SyncValsverifier → artifact → classifier → verdict
SyncVals · Trajectory

adv-robust-pgd

claude-code claude-opus-4-8 ✓ resolved GOOD_SUCCESS ↑ View task
Solved from the instruction alone, tests/ and solution/ were withheld from the agent's workspace and restored only for grading.
Reward = tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.
Classification , post-hoc; cannot change the reward
GOOD_SUCCESSLegitimate solve, implements the asked-for behavior; tests verify real functionality.
SubtypeLegitimate Solution
EvidenceTrial passed with reward=1.0. Verifier output shows: 'COMPONENT CONFORMANCE SUITE: 201/201 cases passed (all milestones pass: True)' and 'FINAL METRIC: PGD robust accuracy = 0.66 >= threshold 0.6'. The agent successfully implemented a complete numpy-only deep learning framework including reverse-mode autodiff with correct backward passes for all operations (conv2d with col2im adjoint, pooling, normalization, recurrent cells, 10 adversarial training losses), multiple optimizers, and an attack zoo, then trained a CNN to reach the robust accuracy threshold. The verifier re-graded the agent's train.py locally after an OOM kill in the e2b sandbox, confirming the solution is correct.
Root causeThe agent correctly solved an exceptionally hard task requiring implementation of a complete deep learning framework from scratch with proper handling of complex backward passes, adversarial training, and robustness metrics.
RecommendationN/A - task is fine
Trajectory
Tool-by-tool agent trajectory
115 tool calls · 4 tool types · 133 steps
Agent session started, model: claude-opus-4-8 · 29 tools available · cwd: /app
I'll start by exploring the structure of the task and the stub files.

List app structure

shell
ls -la && echo "---SUBMISSION---" && ls -la submission/ && echo "---DATA---" && ls -la data/
output
total 101
drwxr-xr-x  4 user user  4096 Jun 30 22:24 .
drwxr-xr-x 21 root root  4096 Jun 30 22:24 ..
-rw-r--r--  1 user user 14238 Jun 30 22:24 PROMPT.txt
-rw-r--r--  1 user user     5 Jun 30 22:24 agent.pid
-rw-r--r--  1 user user     0 Jun 30 22:24 agent.stderr
-rw-r--r--  1 user user  4517 Jun 30 22:24 agent_trajectory.jsonl
drwxr-xr-x  2 user user    60 Jun 30 22:24 data
-rw-r--r--  1 user user     0 Jun 30 22:24 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 22:24 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 22:24 .
drwxr-xr-x 4 user user  4096 Jun 30 22:24 ..
-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 22:24 .
drwxr-xr-x 4 user user    4096 Jun 30 22:24 ..
-rw-r--r-- 1 user user 2967336 Jun 30 11:01 train.npz

View run_checks and test.sh

shell
cat run_checks.py | head -50 && echo "===TEST.SH===" && cat test.sh
output
#!/usr/bin/env python3
"""PUBLIC component check runner for the from-scratch numpy framework + the adversarial machinery.

Run this AS OFTEN AS YOU LIKE while you implement to get a per-check progress signal:

    python3 /app/run_checks.py                       # check the framework in /app/submission
    python3 /app/run_checks.py --submission /some/dir
    python3 /app/run_checks.py --only attack         # focus a single milestone

It imports YOUR modules from /app/submission (autograd.py / nn.py / optim.py / data.py / attack.py
/ advtrain.py), exactly as the grader does, and runs a PUBLIC portion of the component conformance
suite, printing a clear per-check report. Each FAIL line names the exact op / layer / optimizer /
attack / adversarial-training property that is wrong.

HOW THIS RELATES TO YOUR GRADE
------------------------------
This runner is a DEBUGGING AID, not the grader. It checks YOUR framework against self-consistent
references it computes on its OWN:
  * autograd_grads : central FINITE-DIFFERENCE of YOUR forward vs YOUR analytic backward (every op,
                     incl. the convolution input-gradient, the pools, and the full SmallCNN -- both
                     grad-wrt-PARAMS and grad-wrt-INPUT, the attack gradient).
  * nn_forward     : mathematical PROPERTIES your forward must satisfy (relu/softmax/conv arithmetic
                     /SmallCNN shape).
  * optim          : YOUR optimizer step vs the closed-form update re-derived here in numpy, for

…[truncated 2310 chars]
Let me read all the stub files to understand the contracts.

/app/submission/autograd.py

contents
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]
Let me read the rest of the stub files.

/app/submission/nn.py

contents
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

contents
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/data.py

contents
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/attack.py

contents
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

contents
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/train.py

contents
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]
Now let me examine the run_checks.py to understand exact test expectations, and inspect the data.

Inspect data shapes

shell
python3 -c "
import numpy as np
d=np.load('data/train.npz')
print('X',d['X'].shape,d['X'].dtype,d['X'].min(),d['X'].max())
print('y',d['y'].shape,d['y'].dtype, np.bincount(d['y']))
print('classes',len(set(d['y'].tolist())))
"
output
X (3600, 1, 16, 16) float32 0.0 1.0
y (3600,) int64 [593 592 629 596 599 591]
classes 6

Find detach usage in checks

shell
grep -n "detach\|stop_grad\|stopgrad\|\.detach" run_checks.py | head -40
output
976:    # TRADES stop-gradient: agent TRADES grad must equal a hand-built detached-target reference.
977:    def trades_stopgrad():
990:    prop("trades_stopgrad", trades_stopgrad)
1009:    # CLP stop-gradient (pairing target detached): agent CLP grad equals a detached-target reference.
1010:    def clp_stopgrad():
1025:    prop("clp_stopgrad", clp_stopgrad)
1027:    # KL-AT stop-gradient + adv-as-p argument order: agent grad equals the detached-target reference.
1028:    def kl_at_stopgrad():
1041:    prop("kl_at_stopgrad", kl_at_stopgrad)

/app/run_checks.py

contents
900	    prop("input_grad_fd", input_grad_fd)
901	    prop("cw_loss_input_grad_fd", cw_input_grad_fd)
902	    prop("dlr_loss_grad", dlr_loss_grad)
903	    prop("dlr_loss_input_grad_fd", dlr_input_grad_fd)
904	    prop("fgsm_step", fgsm_step)
905	    prop("pgd_projection", lambda: proj("pgd", lambda m, x, y, e: atk.pgd_attack(m, x, y, e, 10, 0.03, rng=np.random.default_rng(sb + 22)), 0.1))
906	    prop("mi_fgsm_projection", lambda: proj("mi", lambda m, x, y, e: atk.mi_fgsm(m, x, y, e, 10, 0.03, mu=1.0, rng=np.random.default_rng(sb + 23)), 0.1))
907	    prop("cw_pgd_projection", lambda: proj("cw", lambda m, x, y, e: atk.cw_pgd(m, x, y, e, 10, 0.03, rng=np.random.default_rng(sb + 24)), 0.1))
908	    prop("pgd_l2_projection", lambda: proj("l2", lambda m, x, y, e: atk.pgd_l2(m, x, y, e, 10, 0.2, rng=np.random.default_rng(sb + 25)), 1.0, l2=True))
909	    prop("bim_projection", lambda: proj("bim", lambda m, x, y, e: atk.bim(m, x, y, e, 10, 0.03), 0.1))
910	    prop("rfgsm_projection", lambda: proj("rfgsm", lambda m, x, y, e: atk.rfgsm(m, x, y, e, rng=np.random.default_rng(sb + 26)), 0.1))
911	    prop("ni_fgsm_projection", lambda: proj("ni", lambda m, x, y, e: atk.ni_fgsm(m, x, y, e, 10, 0.03, mu=1.0, rng=np.random.default_rng(sb + 27)), 0.1))
912	    prop("pgd_restarts_projection", lambda: proj("restarts", lambda m, x, y, e: atk.pgd_restarts(m, x, y, e, 10, 0.03, restarts=3, rng=np.random.default_rng(sb + 28)), 0.1))
913	    prop("dlr_pgd_projection", lambda: proj("dlrpgd", lambda 
…[truncated 7635 chars]

/app/run_checks.py

contents
55	
56	
57	def _load_module(name, path, inject=None):
58	    saved = {}
59	    if inject:
60	        for k, v in inject.items():
61	            saved[k] = sys.modules.get(k)
62	            sys.modules[k] = v
63	    try:
64	        spec = importlib.util.spec_from_file_location(name, str(path))
65	        mod = importlib.util.module_from_spec(spec)
66	        spec.loader.exec_module(mod)
67	        return mod
68	    finally:
69	        if inject:
70	            for k, old in saved.items():
71	                if old is None:
72	                    sys.modules.pop(k, None)
73	                else:
74	                    sys.modules[k] = old
75	
76	
77	def load_framework(root):
78	    ag = _load_module("agent_autograd", root / "autograd.py")
79	    nn = _load_module("agent_nn", root / "nn.py", inject={"autograd": ag})
80	    opt = _load_module("agent_optim", root / "optim.py", inject={"autograd": ag})
81	    data = _load_module("agent_data", root / "data.py")
82	    atk = _load_module("agent_attack", root / "attack.py", inject={"autograd": ag})
83	    at = _load_module("agent_advtrain", root / "advtrain.py", inject={"autograd": ag})
84	    return {"autograd": ag, "nn": nn, "optim": opt, "data": data, "attack": atk, "advtrain": at}
85	
86	
87	def _num_grad_input(forward, x, eps=EPS):
88	    g = np.zeros_like(x)
89	    flat = x.reshape(-1)
90	    gf = g.reshape(-1)
91	    for i in range(flat.size):
92	        old = flat[i]
93	        flat[i] = old + eps
94	        fp = float(np.sum(
…[truncated 3679 chars]

/app/run_checks.py

contents
175	    case("min_axis_keep", lambda t: ag.min(t, axis=0, keepdims=True), (4, 5), 21)
176	    case("transpose", lambda t: ag.transpose(t), (4, 5), 22)
177	    case("transpose_axes", lambda t: ag.transpose(t, (0, 2, 1)), (2, 4, 5), 23)
178	    case("reshape", lambda t: ag.reshape(t, (5, 4)), (4, 5), 24)
179	    case("getitem_row", lambda t: t[1], (4, 5), 25)
180	    case("getitem_slice", lambda t: t[:, 1:4], (4, 5), 26)
181	    case("concat", lambda t: ag.concat([t, ag.mul(t, 2.0)], axis=1), (4, 5), 27)
182	    case("stack", lambda t: ag.stack([t, ag.mul(t, 2.0)], axis=0), (4, 5), 28)
183	    case("matmul", lambda t: ag.matmul(t, T(np.ones((5, 3)))), (4, 5), 29)
184	    case("matmul_batched", lambda t: ag.matmul(t, T(np.ones((2, 5, 3)))), (2, 4, 5), 30)
185	    case("relu", lambda t: ag.relu(t), (4, 5), 31)
186	    case("leaky_relu", lambda t: ag.leaky_relu(t, 0.05), (4, 5), 32)
187	    case("sigmoid", lambda t: ag.sigmoid(t), (4, 5), 33)
188	    case("tanh", lambda t: ag.tanh(t), (4, 5), 34)
189	    case("gelu", lambda t: ag.gelu(t), (4, 5), 35)
190	    case("softmax", lambda t: ag.mul(ag.softmax(t, -1), T(np.arange(5.0) + 1.0)), (4, 5), 36)
191	    case("log_softmax", lambda t: ag.mul(ag.log_softmax(t, -1), T(np.arange(5.0) + 1.0)), (4, 5), 37)
192	
193	    # extra nonlinearities
194	    case("softplus", lambda t: ag.softplus(t), (4, 5), 200)
195	    case("silu", lambda t: ag.silu(t), (4, 5), 201)
196	    case("mish", lambda t: ag.mish(t), (4, 5), 202)
197	    case("elu", la
…[truncated 8612 chars]

/app/run_checks.py

contents
334	        X = np.random.default_rng(sb + seed + 1).standard_normal((3, 1, 8, 8)); tgt = np.array([0, 1, 2])
335	
336	        def loss_now():
337	            return ag.cross_entropy(m(X), tgt)
338	        for p in m.params():
339	            p.grad = None
340	        loss_now().backward()
341	        p = m.named_params()[key]
342	        gnum = _param_grad_fd(loss_now, p)
343	        results.append((f"grad/{name}", _max_abs(p.grad, gnum) < TOL_GRAD, f"max_abs={_max_abs(p.grad, gnum):.2e}"))
344	    guard("cnn_grad_fc2.bias", lambda: cnn_param_grad("cnn_grad_fc2.bias", "fc2.bias", 70))
345	    guard("cnn_grad_conv1.weight", lambda: cnn_param_grad("cnn_grad_conv1.weight", "conv1.weight", 71))
346	    guard("cnn_grad_conv2.weight", lambda: cnn_param_grad("cnn_grad_conv2.weight", "conv2.weight", 72))
347	    guard("cnn_grad_fc1.weight", lambda: cnn_param_grad("cnn_grad_fc1.weight", "fc1.weight", 73))
348	
349	    def cnn_input_grad():
350	        m = nn.SmallCNN(n_classes=4, c_in=1, img=8, c1=6, c2=8, ff=16, seed=sb + 80)
351	        x0 = np.random.default_rng(sb + 81).standard_normal((3, 1, 8, 8)); tgt = np.array([0, 1, 2])
352	        xt = T(x0.copy(), requires_grad=True)
353	        ag.cross_entropy(m(xt), tgt).backward()
354	        gnum = _num_grad_input(lambda xv: np.array(float(ag.cross_entropy(m(T(xv)), tgt).data)), x0.copy())
355	        results.append(("grad/cnn_input_grad", _max_abs(xt.grad, gnum) < TOL_GRAD, f"max_abs={_max_abs(xt.grad, gnum):.2e}"))
356	    guard("c
…[truncated 5861 chars]

/app/run_checks.py

contents
473	        logp = x - np.log(np.exp(x - x.max(-1, keepdims=True)).sum(-1, keepdims=True)) - x.max(-1, keepdims=True)
474	        ref = float(-logp[np.arange(5), tgt].mean())
475	        return abs(float(ag.cross_entropy(T(x), tgt).data) - ref) < 1e-6, ""
476	
477	    def kl_nonneg():
478	        x = np.random.default_rng(sb + 8).standard_normal((4, 5))
479	        return float(ag.kl_div(T(x), T(x)).data) < 1e-9, "KL(p||p)==0"
480	
481	    def layernorm_stats():
482	        x = np.random.default_rng(sb + 9).standard_normal((4, 6))
483	        y = ag.layernorm(T(x), T(np.ones(6)), T(np.zeros(6))).data
484	        return abs(y.mean(-1)).max() < 1e-9 and abs(y.std(-1) - 1.0).max() < 1e-4, ""
485	
486	    def conv_arith():
487	        # a known 1-filter average-ish conv must match a direct numpy correlation on a tiny case.
488	        x = np.random.default_rng(sb + 10).standard_normal((2, 1, 5, 5))
489	        W = np.random.default_rng(sb + 11).standard_normal((1, 1, 3, 3)); b = np.zeros(1)
490	        out = ag.conv2d(T(x), T(W), T(b), pad=1).data
491	        xp = np.pad(x, ((0, 0), (0, 0), (1, 1), (1, 1)))
492	        ref = np.zeros((2, 1, 5, 5))
493	        for oh in range(5):
494	            for ow in range(5):
495	                ref[:, 0, oh, ow] = (xp[:, 0, oh:oh + 3, ow:ow + 3] * W[0, 0]).sum(axis=(1, 2))
496	        return _max_abs(out, ref) < 1e-6, ""
497	
498	    def maxpool_def():
499	        x = np.random.default_rng(sb + 12).standard_normal((2, 2, 4, 4))
500	        
…[truncated 6028 chars]

/app/run_checks.py

contents
633	            return p - lr * g / (np.sqrt(s) + eps)
634	        return f
635	
636	    def rmsprop_step(lr, alpha, eps, wd):
637	        def f(i, p, g, t, st):
638	            sk = ("s", i)
639	            s = st.get(sk, np.zeros_like(p))
640	            g = g + wd * p
641	            s = alpha * s + (1 - alpha) * (g * g)
642	            st[sk] = s
643	            return p - lr * g / (np.sqrt(s) + eps)
644	        return f
645	
646	    def adam_step(lr, b1, b2, eps, wd):
647	        def f(i, p, g, t, st):
648	            mk_, vk = ("m", i), ("v", i)
649	            m = st.get(mk_, np.zeros_like(p)); v = st.get(vk, np.zeros_like(p))
650	            g = g + wd * p
651	            m = b1 * m + (1 - b1) * g; v = b2 * v + (1 - b2) * (g * g)
652	            st[mk_], st[vk] = m, v
653	            return p - lr * (m / (1 - b1 ** t)) / (np.sqrt(v / (1 - b2 ** t)) + eps)
654	        return f
655	
656	    def adamw_step(lr, b1, b2, eps, wd, no_decay_ndim=1):
657	        def f(i, p, g, t, st):
658	            mk_, vk = ("m", i), ("v", i)
659	            m = st.get(mk_, np.zeros_like(p)); v = st.get(vk, np.zeros_like(p))
660	            m = b1 * m + (1 - b1) * g; v = b2 * v + (1 - b2) * (g * g)
661	            st[mk_], st[vk] = m, v
662	            wd_eff = 0.0 if p.ndim <= no_decay_ndim else wd
663	            return p - lr * ((m / (1 - b1 ** t)) / (np.sqrt(v / (1 - b2 ** t)) + eps) + wd_eff * p)
664	        return f
665	
666	    def adamax_step(lr, b1, b2, eps, wd):
667	        def f(
…[truncated 7660 chars]

/app/run_checks.py

contents
813	        except Exception as e:  # noqa: BLE001
814	            results.append((f"attack/{name}", False, f"raised {type(e).__name__}: {e}"))
815	
816	    def _model(seed):
817	        return nn.SmallCNN(n_classes=4, c_in=1, img=8, c1=6, c2=8, ff=16, seed=sb + seed)
818	
819	    def input_grad_fd():
820	        m = _model(11)
821	        x0 = np.random.default_rng(sb + 12).standard_normal((3, 1, 8, 8)); y = np.array([0, 1, 2])
822	        gan = np.asarray(atk.input_grad(m, x0, y), np.float64)
823	        gnum = _num_grad_input(lambda xv: np.array(float(ag.cross_entropy(m(ag.Tensor(xv)), y).data)), x0.copy())
824	        return _max_abs(gan, gnum) < TOL_GRAD, f"max_abs={_max_abs(gan, gnum):.2e}"
825	
826	    def cw_input_grad_fd():
827	        m = _model(13)
828	        x0 = np.random.default_rng(sb + 14).standard_normal((3, 1, 8, 8)); y = np.array([0, 1, 2])
829	        lf = lambda lg, t: atk.cw_margin_loss(lg, t, 0.0)
830	        gan = np.asarray(atk.loss_input_grad(m, x0, y, lf), np.float64)
831	        gnum = _num_grad_input(lambda xv: np.array(float(lf(m(ag.Tensor(xv)), y).data)), x0.copy())
832	        return _max_abs(gan, gnum) < TOL_GRAD, f"max_abs={_max_abs(gan, gnum):.2e}"
833	
834	    def fgsm_step():
835	        m = _model(16)
836	        x = np.clip(np.random.default_rng(sb + 17).random((4, 1, 8, 8)), 0, 1); y = np.array([0, 1, 2, 3]); eps = 0.1
837	        xadv = np.asarray(atk.fgsm(m, x, y, eps), np.float64)
838	        g = np.asarray(atk.input_grad(m, x, y), 
…[truncated 3900 chars]

/app/run_checks.py

contents
1059	
1060	    def run(name, loss_kind):
1061	        try:
1062	            m = nn.SmallCNN(n_classes=4, c_in=1, img=8, c1=6, c2=8, ff=16, seed=sb + 5)
1063	            o = opt.AdamW(m.params(), lr=5e-3, betas=(0.9, 0.99), weight_decay=0.0)
1064	            losses = []
1065	            bs = 8
1066	            for step in range(8):
1067	                s = (step * bs) % 24
1068	                idx = np.arange(s, s + bs)
1069	                xb, yb = X[idx], y[idx]
1070	                xadv = atk.pgd_attack(m, xb, yb, 0.1, 4, 0.03, rng=np.random.default_rng(sb + 1000 + step))
1071	                loss = at.pgd_at_loss(m, xadv, yb) if loss_kind == "at" else at.trades_loss(m, xb, xadv, yb, beta=4.0)[0]
1072	                o.zero_grad(); loss.backward(); opt.clip_grad_norm(m.params(), 5.0); o.step()
1073	                losses.append(float(loss.data))
1074	            finite = all(math.isfinite(x) for x in losses)
1075	            results.append((f"integration/{name}_finite", finite, f"{losses[0]:.3f}->{losses[-1]:.3f}"))
1076	            results.append((f"integration/{name}_decreases", finite and losses[-1] < losses[0] - 1e-3, f"{losses[0]:.4f}->{losses[-1]:.4f}"))
1077	        except Exception as e:  # noqa: BLE001
1078	            results.append((f"integration/{name}_finite", False, f"raised {type(e).__name__}: {e}"))
1079	            results.append((f"integration/{name}_decreases", False, "n/a"))
1080	
1081	    run("pgd_at", "at")
1082	    run("trades", "trades")
1083	
1084	 
…[truncated 4161 chars]

Find tolerance constants

shell
grep -n "TOL_GRAD\|TOL_PROP\|TOL_OPT\|EPS =\|PUBLIC_SEED_BASE" run_checks.py | head
output
50:TOL_GRAD = 2e-3
51:TOL_PROP = 1e-6
52:TOL_OPT = 1e-7
53:EPS = 1e-5
54:PUBLIC_SEED_BASE = 1000
145:            results.append((f"grad/{name}", err < TOL_GRAD, f"max_abs={err:.2e}"))
220:        results.append(("grad/groupnorm_x", _max_abs(lt.grad, gnum) < TOL_GRAD, ""))
229:        results.append(("grad/rmsnorm_x", _max_abs(lt.grad, gnum) < TOL_GRAD, ""))
238:        results.append(("grad/layernorm_x", _max_abs(lt.grad, gnum) < TOL_GRAD, ""))
247:        results.append(("grad/batchnorm_x", _max_abs(lt.grad, gnum) < TOL_GRAD, ""))
I now have a thorough understanding. Let me write the autograd engine first.
write · /app/submission/autograd.py
"""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)


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, 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 _expand_grad(grad, shape, axis, keepdims):
    grad = np.asarray(grad, np.float64)
    if axis is None:
        return np.broadcast_to(grad, shape).astype(np.float64).copy()
    axes = axis if isinstance(axis, tuple) else (axis,)
    axes = tuple(ax % len(shape) for ax in axes)
    if not keepdims:
        for ax in sorted(axes):
            grad = np.expand_dims(grad, ax)
    return np.broadcast_to(grad, shape).astype(np.float64).copy()


def _reduce_n(shape, axis):
    if axis is None:
        n = 1
        for s in shape:
            n *= s
        return n
    axes = axis if isinstance(axis, tuple) else (axis,)
    n = 1
    for ax in axes:
        n *= shape[ax]
    return n


class Tensor:
    __slots__ = ("data", "grad", "requires_grad", "_backward", "_parents")

    def __init__(self, data, requires_grad: bool = False, _parents: Iterable["Tensor"] = ()):
        self
…[truncated 25849 chars]

contents
The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
Now nn.py forwards:

/app/submission/nn.py

− removed
    def forward(self, x: Tensor) -> Tensor:
        """ag.conv2d(x, weight, bias, pad)."""
        raise NotImplementedError("implement Conv2d.forward")
+ added
    def forward(self, x: Tensor) -> Tensor:
        """ag.conv2d(x, weight, bias, pad)."""
        return ag.conv2d(x, self.weight, self.bias, self.pad)

contents
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

− removed
    def forward(self, x: Tensor) -> Tensor:
        """ag.maxpool2d(x, k)."""
        raise NotImplementedError("implement MaxPool2d.forward")
+ added
    def forward(self, x: Tensor) -> Tensor:
        """ag.maxpool2d(x, k)."""
        return ag.maxpool2d(x, self.k)

contents
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

− removed
    def forward(self, x: Tensor) -> Tensor:
        """ag.avgpool2d(x, k)."""
        raise NotImplementedError("implement AvgPool2d.forward")
+ added
    def forward(self, x: Tensor) -> Tensor:
        """ag.avgpool2d(x, k)."""
        return ag.avgpool2d(x, self.k)

contents
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

− removed
    def forward(self, x: Tensor) -> Tensor:
        """ag.matmul(x, weight) + bias."""
        raise NotImplementedError("implement Linear.forward")
+ added
    def forward(self, x: Tensor) -> Tensor:
        """ag.matmul(x, weight) + bias."""
        return ag.add(ag.matmul(x, self.weight), self.bias)

contents
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

− removed
    def forward(self, x: Tensor) -> Tensor:
        """ag.layernorm(x, weight, bias, eps)."""
        raise NotImplementedError("implement LayerNorm.forward")
+ added
    def forward(self, x: Tensor) -> Tensor:
        """ag.layernorm(x, weight, bias, eps)."""
        return ag.layernorm(x, self.weight, self.bias, self.eps)

contents
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

− removed
    def forward(self, x: Tensor) -> Tensor:
        """ag.batchnorm(x, weight, bias, eps)."""
        raise NotImplementedError("implement BatchNorm1d.forward")
+ added
    def forward(self, x: Tensor) -> Tensor:
        """ag.batchnorm(x, weight, bias, eps)."""
        return ag.batchnorm(x, self.weight, self.bias, self.eps)

contents
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

− removed
    def forward(self, x):
        """linear -> relu (between layers) -> ... -> linear (no final activation)."""
        raise NotImplementedError("implement MLP.forward")
+ added
    def forward(self, x):
        """linear -> relu (between layers) -> ... -> linear (no final activation)."""
        if not isinstance(x, Tensor):
            x = Tensor(x)
        for i, lin in enumerate(self.layers):
            x = lin(x)
            if i < len(self.layers) - 1:
                x = ag.relu(x)
        return x

contents
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

− removed
    def forward(self, x):
        """for each conv: pool(relu(conv(x))); then flatten -> relu(fc1) -> fc2."""
        raise NotImplementedError("implement ConvNet.forward")
+ added
    def forward(self, x):
        """for each conv: pool(relu(conv(x))); then flatten -> relu(fc1) -> fc2."""
        if not isinstance(x, Tensor):
            x = Tensor(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)

contents
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

− removed
    def forward(self, x: Tensor) -> Tensor:
        """ag.groupnorm(x, weight, bias, num_groups, eps)."""
        raise NotImplementedError("implement GroupNorm.forward")
+ added
    def forward(self, x: Tensor) -> Tensor:
        """ag.groupnorm(x, weight, bias, num_groups, eps)."""
        return ag.groupnorm(x, self.weight, self.bias, self.num_groups, self.eps)

contents
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

− removed
    def forward(self, x: Tensor) -> Tensor:
        """ag.rmsnorm(x, weight, eps)."""
        raise NotImplementedError("implement RMSNorm.forward")
+ added
    def forward(self, x: Tensor) -> Tensor:
        """ag.rmsnorm(x, weight, eps)."""
        return ag.rmsnorm(x, self.weight, self.eps)

contents
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

− removed
    def forward(self, x: Tensor, h: Tensor) -> Tensor:
        """h' = tanh(x @ weight_ih^T + bias_ih + h @ weight_hh^T + bias_hh)."""
        raise NotImplementedError("implement RNNCell.forward")
+ added
    def forward(self, x: Tensor, h: Tensor) -> Tensor:
        """h' = tanh(x @ weight_ih^T + bias_ih + h @ weight_hh^T + bias_hh)."""
        if not isinstance(x, Tensor):
            x = Tensor(x)
        if not isinstance(h, Tensor):
            h = Tensor(h)
        pre = 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))
        return ag.tanh(pre)

contents
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

− removed
    def forward(self, x: Tensor, h: Tensor) -> Tensor:
        """the GRU update above (slice the 3 gates out of the (N,3H) pre-activations)."""
        raise NotImplementedError("implement GRUCell.forward")
+ added
    def forward(self, x: Tensor, h: Tensor) -> Tensor:
        """the GRU update above (slice the 3 gates out of the (N,3H) pre-activations)."""
        if not isinstance(x, Tensor):
            x = Tensor(x)
        if not isinstance(h, Tensor):
            h = Tensor(h)
        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)
        r = ag.sigmoid(ag.add(gi[:, 0:H], gh[:, 0:H]))
        z = ag.sigmoid(ag.add(gi[:, H:2 * H], gh[:, H:2 * H]))
        n = ag.tanh(ag.add(gi[:, 2 * H:3 * H], ag.mul(r, gh[:, 2 * H:3 * H])))
        one = Tensor(np.ones((1, H)))
        return ag.add(ag.mul(ag.sub(one, z), n), ag.mul(z, h))

contents
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

− removed
    def forward(self, x: Tensor, h: Tensor, c: Tensor):
        """the LSTM update above; return (h', c')."""
        raise NotImplementedError("implement LSTMCell.forward")
+ added
    def forward(self, x: Tensor, h: Tensor, c: Tensor):
        """the LSTM update above; return (h', c')."""
        if not isinstance(x, Tensor):
            x = Tensor(x)
        if not isinstance(h, Tensor):
            h = Tensor(h)
        if not isinstance(c, Tensor):
            c = Tensor(c)
        H = self.hidden_size
        pre = 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(pre[:, 0:H])
        f = ag.sigmoid(pre[:, H:2 * H])
        g = ag.tanh(pre[:, 2 * H:3 * H])
        o = ag.sigmoid(pre[:, 3 * H:4 * H])
        c2 = ag.add(ag.mul(f, c), ag.mul(i, g))
        h2 = ag.mul(o, ag.tanh(c2))
        return h2, c2

contents
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

− removed
    def forward(self, x):
        """h = pool(relu(conv1(x))); res = conv2b(relu(conv2a(h))); h = relu(h + res); flatten -> fc."""
        raise NotImplementedError("implement SmallResCNN.forward")
+ added
    def forward(self, x):
        """h = pool(relu(conv1(x))); res = conv2b(relu(conv2a(h))); h = relu(h + res); flatten -> fc."""
        if not isinstance(x, Tensor):
            x = Tensor(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)

contents
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

− removed
    def forward(self, x) -> Tensor:
        """conv1 -> relu -> pool -> conv2 -> relu -> pool -> flatten -> fc1 -> relu -> fc2."""
        raise NotImplementedError("implement SmallCNN.forward")
+ added
    def forward(self, x) -> Tensor:
        """conv1 -> relu -> pool -> conv2 -> relu -> pool -> flatten -> fc1 -> relu -> fc2."""
        if not isinstance(x, Tensor):
            x = Tensor(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)

contents
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
Now data.py:

/app/submission/data.py

− removed
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")
+ added
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 = len(X)
    perm = rng.permutation(n)
    n_val = int(round(n * val_frac))
    val_idx = perm[:n_val]
    tr_idx = perm[n_val:]
    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 = len(X)
    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 = len(X)
    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((len(y), n_classes), dtype=np.float64)
    oh[np.arange(len(y)), y] = 1.0
    return oh


def clip01(X: np.ndarray) -> np.ndarray:
    """Clamp X into the [0,1] pixel
…[truncated 68 chars]

contents
The file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
Now optim.py:

/app/submission/optim.py

− removed
    def step(self):
        raise NotImplementedError("implement SGD.step")
+ added
    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._v[i] = self.momentum * self._v[i] + g
            v = self._v[i]
            step = (g + self.momentum * v) if self.nesterov else v
            p.data -= self.lr * step

contents
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

− removed
    def step(self):
        raise NotImplementedError("implement Adagrad.step")
+ added
    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)

contents
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

− removed
    def step(self):
        raise NotImplementedError("implement RMSprop.step")
+ added
    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)

contents
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

− removed
    def step(self):
        raise NotImplementedError("implement Adam.step")
+ added
    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)

contents
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

− removed
    def step(self):
        raise NotImplementedError("implement AdamW.step")
+ added
    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.ndim <= self.no_decay_ndim else self.weight_decay
            p.data -= self.lr * (mhat / (np.sqrt(vhat) + self.eps) + wd_eff * p.data)

contents
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

− removed
    def step(self):
        raise NotImplementedError("implement Adamax.step")
+ added
    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)

contents
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

− removed
    def step(self):
        raise NotImplementedError("implement NAdam.step")
+ added
    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)

contents
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

− removed
    def step(self):
        raise NotImplementedError("implement Lion.step")
+ added
    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) * g

contents
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

− removed
    def step(self):
        raise NotImplementedError("implement Adadelta.step")
+ added
    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
            self._d[i] = self.rho * self._d[i] + (1 - self.rho) * (delta * delta)
            p.data -= self.lr * delta

contents
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

− removed
    If total > max_norm, scale
    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")
+ added
    If total > max_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


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)

contents
<tool_use_error>String to replace not found in file.
String:     If total > max_norm, scale
    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")</tool_use_error>

/app/submission/optim.py

− removed
    raise NotImplementedError("implement clip_grad_norm")
+ added
    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

contents
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

− removed
    raise NotImplementedError("implement clip_grad_value")
+ added
    for p in params:
        if p.grad is not None:
            p.grad = np.clip(p.grad, -clip_value, clip_value)

contents
The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
Now the schedulers:

/app/submission/optim.py

− removed
    raise NotImplementedError("implement cosine_lr")
+ added
    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))

contents
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

− removed
    raise NotImplementedError("implement warmup_lr")
+ added
    return base_lr * (step + 1) / warmup if step < warmup else base_lr

contents
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

− removed
    raise NotImplementedError("implement step_lr")
+ added
    return base_lr * (gamma ** (step // step_size))

contents
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

− removed
    raise NotImplementedError("implement exponential_lr")
+ added
    return base_lr * (gamma ** step)

contents
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

− removed
    raise NotImplementedError("implement polynomial_lr")
+ added
    prog = min(1.0, max(0.0, step / total))
    return (base_lr - min_lr) * ((1.0 - prog) ** power) + min_lr

contents
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

− removed
    raise NotImplementedError("implement linear_lr")
+ added
    prog = min(1.0, max(0.0, step / total))
    return base_lr + (min_lr - base_lr) * prog

contents
The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
Let me run the checks so far for the non-attack milestones:

Run autograd grad checks

shell
python3 run_checks.py --only autograd_grads 2>&1 | tail -40
output
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=1.09e-09)
  grad/conv2d_weight                       OK    (max_abs=3.38e-09)
  grad/conv2d_bias                         OK  
  grad/conv2d_pad0_input                   OK    (max_abs=7.79e-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=1.21e-11)
  grad/cnn_grad_conv1.weight               OK    (max_abs=2.55e-11)
  grad/cnn_grad_conv2.weight               OK    (max_abs=2.61e-11)
  grad/cnn_grad_fc1.weight                 OK    (ma
…[truncated 815 chars]
All autograd grad checks pass. Let me run nn_forward, optim, and data:

Run nn_forward optim data checks

shell
for ms in nn_forward optim data; do echo "=== $ms ==="; python3 run_checks.py --only $ms 2>&1 | tail -3; done
output
=== nn_forward ===
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.
=== optim ===
optim 20/20
TOTAL  20/20 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 3/3
TOTAL  3/3 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.
Now attack.py:

/app/submission/attack.py

− removed
def loss_input_grad(model, x: np.ndarray, y: np.ndarray,
                    loss_fn: Callable[[Tensor, np.ndarray], Tensor]) -> np.ndarray:
    """d/dx of a scalar attack loss ``loss_fn(logits, y)`` w.r.t. the input image (ndarray). Wrap x
    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")


def cw_margin_loss(logits, y, kappa: float = 0.0) -> Tensor:
    """Untargeted CW margin loss (mean over batch). For each example margin = z_y - max_{j!=y} z_j;
    return mean(max(-margin, -kappa)). Ascending it drives the true logit below the best other.
    Build it with autograd ops so the input-gradient flows; returns a scalar Tensor."""
    raise NotImplementedError("implement cw_margin_loss")


def dlr_loss(logits, y, eps: float = 1e-12) -> Tensor:
    """Difference-of-Logits-Ratio loss (smooth, fully-differentiable variant). With true class y:
        DLR_i = -(z_y - max_{j!=y} z_j) / (max_j z_j - mean_j z_j + eps); return the batch mean.
    Build the WHOLE expression (numerator AND the spread normalizer) from autograd ops (max/mean) so
    it differentiates exactly and the input-gradient flows; returns a scalar Tensor."""
    raise NotImplementedError("implement dlr_loss")



…[truncated 4907 chars]
+ added
_BIG = 1e9


def loss_input_grad(model, x: np.ndarray, y: np.ndarray,
                    loss_fn: Callable[[Tensor, np.ndarray], Tensor]) -> np.ndarray:
    xt = Tensor(np.asarray(x, np.float64), requires_grad=True)
    logits = model(xt)
    loss = loss_fn(logits, y)
    loss.backward()
    return np.asarray(xt.grad, np.float64)


def input_grad(model, x: np.ndarray, y: np.ndarray) -> np.ndarray:
    return loss_input_grad(model, x, y, lambda lg, t: ag.cross_entropy(lg, t))


def cw_margin_loss(logits, y, kappa: float = 0.0) -> Tensor:
    logits = logits if isinstance(logits, Tensor) else Tensor(logits)
    n, c = logits.shape
    y = np.asarray(y).astype(int)
    oh = np.zeros((n, c), dtype=np.float64)
    oh[np.arange(n), y] = 1.0
    z_y = ag.sum(ag.mul(logits, Tensor(oh)), axis=1)
    other = ag.sub(logits, Tensor(oh * _BIG))
    max_other = ag.max(other, axis=1)
    margin = ag.sub(z_y, max_other)
    neg = ag.mul(margin, -1.0)
    return ag.mean(ag.maximum(neg, Tensor(np.full(n, -kappa))))


def dlr_loss(logits, y, eps: float = 1e-12) -> Tensor:
    logits = logits if isinstance(logits, Tensor) else Tensor(logits)
    n, c = logits.shape
    y = np.asarray(y).astype(int)
    oh = np.zeros((n, c), dtype=np.float64)
    oh[np.arange(n), y] = 1.0
    z_y = ag.sum(ag.mul(logits, Tensor(oh)), axis=1)
    other = ag.sub(logits, Tensor(oh * _BIG))
    max_other = ag.max(other, axis=1)
    max_all = ag.max(logits, axis=1)
    mean_all = ag.mean(logits, axis=1)
    num = ag.s
…[truncated 7521 chars]

contents
The file /app/submission/attack.py has been updated successfully. (file state is current in your context , no need to Read it back)

Run attack checks

shell
python3 run_checks.py --only attack 2>&1 | tail -25
output
[attack]  18/18
  attack/input_grad_fd                     OK    (max_abs=2.73e-11)
  attack/cw_loss_input_grad_fd             OK    (max_abs=4.13e-12)
  attack/dlr_loss_grad                     OK    (max_abs=1.04e-09)
  attack/dlr_loss_input_grad_fd            OK    (max_abs=3.51e-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.
All attack checks pass. Now advtrain.py:

/app/submission/advtrain.py

− removed
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 1782 chars]
+ added
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(x_adv), 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))."""
    clean = model(x_clean)
    adv = model(x_adv)
    ce = ag.cross_entropy(clean, y)
    tgt = _detach(clean)
    kl = ag.kl_div(tgt, adv)
    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."""
    clean = model(x_clean)
    adv = model(x_adv)
    n, c = adv.shape
    y = np.asarray(y).astype(int)
    oh = np.zeros((n, c), dtype=np.float64)
    oh[np.arange(n), y] = 1.0
    # boosted CE on adv = CE(adv,y) - mean log(1 - max_{j!=y} p_adv_j)
    ce = ag.cross_entropy(adv, y)
    p_adv = ag.softmax(adv, axis=1)
    other = ag.sub(p_adv, Tensor(oh * 1e9))
    max_other = ag.max(other, axis=1)
   
…[truncated 4305 chars]

contents
The file /app/submission/advtrain.py has been updated successfully. (file state is current in your context , no need to Read it back)

Run advtrain and integration checks

shell
python3 run_checks.py --only advtrain 2>&1 | tail -20; echo "=== integration ==="; python3 run_checks.py --only integration 2>&1 | tail -6
output
[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=5.12e-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=2.14e-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/trades_decreases             OK    (1.3933->1.3367)

------------------------------------------------------------------------
integration 4/4
TOTAL  4/4 public checks passing
All public checks pass. Now make sure model.npz cle
…[truncated 114 chars]
All milestones pass. Let me run the full suite:

Run full public check suite

shell
python3 run_checks.py 2>&1 | tail -8
output
data/get_batch_shape                     OK    ((16, 1, 8, 8),(16,))
  data/train_val_split                     OK    (30/10)
  data/to_onehot                           OK  

------------------------------------------------------------------------
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.
All 163 public checks pass. Now let me implement the training loop:

/app/submission/train.py

− removed
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")
+ added
def _robust_acc(model, X, y, eps, steps, alpha, rng, batch=256):
    correct = 0
    n = len(X)
    for s in range(0, n, batch):
        xb = X[s:s + batch]
        yb = y[s:s + batch]
        # clean correctness
        clean_pred = model(xb).data.argmax(1)
        xadv = A.pgd_attack(model, xb, yb, eps, steps, alpha, rng=rng)
        adv_pred = model(xadv).data.argmax(1)
        ok = (clean_pred == yb) & (adv_pred == yb)
        correct += int(ok.sum())
    return correct / n


def train(train_path, out_path, epochs=20, batch_size=128, lr=1.2e-3, eps=0.12,
          at_steps=10, alpha=0.035, 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)

    model = SmallCNN(n_classes=n_classes, c_in=c_in, img=img, c1=C1, c2=C2, ff=FF, seed=seed)
    opt = O.AdamW(model.params(), lr=lr, betas=(0.9, 0.999), weight_decay=weight_decay,
                  no_decay_ndim=1)

    total_steps = epochs * max(1, len(Xtr) // batch_size)
    step = 0
    best_robust = -1.0
    best_sd = model.state_dict()
    cfg = {"n_classes": n_classes, "c_in": c_in, "img": img, "c1": C1, "c2": C2, "ff": FF
…[truncated 1253 chars]

contents
The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Let me do a quick timing test with 1 epoch:

Time one epoch

shell
time python3 submission/train.py --epochs 1 --out /tmp/test_model.npz 2>&1 | tail -5
output
real	0m1.389s
user	0m1.367s
sys	0m0.441s

Run one epoch with output

shell
python3 submission/train.py --epochs 1 --out /tmp/test_model.npz 2>&1 | tail -10
output
(Bash completed with no output)

Run one epoch unbuffered

shell
python3 -u submission/train.py --epochs 1 --out /tmp/test_model.npz; echo "EXIT=$?"
output
/bin/bash: line 1:  1539 Killed                  python3 -u submission/train.py --epochs 1 --out /tmp/test_model.npz
EXIT=137

Check memory

shell
free -m; echo "---"; nproc
output
total        used        free      shared  buff/cache   available
Mem:             481         254         179           1          61         227
Swap:              0           0           0
---
2
Memory is very tight (227MB). Let me reduce batch sizes and make eval memory-light:

/app/submission/train.py

− removed
def _robust_acc(model, X, y, eps, steps, alpha, rng, batch=256):
    correct = 0
    n = len(X)
    for s in range(0, n, batch):
        xb = X[s:s + batch]
        yb = y[s:s + batch]
        # clean correctness
        clean_pred = model(xb).data.argmax(1)
        xadv = A.pgd_attack(model, xb, yb, eps, steps, alpha, rng=rng)
        adv_pred = model(xadv).data.argmax(1)
        ok = (clean_pred == yb) & (adv_pred == yb)
        correct += int(ok.sum())
    return correct / n
+ added
def _predict(model, X, batch=64):
    preds = []
    for s in range(0, len(X), batch):
        preds.append(model(X[s:s + batch]).data.argmax(1))
    return np.concatenate(preds)


def _robust_acc(model, X, y, eps, steps, alpha, rng, batch=64):
    correct = 0
    n = len(X)
    for s in range(0, n, batch):
        xb = X[s:s + batch]
        yb = y[s:s + batch]
        clean_pred = model(xb).data.argmax(1)
        xadv = A.pgd_attack(model, xb, yb, eps, steps, alpha, rng=rng)
        adv_pred = model(xadv).data.argmax(1)
        ok = (clean_pred == yb) & (adv_pred == yb)
        correct += int(ok.sum())
    return correct / n

contents
The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)

/app/submission/train.py

− removed
def train(train_path, out_path, epochs=20, batch_size=128, lr=1.2e-3, eps=0.12,
          at_steps=10, alpha=0.035, grad_clip=1.0, weight_decay=5e-4, val_frac=0.12,
          sel_steps=10, seed=0, log_every=1):
+ added
def train(train_path, out_path, epochs=20, batch_size=64, lr=1.0e-3, eps=0.12,
          at_steps=10, alpha=0.035, grad_clip=1.0, weight_decay=5e-4, val_frac=0.12,
          sel_steps=10, seed=0, log_every=1):

contents
The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)

/app/submission/train.py

− removed
        ra = _robust_acc(model, Xval, yval, eps, sel_steps, alpha,
                         np.random.default_rng(12345 + epoch))
        if ra >= best_robust:
            best_robust = ra
            best_sd = model.state_dict()
        if log_every and (epoch % log_every == 0):
            ca = float((model(Xval).data.argmax(1) == yval).mean())
+ added
        nsel = builtins.min(len(Xval), 384)
        ra = _robust_acc(model, Xval[:nsel], yval[:nsel], eps, sel_steps, alpha,
                         np.random.default_rng(12345 + epoch))
        if ra >= best_robust:
            best_robust = ra
            best_sd = model.state_dict()
        if log_every and (epoch % log_every == 0):
            ca = float((_predict(model, Xval[:nsel]) == yval[:nsel]).mean())

contents
The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)

/app/submission/train.py

− removed
import argparse
import json
import os
import sys

import numpy as np
+ added
import argparse
import builtins
import json
import os
import sys

import numpy as np

contents
The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)

Run one epoch with memory tracking

shell
/usr/bin/time -v python3 -u submission/train.py --epochs 1 --out /tmp/test_model.npz 2>&1 | grep -E "epoch|saved|Maximum resident|Killed|Error" | tail -10
output
(Bash completed with no output)

Run one epoch direct

shell
python3 -u submission/train.py --epochs 1 --out /tmp/test_model.npz; echo "EXIT=$?"
Final diff , what the agent changed

No file changes, the agent left the workspace untouched.

Verifier output , the proof behind the reward
tests/test.sh
=== 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 = 0.66  >= threshold 0.6

REWARD = 1   (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_7df0dc0bcb534e53. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.

Trial trial_7df0dc0bcb534e53 · verifier authoritative; classifier explanatory.