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optimization/test.py
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optimization/test.py
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# Copyright 2023 The OPRO Authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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r"""The .py file for prompt optimization.
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Usage:
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Step 1: edit the starting instructions by modifying `initial_instructions`
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Step 2: edit the training ratio by modifying `train_ratio`
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Step 3: check if the model configs (like batch size) are the same as the actual serving configs
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Step 4: run
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```
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python optimize_instructions.py \
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--optimizer="gpt-3.5-turbo" --scorer="text-bison" \
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--instruction_pos="A_begin" --dataset="gsm8k" --task="train"
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```
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The outputs will then be written to `outputs/optimization-results/` in the opro folder.
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Notes:
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1. One or more API keys may need to be provided:
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- When using a Google-Cloud-served model (like text-bison at https://developers.generativeai.google/tutorials/text_quickstart), add `--palm_api_key=<your_key>`
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- When using an OpenAI model, add `--openai_api_key=”<your_key>”`
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2. The initial instructions should be provided in the "initial_instructions"
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variable.
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"""
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import datetime
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import functools
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import os
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import sys
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OPRO_ROOT_PATH = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
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)
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sys.path.insert(0, OPRO_ROOT_PATH)
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from absl import app
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from absl import flags
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import google.generativeai as palm
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import numpy as np
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import openai
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from opro import prompt_utils
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from opro.optimization import opt_utils
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import pandas as pd
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ROOT_DATA_FOLDER_PATH = os.path.join(OPRO_ROOT_PATH, "data")
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_LOCAL_MODEL_PATH = flags.DEFINE_string("local_model_path", None, "Path to local vLLM model.")
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_OPENAI_API_KEY = flags.DEFINE_string(
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"openai_api_key", "", "The OpenAI API key."
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)
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_PALM_API_KEY = flags.DEFINE_string("palm_api_key", "", "The PaLM API key.")
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_SCORER = flags.DEFINE_string(
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"scorer", "text-bison", "The name of the scorer LLM."
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)
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_OPTIMIZER = flags.DEFINE_string(
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"optimizer", "gpt-3.5-turbo", "The name of the optimizer LLM."
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)
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_DATASET = flags.DEFINE_string(
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"dataset", "gsm8k", "The name of dataset to search for instructions on."
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)
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_TASK = flags.DEFINE_string(
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"task",
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"train",
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"The name of task within the above dataset to search for instructions on.",
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)
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_INSTRUCTION_POS = flags.DEFINE_string(
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"instruction_pos",
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"A_begin",
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"The position of the instruction to search for.",
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)
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_META_PROMPT_TYPE = flags.DEFINE_string(
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"meta_prompt_type",
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"both_instructions_and_exemplars",
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"The type of meta-prompt: whether to have both previous instructions and"
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" dataset exemplars (often for fine-tuned optimizers), or to have only"
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" previous instructions (often for pre-trained optimizers).",
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)
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def main(_):
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openai_api_key = _OPENAI_API_KEY.value
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palm_api_key = _PALM_API_KEY.value
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scorer_llm_name = _SCORER.value
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optimizer_llm_name = _OPTIMIZER.value
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dataset_name = _DATASET.value.lower()
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task_name = _TASK.value
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meta_prompt_type = _META_PROMPT_TYPE.value
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local_model_path = _LOCAL_MODEL_PATH.value
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assert dataset_name in {
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"mmlu",
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"bbh",
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"gsm8k",
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}, "The lower-case dataset name must be one of mmlu, bbh, or gsm8k."
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if dataset_name == "mmlu":
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assert task_name in {
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"STEM",
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"humanities",
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"social sciences",
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"otheran (business, health, misc.)",
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} # for now only support searching on one MMLU category
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elif dataset_name == "bbh":
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assert task_name in {
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"boolean_expressions",
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"causal_judgement",
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"date_understanding",
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"disambiguation_qa",
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"dyck_languages",
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"formal_fallacies",
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"geometric_shapes",
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"hyperbaton",
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"logical_deduction_five_objects",
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"logical_deduction_seven_objects",
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"logical_deduction_three_objects",
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"movie_recommendation",
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"multistep_arithmetic_two",
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"navigate",
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"object_counting",
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"penguins_in_a_table",
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"reasoning_about_colored_objects",
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"ruin_names",
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"salient_translation_error_detection",
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"snarks",
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"sports_understanding",
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"temporal_sequences",
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"tracking_shuffled_objects_five_objects",
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"tracking_shuffled_objects_seven_objects",
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"tracking_shuffled_objects_three_objects",
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"web_of_lies",
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"word_sorting",
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}
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else:
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assert dataset_name == "gsm8k"
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assert task_name in {"train", "test"}
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assert scorer_llm_name in {
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"text-bison",
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"gpt-3.5-turbo",
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"gpt-4",
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"local",
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}
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assert optimizer_llm_name in {
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"text-bison",
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"gpt-3.5-turbo",
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"gpt-4",
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"local",
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}
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assert meta_prompt_type in {
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"both_instructions_and_exemplars",
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"instructions_only",
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}
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instruction_pos = _INSTRUCTION_POS.value
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assert instruction_pos in {
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"before_Q",
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"Q_begin",
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"Q_end",
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"A_begin",
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}, (
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"The instruction position should be either before the question, or at the"
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" beginning of the question, at the end of the question, or at the"
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" beginning of the answer."
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)
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print(
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f"scorer: {scorer_llm_name}, optimizer: {optimizer_llm_name}, dataset:"
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f" {dataset_name}, task: {task_name}, instruction_pos: {instruction_pos}"
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)
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# make sure the scorer and optimizer models are callable
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if scorer_llm_name in {"gpt-3.5-turbo", "gpt-4"}:
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assert openai_api_key, "The OpenAI API key must be provided."
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openai.api_key = openai_api_key
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elif scorer_llm_name == "text-bison":
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assert scorer_llm_name == "text-bison"
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assert (
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palm_api_key
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), "A PaLM API key is needed when prompting the text-bison model."
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palm.configure(api_key=palm_api_key)
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elif scorer_llm_name == "local":
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assert local_model_path, "The local model path must be provided."
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assert os.path.exists(local_model_path), (
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f"The local model path {local_model_path} does not exist."
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)
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# set the local model path for vLLM
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# prompt_utils.call_local_server_func(local_model_path)
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else:
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raise ValueError(
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f"Unknown scorer_llm_name: {scorer_llm_name}. "
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"It should be one of text-bison, gpt-3.5-turbo, gpt-4, or local."
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)
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if optimizer_llm_name in {"gpt-3.5-turbo", "gpt-4"}:
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assert openai_api_key, "The OpenAI API key must be provided."
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openai.api_key = openai_api_key
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elif optimizer_llm_name == "text-bison":
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assert optimizer_llm_name == "text-bison"
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assert (
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palm_api_key
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), "A PaLM API key is needed when prompting the text-bison model."
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palm.configure(api_key=palm_api_key)
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elif optimizer_llm_name == "local":
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assert local_model_path, "The local model path must be provided."
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assert os.path.exists(local_model_path), (
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f"The local model path {local_model_path} does not exist."
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)
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# set the local model path for vLLM
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# prompt_utils.call_local_server_func(local_model_path)
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else:
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raise ValueError(
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f"Unknown scorer_llm_name: {optimizer_llm_name}. "
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"It should be one of text-bison, gpt-3.5-turbo, gpt-4, or local."
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)
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if dataset_name == "mmlu":
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root_data_folder_path = os.path.join(ROOT_DATA_FOLDER_PATH, "MMLU-data")
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elif dataset_name == "bbh":
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root_data_folder_path = os.path.join(
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ROOT_DATA_FOLDER_PATH, "BIG-Bench-Hard-data/"
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)
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else:
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assert dataset_name == "gsm8k"
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root_data_folder_path = os.path.join(ROOT_DATA_FOLDER_PATH, "gsm_data")
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# =================== create the result directory ==========================
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datetime_str = (
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str(datetime.datetime.now().replace(microsecond=0))
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.replace(" ", "-")
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.replace(":", "-")
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)
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save_folder = os.path.join(
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OPRO_ROOT_PATH,
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"outputs",
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"optimization-results",
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f"{dataset_name.upper()}-{task_name}-s-{scorer_llm_name}-o-{optimizer_llm_name}-{datetime_str}/",
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)
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result_by_instruction_folder = os.path.join(
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save_folder, "result_by_instruction"
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)
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os.makedirs(result_by_instruction_folder)
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print(f"result directory:\n{save_folder}")
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# ====================== scorer model configs ==============================
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# difference between num_decodes and batch_size:
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# - num_decodes: how many outputs we actually want for each input
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# - batch_size: the batch size in model serving, should equal to that in
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# model serving config
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# 常量定义
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DEFAULT_MAX_TOKENS = 1024
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DEFAULT_TEMPERATURE = 0.0
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PALM_MODEL_NAME = "text-bison-001"
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if scorer_llm_name == "text-bison":
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config = {
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"temperature": DEFAULT_TEMPERATURE,
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"max_decode_steps": DEFAULT_MAX_TOKENS,
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"batch_size": 1,
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"num_servers": 1,
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}
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call_scorer_server_func = functools.partial(
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prompt_utils.call_palm_server_from_cloud,
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model=PALM_MODEL_NAME,
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**config
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)
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scorer_llm_dict = {"model_type": "text-bison", **config}
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elif scorer_llm_name in {"gpt-3.5-turbo", "gpt-4"}:
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config = {
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"temperature": DEFAULT_TEMPERATURE,
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"max_decode_steps": DEFAULT_MAX_TOKENS,
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"batch_size": 1,
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"num_servers": 1,
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}
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call_scorer_server_func = functools.partial(
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prompt_utils.call_openai_server_func,
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model=scorer_llm_name.lower(),
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**config
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)
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scorer_llm_dict = {"model_type": scorer_llm_name.lower(), **config}
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elif scorer_llm_name == "local":
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print(f"[DEBUG] local_model_path: {local_model_path}")
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assert local_model_path, "Local model path must be provided."
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config = {
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"temperature": DEFAULT_TEMPERATURE,
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"max_decode_steps": DEFAULT_MAX_TOKENS,
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"batch_size": 8,
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"num_servers": 8,# number of servers to use for local model
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}
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call_scorer_server_func = functools.partial(
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prompt_utils.call_local_server_func,
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local_model_path=local_model_path,
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**config
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)
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scorer_llm_dict = {"model_type": "local", **config}
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else:
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raise ValueError(f"Unsupported model: {scorer_llm_name}")
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# if scorer_llm_name == "text-bison":
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# # when prompting text-bison with Cloud API
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# scorer_finetuned_palm_temperature = 0.0
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# scorer_finetuned_palm_max_decode_steps = 1024
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# scorer_finetuned_palm_batch_size = 1
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# scorer_finetuned_palm_num_servers = 1
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# scorer_finetuned_palm_dict = dict()
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# scorer_finetuned_palm_dict["temperature"] = (
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# scorer_finetuned_palm_temperature
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# )
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# scorer_finetuned_palm_dict["num_servers"] = (
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# scorer_finetuned_palm_num_servers
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# )
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# scorer_finetuned_palm_dict["batch_size"] = scorer_finetuned_palm_batch_size
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# scorer_finetuned_palm_dict["max_decode_steps"] = (
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# scorer_finetuned_palm_max_decode_steps
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# )
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# call_scorer_finetuned_palm_server_func = functools.partial(
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# prompt_utils.call_palm_server_from_cloud,
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# model="text-bison-001",
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# temperature=scorer_finetuned_palm_dict["temperature"],
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# max_decode_steps=scorer_finetuned_palm_dict["max_decode_steps"],
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# )
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# scorer_llm_dict = {
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# "model_type": scorer_llm_name.lower(),
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# }
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# scorer_llm_dict.update(scorer_finetuned_palm_dict)
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# call_scorer_server_func = call_scorer_finetuned_palm_server_func
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# elif scorer_llm_name in {"gpt-3.5-turbo", "gpt-4"}:
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# # assert scorer_llm_name.lower() in {"gpt-3.5-turbo", "gpt-4"}
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# scorer_gpt_max_decode_steps = 1024
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# scorer_gpt_temperature = 0.0
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# scorer_gpt_dict = dict()
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# scorer_gpt_dict["max_decode_steps"] = scorer_gpt_max_decode_steps
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# scorer_gpt_dict["temperature"] = scorer_gpt_temperature
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# scorer_gpt_dict["num_decodes"] = 1
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# scorer_gpt_dict["batch_size"] = 1
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# scorer_gpt_dict["num_servers"] = 1
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# scorer_llm_dict = {
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# "model_type": scorer_llm_name.lower(),
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# }
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# scorer_llm_dict.update(scorer_gpt_dict)
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# call_scorer_server_func = functools.partial(
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# prompt_utils.call_openai_server_func,
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# model=scorer_llm_name.lower(),
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# max_decode_steps=scorer_gpt_max_decode_steps,
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# temperature=scorer_gpt_temperature,
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# )
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# elif scorer_llm_name == "local":
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# # local vLLM model
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# scorer_local_max_decode_steps = 1024
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# scorer_local_temperature = 0.0
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# call_scorer_server_func = functools.partial(
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# prompt_utils.call_local_model_server_func,
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# model_path=local_model_path,
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# max_decode_steps=scorer_local_max_decode_steps,
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# temperature=scorer_local_temperature,
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# )
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# else:
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# raise ValueError(
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# f"Unknown scorer_llm_name: {scorer_llm_name}. "
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# "It should be one of text-bison, gpt-3.5-turbo, gpt-4, or local."
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# )
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# ====================== optimizer model configs ============================
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if optimizer_llm_name.lower() == "text-bison":
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# PaLM text-bison 模型配置
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optimizer_llm_dict = {
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"model_type": "text-bison",
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"temperature": 1.0, # 更高的随机性以生成多样化解
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"max_decode_steps": 1024, # 最大生成长度
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"batch_size": 1, # 单样本处理
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"num_decodes": 8, # 生成8个候选结果
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"num_servers": 1 # 单服务器
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}
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call_optimizer_server_func = functools.partial(
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prompt_utils.call_palm_server_from_cloud,
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model="text-bison-001",
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temperature=optimizer_llm_dict["temperature"],
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max_decode_steps=optimizer_llm_dict["max_decode_steps"],
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)
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elif optimizer_llm_name.lower() in {"gpt-3.5-turbo", "gpt-4"}:
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# GPT 模型配置
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optimizer_llm_dict = {
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"model_type": optimizer_llm_name.lower(),
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"temperature": 1.0, # 更高的随机性
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||||
"max_decode_steps": 512, # 较短的最大长度
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"batch_size": 1,
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"num_decodes": 1 , # 单次生成
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||||
"num_servers": 1 # 单服务器
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||||
}
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||||
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||||
call_optimizer_server_func = functools.partial(
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||||
prompt_utils.call_openai_server_func,
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model=optimizer_llm_name,
|
||||
max_decode_steps=optimizer_llm_dict["max_decode_steps"],
|
||||
temperature=optimizer_llm_dict["temperature"],
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||||
)
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||||
elif optimizer_llm_name.lower() == "local":
|
||||
assert local_model_path, "Local model path must be provided."
|
||||
optimizer_llm_dict = {
|
||||
"model_type": optimizer_llm_name.lower(),
|
||||
"temperature": 1.0, # 更高的随机性
|
||||
"max_decode_steps": 512, # 较短的最大长度
|
||||
"batch_size": 8,
|
||||
"num_decodes": 1 , # 单次生成
|
||||
"num_servers": 8 # 单服务器
|
||||
}
|
||||
call_optimizer_server_func = functools.partial(
|
||||
prompt_utils.call_local_server_func,
|
||||
local_model_path=local_model_path,
|
||||
max_decode_steps=optimizer_llm_dict["max_decode_steps"],
|
||||
temperature=optimizer_llm_dict["temperature"],
|
||||
)
|
||||
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Unsupported optimizer model: {optimizer_llm_name}. "
|
||||
"Must be one of: text-bison, gpt-3.5-turbo, gpt-4"
|
||||
)
|
||||
|
||||
# if optimizer_llm_name.lower() == "text-bison":
|
||||
# # when prompting text-bison with Cloud API
|
||||
# optimizer_finetuned_palm_temperature = 1.0
|
||||
# optimizer_finetuned_palm_num_decodes = 8
|
||||
# optimizer_finetuned_palm_max_decode_steps = 1024
|
||||
# optimizer_finetuned_palm_batch_size = 1
|
||||
# optimizer_finetuned_palm_num_servers = 1
|
||||
# optimizer_finetuned_palm_dict = dict()
|
||||
# optimizer_finetuned_palm_dict["temperature"] = (
|
||||
# optimizer_finetuned_palm_temperature
|
||||
# )
|
||||
# optimizer_finetuned_palm_dict["num_decodes"] = (
|
||||
# optimizer_finetuned_palm_num_decodes
|
||||
# )
|
||||
# optimizer_finetuned_palm_dict["batch_size"] = (
|
||||
# optimizer_finetuned_palm_batch_size
|
||||
# )
|
||||
# optimizer_finetuned_palm_dict["num_servers"] = (
|
||||
# optimizer_finetuned_palm_num_servers
|
||||
# )
|
||||
# optimizer_finetuned_palm_dict["max_decode_steps"] = (
|
||||
# optimizer_finetuned_palm_max_decode_steps
|
||||
# )
|
||||
|
||||
# call_optimizer_finetuned_palm_server_func = functools.partial(
|
||||
# prompt_utils.call_palm_server_from_cloud,
|
||||
# model="text-bison-001",
|
||||
# temperature=optimizer_finetuned_palm_dict["temperature"],
|
||||
# max_decode_steps=optimizer_finetuned_palm_dict["max_decode_steps"],
|
||||
# )
|
||||
|
||||
# optimizer_llm_dict = {
|
||||
# "model_type": optimizer_llm_name.lower(),
|
||||
# }
|
||||
# optimizer_llm_dict.update(optimizer_finetuned_palm_dict)
|
||||
# call_optimizer_server_func = call_optimizer_finetuned_palm_server_func
|
||||
|
||||
# else:
|
||||
# assert optimizer_llm_name in {"gpt-3.5-turbo", "gpt-4"}
|
||||
# optimizer_gpt_max_decode_steps = 512
|
||||
# optimizer_gpt_temperature = 1.0
|
||||
|
||||
# optimizer_llm_dict = dict()
|
||||
# optimizer_llm_dict["max_decode_steps"] = optimizer_gpt_max_decode_steps
|
||||
# optimizer_llm_dict["temperature"] = optimizer_gpt_temperature
|
||||
# optimizer_llm_dict["batch_size"] = 1
|
||||
# optimizer_llm_dict["num_decodes"] = 1
|
||||
# call_optimizer_server_func = functools.partial(
|
||||
# prompt_utils.call_openai_server_func,
|
||||
# model=optimizer_llm_name,
|
||||
# max_decode_steps=optimizer_gpt_max_decode_steps,
|
||||
# temperature=optimizer_gpt_temperature,
|
||||
# )
|
||||
|
||||
# ====================== try calling the servers ============================
|
||||
print("\n======== testing the scorer and optimizer servers ===========")
|
||||
scorer_test_output = call_scorer_server_func(
|
||||
"Does the sun rise from the north? Just answer yes or no."
|
||||
)
|
||||
print(f"number of scorer output decodes: {len(scorer_test_output)}")
|
||||
print(f"scorer test output: {scorer_test_output}")
|
||||
optimizer_test_output = call_optimizer_server_func(
|
||||
"Does the sun rise from the north? Just answer yes or no.",
|
||||
temperature=1.0,
|
||||
)
|
||||
print(f"number of optimizer output decodes: {len(optimizer_test_output)}")
|
||||
print(f"optimizer test output: {optimizer_test_output}")
|
||||
print("Finished testing the servers.")
|
||||
|
||||
# ====================== read data ============================
|
||||
print("\n================ prompt optimization settings ==============")
|
||||
# from https://github.com/hendrycks/test/blob/master/categories.py
|
||||
subcategories = {
|
||||
"abstract_algebra": ["math"],
|
||||
"anatomy": ["health"],
|
||||
"astronomy": ["physics"],
|
||||
"business_ethics": ["business"],
|
||||
"clinical_knowledge": ["health"],
|
||||
"college_biology": ["biology"],
|
||||
"college_chemistry": ["chemistry"],
|
||||
"college_computer_science": ["computer science"],
|
||||
"college_mathematics": ["math"],
|
||||
"college_medicine": ["health"],
|
||||
"college_physics": ["physics"],
|
||||
"computer_security": ["computer science"],
|
||||
"conceptual_physics": ["physics"],
|
||||
"econometrics": ["economics"],
|
||||
"electrical_engineering": ["engineering"],
|
||||
"elementary_mathematics": ["math"],
|
||||
"formal_logic": ["philosophy"],
|
||||
"global_facts": ["other"],
|
||||
"high_school_biology": ["biology"],
|
||||
"high_school_chemistry": ["chemistry"],
|
||||
"high_school_computer_science": ["computer science"],
|
||||
"high_school_european_history": ["history"],
|
||||
"high_school_geography": ["geography"],
|
||||
"high_school_government_and_politics": ["politics"],
|
||||
"high_school_macroeconomics": ["economics"],
|
||||
"high_school_mathematics": ["math"],
|
||||
"high_school_microeconomics": ["economics"],
|
||||
"high_school_physics": ["physics"],
|
||||
"high_school_psychology": ["psychology"],
|
||||
"high_school_statistics": ["math"],
|
||||
"high_school_us_history": ["history"],
|
||||
"high_school_world_history": ["history"],
|
||||
"human_aging": ["health"],
|
||||
"human_sexuality": ["culture"],
|
||||
"international_law": ["law"],
|
||||
"jurisprudence": ["law"],
|
||||
"logical_fallacies": ["philosophy"],
|
||||
"machine_learning": ["computer science"],
|
||||
"management": ["business"],
|
||||
"marketing": ["business"],
|
||||
"medical_genetics": ["health"],
|
||||
"miscellaneous": ["other"],
|
||||
"moral_disputes": ["philosophy"],
|
||||
"moral_scenarios": ["philosophy"],
|
||||
"nutrition": ["health"],
|
||||
"philosophy": ["philosophy"],
|
||||
"prehistory": ["history"],
|
||||
"professional_accounting": ["other"],
|
||||
"professional_law": ["law"],
|
||||
"professional_medicine": ["health"],
|
||||
"professional_psychology": ["psychology"],
|
||||
"public_relations": ["politics"],
|
||||
"security_studies": ["politics"],
|
||||
"sociology": ["culture"],
|
||||
"us_foreign_policy": ["politics"],
|
||||
"virology": ["health"],
|
||||
"world_religions": ["philosophy"],
|
||||
}
|
||||
|
||||
categories = {
|
||||
"STEM": [
|
||||
"physics",
|
||||
"chemistry",
|
||||
"biology",
|
||||
"computer science",
|
||||
"math",
|
||||
"engineering",
|
||||
],
|
||||
"humanities": ["history", "philosophy", "law"],
|
||||
"social sciences": [
|
||||
"politics",
|
||||
"culture",
|
||||
"economics",
|
||||
"geography",
|
||||
"psychology",
|
||||
],
|
||||
"other (business, health, misc.)": ["other", "business", "health"],
|
||||
}
|
||||
|
||||
if dataset_name == "mmlu":
|
||||
# EITHER: filter by category
|
||||
# category_names = [
|
||||
# "STEM",
|
||||
# "humanities",
|
||||
# "social sciences",
|
||||
# "other (business, health, misc.)",
|
||||
# ]
|
||||
category_names = [task_name]
|
||||
folder_name = "test" # one of {'auxiliary_train', 'dev', 'val', 'test'}
|
||||
task_names = []
|
||||
for task_csv_name in os.listdir(
|
||||
os.path.join(root_data_folder_path, folder_name)
|
||||
):
|
||||
task_names.append(task_csv_name.split(".")[0])
|
||||
|
||||
tasks_in_category = []
|
||||
for category_name in category_names:
|
||||
for task_name in task_names:
|
||||
for subname in subcategories:
|
||||
if subname in task_name:
|
||||
if subcategories[subname][0] in categories[category_name]:
|
||||
tasks_in_category.append(task_name)
|
||||
break
|
||||
|
||||
tasks_all = [(folder_name, task_name) for task_name in tasks_in_category]
|
||||
multiple_choice_tasks = set([item[1] for item in tasks_all])
|
||||
boolean_tasks = set()
|
||||
numerical_output_tasks = set()
|
||||
|
||||
# OR: filter by task
|
||||
# tasks_all = [
|
||||
# # ('test', 'abstract_algebra_test'),
|
||||
# # ('test', 'college_computer_science_test'),
|
||||
# # ('test', 'college_mathematics_test'),
|
||||
# # ('test', 'college_physics_test'),
|
||||
# # ('test', 'elementary_mathematics_test'),
|
||||
# # ('test', 'global_facts_test'),
|
||||
# # ('test', 'high_school_physics_test'),
|
||||
# # ('test', 'machine_learning_test'),
|
||||
# # ('test', 'management_test'),
|
||||
# # ('test', 'medical_genetics_test'),
|
||||
# # ('test', 'moral_scenarios_test'),
|
||||
# # ('test', 'professional_psychology_test'),
|
||||
# # ('test', 'public_relations_test'),
|
||||
# # ('test', 'professional_law_test'),
|
||||
# # ('test', 'high_school_psychology_test'),
|
||||
# # ('test', 'high_school_world_history_test'),
|
||||
# # ('test', 'human_aging_test'),
|
||||
# # ('test', 'miscellaneous_test'),
|
||||
# # ('test', 'moral_scenarios_test'),
|
||||
# ('test', 'professional_psychology_test'),
|
||||
# # ('test', 'security_studies_test'),
|
||||
# ]
|
||||
|
||||
elif dataset_name == "bbh":
|
||||
tasks_all = [task_name]
|
||||
assert (
|
||||
len(tasks_all) == 1
|
||||
), "for now only support prompt optimization on one BBH task"
|
||||
|
||||
# all BBH tasks are as below
|
||||
# tasks_all = [
|
||||
# 'boolean_expressions',
|
||||
# 'causal_judgement',
|
||||
# 'date_understanding',
|
||||
# 'disambiguation_qa',
|
||||
# 'dyck_languages',
|
||||
# 'formal_fallacies',
|
||||
# 'geometric_shapes',
|
||||
# 'hyperbaton',
|
||||
# 'logical_deduction_five_objects',
|
||||
# 'logical_deduction_seven_objects',
|
||||
# 'logical_deduction_three_objects',
|
||||
# 'movie_recommendation',
|
||||
# 'multistep_arithmetic_two',
|
||||
# 'navigate',
|
||||
# 'object_counting',
|
||||
# 'penguins_in_a_table',
|
||||
# 'reasoning_about_colored_objects',
|
||||
# 'ruin_names',
|
||||
# 'salient_translation_error_detection',
|
||||
# 'snarks',
|
||||
# 'sports_understanding',
|
||||
# 'temporal_sequences',
|
||||
# 'tracking_shuffled_objects_five_objects',
|
||||
# 'tracking_shuffled_objects_seven_objects',
|
||||
# 'tracking_shuffled_objects_three_objects',
|
||||
# 'web_of_lies',
|
||||
# 'word_sorting'
|
||||
# ]
|
||||
numerical_output_tasks = {
|
||||
"object_counting",
|
||||
"multistep_arithmetic_two",
|
||||
}
|
||||
|
||||
multiple_choice_tasks = {
|
||||
"date_understanding",
|
||||
"disambiguation_qa",
|
||||
"geometric_shapes",
|
||||
"hyperbaton",
|
||||
"logical_deduction_five_objects",
|
||||
"logical_deduction_seven_objects",
|
||||
"logical_deduction_three_objects",
|
||||
"movie_recommendation",
|
||||
"penguins_in_a_table",
|
||||
"reasoning_about_colored_objects",
|
||||
"ruin_names",
|
||||
"salient_translation_error_detection",
|
||||
"snarks",
|
||||
"temporal_sequences",
|
||||
"tracking_shuffled_objects_five_objects",
|
||||
"tracking_shuffled_objects_seven_objects",
|
||||
"tracking_shuffled_objects_three_objects",
|
||||
}
|
||||
|
||||
boolean_tasks = {
|
||||
"boolean_expressions", # True or False
|
||||
"causal_judgement", # yes or no
|
||||
"formal_fallacies", # valid or invalid
|
||||
"navigate", # yes or no
|
||||
"sports_understanding", # yes or no
|
||||
"web_of_lies", # yes or no
|
||||
}
|
||||
|
||||
else:
|
||||
assert dataset_name in {"gsm8k"}
|
||||
tasks_all = [task_name]
|
||||
multiple_choice_tasks = set()
|
||||
boolean_tasks = set()
|
||||
numerical_output_tasks = set(tasks_all)
|
||||
|
||||
if dataset_name == "mmlu":
|
||||
raw_data = pd.DataFrame()
|
||||
prediction_treat_as_number = False
|
||||
prediction_treat_as_bool = False
|
||||
elif dataset_name == "bbh":
|
||||
raw_data = []
|
||||
prediction_treat_as_number = bool(
|
||||
tasks_all[0] in numerical_output_tasks
|
||||
) # for now only check the first task
|
||||
prediction_treat_as_bool = bool(
|
||||
tasks_all[0] in boolean_tasks
|
||||
) # for now only check the first task
|
||||
print(
|
||||
f"prediction_treat_as_number: {prediction_treat_as_number},"
|
||||
f" prediction_treat_as_bool: {prediction_treat_as_bool}"
|
||||
)
|
||||
else:
|
||||
assert dataset_name == "gsm8k"
|
||||
raw_data = pd.DataFrame()
|
||||
prediction_treat_as_number = True
|
||||
prediction_treat_as_bool = False
|
||||
|
||||
for t in tasks_all:
|
||||
if dataset_name == "mmlu":
|
||||
folder_name = t[0]
|
||||
task_name = t[1]
|
||||
single_task_df = pd.read_csv(
|
||||
os.path.join(root_data_folder_path, f"{folder_name}/{task_name}.csv"),
|
||||
index_col=None,
|
||||
header=None,
|
||||
)
|
||||
raw_data = pd.concat([raw_data, single_task_df])
|
||||
elif dataset_name == "bbh":
|
||||
task_name = t
|
||||
single_task_list = opt_utils.load_bbh_task_data(
|
||||
task_name, base_dir=root_data_folder_path
|
||||
)
|
||||
raw_data += single_task_list
|
||||
else:
|
||||
assert dataset_name == "gsm8k"
|
||||
task_name = t
|
||||
f_gsm = os.path.join(root_data_folder_path, f"gsm_{task_name}.tsv")
|
||||
single_task_df = pd.read_csv(f_gsm, sep="\t", header=None)
|
||||
raw_data = pd.concat([raw_data, single_task_df])
|
||||
|
||||
if dataset_name == "mmlu":
|
||||
num_examples = raw_data.shape[0]
|
||||
elif dataset_name == "bbh":
|
||||
num_examples = len(raw_data)
|
||||
else:
|
||||
assert dataset_name in {"gsm8k"}
|
||||
num_examples = raw_data.shape[0]
|
||||
print(f"number of examples in the current task: {num_examples}")
|
||||
|
||||
# ================ split data into train/val/test ==========================
|
||||
if dataset_name == "mmlu":
|
||||
train_ratio = 0.8
|
||||
eval_ratio = 0.2
|
||||
elif dataset_name == "gsm8k":
|
||||
train_ratio = 0.035
|
||||
eval_ratio = 0
|
||||
else:
|
||||
assert dataset_name == "bbh"
|
||||
train_ratio = 0.2
|
||||
eval_ratio = 0
|
||||
|
||||
# train-validation-test split
|
||||
# It is important to sort the indices, as this ensures the is_multiple_choice
|
||||
# Boolean variables match the data points.
|
||||
assert train_ratio + eval_ratio <= 1
|
||||
test_ratio = 1 - train_ratio - eval_ratio
|
||||
print(
|
||||
f"train_ratio: {train_ratio}, eval_ratio: {eval_ratio}, "
|
||||
f"test_ratio: {test_ratio}"
|
||||
)
|
||||
np.random.seed(0)
|
||||
train_index = np.sort(
|
||||
np.array(
|
||||
np.random.choice(
|
||||
num_examples, size=int(train_ratio * num_examples), replace=False
|
||||
)
|
||||
)
|
||||
)
|
||||
eval_and_test_index = np.sort(
|
||||
np.array(list(set(np.arange(num_examples)) - set(train_index)))
|
||||
)
|
||||
eval_index = np.sort(
|
||||
np.array(
|
||||
np.random.choice(
|
||||
eval_and_test_index,
|
||||
size=int(eval_ratio * num_examples),
|
||||
replace=False,
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
# ========== set other optimization experiment hyperparameters ==============
|
||||
if scorer_llm_name == "text-bison":
|
||||
old_instruction_score_threshold = 0.0 # 完全保留旧指令 表示不过滤任何历史指令（即使质量很低的旧指令也会保留）。
|
||||
# old_instruction_score_threshold = 0.15 # for GSM8K
|
||||
elif scorer_llm_name == "local":
|
||||
old_instruction_score_threshold = 0.3
|
||||
else:
|
||||
assert scorer_llm_name in {"gpt-3.5-turbo", "gpt-4"} # 模型校验
|
||||
old_instruction_score_threshold = 0.3 # 过滤低质量旧指令
|
||||
|
||||
if scorer_llm_name == "text-bison":
|
||||
extract_final_answer_by_prompting_again = False # 是否通过二次提示提取最终答案（例如从冗长响应中提取关键内容）
|
||||
include_qa = False # 是否在元提示中包含问答对
|
||||
evaluate_in_parallel = False # 是否并行评估
|
||||
elif scorer_llm_name == "local":
|
||||
extract_final_answer_by_prompting_again = True
|
||||
include_qa = True
|
||||
evaluate_in_parallel = True
|
||||
else:
|
||||
assert scorer_llm_name in {"gpt-3.5-turbo", "gpt-4"}
|
||||
extract_final_answer_by_prompting_again = False
|
||||
include_qa = False
|
||||
evaluate_in_parallel = False
|
||||
|
||||
optimizer_llm_temperature = optimizer_llm_dict["temperature"]
|
||||
|
||||
num_few_shot_questions_for_instruction_refinement = 3 # number of few-shot questions 每次优化指令时参考的少样本示例数量（Few-shot QA对）。
|
||||
|
||||
# To change the number of generated instructions in each step, one should
|
||||
# edit the value of the variable below, instead of editing the number of
|
||||
# decodes in model parameters, because those values are limited by model
|
||||
# serving configs.
|
||||
num_generated_instructions_in_each_step = 3 # number of generated instructions in each step 每轮搜索生成的候选指令数量。
|
||||
num_search_steps = 50 # number of search steps 总优化迭代次数。
|
||||
|
||||
initial_instructions = [
|
||||
"Let's solve the problem.",
|
||||
# "",
|
||||
# "The answer is",
|
||||
]
|
||||
few_shot_qa_pairs = True #是否使用少样本示例指导指令生成。
|
||||
# one of {'accumulative_most_frequent', 'current_most_frequent', 'random',
|
||||
# 'constant'}
|
||||
few_shot_selection_criteria = "random" #对多样性要求高时用 random，稳定性要求高时用 most_frequent。
|
||||
# whether to evaluate generated instructions on the exemplars in meta-prompt
|
||||
evaluate_generated_ins_on_few_shot = False # 是否评估新指令 开发阶段设为 True调试指令质量。
|
||||
# whether to evaluate old instructions on the exemplars in the meta-prompt
|
||||
evaluate_old_ins_on_few_shot = False # 是否评估旧指令 生产阶段设为 False加速运行。
|
||||
# every this number of steps, compute the accuracies of current-step
|
||||
# instructions on the validation set
|
||||
eval_interval = 3 # 每N步在验证集上测试当前指令的准确率。
|
||||
|
||||
max_num_instructions = (
|
||||
20 # 元提示中保留的历史指令数量上限。
|
||||
)
|
||||
# 将连续分数离散化为N档（如0-100整数），简化模型理解。
|
||||
num_score_buckets = 100
|
||||
# whether to put old instructions and scores to before exemplars in
|
||||
# 控制元提示中历史指令和少样本示例的顺序。
|
||||
meta_prompt_instructions_before_exemplars = True
|
||||
|
||||
# ===================== run prompt optimization ======================
|
||||
|
||||
assert few_shot_selection_criteria in {
|
||||
"accumulative_most_frequent",
|
||||
"current_most_frequent",
|
||||
"random",
|
||||
"constant",
|
||||
}
|
||||
evolution_kwargs = {
|
||||
"num_search_steps": num_search_steps,
|
||||
"old_instruction_score_threshold": old_instruction_score_threshold,
|
||||
"scorer_llm_dict": scorer_llm_dict,
|
||||
"optimizer_llm_dict": optimizer_llm_dict,
|
||||
"extract_final_answer_by_prompting_again": (
|
||||
extract_final_answer_by_prompting_again
|
||||
),
|
||||
"include_qa": include_qa,
|
||||
"evaluate_in_parallel": evaluate_in_parallel,
|
||||
"tasks_all": tasks_all,
|
||||
"train_ratio": train_ratio,
|
||||
"eval_ratio": eval_ratio,
|
||||
"test_ratio": test_ratio,
|
||||
"train_index": train_index,
|
||||
"eval_index": eval_index,
|
||||
"dataset_name": dataset_name,
|
||||
"task_name": task_name,
|
||||
"num_examples": num_examples,
|
||||
"root_data_folder_path": root_data_folder_path,
|
||||
"optimizer_llm_temperature": optimizer_llm_temperature,
|
||||
# "optimizer_llm_temperature_schedule": (
|
||||
# optimizer_llm_temperature_schedule
|
||||
# ),
|
||||
# "optimizer_llm_temperature_end": optimizer_llm_temperature_end,
|
||||
"initial_instructions": initial_instructions,
|
||||
"multiple_choice_tasks": multiple_choice_tasks,
|
||||
"raw_data": raw_data,
|
||||
"call_scorer_server_func": call_scorer_server_func,
|
||||
"call_optimizer_server_func": call_optimizer_server_func,
|
||||
"instruction_pos": instruction_pos,
|
||||
"prediction_treat_as_number": prediction_treat_as_number,
|
||||
"prediction_treat_as_bool": prediction_treat_as_bool,
|
||||
"result_by_instruction_folder": result_by_instruction_folder,
|
||||
"few_shot_qa_pairs": few_shot_qa_pairs,
|
||||
"num_score_buckets": num_score_buckets,
|
||||
"max_num_instructions": max_num_instructions,
|
||||
"meta_prompt_type": meta_prompt_type,
|
||||
"meta_prompt_instructions_before_exemplars": (
|
||||
meta_prompt_instructions_before_exemplars
|
||||
),
|
||||
"few_shot_selection_criteria": few_shot_selection_criteria,
|
||||
"optimizer_llm_name": optimizer_llm_name,
|
||||
"num_generated_instructions_in_each_step": (
|
||||
num_generated_instructions_in_each_step
|
||||
),
|
||||
"evaluate_generated_ins_on_few_shot": evaluate_generated_ins_on_few_shot,
|
||||
"num_few_shot_questions_for_instruction_refinement": (
|
||||
num_few_shot_questions_for_instruction_refinement
|
||||
),
|
||||
"evaluate_old_ins_on_few_shot": evaluate_old_ins_on_few_shot,
|
||||
"eval_interval": eval_interval,
|
||||
"save_folder": save_folder,
|
||||
}
|
||||
|
||||
opt_utils.run_evolution(**evolution_kwargs)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run(main)
|
||||
Reference in New Issue
Block a user