# Source code for paddle.fluid.lod_tensor

```
# Copyright (c) 2018 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.
from __future__ import print_function
from . import core
import numpy as np
__all__ = ['create_lod_tensor', 'create_random_int_lodtensor']
[docs]def create_lod_tensor(data, recursive_seq_lens, place):
"""
Create a lod tensor from a numpy array, a list, or an existing lod tensor.
Create a lod tensor by doing the following:
1. Check that the length-based level of detail (LoD) also known as
recursive_sequence_lengths of the input is valid.
2. Convert recursive_sequence_lengths to a offset-based LoD.
3. Copy the data from a numpy array, a list or a existing lod tensor to
CPU or GPU device (based on input place).
4. Set the level of detail (LoD) using the offset-based LoD.
Examples:
Suppose we want LoDTensor to hold data for sequences of word, where each
word is represented by an integer. If we want to create a LoDTensor to
represent two sentences, one of 2 words, and one of 3 words.
Then :code:`data` can be a numpy array of integers with shape (5, 1).
:code:`recursive_seq_lens` will be [[2, 3]], indicating the length(# of words) in each
sentence. This length-based :code:`recursive_seq_lens` [[2, 3]] will be converted to
offset-based LoD [[0, 2, 5]] inside the function call.
Please reference :ref:`api_guide_low_level_lod_tensor` for more details
regarding LoD.
Args:
data(numpy.ndarray|list|LoDTensor): a numpy array or a LoDTensor or a
list holding the data to be copied.
recursive_seq_lens(list): a list of lists indicating the length-based level of detail
info specified by the user.
place(Place): CPU or GPU place indicating where the data in the new
LoDTensor will be stored.
Returns:
A fluid LoDTensor object with tensor data and recursive_seq_lens info.
"""
if isinstance(data, core.LoDTensor):
return create_lod_tensor(np.array(data), recursive_seq_lens, place)
elif isinstance(data, list):
# When input data is a list, it only deal with the case where the base element
# is an index of shape [1] and dtype int64 (e.g., word id). Hence, the generated
# LoDTensor will be of shape [n, 1] and dtype int64, where `n` is the total number
# of words or other indexes in the sequence.
new_recursive_seq_lens = []
for seq in data:
new_recursive_seq_lens.append(len(seq))
assert [
new_recursive_seq_lens
] == recursive_seq_lens, "data and recursive_seq_lens do not match"
flattened_data = np.concatenate(data, axis=0)
flattened_data = flattened_data.reshape([len(flattened_data), 1])
return create_lod_tensor(flattened_data, recursive_seq_lens, place)
elif isinstance(data, np.ndarray):
tensor = core.LoDTensor()
tensor.set(data, place)
tensor.set_recursive_sequence_lengths(recursive_seq_lens)
assert tensor.has_valid_recursive_sequence_lengths(
), "the provided lod info is invalid"
return tensor
else:
raise TypeError(
"data should be either a LoDTensor, a Numpy array or a list")
[docs]def create_random_int_lodtensor(recursive_seq_lens, base_shape, place, low,
high):
"""
Create a LoDTensor containing random integers.
This function is frequently used in the book examples. So we revised it
based on the new create_lod_tensor API and put it here in the lod_tensor
module to simplify the code.
The function does the following:
1. Calculate the overall shape of the LoDTensor based on the length-based
:code:`recursive_seq_lens` input and the shape of the basic element in
:code:`base_shape`.
2. Create a numpy array of this shape.
3. Create the LoDTensor using create_lod_tensor API.
Suppose we want LoDTensor to hold data for sequences of word, where each
word is represented by an integer. If we want to create a LoDTensor to
represent two sentences, one of 2 words, and one of 3 words. Then
'base_shape' is [1], input length-based 'recursive_seq_lens' is [[2, 3]].
Then the overall shape of the LoDTensor would be [5, 1], holding 5 words
for two sentences.
Args:
recursive_seq_lens(list): a list of lists indicating the length-based
level of detail info specified by the user.
base_shape(list): the shape of the basic element to be held by the
LoDTensor.
place(Place): CPU or GPU place indicating where the data in the new
LoDTensor will be stored.
low(int): the lower bound of the random integers.
high(int): the upper bound of the random integers.
Returns:
A fluid LoDTensor object with tensor data and recursive_seq_lens info.
"""
assert isinstance(base_shape, list), "base_shape should be a list"
# append the total number of basic elements to the front of its shape
overall_shape = [sum(recursive_seq_lens[-1])] + base_shape
# the range of integer data elements is [low, high]
data = np.random.random_integers(low, high, overall_shape).astype("int64")
return create_lod_tensor(data, recursive_seq_lens, place)
```