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', 'tiiuae/falcon-7b', 'tiiuae/falcon-40b', 'tiiuae/falcon-180B', 'meta-llama/Llama-2-7b', 'meta-llama/Llama-2-13b', 'meta-llama/Llama-2-70b', 'h2oai/h2ogpt-4096-llama2-7b', 'h2oai/h2ogpt-4096-llama2-13b', 'h2oai/h2ogpt-4096-llama2-70b', 'h2oai/h2ogpt-16k-codellama-7b', 'h2oai/h2ogpt-16k-codellama-13b', 'h2oai/h2ogpt-16k-codellama-34b', 'h2oai/h2ogpt-16k-codellama-7b-python', 'h2oai/h2ogpt-16k-codellama-13b-python', 'h2oai/h2ogpt-16k-codellama-34b-python', ], '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', 'TheBloke/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GPTQ', ], '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': ['TheBloke/llama-30b-supercot-SuperHOT-8K-fp16'], # https://huggingface.co./TheBloke/llama-30b-supercot-SuperHOT-8K-fp16#prompting '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": [], "mptinstruct": ['mosaicml/mpt-30b-instruct', 'mosaicml/mpt-7b-instruct', 'mosaicml/mpt-30b-instruct'], "mptchat": ['mosaicml/mpt-7b-chat', 'mosaicml/mpt-30b-chat', 'TheBloke/mpt-30B-chat-GGML'], "vicuna11": ['lmsys/vicuna-33b-v1.3', 'lmsys/vicuna-7b-v1.5', 'lmsys/vicuna-13b-v1.5'], "one_shot": ['lmsys/fastchat-t5-3b-v1.0'], "falcon": ['tiiuae/falcon-40b-instruct', 'tiiuae/falcon-7b-instruct'], "llama2": [ 'meta-llama/Llama-2-7b-chat-hf', 'meta-llama/Llama-2-13b-chat-hf', 'meta-llama/Llama-2-34b-chat-hf', 'meta-llama/Llama-2-70b-chat-hf', 'h2oai/h2ogpt-oasst1-4096-llama2-7b', 'h2oai/h2ogpt-oasst1-4096-llama2-13b', 'h2oai/h2ogpt-oasst1-4096-llama2-70b', 'llama', 'TheBloke/Llama-2-7b-Chat-GPTQ', 'TheBloke/Llama-2-7b-chat-fp16', 'TheBloke/Llama-2-13b-chat-fp16', 'TheBloke/Llama-2-70b-chat-fp16', 'h2oai/h2ogpt-4096-llama2-7b-chat', 'h2oai/h2ogpt-4096-llama2-13b-chat', 'h2oai/h2ogpt-4096-llama2-70b-chat', 'h2oai/h2ogpt-16k-codellama-7b-instruct', 'h2oai/h2ogpt-16k-codellama-13b-instruct', 'h2oai/h2ogpt-16k-codellama-34b-instruct', ], "beluga": ['stabilityai/StableBeluga2', 'psmathur/orca_mini_v3_7b'], "wizard3nospace": ['WizardLM/WizardLM-13B-V1.2'], "falcon_chat": ['tiiuae/falcon-180B-chat'], # 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, system_prompt=None, histi=-1): 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.get('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 = ':' 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: """ # but only allow terminate after prompt is found correctly, else can't terminate terminate_response = ['### Human:', '### Human: ', ' ### Human:', '### Assistant:'] 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 = eos 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 = '' # llama eos humanstr = prompt_tokens botstr = answer_tokens terminate_response = [humanstr, PreResponse, eos] chat_sep = eos 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 = "" 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 = "" 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 = '' 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 elif prompt_type in [PromptType.mptinstruct.value, str(PromptType.mptinstruct.value), PromptType.mptinstruct.name]: # https://huggingface.co./mosaicml/mpt-30b-instruct#formatting promptA = 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 """ terminate_response = None chat_turn_sep = chat_sep = '\n' humanstr = PreInstruct botstr = PreResponse elif prompt_type in [PromptType.mptchat.value, str(PromptType.mptchat.value), PromptType.mptchat.name]: # https://huggingface.co./TheBloke/mpt-30B-chat-GGML#prompt-template promptA = promptB = """<|im_start|>system\nA conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.\n<|im_end|>""" if not ( chat and reduced) else '' PreInstruct = """<|im_start|>user """ PreInput = None PreResponse = """<|im_end|><|im_start|>assistant """ terminate_response = ['<|im_end|>'] chat_sep = '' chat_turn_sep = '<|im_end|>' humanstr = PreInstruct botstr = PreResponse elif prompt_type in [PromptType.falcon.value, str(PromptType.falcon.value), PromptType.falcon.name]: promptA = promptB = "" if not (chat and reduced) else '' PreInstruct = """User: """ PreInput = None PreResponse = """Assistant:""" terminate_response = ['\nUser', "<|endoftext|>"] chat_sep = '\n\n' chat_turn_sep = '\n\n' humanstr = PreInstruct botstr = PreResponse if making_context: # when making context, want it to appear as-if LLM generated, which starts with space after : PreResponse = 'Assistant: ' 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 # generates_leading_space = True elif prompt_type in [PromptType.guanaco.value, str(PromptType.guanaco.value), PromptType.guanaco.name]: # https://huggingface.co./TheBloke/guanaco-65B-GPTQ 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.llama2.value, str(PromptType.llama2.value), PromptType.llama2.name]: if system_prompt in [None, 'None', 'auto']: # automatic system_prompt = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.""" # too much safety, hurts accuracy if system_prompt: sys_msg = """<>\n%s\n<>\n\n""" % system_prompt else: sys_msg = '' if not (chat and reduced): promptA = promptB = '' else: promptA = promptB = '' PreInput = None PreInstruct = "[INST] " if making_context and histi == 0 or not making_context and not (chat and reduced): PreInstruct += sys_msg PreResponse = "[/INST]" terminate_response = ["[INST]", ""] chat_sep = ' ' chat_turn_sep = ' ' humanstr = '[INST]' botstr = '[/INST]' if making_context: PreResponse += " " elif prompt_type in [PromptType.beluga.value, str(PromptType.beluga.value), PromptType.beluga.name]: if system_prompt in [None, 'None', 'auto']: # automatic system_prompt = "You are Stable Beluga, an AI that follows instructions extremely well. Help as much as you can. Remember, be safe, and don't do anything illegal." if system_prompt: sys_msg = """### System:\n%s\n\n""" % system_prompt else: sys_msg = '' if sys_msg and not (chat and reduced): # too much safety, hurts accuracy promptA = promptB = sys_msg else: promptA = promptB = '' PreInput = None PreInstruct = "### User:\n" PreResponse = "\n### Assistant:\n" terminate_response = ['### Assistant:', ""] chat_sep = '\n' chat_turn_sep = '\n\n' humanstr = '### User:' botstr = '### Assistant:' elif prompt_type in [PromptType.wizard3nospace.value, str(PromptType.wizard3nospace.value), PromptType.wizard3nospace.name]: # https://huggingface.co./WizardLM/WizardLM-13B-V1.2/discussions/3 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.one_shot.value, str(PromptType.one_shot.value), PromptType.one_shot.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. ### Human: Got any creative ideas for a 10 year old’s birthday? ### Assistant: Of course! Here are some creative ideas for a 10-year-old's birthday party: 1. Treasure Hunt: Organize a treasure hunt in your backyard or nearby park. Create clues and riddles for the kids to solve, leading them to hidden treasures and surprises. 2. Science Party: Plan a science-themed party where kids can engage in fun and interactive experiments. You can set up different stations with activities like making slime, erupting volcanoes, or creating simple chemical reactions. 3. Outdoor Movie Night: Set up a backyard movie night with a projector and a large screen or white sheet. Create a cozy seating area with blankets and pillows, and serve popcorn and snacks while the kids enjoy a favorite movie under the stars. 4. DIY Crafts Party: Arrange a craft party where kids can unleash their creativity. Provide a variety of craft supplies like beads, paints, and fabrics, and let them create their own unique masterpieces to take home as party favors. 5. Sports Olympics: Host a mini Olympics event with various sports and games. Set up different stations for activities like sack races, relay races, basketball shooting, and obstacle courses. Give out medals or certificates to the participants. 6. Cooking Party: Have a cooking-themed party where the kids can prepare their own mini pizzas, cupcakes, or cookies. Provide toppings, frosting, and decorating supplies, and let them get hands-on in the kitchen. 7. Superhero Training Camp: Create a superhero-themed party where the kids can engage in fun training activities. Set up an obstacle course, have them design their own superhero capes or masks, and organize superhero-themed games and challenges. 8. Outdoor Adventure: Plan an outdoor adventure party at a local park or nature reserve. Arrange activities like hiking, nature scavenger hunts, or a picnic with games. Encourage exploration and appreciation for the outdoors. Remember to tailor the activities to the birthday child's interests and preferences. Have a great celebration!""" if not ( chat and reduced) else '' PreInstruct = """ ### Human: """ PreInput = None PreResponse = """ ### Assistant:""" # but only allow terminate after prompt is found correctly, else can't terminate terminate_response = ['### Human:', '### Human: ', ' ### Human:', '### Assistant:'] chat_turn_sep = chat_sep = '\n' humanstr = PreInstruct botstr = PreResponse elif prompt_type in [PromptType.falcon_chat.value, str(PromptType.falcon_chat.value), PromptType.falcon_chat.name]: if system_prompt in [None, 'None', 'auto']: # automatic system_prompt = "You are an intelligent and helpful assistant." if system_prompt: sys_msg = "System: %s\n" % system_prompt else: sys_msg = '' if sys_msg and not (chat and reduced): # too much safety, hurts accuracy promptA = promptB = sys_msg else: promptA = promptB = '' PreInstruct = """User: """ PreInput = None PreResponse = """Falcon:""" terminate_response = ['\nUser:', "<|endoftext|>", " User:", "###"] chat_sep = '\n' chat_turn_sep = '\n' humanstr = PreInstruct botstr = PreResponse if making_context: # when making context, want it to appear as-if LLM generated, which starts with space after : PreResponse = botstr + ' ' 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, system_prompt=system_prompt) 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, system_prompt=None, histi=-1): 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, system_prompt = get_prompt(prompt_type, prompt_dict, chat, context, reduced, making_context, system_prompt=system_prompt, histi=histi) # 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=False, allowed_repeat_line_length=10, system_prompt=None): 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 self.system_prompt = system_prompt 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, self.system_prompt = \ get_prompt(self.prompt_type, self.prompt_dict, chat, context, reduced, making_context, system_prompt=system_prompt) self.pre_response = self.PreResponse @property def stop_sequences(self): terminate_response = self.terminate_response or [] stop_sequences = list(set(terminate_response + [self.PreResponse])) stop_sequences = [x for x in stop_sequences if x] return stop_sequences def generate_prompt(self, data_point, reduced=False, context_from_history=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: :param context_from_history: whether context is from reduced=True version of history in prompt form In which case we need to put promptA at very front to recover correct behavior :return: """ if context_from_history is None and data_point.get('context'): context_from_history = True reduced = True 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, histi=-1, system_prompt=self.system_prompt) 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') and context_from_history: 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, only_new_text=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 = ['', '', '<|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) allow_terminate = True elif only_new_text: # only use terminate, that will have other variations of cleaning that include \n etc. not just simple human bot that will leave residual \n allow_terminate = True elif prompt is None: allow_terminate = True # then use most basic parsing like pipeline if not self.botstr: pass else: 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: # 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' output = self.fix_text(self.prompt_type, output) 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 @staticmethod def fix_text(prompt_type1, text1): if prompt_type1 == 'human_bot': # hack bug in vLLM with stopping, stops right, but doesn't return last token hfix = '