ChatGPTBook/UniLMProj 代码地址
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README.md
本项目为书籍《ChatGPT原理与实战:大型语言模型的算法、技术和私有化》中第3章《预训练语言模型》实战部分代码-基于夸夸闲聊数据的UniLM模型实战。
项目简介
针对夸夸闲聊数据集,利用UniLM模型进行模型训练及测试,更深入地了解预训练语言模型的使用方法,完成一个生成式闲聊机器人任务。
项目主要结构如下:
- data 存放数据的文件夹
- dirty_word.txt 敏感词数据
- douban_kuakua_qa.txt 原始语料 【数据量:大概20M的样子】==》用于增量训练
- sample.json 处理后的语料样例
- kuakua_robot_model 已训练好的模型路径
- config.json
- pytorch_model.bin
- vocab.txt
- pretrain_model UniLm预训练文件路径
- config.json
- pytorch_model.bin
- vocab.txt
- chatbot.py 模型推理文件
- configuration_unilm.py UniLm配置文件
- data_helper.py 数据预处理文件
- data_set.py 数据类文件
- modeling_unilm.py UniLm模型文件
- train.py 模型训练文件
- dirty_recognize.py 敏感词检测文件
增量训练的数据样例:
Q: 要去打球赛了求表扬 A: 真棒好好打乒乓球! Q: 要去打球赛了求表扬 A: 是篮球哈哈哈 Q: 要去打球赛了求表扬 A: 篮板王就是你! Q: 要去打球赛了求表扬 A: 加油别把鞋踢脏喽 Q: 要去打球赛了求表扬 A: 多买点儿币! Q: 要去打球赛了求表扬 A: 已经脏了 Q: 要去打球赛了求表扬 A: 好滴 Q: 要去打球赛了求表扬 A: 这个配色是是真心不太合我的胃口,还有为什么白鞋要配黑袜子 Q: 要去打球赛了求表扬 A: 这不是表扬组吗hhh你咋来拆台 Q: 要去打球赛了求表扬 A: 我不是,我没有,别瞎说哈 Q: 要去打球赛了求表扬 A: 全场最帅(・ัω・ั),卡胃踩脚拇指戳肋骨无毒神掌天下无敌,然后需要代打嘛 Q: 要去打球赛了求表扬 A: 你走! Q: 要去打球赛了求表扬 A: 8要! Q: 要去打球赛了求表扬 A: 我不,我还想问问什么鞋码,多高多重,打什么位置的
注意:由于GitHub不方便放模型文件,因此data文件中douban_kuakua_qa.txt文件、kuakua_robot_model文件夹和pretrain_model文件夹中的模型bin文件,请从百度云盘中下载。【bert模型大小:400MB,用于增量训练的模型,应该是来自https://huggingface.co/bert-base-chinese/tree/main下载的原始bert文件】
文件名称 | 下载地址 | 提取码 |
---|---|---|
pretrain_model | 百度云 | 7h4a |
kuakua_robot_model | 百度云 | j954 |
data | 百度云 | 3sz3 |
由于敏感词表中包含大量敏感词,导致百度云的data链接会失效,因此将敏感词之间放到项目的data目录下。
环境配置
模型训练或推理所需环境,请参考requirements.txt文件。
数据处理
数据预处理需要运行data_helper.py文件,会在data文件夹中生成训练集和测试集文件。
命令如下:
python3 data_helper.py
注意:如果需要修改数据生成路径或名称,请修改data_helper.py文件147-150行,自行定义。
模型训练
模型训练需要运行train.py文件,会自动生成output_dir文件夹,存放每个epoch保存的模型文件。
命令如下:
python3 train.py --device 0 \ --data_dir "data/" \ --src_file "train.json" \ --model_name_or_path "pretrain_model/" \ --max_seq_length 256 \ --train_batch_size 16 \ --num_train_epochs 10
注意:当服务器资源不同或读者更换数据等时,可以在模型训练时修改响应参数,详细参数说明见代码或阅读书3.5.4小节。
模型训练示例如下:
模型推理
模型训练需要运行chatbot.py文件,可以采用项目中以提供的模型,也可以采用自己训练后的模型。
命令如下:
python3 chatbot.py --device 0 --topk 3 --topp 0.95 --max_len 32
注意:如果修改模型路径,请修改--model_path参数。
USER:工作不开心
CHAT-BOT:我也是,不过现在开始努力找工作啦!一起加油鸭~
USER:工作不开心
CHAT-BOT:那就祝福你,能找到喜欢的工作吧。
USER:工作不开心
CHAT-BOT:你也要加油鸭!
USER:工作不开心
CHAT-BOT:我也是,不过总有一天就会好起来的
样例2
USER:我分手了
CHAT-BOT:分手快乐,你会遇到更好的。
USER:我分手了
CHAT-BOT:旧的不去新的不来,你会遇见更好的
USER:我分手了
CHAT-BOT:分的好,下一个更乖
样例3
USER:好想去吃火锅,但是我在减肥
CHAT-BOT:你好棒!一定会瘦下来的!
USER:好像去吃火锅,但是我在减肥
CHAT-BOT:吃火锅是为了能更好的减肥,你真棒!
我自己没有gpu,训练了一天,运行的流程如下:
D:\book\ChatGPTBook-main\UniLMProj 的目录 2023-09-25 17:54 <DIR> . 2023-09-25 17:22 <DIR> .. 2023-09-25 17:22 5,530 chatbot.py 2023-09-25 17:22 2,153 configuration_unilm.py 2023-09-25 21:04 <DIR> data 2023-09-25 17:22 4,375 data_helper.py 2023-09-25 17:22 9,173 data_set.py 2023-09-25 17:22 1,304 dirty_recognize.py 2023-09-25 17:22 <DIR> images 2023-09-25 17:22 <DIR> kuakua_robot_model 2023-09-25 17:22 13,452 modeling_unilm.py 2023-09-25 17:22 <DIR> pretrain_model 2023-09-25 17:22 4,199 README.md 2023-09-25 17:22 88 requirements.txt 2023-09-25 17:22 8,337 train.py 2023-09-25 17:22 1,861 trie.py 2023-09-25 17:54 <DIR> __pycache__ 10 个文件 50,472 字节 7 个目录 175,152,689,152 可用字节 D:\book\ChatGPTBook-main\UniLMProj>python data_helper.py total number of data: 121687 D:\book\ChatGPTBook-main\UniLMProj>dir data 驱动器 D 中的卷是 Data 卷的序列号是 CA99-555E D:\book\ChatGPTBook-main\UniLMProj\data 的目录 2023-09-25 21:06 <DIR> . 2023-09-25 17:54 <DIR> .. 2023-09-25 17:22 245,546 dirty_words.txt 2023-09-25 17:56 21,620,763 douban_kuakua_qa.txt 2023-09-25 17:22 446 sample.json 2023-09-25 21:06 14,272,447 train.json 4 个文件 36,139,202 字节 2 个目录 175,138,414,592 可用字节 D:\book\ChatGPTBook-main\UniLMProj>python train.py Traceback (most recent call last): File "D:\book\ChatGPTBook-main\UniLMProj\train.py", line 18, in <module> import torch ModuleNotFoundError: No module named 'torch' D:\book\ChatGPTBook-main\UniLMProj>pip install torch D:\book\ChatGPTBook-main\UniLMProj>pip install torch D:\book\ChatGPTBook-main\UniLMProj> D:\book\ChatGPTBook-main\UniLMProj>python train.py Traceback (most recent call last): File "D:\book\ChatGPTBook-main\UniLMProj\train.py", line 20, in <module> from transformers import BertTokenizer ModuleNotFoundError: No module named 'transformers' D:\book\ChatGPTBook-main\UniLMProj>pip install transformers D:\book\ChatGPTBook-main\UniLMProj>python train.py Traceback (most recent call last): File "D:\book\ChatGPTBook-main\UniLMProj\train.py", line 170, in <module> main() File "D:\book\ChatGPTBook-main\UniLMProj\train.py", line 86, in main model = UnilmForSeq2Seq.from_pretrained(args.model_name_or_path, config=config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Python311\Lib\site-packages\transformers\modeling_utils.py", line 2740, in from_pretrained raise EnvironmentError( OSError: Error no file named pytorch_model.bin, tf_model.h5, model.ckpt.index or flax_model.msgpack found in directory pretrain_model/. D:\book\ChatGPTBook-main\UniLMProj>python train.py Loading Train Dataset data/ convert seq2seq example: 108730it [00:29, 3741.34it/s] Load 108730 data C:\Python311\Lib\site-packages\transformers\optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning warnings.warn( 09/25/2023 21:45:57 - INFO - __main__ - ***** CUDA.empty_cache() ***** 09/25/2023 21:45:57 - INFO - __main__ - ***** Running training ***** 09/25/2023 21:45:57 - INFO - __main__ - Batch size = 16 09/25/2023 21:45:57 - INFO - __main__ - Num steps = 67960 Epoch: 0%| | 0/10 [00:00<?, ?it/s]C:\Python311\Lib\site-packages\torch\optim\lr_scheduler.py:265: UserWarning: To get the last learning rate computed by the scheduler, please use `get_last_lr()`. warnings.warn("To get the last learning rate computed by the scheduler, " Iter (loss=3.279): 100%|████████████████████████████████████████████████████████| 6796/6796 [25:32:28<00:00, 13.53s/it] 09/26/2023 23:18:26 - INFO - __main__ - ** ** * Saving fine-tuned model and optimizer ** ** * 32:28<00:00, 9.24s/it] Epoch: 10%|██████▌ | 1/10 [25:32:29<229:52:22, 91949.11s/it] Iter (loss=3.525): 31%|████████████████▎ | 2096/6796 [10:55:33<16:16:17, 12.46s/it]
训练了一天,最后的聊天效果:
D:\book\ChatGPTBook-main\UniLMProj>python chatbot.py --topk 3 --topp 0.95 --max_len 32 --model_name_or_path output_dir\checkpoint-6796\ cpu Chitchat Robot Starting user:你好啊 chat-bot:这么可爱的人真的很有缘分呢,你是最棒了! user:终于训练好了一个大模型机器人了 chat-bot:好厉害!我也想学,但是还是没有做到 user:哈哈哈 chat-bot:你真的很可爱!我也是,但不想被表扬。 user:大模型是什么呢? chat-bot:我们一起努力吧! user:你是谁? chat-bot:我是你! user:你逗我呢 chat-bot:你也是个可爱的小可爱,我们一起夸
我们来分析下模型源码:
# -*- coding:utf-8 -*- # @project: ChatGPT # @filename: modeling_unilm # @author: 刘聪NLP # @zhihu: https://www.zhihu.com/people/LiuCongNLP # @contact: [email protected] # @time: 2023/2/26 15:11 """ 文件说明: """ import copy import math import logging import torch from torch import nn import torch.nn.functional as F from torch.nn.modules.loss import _Loss from transformers.modeling_utils import PreTrainedModel from configuration_unilm import UnilmConfig from transformers.models.bert.modeling_bert import load_tf_weights_in_bert, BertPooler, BertIntermediate, BertOutput, \ BertSelfOutput, BertOnlyMLMHead, BertEmbeddings logger = logging.getLogger(__name__) BertLayerNorm = torch.nn.LayerNorm class BertSelfAttention(nn.Module): def __init__(self, config): super(BertSelfAttention, self).__init__() if config.hidden_size % config.num_attention_heads != 0: raise ValueError( "The hidden size (%d) is not a multiple of the number of attention " "heads (%d)" % (config.hidden_size, config.num_attention_heads)) self.num_attention_heads = config.num_attention_heads self.attention_head_size = int( config.hidden_size / config.num_attention_heads) self.all_head_size = self.num_attention_heads * self.attention_head_size self.query = nn.Linear(config.hidden_size, self.all_head_size) self.key = nn.Linear(config.hidden_size, self.all_head_size) self.value = nn.Linear(config.hidden_size, self.all_head_size) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) def transpose_for_scores(self, x): sz = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) x = x.view(*sz) return x.permute(0, 2, 1, 3) def forward(self, hidden_states, attention_mask, history_states=None): if history_states is None: mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(hidden_states) mixed_value_layer = self.value(hidden_states) else: x_states = torch.cat((history_states, hidden_states), dim=1) mixed_query_layer = self.query(hidden_states) mixed_key_layer = self.key(x_states) mixed_value_layer = self.value(x_states) query_layer = self.transpose_for_scores(mixed_query_layer) key_layer = self.transpose_for_scores(mixed_key_layer) value_layer = self.transpose_for_scores(mixed_value_layer) attention_scores = torch.matmul( query_layer / math.sqrt(self.attention_head_size), key_layer.transpose(-1, -2)) attention_scores = attention_scores + attention_mask attention_probs = nn.Softmax(dim=-1)(attention_scores) attention_probs = self.dropout(attention_probs) context_layer = torch.matmul(attention_probs, value_layer) context_layer = context_layer.permute(0, 2, 1, 3).contiguous() new_context_layer_shape = context_layer.size()[ :-2] + (self.all_head_size,) context_layer = context_layer.view(*new_context_layer_shape) return context_layer class BertAttention(nn.Module): def __init__(self, config): super(BertAttention, self).__init__() self.self = BertSelfAttention(config) self.output = BertSelfOutput(config) def forward(self, input_tensor, attention_mask, history_states=None): self_output = self.self( input_tensor, attention_mask, history_states=history_states) attention_output = self.output(self_output, input_tensor) return attention_output class BertLayer(nn.Module): def __init__(self, config): super(BertLayer, self).__init__() self.attention = BertAttention(config) self.intermediate = BertIntermediate(config) self.output = BertOutput(config) def forward(self, hidden_states, attention_mask, history_states=None): attention_output = self.attention( hidden_states, attention_mask, history_states=history_states) intermediate_output = self.intermediate(attention_output) layer_output = self.output(intermediate_output, attention_output) return layer_output class BertEncoder(nn.Module): def __init__(self, config): super(BertEncoder, self).__init__() layer = BertLayer(config) self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(config.num_hidden_layers)]) def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True, prev_embedding=None, prev_encoded_layers=None): assert (prev_embedding is None) == (prev_encoded_layers is None) all_encoder_layers = [] if (prev_embedding is not None) and (prev_encoded_layers is not None): history_states = prev_embedding for i, layer_module in enumerate(self.layer): hidden_states = layer_module( hidden_states, attention_mask, history_states=history_states) if output_all_encoded_layers: all_encoder_layers.append(hidden_states) if prev_encoded_layers is not None: history_states = prev_encoded_layers[i] else: for layer_module in self.layer: hidden_states = layer_module( hidden_states, attention_mask) if output_all_encoded_layers: all_encoder_layers.append(hidden_states) if not output_all_encoded_layers: all_encoder_layers.append(hidden_states) return all_encoder_layers class UnilmPreTrainedModel(PreTrainedModel): config_class = UnilmConfig load_tf_weights = load_tf_weights_in_bert base_model_prefix = "unilm" def _init_weights(self, module): if isinstance(module, (nn.Linear, nn.Embedding)): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) elif isinstance(module, BertLayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) if isinstance(module, nn.Linear) and module.bias is not None: module.bias.data.zero_() class UnilmModel(UnilmPreTrainedModel): def __init__(self, config): super(UnilmModel, self).__init__(config) self.embeddings = BertEmbeddings(config) self.encoder = BertEncoder(config) self.pooler = BertPooler(config) self.init_weights() def get_extended_attention_mask(self, input_ids, token_type_ids, attention_mask): if attention_mask is None: attention_mask = torch.ones_like(input_ids) if token_type_ids is None: token_type_ids = torch.zeros_like(input_ids) if attention_mask.dim() == 2: extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) elif attention_mask.dim() == 3: extended_attention_mask = attention_mask.unsqueeze(1) else: raise NotImplementedError extended_attention_mask = extended_attention_mask.to( dtype=next(self.parameters()).dtype) # fp16 compatibility extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 return extended_attention_mask def forward(self, input_ids, token_type_ids=None, attention_mask=None, output_all_encoded_layers=True): extended_attention_mask = self.get_extended_attention_mask( input_ids, token_type_ids, attention_mask) embedding_output = self.embeddings( input_ids, token_type_ids) encoded_layers = self.encoder(embedding_output, extended_attention_mask, output_all_encoded_layers=output_all_encoded_layers) sequence_output = encoded_layers[-1] pooled_output = self.pooler(sequence_output) if not output_all_encoded_layers: encoded_layers = encoded_layers[-1] return encoded_layers, pooled_output class LabelSmoothingLoss(_Loss): def __init__(self, label_smoothing=0, tgt_vocab_size=0, ignore_index=0, size_average=None, reduce=None, reduction='mean'): assert 0.0 < label_smoothing <= 1.0 self.ignore_index = ignore_index super(LabelSmoothingLoss, self).__init__( size_average=size_average, reduce=reduce, reduction=reduction) assert label_smoothing > 0 assert tgt_vocab_size > 0 smoothing_value = label_smoothing / (tgt_vocab_size - 2) one_hot = torch.full((tgt_vocab_size,), smoothing_value) one_hot[self.ignore_index] = 0 self.register_buffer('one_hot', one_hot.unsqueeze(0)) self.confidence = 1.0 - label_smoothing self.tgt_vocab_size = tgt_vocab_size def forward(self, output, target): assert self.tgt_vocab_size == output.size(2) batch_size, num_pos = target.size(0), target.size(1) output = output.view(-1, self.tgt_vocab_size) target = target.view(-1) model_prob = self.one_hot.repeat(target.size(0), 1) model_prob.scatter_(1, target.unsqueeze(1), self.confidence) model_prob.masked_fill_((target == self.ignore_index).unsqueeze(1), 0) return F.kl_div(output, model_prob.type_as(output), reduction='none').view(batch_size, num_pos, -1).sum(2) class UnilmForSeq2Seq(UnilmPreTrainedModel): """UniLM模型进行Seq2Seq的训练模型类""" def __init__(self, config): """模型初始化函数,定义模型训练所需的各个模块""" super(UnilmForSeq2Seq, self).__init__(config) self.bert = UnilmModel(config) self.cls = BertOnlyMLMHead(config) self.mask_lm = nn.CrossEntropyLoss(reduction='none') if hasattr(config, 'label_smoothing') and config.label_smoothing: self.mask_lm_smoothed = LabelSmoothingLoss(config.label_smoothing, config.vocab_size, ignore_index=0, reduction='none') else: self.mask_lm_smoothed = None self.init_weights() self.tie_weights() def tie_weights(self): """权重加载,加载预训练模型的embeddings部分权重""" self._tie_or_clone_weights(self.cls.predictions.decoder, self.bert.embeddings.word_embeddings) def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, masked_pos=None, masked_weights=None): """模型forward,向前传递函数""" # 获取Encoder部分的序列输出,维度[bs,seq_len,hidden_size] sequence_output, __ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) def gather_seq_out_by_pos(seq, pos): return torch.gather(seq, 1, pos.unsqueeze(2).expand(-1, -1, seq.size(-1))) def loss_mask_and_normalize(loss, mask): mask = mask.type_as(loss) loss = loss * mask denominator = torch.sum(mask) + 1e-5 return (loss / denominator).sum() if masked_lm_labels is None: if masked_pos is None: prediction_scores = self.cls(sequence_output) else: sequence_output_masked = gather_seq_out_by_pos(sequence_output, masked_pos) prediction_scores = self.cls(sequence_output_masked) return prediction_scores # 获取被掩码位置的向量 sequence_output_masked = gather_seq_out_by_pos(sequence_output, masked_pos) prediction_scores_masked = self.cls(sequence_output_masked) if self.mask_lm_smoothed: masked_lm_loss = self.mask_lm_smoothed(F.log_softmax(prediction_scores_masked.float(), dim=-1), masked_lm_labels) else: masked_lm_loss = self.mask_lm(prediction_scores_masked.transpose(1, 2).float(), masked_lm_labels) # 计算[Mask]标记的损失值 masked_lm_loss = loss_mask_and_normalize(masked_lm_loss.float(), masked_weights) return masked_lm_loss class UnilmForSeq2SeqDecodeSample(UnilmPreTrainedModel): """UniLM模型进行Seq2Seq的模型解码类""" def __init__(self, config): """模型初始化函数,定义模型训练所需的各个模块""" super(UnilmForSeq2SeqDecodeSample, self).__init__(config) self.bert = UnilmModel(config) self.cls = BertOnlyMLMHead(config) self.init_weights() self.tie_weights() def tie_weights(self): """权重加载,加载预训练模型的embeddings部分权重""" self._tie_or_clone_weights(self.cls.predictions.decoder, self.bert.embeddings.word_embeddings) def forward(self, input_ids, token_type_ids, attention_mask): # 获取Encoder部分的序列输出,维度[bs,seq_len,hidden_size] sequence_output, __ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) # 获取最优一个节点的输出 last_hidden = sequence_output[:, -1:, :] # 将其映射到词表中,为后面解码提供内容 prediction_scores = self.cls(last_hidden) return prediction_scores
文件定义了一个基于UniLM(Unified Language Model)的Seq2Seq模型,主要用于序列生成任务。UniLM是一种预训练的语言模型,它在单一的语言模型架构下整合了双向和单向的语言模型。
文件中定义了以下几个主要的类:
1. BertSelfAttention:这是一个自注意力机制的实现,用于计算输入序列中每个元素的注意力分数。
2. BertAttention、BertLayer、BertEncoder:这些类是BERT模型的主要组成部分,用于处理输入序列并生成隐藏状态。
3. UnilmPreTrainedModel:这是一个预训练模型的基类,定义了权重初始化和加载预训练权重的方法。
4. UnilmModel:这是UniLM模型的主要实现,它包含了BERT的嵌入层、编码器和池化层。
5. LabelSmoothingLoss:这是一个实现了标签平滑的损失函数,用于训练过程中减少模型对于标签的过拟合。
6. UnilmForSeq2Seq:这是一个用于序列到序列任务的UniLM模型,它在UnilmModel的基础上添加了一个预测头,用于预测下一个词。
7. UnilmForSeq2SeqDecodeSample:这是一个用于序列到序列任务的解码器,它使用UnilmModel生成的隐藏状态,通过预测头生成下一个词的预测。
总的来说,这个文件定义的模型结构主要用于处理序列到序列的任务,如机器翻译、文本摘要等。模型的最终目标是根据输入的序列生成一个新的序列。
【模型训练】
这个训练代码使用的模型是UnilmForSeq2Seq,这是一个基于UniLM(Unified Language Model)的序列到序列模型。这个模型主要用于处理序列生成任务,如机器翻译、文本摘要等。在代码中,模型的加载过程如下:config = UnilmConfig.from_pretrained(args.model_name_or_path) tokenizer = BertTokenizer.from_pretrained(args.model_name_or_path, do_lower_case=args.do_lower_case) model = UnilmForSeq2Seq.from_pretrained(args.model_name_or_path, config=config) model.to(device) 这段代码首先从预训练模型的路径加载UniLM的配置和BERT的分词器,然后使用这些配置和分词器从预训练模型的路径加载UnilmForSeq2Seq模型,并将模型移动到指定的设备上(如果有GPU则使用GPU,否则使用CPU)。
class UnilmForSeq2Seq(UnilmPreTrainedModel): """UniLM模型进行Seq2Seq的训练模型类""" def __init__(self, config): """模型初始化函数,定义模型训练所需的各个模块""" super(UnilmForSeq2Seq, self).__init__(config) self.bert = UnilmModel(config) self.cls = BertOnlyMLMHead(config) self.mask_lm = nn.CrossEntropyLoss(reduction='none') if hasattr(config, 'label_smoothing') and config.label_smoothing: self.mask_lm_smoothed = LabelSmoothingLoss(config.label_smoothing, config.vocab_size, ignore_index=0, reduction='none') else: self.mask_lm_smoothed = None self.init_weights() self.tie_weights() def tie_weights(self): """权重加载,加载预训练模型的embeddings部分权重""" self._tie_or_clone_weights(self.cls.predictions.decoder, self.bert.embeddings.word_embeddings) def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, masked_pos=None, masked_weights=None): """模型forward,向前传递函数""" # 获取Encoder部分的序列输出,维度[bs,seq_len,hidden_size] sequence_output, __ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False) def gather_seq_out_by_pos(seq, pos): return torch.gather(seq, 1, pos.unsqueeze(2).expand(-1, -1, seq.size(-1))) def loss_mask_and_normalize(loss, mask): mask = mask.type_as(loss) loss = loss * mask denominator = torch.sum(mask) + 1e-5 return (loss / denominator).sum() if masked_lm_labels is None: if masked_pos is None: prediction_scores = self.cls(sequence_output) else: sequence_output_masked = gather_seq_out_by_pos(sequence_output, masked_pos) prediction_scores = self.cls(sequence_output_masked) return prediction_scores # 获取被掩码位置的向量 sequence_output_masked = gather_seq_out_by_pos(sequence_output, masked_pos) prediction_scores_masked = self.cls(sequence_output_masked) if self.mask_lm_smoothed: masked_lm_loss = self.mask_lm_smoothed(F.log_softmax(prediction_scores_masked.float(), dim=-1), masked_lm_labels) else: masked_lm_loss = self.mask_lm(prediction_scores_masked.transpose(1, 2).float(), masked_lm_labels) # 计算[Mask]标记的损失值 masked_lm_loss = loss_mask_and_normalize(masked_lm_loss.float(), masked_weights) return masked_lm_loss
我们重点看看这个模型类:
UnilmForSeq2Seq是一个基于UniLM模型的序列到序列模型,主要用于处理序列生成任务,如机器翻译、文本摘要等。下面是UnilmForSeq2Seq模型的主要组成部分及其功能:
1. self.bert = UnilmModel(config):这是UniLM模型的主体部分,包括BERT的嵌入层、编码器和池化层。这部分用于处理输入序列并生成隐藏状态。
2. self.cls = BertOnlyMLMHead(config):这是一个预测头,用于预测下一个词。它接收UnilmModel生成的隐藏状态,并输出每个词的预测分数。
3. self.mask_lm = nn.CrossEntropyLoss(reduction='none'):这是一个交叉熵损失函数,用于计算预测和真实标签之间的损失。
4. self.mask_lm_smoothed = LabelSmoothingLoss(config.label_smoothing, config.vocab_size, ignore_index=0, reduction='none'):这是一个实现了标签平滑的损失函数,用于训练过程中减少模型对于标签的过拟合。
5. forward函数:这是模型的前向传播函数,它接收输入序列、注意力掩码和标签,然后通过UnilmModel和预测头计算预测分数,最后使用损失函数计算损失。
总的来说,UnilmForSeq2Seq模型的主要功能是根据输入的序列生成一个新的序列,并通过计算预测和真实标签之间的损失进行训练。
标签:transformer,--,模型,attention,mask,masked,output,config,self From: https://www.cnblogs.com/bonelee/p/17732317.html