tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.List app structure
ls -la && echo "---SUBMISSION---" && ls -la submission/ && echo "---DATA---" && ls -la data/
total 89 drwxr-xr-x 4 user user 4096 Jun 30 22:48 . drwxr-xr-x 21 root root 4096 Jun 30 22:47 .. -rw-r--r-- 1 user user 14914 Jun 30 22:48 PROMPT.txt -rw-r--r-- 1 user user 5 Jun 30 22:48 agent.pid -rw-r--r-- 1 user user 0 Jun 30 22:48 agent.stderr -rw-r--r-- 1 user user 4552 Jun 30 22:48 agent_trajectory.jsonl drwxr-xr-x 2 user user 60 Jun 30 22:48 data -rw-r--r-- 1 user user 0 Jun 30 22:48 run.log -rwxr-xr-x 1 user user 45855 Jun 30 11:01 run_checks.py drwxr-xr-x 3 user user 128 Jun 30 22:48 submission -rwxr-xr-x 1 user user 1318 Jun 30 11:01 test.sh -rwxr-xr-x 1 user user 629 Jun 30 11:01 timer.sh ---SUBMISSION--- total 100 drwxr-xr-x 3 user user 128 Jun 30 22:48 . drwxr-xr-x 4 user user 4096 Jun 30 22:48 .. drwxr-xr-x 2 user user 4096 Jun 30 22:48 __pycache__ -rw-r--r-- 1 user user 48121 Jun 30 11:01 autograd.py -rw-r--r-- 1 user user 1692 Jun 30 11:01 data.py -rw-r--r-- 1 user user 23705 Jun 30 11:01 nn.py -rw-r--r-- 1 user user 12275 Jun 30 11:01 optim.py -rw-r--r-- 1 user user 2900 Jun 30 11:01 train.py ---DATA--- total 11108 drwxr-xr-x 2 user user 60 Jun 30 22:48 . drwxr-xr-x 4 user user 4096 Jun 30 22:48 .. -rw-r--r-- 1 user user 3953364 Jun 30 11:01 reweight.npz -rw-r--r-- 1 user user 7411924 Jun 30 11:01 train.npz
Count lines in files
wc -l submission/*.py run_checks.py test.sh 2>/dev/null
1306 submission/autograd.py
44 submission/data.py
543 submission/nn.py
272 submission/optim.py
65 submission/train.py
937 run_checks.py
24 test.sh
3191 total/app/submission/autograd.py
1 """Reverse-mode automatic differentiation over numpy arrays. 2 3 YOU MUST IMPLEMENT EVERYTHING MARKED ``# TODO`` / ``raise NotImplementedError`` using numpy 4 ONLY. No torch/jax/tensorflow/autograd/etc. The grader's conformance suite differentiates each 5 op below with central finite differences and compares against YOUR analytic backward, so every 6 backward must be correct (a single wrong reduction / Jacobian / group-weighting fails the checks). 7 8 A ``Tensor`` wraps one ``numpy.ndarray`` (stored as ``float64`` in ``self.data``) and records 9 the op that produced it so ``.backward()`` can accumulate ``.grad`` into every leaf. 10 11 CONTRACT (the grader builds its own reference against this exact API): 12 * ``Tensor(data, requires_grad=False)`` -- ``self.data`` is ``float64``; ``self.grad`` is None 13 until backward, then a same-shape ``float64`` array. requires_grad leaves accumulate grad. 14 * Every op returns a NEW Tensor whose ``requires_grad`` is the OR of its differentiable inputs, 15 and whose ``_backward`` closure does ``input._accum(local_grad)`` (so multiple paths add up). 16 * ``t.backward(grad=None)`` -- seeds ``grad`` (all-ones for a scalar) and walks the graph in 17 reverse topological order, calling each node's ``_backward``. Repeated backward accumulates. 18 * BROADCASTING: binary ops broadcast like numpy; the backward MUST reduce (sum) the upstream 19 gradient back to each input's ORIGINAL shape (use the provided ``_unbroadcast`` helper). 20 * …[truncated 52049 chars]
/app/submission/nn.py
1 """Neural-network layers + models + the robustness machinery, built on YOUR autograd engine. 2 3 Implement every ``# TODO`` forward (and the robustness-helper bodies). The parameters + 4 ``named_params`` naming + the checkpoint round-trip are already wired; you compose the autograd 5 ops. The grader checks each layer's forward against its OWN reference AND finite-difference-checks 6 the gradients that flow through your autograd, so the composition must be exactly right. 7 8 THE FINAL-METRIC MODEL is the pinned ``SmallCNN`` (a conv FEATURE EXTRACTOR + a single linear 9 CLASSIFIER). The split is what makes last-layer retraining (DFR) on a group-balanced set 10 expressible against the pinned graph: freeze the extractor and re-fit ONLY ``classifier``. 11 ``featurize(X, training)`` returns the post-ReLU embedding so you can freeze the extractor. 12 13 The other layers/models (Linear / Conv2d / BatchNorm2d / BatchNorm1d / LayerNorm / Dropout / 14 AvgPool2d / MLP / ResidualMLP / a TinyConvNet) are exercised by the conformance suite across a 15 WIDE surface -- each is an independent potential bug. The robustness machinery (``GroupDROState`` 16 exponentiated-gradient adversary-weight update, ``balanced_group_weights``, 17 ``class_balanced_weights``, ``log_class_prior``) is the domain-specific heart this task adds; the 18 ``robust`` milestone checks them. 19 20 SmallCNN naming contract (do not change -- the verifier indexes by these exact keys): 21 conv1.weight (Wd,3,3,3) | con …[truncated 24273 chars]
/app/submission/optim.py
1 """Optimizers + LR schedulers + grad clipping over a flat list of autograd ``Tensor`` parameters. 2 3 Implement the ``# TODO`` update math. All optimizers operate IN PLACE on ``param.data`` using 4 ``param.grad``. ``zero_grad()`` resets every grad to None. ``t`` is the 1-based step count. The 5 grader runs one (and several) steps of each and compares your updated params against its 6 reference within a tight tolerance, so the math must be EXACTLY right -- every optimizer / 7 scheduler / clipper is an independent potential bug. 8 9 The training recipe uses AdamW (decoupled weight decay) + ``clip_grad_norm`` for the ERM 10 extractor and the last-layer (DFR) retrain; the rest of the zoo is graded for parity. 11 12 SGD : g = grad + wd*p; v = mu*v + g; upd = (g + mu*v) if nesterov else v; p -= lr*upd 13 Adagrad : g = grad + wd*p; s += g^2; p -= lr*g/(sqrt(s)+eps) 14 RMSprop : g = grad + wd*p; s = alpha*s + (1-alpha)*g^2; p -= lr*g/(sqrt(s)+eps) 15 Adam : g = grad + wd*p (COUPLED L2); m,v EMA; mhat=m/(1-b1^t); vhat=v/(1-b2^t); 16 p -= lr*mhat/(sqrt(vhat)+eps) 17 AdamW : DECOUPLED wd; m,v EMA; p -= lr*( mhat/(sqrt(vhat)+eps) + wd*p ) 18 Adamax : m=b1*m+(1-b1)*g; u=max(b2*u, |g|); p -= (lr/(1-b1^t))*m/(u+eps) (infinity-norm) 19 NAdam : m,v EMA; mhat=m/(1-b1^t); vhat=v/(1-b2^t); 20 mnes = b1*mhat + (1-b1)*g/(1-b1^t); p -= lr*mnes/(sqrt(vhat)+eps) 21 Lion : upd = sign(b1*m + (1-b1)*g); p -= lr*(upd + wd*p); m = b2*m + (1-b2 …[truncated 11759 chars]
/app/submission/data.py
1 """Data loading + group helpers for the worst-group / spurious-correlation task.
2
3 Implement the ``# TODO`` bodies (numpy only). The splits ship as ``.npz`` files with arrays
4 ``X`` (N,3,S,S) float, ``y`` (the SHAPE class to predict) and ``g`` (the spurious background-color
5 id). A "group" is the pair ``(y, g)``; ``group_ids(y, g, n_classes) = y*n_classes + g`` flattens it
6 to a single id in ``[0, n_classes**2)``.
7 """
8 from __future__ import annotations
9
10 from typing import Dict, Tuple
11
12 import numpy as np
13
14
15 def load_npz(path: str) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
16 """Load (X, y, g) from a .npz; return float64 X, int64 y, int64 g. (Provided.)"""
17 d = np.load(path)
18 return (np.asarray(d["X"], dtype=np.float64),
19 np.asarray(d["y"], dtype=np.int64),
20 np.asarray(d["g"], dtype=np.int64))
21
22
23 def group_ids(y: np.ndarray, g: np.ndarray, n_classes: int) -> np.ndarray:
24 """Flatten (y, g) to a single group id y*n_classes + g (int64)."""
25 # TODO
26 raise NotImplementedError("group_ids")
27
28
29 def iterate(X, y, g, batch_size, rng):
30 """Yield (Xb, yb, gb) minibatches over a random permutation of the dataset."""
31 # TODO
32 raise NotImplementedError("iterate")
33
34
35 def worst_group_acc(preds, y, g, n_classes) -> Tuple[float, float, Dict[int, float]]:
36 """Return (min over (y,g) groups of within-group accuracy, average accuracy, per-group dict)."""
37
…[truncated 318 chars]/app/submission/train.py
1 """Training entry point. Implement your worst-group-robust recipe, train the pinned SmallCNN,
2 and save the checkpoint the grader will load.
3
4 Run it however you like (e.g. ``python3 /app/submission/train.py``). It MUST end by writing
5 ``/app/submission/model.npz`` via ``save_checkpoint`` below. The grader loads that file into its
6 OWN reference SmallCNN (architecture pinned to WIDTH/EMBED_DIM below -- keep these) and evaluates
7 WORST-GROUP accuracy on a SEALED, group-balanced test set; you pass the metric part iff
8 worst-group accuracy >= the frozen threshold. (You ALSO must pass the component conformance suite
9 over autograd.py / nn.py / optim.py.)
10
11 A naive cross-entropy ("ERM") run on the correlated train split will reach high AVERAGE accuracy
12 but ~0 worst-group accuracy -- it keys on the spurious background color. Removing that reliance is
13 the task. One well-known cheap recipe: train the feature extractor on the correlated data, then
14 RE-FIT ONLY the final ``classifier`` on the GROUP-BALANCED reweight split with a group-balanced
15 (group-weighted) loss so the head stops keying off color. You are free to use any method, as long
16 as you submit a state_dict for the pinned architecture.
17 """
18 from __future__ import annotations
19
20 import argparse
21 import json
22 import os
23 import sys
24
25 import numpy as np
26
27 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
28 sys.path.insert(0, "/app")
29 sys.path.insert(0, "/app/su
…[truncated 1589 chars]/app/run_checks.py
1 #!/usr/bin/env python3 2 """PUBLIC component check runner for the from-scratch numpy framework (worst-group / spurious- 3 correlation task). 4 5 Run this AS OFTEN AS YOU LIKE while you implement to get a per-check progress signal: 6 7 python3 /app/run_checks.py # check the framework in /app/submission 8 python3 /app/run_checks.py --submission /dir 9 python3 /app/run_checks.py --only robust # focus a single milestone 10 11 It imports YOUR modules from /app/submission (autograd.py / nn.py / optim.py / data.py), exactly 12 as the grader does, and runs a PUBLIC portion of the component conformance suite, printing a clear 13 per-check report. Each FAIL line names the exact op / layer / optimizer / robustness-loss that is 14 wrong. 15 16 HOW THIS RELATES TO YOUR GRADE 17 ------------------------------ 18 This runner is a DEBUGGING AID, not the grader. It checks your framework against self-consistent 19 references it computes on its own: 20 * autograd_grads : central FINITE-DIFFERENCE of YOUR forward vs YOUR analytic backward. 21 * nn_forward : mathematical PROPERTIES your forward must satisfy. 22 * optim : YOUR optimizer step vs the closed-form update re-derived here in numpy, for 23 every optimizer + the LR schedulers + both grad clippers. 24 * robust : finite-diff of YOUR robustness-loss backward + the per-group-normalization / 25 Group-DRO / weight-helper PROPERTI …[truncated 47999 chars]
Inspect test.sh and data
cat test.sh && echo "---" && python3 -c "import numpy; print(numpy.__version__)" && 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 vals',np.unique(d['y']),'g vals',np.unique(d['g'])); import collections; print('counts', collections.Counter(zip(d['y'].tolist(),d['g'].tolist())))" 2>&1 | head -40#!/usr/bin/env bash
# Smoke test (NOT the grader): confirms the environment is wired -- numpy imports, the train +
# reweight splits load, 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
for f in ("train.npz", "reweight.npz"):
assert os.path.exists(f"/app/data/{f}"), f"missing /app/data/{f}"
d = np.load("/app/data/train.npz"); r = np.load("/app/data/reweight.npz")
print(f"train X {d['X'].shape} y {d['y'].shape} g {d['g'].shape}; reweight X {r['X'].shape}")
assert r["X"].shape[1:] == d["X"].shape[1:], (r["X"].shape, d["X"].shape)
for f in ("autograd.py", "nn.py", "optim.py", "data.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"
print("submission stubs present:", sorted(os.listdir("/app/submission")))
print("numpy", np.__version__)
PY
# The visible check runner is present + runnable (it exits 1 on the unfilled stub, which is
# expected -- the stubs raise NotImplementedError until you implement them).
python3 /app/run_checks.py --quiet || true
echo "app_test smoke OK (run 'python3 /app/run_checks.py' to test your implementation)"
---
2.2.6
{'X': ((2400, 3, 16, 16), dtype('float32')), 'y': ((2400,), dtype('int64')), 'g': ((2400,), dtype('int64'))}
y vals [0 1 2 3] g vals [0 1 2 3]
counts Counter({(2, 2): 586,
…[truncated 173 chars]Inspect reweight data
python3 -c "import numpy as np, collections; d=np.load('data/reweight.npz'); print({k:v.shape for k,v in d.items()}); print('counts',sorted(collections.Counter(zip(d['y'].tolist(),d['g'].tolist())).items()))"{'X': (1280, 3, 16, 16), 'y': (1280,), 'g': (1280,)}
counts [((0, 0), 80), ((0, 1), 80), ((0, 2), 80), ((0, 3), 80), ((1, 0), 80), ((1, 1), 80), ((1, 2), 80), ((1, 3), 80), ((2, 0), 80), ((2, 1), 80), ((2, 2), 80), ((2, 3), 80), ((3, 0), 80), ((3, 1), 80), ((3, 2), 80), ((3, 3), 80)]/app/submission/autograd.py
class Tensor:
__slots__ = ("data", "grad", "requires_grad", "_backward", "_parents")def _expand(g: np.ndarray, in_shape: tuple, axis, keepdims: bool) -> np.ndarray:
"""Broadcast a reduced gradient ``g`` back to ``in_shape`` (inverse of a sum/mean reduction)."""
g = _as_f64(g)
if axis is None:
return np.ones(in_shape, dtype=np.float64) * g
axes = (axis,) if isinstance(axis, int) else tuple(axis)
axes = tuple(ax % len(in_shape) for ax in axes)
if not keepdims:
shp = list(in_shape)
for ax in axes:
shp[ax] = 1
g = g.reshape(shp)
return np.ones(in_shape, dtype=np.float64) * g
class Tensor:
__slots__ = ("data", "grad", "requires_grad", "_backward", "_parents")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/autograd.py
raise NotImplementedError("Tensor.backward") topo: List["Tensor"] = []
visited = set()
stack = [(self, False)]
while stack:
node, processed = stack.pop()
if processed:
topo.append(node)
continue
if id(node) in visited:
continue
visited.add(id(node))
stack.append((node, True))
for p in node._parents:
if id(p) not in visited:
stack.append((p, False))
if grad is None:
grad = np.ones_like(self.data)
self._accum(_as_f64(grad))
for node in reversed(topo):
node._backward()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/autograd.py
raise NotImplementedError("add backward") # TODO g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(g, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(g, b.data.shape))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/autograd.py
raise NotImplementedError("sub backward") # TODO g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(g, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(-g, b.data.shape))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/autograd.py
raise NotImplementedError("mul backward") # TODO g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(g * b.data, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(g * a.data, b.data.shape))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/autograd.py
raise NotImplementedError("div backward") # TODO g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(g / b.data, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(-g * a.data / (b.data * b.data), b.data.shape))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/autograd.py
raise NotImplementedError("power backward") # TODO if a.requires_grad:
a._accum(out.grad * pf * a.data ** (pf - 1.0))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/autograd.py
raise NotImplementedError("maximum backward") # TODO g = out.grad
am = (a.data > b.data).astype(np.float64)
bm = (b.data > a.data).astype(np.float64)
eq = (a.data == b.data).astype(np.float64) * 0.5
if a.requires_grad:
a._accum(_unbroadcast(g * (am + eq), a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(g * (bm + eq), b.data.shape))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/autograd.py
raise NotImplementedError("minimum backward") # TODO g = out.grad
am = (a.data < b.data).astype(np.float64)
bm = (b.data < a.data).astype(np.float64)
eq = (a.data == b.data).astype(np.float64) * 0.5
if a.requires_grad:
a._accum(_unbroadcast(g * (am + eq), a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(g * (bm + eq), b.data.shape))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/autograd.py
raise NotImplementedError("where backward") # TODO g = out.grad
cf = c.astype(np.float64)
if a.requires_grad:
a._accum(_unbroadcast(g * cf, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(g * (1.0 - cf), b.data.shape))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/autograd.py
raise NotImplementedError("exp backward") # TODO if a.requires_grad:
a._accum(out.grad * e)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/autograd.py
raise NotImplementedError("log backward") # TODO if a.requires_grad:
a._accum(out.grad / a.data)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/autograd.py
raise NotImplementedError("sqrt backward") # TODO if a.requires_grad:
a._accum(out.grad * 0.5 / s)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/autograd.py
raise NotImplementedError("abs backward") # TODO if a.requires_grad:
a._accum(out.grad * np.sign(a.data))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/autograd.py
raise NotImplementedError("clip backward") # TODO if a.requires_grad:
mask = (a.data > lo) & (a.data < hi)
a._accum(out.grad * mask)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/autograd.py
raise NotImplementedError("sum backward") # TODO if a.requires_grad:
a._accum(_expand(out.grad, a.data.shape, axis, keepdims))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/autograd.py
raise NotImplementedError("mean backward") # TODO if a.requires_grad:
a._accum(_expand(out.grad, a.data.shape, axis, keepdims) / n)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/autograd.py
raise NotImplementedError("max backward") # TODO if a.requires_grad:
mask = (a.data == m).astype(np.float64)
counts = mask.sum(axis=axis, keepdims=True)
gb = _expand(out.grad, a.data.shape, axis, keepdims)
a._accum(mask * gb / counts)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/autograd.py
raise NotImplementedError("min backward") # TODO if a.requires_grad:
mask = (a.data == m).astype(np.float64)
counts = mask.sum(axis=axis, keepdims=True)
gb = _expand(out.grad, a.data.shape, axis, keepdims)
a._accum(mask * gb / counts)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/autograd.py
raise NotImplementedError("transpose backward") # TODO if a.requires_grad:
if axes is None:
a._accum(np.transpose(out.grad))
else:
inv = np.argsort(axes)
a._accum(np.transpose(out.grad, inv))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/autograd.py
raise NotImplementedError("reshape backward") # TODO if a.requires_grad:
a._accum(out.grad.reshape(a.data.shape))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/autograd.py
raise NotImplementedError("getitem backward") # TODO if a.requires_grad:
ga = np.zeros_like(a.data)
np.add.at(ga, idx, out.grad)
a._accum(ga)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/autograd.py
raise NotImplementedError("concat backward") # TODO g = out.grad
start = 0
for t, sz in zip(ts, sizes):
sl = [slice(None)] * g.ndim
sl[axis] = slice(start, start + sz)
if t.requires_grad:
t._accum(g[tuple(sl)])
start += szThe file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
raise NotImplementedError("stack backward") # TODO g = out.grad
for i, t in enumerate(ts):
if t.requires_grad:
t._accum(np.take(g, i, axis=axis))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/autograd.py
raise NotImplementedError("matmul backward") # TODO g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(g @ np.swapaxes(b.data, -1, -2), a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(np.swapaxes(a.data, -1, -2) @ g, b.data.shape))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/autograd.py
raise NotImplementedError("relu backward") # TODO if a.requires_grad:
a._accum(out.grad * (a.data > 0.0))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/autograd.py
raise NotImplementedError("leaky_relu backward") # TODO if a.requires_grad:
a._accum(out.grad * np.where(a.data > 0.0, 1.0, sl))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/autograd.py
raise NotImplementedError("sigmoid backward") # TODO if a.requires_grad:
a._accum(out.grad * s * (1.0 - s))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/autograd.py
raise NotImplementedError("tanh backward") # TODO if a.requires_grad:
a._accum(out.grad * (1.0 - t * t))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/autograd.py
raise NotImplementedError("gelu backward") # TODO if a.requires_grad:
pdf = np.exp(-0.5 * x * x) / np.sqrt(2.0 * np.pi)
a._accum(out.grad * (cdf + x * pdf))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/autograd.py
raise NotImplementedError("softmax backward") # TODO if a.requires_grad:
g = out.grad
tmp = (g * s).sum(axis=axis, keepdims=True)
a._accum(s * (g - tmp))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/autograd.py
raise NotImplementedError("log_softmax backward") # TODO if a.requires_grad:
g = out.grad
a._accum(g - s * g.sum(axis=axis, keepdims=True))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/autograd.py
raise NotImplementedError("softplus backward") # TODO if a.requires_grad:
sig = 1.0 / (1.0 + np.exp(-bx))
a._accum(out.grad * sig)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/autograd.py
raise NotImplementedError("silu backward") # TODO if a.requires_grad:
a._accum(out.grad * (sig + a.data * sig * (1.0 - sig)))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/autograd.py
raise NotImplementedError("elu backward") # TODO if a.requires_grad:
a._accum(out.grad * np.where(x > 0.0, 1.0, al * ex))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/autograd.py
raise NotImplementedError("mish backward") # TODO if a.requires_grad:
sig = 1.0 / (1.0 + np.exp(-x))
a._accum(out.grad * (t + x * (1.0 - t * t) * sig))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/autograd.py
raise NotImplementedError("hardtanh backward") # TODO if a.requires_grad:
mask = (a.data > lo) & (a.data < hi)
a._accum(out.grad * mask)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/autograd.py
raise NotImplementedError("var backward") # TODO if a.requires_grad:
gb = _expand(out.grad, a.data.shape, axis, keepdims)
a._accum(gb * 2.0 * (a.data - mu) / (n - ddof))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/autograd.py
raise NotImplementedError("std backward") # TODO if a.requires_grad:
gb = _expand(out.grad, a.data.shape, axis, keepdims)
a._accum(gb * (a.data - mu) / (n * skeep))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/autograd.py
raise NotImplementedError("cumsum backward") # TODO if a.requires_grad:
g = out.grad
gr = np.flip(np.cumsum(np.flip(g, axis=axis), axis=axis), axis=axis)
a._accum(gr)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/autograd.py
raise NotImplementedError("mse_loss backward") # TODO if pred.requires_grad:
pred._accum(2.0 * diff / n * out.grad)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/autograd.py
raise NotImplementedError("cross_entropy backward") # TODO if logits.requires_grad:
y = np.zeros_like(sm)
y[np.arange(n), t] = 1.0
logits._accum((sm - y) / n * out.grad)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/autograd.py
raise NotImplementedError("group_weighted_ce backward") # TODO if logits.requires_grad:
y = np.zeros_like(sm)
y[np.arange(n), t] = 1.0
logits._accum((sm - y) * scale[:, None] * out.grad)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/autograd.py
raise NotImplementedError("reweighted_ce backward") # TODO if logits.requires_grad:
y = np.zeros_like(sm)
y[np.arange(n), t] = 1.0
logits._accum((sm - y) * (w / wsum)[:, None] * out.grad)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/autograd.py
raise NotImplementedError("group_dro_loss backward") # TODO if logits.requires_grad:
y = np.zeros_like(sm)
y[np.arange(n), t] = 1.0
logits._accum((sm - y) * scale[:, None] * out.grad)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/autograd.py
raise NotImplementedError("logit_adjusted_ce backward") # TODO if logits.requires_grad:
y = np.zeros_like(sm)
y[np.arange(n), t] = 1.0
logits._accum((sm - y) / n * out.grad)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/autograd.py
raise NotImplementedError("focal_loss backward") # TODO if logits.requires_grad:
y = np.zeros_like(sm)
y[np.arange(n), t] = 1.0
dfl_dp = g * (1.0 - p) ** (g - 1.0) * np.log(pc) - (1.0 - p) ** g / pc
coef = dfl_dp * p
logits._accum(coef[:, None] * (y - sm) / n * out.grad)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/autograd.py
raise NotImplementedError("irm_penalty backward") # TODO if logits.requires_grad:
sx = (sm * x).sum(axis=-1, keepdims=True)
inner = (sm - y) + sm * (x - sx)
logits._accum(2.0 * grad_w * (1.0 / n) * inner * out.grad)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/autograd.py
raise NotImplementedError("gce_loss backward") # TODO if logits.requires_grad:
y = np.zeros_like(sm)
y[np.arange(n), t] = 1.0
coef = -(pc ** qf)
logits._accum(coef[:, None] * (y - sm) / n * out.grad)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/autograd.py
raise NotImplementedError("vrex_penalty backward") # TODO if r.requires_grad:
r._accum((2.0 / K) * (r.data - mu) * out.grad)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/autograd.py
raise NotImplementedError("ldam_loss backward") # TODO if logits.requires_grad:
y = np.zeros_like(sm)
y[np.arange(n), t] = 1.0
logits._accum(sc * (sm - y) / n * out.grad)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/autograd.py
raise NotImplementedError("spectral_decoupling backward") # TODO if logits.requires_grad:
logits._accum(lm * x / n * out.grad)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/autograd.py
raise NotImplementedError("layernorm backward") # TODO g = out.grad
if gamma.requires_grad:
gamma._accum(_unbroadcast(g * xhat, gamma.data.shape))
if beta.requires_grad:
beta._accum(_unbroadcast(g, beta.data.shape))
if a.requires_grad:
gy = g * gamma.data
mgy = gy.mean(axis=-1, keepdims=True)
mgyx = (gy * xhat).mean(axis=-1, keepdims=True)
a._accum(inv * (gy - mgy - xhat * mgyx))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/autograd.py
raise NotImplementedError("batchnorm backward") # TODO g = out.grad
if gamma.requires_grad:
gamma._accum(_unbroadcast(g * xhat, gamma.data.shape))
if beta.requires_grad:
beta._accum(_unbroadcast(g, beta.data.shape))
if a.requires_grad:
gy = g * gamma.data
mgy = gy.mean(axis=0, keepdims=True)
mgyx = (gy * xhat).mean(axis=0, keepdims=True)
a._accum(inv * (gy - mgy - xhat * mgyx))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/autograd.py
raise NotImplementedError("batchnorm2d backward") # TODO g = out.grad
gC = gamma.data.reshape(1, C, 1, 1)
if gamma.requires_grad:
gamma._accum((g * xhat).sum(axis=(0, 2, 3)))
if beta.requires_grad:
beta._accum(g.sum(axis=(0, 2, 3)))
if x.requires_grad:
gy = g * gC
if training:
mgy = gy.mean(axis=(0, 2, 3), keepdims=True)
mgyx = (gy * xhat).mean(axis=(0, 2, 3), keepdims=True)
x._accum(inv * (gy - mgy - xhat * mgyx))
else:
x._accum(gy * inv)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/autograd.py
raise NotImplementedError("rms_norm backward") # TODO g = out.grad
if gamma.requires_grad:
gamma._accum(_unbroadcast(g * xhat, gamma.data.shape))
if a.requires_grad:
gy = g * gamma.data
s = (gy * x).sum(axis=-1, keepdims=True)
a._accum(r * gy - (r ** 3 / D) * x * s)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/autograd.py
raise NotImplementedError("groupnorm2d backward") # TODO g = out.grad
gC = gamma.data.reshape(1, C, 1, 1)
if gamma.requires_grad:
gamma._accum((g * xhat).sum(axis=(0, 2, 3)))
if beta.requires_grad:
beta._accum(g.sum(axis=(0, 2, 3)))
if x.requires_grad:
gy = (g * gC).reshape(N, G, m)
xhg = xhat.reshape(N, G, m)
mgy = gy.mean(axis=2, keepdims=True)
mgyx = (gy * xhg).mean(axis=2, keepdims=True)
dxg = inv * (gy - mgy - xhg * mgyx)
x._accum(dxg.reshape(N, C, H, W))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/autograd.py
raise NotImplementedError("conv2d backward") # TODO g = out.grad.reshape(N, Cout, OH * OW)
if weight.requires_grad:
dWm = np.einsum("nop,nkp->ok", g, cols)
weight._accum(dWm.reshape(Cout, Cin, kh, kw))
if bias.requires_grad:
bias._accum(out.grad.sum(axis=(0, 2, 3)))
if x.requires_grad:
dcols = np.einsum("ok,nop->nkp", Wm, g)
dcols = dcols.reshape(N, Cin, kh, kw, OH, OW)
H, W = x.data.shape[2], x.data.shape[3]
Hp, Wp = H + 2 * pad, W + 2 * pad
dxp = np.zeros((N, Cin, Hp, Wp), dtype=np.float64)
for i in range(kh):
for j in range(kw):
dxp[:, :, i:i + st * OH:st, j:j + st * OW:st] += dcols[:, :, i, j]
dx = dxp[:, :, pad:pad + H, pad:pad + W] if pad > 0 else dxp
x._accum(dx)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/autograd.py
raise NotImplementedError("maxpool2d backward") # TODO if x.requires_grad:
Hk, Wk = H // k, W // k
xr2 = xd.reshape(N, C, Hk, k, Wk, k)
m = outd.reshape(N, C, Hk, 1, Wk, 1)
mask = (xr2 == m).astype(np.float64)
counts = mask.sum(axis=(3, 5), keepdims=True)
gb = out.grad.reshape(N, C, Hk, 1, Wk, 1)
dxr = mask * gb / counts
x._accum(dxr.reshape(N, C, H, W))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/autograd.py
raise NotImplementedError("maxpool2d_stride backward") # TODO if x.requires_grad:
m = outd.reshape(N, C, 1, 1, OH, OW)
mask = (win == m).astype(np.float64)
counts = mask.sum(axis=(2, 3), keepdims=True)
gb = out.grad.reshape(N, C, 1, 1, OH, OW)
contrib = mask * gb / counts
dxp = np.zeros_like(xp)
for i in range(k):
for j in range(k):
dxp[:, :, i:i + st * OH:st, j:j + st * OW:st] += contrib[:, :, i, j]
dx = dxp[:, :, pad:pad + H, pad:pad + W] if pad else dxp
x._accum(dx)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/autograd.py
raise NotImplementedError("avgpool2d backward") # TODO if x.requires_grad:
Hk, Wk = H // k, W // k
gb = out.grad.reshape(N, C, Hk, 1, Wk, 1) / (k * k)
dxr = np.ones((N, C, Hk, k, Wk, k), dtype=np.float64) * gb
x._accum(dxr.reshape(N, C, H, W))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/autograd.py
raise NotImplementedError("global_avg_pool backward") # TODO if x.requires_grad:
gb = out.grad.reshape(N, C, 1, 1) / (H * W)
x._accum(np.ones((N, C, H, W), dtype=np.float64) * gb)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/autograd.py
raise NotImplementedError("embedding backward") # TODO if weight.requires_grad:
gw = np.zeros_like(weight.data)
np.add.at(gw, idx, out.grad)
weight._accum(gw)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/autograd.py
def _bw_id():
raise NotImplementedError("dropout backward") # TODO
out._backward = _bw_id def _bw_id():
if a.requires_grad:
a._accum(out.grad)
out._backward = _bw_idThe file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
raise NotImplementedError("dropout backward") # TODO if a.requires_grad:
a._accum(out.grad * keep)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
Check remaining stubs in autograd
cd /app/submission && grep -n "NotImplementedError\|# TODO" autograd.py
3:YOU MUST IMPLEMENT EVERYTHING MARKED ``# TODO`` / ``raise NotImplementedError`` using numpy
/app/submission/optim.py
# TODO: implement the SGD update (see module docstring). Update p.data in place.
raise NotImplementedError("SGD.step") 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
upd = (g + self.momentum * v) if self.nesterov else v
p.data -= self.lr * updThe 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
# TODO: implement the Adagrad update (see module docstring). Update p.data in place.
raise NotImplementedError("Adagrad.step") 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._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
# TODO: implement the RMSprop update (see module docstring). Update p.data in place.
raise NotImplementedError("RMSprop.step") 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
# TODO: implement the Adam update (see module docstring). Update p.data in place.
raise NotImplementedError("Adam.step") self.t += 1
t = self.t
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 ** t)
vhat = self._v[i] / (1 - self.b2 ** 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
# TODO: implement the AdamW update (see module docstring). Update p.data in place.
raise NotImplementedError("AdamW.step") self.t += 1
t = self.t
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 ** t)
vhat = self._v[i] / (1 - self.b2 ** t)
p.data -= self.lr * (mhat / (np.sqrt(vhat) + self.eps) + self.weight_decay * 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
# TODO: implement the Adamax update (see module docstring). Update p.data in place.
raise NotImplementedError("Adamax.step") self.t += 1
t = self.t
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 ** 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
# TODO: implement the NAdam update (see module docstring). Update p.data in place.
raise NotImplementedError("NAdam.step") self.t += 1
t = self.t
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 ** t)
vhat = self._v[i] / (1 - self.b2 ** t)
mnes = self.b1 * mhat + (1 - self.b1) * g / (1 - self.b1 ** 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
# TODO: implement the Lion update (see module docstring). Update p.data in place.
raise NotImplementedError("Lion.step") for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad
upd = np.sign(self.b1 * self._m[i] + (1 - self.b1) * g)
p.data -= self.lr * (upd + 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
# TODO: implement the RAdam update (see module docstring). Update p.data in place.
raise NotImplementedError("RAdam.step") self.t += 1
t = self.t
b1, b2, eps = self.b1, self.b2, self.eps
bc1 = 1 - b1 ** t
bc2 = 1 - b2 ** t
rinf = 2 / (1 - b2) - 1
rt = rinf - 2 * t * (b2 ** t) / bc2
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._m[i] = b1 * self._m[i] + (1 - b1) * g
self._v[i] = b2 * self._v[i] + (1 - b2) * (g * g)
mhat = self._m[i] / bc1
if rt > 4:
l = np.sqrt(bc2 / (self._v[i] + eps))
r = math.sqrt(((rt - 4) * (rt - 2) * rinf) / ((rinf - 4) * (rinf - 2) * rt))
p.data -= self.lr * mhat * r * l
else:
p.data -= self.lr * mhatThe 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
# TODO: implement the AdaBelief update (see module docstring). Update p.data in place.
raise NotImplementedError("AdaBelief.step") self.t += 1
t = self.t
b1, b2, eps = self.b1, self.b2, self.eps
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad
self._m[i] = b1 * self._m[i] + (1 - b1) * g
d = g - self._m[i]
self._s[i] = b2 * self._s[i] + (1 - b2) * (d * d) + eps
mhat = self._m[i] / (1 - b1 ** t)
shat = self._s[i] / (1 - b2 ** t)
p.data -= self.lr * (mhat / (np.sqrt(shat) + eps) + self.weight_decay * 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
# TODO: implement (see docstring).
raise NotImplementedError("clip_grad_norm") 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 totalThe 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
# TODO: implement (see docstring).
raise NotImplementedError("clip_grad_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
# TODO: implement (see docstring).
raise NotImplementedError("cosine_lr") 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))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
# TODO: implement (see docstring).
raise NotImplementedError("warmup_lr")return base_lr * (step + 1) / warmup if step < warmup else base_lr
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
# TODO: implement (see docstring).
raise NotImplementedError("step_lr")return base_lr * (gamma ** (step // step_size))
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
# TODO: implement (see docstring).
raise NotImplementedError("exponential_lr")return base_lr * (gamma ** step)
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
# TODO: implement (see docstring).
raise NotImplementedError("polynomial_lr") if step >= total:
return min_lr
return (base_lr - min_lr) * ((1 - step / total) ** power) + min_lrThe 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
# TODO: implement (see docstring).
raise NotImplementedError("cosine_restart_lr") s = step
T_ = period
while s >= T_:
s -= T_
T_ *= t_mult
return min_lr + 0.5 * (base_lr - min_lr) * (1 + math.cos(math.pi * s / max(1, T_)))The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run optim checks
cd /app && OMP_NUM_THREADS=1 python3 run_checks.py --only optim 2>&1 | tail -30
[optim] 21/21 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/RAdam_12step OK (max_abs=0.0e+00) optim/AdaBelief_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_cosine_restart_lr OK (match) ---------------------------------------------------------------------- optim 21/21 TOTAL 21/21 …[truncated 165 chars]
/app/submission/nn.py
def forward(self, x: Tensor) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("forward")
def named_params(self):
d = {"weight": self.weight} def forward(self, x: Tensor) -> Tensor:
out = ag.matmul(x, self.weight)
if self.bias is not None:
out = ag.add(out, self.bias)
return out
def named_params(self):
d = {"weight": self.weight}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
def forward(self, x: Tensor) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("forward")
def named_params(self):
return {"weight": self.weight, "bias": self.bias} def forward(self, x: Tensor) -> Tensor:
return ag.conv2d(x, self.weight, self.bias, pad=self.pad, stride=1)
def named_params(self):
return {"weight": self.weight, "bias": 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
def forward(self, x: Tensor, training: bool = True) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("forward")
def named_params(self):
return {"gamma": self.gamma, "beta": self.beta}
def buffers(self): def forward(self, x: Tensor, training: bool = True) -> Tensor:
return ag.batchnorm2d(x, self.gamma, self.beta, self.running_mean, self.running_var,
training=training, momentum=self.momentum, eps=self.eps)
def named_params(self):
return {"gamma": self.gamma, "beta": self.beta}
def buffers(self):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
def forward(self, x: Tensor) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("forward")
def named_params(self):
return {"gamma": self.gamma, "beta": self.beta}
class LayerNorm(Module): def forward(self, x: Tensor) -> Tensor:
return ag.batchnorm(x, self.gamma, self.beta, eps=self.eps)
def named_params(self):
return {"gamma": self.gamma, "beta": self.beta}
class LayerNorm(Module):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
def forward(self, x: Tensor) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("forward")
def named_params(self):
return {"gamma": self.gamma, "beta": self.beta}
class Dropout(Module): def forward(self, x: Tensor) -> Tensor:
return ag.layernorm(x, self.gamma, self.beta, eps=self.eps)
def named_params(self):
return {"gamma": self.gamma, "beta": self.beta}
class Dropout(Module):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
def forward(self, x: Tensor, training: bool = False, rng=None) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("forward") def forward(self, x: Tensor, training: bool = False, rng=None) -> Tensor:
return ag.dropout(x, self.p, training, rng)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
def forward(self, x: Tensor) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("forward")
def named_params(self):
return {}
class MaxPool2d(Module): def forward(self, x: Tensor) -> Tensor:
return ag.avgpool2d(x, self.k)
def named_params(self):
return {}
class MaxPool2d(Module):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
def forward(self, x: Tensor) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("forward")
def named_params(self):
return {}
class RMSNorm(Module): def forward(self, x: Tensor) -> Tensor:
return ag.maxpool2d(x, self.k)
def named_params(self):
return {}
class RMSNorm(Module):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
def forward(self, x: Tensor) -> Tensor:
# TODO: implement (compose ag.rms_norm). See the docstring/contract.
raise NotImplementedError("forward") def forward(self, x: Tensor) -> Tensor:
return ag.rms_norm(x, self.gamma, eps=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
def forward(self, x: Tensor) -> Tensor:
# TODO: implement (compose ag.groupnorm2d). See the docstring/contract.
raise NotImplementedError("forward") def forward(self, x: Tensor) -> Tensor:
return ag.groupnorm2d(x, self.gamma, self.beta, self.num_groups, eps=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
def forward(self, X, training: bool = True) -> Tensor:
# TODO: implement (stem->gn0->silu; then 2 residual blocks
# r=h; h=silu(b*n1(b*c1(h))); h=b*n2(b*c2(h)); h=silu(h + r);
# then strided downsample via ag.conv2d(h, down.weight, down.bias, pad=1, stride=2) + silu;
# then global-avg-pool over axes (2,3); then the linear head). Compose ag.* ops.
raise NotImplementedError("forward") def forward(self, X, training: bool = True) -> Tensor:
h = ag.silu(self.gn0(self.stem(X)))
# block 1
r = h
h = ag.silu(self.b1n1(self.b1c1(h)))
h = self.b1n2(self.b1c2(h))
h = ag.silu(ag.add(h, r))
# block 2
r = h
h = ag.silu(self.b2n1(self.b2c1(h)))
h = self.b2n2(self.b2c2(h))
h = ag.silu(ag.add(h, r))
# strided downsample
h = ag.conv2d(h, self.down.weight, self.down.bias, pad=1, stride=2)
h = ag.silu(h)
h = ag.global_avg_pool(h)
return self.head(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
def featurize(self, X, training: bool = True) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("featurize")
def forward(self, X, training: bool = True) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("forward") def featurize(self, X, training: bool = True) -> Tensor:
h = ag.relu(self.bn1(self.conv1(X), training=training))
h = ag.maxpool2d(h, 2)
h = ag.relu(self.bn2(self.conv2(h), training=training))
h = ag.maxpool2d(h, 2)
h = ag.relu(self.bn3(self.conv3(h), training=training))
h = ag.global_avg_pool(h)
h = ag.relu(self.embed(h))
return h
def forward(self, X, training: bool = True) -> Tensor:
feat = self.featurize(X, training=training)
return self.classifier(feat)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
def forward(self, x) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("forward")
def named_params(self) -> Dict[str, Tensor]:
d: Dict[str, Tensor] = {}
for i, lin in enumerate(self.layers): def forward(self, x) -> Tensor:
h = x
n = len(self.layers)
for i, lin in enumerate(self.layers):
h = lin(h)
if i < n - 1:
h = ag.relu(h)
return h
def named_params(self) -> Dict[str, Tensor]:
d: Dict[str, Tensor] = {}
for i, lin in enumerate(self.layers):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
def forward(self, x) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("forward")
def named_params(self) -> Dict[str, Tensor]:
d: Dict[str, Tensor] = {}
for k, v in self.proj_in.named_params().items(): def forward(self, x) -> Tensor:
h = self.proj_in(x)
for blk in self.blocks:
r = h
z = blk["ln"](h)
z = blk["fc1"](z)
z = ag.gelu(z)
z = blk["fc2"](z)
h = ag.add(r, z)
return self.proj_out(h)
def named_params(self) -> Dict[str, Tensor]:
d: Dict[str, Tensor] = {}
for k, v in self.proj_in.named_params().items():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
def forward(self, X, training: bool = True) -> Tensor:
# TODO: implement (compose ag.* ops). See the docstring/contract.
raise NotImplementedError("forward")
def named_params(self) -> Dict[str, Tensor]:
d: Dict[str, Tensor] = {}
for name in ("conv1", "bn1", "conv2", "bn2", "head"): def forward(self, X, training: bool = True) -> Tensor:
h = ag.relu(self.bn1(self.conv1(X), training=training))
h = ag.avgpool2d(h, 2)
h = ag.relu(self.bn2(self.conv2(h), training=training))
h = ag.maxpool2d(h, 2)
n = h.shape[0]
h = ag.reshape(h, (n, self.W * 2 * self.side * self.side))
return self.head(h)
def named_params(self) -> Dict[str, Tensor]:
d: Dict[str, Tensor] = {}
for name in ("conv1", "bn1", "conv2", "bn2", "head"):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
# TODO: implement (numpy only). See docstring.
raise NotImplementedError("balanced_group_weights") gids = np.asarray(group_ids).astype(np.int64).reshape(-1)
if n_groups is None:
n_groups = int(gids.max()) + 1
present = np.unique(gids)
w = np.zeros(int(n_groups), dtype=np.float64)
w[present] = 1.0 / len(present)
return wThe 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
# TODO: implement (numpy only). See docstring.
raise NotImplementedError("class_balanced_weights") t = np.asarray(targets).astype(np.int64).reshape(-1)
if n_classes is None:
n_classes = int(t.max()) + 1
counts = np.bincount(t, minlength=int(n_classes)).astype(np.float64)
if beta == 0.0:
cw = np.where(counts > 0, 1.0 / np.where(counts > 0, counts, 1.0), 0.0)
else:
eff = (1.0 - beta ** counts) / (1.0 - beta)
cw = np.where(counts > 0, 1.0 / np.where(eff > 0, eff, 1.0), 0.0)
w = cw[t]
w = w / w.mean()
return wThe 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
# TODO: implement (numpy only). See docstring.
raise NotImplementedError("log_class_prior") t = np.asarray(targets).astype(np.int64).reshape(-1)
if n_classes is None:
n_classes = int(t.max()) + 1
counts = np.bincount(t, minlength=int(n_classes)).astype(np.float64)
N = len(t)
prior = counts / N
return np.where(counts > 0, np.log(np.where(prior > 0, prior, 1.0)), np.log(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
# TODO: implement (numpy only). See docstring.
raise NotImplementedError("ldam_margins") t = np.asarray(targets).astype(np.int64).reshape(-1)
if n_classes is None:
n_classes = int(t.max()) + 1
counts = np.bincount(t, minlength=int(n_classes)).astype(np.float64)
inv = np.where(counts > 0, np.where(counts > 0, counts, 1.0) ** (-0.25), 0.0)
mx = inv.max()
if mx <= 0:
return np.zeros(int(n_classes), dtype=np.float64)
return inv / mx * max_marginThe 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
# TODO: implement (numpy only). See docstring.
raise NotImplementedError("effective_number_weights") t = np.asarray(targets).astype(np.int64).reshape(-1)
if n_classes is None:
n_classes = int(t.max()) + 1
counts = np.bincount(t, minlength=int(n_classes)).astype(np.float64)
present = counts > 0
eff = (1.0 - beta ** counts) / (1.0 - beta)
w = np.where(present, 1.0 / np.where(eff > 0, eff, 1.0), 0.0)
w = w / w[present].mean()
return wThe 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
def update(self, group_losses, present=None):
# TODO: implement the masked EMA update (see docstring). Return self.l.copy().
raise NotImplementedError("EMAGroupLoss.update")
def worst_group(self):
# TODO: implement (argmax of the current EMA). See docstring.
raise NotImplementedError("EMAGroupLoss.worst_group") def update(self, group_losses, present=None):
new = np.asarray(group_losses, dtype=np.float64).reshape(-1)
if present is None:
mask = np.ones(self.n_groups, dtype=bool)
else:
mask = np.asarray(present, dtype=bool).reshape(-1)
for k in range(self.n_groups):
if not mask[k]:
continue
if not self._init[k]:
self.l[k] = new[k]
self._init[k] = True
else:
self.l[k] = self.beta * self.l[k] + (1.0 - self.beta) * new[k]
return self.l.copy()
def worst_group(self):
return int(np.argmax(self.l))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
def update(self, group_losses):
# TODO: exponentiated-gradient update of self.q on group_losses; renormalize.
raise NotImplementedError("GroupDROState.update") def update(self, group_losses):
loss = np.asarray(group_losses, dtype=np.float64).reshape(-1)
self.q = self.q * np.exp(self.eta_q * loss)
self.q = self.q / self.q.sum()
return self.q.copy()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
"""Flatten (y, g) to a single group id y*n_classes + g (int64)."""
# TODO
raise NotImplementedError("group_ids") """Flatten (y, g) to a single group id y*n_classes + g (int64)."""
return (np.asarray(y, dtype=np.int64) * int(n_classes) + np.asarray(g, dtype=np.int64)).astype(np.int64)The file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/data.py
"""Yield (Xb, yb, gb) minibatches over a random permutation of the dataset."""
# TODO
raise NotImplementedError("iterate") """Yield (Xb, yb, gb) minibatches over a random permutation of the dataset."""
X = np.asarray(X); y = np.asarray(y); g = np.asarray(g)
n = X.shape[0]
perm = rng.permutation(n)
for i in range(0, n, batch_size):
idx = perm[i:i + batch_size]
yield X[idx], y[idx], g[idx]The file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/data.py
"""Return (min over (y,g) groups of within-group accuracy, average accuracy, per-group dict)."""
# TODO
raise NotImplementedError("worst_group_acc") """Return (min over (y,g) groups of within-group accuracy, average accuracy, per-group dict)."""
preds = np.asarray(preds).reshape(-1)
y = np.asarray(y).reshape(-1)
g = np.asarray(g).reshape(-1)
correct = (preds == y)
gid = y * int(n_classes) + g
per = {}
for k in np.unique(gid):
mask = gid == k
per[int(k)] = float(correct[mask].mean())
wga = min(per.values()) if per else 0.0
avg = float(correct.mean()) if len(correct) else 0.0
return wga, avg, perThe file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/data.py
"""Boolean mask selecting ~frac of EACH (y,g) group (keeps both slices group-balanced)."""
# TODO
raise NotImplementedError("stratified_group_split") """Boolean mask selecting ~frac of EACH (y,g) group (keeps both slices group-balanced)."""
y = np.asarray(y).reshape(-1)
g = np.asarray(g).reshape(-1)
gid = y * int(n_classes) + g
mask = np.zeros(len(y), dtype=bool)
for k in np.unique(gid):
idx = np.where(gid == k)[0]
rng.shuffle(idx)
ntake = int(round(frac * len(idx)))
ntake = max(0, min(len(idx), ntake))
mask[idx[:ntake]] = True
return maskThe file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run full check suite
OMP_NUM_THREADS=1 python3 run_checks.py 2>&1 | grep -E "FAIL|TOTAL|^\[|^---|^[a-z].*[0-9]/[0-9]" | head -60
[autograd_grads] 81/81 [nn_forward] 21/21 [optim] 21/21 [robust] 13/13 [robust2] 8/8 [data] 3/3 ---------------------------------------------------------------------- autograd_grads 81/81 | nn_forward 21/21 | optim 21/21 | robust 13/13 | robust2 8/8 | data 3/3 TOTAL 147/147 public checks passing
"""Training entry point: worst-group-robust SmallCNN via ERM pretrain + DFR last-layer retrain.
Recipe:
1. ERM-train the full SmallCNN on the (spuriously correlated) train split with AdamW + grad clip.
This learns a feature extractor that encodes the shape (among other things) and populates the
BatchNorm running buffers used at eval time.
2. DFR: freeze the extractor, recompute penultimate features (eval/BN-eval mode) on the
GROUP-BALANCED reweight split, and re-fit ONLY the linear classifier with a group-balanced
(group_weighted_ce) loss so the head stops keying off the spurious background color.
3. Save the pinned-architecture checkpoint the grader loads.
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import numpy as np
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, "/app")
sys.path.insert(0, "/app/submission")
import autograd as ag # noqa: E402,F401
from autograd import Tensor # noqa: E402
import data as D # noqa: E402,F401
import optim as O # noqa: E402,F401
import nn as NN # noqa: E402
from nn import SmallCNN # noqa: E402,F401
# Pinned architecture (MUST match the grader's reference; do not change).
WIDTH = 16
EMBED_DIM = 48
def save_checkpoint(path, state_dict, n_classes, img_size):
os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
arrs = {k: np.asarray(v, dtype=np.float32) for k, v in s
…[truncated 5205 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Smoke train 3 epochs
cd /app/submission && OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 timeout 300 python3 train.py --epochs 3 2>&1 | tail -20
(Bash completed with no output)
Smoke train unbuffered
OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 timeout 300 python3 -u train.py --epochs 3 2>&1 | tail -20; echo "EXIT=${PIPESTATUS[0]}"n_classes=4 img_size=16 train=(2400, 3, 16, 16) reweight=(1280, 3, 16, 16) Phase 1: ERM pretrain ... EXIT=137
Time 5 batches and memory
OMP_NUM_THREADS=1 python3 -u -c "
import time,numpy as np, resource
import autograd as ag, optim as O
from autograd import Tensor
import data as D
from nn import SmallCNN
Xtr,ytr,gtr=D.load_npz('/app/data/train.npz')
m=SmallCNN(n_classes=4,img_size=16,width=16,embed_dim=48,seed=0)
p=m.params()
opt=O.AdamW(p,lr=1e-3,weight_decay=1e-4)
t0=time.time()
bs=128
for i in range(5):
xb=Xtr[i*bs:(i+1)*bs]; yb=ytr[i*bs:(i+1)*bs]
opt.zero_grad()
loss=ag.cross_entropy(m(xb,training=True),yb)
loss.backward()
O.clip_grad_norm(p,5.0)
opt.step()
print('batch',i,'loss',float(loss.data),'t',round(time.time()-t0,2),'mem_MB',round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024,1))
" 2>&1 | tail -20batch 0 loss 1.5429725751578092 t 0.35 mem_MB 126.4 batch 1 loss 1.3161780557628497 t 0.76 mem_MB 195.3
Check memory with gc.collect
free -m 2>/dev/null | head -3; echo "---"; OMP_NUM_THREADS=1 python3 -u -c "
import time,numpy as np, resource, gc
import autograd as ag, optim as O
from nn import SmallCNN
import data as D
Xtr,ytr,gtr=D.load_npz('/app/data/train.npz')
m=SmallCNN(n_classes=4,img_size=16,width=16,embed_dim=48,seed=0)
p=m.params(); opt=O.AdamW(p,lr=1e-3,weight_decay=1e-4)
bs=128
for i in range(12):
xb=Xtr[i*bs:(i+1)*bs]; yb=ytr[i*bs:(i+1)*bs]
opt.zero_grad()
loss=ag.cross_entropy(m(xb,training=True),yb); loss.backward()
O.clip_grad_norm(p,5.0); opt.step()
del loss; gc.collect()
print('batch',i,'mem_MB',round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024,1),flush=True)
" 2>&1 | tail -20total used free shared buff/cache available Mem: 481 250 180 1 65 231 Swap: 0 0 0 --- batch 0 mem_MB 126.4 batch 1 mem_MB 129.0 batch 2 mem_MB 129.0 batch 3 mem_MB 129.3 batch 4 mem_MB 129.3 batch 5 mem_MB 129.3 batch 6 mem_MB 129.3 batch 7 mem_MB 129.3 batch 8 mem_MB 129.3 batch 9 mem_MB 129.3 batch 10 mem_MB 129.3 batch 11 mem_MB 129.3
/app/submission/train.py
import argparse import json import os import sys import numpy as np
import argparse import gc import json import os import sys import numpy as np
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
O.clip_grad_norm(params, 5.0)
opt.step()
step += 1
return model O.clip_grad_norm(params, 5.0)
opt.step()
del logits, loss
gc.collect()
step += 1
return modelThe 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
loss = ag.group_weighted_ce(logits, y, gid)
loss.backward()
opt.step()
Wsum += W.data loss = ag.group_weighted_ce(logits, y, gid)
loss.backward()
opt.step()
del logits, loss
gc.collect()
Wsum += W.dataThe 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
f = model.featurize(xb, training=training)
feats.append(np.asarray(f.data))
return np.concatenate(feats, axis=0) f = model.featurize(xb, training=training)
feats.append(np.asarray(f.data))
del f
gc.collect()
return np.concatenate(feats, axis=0)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
out.append(np.asarray(model(xb, training=False).data))
return np.concatenate(out, axis=0) out.append(np.asarray(model(xb, training=False).data))
gc.collect()
return np.concatenate(out, axis=0)The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Launch training in background
OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 nohup python3 -u train.py --epochs 20 > /app/train.out 2>&1 & echo "started pid $!"
started pid 1521
Check training progress after 90s
sleep 90; cat /app/train.out; echo "---running?---"; ps aux | grep "[t]rain.py" | awk '{print $6/1024" MB"}'<tool_use_error>Blocked: sleep 90 followed by: cat /app/train.out echo "---running?---" ps aux grep "[t]rain.py" awk '{print $6/1024" MB"}'. To wait for a condition, use Monitor with an until-loop (e.g. `until <check>; do sleep 2; done`). To wait for a command you started, use run_in_background: true. Do not chain shorter sleeps to work around this block.</tool_use_error>Wait until ERM done or process exits
until ! ps -p 1521 > /dev/null 2>&1 || grep -q "Phase 2" /app/train.out 2>/dev/null; do sleep 5; done; cat /app/train.out; echo "---mem---"; ps aux | grep "[t]rain.py" | awk '{print $6/1024" MB"}'n_classes=4 img_size=16 train=(2400, 3, 16, 16) reweight=(1280, 3, 16, 16) Phase 1: ERM pretrain ... ERM head on reweight: worst-group=0.000 avg=0.352 Phase 2: DFR last-layer retrain ... ---mem--- 0.101562 MB 233.605 MB 112.422 MB
Wait for training completion
until ! ps -p 1521 > /dev/null 2>&1; do sleep 5; done; cat /app/train.out
n_classes=4 img_size=16 train=(2400, 3, 16, 16) reweight=(1280, 3, 16, 16) Phase 1: ERM pretrain ... ERM head on reweight: worst-group=0.000 avg=0.352 Phase 2: DFR last-layer retrain ... DFR head val: worst-group=0.625 avg=0.805 Final model on reweight: worst-group=0.600 avg=0.824 saved checkpoint -> /app/submission/model.npz
"""Dev harness: experiment with full-network fine-tuning on the balanced reweight set."""
import gc, sys, os, numpy as np
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
import autograd as ag, optim as O, data as D
from autograd import Tensor
from nn import SmallCNN
def load_ckpt(path, n_classes, img_size):
d = np.load(path)
sd = {k: d[k] for k in d.files if k != "__config__"}
m = SmallCNN(n_classes=n_classes, img_size=img_size, width=16, embed_dim=48, seed=0)
m.load_state_dict(sd)
return m
def worst_group(preds, y, g, nc):
gid = y*nc+g; accs=[]
for k in np.unique(gid):
mm=gid==k; accs.append((preds[mm]==y[mm]).mean())
return float(min(accs)), float((preds==y).mean())
def logits_batched(m, X, bs=256):
out=[]
for i in range(0,X.shape[0],bs):
out.append(np.asarray(m(X[i:i+bs],training=False).data)); gc.collect()
return np.concatenate(out,0)
def finetune(m, X, y, g, nc, epochs, lr, bs=128, seed=0, wd=1e-4):
rng=np.random.default_rng(seed)
p=m.params(); opt=O.AdamW(p, lr=lr, weight_decay=wd)
N=X.shape[0]; nsteps=epochs*((N+bs-1)//bs); step=0
gid_all=(y*nc+g).astype(np.int64)
for ep in range(epochs):
perm=rng.permutation(N)
for i in range(0,N,bs):
idx=perm[i:i+bs]
opt.lr=O.cosine_lr(step,nsteps,lr,warmup=max(1,nsteps//20),min_lr=lr*0.05)
opt.zero_grad()
logits=m(X[idx],training=True)
loss=ag.group_weighted_ce
…[truncated 863 chars]File created successfully at: /app/submission/exp.py (file state is current in your context , no need to Read it back)
Run fine-tune experiment in background
OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 nohup python3 -u exp.py > /app/exp.out 2>&1 & echo "pid $!"
pid 1619
Wait for experiment
until ! ps -p 1619 > /dev/null 2>&1; do sleep 5; done; cat /app/exp.out
before ft val (0.5833333333333334, 0.8333333333333334) after 5 epochs val (0.6666666666666666, 0.875) after 10 epochs val (0.7916666666666666, 0.9114583333333334) after 15 epochs val (0.75, 0.9270833333333334) after 20 epochs val (0.75, 0.9427083333333334) after 25 epochs val (0.7916666666666666, 0.9453125) after 30 epochs val (0.7916666666666666, 0.953125)
/app/submission/exp.py
for stint in range(6):
finetune(m, Xrw[fit_mask], yrw[fit_mask], grw[fit_mask], nc, epochs=5, lr=3e-4, seed=stint)
vp=logits_batched(m,Xrw[val_mask]).argmax(1)
print(f'after {5*(stint+1)} epochs val', worst_group(vp,yrw[val_mask],grw[val_mask],nc), flush=True) for stint in range(8):
finetune(m, Xrw[fit_mask], yrw[fit_mask], grw[fit_mask], nc, epochs=5, lr=3e-4, seed=stint)
vp=logits_batched(m,Xrw[val_mask]).argmax(1)
print(f'after {5*(stint+1)} epochs val', worst_group(vp,yrw[val_mask],grw[val_mask],nc), flush=True)
# DFR head refit on top of fine-tuned extractor
def featb(X,bs=256):
fs=[]
for i in range(0,X.shape[0],bs):
fs.append(np.asarray(m.featurize(X[i:i+bs],training=False).data)); gc.collect()
return np.concatenate(fs,0)
Ff=featb(Xrw[fit_mask]); Fv=featb(Xrw[val_mask])
gidf=(yrw[fit_mask]*nc+grw[fit_mask]).astype(np.int64)
W=Tensor(np.random.default_rng(0).standard_normal((48,nc))*0.01,requires_grad=True)
b=Tensor(np.zeros(nc),requires_grad=True)
opt=O.AdamW([W,b],lr=5e-2,weight_decay=1e-3)
for s in range(500):
opt.zero_grad(); lo=ag.add(ag.matmul(Tensor(Ff),W),b)
loss=ag.group_weighted_ce(lo,yrw[fit_mask],gidf); loss.backward(); opt.step(); del lo,loss; gc.collect()
vp=(Fv@W.data+b.data).argmax(1)
print('DFR-on-finetuned val', worst_group(vp,yrw[val_mask],grw[val_mask],nc), flush=True)The file /app/submission/exp.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run extended fine-tune + DFR experiment
OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 nohup python3 -u exp.py > /app/exp.out 2>&1 & echo "pid $!"
pid 1654
- [numpy-dl-framework-task](numpy-dl-framework-task.md) , from-scratch numpy autodiff + worst-group robust SmallCNN training task in /app
File created successfully at: /home/user/.claude/projects/-app/memory/MEMORY.md (file state is current in your context , no need to Read it back)
--- name: numpy-dl-framework-task description: Constraints and recipe for the /app numpy-only DL framework + worst-group robust training task metadata: type: project --- Task in `/app`: implement numpy-only reverse-mode autodiff framework (`submission/autograd.py`, `nn.py`, `optim.py`, `data.py`) then train worst-group-robust SmallCNN -> `submission/model.npz`. Check with `python3 /app/run_checks.py` (147 public checks). All 147 pass as of this session. **Hard env constraints:** - Machine has only ~481MB RAM. The autograd graph creates reference cycles (`out._backward` closure captures `out`), so **`gc.collect()` per training batch is required** or the run OOMs (SIGKILL/exit 137). With gc.collect each batch, memory stays ~129MB. - Set `OMP_NUM_THREADS=1` etc. float64 throughout. ~0.4s/batch for SmallCNN bs=128. - ARG_MAX 65536 , write large files in pieces. **Data:** train.npz (2400, spurious color~shape ~97%), reweight.npz (1280, group-balanced 80/group, 16 groups = y*4+g). 4 shape classes, 4 colors, 16x16x3 images in [0,1]. Feed raw X (no input normalization , grader feeds raw; rely on BN). **Recipe:** ERM pretrain on train -> fine-tune WHOLE net on balanced reweight with group_weighted_ce (this beats DFR-head-only: ~0.79 worst-group val vs 0.6 for DFR alone). SmallCNN pinned width=16 embed_dim=48; chose **maxpool2d** for the intermediate "pool" (ambiguous in spec , risk if grader uses avgpool).
File created successfully at: /home/user/.claude/projects/-app/memory/numpy-dl-framework-task.md (file state is current in your context , no need to Read it back)
Wait for experiment results
until ! ps -p 1654 > /dev/null 2>&1; do sleep 5; done; cat /app/exp.out
before ft val (0.5833333333333334, 0.8333333333333334) after 5 epochs val (0.6666666666666666, 0.875) after 10 epochs val (0.7916666666666666, 0.9114583333333334) after 15 epochs val (0.75, 0.9270833333333334) after 20 epochs val (0.75, 0.9427083333333334) after 25 epochs val (0.7916666666666666, 0.9453125) after 30 epochs val (0.7916666666666666, 0.953125) after 35 epochs val (0.9166666666666666, 0.96875) after 40 epochs val (0.9166666666666666, 0.9713541666666666) DFR-on-finetuned val (0.875, 0.9583333333333334)
"""Training entry point: worst-group-robust SmallCNN.
Recipe:
1. ERM-pretrain the full SmallCNN on the (spuriously correlated) train split (AdamW + grad clip)
to learn general conv features and seed BatchNorm running buffers.
2. Fine-tune the WHOLE network on the GROUP-BALANCED reweight split with the group-balanced
(group_weighted_ce) loss, in several short cosine "stints". Because the balanced data carries
no background-color->shape signal, this drives the network to predict the SHAPE, lifting
worst-group accuracy far above the ERM baseline (and above last-layer-only DFR).
3. Save the pinned-architecture checkpoint the grader loads.
A held-out group-stratified split of the reweight set is used only to PRINT an honest worst-group
estimate; the saved model is fine-tuned on ALL of the reweight set.
"""
from __future__ import annotations
import argparse
import gc
import json
import os
import sys
import numpy as np
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, "/app")
sys.path.insert(0, "/app/submission")
import autograd as ag # noqa: E402,F401
from autograd import Tensor # noqa: E402,F401
import data as D # noqa: E402,F401
import optim as O # noqa: E402,F401
from nn import SmallCNN # noqa: E402
WIDTH = 16
EMBED_DIM = 48
def save_checkpoint(path, state_dict, n_classes, img_size):
os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
arrs = {k: np.asarra
…[truncated 4756 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run final training in background
OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 nohup python3 -u train.py > /app/train.out 2>&1 & echo "pid $!"
pid 1691
Wait for VAL estimate
until ! ps -p 1691 > /dev/null 2>&1 || grep -q "VAL worst" /app/train.out 2>/dev/null; do sleep 10; done; cat /app/train.out
n_classes=4 img_size=16 train=(2400, 3, 16, 16) reweight=(1280, 3, 16, 16) Phase 1: ERM pretrain ... ERM worst-group/avg on reweight: (0.0, 0.284375) Phase 2a: validation estimate (fine-tune on fit split) ... VAL worst-group/avg: (0.7916666666666666, 0.9401041666666666) Phase 2b: final fine-tune on ALL reweight ...
Wait for training completion
until ! ps -p 1691 > /dev/null 2>&1; do sleep 10; done; cat /app/train.out; echo "---"; ls -la /app/submission/model.npz
n_classes=4 img_size=16 train=(2400, 3, 16, 16) reweight=(1280, 3, 16, 16) Phase 1: ERM pretrain ... ERM worst-group/avg on reweight: (0.0, 0.284375) Phase 2a: validation estimate (fine-tune on fit split) ... VAL worst-group/avg: (0.7916666666666666, 0.9401041666666666) Phase 2b: final fine-tune on ALL reweight ... FINAL (in-sample) worst-group/avg on reweight: (0.9375, 0.9875) saved checkpoint -> /app/submission/model.npz --- -rw-r--r-- 1 user user 71712 Jun 30 23:11 /app/submission/model.npz
Inspect image structure for augmentation
OMP_NUM_THREADS=1 python3 -c "
import numpy as np
d=np.load('/app/data/reweight.npz'); X,y,g=d['X'],d['y'],d['g']
# For each class, look at a sample image's structure (mean over channels), and color signature
for cls in range(4):
i=np.where(y==cls)[0][0]
img=X[i]
print('class',cls,'g',g[i],'channel means',np.round(img.reshape(3,-1).mean(1),3),'shape-map nonzero frac', round(float((img.max(0)>0.1).mean()),3))
# check if shape occupies whole image or a tile
print('img min/max', X.min(), X.max())
# symmetry check: is a class image roughly symmetric under flips?
i=np.where(y==0)[0][0]; im=X[i].max(0)
print('hflip diff', round(float(np.abs(im-im[:,::-1]).mean()),4),'vflip diff',round(float(np.abs(im-im[::-1]).mean()),4))
"class 0 g 1 channel means [0.285 0.489 0.823] shape-map nonzero frac 1.0 class 1 g 0 channel means [0.802 0.236 0.236] shape-map nonzero frac 1.0 class 2 g 0 channel means [0.794 0.236 0.235] shape-map nonzero frac 1.0 class 3 g 0 channel means [0.792 0.233 0.232] shape-map nonzero frac 1.0 img min/max 0.0 1.0 hflip diff 0.0709 vflip diff 0.075
"""Dev: compare fine-tune variants (flip aug, combined balanced sampling) on a fixed val split."""
import gc, sys, os, numpy as np
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
import autograd as ag, optim as O, data as D
from nn import SmallCNN
NC=4; IMG=16
def wg(preds,y,g):
gid=y*NC+g; a=[(preds[gid==k]==y[gid==k]).mean() for k in np.unique(gid)]
return float(min(a)), float((preds==y).mean())
def logits_b(m,X,bs=256):
o=[]
for i in range(0,X.shape[0],bs):
o.append(np.asarray(m(X[i:i+bs],training=False).data)); gc.collect()
return np.concatenate(o,0)
def aug(xb, rng):
xb=xb.copy()
h=rng.random(xb.shape[0])<0.5; v=rng.random(xb.shape[0])<0.5
xb[h]=xb[h,:,:,::-1]; xb[v]=xb[v,:,::-1,:]
return xb
def erm(m,X,y,epochs,seed):
rng=np.random.default_rng(seed); p=m.params(); opt=O.AdamW(p,lr=1e-3,weight_decay=1e-4)
N=X.shape[0]; ns=epochs*((N+127)//128); st=0
for ep in range(epochs):
perm=rng.permutation(N)
for i in range(0,N,128):
idx=perm[i:i+128]; opt.lr=O.cosine_lr(st,ns,1e-3,warmup=max(1,ns//20),min_lr=2e-5)
opt.zero_grad(); l=ag.cross_entropy(m(X[idx],training=True),y[idx]); l.backward()
O.clip_grad_norm(p,5.0); opt.step(); del l; gc.collect(); st+=1
return m
def ft_reweight(m,X,y,gid,stints,ep,lr,seed,use_aug=False):
rng=np.random.default_rng(seed); p=m.params()
for s in range(stints):
opt=O.AdamW(p,lr=lr,weight_decay=1e-4); N=
…[truncated 2767 chars]File created successfully at: /app/submission/exp2.py (file state is current in your context , no need to Read it back)
Run variant comparison experiment
OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 nohup python3 -u exp2.py > /app/exp2.out 2>&1 & echo "pid $!"
pid 1806
Wait for variant experiment
until ! ps -p 1806 > /dev/null 2>&1; do sleep 10; done; cat /app/exp2.out
reweight-only (0.75, 0.9453125) reweight+aug (0.25, 0.8411458333333334) combined-balanced (0.875, 0.9713541666666666) combined-bal+aug (0.75, 0.9505208333333334)
"""Training entry point: worst-group-robust SmallCNN.
Recipe (validated on a held-out group-stratified split of the reweight set):
1. ERM-pretrain the full SmallCNN on the (spuriously correlated) train split (AdamW + grad clip)
to learn general conv features and seed BatchNorm running buffers.
2. Fine-tune the WHOLE network with GROUP-BALANCED minibatch sampling over the COMBINED
train+reweight pool: every minibatch draws an equal number of samples from each (shape,color)
group, so the background color carries no class signal and the network must predict the SHAPE.
This lifts worst-group accuracy far above the ERM baseline and above reweight-only fine-tuning.
3. Save the pinned-architecture checkpoint the grader loads.
(Image augmentation via flips was tried and REMOVED -- the shapes are orientation-dependent, so
flips are not label-preserving and hurt worst-group accuracy.)
"""
from __future__ import annotations
import argparse
import gc
import json
import os
import sys
import numpy as np
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, "/app")
sys.path.insert(0, "/app/submission")
import autograd as ag # noqa: E402,F401
from autograd import Tensor # noqa: E402,F401
import data as D # noqa: E402,F401
import optim as O # noqa: E402,F401
from nn import SmallCNN # noqa: E402
WIDTH = 16
EMBED_DIM = 48
def save_checkpoint(path, state_dict, n_classes, img_size):
os
…[truncated 5345 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run final training with combined-balanced recipe
OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 nohup python3 -u train.py > /app/train.out 2>&1 & echo "pid $!"
pid 1944
Re-run full check suite
cd /app && OMP_NUM_THREADS=1 python3 run_checks.py 2>&1 | tail -8
data/group_ids OK ([ 1 4 11 14]) data/worst_group_acc OK (wga=0.00 avg=0.80) data/stratified_group_split OK (selected 33/60) ---------------------------------------------------------------------- autograd_grads 81/81 | nn_forward 21/21 | optim 21/21 | robust 13/13 | robust2 8/8 | data 3/3 TOTAL 147/147 public checks passing All public checks pass. Now make sure model.npz clears the held-out worst-group accuracy threshold; the grader re-checks with DIFFERENT seeds.
Wait for final training
until ! ps -p 1944 > /dev/null 2>&1; do sleep 10; done; cat /app/train.out
n_classes=4 img_size=16 train=(2400, 3, 16, 16) reweight=(1280, 3, 16, 16) Phase 1: ERM pretrain ... ERM worst-group/avg on reweight: (0.0, 0.26015625) Phase 2a: validation estimate (balanced fine-tune on train+reweight_fit) ... VAL worst-group/avg: (0.875, 0.9765625) Phase 2b: final balanced fine-tune on train+ALL reweight ... FINAL (in-sample) worst-group/avg on reweight: (0.9625, 0.99453125) saved checkpoint -> /app/submission/model.npz
Verify checkpoint loads strict and evaluate
cd /app/submission && OMP_NUM_THREADS=1 python3 -c "
import numpy as np, json, gc
import data as D
from nn import SmallCNN
d=np.load('model.npz')
cfg=json.loads(bytes(d['__config__']).decode()); print('config',cfg)
sd={k:d[k] for k in d.files if k!='__config__'}
m=SmallCNN(n_classes=cfg['n_classes'],img_size=cfg['img_size'],width=cfg['width'],embed_dim=cfg['embed_dim'],seed=999)
m.load_state_dict(sd, strict=True) # grader-style strict load into a DIFFERENT seed
print('loaded strict OK; keys=',len(sd))
Xrw,yrw,grw=D.load_npz('/app/data/reweight.npz')
o=[]
for i in range(0,len(Xrw),256):
o.append(np.asarray(m(Xrw[i:i+256],training=False).data)); gc.collect()
pred=np.concatenate(o,0).argmax(1)
gid=yrw*4+grw; accs={int(k):float((pred[gid==k]==yrw[gid==k]).mean()) for k in np.unique(gid)}
print('worst-group',min(accs.values()),'avg',float((pred==yrw).mean()))
print('per-group',{k:round(v,2) for k,v in sorted(accs.items())})
print('param shapes ok:', all(sd[k].shape==v.data.shape for k,v in m.named_params().items()))
"config {'n_classes': 4, 'img_size': 16, 'width': 16, 'embed_dim': 48}
loaded strict OK; keys= 22
worst-group 0.9625 avg 0.99453125
per-group {0: 1.0, 1: 1.0, 2: 1.0, 3: 0.99, 4: 1.0, 5: 1.0, 6: 1.0, 7: 1.0, 8: 1.0, 9: 0.97, 10: 0.96, 11: 0.99, 12: 1.0, 13: 1.0, 14: 1.0, 15: 1.0}
param shapes ok: TrueClean dev files and scan forbidden imports
rm -f exp.py exp2.py && rm -rf __pycache__ && echo "removed dev files" && echo "--- forbidden import scan ---" && grep -rEl "torch|tensorflow|jax|tinygrad|micrograd|autograd_lib|chainer|flax|keras|theano|cupy|mxnet|^import autograd\b" *.py | xargs -r grep -nE "import (torch|tensorflow|jax|tinygrad|chainer|flax|keras|theano|cupy|mxnet)" || echo "NONE found"; echo "--- files ---"; ls -la
removed dev files --- forbidden import scan --- NONE found --- files --- total 196 drwxr-xr-x 2 user user 4096 Jun 30 23:28 . drwxr-xr-x 4 user user 4096 Jun 30 23:12 .. -rw-r--r-- 1 user user 57692 Jun 30 22:53 autograd.py -rw-r--r-- 1 user user 2570 Jun 30 22:56 data.py -rw-r--r-- 1 user user 71712 Jun 30 23:28 model.npz -rw-r--r-- 1 user user 25713 Jun 30 22:56 nn.py -rw-r--r-- 1 user user 15868 Jun 30 22:54 optim.py -rw-r--r-- 1 user user 6845 Jun 30 23:22 train.py
Run smoke test and full checks
cd /app && bash test.sh 2>&1 | tail -6; echo "=== FULL CHECKS ==="; OMP_NUM_THREADS=1 python3 run_checks.py 2>&1 | tail -4
numpy 2.2.6 ---------------------------------------------------------------------- autograd_grads 81/81 | nn_forward 21/21 | optim 21/21 | robust 13/13 | robust2 8/8 | data 3/3 TOTAL 147/147 public checks passing All public checks pass. Now make sure model.npz clears the held-out worst-group accuracy threshold; the grader re-checks with DIFFERENT seeds. app_test smoke OK (run 'python3 /app/run_checks.py' to test your implementation) === FULL CHECKS === ---------------------------------------------------------------------- autograd_grads 81/81 | nn_forward 21/21 | optim 21/21 | robust 13/13 | robust2 8/8 | data 3/3 TOTAL 147/147 public checks passing All public checks pass. Now make sure model.npz clears the held-out worst-group accuracy threshold; the grader re-checks with DIFFERENT seeds.
No file changes, the agent left the workspace untouched.
=== SyncVals verifier (local re-grade of the agent's own checkpoint) === COMPONENT CONFORMANCE SUITE: 194/194 cases passed (all milestones pass: True) FINAL METRIC: worst-group accuracy = 0.9433 >= threshold 0.86 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_e3def5ba1c0f4cc4. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_e3def5ba1c0f4cc4 · verifier authoritative; classifier explanatory.