import time from enum import Enum class PromptType(Enum): plain = 0 instruct = 1 quality = 2 human_bot = 3 dai_faq = 4 summarize = 5 simple_instruct = 6 instruct_vicuna = 7 instruct_with_end = 8 human_bot_orig = 9 prompt_answer = 10 open_assistant = 11 wizard_lm = 12 wizard_mega = 13 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', '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 'gptj', # internally handles prompting 'llama', # internally handles prompting ], '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-en-2048-open-llama-7b-preview-300bt', 'h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2', ], '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', # private ], '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'], } 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, chat, context, reduced): if prompt_type in [-1, "-1", "plain"]: promptA = promptB = PreInstruct = PreInput = PreResponse = '' terminate_response = [] chat_sep = '' humanstr = '' botstr = '' elif prompt_type == 'simple_instruct': promptA = promptB = PreInstruct = PreInput = PreResponse = None terminate_response = [] chat_sep = '\n' humanstr = '' botstr = '' elif prompt_type in [0, "0", "instruct"] or prompt_type in [7, "7", "instruct_with_end"]: 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 [7, "7", "instruct_with_end"]: terminate_response = ['### End'] else: terminate_response = None chat_sep = '\n' humanstr = PreInstruct botstr = PreResponse elif prompt_type in [1, "1", "quality"]: 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_sep = '\n' humanstr = PreInstruct # first thing human says botstr = PreResponse # first thing bot says elif prompt_type in [2, "2", "human_bot", 9, "9", "human_bot_orig"]: human = ':' bot = ":" if reduced or context or prompt_type in [2, "2", "human_bot"]: 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 = human promptB = promptA = '%s%s ' % (preprompt, start) PreInstruct = "" PreInput = None if reduced: # 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 = [start, PreResponse] chat_sep = '\n' humanstr = human # tag before human talks botstr = bot # tag before bot talks elif prompt_type in [3, "3", "dai_faq"]: 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_sep = terminate_response humanstr = PreInstruct botstr = PreResponse elif prompt_type in [5, "5", "summarize"]: promptA = promptB = PreInput = '' PreInstruct = '## Main Text\n\n' PreResponse = '\n\n## Summary\n\n' terminate_response = None chat_sep = '\n' humanstr = PreInstruct botstr = PreResponse elif prompt_type in [6, "6", "instruct_vicuna"]: 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_sep = '\n' humanstr = PreInstruct botstr = PreResponse elif prompt_type in [10, "10", "prompt_answer"]: preprompt = '' prompt_tokens = "<|prompt|>" answer_tokens = "<|answer|>" start = prompt_tokens promptB = promptA = '%s%s' % (preprompt, start) PreInstruct = "" PreInput = None PreResponse = answer_tokens eos = '<|endoftext|>' # neox eos terminate_response = [start, PreResponse, eos] chat_sep = eos humanstr = prompt_tokens botstr = answer_tokens elif prompt_type in [11, "11", "open_assistant"]: # From added_tokens.json preprompt = '' prompt_tokens = "<|prompter|>" answer_tokens = "<|assistant|>" start = prompt_tokens promptB = promptA = '%s%s' % (preprompt, start) PreInstruct = "" PreInput = None PreResponse = answer_tokens pend = "<|prefix_end|>" eos = "" terminate_response = [start, PreResponse, pend, eos] chat_sep = eos humanstr = prompt_tokens botstr = answer_tokens elif prompt_type in [12, "12", "wizard_lm"]: # 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" eos = "" terminate_response = [PreResponse, eos] chat_sep = eos humanstr = promptA botstr = PreResponse elif prompt_type in [13, "13", "wizard_mega"]: preprompt = '' start = '' promptB = promptA = '%s%s' % (preprompt, start) PreInstruct = """ ### Instruction: """ PreInput = None PreResponse = """ ### Assistant: """ terminate_response = [PreResponse] chat_sep = '\n' humanstr = PreInstruct botstr = PreResponse else: raise RuntimeError("No such prompt_type=%s" % prompt_type) return promptA, promptB, PreInstruct, PreInput, PreResponse, terminate_response, chat_sep, humanstr, botstr def generate_prompt(data_point, prompt_type, chat, reduced): 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) assert prompt_type in prompt_types, "Bad prompt type: %s" % prompt_type promptA, promptB, PreInstruct, PreInput, PreResponse, \ terminate_response, chat_sep, humanstr, botstr = get_prompt(prompt_type, chat, context, reduced) prompt = context if not reduced else '' 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_newline(prompt_type, prompt) elif instruction and input and PreInstruct is None and PreInput is not None: prompt += f"""{PreInput}{instruction} {input}""" prompt = inject_newline(prompt_type, prompt) elif input and instruction and PreInput is None and PreInstruct is not None: prompt += f"""{PreInstruct}{instruction} {input}""" prompt = inject_newline(prompt_type, prompt) elif instruction and PreInstruct is not None: prompt += f"""{PreInstruct}{instruction}""" prompt = inject_newline(prompt_type, prompt) elif input and PreInput is not None: prompt += f"""{PreInput}{input}""" prompt = inject_newline(prompt_type, prompt) elif input and instruction and PreInput is not None: prompt += f"""{PreInput}{instruction}{input}""" prompt = inject_newline(prompt_type, prompt) elif input and instruction and PreInstruct is not None: prompt += f"""{PreInstruct}{instruction}{input}""" prompt = inject_newline(prompt_type, prompt) elif input and instruction: # i.e. for simple_instruct prompt += f"""{instruction}: {input}""" prompt = inject_newline(prompt_type, prompt) elif input: prompt += f"""{input}""" prompt = inject_newline(prompt_type, prompt) elif instruction: prompt += f"""{instruction}""" prompt = inject_newline(prompt_type, prompt) 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 def inject_newline(prompt_type, prompt): if prompt_type not in [-1, '-1', 'plain', 'simple_instruct']: # only add new line if structured prompt, while 'plain' is just generation of next tokens from input prompt += '\n' return prompt class Prompter(object): def __init__(self, prompt_type, debug=False, chat=False, stream_output=False, repeat_penalty=True, allowed_repeat_line_length=10): self.prompt_type = prompt_type data_point = dict(instruction='', input='', output='') _, self.pre_response, self.terminate_response, self.chat_sep = \ generate_prompt(data_point, prompt_type, chat, False) 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 self.promptA, self.promptB, self.PreInstruct, self.PreInput, self.PreResponse, \ self.terminate_response, self.chat_sep, self.humanstr, self.botstr = \ get_prompt(prompt_type, chat, context, reduced) def generate_prompt(self, data_point): reduced = False prompt, _, _, _ = generate_prompt(data_point, self.prompt_type, self.chat, reduced) if self.debug: print("prompt: ", prompt, flush=True) self.prompt = prompt return prompt def get_response(self, outputs, prompt=None, sanitize_bot_response=True): if isinstance(outputs, str): outputs = [outputs] if self.debug: print("output:\n", '\n\n'.join(outputs), flush=True) if prompt is not None: self.prompt = prompt def clean_response(response): meaningless_words = ['', '', '<|endoftext|>'] for word in meaningless_words: response = response.replace(word, "") if sanitize_bot_response: from better_profanity import profanity response = profanity.censor(response) response = response.strip("\n") 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 [0, '0', 'plain']: 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].strip().split(self.humanstr)[0].strip()) else: # i.e. use after bot but only up to next bot output = clean_response(output.split(self.botstr)[1].strip().split(self.botstr)[0].strip()) else: # output = clean_response(output.strip()) # 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).strip() if self.repeat_penalty: output = clean_repeats(output).strip() 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].strip() else: output = output.strip() else: output = output.strip() 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", '\n\n'.join(outputs), flush=True) return output