class NERDataset(Dataset):
"""
针对特定数据集，定义一个相关的取数据的方式
"""
def __init__(self, sents_src, sents_tgt):
## 一般init函数是加载所有数据
super(NERDataset, self).__init__()
# 读原始数据
# self.sents_src, self.sents_tgt = read_corpus(poem_corpus_dir)
self.sents_src = sents_src
self.sents_tgt = sents_tgt
self.idx2word = {k: v for v, k in word2idx.items()}
self.tokenizer = Tokenizer(word2idx)
def __getitem__(self, i):
## 得到单个数据
# print(i)
src = self.sents_src[i]
tgt = self.sents_tgt[i]
token_ids, token_type_ids = self.tokenizer.encode(src)
if len(token_ids) != len(tgt):
print(len(token_ids))
print(len(tgt))
print(src)
print(self.tokenizer.decode(token_ids))
print(tgt)
self.__getitem__(i + 1)
output = {
"token_ids": token_ids,
"token_type_ids": token_type_ids,
"target_id": tgt
}
return output
def __len__(self):
return len(self.sents_src)
class Seq2SeqModel(nn.Module):
"""
"""
def __init__(self, vocab_path, model_name="roberta"):
super(Seq2SeqModel, self).__init__()
self.word2ix = load_chinese_base_vocab(vocab_path)
self.tokenizer = Tokenizer(self.word2ix)
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertLMPredictionHead
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(config, self.bert.embeddings.word_embeddings.weight)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertLMPredictionHead
config = BertConfig(len(self.word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(config, self.bert.embeddings.word_embeddings.weight)
else :
raise Exception("model_name_err")
self.hidden_dim = config.hidden_size
self.vocab_size = config.vocab_size
def compute_loss(self, predictions, labels, target_mask):
"""
target_mask : 句子a部分和pad部分全为0， 而句子b部分为1
"""
predictions = predictions.view(-1, self.vocab_size)
labels = labels.view(-1)
target_mask = target_mask.view(-1).float()
loss = nn.CrossEntropyLoss(ignore_index=0, reduction="none")
return (loss(predictions, labels) * target_mask).sum() / target_mask.sum() ## 通过mask 取消 pad 和句子a部分预测的影响
def forward(self, input_tensor, token_type_id, position_enc=None, labels=None, device="cpu"):
## 传入输入，位置编码，token type id ，还有句子a 和句子b的长度，注意都是传入一个batch数据
## 传入的几个值，在seq2seq 的batch iter 函数里面都可以返回
input_shape = input_tensor.shape
batch_size = input_shape[0]
seq_len = input_shape[1]
## 构建特殊的mask
ones = torch.ones((1, 1, seq_len, seq_len), dtype=torch.float32, device=device)
a_mask = ones.tril() # 下三角矩阵
s_ex12 = token_type_id.unsqueeze(1).unsqueeze(2).float()
s_ex13 = token_type_id.unsqueeze(1).unsqueeze(3).float()
a_mask = (1.0 - s_ex12) * (1.0 - s_ex13) + s_ex13 * a_mask
enc_layers, _ = self.bert(input_tensor, position_ids=position_enc, token_type_ids=token_type_id, attention_mask=a_mask,
output_all_encoded_layers=True)
squence_out = enc_layers[-1] ## 取出来最后一层输出
predictions = self.decoder(squence_out)
if labels is not None:
## 计算loss
## 需要构建特殊的输出mask 才能计算正确的loss
# 预测的值不用取最后sep符号的结果 因此是到-1
predictions = predictions[:, :-1].contiguous()
target_mask = token_type_id[:, 1:].contiguous()
loss = self.compute_loss(predictions, labels, target_mask)
return predictions, loss
else :
return predictions
def generate(self, text, out_max_length=80, beam_size=1, device="cpu", is_poem=False):
# 对 一个 句子生成相应的结果
## 通过输出最大长度得到输入的最大长度，这里问题不大，如果超过最大长度会进行截断
self.out_max_length = out_max_length
input_max_length = max_length - out_max_length
# print(text)
token_ids, token_type_ids = self.tokenizer.encode(text, max_length=input_max_length)
token_ids = torch.tensor(token_ids, device=device).view(1, -1)
token_type_ids = torch.tensor(token_type_ids, device=device).view(1, -1)
if is_poem:## 古诗的beam-search稍有不同
out_puts_ids = self.poem_beam_search(token_ids, token_type_ids, self.word2ix, beam_size=beam_size, device=device)
else :
out_puts_ids = self.beam_search(token_ids, token_type_ids, self.word2ix, beam_size=beam_size, device=device)
# 解码 得到相应输出
return self.tokenizer.decode(out_puts_ids)
def poem_beam_search(self, token_ids, token_type_ids, word2ix, beam_size=1, device="cpu"):
"""
专门针对写诗的beam-search
"""
ix2word = {v: k for k, v in word2ix.items()}
sep_id = word2ix["[SEP]"]
douhao_id = word2ix["，"]# 逗号
juhao_id = word2ix["。"]# 句号
# 用来保存输出序列
output_ids = [[]]
word_list = {} # 保证不重复生成
last_chars = []
yayun_save = -1
# 用来保存累计得分
output_scores = torch.zeros(token_ids.shape[0], device=device)
flag = 0 # 判断第一次遇到逗号
for step in range(self.out_max_length):
scores = self.forward(token_ids, token_type_ids, device=device)
if step == 0:
# 重复beam-size次 输入ids
token_ids = token_ids.view(1, -1).repeat(beam_size, 1)
token_type_ids = token_type_ids.view(1, -1).repeat(beam_size, 1)
## 计算log 分值 (beam_size, vocab_size)
logit_score = torch.log_softmax(scores, dim=-1)[:, -1]
logit_score = output_scores.view(-1, 1) + logit_score # 累计得分
## 取topk的时候我们是展平了然后再去调用topk函数
# 展平
logit_score = logit_score.view(-1)
hype_score, hype_pos = torch.topk(logit_score, beam_size)
indice1 = hype_pos / scores.shape[-1] # 行索引
indice2 = hype_pos % scores.shape[-1] # 列索引
# 下面需要更新一下输出了
new_hype_scores = []
new_hype_ids = []
next_chars = [] # 用来保存新预测出来的一个字符，继续接到输入序列后面，再去预测新字符
index = 0
for i_1, i_2, score in zip(indice1, indice2, hype_score):
i_1 = i_1.item()
i_2 = i_2.item()
score = score.item()
if i_2 != douhao_id and i_2 != juhao_id:
if i_2 not in word_list.keys():
word_list[i_2] = 1
else :
# 加惩罚
word_list[i_2] += 1
score -= 1 * word_list[i_2]
hype_score[index] -= 1 * word_list[i_2]
if flag == 0 and i_2 == douhao_id:
if len(last_chars) - 1 < index:
# 说明刚开始预测便预测到逗号了，上一个字符还没有存储
break
flag += 1
word = ix2word[last_chars[index]]# 找到上一个字符 记住其押韵情况
for i, each_yayun in enumerate(yayun_list):
if word in each_yayun:
yayun_save = i
break
if i_2 == juhao_id:
word = ix2word[last_chars[index]]
# 找押韵 给奖励
if word in yayun_list[yayun_save]:
score += 5
hype_score[index] += 5
else:
score -= 2
hype_score[index] -= 2
hype_id = output_ids[i_1] + [i_2] # 保存所有输出的序列，而不仅仅是新预测的单个字符
if i_2 == sep_id:
# 说明解码到最后了
if score == torch.max(hype_score).item():
return hype_id[: -1]
else:
# 完成一个解码了，但这个解码得分并不是最高，因此的话需要跳过这个序列
beam_size -= 1
else :
new_hype_ids.append(hype_id)
new_hype_scores.append(score)
next_chars.append(i_2) # 收集一下，需要连接到当前的输入序列之后
index += 1
output_ids = new_hype_ids
last_chars = next_chars.copy() # 记录一下上一个字符
output_scores = torch.tensor(new_hype_scores, dtype=torch.float32, device=device)
# 现在需要重新构造输入数据了，用上一次输入连接上这次新输出的字符，再输入bert中预测新字符
token_ids = token_ids[:len(output_ids)].contiguous() # 截取，因为要过滤掉已经完成预测的序列
token_type_ids = token_type_ids[: len(output_ids)].contiguous()
next_chars = torch.tensor(next_chars, dtype=torch.long, device=device).view(-1, 1)
next_token_type_ids = torch.ones_like(next_chars, device=device)
# 连接
token_ids = torch.cat((token_ids, next_chars), dim=1)
token_type_ids = torch.cat((token_type_ids, next_token_type_ids), dim=1)
if beam_size < 1:
break
# 如果达到最大长度的话 直接把得分最高的输出序列返回把
return output_ids[output_scores.argmax().item()]
def beam_search(self, token_ids, token_type_ids, word2ix, beam_size=1, device="cpu"):
"""
beam-search操作
"""
sep_id = word2ix["[SEP]"]
# 用来保存输出序列
output_ids = [[]]
# 用来保存累计得分
output_scores = torch.zeros(token_ids.shape[0], device=device)
for step in range(self.out_max_length):
scores = self.forward(token_ids, token_type_ids, device=device)
if step == 0:
# 重复beam-size次 输入ids
token_ids = token_ids.view(1, -1).repeat(beam_size, 1)
token_type_ids = token_type_ids.view(1, -1).repeat(beam_size, 1)
## 计算log 分值 (beam_size, vocab_size)
logit_score = torch.log_softmax(scores, dim=-1)[:, -1]
logit_score = output_scores.view(-1, 1) + logit_score # 累计得分
## 取topk的时候我们是展平了然后再去调用topk函数
# 展平
logit_score = logit_score.view(-1)
hype_score, hype_pos = torch.topk(logit_score, beam_size)
indice1 = hype_pos / scores.shape[-1] # 行索引
indice2 = hype_pos % scores.shape[-1] # 列索引
# 下面需要更新一下输出了
new_hype_scores = []
new_hype_ids = []
# 为啥有这个[],就是因为要过滤掉结束的序列。
next_chars = [] # 用来保存新预测出来的一个字符，继续接到输入序列后面，再去预测新字符
for i_1, i_2, score in zip(indice1, indice2, hype_score):
i_1 = i_1.item()
i_2 = i_2.item()
score = score.item()
hype_id = output_ids[i_1] + [i_2] # 保存所有输出的序列，而不仅仅是新预测的单个字符
if i_2 == sep_id:
# 说明解码到最后了
if score == torch.max(hype_score).item():
# 说明找到得分最大的那个序列了 直接返回即可
return hype_id[: -1]
else:
# 完成一个解码了，但这个解码得分并不是最高，因此的话需要跳过这个序列
beam_size -= 1
else :
new_hype_ids.append(hype_id)
new_hype_scores.append(score)
next_chars.append(i_2) # 收集一下，需要连接到当前的输入序列之后
output_ids = new_hype_ids
output_scores = torch.tensor(new_hype_scores, dtype=torch.float32, device=device)
# 现在需要重新构造输入数据了，用上一次输入连接上这次新输出的字符，再输入bert中预测新字符
token_ids = token_ids[:len(output_ids)].contiguous() # 截取，因为要过滤掉已经完成预测的序列
token_type_ids = token_type_ids[: len(output_ids)].contiguous()
next_chars = torch.tensor(next_chars, dtype=torch.long, device=device).view(-1, 1)
next_token_type_ids = torch.ones_like(next_chars, device=device)
# 连接
token_ids = torch.cat((token_ids, next_chars), dim=1)
token_type_ids = torch.cat((token_type_ids, next_token_type_ids), dim=1)
if beam_size < 1:
break
# 如果达到最大长度的话 直接把得分最高的输出序列返回把
return output_ids[output_scores.argmax().item()]
class SimBertModel(BasicBert):
"""
"""
def __init__(self, word2ix, model_name="roberta", tokenizer=None):
super(SimBertModel, self).__init__()
self.word2ix = word2ix
if tokenizer is None:
self.tokenizer = Tokenizer(word2ix)
else:
self.tokenizer = tokenizer
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertLMPredictionHead
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(config, self.bert.embeddings.word_embeddings.weight)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertLMPredictionHead
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(config, self.bert.embeddings.word_embeddings.weight)
else :
raise Exception("model_name_err")
self.hidden_dim = config.hidden_size
self.vocab_size = len(word2ix)
def compute_loss(self, predictions, labels, target_mask):
loss1 = self.compute_loss_of_seq2seq(predictions, labels, target_mask)
loss2 = self.compute_loss_of_similarity(predictions[:, 0]) ## 拿出cls向量
return loss1 + loss2
def compute_loss_of_seq2seq(self, predictions, labels, target_mask):
predictions = predictions.view(-1, self.vocab_size)
labels = labels.view(-1)
target_mask = target_mask.view(-1).float()
loss = nn.CrossEntropyLoss(ignore_index=0, reduction="none")
return (loss(predictions, labels) * target_mask).sum() / target_mask.sum() ## 通过mask 取消 pad 和句子a部分预测的影响
def compute_loss_of_similarity(self, y_pred):
y_true = self.get_labels_of_similarity(y_pred) # 构建标签
y_true = y_true.to(self.device)
norm_a = torch.nn.functional.normalize(y_pred, dim=-1, p=2)
# y_pred = K.l2_normalize(y_pred, axis=1) # 句向量归一化
similarities = norm_a.matmul(norm_a.t())
# similarities = K.dot(y_pred, K.transpose(y_pred)) # 相似度矩阵
similarities = similarities - (torch.eye(y_pred.shape[0]) * 1e12).to(self.device) # 排除对角线
similarities = similarities * 30 # scale
similarities = similarities
loss_f = nn.CrossEntropyLoss()
loss = loss_f(similarities, y_true)
# loss = K.categorical_crossentropy(
# y_true, similarities, from_logits=True
# )
return loss
def get_labels_of_similarity(self, y_pred):
idxs = torch.arange(0, y_pred.shape[0])
idxs_1 = idxs[None, :]
idxs_2 = (idxs + 1 - idxs % 2 * 2)[:, None]
labels = (idxs_1 == idxs_2).float().argmax(dim=-1).long()
return labels
def forward(self, input_tensor, token_type_id, position_enc=None, labels=None):
## 传入输入，位置编码，token type id ，还有句子a 和句子b的长度，注意都是传入一个batch数据
## 传入的几个值，在seq2seq 的batch iter 函数里面都可以返回
input_tensor = input_tensor.to(self.device)
token_type_id = token_type_id.to(self.device)
if position_enc is not None:
position_enc = position_enc.to(self.device)
if labels is not None :
labels = labels.to(self.device)
input_shape = input_tensor.shape
batch_size = input_shape[0]
seq_len = input_shape[1]
## 构建特殊的mask
ones = torch.ones((1, 1, seq_len, seq_len), dtype=torch.float32, device=self.device)
a_mask = ones.tril() # 下三角矩阵
s_ex12 = token_type_id.unsqueeze(1).unsqueeze(2).float()
s_ex13 = token_type_id.unsqueeze(1).unsqueeze(3).float()
a_mask = (1.0 - s_ex12) * (1.0 - s_ex13) + s_ex13 * a_mask
enc_layers, _ = self.bert(input_tensor, position_ids=position_enc, token_type_ids=token_type_id, attention_mask=a_mask,
output_all_encoded_layers=True)
squence_out = enc_layers[-1] ## 取出来最后一层输出
predictions = self.decoder(squence_out)
if labels is not None:
## 计算loss
## 需要构建特殊的输出mask 才能计算正确的loss
# 预测的值不用取最后sep符号的结果 因此是到-1
predictions = predictions[:, :-1].contiguous()
target_mask = token_type_id[:, 1:].contiguous()
loss = self.compute_loss(predictions, labels, target_mask)
return predictions, loss
else :
return predictions
def generate(self, text, out_max_length=40, beam_size=1, is_poem=False, max_length=256):
# 对 一个 句子生成相应的结果
## 通过输出最大长度得到输入的最大长度，这里问题不大，如果超过最大长度会进行截断
self.out_max_length = out_max_length
input_max_length = max_length - out_max_length
# print(text)
try:
token_ids, token_type_ids = self.tokenizer.encode(text, max_length=input_max_length)
except:
# 可能是transformer的tokenizer
tokenizer_out = self.tokenizer.encode_plus(text, max_length=input_max_length, truncation=True)
token_ids = tokenizer_out["input_ids"]
token_type_ids = tokenizer_out["token_type_ids"]
token_ids = torch.tensor(token_ids, device=self.device).view(1, -1)
token_type_ids = torch.tensor(token_type_ids, device=self.device).view(1, -1)
if is_poem:## 古诗的beam-search稍有不同
out_puts_ids = self.beam_search_poem(text, token_ids, token_type_ids, self.word2ix, beam_size=beam_size, device=self.device)
else :
out_puts_ids = self.beam_search(token_ids, token_type_ids, self.word2ix, beam_size=beam_size, device=self.device)
return self.tokenizer.decode(out_puts_ids.cpu().numpy())
def sample_generate(self, text, out_max_length=40, top_k=30, top_p=0.0, max_length=256):
input_max_length = max_length - out_max_length
token_ids, token_type_ids = self.tokenizer.encode(text, max_length=input_max_length)
token_ids = torch.tensor(token_ids, device=self.device, dtype=torch.long).view(1, -1)
token_type_ids = torch.tensor(token_type_ids, device=self.device, dtype=torch.long).view(1, -1)
device = self.device
output_ids = []
sep_id = self.word2ix["[SEP]"]
with torch.no_grad():
for step in range(out_max_length):
scores = self.forward(token_ids, token_type_ids)
logit_score = torch.log_softmax(scores[:, -1], dim=-1).squeeze(0)
logit_score[self.word2ix["[UNK]"]] = -float('Inf')
filtered_logits = top_k_top_p_filtering(logit_score, top_k=top_k, top_p=top_p)
next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
if sep_id == next_token.item():
break
output_ids.append(next_token.item())
token_ids = torch.cat((token_ids, next_token.long().unsqueeze(0)), dim=1)
token_type_ids = torch.cat([token_type_ids, torch.ones((1, 1), device=device, dtype=torch.long)], dim=1)
return self.tokenizer.decode(np.array(output_ids))
def beam_search(self, token_ids, token_type_ids, word2ix, beam_size=1, device="cpu"):
"""
beam-search操作
"""
sep_id = word2ix["[SEP]"]
# 用来保存输出序列
output_ids = torch.empty(1, 0, device=device, dtype=torch.long)
# 用来保存累计得分
with torch.no_grad():
output_scores = torch.zeros(token_ids.shape[0], device=device)
for step in range(self.out_max_length):
if step == 0:
scores = self.forward(token_ids, token_type_ids)
# 重复beam-size次 输入ids
token_ids = token_ids.view(1, -1).repeat(beam_size, 1)
token_type_ids = token_type_ids.view(1, -1).repeat(beam_size, 1)
else:
scores = self.forward(new_input_ids, new_token_type_ids)
logit_score = torch.log_softmax(scores[:, -1], dim=-1)
logit_score = output_scores.view(-1, 1) + logit_score # 累计得分
## 取topk的时候我们是展平了然后再去调用topk函数
# 展平
logit_score = logit_score.view(-1)
hype_score, hype_pos = torch.topk(logit_score, beam_size)
indice1 = (hype_pos // scores.shape[-1]) # 行索引
indice2 = (hype_pos % scores.shape[-1]).long().reshape(-1, 1) # 列索引
# 更新得分
output_scores = hype_score
output_ids = torch.cat([output_ids[indice1], indice2], dim=1).long()
new_input_ids = torch.cat([token_ids, output_ids], dim=1)
new_token_type_ids = torch.cat([token_type_ids, torch.ones_like(output_ids)], dim=1)
end_counts = (output_ids == sep_id).sum(1) # 统计出现的end标记
best_one = output_scores.argmax()
if end_counts[best_one] == 1:
# 说明出现终止了～
return output_ids[best_one][:-1]
else :
# 保留未完成部分
flag = (end_counts < 1) # 标记未完成序列
if not flag.all(): # 如果有已完成的
token_ids = token_ids[flag]
token_type_ids = token_type_ids[flag]
new_input_ids = new_input_ids[flag]
new_token_type_ids = new_token_type_ids[flag]
output_ids = output_ids[flag] # 扔掉已完成序列
output_scores = output_scores[flag] # 扔掉已完成序列
end_counts = end_counts[flag] # 扔掉已完成end计数
beam_size = flag.sum() # topk相应变化
return output_ids[output_scores.argmax()]
class Seq2SeqModel(nn.Module):
"""
"""
def __init__(self, word2ix, model_name="roberta"):
super(Seq2SeqModel, self).__init__()
self.word2ix = word2ix
self.tokenizer = Tokenizer(word2ix)
config = ""
if model_name == "roberta":
from bert_seq2seq.model.roberta_model import BertModel, BertConfig, BertLMPredictionHead
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(
config, self.bert.embeddings.word_embeddings.weight)
elif model_name == "bert":
from bert_seq2seq.model.bert_model import BertConfig, BertModel, BertLMPredictionHead
config = BertConfig(len(word2ix))
self.bert = BertModel(config)
self.decoder = BertLMPredictionHead(
config, self.bert.embeddings.word_embeddings.weight)
else:
raise Exception("model_name_err")
self.hidden_dim = config.hidden_size
self.vocab_size = len(word2ix)
def compute_loss(self, predictions, labels, target_mask):
"""
target_mask : 句子a部分和pad部分全为0， 而句子b部分为1
"""
predictions = predictions.view(-1, self.vocab_size)
labels = labels.view(-1)
target_mask = target_mask.view(-1).float()
loss = nn.CrossEntropyLoss(ignore_index=0, reduction="none")
return (loss(predictions, labels) * target_mask
).sum() / target_mask.sum() ## 通过mask 取消 pad 和句子a部分预测的影响
def forward(self,
input_tensor,
token_type_id,
position_enc=None,
labels=None,
device="cpu"):
## 传入输入，位置编码，token type id ，还有句子a 和句子b的长度，注意都是传入一个batch数据
## 传入的几个值，在seq2seq 的batch iter 函数里面都可以返回
input_shape = input_tensor.shape
batch_size = input_shape[0]
seq_len = input_shape[1]
## 构建特殊的mask
ones = torch.ones((1, 1, seq_len, seq_len),
dtype=torch.float32,
device=device)
a_mask = ones.tril() # 下三角矩阵
s_ex12 = token_type_id.unsqueeze(1).unsqueeze(2).float()
s_ex13 = token_type_id.unsqueeze(1).unsqueeze(3).float()
a_mask = (1.0 - s_ex12) * (1.0 - s_ex13) + s_ex13 * a_mask
enc_layers, _ = self.bert(input_tensor,
position_ids=position_enc,
token_type_ids=token_type_id,
attention_mask=a_maask,
output_all_encoded_layers=True)
squence_out = enc_layers[-1] ## 取出来最后一层输出
predictions = self.decoder(squence_out)
if labels is not None:
## 计算loss
## 需要构建特殊的输出mask 才能计算正确的loss
# 预测的值不用取最后sep符号的结果 因此是到-1
predictions = predictions[:, :-1].contiguous()
target_mask = token_type_id[:, 1:].contiguous()
loss = self.compute_loss(predictions, labels, target_mask)
return predictions, loss
else:
return predictions
def generate(self,
text,
out_max_length=40,
beam_size=1,
device="cpu",
is_poem=False,
max_length=256):
# 对 一个 句子生成相应的结果
## 通过输出最大长度得到输入的最大长度，这里问题不大，如果超过最大长度会进行截断
self.out_max_length = out_max_length
input_max_length = max_length - out_max_length
# print(text)
token_ids, token_type_ids = self.tokenizer.encode(
text, max_length=input_max_length)
token_ids = torch.tensor(token_ids, device=device).view(1, -1)
token_type_ids = torch.tensor(token_type_ids,
device=device).view(1, -1)
if is_poem: ## 古诗的beam-search稍有不同
out_puts_ids = self.beam_search_poem(text,
token_ids,
token_type_ids,
self.word2ix,
beam_size=beam_size,
device=device)
else:
out_puts_ids = self.beam_search(token_ids,
token_type_ids,
self.word2ix,
beam_size=beam_size,
device=device)
# 解码 得到相应输出
# if err is False:
# return self.tokenizer.decode(out_puts_ids)
return self.tokenizer.decode(out_puts_ids.cpu().numpy())
# def poem_beam_search(self, token_ids, token_type_ids, word2ix, beam_size=1, device="cpu"):
# """
# 专门针对写诗的beam-search
# """
# ix2word = {v: k for k, v in word2ix.items()}
# sep_id = word2ix["[SEP]"]
# douhao_id = word2ix["，"]# 逗号
# juhao_id = word2ix["。"]# 句号
# # 用来保存输出序列
# output_ids = [[]]
# # word_list = {} # 保证不重复生成
# repeat_list = [[], [], [], [], []]
# last_chars = []
# yayun_save = -1
# # 用来保存累计得分
# output_scores = torch.zeros(token_ids.shape[0], device=device)
# flag = 0 # 判断第一次遇到逗号
# for step in range(self.out_max_length):
# scores = self.forward(token_ids, token_type_ids, device=device)
# if step == 0:
# # 重复beam-size次 输入ids
# token_ids = token_ids.view(1, -1).repeat(beam_size, 1)
# token_type_ids = token_type_ids.view(1, -1).repeat(beam_size, 1)
# ## 计算log 分值 (beam_size, vocab_size)
# logit_score = torch.log_softmax(scores, dim=-1)[:, -1]
# logit_score = output_scores.view(-1, 1) + logit_score # 累计得分
# ## 取topk的时候我们是展平了然后再去调用topk函数
# # 展平
# logit_score = logit_score.view(-1)
# hype_score, hype_pos = torch.topk(logit_score, beam_size)
# indice1 = hype_pos // scores.shape[-1] # 行索引
# indice2 = hype_pos % scores.shape[-1] # 列索引
# # 下面需要更新一下输出了
# new_hype_scores = []
# new_hype_ids = []
# new_repeat_list = []
# next_chars = [] # 用来保存新预测出来的一个字符，继续接到输入序列后面，再去预测新字符
# index = 0
# for i_1, i_2, score in zip(indice1, indice2, hype_score):
# i_1 = i_1.item()
# i_2 = i_2.item()
# score = score.item()
# if i_2 != douhao_id and i_2 != juhao_id:
# if i_2 in repeat_list[i_1]:
# # 说明出现重复了
# # 扣分
# score -= 1
# hype_score[i_1] -= 1
# else :
# repeat_list[i_1].append(i_2)
# # if i_2 not in word_list.keys():
# # word_list[i_2] = 1
# # else :
# # # 加惩罚
# # word_list[i_2] += 1
# # score -= 1 * word_list[i_2]
# # hype_score[index] -= 1 * word_list[i_2]
# if flag == 0 and i_2 == douhao_id and len(last_chars) != 0:
# flag += 1
# word = ix2word[last_chars[index]]# 找到上一个字符 记住其押韵情况
# for i, each_yayun in enumerate(yayun_list):
# if word in each_yayun:
# yayun_save = i
# break
# if i_2 == juhao_id and len(last_chars) != 0:
# word = ix2word[last_chars[i_1]]
# # 找押韵 给奖励
# if word in yayun_list[yayun_save]:
# score += 2
# hype_score[i_1] += 2
# else:
# score -= 2
# hype_score[i_1] -= 2
# hype_id = output_ids[i_1] + [i_2] # 保存所有输出的序列，而不仅仅是新预测的单个字符
# if i_2 == sep_id:
# # 说明解码到最后了
# if score == torch.max(hype_score).item():
# return hype_id[: -1], False
# else:
# # 完成一个解码了，但这个解码得分并不是最高，因此的话需要跳过这个序列
# beam_size -= 1
# else :
# new_hype_ids.append(hype_id)
# new_hype_scores.append(score)
# next_chars.append(i_2) # 收集一下，需要连接到当前的输入序列之后
# new_repeat_list.append(repeat_list[i_1])
# index += 1
# output_ids = new_hype_ids
# repeat_list = new_repeat_list ## 重复会扣分
# last_chars = next_chars.copy() # 记录一下上一个字符
# output_scores = torch.tensor(new_hype_scores, dtype=torch.float32, device=device)
# # 现在需要重新构造输入数据了，用上一次输入连接上这次新输出的字符，再输入bert中预测新字符
# token_ids = token_ids[:len(output_ids)].contiguous() # 截取，因为要过滤掉已经完成预测的序列
# token_type_ids = token_type_ids[: len(output_ids)].contiguous()
# next_chars = torch.tensor(next_chars, dtype=torch.long, device=device).view(-1, 1)
# next_token_type_ids = torch.ones_like(next_chars, device=device)
# # 连接
# token_ids = torch.cat((token_ids, next_chars), dim=1)
# token_type_ids = torch.cat((token_type_ids, next_token_type_ids), dim=1)
# if beam_size < 1:
# break
# # 如果达到最大长度的话 直接把得分最高的输出序列返回把
# err = False
# try:
# return output_ids[output_scores.argmax().item()], err
# except:
# err = True
# return "本次解码出现错误", err
def beam_search(self,
token_ids,
token_type_ids,
word2ix,
beam_size=1,
device="cpu"):
"""
beam-search操作
"""
sep_id = word2ix["[SEP]"]
# 用来保存输出序列
output_ids = torch.empty(1, 0, device=device, dtype=torch.long)
# 用来保存累计得分
with torch.no_grad():
output_scores = torch.zeros(token_ids.shape[0], device=device)
for step in range(self.out_max_length):
if step == 0:
scores = self.forward(token_ids,
token_type_ids,
device=device)
# 重复beam-size次 输入ids
token_ids = token_ids.view(1, -1).repeat(beam_size, 1)
token_type_ids = token_type_ids.view(1, -1).repeat(
beam_size, 1)
else:
scores = self.forward(new_input_ids,
new_token_type_ids,
device=device)
logit_score = torch.log_softmax(scores[:, -1], dim=-1)
logit_score = output_scores.view(-1, 1) + logit_score # 累计得分
## 取topk的时候我们是展平了然后再去调用topk函数
# 展平
logit_score = logit_score.view(-1)
hype_score, hype_pos = torch.topk(logit_score, beam_size)
indice1 = (hype_pos // scores.shape[-1]) # 行索引
indice2 = (hype_pos % scores.shape[-1]).long().reshape(
-1, 1) # 列索引
# 更新得分
output_scores = hype_score
output_ids = torch.cat([output_ids[indice1], indice2],
dim=1).long()
new_input_ids = torch.cat([token_ids, output_ids], dim=1)
new_token_type_ids = torch.cat(
[token_type_ids,
torch.ones_like(output_ids)], dim=1)
end_counts = (output_ids == sep_id).sum(1) # 统计出现的end标记
best_one = output_scores.argmax()
if end_counts[best_one] == 1:
# 说明出现终止了～
return output_ids[best_one][:-1]
else:
# 保留未完成部分
flag = (end_counts < 1) # 标记未完成序列
if not flag.all(): # 如果有已完成的
token_ids = token_ids[flag]
token_type_ids = token_type_ids[flag]
new_input_ids = new_input_ids[flag]
new_token_type_ids = new_token_type_ids[flag]
output_ids = output_ids[flag] # 扔掉已完成序列
output_scores = output_scores[flag] # 扔掉已完成序列
end_counts = end_counts[flag] # 扔掉已完成end计数
beam_size = flag.sum() # topk相应变化
return output_ids[output_scores.argmax()]
def beam_search_poem(self,
text,
token_ids,
token_type_ids,
word2ix,
beam_size=1,
device="cpu"):
"""
beam-search操作
"""
yayun_pos = []
title = text.split("##")[0]
if "五言律诗" in text:
yayun_pos = [10, 22, 34, 46]
elif "五言绝句" in text:
yayun_pos = [10, 22]
elif "七言律诗" in text:
yayun_pos = [14, 30, 46, 62]
elif "七言绝句" in text:
yayun_pos = [14, 30]
sep_id = word2ix["[SEP]"]
douhao_id = word2ix["，"] # 逗号
ix2word = {v: k for k, v in word2ix.items()}
juhao_id = word2ix["。"] # 句号
repeat_word = [[] for i in range(beam_size)]
# 用来保存输出序列
output_ids = torch.empty(1, 0, device=device, dtype=torch.long)
last_chars = torch.empty(1, 0, device=device, dtype=torch.long)
yayun_chars = (-1) * torch.ones(beam_size, dtype=torch.long)
start = 0
with torch.no_grad():
output_scores = torch.zeros(token_ids.shape[0], device=device)
for step in range(self.out_max_length):
if step == 0:
scores = self.forward(token_ids,
token_type_ids,
device=device)
# 重复beam-size次 输入ids
token_ids = token_ids.view(1, -1).repeat(beam_size, 1)
token_type_ids = token_type_ids.view(1, -1).repeat(
beam_size, 1)
else:
scores = self.forward(new_input_ids,
new_token_type_ids,
device=device)
logit_score = torch.log_softmax(scores[:, -1], dim=-1)
for i, char in enumerate(last_chars):
for word in repeat_word[i]:
logit_score[i, word] -= 5
for word in title:
ix = word2ix.get(word, -1)
if ix != -1:
logit_score[i, ix] += 2
if step in yayun_pos:
# print("step is " + str(step))
# print("yayun_chars is " + str(yayun_chars))
for i, char in enumerate(last_chars):
if yayun_chars[i].item() != -1:
yayuns = yayun_list[yayun_chars[i].item()]
for char in yayuns:
ix = word2ix.get(char, -1)
if ix != -1:
# print("char is " + str(char))
logit_score[i, ix] += 10
logit_score = output_scores.view(-1, 1) + logit_score # 累计得分
## 取topk的时候我们是展平了然后再去调用topk函数
# 展平
logit_score = logit_score.view(-1)
hype_score, hype_pos = torch.topk(logit_score, beam_size)
indice1 = (hype_pos // scores.shape[-1]) # 行索引
indice2 = (hype_pos % scores.shape[-1]).long().reshape(
-1, 1) # 列索引
for index, each_out in zip(indice1, indice2):
index = index.item()
each_out = each_out.item()
if each_out in repeat_word[index]:
pass
# repeat_word[index].append(each_out)
# hype_score[index] -= 2 * repeat_word[index].count(each_out)
else:
repeat_word[index].append(each_out)
if start < beam_size and each_out == douhao_id and len(
last_chars) != 0:
start += 1
word = ix2word[
last_chars[index].item()] # 找到上一个字符 记住其押韵情况
for i, each_yayun in enumerate(yayun_list):
if word in each_yayun:
yayun_chars[index] = i
break
# if each_out == juhao_id and len(last_chars) != 0:
# word = ix2word[last_chars[index].item()]
# if yayun_chars[index].item() != -1 and word in yayun_list[yayun_chars[index].item()]:
# hype_score[index] += 10
# else:
# hype_score[index] -= 5
# 更新得分
output_scores = hype_score
last_chars = indice2
output_ids = torch.cat([output_ids[indice1], indice2],
dim=1).long()
new_input_ids = torch.cat([token_ids, output_ids], dim=1)
new_token_type_ids = torch.cat(
[token_type_ids,
torch.ones_like(output_ids)], dim=1)
end_counts = (output_ids == sep_id).sum(1) # 统计出现的end标记
best_one = output_scores.argmax()
if end_counts[best_one] == 1:
# 说明出现终止了～
# print(repeat_word)
# print(yayun_chars)
return output_ids[best_one][:-1]
else:
# 保留未完成部分
flag = (end_counts < 1) # 标记未完成序列
if not flag.all(): # 如果有已完成的
token_ids = token_ids[flag]
token_type_ids = token_type_ids[flag]
last_chars = last_chars[flag]
yayun_chars = yayun_chars[flag]
new_input_ids = new_input_ids[flag]
new_token_type_ids = new_token_type_ids[flag]
output_ids = output_ids[flag] # 扔掉已完成序列
output_scores = output_scores[flag] # 扔掉已完成序列
end_counts = end_counts[flag] # 扔掉已完成end计数
beam_size = flag.sum() # topk相应变化
flag = flag.long()
new_repeat_word = []
for index, i in enumerate(flag):
if i.item() == 1:
new_repeat_word.append(repeat_word[index])
repeat_word = new_repeat_word
# print(repeat_word)
# print(yayun_chars)
return output_ids[output_scores.argmax()]
def beam_search_poem_v2(self,
text,
token_ids,
token_type_ids,
word2ix,
beam_size=1,
device="cpu"):
"""
beam-search操作
"""
yayun_pos = []
if "五言律诗" in text:
yayun_pos = [10, 22, 34, 46]
elif "五言绝句" in text:
yayun_pos = [10, 22]
elif "七言律诗" in text:
yayun_pos = [14, 30, 46, 62]
elif "七言绝句" in text:
yayun_pos = [14, 30]
sep_id = word2ix["[SEP]"]
douhao_id = word2ix["，"] # 逗号
ix2word = {v: k for k, v in word2ix.items()}
juhao_id = word2ix["。"] # 句号
repeat_word = []
# 用来保存输出序列
output_ids = torch.empty(1, 0, device=device, dtype=torch.long)
last_chars = torch.empty(1, 0, device=device, dtype=torch.long)
yayun_chars = (-1) * torch.ones(beam_size, dtype=torch.long)
start = 0
with torch.no_grad():
output_scores = torch.zeros(token_ids.shape[0], device=device)
for step in range(self.out_max_length):
if step == 0:
scores = self.forward(token_ids,
token_type_ids,
device=device)
# 重复beam-size次 输入ids
token_ids = token_ids.view(1, -1).repeat(beam_size, 1)
token_type_ids = token_type_ids.view(1, -1).repeat(
beam_size, 1)
else:
scores = self.forward(new_input_ids,
new_token_type_ids,
device=device)
logit_score = torch.log_softmax(scores[:, -1], dim=-1)
# if len(last_chars) != 0:
# logit_score[last_chars] -= 5
for i, char in enumerate(last_chars):
logit_score[i, char] -= 2
for word in repeat_word:
logit_score[i, word] -= 1
if step in yayun_pos:
# print("step is " + str(step))
# print("yayun_chars is " + str(yayun_chars))
for i, char in enumerate(last_chars):
if yayun_chars[i].item() != -1:
yayuns = yayun_list[yayun_chars[i].item()]
for char in yayuns:
ix = word2ix.get(char, -1)
if ix != -1:
# print("char is " + str(char))
logit_score[i, ix] += 3
logit_score = output_scores.view(-1, 1) + logit_score # 累计得分
## 取topk的时候我们是展平了然后再去调用topk函数
# 展平
logit_score = logit_score.view(-1)
hype_score, hype_pos = torch.topk(logit_score, beam_size)
indice1 = (hype_pos // scores.shape[-1]) # 行索引
indice2 = (hype_pos % scores.shape[-1]).long().reshape(
-1, 1) # 列索引
for index, each_out in zip(indice1, indice2):
index = index.item()
each_out = each_out.item()
if each_out in repeat_word:
pass
# repeat_word[index].append(each_out)
# hype_score[index] -= 2 * repeat_word[index].count(each_out)
else:
repeat_word.append(each_out)
if start < beam_size and each_out == douhao_id and len(
last_chars) != 0:
start += 1
word = ix2word[
last_chars[index].item()] # 找到上一个字符 记住其押韵情况
for i, each_yayun in enumerate(yayun_list):
if word in each_yayun:
yayun_chars[index] = i
break
# if each_out == juhao_id and len(last_chars) != 0:
# word = ix2word[last_chars[index].item()]
# if yayun_chars[index].item() != -1 and word in yayun_list[yayun_chars[index].item()]:
# hype_score[index] += 10
# else:
# hype_score[index] -= 5
# 更新得分
output_scores = hype_score
last_chars = indice2
output_ids = torch.cat([output_ids[indice1], indice2],
dim=1).long()
new_input_ids = torch.cat([token_ids, output_ids], dim=1)
new_token_type_ids = torch.cat(
[token_type_ids,
torch.ones_like(output_ids)], dim=1)
end_counts = (output_ids == sep_id).sum(1) # 统计出现的end标记
best_one = output_scores.argmax()
if end_counts[best_one] == 1:
# 说明出现终止了～
# print(repeat_word)
# print(yayun_chars)
return output_ids[best_one]
else:
# 保留未完成部分
flag = (end_counts < 1) # 标记未完成序列
if not flag.all(): # 如果有已完成的
token_ids = token_ids[flag]
token_type_ids = token_type_ids[flag]
last_chars = last_chars[flag]
yayun_chars = yayun_chars[flag]
new_input_ids = new_input_ids[flag]
new_token_type_ids = new_token_type_ids[flag]
output_ids = output_ids[flag] # 扔掉已完成序列
output_scores = output_scores[flag] # 扔掉已完成序列
end_counts = end_counts[flag] # 扔掉已完成end计数
beam_size = flag.sum() # topk相应变化
flag = flag.long()
# print(repeat_word)
# print(yayun_chars)
return output_ids[output_scores.argmax()]
class GPT2(BasicGPT):
def __init__(self, word2ix, tokenizer=None):
super().__init__()
self.word2ix = word2ix
if tokenizer is not None:
self.tokenizer = tokenizer
else:
self.tokenizer = Tokenizer(word2ix)
self.config = GPT2Config(len(word2ix))
self.model = GPT2LMHeadModel(self.config)
def sample_generate(self, text, input_max_length=256, out_max_length=200, top_k=30, top_p=0.0, add_eos=False):
token_ids, _ = self.tokenizer.encode(text, max_length=input_max_length)
if not add_eos:
token_ids = torch.tensor(token_ids, device=self.device, dtype=torch.long)[:-1].view(1, -1)
else:
token_ids = torch.tensor(token_ids, device=self.device, dtype=torch.long).view(1, -1)
output_ids = []
sep_id = self.word2ix["[SEP]"]
with torch.no_grad():
for step in range(out_max_length):
_, scores = self.model(token_ids)
logit_score = torch.log_softmax(scores[:, -1], dim=-1).squeeze(0)
logit_score[self.word2ix["[UNK]"]] = -float('Inf')
filtered_logits = top_k_top_p_filtering(logit_score, top_k=top_k, top_p=top_p)
next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
if sep_id == next_token.item():
break
output_ids.append(next_token.item())
token_ids = torch.cat((token_ids, next_token.long().unsqueeze(0)), dim=1)
return self.tokenizer.decode(np.array(output_ids))
def sample_generate_english(self, text, input_max_length=256, out_max_length=200, top_k=30, top_p=0.0, add_eos=False):
token_ids = self.tokenizer.encode(text, max_length=input_max_length, truncation=True)
if add_eos:
token_ids = token_ids + [self.word2ix["<EOS>"]]
token_ids = torch.tensor(token_ids, device=self.device, dtype=torch.long).view(1, -1)
output_ids = []
sep_id = self.word2ix["<EOS>"]
with torch.no_grad():
for step in range(out_max_length):
_, scores = self.model(token_ids)
# print(scores.shape)
logit_score = torch.log_softmax(scores[:, -1], dim=-1).squeeze(0)
# print(logit_score.shape)
logit_score[self.word2ix["unk"]] = -float('Inf')
filtered_logits = top_k_top_p_filtering(logit_score, top_k=top_k, top_p=top_p)
next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
if sep_id == next_token.item():
break
# pass
output_ids.append(next_token.item())
token_ids = torch.cat((token_ids, next_token.long().unsqueeze(0)), dim=1)
return self.tokenizer.decode(output_ids)
def _make_causal_mask(self, input_ids_shape: torch.Size):
bsz, tgt_len = input_ids_shape
mask = torch.full((tgt_len, tgt_len), 0.0).to(self.device)
mask_cond = torch.arange(mask.size(-1)).to(self.device)
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 1.0)
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len)
def forward(self, x, labels=None):
if labels is not None:
labels = labels.to(self.device)
x = x.to(self.device)
# input_ids = torch.tensor([[1, 2, 3, 5, -100], [4, 5, 6, -100, -100]])
attention_mask = self._make_causal_mask(x.shape)
pad_mask = (labels != -100).float()
attention_mask = attention_mask * pad_mask.unsqueeze(1).unsqueeze(1)
loss, lm_logit = self.model(x, labels=labels, attention_mask=attention_mask)
return loss, lm_logit
