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"""
this isn't a bert based model, i just liked the name and named it
--> decoder-only model, uses RMS normalization and GELU activation function
--> one masked-attention and other unmasked
--> attention layers have relational positional-embeddings
"""
import json
with open('config.json', 'r', encoding='utf-8') as file:
params = json.load(file)
# required parameters
block_size = params['block_size']
d_model = params['d_model']
n_head = params['n_heads']
n_layers = params['n_layers']
learning_rate = params['learning_rate']
dropout = params['dropout']
norm_eps = params['norm_eps']
import torch
import torch.nn as nn
from torch.nn import functional as F
device = 'cuda' if torch.cuda.is_available() else 'cpu'
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
output = self._norm(x.float()).type_as(x)
return output * self.weight
class SingleHead(nn.Module):
def __init__(self,
head_size: int,
d_model: int,
block_size: int,
dropout: float):
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)
self.rel_pos_embd = nn.Parameter(torch.randn(block_size, block_size, head_size))
self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
def forward(self, x: torch.Tensor, mask: bool= False):
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)
if mask is True:
scores = scores.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
rel_pos_scores = torch.einsum('btc,tvc->btv', query, self.rel_pos_embd[:T, :T])
scores = scores + rel_pos_scores
att_mat = F.softmax(scores, dim=-1)
att_mat = self.dropout(att_mat)
value = self.value(x)
output = torch.matmul(att_mat, value)
return output
class MultiHeadAttention(nn.Module):
def __init__(self,
d_model: int,
block_size: int,
n_head : int,
dropout: float):
head_size = d_model // n_head
super().__init__()
self.heads = nn.ModuleList([SingleHead(d_model=d_model, dropout=dropout, block_size=block_size, head_size=head_size) for _ in range(n_head)])
self.projection = nn.Linear(d_model, d_model)
self.dropout = nn.Dropout(dropout)
def forward(self, x: torch.Tensor, mask: bool):
out = torch.cat([h(x, mask) for h in self.heads], dim=-1)
out = self.dropout(self.projection(out))
return out
class FeedForward(nn.Module):
def __init__(self, d_model, dropout):
super().__init__()
self.net = nn.Sequential(
nn.Linear(d_model, 5 * d_model),
nn.GELU(),
nn.Linear(5 * d_model, d_model),
nn.Dropout(dropout),
)
def forward(self, x: torch.Tensor):
return self.net(x)
class DecoderBlock(nn.Module):
def __init__(self, d_model: int,
block_size: int,
n_head: int,
norm_eps: float,
dropout: float):
super().__init__()
self.self_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.norm = RMSNorm(d_model, eps=norm_eps)
def forward(self, x: torch.Tensor):
x_out = self.self_att(self.norm(x), mask=True)
x_out = x + self.dropout(x_out)
del x
x = self.self_att(self.norm(x_out, mask=False))
x = x_out + self.dropout(x)
del x_out
x_out = self.ffwd(self.norm(x))
x_out = x + self.dropout(x_out)
del x
return x_out
class Transformer(nn.Module):
def __init__(self, vocab_size: int):
super().__init__()
self.block_size = block_size
self.token_embeddings = nn.Embedding(vocab_size, d_model)
self.decoder = nn.Sequential(*[DecoderBlock(n_head=n_head, d_model=d_model, dropout=dropout, norm_eps=norm_eps, block_size=block_size) for _ in range(n_layers)])
self.norm_final = RMSNorm(d_model, eps=norm_eps)
self.linear_final = nn.Linear(d_model, vocab_size)
self.dropout = nn.Dropout(dropout)
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
x = self.token_embeddings(idx)
x = self.decoder(x)
logits = self.linear_final(self.norm_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
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
self.eval()
for _ in range(max_new_tokens):
idx_cond = idx if idx.size(1) <= self.block_size else idx[:, -self.block_size:]
logits, _ = self(idx_cond)
logits = logits[:, -1, :] / temperature
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float('Inf')
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, idx_next), dim=1)
return idx |