tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.List app structure
ls -la && echo "---SUBMISSION---" && ls -la submission/ && echo "---DATA---" && ls -la data/ && echo "---wc---" && wc -l submission/*.py 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 4635 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
---wc---
1381 submission/autograd.py
42 submission/data.py
869 submission/nn.py
306 submission/optim.py
64 submission/train.py
1522 run_checks.py
4184 total/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/run_checks.py
<system-reminder>[Truncated: PARTIAL view , showing lines 1-871 of 1523 total (37115 tokens, cap 25000). Call Read with offset=872 limit=871 for the next page, or Grep to find a specific section. Do NOT answer from this page alone if the answer may be further in the file.]</system-reminder> 1 #!/usr/bin/env python3 2 """PUBLIC component check runner for the from-scratch numpy QAT framework. 3 4 Run this AS OFTEN AS YOU LIKE while you implement, to get a per-check progress signal: 5 6 python3 /app/run_checks.py # check the framework in /app/submission 7 python3 /app/run_checks.py --submission /some/dir 8 python3 /app/run_checks.py --only autograd_grads # run one milestone 9 python3 /app/run_checks.py --quiet # summary line only 10 11 It imports YOUR modules from /app/submission (autograd.py / nn.py / optim.py / data.py), exactly as 12 the grader does, and runs a PUBLIC portion of the component conformance suite, printing a clear 13 per-check report so you can implement -> run -> read failures -> fix -> repeat. 14 15 HOW THIS RELATES TO YOUR GRADE 16 ------------------------------ 17 This runner is a DEBUGGING AID, not the grader. It checks YOUR framework against references it can 18 compute on its own: 19 * autograd_grads : central FINITE-DIFFERENCE of YOUR forward vs YOUR analytic backward, for every 20 differentiable op + the domain ops (conv2d/avgpool2d/maxpool2d/batchnorm2d/ 21 …[truncated 48708 chars]
/app/run_checks.py
872 [x[0, 0, 2:4, 0:2].max(), x[0, 0, 2:4, 2:4].max()]])
873 return _max_abs(y[0, 0], ref) < 1e-9 and y.shape == (1, 1, 2, 2), ""
874
875 def avgpool_def():
876 x = np.random.default_rng(sb + 20).standard_normal((1, 1, 4, 4))
877 y = ag.avgpool2d(T(x), 2).data
878 return abs(y[0, 0, 0, 0] - x[0, 0, 0:2, 0:2].mean()) < 1e-9, ""
879
880 def fakequant_grid_w():
881 x = np.random.default_rng(sb + 21).standard_normal((6, 6)) * 2.0
882 s = 0.5
883 out = ag.fake_quant(T(x), T([s]), 2, 1).data
884 levels = np.round(out / s)
885 in_grid = np.all((levels >= -2) & (levels <= 1))
886 on_grid = _max_abs(out, levels * s) < 1e-9
887 return bool(in_grid and on_grid), f"unique={np.unique(out).size}"
888
889 def fakequant_grid_a():
890 x = np.random.default_rng(sb + 22).standard_normal((6, 6)) * 2.0
891 s = 0.4
892 out = ag.fake_quant(T(x), T([s]), 0, 3).data
893 levels = np.round(out / s)
894 return bool(np.all((levels >= 0) & (levels <= 3))), f"unique={np.unique(out).size}"
895
896 def linear_def():
897 rng = np.random.default_rng(sb + 23)
898 lin = nn.Linear(5, 3, np.random.default_rng(sb + 90))
899 x = rng.standard_normal((4, 5))
900 return _max_abs(lin(T(x)).data, x @ lin.weight.data + lin.bias.data) < 1e-6, ""
901
902 def mlp_shape():
903 y = nn.MLP(8, 16, np.random.default_
…[truncated 31859 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``.)
"""
topo: List[Tensor] = []
visited = set()
def build(t: "Tensor"):
if id(t) in visited:
return
visited.add(id(t))
for p in t._parents:
build(p)
topo.append(t)
build(self)
if grad is None:
grad = np.ones_like(self.data)
else:
grad = _as_f64(grad)
self._accum(grad)
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():
if a.requires_grad:
a._accum(_unbroadcast(out.grad, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(out.grad, b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("sub backward") # TODO def _bw():
if a.requires_grad:
a._accum(_unbroadcast(out.grad, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(-out.grad, b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("mul backward") # TODO def _bw():
if a.requires_grad:
a._accum(_unbroadcast(out.grad * b.data, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(out.grad * a.data, b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("div backward") # TODO def _bw():
if a.requires_grad:
a._accum(_unbroadcast(out.grad / b.data, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(-out.grad * a.data / (b.data ** 2), b.data.shape))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("power backward") # TODO def _bw():
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():
a_big = (a.data > b.data).astype(np.float64)
b_big = (b.data > a.data).astype(np.float64)
tie = (a.data == b.data).astype(np.float64) * 0.5
if a.requires_grad:
a._accum(_unbroadcast(out.grad * (a_big + tie), a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(out.grad * (b_big + tie), 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():
a_sm = (a.data < b.data).astype(np.float64)
b_sm = (b.data < a.data).astype(np.float64)
tie = (a.data == b.data).astype(np.float64) * 0.5
if a.requires_grad:
a._accum(_unbroadcast(out.grad * (a_sm + tie), a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(out.grad * (b_sm + tie), 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():
if a.requires_grad:
a._accum(_unbroadcast(out.grad * c, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(out.grad * (~c), 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:
mask = (a.data > lo) & (a.data < hi)
a._accum(out.grad * mask)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("sum backward") # TODO def _bw():
if a.requires_grad:
g = out.grad
if axis is not None and not keepdims:
g = np.expand_dims(g, axis=axis)
a._accum(np.broadcast_to(g, a.data.shape).copy())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 = out.grad
if axis is not None and not keepdims:
g = np.expand_dims(g, axis=axis)
n = a.data.size // out.data.size
a._accum(np.broadcast_to(g, a.data.shape).copy() / 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:
md = a.data.max(axis=axis, keepdims=True)
g = out.grad
if axis is not None and not keepdims:
g = np.expand_dims(g, axis=axis)
elif axis is None:
g = np.reshape(g, (1,) * a.data.ndim)
mask = (a.data == md).astype(np.float64)
cnt = mask.sum(axis=axis, keepdims=True)
a._accum(mask / cnt * 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("min backward") # TODO def _bw():
if a.requires_grad:
md = a.data.min(axis=axis, keepdims=True)
g = out.grad
if axis is not None and not keepdims:
g = np.expand_dims(g, axis=axis)
elif axis is None:
g = np.reshape(g, (1,) * a.data.ndim)
mask = (a.data == md).astype(np.float64)
cnt = mask.sum(axis=axis, keepdims=True)
a._accum(mask / cnt * g)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("concat backward") # TODO def _bw():
offset = 0
for t in ts:
sz = t.data.shape[axis]
if t.requires_grad:
sl = [slice(None)] * out.data.ndim
sl[axis] = slice(offset, offset + sz)
t._accum(out.grad[tuple(sl)].copy())
offset += szThe file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("stack backward") # TODO def _bw():
for i, t in enumerate(ts):
if t.requires_grad:
sl = [slice(None)] * out.data.ndim
sl[axis] = i
t._accum(out.grad[tuple(sl)].copy())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, tuple(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:
g = np.zeros_like(a.data)
np.add.at(g, idx, out.grad)
a._accum(g)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("matmul backward") # TODO def _bw():
if a.requires_grad:
da = out.grad @ np.swapaxes(b.data, -1, -2)
a._accum(_unbroadcast(da, a.data.shape))
if b.requires_grad:
db = np.swapaxes(a.data, -1, -2) @ out.grad
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(-a.data * a.data / 2.0) / 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
a._accum(s * (g - (g * s).sum(axis=axis, keepdims=True)))The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("log_softmax backward") # TODO def _bw():
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:
pred._accum(out.grad * (2.0 / pred.data.size) * (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(xd.ndim - 1))
gamma._accum((g * xhat).sum(axis=ax))
if beta.requires_grad:
ax = tuple(range(xd.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_mat = out.grad.reshape(N, Cout, OH * OW)
if weight.requires_grad:
dW = np.einsum("nop,ncp->oc", dout_mat, cols)
weight._accum(dW.reshape(Cout, Cin, KH, KW))
if has_bias and bias.requires_grad:
bias._accum(dout_mat.sum(axis=(0, 2)))
if x.requires_grad:
dcols = np.einsum("oc,nop->ncp", Wm, dout_mat)
dxp = _col2im(dcols, xp.shape, KH, KW, stride, OH, OW)
if pad > 0:
dx = dxp[:, :, pad:pad + H, pad:pad + W]
else:
dx = dxp
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("avgpool2d backward") # TODO def _bw():
if x.requires_grad:
g = out.grad / (k * k)
gexp = np.zeros((N, C, OH, k, OW, k), dtype=np.float64)
gexp[...] = g[:, :, :, None, :, None]
dx = np.zeros_like(x.data)
dx[:, :, :OH * k, :OW * k] = gexp.reshape(N, C, OH * k, OW * k)
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("maxpool2d backward") # TODO def _bw():
if x.requires_grad:
mx = out.data[:, :, :, None, :, None]
mask = (xr == mx)
# break ties: keep only first per window
flat = mask.reshape(N, C, OH, OW, k * k) if False else None
g = out.grad[:, :, :, None, :, None] * mask
dx = np.zeros_like(x.data)
dx[:, :, :OH * k, :OW * k] = g.reshape(N, C, OH * k, OW * k)
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
mx = out.data[:, :, :, None, :, None]
mask = (xr == mx)
# break ties: keep only first per window
flat = mask.reshape(N, C, OH, OW, k * k) if False else None
g = out.grad[:, :, :, None, :, None] * mask mx = out.data[:, :, :, None, :, None]
mask = (xr == mx)
g = out.grad[:, :, :, None, :, None] * maskThe 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
mask = (r >= Qn) & (r <= Qp)
if v.requires_grad:
v._accum(g * mask)
if scale.requires_grad:
ds = np.where(r < Qn, Qn, np.where(r > Qp, Qp, np.round(r) - r))
gs = float((g * ds).sum()) * grad_scale
scale._accum(np.array(gs).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 ** 2))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 = out.grad
if axis is not None and not keepdims:
g = np.expand_dims(g, axis=axis)
elif axis is None:
g = np.reshape(g, (1,) * a.data.ndim)
N = a.data.size // (out.data.size if out.data.ndim else 1)
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 = out.grad
if axis is not None and not keepdims:
g = np.expand_dims(g, axis=axis)
elif axis is None:
g = np.reshape(g, (1,) * a.data.ndim)
N = a.data.size // sd.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:
gd = np.zeros_like(a.data)
np.put_along_axis(gd, idx, 0, axis=axis)
np.add.at(gd, np.broadcast_arrays(*np.indices(idx.shape, sparse=False))
if False else None, 0) if False else None
# accumulate via add_along: build index grid
grid = list(np.indices(idx.shape, sparse=False))
grid[axis] = idx
np.add.at(gd, tuple(grid), out.grad)
a._accum(gd)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:
gd = np.zeros_like(a.data)
np.put_along_axis(gd, idx, 0, axis=axis)
np.add.at(gd, np.broadcast_arrays(*np.indices(idx.shape, sparse=False))
if False else None, 0) if False else None
# accumulate via add_along: build index grid
grid = list(np.indices(idx.shape, sparse=False))
grid[axis] = idx
np.add.at(gd, tuple(grid), out.grad)
a._accum(gd) def _bw():
if a.requires_grad:
gd = np.zeros_like(a.data)
grid = list(np.indices(idx.shape, sparse=False))
grid[axis] = idx
np.add.at(gd, tuple(grid), out.grad)
a._accum(gd)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.copy())
else:
x._accum(out.grad[:, :, pad:-pad, pad:-pad].copy())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:
tsp = np.tanh(sp)
with np.errstate(over="ignore"):
sig = 1.0 / (1.0 + np.exp(-x))
a._accum(out.grad * (tsp + x * (1.0 - tsp * tsp) * 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:
grad_local = np.where(x > 0.0, 1.0, alpha * np.exp(np.minimum(x, 0.0)))
a._accum(out.grad * grad_local)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:
mask = (a.data > lo) & (a.data < hi)
a._accum(out.grad * mask)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("hardsigmoid backward") # TODO def _bw():
if a.requires_grad:
mask = (z > 0.0) & (z < 1.0)
a._accum(out.grad / 6.0 * mask)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("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:
gx = (g * gamma.data.reshape(1, C, 1, 1)).reshape(N, G, cg * H * W)
xhat_g = xhat.reshape(N, G, cg * H * W)
M = cg * H * W
dxg = inv / M * (M * gx - gx.sum(axis=2, keepdims=True)
- xhat_g * (gx * xhat_g).sum(axis=2, keepdims=True))
x._accum(dxg.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
mask = (r >= Qn) & (r <= Qp)
if v.requires_grad:
v._accum(g * mask)
if scale.requires_grad:
d = np.where(r < Qn, Qn, np.where(r > Qp, Qp, np.round(r) - r))
gd = g * d
sum_axes = tuple(ax for ax in range(v.data.ndim) if ax != axis)
scale._accum(gd.sum(axis=sum_axes) * grad_scale)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
mask = (r >= Qn) & (r <= Qp)
if v.requires_grad:
v._accum(g * mask)
if scale.requires_grad:
ds = np.where(r < Qn, Qn - z, np.where(r > Qp, Qp - z, (q - z) - (r - z)))
gs = float((g * ds).sum()) * grad_scale
scale._accum(np.array(gs).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 = np.flip(np.cumsum(np.flip(out.grad, axis=axis), axis=axis), axis=axis)
a._accum(g)The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/autograd.py
def _bw():
raise NotImplementedError("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=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():
wa = np.exp(a.data - out_data)
wb = np.exp(b.data - out_data)
if a.requires_grad:
a._accum(_unbroadcast(out.grad * wa, a.data.shape))
if b.requires_grad:
b._accum(_unbroadcast(out.grad * 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
proj = (y * g).sum(axis=axis, keepdims=True)
a._accum((g - y * proj) / 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(xd.ndim - 1))
gamma._accum((g * xhat).sum(axis=ax))
if x.requires_grad:
gg = g * gamma.data
dx = inv * gg - (xd * inv ** 3 / D) * (gg * xd).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("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 * g_).reshape(N, C, M)
xhat_g = xhat.reshape(N, C, M)
dxg = inv / M * (M * gx - gx.sum(axis=2, keepdims=True)
- xhat_g * (gx * xhat_g).sum(axis=2, keepdims=True))
x._accum(dxg.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:
grad_local = np.where(quad, diff, delta * np.sign(diff))
pred._accum(out.grad * grad_local / 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:
gd = np.zeros_like(weight.data)
np.add.at(gd, idx, out.grad)
weight._accum(gd)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).reshape(N, groups, cog, OH * OW)
if weight.requires_grad:
dWm = np.einsum("ngop,ngcp->goc", dout, cols_g)
weight._accum(dWm.reshape(Cout, cig, KH, KW))
if has_bias and bias.requires_grad:
bias._accum(out.grad.reshape(N, Cout, OH * OW).sum(axis=(0, 2)))
if x.requires_grad:
dcols_g = np.einsum("goc,ngop->ngcp", Wm, dout)
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:
dx = dxp[:, :, pad:pad + H, pad:pad + W]
else:
dx = dxp
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("conv_transpose2d backward") # TODO def _bw():
if has_bias and bias.requires_grad:
bias._accum(out.grad.sum(axis=(0, 2, 3)))
if pad > 0:
gfull = np.zeros((N, Cout, OHf, OWf), dtype=np.float64)
gfull[:, :, pad:OHf - pad, pad:OWf - pad] = out.grad
else:
gfull = out.grad
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 weight.requires_grad:
dW = np.einsum("ncij,noijKL->coKL", xd, gcontrib)
weight._accum(dW)
if x.requires_grad:
dx = np.einsum("coKL,noijKL->ncij", Wm, gcontrib)
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("avgpool2d_s backward") # TODO def _bw():
if x.requires_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] += \
out.grad[:, :, oi, oj][:, :, None, None] / (k * k)
if pad > 0:
dx = dxp[:, :, pad:pad + H, pad:pad + W]
else:
dx = dxp
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("maxpool2d_s backward") # TODO def _bw():
if x.requires_grad:
dxp = np.zeros((N, C, Hp, Wp), dtype=np.float64)
n_grid = np.arange(N)[:, None] * np.ones((1, C), dtype=np.int64)
c_grid = np.ones((N, 1), dtype=np.int64) * np.arange(C)[None, :]
for oi in range(OH):
for oj in range(OW):
rows = oi * stride + argi[:, :, oi, oj]
cols = oj * stride + argj[:, :, oi, oj]
np.add.at(dxp, (n_grid, c_grid, rows, cols), out.grad[:, :, oi, oj])
if pad > 0:
dx = dxp[:, :, pad:pad + H, pad:pad + W]
else:
dx = dxp
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_lsq_plus backward (STE v-grad + beta-grad + LSQ scale grad)") # TODO def _bw():
g = out.grad
mask = (r >= Qn) & (r <= Qp)
if v.requires_grad:
v._accum(g * mask)
if beta.requires_grad:
gb = float((g * (~mask)).sum())
beta._accum(np.array(gb).reshape(beta.data.shape))
if scale.requires_grad:
ds = np.where(r < Qn, Qn, np.where(r > Qp, Qp, np.round(r) - r))
gs = float((g * ds).sum()) * grad_scale
scale._accum(np.array(gs).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 autograd stubs and run quant_grads
grep -n "NotImplementedError" submission/autograd.py; echo "---run---"; python3 run_checks.py --only quant_grads 2>&1 | tail -30
3:YOU MUST IMPLEMENT EVERYTHING MARKED ``# TODO`` / ``raise NotImplementedError`` using numpy ---run--- [quant_grads] 21/21 quant/ste_indicator_w OK (err=0.0e+00) quant/ste_indicator_a OK (err=0.0e+00) quant/scale_clip_low OK (got=-8.0000 exp=-8.0000) quant/scale_clip_high OK (got=6.9282 exp=6.9282) quant/scale_inrange_w OK (got=-0.1759 exp=-0.1759) quant/scale_inrange_a OK (got=-0.2045 exp=-0.2045) quant/ste_indicator_int3 OK (err=0.0e+00) quant/ste_indicator_int4 OK (err=0.0e+00) quant/grid_int2_w OK (levels=4<= 4) quant/grid_int3_w OK (levels=8<= 8) quant/grid_int4_w OK (levels=16<= 16) quant/perchan_ste_w OK (ste=True grid=True) quant/perchan_ste_int3 OK (ste=True grid=True) quant/perchan_scale_inrange OK quant/affine_ste_z0 OK (ste=True) quant/affine_ste_z1 OK (ste=True) quant/affine_ste_signed OK (ste=True) quant/lsqplus_w OK (ste=True beta=True scale=True) quant/lsqplus_a OK (ste=True beta=True scale=True) quant/lsqplus_int3 OK (ste=True beta=True scale=True) quant/lsqplus_redu …[truncated 296 chars]
/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,
running_mean=self.running_mean, running_var=self.running_var,
momentum=self.momentum, training=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)
Check for batchnorm1d in autograd
grep -n "def batchnorm1d\|def batchnorm2d\|batchnorm1d" submission/autograd.py
833:def batchnorm2d(x, gamma, beta, eps: float = 1e-5,
/app/submission/autograd.py
855 if running_mean is not None and running_var is not None: 856 ubvar = var * (M / _bi_max(1, M - 1)) 857 running_mean[:] = (1 - momentum) * running_mean + momentum * mu.reshape(-1) 858 running_var[:] = (1 - momentum) * running_var + momentum * ubvar.reshape(-1) 859 else: 860 mu = running_mean.reshape(1, C, 1, 1) 861 var = running_var.reshape(1, C, 1, 1) 862 inv = 1.0 / np.sqrt(var + eps) 863 xhat = (xd - mu) * inv 864 g_ = gamma.data.reshape(1, C, 1, 1) 865 out = _mk(g_ * xhat + beta.data.reshape(1, C, 1, 1), (x, gamma, beta), 866 x.requires_grad or gamma.requires_grad or beta.requires_grad) 867 868 def _bw(): 869 g = out.grad 870 if gamma.requires_grad: 871 gamma._accum((g * xhat).sum(axis=(0, 2, 3))) 872 if beta.requires_grad: 873 beta._accum(g.sum(axis=(0, 2, 3))) 874 if x.requires_grad:
/app/submission/autograd.py
874 if x.requires_grad: 875 gx = g * g_ 876 if training: 877 dx = inv / M * (M * gx - gx.sum(axis=(0, 2, 3), keepdims=True) 878 - xhat * (gx * xhat).sum(axis=(0, 2, 3), keepdims=True)) 879 else: 880 dx = gx * inv 881 x._accum(dx) 882 883 out._backward = _bw 884 return out 885 886 887 # ============================================================ LSQ fake-quant (DOMAIN; the crux) 888 def fake_quant(v, scale, n_levels_neg: int, n_levels_pos: int, 889 grad_scale: Optional[float] = None) -> Tensor:
/app/submission/autograd.py
out._backward = _bw
return out
# ============================================================ LSQ fake-quant (DOMAIN; the crux) out._backward = _bw
return out
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 per feature C. x:(N,C); gamma,beta:(C,). Same semantics as
batchnorm2d but reduced over axis 0 only (M=N)."""
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 440 chars]The file /app/submission/autograd.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/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,
running_mean=self.running_mean, running_var=self.running_var,
momentum=self.momentum, training=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") h = X if isinstance(X, Tensor) else Tensor(X)
h = self.stem(h)
for b in self.blocks:
h = b(h)
N = h.data.shape[0]
C = h.data.shape[1]
Hs = h.data.shape[2]
h = ag.avgpool2d(h, Hs)
h = ag.reshape(h, (N, C))
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") h = X if isinstance(X, Tensor) else Tensor(X)
N = h.data.shape[0]
h = ag.reshape(h, (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 = Tensor(1.0)
return ag.add(ag.mul(ag.sub(one, 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") h = X if isinstance(X, Tensor) else Tensor(X)
h = self.stem(h)
for b in self.blocks:
h = b(h)
N = h.data.shape[0]
C = h.data.shape[1]
Hs = h.data.shape[2]
h = ag.avgpool2d(h, Hs)
h = ag.reshape(h, (N, C))
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)
Run nn_forward and autograd_grads
grep -rn "NotImplementedError" submission/nn.py | grep -v "raise NotImplementedError$" | head; echo "===nn_forward==="; python3 run_checks.py --only nn_forward 2>&1 | tail -8; echo "===autograd_grads==="; python3 run_checks.py --only autograd_grads 2>&1 | grep -E "FAIL|autograd_grads [0-9]"
===nn_forward=== fwd/fake_quant_lsq_plus_grid OK (unique=4) fwd/QuantSepCNN_shape OK fwd/QuantSepCNN_state_dict_roundtrip OK (keys=20) ------------------------------------------------------------------------ nn_forward 53/53 TOTAL 53/53 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). ===autograd_grads=== autograd_grads 115/115
/app/submission/optim.py
def step(self):
# 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") def step(self):
idx = 0
for plist, wd_over in self._groups:
wd = self.weight_decay if wd_over is None else wd_over
for p in plist:
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
def step(self):
# TODO: AdamW with bias correction + DECOUPLED, per-group weight decay (see docstring).
raise NotImplementedError("AdamW.step") def step(self):
self.t += 1
b1, b2, eps = self.b1, self.b2, self.eps
idx = 0
for plist, wd_over in self._groups:
wd = self.weight_decay if wd_over is None else wd_over
for p in plist:
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] / (1 - b1 ** self.t)
vhat = self._v[idx] / (1 - b2 ** self.t)
p.data -= self.lr * (mhat / (np.sqrt(vhat) + 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
def step(self):
# TODO: Adam with bias correction + COUPLED, per-group weight decay (see docstring).
raise NotImplementedError("Adam.step") def step(self):
self.t += 1
b1, b2, eps = self.b1, self.b2, self.eps
idx = 0
for plist, wd_over in self._groups:
wd = self.weight_decay if wd_over is None else wd_over
for p in plist:
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] / (1 - b1 ** self.t)
vhat = self._v[idx] / (1 - b2 ** self.t)
p.data -= self.lr * mhat / (np.sqrt(vhat) + 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
def step(self):
# TODO: RMSprop update with per-group weight decay (see docstring).
raise NotImplementedError("RMSprop.step") def step(self):
idx = 0
for plist, wd_over in self._groups:
wd = self.weight_decay if wd_over is None else wd_over
for p in plist:
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
def step(self):
# TODO: Adagrad update with per-group weight decay (see docstring).
raise NotImplementedError("Adagrad.step") def step(self):
idx = 0
for plist, wd_over in self._groups:
wd = self.weight_decay if wd_over is None else wd_over
for p in plist:
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
def step(self):
# TODO: Lion update with per-group (decoupled) weight decay (see docstring).
raise NotImplementedError("Lion.step") def step(self):
b1, b2 = self.b1, self.b2
idx = 0
for plist, wd_over in self._groups:
wd = self.weight_decay if wd_over is None else wd_over
for p in plist:
if p.grad is not None:
g = p.grad
upd = np.sign(b1 * self._m[idx] + (1 - b1) * g)
p.data -= self.lr * (upd + wd * p.data)
self._m[idx] = b2 * self._m[idx] + (1 - 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
``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
prog = min(1.0, max(0.0, (step - warmup) / max(1, total - warmup)))
return min_lr + 0.5 * (base_lr - min_lr) * (1 + math.cos(math.pi * prog))The file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
constant ``base_lr``."""
# TODO
raise NotImplementedError("warmup_lr") constant ``base_lr``."""
if step < warmup:
return base_lr * (step + 1) / warmup
return base_lrThe file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/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 ** step)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
# TODO: compute the global L2 norm over all param grads; scale in place if it exceeds max_norm.
raise NotImplementedError("clip_grad_norm") total = math.sqrt(sum(float((p.grad ** 2).sum()) for p in params if p.grad is not None))
if total > max_norm:
scale = max_norm / (total + 1e-6)
for p in params:
if p.grad is not None:
p.grad = p.grad * scale
return totalThe file /app/submission/optim.py has been updated successfully. (file state is current in your context , no need to Read it back)
/app/submission/optim.py
def step(self):
# TODO: implement the Adamax update honouring per-group weight decay.
raise NotImplementedError("Adamax.step") def step(self):
self.t += 1
b1, b2, eps = self.b1, self.b2, self.eps
idx = 0
for plist, wd_over in self._groups:
wd = self.weight_decay if wd_over is None else wd_over
for p in plist:
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 / (1 - b1 ** self.t)) * self._m[idx] / (self._u[idx] + 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
def step(self):
# TODO: implement the NAdam update honouring per-group weight decay.
raise NotImplementedError("NAdam.step") def step(self):
self.t += 1
b1, b2, eps = self.b1, self.b2, self.eps
idx = 0
for plist, wd_over in self._groups:
wd = self.weight_decay if wd_over is None else wd_over
for p in plist:
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] / (1 - b1 ** (self.t + 1)) + (1 - b1) * g / (1 - b1 ** self.t)
vhat = self._v[idx] / (1 - b2 ** self.t)
p.data -= self.lr * mhat / (np.sqrt(vhat) + 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
def step(self):
# TODO: implement the Adadelta update honouring per-group weight decay.
raise NotImplementedError("Adadelta.step") def step(self):
rho, eps = self.rho, self.eps
idx = 0
for plist, wd_over in self._groups:
wd = self.weight_decay if wd_over is None else wd_over
for p in plist:
if p.grad is not None:
g = p.grad + wd * p.data
self._s[idx] = rho * self._s[idx] + (1 - rho) * (g * g)
delta = np.sqrt(self._d[idx] + eps) / np.sqrt(self._s[idx] + eps) * g
p.data -= self.lr * delta
self._d[idx] = rho * self._d[idx] + (1 - 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
def step(self):
# TODO: implement the Nesterov-SGD update honouring per-group weight decay.
raise NotImplementedError("NesterovSGD.step") def step(self):
mu = self.momentum
idx = 0
for plist, wd_over in self._groups:
wd = self.weight_decay if wd_over is None else wd_over
for p in plist:
if p.grad is not None:
g = p.grad + wd * p.data
self._v[idx] = mu * self._v[idx] + g
p.data -= self.lr * (g + mu * 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
"""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)."""
prog = min(1.0, max(0.0, step / total))
return min_lr + (base_lr - min_lr) * ((1 - prog) ** power)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
"""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."""
prog = min(1.0, max(0.0, step / total))
return base_lr + (min_lr - base_lr) * progThe 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)."""
return base_lr * (gamma ** sum(1 for m in milestones if step >= m))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
"""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 + 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 all public checks
python3 run_checks.py 2>&1 | grep -E "FAIL|\[|TOTAL|^optim|^data|^integration"
[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
Inspect dataset
python3 -c "
import numpy as np
d=np.load('data/train.npz')
print('keys',list(d.keys()))
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))
"keys ['X', 'y'] X (2500, 3, 16, 16) uint8 0 255 y (2500,) int64 [250 250 250 250 250 250 250 250 250 250]
/app/submission/train.py
def train(data_path, out_path, epochs=30, seed=0, n_classes=10):
# TODO: load + normalize the data; build QuantCNN(n_classes, WIDTHS, seed); initialize the LSQ
# step sizes; build the optimizer with weight decay only on the conv/head weights; run the QAT
# loop (forward -> cross_entropy -> backward -> clip_grad_norm -> step) with an LR schedule;
# keep your best epoch; BAKE the quantized weights; save the state_dict to out_path.
raise NotImplementedError("implement the QAT training loop")W_NEG, W_POS = 2, 1
A_NEG, A_POS = 0, 3
def init_lsq_scales(model, Xcal):
"""Initialize weight-quant step sizes from the weights and activation-quant step sizes from a
calibration batch (post-relu magnitudes)."""
nps = model.named_params()
for nm, p in nps.items():
if nm.endswith("wq.scale"):
wkey = nm[:-len("wq.scale")] + "weight"
p.data[...] = max(2.0 * float(np.mean(np.abs(nps[wkey].data))) / np.sqrt(W_POS), 1e-3)
elif nm.endswith("aq.scale"):
p.data[...] = 1.0
# calibrate activation scales by pushing a batch through stem + blocks
h = ag.Tensor(Xcal)
blocks = [model.stem] + list(model.blocks)
for blk in blocks:
wq = blk.conv.wq.quant(blk.conv.weight)
c = ag.conv2d(h, wq, bias=None, stride=blk.conv.stride, pad=1)
bn = blk.bn(c)
r = ag.relu(bn)
pos_mean = float(np.mean(np.abs(r.data)))
blk.aq.scale.data[...] = max(2.0 * pos_mean / np.sqrt(A_POS), 1e-3)
h = blk.aq(r)
def evaluate(model, Xn, y, batch=250):
model.set_training(False)
correct = 0
for i in range(0, len(Xn), batch):
xb = Xn[i:i + batch]
logits = model(xb).data
correct += int(np.sum(np.argmax(logits, axis=1) == y[i:i + batch]))
model.set_training(True)
return correct / len(Xn)
def train(data_path, out_path, epochs=40, seed=0, n_classes=10):
from nn import QuantCNN
X, y = D.load_npz(data_path)
Xn = D.normalize(X)
…[truncated 2415 chars]The file /app/submission/train.py has been updated successfully. (file state is current in your context , no need to Read it back)
Smoke test training 2 epochs
cd /app/submission && timeout 300 python3 train.py --epochs 2 --out /tmp/test_ckpt.npz 2>&1 | tail -15
No file changes, the agent left the workspace untouched.
=== SyncVals verifier (local re-grade of the agent's own checkpoint) === COMPONENT CONFORMANCE SUITE: 278/278 cases passed (all milestones pass: True) FINAL METRIC: held-out INT2 top-1 = 0.832 >= threshold 0.7 REWARD = 1 (1 iff every component milestone passes AND the sealed held-out metric clears its threshold) NOTE: the in-sandbox training in the 481MB e2b box was OOM-killed (0-byte checkpoint), so the raw in-sandbox reward was an infrastructure artifact. This reward is from a faithful LOCAL re-train of the agent's OWN train.py (24GB, no OOM) with its own default hyperparameters, then re-graded by the real verifier.
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_3dacecfb069a487f. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_3dacecfb069a487f · verifier authoritative; classifier explanatory.