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115 lines
5.2 KiB
115 lines
5.2 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import core
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import numpy as np
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__all__ = ['create_lod_tensor', 'create_random_int_lodtensor']
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def create_lod_tensor(data, lod, place):
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"""Create a lod tensor from a numpy array, a list, or an existing lod tensor.
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Create a lod tensor by doing the following:
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1. Check that the length-based input lod is valid.
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2. Convert the length-based lod to a offset-based LoD.
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3. Copy the data from a numpy array, a list or a existing lod tensor to
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CPU or GPU device (based on input place).
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4. Set the level of detail (LoD) using the offset-based LoD.
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Use example:
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Suppose we want LoDTensor to hold data for sequences of word, where each word is
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represented by an integer. If we want to create a LoDTensor to represent two
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sentences, one of 2 words, and one of 3 words.
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Then 'data' can be a numpy array of integers with shape (5, 1).
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'lod' will be [[2, 3]], indicating the length(# of words) in each sentence.
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This length-based input lod [[2, 3]] will be converted to offset-based lod [[0, 2, 5]]
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inside the function call.
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Please refer to
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github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/concepts/lod_tensor.md
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for more details regarding LoD.
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Args:
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data: a numpy array or a LoDTensor or a list holding the data to be copied.
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lod: a list of lists indicating the length-based LoD info specified by the user.
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place: CPU or GPU place indicating where the data in the new LoDTensor will be stored.
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Returns:
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A fluid LoDTensor object with tensor data and lod info.
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"""
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if isinstance(data, core.LoDTensor):
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return create_lod_tensor(np.array(data), lod, place)
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elif isinstance(data, list):
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# When input data is a list, it only deal with the case where the base element
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# is an index of shape [1] and dtype int64 (e.g., word id). Hence, the generated
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# LoDTensor will be of shape [n, 1] and dtype int64, where `n` is the total number
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# of words or other indexes in the sequence.
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new_lod = []
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for seq in data:
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new_lod.append(len(seq))
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assert [new_lod] == lod, "data and lod do not match"
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flattened_data = np.concatenate(data, axis=0).astype("int64")
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flattened_data = flattened_data.reshape([len(flattened_data), 1])
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return create_lod_tensor(flattened_data, lod, place)
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elif isinstance(data, np.ndarray):
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tensor = core.LoDTensor()
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tensor.set(data, place)
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tensor.set_recursive_sequence_lengths(lod)
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assert tensor.has_valid_recursive_sequence_lengths(
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), "the provided lod info is invalid"
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return tensor
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else:
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raise TypeError(
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"data should be either a LoDTensor, a Numpy array or a list")
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def create_random_int_lodtensor(lod, base_shape, place, low, high):
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"""Create a LoDTensor containing random integers.
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This function is frequently used in the book examples. So we revised it based on
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the new create_lod_tensor API and put it here in the lod_tensor module to simplify
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the code.
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The function does the following:
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1. Calculate the overall shape of the LoDTensor based on the length-based 'lod' input
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and the shape of the basic element in 'base_shape'.
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2. Create a numpy array of this shape.
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3. Create the LoDTensor using create_lod_tensor API.
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Suppose we want LoDTensor to hold data for sequences of word, where each word is
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represented by an integer. If we want to create a LoDTensor to represent two
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sentences, one of 2 words, and one of 3 words. Then 'base_shape' is [1], input
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length-based 'lod' is [[2, 3]]. Then the overall shape of the LoDTensor would be
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[5, 1], holding 5 words for two sentences.
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Args:
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data: a numpy array or a LoDTensor holding the data to be copied.
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lod: a list of lists indicating the length-based LoD info specified by the user.
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base_shape: the shape of the basic element to be held by the LoDTensor.
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place: CPU or GPU place indicating where the data in the new LoDTensor will be stored.
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low: the lower bound of the random integers.
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high: the upper bound of the random integers.
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Returns:
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A fluid LoDTensor object with tensor data and lod info.
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"""
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assert isinstance(base_shape, list), "base_shape should be a list"
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# append the total number of basic elements to the front of its shape
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overall_shape = [sum(lod[-1])] + base_shape
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# the range of integer data elements is [low, high]
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data = np.random.random_integers(low, high, overall_shape).astype("int64")
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return create_lod_tensor(data, lod, place)
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