paddlenlp.transformers.skep.modeling 源代码

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# limitations under the License.

from typing import Optional, Tuple
from paddle import Tensor
import paddle
import paddle.nn as nn

from paddlenlp.layers.crf import LinearChainCrf, LinearChainCrfLoss
from paddlenlp.utils.log import logger
from paddlenlp.utils.tools import compare_version

if compare_version(paddle.version.full_version, "2.2.0") >= 0:
    # paddle.text.ViterbiDecoder is supported by paddle after version 2.2.0
    from paddle.text import ViterbiDecoder
else:
    from paddlenlp.layers.crf import ViterbiDecoder

from ..model_outputs import (BaseModelOutputWithPoolingAndCrossAttentions,
                             SequenceClassifierOutput, TokenClassifierOutput,
                             QuestionAnsweringModelOutput,
                             MultipleChoiceModelOutput, MaskedLMOutput,
                             CausalLMOutputWithCrossAttentions, tuple_output)
from .. import PretrainedModel, register_base_model

__all__ = [
    'SkepModel', 'SkepPretrainedModel', 'SkepForSequenceClassification',
    'SkepForTokenClassification', 'SkepCrfForTokenClassification'
]


class SkepEmbeddings(nn.Layer):
    """
    Include embeddings from word, position and token_type embeddings
    """

    def __init__(self,
                 vocab_size: int,
                 hidden_size: Optional[int] = 768,
                 hidden_dropout_prob: Optional[float] = 0.1,
                 max_position_embeddings: Optional[int] = 512,
                 type_vocab_size: Optional[int] = 16,
                 pad_token_id: Optional[int] = 0):
        super(SkepEmbeddings, self).__init__()
        self.word_embeddings = nn.Embedding(vocab_size,
                                            hidden_size,
                                            padding_idx=pad_token_id)
        self.position_embeddings = nn.Embedding(max_position_embeddings,
                                                hidden_size)
        self.type_vocab_size = type_vocab_size
        if self.type_vocab_size != 0:
            self.token_type_embeddings = nn.Embedding(type_vocab_size,
                                                      hidden_size)
        self.layer_norm = nn.LayerNorm(hidden_size)
        self.dropout = nn.Dropout(hidden_dropout_prob)

    def forward(self,
                input_ids: Optional[Tensor] = None,
                token_type_ids: Optional[Tensor] = None,
                position_ids: Optional[Tensor] = None,
                inputs_embeds: Optional[Tensor] = None,
                past_key_values_length: Optional[int] = 0):

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        if position_ids is None:
            input_shape = paddle.shape(inputs_embeds)[:-1]
            # maybe need use shape op to unify static graph and dynamic graph
            ones = paddle.ones(input_shape, dtype="int64")
            seq_length = paddle.cumsum(ones, axis=1)
            position_ids = seq_length - ones

            if past_key_values_length > 0:
                position_ids = position_ids + past_key_values_length

            position_ids.stop_gradient = True

        position_embeddings = self.position_embeddings(position_ids)
        embeddings = inputs_embeds + position_embeddings
        if self.type_vocab_size != 0:
            if token_type_ids is None:
                token_type_ids_shape = paddle.shape(inputs_embeds)[:-1]
                token_type_ids = paddle.zeros(token_type_ids_shape,
                                              dtype="int64")
            token_type_embeddings = self.token_type_embeddings(token_type_ids)
            embeddings += token_type_embeddings
        elif token_type_ids is not None:
            logger.warning(
                "There is no need to pass the token type ids to SKEP based on RoBERTa model."
                "The input token type ids will be ignored.")

        embeddings = self.layer_norm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class SkepPooler(nn.Layer):
    """
    The pooling layer on skep model.
    """

    def __init__(self, hidden_size: int):
        super(SkepPooler, self).__init__()
        self.dense = nn.Linear(hidden_size, hidden_size)
        self.activation = nn.Tanh()

    def forward(self, hidden_states: Tensor):
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(first_token_tensor)
        pooled_output = self.activation(pooled_output)
        return pooled_output


[文档]class SkepPretrainedModel(PretrainedModel): r""" An abstract class for pretrained Skep models. It provides Skep related `model_config_file`, `pretrained_init_configuration`, `resource_files_names`, `pretrained_resource_files_map`, `base_model_prefix` for downloading and loading pretrained models. See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details. """ pretrained_init_configuration = { "skep_ernie_1.0_large_ch": { "attention_probs_dropout_prob": 0.1, "hidden_act": "relu", "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "intermediate_size": 4096, # special for ernie-large "max_position_embeddings": 512, "num_attention_heads": 16, "num_hidden_layers": 24, "type_vocab_size": 4, "vocab_size": 12800, "pad_token_id": 0, }, "skep_ernie_2.0_large_en": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "intermediate_size": 4096, # special for ernie-large "max_position_embeddings": 512, "num_attention_heads": 16, "num_hidden_layers": 24, "type_vocab_size": 4, "vocab_size": 30522, "pad_token_id": 0, }, "skep_roberta_large_en": { "attention_probs_dropout_prob": 0.1, "intermediate_size": 4096, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "max_position_embeddings": 514, "num_attention_heads": 16, "num_hidden_layers": 24, "type_vocab_size": 0, "vocab_size": 50265, "pad_token_id": 1, }, } pretrained_resource_files_map = { "model_state": { "skep_ernie_1.0_large_ch": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_ernie_1.0_large_ch.pdparams", "skep_ernie_2.0_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_ernie_2.0_large_en.pdparams", "skep_roberta_large_en": "https://bj.bcebos.com/paddlenlp/models/transformers/skep/skep_roberta_large_en.pdparams", } } base_model_prefix = "skep"
[文档] def init_weights(self, layer): """ Initialization hook """ if isinstance(layer, (nn.Linear, nn.Embedding)): # only support dygraph, use truncated_normal and make it inplace # and configurable later if isinstance(layer.weight, paddle.Tensor): layer.weight.set_value( paddle.tensor.normal(mean=0.0, std=self.initializer_range if hasattr( self, "initializer_range") else self.skep.config["initializer_range"], shape=layer.weight.shape)) elif isinstance(layer, nn.LayerNorm): layer._epsilon = 1e-5
[文档]@register_base_model class SkepModel(SkepPretrainedModel): r""" The bare SKEP Model outputting raw hidden-states. This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`. Refer to the superclass documentation for the generic methods. This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation /docs/en/api/paddle/fluid/dygraph/layers/Layer_en.html>`__ subclass. Use it as a regular Paddle Layer and refer to the Paddle documentation for all matter related to general usage and behavior. More details refer to `SKEP <https://www.aclweb.org/anthology/2020.acl-main.374>`. Args: vocab_size (int): Vocabulary size of `inputs_ids` in `SKEPModel`. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `SKEPModel`. hidden_size (int, optional): Dimensionality of the embedding layer, encoder layers and the pooler layer. Defaults to `768`. num_hidden_layers (int, optional): Number of hidden layers in the Transformer encoder. Defaults to `12`. num_attention_heads (int, optional): Number of attention heads for each attention layer in the Transformer encoder. Defaults to `12`. intermediate_size (int, optional): Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors to ff layers are firstly projected from `hidden_size` to `intermediate_size`, and then projected back to `hidden_size`. Typically `intermediate_size` is larger than `hidden_size`. Defaults to `3072`. hidden_act (str, optional): The non-linear activation function in the feed-forward layer. ``"gelu"``, ``"relu"`` and any other paddle supported activation functions are supported. Defaults to ``"gelu"``. hidden_dropout_prob (float, optional): The dropout probability for all fully connected layers in the embeddings and encoder. Defaults to ``0.1``. attention_probs_dropout_prob (float, optional): The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target. Defaults to `0.1`. max_position_embeddings (int, optional): The maximum value of the dimensionality of position encoding. The dimensionality of position encoding is the dimensionality of the sequence in `TinyBertModel`. Defaults to `512`. type_vocab_size (int, optional): The vocabulary size of the `token_type_ids` passed when calling `~transformers.SkepModel`. Defaults to `2`. initializer_range (float, optional): The standard deviation of the normal initializer. Defaults to `0.02`. .. note:: A normal_initializer initializes weight matrices as normal distributions. See :meth:`SkepPretrainedModel.init_weights()` for how weights are initialized in `SkepModel`. pad_token_id(int, optional): The index of padding token in the token vocabulary. Defaults to `0`. """ def __init__(self, vocab_size: int, hidden_size: Optional[int] = 768, num_hidden_layers: Optional[int] = 12, num_attention_heads: Optional[int] = 12, intermediate_size: Optional[int] = 3072, hidden_act: Optional[str] = "gelu", hidden_dropout_prob: Optional[float] = 0.1, attention_probs_dropout_prob: Optional[float] = 0.1, max_position_embeddings: Optional[int] = 512, type_vocab_size: Optional[int] = 2, initializer_range: Optional[float] = 0.02, pad_token_id: Optional[int] = 0): super(SkepModel, self).__init__() self.pad_token_id = pad_token_id self.initializer_range = initializer_range self.embeddings = SkepEmbeddings(vocab_size, hidden_size, hidden_dropout_prob, max_position_embeddings, type_vocab_size, pad_token_id) encoder_layer = nn.TransformerEncoderLayer( hidden_size, num_attention_heads, intermediate_size, dropout=hidden_dropout_prob, activation=hidden_act, attn_dropout=attention_probs_dropout_prob, act_dropout=0) self.encoder = nn.TransformerEncoder(encoder_layer, num_hidden_layers) self.pooler = SkepPooler(hidden_size) self.apply(self.init_weights)
[文档] def forward(self, input_ids: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, position_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, past_key_values: Optional[Tuple[Tuple[Tensor]]] = None, use_cache: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None): r""" The SkepModel forward method, overrides the `__call__()` special method. Args: input_ids (Tensor): Indices of input sequence tokens in the vocabulary. They are numerical representations of tokens that build the input sequence. Its data type should be `int64` and it has a shape of [batch_size, sequence_length]. token_type_ids (Tensor, optional): Segment token indices to indicate different portions of the inputs. Selected in the range ``[0, type_vocab_size - 1]``. If `type_vocab_size` is 2, which means the inputs have two portions. Indices can either be 0 or 1: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. Its data type should be `int64` and it has a shape of [batch_size, sequence_length]. Defaults to `None`, which means we don't add segment embeddings. position_ids (Tensor, optionals): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0, max_position_embeddings - 1]``. Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to `None`. attention_mask (`Tensor`, optional): Mask used in multi-head attention to avoid performing attention to some unwanted positions, usually the paddings or the subsequent positions. Its data type can be int, float and bool. When the data type is bool, the `masked` tokens have `False` values and the others have `True` values. When the data type is int, the `masked` tokens have `0` values and the others have `1` values. When the data type is float, the `masked` tokens have `-INF` values and the others have `0` values. It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`. For example, its shape can be [batch_size, sequence_length], [batch_size, sequence_length, sequence_length], [batch_size, num_attention_heads, sequence_length, sequence_length]. Defaults to `None`, which means nothing needed to be prevented attention to. inputs_embeds (Tensor, optional): If you want to control how to convert `inputs_ids` indices into associated vectors, you can pass an embedded representation directly instead of passing `inputs_ids`. past_key_values (tuple(tuple(Tensor)), optional): The length of tuple equals to the number of layers, and each inner tuple haves 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`) which contains precomputed key and value hidden states of the attention blocks. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, optional): If set to `True`, `past_key_values` key value states are returned. Defaults to `None`. output_hidden_states (bool, optional): Whether to return the hidden states of all layers. Defaults to `False`. output_attentions (bool, optional): Whether to return the attentions tensors of all attention layers. Defaults to `False`. return_dict (bool, optional): Whether to return a :class:`~paddlenlp.transformers.model_outputs.ModelOutput` object. If `False`, the output will be a tuple of tensors. Defaults to `False`. Returns: An instance of :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions` if `return_dict=True`. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions`. if the reuslt is tuple: Returns tuple (`sequence_output`, `pooled_output`). With the fields: - `sequence_output` (Tensor): Sequence of hidden-states at the last layer of the model. It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size]. - `pooled_output` (Tensor): The output of first token (`[CLS]`) in sequence. We "pool" the model by simply taking the hidden state corresponding to the first token. Its data type should be float32 and its shape is [batch_size, hidden_size]. Example: .. code-block:: import paddle from paddlenlp.transformers import SkepModel, SkepTokenizer tokenizer = SkepTokenizer.from_pretrained('skep_ernie_2.0_large_en') model = SkepModel.from_pretrained('skep_ernie_2.0_large_en') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP! ") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} output = model(**inputs) """ if input_ids is not None and inputs_embeds is not None: raise ValueError( "You cannot specify both input_ids and inputs_embeds at the same time." ) # init the default bool value output_attentions = output_attentions if output_attentions is not None else False output_hidden_states = output_hidden_states if output_hidden_states is not None else False return_dict = return_dict if return_dict is not None else False use_cache = use_cache if use_cache is not None else False past_key_values_length = 0 if past_key_values is not None: past_key_values_length = past_key_values[0][0].shape[2] if attention_mask is None: attention_mask = paddle.unsqueeze( (input_ids.astype("int64") == self.pad_token_id).astype( self.pooler.dense.weight.dtype) * -1e4, axis=[1, 2]) if past_key_values is not None: batch_size = paddle.shape(past_key_values[0][0])[0] past_mask = paddle.zeros( [batch_size, 1, 1, past_key_values_length], dtype=attention_mask.dtype) attention_mask = paddle.concat([past_mask, attention_mask], axis=-1) # For 2D attention_mask from tokenizer elif attention_mask.ndim == 2: attention_mask = paddle.unsqueeze( attention_mask, axis=[1, 2]).astype(paddle.get_default_dtype()) attention_mask = (1.0 - attention_mask) * -1e4 attention_mask.stop_gradient = True embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length) self.encoder._use_cache = use_cache # To be consistent with HF encoder_outputs = self.encoder( embedding_output, attention_mask, cache=past_key_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) if isinstance(encoder_outputs, type(input_ids)): encoder_outputs = (encoder_outputs, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions)
[文档] def get_input_embeddings(self) -> nn.Embedding: """get skep input word embedding Returns: nn.Embedding: the input word embedding of skep mdoel """ return self.embeddings.word_embeddings
[文档] def set_input_embeddings(self, embedding: nn.Embedding) -> nn.Embedding: """set skep input embedding Returns: nn.Embedding: the instance of new word embedding """ self.embeddings.word_embeddings = embedding
[文档]class SkepForSequenceClassification(SkepPretrainedModel): r""" SKEP Model with a linear layer on top of the pooled output, designed for sequence classification/regression tasks like GLUE tasks. Args: skep (:class:`SkepModel`): An instance of SkepModel. num_classes (int, optional): The number of classes. Defaults to `2`. dropout (float, optional): The dropout probability for output of SKEP. If None, use the same value as `hidden_dropout_prob` of `SkepModel` instance `skep`. Defaults to None. """ def __init__(self, skep, num_classes=2, dropout=None): super(SkepForSequenceClassification, self).__init__() self.num_classes = num_classes self.skep = skep # allow skep to be config self.dropout = nn.Dropout(dropout if dropout is not None else self.skep. config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.skep.config["hidden_size"], num_classes) self.apply(self.init_weights)
[文档] def forward(self, input_ids: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, position_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, labels: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None): r""" The SkepForSequenceClassification forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`SkepModel`. token_type_ids (Tensor, optional): See :class:`SkepModel`. position_ids (Tensor, `optional`): See :class:`SkepModel`. attention_mask (Tensor, optional): See :class:`SkepModel`. labels (Tensor of shape `(batch_size,)`, optional): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., num_classes - 1]`. If `num_classes == 1` a regression loss is computed (Mean-Square loss), If `num_classes > 1` a classification loss is computed (Cross-Entropy). inputs_embeds(Tensor, optional): See :class:`SkepModel`. output_hidden_states (bool, optional): Whether to return the hidden states of all layers. Defaults to `False`. output_attentions (bool, optional): Whether to return the attentions tensors of all attention layers. Defaults to `False`. return_dict (bool, optional): Whether to return a :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` object. If `False`, the output will be a tuple of tensors. Defaults to `False`. Returns: An instance of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` if `return_dict=True`. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput`. Example: .. code-block:: import paddle from paddlenlp.transformers import SkepForSequenceClassification, SkepTokenizer tokenizer = SkepTokenizer.from_pretrained('skep_ernie_2.0_large_en') model = SkepForSequenceClassification.from_pretrained('skep_ernie_2.0_large_en') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs) """ outputs = self.skep(input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.num_classes == 1: loss_fct = paddle.nn.MSELoss() loss = loss_fct(logits, labels) elif labels.dtype == paddle.int64 or labels.dtype == paddle.int32: loss_fct = paddle.nn.CrossEntropyLoss() loss = loss_fct(logits.reshape((-1, self.num_classes)), labels.reshape((-1, ))) else: loss_fct = paddle.nn.BCEWithLogitsLoss() loss = loss_fct(logits, labels) if not return_dict: output = (logits, ) + outputs[2:] return tuple_output(output, loss) return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
[文档]class SkepForTokenClassification(SkepPretrainedModel): r""" SKEP Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks. Args: skep (:class:`SkepModel`): An instance of SkepModel. num_classes (int, optional): The number of classes. Defaults to `2`. dropout (float, optional): The dropout probability for output of SKEP. If None, use the same value as `hidden_dropout_prob` of `SkepModel` instance `skep`. Defaults to None. """ def __init__(self, skep, num_classes=2, dropout=None): super(SkepForTokenClassification, self).__init__() self.num_classes = num_classes self.skep = skep # allow skep to be config self.dropout = nn.Dropout(dropout if dropout is not None else self.skep. config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.skep.config["hidden_size"], num_classes) self.apply(self.init_weights)
[文档] def forward(self, input_ids: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, position_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, labels: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None): r""" The SkepForTokenClassification forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`SkepModel`. token_type_ids (Tensor, optional): See :class:`SkepModel`. position_ids (Tensor, optional): See :class:`SkepModel`. attention_mask (Tensor, optional): See :class:`SkepModel`. labels (Tensor of shape `(batch_size, sequence_length)`, optional): Labels for computing the token classification loss. Indices should be in `[0, ..., num_classes - 1]`. inputs_embeds(Tensor, optional): See :class:`SkepModel`. output_hidden_states (bool, optional): Whether to return the hidden states of all layers. Defaults to `False`. output_attentions (bool, optional): Whether to return the attentions tensors of all attention layers. Defaults to `False`. return_dict (bool, optional): Whether to return a :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` object. If `False`, the output will be a tuple of tensors. Defaults to `False`. Returns: An instance of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` if `return_dict=True`. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput`. Example: .. code-block:: import paddle from paddlenlp.transformers import SkepForTokenClassification, SkepTokenizer tokenizer = SkepTokenizer.from_pretrained('skep_ernie_2.0_large_en') model = SkepForTokenClassification.from_pretrained('skep_ernie_2.0_large_en') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs) """ outputs = self.skep(input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = paddle.nn.CrossEntropyLoss() loss = loss_fct(logits.reshape((-1, self.num_classes)), labels.reshape((-1, ))) if not return_dict: output = (logits, ) + outputs[2:] return tuple_output(output, loss) return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )
[文档]class SkepCrfForTokenClassification(SkepPretrainedModel): r""" SKEPCRF Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks. Args: skep (:class:`SkepModel`): An instance of SkepModel. num_classes (int): The number of classes. """ def __init__(self, skep: SkepModel, num_classes: int = 3): super().__init__() self.num_classes = num_classes self.skep = skep # allow skep to be config gru_hidden_size = 128 self.gru = nn.GRU(self.skep.config["hidden_size"], gru_hidden_size, num_layers=2, direction='bidirect') self.fc = nn.Linear( gru_hidden_size * 2, self.num_classes, weight_attr=paddle.ParamAttr( initializer=nn.initializer.Uniform(low=-0.1, high=0.1), regularizer=paddle.regularizer.L2Decay(coeff=1e-4))) self.crf = LinearChainCrf(self.num_classes, crf_lr=0.2, with_start_stop_tag=False) self.crf_loss = LinearChainCrfLoss(self.crf) self.viterbi_decoder = ViterbiDecoder(self.crf.transitions, False)
[文档] def forward(self, input_ids: Optional[Tensor] = None, token_type_ids: Optional[Tensor] = None, position_ids: Optional[Tensor] = None, attention_mask: Optional[Tensor] = None, seq_lens: Optional[Tensor] = None, labels: Optional[Tensor] = None, inputs_embeds: Optional[Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None): r""" The SkepCrfForTokenClassification forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`SkepModel`. token_type_ids (Tensor, optional): See :class:`SkepModel`. position_ids (Tensor, optional): See :class:`SkepModel`. attention_mask (Tensor, optional): See :class:`SkepModel`. seq_lens (Tensor, optional): The input length tensor storing real length of each sequence for correctness. Its data type should be int64 and its shape is `[batch_size]`. Defaults to `None`. labels (Tensor, optional): The input label tensor. Its data type should be int64 and its shape is `[batch_size, sequence_length]`. inputs_embeds(Tensor, optional): See :class:`SkepModel`. output_hidden_states (bool, optional): Whether to return the hidden states of all layers. Defaults to `False`. output_attentions (bool, optional): Whether to return the attentions tensors of all attention layers. Defaults to `False`. return_dict (bool, optional): Whether to return a :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` object. If `False`, the output will be a tuple of tensors. Defaults to `False`. Returns: An instance of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` if `return_dict=True`. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput`. if return_dict is False, Returns tensor `loss` if `labels` is not None. Otherwise, returns tensor `prediction`. - `loss` (Tensor): The crf loss. Its data type is float32 and its shape is `[batch_size]`. - `prediction` (Tensor): The prediction tensor containing the highest scoring tag indices. Its data type is int64 and its shape is `[batch_size, sequence_length]`. """ outputs = self.skep(input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict) bigru_output, _ = self.gru(outputs[0]) #, sequence_length=seq_lens) emission = self.fc(bigru_output) if seq_lens is None: # compute seq length according to the attention mask if attention_mask is not None: seq_lens = paddle.sum(attention_mask, axis=1, dtype="int64") else: input_ids_shape = paddle.shape(input_ids) seq_lens = paddle.ones(shape=[input_ids_shape[0]], dtype="int64") * input_ids_shape[1] loss, prediction = None, None if labels is not None: loss = self.crf_loss(emission, seq_lens, labels) else: _, prediction = self.viterbi_decoder(emission, seq_lens) # FIXME(wj-Mcat): the output of this old version model is single tensor when return_dict is False if not return_dict: # when loss is None, return prediction if labels is not None: return loss return prediction return TokenClassifierOutput( loss=loss, logits=prediction, hidden_states=outputs.hidden_states, attentions=outputs.attentions, )