wehub-resource-sync
1388 lines
61 KiB
Python
1388 lines
61 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import Optional, Tuple
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle import Tensor
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from ...layers import Linear as TransposedLinear
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from ...utils.converter import StateDictNameMapping, init_name_mappings
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from .. import PretrainedModel, register_base_model
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from ..model_outputs import (
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BaseModelOutputWithPoolingAndCrossAttentions,
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CausalLMOutputWithCrossAttentions,
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MaskedLMOutput,
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MultipleChoiceModelOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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)
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from .configuration import PRETRAINED_INIT_CONFIGURATION, RobertaConfig
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__all__ = [
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"RobertaModel",
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"RobertaPretrainedModel",
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"RobertaForSequenceClassification",
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"RobertaForTokenClassification",
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"RobertaForQuestionAnswering",
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"RobertaForMaskedLM",
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"RobertaForMultipleChoice",
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"RobertaForCausalLM",
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]
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def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length):
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"""
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Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
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are ignored. This is modified from fairseq's `utils.make_positions`.
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Args:
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x: paddle.Tensor x:
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Returns: paddle.Tensor
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"""
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if past_key_values_length is None:
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past_key_values_length = 0
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# The series of casts and type-conversions here are carefully balanced to both work with ONNX export and XLA.
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mask = (input_ids != padding_idx).cast("int64")
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incremental_indices = (paddle.cumsum(mask, axis=1) + past_key_values_length) * mask
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return incremental_indices + padding_idx
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class RobertaEmbeddings(nn.Layer):
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r"""
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Include embeddings from word, position and token_type embeddings.
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"""
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def __init__(self, config: RobertaConfig):
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super(RobertaEmbeddings, self).__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
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self.layer_norm = nn.LayerNorm(config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.padding_idx = config.pad_token_id
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self.cls_token_id = config.cls_token_id
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def forward(
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self,
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input_ids: Optional[Tensor] = None,
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token_type_ids: Optional[Tensor] = None,
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position_ids: Optional[Tensor] = None,
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inputs_embeds: Optional[Tensor] = None,
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past_key_values_length: Optional[int] = None,
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):
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if input_ids is not None:
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inputs_embeds = self.word_embeddings(input_ids)
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if position_ids is None:
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if input_ids is not None:
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position_ids = create_position_ids_from_input_ids(
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input_ids, padding_idx=self.padding_idx, past_key_values_length=past_key_values_length
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)
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else:
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position_ids = self.create_position_ids_from_inputs_embeds(inputs_embeds)
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position_ids.stop_gradient = True
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if token_type_ids is None:
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input_shape = inputs_embeds.shape[:-1]
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token_type_ids = paddle.zeros(input_shape, dtype="int64")
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position_embeddings = self.position_embeddings(position_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + position_embeddings + token_type_embeddings
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embeddings = self.layer_norm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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def create_position_ids_from_inputs_embeds(self, inputs_embeds):
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"""
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We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
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Args:
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input_shape: paddle.Tensor
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Returns: paddle.Tensor
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"""
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input_shape = inputs_embeds.shape[:-1]
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sequence_length = input_shape[1]
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position_ids = paddle.arange(self.padding_idx + 1, sequence_length + self.padding_idx + 1, dtype="int64")
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return position_ids.unsqueeze(0).expand(input_shape)
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class RobertaPooler(nn.Layer):
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def __init__(self, hidden_size):
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super(RobertaPooler, self).__init__()
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self.dense = nn.Linear(hidden_size, hidden_size)
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self.activation = nn.Tanh()
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def forward(self, hidden_states):
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# We "pool" the model by simply taking the hidden state corresponding
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# to the first token.
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first_token_tensor = hidden_states[:, 0]
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pooled_output = self.dense(first_token_tensor)
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pooled_output = self.activation(pooled_output)
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return pooled_output
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class RobertaPretrainedModel(PretrainedModel):
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r"""
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An abstract class for pretrained RoBerta models. It provides RoBerta related
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`model_config_file`, `pretrained_init_configuration`, `resource_files_names`,
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`pretrained_resource_files_map`, `base_model_prefix` for downloading and
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loading pretrained models.
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See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
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"""
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pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
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config_class = RobertaConfig
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pretrained_resource_files_map = {
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"model_state": {
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"hfl/roberta-wwm-ext": "https://bj.bcebos.com/paddlenlp/models/transformers/roberta_base/roberta_chn_base.pdparams",
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"hfl/roberta-wwm-ext-large": "https://bj.bcebos.com/paddlenlp/models/transformers/roberta_large/roberta_chn_large.pdparams",
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"hfl/rbt6": "https://bj.bcebos.com/paddlenlp/models/transformers/rbt6/rbt6_chn_large.pdparams",
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"hfl/rbt4": "https://bj.bcebos.com/paddlenlp/models/transformers/rbt4/rbt4_chn_large.pdparams",
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"hfl/rbt3": "https://bj.bcebos.com/paddlenlp/models/transformers/rbt3/rbt3_chn_large.pdparams",
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"hfl/rbtl3": "https://bj.bcebos.com/paddlenlp/models/transformers/rbtl3/rbtl3_chn_large.pdparams",
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}
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}
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base_model_prefix = "roberta"
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@classmethod
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def _get_name_mappings(cls, config: RobertaConfig) -> list[StateDictNameMapping]:
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mappings = [
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"embeddings.word_embeddings.weight",
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"embeddings.position_embeddings.weight",
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"embeddings.token_type_embeddings.weight",
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["embeddings.LayerNorm.weight", "embeddings.layer_norm.weight"],
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["embeddings.LayerNorm.bias", "embeddings.layer_norm.bias"],
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]
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for layer_index in range(config.num_hidden_layers):
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layer_mappings = [
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[
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f"encoder.layer.{layer_index}.attention.self.query.weight",
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f"encoder.layers.{layer_index}.self_attn.q_proj.weight",
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"transpose",
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],
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[
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f"encoder.layer.{layer_index}.attention.self.query.bias",
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f"encoder.layers.{layer_index}.self_attn.q_proj.bias",
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],
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[
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f"encoder.layer.{layer_index}.attention.self.key.weight",
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f"encoder.layers.{layer_index}.self_attn.k_proj.weight",
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"transpose",
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],
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[
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f"encoder.layer.{layer_index}.attention.self.key.bias",
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f"encoder.layers.{layer_index}.self_attn.k_proj.bias",
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],
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[
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f"encoder.layer.{layer_index}.attention.self.value.weight",
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f"encoder.layers.{layer_index}.self_attn.v_proj.weight",
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"transpose",
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],
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[
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f"encoder.layer.{layer_index}.attention.self.value.bias",
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f"encoder.layers.{layer_index}.self_attn.v_proj.bias",
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],
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[
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f"encoder.layer.{layer_index}.attention.output.dense.weight",
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f"encoder.layers.{layer_index}.self_attn.out_proj.weight",
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"transpose",
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],
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[
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f"encoder.layer.{layer_index}.attention.output.dense.bias",
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f"encoder.layers.{layer_index}.self_attn.out_proj.bias",
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],
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[
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f"encoder.layer.{layer_index}.attention.output.LayerNorm.weight",
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f"encoder.layers.{layer_index}.norm1.weight",
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],
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[
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f"encoder.layer.{layer_index}.attention.output.LayerNorm.bias",
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f"encoder.layers.{layer_index}.norm1.bias",
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],
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[
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f"encoder.layer.{layer_index}.intermediate.dense.weight",
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f"encoder.layers.{layer_index}.linear1.weight",
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"transpose",
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],
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[f"encoder.layer.{layer_index}.intermediate.dense.bias", f"encoder.layers.{layer_index}.linear1.bias"],
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[
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f"encoder.layer.{layer_index}.output.dense.weight",
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f"encoder.layers.{layer_index}.linear2.weight",
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"transpose",
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],
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[f"encoder.layer.{layer_index}.output.dense.bias", f"encoder.layers.{layer_index}.linear2.bias"],
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[f"encoder.layer.{layer_index}.output.LayerNorm.weight", f"encoder.layers.{layer_index}.norm2.weight"],
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[f"encoder.layer.{layer_index}.output.LayerNorm.bias", f"encoder.layers.{layer_index}.norm2.bias"],
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]
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mappings.extend(layer_mappings)
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init_name_mappings(mappings)
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# Other than RobertaModel, other architectures will prepend model prefix
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if config.architectures is not None and "RobertaModel" not in config.architectures:
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for mapping in mappings:
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mapping[0] = "roberta." + mapping[0]
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if cls.__name__ != "RobertaModel":
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for mapping in mappings:
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mapping[1] = "roberta." + mapping[1]
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mappings.extend(
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[
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["pooler.dense.weight", "roberta.pooler.dense.weight", "transpose"],
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["pooler.dense.bias", "roberta.pooler.dense.bias"],
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]
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)
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if config.architectures is not None:
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if "RobertaForSequenceClassification" in config.architectures:
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mappings.extend(
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[
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["classifier.out_proj.weight", None, "transpose"],
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"classifier.out_proj.bias",
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["classifier.dense.weight", None, "transpose"],
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"classifier.dense.bias",
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]
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)
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if "RobertaForMaskedLM" in config.architectures:
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mappings.extend(
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[
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"lm_head.bias",
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"lm_head.dense.weight",
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"lm_head.dense.bias",
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"lm_head.layer_norm.weight",
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"lm_head.layer_norm.bias",
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]
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)
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if (
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"RobertaForTokenClassification" in config.architectures
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or "RobertaForMultipleChoice" in config.architectures
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):
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mappings.extend(
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[
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["classifier.weight", None, "transpose"],
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"classifier.bias",
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]
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)
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if "RobertaForQuestionAnswering" in config.architectures:
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mappings.extend(
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[
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["qa_outputs.weight", "classifier.weight", "transpose"],
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["qa_outputs.bias", "classifier.bias"],
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]
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)
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init_name_mappings(mappings)
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return [StateDictNameMapping(*mapping) for mapping in mappings]
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def _init_weights(self, layer):
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"""Initialization hook"""
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if isinstance(layer, (nn.Linear, nn.Embedding)):
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# only support dygraph, use truncated_normal and make it inplace
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# and configurable later
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layer.weight.set_value(
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paddle.tensor.normal(
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mean=0.0,
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std=self.config.initializer_range,
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shape=layer.weight.shape,
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)
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)
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elif isinstance(layer, nn.LayerNorm):
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layer._epsilon = self.config.layer_norm_eps
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@register_base_model
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class RobertaModel(RobertaPretrainedModel):
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r"""
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The bare Roberta Model outputting raw hidden-states.
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This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
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Refer to the superclass documentation for the generic methods.
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This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
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/docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
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and refer to the Paddle documentation for all matter related to general usage and behavior.
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Args:
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vocab_size (int):
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Vocabulary size of `inputs_ids` in `RobertaModel`. Also is the vocab size of token embedding matrix.
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Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `RobertaModel`.
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hidden_size (int, optional):
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Dimensionality of the embedding layer, encoder layers and pooler layer. Defaults to `768`.
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num_hidden_layers (int, optional):
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Number of hidden layers in the Transformer encoder. Defaults to `12`.
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num_attention_heads (int, optional):
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Number of attention heads for each attention layer in the Transformer encoder.
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Defaults to `12`.
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intermediate_size (int, optional):
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Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors
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to ff layers are firstly projected from `hidden_size` to `intermediate_size`,
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and then projected back to `hidden_size`. Typically `intermediate_size` is larger than `hidden_size`.
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Defaults to `3072`.
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hidden_act (str, optional):
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The non-linear activation function in the feed-forward layer.
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``"gelu"``, ``"relu"`` and any other paddle supported activation functions
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are supported. Defaults to ``"gelu"``.
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hidden_dropout_prob (float, optional):
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The dropout probability for all fully connected layers in the embeddings and encoder.
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Defaults to `0.1`.
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attention_probs_dropout_prob (float, optional):
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The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target.
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Defaults to `0.1`.
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max_position_embeddings (int, optional):
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The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input
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sequence. Defaults to `512`.
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type_vocab_size (int, optional):
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The vocabulary size of the `token_type_ids` passed when calling `~transformers.RobertaModel`.
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Defaults to `2`.
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initializer_range (float, optional):
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The standard deviation of the normal initializer. Defaults to 0.02.
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.. note::
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A normal_initializer initializes weight matrices as normal distributions.
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See :meth:`RobertaPretrainedModel._init_weights()` for how weights are initialized in `RobertaModel`.
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pad_token_id(int, optional):
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The index of padding token in the token vocabulary.
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Defaults to `0`.
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cls_token_id(int, optional):
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The index of cls token in the token vocabulary.
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Defaults to `101`.
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"""
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def __init__(self, config: RobertaConfig, add_pooling_layer=True):
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super(RobertaModel, self).__init__(config)
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self.pad_token_id = config.pad_token_id
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self.initializer_range = config.initializer_range
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self.layer_norm_eps = config.layer_norm_eps
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self.embeddings = RobertaEmbeddings(config)
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encoder_layer = nn.TransformerEncoderLayer(
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config.hidden_size,
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config.num_attention_heads,
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config.intermediate_size,
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dropout=config.hidden_dropout_prob,
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activation=config.hidden_act,
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attn_dropout=config.attention_probs_dropout_prob,
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act_dropout=0,
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)
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self.encoder = nn.TransformerEncoder(encoder_layer, config.num_hidden_layers)
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self.pooler = RobertaPooler(config.hidden_size) if add_pooling_layer else None
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def get_input_embeddings(self):
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return self.embeddings.word_embeddings
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def set_input_embeddings(self, value):
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self.embeddings.word_embeddings = value
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def forward(
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self,
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input_ids: Optional[Tensor] = None,
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token_type_ids: Optional[Tensor] = None,
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position_ids: Optional[Tensor] = None,
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attention_mask: Optional[Tensor] = None,
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past_key_values: Optional[Tuple[Tuple[Tensor]]] = None,
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inputs_embeds: Optional[Tensor] = None,
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use_cache: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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):
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r"""
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Args:
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input_ids (Tensor):
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Indices of input sequence tokens in the vocabulary. They are
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numerical representations of tokens that build the input sequence.
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It's data type should be `int64` and has a shape of [batch_size, sequence_length].
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token_type_ids (Tensor, optional):
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Segment token indices to indicate first and second portions of the inputs.
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Indices can be either 0 or 1:
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- 0 corresponds to a **sentence A** token,
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- 1 corresponds to a **sentence B** token.
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It's data type should be `int64` and has a shape of [batch_size, sequence_length].
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Defaults to None, which means no segment embeddings is added to token embeddings.
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position_ids (Tensor, optional):
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Indices of positions of each input sequence tokens in the position embeddings.
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Selected in the range ``[0, max_position_embeddings - 1]``.
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It's data type should be `int64` and has a shape of [batch_size, sequence_length].
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Defaults to `None`.
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attention_mask (Tensor, optional):
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Mask used in multi-head attention to avoid performing attention to some unwanted positions,
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usually the paddings or the subsequent positions.
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Its data type can be int, float and bool.
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When the data type is bool, the `masked` tokens have `False` values and the others have `True` values.
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When the data type is int, the `masked` tokens have `0` values and the others have `1` values.
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When the data type is float, the `masked` tokens have `-INF` values and the others have `0` values.
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It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
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For example, its shape can be [batch_size, sequence_length], [batch_size, sequence_length, sequence_length],
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[batch_size, num_attention_heads, sequence_length, sequence_length].
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Defaults to `None`, which means nothing needed to be prevented attention to.
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past_key_values (tuple(tuple(Tensor)), optional):
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The length of tuple equals to the number of layers, and each inner
|
|
tuple haves 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`)
|
|
which contains precomputed key and value hidden states of the attention blocks.
|
|
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that
|
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
|
`input_ids` of shape `(batch_size, sequence_length)`.
|
|
inputs_embeds (Tensor, optional):
|
|
If you want to control how to convert `inputs_ids` indices into associated vectors, you can
|
|
pass an embedded representation directly instead of passing `inputs_ids`.
|
|
use_cache (`bool`, optional):
|
|
If set to `True`, `past_key_values` key value states are returned.
|
|
Defaults to `None`.
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.ModelOutput` object. If `False`, the output
|
|
will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions` if
|
|
`return_dict=True`. Otherwise it returns a tuple of tensors corresponding
|
|
to ordered and not None (depending on the input arguments) fields of
|
|
:class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import RobertaModel, RobertaTokenizer
|
|
|
|
tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext')
|
|
model = RobertaModel.from_pretrained('roberta-wwm-ext')
|
|
|
|
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)
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time.")
|
|
|
|
past_key_values_length = None
|
|
if past_key_values is not None:
|
|
past_key_values_length = past_key_values[0][0].shape[2]
|
|
if attention_mask is None:
|
|
attention_mask = paddle.unsqueeze(
|
|
(input_ids == self.pad_token_id).astype(paddle.get_default_dtype()) * -1e4, axis=[1, 2]
|
|
)
|
|
if past_key_values is not None:
|
|
batch_size = past_key_values[0][0].shape[0]
|
|
past_mask = paddle.zeros([batch_size, 1, 1, past_key_values_length], dtype=attention_mask.dtype)
|
|
attention_mask = paddle.concat([past_mask, attention_mask], axis=-1)
|
|
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
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids,
|
|
position_ids=position_ids,
|
|
token_type_ids=token_type_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
|
|
self.encoder._use_cache = use_cache # To be consistent with HF
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
src_mask=attention_mask,
|
|
cache=past_key_values,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
if isinstance(encoder_outputs, type(embedding_output)):
|
|
sequence_output = encoder_outputs
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
return (sequence_output, pooled_output)
|
|
else:
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPoolingAndCrossAttentions(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
past_key_values=encoder_outputs.past_key_values,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class RobertaForQuestionAnswering(RobertaPretrainedModel):
|
|
r"""
|
|
Roberta 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:
|
|
roberta (:class:`RobertaModel`):
|
|
An instance of RobertaModel.
|
|
"""
|
|
|
|
def __init__(self, config: RobertaConfig):
|
|
super(RobertaForQuestionAnswering, self).__init__(config)
|
|
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
self.classifier = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[Tensor] = None,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
inputs_embeds: Optional[Tensor] = None,
|
|
start_positions: Optional[Tensor] = None,
|
|
end_positions: Optional[Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`RobertaModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
position_ids (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
inputs_embeds (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
start_positions (Tensor of shape `(batch_size,)`, optional):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
end_positions (Tensor of shape `(batch_size,)`, optional):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import RobertaForSequenceClassification, RobertaTokenizer
|
|
|
|
tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext')
|
|
model = RobertaForSequenceClassification.from_pretrained('roberta-wwm-ext')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
logits = model(**inputs)
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
outputs = self.roberta(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.classifier(sequence_output)
|
|
logits = paddle.transpose(logits, perm=[2, 0, 1])
|
|
start_logits, end_logits = paddle.unstack(x=logits, axis=0)
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if start_positions.ndim > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if start_positions.ndim > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.shape[1]
|
|
start_positions = start_positions.clip(0, ignored_index)
|
|
end_positions = end_positions.clip(0, ignored_index)
|
|
|
|
loss_fct = paddle.nn.CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class RobertaClassificationHead(nn.Layer):
|
|
"""Head for sentence-level classification tasks."""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
classifier_dropout = (
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.dropout = nn.Dropout(classifier_dropout)
|
|
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(self, features, **kwargs):
|
|
x = features[:, 0, :] # take <s> token (equiv. to [CLS])
|
|
x = self.dropout(x)
|
|
x = self.dense(x)
|
|
x = paddle.tanh(x)
|
|
x = self.dropout(x)
|
|
x = self.out_proj(x)
|
|
return x
|
|
|
|
|
|
class RobertaForSequenceClassification(RobertaPretrainedModel):
|
|
r"""
|
|
Roberta Model with a linear layer on top of the output layer,
|
|
designed for sequence classification/regression tasks like GLUE tasks.
|
|
|
|
Args:
|
|
roberta (:class:`RobertaModel`):
|
|
An instance of `RobertaModel`.
|
|
num_classes (int, optional):
|
|
The number of classes. Defaults to `2`.
|
|
dropout (float, optional):
|
|
The dropout probability for output of Roberta.
|
|
If None, use the same value as `hidden_dropout_prob`
|
|
of `RobertaModel` instance `roberta`. Defaults to `None`.
|
|
"""
|
|
|
|
def __init__(self, config: RobertaConfig):
|
|
super(RobertaForSequenceClassification, self).__init__(config)
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
|
|
self.dropout = nn.Dropout(
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.classifier = RobertaClassificationHead(config)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[Tensor] = None,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
inputs_embeds: Optional[Tensor] = None,
|
|
labels: Optional[Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`RobertaModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
position_ids (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
inputs_embeds (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
labels (Tensor of shape `(batch_size,)`, optional):
|
|
Labels for computing the sequence classification/regression loss.
|
|
Indices should be in `[0, ..., num_classes - 1]`. If `num_classes == 1`
|
|
a regression loss is computed (Mean-Square loss), If `num_classes > 1`
|
|
a classification loss is computed (Cross-Entropy).
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import RobertaForSequenceClassification, RobertaTokenizer
|
|
|
|
tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext')
|
|
model = RobertaForSequenceClassification.from_pretrained('roberta-wwm-ext')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
logits = model(**inputs)
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
outputs = self.roberta(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.num_labels == 1:
|
|
loss_fct = paddle.nn.MSELoss()
|
|
loss = loss_fct(logits, labels)
|
|
elif labels.dtype == paddle.int64 or labels.dtype == paddle.int32:
|
|
loss_fct = paddle.nn.CrossEntropyLoss()
|
|
loss = loss_fct(logits.reshape((-1, self.config.num_labels)), labels.reshape((-1,)))
|
|
else:
|
|
loss_fct = paddle.nn.BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class RobertaForTokenClassification(RobertaPretrainedModel):
|
|
r"""
|
|
Roberta Model with a linear layer on top of the hidden-states output layer,
|
|
designed for token classification tasks like NER tasks.
|
|
|
|
Args:
|
|
roberta (:class:`RobertaModel`):
|
|
An instance of `RobertaModel`.
|
|
num_classes (int, optional):
|
|
The number of classes. Defaults to `2`.
|
|
dropout (float, optional):
|
|
The dropout probability for output of Roberta.
|
|
If None, use the same value as `hidden_dropout_prob`
|
|
of `RobertaModel` instance `roberta`. Defaults to `None`.
|
|
"""
|
|
|
|
def __init__(self, config: RobertaConfig):
|
|
super(RobertaForTokenClassification, self).__init__(config)
|
|
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
self.dropout = nn.Dropout(
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[Tensor] = None,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
inputs_embeds: Optional[Tensor] = None,
|
|
labels: Optional[Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`RobertaModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
position_ids (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
inputs_embeds (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
labels (Tensor of shape `(batch_size, sequence_length)`, optional):
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., num_classes - 1]`.
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import RobertaForTokenClassification, RobertaTokenizer
|
|
|
|
tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext')
|
|
model = RobertaForTokenClassification.from_pretrained('roberta-wwm-ext')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
logits = model(**inputs)
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
outputs = self.roberta(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = paddle.nn.CrossEntropyLoss()
|
|
loss = loss_fct(logits.reshape((-1, self.config.num_labels)), labels.reshape((-1,)))
|
|
if not return_dict:
|
|
|
|
output = (logits,) + outputs[2:]
|
|
if loss is not None:
|
|
return (loss,) + output
|
|
if len(output) == 1:
|
|
return output[0]
|
|
return output
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class RobertaForMultipleChoice(RobertaPretrainedModel):
|
|
"""
|
|
RoBerta Model with a linear layer on top of the hidden-states output layer,
|
|
designed for multiple choice tasks like RocStories/SWAG tasks.
|
|
|
|
Args:
|
|
bert (:class:`RobertaModel`):
|
|
An instance of RobertaModel.
|
|
num_choices (int, optional):
|
|
The number of choices. Defaults to `2`.
|
|
dropout (float, optional):
|
|
The dropout probability for output of Bert.
|
|
If None, use the same value as `hidden_dropout_prob` of `RobertaModel`
|
|
instance `bert`. Defaults to None.
|
|
"""
|
|
|
|
def __init__(self, config: RobertaConfig):
|
|
super(RobertaForMultipleChoice, self).__init__(config)
|
|
self.roberta = RobertaModel(config)
|
|
self.dropout = nn.Dropout(
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[Tensor] = None,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
inputs_embeds: Optional[Tensor] = None,
|
|
labels: Optional[Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
The RobertaForMultipleChoice forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`RobertaModel` and shape as [batch_size, num_choice, sequence_length].
|
|
token_type_ids(Tensor, optional):
|
|
See :class:`RobertaModel` and shape as [batch_size, num_choice, sequence_length].
|
|
position_ids(Tensor, optional):
|
|
See :class:`RobertaModel` and shape as [batch_size, num_choice, sequence_length].
|
|
attention_mask (list, optional):
|
|
See :class:`RobertaModel` and shape as [batch_size, num_choice, sequence_length].
|
|
inputs_embeds (list, optional):
|
|
See :class:`RobertaModel` and shape as [batch_size, num_choice, sequence_length].
|
|
labels (Tensor of shape `(batch_size, )`, optional):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
|
|
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
|
|
`input_ids` above)
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import BertForMultipleChoice, BertTokenizer
|
|
from paddlenlp.data import Pad, Dict
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
|
model = BertForMultipleChoice.from_pretrained('bert-base-uncased', 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]
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
if input_ids is not None:
|
|
num_choices = input_ids.shape[1]
|
|
elif inputs_embeds is not None:
|
|
num_choices = inputs_embeds.shape[1]
|
|
|
|
input_ids = input_ids.reshape((-1, input_ids.shape[-1])) if input_ids is not None else None
|
|
inputs_embeds = (
|
|
inputs_embeds.reshape((-1, inputs_embeds.shape[-2], inputs_embeds.shape[-1]))
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
position_ids = position_ids.reshape((-1, position_ids.shape[-1])) if position_ids is not None else None
|
|
token_type_ids = token_type_ids.reshape((-1, token_type_ids.shape[-1])) if token_type_ids is not None else None
|
|
attention_mask = attention_mask.reshape((-1, attention_mask.shape[-1])) if attention_mask is not None else None
|
|
|
|
outputs = self.roberta(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
pooled_output = outputs[1]
|
|
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.reshape((-1, num_choices))
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = paddle.nn.CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
if not return_dict:
|
|
output = (reshaped_logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class RobertaForMaskedLM(RobertaPretrainedModel):
|
|
"""
|
|
Roberta Model with a `masked language modeling` head on top.
|
|
|
|
Args:
|
|
bert (:class:RobertaModel`):
|
|
An instance of :class:`RobertaModel`.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: RobertaConfig):
|
|
super(RobertaForMaskedLM, self).__init__(config)
|
|
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
self.lm_head = RobertaLMHead(config)
|
|
self.tie_weights()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head.decoder = new_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[Tensor] = None,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
inputs_embeds: Optional[Tensor] = None,
|
|
labels: Optional[Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`RobertaModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
position_ids (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
inputs_embeds (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
labels (Tensor of shape `(batch_size, sequence_length)`, optional):
|
|
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
|
|
vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
|
|
loss is only computed for the tokens with labels in `[0, ..., vocab_size]`
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.MaskedLMOutput` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.MaskedLMOutput` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.MaskedLMOutput`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import RobertaForMaskedLM, RobertaTokenizer
|
|
|
|
tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext')
|
|
model = RobertaForMaskedLM.from_pretrained('roberta-wwm-ext')
|
|
|
|
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, 13, 30522]
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
outputs = self.roberta(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.lm_head(sequence_output)
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
loss_fct = paddle.nn.CrossEntropyLoss() # -100 index = padding token
|
|
masked_lm_loss = loss_fct(
|
|
prediction_scores.reshape((-1, prediction_scores.shape[-1])), labels.reshape((-1,))
|
|
)
|
|
if not return_dict:
|
|
output = (prediction_scores,) + outputs[2:]
|
|
return (
|
|
((masked_lm_loss,) + output)
|
|
if masked_lm_loss is not None
|
|
else (output[0] if len(output) == 1 else output)
|
|
)
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class RobertaLMHead(nn.Layer):
|
|
"""Roberta Head for masked language modeling."""
|
|
|
|
def __init__(self, config: RobertaConfig):
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
|
|
|
|
self.decoder = TransposedLinear(config.hidden_size, config.vocab_size)
|
|
# link bias to load pretrained weights
|
|
self.bias = self.decoder.bias
|
|
|
|
def forward(self, features, **kwargs):
|
|
x = self.dense(features)
|
|
x = F.gelu(x)
|
|
x = self.layer_norm(x)
|
|
|
|
# project back to size of vocabulary with bias
|
|
x = self.decoder(x)
|
|
|
|
return x
|
|
|
|
|
|
class RobertaForCausalLM(RobertaPretrainedModel):
|
|
"""
|
|
Roberta Model with a `Causal language modeling` head on top.
|
|
|
|
Args:
|
|
bert (:class:RobertaModel`):
|
|
An instance of :class:`RobertaModel`.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: RobertaConfig):
|
|
super().__init__(config)
|
|
self.roberta = RobertaModel(config, add_pooling_layer=False)
|
|
self.lm_head = RobertaLMHead(config)
|
|
|
|
self.tie_weights()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head.decoder = new_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[Tensor] = None,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
inputs_embeds: Optional[Tensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[Tensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
labels: Optional[Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`RobertaModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
position_ids (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
inputs_embeds (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
past_key_values (tuple(tuple(Tensor)), optional):
|
|
See :class:`RobertaModel`.
|
|
use_cache (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`RobertaModel`.
|
|
labels (Tensor of shape `(batch_size, sequence_length)`, optional):
|
|
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
|
|
`[-100, 0, ..., vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
|
|
ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., vocab_size]`.
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.CausalLMOutputWithCrossAttentions` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.CausalLMOutputWithCrossAttentions` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.CausalLMOutputWithCrossAttentions`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import RobertaForCausalLM, RobertaTokenizer
|
|
|
|
tokenizer = RobertaTokenizer.from_pretrained('roberta-wwm-ext')
|
|
model = RobertaForCausalLM.from_pretrained('roberta-wwm-ext')
|
|
|
|
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, 13, 30522]
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
if labels is not None:
|
|
use_cache = False
|
|
outputs = self.roberta(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.lm_head(sequence_output)
|
|
|
|
lm_loss = None
|
|
if labels is not None:
|
|
# we are doing next-token prediction; shift prediction scores and input ids by one
|
|
shifted_prediction_scores = prediction_scores[:, :-1, :]
|
|
labels = labels[:, 1:]
|
|
loss_fct = paddle.nn.CrossEntropyLoss()
|
|
lm_loss = loss_fct(
|
|
shifted_prediction_scores.reshape((-1, prediction_scores.shape[-1])), labels.reshape((-1,))
|
|
)
|
|
if not return_dict:
|
|
output = (prediction_scores,) + outputs[2:]
|
|
return ((lm_loss,) + output) if lm_loss is not None else (output[0] if len(output) == 1 else output)
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=lm_loss,
|
|
logits=prediction_scores,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs):
|
|
input_shape = input_ids.shape
|
|
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
|
|
if attention_mask is None:
|
|
attention_mask = input_ids.new_ones(input_shape)
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# cut decoder_input_ids if past is used
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if past is not None:
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input_ids = input_ids[:, -1:]
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return {"input_ids": input_ids, "attention_mask": attention_mask, "past_key_values": past}
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def _reorder_cache(self, past, beam_idx):
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reordered_past = ()
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for layer_past in past:
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reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
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return reordered_past
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