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import transformers
from utils import printf
import copy
class prompt:
def __init__(self, tokenizer, max_len, add_eos=True):
self.tokenizer = tokenizer
self.max_len = max_len
self.add_eos=add_eos
class instruct_prompt(prompt):
prompt = (
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:"
)
prompt_input = (
"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
"### Instruction:{instruction}\n\n### Input:{input}\n\n### Response:"
)
prompt_history = "User:{input}\n\nAssistant:{output}\n\n"
prompt_post = "User:{input}\n\nAssistant:"
def preprocess_gen(self, data_point):
if 'history' not in data_point:
# single instruction format {'instruction':..,'input':..}
if 'input' in data_point:
user_prompt = self.prompt_input.format_map(data_point)
else:
user_prompt = self.prompt.format_map(data_point)
else:
# multi turn format {'history':[..], 'input':[..]}
user_prompt = "\n".join(["User:" + i['input']+"\n"+"Assistant:" + i['output'] for i in data_point['history']]) + "\nUser:" + data_point['input'] + "\nAssistant:"
user_prompt = user_prompt[-self.max_len:]
user_prompt=self.prompt.format_map({'instruction':user_prompt})
input_ids = self.tokenizer(user_prompt)["input_ids"]
return input_ids
def preprocess_train(self, data_point):
# single instruction format {'instruction':..,'input':..,'output':..}
if 'instruction' in data_point:
if 'input' in data_point:
user_prompt = self.prompt_input.format_map(data_point)
else:
user_prompt = self.prompt.format_map(data_point)
output = data_point["output"]
# multi turn format {'input':[..], 'output':[..]}
else:
user_prompt = ''
lens = len(data_point['input'])
for i in range(lens-1):
user_prompt += self.prompt_history.format_map({'input':data_point['input'][i],'output':data_point['output'][i]})
user_prompt += self.prompt_post.format_map({'input':data_point['input'][-1]})
user_prompt = self.prompt.format_map({'instruction': user_prompt})
output = data_point['output'][-1]
len_user_prompt_tokens = (len(self.tokenizer(
user_prompt,
truncation=True,
max_length=self.max_len + 1,
)["input_ids"])- 1) # no eos token
full_tokens = self.tokenizer(
user_prompt + output,
truncation=True,
max_length=self.max_len + 1,
padding="max_length",
)["input_ids"][:-1]
return {
"input_ids": full_tokens,
"labels": [-100] * len_user_prompt_tokens
+ full_tokens[len_user_prompt_tokens:],
"attention_mask": [1] * (len(full_tokens)),
}
def data_collator(self,):
return transformers.DataCollatorForLanguageModeling(self.tokenizer, mlm=False)
def postprocess(self, text, render=True):
#import pdb;pdb.set_trace()
printf(text)
output = text.split("### Response:")[1].strip()
output = output.replace("Belle", "Vicuna")
printf(output)
if '###' in output:
output = output.split("###")[0]
if 'User' in output:
output = output.split("User")[0]
output = output.replace('�','').replace('</s>', '')
if render:
# fix gradio chatbot markdown code render bug
lines = output.split("\n")
for i, line in enumerate(lines):
if "```" in line:
if line != "```":
lines[i] = f'<pre><code class="language-{lines[i][3:]}">'
else:
lines[i] = '</code></pre>'
else:
if i > 0:
lines[i] = "<br/>" + line.replace("<", "<").replace(">", ">").replace("__", '\_\_')
output = "".join(lines)
# output = output.replace('<br/><pre>','\n<pre>') work for html; but not for gradio
return output
class chat_prompt(prompt):
prompt_pre = (
"The following is a conversation between an AI assistant called Assistant and a human user called User. "
"The assistant is intelligent, knowledgeable and polite to answer questions of user.\n\n"
)
prompt_history = "User:{input}\n\nAssistant:{output}\n\n"
prompt_post = "User:{input}\n\nAssistant:"
def preprocess_gen(self, data_point):
user_prompt = self.prompt_pre
len_avail = self.max_len - len(self.tokenizer(user_prompt, add_special_tokens=False)['input_ids'])
input_prompt = self.prompt_post.format_map({'input':data_point['input']})
len_avail -= len(self.tokenizer(input_prompt, add_special_tokens=False)['input_ids'])
lens = len(data_point['history'])
tokenized_lens = []
for i in range(lens):
tmp_prompt = self.prompt_history.format_map(data_point['history'][i])
tokenized_lens.append(len(self.tokenizer(tmp_prompt,add_special_tokens=False)["input_ids"]))
# 启发式：/2 优先除前面的
i = 0
while sum(tokenized_lens) > len_avail and i < lens:
history = data_point['history'][i]
tmp_len1 = len(history['input'])
tmp_len2 = len(history['output'])
if tmp_len2 > tmp_len1:
history['output'] = history['output'][:tmp_len2//2]
else:
history['input'] = history['input'][:tmp_len1//2]
prompt = self.prompt_history.format_map(history)
single_len =(len(self.tokenizer(prompt,add_special_tokens=False)["input_ids"]))
tokenized_lens[i] = single_len
i += 1
total_len = sum(tokenized_lens)
# 还不够的话 直接截断
while total_len > len_avail and i < lens - 1 :
total_len -= tokenized_lens[i]
data_point['history'] = data_point['history'][1:]
i += 1
# 最终合并
for i in range(lens):
user_prompt += self.prompt_history.format_map(data_point['history'][i])
user_prompt += input_prompt
printf({'real_input:':user_prompt})
inputs = self.tokenizer(user_prompt)["input_ids"]
return inputs
def preprocess_train(self, data_point):
user_prompt = self.prompt_pre
lens = len(data_point['input'])
# print("Length of data_point['input']: ", len(data_point['input']))
# print("Length of data_point['output']: ", len(data_point['output']))
# for i in range(lens-1):
# user_prompt += self.prompt_history.format_map({'input':data_point['input'][i].strip(),'output':data_point['output'][i].strip()})
user_prompt += self.prompt_post.format_map({'input':data_point['input'].strip()})
len_user_prompt_tokens = len(self.tokenizer(
user_prompt,
truncation=True,
max_length=self.max_len,
)["input_ids"]) - 1 # remove extra eos
if self.add_eos:
full_tokens = self.tokenizer(
user_prompt + data_point["output"].strip(),
truncation=True,
padding=False,
max_length=self.max_len,
)["input_ids"] # need eos
else:
full_tokens = self.tokenizer(
user_prompt + data_point["output"].strip(),
truncation=True,
padding=False,
max_length=self.max_len+1,
)["input_ids"][:-1] # delete eos
return {
"input_ids": full_tokens,
"labels": [-100] * len_user_prompt_tokens + full_tokens[len_user_prompt_tokens:],
"attention_mask": [1] * (len(full_tokens)),
}
def data_collator(self,):
return transformers.DataCollatorForSeq2Seq(self.tokenizer)
def postprocess(self, text, render=False):
output = text.split("Assistant:")[-1].strip()
if 'User:' in output:
output = output.split("User:")[0]
output = output.replace('�','')
if render:
# fix gradio chatbot markdown code render bug
lines = output.split("\n")
for i, line in enumerate(lines):
if "```" in line:
if line != "```":
lines[i] = f'<pre><code class="language-{lines[i][3:]}">'
else:
lines[i] = '</code></pre>'
else:
if i > 0:
lines[i] = "<br/>" + line.replace("<", "<").replace(">", ">").replace("__", '\_\_')
output = "".join(lines)
# output = output.replace('<br/><pre>','\n<pre>') work for html; but not for gradio
return output
def get_data_collator():
return transformers.DataCollatorForLanguageModeling
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