paddlenlp.transformers.electra.modeling 源代码

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2019 The Google AI Language Team Authors and The HuggingFace Inc. team.
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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.
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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
# distributed under the License is distributed on an "AS IS" BASIS,
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import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import TransformerEncoderLayer, TransformerEncoder
from paddle.nn.layer.transformer import _convert_attention_mask

from .. import PretrainedModel, register_base_model

__all__ = [
    'ElectraModel', 'ElectraPretrainedModel', 'ElectraForTotalPretraining',
    'ElectraDiscriminator', 'ElectraGenerator', 'ElectraClassificationHead',
    'ElectraForSequenceClassification', 'ElectraForTokenClassification',
    'ElectraPretrainingCriterion', 'ElectraForMultipleChoice',
    'ElectraForQuestionAnswering', 'ElectraForMaskedLM',
    'ElectraForPretraining', 'ErnieHealthForTotalPretraining',
    'ErnieHealthPretrainingCriterion', 'ErnieHealthDiscriminator'
]


def get_activation(activation_string):
    if activation_string in ACT2FN:
        return ACT2FN[activation_string]
    else:
        raise KeyError("function {} not found in ACT2FN mapping {}".format(
            activation_string, list(ACT2FN.keys())))


def mish(x):
    return x * F.tanh(F.softplus(x))


def linear_act(x):
    return x


def swish(x):
    return x * F.sigmoid(x)


ACT2FN = {
    "relu": F.relu,
    "gelu": F.gelu,
    "tanh": F.tanh,
    "sigmoid": F.sigmoid,
    "mish": mish,
    "linear": linear_act,
    "swish": swish,
}


class TransformerEncoderLayerPro(TransformerEncoderLayer):
    def __init__(self,
                 d_model,
                 nhead,
                 dim_feedforward,
                 dropout=0.1,
                 activation="relu",
                 attn_dropout=None,
                 act_dropout=None,
                 normalize_before=False,
                 weight_attr=None,
                 bias_attr=None):
        super(TransformerEncoderLayerPro, self).__init__(
            d_model, nhead, dim_feedforward, dropout, activation, attn_dropout,
            act_dropout, normalize_before, weight_attr, bias_attr)

    def forward(self, src, src_mask=None, cache=None, output_attentions=False):
        self.self_attn.need_weights = output_attentions
        src_mask = _convert_attention_mask(src_mask, src.dtype)
        attentions = None

        residual = src
        if self.normalize_before:
            src = self.norm1(src)
        if cache is None:
            src = self.self_attn(src, src, src, src_mask)
            if output_attentions:
                src, attentions = src
        else:
            output = self.self_attn(src, src, src, src_mask, cache)
            if output_attentions:
                src, attentions, incremental_cache = output
            else:
                src, incremental_cache = output

        src = residual + self.dropout1(src)
        if not self.normalize_before:
            src = self.norm1(src)

        residual = src
        if self.normalize_before:
            src = self.norm2(src)
        src = self.linear2(self.dropout(self.activation(self.linear1(src))))
        src = residual + self.dropout2(src)
        if not self.normalize_before:
            src = self.norm2(src)
        if output_attentions:
            src = (src, attentions)
        return src if cache is None else (src, incremental_cache)


class TransformerEncoderPro(TransformerEncoder):
    def __init__(self, encoder_layer, num_layers, norm=None):
        super(TransformerEncoderPro, self).__init__(encoder_layer, num_layers,
                                                    norm)

    def forward(self,
                src,
                src_mask=None,
                cache=None,
                output_attentions=False,
                output_hidden_states=False):
        src_mask = _convert_attention_mask(src_mask, src.dtype)

        output = src
        new_caches = []
        all_attentions = []
        all_hidden_states = []
        for i, mod in enumerate(self.layers):
            if cache is None:
                output = mod(output, src_mask=src_mask)
            else:
                output, new_cache = mod(output,
                                        src_mask=src_mask,
                                        cache=cache[i])
                new_caches.append(new_cache)
            if output_attentions:
                all_attentions.append(output[1])
                output = output[0]
            if output_hidden_states:
                all_hidden_states.append(output)

        if self.norm is not None:
            output = self.norm(output)

        if output_attentions or output_hidden_states:
            output = (output, all_attentions, all_hidden_states)

        return output if cache is None else (output, new_caches)


class ElectraEmbeddings(nn.Layer):
    """Construct the embeddings from word, position and token_type embeddings."""

    def __init__(self, vocab_size, embedding_size, hidden_dropout_prob,
                 max_position_embeddings, type_vocab_size, layer_norm_eps):
        super(ElectraEmbeddings, self).__init__()
        self.word_embeddings = nn.Embedding(vocab_size, embedding_size)
        self.position_embeddings = nn.Embedding(max_position_embeddings,
                                                embedding_size)
        self.token_type_embeddings = nn.Embedding(type_vocab_size,
                                                  embedding_size)

        self.layer_norm = nn.LayerNorm(embedding_size, epsilon=layer_norm_eps)
        self.dropout = nn.Dropout(hidden_dropout_prob)

    def forward(self, input_ids, token_type_ids=None, position_ids=None):
        if position_ids is None:
            ones = paddle.ones_like(input_ids, dtype="int64")
            seq_length = paddle.cumsum(ones, axis=-1)
            position_ids = seq_length - ones
            position_ids.stop_gradient = True
        position_ids = position_ids.astype("int64")

        if token_type_ids is None:
            token_type_ids = paddle.zeros_like(input_ids, dtype="int64")

        input_embeddings = self.word_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)

        embeddings = input_embeddings + position_embeddings + token_type_embeddings

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

        return embeddings


class ElectraDiscriminatorPredictions(nn.Layer):
    """Prediction layer for the discriminator, made up of two dense layers."""

    def __init__(self, hidden_size, hidden_act):
        super(ElectraDiscriminatorPredictions, self).__init__()

        self.dense = nn.Linear(hidden_size, hidden_size)
        self.dense_prediction = nn.Linear(hidden_size, 1)
        self.act = get_activation(hidden_act)

    def forward(self, discriminator_hidden_states):
        hidden_states = self.dense(discriminator_hidden_states)
        hidden_states = self.act(hidden_states)
        logits = self.dense_prediction(hidden_states).squeeze()

        return logits


class ElectraGeneratorPredictions(nn.Layer):
    """Prediction layer for the generator, made up of two dense layers."""

    def __init__(self, embedding_size, hidden_size, hidden_act):
        super(ElectraGeneratorPredictions, self).__init__()

        self.layer_norm = nn.LayerNorm(embedding_size)
        self.dense = nn.Linear(hidden_size, embedding_size)
        self.act = get_activation(hidden_act)

    def forward(self, generator_hidden_states):
        hidden_states = self.dense(generator_hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.layer_norm(hidden_states)

        return hidden_states


[文档]class ElectraPretrainedModel(PretrainedModel): """ An abstract class for pretrained Electra models. It provides Electra 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. """ base_model_prefix = "electra" model_config_file = "model_config.json" # pretrained general configuration gen_weight = 1.0 disc_weight = 50.0 tie_word_embeddings = True untied_generator_embeddings = False use_softmax_sample = True # model init configuration pretrained_init_configuration = { "electra-small": { "attention_probs_dropout_prob": 0.1, "embedding_size": 128, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 256, "initializer_range": 0.02, "intermediate_size": 1024, "max_position_embeddings": 512, "num_attention_heads": 4, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 30522 }, "electra-base": { "attention_probs_dropout_prob": 0.1, "embedding_size": 768, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 30522 }, "electra-large": { "attention_probs_dropout_prob": 0.1, "embedding_size": 1024, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "max_position_embeddings": 512, "num_attention_heads": 16, "num_hidden_layers": 24, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 30522 }, "chinese-electra-small": { "attention_probs_dropout_prob": 0.1, "embedding_size": 128, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 256, "initializer_range": 0.02, "intermediate_size": 1024, "max_position_embeddings": 512, "num_attention_heads": 4, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 21128 }, "chinese-electra-base": { "attention_probs_dropout_prob": 0.1, "embedding_size": 768, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 21128 }, "ernie-health-chinese": { "attention_probs_dropout_prob": 0.1, "embedding_size": 768, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 22608, "layer_norm_eps": 1e-5 }, } resource_files_names = {"model_state": "model_state.pdparams"} pretrained_resource_files_map = { "model_state": { "electra-small": "https://bj.bcebos.com/paddlenlp/models/transformers/electra/electra-small.pdparams", "electra-base": "https://bj.bcebos.com/paddlenlp/models/transformers/electra/electra-base.pdparams", "electra-large": "https://bj.bcebos.com/paddlenlp/models/transformers/electra/electra-large.pdparams", "chinese-electra-small": "https://bj.bcebos.com/paddlenlp/models/transformers/chinese-electra-small/chinese-electra-small.pdparams", "chinese-electra-base": "https://bj.bcebos.com/paddlenlp/models/transformers/chinese-electra-base/chinese-electra-base.pdparams", "ernie-health-chinese": "https://paddlenlp.bj.bcebos.com/models/transformers/ernie-health-chinese/ernie-health-chinese.pdparams" } }
[文档] def init_weights(self): """ Initializes and tie weights if needed. """ # Initialize weights self.apply(self._init_weights) # Tie weights if needed self.tie_weights()
[文档] def tie_weights(self): """ Tie the weights between the input embeddings and the output embeddings. """ if hasattr(self, "get_output_embeddings") and hasattr( self, "get_input_embeddings"): output_embeddings = self.get_output_embeddings() if output_embeddings is not None: self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
def _init_weights(self, layer): """ Initialize the weights """ if isinstance(layer, (nn.Linear, nn.Embedding)): layer.weight.set_value( paddle.tensor.normal( mean=0.0, std=self.initializer_range if hasattr(self, "initializer_range") else self.electra.config["initializer_range"], shape=layer.weight.shape)) elif isinstance(layer, nn.LayerNorm): layer.bias.set_value(paddle.zeros_like(layer.bias)) layer.weight.set_value(paddle.full_like(layer.weight, 1.0)) layer._epsilon = getattr(self, "layer_norm_eps", 1e-12) if isinstance(layer, nn.Linear) and layer.bias is not None: layer.bias.set_value(paddle.zeros_like(layer.bias)) def _tie_or_clone_weights(self, output_embeddings, input_embeddings): """Tie or clone layer weights""" if output_embeddings.weight.shape == input_embeddings.weight.shape: output_embeddings.weight = input_embeddings.weight elif output_embeddings.weight.shape == input_embeddings.weight.t( ).shape: output_embeddings.weight.set_value(input_embeddings.weight.t()) else: raise ValueError( "when tie input/output embeddings, the shape of output embeddings: {}" "should be equal to shape of input embeddings: {}" "or should be equal to the shape of transpose input embeddings: {}". format(output_embeddings.weight.shape, input_embeddings.weight. shape, input_embeddings.weight.t().shape)) if getattr(output_embeddings, "bias", None) is not None: if output_embeddings.weight.shape[ -1] != output_embeddings.bias.shape[0]: raise ValueError( "the weight lase shape: {} of output_embeddings is not equal to the bias shape: {}" "please check output_embeddings configuration".format( output_embeddings.weight.shape[ -1], output_embeddings.bias.shape[0]))
[文档]@register_base_model class ElectraModel(ElectraPretrainedModel): """ The bare Electra Model transformer 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. Args: vocab_size (int): Vocabulary size of `inputs_ids` in `ElectraModel`. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `ElectraModel`. embedding_size (int, optional): Dimensionality of the embedding layer. hidden_size (int, optional): Dimensionality of the encoder layer and pooler layer. num_hidden_layers (int, optional): Number of hidden layers in the Transformer encoder. num_attention_heads (int, optional): Number of attention heads for each attention layer in the Transformer encoder. 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`. 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. hidden_dropout_prob (float, optional): The dropout probability for all fully connected layers in the embeddings and encoder. attention_probs_dropout_prob (float, optional): The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target. max_position_embeddings (int, optional): The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input sequence. type_vocab_size (int, optional): The vocabulary size of `token_type_ids`. initializer_range (float, optional): The standard deviation of the normal initializer. .. note:: A normal_initializer initializes weight matrices as normal distributions. See :meth:`ElectraPretrainedModel.init_weights()` for how weights are initialized in `ElectraModel`. pad_token_id (int, optional): The index of padding token in the token vocabulary. """ def __init__(self, vocab_size, embedding_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, pad_token_id, layer_norm_eps=1e-12): super(ElectraModel, self).__init__() self.pad_token_id = pad_token_id self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.embeddings = ElectraEmbeddings( vocab_size, embedding_size, hidden_dropout_prob, max_position_embeddings, type_vocab_size, layer_norm_eps) if embedding_size != hidden_size: self.embeddings_project = nn.Linear(embedding_size, hidden_size) encoder_layer = TransformerEncoderLayerPro( 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 = TransformerEncoderPro(encoder_layer, num_hidden_layers) self.init_weights() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value
[文档] def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, output_attentions=False, output_hidden_states=False): r''' The ElectraModel 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, optional): 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 on 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]`. Defaults to `None`, which means nothing needed to be prevented attention to. Returns: Tensor: Returns tensor `encoder_outputs`, which is the output at the last layer of the model. Its data type should be float32 and has a shape of [batch_size, sequence_length, hidden_size]. Example: .. code-block:: import paddle from paddlenlp.transformers import ElectraModel, ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraModel.from_pretrained('electra-small') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} output = model(**inputs) ''' if attention_mask is None: attention_mask = paddle.unsqueeze( (input_ids == self.pad_token_id ).astype(paddle.get_default_dtype()) * -1e4, axis=[1, 2]) else: if attention_mask.ndim == 2: attention_mask = attention_mask.unsqueeze(axis=[1, 2]) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids) if hasattr(self, "embeddings_project"): embedding_output = self.embeddings_project(embedding_output) encoder_outputs = self.encoder( embedding_output, attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states) return encoder_outputs
[文档]class ElectraDiscriminator(ElectraPretrainedModel): """ The Electra Discriminator can detect the tokens that are replaced by the Electra Generator. Args: electra (:class:`ElectraModel`): An instance of :class:`ElectraModel`. """ def __init__(self, electra): super(ElectraDiscriminator, self).__init__() self.electra = electra self.discriminator_predictions = ElectraDiscriminatorPredictions( self.electra.config["hidden_size"], self.electra.config["hidden_act"]) self.init_weights()
[文档] def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None): r""" Args: input_ids (Tensor): See :class:`ElectraModel`. token_type_ids (Tensor, optional): See :class:`ElectraModel`. position_ids (Tensor, optional): See :class:`ElectraModel`. attention_mask (Tensor, optional): See :class:`ElectraModel`. Returns: Tensor: Returns tensor `logits`, the prediction result of replaced tokens. Its data type should be float32 and if batch_size>1, its shape is [batch_size, sequence_length], if batch_size=1, its shape is [sequence_length]. Example: .. code-block:: import paddle from paddlenlp.transformers import ElectraDiscriminator, ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraDiscriminator.from_pretrained('electra-small') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs) """ discriminator_sequence_output = self.electra( input_ids, token_type_ids, position_ids, attention_mask) logits = self.discriminator_predictions(discriminator_sequence_output) return logits
[文档]class ElectraGenerator(ElectraPretrainedModel): """ The Electra Generator will replace some tokens of the given sequence, it is trained as a masked language model. Args: electra (:class:`ElectraModel`): An instance of :class:`ElectraModel`. """ def __init__(self, electra): super(ElectraGenerator, self).__init__() self.electra = electra self.generator_predictions = ElectraGeneratorPredictions( self.electra.config["embedding_size"], self.electra.config["hidden_size"], self.electra.config["hidden_act"]) if not self.tie_word_embeddings: self.generator_lm_head = nn.Linear( self.electra.config["embedding_size"], self.electra.config["vocab_size"]) else: self.generator_lm_head_bias = self.create_parameter( shape=[self.electra.config["vocab_size"]], dtype=paddle.get_default_dtype(), is_bias=True) self.init_weights() def get_input_embeddings(self): return self.electra.embeddings.word_embeddings
[文档] def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None): r""" Args: input_ids (Tensor): See :class:`ElectraModel`. token_type_ids (Tensor, optional): See :class:`ElectraModel`. position_ids (Tensor, optional): See :class:`ElectraModel`. attention_mask (Tensor, optional): See :class:`ElectraModel`. Returns: Tensor: Returns tensor `prediction_scores`, the scores of Electra Generator. Its data type should be int64 and its shape is [batch_size, sequence_length, vocab_size]. Example: .. code-block:: import paddle from paddlenlp.transformers import ElectraGenerator, ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraGenerator.from_pretrained('electra-small') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} prediction_scores = model(**inputs) """ generator_sequence_output = self.electra(input_ids, token_type_ids, position_ids, attention_mask) prediction_scores = self.generator_predictions( generator_sequence_output) if not self.tie_word_embeddings: prediction_scores = self.generator_lm_head(prediction_scores) else: prediction_scores = paddle.add(paddle.matmul( prediction_scores, self.get_input_embeddings().weight, transpose_y=True), self.generator_lm_head_bias) return prediction_scores
[文档]class ElectraClassificationHead(nn.Layer): """ Perform sentence-level classification tasks. Args: hidden_size (int): Dimensionality of the embedding layer. hidden_dropout_prob (float): The dropout probability for all fully connected layers. num_classes (int): The number of classes. activation (str): The activation function name between layers. """ def __init__(self, hidden_size, hidden_dropout_prob, num_classes, activation): super(ElectraClassificationHead, self).__init__() self.dense = nn.Linear(hidden_size, hidden_size) self.dropout = nn.Dropout(hidden_dropout_prob) self.out_proj = nn.Linear(hidden_size, num_classes) self.act = get_activation(activation)
[文档] def forward(self, features, **kwargs): r""" The ElectraClassificationHead forward method, overrides the __call__() special method. Args: features(Tensor): Input sequence, usually the `sequence_output` of electra model. Its data type should be float32 and its shape is [batch_size, sequence_length, hidden_size]. Returns: Tensor: Returns a tensor of the input text classification logits. Shape as `[batch_size, num_classes]` and dtype as float32. """ x = features[:, 0, :] # take <s> token (equiv. to [CLS]) x = self.dropout(x) x = self.dense(x) x = self.act(x) x = self.dropout(x) x = self.out_proj(x) return x
[文档]class ErnieHealthDiscriminator(ElectraPretrainedModel): """ The Discriminators in ERNIE-Health (https://arxiv.org/abs/2110.07244), including - token-level Replaced Token Detection (RTD) task - token-level Multi-Token Selection (MTS) task - sequence-level Contrastive Sequence Prediction (CSP) task. Args: electra (:class:`ElectraModel`): An instance of :class:`ElectraModel`. """ def __init__(self, electra): super(ErnieHealthDiscriminator, self).__init__() self.electra = electra self.discriminator_rtd = ElectraDiscriminatorPredictions( self.electra.config["hidden_size"], self.electra.config["hidden_act"]) self.discriminator_mts = nn.Linear(self.electra.config["hidden_size"], self.electra.config["hidden_size"]) self.activation_mts = get_activation(self.electra.config["hidden_act"]) self.bias_mts = nn.Embedding(self.electra.config["vocab_size"], 1) self.discriminator_csp = ElectraClassificationHead( self.electra.config["hidden_size"], self.electra.config["hidden_dropout_prob"], num_classes=128, activation='gelu') self.init_weights()
[文档] def forward(self, input_ids, candidate_ids, token_type_ids=None, position_ids=None, attention_mask=None): r""" Args: input_ids (Tensor): See :class:`ElectraModel`. candidate_ids (Tensor): The candidate indices of input sequence tokens in the vocabulary for MTS task. Its data type should be `int64` and it has a shape of [batch_size, sequence_length]. token_type_ids (Tensor, optional): See :class:`ElectraModel`. position_ids (Tensor, optional): See :class:`ElectraModel`. attention_mask (Tensor, optional): See :class:`ElectraModel`. Returns: Tensor: Returns list of tensors, the prediction results of RTD, MTS and CSP. The logits' data type should be float32 and if batch_size > 1, - the shape of `logits_rtd` is [batch_size, sequence_length], - the shape of `logits_mts` is [batch_size, sequence_length, num_candidate], - the shape of `logits_csp` is [batch_size, 128]. If batch_size=1, the shapes are [sequence_length], [sequence_length, num_cadidate], [128], separately. """ discriminator_sequence_output = self.electra( input_ids, token_type_ids, position_ids, attention_mask) logits_rtd = self.discriminator_rtd(discriminator_sequence_output) cands_embs = self.electra.embeddings.word_embeddings(candidate_ids) hidden_mts = self.discriminator_mts(discriminator_sequence_output) hidden_mts = self.activation_mts(hidden_mts) hidden_mts = paddle.matmul( hidden_mts.unsqueeze(2), cands_embs, transpose_y=True).squeeze(2) logits_mts = paddle.add(hidden_mts, self.bias_mts(candidate_ids).squeeze(3)) logits_csp = self.discriminator_csp(discriminator_sequence_output) return logits_rtd, logits_mts, logits_csp
[文档]class ElectraForSequenceClassification(ElectraPretrainedModel): """ Electra Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks. Args: electra (:class:`ElectraModel`): An instance of ElectraModel. num_classes (int, optional): The number of classes. Defaults to `2`. dropout (float, optional): The dropout probability for output of Electra. If None, use the same value as `hidden_dropout_prob` of `ElectraModel` instance `electra`. Defaults to None. activation (str, optional): The activation function name for classifier. Defaults to "gelu". layer_norm_eps (float, optional): The epsilon to initialize nn.LayerNorm layers. Defaults to 1e-12. """ def __init__(self, electra, num_classes=2, dropout=None, activation="gelu"): super(ElectraForSequenceClassification, self).__init__() self.num_classes = num_classes self.electra = electra self.classifier = ElectraClassificationHead( hidden_size=self.electra.config["hidden_size"], hidden_dropout_prob=dropout if dropout is not None else self.electra.config["hidden_dropout_prob"], num_classes=self.num_classes, activation=activation) self.init_weights()
[文档] def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None): r""" The ElectraForSequenceClassification forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`ElectraModel`. token_type_ids (Tensor, optional): See :class:`ElectraModel`. position_ids(Tensor, optional): See :class:`ElectraModel`. attention_mask (list, optional): See :class:`ElectraModel`. Returns: Tensor: Returns tensor `logits`, a tensor of the input text classification logits. Shape as `[batch_size, num_classes]` and dtype as float32. Example: .. code-block:: import paddle from paddlenlp.transformers import ElectraForSequenceClassification from paddlenlp.transformers import ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraForSequenceClassification.from_pretrained('electra-small') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs) """ sequence_output = self.electra(input_ids, token_type_ids, position_ids, attention_mask) logits = self.classifier(sequence_output) return logits
[文档]class ElectraForTokenClassification(ElectraPretrainedModel): """ Electra Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks. Args: electra (:class:`ElectraModel`): An instance of ElectraModel. num_classes (int, optional): The number of classes. Defaults to `2`. dropout (float, optional): The dropout probability for output of Electra. If None, use the same value as `hidden_dropout_prob` of `ElectraModel` instance `electra`. Defaults to None. """ def __init__(self, electra, num_classes=2, dropout=None): super(ElectraForTokenClassification, self).__init__() self.num_classes = num_classes self.electra = electra self.dropout = nn.Dropout(dropout if dropout is not None else self.electra.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.electra.config["hidden_size"], self.num_classes) self.init_weights()
[文档] def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None): r""" The ElectraForTokenClassification forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`ElectraModel`. token_type_ids (Tensor, optional): See :class:`ElectraModel`. position_ids(Tensor, optional): See :class:`ElectraModel`. attention_mask (list, optional): See :class:`ElectraModel`. Returns: Tensor: Returns tensor `logits`, a tensor of the input token classification logits. Shape as `[batch_size, sequence_length, num_classes]` and dtype as `float32`. Example: .. code-block:: import paddle from paddlenlp.transformers import ElectraForTokenClassification from paddlenlp.transformers import ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraForTokenClassification.from_pretrained('electra-small') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs) """ sequence_output = self.electra(input_ids, token_type_ids, position_ids, attention_mask) sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) return logits
[文档]class ElectraForTotalPretraining(ElectraPretrainedModel): """ Electra Model for pretraining tasks. Args: generator (:class:`ElectraGenerator`): An instance of :class:`ElectraGenerator`. discriminator (:class:`ElectraDiscriminator`): An instance of :class:`ElectraDiscriminator`. """ pretrained_init_configuration = { "electra-small-generator": { "attention_probs_dropout_prob": 0.1, "embedding_size": 128, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 256, "initializer_range": 0.02, "intermediate_size": 1024, "max_position_embeddings": 512, "num_attention_heads": 4, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 30522 }, "electra-base-generator": { "attention_probs_dropout_prob": 0.1, "embedding_size": 768, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 256, "initializer_range": 0.02, "intermediate_size": 1024, "max_position_embeddings": 512, "num_attention_heads": 4, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 30522 }, "electra-large-generator": { "attention_probs_dropout_prob": 0.1, "embedding_size": 1024, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 256, "initializer_range": 0.02, "intermediate_size": 1024, "max_position_embeddings": 512, "num_attention_heads": 4, "num_hidden_layers": 24, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 30522 }, "electra-small-discriminator": { "attention_probs_dropout_prob": 0.1, "embedding_size": 128, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 256, "initializer_range": 0.02, "intermediate_size": 1024, "max_position_embeddings": 512, "num_attention_heads": 4, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 30522 }, "electra-base-discriminator": { "attention_probs_dropout_prob": 0.1, "embedding_size": 768, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 30522 }, "electra-large-discriminator": { "attention_probs_dropout_prob": 0.1, "embedding_size": 1024, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "max_position_embeddings": 512, "num_attention_heads": 16, "num_hidden_layers": 24, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 30522 }, } def __init__(self, generator, discriminator): super(ElectraForTotalPretraining, self).__init__() self.generator = generator self.discriminator = discriminator self.initializer_range = discriminator.electra.initializer_range self.init_weights() def get_input_embeddings(self): if not self.untied_generator_embeddings: return self.generator.electra.embeddings.word_embeddings else: return None def get_output_embeddings(self): if not self.untied_generator_embeddings: return self.discriminator.electra.embeddings.word_embeddings else: return None def get_discriminator_inputs(self, inputs, raw_inputs, generator_logits, generator_labels, use_softmax_sample): # get generator token result sampled_tokens = (self.sample_from_softmax(generator_logits, use_softmax_sample)).detach() sampled_tokids = paddle.argmax(sampled_tokens, axis=-1) # update token only at mask position # generator_labels : [B, L], L contains -100(unmasked) or token value(masked) # mask_positions : [B, L], L contains 0(unmasked) or 1(masked) umask_positions = paddle.zeros_like(generator_labels) mask_positions = paddle.ones_like(generator_labels) mask_positions = paddle.where(generator_labels == -100, umask_positions, mask_positions) updated_inputs = self.update_inputs(inputs, sampled_tokids, mask_positions) # use inputs and updated_input to get discriminator labels labels = mask_positions * (paddle.ones_like(inputs) - paddle.equal( updated_inputs, raw_inputs).astype("int32")) return updated_inputs, labels, sampled_tokids def sample_from_softmax(self, logits, use_softmax_sample=True): if use_softmax_sample: #uniform_noise = paddle.uniform(logits.shape, dtype="float32", min=0, max=1) uniform_noise = paddle.rand( logits.shape, dtype=paddle.get_default_dtype()) gumbel_noise = -paddle.log(-paddle.log(uniform_noise + 1e-9) + 1e-9) else: gumbel_noise = paddle.zeros_like(logits) # softmax_sample equal to sampled_tokids.unsqueeze(-1) softmax_sample = paddle.argmax( F.softmax(logits + gumbel_noise), axis=-1) # one hot return F.one_hot(softmax_sample, logits.shape[-1]) def update_inputs(self, sequence, updates, positions): shape = sequence.shape assert (len(shape) == 2), "the dimension of inputs should be [B, L]" B, L = shape N = positions.shape[1] assert ( N == L), "the dimension of inputs and mask should be same as [B, L]" updated_sequence = (( (paddle.ones_like(sequence) - positions) * sequence) + (positions * updates)) return updated_sequence
[文档] def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None, raw_input_ids=None, generator_labels=None): r""" The ElectraForPretraining forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`ElectraModel`. token_type_ids (Tensor, optional): See :class:`ElectraModel`. position_ids(Tensor, optional): See :class:`ElectraModel`. attention_mask (list, optional): See :class:`ElectraModel`. raw_input_ids(Tensor, optional): Raw inputs used to get discriminator labels. Its data type should be `int64` and it has a shape of [batch_size, sequence_length]. generator_labels(Tensor, optional): Labels to compute the discriminator inputs. Its data type should be int64 and its shape is [batch_size, sequence_length]. The value for unmasked tokens should be -100 and value for masked tokens should be 0. Returns: tuple: Returns tuple (generator_logits, disc_logits, disc_labels, attention_mask). With the fields: - `generator_logits` (Tensor): The scores of Electra Generator. Its data type should be int64 and its shape is [batch_size, sequence_length, vocab_size]. - `disc_logits` (Tensor): The the prediction result of replaced tokens. Its data type should be float32 and if batch_size>1, its shape is [batch_size, sequence_length], if batch_size=1, its shape is [sequence_length]. - `disc_labels` (Tensor): The labels of electra discriminator. Its data type should be int32, and its shape is [batch_size, sequence_length]. - `attention_mask` (Tensor): See :class:`ElectraModel`. Its data type should be bool. """ assert ( generator_labels is not None ), "generator_labels should not be None, please check DataCollatorForLanguageModeling" generator_logits = self.generator(input_ids, token_type_ids, position_ids, attention_mask) disc_inputs, disc_labels, generator_predict_tokens = self.get_discriminator_inputs( input_ids, raw_input_ids, generator_logits, generator_labels, self.use_softmax_sample) disc_logits = self.discriminator(disc_inputs, token_type_ids, position_ids, attention_mask) if attention_mask is None: attention_mask = ( input_ids != self.discriminator.electra.config["pad_token_id"]) else: attention_mask = attention_mask.astype("bool") return generator_logits, disc_logits, disc_labels, attention_mask
class ElectraPooler(nn.Layer): def __init__(self, hidden_size, pool_act="gelu"): super(ElectraPooler, self).__init__() self.dense = nn.Linear(hidden_size, hidden_size) self.activation = get_activation(pool_act) self.pool_act = pool_act def forward(self, hidden_states): # 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 ErnieHealthForTotalPretraining(ElectraForTotalPretraining): """ ERNIE-Health Model for pretraining task. Args: generator (:class:`ElectraGenerator`): An instance of :class:`ElectraGenerator`. discriminator (:class:`ErnieHealthDiscriminator): An instance of :class:`ErnieHealthDiscriminator`. """ pretrained_init_configuration = { "ernie-health-chinese-generator": { "attention_probs_dropout_prob": 0.1, "embedding_size": 768, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 256, "initializer_range": 0.02, "intermediate_size": 1024, "max_position_embeddings": 512, "num_attention_heads": 4, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 22608, "layer_norm_eps": 1e-12 }, "ernie-health-chinese-discriminator": { "attention_probs_dropout_prob": 0.1, "embedding_size": 768, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 0, "type_vocab_size": 2, "vocab_size": 22608, "layer_norm_eps": 1e-12 } } def get_discriminator_inputs_ernie_health( self, inputs, raw_inputs, generator_logits, generator_labels, use_softmax_sample): updated_inputs, labels, sampled_tokids = self.get_discriminator_inputs( inputs, raw_inputs, generator_logits, generator_labels, use_softmax_sample) # Get negative samples to construct candidates. neg_samples_ids = self.sample_negatives_from_softmax( generator_logits, raw_inputs, use_softmax_sample) candidate_ids = paddle.concat( [raw_inputs.unsqueeze(2), neg_samples_ids], axis=2).detach() return updated_inputs, labels, sampled_tokids, candidate_ids
[文档] def sample_negatives_from_softmax(self, logits, raw_inputs, use_softmax_sample=True): r""" Sample K=5 non-original negative samples for candidate set. Returns: Tensor: Returns tensor `neg_samples_ids`, a tensor of the negative samples of original inputs. Shape as ` [batch_size, sequence_length, K, vocab_size]` and dtype as `int64`. """ K = 5 # Initialize excluded_ids by original inputs in one-hot encoding. # Its shape is [batch_size, sequence_length, vocab_size]. excluded_ids = F.one_hot(raw_inputs, logits.shape[-1]) * -10000 neg_sample_one_hot = None neg_samples_ids = None for sample in range(K): # Update excluded_ids. if neg_sample_one_hot is not None: excluded_ids = excluded_ids + neg_sample_one_hot * -10000 if use_softmax_sample: uniform_noise = paddle.rand( logits.shape, dtype=paddle.get_default_dtype()) gumbel_noise = -paddle.log(-paddle.log(uniform_noise + 1e-9) + 1e-9) else: gumbel_noise = paddle.zeros_like(logits) sampled_ids = paddle.argmax( F.softmax(logits + gumbel_noise + excluded_ids), axis=-1) # One-hot encoding of sample_ids. neg_sample_one_hot = F.one_hot(sampled_ids, logits.shape[-1]) if neg_samples_ids is None: neg_samples_ids = sampled_ids.unsqueeze(2) else: neg_samples_ids = paddle.concat( [neg_samples_ids, sampled_ids.unsqueeze(2)], axis=2) return neg_samples_ids
[文档] def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None, raw_input_ids=None, generator_labels=None): assert ( generator_labels is not None ), "generator_labels should not be None, please check DataCollator" generator_logits = self.generator(input_ids, token_type_ids, position_ids, attention_mask) disc_input_list = self.get_discriminator_inputs_ernie_health( input_ids, raw_input_ids, generator_logits, generator_labels, self.use_softmax_sample) disc_inputs, disc_labels, _, disc_candidates = disc_input_list logits_rtd, logits_mts, logits_csp = self.discriminator( disc_inputs, disc_candidates, token_type_ids, position_ids, attention_mask) if attention_mask is None: pad_id = self.generator.electra.pad_token_id attention_mask = (input_ids != pad_id) else: attention_mask = attention_mask.astype('bool') return generator_logits, logits_rtd, logits_mts, logits_csp, disc_labels, attention_mask
[文档]class ElectraForMultipleChoice(ElectraPretrainedModel): """ Electra Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks. Args: electra (:class:`ElectraModel`): An instance of ElectraModel. num_choices (int, optional): The number of choices. Defaults to `2`. dropout (float, optional): The dropout probability for output of Electra. If None, use the same value as `hidden_dropout_prob` of `ElectraModel` instance `electra`. Defaults to None. """ def __init__(self, electra, num_choices=2, dropout=None): super(ElectraForMultipleChoice, self).__init__() self.num_choices = num_choices self.electra = electra self.sequence_summary = ElectraPooler( self.electra.config["hidden_size"], pool_act="gelu") self.dropout = nn.Dropout(dropout if dropout is not None else self.electra.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.electra.config["hidden_size"], 1) self.init_weights()
[文档] def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None): r""" The ElectraForMultipleChoice forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`ElectraModel` and shape as [batch_size, num_choice, sequence_length]. token_type_ids (Tensor, optional): See :class:`ElectraModel` and shape as [batch_size, num_choice, sequence_length]. position_ids(Tensor, optional): See :class:`ElectraModel` and shape as [batch_size, num_choice, sequence_length]. attention_mask (list, optional): See :class:`ElectraModel` and shape as [batch_size, num_choice, sequence_length]. Returns: Tensor: Returns tensor `reshaped_logits`, a tensor of the multiple choice classification logits. Shape as `[batch_size, num_choice]` and dtype as `float32`. Example: .. code-block:: import paddle from paddlenlp.transformers import ElectraForMultipleChoice, ElectraTokenizer from paddlenlp.data import Pad, Dict tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraForMultipleChoice.from_pretrained('electra-small', num_choices=2) data = [ { "question": "how do you turn on an ipad screen?", "answer1": "press the volume button.", "answer2": "press the lock button.", "label": 1, }, { "question": "how do you indent something?", "answer1": "leave a space before starting the writing", "answer2": "press the spacebar", "label": 0, }, ] text = [] text_pair = [] for d in data: text.append(d["question"]) text_pair.append(d["answer1"]) text.append(d["question"]) text_pair.append(d["answer2"]) inputs = tokenizer(text, text_pair) batchify_fn = lambda samples, fn=Dict( { "input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id), # input_ids "token_type_ids": Pad( axis=0, pad_val=tokenizer.pad_token_type_id ), # token_type_ids } ): fn(samples) inputs = batchify_fn(inputs) reshaped_logits = model( input_ids=paddle.to_tensor(inputs[0], dtype="int64"), token_type_ids=paddle.to_tensor(inputs[1], dtype="int64"), ) print(reshaped_logits.shape) # [2, 2] """ input_ids = input_ids.reshape( (-1, input_ids.shape[-1])) # flat_input_ids: [bs*num_choice,seq_l] if token_type_ids is not None: token_type_ids = token_type_ids.reshape( (-1, token_type_ids.shape[-1])) if position_ids is not None: position_ids = position_ids.reshape((-1, position_ids.shape[-1])) if attention_mask is not None: attention_mask = attention_mask.reshape( (-1, attention_mask.shape[-1])) sequence_output = self.electra(input_ids, token_type_ids, position_ids, attention_mask) pooled_output = self.sequence_summary(sequence_output) pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) # logits: (bs*num_choice,1) reshaped_logits = logits.reshape( (-1, self.num_choices)) # logits: (bs, num_choice) return reshaped_logits
[文档]class ElectraPretrainingCriterion(paddle.nn.Layer): ''' Args: vocab_size(int): Vocabulary size of `inputs_ids` in `ElectraModel`. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `ElectraModel`. gen_weight(float): The weight of the Electra Generator. disc_weight(float): The weight of the Electra Discriminator. ''' def __init__(self, vocab_size, gen_weight, disc_weight): super(ElectraPretrainingCriterion, self).__init__() self.vocab_size = vocab_size self.gen_weight = gen_weight self.disc_weight = disc_weight self.gen_loss_fct = nn.CrossEntropyLoss(reduction='none') self.disc_loss_fct = nn.BCEWithLogitsLoss(reduction='none')
[文档] def forward(self, generator_prediction_scores, discriminator_prediction_scores, generator_labels, discriminator_labels, attention_mask): """ Args: generator_prediction_scores(Tensor): The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length, vocab_size]. discriminator_prediction_scores(Tensor): The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length] or [sequence length] if batch_size=1. generator_labels(Tensor): The labels of the generator, its dimensionality is equal to `generator_prediction_scores`. Its data type should be int64 and its shape is [batch_size, sequence_size, 1]. discriminator_labels(Tensor): The labels of the discriminator, its dimensionality is equal to `discriminator_prediction_scores`. The labels should be numbers between 0 and 1. Its data type should be float32 and its shape is [batch_size, sequence_size] or [sequence length] if batch_size=1. attention_mask(Tensor): See :class:`ElectraModel`. Returns: Tensor: The pretraining loss, equals to weighted generator loss plus the weighted discriminator loss. Its data type should be float32 and its shape is [1]. """ # generator loss gen_loss = self.gen_loss_fct( paddle.reshape(generator_prediction_scores, [-1, self.vocab_size]), paddle.reshape(generator_labels, [-1])) # todo: we can remove 4 lines after when CrossEntropyLoss(reduction='mean') improved umask_positions = paddle.zeros_like(generator_labels).astype( paddle.get_default_dtype()) mask_positions = paddle.ones_like(generator_labels).astype( paddle.get_default_dtype()) mask_positions = paddle.where(generator_labels == -100, umask_positions, mask_positions) if mask_positions.sum() == 0: gen_loss = paddle.to_tensor([0.0]) else: gen_loss = gen_loss.sum() / mask_positions.sum() # discriminator loss seq_length = discriminator_labels.shape[1] disc_loss = self.disc_loss_fct( paddle.reshape(discriminator_prediction_scores, [-1, seq_length]), discriminator_labels.astype(paddle.get_default_dtype())) if attention_mask is not None: umask_positions = paddle.ones_like(discriminator_labels).astype( paddle.get_default_dtype()) mask_positions = paddle.zeros_like(discriminator_labels).astype( paddle.get_default_dtype()) use_disc_loss = paddle.where(attention_mask, disc_loss, mask_positions) umask_positions = paddle.where(attention_mask, umask_positions, mask_positions) disc_loss = use_disc_loss.sum() / umask_positions.sum() else: total_positions = paddle.ones_like(discriminator_labels).astype( paddle.get_default_dtype()) disc_loss = disc_loss.sum() / total_positions.sum() return self.gen_weight * gen_loss + self.disc_weight * disc_loss
[文档]class ErnieHealthPretrainingCriterion(paddle.nn.Layer): ''' Args: vocab_size(int): Vocabulary size of `inputs_ids` in `ElectraModel`. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `ElectraModel`. gen_weight(float): The weight of the Electra Generator. disc_weight(float): The weight of the Electra Discriminator. ''' def __init__(self, vocab_size, gen_weight): super(ErnieHealthPretrainingCriterion, self).__init__() self.vocab_size = vocab_size self.gen_weight = gen_weight self.rtd_weight = 50.0 self.mts_weight = 20.0 self.csp_weight = 1.0 self.gen_loss_fct = nn.CrossEntropyLoss(reduction='none') self.disc_rtd_loss_fct = nn.BCEWithLogitsLoss(reduction='none') self.disc_csp_loss_fct = nn.CrossEntropyLoss(reduction='none') self.disc_mts_loss_fct = nn.CrossEntropyLoss(reduction='none') self.temperature = 0.07
[文档] def forward(self, generator_logits, generator_labels, logits_rtd, logits_mts, logits_csp, discriminator_labels, attention_mask): """ Args: generator_logits(Tensor): The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length, vocab_size]. generator_labels(Tensor): The labels of the generator, its dimensionality is equal to `generator_prediction_scores`. Its data type should be int64 and its shape is [batch_size, sequence_size, 1]. logits_rtd(Tensor): The scores of masked token prediction. Its data type should be float32. and its shape is [batch_size, sequence_length] or [sequence length] if batch_size=1. discriminator_labels(Tensor): The labels of the discriminator, its dimensionality is equal to `discriminator_prediction_scores`. The labels should be numbers between 0 and 1. Its data type should be float32 and its shape is [batch_size, sequence_size] or [sequence length] if batch_size=1. attention_mask(Tensor): See :class:`ElectraModel`. Returns: Tensor: The pretraining loss, equals to weighted generator loss plus the weighted discriminator loss. Its data type should be float32 and its shape is [1]. """ # generator loss gen_loss = self.gen_loss_fct( paddle.reshape(generator_logits, [-1, self.vocab_size]), paddle.reshape(generator_labels, [-1])) # todo: we can remove 4 lines after when CrossEntropyLoss(reduction='mean') improved umask_positions = paddle.zeros_like(generator_labels).astype( paddle.get_default_dtype()) mask_positions = paddle.ones_like(generator_labels).astype( paddle.get_default_dtype()) mask_positions = paddle.where(generator_labels == -100, umask_positions, mask_positions) if mask_positions.sum() == 0: gen_loss = paddle.to_tensor([0.0]) else: gen_loss = gen_loss.sum() / mask_positions.sum() # RTD discriminator loss seq_length = discriminator_labels.shape[1] rtd_labels = discriminator_labels disc_rtd_loss = self.disc_rtd_loss_fct( paddle.reshape(logits_rtd, [-1, seq_length]), rtd_labels.astype(logits_rtd.dtype)) if attention_mask is not None: umask_positions = paddle.ones_like(rtd_labels).astype( paddle.get_default_dtype()) mask_positions = paddle.zeros_like(rtd_labels).astype( paddle.get_default_dtype()) umask_positions = paddle.where(attention_mask, umask_positions, mask_positions) # Mask has different meanings here. It denotes [mask] token in # generator and denotes [pad] token in discriminator. disc_rtd_loss = paddle.where(attention_mask, disc_rtd_loss, mask_positions) disc_rtd_loss = disc_rtd_loss.sum() / umask_positions.sum() else: total_positions = paddle.ones_like(rtd_labels).astype( paddle.get_default_dtype()) disc_rtd_loss = disc_rtd_loss.sum() / total_positions.sum() # MTS discriminator loss replaced_positions = discriminator_labels.astype("bool") mts_labels = paddle.zeros( [logits_mts.shape[0] * logits_mts.shape[1]], dtype=generator_labels.dtype).detach() disc_mts_loss = self.disc_mts_loss_fct( paddle.reshape(logits_mts, [-1, logits_mts.shape[-1]]), mts_labels) disc_mts_loss = paddle.reshape(disc_mts_loss, [-1, seq_length]) original_positions = paddle.zeros_like(replaced_positions).astype( paddle.get_default_dtype()) disc_mts_loss = paddle.where(replaced_positions, disc_mts_loss, original_positions) if discriminator_labels.sum() == 0: disc_mts_loss = paddle.to_tensor([0.0]) else: disc_mts_loss = disc_mts_loss.sum() / discriminator_labels.sum() # CSP discriminator loss logits_csp = F.normalize(logits_csp, axis=-1) # Gather from all devices (split first) logit_csp_0, logit_csp_1 = paddle.split( logits_csp, num_or_sections=2, axis=0) if paddle.distributed.get_world_size() > 1: csp_list_0, csp_list_1 = [], [] paddle.distributed.all_gather(csp_list_0, logit_csp_0) paddle.distributed.all_gather(csp_list_1, logit_csp_1) logit_csp_0 = paddle.concat(csp_list_0, axis=0) logit_csp_1 = paddle.concat(csp_list_1, axis=0) batch_size = logit_csp_0.shape[0] logits_csp = paddle.concat([logit_csp_0, logit_csp_1], axis=0) # Similarity matrix logits_csp = paddle.matmul(logits_csp, logits_csp, transpose_y=True) # Temperature scale logits_csp = logits_csp / self.temperature # Mask self-contrast mask = -1e4 * paddle.eye(logits_csp.shape[0]) logits_csp = logits_csp + mask # Create labels for bundle csp_labels = paddle.concat( [ paddle.arange(batch_size, 2 * batch_size), paddle.arange(batch_size) ], axis=0) # Calculate SimCLR loss disc_csp_loss = self.disc_csp_loss_fct(logits_csp, csp_labels) disc_csp_loss = disc_csp_loss.sum() / (batch_size * 2) loss = self.gen_weight * gen_loss + self.rtd_weight * disc_rtd_loss + \ self.mts_weight * disc_mts_loss + self.csp_weight * disc_csp_loss return loss, gen_loss, disc_rtd_loss, disc_mts_loss, disc_csp_loss
[文档]class ElectraForQuestionAnswering(ElectraPretrainedModel): """ Electra Model with a linear layer on top of the hidden-states output to compute `span_start_logits` and `span_end_logits`, designed for question-answering tasks like SQuAD. Args: electra (:class:`ElectraModel`): An instance of ElectraModel. """ def __init__(self, electra): super(ElectraForQuestionAnswering, self).__init__() self.electra = electra self.classifier = nn.Linear(self.electra.config["hidden_size"], 2) self.init_weights()
[文档] def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None): r""" The ElectraForQuestionAnswering forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`ElectraModel`. token_type_ids (Tensor, optional): See :class:`ElectraModel`. position_ids(Tensor, optional): See :class:`ElectraModel`. attention_mask (list, optional): See :class:`ElectraModel`. Returns: tuple: Returns tuple (`start_logits`, `end_logits`). With the fields: - `start_logits` (Tensor): A tensor of the input token classification logits, indicates the start position of the labelled span. Its data type should be float32 and its shape is [batch_size, sequence_length]. - `end_logits` (Tensor): A tensor of the input token classification logits, indicates the end position of the labelled span. Its data type should be float32 and its shape is [batch_size, sequence_length]. Example: .. code-block:: import paddle from paddlenlp.transformers import ElectraForQuestionAnswering, ElectraTokenizer tokenizer = ElectraTokenizer.from_pretrained('electra-small') model = ElectraForQuestionAnswering.from_pretrained('electra-small') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} outputs = model(**inputs) start_logits = outputs[0] end_logits = outputs[1] """ sequence_output = self.electra( input_ids, token_type_ids, position_ids=position_ids, attention_mask=attention_mask) logits = self.classifier(sequence_output) logits = paddle.transpose(logits, perm=[2, 0, 1]) start_logits, end_logits = paddle.unstack(x=logits, axis=0) return start_logits, end_logits
# ElectraForMaskedLM is the same as ElectraGenerator ElectraForMaskedLM = ElectraGenerator ElectraForPretraining = ElectraForTotalPretraining