File size: 8,603 Bytes
6c3cf9d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | """Learned raw-input algorithm selection for the exact U2 executor.
This research wrapper contains one learned router and one frozen U2 executor.
The router sees only raw base-16 digits, argument roles, and lengths. It emits
the U2 algorithm instruction; it does not compute or inspect the answer.
"""
from __future__ import annotations
from collections.abc import Sequence
from dataclasses import dataclass
import torch
import torch.nn as nn
from src.universal_self_clocked_u1 import UniversalU1Prediction
from src.universal_self_clocked_u2 import (
ALGORITHM_COUNT,
UniversalSelfClockedU2,
)
class RawInputAlgorithmRouter(nn.Module):
"""Small input-capable router over three raw base-16 rows."""
def __init__(
self,
*,
maximum_digits: int = 1024,
digit_dimension: int = 16,
length_dimension: int = 8,
hidden_dimension: int = 64,
algorithm_count: int = ALGORITHM_COUNT,
feature_mode: str = "token_summary",
) -> None:
super().__init__()
if min(
maximum_digits,
digit_dimension,
length_dimension,
hidden_dimension,
algorithm_count,
) < 1:
raise ValueError("all router dimensions must be positive")
self.maximum_digits = int(maximum_digits)
self.digit_dimension = int(digit_dimension)
self.length_dimension = int(length_dimension)
self.hidden_dimension = int(hidden_dimension)
self.algorithm_count = int(algorithm_count)
if feature_mode not in {"token_summary", "structural_lengths"}:
raise ValueError("unknown router feature_mode")
self.feature_mode = str(feature_mode)
self.digit_embedding = nn.Embedding(16, self.digit_dimension)
self.role_embedding = nn.Embedding(3, self.digit_dimension)
self.length_embedding = nn.Embedding(
self.maximum_digits + 1, self.length_dimension
)
per_argument = 3 * self.digit_dimension + self.length_dimension
network_input = 3 * per_argument if feature_mode == "token_summary" else 9
self.network = nn.Sequential(
nn.Linear(network_input, self.hidden_dimension),
nn.GELU(),
nn.Linear(self.hidden_dimension, self.algorithm_count),
)
self.config = {
"maximum_digits": self.maximum_digits,
"digit_dimension": self.digit_dimension,
"length_dimension": self.length_dimension,
"hidden_dimension": self.hidden_dimension,
"algorithm_count": self.algorithm_count,
"feature_mode": self.feature_mode,
}
def _row_lengths(self, rows: Sequence[Sequence[int]]) -> torch.Tensor:
if not rows:
raise ValueError("router arguments must be nonempty")
device = self.digit_embedding.weight.device
lengths = torch.tensor([len(row) for row in rows], device=device)
if bool((lengths < 1).any().item()):
raise ValueError("router argument rows must be nonempty")
if bool((lengths > self.maximum_digits).any().item()):
raise ValueError("router argument exceeds maximum_digits")
for row in rows:
if any(digit < 0 or digit > 15 for digit in row):
raise ValueError("router digits must be in [0, 15]")
return lengths
def _argument_features(
self, rows: Sequence[Sequence[int]], role: int
) -> torch.Tensor:
device = self.digit_embedding.weight.device
lengths = self._row_lengths(rows)
width = int(lengths.max().item())
digits = torch.zeros((len(rows), width), dtype=torch.long, device=device)
mask = torch.zeros((len(rows), width), dtype=torch.bool, device=device)
for index, row in enumerate(rows):
values = torch.tensor(row, dtype=torch.long, device=device)
if bool(((values < 0) | (values > 15)).any().item()):
raise ValueError("router digits must be in [0, 15]")
digits[index, : len(row)] = values
mask[index, : len(row)] = True
role_ids = torch.full_like(digits, int(role))
embedded = self.digit_embedding(digits) + self.role_embedding(role_ids)
mean = (embedded * mask.unsqueeze(-1)).sum(1) / lengths.unsqueeze(-1)
first = embedded[:, 0]
last_indices = (lengths - 1).view(-1, 1, 1).expand(
-1, 1, self.digit_dimension
)
last = embedded.gather(1, last_indices).squeeze(1)
length = self.length_embedding(lengths)
return torch.cat((mean, first, last, length), -1)
def _structural_length_features(
self,
a_rows: Sequence[Sequence[int]],
b_rows: Sequence[Sequence[int]],
modulus_rows: Sequence[Sequence[int]],
) -> torch.Tensor:
lengths = torch.stack(
(
self._row_lengths(a_rows),
self._row_lengths(b_rows),
self._row_lengths(modulus_rows),
),
-1,
).float()
linear = lengths / float(self.maximum_digits)
logarithmic = torch.log2(lengths) / torch.log2(
torch.tensor(
float(self.maximum_digits),
device=lengths.device,
)
).clamp_min(1.0)
operand_minimum = logarithmic[:, :2].min(-1).values
operand_maximum = logarithmic[:, :2].max(-1).values
modulus = logarithmic[:, 2]
minimum_is_shorter = lengths[:, :2].min(-1).values.lt(lengths[:, 2])
return torch.cat(
(
linear,
logarithmic,
(operand_minimum - modulus).unsqueeze(-1),
(operand_maximum - modulus).unsqueeze(-1),
minimum_is_shorter.float().unsqueeze(-1),
),
-1,
)
def forward(
self,
a_rows: Sequence[Sequence[int]],
b_rows: Sequence[Sequence[int]],
modulus_rows: Sequence[Sequence[int]],
) -> torch.Tensor:
batch = len(a_rows)
if not batch or len(b_rows) != batch or len(modulus_rows) != batch:
raise ValueError("router raw arguments must be nonempty and aligned")
if self.feature_mode == "structural_lengths":
features = self._structural_length_features(
a_rows, b_rows, modulus_rows
)
else:
features = torch.cat(
(
self._argument_features(a_rows, 0),
self._argument_features(b_rows, 1),
self._argument_features(modulus_rows, 2),
),
-1,
)
return self.network(features)
def route_ids(
self,
a_rows: Sequence[Sequence[int]],
b_rows: Sequence[Sequence[int]],
modulus_rows: Sequence[Sequence[int]],
) -> torch.Tensor:
return self.forward(a_rows, b_rows, modulus_rows).argmax(-1)
@dataclass(frozen=True)
class AutoRoutedU3Prediction:
answers: torch.Tensor
algorithm_ids: torch.Tensor
executor: UniversalU1Prediction
class AutoRoutedUniversalU3(nn.Module):
"""One checkpoint combining the learned router and frozen U2 executor."""
def __init__(
self,
*,
executor: UniversalSelfClockedU2,
router: RawInputAlgorithmRouter,
) -> None:
super().__init__()
self.executor = executor
self.router = router
self.config = {
"executor": dict(executor.config),
"router": dict(router.config),
}
def predict_encoded(
self,
a_rows: Sequence[Sequence[int]],
b_rows: Sequence[Sequence[int]],
modulus_rows: Sequence[Sequence[int]],
) -> AutoRoutedU3Prediction:
algorithm_ids = self.router.route_ids(a_rows, b_rows, modulus_rows)
execution = self.executor.predict_encoded_ids(
a_rows,
b_rows,
modulus_rows,
algorithm_ids.detach().cpu().tolist(),
)
return AutoRoutedU3Prediction(
execution.answers,
algorithm_ids,
execution,
)
def forward(
self,
a_rows: Sequence[Sequence[int]],
b_rows: Sequence[Sequence[int]],
modulus_rows: Sequence[Sequence[int]],
) -> AutoRoutedU3Prediction:
return self.predict_encoded(a_rows, b_rows, modulus_rows)
__all__ = [
"AutoRoutedU3Prediction",
"AutoRoutedUniversalU3",
"RawInputAlgorithmRouter",
]
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