qat-int2-cifar

total 117
drwxr-xr-x  4 user user  4096 Jun 30 22:13 .
drwxr-xr-x 21 root root  4096 Jun 30 22:13 ..
-rw-r--r--  1 user user 15364 Jun 30 22:13 PROMPT.txt
-rw-r--r--  1 user user     5 Jun 30 22:13 agent.pid
-rw-r--r--  1 user user     0 Jun 30 22:13 agent.stderr
-rw-r--r--  1 user user  4644 Jun 30 22:13 agent_trajectory.jsonl
drwxr-xr-x  2 user user    60 Jun 30 22:13 data
-rw-r--r--  1 user user     0 Jun 30 22:13 run.log
-rwxr-xr-x  1 user user 76773 Jun 30 10:02 run_checks.py
drwxr-xr-x  2 user user   128 Jun 30 22:13 submission
-rwxr-xr-x  1 user user   804 Jun 30 10:02 test.sh
-rwxr-xr-x  1 user user   629 Jun 30 10:02 timer.sh
---SUBMISSION---
total 112
drwxr-xr-x 2 user user   128 Jun 30 22:13 .
drwxr-xr-x 4 user user  4096 Jun 30 22:13 ..
-rw-r--r-- 1 user user 50472 Jun 30 10:02 autograd.py
-rw-r--r-- 1 user user  1661 Jun 30 10:02 data.py
-rw-r--r-- 1 user user 33295 Jun 30 10:02 nn.py
-rw-r--r-- 1 user user 11509 Jun 30 10:02 optim.py
-rw-r--r-- 1 user user  2827 Jun 30 10:02 train.py
---DATA---
total 1900
drwxr-xr-x 2 user user      60 Jun 30 22:13 .
drwxr-xr-x 4 user user    4096 Jun 30 22:13 ..
-rw-r--r-- 1 user user 1940490 Jun 30 10:02 train.npz
---CHECKS---
 1522 run_checks.py
   18 test.sh
 1540 total

#!/usr/bin/env bash
# Smoke test (NOT the grader): confirms the environment is wired -- numpy imports, the train split
# loads, and the submission stubs are present. It does NOT check correctness (the stubs raise
# NotImplementedError until you implement them).
set -uo pipefail

python3 - <<'PY'
import numpy as np, os
assert os.path.exists("/app/data/train.npz"), "missing train.npz"
d = np.load("/app/data/train.npz")
X, y = d["X"], d["y"]
print(f"train: X={X.shape} {X.dtype}, y={y.shape}, classes={len(set(y.tolist()))}")
for f in ("autograd.py", "nn.py", "optim.py", "data.py", "train.py"):
    assert os.path.exists(f"/app/submission/{f}"), f"missing /app/submission/{f}"
print("submission stubs present:", os.listdir("/app/submission"))
print("numpy", np.__version__)
PY
echo "app_test smoke OK"
===TIMER===
#!/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

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]

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]

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]

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]

17:This runner is a DEBUGGING AID, not the grader. It checks YOUR framework against references it can
33:The REAL grader (the verifier) runs the SAME categories but with its OWN reference framework,
37:  * The grader ALSO bit-audits + loads your trained qat_model.npz into its own reference QuantCNN
64:def _load_module(name, path, inject=None):
84:def load_framework(root):
93:def _num_grad_input(forward, x, eps=EPS):
108:def _max_abs(a, b):
112:def _bsum(it):
119:def _imax(a, b):
124:def check_autograd_grads(fw, sb):
130:    def case(name, build, shape, seed, upstream=None):
135:            def wrapped(t):
193:    def two_path():
204:    def loss_grad(name, lossfn, shape, seed, inttgt):
218:    def norm_param(name, which, seed):
237:    def conv_grad(name, which, seed):
265:    def pool_grad(name, op, seed, jitter=False):
281:    def bn_grad(name, dim, which, seed):
316:    def layer_grad(name, make, in_shape, seed):
334:    # composes -- the grader checks these grads to the reference exactly).
335:    def quant_model_backprop(name, build, make_X, seed):
382:    def gn_x():
395:    def gn_param(name, which, seed):
413:    def cell_grad(name, make, in_shape, hid, seed, two_state=False):
424:                def fwd(xv):
438:    def cell_param_grad(name, make, in_shape, hid, pkey, seed, two_state=False):
450:                def fwd(pv):
455:                def fwd(pv):
479:    def hub_grad(name, seed, scale_in, delta):
493:    def kl_grad_pub():
505:    def emb_grad_pub():
517:  
…[truncated 3165 chars]

1059:    def lsqplus_grid():
1065:    def qsepcnn_shape():
1070:    def qsepcnn_roundtrip():
1106:def check_optim(fw, sb):
1112:    def mk(seed):
1116:    def run(name, opt_fn, ref_step, nsteps, gseed):
1130:            results.append((f"optim/{name}", err < TOL_OPT, f"max_abs={err:.1e}"))
1134:    def sgd_step(lr, mu, wd):
1135:        def f(i, p, g, t, st):
1143:    def adagrad_step(lr, eps, wd):
1144:        def f(i, p, g, t, st):
1152:    def rmsprop_step(lr, alpha, eps, wd):
1153:        def f(i, p, g, t, st):
1161:    def adam_step(lr, b1, b2, eps, wd, decoupled):
1162:        def f(i, p, g, t, st):
1174:    def lion_step(lr, b1, b2, wd):
1175:        def f(i, p, g, t, st):
1196:    def adamax_step(lr, b1, b2, eps, wd):
1197:        def f(i, p, g, t, st):
1206:    def nadam_step(lr, b1, b2, eps, wd):
1207:        def f(i, p, g, t, st):
1218:    def adadelta_step(lr, rho, eps, wd):
1219:        def f(i, p, g, t, st):
1230:    def nesterov_step(lr, mu, wd):
1231:        def f(i, p, g, t, st):
1248:    def group_case():
1250:            def build():
1268:            ok = _max_abs(a0, r0) < TOL_OPT and _max_abs(a1, r1) < TOL_OPT
1274:    def split_case():
1293:    def clip_case(name, scale_in, max_norm):
1304:            results.append((f"optim/{name}", err < TOL_OPT and abs(ret - total) < 1e-6, f"max_abs={err:.1e}"))
1310:    def sched(name, fn, ref):
1313:            results.append((f"optim/{name}", err < TOL_SCHED, f"max_abs={err:.1e}"))
1317:    def ref_cosine(s):
1328:
…[truncated 424 chars]

84	def load_framework(root):
85	    ag = _load_module("agent_autograd", root / "autograd.py")
86	    nn = _load_module("agent_nn", root / "nn.py", inject={"autograd": ag})
87	    opt = _load_module("agent_optim", root / "optim.py", inject={"autograd": ag})
88	    data = _load_module("agent_data", root / "data.py")
89	    return {"autograd": ag, "nn": nn, "optim": opt, "data": data}
90	
91	
92	# --------------------------------------------------------------------------- numeric helpers
93	def _num_grad_input(forward, x, eps=EPS):
94	    g = np.zeros_like(x)
95	    flat = x.reshape(-1)
96	    gf = g.reshape(-1)
97	    for i in range(flat.size):
98	        old = flat[i]
99	        flat[i] = old + eps
100	        fp = float(np.sum(forward(x)))
101	        flat[i] = old - eps
102	        fm = float(np.sum(forward(x)))
103	        flat[i] = old
104	        gf[i] = (fp - fm) / (2 * eps)
105	    return g
106	
107	
108	def _max_abs(a, b):
109	    return float(np.max(np.abs(np.asarray(a, np.float64) - np.asarray(b, np.float64))))
110	
111	
112	def _bsum(it):
113	    s = 0
114	    for x in it:
115	        s += int(bool(x))
116	    return s
117	
118	
119	def _imax(a, b):
120	    return a if a > b else b
121	
122	
123	# ======================================================================= autograd_grads
124	def check_autograd_grads(fw, sb):
125	    ag = fw["autograd"]
126	    nn = fw["nn"]
127	    T = ag.Tensor
128	    results = []
129	
130	    def case(name, build, shape, seed, upstrea
…[truncated 12793 chars]

344	                    p.data[...] = 1.0
345	            m.set_training(True)
346	            X = make_X(sb + seed + 5)
347	            tgt = np.array([0, 2])
348	            for p in m.params():
349	                p.grad = None
350	            ag.cross_entropy(m(X), tgt).backward()
351	            allfinite = all(p.grad is not None and np.all(np.isfinite(p.grad)) for p in m.params())
352	            ngrad = sum(1 for p in m.params() if p.grad is not None)
353	            results.append((f"grad/{name}", allfinite and ngrad == len(m.params()),
354	                            f"{ngrad}/{len(m.params())} grads, finite={allfinite}"))
355	        except Exception as e:  # noqa: BLE001
356	            results.append((f"grad/{name}", False, f"raised {type(e).__name__}: {e}"))
357	    quant_model_backprop("QuantCNN_backprop", lambda s: nn.QuantCNN(6, widths=(8, 16), seed=s),
358	                         lambda s: np.random.default_rng(s).standard_normal((2, 3, 16, 16)), 90)
359	    quant_model_backprop("QuantMLP_backprop", lambda s: nn.QuantMLP(6, img_size=8, hidden=(32, 16), seed=s),
360	                         lambda s: np.random.default_rng(s).standard_normal((2, 3, 8, 8)), 100)
361	
362	    # ---- EXTRA generic ops (same coverage as the held-out grader) ----
363	    case("square", lambda t: ag.square(t), (4, 5), 110)
364	    case("rsqrt", lambda t: ag.rsqrt(ag.add(t, T(5.0))), (4, 5), 111)
365	    case("reciprocal", lambda t: ag.reciprocal(ag.add(t, T(5.0))), (4, 5), 112)
366	
…[truncated 14060 chars]

583	# ======================================================================= quant_grads (LSQ / STE)
584	def check_quant_grads(fw, sb):
585	    ag = fw["autograd"]
586	    results = []
587	
588	    def grads(v0, s0, Qn, Qp, up):
589	        vt = ag.Tensor(v0.copy(), requires_grad=True)
590	        st = ag.Tensor(np.array([s0]), requires_grad=True)
591	        out = ag.fake_quant(vt, st, Qn, Qp)
592	        out.backward(up.copy())
593	        return np.asarray(vt.grad, np.float64), np.asarray(st.grad, np.float64), out.data.copy()
594	
595	    def ste_indicator(name, seed, Qn, Qp, s0):
596	        try:
597	            rng = np.random.default_rng(sb + seed)
598	            v0 = rng.standard_normal((5, 5)) * 2.0
599	            up = rng.standard_normal((5, 5))
600	            gv, _, _ = grads(v0, s0, Qn, Qp, up)
601	            r = v0 / s0
602	            middle = (r >= -Qn) & (r <= Qp)
603	            expected = up * middle
604	            ok = _max_abs(gv, expected) < 1e-9 and np.all(gv[~middle] == 0.0)
605	            results.append((f"quant/{name}", ok, f"err={_max_abs(gv, expected):.1e}"))
606	        except Exception as e:  # noqa: BLE001
607	            results.append((f"quant/{name}", False, f"raised {type(e).__name__}: {e}"))
608	    ste_indicator("ste_indicator_w", 60, 2, 1, 0.7)
609	    ste_indicator("ste_indicator_a", 61, 0, 3, 0.5)
610	
611	    def scale_clip(name, seed, Qn, Qp, s0, fill, bound):
612	        try:
613	            v0 = np.full((4, 4), fill)
614	      
…[truncated 16955 chars]

922	
923	    def qmlp_roundtrip():
924	        m = nn.QuantMLP(10, img_size=16, hidden=(64, 32), seed=sb + 31)
925	        sd = m.state_dict()
926	        m2 = nn.QuantMLP(10, img_size=16, hidden=(64, 32), seed=sb + 32)
927	        m2.load_state_dict(sd)
928	        return True, f"keys={len(sd)}"
929	
930	    # ---- EXTRA op / activation / layer property checks (match the held-out coverage) ----
931	    def square_def():
932	        x = np.random.default_rng(sb + 40).standard_normal((4, 5))
933	        return _max_abs(ag.square(T(x)).data, x * x) < 1e-9, ""
934	
935	    def rsqrt_def():
936	        x = np.abs(np.random.default_rng(sb + 41).standard_normal((4, 5))) + 0.5
937	        return _max_abs(ag.rsqrt(T(x)).data, 1.0 / np.sqrt(x)) < 1e-9, ""
938	
939	    def var_def():
940	        x = np.random.default_rng(sb + 42).standard_normal((4, 5))
941	        return _max_abs(ag.var(T(x), axis=1).data, x.var(axis=1)) < 1e-9, ""
942	
943	    def std_def():
944	        x = np.random.default_rng(sb + 43).standard_normal((4, 5))
945	        return _max_abs(ag.std(T(x), axis=1).data, x.std(axis=1)) < 1e-9, ""
946	
947	    def softplus_def():
948	        x = np.random.default_rng(sb + 44).standard_normal((4, 5))
949	        return _max_abs(ag.softplus(T(x)).data, np.log1p(np.exp(x))) < 1e-6, ""
950	
951	    def silu_def():
952	        x = np.random.default_rng(sb + 45).standard_normal((4, 5))
953	        return _max_abs(ag.silu(T(x)).data, x / (1 + np.exp(-x))) < 1e-6, ""
954	
955	    d
…[truncated 9362 chars]

1122	            for s in range(nsteps):
1123	                grng = np.random.default_rng(gseed + s)
1124	                grads = [grng.standard_normal(p.data.shape) for p in ps]
1125	                for p, g in zip(ps, grads):
1126	                    p.grad = g.copy()
1127	                o.step()
1128	                ref = [ref_step(i, ref[i], grads[i].copy(), s + 1, st) for i in range(len(ps))]
1129	            err = max(_max_abs(p.data, ref[i]) for i, p in enumerate(ps))
1130	            results.append((f"optim/{name}", err < TOL_OPT, f"max_abs={err:.1e}"))
1131	        except Exception as e:  # noqa: BLE001
1132	            results.append((f"optim/{name}", False, f"raised {type(e).__name__}: {e}"))
1133	
1134	    def sgd_step(lr, mu, wd):
1135	        def f(i, p, g, t, st):
1136	            v = st.get(("v", i), np.zeros_like(p))
1137	            g = g + wd * p
1138	            v = mu * v + g
1139	            st[("v", i)] = v
1140	            return p - lr * v
1141	        return f
1142	
1143	    def adagrad_step(lr, eps, wd):
1144	        def f(i, p, g, t, st):
1145	            s = st.get(("s", i), np.zeros_like(p))
1146	            g = g + wd * p
1147	            s = s + g * g
1148	            st[("s", i)] = s
1149	            return p - lr * g / (np.sqrt(s) + eps)
1150	        return f
1151	
1152	    def rmsprop_step(lr, alpha, eps, wd):
1153	        def f(i, p, g, t, st):
1154	            s = st.get(("s", i), np.zeros_like(p))
1155	            g = g + wd * p
1156	  
…[truncated 16049 chars]

40	reward = 1 from the grader iff (every component milestone fully passes on the held-out cases) AND
41	(the bit-audit passes) AND (held-out top-1 >= the threshold). Drive THIS runner to all-green first,
42	then train. Exit code is 0 iff every public check here passes.
43	"""
44	from __future__ import annotations
45	
46	import argparse
47	import importlib.util
48	import math
49	import sys
50	from pathlib import Path
51	
52	import numpy as np
53	
54	TOL_GRAD = 2e-3
55	TOL_PROP = 1e-6
56	TOL_OPT = 1e-7
57	TOL_SCHED = 1e-9
58	EPS = 1e-5
59	
60	PUBLIC_SEED_BASE = 1000   # the GRADER uses a DIFFERENT base; do not curve-fit to these seeds.
61	
62	
63	# --------------------------------------------------------------------------- module loading
64	def _load_module(name, path, inject=None):
65	    saved = {}
66	    if inject:
67	        for k, v in inject.items():
68	            saved[k] = sys.modules.get(k)
69	            sys.modules[k] = v
70	    try:
71	        spec = importlib.util.spec_from_file_location(name, str(path))
72	        mod = importlib.util.module_from_spec(spec)
73	        spec.loader.exec_module(mod)
74	        return mod
75	    finally:
76	        if inject:
77	            for k, old in saved.items():
78	                if old is None:
79	                    sys.modules.pop(k, None)
80	                else:
81	                    sys.modules[k] = old
82	
83

batchnorm1d: 0
conv2d: 2
conv2d_gen: 1
conv_transpose2d: 1
avgpool2d_s: 1
maxpool2d_s: 1
--- data shape ---
(2500, 3, 16, 16) uint8 (2500,) [0 1 2 3 4 5 6 7 8 9]

File created successfully at: /app/submission/_patch_ag.py (file state is current in your context , no need to Read it back)

patch batch 1 OK, replaced 34

File created successfully at: /app/submission/_patch_ag2.py (file state is current in your context , no need to Read it back)

patch batch 2 OK, replaced 21

File created successfully at: /app/submission/_patch_ag3.py (file state is current in your context , no need to Read it back)

patch batch 3 OK, replaced 15 + batchnorm1d
import ok
['Callable', 'Iterable', 'List', 'Optional', 'Tensor', 'abs', 'add', 'annotations', 'avgpool2d', 'avgpool2d_s', 'batchnorm1d', 'batchnorm2d', 'clip', 'concat', 'conv2d', 'conv2d_gen', 'conv_transpose2d', 'cross_entropy', 'cumsum', 'div', 'elu', 'embedding', 'exp', 'fake_quant', 'fake_quant_affine', 'fake_quant_lsq_plus', 'fake_quant_per_channel', 'flip', 'gather', 'gelu', 'getitem', 'groupnorm', 'hardsigmoid', 'hardtanh', 'huber_loss', 'instance_norm', 'kl_div', 'l2_normalize', 'layernorm', 'leaky_relu', 'log', 'log_softmax', 'logaddexp', 'logsumexp', 'matmul', 'max', 'maximum', 'maxpool2d', 'maxpool2d_s', 'mean']

File created successfully at: /app/submission/_patch_nn.py (file state is current in your context , no need to Read it back)

nn patched, replaced 32

File created successfully at: /app/submission/_patch_opt.py (file state is current in your context , no need to Read it back)

optim patched, replaced 19

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

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

------------------------------------------------------------------------
autograd_grads 114/115 | quant_grads 21/21 | nn_forward 53/53 | optim 25/25 | integration 6/6 | data 2/2
TOTAL  221/222 public checks passing
Fix the FAIL lines above (each is a backward/forward/optimizer bug). Re-run until all green, then train.

grad/pad2d                               FAIL  (raised NameError: name 'a' is not defined)
Fix the FAIL lines above (each is a backward/forward/optimizer bug). Re-run until all green, then train.

<tool_use_error>File has been modified since read, either by the user or by a linter. Read it again before attempting to write it.</tool_use_error>

fixed pad2d

------------------------------------------------------------------------
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).

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

real	0m0.836s
user	0m0.768s
sys	0m0.136s

(Bash completed with no output)

/bin/bash: line 1:  1535 Killed                  python3 -u train.py --epochs 2 --out /tmp/qat_test.npz
EXIT=137

forward...
logits (128, 10)
loss 2.302585092994046
backward done
maxrss MB 195.04296875
EXIT=0

total        used        free      shared  buff/cache   available
Mem:             481         229         174           1          91         252
---
0 2.302585092994046 rss 196
/bin/bash: line 32:  1549 Killed                  python3 -u - <<'PY'
import numpy as np, resource, sys
import autograd as ag, data as D, optim as O
from nn import QuantCNN
X,y=D.load_npz("/app/data/train.npz"); Xn=D.normalize(X)
m=QuantCNN(10,(16,32),seed=0); m.set_training(True)
g=O.split_decay_params(m)
opt=O.Adam([{"params":g["decay"],"weight_decay":5e-4},{"params":g["no_decay"],"weight_decay":0.0}],lr=4e-3)
drng=np.random.default_rng(7)
for i,(xb,yb) in enumerate(D.iter_minibatches(Xn[:2000],y[:2000],128,drng)):
    logits=m(xb); loss=ag.cross_entropy(logits,yb)
    opt.zero_grad(); loss.backward(); O.clip_grad_norm(m.params(),3.0); opt.step()
    if i%4==0:
        print(i, float(loss.data), "rss", resource.getrusage(resource.RUSAGE_SELF).ru_maxrss//1024); sys.stdout.flush()
print("epoch done")
PY

EXIT=137

0 rss 123
8 rss 126
16 rss 126
24 rss 127
epoch done rss 127
EXIT=0

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

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

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

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

Traceback (most recent call last):
  File "/app/submission/train.py", line 154, in <module>
    main()
  File "/app/submission/train.py", line 150, in main
    train(args.data, args.out, epochs=args.epochs, seed=args.seed, n_classes=args.n_classes)
  File "/app/submission/train.py", line 122, in train
    print(f"epoch {ep+1}/{epochs} lr={lr:.4f} loss={float(loss.data):.3f} val={acc:.3f} tr={tr_acc:.3f}", flush=True)
                                                          ^^^^
UnboundLocalError: cannot access local variable 'loss' where it is not associated with a value

real	0m10.547s
user	0m9.795s
sys	0m0.840s
EXIT=0

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

epoch 1/8 lr=0.0040 loss=2.288 val=0.133 tr=0.165
epoch 2/8 lr=0.0038 loss=2.051 val=0.135 tr=0.196
epoch 3/8 lr=0.0033 loss=1.668 val=0.340 tr=0.409
epoch 4/8 lr=0.0025 loss=1.324 val=0.645 tr=0.694
epoch 5/8 lr=0.0016 loss=0.939 val=0.550 tr=0.622
epoch 6/8 lr=0.0008 loss=0.919 val=0.772 tr=0.829
epoch 7/8 lr=0.0003 loss=0.736 val=0.800 tr=0.837
epoch 8/8 lr=0.0001 loss=0.791 val=0.820 tr=0.849
best_val=0.820 baked_val=0.820 saved->/tmp/qat_test.npz

real	1m24.139s
user	1m17.647s
sys	0m6.574s
EXIT=0

audit ok ; total keys 24
sample weight uniques: 4 3

started pid 1582

<tool_use_error>Blocked: sleep 180 followed by: tail -8 /tmp/train_full.log. 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>

Command running in background with ID: b2wdjci7u. Output is being written to: /tmp/claude-1000/-app/18edea3b-2147-48c9-a1cb-d8972ca3194c/tasks/b2wdjci7u.output. You will be notified when it completes. To check interim output, use Read on that file path.

<tool_use_error>Blocked: sleep 60 followed by: tail -6 /tmp/train_full.log. 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>

1	epoch 1/36 lr=0.0040 loss=2.288 val=0.133 tr=0.165
2

__pycache__
autograd.py
data.py
nn.py
optim.py
train.py

Command running in background with ID: bo7qgd1fd. Output is being written to: /tmp/claude-1000/-app/18edea3b-2147-48c9-a1cb-d8972ca3194c/tasks/bo7qgd1fd.output. You will be notified when it completes. To check interim output, use Read on that file path.

Next wakeup scheduled for 22:35:00 (in 412s). Nothing more to do this turn , the harness re-invokes you when the wakeup fires or a task-notification arrives.

1	epoch 1/36 lr=0.0040 loss=2.288 val=0.133 tr=0.165
2	epoch 2/36 lr=0.0040 loss=2.070 val=0.107 tr=0.138
3	epoch 3/36 lr=0.0040 loss=1.621 val=0.383 tr=0.450
4	epoch 4/36 lr=0.0039 loss=1.209 val=0.655 tr=0.686
5

Wasted call , file unchanged since your last Read. Refer to that earlier tool_result instead.