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MicroGPT: Minimal PyTorch decoder-only GPT.
Same architecture as advance-torch-gpt.py but with tiny config for fast iteration.
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-5):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, x):
return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) * self.weight
class CausalSelfAttention(nn.Module):
def __init__(self, n_embd, n_head, block_size, dropout):
super().__init__()
assert n_embd % n_head == 0
self.n_head = n_head
self.head_dim = n_embd // n_head
self.q = nn.Linear(n_embd, n_embd, bias=False)
self.k = nn.Linear(n_embd, n_embd, bias=False)
self.v = nn.Linear(n_embd, n_embd, bias=False)
self.proj = nn.Linear(n_embd, n_embd, bias=False)
self.attn_drop = nn.Dropout(dropout)
self.resid_drop = nn.Dropout(dropout)
self.register_buffer('mask', torch.tril(torch.ones(block_size, block_size)).view(1, 1, block_size, block_size))
def forward(self, x):
B, T, C = x.shape
q = self.q(x).view(B, T, self.n_head, self.head_dim).transpose(1, 2)
k = self.k(x).view(B, T, self.n_head, self.head_dim).transpose(1, 2)
v = self.v(x).view(B, T, self.n_head, self.head_dim).transpose(1, 2)
att = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
att = att.masked_fill(self.mask[:, :, :T, :T] == 0, float('-inf'))
att = F.softmax(att, dim=-1)
att = self.attn_drop(att)
y = att @ v
y = y.transpose(1, 2).contiguous().view(B, T, C)
return self.resid_drop(self.proj(y))
class MLP(nn.Module):
def __init__(self, n_embd, dropout):
super().__init__()
self.fc = nn.Linear(n_embd, 4 * n_embd, bias=False)
self.proj = nn.Linear(4 * n_embd, n_embd, bias=False)
self.drop = nn.Dropout(dropout)
def forward(self, x):
x = F.gelu(self.fc(x))
x = self.proj(x)
return self.drop(x)
class Block(nn.Module):
def __init__(self, n_embd, n_head, block_size, dropout):
super().__init__()
self.ln1 = RMSNorm(n_embd)
self.attn = CausalSelfAttention(n_embd, n_head, block_size, dropout)
self.ln2 = RMSNorm(n_embd)
self.mlp = MLP(n_embd, dropout)
def forward(self, x):
x = x + self.attn(self.ln1(x))
x = x + self.mlp(self.ln2(x))
return x
class MicroGPT(nn.Module):
"""Minimal decoder-only GPT. Drop-in compatible with advance-torch-gpt.py's GPT class."""
def __init__(self, vocab_size, block_size, n_layer=2, n_head=4, n_embd=128, dropout=0.1):
super().__init__()
self.block_size = block_size
self.wte = nn.Embedding(vocab_size, n_embd)
self.wpe = nn.Embedding(block_size, n_embd)
self.blocks = nn.ModuleList([Block(n_embd, n_head, block_size, dropout) for _ in range(n_layer)])
self.ln_f = RMSNorm(n_embd)
self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)
self.lm_head.weight = self.wte.weight
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, idx, targets=None):
B, T = idx.shape
if T > self.block_size:
raise ValueError(f'block size exceeded: {T} > {self.block_size}')
pos = torch.arange(T, device=idx.device)
x = self.wte(idx) + self.wpe(pos)
for block in self.blocks:
x = block(x)
x = self.ln_f(x)
logits = self.lm_head(x)
loss = None
if targets is not None:
loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), targets.reshape(-1))
return logits, loss
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=40):
self.eval()
for _ in range(max_new_tokens):
idx_cond = 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
@torch.no_grad()
def generate_text(self, idx, max_new_tokens, temperature=1.0, top_k=40):
"""Alias for generate, returns tokens only."""
return self.generate(idx, max_new_tokens, temperature, top_k) |