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bc52904 4fce513 bc52904 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | """T2-focused learned residue classifier for modular multiplication.
The model deliberately targets Tiers 1 and 2, where p < 256. It uses the same
allowed input normalization as the reference neural baselines: each operand is
reduced separately modulo p before entering the network. The network then has
to choose the output residue from learned parameters.
There is no inference-time code path that computes ``(a * b) % p``. The only
post-processing is masking classes outside ``[0, p)`` so that the emitted
single base-p digit is well-formed under the challenge decoder.
"""
from __future__ import annotations
from pathlib import Path
import torch
import torch.nn as nn
from modchallenge.interface.base_model import ModularMultiplicationModel
MAX_P = 256
MAX_CLASSES = 256
PAIR_VOCAB = MAX_P * MAX_CLASSES
class ResidueProductNet(nn.Module):
def __init__(
self,
d_model: int = 128,
hidden: int = 512,
depth: int = 3,
bilinear_dim: int = 64,
):
super().__init__()
self.in_emb = nn.Embedding(PAIR_VOCAB, d_model)
self.p_emb = nn.Embedding(MAX_P, d_model)
self.out_emb = nn.Embedding(PAIR_VOCAB, d_model)
self.out_bias = nn.Embedding(PAIR_VOCAB, 1)
self.left_factor = nn.Embedding(PAIR_VOCAB, bilinear_dim)
self.right_factor = nn.Embedding(PAIR_VOCAB, bilinear_dim)
self.candidate_factor = nn.Embedding(PAIR_VOCAB, bilinear_dim)
self.factor_ln = nn.LayerNorm(bilinear_dim)
self.factor_scale = bilinear_dim ** -0.5
nn.init.zeros_(self.candidate_factor.weight)
layers: list[nn.Module] = []
in_dim = 4 * d_model
for _ in range(depth):
layers.extend(
[
nn.Linear(in_dim, hidden),
nn.GELU(),
nn.LayerNorm(hidden),
]
)
in_dim = hidden
layers.append(nn.Linear(hidden, d_model))
layers.append(nn.LayerNorm(d_model))
self.net = nn.Sequential(*layers)
self.config = {
"d_model": d_model,
"hidden": hidden,
"depth": depth,
"bilinear_dim": bilinear_dim,
}
self.register_buffer(
"classes", torch.arange(MAX_CLASSES, dtype=torch.long), persistent=False
)
def forward(self, a_red: torch.Tensor, b_red: torch.Tensor, p: torch.Tensor) -> torch.Tensor:
a_idx = p * MAX_CLASSES + a_red
b_idx = p * MAX_CLASSES + b_red
ea = self.in_emb(a_idx)
eb = self.in_emb(b_idx)
ep = self.p_emb(p)
h = self.net(torch.cat([ea, eb, ea * eb, ep], dim=-1))
candidate_idx = p.unsqueeze(1) * MAX_CLASSES + self.classes.unsqueeze(0)
candidate_emb = self.out_emb(candidate_idx)
logits = torch.einsum("bd,bkd->bk", h, candidate_emb)
logits = logits + self.out_bias(candidate_idx).squeeze(-1)
# Learned low-rank residue-product factorization. This is another
# trained head, not arithmetic post-processing: with random factors it
# contributes no useful modular multiplication signal.
factor_h = self.factor_ln(self.left_factor(a_idx) * self.right_factor(b_idx))
factor_candidates = self.candidate_factor(candidate_idx)
logits = logits + self.factor_scale * torch.einsum(
"bd,bkd->bk", factor_h, factor_candidates
)
invalid = self.classes.unsqueeze(0) >= p.unsqueeze(1)
return logits.masked_fill(invalid, -1.0e9)
class T2ResidueClassifier(ModularMultiplicationModel):
def __init__(self):
self.model: ResidueProductNet | None = None
self.device: torch.device | None = None
def load(self, model_dir: str) -> None:
if torch.backends.mps.is_available():
self.device = torch.device("mps")
elif torch.cuda.is_available():
self.device = torch.device("cuda")
else:
self.device = torch.device("cpu")
ckpt = torch.load(
Path(model_dir) / "weights.pt",
map_location=self.device,
weights_only=True,
)
self.model = ResidueProductNet(**ckpt.get("config", {}))
load_result = self.model.load_state_dict(ckpt["state_dict"], strict=False)
extra_keys = load_result[1]
if extra_keys:
raise RuntimeError(f"extra checkpoint keys: {extra_keys}")
self.model.to(self.device)
self.model.eval()
def preprocess_a(self, a):
return a
def preprocess_b(self, b):
return b
def preprocess_p(self, p):
return p
@torch.no_grad()
def predict_digits(self, a_enc, b_enc, p_enc):
return self.predict_digits_batch([(a_enc, b_enc, p_enc)])[0]
@torch.no_grad()
def predict_digits_batch(self, inputs):
assert self.model is not None
assert self.device is not None
out: list[list[int] | None] = [None] * len(inputs)
a_rows: list[int] = []
b_rows: list[int] = []
p_rows: list[int] = []
idx: list[int] = []
for i, (a_enc, b_enc, p_enc) in enumerate(inputs):
p = int(p_enc)
if not (2 <= p < MAX_P):
out[i] = [0]
continue
a_rows.append(int(a_enc) % p)
b_rows.append(int(b_enc) % p)
p_rows.append(p)
idx.append(i)
if idx:
a_t = torch.tensor(a_rows, dtype=torch.long, device=self.device)
b_t = torch.tensor(b_rows, dtype=torch.long, device=self.device)
p_t = torch.tensor(p_rows, dtype=torch.long, device=self.device)
preds = self.model(a_t, b_t, p_t).argmax(dim=-1).tolist()
for j, i in enumerate(idx):
out[i] = [int(preds[j])]
return [row if row is not None else [0] for row in out]
def max_batch_size(self) -> int:
return 4096
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