tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.Explore repository 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
/app/submission/autograd.py
/app/submission/data.py
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]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
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]/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]
/app/run_checks.py
1 #!/usr/bin/env python3 2 """PUBLIC component check runner for the from-scratch numpy QAT framework. 3 4 Run this AS OFTEN AS YOU LIKE while you implement, to get a per-check progress signal: 5 6 python3 /app/run_checks.py # check the framework in /app/submission 7 python3 /app/run_checks.py --submission /some/dir 8 python3 /app/run_checks.py --only autograd_grads # run one milestone 9 python3 /app/run_checks.py --quiet # summary line only 10 11 It imports YOUR modules from /app/submission (autograd.py / nn.py / optim.py / data.py), exactly as 12 the grader does, and runs a PUBLIC portion of the component conformance suite, printing a clear 13 per-check report so you can implement -> run -> read failures -> fix -> repeat. 14 15 HOW THIS RELATES TO YOUR GRADE 16 ------------------------------ 17 This runner is a DEBUGGING AID, not the grader. It checks YOUR framework against references it can 18 compute on its own: 19 * autograd_grads : central FINITE-DIFFERENCE of YOUR forward vs YOUR analytic backward, for every 20 differentiable op + the domain ops (conv2d/avgpool2d/maxpool2d/batchnorm2d/ 21 batchnorm1d/layernorm) + end-to-end through the full QuantCNN / QuantMLP. 22 * quant_grads : the LSQ fake_quant STE indicator + scale-gradient CLIP RULES (the exact 23 structural identities the straight-through estimator must satisfy). 24 * nn_forward : …[truncated 3362 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)
topo = []
visited = set()
…[truncated 47602 chars]The file /app/submission/autograd.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(Xn)
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)
"""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_params, wd_override in self._groups:
wd = self.weight_decay if
…[truncated 11259 chars]The file /app/submission/optim.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))
# ===================================================================== generic layers
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) ->
…[truncated 25619 chars]The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run public checks
python3 run_checks.py 2>&1 | head -100
[autograd_grads] 115/115 grad/add_bcast OK (max_abs=3.17e-10) grad/sub_bcast OK (max_abs=3.17e-10) grad/mul_bcast OK (max_abs=1.02e-10) grad/div OK (max_abs=1.89e-11) grad/div_denom OK (max_abs=6.86e-11) grad/power2 OK (max_abs=2.34e-10) grad/power3 OK (max_abs=2.06e-10) grad/maximum OK (max_abs=6.55e-12) grad/minimum OK (max_abs=6.55e-12) grad/where OK (max_abs=7.48e-11) grad/exp OK (max_abs=1.72e-10) grad/log OK (max_abs=2.05e-10) grad/sqrt OK (max_abs=3.39e-10) grad/abs OK (max_abs=5.10e-11) grad/clip OK (max_abs=6.55e-12) grad/matmul OK (max_abs=1.14e-10) grad/matmul_batched OK (max_abs=2.42e-10) grad/transpose OK (max_abs=5.10e-11) grad/transpose_axes OK (max_abs=5.10e-11) grad/reshape OK (max_abs=3.79e-11) grad/getitem_index OK (max_abs=3.79e-11) grad/getitem_slice OK …[truncated 4613 chars]
See remaining check results
python3 run_checks.py 2>&1 | tail -80
fwd/fake_quant_grid_w OK (unique=4) fwd/fake_quant_grid_a OK (unique=4) fwd/Linear_def OK fwd/MLP_shape OK (shape=(2, 3, 8)) fwd/QuantCNN_shape OK fwd/QuantMLP_shape OK fwd/QuantCNN_state_dict_roundtrip OK (keys=24) fwd/QuantMLP_state_dict_roundtrip OK (keys=19) fwd/square_def OK fwd/rsqrt_def OK fwd/var_def OK fwd/std_def OK fwd/softplus_def OK fwd/silu_def OK fwd/mish_def OK fwd/elu_def OK fwd/hardsigmoid_def OK fwd/groupnorm_zero_mean_unit_var OK fwd/fake_quant_grid_int3 OK (unique=8) fwd/fake_quant_grid_int4 OK (unique=14) fwd/RNNCell_shape OK (shape=(3, 4)) fwd/GRUCell_shape OK (shape=(3, 4)) fwd/LSTMCell_shape OK fwd/PerChannelWeightQuant_grid OK fwd/AffineActQuant_def OK fwd/cumsum_def OK fwd/logsumexp_def OK fwd/l2_normalize_def OK fwd/rms_norm_stats …[truncated 2975 chars]
Inspect training data
python3 -c "
import numpy as np
d = np.load('data/train.npz')
X = d['X']; y = d['y']
print('X shape:', X.shape, 'dtype:', X.dtype)
print('y shape:', y.shape, 'classes:', np.unique(y))
print('X range:', X.min(), X.max())
print('class counts:', np.bincount(y.astype(int)))
"X shape: (2500, 3, 16, 16) dtype: uint8 y shape: (2500,) classes: [0 1 2 3 4 5 6 7 8 9] X range: 0 255 class counts: [250 250 250 250 250 250 250 250 250 250]
"""QAT training loop for INT-2 QuantCNN."""
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_weight_scale(w, Qp):
"""Initialize LSQ scale for a weight tensor."""
mean_abs = float(np.abs(w).mean())
if mean_abs < 1e-8:
return 0.1
return max(2.0 * mean_abs / max(Qp, 1), 1e-6)
def _calibrate_activations(model, Xn, rng, cal_size=256):
"""Run a calibration forward pass to get activation statistics.
Temporarily set activation quantizer scales to 100 so nothing is clipped."""
# Save current aq scales
saved = []
for aq in [model.stem.aq] + [b.aq for b in model.blocks]:
saved.append(float(aq.scale.data))
aq.scale.data[:] = 100.0
model.set_training(False)
indices = rng.integers(0, len(Xn), min(cal_size, len(Xn)), endpoint=False)
Xbatch = Xn[indices]
# Forward pass – we need to hook activations. Do it manually by temporarily
# replacing each ConvBlock.aq with a recorder.
# Instead, just use a simpler calibration: forward the stem and each block separately
# and observe the output range.
# Reset scales
for i, aq in enumerate([model.stem.aq] + [b.aq for b in model.blocks]):
aq.scale.data[:] = saved[i]
model
…[truncated 6154 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Start training in background
python3 submission/train.py --epochs 80 2>&1 | tail -30 & echo "Training started in background, PID=$!"
Training started in background, PID=1568
Check training progress
sleep 30 && jobs && ps aux | grep train.py | grep -v grep
<tool_use_error>Blocked: sleep 30 followed by: jobs ps aux grep train.py grep -v grep. 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>
Quick test training run (3 epochs)
python3 submission/train.py --epochs 3 2>&1
Exit code 137 /bin/bash: line 1: 1574 Killed python3 submission/train.py --epochs 3 2>&1
Quick timing test with batch_size=32
timeout 60 python3 -c "
import sys
sys.path.insert(0, '/app/submission')
sys.path.insert(0, '/app')
import numpy as np
import autograd as ag
import data as D
import optim as O
from nn import QuantCNN
model = QuantCNN(10, (16,32), seed=0)
model.set_training(True)
X, y = D.load_npz('/app/data/train.npz')
Xn = D.normalize(X)
# Init scales
for block_list in [model.stem] + model.blocks:
pass
# Init weight scales
def init_wq(m):
m.stem.conv.wq.scale.data[:] = 2.0 * np.abs(m.stem.conv.weight.data).mean()
for b in m.blocks:
b.conv.wq.scale.data[:] = 2.0 * np.abs(b.conv.weight.data).mean()
m.head_wq.scale.data[:] = 2.0 * np.abs(m.head_w.data).mean()
m.stem.aq.scale.data[:] = 0.5
for b in m.blocks:
b.aq.scale.data[:] = 0.5
init_wq(model)
rng = np.random.default_rng(0)
import time
t0 = time.time()
for Xb, yb in D.iter_minibatches(Xn, y, 32, rng):
logits = model(ag.Tensor(Xb))
loss = ag.cross_entropy(logits, yb)
loss.backward()
print('loss:', float(loss.data), 'time:', time.time()-t0)
break
print('One step done in', time.time()-t0, 'seconds')
" 2>&1loss: 2.396346605150601 time: 0.1400458812713623 One step done in 0.14006710052490234 seconds
Test 2-epoch run with batch_size=64
timeout 60 python3 -c "
import sys
sys.path.insert(0, '/app/submission')
sys.path.insert(0, '/app')
import numpy as np
import autograd as ag
import data as D
import optim as O
from nn import QuantCNN, W_NEG, W_POS
model = QuantCNN(10, (16,32), seed=0)
model.set_training(True)
X, y = D.load_npz('/app/data/train.npz')
Xn = D.normalize(X)
# Init weight and act scales
model.stem.conv.wq.scale.data[:] = 2.0 * np.abs(model.stem.conv.weight.data).mean()
for b in model.blocks:
b.conv.wq.scale.data[:] = 2.0 * np.abs(b.conv.weight.data).mean()
model.head_wq.scale.data[:] = 2.0 * np.abs(model.head_w.data).mean()
model.stem.aq.scale.data[:] = 0.5
for b in model.blocks:
b.aq.scale.data[:] = 0.5
groups = O.split_decay_params(model)
opt = O.AdamW([
{'params': groups['decay'], 'weight_decay': 1e-4},
{'params': groups['no_decay'], 'weight_decay': 0.0},
], lr=5e-3)
import time
t0 = time.time()
# Run 2 epochs
for ep in range(2):
rng = np.random.default_rng(ep)
total_loss = 0; nb = 0
for Xb, yb in D.iter_minibatches(Xn, y, 64, rng):
opt.zero_grad()
logits = model(ag.Tensor(Xb))
loss = ag.cross_entropy(logits, yb)
loss.backward()
O.clip_grad_norm(model.params(), 1.0)
opt.step()
total_loss += float(loss.data); nb += 1
print(f'Epoch {ep+1}: loss={total_loss/nb:.4f} time={time.time()-t0:.1f}s')
" 2>&1Exit code 137
/bin/bash: line 93: 1583 Killed timeout 60 python3 -c "
import sys
sys.path.insert(0, '/app/submission')
sys.path.insert(0, '/app')
import numpy as np
import autograd as ag
import data as D
import optim as O
from nn import QuantCNN, W_NEG, W_POS
model = QuantCNN(10, (16,32), seed=0)
model.set_training(True)
X, y = D.load_npz('/app/data/train.npz')
Xn = D.normalize(X)
# Init weight and act scales
model.stem.conv.wq.scale.data[:] = 2.0 * np.abs(model.stem.conv.weight.data).mean()
for b in model.blocks:
b.conv.wq.scale.data[:] = 2.0 * np.abs(b.conv.weight.data).mean()
model.head_wq.scale.data[:] = 2.0 * np.abs(model.head_w.data).mean()
model.stem.aq.scale.data[:] = 0.5
for b in model.blocks:
b.aq.scale.data[:] = 0.5
groups = O.split_decay_params(model)
opt = O.AdamW([
{'params': groups['decay'], 'weight_decay': 1e-4},
{'params': groups['no_decay'], 'weight_decay': 0.0},
], lr=5e-3)
import time
t0 = time.time()
# Run 2 epochs
for ep in range(2):
rng = np.random.default_rng(ep)
total_loss = 0; nb = 0
for Xb, yb in D.iter_minibatches(Xn, y, 64, rng):
opt.zero_grad()
logits = model(ag.Tensor(Xb))
loss = ag.cross_entropy(logits, yb)
loss.backward()
O.clip_grad_norm(model.params(), 1.0)
opt.step()
total_loss += float(loss.data); nb += 1
print(f'Epoch {ep+1}: loss={total_loss/nb:.4f} time={time.time()-t0:.1f}s')
" 2>&1Kill any running train.py processes
kill $(pgrep -f "train.py") 2>/dev/null; echo "killed"
Exit code 144
No 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 = 0.8553 >= 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_e1f814c504f14c38. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_e1f814c504f14c38 · verifier authoritative; classifier explanatory.