chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright (c) 2021 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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from __future__ import annotations
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from typing import TYPE_CHECKING
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from paddle import _C_ops
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from ..base.data_feeder import check_type, check_variable_and_dtype
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from ..base.framework import in_dynamic_or_pir_mode
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from ..base.layer_helper import LayerHelper
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from ..nn import Layer
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if TYPE_CHECKING:
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from paddle import Tensor
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__all__ = ['viterbi_decode', 'ViterbiDecoder']
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def viterbi_decode(
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potentials: Tensor,
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transition_params: Tensor,
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lengths: Tensor,
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include_bos_eos_tag: bool = True,
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name: str | None = None,
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) -> Tensor:
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"""
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Decode the highest scoring sequence of tags computed by transitions and potentials and get the viterbi path.
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Args:
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potentials (Tensor): The input tensor of unary emission. This is a 3-D
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tensor with shape of [batch_size, sequence_length, num_tags]. The data type is float32 or float64.
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transition_params (Tensor): The input tensor of transition matrix. This is a 2-D
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tensor with shape of [num_tags, num_tags]. The data type is float32 or float64.
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lengths (Tensor): The input tensor of length of each sequence. This is a 1-D tensor with shape of [batch_size]. The data type is int64.
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include_bos_eos_tag (`bool`, optional): If set to True, the last row and the last column of transitions will be considered
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as start tag, the second to last row and the second to last column of transitions will be considered as stop tag. Defaults to ``True``.
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name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please
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refer to :ref:`api_guide_Name`.
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Returns:
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scores(Tensor): The output tensor containing the score for the Viterbi sequence. The shape is [batch_size]
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and the data type is float32 or float64.
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paths(Tensor): The output tensor containing the highest scoring tag indices. The shape is [batch_size, sequence_length]
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and the data type is int64.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> paddle.seed(2023)
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>>> batch_size, seq_len, num_tags = 2, 4, 3
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>>> emission = paddle.rand((batch_size, seq_len, num_tags), dtype='float32')
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>>> length = paddle.randint(1, seq_len + 1, [batch_size])
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>>> tags = paddle.randint(0, num_tags, [batch_size, seq_len])
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>>> transition = paddle.rand((num_tags, num_tags), dtype='float32')
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>>> scores, path = paddle.text.viterbi_decode(emission, transition, length, False)
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>>> print(scores)
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Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
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[2.57385254, 2.04533720])
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>>> print(path)
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Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True,
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[[0, 0],
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[1, 1]])
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"""
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if in_dynamic_or_pir_mode():
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return _C_ops.viterbi_decode(
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potentials, transition_params, lengths, include_bos_eos_tag
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)
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check_variable_and_dtype(
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potentials, 'input', ['float32', 'float64'], 'viterbi_decode'
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)
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check_variable_and_dtype(
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transition_params,
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'transitions',
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['float32', 'float64'],
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'viterbi_decode',
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)
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check_variable_and_dtype(lengths, 'length', 'int64', 'viterbi_decode')
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check_type(include_bos_eos_tag, 'include_tag', bool, 'viterbi_decode')
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helper = LayerHelper('viterbi_decode', **locals())
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attrs = {'include_bos_eos_tag': include_bos_eos_tag}
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scores = helper.create_variable_for_type_inference(potentials.dtype)
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path = helper.create_variable_for_type_inference('int64')
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helper.append_op(
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type='viterbi_decode',
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inputs={
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'Input': potentials,
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'Transition': transition_params,
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'Length': lengths,
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},
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outputs={'Scores': scores, 'Path': path},
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attrs=attrs,
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)
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return scores, path
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class ViterbiDecoder(Layer):
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"""
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Decode the highest scoring sequence of tags computed by transitions and potentials and get the viterbi path.
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Args:
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transitions (`Tensor`): The transition matrix. Its dtype is float32 and has a shape of `[num_tags, num_tags]`.
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include_bos_eos_tag (`bool`, optional): If set to True, the last row and the last column of transitions will be considered
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as start tag, the second to last row and the second to last column of transitions will be considered as stop tag. Defaults to ``True``.
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name (str|None, optional): The default value is None. Normally there is no need for user to set this property. For more information, please
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refer to :ref:`api_guide_Name`.
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Shape:
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potentials (Tensor): The input tensor of unary emission. This is a 3-D tensor with shape of
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[batch_size, sequence_length, num_tags]. The data type is float32 or float64.
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lengths (Tensor): The input tensor of length of each sequence. This is a 1-D tensor with shape of
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[batch_size]. The data type is int64.
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Returns:
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scores(Tensor): The output tensor containing the score for the Viterbi sequence. The shape is [batch_size]
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and the data type is float32 or float64.
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paths(Tensor): The output tensor containing the highest scoring tag indices. The shape is [batch_size, sequence_length]
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and the data type is int64.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> paddle.seed(2023)
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>>> batch_size, seq_len, num_tags = 2, 4, 3
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>>> emission = paddle.rand((batch_size, seq_len, num_tags), dtype='float32')
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>>> length = paddle.randint(1, seq_len + 1, [batch_size])
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>>> tags = paddle.randint(0, num_tags, [batch_size, seq_len])
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>>> transition = paddle.rand((num_tags, num_tags), dtype='float32')
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>>> decoder = paddle.text.ViterbiDecoder(transition, include_bos_eos_tag=False)
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>>> scores, path = decoder(emission, length)
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>>> print(scores)
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Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
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[2.57385254, 2.04533720])
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>>> print(path)
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Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True,
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[[0, 0],
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[1, 1]])
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"""
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transitions: Tensor
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include_bos_eos_tag: bool
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name: str | None
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def __init__(
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self,
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transitions: Tensor,
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include_bos_eos_tag: bool = True,
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name: str | None = None,
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) -> None:
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super().__init__()
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self.transitions = transitions
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self.include_bos_eos_tag = include_bos_eos_tag
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self.name = name
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def forward(self, potentials: Tensor, lengths: Tensor) -> Tensor:
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return viterbi_decode(
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potentials,
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self.transitions,
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lengths,
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self.include_bos_eos_tag,
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self.name,
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)
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