tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.Explore project structure
find /app -type f | head -50 && echo "---" && ls /app/submission/
/app/run_checks.py /app/test.sh /app/timer.sh /app/PROMPT.txt /app/agent.pid /app/run.log /app/agent_trajectory.jsonl /app/agent.stderr /app/submission/nn.py /app/submission/autograd.py /app/submission/train.py /app/submission/optim.py /app/submission/data.py /app/data/train.npz --- autograd.py data.py nn.py optim.py train.py
Run initial checks to see starting state
python3 /app/run_checks.py --quiet 2>&1 | head -50
------------------------------------------------------------------------ autograd_grads 0/115 | quant_grads 3/21 | nn_forward 42/53 | optim 1/25 | integration 0/6 | data 0/2 TOTAL 46/222 public checks passing Fix the FAIL lines above (each is a backward/forward/optimizer bug). Re-run until all green, then train.
/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 (a) differentiates the 5 differentiable ops below with central finite differences and compares against YOUR analytic 6 backward, and (b) checks the LSQ ``fake_quant`` STE + scale gradient against the reference 7 definition, so every backward must be correct (a single wrong reduction / STE / Jacobian fails 8 the gradient checks and zeroes the reward). 9 10 A ``Tensor`` wraps one ``numpy.ndarray`` (stored as ``float64`` in ``self.data``) and records the 11 op that produced it so ``.backward()`` can accumulate ``.grad`` into every leaf. 12 13 CONTRACT (the grader builds its own reference against this exact API): 14 * ``Tensor(data, requires_grad=False)`` -- ``self.data`` is ``float64``; ``self.grad`` is None 15 until backward, then a same-shape ``float64`` array. requires_grad leaves accumulate grad. 16 * Every op returns a NEW Tensor whose ``requires_grad`` is the OR of its differentiable inputs, 17 and whose ``_backward`` closure does ``input._accum(local_grad)`` (so multiple paths add up). 18 * ``t.backward(grad=None)`` -- seeds ``grad`` (all-ones for a scalar) and walks the graph in 19 reverse topological order, calling each node's ``_backward``. Repeated backward accumulates. 20 * BROADCASTING: binary ops broadcast like numpy; the ba …[truncated 54775 chars]
/app/submission/nn.py
1 """QAT layers + a small INT2 CNN, built on YOUR autograd engine.
2
3 Implement every ``# TODO`` forward (the parameters + ``named_params``/``buffers`` naming are
4 already wired for you; you compose the autograd ops). The grader checks each layer's forward
5 against its OWN reference AND finite-difference-checks the gradients that flow through your
6 autograd, so the composition must be exactly right.
7
8 CHECKPOINT NAMING CONTRACT (the grader loads YOUR ``qat_model.npz`` into its own reference
9 QuantCNN by these exact keys/shapes -- do not rename):
10 stem.conv.weight (W0,3,3,3) | stem.conv.wq.scale (1,) | stem.bn.gamma/.beta (W0,) | stem.aq.scale (1,)
11 blocks.{i}.conv.weight (Cout,Cin,3,3) | blocks.{i}.conv.wq.scale (1,)
12 blocks.{i}.bn.gamma/.beta (Cout,) | blocks.{i}.aq.scale (1,)
13 head.weight (Cfeat,n_classes) | head.wq.scale (1,) | head.bias (n_classes,)
14 BN running stats are BUFFERS (saved alongside params, NOT trained by the optimizer, NOT audited):
15 stem.bn.running_mean/.running_var (W0,) | blocks.{i}.bn.running_mean/.running_var (Cout,)
16
17 Quantization: every conv/head WEIGHT is LSQ-quantized to a signed 2-bit grid {-2,-1,0,1} via a
18 per-tensor learnable step before use; every block activation (post-relu) is LSQ-quantized to an
19 unsigned 2-bit grid {0,1,2,3}. Conv = 3x3 same-padding cross-correlation. Linear head = x@weight+bias.
20 """
21 from __future__ import annotations
22
23 from typing import Dict, List
24
25 import numpy as n
…[truncated 35167 chars]/app/submission/optim.py
/app/submission/data.py
1 """Optimizers over autograd ``Tensor`` parameters.
2
3 Implement the ``# TODO`` update math. Both optimizers update ``param.data`` IN PLACE using
4 ``param.grad``. The grader runs one (and several) steps and compares your updated params against
5 its reference within a tight tolerance, so the math must be exact.
6
7 SGD (with momentum ``mu`` and coupled weight decay ``wd``):
8 g = grad + wd * param; v = mu * v + g; param -= lr * v
9
10 AdamW (DECOUPLED weight decay -- the decay is applied to the PARAM, not folded into the moments):
11 m = b1*m + (1-b1)*g; v = b2*v + (1-b2)*g^2
12 mhat = m / (1 - b1^t); vhat = v / (1 - b2^t) # t = 1-based step count
13 param -= lr * ( mhat / (sqrt(vhat) + eps) + wd * param )
14
15 PARAM GROUPS: both optimizers must accept EITHER a flat param list OR a list of
16 ``{"params": [...], "weight_decay": wd}`` dicts, so weight decay can be applied to SOME params
17 and not others. This matters for low-bit LSQ QAT: the per-tensor quantizer ``scale`` params,
18 BatchNorm gamma/beta, and biases must be EXCLUDED from weight decay. ``_normalize_groups`` and
19 ``split_decay_params`` are provided; honour the per-group weight decay in ``step``.
20 """
21 from __future__ import annotations
22
23 import math
24
25 from typing import Dict, List
26
27 import numpy as np
28
29 from autograd import Tensor
30
31
32 def split_decay_params(model) -> Dict[str, List[Tensor]]:
33 """Partition a model's named params into weight
…[truncated 11129 chars]1 """Image data loading + normalization + batching.
2
3 The dataset is a procedurally-generated CIFAR-style set: ``X`` is ``uint8`` (N,3,S,S) in [0,255],
4 ``y`` is the integer class label in [0, n_classes). The shipped training split is at
5 ``/app/data/train.npz``; the SEALED held-out split is regenerated by the verifier (never shipped).
6
7 Implement the ``# TODO`` bodies (numpy only). The verifier normalizes the held-out split with the
8 SAME transform, so match it exactly.
9
10 Contract:
11 load_npz(path) -> (X uint8 (N,3,S,S), y int64 (N,)) [provided]
12 normalize(X) -> float64: x = (X/255 - 0.5) / 0.25 [per-channel, the grader's transform]
13 iter_minibatches(Xn, y, batch_size, rng, shuffle=True) -> yields (xb, yb)
14 """
15 from __future__ import annotations
16
17 from typing import Tuple
18
19 import numpy as np
20
21 NORM_MEAN = 0.5
22 NORM_STD = 0.25
23
24
25 def load_npz(path: str) -> Tuple[np.ndarray, np.ndarray]:
26 d = np.load(path)
27 X = np.asarray(d["X"]).astype(np.uint8)
28 y = np.asarray(d["y"]).astype(np.int64).reshape(-1)
29 return X, y
30
31
32 def normalize(X: np.ndarray) -> np.ndarray:
33 """uint8 (N,3,S,S) -> float64, x = (X/255 - NORM_MEAN) / NORM_STD."""
34 # TODO: cast to float, scale to [0,1], subtract NORM_MEAN, divide by NORM_STD.
35 raise NotImplementedError("normalize")
36
37
38 def iter_minibatches(Xn: np.ndarray, y: np.ndarray, batch_size: int,
39 rng: np.rando
…[truncated 281 chars]/app/submission/train.py
1 """Training entry point. Implement the QAT loop, train your INT2 CNN on /app/data/train.npz, and 2 SAVE the checkpoint to /app/submission/qat_model.npz (numpy .npz of the model state_dict: every 3 param + every BN running buffer, by the nn.QuantCNN naming contract). 4 5 The grader (verify.py) will: 6 (1) run the conformance suite over your autograd/nn/optim, 7 (2) BIT-AUDIT every conv/head weight tensor in your checkpoint to <= 4 distinct float values, 8 (3) load your weights into its OWN QuantCNN and score held-out top-1 on a SEALED split. 9 10 So at SAVE time you must BAKE the quantized weights back into each ``weight`` tensor (replace it 11 with round(clip(w/s, -W_NEG, W_POS)) * s using the learned wq.scale) so the saved weights already 12 lie on the 2-bit grid and pass the audit. Reaching the top-1 floor at INT-2 needs a real recipe: 13 sensible LSQ STEP-SIZE INITIALIZATION (a default scale of 1.0 quantizes the small init weights to 14 all-zeros -> a dead network at chance), per-tensor learnable scales, weight decay EXCLUDED from the 15 scale/BN/bias params, gradient clipping, a decaying LR, and enough epochs. Naive post-training 16 rounding or under-training lands at chance. 17 18 This file is a skeleton; the loop body is yours to write. 19 """ 20 from __future__ import annotations 21 22 import argparse 23 import os 24 import sys 25 26 import numpy as np 27 28 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) 29 sys.path.insert(0, "/app") 30 3 …[truncated 1513 chars]
"""Reverse-mode automatic differentiation over numpy arrays."""
from __future__ import annotations
from typing import Callable, Iterable, List, Optional
import numpy as np
_bi_max = max
_bi_min = min
def _as_f64(x) -> np.ndarray:
return np.asarray(x, dtype=np.float64)
def _unbroadcast(grad: np.ndarray, shape: tuple) -> np.ndarray:
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)
class Tensor:
__slots__ = ("data", "grad", "requires_grad", "_backward", "_parents")
def __init__(self, data, requires_grad: bool = False, _parents: Iterable["Tensor"] = ()):
self.data: np.ndarray = _as_f64(data)
self.grad: Optional[np.ndarray] = None
self.requires_grad: bool = bool(requires_grad)
self._backward: Callable[[], None] = lambda: None
self._parents: tuple = tuple(_parents)
@property
def shape(self):
return self.data.shape
@property
def ndim(self):
return self.data.ndim
def zero_grad(self):
self.grad = None
def _accum(self, g: np.ndarray):
if self.grad is None:
self.grad = np.zeros_like(self.data)
self.grad += g
def backward(self, grad: Optional[np.ndarray] = None):
if grad is None:
grad = np.ones_like(self.data)
self._accum(grad)
topo
…[truncated 49821 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
def gather(a, idx, axis: int) -> Tensor:
a = _ensure(a)
idx = np.asarray(idx)
out = _mk(np.take_along_axis(a.data, idx, axis=axis), (a,), a.requires_grad)
def _bw():
g = out.grad
if g is None: return
if a.requires_grad:
da = np.zeros_like(a.data)
np.add.at(da, tuple(
idx if i == axis else np.arange(a.data.shape[i]).reshape(
[-1 if i == j else 1 for j in range(a.data.ndim)])
for i in range(a.data.ndim)
), g)
a._accum(da)
out._backward = _bw
return outdef gather(a, idx, axis: int) -> Tensor:
a = _ensure(a)
idx = np.asarray(idx)
out = _mk(np.take_along_axis(a.data, idx, axis=axis), (a,), a.requires_grad)
def _bw():
g = out.grad
if g is None: return
if a.requires_grad:
da = np.zeros_like(a.data)
np.add.at(da, _gather_idx(idx, axis, a.data.ndim), g)
a._accum(da)
out._backward = _bw
return out
def _gather_idx(idx, axis, ndim):
"""Build the index tuple for scatter-add in gather backward."""
full = []
for i in range(ndim):
if i == axis:
full.append(idx)
else:
shape = [1] * ndim
shape[i] = -1
full.append(np.arange(idx.shape[i]).reshape(shape))
return tuple(full)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
"""QAT layers + a small INT2 CNN, built on YOUR autograd engine."""
from __future__ import annotations
from typing import Dict, List
import numpy as np
import autograd as ag
from autograd import Tensor
W_NEG, W_POS = 2, 1 # signed weights: {-2,-1,0,1}
A_NEG, A_POS = 0, 3 # unsigned activations (post-relu): {0,1,2,3}
class Module:
def params(self) -> List[Tensor]:
return list(self.named_params().values())
def named_params(self) -> Dict[str, Tensor]:
raise NotImplementedError
def buffers(self) -> Dict[str, np.ndarray]:
return {}
def __call__(self, *a, **k):
return self.forward(*a, **k)
def _kaiming(rng, fan_in, shape, gain=1.0):
return rng.standard_normal(shape) * (gain / np.sqrt(fan_in))
class Linear(Module):
def __init__(self, n_in, n_out, rng, bias=True, gain=1.0):
self.weight = Tensor(_kaiming(rng, n_in, (n_in, n_out), gain), requires_grad=True)
self.bias = Tensor(np.zeros(n_out), requires_grad=True) if bias else None
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}
if self.bias is not None:
d["bias"] = self.bias
return d
class ReLU(Module):
def forward(self, x: Tensor) -> Tensor:
return ag.relu(x)
def named_params(self):
return {}
clas
…[truncated 25587 chars]The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
"""Optimizers over autograd Tensor parameters."""
from __future__ import annotations
import math
from typing import Dict, List
import numpy as np
from autograd import Tensor
def split_decay_params(model) -> Dict[str, List[Tensor]]:
decay, no_decay = [], []
for name, p in model.named_params().items():
lname = name.lower()
if lname.endswith("scale") or "bn." in lname or lname.endswith("gamma") \
or lname.endswith("beta") or lname.endswith("bias"):
no_decay.append(p)
else:
decay.append(p)
return {"decay": decay, "no_decay": no_decay}
def _normalize_groups(params):
plist = list(params)
if plist and isinstance(plist[0], dict):
return [(list(g["params"]), g.get("weight_decay", None)) for g in plist]
return [(plist, None)]
class Optimizer:
def __init__(self, params):
self._groups = _normalize_groups(params)
self.params: List[Tensor] = [p for g, _ in self._groups for p in g]
def zero_grad(self):
for p in self.params:
p.grad = None
class SGD(Optimizer):
def __init__(self, params, lr=0.1, momentum=0.0, weight_decay=0.0):
super().__init__(params)
self.lr = lr
self.momentum = momentum
self.weight_decay = weight_decay
self._v = [np.zeros_like(p.data) for p in self.params]
def step(self):
idx = 0
for group, wd_override in self._groups:
wd = self.weight_decay if wd_ove
…[truncated 11134 chars]The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
"""Image data loading + normalization + batching."""
from __future__ import annotations
from typing import Tuple
import numpy as np
NORM_MEAN = 0.5
NORM_STD = 0.25
def load_npz(path: str) -> Tuple[np.ndarray, np.ndarray]:
d = np.load(path)
X = np.asarray(d["X"]).astype(np.uint8)
y = np.asarray(d["y"]).astype(np.int64).reshape(-1)
return X, y
def normalize(X: np.ndarray) -> np.ndarray:
return (X.astype(np.float64) / 255.0 - NORM_MEAN) / NORM_STD
def iter_minibatches(Xn: np.ndarray, y: np.ndarray, batch_size: int,
rng: np.random.Generator, shuffle: bool = True):
N = len(y)
idx = np.arange(N)
if shuffle:
rng.shuffle(idx)
for start in range(0, N, batch_size):
batch_idx = idx[start:start + batch_size]
yield Xn[batch_idx], y[batch_idx]
The file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run checks to see current status
python3 run_checks.py 2>&1 | tail -30
optim/NesterovSGD_5step OK (max_abs=0.0e+00) optim/param_groups OK optim/split_decay_params OK optim/clip_grad_norm_clips OK (max_abs=0.0e+00) optim/clip_grad_norm_noop OK (max_abs=0.0e+00) optim/sched_cosine OK (max_abs=0.0e+00) optim/sched_warmup OK (max_abs=0.0e+00) optim/sched_step OK (max_abs=0.0e+00) optim/sched_exponential OK (max_abs=0.0e+00) optim/sched_polynomial OK (max_abs=0.0e+00) optim/sched_linear OK (max_abs=1.7e-18) optim/sched_multistep OK (max_abs=0.0e+00) optim/sched_cosine_restarts OK (max_abs=0.0e+00) [integration] 6/6 integration/qcnn_finite OK (1.787->1.018) integration/qcnn_loss_decreases OK (1.7869->min 1.0175) integration/qmlp_finite OK (1.981->0.237) integration/qmlp_loss_decreases OK (1.9807->min 0.2374) integration/qsepcnn_finite OK (1.925->1.655) integration/qsepcnn_loss_decreases OK (1.9249->min 1.6340) [data] 2/2 data/normalize_def OK data/iter_minibatches_cover OK (covered 23/23) ------------------------------------------------------------------------ autograd_grads 115/115 | quant_grads 21/21 | …[truncated 238 chars]
"""Training entry point for QAT INT-2 CNN."""
from __future__ import annotations
import argparse
import os
import sys
import numpy as np
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, "/app")
import autograd as ag
import data as D
import optim as O
from nn import QuantCNN, W_NEG, W_POS, A_NEG, A_POS
WIDTHS = (16, 32)
def init_lsq_scales(model, Xn_batch):
"""Initialize LSQ step sizes from the actual activation distributions."""
# For weights: scale = max(|w|) / Qp
params = model.named_params()
Qp_w = float(W_POS)
Qp_a = float(A_POS)
# Init weight scales from weight magnitude
for name, p in params.items():
if name.endswith("wq.scale"):
# Find the corresponding weight
base = name[:-len("wq.scale")]
wname = base + "weight"
if wname in params:
w = params[wname].data
s = np.max(np.abs(w)) / Qp_w
s = max(s, 1e-4)
p.data[...] = s
else:
p.data[...] = 0.1
# Run a forward pass to init activation scales
model.set_training(False)
x = ag.Tensor(Xn_batch)
with_acts = _collect_activations(model, x)
model.set_training(True)
for name, act_val in with_acts.items():
scale_name = name
if scale_name in params:
s = np.max(np.abs(act_val)) / Qp_a
s = max(s, 1e-4)
params[scale_name].data[...] = s
def _collect
…[truncated 5193 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Quick test of training loop
python3 submission/train.py --epochs 5 2>&1 | head -20
(Bash completed with no output)
Test training loop with 5 epochs
python3 submission/train.py --epochs 5 2>&1
Exit code 137 /bin/bash: line 1: 1491 Killed python3 submission/train.py --epochs 5 2>&1
Check time remaining
cat /app/timer.sh && bash /app/timer.sh 2>&1 | head -5
#!/bin/bash # Reports remaining agent time (mirrors task.toml [agent] timeout_sec). TIMEOUT_SEC=36000 ELAPSED=$(ps -o etimes= -p 1 2>/dev/null | tr -d ' ') if ! [[ "$ELAPSED" =~ ^[0-9]+$ ]]; then START_FILE="$(dirname "$0")/.timer_start" if [ ! -f "$START_FILE" ]; then date +%s >"$START_FILE" fi START_DATE=$(cat "$START_FILE") NOW=$(date +%s) ELAPSED=$((NOW - START_DATE)) fi REMAINING=$((TIMEOUT_SEC - ELAPSED)) if [ $REMAINING -le 0 ]; then echo "Timer expired!" else echo "Remaining time (hours:minutes)": HOURS=$((REMAINING / 3600)) MINUTES=$(((REMAINING % 3600) / 60)) printf "%d:%02d\n" $HOURS $MINUTES fi Remaining time (hours:minutes): 9:44
Quick test of one forward/backward pass
timeout 120 python3 -c "
import sys
sys.path.insert(0, 'submission')
import numpy as np
import data as D
import autograd as ag
import optim as O
from nn import QuantCNN
X_raw, y = D.load_npz('data/train.npz')
print('Data shape:', X_raw.shape, y.shape, 'classes:', np.unique(y))
Xn = D.normalize(X_raw)
model = QuantCNN(n_classes=10, widths=(16,32), seed=0)
model.set_training(True)
# Small test: 1 batch
xb = Xn[:32]
yb = y[:32]
xb_t = ag.Tensor(xb)
logits = model(xb_t)
print('logits shape:', logits.shape)
loss = ag.cross_entropy(logits, yb)
print('loss:', float(loss.data))
loss.backward()
print('stem weight grad:', model.stem.conv.weight.grad is not None)
" 2>&1Data shape: (2500, 3, 16, 16) (2500,) classes: [0 1 2 3 4 5 6 7 8 9] logits shape: (32, 10) loss: 2.302585092994046 stem weight grad: True
Time a few training batches
timeout 120 python3 -c "
import sys, time
sys.path.insert(0, 'submission')
import numpy as np
import data as D
import autograd as ag
import optim as O
from nn import QuantCNN
X_raw, y = D.load_npz('data/train.npz')
Xn = D.normalize(X_raw)
model = QuantCNN(n_classes=10, widths=(16,32), seed=0)
model.set_training(True)
optimizer = O.AdamW(model.params(), lr=1e-2, weight_decay=1e-4)
t0 = time.time()
for i in range(5):
xb = Xn[i*32:(i+1)*32]
yb = y[i*32:(i+1)*32]
optimizer.zero_grad()
xb_t = ag.Tensor(xb)
logits = model(xb_t)
loss = ag.cross_entropy(logits, yb)
loss.backward()
optimizer.step()
print(f'batch {i}: loss={float(loss.data):.4f}, time={time.time()-t0:.2f}s')
" 2>&1batch 0: loss=2.3026, time=0.14s batch 1: loss=2.3030, time=0.28s batch 2: loss=2.3040, time=0.42s batch 3: loss=2.3026, time=0.56s batch 4: loss=2.3065, time=0.68s
Test training for 3 epochs
timeout 300 python3 -c "
import sys, time, traceback
sys.path.insert(0, 'submission')
import numpy as np
import data as D
import autograd as ag
import optim as O
from nn import QuantCNN, W_NEG, W_POS, A_NEG, A_POS
X_raw, y = D.load_npz('data/train.npz')
Xn = D.normalize(X_raw)
N_total = len(y)
model = QuantCNN(n_classes=10, widths=(16,32), seed=0)
model.set_training(True)
# Simple scale init: just use 0.1 for weights, 0.1 for activations
params = model.named_params()
for name, p in params.items():
if 'wq.scale' in name:
# Find corresponding weight
base = name[:-len('wq.scale')]
wname = base + 'weight'
if wname in params:
w = params[wname].data
p.data[...] = max(np.max(np.abs(w)) / float(W_POS), 1e-4)
if 'aq.scale' in name:
p.data[...] = 0.5
groups_dict = O.split_decay_params(model)
param_groups = [
{'params': groups_dict['decay'], 'weight_decay': 1e-4},
{'params': groups_dict['no_decay'], 'weight_decay': 0.0},
]
base_lr = 5e-3
optimizer = O.AdamW(param_groups, lr=base_lr, betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-4)
rng = np.random.default_rng(42)
batch_size = 64
epochs = 3
t0 = time.time()
for epoch in range(epochs):
epoch_loss = 0
n_batches = 0
correct = 0
total = 0
for xb, yb in D.iter_minibatches(Xn, y, batch_size, rng, shuffle=True):
optimizer.zero_grad()
xb_t = ag.Tensor(xb)
logits = model(xb_t)
loss = ag.cross_entropy(logits, yb
…[truncated 412 chars]Exit code 137
/bin/bash: line 131: 1578 Killed timeout 300 python3 -c "
import sys, time, traceback
sys.path.insert(0, 'submission')
import numpy as np
import data as D
import autograd as ag
import optim as O
from nn import QuantCNN, W_NEG, W_POS, A_NEG, A_POS
X_raw, y = D.load_npz('data/train.npz')
Xn = D.normalize(X_raw)
N_total = len(y)
model = QuantCNN(n_classes=10, widths=(16,32), seed=0)
model.set_training(True)
# Simple scale init: just use 0.1 for weights, 0.1 for activations
params = model.named_params()
for name, p in params.items():
if 'wq.scale' in name:
# Find corresponding weight
base = name[:-len('wq.scale')]
wname = base + 'weight'
if wname in params:
w = params[wname].data
p.data[...] = max(np.max(np.abs(w)) / float(W_POS), 1e-4)
if 'aq.scale' in name:
p.data[...] = 0.5
groups_dict = O.split_decay_params(model)
param_groups = [
{'params': groups_dict['decay'], 'weight_decay': 1e-4},
{'params': groups_dict['no_decay'], 'weight_decay': 0.0},
]
base_lr = 5e-3
optimizer = O.AdamW(param_groups, lr=base_lr, betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-4)
rng = np.random.default_rng(42)
batch_size = 64
epochs = 3
t0 = time.time()
for epoch in range(epochs):
epoch_loss = 0
n_batches = 0
correct = 0
total = 0
for xb, yb in D.iter_minibatches(Xn, y, batch_size, rng, shuffle=True):
optimizer.zero_grad()
xb_t = ag.Tensor(xb)
…[truncated 477 chars]Check memory available and usage estimate
free -m && python3 -c "
import numpy as np
# Estimate memory per batch
bs = 64
# im2col for stem: (64, 27, 256)
cols_stem = 64 * 27 * 256 * 8 / 1024 / 1024
# out: (64, 16, 256)
out_stem = 64 * 16 * 256 * 8 / 1024 / 1024
# block 0: (64, 32, 64)
cols_b0 = 64 * (16*9) * (8*8) * 8 / 1024 / 1024
print(f'Stem cols: {cols_stem:.1f} MB')
print(f'Stem out: {out_stem:.1f} MB')
print(f'Block0 cols: {cols_b0:.1f} MB')
# Total per batch estimate
total = (cols_stem + out_stem) * 2 + (cols_b0 * 2) * 2 # factor 2 for bwd
print(f'Total estimate: {total:.1f} MB')
"total used free shared buff/cache available Mem: 481 255 172 1 67 226 Swap: 0 0 0 Stem cols: 3.4 MB Stem out: 2.0 MB Block0 cols: 4.5 MB Total estimate: 28.8 MB
Test with batch size 16 and explicit gc
timeout 60 python3 -c "
import sys, time, gc
sys.path.insert(0, 'submission')
import numpy as np
import data as D
import autograd as ag
import optim as O
from nn import QuantCNN
X_raw, y = D.load_npz('data/train.npz')
Xn = D.normalize(X_raw)
model = QuantCNN(n_classes=10, widths=(16,32), seed=0)
model.set_training(True)
optimizer = O.AdamW(model.params(), lr=1e-2, weight_decay=1e-4)
rng = np.random.default_rng(42)
batch_size = 16
t0 = time.time()
for i, (xb, yb) in enumerate(D.iter_minibatches(Xn, y, batch_size, rng)):
optimizer.zero_grad()
xb_t = ag.Tensor(xb)
logits = model(xb_t)
loss = ag.cross_entropy(logits, yb)
loss.backward()
O.clip_grad_norm(model.params(), 1.0)
optimizer.step()
lv = float(loss.data)
del logits, loss, xb_t
gc.collect()
if i % 10 == 0:
print(f'batch {i}: loss={lv:.4f}, t={time.time()-t0:.2f}s')
if i >= 40:
break
print('done')
" 2>&1batch 0: loss=2.3026, t=0.08s batch 10: loss=2.3069, t=0.75s batch 20: loss=2.3106, t=1.40s batch 30: loss=2.3096, t=2.03s batch 40: loss=2.3086, t=2.69s done
Test different batch sizes with gc
timeout 60 python3 -c "
import sys, time, gc
sys.path.insert(0, 'submission')
import numpy as np
import data as D
import autograd as ag
import optim as O
from nn import QuantCNN
X_raw, y = D.load_npz('data/train.npz')
Xn = D.normalize(X_raw)
model = QuantCNN(n_classes=10, widths=(16,32), seed=0)
model.set_training(True)
optimizer = O.AdamW(model.params(), lr=1e-2, weight_decay=1e-4)
rng = np.random.default_rng(42)
t0 = time.time()
for bs in [32, 64, 128]:
gc.collect()
for i, (xb, yb) in enumerate(D.iter_minibatches(Xn, y, bs, rng)):
optimizer.zero_grad()
xb_t = ag.Tensor(xb)
logits = model(xb_t)
loss = ag.cross_entropy(logits, yb)
loss.backward()
O.clip_grad_norm(model.params(), 1.0)
optimizer.step()
lv = float(loss.data)
del logits, loss, xb_t
gc.collect()
if i >= 2: break
print(f'batch_size={bs}: loss={lv:.4f}, t={time.time()-t0:.2f}s')
print('done')
" 2>&1batch_size=32: loss=2.3035, t=0.40s batch_size=64: loss=2.3013, t=1.19s batch_size=128: loss=2.3054, t=2.89s done
Find optimal batch size for throughput
timeout 60 python3 -c "
import sys, time, gc
sys.path.insert(0, 'submission')
import numpy as np
import data as D
import autograd as ag
import optim as O
from nn import QuantCNN
X_raw, y = D.load_npz('data/train.npz')
Xn = D.normalize(X_raw)
model = QuantCNN(n_classes=10, widths=(16,32), seed=0)
model.set_training(True)
optimizer = O.AdamW(model.params(), lr=1e-2, weight_decay=1e-4)
rng = np.random.default_rng(42)
for bs in [16, 32, 64]:
gc.collect()
t0 = time.time()
n = 0
for xb, yb in D.iter_minibatches(Xn, y, bs, rng, shuffle=False):
optimizer.zero_grad()
xb_t = ag.Tensor(xb)
logits = model(xb_t)
loss = ag.cross_entropy(logits, yb)
loss.backward()
optimizer.step()
del logits, loss, xb_t
gc.collect()
n += len(yb)
if n >= 500: break
t1 = time.time()
print(f'bs={bs}: {n} samples in {t1-t0:.2f}s = {n/(t1-t0):.0f} samples/sec')
" 2>&1bs=16: 512 samples in 2.13s = 241 samples/sec bs=32: 512 samples in 2.18s = 235 samples/sec bs=64: 512 samples in 2.17s = 236 samples/sec
"""Training entry point for QAT INT-2 CNN."""
from __future__ import annotations
import argparse
import gc
import os
import sys
import numpy as np
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, "/app")
import autograd as ag
import data as D
import optim as O
from nn import QuantCNN, W_NEG, W_POS, A_NEG, A_POS
WIDTHS = (16, 32)
def init_weight_scales(model):
"""Set each weight-quantizer step to max(|w|)/Qp so weights span the grid."""
params = model.named_params()
Qp_w = float(W_POS)
for name, p in params.items():
if name.endswith("wq.scale"):
base = name[:-len("wq.scale")]
wname = base + "weight"
if wname in params:
w = params[wname].data
s = max(float(np.max(np.abs(w))) / Qp_w, 1e-4)
p.data[...] = s
def init_act_scales(model, Xn, y, batch_size=128):
"""Run one forward pass (BN in training mode) and set activation scales
from the observed activation ranges (no grad needed)."""
rng0 = np.random.default_rng(999)
params = model.named_params()
Qp_a = float(A_POS)
# Collect activations by instrumenting the forward
# We run block-by-block manually
model.set_training(True)
n_batches = 0
act_accum = {} # name -> list of max values
for xb, yb in D.iter_minibatches(Xn, y, batch_size, rng0, shuffle=True):
xb_t = ag.Tensor(xb)
# Manually run stem
h = model.stem.conv(xb_
…[truncated 5827 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Test training for 10 epochs
timeout 300 python3 submission/train.py --epochs 10 2>&1
Command running in background with ID: by5z1mnk6. Output is being written to: /tmp/claude-1000/-app/db724dd1-4684-4463-91c3-a513dd2c5506/tasks/by5z1mnk6.output. You will be notified when it completes. To check interim output, use Read on that file path.
Check training progress after 30 seconds
sleep 30 && cat /tmp/claude-1000/-app/db724dd1-4684-4463-91c3-a513dd2c5506/tasks/by5z1mnk6.output 2>&1
<tool_use_error>Blocked: sleep 30 followed by: cat /tmp/claude-1000/-app/db724dd1-4684-4463-91c3-a513dd2c5506/tasks/by5z1mnk6.output. 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>
/tmp/claude-1000/-app/db724dd1-4684-4463-91c3-a513dd2c5506/tasks/by5z1mnk6.output
<system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder>
Check current training output
cat /tmp/claude-1000/-app/db724dd1-4684-4463-91c3-a513dd2c5506/tasks/by5z1mnk6.output
(Bash completed with no output)
Check if training is still running
jobs -l 2>&1; ps aux | grep train | grep -v grep
<persisted-output> Output too large (30.4KB). Full output saved to: /home/user/.claude/projects/-app/db724dd1-4684-4463-91c3-a513dd2c5506/tool-results/bah72dn6e.txt Preview (first 2KB): user 1330 0.0 0.0 2504 104 ? S 22:11 0:00 timeout 7200 claude -p You are an autonomous engineer in a fresh Linux sandbox. Working dir is /app. Implement the solution by editing the stub files under /app/submission/ . Run `python3 /app/run_checks.py` for the public component checks and `bash /app/test.sh` for an env smoke. Save all required outputs (e.g. the trained checkpoint) before you finish. numpy is the only allowed dependency. Work fully autonomously, do not ask questions, keep iterating until checks pass or you run out of time. ----- TASK ----- # Build a QAT framework from scratch, then train an INT-2 CNN with it You are given a **stubbed, numpy-only deep-learning framework** and a small CIFAR-style image dataset. Your job has two parts, and **both are graded**: 1. **Implement the entire framework from scratch** ??? fill in every `# TODO` / `raise NotImplementedError` in the files under `/app/submission/`. This includes a reverse-mode automatic-differentiation engine (you implement the backward pass for every operation yourself), the convolution / batch-norm / pooling ops, the **Learned Step Size Quantization (LSQ) fake-quantizer** with the **straight-through estimator**, the neural-network layers, the optimizers, and the data pipeline. 2. ** …[truncated 711 chars]
Read training output
cat /tmp/claude-1000/-app/db724dd1-4684-4463-91c3-a513dd2c5506/tasks/by5z1mnk6.output
Scale init done stem.conv.wq.scale: 1.0267 stem.aq.scale: 0.8112 blocks.0.conv.wq.scale: 0.4596 blocks.0.aq.scale: 1.5803 blocks.1.conv.wq.scale: 0.3288 blocks.1.aq.scale: 1.7614 head.wq.scale: 0.5377 Epoch 1/10 | loss=2.3667 | train=0.093 | val=0.118 | lr=0.005000 Epoch 2/10 | loss=2.1771 | train=0.231 | val=0.329 | lr=0.004853 Epoch 3/10 | loss=1.5478 | train=0.536 | val=0.530 | lr=0.004423 Epoch 4/10 | loss=1.0449 | train=0.718 | val=0.796 | lr=0.003762 Epoch 5/10 | loss=0.7679 | train=0.826 | val=0.824 | lr=0.002949 Epoch 10/10 | loss=0.3934 | train=0.924 | val=0.937 | lr=0.000010 Best val_acc: 0.9372 Saved checkpoint to /app/submission/qat_model.npz
Check checkpoint and bit-audit
python3 -c "
import numpy as np
sd = np.load('submission/qat_model.npz')
print('Keys:', sorted(sd.keys()))
print()
for k in sorted(sd.keys()):
v = sd[k]
if k.endswith('.weight') or k == 'head.weight':
uvals = np.unique(v)
print(f'{k}: shape={v.shape}, unique={len(uvals)} values: {uvals[:8]}')
" 2>&1Keys: ['blocks.0.aq.scale', 'blocks.0.bn.beta', 'blocks.0.bn.gamma', 'blocks.0.bn.running_mean', 'blocks.0.bn.running_var', 'blocks.0.conv.weight', 'blocks.0.conv.wq.scale', 'blocks.1.aq.scale', 'blocks.1.bn.beta', 'blocks.1.bn.gamma', 'blocks.1.bn.running_mean', 'blocks.1.bn.running_var', 'blocks.1.conv.weight', 'blocks.1.conv.wq.scale', 'head.bias', 'head.weight', 'head.wq.scale', 'stem.aq.scale', 'stem.bn.beta', 'stem.bn.gamma', 'stem.bn.running_mean', 'stem.bn.running_var', 'stem.conv.weight', 'stem.conv.wq.scale'] blocks.0.conv.weight: shape=(32, 16, 3, 3), unique=4 values: [-0.22941945 -0.11470973 0. 0.11470973] blocks.1.conv.weight: shape=(32, 32, 3, 3), unique=4 values: [-0.24754164 -0.12377082 0. 0.12377082] head.weight: shape=(32, 10), unique=3 values: [-1.0043414 0. 1.0043414] stem.conv.weight: shape=(16, 3, 3, 3), unique=4 values: [-0.5662906 -0.2831453 -0. 0.2831453]
Verify all checks still pass
python3 run_checks.py --quiet 2>&1
------------------------------------------------------------------------ autograd_grads 115/115 | quant_grads 21/21 | nn_forward 53/53 | optim 25/25 | integration 6/6 | data 2/2 TOTAL 222/222 public checks passing All public checks pass. Now train qat_model.npz to clear the held-out top-1 threshold (the grader re-checks components with DIFFERENT seeds).
Start full 80-epoch training
python3 submission/train.py --epochs 80 2>&1 & echo "Training started as PID $!"
Training started as PID 1646
Wait for training to complete
until [ ! -d /proc/1646 ]; do sleep 2; done; echo "Training complete"
Command running in background with ID: bs6a37m8a. Output is being written to: /tmp/claude-1000/-app/db724dd1-4684-4463-91c3-a513dd2c5506/tasks/bs6a37m8a.output. You will be notified when it completes. To check interim output, use Read on that file path.
Confirm training is running
ps aux | grep train.py | grep -v grep
<persisted-output> Output too large (30.5KB). Full output saved to: /home/user/.claude/projects/-app/db724dd1-4684-4463-91c3-a513dd2c5506/tool-results/brq51ripi.txt Preview (first 2KB): user 1330 0.0 0.0 2504 104 ? S 22:11 0:00 timeout 7200 claude -p You are an autonomous engineer in a fresh Linux sandbox. Working dir is /app. Implement the solution by editing the stub files under /app/submission/ . Run `python3 /app/run_checks.py` for the public component checks and `bash /app/test.sh` for an env smoke. Save all required outputs (e.g. the trained checkpoint) before you finish. numpy is the only allowed dependency. Work fully autonomously, do not ask questions, keep iterating until checks pass or you run out of time. ----- TASK ----- # Build a QAT framework from scratch, then train an INT-2 CNN with it You are given a **stubbed, numpy-only deep-learning framework** and a small CIFAR-style image dataset. Your job has two parts, and **both are graded**: 1. **Implement the entire framework from scratch** ??? fill in every `# TODO` / `raise NotImplementedError` in the files under `/app/submission/`. This includes a reverse-mode automatic-differentiation engine (you implement the backward pass for every operation yourself), the convolution / batch-norm / pooling ops, the **Learned Step Size Quantization (LSQ) fake-quantizer** with the **straight-through estimator**, the neural-network layers, the optimizers, and the data pipeline. 2. ** …[truncated 711 chars]
Wait for background training process
wait 1646 2>/dev/null; echo "wait complete"
wait complete
Check training log
cat /tmp/run_log_train.txt 2>/dev/null || ls /tmp/ | head -20
claude-1000 systemd-private-87c33bad8a594d4caefd62a7781643a2-chrony-wait.service-c3XH0Q systemd-private-87c33bad8a594d4caefd62a7781643a2-chrony.service-1C5zZP
Check if checkpoint was updated by the 80-epoch run
python3 -c "
import numpy as np
sd = np.load('submission/qat_model.npz')
for k in ['stem.conv.weight','blocks.0.conv.weight','head.weight']:
v = sd[k]
print(f'{k}: unique={len(np.unique(v))} values')
" 2>&1stem.conv.weight: unique=4 values blocks.0.conv.weight: unique=4 values head.weight: unique=3 values
Check checkpoint file modification time
# Check what happened to the 80-epoch training ls -la /app/submission/qat_model.npz
-rw-r--r-- 1 user user 66086 Jun 30 22:32 /app/submission/qat_model.npz
Check checkpoint accuracy on training data
date && ls -la /app/submission/qat_model.npz && python3 -c "
import numpy as np
import sys
sys.path.insert(0,'submission')
import data as D
import autograd as ag
from nn import QuantCNN
X_raw, y = D.load_npz('data/train.npz')
Xn = D.normalize(X_raw)
model = QuantCNN(n_classes=10, widths=(16,32), seed=0)
sd = {k:v for k,v in np.load('submission/qat_model.npz').items()}
model.load_state_dict(sd)
model.set_training(False)
correct=0
total=0
import gc
rng=__import__('numpy').random.default_rng(42)
for xb,yb in D.iter_minibatches(Xn,y,128,rng,shuffle=False):
xb_t = ag.Tensor(xb)
logits = model(xb_t)
preds = logits.data.argmax(axis=1)
correct += int((preds==yb).sum())
total += len(yb)
del logits, xb_t; gc.collect()
print(f'Train acc from checkpoint: {correct/total:.4f}')
" 2>&1No file changes, the agent left the workspace untouched.
=== SyncVals verifier (local re-grade of the agent's own checkpoint) === COMPONENT CONFORMANCE SUITE: 276/278 cases passed (all milestones pass: False) failing milestones: integration (21/23) FINAL METRIC: held-out INT2 top-1 = 0.912 >= threshold 0.7 REWARD = 0 (1 iff every component milestone passes AND the sealed held-out metric clears its threshold) NOTE: the in-sandbox training in the 481MB e2b box was OOM-killed (0-byte checkpoint), so the raw in-sandbox reward was an infrastructure artifact. This reward is from a faithful LOCAL re-train of the agent's OWN train.py (24GB, no OOM) with its own default hyperparameters, then re-graded by the real verifier.
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_719a33bbb76a40f9. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_719a33bbb76a40f9 · verifier authoritative; classifier explanatory.