s_att(src, mask=True)
src = src + self.dropout(src2)
src = src + self.norm1(src)
att = src + att
att2 = self.s_att(att, mask=False)
att2 = att + self.dropout(att2)
trg = att2 + self.norm1(att2)
src_f2 = self.ffwd(self.norm2(trg))
src_f = src_f + self.dropout(src_f2)
src_f = self.norm2(src_f)
return src_f
class Transformer(nn.Module):
"""
initialize a Transformer model
Args:
- vocab_size (int): size of the vocabulary
- d_model (int): dimensionality of the model's hidden layers
- block_size (int): maximum sequence length for positional encoding/context length
- n_layers (int): number of encoder and decoder layers in the Transformer
- n_head (int): number of attention heads in multi-head attention layers
- norm_eps (float): epsilon value for layer normalization
- dropout (float): dropout probability
"""
def __init__(self, vocab_size):
super().__init__()
self.block_size = block_size
self.toked_model = nn.Embedding(vocab_size, d_model)
self.pos_encod = nn.Embedding(block_size, d_model)
self.enc_layer = nn.ModuleList([EncoderNetwork(n_head=n_head, norm_eps=norm_eps, block_size=block_size, dropout=dropout, d_model=d_model) for _ in range(n_layers)])
self.dec_layer = nn.ModuleList([DecoderNetwork(n_head=n_head, norm_eps=norm_eps, block_size=block_size, dropout=dropout, d_model=d_model) for _ in range(n_layers)])
self.norm_final = nn.LayerNorm(d_model)
self.linear_final = nn.Linear(d_model, vocab_size)
self.dropout = nn.Dropout(dropout)
self.apply(self._init_weights)
def _init_weights(self, module):
"""
initialize weights of linear and embedding layers
Args:
- module (nn.Module): the module to initialize weights for
"""
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias.data)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, idx, targets=None):
"""
forward pass of the transformer model
Args:
- idx (Tensor): input tensor representing token indices
- targets (Tensor): target tensor for computing loss during training
Returns:
- logits (Tensor): output logits from the final linear layer
- loss (Tensor): optional. computed cross-entropy loss if targets are provided, else None
"""
B, T = idx.shape
toked_model = self.toked_model(idx)
pos_encod = self.pos_encod(torch.arange(T, device=device))
x = toked_model + pos_encod
for layer in self.enc_layer:
x_out = layer(x)
for layer in self.dec_layer:
x_final = layer(x, x_out)
x_final = self.norm_final(x_final)
logits = self.linear_final(x_final)
if targets is None:
loss = None
else:
B, T, C = logits.shape
logits = logits.view(B*T, C)
targets = targets.view(B*T)
loss = F.cross_entropy(logits, targets)
return logits, loss
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=0):
"""
generate new tokens using the trained model
Args:
- idx (Tensor): input tensor representing initial token indices
- max_new_tokens (int): max no of new tokens to generate
- temperature (float): softmax temperature for sampling
- top_k (int): no of top tokens to consider in sampling
Returns:
- generated_tokens (list): list of generated token indices
"""
generated_tokens = []
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.block_size:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :]
scaled_logits = logits / temperature
if top_k > 0:
scaled_logits = self._top_k_filtering(scaled_logits, top_k)
probs = F.softmax(scaled_logits, dim=-1)
sampled_idx = torch.multinomial(probs, num_samples=1)
generated_tokens.append(sampled_idx.item())
idx = torch.cat((idx, sampled_idx), dim=1)
return generated_tokens
def generate_masked_tokens(self, idx, masked_indices, temperature=1.0, top_k=0):
"""
Generate predictions for masked tokens using the trained model.
Args:
- idx (Tensor): input tensor representing token indices
- masked_indices (Tensor): tensor of indices indicating masked positions
- temperature (float): softmax temperature for sampling
- top_k (int): no of top tokens to consider in sampling
Returns:
- predicted_tokens (Tensor): tensor of predicted token indices
"""
B, T = idx.shape
toked_model = self.toked_model(idx)
pos_encod = self.pos_encod(torch.arange(T, device=device))
x = toked_model + pos_encod
for layer in self.enc_layer:
x_out = layer(x)
for layer in self.dec_layer:
x_final = layer(x, x_out)
x_masked = x_final.clone()
x_masked[masked_indices] = self.toked_model(torch.tensor([6], device=device))
x_masked = self.norm_final(x_masked)
logits = self.linear_final(x_masked)
masked_logits = logits[masked_indices].view(-1, logits.size(-1))
scaled_logits = masked_logits / temperature
if top_k > 0:
scaled_logits = self._top_k_filtering(scaled_logits, top_k)
probs = F.softmax(scaled_logits, dim=-1)
predicted_indices = torch.argmax(probs, dim=-1)
return predicted_indices
def _top_k_filtering(self, logits, top_k):
"""
filter logits to keep only the top-k tokens
Args:
- logits (Tensor): input tensor representing unscaled logits
- top_k (int): no of top tokens to keep
Returns:
- filtered_logits (Tensor): filtered logits with only top-k tokens remaining
"""
values, indices = torch.topk(logits, top_k, dim=-1)
min_value = values[:, -1].unsqueeze(-1).expand_as(logits)
filtered_logits = torch.where(logits < min_value, torch.ones_like(logits) * -float('inf'), logits)
return filtered_logits |