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"""
simple BERT architecture model, paired with one more layer of
masked self-attention, to predict next token
"""
import torch
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
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# hyperparams
batch_size = 8
block_size = 32
max_iters = 10
eval_interval = 10
learning_rate = 3e-4
eval_iters = 5
d_model = 256
n_layer = 16
n_head = 12
dropout = 0.2
norm_eps = 1e-5
class SWiGLU(nn.Module):
""" SWiGLU(x) = σ(x) ⊙ ReLU(x) + (1−σ(x)) ⊙ x """
def forward(self, x):
sigmoid_output = torch.sigmoid(x)
relu_output = F.relu(x)
out = sigmoid_output * relu_output + (1 - sigmoid_output) * x
return out
class UnMaskedHead(nn.Module):
""" single head of self attention """
def __init__(self, d_model, head_size, dropout):
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=True)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
B, T, C = x.shape
key = self.key(x)
query = self.query(x)
weights = query @ key.transpose(-2, -1) * key.shape[-1]**-0.5
weights = F.softmax(weights, dim=-1)
weights = self.dropout(weights)
value = self.value(x)
out = weights @ value
return out
class MaskedHead(nn.Module):
""" one head of self-attention """
def __init__(self, head_size, dropout, d_model):
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=True)
self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
self.dropout = nn.Dropout(dropout)
def forward(self, x):
B,T,C = x.shape
k = self.key(x)
q = self.query(x)
wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T)
wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
wei = F.softmax(wei, dim=-1) # (B, T, T)
wei = self.dropout(wei)
v = self.value(x)
out = wei @ v
return out
class MultiUnMasked(nn.Module):
def __init__(self, d_model, n_head, dropout):
head_size = d_model // n_head
super().__init__()
self.heads = nn.ModuleList([UnMaskedHead(d_model=d_model, dropout=dropout, head_size=head_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):
out = torch.cat([h(x) for h in self.heads], dim=-1)
out = self.dropout(self.proj(out))
return out
class MultiMasked(nn.Module):
def __init__(self, d_model, n_head, dropout):
head_size = d_model // n_head
super().__init__()
self.heads = nn.ModuleList([MaskedHead(d_model=d_model, dropout=dropout, head_size=head_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):
out = torch.cat([h(x) for h in self.heads], dim=-1)
out = self.dropout(self.proj(out))
return out
class FeedForward(nn.Module):
def __init__(self, d_model, dropout):
super().__init__()
self.net = nn.Sequential(
nn.Linear(d_model, 4*d_model),
nn.GELU(),
nn.Linear(4*d_model, d_model),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class Block(nn.Module):
def __init__(self, d_model, n_head, norm_eps, dropout):
super().__init__()
self.sa_masked = MultiMasked(n_head=n_head, d_model=d_model, dropout=dropout)
self.sa_unmasked = MultiUnMasked(n_head=n_head, d_model=d_model, dropout=dropout)
self.ffwd = FeedForward(d_model, dropout=dropout)
self.norm1 = nn.LayerNorm(d_model, eps=norm_eps)
self.norm2 = nn.LayerNorm(d_model, eps=norm_eps)
def forward(self, x):
x2 = x + self.sa_unmasked(self.norm1(x))
x = x2 + self.norm2(self.ffwd(x2))
x2 = x + self.sa_masked(self.norm1(x))
x = x2 + self.norm2(self.ffwd(x2))
return x
class EnigmaBERT(nn.Module):
def __init__(self, vocab_size):
super().__init__()
self.toked_model = nn.Embedding(vocab_size, d_model)
self.pos_encod = nn.Embedding(block_size, d_model)
self.block = nn.Sequential(*[Block(d_model=d_model, dropout=dropout, norm_eps=norm_eps, n_head=n_head) for _ in range(n_layer)])
self.norm_final = nn.LayerNorm(d_model, eps=norm_eps)
self.linear_final = nn.Linear(d_model, vocab_size)
self.apply(self._init_weights)
def _init_weights(self, module):
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):
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
x = self.block(x)
x = self.norm_final(x)
logits = self.linear_final(x)
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):
generated_tokens = []
for _ in range(max_new_tokens):
idx_cond = idx[:, -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 _top_k_filtering(self, logits, top_k):
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 |