常用网站开发语言优缺点,郑州哪家医院看男科比较专业,汕头seo优化项目,网页视频怎么下载到u盘GPT 结束语设计 以nanogpt为例
目录
GPT 结束语设计 以nanogpt为例
1、简述
2、分词设计
3、结束语断点 1、简述
在手搓gpt的时候#xff0c;可能会遇到一些性能问题#xff0c;即关于是否需要全部输出或者怎么节约资源。
在输出语句被max_new_tokens 限制#xff0c…GPT 结束语设计 以nanogpt为例
目录
GPT 结束语设计 以nanogpt为例
1、简述
2、分词设计
3、结束语断点 1、简述
在手搓gpt的时候可能会遇到一些性能问题即关于是否需要全部输出或者怎么节约资源。
在输出语句被max_new_tokens 限制如果出现一些输出句子比较长就会被限制但如果是设计时候没有设计结束语就会出现全部输出的问题。
如果只需要一部分的语句或者是某一些特定的场景设计例如
1、gpt自动化操作
2、输出美观
3、一些较小的业务场景特定处理的业务
以上的业务场景都是设计的时候为特定模型即小大模型通常不需要较大的参数所以在设计时候如果考虑到轻量化和小型化参数1M至100M之间的小大模型。
基于成本和开发快速考虑可以使用nanogpt用于训练和开发然后再进一步的微调迭代所需要的性能和效果基本可以满足部分要求迭代速度较快适合单人或小团队开发特定场景。 2、分词设计
以下是关于之前做过的一个开发场景音乐生成按键的场景 分词中加入了end的作为特定的结束语如果后续扩展可以通过end前后设计一些音乐风格的标识符这样通过风格的标识来达到风格的统一。 # 自定义词典 word_dict set([\n, , , ., 0, 1, 2, 3, 4 6, 7, 8, 9, :, a, b, c, d, e, f, g, h,j, n,m,q,w,r,t,y,u, s, v, x, z,96,97,98,99,100, 101,102,103,104,105,end])
seg_list max_forward_matching(data, word_dict, max(len(word) for word in word_dict)) words list(seg_list) # 创建一个默认字典来存储词汇到ID的映射 word_to_id defaultdict(lambda: len(word_to_id)) # 创建一个列表来存储ID到词汇的映射可选 id_to_word [] # 构建词汇到ID的映射 for word in words: word_id word_to_id[word] # ID到词汇的映射可以这样做 if word_id len(word_to_id): # 只有当新的ID被分配时才添加到id_to_word中 id_to_word.append(word)
import os
import pickle
import requests
import numpy as np
from collections import defaultdict
# download the tiny shakespeare dataset
input_file_path os.path.join(os.path.dirname(__file__), music.txt)
if not os.path.exists(input_file_path):data_url https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txtwith open(input_file_path, w) as f:f.write(requests.get(data_url).text)with open(input_file_path, r,encodingutf-8) as f:data f.read()
print(flength of dataset in characters: {len(data):,})# get all the unique characters that occur in this text
def max_forward_matching(text, word_dict, max_len):result []index 0while index len(text):found Falsefor size in range(max_len, 0, -1): # 从最大长度开始尝试匹配piece text[index:index size]if piece in word_dict:result.append(piece)index sizefound Truebreakif not found: # 如果没有找到匹配的词则按字符输出result.append(text[index])index 1return result#自建一套
# 自定义词典
word_dict set([\n, , , ., 0, 1, 2, 3, 46, 7, 8, 9, :, a, b, c, d,e, f, g, h,j, n,m,q,w,r,t,y,u,s, v, x, z,96,97,98,99,100,101,102,103,104,105,end])seg_list max_forward_matching(data, word_dict, max(len(word) for word in word_dict))
words list(seg_list)
# 创建一个默认字典来存储词汇到ID的映射
word_to_id defaultdict(lambda: len(word_to_id))
# 创建一个列表来存储ID到词汇的映射可选
id_to_word []
# 构建词汇到ID的映射
for word in words:word_id word_to_id[word]# ID到词汇的映射可以这样做if word_id len(word_to_id): # 只有当新的ID被分配时才添加到id_to_word中id_to_word.append(word)chars list(word_to_id)
print(chars)
vocab_size len(chars)print(all the unique characters:, .join(chars))
print(fvocab size: {vocab_size:,})
#Myzzb That is need about jieba to cut text
print(chars)
# create a mapping from characters to integers
stoi { ch:i for i,ch in enumerate(chars) }
print(stoi)
itos { i:ch for i,ch in enumerate(chars) }
print(itos)def encode(s):seg_list max_forward_matching(data, word_dict, max(len(word) for word in word_dict))words list(seg_list)# 创建一个默认字典来存储词汇到ID的映射word_to_id defaultdict(lambda: len(word_to_id))# 创建一个列表来存储ID到词汇的映射id_to_word []# 构建词汇到ID的映射for word in words:word_id word_to_id[word]# 如果你也需要ID到词汇的映射可以这样做if word_id len(word_to_id): # 只有当新的ID被分配时才添加到id_to_word中id_to_word.append(word)return [word_to_id[word] for word in words] # encoder: take a string, output a list of integers
def decode(l):seg_list max_forward_matching(data, word_dict, max(len(word) for word in word_dict))words list(seg_list)# 创建一个默认字典来存储词汇到ID的映射word_to_id defaultdict(lambda: len(word_to_id))# 创建一个列表来存储ID到词汇的映射可选id_to_word []# 构建词汇到ID的映射for word in words:word_id word_to_id[word]# 如果你也需要ID到词汇的映射可以这样做if word_id len(word_to_id): # 只有当新的ID被分配时才添加到id_to_word中id_to_word.append(word)return .join([word_to_id[word] for word in words]) # decoder: take a list of integers, output a string
# create the train and test splits
n len(data)
train_data data[:int(n*0.95)]#这里因为没写字典排序所以训练集和测试集懒得分开
val_data data[int(n*0.95):]
# print(val_data)
# encode both to integers
train_ids encode(train_data)
print(train_ids)
val_ids encode(val_data)
print(val_ids)
# print(val_ids)
print(ftrain has {len(train_ids):,} tokens)
print(fval has {len(val_ids):,} tokens)# export to bin files
train_ids np.array(train_ids, dtypenp.uint16)
val_ids np.array(val_ids, dtypenp.uint16)
train_ids.tofile(os.path.join(os.path.dirname(__file__), train.bin))
val_ids.tofile(os.path.join(os.path.dirname(__file__), val.bin))# save the meta information as well, to help us encode/decode later
meta {vocab_size: vocab_size,itos: itos,stoi: stoi,
}
with open(os.path.join(os.path.dirname(__file__), meta.pkl), wb) as f:pickle.dump(meta, f) 3、结束语断点 通过在推理过程中检测新生成的编码是否和结束语一致以上在设计的过程中通过字典分词然后再分配的编码是可以通过代码获取对应的结束语的编码。
通过在分词的时候进行对部分结束语进行输出例子
print(encode(\n))
print(encode(\t))源码添加上即可知道结束语的编码是多少 Prepare the Shakespeare dataset for character-level language modeling.
So instead of encoding with GPT-2 BPE tokens, we just map characters to ints.
Will save train.bin, val.bin containing the ids, and meta.pkl containing the
encoder and decoder and some other related info.import os
import pickle
import requests
import numpy as np# download the tiny shakespeare dataset
input_file_path os.path.join(os.path.dirname(__file__), say.txt)
if not os.path.exists(input_file_path):data_url https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txtwith open(input_file_path, w) as f:f.write(requests.get(data_url).text)with open(input_file_path, r,encodingutf-8, errorsreplace) as f:data f.read()
print(flength of dataset in characters: {len(data):,})# get all the unique characters that occur in this text
chars sorted(list(set(data)))
vocab_size len(chars)
print(all the unique characters:, .join(chars))
print(fvocab size: {vocab_size:,})# create a mapping from characters to integers
stoi { ch:i for i,ch in enumerate(chars) }
itos { i:ch for i,ch in enumerate(chars) }def encode(s):return [stoi[c] for c in s] # encoder: take a string, output a list of integers
def decode(l):return .join([itos[i] for i in l]) # decoder: take a list of integers, output a stringprint(encode(\n))
print(encode(\t))# create the train and test splits
n len(data)
train_data data[:int(n*0.9)]
val_data data[int(n*0.9):]# encode both to integers
train_ids encode(train_data)
val_ids encode(val_data)
print(ftrain has {len(train_ids):,} tokens)
print(fval has {len(val_ids):,} tokens)# export to bin files
train_ids np.array(train_ids, dtypenp.uint16)
val_ids np.array(val_ids, dtypenp.uint16)
train_ids.tofile(os.path.join(os.path.dirname(__file__), train.bin))
val_ids.tofile(os.path.join(os.path.dirname(__file__), val.bin))# save the meta information as well, to help us encode/decode later
meta {vocab_size: vocab_size,itos: itos,stoi: stoi,
}
with open(os.path.join(os.path.dirname(__file__), meta.pkl), wb) as f:pickle.dump(meta, f)# length of dataset in characters: 1115394
# all the unique characters:
# !$,-.3:;?ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz
# vocab size: 65
# train has 1003854 tokens
# val has 111540 tokens只需要简单添加一句代码即可
# 检查是否生成了结束语 可以获取大部分结束语的编码用于判断 也可以自拟结束语 将其处理为唯一的标识符避免干扰 if 1 in idx_next[0].tolist():break
torch.no_grad()def generate(self, idx, max_new_tokens, temperature1.0, top_kNone):Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and completethe sequence max_new_tokens times, feeding the predictions back into the model each time.Most likely youll want to make sure to be in model.eval() mode of operation for this.for _ in range(max_new_tokens):# if the sequence context is growing too long we must crop it at block_sizeidx_cond idx if idx.size(1) self.config.block_size else idx[:, -self.config.block_size:]# forward the model to get the logits for the index in the sequencelogits, _ self(idx_cond)# pluck the logits at the final step and scale by desired temperaturelogits logits[:, -1, :] / temperature# optionally crop the logits to only the top k optionsif top_k is not None:v, _ torch.topk(logits, min(top_k, logits.size(-1)))logits[logits v[:, [-1]]] -float(Inf)# apply softmax to convert logits to (normalized) probabilitiesprobs F.softmax(logits, dim-1)# sample from the distributionidx_next torch.multinomial(probs, num_samples1)# 检查是否生成了结束语 可以获取大部分结束语的编码用于判断 也可以自拟结束语 将其处理为唯一的标识符避免干扰if 1 in idx_next[0].tolist():break# append sampled index to the running sequence and continueidx torch.cat((idx, idx_next), dim1)return idx
