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import torch
import torch.nn as nn
from torch.nn import functional as F
# Configuration Dataclass (equivalent to GPTConfig in nanoGPT)
class MVTConfig:
vocab_size = 5000 # V: Set by custom tokenizer
block_size = 256 # T_ctx: Context length
n_layer = 8 # N_layer: Number of decoder blocks
n_head = 8 # N_head: Number of attention heads
n_embd = 512 # D_embd: Embedding dimension
batch_size = 16 # B: Batch size
dropout = 0.1
bias = False # Optional bias for linear layers
# Initializing device setup
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# --- 1. Causal Self-Attention Mechanism ---
class CausalSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
assert config.n_embd % config.n_head == 0
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
self.attn_dropout = nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
self.n_head = config.n_head
self.n_embd = config.n_embd
self.dropout = config.dropout
self.block_size = config.block_size
self.register_buffer("mask", torch.tril(torch.ones(config.block_size, config.block_size))
.view(1, 1, config.block_size, config.block_size))
nn.init.normal_(self.c_proj.weight, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
def forward(self, x):
B, T, C = x.size()
q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(self.mask[:, :, :T, :T] == 0, float('-inf'))
att = F.softmax(att, dim=-1)
att = self.attn_dropout(att)
y = att @ v
y = y.transpose(1, 2).contiguous().view(B, T, C)
y = self.resid_dropout(self.c_proj(y))
return y
# --- 2. Feed-Forward Network (MLP) ---
class MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
self.gelu = nn.GELU()
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
self.dropout = nn.Dropout(config.dropout)
nn.init.normal_(self.c_proj.weight, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
def forward(self, x):
x = self.c_fc(x)
x = self.gelu(x)
x = self.c_proj(x)
x = self.dropout(x)
return x
# --- 3. Transformer Block ---
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd, bias=config.bias)
self.attn = CausalSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.n_embd, bias=config.bias)
self.mlp = MLP(config)
def forward(self, x):
x = x + self.attn(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
# --- 4. The MinimalGPT Model ---
class MinimalGPT(nn.Module):
def __init__(self, config):
super().__init__()
# Store config parameters as instance attributes for TorchScript compatibility
self.vocab_size = config.vocab_size
self.block_size = config.block_size
self.n_layer = config.n_layer
self.n_head = config.n_head
self.n_embd = config.n_embd
self.dropout = config.dropout
self.bias = config.bias
self.transformer = nn.ModuleDict(dict(
wte=nn.Embedding(self.vocab_size, self.n_embd),
wpe=nn.Embedding(self.block_size, self.n_embd),
drop=nn.Dropout(self.dropout),
h=nn.ModuleList([Block(config) for _ in range(self.n_layer)]),
ln_f=nn.LayerNorm(self.n_embd, bias=self.bias),
))
self.lm_head = nn.Linear(self.n_embd, self.vocab_size, bias=False)
self.transformer.wte.weight = self.lm_head.weight
print(f"Minimal GPT Model initialized: {sum(p.numel() for p in self.parameters())/1e6:.2f}M parameters")
def forward(self, idx, targets=None):
B, T = idx.size()
assert T <= self.block_size, f"Input sequence length {T} exceeds block size {self.block_size}"
pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
tok_emb = self.transformer.wte(idx)
pos_emb = self.transformer.wpe(pos)
x = self.transformer.drop(tok_emb + pos_emb)
for block in self.transformer.h:
x = block(x)
x = self.transformer.ln_f(x)
logits = self.lm_head(x)
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
else: # Return a dummy loss tensor if targets is None for TorchScript compatibility
loss = torch.tensor(0.0, device=idx.device)
return logits, loss
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