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"""
Loss-reweighting ablation (GPT-5.6-Terra's design, adapted to faithful J-lens).
Tests whether increasing a token's EFFECTIVE frequency/importance reduces its
faithful J-lens norm — without the confounds of the old random-insertion
ablation (which shifted positions, destroyed n-grams, and used an unmatched
control run).
Design per seed (identical init + identical minibatch order for all three):
q-upweight: cross-entropy terms whose target is 'q' are weighted x2.
control: ordinary loss.
ctrl_random: same-total-loss control: weight x2 on the SAME NUMBER of
randomly chosen non-'q' target positions (deterministic per
batch index, so all models share the same control positions).
If increased effective frequency causally reduces the faithful J-lens norm of
'q', the q-upweight model must show a lower norm than BOTH controls.
Steps:
python3 src/loss_reweight.py --step train --mode q --seed 0 [--max_iters 3000]
python3 src/loss_reweight.py --step jlens --mode q --seed 0 [--layers 2,3,4]
python3 src/loss_reweight.py --step summary
"""
import sys, os, argparse, subprocess, pickle
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from typing import Any
import numpy as np
import torch
import torch.nn.functional as F
DATA_DIR = 'data/shakespeare_char'
OUT_ROOT = 'out-loss-reweight'
JLENS_OUT = 'outputs/loss_reweight'
TARGET = 'q'
WEIGHT = 2.0
MODEL_ARGS: dict[str, Any] = dict(n_layer=6, n_head=6, n_embd=384, block_size=128,
bias=False, dropout=0.2)
LOSS_MODES = ('q', 'control', 'ctrl_random')
def _batch(data, blk, bs, g, device):
ix = torch.randint(len(data) - blk, (bs,), generator=g)
x = torch.stack([torch.from_numpy(data[i:i+blk].astype(np.int64)) for i in ix])
y = torch.stack([torch.from_numpy(data[i+1:i+1+blk].astype(np.int64)) for i in ix])
return x.to(device), y.to(device)
def _weighted_loss(logits, y, mode, q_id, batch_k, V, device):
"""Per-token weighted CE. Returns scalar loss."""
logp = F.log_softmax(logits.view(-1, V), dim=-1)
nll = -logp.gather(1, y.view(-1, 1)).squeeze(1) # (B*T,)
w = torch.ones_like(nll)
if mode == 'q':
w[y.view(-1) == q_id] = WEIGHT
elif mode == 'ctrl_random':
g = torch.Generator().manual_seed(1000 + batch_k) # CPU generator (randperm)
n_q = int((y == q_id).sum().item())
flat = torch.arange(y.numel(), device=device)
non_q = flat[y.view(-1) != q_id]
if len(non_q) > 0 and n_q > 0:
pick = non_q[torch.randperm(len(non_q), generator=g)[:min(n_q, len(non_q))]]
w[pick] = WEIGHT
return (nll * w).mean()
def train(mode, seed, max_iters, batch_size):
sys.path.insert(0, '.')
from model import GPT, GPTConfig
torch.manual_seed(seed)
np.random.seed(seed)
device = 'cuda'
train_data = np.memmap(f'{DATA_DIR}/train.bin', dtype=np.uint16, mode='r')
val_data = np.memmap(f'{DATA_DIR}/val.bin', dtype=np.uint16, mode='r')
with open(f'{DATA_DIR}/meta.pkl', 'rb') as f:
meta = pickle.load(f)
q_id = meta['stoi'][TARGET]
args = dict(
n_layer=int(MODEL_ARGS['n_layer']), n_head=int(MODEL_ARGS['n_head']),
n_embd=int(MODEL_ARGS['n_embd']), block_size=int(MODEL_ARGS['block_size']),
bias=bool(MODEL_ARGS['bias']), dropout=float(MODEL_ARGS['dropout']),
vocab_size=int(meta['vocab_size']),
)
model = GPT(GPTConfig(**args)).to(device)
print(f"[{mode}] seed {seed}: params={sum(p.numel() for p in model.parameters())/1e6:.2f}M")
opt = model.configure_optimizers(weight_decay=0.1, learning_rate=1e-3,
betas=(0.9, 0.99), device_type='cuda')
bs = batch_size
blk = args['block_size']
V = args['vocab_size']
# identical minibatch order for every model: per-seed CPU generator, fixed start
# (torch.randint does not accept CUDA generators on torch 2.4)
g = torch.Generator().manual_seed(20260731 + seed)
gval = torch.Generator().manual_seed(777 + seed)
def get_batch(split):
d = train_data if split == 'train' else val_data
gg = g if split == 'train' else gval
return _batch(d, blk, bs, gg, device)
best_val = 1e9
out_dir = f'{OUT_ROOT}/{mode}/seed{seed}'
os.makedirs(out_dir, exist_ok=True)
for it in range(max_iters):
if it % 500 == 0:
model.eval()
lv = []
for _ in range(50):
X, Y = get_batch('val')
with torch.no_grad():
logits = model(X)[0]
lv.append(F.cross_entropy(logits.view(-1, V), Y.view(-1)).item())
v = np.mean(lv)
model.train()
if v < best_val:
best_val = v
torch.save({'model': model.state_dict(), 'model_args': args,
'best_val_loss': best_val}, f'{out_dir}/ckpt.pt')
if it % 1000 == 0:
print(f" iter {it}: val={v:.4f}")
X, Y = get_batch('train')
logits = model(X)[0]
loss = _weighted_loss(logits, Y, mode, q_id, it, V, device)
loss.backward()
opt.step()
opt.zero_grad(set_to_none=True)
print(f"[{mode}] seed {seed} done. best_val={best_val:.4f}")
def jlens(mode, seed, layers, n_prompts):
out_dir = f'{JLENS_OUT}/{mode}/seed{seed}'
cmd = ["python3", "-u", "src/jlens_v3.py",
"--checkpoint", f'{OUT_ROOT}/{mode}/seed{seed}/ckpt.pt',
"--data_dir", DATA_DIR,
"--n_prompts", str(n_prompts),
"--layers", layers,
"--chunk", "16",
"--output_dir", out_dir]
print("running:", " ".join(cmd))
r = subprocess.run(cmd, cwd='/workspace/code')
assert r.returncode == 0, "jlens_v3 failed"
def summary(layers):
with open(f'{DATA_DIR}/meta.pkl', 'rb') as f:
meta = pickle.load(f)
q_id = meta['stoi'][TARGET]
seeds = sorted(set(
d.split('seed')[1] for mode in LOSS_MODES
for d in os.listdir(f'{JLENS_OUT}/{mode}')
if d.startswith('seed')))
print(f"\n{'='*78}")
print(f"LOSS-REWEIGHTING: faithful J-lens norm of '{TARGET}' "
f"({WEIGHT}x CE) vs controls")
print(f"{'='*78}")
print(f"{'seed':<5}{'layer':<6}" + "".join(f"{m:>14}" for m in LOSS_MODES))
for s in seeds:
for l in map(int, layers.split(',')):
row = [s, str(l)]
for m in LOSS_MODES:
d = torch.load(f'{JLENS_OUT}/{m}/seed{s}/layer{l}.pt',
map_location='cpu')
row.append(f"{d['faithful_norms'][q_id]:.4f}")
print(f"{row[0]:<5}{row[1]:<6}" + "".join(f"{v:>14}" for v in row[2:]))
# mean over middle layers per mode
mids = [l for l in map(int, layers.split(','))]
means = {}
for m in LOSS_MODES:
vals = []
for l in mids:
d = torch.load(f'{JLENS_OUT}/{m}/seed{s}/layer{l}.pt',
map_location='cpu')
vals.append(d['faithful_norms'][q_id])
means[m] = np.mean(vals)
print(f" -> mean over layers: q={means['q']:.4f} "
f"control={means['control']:.4f} ctrl_random={means['ctrl_random']:.4f}")
print(f" -> q/control = {means['q']/max(means['control'],1e-9):.3f} "
f"q/ctrl_random = {means['q']/max(means['ctrl_random'],1e-9):.3f}")
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument('--step', required=True, choices=['train', 'jlens', 'summary'])
ap.add_argument('--mode', choices=LOSS_MODES)
ap.add_argument('--seed', type=int, default=0)
ap.add_argument('--max_iters', type=int, default=3000)
ap.add_argument('--batch_size', type=int, default=16)
ap.add_argument('--layers', default='2,3,4')
ap.add_argument('--n_prompts', type=int, default=10)
a = ap.parse_args()
if a.step == 'train':
assert a.mode, "need --mode"
train(a.mode, a.seed, a.max_iters, a.batch_size)
elif a.step == 'jlens':
assert a.mode, "need --mode"
jlens(a.mode, a.seed, a.layers, a.n_prompts)
else:
summary(a.layers)
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