tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.ls -la && echo "---SUBMISSION---" && ls -la submission/ && echo "---DATA---" && ls -la data/ && python3 -c "import numpy as np; d=np.load('data/train.npz'); print({k:(v.shape,v.dtype) for k,v in d.items()}); print('y unique', np.unique(d['y']))"total 101
drwxr-xr-x 4 user user 4096 Jun 30 22:29 .
drwxr-xr-x 21 root root 4096 Jun 30 22:28 ..
-rw-r--r-- 1 user user 14238 Jun 30 22:29 PROMPT.txt
-rw-r--r-- 1 user user 5 Jun 30 22:29 agent.pid
-rw-r--r-- 1 user user 0 Jun 30 22:29 agent.stderr
-rw-r--r-- 1 user user 4625 Jun 30 22:29 agent_trajectory.jsonl
drwxr-xr-x 2 user user 60 Jun 30 22:29 data
-rw-r--r-- 1 user user 0 Jun 30 22:29 run.log
-rwxr-xr-x 1 user user 57740 Jun 30 11:01 run_checks.py
drwxr-xr-x 2 user user 128 Jun 30 22:29 submission
-rwxr-xr-x 1 user user 998 Jun 30 11:01 test.sh
-rwxr-xr-x 1 user user 629 Jun 30 11:01 timer.sh
---SUBMISSION---
total 80
drwxr-xr-x 2 user user 128 Jun 30 22:29 .
drwxr-xr-x 4 user user 4096 Jun 30 22:29 ..
-rw-r--r-- 1 user user 5490 Jun 30 11:01 advtrain.py
-rw-r--r-- 1 user user 8776 Jun 30 11:01 attack.py
-rw-r--r-- 1 user user 18341 Jun 30 11:01 autograd.py
-rw-r--r-- 1 user user 2167 Jun 30 11:01 data.py
-rw-r--r-- 1 user user 15988 Jun 30 11:01 nn.py
-rw-r--r-- 1 user user 8853 Jun 30 11:01 optim.py
-rw-r--r-- 1 user user 3742 Jun 30 11:01 train.py
---DATA---
total 2904
drwxr-xr-x 2 user user 60 Jun 30 22:29 .
drwxr-xr-x 4 user user 4096 Jun 30 22:29 ..
-rw-r--r-- 1 user user 2967336 Jun 30 11:01 train.npz
{'X': ((3600, 1, 16, 16), dtype('float32')), 'y': ((3600,), dtype('int64'))}
y unique [0 1 2 3 4 5]/app/submission/autograd.py
1 """Reverse-mode automatic differentiation over numpy arrays (IMPLEMENT THIS). 2 3 A ``Tensor`` wraps a single ``numpy.ndarray`` (``float64``) and records the operation that 4 produced it so that ``.backward()`` can walk the graph in reverse-topological order and 5 accumulate ``.grad`` (the gradient of the scalar on which ``.backward()`` was called w.r.t. 6 this tensor) into every leaf. 7 8 This task is ADVERSARIAL ROBUSTNESS: the model is a small convolutional classifier and the 9 attacks (PGD/FGSM/MI-FGSM/PGD-L2/CW) ascend on the gradient of an attack loss w.r.t. the INPUT 10 image, so the autograd engine must (a) flow gradients back to PARAMETERS (for training) AND 11 (b) flow gradients back to the INPUT tensor (for the attack). BOTH directions are exercised by the 12 verifier's finite-difference gradient checks, so the convolution's input-gradient (the ``_col2im`` 13 adjoint) is load-bearing. 14 15 Contract (the verifier's conformance suite differentiates your analytic backward against finite 16 differences and checks your forward against its own reference, so match this exactly): 17 18 * ``Tensor(data, requires_grad=False)`` -- ``data`` is array-like, stored as ``float64`` in 19 ``self.data``; ``self.grad`` starts as ``None`` and becomes a same-shape ``float64`` array after 20 backward. ``requires_grad`` leaves accumulate grad; non-requiring leaves do not. 21 * Every op returns a NEW ``Tensor`` whose ``requires_grad`` is the OR of its differentiable inputs. 22 …[truncated 18629 chars]
/app/submission/nn.py
1 """Neural-network layers + a small convolutional classifier, built on the autograd engine 2 (IMPLEMENT THE FORWARDS). 3 4 Every module exposes ``params()`` -> list[Tensor] (the leaf tensors with ``requires_grad=True``) 5 and ``named_params()`` -> dict[str, Tensor] with STABLE names that match the checkpoint format the 6 verifier loads. ``SmallCNN.state_dict()`` / ``load_state_dict()`` round-trip a plain 7 ``dict[str, np.ndarray]`` (saved as an ``.npz``); the verifier loads YOUR checkpoint into its OWN 8 reference SmallCNN via these names, so the names + shapes below are part of the contract. 9 10 Naming contract for SmallCNN (do not change -- the verifier indexes by these exact keys): 11 conv1.weight (C1, C_in, 3, 3) conv1.bias (C1,) 12 conv2.weight (C2, C1, 3, 3) conv2.bias (C2,) 13 fc1.weight (C2*(H/4)*(W/4), F) fc1.bias (F,) 14 fc2.weight (F, n_classes) fc2.bias (n_classes,) 15 16 Architecture (pinned): for an (N, C_in, H, W) input image batch, 17 conv1 (C_in->C1, 3x3, pad 1) -> relu 18 maxpool 2x2 (H,W -> H/2,W/2) 19 conv2 (C1->C2, 3x3, pad 1) -> relu 20 maxpool 2x2 (H/2,W/2 -> H/4,W/4) 21 flatten -> fc1 (-> F) -> relu -> fc2 (-> n_classes) logits. 22 ``Linear`` computes ``x @ weight + bias`` (weight is (in, out)). No BatchNorm inside SmallCNN (so 23 the attack runs a single deterministic eval-mode forward with no running-stat dependence). 24 25 The other …[truncated 15896 chars]
/app/submission/optim.py
1 """Optimizers + LR schedulers over a flat list of autograd ``Tensor`` parameters (IMPLEMENT THESE). 2 3 Every optimizer operates IN PLACE on ``param.data`` using ``param.grad`` (the gradient accumulated 4 by ``Tensor.backward``). ``zero_grad()`` resets every grad to None. The EXACT update formula each 5 optimizer must match (the verifier checks one and several steps against its own reference) is in 6 its docstring. 7 8 The adversarial-training loop uses AdamW (decoupled decay, with ``no_decay_ndim`` excluding 1-D 9 params/biases) + global grad-norm clipping; the rest of the optimizer zoo + the four LR schedulers 10 are part of the from-scratch framework the conformance suite audits. 11 """ 12 from __future__ import annotations 13 14 import math 15 from typing import List 16 17 import numpy as np 18 19 from autograd import Tensor 20 21 22 class Optimizer: 23 def __init__(self, params: List[Tensor]): 24 self.params = list(params) 25 26 def zero_grad(self): 27 for p in self.params: 28 p.grad = None 29 30 31 class SGD(Optimizer): 32 """SGD + momentum + optional Nesterov + (coupled/L2) weight decay: 33 g = grad + wd*param; v = mu*v + g; step = (g + mu*v) if nesterov else v; param -= lr*step.""" 34 35 def __init__(self, params, lr=0.1, momentum=0.0, weight_decay=0.0, nesterov=False): 36 super().__init__(params) 37 self.lr = lr 38 self.momentum = momentum 39 self.weight_decay = weight …[truncated 8125 chars]
/app/submission/attack.py
1 """White-box evasion attacks built on the from-scratch autograd engine (IMPLEMENT THESE). 2 3 An adversary perturbs each input within a norm budget (L-inf eps in the [0,1] pixel domain by 4 default) and tries to make the classifier WRONG. Every attack ascends on the gradient of an attack 5 loss with respect to the INPUT image, which is exactly the gradient the autograd engine produces 6 when the input is wrapped in a ``requires_grad=True`` Tensor and the loss is backpropagated -- so a 7 correct ``conv2d`` input-gradient (the ``_col2im`` adjoint) is load-bearing. 8 9 THE PRIMITIVE 10 input_grad(model, x, y) d/dx of mean cross-entropy of model(x) vs y (ndarray). 11 loss_input_grad(model, x, y, loss_fn) d/dx of an ARBITRARY scalar attack loss loss_fn(logits,y). 12 13 L-INF ATTACKS 14 fgsm(model, x, y, eps) clip(x + eps*sign(input_grad)). 15 pgd_attack(model, x, y, eps, steps, alpha) iterated FGSM with random start + project to the 16 L-inf eps-ball around x AND the [0,1] box each step. 17 mi_fgsm(model, x, y, eps, steps, alpha, mu) momentum-iterative FGSM: accumulate a decaying 18 momentum of the L1-NORMALIZED gradient, step on its sign, 19 project each step. 20 targeted_pgd(model, x, y_target, eps, steps, alpha) DESCEND CE toward y_target (step on the 21 …[truncated 7820 chars]
/app/submission/advtrain.py
1 """Adversarial-training objectives built on the from-scratch autograd engine (IMPLEMENT THESE).
2
3 Several standard recipes turn a fragile classifier into a robust one. Each crafts adversarial
4 inputs per minibatch with the model's own attack, then takes a gradient step on a robustness-aware
5 loss; the recipes differ in the loss. All run a single forward+backward; the caller does grad-clip
6 + the optimizer step. ``x_clean`` / ``x_adv`` are ndarrays (N, C, H, W); ``y`` is an int (N,).
7
8 pgd_at_loss(model, x_adv, y) Madry PGD-AT: CE on the ADVERSARIAL batch.
9 trades_loss(model, x_clean, x_adv, y, beta) TRADES: CE(clean) + beta*KL(stopgrad(clean)||adv).
10 The clean branch INSIDE the KL is a STOP-GRADIENT
11 target (detached): the KL backprops only through
12 the adversarial branch.
13 mart_loss(model, x_clean, x_adv, y, beta) MART: boosted-CE on the adversarial batch
14 ( CE(adv,y) - mean log(1 - max_{j!=y} p_adv_j) ) +
15 beta * mean_i[ (1 - p_clean_i[y_i]) *
16 KL_i(softmax(adv)||stopgrad softmax(clean)) ].
17 alp_loss(model, x_clean, x_adv, y, lam) Adversarial Logit Pairing:
18 0.5*(CE(clean)+CE(ad
…[truncated 4278 chars]/app/submission/data.py
1 """Data loading + minibatching for the shape-classification set (IMPLEMENT THESE).
2
3 The visible train split ships as ``/app/data/train.npz`` with arrays ``X`` (N, C, H, W) float
4 pixels in [0,1] and ``y`` (N,) int labels. The SEALED held-out split is NOT in the image -- the
5 verifier regenerates it from a secret feature seed and runs its own attack on it.
6
7 Contract:
8 load_npz(path) -> (X float64 (N,C,H,W), y int64 (N,))
9 train_val_split(X, y, val_frac, rng) -> ((Xtr,ytr),(Xval,yval)) random disjoint split (val slice
10 is a held-BACK slice of the VISIBLE train data you may use for best-checkpoint selection --
11 it is NOT the verifier's sealed held-out set)
12 iter_batches(X, y, batch_size, rng) -> yields (xb, yb) random-permutation minibatches
13 get_batch(X, y, batch_size, rng) -> a single random minibatch
14 to_onehot(y, n_classes) -> (N, n_classes) one-hot float matrix
15 clip01(X) -> X clamped into the [0,1] pixel domain
16 """
17 from __future__ import annotations
18
19 from typing import Iterator, Tuple
20
21 import numpy as np
22
23
24 def load_npz(path: str) -> Tuple[np.ndarray, np.ndarray]:
25 raise NotImplementedError("implement load_npz")
26
27
28 def train_val_split(X: np.ndarray, y: np.ndarray, val_frac: float,
29 rng: np.random.Generator):
30 raise NotImplementedError("implement train_val_split")
31
32
33 def iter_batches(X: np.ndarray, y: np.ndarray, batch_size: int,
34
…[truncated 814 chars]/app/submission/train.py
1 """Adversarial-training loop for the small CNN, using the from-scratch numpy framework 2 (IMPLEMENT THIS). 3 4 Standard (clean) training reaches ~99% CLEAN accuracy but ~chance ROBUST accuracy -- it is broken 5 by the verifier's STRONG multi-restart L-inf PGD attack. WEAK robust training is not enough either: 6 single-step (FGSM) adversarial training suffers "catastrophic overfitting" and is shattered by the 7 multi-step / multi-restart eval attack, and adversarial training at a SMALLER perturbation budget 8 than the eval budget leaves the model fragile exactly where it is attacked. The effective recipe is 9 full multi-step PGD ADVERSARIAL TRAINING (Madry et al. 2018) with the perturbation budget MATCHED 10 to the (larger) eval budget: 11 12 * on every minibatch, craft an inner k-step L-inf PGD adversarial batch against the CURRENT model 13 (attack.pgd_attack), at the eps the eval uses, then take the AdamW step on advtrain.pgd_at_loss 14 (cross-entropy on the adversarial batch); 15 * train long enough for robustness to converge (a few epochs is not enough at this eps); 16 * stability details that materially raise the achieved robust accuracy and make it reproducible: 17 (a) exclude biases (1-D params) from weight decay (AdamW no_decay_ndim=1); (b) clip the global 18 gradient norm (the adversarial loss surface is sharp); (c) keep the BEST-by-robust-accuracy 19 checkpoint, evaluated each epoch with the model's own multi-step PGD on a held-BACK slic …[truncated 2473 chars]
/app/run_checks.py
<system-reminder>[Truncated: PARTIAL view , showing lines 1-868 of 1188 total (29062 tokens, cap 25000). Call Read with offset=869 limit=868 for the next page, or Grep to find a specific section. Do NOT answer from this page alone if the answer may be further in the file.]</system-reminder> 1 #!/usr/bin/env python3 2 """PUBLIC component check runner for the from-scratch numpy framework + the adversarial machinery. 3 4 Run this AS OFTEN AS YOU LIKE while you implement to get a per-check progress signal: 5 6 python3 /app/run_checks.py # check the framework in /app/submission 7 python3 /app/run_checks.py --submission /some/dir 8 python3 /app/run_checks.py --only attack # focus a single milestone 9 10 It imports YOUR modules from /app/submission (autograd.py / nn.py / optim.py / data.py / attack.py 11 / advtrain.py), exactly as the grader does, and runs a PUBLIC portion of the component conformance 12 suite, printing a clear per-check report. Each FAIL line names the exact op / layer / optimizer / 13 attack / adversarial-training property that is wrong. 14 15 HOW THIS RELATES TO YOUR GRADE 16 ------------------------------ 17 This runner is a DEBUGGING AID, not the grader. It checks YOUR framework against self-consistent 18 references it computes on its OWN: 19 * autograd_grads : central FINITE-DIFFERENCE of YOUR forward vs YOUR analytic backward (every op, 20 incl. the convolution input-gradient, the pools, an …[truncated 44212 chars]
/app/run_checks.py
869 def dlr_loss_grad():
870 rng = np.random.default_rng(sb + 60)
871 x0 = rng.standard_normal((5, 4)); y = rng.integers(0, 4, size=5)
872 lt = ag.Tensor(x0.copy(), requires_grad=True)
873 atk.dlr_loss(lt, y).backward()
874 gnum = _num_grad_input(lambda xv: np.array(float(atk.dlr_loss(ag.Tensor(xv), y).data)), x0.copy())
875 return _max_abs(lt.grad, gnum) < TOL_GRAD, f"max_abs={_max_abs(lt.grad, gnum):.2e}"
876
877 def dlr_input_grad_fd():
878 m = _model(61)
879 x0 = np.random.default_rng(sb + 62).standard_normal((3, 1, 8, 8)); y = np.array([0, 1, 2])
880 lf = lambda lg, t: atk.dlr_loss(lg, t)
881 gan = np.asarray(atk.loss_input_grad(m, x0, y, lf), np.float64)
882 gnum = _num_grad_input(lambda xv: np.array(float(lf(m(ag.Tensor(xv)), y).data)), x0.copy())
883 return _max_abs(gan, gnum) < TOL_GRAD, f"max_abs={_max_abs(gan, gnum):.2e}"
884
885 def bim_stronger():
886 m = _model(67)
887 x = np.clip(np.random.default_rng(sb + 68).random((8, 1, 8, 8)), 0, 1); y = np.array([0, 1, 2, 3, 0, 1, 2, 3]); eps = 0.12
888 xf = atk.fgsm(m, x, y, eps); xb = atk.bim(m, x, y, eps, 15, 0.025)
889 lf = float(ag.cross_entropy(m(xf), y).data); lb = float(ag.cross_entropy(m(xb), y).data)
890 return lb >= lf - 1e-6, f"fgsm={lf:.3f} bim={lb:.3f}"
891
892 def restarts_worst_case():
893 m = _model(69)
894 x = np.clip(np.random.default_
…[truncated 15653 chars]"""Reverse-mode automatic differentiation over numpy arrays."""
from __future__ import annotations
import math
from typing import Callable, Iterable, List, Optional
import numpy as np
_ERF = np.vectorize(math.erf)
_SQRT2 = math.sqrt(2.0)
_SQRT2PI = math.sqrt(2.0 * math.pi)
def _as_f64(x) -> np.ndarray:
return np.asarray(x, dtype=np.float64)
def _unbroadcast(grad: np.ndarray, shape: tuple) -> np.ndarray:
grad = np.asarray(grad, dtype=np.float64)
while grad.ndim > len(shape):
grad = grad.sum(axis=0)
for i, s in enumerate(shape):
if s == 1 and grad.shape[i] != 1:
grad = grad.sum(axis=i, keepdims=True)
return grad
def _unreduce(g, shape, axis, keepdims):
g = np.asarray(g, dtype=np.float64)
if axis is None:
return np.broadcast_to(g, shape).copy()
ax = axis if isinstance(axis, tuple) else (axis,)
ax = tuple(a % len(shape) for a in ax)
if not keepdims:
g = np.expand_dims(g, ax)
return np.broadcast_to(g, shape).copy()
def _count(shape, axis):
if axis is None:
n = 1
for s in shape:
n *= s
return n
ax = axis if isinstance(axis, tuple) else (axis,)
n = 1
for a in ax:
n *= shape[a]
return n
class Tensor:
__slots__ = ("data", "grad", "requires_grad", "_backward", "_parents")
def __init__(self, data, requires_grad: bool = False, _parents: Iterable["Tensor"] = ()):
self.data: np.ndarray = _as_f64(data)
s
…[truncated 27835 chars]The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
def forward(self, x: Tensor) -> Tensor:
"""ag.conv2d(x, weight, bias, pad)."""
raise NotImplementedError("implement Conv2d.forward") def forward(self, x: Tensor) -> Tensor:
"""ag.conv2d(x, weight, bias, pad)."""
return ag.conv2d(x, self.weight, self.bias, self.pad)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""ag.maxpool2d(x, k)."""
raise NotImplementedError("implement MaxPool2d.forward") """ag.maxpool2d(x, k)."""
return ag.maxpool2d(x, self.k)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""ag.avgpool2d(x, k)."""
raise NotImplementedError("implement AvgPool2d.forward") """ag.avgpool2d(x, k)."""
return ag.avgpool2d(x, self.k)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""ag.matmul(x, weight) + bias."""
raise NotImplementedError("implement Linear.forward") """ag.matmul(x, weight) + bias."""
return ag.add(ag.matmul(x, self.weight), self.bias)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""ag.layernorm(x, weight, bias, eps)."""
raise NotImplementedError("implement LayerNorm.forward") """ag.layernorm(x, weight, bias, eps)."""
return ag.layernorm(x, self.weight, self.bias, self.eps)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""ag.batchnorm(x, weight, bias, eps)."""
raise NotImplementedError("implement BatchNorm1d.forward") """ag.batchnorm(x, weight, bias, eps)."""
return ag.batchnorm(x, self.weight, self.bias, self.eps)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""linear -> relu (between layers) -> ... -> linear (no final activation)."""
raise NotImplementedError("implement MLP.forward") """linear -> relu (between layers) -> ... -> linear (no final activation)."""
for i, lin in enumerate(self.layers):
x = lin(x)
if i < len(self.layers) - 1:
x = ag.relu(x)
return xThe file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""for each conv: pool(relu(conv(x))); then flatten -> relu(fc1) -> fc2."""
raise NotImplementedError("implement ConvNet.forward") """for each conv: pool(relu(conv(x))); then flatten -> relu(fc1) -> fc2."""
x = ag._ensure(x)
for conv in self.convs:
x = self.pool(ag.relu(conv(x)))
n = x.shape[0]
x = ag.reshape(x, (n, -1))
x = ag.relu(self.fc1(x))
return self.fc2(x)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""ag.groupnorm(x, weight, bias, num_groups, eps)."""
raise NotImplementedError("implement GroupNorm.forward") """ag.groupnorm(x, weight, bias, num_groups, eps)."""
return ag.groupnorm(x, self.weight, self.bias, self.num_groups, self.eps)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""ag.rmsnorm(x, weight, eps)."""
raise NotImplementedError("implement RMSNorm.forward") """ag.rmsnorm(x, weight, eps)."""
return ag.rmsnorm(x, self.weight, self.eps)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""h' = tanh(x @ weight_ih^T + bias_ih + h @ weight_hh^T + bias_hh)."""
raise NotImplementedError("implement RNNCell.forward") """h' = tanh(x @ weight_ih^T + bias_ih + h @ weight_hh^T + bias_hh)."""
wi = ag.transpose(self.weight_ih)
wh = ag.transpose(self.weight_hh)
pre = ag.add(ag.add(ag.matmul(x, wi), self.bias_ih),
ag.add(ag.matmul(h, wh), self.bias_hh))
return ag.tanh(pre)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""the GRU update above (slice the 3 gates out of the (N,3H) pre-activations)."""
raise NotImplementedError("implement GRUCell.forward") """the GRU update above (slice the 3 gates out of the (N,3H) pre-activations)."""
H = self.hidden_size
gi = ag.add(ag.matmul(x, ag.transpose(self.weight_ih)), self.bias_ih)
gh = ag.add(ag.matmul(h, ag.transpose(self.weight_hh)), self.bias_hh)
i_r = gi[:, 0:H]; i_z = gi[:, H:2 * H]; i_n = gi[:, 2 * H:3 * H]
h_r = gh[:, 0:H]; h_z = gh[:, H:2 * H]; h_n = gh[:, 2 * H:3 * H]
r = ag.sigmoid(ag.add(i_r, h_r))
z = ag.sigmoid(ag.add(i_z, h_z))
n = ag.tanh(ag.add(i_n, ag.mul(r, h_n)))
one_minus_z = ag.sub(1.0, z)
return ag.add(ag.mul(one_minus_z, n), ag.mul(z, h))The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""the LSTM update above; return (h', c')."""
raise NotImplementedError("implement LSTMCell.forward") """the LSTM update above; return (h', c')."""
H = self.hidden_size
g = ag.add(ag.add(ag.matmul(x, ag.transpose(self.weight_ih)), self.bias_ih),
ag.add(ag.matmul(h, ag.transpose(self.weight_hh)), self.bias_hh))
i = ag.sigmoid(g[:, 0:H])
f = ag.sigmoid(g[:, H:2 * H])
gg = ag.tanh(g[:, 2 * H:3 * H])
o = ag.sigmoid(g[:, 3 * H:4 * H])
c2 = ag.add(ag.mul(f, c), ag.mul(i, gg))
h2 = ag.mul(o, ag.tanh(c2))
return h2, c2The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""h = pool(relu(conv1(x))); res = conv2b(relu(conv2a(h))); h = relu(h + res); flatten -> fc."""
raise NotImplementedError("implement SmallResCNN.forward") """h = pool(relu(conv1(x))); res = conv2b(relu(conv2a(h))); h = relu(h + res); flatten -> fc."""
x = ag._ensure(x)
h = self.pool(ag.relu(self.conv1(x)))
res = self.conv2b(ag.relu(self.conv2a(h)))
h = ag.relu(ag.add(h, res))
n = h.shape[0]
h = ag.reshape(h, (n, -1))
return self.fc(h)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
"""conv1 -> relu -> pool -> conv2 -> relu -> pool -> flatten -> fc1 -> relu -> fc2."""
raise NotImplementedError("implement SmallCNN.forward") """conv1 -> relu -> pool -> conv2 -> relu -> pool -> flatten -> fc1 -> relu -> fc2."""
x = ag._ensure(x)
x = self.pool(ag.relu(self.conv1(x)))
x = self.pool(ag.relu(self.conv2(x)))
n = x.shape[0]
x = ag.reshape(x, (n, -1))
x = ag.relu(self.fc1(x))
return self.fc2(x)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/data.py
def load_npz(path: str) -> Tuple[np.ndarray, np.ndarray]:
raise NotImplementedError("implement load_npz")
def train_val_split(X: np.ndarray, y: np.ndarray, val_frac: float,
rng: np.random.Generator):
raise NotImplementedError("implement train_val_split")
def iter_batches(X: np.ndarray, y: np.ndarray, batch_size: int,
rng: np.random.Generator) -> Iterator[Tuple[np.ndarray, np.ndarray]]:
raise NotImplementedError("implement iter_batches")
def get_batch(X: np.ndarray, y: np.ndarray, batch_size: int,
rng: np.random.Generator) -> Tuple[np.ndarray, np.ndarray]:
"""A single random minibatch (without replacement if possible). Shape-preserving."""
raise NotImplementedError("implement get_batch")
def to_onehot(y: np.ndarray, n_classes: int) -> np.ndarray:
"""(N, n_classes) one-hot float matrix from integer labels y."""
raise NotImplementedError("implement to_onehot")
def clip01(X: np.ndarray) -> np.ndarray:
"""Clamp X into the [0,1] pixel domain (the valid attack range)."""
raise NotImplementedError("implement clip01")def load_npz(path: str) -> Tuple[np.ndarray, np.ndarray]:
d = np.load(path)
X = np.asarray(d["X"], dtype=np.float64)
y = np.asarray(d["y"], dtype=np.int64)
return X, y
def train_val_split(X: np.ndarray, y: np.ndarray, val_frac: float,
rng: np.random.Generator):
n = X.shape[0]
perm = rng.permutation(n)
nval = int(round(n * val_frac))
val_idx = perm[:nval]
tr_idx = perm[nval:]
return (X[tr_idx], y[tr_idx]), (X[val_idx], y[val_idx])
def iter_batches(X: np.ndarray, y: np.ndarray, batch_size: int,
rng: np.random.Generator) -> Iterator[Tuple[np.ndarray, np.ndarray]]:
n = X.shape[0]
perm = rng.permutation(n)
for s in range(0, n, batch_size):
idx = perm[s:s + batch_size]
yield X[idx], y[idx]
def get_batch(X: np.ndarray, y: np.ndarray, batch_size: int,
rng: np.random.Generator) -> Tuple[np.ndarray, np.ndarray]:
"""A single random minibatch (without replacement if possible). Shape-preserving."""
n = X.shape[0]
replace = batch_size > n
idx = rng.choice(n, size=batch_size, replace=replace)
return X[idx], y[idx]
def to_onehot(y: np.ndarray, n_classes: int) -> np.ndarray:
"""(N, n_classes) one-hot float matrix from integer labels y."""
y = np.asarray(y).astype(int)
oh = np.zeros((y.shape[0], n_classes), dtype=np.float64)
oh[np.arange(y.shape[0]), y] = 1.0
return oh
def clip01(X: np.ndarray) -> np.ndarray:
"""Clamp X int
…[truncated 85 chars]The file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
python3 run_checks.py --only autograd_grads 2>&1 | tail -40
grad/prod_axis OK (max_abs=5.65e-10) grad/cumsum_ax1 OK (max_abs=3.79e-11) grad/logsumexp_ax1 OK (max_abs=4.93e-11) grad/pad2d OK (max_abs=5.10e-11) grad/groupnorm_x OK grad/rmsnorm_x OK grad/layernorm_x OK grad/batchnorm_x OK grad/embedding OK grad/mse_loss OK grad/cross_entropy OK grad/kl_div_q OK grad/kl_div_p OK grad/conv2d_input OK (max_abs=1.33e-09) grad/conv2d_weight OK (max_abs=3.61e-09) grad/conv2d_bias OK grad/conv2d_pad0_input OK (max_abs=8.78e-10) grad/maxpool2d OK grad/avgpool2d OK grad/nn.Conv2d_x OK grad/nn.Linear_x OK grad/nn.LayerNorm_x OK grad/nn.BatchNorm1d_x OK grad/cnn_grad_fc2.bias OK (max_abs=1.71e-11) grad/cnn_grad_conv1.weight OK (max_abs=2.36e-11) grad/cnn_grad_conv2.weight OK (max_abs=2.75e-11) grad/cnn_grad_fc1.weight OK (ma …[truncated 815 chars]
python3 run_checks.py --only nn_forward 2>&1 | tail -30 && echo "===DATA===" && python3 run_checks.py --only data 2>&1 | tail -8
[nn_forward] 22/22 fwd/relu_def OK fwd/leaky_relu_def OK fwd/sigmoid_def OK fwd/tanh_def OK fwd/gelu_erf_def OK fwd/softplus_def OK fwd/silu_def OK fwd/elu_def OK fwd/logsumexp_def OK fwd/softmax_rows_sum_1 OK fwd/cross_entropy_value OK fwd/kl_self_zero OK (KL(p||p)==0) fwd/layernorm_row_stats OK fwd/groupnorm_stats OK fwd/rmsnorm_def OK fwd/conv2d_arithmetic OK fwd/maxpool2d_def OK fwd/avgpool2d_def OK fwd/Linear_def OK fwd/SmallCNN_shape OK (shape=(4, 5)) fwd/SmallResCNN_shape OK (shape=(4, 3)) fwd/SmallCNN_state_dict_roundtrip OK (state_dict round-trips) ------------------------------------------------------------------------ nn_forward 22/22 TOTAL 22/22 public checks passing All public checks pass. Now make sure model.npz clears the held-out ROBUST-accuracy threshold; the grader re-checks with DIFFERENT seeds/shapes + its OWN strong PGD. ===DATA=== data/get_batch_shape OK …[truncated 409 chars]
/app/submission/optim.py
def step(self):
raise NotImplementedError("implement SGD.step") def step(self):
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
v = self.momentum * self._v[i] + g
self._v[i] = v
step = (g + self.momentum * v) if self.nesterov else v
p.data -= self.lr * stepThe file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
raise NotImplementedError("implement Adagrad.step") def step(self):
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._s[i] += g * g
p.data -= self.lr * g / (np.sqrt(self._s[i]) + self.eps)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
raise NotImplementedError("implement RMSprop.step") def step(self):
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._s[i] = self.alpha * self._s[i] + (1 - self.alpha) * (g * g)
p.data -= self.lr * g / (np.sqrt(self._s[i]) + self.eps)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
raise NotImplementedError("implement Adam.step") def step(self):
self.t += 1
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._m[i] = self.b1 * self._m[i] + (1 - self.b1) * g
self._v[i] = self.b2 * self._v[i] + (1 - self.b2) * (g * g)
mhat = self._m[i] / (1 - self.b1 ** self.t)
vhat = self._v[i] / (1 - self.b2 ** self.t)
p.data -= self.lr * mhat / (np.sqrt(vhat) + self.eps)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
raise NotImplementedError("implement AdamW.step") def step(self):
self.t += 1
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad
self._m[i] = self.b1 * self._m[i] + (1 - self.b1) * g
self._v[i] = self.b2 * self._v[i] + (1 - self.b2) * (g * g)
mhat = self._m[i] / (1 - self.b1 ** self.t)
vhat = self._v[i] / (1 - self.b2 ** self.t)
wd_eff = 0.0 if p.data.ndim <= self.no_decay_ndim else self.weight_decay
p.data -= self.lr * (mhat / (np.sqrt(vhat) + self.eps) + wd_eff * p.data)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
raise NotImplementedError("implement Adamax.step") def step(self):
self.t += 1
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._m[i] = self.b1 * self._m[i] + (1 - self.b1) * g
self._u[i] = np.maximum(self.b2 * self._u[i], np.abs(g))
p.data -= (self.lr / (1 - self.b1 ** self.t)) * self._m[i] / (self._u[i] + self.eps)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
raise NotImplementedError("implement NAdam.step") def step(self):
self.t += 1
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._m[i] = self.b1 * self._m[i] + (1 - self.b1) * g
self._v[i] = self.b2 * self._v[i] + (1 - self.b2) * (g * g)
mhat = self._m[i] / (1 - self.b1 ** self.t)
vhat = self._v[i] / (1 - self.b2 ** self.t)
mnes = self.b1 * mhat + (1 - self.b1) * g / (1 - self.b1 ** self.t)
p.data -= self.lr * mnes / (np.sqrt(vhat) + self.eps)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
raise NotImplementedError("implement Lion.step") def step(self):
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad
c = np.sign(self.b1 * self._m[i] + (1 - self.b1) * g)
p.data -= self.lr * (c + self.weight_decay * p.data)
self._m[i] = self.b2 * self._m[i] + (1 - self.b2) * gThe file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
raise NotImplementedError("implement Adadelta.step") def step(self):
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._s[i] = self.rho * self._s[i] + (1 - self.rho) * (g * g)
delta = np.sqrt(self._d[i] + self.eps) / np.sqrt(self._s[i] + self.eps) * g
p.data -= self.lr * delta
self._d[i] = self.rho * self._d[i] + (1 - self.rho) * (delta * delta)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
scale every grad by max_norm/(total+1e-6)."""
raise NotImplementedError("implement clip_grad_norm") scale every grad by max_norm/(total+1e-6)."""
total = 0.0
for p in params:
if p.grad is not None:
total += float((p.grad ** 2).sum())
total = math.sqrt(total)
if total > max_norm:
scale = max_norm / (total + 1e-6)
for p in params:
if p.grad is not None:
p.grad = p.grad * scale
return total<tool_use_error>String to replace not found in file.
String: scale every grad by max_norm/(total+1e-6)."""
raise NotImplementedError("implement clip_grad_norm")</tool_use_error>/app/submission/optim.py
186 for i, p in enumerate(self.params): 187 if p.grad is None: 188 continue 189 g = p.grad + self.weight_decay * p.data 190 self._m[i] = self.b1 * self._m[i] + (1 - self.b1) * g 191 self._v[i] = self.b2 * self._v[i] + (1 - self.b2) * (g * g) 192 mhat = self._m[i] / (1 - self.b1 ** self.t) 193 vhat = self._v[i] / (1 - self.b2 ** self.t) 194 mnes = self.b1 * mhat + (1 - self.b1) * g / (1 - self.b1 ** self.t) 195 p.data -= self.lr * mnes / (np.sqrt(vhat) + self.eps) 196 197
/app/submission/optim.py
218 class Adadelta(Optimizer):
219 """Adadelta (Zeiler 2012), coupled wd (lr is a multiplier, default 1.0):
220 g = grad + wd*param; s = rho*s + (1-rho)*g*g;
221 delta = sqrt(d + eps)/sqrt(s + eps) * g; param -= lr*delta; d = rho*d + (1-rho)*delta*delta."""
222
223 def __init__(self, params, lr=1.0, rho=0.9, eps=1e-6, weight_decay=0.0):
224 super().__init__(params)
225 self.lr, self.rho, self.eps, self.weight_decay = lr, rho, eps, weight_decay
226 self._s = [np.zeros_like(p.data) for p in self.params]
227 self._d = [np.zeros_like(p.data) for p in self.params]
228
229 def step(self):
230 for i, p in enumerate(self.params):
231 if p.grad is None:
232 continue
233 g = p.grad + self.weight_decay * p.data
234 self._s[i] = self.rho * self._s[i] + (1 - self.rho) * (g * g)
235 delta = np.sqrt(self._d[i] + self.eps) / np.sqrt(self._s[i] + self.eps) * g
236 p.data -= self.lr * delta
237 self._d[i] = self.rho * self._d[i] + (1 - self.rho) * (delta * delta)
238
239
240 # ===================================================================== grad clipping
241 def clip_grad_norm(params: List[Tensor], max_norm: float) -> float:
242 """Global L2 gradient-norm clipping. Return the pre-clip total norm. If total > max_norm, scale
243 every grad by max_norm/(total+1e-6)."""
244 raise NotImplementedError("implement clip_grad_nor
…[truncated 2092 chars]/app/submission/optim.py
every grad by max_norm/(total+1e-6)."""
raise NotImplementedError("implement clip_grad_norm")
def clip_grad_value(params: List[Tensor], clip_value: float) -> None:
"""Element-wise gradient clipping: clamp every grad to [-clip_value, clip_value]."""
raise NotImplementedError("implement clip_grad_value") every grad by max_norm/(total+1e-6)."""
total = 0.0
for p in params:
if p.grad is not None:
total += float((p.grad ** 2).sum())
total = math.sqrt(total)
if total > max_norm:
scale = max_norm / (total + 1e-6)
for p in params:
if p.grad is not None:
p.grad = p.grad * scale
return total
def clip_grad_value(params: List[Tensor], clip_value: float) -> None:
"""Element-wise gradient clipping: clamp every grad to [-clip_value, clip_value]."""
for p in params:
if p.grad is not None:
p.grad = np.clip(p.grad, -clip_value, clip_value)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
else: min_lr + 0.5*(base_lr-min_lr)*(1+cos(pi*progress)), progress=clip((step-warmup)/(total-warmup),0,1)."""
raise NotImplementedError("implement cosine_lr")
def warmup_lr(step: int, base_lr: float, warmup: int) -> float:
"""Linear warmup to base_lr over warmup steps, flat after: base_lr*(step+1)/warmup if step<warmup else base_lr."""
raise NotImplementedError("implement warmup_lr")
def step_lr(step: int, base_lr: float, step_size: int, gamma: float = 0.1) -> float:
"""Step decay: base_lr * gamma ** (step // step_size)."""
raise NotImplementedError("implement step_lr")
def exponential_lr(step: int, base_lr: float, gamma: float = 0.95) -> float:
"""Exponential decay: base_lr * gamma ** step."""
raise NotImplementedError("implement exponential_lr")
def polynomial_lr(step: int, total: int, base_lr: float, power: float = 2.0,
min_lr: float = 0.0) -> float:
"""Polynomial decay to min_lr over total steps: progress=clip(step/total,0,1);
lr = (base_lr-min_lr)*(1-progress)**power + min_lr."""
raise NotImplementedError("implement polynomial_lr")
def linear_lr(step: int, total: int, base_lr: float, min_lr: float = 0.0) -> float:
"""Linear decay base_lr->min_lr over total steps (flat after): progress=clip(step/total,0,1);
lr = base_lr + (min_lr-base_lr)*progress."""
raise NotImplementedError("implement linear_lr") else: min_lr + 0.5*(base_lr-min_lr)*(1+cos(pi*progress)), progress=clip((step-warmup)/(total-warmup),0,1)."""
if warmup > 0 and step < warmup:
return base_lr * (step + 1) / warmup
prog = min(1.0, max(0.0, (step - warmup) / max(1, total - warmup)))
return min_lr + 0.5 * (base_lr - min_lr) * (1 + math.cos(math.pi * prog))
def warmup_lr(step: int, base_lr: float, warmup: int) -> float:
"""Linear warmup to base_lr over warmup steps, flat after: base_lr*(step+1)/warmup if step<warmup else base_lr."""
if step < warmup:
return base_lr * (step + 1) / warmup
return base_lr
def step_lr(step: int, base_lr: float, step_size: int, gamma: float = 0.1) -> float:
"""Step decay: base_lr * gamma ** (step // step_size)."""
return base_lr * (gamma ** (step // step_size))
def exponential_lr(step: int, base_lr: float, gamma: float = 0.95) -> float:
"""Exponential decay: base_lr * gamma ** step."""
return base_lr * (gamma ** step)
def polynomial_lr(step: int, total: int, base_lr: float, power: float = 2.0,
min_lr: float = 0.0) -> float:
"""Polynomial decay to min_lr over total steps: progress=clip(step/total,0,1);
lr = (base_lr-min_lr)*(1-progress)**power + min_lr."""
prog = min(1.0, max(0.0, step / total))
return (base_lr - min_lr) * ((1.0 - prog) ** power) + min_lr
def linear_lr(step: int, total: int, base_lr: float, min_lr: float = 0.0) -> float:
"""Linear decay base_lr->min_lr over to
…[truncated 194 chars]The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
python3 run_checks.py --only optim 2>&1 | tail -35
[optim] 20/20 optim/SGD_1step OK (max_abs=0.0e+00) optim/SGD_momentum_5step OK (max_abs=0.0e+00) optim/SGD_nesterov_5step OK (max_abs=0.0e+00) optim/Adagrad_5step OK (max_abs=0.0e+00) optim/RMSprop_5step OK (max_abs=0.0e+00) optim/Adam_10step_wd OK (max_abs=0.0e+00) optim/AdamW_10step_wd OK (max_abs=0.0e+00) optim/Adamax_8step OK (max_abs=0.0e+00) optim/NAdam_8step OK (max_abs=0.0e+00) optim/Lion_8step_wd OK (max_abs=0.0e+00) optim/Adadelta_8step OK (max_abs=0.0e+00) optim/clip_grad_norm_clips OK (max_abs=0.0e+00) optim/clip_grad_norm_noop OK (max_abs=0.0e+00) optim/clip_grad_value OK (max_abs=0.0e+00) optim/sched_cosine_lr OK (match) optim/sched_warmup_lr OK (match) optim/sched_step_lr OK (match) optim/sched_exponential_lr OK (match) optim/sched_polynomial_lr OK (match) optim/sched_linear_lr OK (match) ------------------------------------------------------------------------ optim 20/20 TOTAL 20/20 public checks passing All public checks pass. Now make sure model.npz clears the held-out ROBUST-accuracy …[truncated 81 chars]
/app/submission/attack.py
in a requires_grad=True Tensor, forward, compute loss_fn, backprop, return xt.grad."""
raise NotImplementedError("implement loss_input_grad")
def input_grad(model, x: np.ndarray, y: np.ndarray) -> np.ndarray:
"""d/dx of mean CROSS-ENTROPY of model(x) vs y (the standard attack gradient)."""
raise NotImplementedError("implement input_grad") in a requires_grad=True Tensor, forward, compute loss_fn, backprop, return xt.grad."""
xt = Tensor(np.asarray(x, dtype=np.float64), requires_grad=True)
logits = model(xt)
loss = loss_fn(logits, y)
loss.backward()
return np.asarray(xt.grad, dtype=np.float64)
def input_grad(model, x: np.ndarray, y: np.ndarray) -> np.ndarray:
"""d/dx of mean CROSS-ENTROPY of model(x) vs y (the standard attack gradient)."""
return loss_input_grad(model, x, y, lambda lg, t: ag.cross_entropy(lg, t))The file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
Build it with autograd ops so the input-gradient flows; returns a scalar Tensor."""
raise NotImplementedError("implement cw_margin_loss") Build it with autograd ops so the input-gradient flows; returns a scalar Tensor."""
logits = ag._ensure(logits)
N, C = logits.shape
y = np.asarray(y).astype(int)
idx = (np.arange(N), y)
onehot = np.zeros((N, C), dtype=bool)
onehot[idx] = True
z_y = ag.getitem(logits, idx)
masked = ag.where(onehot, Tensor(np.full((N, C), -1e30)), logits)
other_max = ag.max(masked, axis=-1)
margin = ag.sub(z_y, other_max)
per = ag.maximum(ag.mul(margin, -1.0), -float(kappa))
return ag.mean(per)The file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
it differentiates exactly and the input-gradient flows; returns a scalar Tensor."""
raise NotImplementedError("implement dlr_loss") it differentiates exactly and the input-gradient flows; returns a scalar Tensor."""
logits = ag._ensure(logits)
N, C = logits.shape
y = np.asarray(y).astype(int)
idx = (np.arange(N), y)
onehot = np.zeros((N, C), dtype=bool)
onehot[idx] = True
z_y = ag.getitem(logits, idx)
masked = ag.where(onehot, Tensor(np.full((N, C), -1e30)), logits)
other_max = ag.max(masked, axis=-1)
max_all = ag.max(logits, axis=-1)
mean_all = ag.mean(logits, axis=-1)
num = ag.sub(z_y, other_max)
denom = ag.add(ag.sub(max_all, mean_all), float(eps))
dlr = ag.mul(ag.div(num, denom), -1.0)
return ag.mean(dlr)The file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
"""Project x_adv into the L-inf eps-ball around x, then into [lo,hi]."""
raise NotImplementedError("implement project_linf") """Project x_adv into the L-inf eps-ball around x, then into [lo,hi]."""
xa = np.clip(x_adv, x - eps, x + eps)
return np.clip(xa, lo, hi)The file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
"""Project x_adv so the PER-EXAMPLE L-2 norm of (x_adv - x) is <= eps, then clip to [lo,hi]."""
raise NotImplementedError("implement project_l2") """Project x_adv so the PER-EXAMPLE L-2 norm of (x_adv - x) is <= eps, then clip to [lo,hi]."""
delta = x_adv - x
N = delta.shape[0]
flat = delta.reshape(N, -1)
norm = np.linalg.norm(flat, axis=1, keepdims=True)
factor = np.minimum(1.0, eps / (norm + 1e-12))
flat = flat * factor
xa = x + flat.reshape(delta.shape)
return np.clip(xa, lo, hi)The file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
"""One-step FGSM: clip(x + eps*sign(input_grad))."""
raise NotImplementedError("implement fgsm") """One-step FGSM: clip(x + eps*sign(input_grad))."""
g = input_grad(model, x, y)
xadv = x + eps * np.sign(g)
return np.clip(xadv, clip[0], clip[1])The file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
"""Iterated FGSM with random start + L-inf projection (the standard PGD attack)."""
raise NotImplementedError("implement pgd_attack") """Iterated FGSM with random start + L-inf projection (the standard PGD attack)."""
lo, hi = clip
x0 = x if x0 is None else x0
if rng is None:
xadv = x.copy()
else:
xadv = x + rng.uniform(-eps, eps, size=x.shape)
xadv = project_linf(xadv, x0, eps, lo, hi)
for _ in range(steps):
g = input_grad(model, xadv, y)
xadv = xadv + alpha * np.sign(g)
xadv = project_linf(xadv, x0, eps, lo, hi)
return xadvThe file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
project to the L-inf ball + box each step."""
raise NotImplementedError("implement mi_fgsm") project to the L-inf ball + box each step."""
lo, hi = clip
N = x.shape[0]
if rng is None:
xadv = x.copy()
else:
xadv = project_linf(x + rng.uniform(-eps, eps, size=x.shape), x, eps, lo, hi)
gmom = np.zeros_like(x)
for _ in range(steps):
g = input_grad(model, xadv, y)
gnorm = np.sum(np.abs(g.reshape(N, -1)), axis=1).reshape([N] + [1] * (g.ndim - 1))
gmom = mu * gmom + g / (gnorm + 1e-12)
xadv = xadv + alpha * np.sign(gmom)
xadv = project_linf(xadv, x, eps, lo, hi)
return xadvThe file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
"""Targeted PGD: DESCEND CE toward y_target (step on the NEGATIVE gradient sign); project each step."""
raise NotImplementedError("implement targeted_pgd") """Targeted PGD: DESCEND CE toward y_target (step on the NEGATIVE gradient sign); project each step."""
lo, hi = clip
if rng is None:
xadv = x.copy()
else:
xadv = project_linf(x + rng.uniform(-eps, eps, size=x.shape), x, eps, lo, hi)
for _ in range(steps):
g = input_grad(model, xadv, y_target)
xadv = xadv - alpha * np.sign(g)
xadv = project_linf(xadv, x, eps, lo, hi)
return xadvThe file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
"""PGD ascending the untargeted CW margin loss (cw_margin_loss); project to the ball + box each step."""
raise NotImplementedError("implement cw_pgd") """PGD ascending the untargeted CW margin loss (cw_margin_loss); project to the ball + box each step."""
lo, hi = clip
lf = lambda lg, t: cw_margin_loss(lg, t, kappa)
if rng is None:
xadv = x.copy()
else:
xadv = project_linf(x + rng.uniform(-eps, eps, size=x.shape), x, eps, lo, hi)
for _ in range(steps):
g = loss_input_grad(model, xadv, y, lf)
xadv = xadv + alpha * np.sign(g)
xadv = project_linf(xadv, x, eps, lo, hi)
return xadvThe file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
into the L-2 eps-ball + pixel box each step."""
raise NotImplementedError("implement pgd_l2") into the L-2 eps-ball + pixel box each step."""
lo, hi = clip
N = x.shape[0]
if rng is None:
xadv = x.copy()
else:
d = rng.standard_normal(x.shape)
dn = np.linalg.norm(d.reshape(N, -1), axis=1, keepdims=True)
d = d / (dn.reshape([N] + [1] * (x.ndim - 1)) + 1e-12) * eps * rng.random((N,) + (1,) * (x.ndim - 1))
xadv = project_l2(x + d, x, eps, lo, hi)
for _ in range(steps):
g = input_grad(model, xadv, y)
gn = np.linalg.norm(g.reshape(N, -1), axis=1).reshape([N] + [1] * (g.ndim - 1))
xadv = xadv + alpha * g / (gn + 1e-12)
xadv = project_l2(xadv, x, eps, lo, hi)
return xadvThe file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
x_adv += alpha*sign(input_grad); project to the L-inf eps-ball + box."""
raise NotImplementedError("implement bim") x_adv += alpha*sign(input_grad); project to the L-inf eps-ball + box."""
lo, hi = clip
xadv = x.copy()
for _ in range(steps):
g = input_grad(model, xadv, y)
xadv = xadv + alpha * np.sign(g)
xadv = project_linf(xadv, x, eps, lo, hi)
return xadvThe file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
size alpha; project to the eps-ball + box. Default alpha = eps/2."""
raise NotImplementedError("implement rfgsm") size alpha; project to the eps-ball + box. Default alpha = eps/2."""
lo, hi = clip
if alpha is None:
alpha = eps / 2.0
if rng is None:
rng = np.random.default_rng()
xstart = x + (eps - alpha) * np.sign(rng.uniform(-1.0, 1.0, size=x.shape))
xstart = np.clip(xstart, lo, hi)
g = input_grad(model, xstart, y)
xadv = xstart + alpha * np.sign(g)
return project_linf(xadv, x, eps, lo, hi)The file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
g_mom = mu*g_mom + grad/||grad||_1, step on sign(g_mom); project each step."""
raise NotImplementedError("implement ni_fgsm") g_mom = mu*g_mom + grad/||grad||_1, step on sign(g_mom); project each step."""
lo, hi = clip
N = x.shape[0]
if rng is None:
xadv = x.copy()
else:
xadv = project_linf(x + rng.uniform(-eps, eps, size=x.shape), x, eps, lo, hi)
gmom = np.zeros_like(x)
for _ in range(steps):
xnes = xadv + alpha * mu * gmom
g = input_grad(model, xnes, y)
gnorm = np.sum(np.abs(g.reshape(N, -1)), axis=1).reshape([N] + [1] * (g.ndim - 1))
gmom = mu * gmom + g / (gnorm + 1e-12)
xadv = xadv + alpha * np.sign(gmom)
xadv = project_linf(xadv, x, eps, lo, hi)
return xadvThe file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
adversarial input with the HIGHEST per-example cross-entropy (worst case). Return that batch."""
raise NotImplementedError("implement pgd_restarts") adversarial input with the HIGHEST per-example cross-entropy (worst case). Return that batch."""
if rng is None:
rng = np.random.default_rng()
N = x.shape[0]
best = None
best_ce = np.full(N, -np.inf)
for _ in range(restarts):
xadv = pgd_attack(model, x, y, eps, steps, alpha, clip=clip, rng=rng)
logits = model(Tensor(xadv))
ce = np.asarray(ag.nll_per_sample(logits, y).data, dtype=np.float64)
if best is None:
best = xadv.copy()
upd = ce > best_ce
best[upd] = xadv[upd]
best_ce[upd] = ce[upd]
return bestThe file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/attack.py
"""PGD ascending the DLR loss (dlr_loss) instead of CE; project to the L-inf ball + box each step."""
raise NotImplementedError("implement dlr_pgd") """PGD ascending the DLR loss (dlr_loss) instead of CE; project to the L-inf ball + box each step."""
lo, hi = clip
lf = lambda lg, t: dlr_loss(lg, t)
if rng is None:
xadv = x.copy()
else:
xadv = project_linf(x + rng.uniform(-eps, eps, size=x.shape), x, eps, lo, hi)
for _ in range(steps):
g = loss_input_grad(model, xadv, y, lf)
xadv = xadv + alpha * np.sign(g)
xadv = project_linf(xadv, x, eps, lo, hi)
return xadvThe file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)
python3 run_checks.py --only attack 2>&1 | tail -25
[attack] 18/18 attack/input_grad_fd OK (max_abs=2.12e-11) attack/cw_loss_input_grad_fd OK (max_abs=2.41e-12) attack/dlr_loss_grad OK (max_abs=1.04e-09) attack/dlr_loss_input_grad_fd OK (max_abs=2.81e-11) attack/fgsm_step OK attack/pgd_projection OK attack/mi_fgsm_projection OK attack/cw_pgd_projection OK attack/pgd_l2_projection OK attack/bim_projection OK attack/rfgsm_projection OK attack/ni_fgsm_projection OK attack/pgd_restarts_projection OK attack/dlr_pgd_projection OK attack/pgd_stronger_than_fgsm OK (fgsm=1.450 pgd=1.469) attack/bim_stronger_than_fgsm OK (fgsm=1.411 bim=1.423) attack/pgd_restarts_worst_case OK (single=1.521 restarts=1.525) attack/targeted_pgd_lowers_target_ce OK (1.388->1.333) ------------------------------------------------------------------------ attack 18/18 TOTAL 18/18 public checks passing All public checks pass. Now make sure model.npz clears the held-out ROBUST-accuracy threshold; the grader re-checks with DIFFERENT seeds/shapes + its OWN strong PGD.
/app/submission/advtrain.py
def pgd_at_loss(model, x_adv: np.ndarray, y: np.ndarray) -> Tensor:
"""Madry PGD-AT loss: cross-entropy on the adversarial batch (trains parameters)."""
raise NotImplementedError("implement pgd_at_loss")
def trades_loss(model, x_clean: np.ndarray, x_adv: np.ndarray, y: np.ndarray,
beta: float = 6.0) -> Tuple[Tensor, Dict[str, float]]:
"""TRADES loss = CE(clean, y) + beta * KL(stopgrad(softmax(clean)) || softmax(adv))."""
raise NotImplementedError("implement trades_loss")
def mart_loss(model, x_clean: np.ndarray, x_adv: np.ndarray, y: np.ndarray,
beta: float = 5.0) -> Tuple[Tensor, Dict[str, float]]:
"""MART loss (boosted CE on adv + misclassification-aware weighted KL with the clean target
DETACHED); see the module docstring for the exact form."""
raise NotImplementedError("implement mart_loss")
def alp_loss(model, x_clean: np.ndarray, x_adv: np.ndarray, y: np.ndarray,
lam: float = 0.5) -> Tuple[Tensor, Dict[str, float]]:
"""Adversarial Logit Pairing: 0.5*(CE(clean)+CE(adv)) + lam*mean||z_clean - z_adv||^2 (no detach)."""
raise NotImplementedError("implement alp_loss")
def label_smoothed_ce(logits, y, eps_ls: float = 0.1, n_classes: int = None) -> Tensor:
"""CE against a label-smoothed target ((1-eps_ls)*onehot + eps_ls/C*uniform), via log_softmax."""
raise NotImplementedError("implement label_smoothed_ce")
def robust_ce_loss(model, x_clean: np.ndarray, x_adv: np.ndarray, y: np.nd
…[truncated 212 chars]def _detach(t: Tensor) -> Tensor:
return Tensor(t.data.copy())
def pgd_at_loss(model, x_adv: np.ndarray, y: np.ndarray) -> Tensor:
"""Madry PGD-AT loss: cross-entropy on the adversarial batch (trains parameters)."""
return ag.cross_entropy(model(Tensor(np.asarray(x_adv, dtype=np.float64))), y)
def trades_loss(model, x_clean: np.ndarray, x_adv: np.ndarray, y: np.ndarray,
beta: float = 6.0) -> Tuple[Tensor, Dict[str, float]]:
"""TRADES loss = CE(clean, y) + beta * KL(stopgrad(softmax(clean)) || softmax(adv))."""
cl = model(Tensor(np.asarray(x_clean, dtype=np.float64)))
ad = model(Tensor(np.asarray(x_adv, dtype=np.float64)))
ce = ag.cross_entropy(cl, y)
tgt = _detach(cl)
kl = ag.kl_div(tgt, ad)
loss = ag.add(ce, ag.mul(kl, float(beta)))
return loss, {"ce": float(ce.data), "kl": float(kl.data), "loss": float(loss.data)}
def mart_loss(model, x_clean: np.ndarray, x_adv: np.ndarray, y: np.ndarray,
beta: float = 5.0) -> Tuple[Tensor, Dict[str, float]]:
"""MART loss (boosted CE on adv + misclassification-aware weighted KL with the clean target
DETACHED); see the module docstring for the exact form."""
cl = model(Tensor(np.asarray(x_clean, dtype=np.float64)))
ad = model(Tensor(np.asarray(x_adv, dtype=np.float64)))
y = np.asarray(y).astype(int)
N, C = ad.shape
idx = (np.arange(N), y)
onehot = np.zeros((N, C), dtype=bool)
onehot[idx] = True
# boosted CE: CE(adv,y) - mea
…[truncated 2784 chars]The file /app/submission/advtrain.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/advtrain.py
through the adversarial branch; the clean branch still trains via its CE term)."""
raise NotImplementedError("implement clp_loss") through the adversarial branch; the clean branch still trains via its CE term)."""
zc = model(Tensor(np.asarray(x_clean, dtype=np.float64)))
za = model(Tensor(np.asarray(x_adv, dtype=np.float64)))
ce = ag.mul(ag.add(ag.cross_entropy(zc, y), ag.cross_entropy(za, y)), 0.5)
zc_det = _detach(zc)
diff = ag.sub(zc_det, za)
pair = ag.mean(ag.mul(diff, diff))
loss = ag.add(ce, ag.mul(pair, float(lam)))
return loss, {"ce": float(ce.data), "pair": float(pair.data), "loss": float(loss.data)}The file /app/submission/advtrain.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/advtrain.py
as the second ("q") -- the opposite argument order from TRADES."""
raise NotImplementedError("implement kl_at_loss") as the second ("q") -- the opposite argument order from TRADES."""
cl = model(Tensor(np.asarray(x_clean, dtype=np.float64)))
ad = model(Tensor(np.asarray(x_adv, dtype=np.float64)))
ce = ag.cross_entropy(ad, y)
tgt = _detach(cl)
kl = ag.kl_div(ad, tgt)
loss = ag.add(ce, ag.mul(kl, float(beta)))
return loss, {"ce": float(ce.data), "kl": float(kl.data), "loss": float(loss.data)}The file /app/submission/advtrain.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/advtrain.py
"""AT with logit squeezing: L = CE(adv,y) + lam*mean(z_adv**2) (penalize large logits)."""
raise NotImplementedError("implement logit_squeeze_at_loss")
def ls_at_loss(model, x_adv: np.ndarray, y: np.ndarray,
eps_ls: float = 0.1) -> Tuple[Tensor, Dict[str, float]]:
"""Label-smoothed AT: L = label_smoothed_ce(model(adv), y, eps_ls)."""
raise NotImplementedError("implement ls_at_loss") """AT with logit squeezing: L = CE(adv,y) + lam*mean(z_adv**2) (penalize large logits)."""
za = model(Tensor(np.asarray(x_adv, dtype=np.float64)))
ce = ag.cross_entropy(za, y)
sq = ag.mean(ag.mul(za, za))
loss = ag.add(ce, ag.mul(sq, float(lam)))
return loss, {"ce": float(ce.data), "sq": float(sq.data), "loss": float(loss.data)}
def ls_at_loss(model, x_adv: np.ndarray, y: np.ndarray,
eps_ls: float = 0.1) -> Tuple[Tensor, Dict[str, float]]:
"""Label-smoothed AT: L = label_smoothed_ce(model(adv), y, eps_ls)."""
za = model(Tensor(np.asarray(x_adv, dtype=np.float64)))
loss = label_smoothed_ce(za, y, eps_ls)
return loss, {"loss": float(loss.data)}The file /app/submission/advtrain.py has been updated successfully. (file state is current in your context , no need to Read it back)
python3 run_checks.py --only advtrain 2>&1 | tail -25 && echo "===INTEGRATION===" && python3 run_checks.py --only integration 2>&1 | tail -8
[advtrain] 14/14 advtrain/at_param_grad OK (max_abs=2.56e-11) advtrain/trades_param_grad OK (max_abs=5.66e-04) advtrain/mart_param_grad OK (max_abs=1.14e-04) advtrain/alp_param_grad OK (max_abs=2.60e-11) advtrain/robust_ce_param_grad OK (max_abs=4.29e-11) advtrain/clp_param_grad OK (max_abs=2.84e-05) advtrain/kl_at_param_grad OK (max_abs=1.62e-03) advtrain/logit_squeeze_param_grad OK (max_abs=4.80e-11) advtrain/ls_at_param_grad OK (max_abs=2.76e-11) advtrain/label_smoothed_ce_grad OK advtrain/trades_stopgrad OK (max_abs=0.00e+00) advtrain/alp_pairing OK (max_abs=0.00e+00) advtrain/clp_stopgrad OK (max_abs=0.00e+00) advtrain/kl_at_stopgrad OK (max_abs=0.00e+00) ------------------------------------------------------------------------ advtrain 14/14 TOTAL 14/14 public checks passing All public checks pass. Now make sure model.npz clears the held-out ROBUST-accuracy threshold; the grader re-checks with DIFFERENT seeds/shapes + its OWN strong PGD. ===INTEGRATION=== integration/pgd_at_decreases OK (1.4247->1.3885) integration/trades_finite OK (1.393->1.337) integration/trades_decreases OK (1.3933->1.3367) --------------------------------------------- …[truncated 242 chars]
python3 run_checks.py 2>&1 | tail -5
------------------------------------------------------------------------ autograd_grads 82/82 | nn_forward 22/22 | optim 20/20 | attack 18/18 | advtrain 14/14 | integration 4/4 | data 3/3 TOTAL 163/163 public checks passing All public checks pass. Now make sure model.npz clears the held-out ROBUST-accuracy threshold; the grader re-checks with DIFFERENT seeds/shapes + its OWN strong PGD.
cat test.sh && echo "---" && grep -rEl "torch|jax|tensorflow|tinygrad|micrograd|autograd_lib|^import autograd$" submission/*.py 2>/dev/null; echo "checking imports done"
#!/usr/bin/env bash
# Smoke test (NOT the grader): confirms the environment is wired -- numpy imports, the training
# split loads, and the submission stubs are present. It does NOT check correctness (the stubs raise
# NotImplementedError until you implement them).
set -uo pipefail
python3 - <<'PY'
import numpy as np, os
p = "/app/data/train.npz"
assert os.path.exists(p), "missing train.npz"
d = np.load(p)
print(f"train.npz: X{d['X'].shape} {d['X'].dtype} y{d['y'].shape} classes={len(set(d['y'].tolist()))}")
for f in ("autograd.py", "nn.py", "optim.py", "data.py", "attack.py", "advtrain.py", "train.py"):
assert os.path.exists(f"/app/submission/{f}"), f"missing /app/submission/{f}"
assert os.path.exists("/app/run_checks.py"), "missing /app/run_checks.py (public component runner)"
print("submission stubs present:", sorted(os.listdir("/app/submission")))
print("numpy", np.__version__)
PY
echo "app_test smoke OK (run 'python3 /app/run_checks.py' for the per-check progress report)"
---
submission/nn.py
checking imports done/app/submission/train.py
def train(train_path, out_path, epochs=12, batch_size=128, lr=1.2e-3, eps=0.12,
at_steps=7, alpha=0.03, grad_clip=1.0, weight_decay=5e-4, val_frac=0.15,
sel_steps=7, seed=0, log_every=1):
"""Run multi-step PGD adversarial training and save the best-by-robust-val checkpoint to
``out_path``. See the module docstring for the recipe."""
raise NotImplementedError("implement the adversarial-training loop")def _robust_acc(model, X, y, eps, steps, alpha, restarts, rng, batch=256):
"""Worst-case robust accuracy under multi-restart PGD on (X, y)."""
n = X.shape[0]
correct = np.zeros(n, dtype=bool)
# clean correctness first
for s in range(0, n, batch):
xb = X[s:s + batch]
logits = model(A.Tensor(xb)).data
correct[s:s + batch] = (logits.argmax(1) == y[s:s + batch])
robust = correct.copy()
for s in range(0, n, batch):
xb = X[s:s + batch]; yb = y[s:s + batch]
sl = slice(s, s + batch)
if not robust[sl].any():
continue
xadv = A.pgd_restarts(model, xb, yb, eps, steps, alpha, restarts=restarts, rng=rng)
pred = model(A.Tensor(xadv)).data.argmax(1)
robust[sl] = robust[sl] & (pred == yb)
return float(robust.mean())
def train(train_path, out_path, epochs=30, batch_size=128, lr=1e-3, eps=0.12,
at_steps=10, alpha=0.03, grad_clip=1.0, weight_decay=5e-4, val_frac=0.12,
sel_steps=10, seed=0, log_every=1):
"""Run multi-step PGD adversarial training and save the best-by-robust-val checkpoint to
``out_path``. See the module docstring for the recipe."""
rng = np.random.default_rng(seed)
X, y = D.load_npz(train_path)
n_classes = int(y.max()) + 1
c_in, img = X.shape[1], X.shape[2]
(Xtr, ytr), (Xval, yval) = D.train_val_split(X, y, val_frac, rng)
print(f"data: train={Xtr.shape} val={Xval.shape} classes={n_classes} img={img} c_in={c_in}")
…[truncated 1889 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
cd /app/submission && time python3 -c "
import train as Tr
import numpy as np
sd, br = Tr.train('/app/data/train.npz', '/tmp/test_model.npz', epochs=1, seed=0)
" 2>&1 | tail -15No file changes, the agent left the workspace untouched.
=== SyncVals verifier (local re-grade of the agent's own checkpoint) === COMPONENT CONFORMANCE SUITE: 201/201 cases passed (all milestones pass: True) FINAL METRIC: PGD robust accuracy = 0.5675 < threshold 0.6 REWARD = 0 (1 iff every component milestone passes AND the sealed held-out metric clears its threshold) NOTE: the in-sandbox training in the 481MB e2b box was OOM-killed (0-byte checkpoint), so the raw in-sandbox reward was an infrastructure artifact. This reward is from a faithful LOCAL re-train of the agent's OWN train.py (24GB, no OOM) with its own default hyperparameters, then re-graded by the real verifier.
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_17b58591aaf6490f. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_17b58591aaf6490f · verifier authoritative; classifier explanatory.