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/ && echo "---RUNCHECKS head---" && head -50 run_checks.py
total 89 drwxr-xr-x 4 user user 4096 Jun 30 22:32 . drwxr-xr-x 21 root root 4096 Jun 30 22:32 .. -rw-r--r-- 1 user user 12853 Jun 30 22:32 PROMPT.txt -rw-r--r-- 1 user user 5 Jun 30 22:32 agent.pid -rw-r--r-- 1 user user 0 Jun 30 22:32 agent.stderr -rw-r--r-- 1 user user 4610 Jun 30 22:32 agent_trajectory.jsonl drwxr-xr-x 2 user user 60 Jun 30 22:32 data -rw-r--r-- 1 user user 0 Jun 30 22:32 run.log -rwxr-xr-x 1 user user 47612 Jun 30 11:01 run_checks.py drwxr-xr-x 3 user user 128 Jun 30 22:32 submission -rwxr-xr-x 1 user user 1376 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 22:32 . drwxr-xr-x 4 user user 4096 Jun 30 22:32 .. drwxr-xr-x 2 user user 4096 Jun 30 22:32 __pycache__ -rw-r--r-- 1 user user 27741 Jun 30 11:01 autograd.py -rw-r--r-- 1 user user 2625 Jun 30 11:01 data.py -rw-r--r-- 1 user user 21670 Jun 30 11:01 nn.py -rw-r--r-- 1 user user 6253 Jun 30 11:01 optim.py -rw-r--r-- 1 user user 20833 Jun 30 11:01 ssl.py -rw-r--r-- 1 user user 3551 Jun 30 11:01 train.py ---DATA--- total 1804 drwxr-xr-x 2 user user 60 Jun 30 22:32 . drwxr-xr-x 4 user user 4096 Jun 30 22:32 .. -rw-r--r-- 1 user user 59874 Jun 30 11:01 labeled.npz -rw-r--r-- 1 user user 1778166 Jun 30 11:01 unlabeled.npz ---RUNCHECKS head--- #!/usr/bin/env python3 """PUBLIC component check runner for the from-scratch numpy DL framework + FixMatch SSL machinery. Run this AS …[truncated 2978 chars]
/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 fails the gradient 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 * Numerically …[truncated 29481 chars]
/app/submission/nn.py
1 """Neural-network layers + several from-scratch models, built on YOUR autograd engine.
2
3 Implement every ``# TODO`` forward (the parameters + the ``named_params`` naming are already
4 wired for you; you compose the autograd ops). The grader checks each layer's forward against its
5 OWN reference AND finite-difference-checks the gradients that flow through your autograd, so the
6 composition must be exactly right.
7
8 LAYER / MODEL CHECKLIST (forward + grad checked):
9 Linear Embedding LayerNorm BatchNorm1d Dropout Conv2d MaxPool2d AvgPool2d
10 MultiHeadSelfAttention MLP TransformerBlock
11 RNNCell LSTMCell GRUCell
12 HiddenBlock + MLPClassifier (THE METRIC MODEL) | CNNClassifier (images)
13 | SeqClassifier (rnn/lstm/gru sequence)
14
15 CHECKPOINT NAMING CONTRACT for the METRIC model (model.npz; the grader loads YOUR ``.npz`` into
16 its own reference MLPClassifier by these exact keys/shapes -- do not rename):
17 MLPClassifier (model.npz):
18 layers.{i}.fc.weight (d_in_i, d_hidden) | layers.{i}.fc.bias (d_hidden,)
19 layers.{i}.ln.gamma/.beta (d_hidden,)
20 head.weight (d_hidden, num_classes) | head.bias (num_classes,)
21 (layer 0 has d_in_0 = in_dim; layers 1.. have d_in_i = d_hidden.)
22 CNNClassifier:
23 conv1.weight (C,1,3,3) | conv1.bias (C,) | conv2.weight (C,C,3,3) | conv2.bias (C,)
24 fc.weight (C*(img//4)*(img//4), n_classes) | fc.bias (n_classes,)
25 SeqClassifier, cell in {rnn (G=1), lstm (G=4), gru (G=3)}:
26 embed
…[truncated 22234 chars]/app/submission/optim.py
1 """Optimizers + LR schedulers over a flat list of autograd ``Tensor`` parameters. 2 3 Implement the ``# TODO`` update math + schedulers. Every optimizer updates ``param.data`` IN 4 PLACE using ``param.grad``. The grader runs one (and several) step(s) and compares your updated 5 params against its reference within a tight tolerance, so the math must be exact. ``t`` is the 6 1-based step count (track it yourself where bias correction is needed). 7 8 SGD (momentum ``mu``, coupled weight decay ``wd``, optional Nesterov): 9 g = grad + wd * param 10 v = mu * v + g 11 update = (g + mu * v) if nesterov else v 12 param -= lr * update 13 14 Adagrad: g = grad + wd*param ; s += g^2 ; param -= lr * g / (sqrt(s) + eps) 15 RMSprop: g = grad + wd*param ; s = alpha*s + (1-alpha)*g^2 ; param -= lr*g/(sqrt(s)+eps) 16 17 Adam (COUPLED / L2 weight decay folded into the gradient): 18 g = grad + wd*param 19 m = b1*m + (1-b1)*g ; v = b2*v + (1-b2)*g^2 20 mhat = m/(1-b1^t) ; vhat = v/(1-b2^t) 21 param -= lr * mhat / (sqrt(vhat) + eps) 22 23 AdamW (DECOUPLED weight decay -- decay the PARAM, not the moments): 24 m = b1*m + (1-b1)*g ; v = b2*v + (1-b2)*g^2 25 mhat = m/(1-b1^t) ; vhat = v/(1-b2^t) 26 param -= lr * ( mhat/(sqrt(vhat)+eps) + wd*param ) 27 28 Lion (sign of an interpolated momentum; decoupled weight decay): 29 update = sign( b1*m + (1-b1)*g ) 30 param -= lr * ( update + wd*param ) 31 m = b2*m + (1-b2)*g …[truncated 5297 chars]
/app/submission/data.py
1 """Data loading + minibatch iteration for the FixMatch task, plus tiny synthetic dataset builders
2 the component suite uses to exercise the CNN / sequence models.
3
4 Implement every ``# TODO`` body. ``/app/data/labeled.npz`` ships the FEW labeled examples (arrays
5 ``X`` (N_lab, dim) standardized features + ``y`` (N_lab,) integer labels) and
6 ``/app/data/unlabeled.npz`` ships the large UNLABELED pool (array ``X`` only).
7
8 Contract:
9 load_npz(path) -> (X float64 (N, dim), y int64 (N,) or None if the file has no ``y``)
10 n_classes(y) -> int (max label + 1)
11 epoch_batches(n, batch_size, rng) -> yields arrays of shuffled indices (last batch may be short)
12 get_batch(X, y, idx) -> (Xb float64 (B, dim), yb int64 (B,) or None, idx int64 (B,))
13 make_image_dataset(n, seed, img, n_classes) -> (X (n,1,img,img), y (n,)) for the CNN model
14 make_seq_dataset(n, seed, T, vocab, n_classes) -> (X (n,T) int, y (n,)) for the seq model
15 """
16 from __future__ import annotations
17
18 from typing import Optional, Tuple
19
20 import numpy as np
21
22
23 def load_npz(path: str) -> Tuple[np.ndarray, Optional[np.ndarray]]:
24 # TODO: np.load the file; return (X as float64, y as int64 reshaped to 1-D or None if absent).
25 raise NotImplementedError("load_npz")
26
27
28 def n_classes(y: np.ndarray) -> int:
29 # TODO: return int(max(y)) + 1
30 raise NotImplementedError("n_classes")
31
32
33 def epoch_batches(n: int, batch_size: int, rng: np.random.G
…[truncated 1281 chars]/app/submission/ssl.py
1 """FixMatch (+ related MixMatch / UDA) semi-supervised-learning machinery, on top of YOUR numpy 2 autograd engine. THIS IS THE DOMAIN DELIVERABLE -- implement every ``# TODO`` body. 3 4 Each differentiable loss below is an autograd OP: it must build a scalar Tensor whose ``_backward`` 5 adds the correct analytic gradient into the input logits (the grader finite-difference gradient- 6 checks each one). The augmentation / gating / alignment / EMA pieces are pure numpy recursions the 7 grader checks for exactness. A wrong gradient, gate, alignment, sharpen, mixup, interleave, or EMA 8 update fails the domain milestone -> reward 0. 9 10 CHECKLIST (each is a separate graded check): 11 AUGMENTATION (numpy, label-preserving, no grad): 12 weak_augment strong_augment cutout mixup interleave / de_interleave 13 PSEUDO-LABELING / DISTRIBUTION CONTROL (numpy, no grad): 14 pseudo_label confidence_mask sharpen DistributionAligner EMAScalar 15 DIFFERENTIABLE LOSSES (autograd ops, hand-written analytic backward): 16 soft_cross_entropy consistency_loss mse_consistency kl_divergence 17 entropy_loss class_balance_loss fixmatch_loss 18 EMA TEACHER (numpy recursion): 19 EMATeacher 20 21 NUMERICAL CONVENTIONS (MUST match): 22 * soft_cross_entropy / consistency: per-row loss ``-sum_c target[c]*logp[c]`` weighted by 23 ``weight[n]`` and AVERAGED OVER THE FULL BATCH N (divide by N, not by the number of confident 24 rows). Backward wrt logits: `` …[truncated 20837 chars]
/app/submission/train.py
1 """FixMatch semi-supervised training loop using YOUR from-scratch numpy framework + YOUR SSL 2 machinery in ssl.py. 3 4 Implement the ``# TODO`` training loop. Per step: 5 * supervised: cross-entropy on a WEAKLY-augmented labeled batch; 6 * unlabeled: forward a WEAKLY-augmented view -> softmax -> (distribution-align) -> confidence-gated 7 pseudo-labels; then minimize the masked consistency loss between the STRONGLY-augmented view and 8 those (stop-gradient) pseudo-labels, weighted by lambda_u; 9 * backward -> grad-clip -> optimizer step (an LR warmup + cosine schedule is a good choice); 10 * EMA-update a teacher copy of the weights. 11 12 Save an ``.npz`` checkpoint of the EMA-averaged classifier state_dict so the grader can load your 13 weights into ITS reference classifier and score top-1 on the SEALED held-out TEST split. 14 15 PIN the architecture to match the grader (it rebuilds exactly this graph and loads your weights): 16 in_dim = X.shape[1], num_classes = max(y)+1, d_hidden = 256, n_layer = 3. 17 18 NOTE: a supervised-only run on the few labels overfits them and generalizes near the supervised 19 floor; a naive / collapsed FixMatch run (no distribution alignment / no supervised warmup / 20 mis-tuned gate) collapses to a constant predictor near chance. You need the full FixMatch recipe, 21 with correct component gradients, to clear the threshold. 22 """ 23 from __future__ import annotations 24 25 import argparse 26 import json 27 import math 2 …[truncated 2303 chars]
/app/run_checks.py
60
61 # Public seed base. The GRADER uses a DIFFERENT base, so do not curve-fit to these seeds.
62 PUBLIC_SEED_BASE = 1000
63
64
65 # --------------------------------------------------------------------------- module loading
66 def _load_module(name, path, inject=None):
67 saved = {}
68 if inject:
69 for k, v in inject.items():
70 saved[k] = sys.modules.get(k)
71 sys.modules[k] = v
72 try:
73 spec = importlib.util.spec_from_file_location(name, str(path))
74 mod = importlib.util.module_from_spec(spec)
75 spec.loader.exec_module(mod)
76 return mod
77 finally:
78 if inject:
79 for k, old in saved.items():
80 if old is None:
81 sys.modules.pop(k, None)
82 else:
83 sys.modules[k] = old
84
85
86 def load_framework(root):
87 ag = _load_module("agent_autograd", root / "autograd.py")
88 nn = _load_module("agent_nn", root / "nn.py", inject={"autograd": ag})
89 opt = _load_module("agent_optim", root / "optim.py", inject={"autograd": ag})
90 ssl = _load_module("agent_ssl", root / "ssl.py", inject={"autograd": ag})
91 data = _load_module("agent_data", root / "data.py")
92 return {"autograd": ag, "nn": nn, "optim": opt, "ssl": ssl, "data": data}
93
94
95 # --------------------------------------------------------------------------- numeric helpers
96 def _num_grad_input(forward, x, eps=EPS):
…[truncated 46583 chars]Check tolerances and data
grep -n "EPS\|TOL_" /app/run_checks.py | head -20; echo "---TEST.SH---"; cat /app/test.sh; echo "---DATA SHAPES---"; python3 -c "
import numpy as np
d=np.load('/app/data/labeled.npz'); print('labeled X',d['X'].shape,'y',d['y'].shape,'classes',d['y'].max()+1, np.bincount(d['y']))
u=np.load('/app/data/unlabeled.npz'); print('unlabeled X',u['X'].shape)
"55:TOL_GRAD = 2e-3
56:TOL_PROP = 1e-6
57:TOL_OPT = 1e-7
58:TOL_SCHED = 1e-9
59:EPS = 1e-5
96:def _num_grad_input(forward, x, eps=EPS):
145: results.append((f"grad/{name}", err < TOL_GRAD, f"max_abs={err:.2e}"))
216: results.append((f"grad/{name}", _max_abs(np.asarray(pt.grad), _num_grad_input(fwd, p0.copy())) < TOL_GRAD, ""))
231: results.append(("grad/cross_entropy", _max_abs(np.asarray(lt.grad), gnum) < TOL_GRAD, ""))
243: results.append(("grad/embedding", _max_abs(np.asarray(Wt.grad), gnum) < TOL_GRAD, ""))
256: results.append((f"grad/{name}", _max_abs(np.asarray(xt.grad), gnum) < TOL_GRAD, ""))
295: results.append((f"grad/{name}", _max_abs(np.asarray(xt.grad), gnum) < TOL_GRAD, ""))
318: flat[i] = old + EPS; lp = float(loss_now().data)
319: flat[i] = old - EPS; lm = float(loss_now().data)
321: gf[i] = (lp - lm) / (2 * EPS)
322: results.append((f"grad/{name}", _max_abs(gan, gnum) < TOL_GRAD, ""))
352: return err < TOL_PROP and bool((s >= 0).all()), f"row_sum_err={err:.1e}"
360: return _max_abs(ag.relu(T(x)).data, np.maximum(x, 0)) < TOL_PROP, ""
490: results.append((f"optim/{name}", err < TOL_OPT, f"max_abs={err:.1e}"))
581: results.append((f"optim/{name}", err < TOL_OPT and abs(ret - total) < 1e-6, f"max_abs={err:.1e}"))
---TEST.SH---
#!/usr/bin/env bash
# Smoke test (NOT the grader): confirms the environment is wire
…[truncated 1405 chars]/app/submission/autograd.py
def _unbroadcast(grad: np.ndarray, shape: tuple) -> np.ndarray:
"""Sum ``grad`` (a broadcasted shape) back down to ``shape``: drop leading extra axes, then
sum size-1 axes with keepdims. Use this in every binary-op backward."""
while grad.ndim > len(shape):
grad = grad.sum(axis=0)
for ax, sz in enumerate(shape):
if sz == 1 and grad.shape[ax] != 1:
grad = grad.sum(axis=ax, keepdims=True)
return grad.reshape(shape)def _unbroadcast(grad: np.ndarray, shape: tuple) -> np.ndarray:
"""Sum ``grad`` (a broadcasted shape) back down to ``shape``: drop leading extra axes, then
sum size-1 axes with keepdims. Use this in every binary-op backward."""
while grad.ndim > len(shape):
grad = grad.sum(axis=0)
for ax, sz in enumerate(shape):
if sz == 1 and grad.shape[ax] != 1:
grad = grad.sum(axis=ax, keepdims=True)
return grad.reshape(shape)
def _restore(g: np.ndarray, axis, keepdims: bool, ndim: int) -> np.ndarray:
"""Re-insert reduced (size-1) axes into ``g`` so it broadcasts back over a reduction's input."""
g = np.asarray(g)
if axis is None or keepdims:
return g
ax = axis if isinstance(axis, tuple) else (axis,)
for x in sorted(z % ndim for z in ax):
g = np.expand_dims(g, x)
return g
def _reduce_count(shape, axis) -> float:
if axis is None:
n = 1
for s in shape:
n *= s
return float(n)
ax = axis if isinstance(axis, tuple) else (axis,)
n = 1
for x in ax:
n *= shape[x]
return float(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
TODO: implement the topological walk + seeding. (The per-op ``_backward`` closures do
the local accumulation; here you only orchestrate the order + seed ``self.grad``.)
"""
raise NotImplementedError("Tensor.backward") TODO: implement the topological walk + seeding. (The per-op ``_backward`` closures do
the local accumulation; here you only orchestrate the order + seed ``self.grad``.)
"""
topo: List["Tensor"] = []
visited = set()
def build(t: "Tensor"):
if id(t) in visited:
return
visited.add(id(t))
for p in t._parents:
build(p)
topo.append(t)
build(self)
if grad is None:
grad = np.ones_like(self.data)
self._accum(_as_f64(grad))
for t in reversed(topo):
t._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
def _bw():
raise NotImplementedError("add backward") # TODO def _bw():
a._accum(_unbroadcast(out.grad, a.data.shape))
b._accum(_unbroadcast(out.grad, 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
def _bw():
raise NotImplementedError("sub backward") # TODO def _bw():
a._accum(_unbroadcast(out.grad, a.data.shape))
b._accum(_unbroadcast(-out.grad, 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
def _bw():
raise NotImplementedError("mul backward") # TODO def _bw():
a._accum(_unbroadcast(out.grad * b.data, a.data.shape))
b._accum(_unbroadcast(out.grad * 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
def _bw():
raise NotImplementedError("div backward") # TODO def _bw():
a._accum(_unbroadcast(out.grad / b.data, a.data.shape))
b._accum(_unbroadcast(-out.grad * a.data / (b.data ** 2), 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
def _bw():
raise NotImplementedError("power backward") # TODO def _bw():
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
def _bw():
raise NotImplementedError("maximum backward") # TODO def _bw():
ga = np.where(a.data > b.data, 1.0, np.where(a.data == b.data, 0.5, 0.0))
a._accum(_unbroadcast(out.grad * ga, a.data.shape))
b._accum(_unbroadcast(out.grad * (1.0 - ga), 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
def _bw():
raise NotImplementedError("minimum backward") # TODO def _bw():
ga = np.where(a.data < b.data, 1.0, np.where(a.data == b.data, 0.5, 0.0))
a._accum(_unbroadcast(out.grad * ga, a.data.shape))
b._accum(_unbroadcast(out.grad * (1.0 - ga), 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
def _bw():
raise NotImplementedError("where backward") # TODO def _bw():
a._accum(_unbroadcast(np.where(c, out.grad, 0.0), a.data.shape))
b._accum(_unbroadcast(np.where(c, 0.0, out.grad), 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
def _bw():
raise NotImplementedError("exp backward") # TODO def _bw():
a._accum(out.grad * out.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
def _bw():
raise NotImplementedError("log backward") # TODO def _bw():
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
def _bw():
raise NotImplementedError("sqrt backward") # TODO def _bw():
a._accum(out.grad * 0.5 / out.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
def _bw():
raise NotImplementedError("abs backward") # TODO def _bw():
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
def _bw():
raise NotImplementedError("clip backward") # TODO def _bw():
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
def _bw():
raise NotImplementedError("sum backward") # TODO def _bw():
g = _restore(out.grad, axis, keepdims, a.data.ndim)
a._accum(np.broadcast_to(g, a.data.shape).astype(np.float64))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():
raise NotImplementedError("mean backward") # TODO def _bw():
n = _reduce_count(a.data.shape, axis)
g = _restore(out.grad, axis, keepdims, a.data.ndim)
a._accum(np.broadcast_to(g, a.data.shape).astype(np.float64) / 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
def _bw():
raise NotImplementedError("max backward") # TODO def _bw():
out_full = _restore(out.data, axis, keepdims, a.data.ndim)
g = _restore(out.grad, axis, keepdims, a.data.ndim)
mask = (a.data == out_full).astype(np.float64)
counts = mask.sum(axis=axis, keepdims=True)
a._accum(mask * (g / 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
def _bw():
raise NotImplementedError("min backward") # TODO def _bw():
out_full = _restore(out.data, axis, keepdims, a.data.ndim)
g = _restore(out.grad, axis, keepdims, a.data.ndim)
mask = (a.data == out_full).astype(np.float64)
counts = mask.sum(axis=axis, keepdims=True)
a._accum(mask * (g / 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
def _bw():
raise NotImplementedError("transpose backward") # TODO def _bw():
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
def _bw():
raise NotImplementedError("reshape backward") # TODO def _bw():
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
def _bw():
raise NotImplementedError("getitem backward") # TODO def _bw():
g = np.zeros_like(a.data)
np.add.at(g, idx, out.grad)
a._accum(g)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():
raise NotImplementedError("concat backward") # TODO def _bw():
g = out.grad
ax = axis % g.ndim
start = 0
for t in ts:
sz = t.data.shape[ax]
sl = [slice(None)] * g.ndim
sl[ax] = slice(start, start + sz)
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
def _bw():
raise NotImplementedError("stack backward") # TODO def _bw():
g = out.grad
ax = axis % g.ndim
for i, t in enumerate(ts):
sl = [slice(None)] * g.ndim
sl[ax] = i
t._accum(g[tuple(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
def _bw():
raise NotImplementedError("matmul backward") # TODO def _bw():
g = out.grad
da = g @ np.swapaxes(b.data, -1, -2)
db = np.swapaxes(a.data, -1, -2) @ g
a._accum(_unbroadcast(da, a.data.shape))
b._accum(_unbroadcast(db, 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
def _bw():
raise NotImplementedError("relu backward") # TODO def _bw():
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
def _bw():
raise NotImplementedError("leaky_relu backward") # TODO def _bw():
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
def _bw():
raise NotImplementedError("sigmoid backward") # TODO def _bw():
a._accum(out.grad * out.data * (1.0 - out.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
def _bw():
raise NotImplementedError("tanh backward") # TODO def _bw():
a._accum(out.grad * (1.0 - out.data * out.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
def _bw():
raise NotImplementedError("softplus backward") # TODO def _bw():
a._accum(out.grad * (1.0 / (1.0 + np.exp(-bx))))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():
raise NotImplementedError("silu backward") # TODO def _bw():
a._accum(out.grad * s * (1.0 + x * (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
def _bw():
raise NotImplementedError("mish backward") # TODO def _bw():
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
def _bw():
raise NotImplementedError("elu backward") # TODO def _bw():
a._accum(out.grad * np.where(pos, 1.0, al * np.exp(np.minimum(x, 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
def _bw():
raise NotImplementedError("logsumexp backward") # TODO def _bw():
sm = e / s
g = out.grad if keepdims else np.expand_dims(out.grad, axis)
a._accum(g * sm)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():
raise NotImplementedError("var backward") # TODO def _bw():
n = _reduce_count(x.shape, axis)
g = _restore(out.grad, axis, keepdims, x.ndim)
a._accum(g * (2.0 / n) * xc)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():
raise NotImplementedError("std backward") # TODO def _bw():
n = _reduce_count(x.shape, axis)
g = _restore(out.grad, axis, keepdims, x.ndim)
a._accum(g * xc / (n * sd))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():
raise NotImplementedError("cumsum backward") # TODO def _bw():
g = out.grad
rc = np.flip(np.cumsum(np.flip(g, axis=axis), axis=axis), axis=axis)
a._accum(rc)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():
raise NotImplementedError("outer backward") # TODO def _bw():
g = out.grad
a._accum(g @ b.data)
b._accum(a.data @ g)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():
raise NotImplementedError("gelu backward") # TODO def _bw():
pdf = np.exp(-x * x / 2.0) / 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
def _bw():
raise NotImplementedError("softmax backward") # TODO def _bw():
g = out.grad
a._accum(s * (g - (g * s).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
def _bw():
raise NotImplementedError("log_softmax backward") # TODO def _bw():
g = out.grad
sm = np.exp(out.data)
a._accum(g - sm * 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
def _bw():
raise NotImplementedError("cross_entropy backward") # TODO def _bw():
sm = np.exp(logp)
g = sm.copy()
g[np.arange(n), t] -= 1.0
g = g / n
logits._accum(g * 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
def _bw():
raise NotImplementedError("mse_loss backward") # TODO def _bw():
pred._accum(out.grad * 2.0 * (pred.data - tgt) / pred.data.size)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():
raise NotImplementedError("layernorm backward") # TODO def _bw():
g = out.grad
lead = tuple(range(g.ndim - 1))
gamma._accum((g * xhat).sum(axis=lead))
beta._accum(g.sum(axis=lead))
gx = g * gamma.data
dx = inv * (gx - gx.mean(axis=-1, keepdims=True)
- xhat * (gx * xhat).mean(axis=-1, keepdims=True))
a._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
def _bw():
raise NotImplementedError("batchnorm backward") # TODO def _bw():
g = out.grad
gamma._accum((g * xhat).sum(axis=0))
beta._accum(g.sum(axis=0))
gx = g * gamma.data
dx = inv * (gx - gx.mean(axis=0, keepdims=True)
- xhat * (gx * xhat).mean(axis=0, keepdims=True))
a._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
def _bw():
raise NotImplementedError("embedding backward") # TODO def _bw():
g = np.zeros_like(weight.data)
np.add.at(g, idx, out.grad)
weight._accum(g)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 (eval) backward") # TODO
out._backward = _bw_id
return out
raise NotImplementedError("dropout (training) -- optional") def _bw_id():
a._accum(out.grad)
out._backward = _bw_id
return out
if rng is None:
rng = np.random.default_rng()
mask = (rng.random(a.data.shape) >= p).astype(np.float64) / (1.0 - p)
out = _mk(a.data * mask, (a,), a.requires_grad)
def _bw():
a._accum(out.grad * mask)
out._backward = _bw
return outThe 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
TODO: implement the FORWARD and the backward. (Unlike the other ops, the forward is NOT
pre-filled for you -- build the output, then differentiate it.)
"""
raise NotImplementedError("conv2d") TODO: implement the FORWARD and the backward. (Unlike the other ops, the forward is NOT
pre-filled for you -- build the output, then differentiate it.)
"""
x = _ensure(x)
weight = _ensure(weight)
bias_t = _ensure(bias) if bias is not None else None
xd = x.data
wd = weight.data
N, Cin, H, W = xd.shape
Cout, Cin2, KH, KW = wd.shape
s, p = int(stride), int(padding)
xp = _pad2d(xd, p)
OH = (H + 2 * p - KH) // s + 1
OW = (W + 2 * p - KW) // s + 1
cols = np.zeros((N, Cin, KH, KW, OH, OW))
for i in range(KH):
for j in range(KW):
cols[:, :, i, j, :, :] = xp[:, :, i:i + s * OH:s, j:j + s * OW:s]
colsr = cols.reshape(N, Cin * KH * KW, OH * OW)
wr = wd.reshape(Cout, Cin * KH * KW)
out_data = np.einsum('oc,ncl->nol', wr, colsr).reshape(N, Cout, OH, OW)
if bias_t is not None:
out_data = out_data + bias_t.data.reshape(1, Cout, 1, 1)
parents = (x, weight) + ((bias_t,) if bias_t is not None else ())
rg = x.requires_grad or weight.requires_grad or (bias_t is not None and bias_t.requires_grad)
out = _mk(out_data, parents, rg)
def _bw():
g = out.grad
gr = g.reshape(N, Cout, OH * OW)
dw = np.einsum('nol,ncl->oc', gr, colsr).reshape(wd.shape)
weight._accum(dw)
if bias_t is not None:
bias_t._accum(g.sum(axis=(0, 2, 3)))
dcol = np.einsum('oc,nol->ncl', wr, gr).reshape(N, Cin, KH, KW, OH, OW)
dxp = np.zeros_li
…[truncated 270 chars]The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
TODO: implement the FORWARD and the backward.
"""
raise NotImplementedError("maxpool2d") TODO: implement the FORWARD and the backward.
"""
x = _ensure(x)
xd = x.data
N, C, H, W = xd.shape
k, s = int(kernel), int(stride)
OH = (H - k) // s + 1
OW = (W - k) // s + 1
win = np.zeros((N, C, k, k, OH, OW))
for i in range(k):
for j in range(k):
win[:, :, i, j, :, :] = xd[:, :, i:i + s * OH:s, j:j + s * OW:s]
wr = win.reshape(N, C, k * k, OH, OW)
out_data = wr.max(axis=2)
out = _mk(out_data, (x,), x.requires_grad)
def _bw():
g = out.grad
mx = wr.max(axis=2, keepdims=True)
mask = (wr == mx).astype(np.float64)
counts = mask.sum(axis=2, keepdims=True)
dist = mask / counts * g[:, :, None, :, :]
dist = dist.reshape(N, C, k, k, OH, OW)
dx = np.zeros_like(xd)
for i in range(k):
for j in range(k):
dx[:, :, i:i + s * OH:s, j:j + s * OW:s] += dist[:, :, i, j, :, :]
x._accum(dx)
out._backward = _bw
return outThe 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
TODO: implement the FORWARD and the backward.
"""
raise NotImplementedError("avgpool2d") TODO: implement the FORWARD and the backward.
"""
x = _ensure(x)
xd = x.data
N, C, H, W = xd.shape
k, s = int(kernel), int(stride)
OH = (H - k) // s + 1
OW = (W - k) // s + 1
win = np.zeros((N, C, k, k, OH, OW))
for i in range(k):
for j in range(k):
win[:, :, i, j, :, :] = xd[:, :, i:i + s * OH:s, j:j + s * OW:s]
wr = win.reshape(N, C, k * k, OH, OW)
out_data = wr.mean(axis=2)
out = _mk(out_data, (x,), x.requires_grad)
def _bw():
g = out.grad
dist = np.broadcast_to(g[:, :, None, :, :] / (k * k), (N, C, k * k, OH, OW))
dist = dist.reshape(N, C, k, k, OH, OW)
dx = np.zeros_like(xd)
for i in range(k):
for j in range(k):
dx[:, :, i:i + s * OH:s, j:j + s * OW:s] += dist[:, :, i, j, :, :]
x._accum(dx)
out._backward = _bw
return outThe file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run autograd checks
python3 run_checks.py --only autograd_grads 2>&1 | tail -40
grad/std_axis OK (max_abs=9.24e-11) grad/cumsum OK (max_abs=5.21e-10) grad/softmax OK (max_abs=2.11e-10) grad/softmax_3d OK (max_abs=2.35e-10) grad/log_softmax OK (max_abs=1.04e-09) grad/conv2d OK (max_abs=6.99e-10) grad/conv2d_stride OK (max_abs=2.50e-10) grad/maxpool2d OK (max_abs=3.17e-10) grad/avgpool2d OK (max_abs=3.49e-11) grad/layernorm_x OK (max_abs=4.11e-10) grad/batchnorm_x OK (max_abs=3.42e-10) grad/layernorm_gamma OK grad/layernorm_beta OK grad/batchnorm_gamma OK grad/batchnorm_beta OK grad/cross_entropy OK grad/embedding OK grad/nn.Linear_x FAIL (raised NotImplementedError: Linear.forward) grad/nn.RMSNorm_x FAIL (raised NotImplementedError: RMSNorm.forward) grad/nn.SwiGLU_x FAIL (raised NotImplementedError: SwiGLU.forward) grad/nn.LayerNorm_x FAIL (raised NotImplementedError: LayerNorm.forward) grad/nn.BatchNorm1d_x FAIL (raised NotImplementedError: BatchNorm1d.forward) grad/nn.HiddenBlock_x FAIL (raised NotImplementedError: …[truncated 1404 chars]
/app/submission/nn.py
def forward(self, x: Tensor) -> Tensor:
# TODO: matmul x@weight then add bias (if present). Use ag.matmul / ag.add.
raise NotImplementedError("Linear.forward") 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 outThe 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: return ag.embedding(self.weight, idx)
raise NotImplementedError("Embedding.forward")return ag.embedding(self.weight, idx)
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: return ag.layernorm(x, self.gamma, self.beta, self.eps)
raise NotImplementedError("LayerNorm.forward")return ag.layernorm(x, self.gamma, self.beta, 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
# TODO: ms = mean(x*x, -1, keepdims); inv = (ms+eps)**-0.5; return x*inv*gamma (autograd ops)
raise NotImplementedError("RMSNorm.forward") ms = ag.mean(ag.mul(x, x), axis=-1, keepdims=True)
inv = ag.power(ag.add(ms, self.eps), -0.5)
return ag.mul(ag.mul(x, inv), self.gamma)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: g = ag.silu(self.w_gate(x)); u = self.w_up(x); return self.w_down(ag.mul(g, u))
raise NotImplementedError("SwiGLU.forward") g = ag.silu(self.w_gate(x))
u = self.w_up(x)
return self.w_down(ag.mul(g, u))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: return ag.batchnorm(x, self.gamma, self.beta, self.eps)
raise NotImplementedError("BatchNorm1d.forward")return ag.batchnorm(x, self.gamma, self.beta, 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
# TODO: return ag.dropout(x, self.p, training, rng)
raise NotImplementedError("Dropout.forward")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
# TODO: return ag.conv2d(x, self.weight, self.bias, self.stride, self.padding)
raise NotImplementedError("Conv2d.forward")return ag.conv2d(x, self.weight, self.bias, self.stride, self.padding)
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: return ag.maxpool2d(x, self.kernel, self.stride)
raise NotImplementedError("MaxPool2d.forward")return ag.maxpool2d(x, self.kernel, self.stride)
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: return ag.avgpool2d(x, self.kernel, self.stride)
raise NotImplementedError("AvgPool2d.forward")return ag.avgpool2d(x, self.kernel, self.stride)
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 causal multi-head self-attention as described in the docstring.
raise NotImplementedError("MultiHeadSelfAttention.forward") B, T, _ = x.shape
H, dh = self.n_head, self.d_head
qkv = self.qkv(x) # (B, T, 3*d_model)
qkv = ag.reshape(qkv, (B, T, 3, H, dh))
qkv = ag.transpose(qkv, (2, 0, 3, 1, 4)) # (3, B, H, T, dh)
q, k, v = qkv[0], qkv[1], qkv[2] # (B, H, T, dh)
kt = ag.transpose(k, (0, 1, 3, 2)) # (B, H, dh, T)
scores = ag.mul(ag.matmul(q, kt), 1.0 / np.sqrt(dh)) # (B, H, T, T)
mask = np.triu(np.ones((T, T)), k=1) * -1e9
scores = ag.add(scores, Tensor(mask.reshape(1, 1, T, T)))
att = ag.softmax(scores, axis=-1)
out = ag.matmul(att, v) # (B, H, T, dh)
out = ag.transpose(out, (0, 2, 1, 3)) # (B, T, H, dh)
out = ag.reshape(out, (B, T, self.d_model))
return self.proj(out)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: fc2(gelu(fc1(x)))
raise NotImplementedError("MLP.forward")return self.fc2(ag.gelu(self.fc1(x)))
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: pre-norm residual wiring (see docstring).
raise NotImplementedError("TransformerBlock.forward") x = ag.add(x, self.attn(self.ln1(x)))
x = ag.add(x, self.mlp(self.ln2(x)))
return xThe file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: tanh(x@W_ih + b_ih + h@W_hh + b_hh)
raise NotImplementedError("RNNCell.forward") z = ag.add(ag.add(ag.matmul(x, self.W_ih), self.b_ih),
ag.add(ag.matmul(h, self.W_hh), self.b_hh))
return ag.tanh(z)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 the LSTM cell update; return (h_new, c_new).
raise NotImplementedError("LSTMCell.forward") h, c = state
H = self.hidden
z = ag.add(ag.add(ag.matmul(x, self.W_ih), self.b_ih),
ag.add(ag.matmul(h, self.W_hh), self.b_hh))
i = ag.sigmoid(z[:, 0 * H:1 * H])
f = ag.sigmoid(z[:, 1 * H:2 * H])
g = ag.tanh(z[:, 2 * H:3 * H])
o = ag.sigmoid(z[:, 3 * H:4 * H])
c_new = ag.add(ag.mul(f, c), ag.mul(i, g))
h_new = ag.mul(o, ag.tanh(c_new))
return h_new, c_newThe 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 the GRU cell update; return h_new.
raise NotImplementedError("GRUCell.forward") H = self.hidden
gi = ag.add(ag.matmul(x, self.W_ih), self.b_ih)
gh = ag.add(ag.matmul(h, self.W_hh), self.b_hh)
r = ag.sigmoid(ag.add(gi[:, 0 * H:1 * H], gh[:, 0 * H:1 * H]))
z = ag.sigmoid(ag.add(gi[:, 1 * H:2 * H], gh[:, 1 * H:2 * H]))
n = ag.tanh(ag.add(gi[:, 2 * H:3 * H], ag.mul(r, gh[:, 2 * H:3 * H])))
h_new = ag.add(ag.mul(ag.sub(1.0, z), n), ag.mul(z, h))
return h_newThe 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: relu(layernorm(linear(x))) -- compose self.fc, self.ln and ag.relu.
raise NotImplementedError("HiddenBlock.forward")return ag.relu(self.ln(self.fc(x)))
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: h = Tensor(X) if X is a raw array; for blk in self.layers: h = blk(h);
# return self.head(h)
raise NotImplementedError("MLPClassifier.forward") h = X if isinstance(X, Tensor) else Tensor(X)
for blk in self.layers:
h = blk(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
# TODO: implement the conv->relu->maxpool x2 -> flatten -> fc pipeline (see docstring).
# Wrap a raw ndarray x in a Tensor first; reshape with the batch size N as dim 0.
raise NotImplementedError("CNNClassifier.forward") h = x if isinstance(x, Tensor) else Tensor(x)
N = h.shape[0]
h = ag.maxpool2d(ag.relu(self.conv1(h)), 2, 2)
h = ag.maxpool2d(ag.relu(self.conv2(h)), 2, 2)
h = ag.reshape(h, (N, self.feat))
return self.fc(h)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: embed idx, loop the cell over time from a zero state, classify the final hidden.
raise NotImplementedError("SeqClassifier.forward") idx = np.asarray(idx).astype(np.int64)
N, T = idx.shape
emb = self.embed(idx) # (N, T, d_model)
h = Tensor(np.zeros((N, self.hidden)))
if self.cell_kind == "lstm":
c = Tensor(np.zeros((N, self.hidden)))
for t in range(T):
h, c = self.cell(emb[:, t, :], (h, c))
else:
for t in range(T):
h = self.cell(emb[:, t, :], h)
return self.fc(h)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
# TODO: SGD(+momentum, +coupled weight decay, +optional Nesterov) update.
raise NotImplementedError("SGD.step") def step(self):
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
v = self.momentum * self._v[i] + g
self._v[i] = v
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
def step(self):
# TODO: Adagrad update (accumulate squared grads).
raise NotImplementedError("Adagrad.step") def step(self):
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._s[i] = self._s[i] + g * g
p.data -= self.lr * g / (np.sqrt(self._s[i]) + self.eps)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
# TODO: RMSprop update (EMA of squared grads).
raise NotImplementedError("RMSprop.step") def step(self):
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._s[i] = self.alpha * self._s[i] + (1.0 - self.alpha) * (g * g)
p.data -= self.lr * g / (np.sqrt(self._s[i]) + self.eps)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
# TODO: Adam update with bias correction + COUPLED (L2) weight decay.
raise NotImplementedError("Adam.step") def step(self):
self.t += 1
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad + self.weight_decay * p.data
self._m[i] = self.b1 * self._m[i] + (1.0 - self.b1) * g
self._v[i] = self.b2 * self._v[i] + (1.0 - self.b2) * (g * g)
mhat = self._m[i] / (1.0 - self.b1 ** self.t)
vhat = self._v[i] / (1.0 - self.b2 ** self.t)
p.data -= self.lr * mhat / (np.sqrt(vhat) + self.eps)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
# TODO: AdamW update with bias correction + DECOUPLED weight decay.
raise NotImplementedError("AdamW.step") def step(self):
self.t += 1
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad
self._m[i] = self.b1 * self._m[i] + (1.0 - self.b1) * g
self._v[i] = self.b2 * self._v[i] + (1.0 - self.b2) * (g * g)
mhat = self._m[i] / (1.0 - self.b1 ** self.t)
vhat = self._v[i] / (1.0 - self.b2 ** self.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
def step(self):
# TODO: Lion update (sign of interpolated momentum; momentum updated AFTER the step).
raise NotImplementedError("Lion.step") def step(self):
for i, p in enumerate(self.params):
if p.grad is None:
continue
g = p.grad
upd = np.sign(self.b1 * self._m[i] + (1.0 - self.b1) * g)
p.data -= self.lr * (upd + self.weight_decay * p.data)
self._m[i] = self.b2 * self._m[i] + (1.0 - 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: compute the global L2 norm over all param grads; scale in place if it exceeds max_norm.
raise NotImplementedError("clip_grad_norm") total = math.sqrt(sum(float((p.grad ** 2).sum()) for p in params if p.grad is not None))
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
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) * (1.0 + 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
"""Step decay: base_lr * gamma ** (step // step_size)."""
# TODO
raise NotImplementedError("step_lr") """Step decay: base_lr * gamma ** (step // step_size)."""
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
"""Exponential decay: base_lr * gamma ** step."""
# TODO
raise NotImplementedError("exponential_lr") """Exponential decay: base_lr * gamma ** step."""
return base_lr * gamma ** stepThe file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
"""Linear warmup to ``base_lr`` over ``warmup`` steps, then constant ``base_lr``."""
# TODO
raise NotImplementedError("warmup_lr") """Linear warmup to ``base_lr`` over ``warmup`` steps, then constant ``base_lr``."""
if step < warmup:
return base_lr * (step + 1) / warmup
return base_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/data.py
def load_npz(path: str) -> Tuple[np.ndarray, Optional[np.ndarray]]:
# TODO: np.load the file; return (X as float64, y as int64 reshaped to 1-D or None if absent).
raise NotImplementedError("load_npz")
def n_classes(y: np.ndarray) -> int:
# TODO: return int(max(y)) + 1
raise NotImplementedError("n_classes")
def epoch_batches(n: int, batch_size: int, rng: np.random.Generator):
# TODO: yield successive slices of a single rng.permutation(n) of size batch_size.
raise NotImplementedError("epoch_batches")
def get_batch(X: np.ndarray, y: Optional[np.ndarray], idx: np.ndarray):
# TODO: gather X[idx] (float64) and y[idx] (int64, or None if y is None); return (Xb, yb, idx).
raise NotImplementedError("get_batch")def load_npz(path: str) -> Tuple[np.ndarray, Optional[np.ndarray]]:
d = np.load(path)
X = np.asarray(d["X"], dtype=np.float64)
y = None
if "y" in d.files:
y = np.asarray(d["y"], dtype=np.int64).reshape(-1)
return X, y
def n_classes(y: np.ndarray) -> int:
return int(np.max(y)) + 1
def epoch_batches(n: int, batch_size: int, rng: np.random.Generator):
perm = rng.permutation(n)
for start in range(0, n, batch_size):
yield perm[start:start + batch_size]
def get_batch(X: np.ndarray, y: Optional[np.ndarray], idx: np.ndarray):
idx = np.asarray(idx).astype(np.int64)
Xb = np.asarray(X[idx], dtype=np.float64)
yb = None if y is None else np.asarray(y[idx], dtype=np.int64)
return Xb, yb, idxThe 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
# TODO: build per-class templates, pick labels, return template[y] + noise and the labels.
raise NotImplementedError("make_image_dataset") rng = np.random.default_rng(seed)
templates = rng.standard_normal((n_classes, 1, img, img))
y = rng.integers(0, n_classes, size=n).astype(np.int64)
X = templates[y] + rng.standard_normal((n, 1, img, img)) * 0.3
return np.asarray(X, dtype=np.float64), yThe 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
# TODO: sample (n, T) token ids in [0, vocab); label = (row sum) % n_classes.
raise NotImplementedError("make_seq_dataset") rng = np.random.default_rng(seed)
X = rng.integers(0, vocab, size=(n, T)).astype(np.int64)
y = (X.sum(axis=1) % n_classes).astype(np.int64)
return X, yThe file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run all checks quiet
rm -rf submission/__pycache__ && python3 run_checks.py --quiet 2>&1 | tail -15
---------------------------------------------------------------------- autograd_grads 77/77 | nn_forward 20/20 | optim 16/16 | fixmatch 1/23 | integration 0/2 | data 4/4 TOTAL 118/142 public checks passing Fix the FAIL lines above (each is a backward/forward/optimizer/SSL bug). Re-run this until everything is green, then train.
/app/submission/ssl.py
# TODO: return X + rng.normal(0, sigma, X.shape) (X as float64)
raise NotImplementedError("weak_augment") X = np.asarray(X, dtype=np.float64)
return X + rng.normal(0.0, sigma, X.shape)The file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: add jitter, then for each row zero round(mask_frac*dim) randomly-chosen coordinates.
raise NotImplementedError("strong_augment") X = np.asarray(X, dtype=np.float64)
out = X + rng.normal(0.0, sigma, X.shape)
dim = X.shape[1]
k = int(round(mask_frac * dim))
if k > 0:
for i in range(X.shape[0]):
idx = rng.choice(dim, size=k, replace=False)
out[i, idx] = 0.0
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: copy X; for each row pick a random start and zero [start:start+length].
raise NotImplementedError("cutout") out = np.array(X, dtype=np.float64, copy=True)
dim = X.shape[1]
for i in range(X.shape[0]):
start = int(rng.integers(0, dim))
out[i, start:min(start + length, dim)] = 0.0
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: implement the convex combination described above; return (X_mix, y_mix, lam, perm).
raise NotImplementedError("mixup") X = np.asarray(X, dtype=np.float64)
onehot = np.asarray(onehot, dtype=np.float64)
lam = float(rng.beta(alpha, alpha))
lam = max(lam, 1.0 - lam)
n = X.shape[0]
perm = rng.permutation(n)
X_mix = lam * X + (1.0 - lam) * X[perm]
y_mix = lam * onehot + (1.0 - lam) * onehot[perm]
return X_mix, y_mix, lam, permThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: stack to (K, M, ...), swap the leading block axis with K over full blocks, return list.
raise NotImplementedError("interleave")return _interleave_swap(arrays, batch_size)
The file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: invert the interleave permutation.
raise NotImplementedError("de_interleave") return _interleave_swap(arrays, batch_size)
def _interleave_swap(arrays, batch_size):
a0 = np.asarray(arrays[0])
M = a0.shape[0]
bs = int(batch_size)
nb = M // bs
rest = a0.shape[1:]
s = np.stack([np.asarray(a).reshape(nb, bs, *rest) for a in arrays], axis=0) # (K, nb, bs, ...)
s = np.swapaxes(s, 0, 1) # (nb, K, bs, ...)
return [s[i].reshape(M, *rest) for i in range(s.shape[0])]The file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: compute hard/conf/mask/onehot as above.
raise NotImplementedError("pseudo_label") p = np.asarray(weak_probs, dtype=np.float64)
n, c = p.shape
hard = p.argmax(axis=1)
conf = p.max(axis=1)
mask = (conf >= tau).astype(np.float64)
onehot = np.zeros((n, c), dtype=np.float64)
onehot[np.arange(n), hard] = 1.0
return onehot, mask, hardThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: return (probs.max(axis=1) >= tau).astype(float)
raise NotImplementedError("confidence_mask") p = np.asarray(probs, dtype=np.float64)
return (p.max(axis=1) >= tau).astype(np.float64)The file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: pw = probs ** (1/temperature); return pw / pw.sum(axis=1, keepdims=True)
raise NotImplementedError("sharpen") p = np.asarray(probs, dtype=np.float64)
pw = p ** (1.0 / temperature)
return pw / pw.sum(axis=1, keepdims=True)The file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: update self.value with the EMA recursion and return it.
raise NotImplementedError("EMAScalar.update") self.value = self.decay * self.value + (1.0 - self.decay) * float(new)
return self.valueThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: update self.running, rescale by prior/(running+1e-6), renormalize rows, return.
raise NotImplementedError("DistributionAligner.align") p = np.asarray(weak_probs, dtype=np.float64)
self.running = self.momentum * self.running + (1.0 - self.momentum) * p.mean(axis=0)
aligned = p * (self.prior / (self.running + 1e-6))
aligned = aligned / aligned.sum(axis=1, keepdims=True)
return alignedThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: compute sigma (per-class confident-argmax counts), set self.sigma, return beta*tau
# with denom = max(sigma.max(), C - sigma.sum()) (guard against 0).
raise NotImplementedError("FlexMatchThresholder.update") p = np.asarray(weak_probs, dtype=np.float64)
hard = p.argmax(axis=1)
conf = p.max(axis=1)
adm = conf >= self.tau
sigma = np.array([float(np.sum(adm & (hard == c))) for c in range(self.C)], dtype=np.float64)
self.sigma = sigma
denom = max(float(sigma.max()), float(self.C - sigma.sum()))
if denom <= 0.0:
denom = 1.0
return (sigma / denom) * self.tauThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: thr = self.update(weak_probs); return (max prob per row >= thr[argmax]) as 0/1 float.
raise NotImplementedError("FlexMatchThresholder.flex_mask") p = np.asarray(weak_probs, dtype=np.float64)
thr = self.update(p)
hard = p.argmax(axis=1)
conf = p.max(axis=1)
return (conf >= thr[hard]).astype(np.float64)The file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: clip(step/length, 0, 1) (handle length<=0 -> 1.0).
raise NotImplementedError("linear_rampup") if length <= 0:
return 1.0
return float(np.clip(step / length, 0.0, 1.0))The file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: per the docstring (1.0 once step>=length).
raise NotImplementedError("cosine_rampup") if length <= 0:
return 1.0
x = float(np.clip(step / length, 0.0, 1.0))
return 0.5 * (1.0 - np.cos(np.pi * x))The file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: per the docstring.
raise NotImplementedError("exp_rampup") if length <= 0:
return 1.0
x = float(np.clip(step / length, 0.0, 1.0))
return float(np.exp(-5.0 * (1.0 - x) ** 2))The file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: build the (N,C) complementary one-hot on argmin for confident rows only.
raise NotImplementedError("complement_distribution") p = np.asarray(weak_probs, dtype=np.float64)
n, c = p.shape
conf = p.max(axis=1)
least = p.argmin(axis=1)
sel = conf >= tau
out = np.zeros((n, c), dtype=np.float64)
out[np.arange(n)[sel], least[sel]] = 1.0
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: forward value via _stable_log_softmax_np; build out via _mk(val, (logits,)); set out._backward.
raise NotImplementedError("soft_cross_entropy") logp = _stable_log_softmax_np(x)
val = float((w * (-(tgt * logp).sum(axis=1))).sum() / n)
out = _mk(val, (logits,))
def _bw():
sm = np.exp(logp)
tsum = tgt.sum(axis=1, keepdims=True)
g = (w / n)[:, None] * (sm * tsum - tgt)
logits._accum(g * out.grad)
out._backward = _bw
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: forward (sum of squared diffs, weighted, /N); backward through the softmax (see docstring).
raise NotImplementedError("mse_consistency") s = _stable_softmax_np(x)
diff = s - tgt
val = float((w * (diff * diff).sum(axis=1)).sum() / n)
out = _mk(val, (student_logits,))
def _bw():
v = (2.0 * w / n)[:, None] * diff
dot = (v * s).sum(axis=1, keepdims=True)
student_logits._accum(s * (v - dot) * out.grad)
out._backward = _bw
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: forward (include the teacher-entropy term so KL is reported); backward as above.
raise NotImplementedError("kl_divergence") logp = _stable_log_softmax_np(x)
tc = np.clip(tgt, 1e-12, 1.0)
val = float((w * (tgt * (np.log(tc) - logp)).sum(axis=1)).sum() / n)
out = _mk(val, (student_logits,))
def _bw():
sm = np.exp(logp)
tsum = tgt.sum(axis=1, keepdims=True)
g = (w / n)[:, None] * (sm * tsum - tgt)
student_logits._accum(g * out.grad)
out._backward = _bw
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: forward mean entropy; backward per the docstring.
raise NotImplementedError("entropy_loss") p = _stable_softmax_np(x)
logp = _stable_log_softmax_np(x)
H = -(p * logp).sum(axis=1)
val = float(H.sum() / n)
out = _mk(val, (logits,))
def _bw():
g = (1.0 / n) * p * (-logp - H[:, None])
logits._accum(g * out.grad)
out._backward = _bw
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: forward (-H of the batch marginal); backward per the docstring.
raise NotImplementedError("class_balance_loss") p = _stable_softmax_np(x)
pbar = p.mean(axis=0)
pbar_c = np.clip(pbar, 1e-12, 1.0)
val = float((pbar * np.log(pbar_c)).sum())
out = _mk(val, (logits,))
def _bw():
a = 1.0 + np.log(pbar_c)
dot = (p * a[None, :]).sum(axis=1, keepdims=True)
g = (1.0 / n) * p * (a[None, :] - dot)
logits._accum(g * out.grad)
out._backward = _bw
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: stable sigmoid-BCE forward via _mk(val, (z,)); backward (w/N)*(sigmoid(z)-t).
raise NotImplementedError("bce_consistency") s = 1.0 / (1.0 + np.exp(-x))
bce = np.maximum(x, 0.0) - x * t + np.log1p(np.exp(-np.abs(x)))
val = float((w * bce.sum(axis=1)).sum() / n)
out = _mk(val, (z,))
def _bw():
g = (w / n)[:, None] * (s - t)
z._accum(g * out.grad)
out._backward = _bw
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: forward sum-squared-diff averaged over N; backward (2w/N)*(x-t).
raise NotImplementedError("pi_consistency") diff = x - t
val = float((w * (diff * diff).sum(axis=1)).sum() / n)
out = _mk(val, (z,))
def _bw():
g = (2.0 * w / n)[:, None] * diff
z._accum(g * out.grad)
out._backward = _bw
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: forward via _stable_log_softmax_np; backward through softmax per the docstring.
raise NotImplementedError("focal_consistency") logp = _stable_log_softmax_np(x)
p = np.exp(logp)
pc = np.clip(p, 1e-12, 1.0)
nlp = -logp
val = float((w * (t * ((1.0 - p) ** gm) * nlp).sum(axis=1)).sum() / n)
out = _mk(val, (z,))
def _bw():
u = t * (-gm * ((1.0 - p) ** (gm - 1.0)) * nlp - ((1.0 - p) ** gm) / pc)
dot = (u * p).sum(axis=1, keepdims=True)
g = (w / n)[:, None] * p * (u - dot)
z._accum(g * out.grad)
out._backward = _bw
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: forward JS (clip probs to 1e-12 before log); backward through softmax per the docstring.
raise NotImplementedError("js_divergence") p = _stable_softmax_np(x)
m = (p + t) / 2.0
pc = np.clip(p, 1e-12, 1.0)
mc = np.clip(m, 1e-12, 1.0)
tc = np.clip(t, 1e-12, 1.0)
js = 0.5 * (p * np.log(pc / mc)).sum(axis=1) + 0.5 * (t * np.log(tc / mc)).sum(axis=1)
val = float((w * js).sum() / n)
out = _mk(val, (z,))
def _bw():
u = (w / n)[:, None] * 0.5 * (np.log(pc) - np.log(mc))
dot = (u * p).sum(axis=1, keepdims=True)
g = p * (u - dot)
z._accum(g * out.grad)
out._backward = _bw
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: forward -(comp*log(1-p)) (clip 1-p to 1e-12); backward through softmax per the docstring.
raise NotImplementedError("negative_consistency") p = _stable_softmax_np(x)
omp = np.clip(1.0 - p, 1e-12, 1.0)
val = float(-(w * (comp * np.log(omp)).sum(axis=1)).sum() / n)
out = _mk(val, (z,))
def _bw():
u = comp / omp
dot = (u * p).sum(axis=1, keepdims=True)
g = (w / n)[:, None] * p * (u - dot)
z._accum(g * out.grad)
out._backward = _bw
return outThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: onehot, mask = pseudo_label(...); loss = soft_cross_entropy(...); scale by lambda_u.
raise NotImplementedError("consistency_loss") wp = np.asarray(weak_probs, dtype=np.float64)
onehot, mask, hard = pseudo_label(wp, tau)
loss = soft_cross_entropy(strong_logits, onehot, weight=mask)
loss = ag.mul(loss, float(lambda_u))
return loss, float(mask.mean())The file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: sup = ag.cross_entropy(...); cons, mr = consistency_loss(...); return (ag.add(sup, cons), mr).
raise NotImplementedError("fixmatch_loss") sup = ag.cross_entropy(sup_logits, sup_y)
cons, mr = consistency_loss(strong_logits, weak_probs, tau, lambda_u)
return ag.add(sup, cons), mrThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: for each named param, shadow <- decay*shadow + (1-decay)*param.data (in place).
raise NotImplementedError("EMATeacher.update") for k, v in model.named_params().items():
self.shadow[k] = self.decay * self.shadow[k] + (1.0 - self.decay) * v.dataThe file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/ssl.py
# TODO: write self.shadow[k] into each model param's data.
raise NotImplementedError("EMATeacher.copy_to") for k, v in model.named_params().items():
v.data[...] = self.shadow[k]The file /app/submission/ssl.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run full checks
rm -rf submission/__pycache__ && python3 run_checks.py 2>&1 | grep -E "FAIL|TOTAL|fixmatch |integration " | head -40
autograd_grads 77/77 | nn_forward 20/20 | optim 16/16 | fixmatch 31/31 | integration 2/2 | data 4/4 TOTAL 150/150 public checks passing
/app/submission/train.py
in_dim = Xl.shape[1]
C = int(yl.max()) + 1
# TODO: build the model + optimizer + EMA teacher (+ distribution aligner), then run the
# FixMatch training loop (supervised CE + masked consistency with confidence-gated, optionally
# distribution-aligned pseudo-labels), and save_checkpoint(out_path, ema.state_dict(), cfg).
raise NotImplementedError("train") in_dim = Xl.shape[1]
C = int(yl.max()) + 1
rng = np.random.default_rng(seed)
aug = np.random.default_rng(seed + 12345)
model = MLPClassifier(in_dim, C, d_hidden=D_HIDDEN, n_layer=N_LAYER, seed=seed)
opt = O.AdamW(model.params(), lr=3e-3, betas=(0.9, 0.999), weight_decay=5e-4)
ema = S.EMATeacher(model, decay=0.999)
aligner = S.DistributionAligner(C, momentum=0.999)
base_lr = 3e-3
warmup = 200
sup_warmup = 400 # steps of supervised-only before opening the consistency gate
rampup_len = 1500 # ramp lambda_u from 0 to lambda_max
lambda_max = 1.0
tau = 0.95
n_lab = Xl.shape[0]
n_unlab = Xu.shape[0]
bs_l = min(64, n_lab)
bs_u = 320
for step in range(steps):
lr = lr_at(step, steps, base_lr, warmup)
opt.lr = lr
# ---- supervised: weak-augmented labeled batch ----
li = rng.integers(0, n_lab, size=bs_l)
Xl_w = S.weak_augment(Xl[li], aug, sigma=0.1)
sup = ag.cross_entropy(model(Xl_w), yl[li])
# ---- unlabeled consistency ----
if step >= sup_warmup:
ui = rng.integers(0, n_unlab, size=bs_u)
Xu_b = Xu[ui]
Xu_w = S.weak_augment(Xu_b, aug, sigma=0.1)
wp = S._stable_softmax_np(np.asarray(model(Xu_w).data, dtype=np.float64))
wp = aligner.align(wp)
lam = lambda_max * S.linear_rampup(step - sup_warmup, rampup_len)
Xu_s = S.strong_augment(Xu_b, aug, sigma=
…[truncated 694 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Probe data structure
python3 -c "
import numpy as np
d=np.load('data/labeled.npz'); X=d['X']; y=d['y']
print('X mean/std', X.mean(), X.std())
# nearest-centroid train accuracy as a signal-strength probe
cent=np.array([X[y==c].mean(0) for c in range(10)])
pred=((X[:,None,:]-cent[None])**2).sum(-1).argmin(1)
print('nearest-centroid train acc', (pred==y).mean())
# how separable in raw 80-dim
"X mean/std -0.013586603 0.9973769 nearest-centroid train acc 0.945
"""Local tuning harness: split labeled into train/val to gauge FixMatch generalization.
NOT the grader; just a proxy since we have no test labels."""
import sys, os, math, time
sys.path.insert(0, "/app/submission")
import numpy as np
import autograd as ag
import optim as O
import ssl as S
from nn import MLPClassifier
D_HIDDEN, N_LAYER = 256, 3
def lr_at(step, total, base_lr, warmup):
if step < warmup:
return base_lr * (step + 1) / warmup
prog = (step - warmup) / max(1, total - warmup)
return 0.5 * base_lr * (1.0 + math.cos(math.pi * min(1.0, prog)))
def evaluate(model, X, y):
logits = model(X).data
return (logits.argmax(1) == y).mean()
def run(Xl, yl, Xu, Xval, yval, steps=4000, seed=0, supervised_only=False,
sup_warmup=400, rampup_len=1500, lambda_max=1.0, tau=0.95, base_lr=3e-3,
bs_u=320, wd=5e-4, ema_decay=0.999):
in_dim = Xl.shape[1]; C = int(yl.max()) + 1
rng = np.random.default_rng(seed); aug = np.random.default_rng(seed + 12345)
model = MLPClassifier(in_dim, C, d_hidden=D_HIDDEN, n_layer=N_LAYER, seed=seed)
opt = O.AdamW(model.params(), lr=base_lr, weight_decay=wd)
ema = S.EMATeacher(model, decay=ema_decay)
aligner = S.DistributionAligner(C, momentum=0.999)
warmup = 200
n_lab, n_unlab = Xl.shape[0], Xu.shape[0]
bs_l = min(64, n_lab)
for step in range(steps):
opt.lr = lr_at(step, steps, base_lr, warmup)
li = rng.integers(0, n_lab, size=bs_l)
sup = ag.cross_
…[truncated 1874 chars]File created successfully at: /app/eval_harness.py (file state is current in your context , no need to Read it back)
Run eval harness short
timeout 500 python3 eval_harness.py 1500 2>&1 | tail -10
No file changes, the agent left the workspace untouched.
=== SyncVals verifier (local re-grade of the agent's own checkpoint) === COMPONENT CONFORMANCE SUITE: 197/197 cases passed (all milestones pass: True) FINAL METRIC: held-out worst-class top-1 = 0.8339 >= threshold 0.72 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_3e69b52fd68d435c. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_3e69b52fd68d435c · verifier authoritative; classifier explanatory.