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import time
import yaml
import os
from modules import shared
from extensions.openai.defaults import get_default_req_params
from extensions.openai.utils import debug_msg
from extensions.openai.errors import *
from modules.text_generation import encode, generate_reply
def edits(instruction: str, input: str, temperature=1.0, top_p=1.0) -> dict:
created_time = int(time.time() * 1000)
# Request parameters
req_params = get_default_req_params()
stopping_strings = []
# Alpaca is verbose so a good default prompt
default_template = (
"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:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
)
instruction_template = default_template
# Use the special instruction/input/response template for anything trained like Alpaca
if shared.settings['instruction_template']:
if 'Alpaca' in shared.settings['instruction_template']:
stopping_strings.extend(['\n###'])
else:
try:
instruct = yaml.safe_load(open(f"characters/instruction-following/{shared.settings['instruction_template']}.yaml", 'r'))
template = instruct['turn_template']
template = template\
.replace('<|user|>', instruct.get('user', ''))\
.replace('<|bot|>', instruct.get('bot', ''))\
.replace('<|user-message|>', '{instruction}\n{input}')
instruction_template = instruct.get('context', '') + template[:template.find('<|bot-message|>')].rstrip(' ')
if instruct['user']:
stopping_strings.extend(['\n' + instruct['user'], instruct['user']])
except Exception as e:
instruction_template = default_template
print(f"Exception: When loading characters/instruction-following/{shared.settings['instruction_template']}.yaml: {repr(e)}")
print("Warning: Loaded default instruction-following template (Alpaca) for model.")
else:
stopping_strings.extend(['\n###'])
print("Warning: Loaded default instruction-following template (Alpaca) for model.")
edit_task = instruction_template.format(instruction=instruction, input=input)
truncation_length = shared.settings['truncation_length']
token_count = len(encode(edit_task)[0])
max_tokens = truncation_length - token_count
if max_tokens < 1:
err_msg = f"This model maximum context length is {truncation_length} tokens. However, your messages resulted in over {truncation_length - max_tokens} tokens."
raise InvalidRequestError(err_msg, param='input')
req_params['max_new_tokens'] = max_tokens
req_params['truncation_length'] = truncation_length
req_params['temperature'] = temperature
req_params['top_p'] = top_p
req_params['seed'] = shared.settings.get('seed', req_params['seed'])
req_params['add_bos_token'] = shared.settings.get('add_bos_token', req_params['add_bos_token'])
req_params['custom_stopping_strings'] = shared.settings['custom_stopping_strings']
debug_msg({'edit_template': edit_task, 'req_params': req_params, 'token_count': token_count})
generator = generate_reply(edit_task, req_params, stopping_strings=stopping_strings, is_chat=False)
longest_stop_len = max([len(x) for x in stopping_strings] + [0])
answer = ''
for a in generator:
answer = a
# some reply's have an extra leading space to fit the instruction template, just clip it off from the reply.
if edit_task[-1] != '\n' and answer and answer[0] == ' ':
answer = answer[1:]
completion_token_count = len(encode(answer)[0])
resp = {
"object": "edit",
"created": created_time,
"choices": [{
"text": answer,
"index": 0,
}],
"usage": {
"prompt_tokens": token_count,
"completion_tokens": completion_token_count,
"total_tokens": token_count + completion_token_count
}
}
return resp
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