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956b8cf | 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 | from __future__ import annotations
from pathlib import Path
import torch
from torch import nn
from modchallenge.interface.base_model import ModularMultiplicationModel
def ints_to_bits(values: list[int], device: torch.device, width: int) -> torch.Tensor:
"""Convert nonnegative Python integers to fixed-width, MSB-first bits."""
byte_width = (width + 7) // 8
packed_bytes = bytearray().join(
int(value).to_bytes(byte_width, "big") for value in values
)
packed = torch.frombuffer(packed_bytes, dtype=torch.uint8)
packed = packed.reshape(len(values), byte_width).to(device=device)
shifts = torch.arange(7, -1, -1, device=device)
bits = ((packed[:, :, None] >> shifts) & 1).reshape(len(values), byte_width * 8)
return bits[:, byte_width * 8 - width :]
def ints_to_digits(
values: list[int],
radix: int,
width: int,
device: torch.device,
) -> torch.Tensor:
bits_per_digit = radix.bit_length() - 1
mask = radix - 1
rows = [
[
(value >> (bits_per_digit * position)) & mask
for position in range(width - 1, -1, -1)
]
for value in values
]
return torch.tensor(rows, dtype=torch.long, device=device)
class TransitionCell(nn.Module):
def __init__(
self,
radix: int = 2,
dmodel: int = 32,
hidden: int = 64,
layers: int = 2,
bidirectional: bool = True,
) -> None:
super().__init__()
self.input_projection = nn.Linear(3, dmodel)
self.digit_embedding = nn.Embedding(radix, dmodel)
self.recurrent = nn.GRU(
dmodel,
hidden,
num_layers=layers,
batch_first=True,
bidirectional=bidirectional,
)
directions = 2 if bidirectional else 1
self.output = nn.Linear(directions * hidden, 1)
def forward(self, features: torch.Tensor, digits: torch.Tensor) -> torch.Tensor:
embedded = self.input_projection(features)
embedded = embedded + self.digit_embedding(digits)[:, None, :]
hidden, _ = self.recurrent(embedded)
return self.output(hidden).squeeze(-1)
class FastModularModel(ModularMultiplicationModel):
def __init__(self) -> None:
self.model: TransitionCell | None = None
self.device: torch.device | None = None
self.radix = 2
self.max_width = 2048
self.bits_per_digit = 1
def load(self, model_dir: str) -> None:
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
checkpoint = torch.load(
Path(model_dir) / "weights.pt",
map_location=self.device,
weights_only=True,
)
config = checkpoint["config"]
self.radix = int(config["radix"])
self.bits_per_digit = self.radix.bit_length() - 1
self.max_width = int(checkpoint["max_width"])
self.model = TransitionCell(**config)
self.model.load_state_dict(checkpoint["state_dict"])
self.model.to(self.device)
self.model.recurrent.flatten_parameters()
self.model.eval()
if self.device.type == "cuda":
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
def preprocess_a(self, a: str) -> int:
return int(a)
def preprocess_b(self, b: str) -> int:
return int(b)
def preprocess_p(self, p: str) -> int:
return int(p)
@torch.inference_mode()
def predict_digits(self, a_enc, b_enc, p_enc) -> list[int]:
return self.predict_digits_batch([(a_enc, b_enc, p_enc)])[0]
@torch.inference_mode()
def predict_digits_batch(self, inputs) -> list[list[int]]:
output: list[list[int]] = [[0] for _ in inputs]
indices: list[int] = []
a_values: list[int] = []
b_values: list[int] = []
moduli: list[int] = []
for index, (a_enc, b_enc, p_enc) in enumerate(inputs):
p = int(p_enc)
if p < 2 or p.bit_length() > self.max_width:
continue
indices.append(index)
a_values.append(int(a_enc) % p)
b_values.append(int(b_enc) % p)
moduli.append(p)
if not indices:
return output
assert self.device is not None
effective_width = max(p.bit_length() for p in moduli)
effective_width = min(self.max_width, max(8, ((effective_width + 7) // 8) * 8))
digit_width = max(
1,
(max(value.bit_length() for value in b_values) + self.bits_per_digit - 1)
// self.bits_per_digit,
)
p_bits = ints_to_bits(moduli, self.device, effective_width).float()
x_bits = ints_to_bits(a_values, self.device, effective_width).float()
control_digits = ints_to_digits(
b_values,
self.radix,
digit_width,
self.device,
)
state = torch.zeros(
(len(indices), effective_width),
dtype=torch.float32,
device=self.device,
)
for position in range(digit_width):
state = self._step(
state,
x_bits,
p_bits,
control_digits[:, position],
)
rows = state.to(dtype=torch.int64).tolist()
for row_index, output_index in enumerate(indices):
output[output_index] = [int(bit) for bit in rows[row_index]]
return output
def max_batch_size(self) -> int:
return 256
def _step(
self,
state: torch.Tensor,
multiplicand: torch.Tensor,
modulus: torch.Tensor,
digit: torch.Tensor,
) -> torch.Tensor:
assert self.model is not None
features = torch.stack((state, multiplicand, modulus), dim=-1)
if self.device is not None and self.device.type == "cuda":
with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
logits = self.model(features, digit)
else:
logits = self.model(features, digit)
return (logits.float() > 0).float()
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