File size: 2,649 Bytes
da8c244 | 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 | from __future__ import annotations
import torch
from torch import nn
from torch.nn import functional as F
class FastWeightProgrammer(nn.Module):
def __init__(
self,
key_vocab: int = 16,
value_vocab: int = 11,
values: int = 10,
dimension: int = 64,
) -> None:
super().__init__()
self.key_embedding = nn.Embedding(key_vocab, dimension)
self.value_embedding = nn.Embedding(value_vocab, dimension)
self.write_controller = nn.Linear(2 * dimension + 1, 1)
self.readout = nn.Linear(dimension, values)
def forward(
self,
keys: torch.Tensor,
values: torch.Tensor,
writes: torch.Tensor,
*,
return_trace: bool = False,
):
key_vectors = F.normalize(self.key_embedding(keys), dim=-1)
value_vectors = self.value_embedding(values)
controller_input = torch.cat(
[key_vectors, value_vectors, writes.unsqueeze(-1)], dim=-1
)
strengths = torch.sigmoid(self.write_controller(controller_input))
strengths = strengths.squeeze(-1) * writes
memory = torch.einsum(
"bt,btd,bte->bde",
strengths,
key_vectors,
value_vectors,
)
query = key_vectors[:, -1]
retrieved = torch.einsum("bd,bde->be", query, memory)
logits = self.readout(retrieved)
if return_trace:
contributions = (
torch.einsum("btd,bd->bt", key_vectors, query) * strengths
)
return logits, strengths, contributions
return logits
class GRUControl(nn.Module):
def __init__(
self,
key_vocab: int = 16,
value_vocab: int = 11,
values: int = 10,
dimension: int = 16,
) -> None:
super().__init__()
self.key_embedding = nn.Embedding(key_vocab, dimension)
self.value_embedding = nn.Embedding(value_vocab, dimension)
self.recurrent = nn.GRU(2 * dimension + 1, dimension, batch_first=True)
self.readout = nn.Linear(dimension, values)
def forward(
self, keys: torch.Tensor, values: torch.Tensor, writes: torch.Tensor
) -> torch.Tensor:
inputs = torch.cat(
[
self.key_embedding(keys),
self.value_embedding(values),
writes.unsqueeze(-1),
],
dim=-1,
)
hidden, _ = self.recurrent(inputs)
return self.readout(hidden[:, -1])
def parameter_count(module: nn.Module) -> int:
return sum(parameter.numel() for parameter in module.parameters())
|