wehub-resource-sync
732 lines
29 KiB
Python
732 lines
29 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation.
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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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import copy
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import math
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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 .. import PretrainedModel, register_base_model
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from ..activations import ACT2FN
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from .configuration import MPNET_PRETRAINED_INIT_CONFIGURATION, MPNetConfig
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__all__ = [
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"MPNetModel",
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"MPNetPretrainedModel",
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"MPNetForMaskedLM",
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"MPNetForSequenceClassification",
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"MPNetForMultipleChoice",
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"MPNetForTokenClassification",
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"MPNetForQuestionAnswering",
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]
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def create_position_ids_from_input_ids(input_ids, padding_idx=1):
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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`. :param paddle.Tensor x: :return paddle.Tensor:
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"""
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mask = (input_ids != padding_idx).astype(paddle.int64)
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incremental_indices = paddle.cumsum(mask, axis=1).astype(mask.dtype) * mask
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return incremental_indices.astype(paddle.int64) + padding_idx
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class MPNetEmbeddings(nn.Layer):
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"""
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Include embeddings from word and position embeddings.
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"""
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def __init__(self, config: MPNetConfig):
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super(MPNetEmbeddings, self).__init__()
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self.padding_idx = config.pad_token_id
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=self.padding_idx)
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self.position_embeddings = nn.Embedding(
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config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
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)
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self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, input_ids, position_ids=None):
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if position_ids is None:
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position_ids = create_position_ids_from_input_ids(input_ids, self.padding_idx)
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words_embeddings = self.word_embeddings(input_ids)
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position_embeddings = self.position_embeddings(position_ids)
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embeddings = words_embeddings + position_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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class MPNetAttention(nn.Layer):
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def __init__(self, config: MPNetConfig):
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super(MPNetAttention, self).__init__()
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if config.hidden_size % config.num_attention_heads != 0:
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raise ValueError(
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"The hidden size (%d) is not a multiple of the number of attention "
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"heads (%d)" % (config.hidden_size, config.num_attention_heads)
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)
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = config.hidden_size // config.num_attention_heads
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.scale = self.attention_head_size**-0.5
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self.q = nn.Linear(config.hidden_size, self.all_head_size)
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self.k = nn.Linear(config.hidden_size, self.all_head_size)
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self.v = nn.Linear(config.hidden_size, self.all_head_size)
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self.o = nn.Linear(config.hidden_size, config.hidden_size)
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self.attention_dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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self.output_dropout = nn.Dropout(config.hidden_dropout_prob)
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def transpose_for_scores(self, x):
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new_x_shape = x.shape[:-1] + [
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self.num_attention_heads,
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self.attention_head_size,
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]
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x = x.reshape(new_x_shape)
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return x.transpose(perm=(0, 2, 1, 3))
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def forward(self, hidden_states, attention_mask=None, position_bias=None):
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q = self.q(hidden_states)
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k = self.k(hidden_states)
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v = self.v(hidden_states)
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q = self.transpose_for_scores(q)
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k = self.transpose_for_scores(k)
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v = self.transpose_for_scores(v)
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attention_scores = paddle.matmul(q, k, transpose_y=True) * self.scale
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if position_bias is not None:
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attention_scores += position_bias
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if attention_mask is not None:
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attention_scores = attention_scores + attention_mask
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attention_probs = F.softmax(attention_scores, axis=-1)
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attention_probs = self.attention_dropout(attention_probs)
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context_layer = paddle.matmul(attention_probs, v)
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context_layer = context_layer.transpose(perm=(0, 2, 1, 3))
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new_context_layer_shape = context_layer.shape[:-2] + [self.all_head_size]
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context_layer = context_layer.reshape(new_context_layer_shape)
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projected_context_layer = self.o(context_layer)
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projected_context_layer_dropout = self.output_dropout(projected_context_layer)
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layer_normed_context_layer = self.layer_norm(hidden_states + projected_context_layer_dropout)
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return layer_normed_context_layer, attention_scores
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class MPNetLayer(nn.Layer):
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def __init__(self, config: MPNetConfig):
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super(MPNetLayer, self).__init__()
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self.attention = MPNetAttention(config)
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self.ffn = nn.Linear(config.hidden_size, config.intermediate_size)
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self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size)
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self.activation = ACT2FN[config.hidden_act]
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self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states, attention_mask=None, position_bias=None):
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attention_output, layer_att = self.attention(
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hidden_states, attention_mask=attention_mask, position_bias=position_bias
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)
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ffn_output = self.ffn(attention_output)
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ffn_output = self.activation(ffn_output)
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ffn_output = self.ffn_output(ffn_output)
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ffn_output_dropout = self.dropout(ffn_output)
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hidden_states = self.layer_norm(ffn_output_dropout + attention_output)
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return hidden_states, layer_att
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class MPNetEncoder(nn.Layer):
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def __init__(self, config: MPNetConfig):
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super(MPNetEncoder, self).__init__()
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layer = MPNetLayer(config)
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self.layer = nn.LayerList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
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self.relative_attention_bias = nn.Embedding(config.relative_attention_num_buckets, config.num_attention_heads)
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def forward(self, hidden_states, attention_mask=None):
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position_bias = self.compute_position_bias(hidden_states)
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all_encoder_layers = []
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all_encoder_att = []
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for i, layer_module in enumerate(self.layer):
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all_encoder_layers.append(hidden_states)
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hidden_states, layer_att = layer_module(all_encoder_layers[i], attention_mask, position_bias)
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all_encoder_att.append(layer_att)
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all_encoder_layers.append(hidden_states)
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return all_encoder_layers, all_encoder_att
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def compute_position_bias(self, x, position_ids=None, num_buckets=32):
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bsz, qlen, klen = x.shape[0], x.shape[1], x.shape[1]
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if position_ids is not None:
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context_position = position_ids.unsqueeze(2)
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memory_position = position_ids.unsqueeze(1)
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else:
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context_position = paddle.arange(qlen).unsqueeze(1)
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memory_position = paddle.arange(klen).unsqueeze(0)
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relative_position = memory_position - context_position
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rp_bucket = self.relative_position_bucket(relative_position, num_buckets=num_buckets)
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values = self.relative_attention_bias(rp_bucket)
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values = values.transpose(perm=[2, 0, 1]).unsqueeze(0)
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values = values.expand(shape=(bsz, values.shape[1], qlen, klen))
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return values
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@staticmethod
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def relative_position_bucket(relative_position, num_buckets=32, max_distance=128):
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ret = 0
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n = -relative_position
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num_buckets //= 2
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ret += (n < 0).astype(paddle.int64) * num_buckets
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n = paddle.abs(n)
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max_exact = num_buckets // 2
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is_small = n < max_exact
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val_if_large = max_exact + (
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paddle.log(n.astype(paddle.float32) / max_exact)
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/ math.log(max_distance / max_exact)
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* (num_buckets - max_exact)
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).astype(paddle.int64)
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val_if_large = paddle.minimum(val_if_large, paddle.full_like(val_if_large, num_buckets - 1))
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ret += paddle.where(is_small, n, val_if_large)
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return ret
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class MPNetPooler(nn.Layer):
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"""
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Pool the result of MPNetEncoder.
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"""
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def __init__(self, config: MPNetConfig):
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super(MPNetPooler, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.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 MPNetPretrainedModel(PretrainedModel):
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"""
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An abstract class for pretrained MPNet models. It provides MPNet related
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`model_config_file`, `resource_files_names`, `pretrained_resource_files_map`,
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`pretrained_init_configuration`, `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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base_model_prefix = "mpnet"
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pretrained_resource_files_map = {
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"model_state": {
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"mpnet-base": "https://bj.bcebos.com/paddlenlp/models/transformers/mpnet/mpnet-base/model_state.pdparams",
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}
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}
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pretrained_init_configuration = MPNET_PRETRAINED_INIT_CONFIGURATION
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config_class = MPNetConfig
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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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# In the dygraph mode, use the `set_value` to reset the parameter directly,
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# and reset the `state_dict` to update parameter in static mode.
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if isinstance(layer.weight, paddle.Tensor):
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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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@register_base_model
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class MPNetModel(MPNetPretrainedModel):
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"""
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The bare MPNet Model transformer 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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config (:class:`MPNetConfig`):
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An instance of MPNetConfig used to construct MPNetModel.
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"""
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def __init__(self, config: MPNetConfig):
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super(MPNetModel, self).__init__(config)
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self.initializer_range = config.initializer_range
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self.embeddings = MPNetEmbeddings(config)
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self.encoder = MPNetEncoder(config)
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self.pooler = MPNetPooler(config)
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def forward(self, input_ids, position_ids=None, attention_mask=None):
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r"""
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The MPNetModel forward method, overrides the `__call__()` special method.
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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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Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
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position_ids(Tensor, optional):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
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max_position_embeddings - 1]``.
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Shape as `(batch_size, num_tokens)` and dtype as int64. 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 on 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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If its data type is int, the values should be either 0 or 1.
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- **1** for tokens that **not masked**,
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- **0** for tokens that **masked**.
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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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Defaults to `None`, which means nothing needed to be prevented attention to.
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Returns:
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tuple: Returns tuple (`sequence_output`, `pooled_output`).
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With the fields:
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- `sequence_output` (Tensor):
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Sequence of hidden-states at the last layer of the model.
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It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
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- `pooled_output` (Tensor):
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The output of first token (`<s>`) in sequence.
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We "pool" the model by simply taking the hidden state corresponding to the first token.
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Its data type should be float32 and its shape is [batch_size, hidden_size].
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Example:
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.. code-block::
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import paddle
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from paddlenlp.transformers import MPNetModel, MPNetTokenizer
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tokenizer = MPNetTokenizer.from_pretrained('mpnet-base')
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model = MPNetModel.from_pretrained('mpnet-base')
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inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
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inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
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outputs = model(**inputs)
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"""
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if attention_mask is None:
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attention_mask = (input_ids != self.embeddings.padding_idx).astype(input_ids.dtype)
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if attention_mask.ndim == 2:
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attention_mask = attention_mask.unsqueeze(axis=[1, 2])
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attention_mask = (1.0 - attention_mask) * -10000.0
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embedding_output = self.embeddings(input_ids, position_ids)
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encoder_outputs, _ = self.encoder(embedding_output, attention_mask)
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sequence_output = encoder_outputs[-1]
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pooled_output = self.pooler(sequence_output)
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return sequence_output, pooled_output
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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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class MPNetLMHead(nn.Layer):
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"""
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MPNet Model with a `language modeling` head on top for CLM fine-tuning.
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"""
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def __init__(
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self,
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config: MPNetConfig,
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embedding_weights=None,
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):
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super(MPNetLMHead, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.activation = ACT2FN[config.hidden_act]
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self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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self.decoder_weight = (
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self.create_parameter(
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shape=[config.vocab_size, config.hidden_size], dtype=self.dense.weight.dtype, is_bias=False
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)
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if embedding_weights is None
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else embedding_weights
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)
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self.decoder_bias = self.create_parameter(
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shape=[config.vocab_size], dtype=self.decoder_weight.dtype, is_bias=True
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)
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def forward(self, hidden_states):
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hidden_states = self.dense(hidden_states)
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hidden_states = self.activation(hidden_states)
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hidden_states = self.layer_norm(hidden_states)
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hidden_states = paddle.matmul(hidden_states, self.decoder_weight, transpose_y=True) + self.decoder_bias
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return hidden_states
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class MPNetForMaskedLM(MPNetPretrainedModel):
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"""
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MPNet Model with a `language modeling` head on top.
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Args:
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config (:class:`MPNetConfig`):
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An instance of MPNetConfig used to construct MPNetModel.
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"""
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def __init__(self, config: MPNetConfig):
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super(MPNetForMaskedLM, self).__init__(config)
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self.mpnet = MPNetModel(config)
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self.lm_head = MPNetLMHead(config, embedding_weights=self.mpnet.embeddings.word_embeddings.weight)
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def forward(
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self,
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input_ids,
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position_ids=None,
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attention_mask=None,
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labels=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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See :class:`MPNetModel`.
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position_ids (Tensor, optional):
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See :class:`MPNetModel`.
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attention_mask (Tensor, optional):
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See :class:`MPNetModel`.
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labels (Tensor, optional):
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The Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ..., vocab_size]`` Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels in ``[0, ..., vocab_size]`` Its shape is [batch_size, sequence_length].
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Returns:
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tuple: Returns tuple (`masked_lm_loss`, `prediction_scores`, ``sequence_output`).
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With the fields:
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- `masked_lm_loss` (Tensor):
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The masked lm loss. Its data type should be float32 and its shape is [1].
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- `prediction_scores` (Tensor):
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The scores of masked token prediction. Its data type should be float32. Its shape is [batch_size, sequence_length, vocab_size].
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- `sequence_output` (Tensor):
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Sequence of hidden-states at the last layer of the model. Its data type should be float32. Its shape is `[batch_size, sequence_length, hidden_size]`.
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"""
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sequence_output, pooled_output = self.mpnet(
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input_ids, position_ids=position_ids, attention_mask=attention_mask
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|
)
|
|
prediction_scores = self.lm_head(sequence_output)
|
|
|
|
masked_lm_loss = None
|
|
|
|
if labels is not None:
|
|
loss_fct = nn.CrossEntropyLoss()
|
|
masked_lm_loss = loss_fct(
|
|
prediction_scores.reshape(shape=(-1, self.mpnet.config["vocab_size"])),
|
|
labels.reshape(shape=(-1,)),
|
|
)
|
|
return masked_lm_loss, prediction_scores, sequence_output
|
|
|
|
return prediction_scores, sequence_output
|
|
|
|
|
|
class MPNetForSequenceClassification(MPNetPretrainedModel):
|
|
"""
|
|
MPNet Model with a linear layer on top of the output layer,
|
|
designed for sequence classification/regression tasks like GLUE tasks.
|
|
|
|
Args:
|
|
config (:class:`MPNetConfig`):
|
|
An instance of MPNetConfig used to construct MPNetModel.
|
|
"""
|
|
|
|
def __init__(self, config: MPNetConfig):
|
|
super(MPNetForSequenceClassification, self).__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.mpnet = MPNetModel(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, config.num_labels)
|
|
|
|
def forward(self, input_ids, position_ids=None, attention_mask=None):
|
|
r"""
|
|
The MPNetForSequenceClassification forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`MPNetModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`MPNetModel`.
|
|
attention_mask (list, optional):
|
|
See :class:`MPNetModel`.
|
|
|
|
Returns:
|
|
Tensor: Returns tensor `logits`, a tensor of the input text classification logits.
|
|
Shape as `[batch_size, num_classes]` and dtype as float32.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import MPNetForSequenceClassification, MPNetTokenizer
|
|
|
|
tokenizer = MPNetTokenizer.from_pretrained('mpnet-base')
|
|
model = MPNetForSequenceClassification.from_pretrained('mpnet-base')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
outputs = model(**inputs)
|
|
|
|
logits = outputs[0]
|
|
"""
|
|
|
|
_, pooled_output = self.mpnet(input_ids, position_ids=position_ids, attention_mask=attention_mask)
|
|
pooled_output = self.dropout(pooled_output)
|
|
|
|
logits = self.classifier(pooled_output)
|
|
|
|
return logits
|
|
|
|
|
|
class MPNetForMultipleChoice(MPNetPretrainedModel):
|
|
"""
|
|
MPNet Model with a linear layer on top of the hidden-states output layer,
|
|
designed for multiple choice tasks like RocStories/SWAG tasks.
|
|
|
|
Args:
|
|
config (:class:`MPNetConfig`):
|
|
An instance of MPNetConfig used to construct MPNetModel.
|
|
num_choices (int, optional):
|
|
The number of choices. Defaults to `2`.
|
|
"""
|
|
|
|
def __init__(self, config: MPNetConfig, num_choices=2):
|
|
super(MPNetForMultipleChoice, self).__init__(config)
|
|
self.num_choices = num_choices
|
|
self.mpnet = MPNetModel(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, position_ids=None, attention_mask=None):
|
|
r"""
|
|
The MPNetForMultipleChoice forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`MPNetModel` and shape as [batch_size, num_choice, sequence_length].
|
|
position_ids(Tensor, optional):
|
|
See :class:`MPNetModel` and shape as [batch_size, num_choice, sequence_length].
|
|
attention_mask (list, optional):
|
|
See :class:`MPNetModel` and shape as [batch_size, num_choice, sequence_length].
|
|
|
|
Returns:
|
|
Tensor: Returns tensor `reshaped_logits`, a tensor of the multiple choice classification logits.
|
|
Shape as `[batch_size, num_choice]` and dtype as `float32`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
import paddle
|
|
from paddlenlp.transformers import MPNetForMultipleChoice, MPNetTokenizer
|
|
|
|
tokenizer = MPNetTokenizer.from_pretrained('mpnet-base')
|
|
model = MPNetForMultipleChoice.from_pretrained('mpnet-base')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
|
|
logits = model(**inputs)
|
|
|
|
"""
|
|
# input_ids: [bs, num_choice, seq_l]
|
|
input_ids = input_ids.reshape(shape=(-1, input_ids.shape[-1])) # flat_input_ids: [bs*num_choice,seq_l]
|
|
|
|
if position_ids is not None:
|
|
position_ids = position_ids.reshape(shape=(-1, position_ids.shape[-1]))
|
|
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask.reshape(shape=(-1, attention_mask.shape[-1]))
|
|
|
|
_, pooled_output = self.mpnet(input_ids, position_ids=position_ids, attention_mask=attention_mask)
|
|
pooled_output = self.dropout(pooled_output)
|
|
|
|
logits = self.classifier(pooled_output) # logits: (bs*num_choice,1)
|
|
reshaped_logits = logits.reshape(shape=(-1, self.num_choices)) # logits: (bs, num_choice)
|
|
|
|
return reshaped_logits
|
|
|
|
|
|
class MPNetForTokenClassification(MPNetPretrainedModel):
|
|
"""
|
|
MPNet Model with a linear layer on top of the hidden-states output layer,
|
|
designed for token classification tasks like NER tasks.
|
|
|
|
Args:
|
|
config (:class:`MPNetConfig`):
|
|
An instance of MPNetConfig used to construct MPNetModel.
|
|
"""
|
|
|
|
def __init__(self, config: MPNetConfig):
|
|
super(MPNetForTokenClassification, self).__init__(config)
|
|
self.mpnet = MPNetModel(config)
|
|
self.num_labels = config.num_labels
|
|
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, position_ids=None, attention_mask=None):
|
|
r"""
|
|
The MPNetForTokenClassification forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`MPNetModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`MPNetModel`.
|
|
attention_mask (list, optional):
|
|
See :class:`MPNetModel`.
|
|
|
|
Returns:
|
|
Tensor: Returns tensor `logits`, a tensor of the input token classification logits.
|
|
Shape as `[batch_size, sequence_length, num_classes]` and dtype as `float32`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import MPNetForTokenClassification, MPNetTokenizer
|
|
|
|
tokenizer = MPNetTokenizer.from_pretrained('mpnet-base')
|
|
model = MPNetForTokenClassification.from_pretrained('mpnet-base')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
|
|
logits = model(**inputs)
|
|
"""
|
|
sequence_output, _ = self.mpnet(input_ids, position_ids=position_ids, attention_mask=attention_mask)
|
|
sequence_output = self.dropout(sequence_output)
|
|
|
|
logits = self.classifier(sequence_output)
|
|
|
|
return logits
|
|
|
|
|
|
class MPNetForQuestionAnswering(MPNetPretrainedModel):
|
|
"""
|
|
MPNet 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:
|
|
config (:class:`MPNetConfig`):
|
|
An instance of MPNetConfig used to construct MPNetModel.
|
|
"""
|
|
|
|
def __init__(self, config: MPNetConfig):
|
|
super(MPNetForQuestionAnswering, self).__init__(config)
|
|
self.mpnet = MPNetModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(self, input_ids, position_ids=None, attention_mask=None):
|
|
r"""
|
|
The MPNetForQuestionAnswering forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`MPNetModel`.
|
|
position_ids (Tensor, optional):
|
|
See :class:`MPNetModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`MPNetModel`.
|
|
|
|
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 MPNetForQuestionAnswering, MPNetTokenizer
|
|
|
|
tokenizer = MPNetTokenizer.from_pretrained('mpnet-base')
|
|
model = MPNetForQuestionAnswering.from_pretrained('mpnet-base')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
outputs = model(**inputs)
|
|
|
|
start_logits = outputs[0]
|
|
end_logits = outputs[1]
|
|
|
|
"""
|
|
|
|
sequence_output, _ = self.mpnet(input_ids, position_ids=position_ids, attention_mask=attention_mask)
|
|
logits = self.qa_outputs(sequence_output)
|
|
logits = paddle.transpose(logits, perm=[2, 0, 1])
|
|
|
|
start_logits, end_logits = paddle.unstack(x=logits, axis=0)
|
|
|
|
return start_logits, end_logits
|