You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
68 lines
2.9 KiB
68 lines
2.9 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import paddle
|
|
from paddle import Tensor
|
|
|
|
__all__ = ["sinusoid_position_encoding", "scaled_position_encoding"]
|
|
|
|
|
|
def sinusoid_position_encoding(num_positions: int,
|
|
feature_size: int,
|
|
omega: float=1.0,
|
|
start_pos: int=0,
|
|
dtype=None) -> paddle.Tensor:
|
|
# return tensor shape (num_positions, feature_size)
|
|
# NOTE: to be compatible with paddle's to_static, we cannnot raise
|
|
# an exception here, take care of it by yourself
|
|
# if (feature_size % 2 != 0):
|
|
# raise ValueError("size should be divisible by 2")
|
|
dtype = dtype or paddle.get_default_dtype()
|
|
|
|
channel = paddle.arange(0, feature_size, 2, dtype=dtype)
|
|
index = paddle.arange(start_pos, start_pos + num_positions, 1, dtype=dtype)
|
|
denominator = channel / float(feature_size)
|
|
denominator = paddle.to_tensor([10000.0], dtype='float32')**denominator
|
|
p = (paddle.unsqueeze(index, -1) * omega) / denominator
|
|
encodings = paddle.zeros([num_positions, feature_size], dtype=dtype)
|
|
encodings[:, 0::2] = paddle.sin(p)
|
|
encodings[:, 1::2] = paddle.cos(p)
|
|
return encodings
|
|
|
|
|
|
def scaled_position_encoding(num_positions: int,
|
|
feature_size: int,
|
|
omega: Tensor,
|
|
start_pos: int=0,
|
|
dtype=None) -> Tensor:
|
|
# omega: Tensor (batch_size, )
|
|
# return tensor shape (batch_size, num_positions, feature_size)
|
|
# consider renaming this as batched positioning encoding
|
|
if (feature_size % 2 != 0):
|
|
raise ValueError("size should be divisible by 2")
|
|
dtype = dtype or paddle.get_default_dtype()
|
|
|
|
channel = paddle.arange(0, feature_size, 2, dtype=dtype)
|
|
index = paddle.arange(
|
|
start_pos, start_pos + num_positions, 1, dtype=omega.dtype)
|
|
batch_size = omega.shape[0]
|
|
omega = paddle.unsqueeze(omega, [1, 2])
|
|
p = (paddle.unsqueeze(index, -1) *
|
|
omega) / (10000.0**(channel / float(feature_size)))
|
|
encodings = paddle.zeros(
|
|
[batch_size, num_positions, feature_size], dtype=dtype)
|
|
# it is nice to have fancy indexing and inplace operations
|
|
encodings[:, :, 0::2] = paddle.sin(p)
|
|
encodings[:, :, 1::2] = paddle.cos(p)
|
|
return encodings
|