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MicroGPT-TF: GPT with a TensorFlow-inspired design pattern, implemented in pure PyTorch.
Uses Pre-LN (LayerNorm before sublayers) and a slightly different MLP structure
reminiscent of TF Transformer implementations.
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class LayerNorm(nn.Module):
"""Standard LayerNorm (TF-style uses gamma/beta naming)."""
def __init__(self, dim, eps=1e-6):
super().__init__()
self.gamma = nn.Parameter(torch.ones(dim))
self.beta = nn.Parameter(torch.zeros(dim))
self.eps = eps
def forward(self, x):
return F.layer_norm(x, x.shape[-1:], self.gamma, self.beta, self.eps)
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
# TF-style: single dense layer for QKV combined, then split
self.qkv = nn.Linear(n_embd, n_embd * 3, 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
qkv = self.qkv(x).reshape(B, T, 3, self.n_head, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[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):
"""TF-inspired MLP with bias and ReLU (instead of GELU)."""
def __init__(self, n_embd, dropout):
super().__init__()
self.fc1 = nn.Linear(n_embd, 4 * n_embd, bias=True)
self.fc2 = nn.Linear(4 * n_embd, n_embd, bias=True)
self.drop = nn.Dropout(dropout)
def forward(self, x):
x = F.relu(self.fc1(x))
x = self.fc2(x)
return self.drop(x)
class Block(nn.Module):
"""TF-style Pre-LN transformer block."""
def __init__(self, n_embd, n_head, block_size, dropout):
super().__init__()
self.ln1 = LayerNorm(n_embd)
self.attn = CausalSelfAttention(n_embd, n_head, block_size, dropout)
self.ln2 = LayerNorm(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_TF(nn.Module):
"""
GPT with TF-inspired design:
- Pre-LN (LayerNorm before sublayers)
- Combined QKV projection
- ReLU activation instead of GELU
- Bias in linear layers
"""
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 = LayerNorm(n_embd)
self.lm_head = nn.Linear(n_embd, vocab_size, bias=True)
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)
if module.bias is not None:
nn.init.zeros_(module.bias)
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 |