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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):
"""
Initialize the RMSNorm normalization layer.
Args:
dim (int): The dimension of the input tensor.
eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
Attributes:
eps (float): A small value added to the denominator for numerical stability.
weight (nn.Parameter): Learnable scaling parameter.
"""
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
"""
Apply the RMSNorm normalization to the input tensor.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The normalized tensor.
"""
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
"""
Forward pass through the RMSNorm layer.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The output tensor after applying RMSNorm.
"""
output = self._norm(x.float()).type_as(x)
return output * self.weight
class UnMaskedHead(nn.Module):
def __init__(self, head_size, d_model, block_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)
self.rel_pos_embd = nn.Parameter(torch.randn(block_size, block_size, head_size))
def forward(self, x):
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_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 UnMaskedAttention(nn.Module):
def __init__(self, d_model, block_size, dropout, n_head):
head_size = d_model // n_head
super().__init__()
self.heads = nn.ModuleList([UnMaskedHead(d_model=d_model, dropout=dropout, block_size=block_size, 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 MaskedHead(nn.Module):
def __init__(self, d_model, head_size, dropout, block_size):
super().__init__()
self.key = nn.Linear(d_model, head_size, bias=False)
self.query = nn.Linear(d_model, head_size, bias=False)
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)))
def forward(self, x):
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)
scores = scores.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
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 CasualMaskedAttention(nn.Module):
def __init__(self, d_model, block_size, dropout, n_head):
head_size = d_model // n_head
super().__init__()
self.heads = nn.ModuleList([MaskedHead(d_model=d_model, dropout=dropout, block_size=block_size, 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 FinalHead(nn.Module):
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=True)
self.dropout = nn.Dropout(dropout)
def forward(self, x, att):
B, T, C = x.shape
key = self.key(att)
query = self.query(att)
scores = torch.matmul(query, key.transpose(-2, -1)) / (key.shape[-1] ** -0.5)
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 FinalAttention(nn.Module):
def __init__(self, d_model, block_size, dropout, n_head):
head_size = d_model // n_head
super().__init__()
self.heads = nn.ModuleList([FinalHead(d_model=d_model, dropout=dropout, block_size=block_size, 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, att):
out = torch.cat([h(x, att) 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 EncoderNetwork(nn.Module):
def __init__(self, d_model, n_head, norm_eps, dropout, block_size):
super().__init__()
self.s_att = UnMaskedAttention(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, src):
src = self.norm(src)
src_out = src + self.dropout(self.s_att(src))
src = self.norm(src_out)
src_f = src + self.dropout(self.ffwd(src))
del src_out, src
return src_f
class DecoderNetwork(nn.Module):
def __init__(self, d_model, n_head, norm_eps, dropout, block_size):
super().__init__()
self.m_att = CasualMaskedAttention(n_head=n_head, d_model=d_model, dropout=dropout, block_size=block_size)
self.f_att = FinalAttention(d_model=d_model, n_head=n_head, 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, src, att):
m_att_out = self.norm(src)
m_out = src + self.dropout(self.m_att(m_att_out))
f_out = self.f_att(m_out, self.norm(att))
f_out = m_out + self.dropout(f_out)
src_f = self.norm(f_out)
src_f = f_out + self.dropout(self.ffwd(src_f))
del f_out, m_out, m_att_out, src, att
return src_f
class Transformer(nn.Module):
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 = 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):
"""
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 |