wehub-resource-sync
1381 lines
59 KiB
Python
1381 lines
59 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2021 The HuggingFace Inc. team. 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 typing import List, Optional, Tuple
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import paddle
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import paddle.nn as nn
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from paddle import Tensor
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from paddle.common_ops_import import convert_dtype
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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 ..activations import get_activation
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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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tuple_output,
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)
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__all__ = [
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"RoFormerModel",
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"RoFormerPretrainedModel",
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"RoFormerForSequenceClassification",
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"RoFormerForTokenClassification",
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"RoFormerForQuestionAnswering",
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"RoFormerForMaskedLM",
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"RoFormerForMultipleChoice",
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"RoFormerForCausalLM",
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]
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from .configuration import (
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ROFORMER_PRETRAINED_INIT_CONFIGURATION,
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ROFORMER_PRETRAINED_RESOURCE_FILES_MAP,
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RoFormerConfig,
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)
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class RoFormerEmbeddings(nn.Layer):
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"""
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Include embeddings from word and token_type embeddings
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"""
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def __init__(
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self,
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vocab_size,
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embedding_size=768,
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hidden_dropout_prob=0.1,
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type_vocab_size=2,
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):
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super().__init__()
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self.word_embeddings = nn.Embedding(vocab_size, embedding_size)
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self.token_type_embeddings = nn.Embedding(type_vocab_size, embedding_size)
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self.layer_norm = nn.LayerNorm(embedding_size)
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self.dropout = nn.Dropout(hidden_dropout_prob)
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def forward(self, input_ids=None, token_type_ids=None, inputs_embeds=None):
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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if token_type_ids is None:
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token_type_ids_shape = inputs_embeds.shape[:-1]
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token_type_ids = paddle.zeros(token_type_ids_shape, dtype="int64")
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + 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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class RotaryPositionEmbedding(nn.Layer):
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def __init__(self, dim, max_position_embeddings=512):
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super().__init__()
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inv_freq = 1.0 / (10000 ** (paddle.arange(0, dim, 2, dtype=paddle.get_default_dtype()) / dim))
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t = paddle.arange(max_position_embeddings, dtype=paddle.get_default_dtype())
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freqs = paddle.matmul(t.unsqueeze(1), inv_freq.unsqueeze(0))
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self.register_buffer("sin", freqs.sin(), persistable=False)
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self.register_buffer("cos", freqs.cos(), persistable=False)
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def forward(self, x, offset=0):
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# x shape [batch_size, num_heads, seqlen, head_dim]
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seqlen = x.shape[-2]
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sin, cos = (
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self.sin[offset : offset + seqlen, :],
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self.cos[offset : offset + seqlen, :],
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)
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x1, x2 = x[..., 0::2], x[..., 1::2]
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# [cos_nθ, -sin_nθ] [x1]
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# [sin_nθ, cos_nθ] [x2]
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# => [x1 * cos_nθ - x2 * sin_nθ, x1 * sin_nθ + x2 * cos_nθ]
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return paddle.stack([x1 * cos - x2 * sin, x1 * sin + x2 * cos], axis=-1).flatten(-2, -1)
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class MultiHeadAttentionWithRotary(nn.MultiHeadAttention):
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def __init__(
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self,
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embed_dim,
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num_heads,
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dropout=0.0,
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kdim=None,
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vdim=None,
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need_weights=False,
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rotary_value=False,
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max_position_embeddings=512,
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):
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super().__init__(embed_dim, num_heads, dropout, kdim, vdim, need_weights)
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self.rotary_value = rotary_value
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self.rotary = RotaryPositionEmbedding(self.head_dim, max_position_embeddings)
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def _prepare_qkv(self, query, key, value, cache=None):
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q = self.q_proj(query)
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q = paddle.reshape(x=q, shape=[0, 0, self.num_heads, self.head_dim])
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q = paddle.transpose(x=q, perm=[0, 2, 1, 3])
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k, v = self.compute_kv(key, value)
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offset = 0 if cache is None else cache.k.shape[2]
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# rotary q,k,v
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q = self.rotary(q, offset=offset)
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k = self.rotary(k, offset=offset)
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if self.rotary_value:
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v = self.rotary(v, offset=offset)
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if isinstance(cache, self.Cache):
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# for decoder self-attention in inference
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k = paddle.concat([cache.k, k], axis=2)
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v = paddle.concat([cache.v, v], axis=2)
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cache = self.Cache(k, v)
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return (q, k, v) if cache is None else (q, k, v, cache)
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class TransformerEncoderLayerWithRotary(nn.TransformerEncoderLayer):
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def __init__(
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self,
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d_model,
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nhead,
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dim_feedforward,
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dropout=0.1,
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activation="relu",
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attn_dropout=None,
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act_dropout=None,
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normalize_before=False,
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rotary_value=False,
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max_position_embeddings=512,
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**kwargs
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):
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super().__init__(
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d_model,
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nhead,
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dim_feedforward,
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dropout=dropout,
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activation=activation,
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attn_dropout=attn_dropout,
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act_dropout=act_dropout,
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normalize_before=normalize_before,
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)
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self.self_attn = MultiHeadAttentionWithRotary(
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d_model,
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nhead,
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dropout=attn_dropout,
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rotary_value=rotary_value,
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max_position_embeddings=max_position_embeddings,
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)
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self._config.update({"rotary_value": rotary_value, "max_position_embeddings": max_position_embeddings})
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class RoFormerPooler(nn.Layer):
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def __init__(self, hidden_size, pool_act="tanh"):
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super().__init__()
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self.dense = nn.Linear(hidden_size, hidden_size)
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self.activation = get_activation(pool_act)
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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 RoFormerLMPredictionHead(nn.Layer):
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def __init__(self, embedding_size, hidden_size, vocab_size, activation, embedding_weights=None):
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super().__init__()
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self.transform = nn.Linear(hidden_size, embedding_size)
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self.activation = get_activation(activation)
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self.layer_norm = nn.LayerNorm(embedding_size)
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self.decoder_weight = (
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self.create_parameter(
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shape=[vocab_size, embedding_size],
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dtype=self.transform.weight.dtype,
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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(shape=[vocab_size], dtype=self.decoder_weight.dtype, is_bias=True)
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def forward(self, hidden_states):
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hidden_states = self.transform(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 RoFormerOnlyMLMHead(nn.Layer):
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def __init__(self, embedding_size, hidden_size, vocab_size, activation, embedding_weights):
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super().__init__()
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self.predictions = RoFormerLMPredictionHead(
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embedding_size,
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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activation=activation,
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embedding_weights=embedding_weights,
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)
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def forward(self, sequence_output):
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prediction_scores = self.predictions(sequence_output)
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return prediction_scores
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class RoFormerPretrainedModel(PretrainedModel):
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r"""
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An abstract class for pretrained RoFormer models. It provides RoFormer 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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config_class = RoFormerConfig
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resource_files_names = {"model_state": "model_state.pdparams"}
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base_model_prefix = "roformer"
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pretrained_init_configuration = ROFORMER_PRETRAINED_INIT_CONFIGURATION
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pretrained_resource_files_map = ROFORMER_PRETRAINED_RESOURCE_FILES_MAP
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@classmethod
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def _get_name_mappings(cls, config: RoFormerConfig) -> List[StateDictNameMapping]:
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mappings: List[StateDictNameMapping] = []
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model_mappings = [
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"embeddings.word_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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["pooler.dense.weight", None, "transpose"],
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"pooler.dense.bias",
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# for TokenClassification
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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}.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}.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}.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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model_mappings.extend(layer_mappings)
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init_name_mappings(model_mappings)
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# base-model prefix "RoFormerModel"
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if "RoFormerModel" not in config.architectures:
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for mapping in model_mappings:
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mapping[0] = "roformer." + mapping[0]
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mapping[1] = "roformer." + mapping[1]
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if "RoFormerForMaskedLM" in config.architectures:
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model_mappings.extend(
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[
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["cls.predictions.transform.dense.weight", "cls.predictions.transform.weight", "transpose"],
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["cls.predictions.transform.dense.bias", "cls.predictions.transform.bias"],
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["cls.predictions.transform.LayerNorm.weight", "cls.predictions.layer_norm.weight"],
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["cls.predictions.transform.LayerNorm.bias", "cls.predictions.layer_norm.bias"],
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["cls.predictions.decoder.bias", "cls.predictions.decoder_bias"],
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]
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)
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# downstream mappings
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if "RoFormerForQuestionAnswering" in config.architectures:
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model_mappings.extend(
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[["qa_outputs.weight", "classifier.weight", "transpose"], ["qa_outputs.bias", "classifier.bias"]]
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)
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if (
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"RoFormerForMultipleChoice" in config.architectures
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or "RoFormerForSequenceClassification" in config.architectures
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or "RoFormerForTokenClassification" in config.architectures
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):
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model_mappings.extend([["classifier.weight", None, "transpose"]])
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init_name_mappings(model_mappings)
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mappings = [StateDictNameMapping(*mapping, index=index) for index, mapping in enumerate(model_mappings)]
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return 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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# 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.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 RoFormerModel(RoFormerPretrainedModel):
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"""
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The bare RoFormerModel 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:`RoFormerConfig`):
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An instance of RoFormerConfig used to construct RoFormerModel.
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"""
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def __init__(
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self,
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config: RoFormerConfig,
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):
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super().__init__(config)
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self.pad_token_id = config.pad_token_id
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self.eos_token_id = config.eos_token_id
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self.initializer_range = config.initializer_range
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if config.embedding_size != config.hidden_size:
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self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size)
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self.embeddings = RoFormerEmbeddings(
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config.vocab_size,
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config.embedding_size,
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config.hidden_dropout_prob,
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config.type_vocab_size,
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)
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encoder_layer = TransformerEncoderLayerWithRotary(
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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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rotary_value=config.rotary_value,
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max_position_embeddings=config.max_position_embeddings,
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)
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self.encoder = nn.TransformerEncoder(encoder_layer, config.num_hidden_layers)
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self.pooler = RoFormerPooler(config.hidden_size, config.pool_act)
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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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attention_mask: Optional[Tensor] = None,
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inputs_embeds: Optional[Tensor] = None,
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past_key_values: Optional[Tuple[Tuple[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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The RoFormerModel forward method, overrides the `__call__()` special method.
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Args:
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input_ids (Tensor, optional):
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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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attention_mask (Tensor, optional):
|
|
Mask used in multi-head attention to avoid performing attention to some unwanted positions,
|
|
usually the paddings or the subsequent positions.
|
|
Its data type can be int, float and bool.
|
|
When the data type is bool, the `masked` tokens have `False` values and the others have `True` values.
|
|
When the data type is int, the `masked` tokens have `0` values and the others have `1` values.
|
|
When the data type is float, the `masked` tokens have `-INF` values and the others have `0` values.
|
|
It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
|
|
For example, its shape can be [batch_size, sequence_length], [batch_size, sequence_length, sequence_length],
|
|
[batch_size, num_attention_heads, sequence_length, sequence_length].
|
|
Defaults to `None`, which means nothing needed to be prevented attention to.
|
|
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`.
|
|
past_key_values (tuple(tuple(Tensor)), optional):
|
|
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)`.
|
|
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 RoFormerModel, RoFormerTokenizer
|
|
|
|
tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base')
|
|
model = RoFormerModel.from_pretrained('roformer-chinese-char-base')
|
|
|
|
tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd")
|
|
output = model(**tokenized_inputs)
|
|
|
|
"""
|
|
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.")
|
|
|
|
# init the default bool value
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
past_key_values_length = 0
|
|
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(self.pooler.dense.weight.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)
|
|
|
|
# For 2D attention_mask from tokenizer
|
|
elif attention_mask.ndim == 2:
|
|
attention_mask = paddle.unsqueeze(attention_mask, axis=[1, 2]).astype(paddle.get_default_dtype())
|
|
attention_mask = (1.0 - attention_mask) * -1e4
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids=input_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds
|
|
)
|
|
|
|
if hasattr(self, "embeddings_project"):
|
|
embedding_output = self.embeddings_project(embedding_output)
|
|
|
|
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)
|
|
return (sequence_output, pooled_output)
|
|
else:
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output)
|
|
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 RoFormerForQuestionAnswering(RoFormerPretrainedModel):
|
|
r"""
|
|
RoFormer 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:`RoFormerConfig`):
|
|
An instance of RoFormerConfig used to construct RoFormerForQuestionAnswering.
|
|
"""
|
|
|
|
def __init__(self, config: RoFormerConfig):
|
|
super().__init__(config)
|
|
self.roformer = RoFormerModel(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, 2)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[Tensor] = None,
|
|
token_type_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"""
|
|
The RoFormerForQuestionAnswering forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`RoFormerModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
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 RoFormerForQuestionAnswering, RoFormerTokenizer
|
|
|
|
tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base')
|
|
model = RoFormerForQuestionAnswering.from_pretrained('roformer-chinese-char-base')
|
|
|
|
tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd")
|
|
outputs = model(**tokenized_inputs)
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.roformer(
|
|
input_ids,
|
|
token_type_ids=token_type_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)
|
|
start_logits, end_logits = paddle.unstack(x=logits, axis=-1)
|
|
|
|
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 tuple_output(output, total_loss)
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class RoFormerForSequenceClassification(RoFormerPretrainedModel):
|
|
r"""
|
|
RoFormer Model with a linear layer on top of the output layer,
|
|
designed for sequence classification/regression tasks like GLUE tasks.
|
|
|
|
Args:
|
|
config (:class:`RoFormerConfig`):
|
|
An instance of RoFormerConfig used to construct RoFormerForSequenceClassification.
|
|
"""
|
|
|
|
def __init__(self, config: RoFormerConfig):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.roformer = RoFormerModel(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: Optional[Tensor] = None,
|
|
token_type_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 RoFormerForSequenceClassification forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`RoFormerModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
labels (Tensor of shape `(batch_size,)`, optional):
|
|
Labels for computing the sequence classification/regression loss.
|
|
Indices should be in `[0, ..., num_labels - 1]`. If `num_labels == 1`
|
|
a regression loss is computed (Mean-Square loss), If `num_labels > 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 RoFormerForSequenceClassification, RoFormerTokenizer
|
|
|
|
tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base')
|
|
model = RoFormerForSequenceClassification.from_pretrained('roformer-chinese-char-base')
|
|
|
|
tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd")
|
|
logits = model(**tokenized_inputs)
|
|
|
|
"""
|
|
outputs = self.roformer(
|
|
input_ids,
|
|
token_type_ids=token_type_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)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == paddle.int64 or labels.dtype == paddle.int32):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = paddle.nn.MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = paddle.nn.CrossEntropyLoss()
|
|
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = paddle.nn.BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return tuple_output(output, loss)
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class RoFormerForTokenClassification(RoFormerPretrainedModel):
|
|
r"""
|
|
RoFormer Model with a linear layer on top of the hidden-states output layer,
|
|
designed for token classification tasks like NER tasks.
|
|
|
|
Args:
|
|
config (:class:`RoFormerConfig`):
|
|
An instance of RoFormerConfig used to construct RoFormerForTokenClassification.
|
|
"""
|
|
|
|
def __init__(self, config: RoFormerConfig):
|
|
super().__init__(config)
|
|
self.roformer = RoFormerModel(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: Optional[Tensor] = None,
|
|
token_type_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 RoFormerForTokenClassification forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`RoFormerModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
labels (Tensor of shape `(batch_size, sequence_length)`, optional):
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., num_labels - 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 RoFormerForTokenClassification, RoFormerTokenizer
|
|
|
|
tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base')
|
|
model = RoFormerForTokenClassification.from_pretrained('roformer-chinese-char-base')
|
|
|
|
tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd")
|
|
logits = model(**tokenized_inputs)
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
outputs = self.roformer(
|
|
input_ids,
|
|
token_type_ids=token_type_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.num_labels)), labels.reshape((-1,)))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return tuple_output(output, loss)
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class RoFormerForMultipleChoice(RoFormerPretrainedModel):
|
|
"""
|
|
RoFormerModel with a linear layer on top of the hidden-states output layer,
|
|
designed for multiple choice tasks like RocStories/SWAG tasks.
|
|
|
|
Args:
|
|
config (:class:`RoFormerConfig`):
|
|
An instance of RoFormerConfig used to construct RoFormerForMultipleChoice.
|
|
"""
|
|
|
|
def __init__(self, config: RoFormerConfig):
|
|
super().__init__(config)
|
|
self.roformer = RoFormerModel(config)
|
|
self.num_choices = config.num_choices
|
|
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,
|
|
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 RoFormerForMultipleChoice forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`RoFormerModel` and shape as [batch_size, num_choice, sequence_length].
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`RoFormerModel` and shape as [batch_size, num_choice, sequence_length].
|
|
attention_mask (Tensor, optional):
|
|
See :class:`RoFormerModel` and shape as [batch_size, num_choice, sequence_length].
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
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 RoFormerForMultipleChoice, RoFormerTokenizer
|
|
|
|
tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base')
|
|
model = RoFormerForMultipleChoice.from_pretrained('roformer-chinese-char-base')
|
|
|
|
data = [
|
|
{
|
|
"question": "如何打开ipad屏幕？",
|
|
"answer1": "按音量按钮。",
|
|
"answer2": "按下锁定按钮。",
|
|
"label": 1,
|
|
},
|
|
{
|
|
"question": "如何缩进一些文本？",
|
|
"answer1": "在开始写之前留一些空格。",
|
|
"answer2": "按空格键。",
|
|
"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"])
|
|
|
|
tokenized_inputs = tokenizer(text, text_pair, padding=True, return_tensors="pd")
|
|
reshaped_logits = model(**tokenized_inputs)
|
|
print(reshaped_logits.shape)
|
|
# [2, 2]
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
input_ids = input_ids.reshape((-1, input_ids.shape[-1])) if input_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.roformer(
|
|
input_ids,
|
|
token_type_ids=token_type_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, self.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 tuple_output(output, loss)
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class RoFormerForMaskedLM(RoFormerPretrainedModel):
|
|
"""
|
|
RoFormer Model with a `masked language modeling` head on top.
|
|
|
|
Args:
|
|
config (:class:`RoFormerConfig`):
|
|
An instance of RoFormerConfig used to construct RoFormerForMaskedLM.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: RoFormerConfig):
|
|
super().__init__(config)
|
|
self.roformer = RoFormerModel(config)
|
|
self.cls = RoFormerOnlyMLMHead(
|
|
config.embedding_size,
|
|
config.hidden_size,
|
|
config.vocab_size,
|
|
config.hidden_act,
|
|
embedding_weights=self.roformer.embeddings.word_embeddings.weight,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[Tensor] = None,
|
|
token_type_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 RoFormerForMaskedLM forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`RoFormerModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
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 RoFormerForMaskedLM, RoFormerTokenizer
|
|
|
|
tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base')
|
|
model = RoFormerForMaskedLM.from_pretrained('roformer-chinese-char-base')
|
|
|
|
tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd")
|
|
logits = model(**tokenized_inputs)
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.roformer(
|
|
input_ids,
|
|
token_type_ids=token_type_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.cls(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 tuple_output(output, masked_lm_loss)
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class RoFormerForCausalLM(RoFormerPretrainedModel):
|
|
"""
|
|
RoFormer Model with a `Causal language modeling` head on top.
|
|
|
|
Args:
|
|
config (:class:`RoFormerConfig`):
|
|
An instance of RoFormerConfig used to construct RoFormerForCausalLM.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: RoFormerConfig):
|
|
super().__init__(config)
|
|
self.roformer = RoFormerModel(config)
|
|
self.cls = RoFormerOnlyMLMHead(
|
|
config.embedding_size,
|
|
config.hidden_size,
|
|
config.vocab_size,
|
|
config.hidden_act,
|
|
embedding_weights=self.roformer.embeddings.word_embeddings.weight,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[Tensor] = None,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
inputs_embeds: Optional[Tensor] = None,
|
|
labels: Optional[Tensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[Tensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
The RoFormerForCausalLM forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`RoFormerModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
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]`.
|
|
past_key_values (tuple(tuple(Tensor)), optional):
|
|
See :class:`RoFormerModel`.
|
|
use_cache (Tensor, optional):
|
|
See :class:`RoFormerModel`.
|
|
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 RoFormerForCausalLM, RoFormerTokenizer
|
|
|
|
tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-sim-char-ft-base')
|
|
model = RoFormerForCausalLM.from_pretrained('roformer-chinese-sim-char-ft-base')
|
|
|
|
tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd")
|
|
logits = model(**tokenized_inputs)
|
|
print(logits.shape)
|
|
# [1, 11, 12000]
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.roformer(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
attention_mask=attention_mask,
|
|
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.cls(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 tuple_output(output, lm_loss)
|
|
|
|
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, use_cache=False, cache=None, **kwargs):
|
|
# only last token for inputs_ids if past is defined in kwargs
|
|
token_type_ids = kwargs.get("token_type_ids", None)
|
|
attention_mask = kwargs.get("attention_mask", None)
|
|
|
|
if attention_mask is not None:
|
|
if "int" in convert_dtype(attention_mask.dtype):
|
|
attention_mask = (1.0 - attention_mask) * -1e4
|
|
|
|
if cache is not None:
|
|
input_ids = input_ids[:, -1].unsqueeze(-1)
|
|
token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
|
|
if attention_mask.ndim == 4:
|
|
attention_mask = attention_mask[:, -1, -1, :].unsqueeze([1, 2])
|
|
|
|
return {
|
|
"input_ids": input_ids,
|
|
"token_type_ids": token_type_ids,
|
|
"attention_mask": attention_mask,
|
|
"past_key_values": cache,
|
|
"use_cache": use_cache,
|
|
}
|
|
|
|
@staticmethod
|
|
def update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False):
|
|
# Update the model inputs during generation.
|
|
# Note that If `token_type_ids` and `attention_mask` in `model_kwargs`
|
|
# and they contain pad value, the result vectors updated by this method
|
|
# may be different from expected. In this case, you need to rewrite the
|
|
# method.
|
|
|
|
# update cache
|
|
if isinstance(outputs, tuple):
|
|
model_kwargs["cache"] = outputs[1]
|
|
|
|
# update token_type_ids with last value
|
|
if "token_type_ids" in model_kwargs and model_kwargs["token_type_ids"] is not None:
|
|
token_type_ids = model_kwargs["token_type_ids"]
|
|
# token type id = 1
|
|
model_kwargs["token_type_ids"] = paddle.concat(
|
|
[token_type_ids, paddle.ones_like(token_type_ids[:, -1:])], axis=-1
|
|
)
|
|
|
|
# update attention_mask
|
|
if not is_encoder_decoder and "attention_mask" in model_kwargs:
|
|
attention_mask = model_kwargs["attention_mask"]
|
|
# nn.Pad2D don't support the data type `bool`
|
|
if convert_dtype(attention_mask.dtype) == "bool":
|
|
attention_mask = paddle.cast(attention_mask, "int64")
|
|
if len(attention_mask.shape) == 4:
|
|
attention_mask = attention_mask.expand((-1, -1, attention_mask.shape[-1], -1))
|
|
attention_mask = nn.Pad2D([0, 0, 0, 1], mode="replicate")(attention_mask)
|
|
attention_mask = nn.Pad2D([0, 1, 0, 0], value=-1e4)(attention_mask)
|
|
dtype = convert_dtype(attention_mask.dtype)
|
|
if "int" in dtype:
|
|
attention_mask[:, :, -1, -1] = 1
|
|
elif "float" in dtype:
|
|
attention_mask[:, :, -1, -1] = 0.0
|
|
else:
|
|
raise ValueError("The data type of input `attention_mask` must " "be bool, int or float")
|
|
else:
|
|
# convert to 4D attention_mask
|
|
attention_mask = paddle.concat(
|
|
[attention_mask, paddle.ones([attention_mask.shape[0], 1], dtype="int64")], axis=-1
|
|
)
|
|
if "int" in convert_dtype(attention_mask.dtype):
|
|
attention_mask = (1.0 - attention_mask) * -1e4
|
|
attention_mask = attention_mask.unsqueeze([1, 2]).expand((-1, -1, attention_mask.shape[-1], -1))
|
|
|
|
token_type_ids = model_kwargs["token_type_ids"]
|
|
mask = token_type_ids[:, None, :] > token_type_ids[:, :, None]
|
|
# we need expand attention_mask
|
|
attention_mask = paddle.where(mask.unsqueeze(1), paddle.to_tensor(-1e4), attention_mask)
|
|
model_kwargs["attention_mask"] = attention_mask
|
|
|
|
return model_kwargs
|