paddlenlp.transformers.ernie.modeling 源代码

# Copyright (c) 2020 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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import paddle
import paddle.nn as nn
import paddle.nn.functional as F

from .. import PretrainedModel, register_base_model

__all__ = [
    'ErnieModel', 'ErniePretrainedModel', 'ErnieForSequenceClassification',
    'ErnieForTokenClassification', 'ErnieForQuestionAnswering',
    'ErnieForPretraining', 'ErniePretrainingCriterion', 'ErnieForMaskedLM',
    'ErnieForMultipleChoice'
]


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

    def __init__(self,
                 vocab_size,
                 hidden_size=768,
                 hidden_dropout_prob=0.1,
                 max_position_embeddings=512,
                 type_vocab_size=2,
                 pad_token_id=0,
                 weight_attr=None,
                 task_type_vocab_size=3,
                 task_id=0,
                 use_task_id=False):
        super(ErnieEmbeddings, self).__init__()

        self.word_embeddings = nn.Embedding(
            vocab_size,
            hidden_size,
            padding_idx=pad_token_id,
            weight_attr=weight_attr)
        self.position_embeddings = nn.Embedding(
            max_position_embeddings, hidden_size, weight_attr=weight_attr)
        self.token_type_embeddings = nn.Embedding(
            type_vocab_size, hidden_size, weight_attr=weight_attr)
        self.use_task_id = use_task_id
        self.task_id = task_id
        if self.use_task_id:
            self.task_type_embeddings = nn.Embedding(
                task_type_vocab_size, hidden_size, weight_attr=weight_attr)
        self.layer_norm = nn.LayerNorm(hidden_size)
        self.dropout = nn.Dropout(hidden_dropout_prob)

    def forward(self,
                input_ids,
                token_type_ids=None,
                position_ids=None,
                task_type_ids=None):
        if position_ids is None:
            # maybe need use shape op to unify static graph and dynamic graph
            #seq_length = input_ids.shape[1]
            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
        if token_type_ids is None:
            token_type_ids = paddle.zeros_like(input_ids, dtype="int64")
        input_embedings = self.word_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)

        embeddings = input_embedings + position_embeddings + token_type_embeddings
        if self.use_task_id:
            if task_type_ids is None:
                task_type_ids = paddle.ones_like(
                    input_ids, dtype="int64") * self.task_id
            task_type_embeddings = self.task_type_embeddings(task_type_ids)
            embeddings = embeddings + task_type_embeddings
        embeddings = self.layer_norm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings


class ErniePooler(nn.Layer):
    def __init__(self, hidden_size, weight_attr=None):
        super(ErniePooler, self).__init__()
        self.dense = nn.Linear(
            hidden_size, hidden_size, weight_attr=weight_attr)
        self.activation = nn.Tanh()

    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 ErniePretrainedModel(PretrainedModel): r""" An abstract class for pretrained ERNIE models. It provides ERNIE related `model_config_file`, `pretrained_init_configuration`, `resource_files_names`, `pretrained_resource_files_map`, `base_model_prefix` for downloading and loading pretrained models. Refer to :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details. """ model_config_file = "model_config.json" pretrained_init_configuration = { # Deprecated, alias for ernie-1.0-base-zh "ernie-1.0": { "attention_probs_dropout_prob": 0.1, "hidden_act": "relu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "max_position_embeddings": 513, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 2, "vocab_size": 18000, "pad_token_id": 0, }, "ernie-1.0-base-zh": { "attention_probs_dropout_prob": 0.1, "hidden_act": "relu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "max_position_embeddings": 513, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 2, "vocab_size": 18000, "pad_token_id": 0, }, "ernie-tiny": { "attention_probs_dropout_prob": 0.1, "hidden_act": "relu", "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "max_position_embeddings": 600, "num_attention_heads": 16, "num_hidden_layers": 3, "type_vocab_size": 2, "vocab_size": 50006, "pad_token_id": 0, }, "ernie-2.0-en": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 4, "vocab_size": 30522, "pad_token_id": 0, }, "ernie-2.0-en-finetuned-squad": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 4, "vocab_size": 30522, "pad_token_id": 0, }, "ernie-2.0-large-en": { "attention_probs_dropout_prob": 0.1, "intermediate_size": 4096, # special for ernie-2.0-large-en "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "max_position_embeddings": 512, "num_attention_heads": 16, "num_hidden_layers": 24, "type_vocab_size": 4, "vocab_size": 30522, "pad_token_id": 0, }, "rocketqa-zh-dureader-query-encoder": { "attention_probs_dropout_prob": 0.1, "hidden_act": "relu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "max_position_embeddings": 513, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 2, "vocab_size": 18000, "pad_token_id": 0, }, "rocketqa-zh-dureader-para-encoder": { "attention_probs_dropout_prob": 0.1, "hidden_act": "relu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "max_position_embeddings": 513, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 2, "vocab_size": 18000, "pad_token_id": 0, }, "rocketqa-v1-marco-query-encoder": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 4, "vocab_size": 30522, "pad_token_id": 0, }, "rocketqa-v1-marco-para-encoder": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 4, "vocab_size": 30522, "pad_token_id": 0, }, "rocketqa-zh-dureader-cross-encoder": { "attention_probs_dropout_prob": 0.1, "hidden_act": "relu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "max_position_embeddings": 513, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 2, "vocab_size": 18000, "pad_token_id": 0, }, "rocketqa-v1-marco-cross-encoder": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "max_position_embeddings": 512, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 4, "vocab_size": 30522, "pad_token_id": 0, }, "ernie-3.0-xbase-zh": { "attention_probs_dropout_prob": 0.0, "hidden_act": "gelu", "hidden_dropout_prob": 0.0, "hidden_size": 1024, "initializer_range": 0.02, "max_position_embeddings": 2048, "num_attention_heads": 16, "num_hidden_layers": 20, "task_type_vocab_size": 16, "type_vocab_size": 4, "use_task_id": True, "vocab_size": 40000 }, "ernie-3.0-base-zh": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "max_position_embeddings": 2048, "num_attention_heads": 12, "num_hidden_layers": 12, "task_type_vocab_size": 3, "type_vocab_size": 4, "use_task_id": True, "vocab_size": 40000 }, "ernie-3.0-medium-zh": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 768, "intermediate_size": 3072, "initializer_range": 0.02, "max_position_embeddings": 2048, "num_attention_heads": 12, "num_hidden_layers": 6, "task_type_vocab_size": 16, "type_vocab_size": 4, "use_task_id": True, "vocab_size": 40000 }, "ernie-3.0-mini-zh": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 384, "intermediate_size": 1536, "initializer_range": 0.02, "max_position_embeddings": 2048, "num_attention_heads": 12, "num_hidden_layers": 6, "task_type_vocab_size": 16, "type_vocab_size": 4, "use_task_id": True, "vocab_size": 40000 }, "ernie-3.0-micro-zh": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 384, "intermediate_size": 1536, "initializer_range": 0.02, "max_position_embeddings": 2048, "num_attention_heads": 12, "num_hidden_layers": 4, "task_type_vocab_size": 16, "type_vocab_size": 4, "use_task_id": True, "vocab_size": 40000 }, "ernie-3.0-nano-zh": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "hidden_dropout_prob": 0.1, "hidden_size": 312, "intermediate_size": 1248, "initializer_range": 0.02, "max_position_embeddings": 2048, "num_attention_heads": 12, "num_hidden_layers": 4, "task_type_vocab_size": 16, "type_vocab_size": 4, "use_task_id": True, "vocab_size": 40000 }, } resource_files_names = {"model_state": "model_state.pdparams"} pretrained_resource_files_map = { "model_state": { # Deprecated, alias for ernie-1.0-base-zh "ernie-1.0": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie/ernie_v1_chn_base.pdparams", "ernie-1.0-base-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie/ernie_v1_chn_base.pdparams", "ernie-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_tiny/ernie_tiny.pdparams", "ernie-2.0-en": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_base/ernie_v2_eng_base.pdparams", "ernie-2.0-en-finetuned-squad": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_base/ernie_v2_eng_base_finetuned_squad.pdparams", "ernie-2.0-large-en": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_v2_large/ernie_v2_eng_large.pdparams", "rocketqa-zh-dureader-query-encoder": "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa_zh_dureader_query_encoder.pdparams", "rocketqa-zh-dureader-para-encoder": "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa_zh_dureader_para_encoder.pdparams", "rocketqa-v1-marco-query-encoder": "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa_v1_marco_query_encoder.pdparams", "rocketqa-v1-marco-para-encoder": "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa_v1_marco_para_encoder.pdparams", "rocketqa-zh-dureader-cross-encoder": "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa_zh_dureader_cross_encoder.pdparams", "rocketqa-v1-marco-cross-encoder": "https://bj.bcebos.com/paddlenlp/models/transformers/rocketqa/rocketqa_v1_marco_cross_encoder.pdparams", "ernie-3.0-base-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_base_zh.pdparams", "ernie-3.0-xbase-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_xbase_zh.pdparams", "ernie-3.0-medium-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_medium_zh.pdparams", "ernie-3.0-mini-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_mini_zh.pdparams", "ernie-3.0-micro-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_micro_zh.pdparams", "ernie-3.0-nano-zh": "https://bj.bcebos.com/paddlenlp/models/transformers/ernie_3.0/ernie_3.0_nano_zh.pdparams", } } base_model_prefix = "ernie"
[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.ernie.config["initializer_range"], shape=layer.weight.shape)) elif isinstance(layer, nn.LayerNorm): layer._epsilon = 1e-12
[文档]@register_base_model class ErnieModel(ErniePretrainedModel): r""" The bare ERNIE 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 `ErnieModel`. 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 `ErnieModel`. hidden_size (int, optional): Dimensionality of the embedding layer, encoder layers and 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, which dictates the maximum supported length of an input sequence. Defaults to `512`. type_vocab_size (int, optional): The vocabulary size of the `token_type_ids`. Defaults to `2`. initializer_range (float, optional): The standard deviation of the normal initializer for initializing all weight matrices. Defaults to `0.02`. .. note:: A normal_initializer initializes weight matrices as normal distributions. See :meth:`ErniePretrainedModel._init_weights()` for how weights are initialized in `ErnieModel`. pad_token_id(int, optional): The index of padding token in the token vocabulary. Defaults to `0`. """ def __init__(self, vocab_size, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, pad_token_id=0, task_type_vocab_size=3, task_id=0, use_task_id=False): super(ErnieModel, self).__init__() self.pad_token_id = pad_token_id self.initializer_range = initializer_range weight_attr = paddle.ParamAttr( initializer=nn.initializer.TruncatedNormal( mean=0.0, std=self.initializer_range)) self.embeddings = ErnieEmbeddings( vocab_size, hidden_size, hidden_dropout_prob, max_position_embeddings, type_vocab_size, pad_token_id, weight_attr, task_type_vocab_size, task_id, use_task_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, weight_attr=weight_attr, normalize_before=False) self.encoder = nn.TransformerEncoder(encoder_layer, num_hidden_layers) self.pooler = ErniePooler(hidden_size, weight_attr) self.apply(self.init_weights)
[文档] def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, task_type_ids=None): r""" Args: input_ids (Tensor): Indices of input sequence tokens in the vocabulary. They are numerical representations of tokens that build the input sequence. It's data type should be `int64` and 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]`. 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]. We use whole-word-mask in ERNIE, so the whole word will have the same value. For example, "使用" as a word, "使" and "用" will have the same value. Defaults to `None`, which means nothing needed to be prevented attention to. Returns: 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 ErnieModel, ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') model = ErnieModel.from_pretrained('ernie-1.0') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} sequence_output, pooled_output = model(**inputs) """ if attention_mask is None: attention_mask = paddle.unsqueeze( (input_ids == self.pad_token_id ).astype(self.pooler.dense.weight.dtype) * -1e4, axis=[1, 2]) # 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, task_type_ids=task_type_ids) encoder_outputs = self.encoder(embedding_output, attention_mask) sequence_output = encoder_outputs pooled_output = self.pooler(sequence_output) return sequence_output, pooled_output
[文档]class ErnieForSequenceClassification(ErniePretrainedModel): r""" Ernie Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks. Args: ernie (ErnieModel): An instance of `paddlenlp.transformers.ErnieModel`. num_classes (int, optional): The number of classes. Default to `2`. dropout (float, optional): The dropout probability for output of ERNIE. If None, use the same value as `hidden_dropout_prob` of `paddlenlp.transformers.ErnieModel` instance. Defaults to `None`. """ def __init__(self, ernie, num_classes=2, dropout=None): super(ErnieForSequenceClassification, self).__init__() self.num_classes = num_classes self.ernie = ernie # allow ernie to be config self.dropout = nn.Dropout(dropout if dropout is not None else self.ernie.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.ernie.config["hidden_size"], num_classes) self.apply(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:`ErnieModel`. token_type_ids (Tensor, optional): See :class:`ErnieModel`. position_ids (Tensor, optional): See :class:`ErnieModel`. attention_mask (Tensor, optional): See :class:`ErnieModel`. 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 ErnieForSequenceClassification, ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') model = ErnieForSequenceClassification.from_pretrained('ernie-1.0') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs) """ _, pooled_output = self.ernie( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask) pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) return logits
[文档]class ErnieForQuestionAnswering(ErniePretrainedModel): """ Ernie 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: ernie (`ErnieModel`): An instance of `ErnieModel`. """ def __init__(self, ernie): super(ErnieForQuestionAnswering, self).__init__() self.ernie = ernie # allow ernie to be config self.classifier = nn.Linear(self.ernie.config["hidden_size"], 2) self.apply(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:`ErnieModel`. token_type_ids (Tensor, optional): See :class:`ErnieModel`. position_ids (Tensor, optional): See :class:`ErnieModel`. attention_mask (Tensor, optional): See :class:`ErnieModel`. 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 ErnieForQuestionAnswering, ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') model = ErnieForQuestionAnswering.from_pretrained('ernie-1.0') 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.ernie( input_ids, token_type_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
[文档]class ErnieForTokenClassification(ErniePretrainedModel): r""" ERNIE Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks. Args: ernie (`ErnieModel`): An instance of `ErnieModel`. num_classes (int, optional): The number of classes. Defaults to `2`. dropout (float, optional): The dropout probability for output of ERNIE. If None, use the same value as `hidden_dropout_prob` of `ErnieModel` instance `ernie`. Defaults to `None`. """ def __init__(self, ernie, num_classes=2, dropout=None): super(ErnieForTokenClassification, self).__init__() self.num_classes = num_classes self.ernie = ernie # allow ernie to be config self.dropout = nn.Dropout(dropout if dropout is not None else self.ernie.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.ernie.config["hidden_size"], num_classes) self.apply(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:`ErnieModel`. token_type_ids (Tensor, optional): See :class:`ErnieModel`. position_ids (Tensor, optional): See :class:`ErnieModel`. attention_mask (Tensor, optional): See :class:`ErnieModel`. 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 ErnieForTokenClassification, ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') model = ErnieForTokenClassification.from_pretrained('ernie-1.0') 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.ernie( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask) sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) return logits
class ErnieLMPredictionHead(nn.Layer): r""" Ernie Model with a `language modeling` head on top. """ def __init__( self, hidden_size, vocab_size, activation, embedding_weights=None, weight_attr=None, ): super(ErnieLMPredictionHead, self).__init__() self.transform = nn.Linear( hidden_size, hidden_size, weight_attr=weight_attr) self.activation = getattr(nn.functional, activation) self.layer_norm = nn.LayerNorm(hidden_size) self.decoder_weight = self.create_parameter( shape=[vocab_size, hidden_size], dtype=self.transform.weight.dtype, attr=weight_attr, is_bias=False) if embedding_weights is None else embedding_weights self.decoder_bias = self.create_parameter( shape=[vocab_size], dtype=self.decoder_weight.dtype, is_bias=True) def forward(self, hidden_states, masked_positions=None): if masked_positions is not None: hidden_states = paddle.reshape(hidden_states, [-1, hidden_states.shape[-1]]) hidden_states = paddle.tensor.gather(hidden_states, masked_positions) # gather masked tokens might be more quick hidden_states = self.transform(hidden_states) hidden_states = self.activation(hidden_states) hidden_states = self.layer_norm(hidden_states) hidden_states = paddle.tensor.matmul( hidden_states, self.decoder_weight, transpose_y=True) + self.decoder_bias return hidden_states class ErniePretrainingHeads(nn.Layer): def __init__( self, hidden_size, vocab_size, activation, embedding_weights=None, weight_attr=None, ): super(ErniePretrainingHeads, self).__init__() self.predictions = ErnieLMPredictionHead( hidden_size, vocab_size, activation, embedding_weights, weight_attr) self.seq_relationship = nn.Linear( hidden_size, 2, weight_attr=weight_attr) def forward(self, sequence_output, pooled_output, masked_positions=None): prediction_scores = self.predictions(sequence_output, masked_positions) seq_relationship_score = self.seq_relationship(pooled_output) return prediction_scores, seq_relationship_score
[文档]class ErnieForPretraining(ErniePretrainedModel): r""" Ernie Model with a `masked language modeling` head and a `sentence order prediction` head on top. """ def __init__(self, ernie): super(ErnieForPretraining, self).__init__() self.ernie = ernie weight_attr = paddle.ParamAttr( initializer=nn.initializer.TruncatedNormal( mean=0.0, std=self.ernie.initializer_range)) self.cls = ErniePretrainingHeads( self.ernie.config["hidden_size"], self.ernie.config["vocab_size"], self.ernie.config["hidden_act"], embedding_weights=self.ernie.embeddings.word_embeddings.weight, weight_attr=weight_attr, ) self.apply(self.init_weights)
[文档] def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, masked_positions=None): r""" Args: input_ids (Tensor): See :class:`ErnieModel`. token_type_ids (Tensor, optional): See :class:`ErnieModel`. position_ids (Tensor, optional): See :class:`ErnieModel`. attention_mask (Tensor, optional): See :class:`ErnieModel`. Returns: tuple: Returns tuple (``prediction_scores``, ``seq_relationship_score``). With the fields: - `prediction_scores` (Tensor): The scores of masked token prediction. Its data type should be float32. If `masked_positions` is None, its shape is [batch_size, sequence_length, vocab_size]. Otherwise, its shape is [batch_size, mask_token_num, vocab_size]. - `seq_relationship_score` (Tensor): The scores of next sentence prediction. Its data type should be float32 and its shape is [batch_size, 2]. """ with paddle.static.amp.fp16_guard(): outputs = self.ernie( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask) sequence_output, pooled_output = outputs[:2] prediction_scores, seq_relationship_score = self.cls( sequence_output, pooled_output, masked_positions) return prediction_scores, seq_relationship_score
[文档]class ErniePretrainingCriterion(paddle.nn.Layer): r""" The loss output of Ernie Model during the pretraining: a `masked language modeling` head and a `next sentence prediction (classification)` head. """ def __init__(self, with_nsp_loss=True): super(ErniePretrainingCriterion, self).__init__() self.with_nsp_loss = with_nsp_loss #self.loss_fn = paddle.nn.loss.CrossEntropyLoss(ignore_index=-1)
[文档] def forward(self, prediction_scores, seq_relationship_score, masked_lm_labels, next_sentence_labels=None): """ Args: prediction_scores(Tensor): The scores of masked token prediction. Its data type should be float32. If `masked_positions` is None, its shape is [batch_size, sequence_length, vocab_size]. Otherwise, its shape is [batch_size, mask_token_num, vocab_size] seq_relationship_score(Tensor): The scores of next sentence prediction. Its data type should be float32 and its shape is [batch_size, 2] masked_lm_labels(Tensor): The labels of the masked language modeling, its dimensionality is equal to `prediction_scores`. Its data type should be int64. If `masked_positions` is None, its shape is [batch_size, sequence_length, 1]. Otherwise, its shape is [batch_size, mask_token_num, 1] next_sentence_labels(Tensor): The labels of the next sentence prediction task, the dimensionality of `next_sentence_labels` is equal to `seq_relation_labels`. Its data type should be int64 and its shape is [batch_size, 1] Returns: Tensor: The pretraining loss, equals to the sum of `masked_lm_loss` plus the mean of `next_sentence_loss`. Its data type should be float32 and its shape is [1]. """ with paddle.static.amp.fp16_guard(): masked_lm_loss = F.cross_entropy( prediction_scores, masked_lm_labels, ignore_index=-1, reduction='none') if not self.with_nsp_loss: return paddle.mean(masked_lm_loss) next_sentence_loss = F.cross_entropy( seq_relationship_score, next_sentence_labels, reduction='none') return paddle.mean(masked_lm_loss), paddle.mean(next_sentence_loss)
class ErnieOnlyMLMHead(nn.Layer): def __init__(self, hidden_size, vocab_size, activation, embedding_weights): super().__init__() self.predictions = ErnieLMPredictionHead( hidden_size=hidden_size, vocab_size=vocab_size, activation=activation, embedding_weights=embedding_weights) def forward(self, sequence_output, masked_positions=None): prediction_scores = self.predictions(sequence_output, masked_positions) return prediction_scores
[文档]class ErnieForMaskedLM(ErniePretrainedModel): """ Ernie Model with a `masked language modeling` head on top. Args: ernie (:class:`ErnieModel`): An instance of :class:`ErnieModel`. """ def __init__(self, ernie): super(ErnieForMaskedLM, self).__init__() self.ernie = ernie self.cls = ErnieOnlyMLMHead( self.ernie.config["hidden_size"], self.ernie.config["vocab_size"], self.ernie.config["hidden_act"], embedding_weights=self.ernie.embeddings.word_embeddings.weight) self.apply(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:`ErnieModel`. token_type_ids (Tensor, optional): See :class:`ErnieModel`. position_ids (Tensor, optional): See :class:`ErnieModel`. attention_mask (Tensor, optional): See :class:`ErnieModel`. Returns: Tensor: Returns tensor `prediction_scores`, The scores of masked token prediction. Its data type should be float32 and shape is [batch_size, sequence_length, vocab_size]. Example: .. code-block:: import paddle from paddlenlp.transformers import ErnieForMaskedLM, ErnieTokenizer tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0') model = ErnieForMaskedLM.from_pretrained('ernie-1.0') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs) print(logits.shape) # [1, 17, 18000] """ outputs = self.ernie( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output, masked_positions=None) return prediction_scores
[文档]class ErnieForMultipleChoice(ErniePretrainedModel): """ Ernie Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks. Args: ernie (:class:`ErnieModel`): An instance of ErnieModel. num_choices (int, optional): The number of choices. Defaults to `2`. dropout (float, optional): The dropout probability for output of Ernie. If None, use the same value as `hidden_dropout_prob` of `ErnieModel` instance `ernie`. Defaults to None. """ def __init__(self, ernie, num_choices=2, dropout=None): super(ErnieForMultipleChoice, self).__init__() self.num_choices = num_choices self.ernie = ernie self.dropout = nn.Dropout(dropout if dropout is not None else self.ernie.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.ernie.config["hidden_size"], 1) self.apply(self.init_weights)
[文档] def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None): r""" The ErnieForMultipleChoice forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`ErnieModel` and shape as [batch_size, num_choice, sequence_length]. token_type_ids(Tensor, optional): See :class:`ErnieModel` and shape as [batch_size, num_choice, sequence_length]. position_ids(Tensor, optional): See :class:`ErnieModel` and shape as [batch_size, num_choice, sequence_length]. attention_mask (list, optional): See :class:`ErnieModel` 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`. """ # input_ids: [bs, num_choice, seq_l] input_ids = input_ids.reshape(shape=( -1, input_ids.shape[-1])) # flat_input_ids: [bs*num_choice,seq_l] if position_ids is not None: position_ids = position_ids.reshape(shape=(-1, position_ids.shape[-1])) if token_type_ids is not None: token_type_ids = token_type_ids.reshape(shape=( -1, token_type_ids.shape[-1])) if attention_mask is not None: attention_mask = attention_mask.reshape( shape=(-1, attention_mask.shape[-1])) _, pooled_output = self.ernie( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask) pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) # logits: (bs*num_choice,1) reshaped_logits = logits.reshape( shape=(-1, self.num_choices)) # logits: (bs, num_choice) return reshaped_logits