File: sinusoid_position_encoding_op.cc

package info (click to toggle)
pytorch 1.13.1%2Bdfsg-4
  • links: PTS, VCS
  • area: main
  • in suites: bookworm
  • size: 139,252 kB
  • sloc: cpp: 1,100,274; python: 706,454; ansic: 83,052; asm: 7,618; java: 3,273; sh: 2,841; javascript: 612; makefile: 323; xml: 269; ruby: 185; yacc: 144; objc: 68; lex: 44
file content (34 lines) | stat: -rw-r--r-- 1,404 bytes parent folder | download | duplicates (2)
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
#include "caffe2/operators/sinusoid_position_encoding_op.h"

namespace caffe2 {
REGISTER_CPU_OPERATOR(
    SinusoidPositionEncoding,
    SinusoidPositionEncodingOp<CPUContext>);

OPERATOR_SCHEMA(SinusoidPositionEncoding)
    .NumInputs(1)
    .NumOutputs(1)
    .SetDoc(R"DOC(
Calculates a sinusoid position encoding tensor as described
in https://arxiv.org/abs/1706.03762. Takes a 2-D tensor
(of size M x K) of positions as input, the embedding size
as an argument, and outputs a position encoding tensor of
size (M x K x embedding_size). Here M is typically the max
sequence length and K is typically the batch size.
The input tensor must satisfy input[m, 0] == input[m, k] for all k.

Encoded as amplitude * SIN(pos/alpha^(i/embedding_size)) if i is even,
else amplitude * COS(pos/alpha^(i/embedding_size)). Here, pos is the position,
alpha and amplitude are tuning parameters, i is the current dimension for
the embedding, and embedding_size is the number of total dimensions in
the embedding.
)DOC")
    .Arg(
        "embedding_size",
        "Desired embedding size/number of dimensions -- defaults to 100")
    .Arg("alpha", "Sinusoid tuning parameter -- defaults to 10000")
    .Arg("amplitude", "Amplitude of Sin/Cos output")
    .Input(0, "positions", "2-D tensor of positions to be encoded")
    .Output(0, "output", "3-D tensor representing the positional encoding");

} // namespace caffe2