import time import pytest from tests.utils import wrap_test_forked from src.enums import source_prefix, source_postfix from src.prompter import generate_prompt example_data_point0 = dict(instruction="Summarize", input="Ducks eat seeds by the lake, then swim in the lake where fish eat small animals.", output="Ducks eat and swim at the lake.") example_data_point1 = dict(instruction="Who is smarter, Einstein or Newton?", output="Einstein.") example_data_point2 = dict(input="Who is smarter, Einstein or Newton?", output="Einstein.") example_data_points = [example_data_point0, example_data_point1, example_data_point2] @wrap_test_forked def test_train_prompt(prompt_type='instruct', data_point=0): example_data_point = example_data_points[data_point] return generate_prompt(example_data_point, prompt_type, '', False, False, False) @wrap_test_forked def test_test_prompt(prompt_type='instruct', data_point=0): example_data_point = example_data_points[data_point] example_data_point.pop('output', None) return generate_prompt(example_data_point, prompt_type, '', False, False, False) @wrap_test_forked def test_test_prompt2(prompt_type='human_bot', data_point=0): example_data_point = example_data_points[data_point] example_data_point.pop('output', None) res = generate_prompt(example_data_point, prompt_type, '', False, False, False) print(res, flush=True) return res prompt_fastchat = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hello! ASSISTANT: Hi!USER: How are you? ASSISTANT: I'm goodUSER: Go to the market? ASSISTANT:""" prompt_humanbot = """: Hello!\n: Hi!\n: How are you?\n: I'm good\n: Go to the market?\n:""" prompt_prompt_answer = "<|prompt|>Hello!<|endoftext|><|answer|>Hi!<|endoftext|><|prompt|>How are you?<|endoftext|><|answer|>I'm good<|endoftext|><|prompt|>Go to the market?<|endoftext|><|answer|>" prompt_prompt_answer_openllama = "<|prompt|>Hello!<|answer|>Hi!<|prompt|>How are you?<|answer|>I'm good<|prompt|>Go to the market?<|answer|>" prompt_mpt_instruct = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction Hello! ### Response Hi! ### Instruction How are you? ### Response I'm good ### Instruction Go to the market? ### Response """ prompt_mpt_chat = """<|im_start|>system A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers. <|im_end|><|im_start|>user Hello!<|im_end|><|im_start|>assistant Hi!<|im_end|><|im_start|>user How are you?<|im_end|><|im_start|>assistant I'm good<|im_end|><|im_start|>user Go to the market?<|im_end|><|im_start|>assistant """ prompt_falcon = """User: Hello! Assistant: Hi! User: How are you? Assistant: I'm good User: Go to the market? Assistant:""" prompt_llama2 = """[INST] Hello! [/INST] Hi! [INST] How are you? [/INST] I'm good [INST] Go to the market? [/INST]""" prompt_llama2_sys = """[INST] <> 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. If 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. <> Hello! [/INST] Hi! [INST] How are you? [/INST] I'm good [INST] Go to the market? [/INST]""" prompt_llama2_pig = """[INST] Who are you? [/INST] I am a big pig who loves to tell kid stories [INST] Hello! [/INST] Hi! [INST] How are you? [/INST] I'm good [INST] Go to the market? [/INST]""" # Fastsys doesn't put space above before final [/INST], I think wrong, since with context version has space. # and llama2 code has space before it always: https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/generation.py prompt_beluga = """### User: Hello! ### Assistant: Hi! ### User: How are you? ### Assistant: I'm good ### User: Go to the market? ### Assistant: """ prompt_beluga_sys = """### System: 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. ### User: Hello! ### Assistant: Hi! ### User: How are you? ### Assistant: I'm good ### User: Go to the market? ### Assistant: """ prompt_falcon180 = """User: Hello! Falcon: Hi! User: How are you? Falcon: I'm good User: Go to the market? Falcon:""" prompt_falcon180_sys = """System: You are an intelligent and helpful assistant. User: Hello! Falcon: Hi! User: How are you? Falcon: I'm good User: Go to the market? Falcon:""" @wrap_test_forked @pytest.mark.parametrize("prompt_type,system_prompt,chat_conversation,expected", [ ('vicuna11', '', None, prompt_fastchat), ('human_bot', '', None, prompt_humanbot), ('prompt_answer', '', None, prompt_prompt_answer), ('prompt_answer_openllama', '', None, prompt_prompt_answer_openllama), ('mptinstruct', '', None, prompt_mpt_instruct), ('mptchat', '', None, prompt_mpt_chat), ('falcon', '', None, prompt_falcon), ('llama2', '', None, prompt_llama2), ('llama2', 'auto', None, prompt_llama2_sys), ('llama2', '', [('Who are you?', 'I am a big pig who loves to tell kid stories')], prompt_llama2_pig), ('beluga', '', None, prompt_beluga), ('beluga', 'auto', None, prompt_beluga_sys), ('falcon_chat', '', None, prompt_falcon180), ('falcon_chat', 'auto', None, prompt_falcon180_sys), ] ) def test_prompt_with_context(prompt_type, system_prompt, chat_conversation, expected): prompt_dict = None # not used unless prompt_type='custom' langchain_mode = 'Disabled' add_chat_history_to_context = True chat = True model_max_length = 2048 memory_restriction_level = 0 keep_sources_in_context = False iinput = '' stream_output = False debug = False from src.prompter import Prompter from src.gen import history_to_context t0 = time.time() history = [["Hello!", "Hi!"], ["How are you?", "I'm good"], ["Go to the market?", None] ] print("duration1: %s %s" % (prompt_type, time.time() - t0), flush=True) t0 = time.time() context = history_to_context(history, langchain_mode=langchain_mode, add_chat_history_to_context=add_chat_history_to_context, prompt_type=prompt_type, prompt_dict=prompt_dict, chat=chat, model_max_length=model_max_length, memory_restriction_level=memory_restriction_level, keep_sources_in_context=keep_sources_in_context, system_prompt=system_prompt, chat_conversation=chat_conversation) print("duration2: %s %s" % (prompt_type, time.time() - t0), flush=True) t0 = time.time() instruction = history[-1][0] # get prompt prompter = Prompter(prompt_type, prompt_dict, debug=debug, chat=chat, stream_output=stream_output, system_prompt=system_prompt) # for instruction-tuned models, expect this: assert prompter.PreResponse assert prompter.PreInstruct assert prompter.botstr assert prompter.humanstr print("duration3: %s %s" % (prompt_type, time.time() - t0), flush=True) t0 = time.time() data_point = dict(context=context, instruction=instruction, input=iinput) prompt = prompter.generate_prompt(data_point) print(prompt) print("duration4: %s %s" % (prompt_type, time.time() - t0), flush=True) assert prompt == expected assert prompt.find(source_prefix) == -1 prompt_fastchat1 = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Go to the market? ASSISTANT:""" prompt_humanbot1 = """: Go to the market?\n:""" prompt_prompt_answer1 = "<|prompt|>Go to the market?<|endoftext|><|answer|>" prompt_prompt_answer_openllama1 = "<|prompt|>Go to the market?<|answer|>" prompt_mpt_instruct1 = """Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction Go to the market? ### Response """ prompt_mpt_chat1 = """<|im_start|>system A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers. <|im_end|><|im_start|>user Go to the market?<|im_end|><|im_start|>assistant """ prompt_falcon1 = """User: Go to the market? Assistant:""" prompt_llama21 = """[INST] Go to the market? [/INST]""" prompt_llama21_sys = """[INST] <> 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. If 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. <> Go to the market? [/INST]""" # Fastsys doesn't put space above before final [/INST], I think wrong, since with context version has space. # and llama2 code has space before it always: https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/generation.py prompt_beluga1_sys = """### System: 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. ### User: Go to the market? ### Assistant: """ prompt_beluga1 = """### User: Go to the market? ### Assistant: """ prompt_falcon1801 = """User: Go to the market? Falcon:""" prompt_falcon1801_sys = """System: You are an intelligent and helpful assistant. User: Go to the market? Falcon:""" @pytest.mark.parametrize("prompt_type,system_prompt,expected", [ ('vicuna11', '', prompt_fastchat1), ('human_bot', '', prompt_humanbot1), ('prompt_answer', '', prompt_prompt_answer1), ('prompt_answer_openllama', '', prompt_prompt_answer_openllama1), ('mptinstruct', '', prompt_mpt_instruct1), ('mptchat', '', prompt_mpt_chat1), ('falcon', '', prompt_falcon1), ('llama2', '', prompt_llama21), ('llama2', 'auto', prompt_llama21_sys), ('beluga', '', prompt_beluga1), ('beluga', 'auto', prompt_beluga1_sys), ('falcon_chat', '', prompt_falcon1801), ('falcon_chat', 'auto', prompt_falcon1801_sys), ] ) @wrap_test_forked def test_prompt_with_no_context(prompt_type, system_prompt, expected): prompt_dict = None # not used unless prompt_type='custom' chat = True iinput = '' stream_output = False debug = False from src.prompter import Prompter context = '' instruction = "Go to the market?" # get prompt prompter = Prompter(prompt_type, prompt_dict, debug=debug, chat=chat, stream_output=stream_output, system_prompt=system_prompt) # for instruction-tuned models, expect this: assert prompter.PreResponse assert prompter.PreInstruct assert prompter.botstr assert prompter.humanstr data_point = dict(context=context, instruction=instruction, input=iinput) prompt = prompter.generate_prompt(data_point) print(prompt) assert prompt == expected assert prompt.find(source_prefix) == -1 @wrap_test_forked def test_source(): prompt = "Who are you?%s\nFOO\n%s" % (source_prefix, source_postfix) assert prompt.find(source_prefix) >= 0 # https://huggingface.co/spaces/tiiuae/falcon-180b-demo/blob/main/app.py def falcon180_format_prompt(message, history, system_prompt): prompt = "" if system_prompt: prompt += f"System: {system_prompt}\n" for user_prompt, bot_response in history: prompt += f"User: {user_prompt}\n" prompt += f"Falcon: {bot_response}\n" # Response already contains "Falcon: " prompt += f"""User: {message} Falcon:""" return prompt @wrap_test_forked def test_falcon180(): prompt = "Who are you?" for system_prompt in ['', "Talk like a Pixie."]: history = [["Who are you?", "I am Falcon, a monster AI model."], ["What can you do?", "I can do well on leaderboard but not actually 1st."]] formatted_prompt = falcon180_format_prompt(prompt, history, system_prompt) print(formatted_prompt)