tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.Explore app directory structure
ls -la && echo "---SUBMISSION---" && ls -la submission/ && echo "---DATA---" && ls -la data/ 2>/dev/null && echo "---CHECKS---" && head -50 run_checks.py
total 117
drwxr-xr-x 4 user user 4096 Jun 30 22:11 .
drwxr-xr-x 21 root root 4096 Jun 30 22:11 ..
-rw-r--r-- 1 user user 15364 Jun 30 22:11 PROMPT.txt
-rw-r--r-- 1 user user 5 Jun 30 22:11 agent.pid
-rw-r--r-- 1 user user 0 Jun 30 22:11 agent.stderr
-rw-r--r-- 1 user user 4609 Jun 30 22:11 agent_trajectory.jsonl
drwxr-xr-x 2 user user 60 Jun 30 22:11 data
-rw-r--r-- 1 user user 0 Jun 30 22:11 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 22:11 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 22:11 .
drwxr-xr-x 4 user user 4096 Jun 30 22:11 ..
-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 22:11 .
drwxr-xr-x 4 user user 4096 Jun 30 22:11 ..
-rw-r--r-- 1 user user 1940490 Jun 30 10:02 train.npz
---CHECKS---
#!/usr/bin/env python3
"""PUBLIC component check runner for the from-scratch numpy QAT framework.
Run this AS OFTEN AS YOU LIKE while you implement, to get a per-check progress signal:
python3 /app/run_checks.py # check the framework in /app/submission
python3 /app/run_che
…[truncated 2695 chars]/app/submission/autograd.py
1 """Reverse-mode automatic differentiation over numpy arrays. 2 3 YOU MUST IMPLEMENT EVERYTHING MARKED ``# TODO`` / ``raise NotImplementedError`` using numpy 4 ONLY. No torch/jax/tensorflow/autograd/etc. The grader's conformance suite (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/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]/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/submission/autograd.py
TODO: implement the topological walk + seeding. (The per-op ``_backward`` closures do the
local accumulation; here you only orchestrate the order + seed ``self.grad``.)
"""
raise NotImplementedError("Tensor.backward") TODO: implement the topological walk + seeding. (The per-op ``_backward`` closures do the
local accumulation; here you only orchestrate the order + seed ``self.grad``.)
"""
if grad is None:
grad = np.ones_like(self.data)
self._accum(_as_f64(grad))
topo: List[Tensor] = []
visited = set()
stack = [(self, False)]
while stack:
node, processed = stack.pop()
if processed:
topo.append(node)
continue
if id(node) in visited:
continue
visited.add(id(node))
stack.append((node, True))
for p in node._parents:
if id(p) not in visited:
stack.append((p, False))
for node in reversed(topo):
node._backward()The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("add backward") # TODO def _bw():
g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(g, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(g, b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("sub backward") # TODO def _bw():
g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(g, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(-g, b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("mul backward") # TODO def _bw():
g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(g * b.data, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(g * a.data, b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("div backward") # TODO def _bw():
g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(g / b.data, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(-g * a.data / (b.data * b.data), b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("power backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * p * a.data ** (p - 1))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("maximum backward") # TODO def _bw():
g = out.grad
ad, bd = a.data, b.data
ga = np.where(ad > bd, 1.0, np.where(ad == bd, 0.5, 0.0))
if a.requires_grad:
a._accum(_unbroadcast(g * ga, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(g * (1.0 - ga), b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("minimum backward") # TODO def _bw():
g = out.grad
ad, bd = a.data, b.data
ga = np.where(ad < bd, 1.0, np.where(ad == bd, 0.5, 0.0))
if a.requires_grad:
a._accum(_unbroadcast(g * ga, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(g * (1.0 - ga), b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("where backward") # TODO def _bw():
g = out.grad
if a.requires_grad:
a._accum(_unbroadcast(np.where(c, g, 0.0), a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(np.where(c, 0.0, g), b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("exp backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * out.data)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("log backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad / a.data)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("sqrt backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * 0.5 / out.data)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("abs backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * np.sign(a.data))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("clip backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * ((a.data > lo) & (a.data < hi)))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
class Tensor:
__slots__ = ("data", "grad", "requires_grad", "_backward", "_parents")def _restore_keepdims(g, axis, keepdims, ndim):
"""Reshape a reduced gradient ``g`` back to keepdims form (size-1 in reduced axes)."""
if axis is None:
return g # scalar; broadcasting handles it
if keepdims:
return g
ax = axis if isinstance(axis, tuple) else (axis,)
ax = tuple(a % ndim for a in ax)
shp = list(g.shape)
for a in sorted(ax):
shp.insert(a, 1)
return g.reshape(shp)
class Tensor:
__slots__ = ("data", "grad", "requires_grad", "_backward", "_parents")The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("sum backward") # TODO def _bw():
if a.requires_grad:
g = _restore_keepdims(out.grad, axis, keepdims, a.data.ndim)
a._accum(np.broadcast_to(g, a.data.shape) + np.zeros_like(a.data))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("mean backward") # TODO def _bw():
if a.requires_grad:
g = _restore_keepdims(out.grad, axis, keepdims, a.data.ndim)
n = a.data.size / out.data.size
a._accum((np.broadcast_to(g, a.data.shape) + np.zeros_like(a.data)) / n)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("max backward") # TODO def _bw():
if a.requires_grad:
g = _restore_keepdims(out.grad, axis, keepdims, a.data.ndim)
od = _restore_keepdims(out.data, axis, keepdims, a.data.ndim)
mask = (a.data == od).astype(np.float64)
cnt = mask.sum(axis=axis, keepdims=True)
a._accum(mask * g / cnt)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("min backward") # TODO def _bw():
if a.requires_grad:
g = _restore_keepdims(out.grad, axis, keepdims, a.data.ndim)
od = _restore_keepdims(out.data, axis, keepdims, a.data.ndim)
mask = (a.data == od).astype(np.float64)
cnt = mask.sum(axis=axis, keepdims=True)
a._accum(mask * g / cnt)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("concat backward") # TODO def _bw():
g = out.grad
offset = 0
for t in ts:
n = t.data.shape[axis]
if t.requires_grad:
sl = [slice(None)] * g.ndim
sl[axis] = slice(offset, offset + n)
t._accum(g[tuple(sl)])
offset += nThe file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("stack backward") # TODO def _bw():
g = out.grad
for i, t in enumerate(ts):
if t.requires_grad:
sl = [slice(None)] * g.ndim
sl[axis] = i
t._accum(g[tuple(sl)])The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("transpose backward") # TODO def _bw():
if a.requires_grad:
if axes is None:
a._accum(np.transpose(out.grad))
else:
inv = np.argsort(axes)
a._accum(np.transpose(out.grad, inv))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("reshape backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad.reshape(a.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("getitem backward") # TODO def _bw():
if a.requires_grad:
dx = np.zeros_like(a.data)
np.add.at(dx, idx, out.grad)
a._accum(dx)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("matmul backward") # TODO def _bw():
g = out.grad
if a.requires_grad:
da = g @ np.swapaxes(b.data, -1, -2)
a._accum(_unbroadcast(da, a.data.shape))
if b.requires_grad:
db = np.swapaxes(a.data, -1, -2) @ g
b._accum(_unbroadcast(db, b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("relu backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * (a.data > 0.0))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("leaky_relu backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * np.where(a.data > 0.0, 1.0, slope))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("sigmoid backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * out.data * (1.0 - out.data))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("tanh backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * (1.0 - out.data * out.data))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("gelu backward") # TODO def _bw():
if a.requires_grad:
pdf = np.exp(-0.5 * a.data * a.data) / np.sqrt(2.0 * np.pi)
a._accum(out.grad * (cdf + a.data * pdf))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("softmax backward") # TODO def _bw():
if a.requires_grad:
g = out.grad
dot = (g * s).sum(axis=axis, keepdims=True)
a._accum(s * (g - dot))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("log_softmax backward") # TODO def _bw():
if a.requires_grad:
g = out.grad
sm = np.exp(out.data)
a._accum(g - sm * g.sum(axis=axis, keepdims=True))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("cross_entropy backward") # TODO def _bw():
if logits.requires_grad:
sm = np.exp(logp)
onehot = np.zeros_like(sm)
onehot[np.arange(n), t] = 1.0
logits._accum(out.grad * (sm - onehot) / n)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("mse_loss backward") # TODO def _bw():
if pred.requires_grad:
n = pred.data.size
pred._accum(out.grad * (2.0 / n) * (pred.data - tgt))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("layernorm backward") # TODO def _bw():
g = out.grad
if gamma.requires_grad:
ax = tuple(range(g.ndim - 1))
gamma._accum((g * xhat).sum(axis=ax))
if beta.requires_grad:
ax = tuple(range(g.ndim - 1))
beta._accum(g.sum(axis=ax))
if x.requires_grad:
gx = g * gamma.data
dx = inv / D * (D * gx - gx.sum(axis=-1, keepdims=True)
- xhat * (gx * xhat).sum(axis=-1, keepdims=True))
x._accum(dx)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("conv2d backward") # TODO (dW, dbias, dx via _col2im) def _bw():
dout = out.grad.reshape(N, Cout, OH * OW)
if weight.requires_grad:
dW = np.einsum("nop,ncp->oc", dout, cols).reshape(Cout, Cin, KH, KW)
weight._accum(dW)
if has_bias and bias.requires_grad:
bias._accum(dout.sum(axis=(0, 2)))
if x.requires_grad:
dcols = np.einsum("oc,nop->ncp", Wm, dout)
dxp = _col2im(dcols, xp.shape, KH, KW, stride, OH, OW)
if pad > 0:
dxp = dxp[:, :, pad:pad + H, pad:pad + W]
x._accum(dxp)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("avgpool2d backward") # TODO def _bw():
if x.requires_grad:
g = out.grad / (k * k) # (N,C,OH,OW)
dx = np.zeros((N, C, OH * k, OW * k), dtype=np.float64)
gg = np.broadcast_to(g[:, :, :, None, :, None], (N, C, OH, k, OW, k))
dx[:] = gg.reshape(N, C, OH * k, OW * k)
full = np.zeros_like(x.data)
full[:, :, :OH * k, :OW * k] = dx
x._accum(full)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("maxpool2d backward") # TODO def _bw():
if x.requires_grad:
g = out.grad # (N,C,OH,OW)
xr2 = xr.reshape(N, C, OH, k, OW, k)
# compute mask of maxima per window
mx = out.data[:, :, :, None, :, None] # (N,C,OH,1,OW,1)
mask = (xr2 == mx)
# if ties, route to first occurrence: pick argmax over flattened k*k
win = xr2.transpose(0, 1, 2, 4, 3, 5).reshape(N, C, OH, OW, k * k)
am = win.argmax(axis=-1) # (N,C,OH,OW)
sel = np.zeros((N, C, OH, OW, k * k), dtype=np.float64)
np.put_along_axis(sel, am[..., None], 1.0, axis=-1)
sel = sel.reshape(N, C, OH, OW, k, k).transpose(0, 1, 2, 4, 3, 5)
grad_blocks = sel * g[:, :, :, None, :, None]
dxr = grad_blocks.reshape(N, C, OH * k, OW * k)
full = np.zeros_like(x.data)
full[:, :, :OH * k, :OW * k] = dxr
x._accum(full)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
xr2 = xr.reshape(N, C, OH, k, OW, k)
# compute mask of maxima per window
mx = out.data[:, :, :, None, :, None] # (N,C,OH,1,OW,1)
mask = (xr2 == mx)
# if ties, route to first occurrence: pick argmax over flattened k*k
win = xr2.transpose(0, 1, 2, 4, 3, 5).reshape(N, C, OH, OW, k * k) xr2 = xr.reshape(N, C, OH, k, OW, k)
# route to argmax (first occurrence) per window
win = xr2.transpose(0, 1, 2, 4, 3, 5).reshape(N, C, OH, OW, k * k)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("batchnorm2d backward") # TODO def _bw():
g = out.grad
if gamma.requires_grad:
gamma._accum((g * xhat).sum(axis=(0, 2, 3)))
if beta.requires_grad:
beta._accum(g.sum(axis=(0, 2, 3)))
if x.requires_grad:
gx = g * g_
if training:
dx = inv / M * (M * gx - gx.sum(axis=(0, 2, 3), keepdims=True)
- xhat * (gx * xhat).sum(axis=(0, 2, 3), keepdims=True))
else:
dx = gx * inv
x._accum(dx)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("fake_quant backward (STE + LSQ scale gradient)") # TODO def _bw():
g = out.grad
if v.requires_grad:
mask = ((r > Qn) & (r < Qp)).astype(np.float64)
v._accum(g * mask)
if scale.requires_grad:
ds_elem = np.where(r < Qn, Qn, np.where(r > Qp, Qp, np.round(r) - r))
ds = float((g * ds_elem).sum()) * grad_scale
scale._accum(np.array(ds).reshape(scale.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("square backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * 2.0 * a.data)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("rsqrt backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * -0.5 * a.data ** (-1.5))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("reciprocal backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * -1.0 / (a.data * a.data))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("var backward") # TODO def _bw():
if a.requires_grad:
g = _restore_keepdims(out.grad, axis, keepdims, a.data.ndim)
n = a.data.size / mu.size if axis is None else a.data.size / out.data.size
# number of averaged elements along reduced axes:
nred = a.data.size / (a.data.size / xc.size * 1) if False else None
N = a.data.size / (mu.size)
a._accum(g * (2.0 / N) * xc)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
if a.requires_grad:
g = _restore_keepdims(out.grad, axis, keepdims, a.data.ndim)
n = a.data.size / mu.size if axis is None else a.data.size / out.data.size
# number of averaged elements along reduced axes:
nred = a.data.size / (a.data.size / xc.size * 1) if False else None
N = a.data.size / (mu.size)
a._accum(g * (2.0 / N) * xc) def _bw():
if a.requires_grad:
g = _restore_keepdims(out.grad, axis, keepdims, a.data.ndim)
N = a.data.size / mu.size
a._accum(g * (2.0 / N) * xc)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("std backward") # TODO def _bw():
if a.requires_grad:
g = _restore_keepdims(out.grad, axis, keepdims, a.data.ndim)
N = a.data.size / mu.size
a._accum(g * xc / (N * sd))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("gather backward") # TODO def _bw():
if a.requires_grad:
dx = np.zeros_like(a.data)
ax = axis % a.data.ndim
grids = list(np.ogrid[tuple(slice(0, s) for s in idx.shape)])
grids[ax] = idx
np.add.at(dx, tuple(grids), out.grad)
a._accum(dx)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("pad2d backward") # TODO def _bw():
if x.requires_grad:
if pad == 0:
x._accum(out.grad)
else:
H, W = x.data.shape[2], x.data.shape[3]
x._accum(out.grad[:, :, pad:pad + H, pad:pad + W])The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("softplus backward") # TODO def _bw():
if a.requires_grad:
with np.errstate(over="ignore"):
sig = 1.0 / (1.0 + np.exp(-bx))
a._accum(out.grad * sig)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("silu backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * (sig + a.data * sig * (1.0 - sig)))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("mish backward") # TODO def _bw():
if a.requires_grad:
t = np.tanh(sp)
with np.errstate(over="ignore"):
sig = 1.0 / (1.0 + np.exp(-x))
a._accum(out.grad * (t + x * (1.0 - t * t) * sig))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("elu backward") # TODO def _bw():
if a.requires_grad:
d = np.where(x > 0.0, 1.0, alpha * np.exp(np.minimum(x, 0.0)))
a._accum(out.grad * d)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("hardtanh backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * ((a.data > lo) & (a.data < hi)))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("hardsigmoid backward") # TODO def _bw():
if a.requires_grad:
a._accum(out.grad * np.where((z > 0.0) & (z < 1.0), 1.0 / 6.0, 0.0))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("groupnorm backward") # TODO def _bw():
g = out.grad
if gamma.requires_grad:
gamma._accum((g * xhat).sum(axis=(0, 2, 3)))
if beta.requires_grad:
beta._accum(g.sum(axis=(0, 2, 3)))
if x.requires_grad:
M = cg * H * W
gx = (g * gamma.data.reshape(1, C, 1, 1)).reshape(N, G, M)
xh = xhat.reshape(N, G, M)
dx = inv / M * (M * gx - gx.sum(axis=2, keepdims=True)
- xh * (gx * xh).sum(axis=2, keepdims=True))
x._accum(dx.reshape(N, C, H, W))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("fake_quant_per_channel backward (STE + per-channel scale grad)") # TODO def _bw():
g = out.grad
if v.requires_grad:
mask = ((r > Qn) & (r < Qp)).astype(np.float64)
v._accum(g * mask)
if scale.requires_grad:
d_elem = np.where(r < Qn, Qn, np.where(r > Qp, Qp, np.round(r) - r))
prod = g * d_elem
sum_axes = tuple(i for i in range(v.data.ndim) if i != (axis % v.data.ndim))
ds = prod.sum(axis=sum_axes) * grad_scale
scale._accum(ds.reshape(scale.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("fake_quant_affine backward (STE + scale grad on shifted grid)") # TODO def _bw():
g = out.grad
if v.requires_grad:
mask = ((r > Qn) & (r < Qp)).astype(np.float64)
v._accum(g * mask)
if scale.requires_grad:
ds_elem = np.where(r < Qn, Qn - z, np.where(r > Qp, Qp - z, (q - z) - (r - z)))
ds = float((g * ds_elem).sum()) * grad_scale
scale._accum(np.array(ds).reshape(scale.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("cumsum backward") # TODO def _bw():
if a.requires_grad:
g = out.grad
a._accum(np.flip(np.cumsum(np.flip(g, axis=axis), axis=axis), axis=axis))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("flip backward") # TODO def _bw():
if a.requires_grad:
a._accum(np.flip(out.grad, axis=axis))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("logsumexp backward") # TODO def _bw():
if a.requires_grad:
g = out.grad
if not keepdims:
g = np.expand_dims(g, axis)
a._accum(sm * g)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("logaddexp backward") # TODO def _bw():
g = out.grad
wa = np.exp(a.data - out_data)
wb = np.exp(b.data - out_data)
if a.requires_grad:
a._accum(_unbroadcast(g * wa, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(g * wb, b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("l2_normalize backward") # TODO def _bw():
if a.requires_grad:
g = out.grad
dot = (y * g).sum(axis=axis, keepdims=True)
a._accum((g - y * dot) / nrm)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("rms_norm backward") # TODO def _bw():
g = out.grad
if gamma.requires_grad:
ax = tuple(range(g.ndim - 1))
gamma._accum((g * xhat).sum(axis=ax))
if x.requires_grad:
gg = g * gamma.data
s = (gg * xd).sum(axis=-1, keepdims=True)
dx = inv * gg - (xd * inv ** 3 / D) * s
x._accum(dx)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("instance_norm backward") # TODO def _bw():
g = out.grad
if gamma.requires_grad:
gamma._accum((g * xhat).sum(axis=(0, 2, 3)))
if beta.requires_grad:
beta._accum(g.sum(axis=(0, 2, 3)))
if x.requires_grad:
gx = (g * gamma.data.reshape(1, C, 1, 1)).reshape(N, C, M)
xh = xhat.reshape(N, C, M)
dx = inv / M * (M * gx - gx.sum(axis=2, keepdims=True)
- xh * (gx * xh).sum(axis=2, keepdims=True))
x._accum(dx.reshape(N, C, H, W))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("huber_loss backward") # TODO def _bw():
if pred.requires_grad:
per_g = np.where(quad, diff, delta * np.sign(diff))
pred._accum(out.grad * per_g / n)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("kl_div backward") # TODO def _bw():
if log_p.requires_grad:
log_p._accum(out.grad * (-q / n))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("embedding backward") # TODO def _bw():
if weight.requires_grad:
dx = np.zeros_like(weight.data)
np.add.at(dx, idx, out.grad)
weight._accum(dx)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("conv2d_gen backward (grouped/dilated dW/db/dx)") # TODO def _bw():
dout = out.grad.reshape(N, Cout, OH * OW)
dout_g = dout.reshape(N, groups, cog, OH * OW)
if weight.requires_grad:
dWm = np.einsum("ngop,ngcp->goc", dout_g, cols_g)
weight._accum(dWm.reshape(Cout, cig, KH, KW))
if has_bias and bias.requires_grad:
bias._accum(dout.sum(axis=(0, 2)))
if x.requires_grad:
dcols_g = np.einsum("goc,ngop->ngcp", Wm, dout_g)
dcols = dcols_g.reshape(N, Cin * KH * KW, OH * OW)
dxp = _col2im_dil(dcols, xp.shape, KH, KW, stride, dilation, OH, OW)
if pad > 0:
dxp = dxp[:, :, pad:pad + H, pad:pad + W]
x._accum(dxp)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("conv_transpose2d backward") # TODO def _bw():
g = out.grad
if has_bias and bias.requires_grad:
bias._accum(g.sum(axis=(0, 2, 3)))
gfull = np.zeros((N, Cout, OHf, OWf), dtype=np.float64)
if pad > 0:
gfull[:, :, pad:OHf - pad, pad:OWf - pad] = g
else:
gfull[:] = g
gcontrib = np.empty((N, Cout, H, W, KH, KW), dtype=np.float64)
for i in range(KH):
for j in range(KW):
gcontrib[:, :, :, :, i, j] = gfull[:, :, i:i + stride * H:stride, j:j + stride * W:stride]
if x.requires_grad:
dx = np.einsum("noijKL,coKL->ncij", gcontrib, Wm)
x._accum(dx)
if weight.requires_grad:
dW = np.einsum("ncij,noijKL->coKL", xd, gcontrib)
weight._accum(dW)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("avgpool2d_s backward") # TODO def _bw():
if x.requires_grad:
g = out.grad
dxp = np.zeros((N, C, Hp, Wp), dtype=np.float64)
for oi in range(OH):
for oj in range(OW):
dxp[:, :, oi * stride:oi * stride + k, oj * stride:oj * stride + k] += \
g[:, :, oi, oj][:, :, None, None] / (k * k)
if pad > 0:
dxp = dxp[:, :, pad:pad + H, pad:pad + W]
x._accum(dxp)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("maxpool2d_s backward") # TODO def _bw():
if x.requires_grad:
g = out.grad
dxp = np.zeros((N, C, Hp, Wp), dtype=np.float64)
ni = np.arange(N)[:, None] * np.ones((1, C), dtype=np.int64)
ci = np.ones((N, 1), dtype=np.int64) * np.arange(C)[None, :]
for oi in range(OH):
for oj in range(OW):
pi = oi * stride + argi[:, :, oi, oj]
pj = oj * stride + argj[:, :, oi, oj]
np.add.at(dxp, (ni, ci, pi, pj), g[:, :, oi, oj])
if pad > 0:
dxp = dxp[:, :, pad:pad + H, pad:pad + W]
x._accum(dxp)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("fake_quant_lsq_plus backward (STE v-grad + beta-grad + LSQ scale grad)") # TODO def _bw():
g = out.grad
mask_in = ((r > Qn) & (r < Qp)).astype(np.float64)
if v.requires_grad:
v._accum(g * mask_in)
if beta.requires_grad:
db = float((g * (1.0 - mask_in)).sum())
beta._accum(np.array(db).reshape(beta.data.shape))
if scale.requires_grad:
ds_elem = np.where(r < Qn, Qn, np.where(r > Qp, Qp, np.round(r) - r))
ds = float((g * ds_elem).sum()) * grad_scale
scale._accum(np.array(ds).reshape(scale.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
Check batchnorm1d presence
cd /app/submission && grep -n "batchnorm1d\|def batchnorm" autograd.py; echo "---nn refs---"; grep -n "ag\.batchnorm1d\|batchnorm1d" nn.py
851:def batchnorm2d(x, gamma, beta, eps: float = 1e-5, ---nn refs--- 220: # TODO: ag.batchnorm1d(x, gamma, beta, eps, running_mean, running_var, momentum, training)
/app/submission/autograd.py
# ============================================================ LSQ fake-quant (DOMAIN; the crux)
def batchnorm1d(x, gamma, beta, eps: float = 1e-5,
running_mean=None, running_var=None, momentum: float = 0.1,
training: bool = True) -> Tensor:
"""BatchNorm over the batch axis N for a (N, C) input. gamma,beta:(C,). POPULATION variance in
training (divide by N); running_var tracks the UNBIASED batch variance (var*N/(N-1))."""
x, gamma, beta = _ensure(x), _ensure(gamma), _ensure(beta)
N, C = x.data.shape
M = N
xd = x.data
if training:
mu = xd.mean(axis=0, keepdims=True)
xc = xd - mu
var = (xc * xc).mean(axis=0, keepdims=True)
inv = 1.0 / np.sqrt(var + eps)
xhat = xc * inv
if running_mean is not None and running_var is not None:
ubvar = var * (M / _bi_max(1, M - 1))
running_mean[:] = (1 - momentum) * running_mean + momentum * mu.reshape(-1)
running_var[:] = (1 - momentum) * running_var + momentum * ubvar.reshape(-1)
else:
mu = running_mean.reshape(1, C)
var = running_var.reshape(1, C)
inv = 1.0 / np.sqrt(var + eps)
xhat = (xd - mu) * inv
g_ = gamma.data.reshape(1, C)
out = _mk(g_ * xhat + beta.data.reshape(1, C), (x, gamma, beta),
x.requires_grad or gamma.requires_grad or beta.requires_grad)
def _bw():
g = out.grad
if gamma.requires_grad:
gamma._accum((g * xhat).sum(axis=0))
if beta.requires_grad:
beta._accum(g.sum(axis=0))
…[truncated 438 chars]The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
Check no remaining TODOs and import works
grep -n "raise NotImplementedError\|# TODO" autograd.py; echo "exit: $?"; python3 -c "import autograd" && echo "IMPORT OK"
3:YOU MUST IMPLEMENT EVERYTHING MARKED ``# TODO`` / ``raise NotImplementedError`` using numpy exit: 0 IMPORT OK
/app/submission/nn.py
# TODO: y = x @ weight (+ bias). Use ag.matmul / ag.add.
raise NotImplementedError("Linear.forward") y = ag.matmul(x, self.weight)
if self.bias is not None:
y = ag.add(y, self.bias)
return yThe file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: out = x + fc2(gelu(fc1(x)))
raise NotImplementedError("MLP.forward")return ag.add(x, self.fc2(ag.gelu(self.fc1(x))))
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.layernorm(x, self.gamma, self.beta, self.eps)
raise NotImplementedError("LayerNorm.forward")return ag.layernorm(x, self.gamma, self.beta, self.eps)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.conv2d(x, self.weight, bias=self.bias, stride=self.stride, pad=self.padding)
raise NotImplementedError("Conv2d.forward")return ag.conv2d(x, self.weight, bias=self.bias, stride=self.stride, pad=self.padding)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.maxpool2d(x, self.k)
raise NotImplementedError("MaxPool2d.forward")return ag.maxpool2d(x, self.k)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.avgpool2d(x, self.k)
raise NotImplementedError("AvgPool2d.forward")return ag.avgpool2d(x, self.k)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.fake_quant(w, self.scale, W_NEG, W_POS)
raise NotImplementedError("WeightQuant.quant")return ag.fake_quant(w, self.scale, W_NEG, W_POS)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.fake_quant(x, self.scale, A_NEG, A_POS)
raise NotImplementedError("ActQuant.forward")return ag.fake_quant(x, self.scale, A_NEG, A_POS)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: ag.batchnorm2d(x, gamma, beta, eps, running_mean, running_var, momentum, training)
raise NotImplementedError("BatchNorm2d.forward") return ag.batchnorm2d(x, self.gamma, self.beta, self.eps,
self.running_mean, self.running_var, self.momentum, self.training)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: ag.batchnorm1d(x, gamma, beta, eps, running_mean, running_var, momentum, training)
raise NotImplementedError("BatchNorm1d.forward") return ag.batchnorm1d(x, self.gamma, self.beta, self.eps,
self.running_mean, self.running_var, self.momentum, self.training)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: wq = self.wq.quant(self.weight); return ag.conv2d(x, wq, bias=None, stride, pad=1)
raise NotImplementedError("QConv.forward") wq = self.wq.quant(self.weight)
return ag.conv2d(x, wq, bias=None, stride=self.stride, pad=1)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: h = aq(relu(bn(conv(x)))) -- in that order.
raise NotImplementedError("ConvBlock.forward")return self.aq(ag.relu(self.bn(self.conv(x))))
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: run the stem + blocks, global-average-pool, then the (2-bit) linear head.
raise NotImplementedError("QuantCNN.forward") if not isinstance(X, Tensor):
X = Tensor(X)
h = self.stem(X)
for b in self.blocks:
h = b(h)
Hs = h.shape[2]
h = ag.avgpool2d(h, Hs)
n = h.shape[0]
h = ag.reshape(h, (n, self.feat))
wq = self.head_wq.quant(self.head_w)
return ag.add(ag.matmul(h, wq), self.head_b)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: wq = self.wq.quant(self.weight); return ag.add(ag.matmul(x, wq), self.bias)
raise NotImplementedError("QLinear.forward") wq = self.wq.quant(self.weight)
return ag.add(ag.matmul(x, wq), self.bias)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: x -> reshape (N, in_dim); for each layer L: h = L["aq"](relu(L["bn"](L["fc"](h))));
# then return self.head(h).
raise NotImplementedError("QuantMLP.forward") if not isinstance(X, Tensor):
X = Tensor(X)
n = X.shape[0]
h = ag.reshape(X, (n, self.in_dim))
for L in self.layers:
h = L["aq"](ag.relu(L["bn"](L["fc"](h))))
return self.head(h)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.silu(x)
raise NotImplementedError("SiLU.forward")return ag.silu(x)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.elu(x, self.alpha)
raise NotImplementedError("ELU.forward")return ag.elu(x, self.alpha)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.groupnorm(x, self.gamma, self.beta, self.num_groups, self.eps)
raise NotImplementedError("GroupNorm.forward")return ag.groupnorm(x, self.gamma, self.beta, self.num_groups, self.eps)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.tanh(x@Wx + h@Wh + b)
raise NotImplementedError("RNNCell.forward")return ag.tanh(ag.add(ag.add(ag.matmul(x, self.Wx), ag.matmul(h, self.Wh)), self.b))
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: implement the GRU update with the named weights above.
raise NotImplementedError("GRUCell.forward") z = ag.sigmoid(ag.add(ag.add(ag.matmul(x, self.Wxz), ag.matmul(h, self.Whz)), self.bz))
r = ag.sigmoid(ag.add(ag.add(ag.matmul(x, self.Wxr), ag.matmul(h, self.Whr)), self.br))
n = ag.tanh(ag.add(ag.add(ag.matmul(x, self.Wxn), ag.matmul(ag.mul(r, h), self.Whn)), self.bn))
one_minus_z = ag.sub(1.0, z)
return ag.add(ag.mul(one_minus_z, n), ag.mul(z, h))The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: implement the LSTM update; return (h2, c2).
raise NotImplementedError("LSTMCell.forward") i = ag.sigmoid(ag.add(ag.add(ag.matmul(x, self.Wxi), ag.matmul(h, self.Whi)), self.bi))
f = ag.sigmoid(ag.add(ag.add(ag.matmul(x, self.Wxf), ag.matmul(h, self.Whf)), self.bf))
g = ag.tanh(ag.add(ag.add(ag.matmul(x, self.Wxg), ag.matmul(h, self.Whg)), self.bg))
o = ag.sigmoid(ag.add(ag.add(ag.matmul(x, self.Wxo), ag.matmul(h, self.Who)), self.bo))
c2 = ag.add(ag.mul(f, c), ag.mul(i, g))
h2 = ag.mul(o, ag.tanh(c2))
return h2, c2The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.fake_quant_per_channel(w, self.scale, self.neg, self.pos, axis=0)
raise NotImplementedError("PerChannelWeightQuant.quant")return ag.fake_quant_per_channel(w, self.scale, self.neg, self.pos, axis=0)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.fake_quant_affine(x, self.scale, self.zero_point, self.neg, self.pos)
raise NotImplementedError("AffineActQuant.forward")return ag.fake_quant_affine(x, self.scale, self.zero_point, self.neg, self.pos)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.rms_norm(x, self.gamma, self.eps)
raise NotImplementedError("RMSNorm.forward")return ag.rms_norm(x, self.gamma, self.eps)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.instance_norm(x, self.gamma, self.beta, self.eps)
raise NotImplementedError("InstanceNorm2d.forward")return ag.instance_norm(x, self.gamma, self.beta, self.eps)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.embedding(self.weight, idx)
raise NotImplementedError("Embedding.forward")return ag.embedding(self.weight, idx)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.conv_transpose2d(x, self.weight, bias=self.bias, stride=self.stride, pad=self.padding)
raise NotImplementedError("ConvTranspose2d.forward")return ag.conv_transpose2d(x, self.weight, bias=self.bias, stride=self.stride, pad=self.padding)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.conv2d_gen(x, self.weight, bias=self.bias, stride=self.stride,
# pad=self.padding, dilation=self.dilation, groups=1)
raise NotImplementedError("DilatedConv2d.forward") return ag.conv2d_gen(x, self.weight, bias=self.bias, stride=self.stride,
pad=self.padding, dilation=self.dilation, groups=1)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: return ag.fake_quant_lsq_plus(x, self.scale, self.beta, self.neg, self.pos)
raise NotImplementedError("LSQPlusActQuant.forward")return ag.fake_quant_lsq_plus(x, self.scale, self.beta, self.neg, self.pos)
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: depthwise conv2d_gen(groups=Cin) on the LSQ-quantized dw_weight, then pointwise
# conv2d_gen(groups=1) on the LSQ-quantized pw_weight.
raise NotImplementedError("QDWSepConv.forward") dwq = self.dw_wq.quant(self.dw_weight)
h = ag.conv2d_gen(x, dwq, bias=None, stride=self.stride, pad=1, dilation=1, groups=self.Cin)
pwq = self.pw_wq.quant(self.pw_weight)
return ag.conv2d_gen(h, pwq, bias=None, stride=1, pad=0, dilation=1, groups=1)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: conv -> bn -> relu -> aq
raise NotImplementedError("QSepBlock.forward")return self.aq(ag.relu(self.bn(self.conv(x))))
The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/nn.py
# TODO: stem -> blocks -> avgpool2d(full window) -> reshape (N,C) -> matmul(LSQ head) + bias
raise NotImplementedError("QuantSepCNN.forward") if not isinstance(X, Tensor):
X = Tensor(X)
h = self.stem(X)
for b in self.blocks:
h = b(h)
Hs = h.shape[2]
h = ag.avgpool2d(h, Hs)
n = h.shape[0]
h = ag.reshape(h, (n, self.feat))
wq = self.head_wq.quant(self.head_w)
return ag.add(ag.matmul(h, wq), self.head_b)The file /app/submission/nn.py has been updated successfully. (file state is current in your context , no need to Read it back)
Check nn.py complete and imports
grep -n "raise NotImplementedError\|# TODO" nn.py; python3 -c "import autograd, nn" && echo "IMPORT OK"
3:Implement every ``# TODO`` forward (the parameters + ``named_params``/``buffers`` naming are 39: raise NotImplementedError IMPORT OK
/app/submission/optim.py
# TODO: SGD(+momentum, +per-group weight decay) update. Iterate self._groups; for each
# group use wd = self.weight_decay if override is None else override; keep a momentum
# buffer per parameter (self._v is indexed in self.params order).
raise NotImplementedError("SGD.step") idx = 0
for group, override in self._groups:
wd = self.weight_decay if override is None else override
for p in group:
if p.grad is not None:
g = p.grad + wd * p.data
self._v[idx] = self.momentum * self._v[idx] + g
p.data -= self.lr * self._v[idx]
idx += 1The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
# TODO: AdamW with bias correction + DECOUPLED, per-group weight decay (see docstring).
raise NotImplementedError("AdamW.step") self.t += 1
b1, b2 = self.b1, self.b2
bc1 = 1 - b1 ** self.t
bc2 = 1 - b2 ** self.t
idx = 0
for group, override in self._groups:
wd = self.weight_decay if override is None else override
for p in group:
if p.grad is not None:
g = p.grad
self._m[idx] = b1 * self._m[idx] + (1 - b1) * g
self._v[idx] = b2 * self._v[idx] + (1 - b2) * (g * g)
mhat = self._m[idx] / bc1
vhat = self._v[idx] / bc2
p.data -= self.lr * (mhat / (np.sqrt(vhat) + self.eps) + wd * p.data)
idx += 1The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
# TODO: Adam with bias correction + COUPLED, per-group weight decay (see docstring).
raise NotImplementedError("Adam.step") self.t += 1
b1, b2 = self.b1, self.b2
bc1 = 1 - b1 ** self.t
bc2 = 1 - b2 ** self.t
idx = 0
for group, override in self._groups:
wd = self.weight_decay if override is None else override
for p in group:
if p.grad is not None:
g = p.grad + wd * p.data
self._m[idx] = b1 * self._m[idx] + (1 - b1) * g
self._v[idx] = b2 * self._v[idx] + (1 - b2) * (g * g)
mhat = self._m[idx] / bc1
vhat = self._v[idx] / bc2
p.data -= self.lr * mhat / (np.sqrt(vhat) + self.eps)
idx += 1The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
# TODO: RMSprop update with per-group weight decay (see docstring).
raise NotImplementedError("RMSprop.step") idx = 0
for group, override in self._groups:
wd = self.weight_decay if override is None else override
for p in group:
if p.grad is not None:
g = p.grad + wd * p.data
self._s[idx] = self.alpha * self._s[idx] + (1 - self.alpha) * (g * g)
p.data -= self.lr * g / (np.sqrt(self._s[idx]) + self.eps)
idx += 1The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
# TODO: Adagrad update with per-group weight decay (see docstring).
raise NotImplementedError("Adagrad.step") idx = 0
for group, override in self._groups:
wd = self.weight_decay if override is None else override
for p in group:
if p.grad is not None:
g = p.grad + wd * p.data
self._s[idx] = self._s[idx] + g * g
p.data -= self.lr * g / (np.sqrt(self._s[idx]) + self.eps)
idx += 1The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
# TODO: Lion update with per-group (decoupled) weight decay (see docstring).
raise NotImplementedError("Lion.step") idx = 0
for group, override in self._groups:
wd = self.weight_decay if override is None else override
for p in group:
if p.grad is not None:
g = p.grad
update = np.sign(self.b1 * self._m[idx] + (1 - self.b1) * g)
p.data -= self.lr * (update + wd * p.data)
self._m[idx] = self.b2 * self._m[idx] + (1 - self.b2) * g
idx += 1The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
# TODO: implement the Adamax update honouring per-group weight decay.
raise NotImplementedError("Adamax.step") self.t += 1
b1, b2 = self.b1, self.b2
bc1 = 1 - b1 ** self.t
idx = 0
for group, override in self._groups:
wd = self.weight_decay if override is None else override
for p in group:
if p.grad is not None:
g = p.grad + wd * p.data
self._m[idx] = b1 * self._m[idx] + (1 - b1) * g
self._u[idx] = np.maximum(b2 * self._u[idx], np.abs(g))
p.data -= (self.lr / bc1) * self._m[idx] / (self._u[idx] + self.eps)
idx += 1The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
# TODO: implement the NAdam update honouring per-group weight decay.
raise NotImplementedError("NAdam.step") self.t += 1
b1, b2 = self.b1, self.b2
bc1 = 1 - b1 ** self.t
bc1n = 1 - b1 ** (self.t + 1)
bc2 = 1 - b2 ** self.t
idx = 0
for group, override in self._groups:
wd = self.weight_decay if override is None else override
for p in group:
if p.grad is not None:
g = p.grad + wd * p.data
self._m[idx] = b1 * self._m[idx] + (1 - b1) * g
self._v[idx] = b2 * self._v[idx] + (1 - b2) * (g * g)
mhat = b1 * self._m[idx] / bc1n + (1 - b1) * g / bc1
vhat = self._v[idx] / bc2
p.data -= self.lr * mhat / (np.sqrt(vhat) + self.eps)
idx += 1The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
# TODO: implement the Adadelta update honouring per-group weight decay.
raise NotImplementedError("Adadelta.step") idx = 0
for group, override in self._groups:
wd = self.weight_decay if override is None else override
for p in group:
if p.grad is not None:
g = p.grad + wd * p.data
self._s[idx] = self.rho * self._s[idx] + (1 - self.rho) * (g * g)
delta = np.sqrt(self._d[idx] + self.eps) / np.sqrt(self._s[idx] + self.eps) * g
p.data -= self.lr * delta
self._d[idx] = self.rho * self._d[idx] + (1 - self.rho) * (delta * delta)
idx += 1The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
# TODO: implement the Nesterov-SGD update honouring per-group weight decay.
raise NotImplementedError("NesterovSGD.step") idx = 0
for group, override in self._groups:
wd = self.weight_decay if override is None else override
for p in group:
if p.grad is not None:
g = p.grad + wd * p.data
self._v[idx] = self.momentum * self._v[idx] + g
p.data -= self.lr * (g + self.momentum * self._v[idx])
idx += 1The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
# TODO: compute the global L2 norm over all param grads; scale in place if it exceeds max_norm.
raise NotImplementedError("clip_grad_norm") total_sq = 0.0
for p in params:
if p.grad is not None:
total_sq += float((p.grad * p.grad).sum())
total_norm = math.sqrt(total_sq)
if total_norm > max_norm:
scale = max_norm / (total_norm + 1e-6)
for p in params:
if p.grad is not None:
p.grad *= scale
return total_normThe file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
``base_lr`` to ``min_lr`` over the remaining (total - warmup) steps. Return the LR at ``step``."""
# TODO
raise NotImplementedError("cosine_lr") ``base_lr`` to ``min_lr`` over the remaining (total - warmup) steps. Return the LR at ``step``."""
if warmup > 0 and step < warmup:
return base_lr * (step + 1) / warmup
denom = max(1, total - warmup)
progress = (step - warmup) / denom
progress = min(1.0, max(0.0, progress))
return min_lr + 0.5 * (base_lr - min_lr) * (1.0 + math.cos(math.pi * progress))The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
constant ``base_lr``."""
# TODO
raise NotImplementedError("warmup_lr") constant ``base_lr``."""
if warmup > 0 and step < warmup:
return base_lr * (step + 1) / warmup
return base_lrThe file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
"""Step decay: base_lr * gamma ** (step // step_size)."""
# TODO
raise NotImplementedError("step_lr") """Step decay: base_lr * gamma ** (step // step_size)."""
return base_lr * gamma ** (step // step_size)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
"""Exponential decay: base_lr * gamma ** step."""
# TODO
raise NotImplementedError("exponential_lr") """Exponential decay: base_lr * gamma ** step."""
return base_lr * gamma ** stepThe file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
"""Polynomial decay: min_lr + (base_lr - min_lr) * (1 - step/total)^power (clamped at total)."""
# TODO
raise NotImplementedError("polynomial_lr") """Polynomial decay: min_lr + (base_lr - min_lr) * (1 - step/total)^power (clamped at total)."""
if step >= total:
return min_lr
return min_lr + (base_lr - min_lr) * (1.0 - step / total) ** powerThe file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
"""Linear decay from base_lr to min_lr over ``total`` steps, then constant at min_lr."""
# TODO
raise NotImplementedError("linear_lr") """Linear decay from base_lr to min_lr over ``total`` steps, then constant at min_lr."""
if step >= total:
return min_lr
return base_lr + (min_lr - base_lr) * (step / total)The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
"""base_lr * gamma^(number of milestones <= step)."""
# TODO
raise NotImplementedError("multistep_lr") """base_lr * gamma^(number of milestones <= step)."""
k = sum(1 for m in milestones if m <= step)
return base_lr * gamma ** kThe file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
"""Cosine annealing with warm restarts of fixed ``period`` (cosine within each period)."""
# TODO
raise NotImplementedError("cosine_restarts_lr") """Cosine annealing with warm restarts of fixed ``period`` (cosine within each period)."""
t = step % period
return min_lr + 0.5 * (base_lr - min_lr) * (1.0 + math.cos(math.pi * t / period))The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/data.py
# TODO: cast to float, scale to [0,1], subtract NORM_MEAN, divide by NORM_STD.
raise NotImplementedError("normalize")return (X.astype(np.float64) / 255.0 - NORM_MEAN) / NORM_STD
The file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/data.py
# TODO: optionally shuffle an index array, then yield contiguous batch_size slices of it.
raise NotImplementedError("iter_minibatches") n = Xn.shape[0]
idx = np.arange(n)
if shuffle:
rng.shuffle(idx)
for start in range(0, n, batch_size):
sel = idx[start:start + batch_size]
yield Xn[sel], y[sel]The file /app/submission/data.py has been updated successfully. (file state is current in your context , no need to Read it back)
Run public conformance checks
cd /app && python3 run_checks.py 2>&1 | tail -60
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 OK fwd/instance_norm_stats OK fwd/conv_transpose2d_shape OK (shape=(2, 4, 7, 7)) fwd/grouped_conv_shape OK (shape=(2, 6, 6, 6)) fwd/embedding_def OK fwd/fake_quant_lsq_plus_grid OK (unique=4) fwd/QuantSepCNN_shape OK fwd/QuantSepCNN_state_dict_roundtrip OK (keys=20) [optim] 25/25 optim/SGD_1step OK (max_abs=0.0e+00) optim/SGD_momentum_5step OK (max_abs=0.0e+00) optim/Adagrad_5step OK (max_abs=0.0e+00) optim/RMSprop_5step OK (max_abs=0.0e+00) optim/Adam_1step OK (max_abs=0.0e+00) optim/Adam_10step_wd OK (max_abs=0.0e+00) optim/AdamW_1step OK (max_abs=0.0e+00) optim/AdamW_10step_wd OK (max_abs=0.0e+00) optim/Lion_5step OK (max_abs=0.0e+00) optim/Adamax …[truncated 1925 chars]
Inspect training data
python3 -c "
import numpy as np
d=np.load('data/train.npz')
X=d['X']; y=d['y']
print('X',X.shape,X.dtype,X.min(),X.max())
print('y',y.shape,y.dtype,np.bincount(y))
"X (2500, 3, 16, 16) uint8 0 255 y (2500,) int64 [250 250 250 250 250 250 250 250 250 250]
"""Training entry point. QAT loop that trains the INT2 QuantCNN and saves qat_model.npz."""
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 # noqa: E402,F401
from autograd import Tensor # noqa: E402
import data as D # noqa: E402,F401
import optim as O # noqa: E402,F401
from nn import QuantCNN, W_POS, A_POS # noqa: E402,F401
WIDTHS = (16, 32)
def _set_scale(scale_t, v, Qp):
s = 2.0 * float(np.mean(np.abs(v))) / np.sqrt(max(1.0, Qp))
scale_t.data[...] = max(s, 1e-3)
def init_lsq_scales(model, xb):
# weight step sizes from the weights themselves
_set_scale(model.stem.conv.wq.scale, model.stem.conv.weight.data, W_POS)
for b in model.blocks:
_set_scale(b.conv.wq.scale, b.conv.weight.data, W_POS)
_set_scale(model.head_wq.scale, model.head_w.data, W_POS)
# activation step sizes from a calibration forward (post-relu pre-quant stats)
model.set_training(True)
h = ag.relu(model.stem.bn(model.stem.conv(Tensor(xb))))
_set_scale(model.stem.aq.scale, h.data, A_POS)
h = model.stem.aq(h)
for b in model.blocks:
pre = ag.relu(b.bn(b.conv(h)))
_set_scale(b.aq.scale, pre.data, A_POS)
h = b.aq(pre)
def accuracy(model, Xn, y, bs=256):
model.set_training(False)
correct = 0
for i in range(0, len(y), bs):
…[truncated 3818 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/train.py
"""Replace each conv/head weight with its fake-quantized value so it lands on the 2-bit grid."""
def q(weight, scale):
s = float(scale.data.reshape(()))
s = s if abs(s) > 1e-12 else 1e-12
r = np.clip(weight.data / s, -float(W_POS) - 1.0 + 1.0, float(W_POS)) # placeholder
# explicit signed 2-bit grid {-2,-1,0,1}: Qn=-2, Qp=1
Qn, Qp = -2.0, 1.0 """Replace each conv/head weight with its fake-quantized value so it lands on the 2-bit grid."""
# explicit signed 2-bit grid {-2,-1,0,1}: Qn=-2, Qp=1
Qn, Qp = -2.0, 1.0The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Quick 5-epoch training smoke test
cd /app/submission && timeout 300 python3 train.py --epochs 5 --out /tmp/test_model.npz 2>&1 | tail -20
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 = 0.836 >= 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_eb3dd39a77654e1f. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_eb3dd39a77654e1f · verifier authoritative; classifier explanatory.