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prompter.py
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import os
import ast
import time
from enums import PromptType # also supports imports from this file from other files
non_hf_types = ['gpt4all_llama', 'llama', 'gptj']
prompt_type_to_model_name = {
'plain': [
'EleutherAI/gpt-j-6B',
'EleutherAI/pythia-6.9b',
'EleutherAI/pythia-12b',
'EleutherAI/pythia-12b-deduped',
'EleutherAI/gpt-neox-20b',
'openlm-research/open_llama_7b_700bt_preview',
'decapoda-research/llama-7b-hf',
'decapoda-research/llama-13b-hf',
'decapoda-research/llama-30b-hf',
'decapoda-research/llama-65b-hf',
'facebook/mbart-large-50-many-to-many-mmt',
'philschmid/bart-large-cnn-samsum',
'philschmid/flan-t5-base-samsum',
'gpt2',
'distilgpt2',
'mosaicml/mpt-7b-storywriter',
'mosaicml/mpt-7b-instruct', # internal code handles instruct
'mosaicml/mpt-7b-chat', # NC, internal code handles instruct
'mosaicml/mpt-30b-instruct', # internal code handles instruct
],
'gptj': ['gptj', 'gpt4all_llama'],
'prompt_answer': [
'h2oai/h2ogpt-gm-oasst1-en-1024-20b',
'h2oai/h2ogpt-gm-oasst1-en-1024-12b',
'h2oai/h2ogpt-gm-oasst1-multilang-1024-20b',
'h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b',
'h2oai/h2ogpt-gm-oasst1-multilang-2048-falcon-7b-v2',
'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3',
'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b',
'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v2',
'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1',
'h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2',
'h2oai/h2ogpt-gm-oasst1-en-xgen-7b-8k',
'h2oai/h2ogpt-gm-oasst1-multilang-xgen-7b-8k',
],
'prompt_answer_openllama': [
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt',
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2',
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-700bt',
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b',
'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-13b',
],
'instruct': [],
'instruct_with_end': ['databricks/dolly-v2-12b'],
'quality': [],
'human_bot': [
'h2oai/h2ogpt-oasst1-512-12b',
'h2oai/h2ogpt-oasst1-512-20b',
'h2oai/h2ogpt-oig-oasst1-256-6_9b',
'h2oai/h2ogpt-oig-oasst1-512-6_9b',
'h2oai/h2ogpt-oig-oasst1-256-6.9b', # legacy
'h2oai/h2ogpt-oig-oasst1-512-6.9b', # legacy
'h2oai/h2ogpt-research-oasst1-512-30b',
'h2oai/h2ogpt-research-oasst1-llama-65b',
'h2oai/h2ogpt-oasst1-falcon-40b',
'h2oai/h2ogpt-oig-oasst1-falcon-40b',
],
'dai_faq': [],
'summarize': [],
'simple_instruct': ['t5-small', 't5-large', 'google/flan-t5', 'google/flan-t5-xxl', 'google/flan-ul2'],
'instruct_vicuna': ['AlekseyKorshuk/vicuna-7b', 'TheBloke/stable-vicuna-13B-HF', 'junelee/wizard-vicuna-13b'],
'human_bot_orig': ['togethercomputer/GPT-NeoXT-Chat-Base-20B'],
"open_assistant": ['OpenAssistant/oasst-sft-7-llama-30b-xor', 'oasst-sft-7-llama-30b'],
"wizard_lm": ['ehartford/WizardLM-7B-Uncensored', 'ehartford/WizardLM-13B-Uncensored'],
"wizard_mega": ['openaccess-ai-collective/wizard-mega-13b'],
"instruct_simple": ['JosephusCheung/Guanaco'],
"wizard_vicuna": ['ehartford/Wizard-Vicuna-13B-Uncensored'],
"wizard2": ['llama', 'mosaicml/mpt-30b-instruct'],
"vicuna11": ['lmsys/vicuna-33b-v1.3'],
# could be plain, but default is correct prompt_type for default TheBloke model ggml-wizardLM-7B.q4_2.bin
}
if os.getenv('OPENAI_API_KEY'):
prompt_type_to_model_name.update({
"openai": ["text-davinci-003", "text-curie-001", "text-babbage-001", "text-ada-001"],
"openai_chat": ["gpt-3.5-turbo", "gpt-3.5-turbo-16k"],
})
inv_prompt_type_to_model_name = {v.strip(): k for k, l in prompt_type_to_model_name.items() for v in l}
inv_prompt_type_to_model_lower = {v.strip().lower(): k for k, l in prompt_type_to_model_name.items() for v in l}
prompt_types_strings = []
for p in PromptType:
prompt_types_strings.extend([p.name])
prompt_types = []
for p in PromptType:
prompt_types.extend([p.name, p.value, str(p.value)])
def get_prompt(prompt_type, prompt_dict, chat, context, reduced, making_context, return_dict=False):
prompt_dict_error = ''
generates_leading_space = False
if prompt_type == PromptType.custom.name and not isinstance(prompt_dict, dict):
try:
prompt_dict = ast.literal_eval(prompt_dict)
except BaseException as e:
prompt_dict_error = str(e)
if prompt_dict_error:
promptA = None
promptB = None
PreInstruct = None
PreInput = ''
PreResponse = ''
terminate_response = None
chat_sep = ''
chat_turn_sep = ''
humanstr = ''
botstr = ''
generates_leading_space = False
elif prompt_type in [PromptType.custom.value, str(PromptType.custom.value),
PromptType.custom.name]:
promptA = prompt_dict.get('promptA', '')
promptB = prompt_dict('promptB', '')
PreInstruct = prompt_dict.get('PreInstruct', '')
PreInput = prompt_dict.get('PreInput', '')
PreResponse = prompt_dict.get('PreResponse', '')
terminate_response = prompt_dict.get('terminate_response', None)
chat_sep = prompt_dict.get('chat_sep', '\n')
chat_turn_sep = prompt_dict.get('chat_turn_sep', '\n')
humanstr = prompt_dict.get('humanstr', '')
botstr = prompt_dict.get('botstr', '')
elif prompt_type in [PromptType.plain.value, str(PromptType.plain.value),
PromptType.plain.name]:
promptA = promptB = PreInstruct = PreInput = PreResponse = None
terminate_response = []
chat_turn_sep = chat_sep = ''
# plain should have None for human/bot, so nothing truncated out, not '' that would truncate after first token
humanstr = None
botstr = None
elif prompt_type == 'simple_instruct':
promptA = promptB = PreInstruct = PreInput = PreResponse = None
terminate_response = []
chat_turn_sep = chat_sep = '\n'
humanstr = None
botstr = None
elif prompt_type in [PromptType.instruct.value, str(PromptType.instruct.value),
PromptType.instruct.name] + [PromptType.instruct_with_end.value,
str(PromptType.instruct_with_end.value),
PromptType.instruct_with_end.name]:
promptA = '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' if not (
chat and reduced) else ''
promptB = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n' if not (
chat and reduced) else ''
PreInstruct = """
### Instruction:
"""
PreInput = """
### Input:
"""
PreResponse = """
### Response:
"""
if prompt_type in [PromptType.instruct_with_end.value, str(PromptType.instruct_with_end.value),
PromptType.instruct_with_end.name]:
terminate_response = ['### End']
else:
terminate_response = None
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.quality.value, str(PromptType.quality.value),
PromptType.quality.name]:
promptA = 'Write a detailed high-quality, accurate, fair, Response with about 100 words by following the Instruction as applied on the Input.\n' if not (
chat and reduced) else ''
promptB = 'Write a detailed high-quality, accurate, fair, Response with about 100 words by following the Instruction.\n' if not (
chat and reduced) else ''
PreInstruct = """
### Instruction:
"""
PreInput = """
### Input:
"""
PreResponse = """
### Response:
"""
terminate_response = None
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct # first thing human says
botstr = PreResponse # first thing bot says
elif prompt_type in [PromptType.human_bot.value, str(PromptType.human_bot.value),
PromptType.human_bot.name] + [PromptType.human_bot_orig.value,
str(PromptType.human_bot_orig.value),
PromptType.human_bot_orig.name]:
human = '<human>:'
bot = "<bot>:"
if reduced or context or prompt_type in [PromptType.human_bot.value, str(PromptType.human_bot.value),
PromptType.human_bot.name]:
preprompt = ''
else:
cur_date = time.strftime('%Y-%m-%d')
cur_time = time.strftime('%H:%M:%S %p %Z')
PRE_PROMPT = """\
Current Date: {}
Current Time: {}
"""
preprompt = PRE_PROMPT.format(cur_date, cur_time)
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = human + ' '
PreInput = None
if making_context:
# when making context, want it to appear as-if LLM generated, which starts with space after :
PreResponse = bot + ' '
else:
# normally LLM adds space after this, because was how trained.
# if add space here, non-unique tokenization will often make LLM produce wrong output
PreResponse = bot
terminate_response = ['\n' + human, '\n' + bot, human, bot, PreResponse]
chat_turn_sep = chat_sep = '\n'
humanstr = human # tag before human talks
botstr = bot # tag before bot talks
generates_leading_space = True
elif prompt_type in [PromptType.dai_faq.value, str(PromptType.dai_faq.value),
PromptType.dai_faq.name]:
promptA = ''
promptB = 'Answer the following Driverless AI question.\n'
PreInstruct = """
### Driverless AI frequently asked question:
"""
PreInput = None
PreResponse = """
### Driverless AI documentation answer:
"""
terminate_response = ['\n\n']
chat_turn_sep = chat_sep = terminate_response
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.summarize.value, str(PromptType.summarize.value),
PromptType.summarize.name]:
promptA = promptB = PreInput = ''
PreInstruct = '## Main Text\n\n'
PreResponse = '\n\n## Summary\n\n'
terminate_response = None
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.instruct_vicuna.value, str(PromptType.instruct_vicuna.value),
PromptType.instruct_vicuna.name]:
promptA = promptB = "A chat between a curious human and an artificial intelligence assistant. " \
"The assistant gives helpful, detailed, and polite answers to the human's questions." if not (
chat and reduced) else ''
PreInstruct = """
### Human:
"""
PreInput = None
PreResponse = """
### Assistant:
"""
terminate_response = [
'### Human:'] # but only allow terminate after prompt is found correctly, else can't terminate
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.prompt_answer.value, str(PromptType.prompt_answer.value),
PromptType.prompt_answer.name]:
preprompt = ''
prompt_tokens = "<|prompt|>"
answer_tokens = "<|answer|>"
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = prompt_tokens
PreInput = None
PreResponse = answer_tokens
eos = '<|endoftext|>' # neox eos
humanstr = prompt_tokens
botstr = answer_tokens
terminate_response = [humanstr, PreResponse, eos]
chat_sep = ''
chat_turn_sep = eos
elif prompt_type in [PromptType.prompt_answer_openllama.value, str(PromptType.prompt_answer_openllama.value),
PromptType.prompt_answer_openllama.name]:
preprompt = ''
prompt_tokens = "<|prompt|>"
answer_tokens = "<|answer|>"
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = prompt_tokens
PreInput = None
PreResponse = answer_tokens
eos = '</s>' # llama eos
humanstr = prompt_tokens
botstr = answer_tokens
terminate_response = [humanstr, PreResponse, eos]
chat_sep = ''
chat_turn_sep = eos
elif prompt_type in [PromptType.open_assistant.value, str(PromptType.open_assistant.value),
PromptType.open_assistant.name]:
# From added_tokens.json
preprompt = ''
prompt_tokens = "<|prompter|>"
answer_tokens = "<|assistant|>"
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = prompt_tokens
PreInput = None
PreResponse = answer_tokens
pend = "<|prefix_end|>"
eos = "</s>"
humanstr = prompt_tokens
botstr = answer_tokens
terminate_response = [humanstr, PreResponse, pend, eos]
chat_turn_sep = chat_sep = eos
elif prompt_type in [PromptType.wizard_lm.value, str(PromptType.wizard_lm.value),
PromptType.wizard_lm.name]:
# https://github.com/ehartford/WizardLM/blob/main/src/train_freeform.py
preprompt = ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = ""
PreInput = None
PreResponse = "\n\n### Response\n"
eos = "</s>"
terminate_response = [PreResponse, eos]
chat_turn_sep = chat_sep = eos
humanstr = promptA
botstr = PreResponse
elif prompt_type in [PromptType.wizard_mega.value, str(PromptType.wizard_mega.value),
PromptType.wizard_mega.name]:
preprompt = ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = """
### Instruction:
"""
PreInput = None
PreResponse = """
### Assistant:
"""
terminate_response = [PreResponse]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.instruct_vicuna2.value, str(PromptType.instruct_vicuna2.value),
PromptType.instruct_vicuna2.name]:
promptA = promptB = "" if not (chat and reduced) else ''
PreInstruct = """
HUMAN:
"""
PreInput = None
PreResponse = """
ASSISTANT:
"""
terminate_response = [
'HUMAN:'] # but only allow terminate after prompt is found correctly, else can't terminate
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.instruct_vicuna3.value, str(PromptType.instruct_vicuna3.value),
PromptType.instruct_vicuna3.name]:
promptA = promptB = "" if not (chat and reduced) else ''
PreInstruct = """
### User:
"""
PreInput = None
PreResponse = """
### Assistant:
"""
terminate_response = [
'### User:'] # but only allow terminate after prompt is found correctly, else can't terminate
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.wizard2.value, str(PromptType.wizard2.value),
PromptType.wizard2.name]:
# https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML
preprompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.""" if not (
chat and reduced) else ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = """
### Instruction:
"""
PreInput = None
PreResponse = """
### Response:
"""
terminate_response = [PreResponse]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.wizard3.value, str(PromptType.wizard3.value),
PromptType.wizard3.name]:
# https://huggingface.co/TheBloke/wizardLM-13B-1.0-GGML
preprompt = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.""" if not (
chat and reduced) else ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = """USER: """
PreInput = None
PreResponse = """ASSISTANT: """
terminate_response = [PreResponse]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.wizard_vicuna.value, str(PromptType.wizard_vicuna.value),
PromptType.wizard_vicuna.name]:
preprompt = ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = """USER: """
PreInput = None
PreResponse = """ASSISTANT: """
terminate_response = [PreResponse]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.instruct_simple.value, str(PromptType.instruct_simple.value),
PromptType.instruct_simple.name]:
promptB = promptA = '' if not (chat and reduced) else ''
PreInstruct = """
### Instruction:
"""
PreInput = """
### Input:
"""
PreResponse = """
### Response:
"""
terminate_response = None
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.openai.value, str(PromptType.openai.value),
PromptType.openai.name]:
preprompt = """The following is a conversation with an AI assistant. The assistant is helpful, creative, clever, and very friendly.""" if not (
chat and reduced) else ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = "\nHuman: "
PreInput = None
PreResponse = "\nAI:"
terminate_response = [PreResponse] + [" Human:", " AI:"]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.gptj.value, str(PromptType.gptj.value),
PromptType.gptj.name]:
preprompt = "### Instruction:\n The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response." if not (
chat and reduced) else ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = "\n### Prompt: "
PreInput = None
PreResponse = "\n### Response: "
terminate_response = [PreResponse] + ["Prompt:", "Response:"]
chat_turn_sep = chat_sep = '\n'
humanstr = PreInstruct
botstr = PreResponse
elif prompt_type in [PromptType.openai_chat.value, str(PromptType.openai_chat.value),
PromptType.openai_chat.name]:
# prompting and termination all handled by endpoint
preprompt = """"""
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
PreInstruct = ""
PreInput = None
PreResponse = ""
terminate_response = []
chat_turn_sep = chat_sep = '\n'
humanstr = None
botstr = None
elif prompt_type in [PromptType.vicuna11.value, str(PromptType.vicuna11.value),
PromptType.vicuna11.name]:
preprompt = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. """ if not (
chat and reduced) else ''
start = ''
promptB = promptA = '%s%s' % (preprompt, start)
eos = '</s>'
PreInstruct = """USER: """
PreInput = None
PreResponse = """ASSISTANT:"""
terminate_response = [PreResponse]
chat_sep = ' '
chat_turn_sep = eos
humanstr = PreInstruct
botstr = PreResponse
if making_context:
# when making context, want it to appear as-if LLM generated, which starts with space after :
PreResponse = PreResponse + ' '
else:
# normally LLM adds space after this, because was how trained.
# if add space here, non-unique tokenization will often make LLM produce wrong output
PreResponse = PreResponse
else:
raise RuntimeError("No such prompt_type=%s" % prompt_type)
if isinstance(terminate_response, (tuple, list)):
assert '' not in terminate_response, "Bad terminate_response"
ret_dict = dict(promptA=promptA, promptB=promptB, PreInstruct=PreInstruct, PreInput=PreInput,
PreResponse=PreResponse, terminate_response=terminate_response, chat_sep=chat_sep,
chat_turn_sep=chat_turn_sep,
humanstr=humanstr, botstr=botstr,
generates_leading_space=generates_leading_space)
if return_dict:
return ret_dict, prompt_dict_error
else:
return tuple(list(ret_dict.values()))
def generate_prompt(data_point, prompt_type, prompt_dict, chat, reduced, making_context):
context = data_point.get('context')
if context is None:
context = ''
instruction = data_point.get('instruction')
input = data_point.get('input')
output = data_point.get('output')
prompt_type = data_point.get('prompt_type', prompt_type)
prompt_dict = data_point.get('prompt_dict', prompt_dict)
assert prompt_type in prompt_types, "Bad prompt type: %s" % prompt_type
promptA, promptB, PreInstruct, PreInput, PreResponse, \
terminate_response, chat_sep, chat_turn_sep, humanstr, botstr, \
generates_leading_space = get_prompt(prompt_type, prompt_dict, chat,
context, reduced, making_context)
# could avoid if reduce=True, but too complex for parent functions to handle
prompt = context
if input and promptA:
prompt += f"""{promptA}"""
elif promptB:
prompt += f"""{promptB}"""
if instruction and PreInstruct is not None and input and PreInput is not None:
prompt += f"""{PreInstruct}{instruction}{PreInput}{input}"""
prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
elif instruction and input and PreInstruct is None and PreInput is not None:
prompt += f"""{PreInput}{instruction}
{input}"""
prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
elif input and instruction and PreInput is None and PreInstruct is not None:
prompt += f"""{PreInstruct}{instruction}
{input}"""
prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
elif instruction and PreInstruct is not None:
prompt += f"""{PreInstruct}{instruction}"""
prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
elif input and PreInput is not None:
prompt += f"""{PreInput}{input}"""
prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
elif input and instruction and PreInput is not None:
prompt += f"""{PreInput}{instruction}{input}"""
prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
elif input and instruction and PreInstruct is not None:
prompt += f"""{PreInstruct}{instruction}{input}"""
prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
elif input and instruction:
# i.e. for simple_instruct
prompt += f"""{instruction}: {input}"""
prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
elif input:
prompt += f"""{input}"""
prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
elif instruction:
prompt += f"""{instruction}"""
prompt = inject_chatsep(prompt_type, prompt, chat_sep=chat_sep)
if PreResponse is not None:
prompt += f"""{PreResponse}"""
pre_response = PreResponse # Don't use strip
else:
pre_response = ''
if output:
prompt += f"""{output}"""
return prompt, pre_response, terminate_response, chat_sep, chat_turn_sep
def inject_chatsep(prompt_type, prompt, chat_sep=None):
if chat_sep:
# only add new line if structured prompt, while 'plain' is just generation of next tokens from input
prompt += chat_sep
return prompt
class Prompter(object):
def __init__(self, prompt_type, prompt_dict, debug=False, chat=False, stream_output=False, repeat_penalty=True,
allowed_repeat_line_length=10):
self.prompt_type = prompt_type
self.prompt_dict = prompt_dict
self.debug = debug
self.chat = chat
self.stream_output = stream_output
self.repeat_penalty = repeat_penalty
self.allowed_repeat_line_length = allowed_repeat_line_length
self.prompt = None
context = "" # not for chat context
reduced = False # not for chat context
making_context = False # not for chat context
self.promptA, self.promptB, self.PreInstruct, self.PreInput, self.PreResponse, \
self.terminate_response, self.chat_sep, self.chat_turn_sep, self.humanstr, self.botstr, \
self.generates_leading_space = \
get_prompt(self.prompt_type, self.prompt_dict, chat, context, reduced, making_context)
self.pre_response = self.PreResponse
def generate_prompt(self, data_point, reduced=None):
"""
data_point['context'] is assumed to be like a system prompt or pre-conversation, not inserted after user prompt
:param data_point:
:param reduced:
:return:
"""
reduced = data_point.get('context') not in ['', None] if reduced is None else reduced
making_context = False # whether really making final prompt or just generating context
prompt, _, _, _, _ = generate_prompt(data_point, self.prompt_type, self.prompt_dict, self.chat, reduced,
making_context)
if self.debug:
print("prompt: %s" % prompt, flush=True)
# if have context, should have always reduced and only preappend promptA/B here
if data_point.get('context'):
if data_point.get('input') and self.promptA:
prompt = self.promptA + prompt
elif self.promptB:
prompt = self.promptB + prompt
self.prompt = prompt
return prompt
def get_response(self, outputs, prompt=None, sanitize_bot_response=False):
if isinstance(outputs, str):
outputs = [outputs]
if self.debug:
print("output:\n%s" % '\n\n'.join(outputs), flush=True)
if prompt is not None:
self.prompt = prompt
def clean_response(response):
meaningless_words = ['<pad>', '</s>', '<|endoftext|>']
for word in meaningless_words:
response = response.replace(word, "")
if sanitize_bot_response:
from better_profanity import profanity
response = profanity.censor(response)
if self.generates_leading_space and isinstance(response, str) and len(response) > 0 and response[0] == ' ':
response = response[1:]
return response
def clean_repeats(response):
lines = response.split('\n')
new_lines = []
[new_lines.append(line) for line in lines if
line not in new_lines or len(line) < self.allowed_repeat_line_length]
if self.debug and len(lines) != len(new_lines):
print("cleaned repeats: %s %s" % (len(lines), len(new_lines)), flush=True)
response = '\n'.join(new_lines)
return response
multi_output = len(outputs) > 1
for oi, output in enumerate(outputs):
if self.prompt_type in [PromptType.plain.value, str(PromptType.plain.value), PromptType.plain.name]:
output = clean_response(output)
elif prompt is None:
# then use most basic parsing like pipeline
if self.botstr in output:
if self.humanstr:
output = clean_response(output.split(self.botstr)[1].split(self.humanstr)[0])
else:
# i.e. use after bot but only up to next bot
output = clean_response(output.split(self.botstr)[1].split(self.botstr)[0])
else:
# output = clean_response(output)
# assume just not printed yet
output = ""
else:
# find first instance of prereponse
# prompt sometimes has odd characters, that mutate length,
# so can't go by length alone
if self.pre_response:
outputi = output.find(prompt)
if outputi >= 0:
output = output[outputi + len(prompt):]
allow_terminate = True
else:
# subtraction is risky due to space offsets sometimes, so only do if necessary
output = output[len(prompt) - len(self.pre_response):]
# [1] to avoid repeated pre_response, just take first (after prompt - pre_response for chat)
if self.pre_response in output:
output = output.split(self.pre_response)[1]
allow_terminate = True
else:
if output:
print("Failure of parsing or not enough output yet: %s" % output, flush=True)
allow_terminate = False
else:
allow_terminate = True
output = output[len(prompt):]
# clean after subtract prompt out, so correct removal of pre_response
output = clean_response(output)
if self.repeat_penalty:
output = clean_repeats(output)
if self.terminate_response and allow_terminate:
finds = []
for term in self.terminate_response:
finds.append(output.find(term))
finds = [x for x in finds if x >= 0]
if len(finds) > 0:
termi = finds[0]
output = output[:termi]
else:
output = output
if multi_output:
# prefix with output counter
output = "\n=========== Output %d\n\n" % (1 + oi) + output
if oi > 0:
# post fix outputs with seperator
output += '\n'
outputs[oi] = output
# join all outputs, only one extra new line between outputs
output = '\n'.join(outputs)
if self.debug:
print("outputclean:\n%s" % '\n\n'.join(outputs), flush=True)
return output