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.
190 lines
7.6 KiB
190 lines
7.6 KiB
# 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.
|
|
|
|
import core
|
|
import numpy as np
|
|
|
|
__all__ = ['create_lod_tensor', 'create_random_int_lodtensor']
|
|
|
|
|
|
def _validate_lod(lod, tensor_height=-1):
|
|
"""Check whether the input length-based lod info is valid.
|
|
|
|
There are several things to check:
|
|
1. lod should be a list of lists. Empty list is fine.
|
|
2. The length of each sublist (a lod level) should be at least one.
|
|
3. Each element in each lod level should be an integer greater than 0.
|
|
4. The sum of one lod level should be equal to the length of the next lod level.
|
|
5. The sum of the last lod level should be equal to the tensor height.
|
|
Bypass this check if user does not provide tensor_height as input.
|
|
|
|
Args:
|
|
lod: the length-based lod info, e.g., [[2, 3], [2, 1, 2, 3, 4]].
|
|
tensor_height: the outermost dimension of the tensor with which the input
|
|
lod is associated with.
|
|
|
|
Returns:
|
|
A boolean indicating whether the input lod is valid or not.
|
|
"""
|
|
assert isinstance(lod, list), "lod should be a list"
|
|
# Empty lod is fine
|
|
if len(lod) == 0:
|
|
return True
|
|
|
|
lod_sum = []
|
|
for level in lod:
|
|
assert isinstance(level, list), "each item in lod should be a list"
|
|
# Each level of lod should have at least one length info
|
|
if len(level) < 1:
|
|
return False
|
|
level_sum = 0
|
|
for lod_len in level:
|
|
# Each length in a level should be > 0
|
|
if lod_len <= 0:
|
|
return False
|
|
level_sum += lod_len
|
|
lod_sum.append(level_sum)
|
|
|
|
for idx, val in enumerate(lod_sum[:-1]):
|
|
# Each level's sum should be equal to
|
|
# the number of items in the next level
|
|
if val != len(lod[idx + 1]):
|
|
return False
|
|
|
|
if tensor_height == -1:
|
|
return True
|
|
else:
|
|
# Last level's sum should be equal to the tensor height
|
|
return lod_sum[-1] == tensor_height
|
|
|
|
|
|
def _convert_lod(lod):
|
|
"""Convert a length-based lod to a offset-based lod.
|
|
|
|
If the length-based lod is [[2, 3], [2, 1, 2, 3, 4]],
|
|
then the offset-based lod is [[0, 2, 5], [0, 2, 3, 5, 8, 12]].
|
|
|
|
Args:
|
|
lod: a length-based lod info.
|
|
|
|
Returns:
|
|
A list of lists as the offset-based lod converted to from the input lod.
|
|
"""
|
|
new_lod = []
|
|
for level in lod:
|
|
cur_len = 0
|
|
new_level = [cur_len]
|
|
for lod_len in level:
|
|
cur_len += lod_len
|
|
new_level.append(cur_len)
|
|
new_lod.append(new_level)
|
|
return new_lod
|
|
|
|
|
|
def create_lod_tensor(data, lod, 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 input lod is valid.
|
|
2. Convert the length-based lod 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.
|
|
|
|
Use example:
|
|
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 'data' can be a numpy array of integers with shape (5, 1).
|
|
'lod' will be [[2, 3]], indicating the length(# of words) in each sentence.
|
|
This length-based input lod [[2, 3]] will be converted to offset-based lod [[0, 2, 5]]
|
|
inside the function call.
|
|
|
|
Please refer to
|
|
github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/concepts/lod_tensor.md
|
|
for more details regarding LoD.
|
|
|
|
Args:
|
|
data: a numpy array or a LoDTensor or a list holding the data to be copied.
|
|
lod: a list of lists indicating the length-based LoD info specified by the user.
|
|
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 lod info.
|
|
"""
|
|
if isinstance(data, core.LoDTensor):
|
|
return create_lod_tensor(np.array(data), lod, 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_lod = []
|
|
for seq in data:
|
|
new_lod.append(len(seq))
|
|
assert [new_lod] == lod, "data and lod do not match"
|
|
flattened_data = np.concatenate(data, axis=0).astype("int64")
|
|
flattened_data = flattened_data.reshape([len(flattened_data), 1])
|
|
return create_lod_tensor(flattened_data, lod, place)
|
|
elif isinstance(data, np.ndarray):
|
|
assert _validate_lod(lod,
|
|
data.shape[0]), "the provided lod info is invalid"
|
|
tensor = core.LoDTensor()
|
|
tensor.set(data, place)
|
|
tensor.set_lod(_convert_lod(lod))
|
|
return tensor
|
|
else:
|
|
raise TypeError(
|
|
"data should be either a LoDTensor, a Numpy array or a list")
|
|
|
|
|
|
def create_random_int_lodtensor(lod, 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 'lod' input
|
|
and the shape of the basic element in '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 'lod' is [[2, 3]]. Then the overall shape of the LoDTensor would be
|
|
[5, 1], holding 5 words for two sentences.
|
|
|
|
Args:
|
|
data: a numpy array or a LoDTensor holding the data to be copied.
|
|
lod: a list of lists indicating the length-based LoD info specified by the user.
|
|
base_shape: the shape of the basic element to be held by the LoDTensor.
|
|
place: CPU or GPU place indicating where the data in the new LoDTensor will be stored.
|
|
low: the lower bound of the random integers.
|
|
high: the upper bound of the random integers.
|
|
|
|
Returns:
|
|
A fluid LoDTensor object with tensor data and lod info.
|
|
"""
|
|
assert isinstance(base_shape, list), "base_shape should be a list"
|
|
converted_lod = _convert_lod(lod)
|
|
# append the total number of basic elements to the front of its shape
|
|
overall_shape = [converted_lod[-1][-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, lod, place)
|