#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

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  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 , 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)