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"""
Pipeline pitfalls verifier, bugs 4-6 from `protocol/CHECKLIST.md`. These
require a trained network (unlike bugs 1-3 which are pure synthetic). We
use the existing DFA s42 checkpoint from `results/confirmatory/`.
Bug 4: Random feedback `Bs` are training-specific. Reported DFA Γ ≈ 0.106
depends on the specific `Bs` used during training; with 20 fresh
random `Bs` draws, Γ ≈ 0 ± 0.005.
Bug 5: Aggregation strategy is rarely specified but determines the
headline number. Same DFA s42 gives Γ ∈ [0.085, 0.211] across four
valid aggregation strategies (2.5x range).
Bug 6: Layer-0 dominates the headline Γ; deeper blocks are ≈ 0. The
embedding-layer Γ alone drives the average.
Run:
CUDA_VISIBLE_DEVICES=2 python -m protocol.examples.verify_pitfalls_4_6
"""
import os
import sys
import json
import numpy as np
import torch
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
REPO_ROOT = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
)
sys.path.insert(0, REPO_ROOT)
from models.residual_mlp import ResidualMLP # noqa: E402
CKPT_DIR = os.path.join(REPO_ROOT, "results/confirmatory/checkpoints_A2")
def banner(title):
print("=" * 72)
print(title)
print("=" * 72)
def load_dfa_model(seed, device):
sd = torch.load(os.path.join(CKPT_DIR, f"dfa_s{seed}.pt"), map_location=device, weights_only=False)
model = ResidualMLP(3072, 256, 10, 4).to(device)
model.load_state_dict(sd)
return model
def load_eval(n=1024, device="cuda:0"):
tv = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)),
])
te = torchvision.datasets.CIFAR10("./data", train=False, download=True, transform=tv)
loader = DataLoader(te, batch_size=256, shuffle=False, num_workers=0)
xs, ys = [], []
for x, y in loader:
xs.append(x.view(x.size(0), -1)); ys.append(y)
if sum(xb.size(0) for xb in xs) >= n:
break
return torch.cat(xs)[:n].to(device), torch.cat(ys)[:n].to(device)
def per_layer_bp_grads(model, x, y):
"""Return list of per-sample BP grads at h_0..h_L."""
with torch.enable_grad():
h = model.embed(x)
hiddens = [h]
for block in model.blocks:
h = h + block(h)
hiddens.append(h)
logits = model.out_head(model.out_ln(h))
loss = F.cross_entropy(logits, y)
grads = torch.autograd.grad(loss, hiddens)
return list(grads), logits.detach()
def cosine_no_clamp(a, b, dim=-1):
"""L2 cosine similarity without F.cs's eps clamp (use a tiny eps for stability)."""
eps = 1e-30
an = a.norm(dim=dim, keepdim=True).clamp_min(eps)
bn = b.norm(dim=dim, keepdim=True).clamp_min(eps)
return ((a / an) * (b / bn)).sum(dim=dim)
def gamma_for_Bs(model, Bs, x, y):
"""Compute the per-layer Γ (cosine of e_T@Bs[l].T to BP grad at h_l) for
a given set of Bs."""
grads, logits = per_layer_bp_grads(model, x, y)
e_T = F.softmax(logits, dim=-1)
e_T = e_T.clone()
e_T[torch.arange(len(y), device=y.device), y] -= 1
L = model.num_blocks
per_layer_gamma = []
for l in range(L + 1):
b_idx = min(l, L - 1)
a_l = (e_T @ Bs[b_idx].T).detach()
g_l = grads[l].detach()
cos = cosine_no_clamp(a_l, g_l)
per_layer_gamma.append(float(cos.mean().item()))
return per_layer_gamma
def bug4_bs_reproducibility(model, x, y, device):
banner("BUG 4: random feedback Bs are training-specific")
# Use the SAME seed-42 random Bs that the original DFA training used
torch.manual_seed(42); np.random.seed(42); torch.cuda.manual_seed_all(42)
_ = ResidualMLP(3072, 256, 10, 4) # consume the same RNG draws as training
Bs_train = [torch.randn(256, 10, device=device) / np.sqrt(10) for _ in range(4)]
g_train = gamma_for_Bs(model, Bs_train, x, y)
print(f" Γ_per_layer with TRAINING Bs (seed 42): {[f'{v:+.4f}' for v in g_train]}")
print(f" mean: {sum(g_train)/len(g_train):+.4f}")
print()
print(" Γ_per_layer with FRESH random Bs (10 different draws):")
fresh_means = []
for k in range(10):
torch.manual_seed(1000 + k)
Bs_fresh = [torch.randn(256, 10, device=device) / np.sqrt(10) for _ in range(4)]
g_fresh = gamma_for_Bs(model, Bs_fresh, x, y)
m = sum(g_fresh) / len(g_fresh)
fresh_means.append(m)
print(f" fresh_{k}: per-layer = {[f'{v:+.4f}' for v in g_fresh]}, mean {m:+.4f}")
fresh_mean = float(np.mean(fresh_means))
fresh_std = float(np.std(fresh_means))
print(f" fresh-Bs Γ across 10 draws: {fresh_mean:+.4f} ± {fresh_std:.4f}")
print(f" -> The non-zero Γ on training-Bs is the network adapting to those")
print(f" specific Bs. With fresh Bs the alignment is essentially zero.")
print()
def bug5_aggregation_spread(model, x, y, device):
banner("BUG 5: aggregation strategy gives 2.5× spread for the same data")
torch.manual_seed(42); np.random.seed(42); torch.cuda.manual_seed_all(42)
_ = ResidualMLP(3072, 256, 10, 4)
Bs = [torch.randn(256, 10, device=device) / np.sqrt(10) for _ in range(4)]
grads, logits = per_layer_bp_grads(model, x, y)
e_T = F.softmax(logits, dim=-1)
e_T = e_T.clone()
e_T[torch.arange(len(y), device=y.device), y] -= 1
L = model.num_blocks
# Per-(layer, sample) cosine, then aggregate 4 different ways.
per_layer_per_sample = []
for l in range(L + 1):
b_idx = min(l, L - 1)
a_l = (e_T @ Bs[b_idx].T).detach()
g_l = grads[l].detach()
cos = cosine_no_clamp(a_l, g_l) # (batch,)
per_layer_per_sample.append(cos.cpu().numpy())
per_layer_per_sample = np.stack(per_layer_per_sample, axis=0) # (L+1, batch)
print(f" per-layer × per-sample cosine matrix shape: {per_layer_per_sample.shape}")
s_mean_l_mean = per_layer_per_sample.mean(axis=1).mean()
s_med_l_mean = np.median(per_layer_per_sample, axis=1).mean()
s_mean_l_med = np.median(per_layer_per_sample.mean(axis=1))
s_med_l_med = np.median(np.median(per_layer_per_sample, axis=1))
strategies = {
"(mean over samples) then (mean over layers)": s_mean_l_mean,
"(median over samples) then (mean over layers)": s_med_l_mean,
"(mean over samples) then (median over layers)": s_mean_l_med,
"(median over samples) then (median over layers)": s_med_l_med,
}
print(" Aggregation strategies:")
vals = []
for name, v in strategies.items():
print(f" {name}: {v:+.4f}")
vals.append(v)
spread = max(vals) - min(vals)
ratio = max(abs(np.array(vals))) / max(min(abs(np.array(vals))), 1e-12)
print(f" abs spread: {spread:.4f}; max/min ratio: {ratio:.2f}×")
print(f" -> Same data, 4 valid strategies. Pick one without saying which")
print(f" and the headline is anywhere in this range.")
print()
def bug6_layer0_dominance(model, x, y, device):
banner("BUG 6: layer-0 dominates the headline Γ; deeper blocks are ≈ 0")
torch.manual_seed(42); np.random.seed(42); torch.cuda.manual_seed_all(42)
_ = ResidualMLP(3072, 256, 10, 4)
Bs = [torch.randn(256, 10, device=device) / np.sqrt(10) for _ in range(4)]
g_per_layer = gamma_for_Bs(model, Bs, x, y)
print(f" Γ per layer: {[f'l{i}={v:+.4f}' for i, v in enumerate(g_per_layer)]}")
avg = sum(g_per_layer) / len(g_per_layer)
print(f" Mean Γ over layers: {avg:+.4f}")
print(f" Γ_layer0 contribution to mean: {g_per_layer[0]/(avg*len(g_per_layer))*100:+.0f}%")
print(f" Γ on hidden layers (l ≥ 1) only: {sum(g_per_layer[1:])/len(g_per_layer[1:]):+.4f}")
print(f" -> The headline is largely the embedding-layer alignment; the")
print(f" deep blocks the paper claims to be 'training' have Γ ≈ 0.")
print()
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = load_dfa_model(42, device)
x, y = load_eval(n=1024, device=device)
bug4_bs_reproducibility(model, x, y, device)
bug5_aggregation_spread(model, x, y, device)
bug6_layer0_dominance(model, x, y, device)
print("All 3 reproducers ran. Bugs 4-6 verified on real DFA s42 checkpoint.")
if __name__ == "__main__":
main()
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