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Minimal single-variant GPT — cheap_qa champion ONLY.
Distilled from the 19-variant code/model_canon_HEAD.py in this repo. Every other gate branch has been
removed; the ONLY conditioning path is the cheap_qa gate:
cheap_qa == qv_variant "dynamic_qa_conditioned"
gate = sigmoid( Linear(2*head_dim -> head_dim) ( cat(q, y) ) )
y = y * gate # applied at G1: post-SDPA, pre-W_O (c_proj)
Champion result (NanoGPT 124M, OpenWebText, 10k iters, seeds 42/1337/123):
baseline 4.4708 -> cheap_qa 4.3897 (-0.0811 CE, -1.81%), 98,304 params (+0.079%).
>>> THE G1 SEAM IS MARKED BELOW BY A "G1 SEAM" BANNER COMMENT.
That block is the entire port surface. Resize the gate for a different attention
by changing head_dim (hs); the gate is always Linear(2*hs -> hs) on cat(q, y).
"""
import math
import inspect
from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F
class LayerNorm(nn.Module):
def __init__(self, ndim, bias):
super().__init__()
self.weight = nn.Parameter(torch.ones(ndim))
self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
def forward(self, input):
return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
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) # <- W_O
hs = config.n_embd // config.n_head # head_dim (64 for the 124M config)
# === cheap_qa gate parameters ===
# cat(q, y) has width 2*hs (=128 when hs=64); gate maps it back to hs (=64).
# This is the ONLY learned gate in this minimal model.
self.qa_gate_proj = nn.Linear(hs * 2, hs, bias=False) # Linear(128 -> 64)
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.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
if not self.flash:
self.register_buffer(
"bias",
torch.tril(torch.ones(config.block_size, config.block_size)).view(
1, 1, config.block_size, config.block_size
),
)
def forward(self, x):
B, T, C = x.size()
hs = C // self.n_head
# Project to q, k, v and split into heads. q and v are (B, n_head, T, hs).
q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
k = k.view(B, T, self.n_head, hs).transpose(1, 2)
q = q.view(B, T, self.n_head, hs).transpose(1, 2)
v = v.view(B, T, self.n_head, hs).transpose(1, 2)
# Scaled dot-product attention -> y is the attention output (the "A" in Q+A).
if self.flash:
y = torch.nn.functional.scaled_dot_product_attention(
q, k, v, is_causal=True, dropout_p=self.dropout if self.training else 0
)
else:
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))
att = F.softmax(att, dim=-1)
att = self.attn_dropout(att)
y = att @ v
# =========================== G1 SEAM ============================
# cheap_qa gate. Inputs available here: q and y, both (B, n_head, T, hs).
# Location: strictly AFTER SDPA, strictly BEFORE the W_O projection (c_proj).
# To port to a different attention: keep this exact 4-line body; only hs changes.
gate_in = torch.cat([q, y], dim=-1) # (B, n_head, T, 2*hs) == cat(query, attn_output)
gate_logit = self.qa_gate_proj(gate_in) # Linear(2*hs -> hs)
gate = torch.sigmoid(gate_logit) # (B, n_head, T, hs) in (0, 1)
y = y * gate # elementwise gate on the attention output
# ========================= END G1 SEAM ==========================
# Merge heads back and apply the output projection W_O.
y = y.transpose(1, 2).contiguous().view(B, T, C)
y = self.resid_dropout(self.c_proj(y)) # c_proj == W_O
return y
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)
def forward(self, x):
x = self.c_fc(x)
x = self.gelu(x)
x = self.c_proj(x)
x = self.dropout(x)
return x
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
self.attn = CausalSelfAttention(config)
self.ln_2 = 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
@dataclass
class GPTConfig:
block_size: int = 1024
vocab_size: int = 50304 # NOTE: 50304 (GPT-2 50257 rounded up for efficiency)
n_layer: int = 12
n_head: int = 12
n_embd: int = 768 # -> head_dim = 768 / 12 = 64, so the gate is Linear(128 -> 64)
dropout: float = 0.0
bias: bool = True
# qv_variant is fixed to cheap_qa in this minimal model; no field needed.
class GPT(nn.Module):
def __init__(self, config):
super().__init__()
assert config.vocab_size is not None
assert config.block_size is not None
self.config = config
self.transformer = nn.ModuleDict(dict(
wte=nn.Embedding(config.vocab_size, config.n_embd),
wpe=nn.Embedding(config.block_size, config.n_embd),
drop=nn.Dropout(config.dropout),
h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
ln_f=LayerNorm(config.n_embd, bias=config.bias),
))
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.transformer.wte.weight = self.lm_head.weight # weight tying
self.apply(self._init_weights)
for pn, p in self.named_parameters():
if pn.endswith('c_proj.weight'):
torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * config.n_layer))
print("number of parameters: %.2fM" % (self.get_num_params() / 1e6,))
def get_num_params(self, non_embedding=True):
n_params = sum(p.numel() for p in self.parameters())
if non_embedding:
n_params -= self.transformer.wpe.weight.numel()
return n_params
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)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, idx, targets=None):
device = idx.device
b, t = idx.size()
assert t <= self.config.block_size
pos = torch.arange(0, t, dtype=torch.long, device=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)
if targets is not None:
logits = self.lm_head(x)
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
else:
logits = self.lm_head(x[:, [-1], :])
loss = None
return logits, loss
def crop_block_size(self, block_size):
assert block_size <= self.config.block_size
self.config.block_size = block_size
self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size])
for block in self.transformer.h:
if hasattr(block.attn, 'bias'):
block.attn.bias = block.attn.bias[:, :, :block_size, :block_size]
def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
param_dict = {pn: p for pn, p in self.named_parameters() if p.requires_grad}
decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
optim_groups = [
{'params': decay_params, 'weight_decay': weight_decay},
{'params': nodecay_params, 'weight_decay': 0.0},
]
num_decay_params = sum(p.numel() for p in decay_params)
num_nodecay_params = sum(p.numel() for p in nodecay_params)
print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
use_fused = fused_available and device_type == 'cuda'
optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, fused=use_fused)
print(f"using fused AdamW: {use_fused}")
return optimizer
def estimate_mfu(self, fwdbwd_per_iter, dt):
N = self.get_num_params()
cfg = self.config
L, H, Q, T = cfg.n_layer, cfg.n_head, cfg.n_embd // cfg.n_head, cfg.block_size
flops_per_token = 6 * N + 12 * L * H * Q * T
flops_per_fwdbwd = flops_per_token * T
flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter
flops_achieved = flops_per_iter * (1.0 / dt)
flops_promised = 312e12
return flops_achieved / flops_promised
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
for _ in range(max_new_tokens):
idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.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
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