paddlenlp.transformers.semantic_search.modeling 源代码

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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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import paddle
import paddle.nn as nn
import paddle.nn.functional as F

from ..ernie.modeling import ErniePretrainedModel

__all__ = ["ErnieDualEncoder", "ErnieCrossEncoder"]


class ErnieEncoder(ErniePretrainedModel):
    def __init__(self, ernie, dropout=None, output_emb_size=None, num_classes=2):
        super(ErnieEncoder, self).__init__()
        self.ernie = ernie  # allow ernie to be config
        self.dropout = nn.Dropout(dropout if dropout is not None else 0.1)
        self.classifier = nn.Linear(self.ernie.config["hidden_size"], num_classes)
        # Compatible to ERNIE-Search for adding extra linear layer
        self.output_emb_size = output_emb_size
        if output_emb_size is not None and output_emb_size > 0:
            weight_attr = paddle.ParamAttr(initializer=paddle.nn.initializer.TruncatedNormal(std=0.02))
            self.emb_reduce_linear = paddle.nn.Linear(
                self.ernie.config["hidden_size"], output_emb_size, weight_attr=weight_attr
            )
        self.apply(self.init_weights)

    def init_weights(self, layer):
        """Initialization hook"""
        if isinstance(layer, nn.LayerNorm):
            layer._epsilon = 1e-12

    def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None):
        sequence_output, pool_output = self.ernie(
            input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask
        )
        return sequence_output, pool_output


[文档]class ErnieDualEncoder(nn.Layer): """ This class encapsulates two ErnieEncoder models into one model, so query embedding and title embedding could be obtained using one model. And this class allows two ErnieEncoder models to be trained at the same time. Example: .. code-block:: import paddle from paddlenlp.transformers import ErnieDualEncoder, ErnieTokenizer model = ErnieDualEncoder("rocketqa-zh-dureader-query-encoder", "rocketqa-zh-dureader-para-encoder") tokenizer = ErnieTokenizer.from_pretrained("rocketqa-zh-dureader-query-encoder") inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} # Get query embedding query_embedding = model.get_pooled_embedding(**inputs) # Get title embedding title_embedding = model.get_pooled_embedding(**inputs, is_query=False) """ def __init__( self, query_model_name_or_path=None, title_model_name_or_path=None, share_parameters=False, output_emb_size=None, dropout=None, reinitialize=False, use_cross_batch=False, ): super().__init__() self.query_ernie, self.title_ernie = None, None self.use_cross_batch = use_cross_batch self.output_emb_size = output_emb_size if query_model_name_or_path is not None: self.query_ernie = ErnieEncoder.from_pretrained(query_model_name_or_path, output_emb_size=output_emb_size) if share_parameters: self.title_ernie = self.query_ernie elif title_model_name_or_path is not None: self.title_ernie = ErnieEncoder.from_pretrained(title_model_name_or_path, output_emb_size=output_emb_size) assert (self.query_ernie is not None) or ( self.title_ernie is not None ), "At least one of query_ernie and title_ernie should not be None" # Compatible to rocketv2 initialization for setting layer._epsilon to 1e-5 if reinitialize: self.apply(self.init_weights)
[文档] def init_weights(self, layer): """Initialization hook""" if isinstance(layer, nn.LayerNorm): layer._epsilon = 1e-5
def get_semantic_embedding(self, data_loader): self.eval() with paddle.no_grad(): for batch_data in data_loader: input_ids, token_type_ids = batch_data input_ids = paddle.to_tensor(input_ids) token_type_ids = paddle.to_tensor(token_type_ids) text_embeddings = self.get_pooled_embedding(input_ids, token_type_ids=token_type_ids) yield text_embeddings
[文档] def get_pooled_embedding( self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, is_query=True ): """Get the first feature of each sequence for classification""" assert (is_query and self.query_ernie is not None) or ( not is_query and self.title_ernie ), "Please check whether your parameter for `is_query` are consistent with DualEncoder initialization." if is_query: sequence_output, _ = self.query_ernie(input_ids, token_type_ids, position_ids, attention_mask) if self.output_emb_size is not None and self.output_emb_size > 0: cls_embedding = self.query_ernie.emb_reduce_linear(sequence_output[:, 0]) else: cls_embedding = sequence_output[:, 0] else: sequence_output, _ = self.title_ernie(input_ids, token_type_ids, position_ids, attention_mask) if self.output_emb_size is not None and self.output_emb_size > 0: cls_embedding = self.title_ernie.emb_reduce_linear(sequence_output[:, 0]) else: cls_embedding = sequence_output[:, 0] return cls_embedding
def cosine_sim( self, query_input_ids, title_input_ids, query_token_type_ids=None, query_position_ids=None, query_attention_mask=None, title_token_type_ids=None, title_position_ids=None, title_attention_mask=None, ): query_cls_embedding = self.get_pooled_embedding( query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask ) title_cls_embedding = self.get_pooled_embedding( title_input_ids, title_token_type_ids, title_position_ids, title_attention_mask, is_query=False ) cosine_sim = paddle.sum(query_cls_embedding * title_cls_embedding, axis=-1) return cosine_sim
[文档] def forward( self, query_input_ids, pos_title_input_ids, neg_title_input_ids, is_prediction=False, query_token_type_ids=None, query_position_ids=None, query_attention_mask=None, pos_title_token_type_ids=None, pos_title_position_ids=None, pos_title_attention_mask=None, neg_title_token_type_ids=None, neg_title_position_ids=None, neg_title_attention_mask=None, ): query_cls_embedding = self.get_pooled_embedding( query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask ) pos_title_cls_embedding = self.get_pooled_embedding( pos_title_input_ids, pos_title_token_type_ids, pos_title_position_ids, pos_title_attention_mask, is_query=False, ) neg_title_cls_embedding = self.get_pooled_embedding( neg_title_input_ids, neg_title_token_type_ids, neg_title_position_ids, neg_title_attention_mask, is_query=False, ) all_title_cls_embedding = paddle.concat(x=[pos_title_cls_embedding, neg_title_cls_embedding], axis=0) if is_prediction: logits = paddle.dot(query_cls_embedding, pos_title_cls_embedding) outputs = {"probs": logits, "q_rep": query_cls_embedding, "p_rep": pos_title_cls_embedding} return outputs if self.use_cross_batch: tensor_list = [] paddle.distributed.all_gather(tensor_list, all_title_cls_embedding) all_title_cls_embedding = paddle.concat(x=tensor_list, axis=0) logits = paddle.matmul(query_cls_embedding, all_title_cls_embedding, transpose_y=True) batch_size = query_cls_embedding.shape[0] labels = paddle.arange(batch_size * self.rank * 2, batch_size * (self.rank * 2 + 1), dtype="int64") labels = paddle.reshape(labels, shape=[-1, 1]) accuracy = paddle.metric.accuracy(input=logits, label=labels) loss = F.cross_entropy(input=logits, label=labels) outputs = {"loss": loss, "accuracy": accuracy} return outputs
[文档]class ErnieCrossEncoder(nn.Layer): """ Example: .. code-block:: import paddle from paddlenlp.transformers import ErnieCrossEncoder, ErnieTokenizer model = ErnieCrossEncoder("rocketqa-zh-dureader-cross-encoder") tokenizer = ErnieTokenizer.from_pretrained("rocketqa-zh-dureader-cross-encoder") inputs = tokenizer("你们好", text_pair="你好") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} # Get embedding of text pair. embedding = model.matching(**inputs) """ def __init__(self, pretrain_model_name_or_path, num_classes=2, reinitialize=False, dropout=None): super().__init__() self.ernie = ErnieEncoder.from_pretrained(pretrain_model_name_or_path, num_classes=num_classes) # Compatible to rocketv2 initialization for setting layer._epsilon to 1e-5 if reinitialize: self.apply(self.init_weights)
[文档] def init_weights(self, layer): """Initialization hook""" if isinstance(layer, nn.LayerNorm): layer._epsilon = 1e-5
[文档] def matching( self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, return_prob_distributation=False ): """Use the pooled_output as the feature for pointwise prediction, eg. RocketQAv1""" _, pooled_output = self.ernie( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask ) pooled_output = self.ernie.dropout(pooled_output) cls_embedding = self.ernie.classifier(pooled_output) probs = F.softmax(cls_embedding, axis=1) if return_prob_distributation: return probs return probs[:, 1]
[文档] def matching_v2(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None): """Use the cls token embedding as the feature for listwise prediction, eg. RocketQAv2""" sequence_output, _ = self.ernie( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask ) pooled_output = self.ernie.dropout(sequence_output[:, 0]) probs = self.ernie.classifier(pooled_output) return probs
[文档] def matching_v3(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None): """Use the pooled_output as the feature for listwise prediction, eg. ERNIE-Search""" sequence_output, pooled_output = self.ernie( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask ) pooled_output = self.ernie.dropout(pooled_output) probs = self.ernie.classifier(pooled_output) return probs
[文档] def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, labels=None): probs = self.matching( input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask, return_prob_distributation=True, ) if labels is not None: accuracy = paddle.metric.accuracy(input=probs, label=labels) loss = F.cross_entropy(input=probs, label=labels) outputs = {"loss": loss, "accuracy": accuracy} return outputs else: return probs[:, 1]