paddlenlp.transformers.luke.modeling 源代码

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2021 The HuggingFace Inc. team.
#
# 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
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# 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.nn as nn
import paddle
import paddle.nn.functional as F

from .. import PretrainedModel, register_base_model
from ...transformers.roberta.modeling import RobertaEmbeddings

import math

__all__ = [
    "LukeModel",
    "LukePretrainedModel",
    "LukeForEntitySpanClassification",
    "LukeForEntityPairClassification",
    "LukeForEntityClassification",
    "LukeForMaskedLM",
    "LukeForQuestionAnswering",
]


def paddle_gather(x, dim, index):
    index_shape = index.shape
    index_flatten = index.flatten()
    if dim < 0:
        dim = len(x.shape) + dim
    nd_index = []
    for k in range(len(x.shape)):
        if k == dim:
            nd_index.append(index_flatten)
        else:
            reshape_shape = [1] * len(x.shape)
            reshape_shape[k] = x.shape[k]
            x_arange = paddle.arange(x.shape[k], dtype=index.dtype)
            x_arange = x_arange.reshape(reshape_shape)
            dim_index = paddle.expand(x_arange, index_shape).flatten()
            nd_index.append(dim_index)
    ind2 = paddle.transpose(paddle.stack(nd_index), [1, 0]).astype("int64")
    paddle_out = paddle.gather_nd(x, ind2).reshape(index_shape)
    return paddle_out


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)


def gelu_new(x):
    """
    Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT). Also see
    the Gaussian Error Linear Units paper: https://arxiv.org/abs/1606.08415
    """
    return F.gelu(x, approximate=True)


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

layer_norm_eps = 1e-6


[文档]class LukePretrainedModel(PretrainedModel): r""" An abstract class for pretrained Luke models. It provides Luke related `model_config_file`, `pretrained_init_configuration`, `resource_files_names`, `pretrained_resource_files_map`, `base_model_prefix` for downloading and loading pretrained models. See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details. """ pretrained_init_configuration = { "luke-base": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "pad_token_id": 1, "hidden_dropout_prob": 0.1, "hidden_size": 768, "initializer_range": 0.02, "intermediate_size": 3072, "max_position_embeddings": 514, "num_attention_heads": 12, "num_hidden_layers": 12, "type_vocab_size": 1, "vocab_size": 50267, }, "luke-large": { "attention_probs_dropout_prob": 0.1, "hidden_act": "gelu", "pad_token_id": 1, "hidden_dropout_prob": 0.1, "hidden_size": 1024, "initializer_range": 0.02, "intermediate_size": 4096, "max_position_embeddings": 514, "num_attention_heads": 16, "num_hidden_layers": 24, "type_vocab_size": 1, "vocab_size": 50267, }, } pretrained_resource_files_map = { "model_state": { "luke-base": "https://bj.bcebos.com/paddlenlp/models/transformers/luke/luke-base/model_state.pdparams", "luke-large": "https://bj.bcebos.com/paddlenlp/models/transformers/luke/luke-large/model_state.pdparams", } } base_model_prefix = "luke"
[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 layer.weight.set_value( paddle.tensor.normal( mean=0.0, std=self.initializer_range if hasattr(self, "initializer_range") else self.luke.config["initializer_range"], shape=layer.weight.shape, ) ) elif isinstance(layer, nn.LayerNorm): layer._epsilon = layer_norm_eps
class LukeSelfOutput(nn.Layer): def __init__(self, hidden_size, hidden_dropout_prob): super(LukeSelfOutput, self).__init__() self.dense = nn.Linear(hidden_size, hidden_size) self.layer_norm = nn.LayerNorm(hidden_size, epsilon=layer_norm_eps) self.dropout = nn.Dropout(hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.layer_norm(hidden_states + input_tensor) return hidden_states class LukeIntermediate(nn.Layer): def __init__(self, hidden_size, hidden_act, intermediate_size): super().__init__() self.dense = nn.Linear(hidden_size, intermediate_size) self.intermediate_act_fn = get_activation(hidden_act) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.intermediate_act_fn(hidden_states) return hidden_states class LukeOutput(nn.Layer): def __init__(self, intermediate_size, hidden_size, hidden_dropout_prob): super(LukeOutput, self).__init__() self.dense = nn.Linear(intermediate_size, hidden_size) self.layer_norm = nn.LayerNorm(hidden_size, epsilon=layer_norm_eps) self.dropout = nn.Dropout(hidden_dropout_prob) def forward(self, hidden_states, input_tensor): hidden_states = self.dense(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.layer_norm(hidden_states + input_tensor) return hidden_states class LukeEmbeddings(RobertaEmbeddings): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ def __init__( self, vocab_size=50267, hidden_size=768, max_position_embeddings=514, type_vocab_size=1, pad_token_id=0, hidden_dropout_prob=0.1, ): super(LukeEmbeddings, self).__init__( vocab_size=vocab_size, hidden_size=hidden_size, hidden_dropout_prob=hidden_dropout_prob, max_position_embeddings=max_position_embeddings, type_vocab_size=type_vocab_size, pad_token_id=pad_token_id, ) def forward( self, input_ids=None, token_type_ids=None, position_ids=None, ): return super(LukeEmbeddings, self).forward( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids ) class LukePooler(nn.Layer): def __init__(self, hidden_size): super(LukePooler, self).__init__() self.dense = nn.Linear(hidden_size, hidden_size) 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 class EntityEmbeddings(nn.Layer): def __init__( self, entity_vocab_size=500000, entity_emb_size=256, hidden_size=768, max_position_embeddings=514, type_vocab_size=1, hidden_dropout_prob=0.1, ): super(EntityEmbeddings, self).__init__() self.entity_emb_size = entity_emb_size self.hidden_size = hidden_size self.entity_embeddings = nn.Embedding(entity_vocab_size, entity_emb_size, padding_idx=0) if entity_emb_size != hidden_size: self.entity_embedding_dense = nn.Linear(entity_emb_size, hidden_size, bias_attr=False) self.position_embeddings = nn.Embedding(max_position_embeddings, hidden_size) self.token_type_embeddings = nn.Embedding(type_vocab_size, hidden_size) self.layer_norm = nn.LayerNorm(hidden_size, epsilon=layer_norm_eps) self.dropout = nn.Dropout(hidden_dropout_prob) def forward(self, entity_ids, position_ids, token_type_ids=None): if token_type_ids is None: token_type_ids = paddle.zeros_like(entity_ids) entity_embeddings = self.entity_embeddings(entity_ids) if self.entity_emb_size != self.hidden_size: entity_embeddings = self.entity_embedding_dense(entity_embeddings) position_embeddings = self.position_embeddings(position_ids.clip(min=0)) position_embedding_mask = (position_ids != -1).astype(position_embeddings.dtype).unsqueeze(-1) position_embeddings = position_embeddings * position_embedding_mask position_embeddings = paddle.sum(position_embeddings, axis=-2) position_embeddings = position_embeddings / position_embedding_mask.sum(axis=-2).clip(min=1e-7) token_type_embeddings = self.token_type_embeddings(token_type_ids) embeddings = entity_embeddings + position_embeddings + token_type_embeddings embeddings = self.layer_norm(embeddings) embeddings = self.dropout(embeddings) return embeddings class LukeSelfAttention(nn.Layer): def __init__(self, num_attention_heads, hidden_size, attention_probs_dropout_prob): super(LukeSelfAttention, self).__init__() self.num_attention_heads = num_attention_heads self.attention_head_size = int(hidden_size / num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(hidden_size, self.all_head_size) self.key = nn.Linear(hidden_size, self.all_head_size) self.value = nn.Linear(hidden_size, self.all_head_size) self.w2e_query = nn.Linear(hidden_size, self.all_head_size) self.e2w_query = nn.Linear(hidden_size, self.all_head_size) self.e2e_query = nn.Linear(hidden_size, self.all_head_size) self.dropout = nn.Dropout(attention_probs_dropout_prob) def transpose_for_scores(self, x): new_x_shape = x.shape[:-1] + [self.num_attention_heads, self.attention_head_size] x = x.reshape(new_x_shape) return x.transpose((0, 2, 1, 3)) def forward( self, word_hidden_states, entity_hidden_states, attention_mask=None, ): word_size = word_hidden_states.shape[1] if entity_hidden_states is None: concat_hidden_states = word_hidden_states else: concat_hidden_states = paddle.concat([word_hidden_states, entity_hidden_states], axis=1) key_layer = self.transpose_for_scores(self.key(concat_hidden_states)) value_layer = self.transpose_for_scores(self.value(concat_hidden_states)) if entity_hidden_states is not None: # compute query vectors using word-word (w2w), word-entity (w2e), entity-word (e2w), entity-entity (e2e) # query layers w2w_query_layer = self.transpose_for_scores(self.query(word_hidden_states)) w2e_query_layer = self.transpose_for_scores(self.w2e_query(word_hidden_states)) e2w_query_layer = self.transpose_for_scores(self.e2w_query(entity_hidden_states)) e2e_query_layer = self.transpose_for_scores(self.e2e_query(entity_hidden_states)) # compute w2w, w2e, e2w, and e2e key vectors used with the query vectors computed above w2w_key_layer = key_layer[:, :, :word_size, :] e2w_key_layer = key_layer[:, :, :word_size, :] w2e_key_layer = key_layer[:, :, word_size:, :] e2e_key_layer = key_layer[:, :, word_size:, :] # compute attention scores based on the dot product between the query and key vectors w2w_attention_scores = paddle.matmul(w2w_query_layer, w2w_key_layer.transpose((0, 1, 3, 2))) w2e_attention_scores = paddle.matmul(w2e_query_layer, w2e_key_layer.transpose((0, 1, 3, 2))) e2w_attention_scores = paddle.matmul(e2w_query_layer, e2w_key_layer.transpose((0, 1, 3, 2))) e2e_attention_scores = paddle.matmul(e2e_query_layer, e2e_key_layer.transpose((0, 1, 3, 2))) # combine attention scores to create the final attention score matrix word_attention_scores = paddle.concat([w2w_attention_scores, w2e_attention_scores], axis=3) entity_attention_scores = paddle.concat([e2w_attention_scores, e2e_attention_scores], axis=3) attention_scores = paddle.concat([word_attention_scores, entity_attention_scores], axis=2) else: query_layer = self.transpose_for_scores(self.query(concat_hidden_states)) attention_scores = paddle.matmul(query_layer, key_layer.transpose((0, 1, 3, 2))) attention_scores = attention_scores / math.sqrt(self.attention_head_size) if attention_mask is not None: # Apply the attention mask is (precomputed for all layers in LukeModel forward() function) attention_scores = attention_scores + attention_mask # Normalize the attention scores to probabilities. attention_probs = nn.functional.softmax(attention_scores, axis=-1) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. attention_probs = self.dropout(attention_probs) context_layer = paddle.matmul(attention_probs, value_layer) context_layer = context_layer.transpose((0, 2, 1, 3)) new_context_layer_shape = context_layer.shape[:-2] + [ self.all_head_size, ] context_layer = context_layer.reshape(new_context_layer_shape) output_word_hidden_states = context_layer[:, :word_size, :] if entity_hidden_states is None: output_entity_hidden_states = None else: output_entity_hidden_states = context_layer[:, word_size:, :] outputs = (output_word_hidden_states, output_entity_hidden_states) return outputs class LukeAttention(nn.Layer): def __init__( self, num_attention_heads, hidden_size, attention_probs_dropout_prob, hidden_dropout_prob, ): super().__init__() self.self = LukeSelfAttention( num_attention_heads=num_attention_heads, hidden_size=hidden_size, attention_probs_dropout_prob=attention_probs_dropout_prob, ) self.output = LukeSelfOutput(hidden_size=hidden_size, hidden_dropout_prob=hidden_dropout_prob) def forward( self, word_hidden_states, entity_hidden_states, attention_mask=None, ): word_size = word_hidden_states.shape[1] self_outputs = self.self(word_hidden_states, entity_hidden_states, attention_mask) if entity_hidden_states is None: concat_self_outputs = self_outputs[0] concat_hidden_states = word_hidden_states else: concat_self_outputs = paddle.concat(self_outputs[:2], axis=1) concat_hidden_states = paddle.concat([word_hidden_states, entity_hidden_states], axis=1) attention_output = self.output(concat_self_outputs, concat_hidden_states) word_attention_output = attention_output[:, :word_size, :] if entity_hidden_states is None: entity_attention_output = None else: entity_attention_output = attention_output[:, word_size:, :] # add attentions if we output them outputs = (word_attention_output, entity_attention_output) + self_outputs[2:] return outputs class LukeLayer(nn.Layer): def __init__( self, num_attention_heads, hidden_size, hidden_act, intermediate_size, attention_probs_dropout_prob, hidden_dropout_prob, ): super(LukeLayer, self).__init__() self.seq_len_dim = 1 self.attention = LukeAttention( num_attention_heads=num_attention_heads, hidden_size=hidden_size, attention_probs_dropout_prob=attention_probs_dropout_prob, hidden_dropout_prob=hidden_dropout_prob, ) self.intermediate = LukeIntermediate( intermediate_size=intermediate_size, hidden_act=hidden_act, hidden_size=hidden_size ) self.output = LukeOutput( intermediate_size=intermediate_size, hidden_size=hidden_size, hidden_dropout_prob=hidden_dropout_prob ) def forward( self, word_hidden_states, entity_hidden_states, attention_mask=None, ): word_size = word_hidden_states.shape[1] self_attention_outputs = self.attention( word_hidden_states, entity_hidden_states, attention_mask, ) if entity_hidden_states is None: concat_attention_output = self_attention_outputs[0] else: concat_attention_output = paddle.concat(self_attention_outputs[:2], axis=1) outputs = self_attention_outputs[2:] # add self attentions if we output attention weights layer_output = self.feed_forward_chunk(concat_attention_output) word_layer_output = layer_output[:, :word_size, :] if entity_hidden_states is None: entity_layer_output = None else: entity_layer_output = layer_output[:, word_size:, :] outputs = (word_layer_output, entity_layer_output) + outputs return outputs def feed_forward_chunk(self, attention_output): intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class LukeEncoder(nn.Layer): def __init__( self, num_attention_heads, hidden_size, hidden_act, intermediate_size, num_hidden_layers, attention_probs_dropout_prob, hidden_dropout_prob, ): super(LukeEncoder, self).__init__() self.layer = nn.LayerList( [ LukeLayer( num_attention_heads=num_attention_heads, hidden_size=hidden_size, hidden_act=hidden_act, intermediate_size=intermediate_size, attention_probs_dropout_prob=attention_probs_dropout_prob, hidden_dropout_prob=hidden_dropout_prob, ) for _ in range(num_hidden_layers) ] ) def forward( self, word_hidden_states, entity_hidden_states, attention_mask=None, ): for i, layer_module in enumerate(self.layer): layer_outputs = layer_module( word_hidden_states, entity_hidden_states, attention_mask, ) word_hidden_states = layer_outputs[0] if entity_hidden_states is not None: entity_hidden_states = layer_outputs[1] return word_hidden_states, entity_hidden_states
[文档]@register_base_model class LukeModel(LukePretrainedModel): """ The bare Luke 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, optional): Vocabulary size of `inputs_ids` in `LukeModel`. 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 `LukeModel`. Defaults to 50267. hidden_size (int, optional): Dimensionality of the embedding layer, encoder layer 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 `514`. type_vocab_size (int, optional): The vocabulary size of `token_type_ids`. Defaults to `1`. entity_vocab_size (int, optional): Vocabulary size of `entity_ids` in `LukeModel`. Also is the vocab size of token entity embedding matrix. Defines the number of different entity that can be represented by the `entity_ids` passed when calling `LukeModel`. Defaults to 500000. entity_emb_size (int, optional): Dimensionality of the entity embedding layer Defaults to `256`. initializer_range (float, optional): The standard deviation of the normal initializer. Defaults to 0.02. .. note:: A normal_initializer initializes weight matrices as normal distributions. See :meth:`BertPretrainedModel.init_weights()` for how weights are initialized in `BertModel`. pad_token_id (int, optional): The index of padding token in the token vocabulary. Defaults to `1`. entity_pad_token_id (int, optional): The index of padding token in the token vocabulary. Defaults to `0`. """ def __init__( self, vocab_size=50267, 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=514, type_vocab_size=1, entity_vocab_size=500000, entity_emb_size=256, initializer_range=0.02, pad_token_id=1, entity_pad_token_id=0, ): super(LukeModel, self).__init__() self.initializer_range = initializer_range self.pad_token_id = pad_token_id self.entity_pad_token_id = entity_pad_token_id self.encoder = LukeEncoder( hidden_act=hidden_act, num_hidden_layers=num_hidden_layers, hidden_size=hidden_size, intermediate_size=intermediate_size, hidden_dropout_prob=hidden_dropout_prob, num_attention_heads=num_attention_heads, attention_probs_dropout_prob=attention_probs_dropout_prob, ) self.embeddings = LukeEmbeddings( pad_token_id=pad_token_id, vocab_size=vocab_size, hidden_size=hidden_size, max_position_embeddings=max_position_embeddings, type_vocab_size=type_vocab_size, hidden_dropout_prob=hidden_dropout_prob, ) self.entity_embeddings = EntityEmbeddings( entity_vocab_size=entity_vocab_size, entity_emb_size=entity_emb_size, hidden_size=hidden_size, max_position_embeddings=max_position_embeddings, type_vocab_size=type_vocab_size, hidden_dropout_prob=hidden_dropout_prob, ) self.pooler = LukePooler(hidden_size=hidden_size) self.apply(self.init_weights)
[文档] def forward( self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, entity_ids=None, entity_position_ids=None, entity_token_type_ids=None, entity_attention_mask=None, ): r""" The LukeModel 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. entity_ids (Tensor, optional): Indices of entity sequence tokens in the entity vocabulary. They are numerical representations of entities that build the entity input sequence. Its data type should be `int64` and it has a shape of [batch_size, entity_sequence_length]. entity_position_ids (Tensor, optional): Indices of positions of each entity sequence tokens in the position embeddings. Selected in the range ``[0, max_position_embeddings - 1]``. Shape as `(batch_size, num_entity_tokens)` and dtype as int64. Defaults to `None`. entity_token_type_ids (Tensor, optional): Segment entity token indices to indicate different portions of the entity 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: entity_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 will be concat with `attention_mask`. Returns: tuple: Returns tuple (`word_hidden_state, entity_hidden_state, pool_output`). With the fields: - `word_hidden_state` (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]. - `entity_hidden_state` (Tensor): Sequence of entity 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 (`<s>`) 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 LukeModel, LukeTokenizer tokenizer = LukeTokenizer.from_pretrained('luke-base') model = LukeModel.from_pretrained('luke-base') text = "Beyoncé lives in Los Angeles." entity_spans = [(0, 7)] inputs = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True) inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} output = model(**inputs) """ input_shape = input_ids.shape batch_size, seq_length = input_shape 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] ) else: if attention_mask.ndim == 2: # attention_mask [batch_size, sequence_length] -> [batch_size, 1, 1, sequence_length] attention_mask = attention_mask.unsqueeze(axis=[1, 2]) attention_mask = (1.0 - attention_mask) * -1e4 if entity_ids is not None: entity_seq_length = entity_ids.shape[1] if entity_attention_mask is None: entity_attention_mask = paddle.unsqueeze( (entity_ids == self.entity_pad_token_id).astype(self.pooler.dense.weight.dtype) * -1e4, axis=[1, 2] ) else: if entity_attention_mask.ndim == 2: # attention_mask [batch_size, sequence_length] -> [batch_size, 1, 1, sequence_length] entity_attention_mask = entity_attention_mask.unsqueeze(axis=[1, 2]) entity_attention_mask = (1.0 - entity_attention_mask) * -1e4 if entity_token_type_ids is None: entity_token_type_ids = paddle.zeros((batch_size, entity_seq_length), dtype="int64") attention_mask = paddle.concat([attention_mask, entity_attention_mask], axis=-1) word_embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, ) if entity_ids is None: entity_embedding_output = None else: entity_embedding_output = self.entity_embeddings(entity_ids, entity_position_ids, entity_token_type_ids) # Fourth, send embeddings through the model encoder_outputs = self.encoder( word_embedding_output, entity_embedding_output, attention_mask=attention_mask, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) return sequence_output, encoder_outputs[1], pooled_output
class LukeLMHead(nn.Layer): """Luke Head for masked language modeling.""" def __init__(self, vocab_size, hidden_size, hidden_act, embedding_weights=None): super(LukeLMHead, self).__init__() self.dense = nn.Linear(hidden_size, hidden_size) self.layer_norm = nn.LayerNorm(hidden_size, epsilon=layer_norm_eps) self.activation = get_activation(hidden_act) self.decoder_weight = ( self.create_parameter(shape=[vocab_size, hidden_size], dtype=self.transform.weight.dtype, 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, features, **kwargs): hidden_state = self.dense(features) hidden_state = self.activation(hidden_state) hidden_state = self.layer_norm(hidden_state) hidden_state = paddle.tensor.matmul(hidden_state, self.decoder_weight, transpose_y=True) + self.decoder_bias return hidden_state class EntityPredictionHeadTransform(nn.Layer): def __init__(self, hidden_act, hidden_size, entity_emb_size): super(EntityPredictionHeadTransform, self).__init__() self.dense = nn.Linear(hidden_size, entity_emb_size) self.transform_act_fn = get_activation(hidden_act) self.layer_norm = nn.LayerNorm(entity_emb_size, epsilon=layer_norm_eps) def forward(self, hidden_states): hidden_states = self.dense(hidden_states) hidden_states = self.transform_act_fn(hidden_states) hidden_states = self.layer_norm(hidden_states) return hidden_states class EntityPredictionHead(nn.Layer): def __init__(self, hidden_size, entity_vocab_size, entity_emb_size, hidden_act): super(EntityPredictionHead, self).__init__() self.transform = EntityPredictionHeadTransform( hidden_size=hidden_size, hidden_act=hidden_act, entity_emb_size=entity_emb_size ) self.decoder = nn.Linear(entity_emb_size, entity_vocab_size) def forward(self, hidden_states): hidden_states = self.transform(hidden_states) hidden_states = self.decoder(hidden_states) return hidden_states
[文档]class LukeForMaskedLM(LukePretrainedModel): """ Luke Model with a `masked language modeling` head on top. Args: luke (:class:`LukeModel`): An instance of :class:`LukeModel`. """ def __init__(self, luke): super(LukeForMaskedLM, self).__init__() self.luke = luke self.vocab_size = self.luke.config["vocab_size"] self.entity_vocab_size = self.luke.config["entity_vocab_size"] self.lm_head = LukeLMHead( vocab_size=self.luke.config["vocab_size"], hidden_size=self.luke.config["hidden_size"], hidden_act=self.luke.config["hidden_act"], embedding_weights=self.luke.embeddings.word_embeddings.weight, ) self.entity_predictions = EntityPredictionHead( hidden_size=self.luke.config["hidden_size"], hidden_act=self.luke.config["hidden_act"], entity_vocab_size=self.luke.config["entity_vocab_size"], entity_emb_size=self.luke.config["entity_emb_size"], ) self.apply(self.init_weights)
[文档] def forward( self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, entity_ids=None, entity_position_ids=None, entity_token_type_ids=None, entity_attention_mask=None, ): r""" The LukeForMaskedLM forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`LukeModel`. token_type_ids (Tensor, optional): See :class:`LukeModel`. position_ids (Tensor, optional): See :class: `LukeModel` attention_mask (list, optional): See :class:`LukeModel`. entity_ids (Tensor, optional): See :class:`LukeModel`. entity_position_ids (Tensor, optional): See :class:`LukeModel`. entity_token_type_ids (Tensor, optional): See :class:`LukeModel`. entity_attention_mask (list, optional): See :class:`LukeModel`. Returns: tuple: Returns tuple (``logits``, ``entity_logits``). With the fields: - `logits` (Tensor): The scores of masked token prediction. Its data type should be float32 and shape is [batch_size, sequence_length, vocab_size]. - `entity_logits` (Tensor): The scores of masked entity prediction. Its data type should be float32 and its shape is [batch_size, entity_length, entity_vocab_size]. Example: .. code-block:: import paddle from paddlenlp.transformers import LukeForMaskedLM, LukeTokenizer tokenizer = LukeTokenizer.from_pretrained('luke-base') model = LukeForMaskedLM.from_pretrained('luke-base') text = "Beyoncé lives in Los Angeles." entity_spans = [(0, 7)] inputs = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True) inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits, entity_logits = model(**inputs) """ outputs = self.luke( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask, entity_ids=entity_ids, entity_position_ids=entity_position_ids, entity_token_type_ids=entity_token_type_ids, entity_attention_mask=entity_attention_mask, ) logits = self.lm_head(outputs[0]) entity_logits = self.entity_predictions(outputs[1]) return logits, entity_logits
[文档]class LukeForEntityClassification(LukePretrainedModel): """ The LUKE model with a classification head on top (a linear layer on top of the hidden state of the first entity token) for entity classification tasks, such as Open Entity. Args: luke (:class:`LukeModel`): An instance of LukeModel. num_classes (int): The number of classes. """ def __init__(self, luke, num_classes): super(LukeForEntityClassification, self).__init__() self.luke = luke self.num_classes = num_classes self.dropout = nn.Dropout(self.luke.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.luke.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, entity_ids=None, entity_position_ids=None, entity_token_type_ids=None, entity_attention_mask=None, ): r""" The LukeForEntityClassification forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`LukeModel`. token_type_ids (Tensor, optional): See :class:`LukeModel`. position_ids (Tensor, optional): See :class: `LukeModel` attention_mask (list, optional): See :class:`LukeModel`. entity_ids (Tensor, optional): See :class:`LukeModel`. entity_position_ids (Tensor, optional): See :class:`LukeModel`. entity_token_type_ids (Tensor, optional): See :class:`LukeModel`. entity_attention_mask (list, optional): See :class:`LukeModel`. Returns: Tensor: Returns tensor `logits`, a tensor of the entity classification logits. Shape as `[batch_size, num_classes]` and dtype as float32. Example: .. code-block:: import paddle from paddlenlp.transformers import LukeForEntityClassification, LukeTokenizer tokenizer = LukeTokenizer.from_pretrained('luke-base') model = LukeForEntityClassification.from_pretrained('luke-base', num_classes=2) text = "Beyoncé lives in Los Angeles." entity_spans = [(0, 7)] inputs = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True) inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs) """ outputs = self.luke( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask, entity_ids=entity_ids, entity_position_ids=entity_position_ids, entity_token_type_ids=entity_token_type_ids, entity_attention_mask=entity_attention_mask, ) feature_vector = outputs[1][:, 0, :] feature_vector = self.dropout(feature_vector) logits = self.classifier(feature_vector) return logits
[文档]class LukeForEntityPairClassification(LukePretrainedModel): """ The LUKE model with a classification head on top (a linear layer on top of the hidden states of the two entity tokens) for entity pair classification tasks, such as TACRED. Args: luke (:class:`LukeModel`): An instance of LukeModel. num_classes (int): The number of classes. """ def __init__(self, luke, num_classes): super(LukeForEntityPairClassification, self).__init__() self.luke = luke self.num_classes = num_classes self.dropout = nn.Dropout(self.luke.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.luke.config["hidden_size"] * 2, num_classes, bias_attr=False) self.apply(self.init_weights)
[文档] def forward( self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, entity_ids=None, entity_position_ids=None, entity_token_type_ids=None, entity_attention_mask=None, ): r""" The LukeForEntityPairClassification forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`LukeModel`. token_type_ids (Tensor, optional): See :class:`LukeModel`. position_ids (Tensor, optional): See :class: `LukeModel` attention_mask (list, optional): See :class:`LukeModel`. entity_ids (Tensor, optional): See :class:`LukeModel`. entity_position_ids (Tensor, optional): See :class:`LukeModel`. entity_token_type_ids (Tensor, optional): See :class:`LukeModel`. entity_attention_mask (list, optional): See :class:`LukeModel`. Returns: Tensor: Returns tensor `logits`, a tensor of the entity pair classification logits. Shape as `[batch_size, num_classes]` and dtype as float32. Example: .. code-block:: import paddle from paddlenlp.transformers import LukeForEntityPairClassification, LukeTokenizer tokenizer = LukeTokenizer.from_pretrained('luke-base') model = LukeForEntityPairClassification.from_pretrained('luke-base', num_classes=2) text = "Beyoncé lives in Los Angeles." entity_spans = [(0, 7), (17, 28)] inputs = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True) inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs) """ outputs = self.luke( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask, entity_ids=entity_ids, entity_position_ids=entity_position_ids, entity_token_type_ids=entity_token_type_ids, entity_attention_mask=entity_attention_mask, ) feature_vector = paddle.concat([outputs[1][:, 0, :], outputs[1][:, 1, :]], axis=1) feature_vector = self.dropout(feature_vector) logits = self.classifier(feature_vector) return logits
[文档]class LukeForEntitySpanClassification(LukePretrainedModel): """ The LUKE model with a span classification head on top (a linear layer on top of the hidden states output) for tasks such as named entity recognition. Args: luke (:class:`LukeModel`): An instance of LukeModel. num_classes (int): The number of classes. """ def __init__(self, luke, num_classes): super(LukeForEntitySpanClassification, self).__init__() self.luke = luke self.num_classes = num_classes self.dropout = nn.Dropout(self.luke.config["hidden_dropout_prob"]) self.classifier = nn.Linear(self.luke.config["hidden_size"] * 3, num_classes) self.apply(self.init_weights)
[文档] def forward( self, entity_start_positions, entity_end_positions, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, entity_ids=None, entity_position_ids=None, entity_token_type_ids=None, entity_attention_mask=None, ): r""" The LukeForEntitySpanClassification forward method, overrides the __call__() special method. Args: entity_start_positions: The start position of entities in sequence. entity_end_positions: The start position of entities in sequence. input_ids (Tensor): See :class:`LukeModel`. token_type_ids (Tensor, optional): See :class:`LukeModel`. position_ids (Tensor, optional): See :class: `LukeModel` attention_mask (list, optional): See :class:`LukeModel`. entity_ids (Tensor, optional): See :class:`LukeModel`. entity_position_ids (Tensor, optional): See :class:`LukeModel`. entity_token_type_ids (Tensor, optional): See :class:`LukeModel`. entity_attention_mask (list, optional): See :class:`LukeModel`. Returns: Tensor: Returns tensor `logits`, a tensor of the entity span classification logits. Shape as `[batch_size, num_entities, num_classes]` and dtype as float32. Example: .. code-block:: import paddle from paddlenlp.transformers import LukeForEntitySpanClassification, LukeTokenizer tokenizer = LukeTokenizer.from_pretrained('luke-base') model = LukeForEntitySpanClassification.from_pretrained('luke-base', num_classes=2) text = "Beyoncé lives in Los Angeles." entity_spans = [(0, 7)] inputs = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True) inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} inputs['entity_start_positions'] = paddle.to_tensor([[1]], dtype='int64') inputs['entity_end_positions'] = paddle.to_tensor([[2]], dtype='int64') logits = model(**inputs) """ outputs = self.luke( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask, entity_ids=entity_ids, entity_position_ids=entity_position_ids, entity_token_type_ids=entity_token_type_ids, entity_attention_mask=entity_attention_mask, ) hidden_size = outputs[0].shape[-1] entity_start_positions = entity_start_positions.unsqueeze(-1).expand((-1, -1, hidden_size)) start_states = paddle_gather(x=outputs[0], index=entity_start_positions, dim=-2) entity_end_positions = entity_end_positions.unsqueeze(-1).expand((-1, -1, hidden_size)) end_states = paddle_gather(x=outputs[0], index=entity_end_positions, dim=-2) feature_vector = paddle.concat([start_states, end_states, outputs[1]], axis=2) feature_vector = self.dropout(feature_vector) logits = self.classifier(feature_vector) return logits
[文档]class LukeForQuestionAnswering(LukePretrainedModel): """ LukeBert Model with question answering tasks. Args: luke (:class:`LukeModel`): An instance of :class:`LukeModel`. """ def __init__(self, luke): super(LukeForQuestionAnswering, self).__init__() self.luke = luke self.qa_outputs = nn.Linear(self.luke.config["hidden_size"], 2) self.apply(self.init_weights)
[文档] def forward( self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None, entity_ids=None, entity_position_ids=None, entity_token_type_ids=None, entity_attention_mask=None, ): r""" The LukeForQuestionAnswering forward method, overrides the __call__() special method. Args: input_ids (Tensor): See :class:`LukeModel`. token_type_ids (Tensor, optional): See :class:`LukeModel`. position_ids (Tensor, optional): See :class: `LukeModel` attention_mask (list, optional): See :class:`LukeModel`. entity_ids (Tensor, optional): See :class:`LukeModel`. entity_position_ids (Tensor, optional): See :class:`LukeModel`. entity_token_type_ids (Tensor, optional): See :class:`LukeModel`. entity_attention_mask (list, optional): See :class:`LukeModel`. 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 LukeForQuestionAnswering, LukeTokenizer tokenizer = LukeTokenizer.from_pretrained('luke-base') model = LukeForQuestionAnswering.from_pretrained('luke-base') text = "Beyoncé lives in Los Angeles." entity_spans = [(0, 7)] inputs = tokenizer(text, entity_spans=entity_spans, add_prefix_space=True) inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} start_logits, end_logits = model(**inputs) """ encoder_outputs = self.luke( input_ids=input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask, entity_ids=entity_ids, entity_position_ids=entity_position_ids, entity_token_type_ids=entity_token_type_ids, entity_attention_mask=entity_attention_mask, ) word_hidden_states = encoder_outputs[0][:, : input_ids.shape[1], :] logits = self.qa_outputs(word_hidden_states) start_logits, end_logits = paddle.split(logits, 2, -1) start_logits = start_logits.squeeze(-1) end_logits = end_logits.squeeze(-1) return start_logits, end_logits