Merge pull request #35 from princeton-nlp/pypi

Pypi

Author: ysymyth

MANIFEST.in

@@ -0,0 +1,4 @@
+include src/tot/data/24/24.csv
+include src/tot/data/crosswords/mini0505_0_100_5.json
+include src/tot/data/crosswords/mini0505.json
+include src/tot/data/text/data_100_random_text.txt

fake.png renamed to pics/fake.png

@@ -0,0 +1,35 @@
+[build-system]
+requires = ["setuptools >= 61.0.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "tot"
+version = "0.1.0"
+description = 'Official Implementation of "Tree of Thoughts: Deliberate Problem Solving with Large Language Models"'
+readme = "README.md"
+requires-python = ">= 3.7"
+authors = [{ name = "Shunyu Yao", email = "shunyuyao.cs@gmail.com" }]
+license = { text = "MIT License" }
+keywords = ["tree-search", "large-language-models", "llm", "prompting", "tree-of-thoughts"]
+classifiers = [
+    "License :: OSI Approved :: MIT License",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.7",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    'Intended Audience :: Science/Research',
+    'Topic :: Scientific/Engineering :: Artificial Intelligence',
+]
+dynamic=["dependencies"]
+
+
+[tool.setuptools.dynamic]
+dependencies = {file = ["requirements.txt"]}
+
+[tool.setuptools.packages.find]
+where = ["src"]  # list of folders that contain the packages (["."] by default)
+
+[project.urls]
+Homepage = "https://github.com/princeton-nlp/tree-of-thought-llm"

teaser.png renamed to pics/teaser.png

@@ -2,44 +2,58 @@
 [![DOI](https://zenodo.org/badge/642099326.svg)](https://zenodo.org/badge/latestdoi/642099326)
 
 <details>
-  <summary>Note: https://github.com/kyegomez/tree-of-thoughts is NOT the correct implementation to replicate paper results. </summary>
-  
-In fact, people have reported that his code [cannot](https://github.com/kyegomez/tree-of-thoughts/issues/52) [properly](https://github.com/kyegomez/tree-of-thoughts/issues/41) [run](https://github.com/kyegomez/tree-of-thoughts/issues/60), and is [probably automatically generated by ChatGPT](fake.png), and [kyegomez has done so for other popular ML methods](https://twitter.com/qbitium/status/1663954096741814272), while intentionally refusing to link to official implementations for his own interests (see https://github.com/kyegomez/tree-of-thoughts/issues/54, https://github.com/kyegomez/tree-of-thoughts/issues/55, https://github.com/kyegomez/tree-of-thoughts/issues/56).
+  <summary>Note: https://github.com/kyegomez/tree-of-thoughts CANNOT replicate paper results. </summary>
+
+In fact, people have reported that his code [cannot](https://github.com/kyegomez/tree-of-thoughts/issues/52) [properly](https://github.com/kyegomez/tree-of-thoughts/issues/41) [run](https://github.com/kyegomez/tree-of-thoughts/issues/60), and is [probably automatically generated by ChatGPT](pics/fake.png), and [kyegomez has done so for other popular ML methods](https://twitter.com/qbitium/status/1663954096741814272), while intentionally refusing to link to official implementations for his own interests (see https://github.com/kyegomez/tree-of-thoughts/issues/54, https://github.com/kyegomez/tree-of-thoughts/issues/55, https://github.com/kyegomez/tree-of-thoughts/issues/56).
 Unfortunately, Google/Github searches go to kyegomez's malicious repo by default as it has more stars. **Please DE-STAR his repo and STAR this to help other people avoid being misled, thanks!**
 </details>
 
 
+![teaser](pics/teaser.png)
 
+Official implementation for paper [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://arxiv.org/abs/2305.10601) with code, prompts, model outputs.
+Also check [its tweet thread](https://twitter.com/ShunyuYao12/status/1659357547474681857) in 1min.
 
 
-![teaser](teaser.png)
 
-Official implementation for paper [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://arxiv.org/abs/2305.10601) with code, prompts, model outputs.
-Also check [its tweet thread](https://twitter.com/ShunyuYao12/status/1659357547474681857) in 1min.
 
 
-Please cite the paper and star this repo if you use ToT and find it interesting/useful. Thanks!
 
-```bibtex
-@misc{yao2023tree,
-      title={{Tree of Thoughts}: Deliberate Problem Solving with Large Language Models}, 
-      author={Shunyu Yao and Dian Yu and Jeffrey Zhao and Izhak Shafran and Thomas L. Griffiths and Yuan Cao and Karthik Narasimhan},
-      year={2023},
-      eprint={2305.10601},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL}
-}
+## Setup
+- Set up OpenAI API key and store in environment variable ``OPENAI_API_KEY`` (see [here](https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety)). 
+
+- Install dependencies and `tot` package (PyPI package coming soon): 
+```bash
+git clone https://github.com/princeton-nlp/tree-of-thought-llm
+cd tree-of-thought-llm
+pip install -r requirements.txt
+pip install -e .  # install `tot` package
 ```
 
 
+## Quick Start
+The following minimal script will attempt to solve the game of 24 with `4 5 6 10` (might be a bit slow as it's using GPT-4):
+```python
+import argparse
+from tot.methods.bfs import solve
+from tot.tasks.game24 import Game24Task
 
-## Setup
-You need to first have an OpenAI API key and store it in the environment variable ``OPENAI_API_KEY`` (see [here](https://help.openai.com/en/articles/5112595-best-practices-for-api-key-safety)). If you use custom base url, set it by environment variable ``OPENAI_API_BASE`` (e.g. https://api.openai.com/v1).
+args = argparse.Namespace(backend='gpt-4', temperature=0.7, task='game24', naive_run=False, prompt_sample=None, method_generate='propose', method_evaluate='value', method_select='greedy', n_generate_sample=1, n_evaluate_sample=3, n_select_sample=5)
 
-Package requirement: ``pip install openai backoff sympy numpy``
+task = Game24Task()
+ys, infos = solve(args, task, 900)
+print(ys[0])
+```
 
+And the output would be something like (note it's not deterministic, and sometimes the output can be wrong):
+```
+10 - 4 = 6 (left: 5 6 6)
+5 * 6 = 30 (left: 6 30)
+30 - 6 = 24 (left: 24)
+Answer: (5 * (10 - 4)) - 6 = 24
+```
 
-## Experiments
+## Paper Experiments
 
 Run experiments via ``sh scripts/{game24, text, crosswords}/{standard_sampling, cot_sampling, bfs}.sh``, except in crosswords we use a DFS algorithm for ToT, which can be run via ``scripts/crosswords/search_crosswords-dfs.ipynb``.
 
@@ -55,13 +69,24 @@ The very simple ``run.py`` implements the ToT + BFS algorithm, as well as the na
 
 
 
-## Trajectories
+## Paper Trajectories
 ``logs/`` contains all the trajectories from the paper's experiments, except for ``logs/game24/gpt-4_0.7_propose1_value3_greedy5_start900_end1000.json`` which was reproduced after the paper (as the original experiment was done in a notebook) and achieved a 69\% score instead of the original 74\% score due to randomness in GPT decoding. We hope to aggregate multiple runs in the future to account for sampling randomness and update the paper, but this shouldn't affect the main conclusions of the paper.
 
+## How to Add A New Task
+Setting up a new task is easy, and mainly involves two steps.
+* Set up a new task class in ``tot/tasks/`` and task files in ``tot/data/``. See ``tot/tasks/game24.py`` for an example. Add the task to ``tot/tasks/__init__.py``.
+* Set up task-specific prompts in ``tot/prompts/``. See ``tot/prompts/game24.py`` for an example. Depending on the nature of the task, choose ``--method_generate`` (choices=[``sample``, ``propose``]) and ``--method_evaluate`` (choices=[``value``, ``vote``]) and their corresponding prompts. 
 
+## Citations
+Please cite the paper and star this repo if you use ToT and find it interesting/useful, thanks! Feel free to contact shunyuyao.cs@gmail.com or open an issue if you have any questions.
 
-## Questions
-Feel free to contact shunyuyao.cs@gmail.com or open an issue if you have any questions.
-
-
-
+```bibtex
+@misc{yao2023tree,
+      title={{Tree of Thoughts}: Deliberate Problem Solving with Large Language Models}, 
+      author={Shunyu Yao and Dian Yu and Jeffrey Zhao and Izhak Shafran and Thomas L. Griffiths and Yuan Cao and Karthik Narasimhan},
+      year={2023},
+      eprint={2305.10601},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

pyproject.toml

@@ -16,3 +16,4 @@ sympy==1.12
 tqdm==4.65.0
 urllib3==2.0.2
 yarl==1.9.2
+pandas==2.0.3

readme.md

@@ -1,108 +1,18 @@
 import os
 import json
-import itertools
 import argparse
-import numpy as np
-from functools import partial
-from models import gpt, gpt_usage
-from tasks import get_task
 
-def get_value(task, x, y, n_evaluate_sample, cache_value=True):
-    value_prompt = task.value_prompt_wrap(x, y)
-    if cache_value and value_prompt in task.value_cache:
-        return task.value_cache[value_prompt]
-    value_outputs = gpt(value_prompt, n=n_evaluate_sample, stop=None)
-    value = task.value_outputs_unwrap(x, y, value_outputs)
-    if cache_value:
-        task.value_cache[value_prompt] = value
-    return value
-
-def get_values(task, x, ys, n_evaluate_sample, cache_value=True):
-    values = []
-    local_value_cache = {}
-    for y in ys:  # each partial output
-        if y in local_value_cache:  # avoid duplicate candidates
-            value = 0
-        else:    
-            value = get_value(task, x, y, n_evaluate_sample, cache_value=cache_value)
-            local_value_cache[y] = value
-        values.append(value)
-    return values
-
-def get_votes(task, x, ys, n_evaluate_sample):
-    vote_prompt = task.vote_prompt_wrap(x, ys)
-    vote_outputs = gpt(vote_prompt, n=n_evaluate_sample, stop=None)
-    values = task.vote_outputs_unwrap(vote_outputs, len(ys))
-    return values
-
-def get_proposals(task, x, y): 
-    propose_prompt = task.propose_prompt_wrap(x, y)
-    proposals = gpt(propose_prompt, n=1, stop=None)[0].split('\n')
-    return [y + _ + '\n' for _ in proposals]
-
-def get_samples(task, x, y, n_generate_sample, prompt_sample, stop):
-    if prompt_sample == 'standard':
-        prompt = task.standard_prompt_wrap(x, y)
-    elif prompt_sample == 'cot':
-        prompt = task.cot_prompt_wrap(x, y)
-    else:
-        raise ValueError(f'prompt_sample {prompt_sample} not recognized')
-    samples = gpt(prompt, n=n_generate_sample, stop=stop)
-    return [y + _ for _ in samples]
-
-def solve(args, task, idx, to_print=True):
-    print(gpt)
-    x = task.get_input(idx)  # input
-    ys = ['']  # current output candidates
-    infos = []
-    for step in range(task.steps):
-        # generation
-        if args.method_generate == 'sample':
-            new_ys = [get_samples(task, x, y, args.n_generate_sample, prompt_sample=args.prompt_sample, stop=task.stops[step]) for y in ys]
-        elif args.method_generate == 'propose':
-            new_ys = [get_proposals(task, x, y) for y in ys]
-        new_ys = list(itertools.chain(*new_ys))
-        ids = list(range(len(new_ys)))
-        # evaluation
-        if args.method_evaluate == 'vote':
-            values = get_votes(task, x, new_ys, args.n_evaluate_sample)
-        elif args.method_evaluate == 'value':
-            values = get_values(task, x, new_ys, args.n_evaluate_sample)
-
-        # selection
-        if args.method_select == 'sample':
-            ps = np.array(values) / sum(values)
-            select_ids = np.random.choice(ids, size=args.n_select_sample, p=ps).tolist()
-        elif args.method_select == 'greedy':
-            select_ids = sorted(ids, key=lambda x: values[x], reverse=True)[:args.n_select_sample]
-        select_new_ys = [new_ys[select_id] for select_id in select_ids]
-
-        # log
-        if to_print: 
-            sorted_new_ys, sorted_values = zip(*sorted(zip(new_ys, values), key=lambda x: x[1], reverse=True))
-            print(f'-- new_ys --: {sorted_new_ys}\n-- sol values --: {sorted_values}\n-- choices --: {select_new_ys}\n')
-        
-        infos.append({'step': step, 'x': x, 'ys': ys, 'new_ys': new_ys, 'values': values, 'select_new_ys': select_new_ys})
-        ys = select_new_ys
-    
-    if to_print: 
-        print(ys)
-    return ys, {'steps': infos}
-
-def naive_solve(args, task, idx, to_print=True):
-    x = task.get_input(idx)  # input
-    ys = get_samples(task, x, '', args.n_generate_sample, args.prompt_sample, stop=None)
-    return ys, {}
+from tot.tasks import get_task
+from tot.methods.bfs import solve, naive_solve
+from tot.models import gpt_usage
 
 def run(args):
-    task = get_task(args.task, args.task_file_path)
+    task = get_task(args.task)
     logs, cnt_avg, cnt_any = [], 0, 0
-    global gpt
-    gpt = partial(gpt, model=args.backend, temperature=args.temperature)
     if args.naive_run:
-        file = f'logs/{args.task}/{args.backend}_{args.temperature}_naive_{args.prompt_sample}_sample_{args.n_generate_sample}_start{args.task_start_index}_end{args.task_end_index}.json'
+        file = f'./logs/{args.task}/{args.backend}_{args.temperature}_naive_{args.prompt_sample}_sample_{args.n_generate_sample}_start{args.task_start_index}_end{args.task_end_index}.json'
     else:
-        file = f'logs/{args.task}/{args.backend}_{args.temperature}_{args.method_generate}{args.n_generate_sample}_{args.method_evaluate}{args.n_evaluate_sample}_{args.method_select}{args.n_select_sample}_start{args.task_start_index}_end{args.task_end_index}.json'
+        file = f'./logs/{args.task}/{args.backend}_{args.temperature}_{args.method_generate}{args.n_generate_sample}_{args.method_evaluate}{args.n_evaluate_sample}_{args.method_select}{args.n_select_sample}_start{args.task_start_index}_end{args.task_end_index}.json'
     os.makedirs(os.path.dirname(file), exist_ok=True)
 
     for i in range(args.task_start_index, args.task_end_index):
@@ -136,7 +46,6 @@ def parse_args():
     args.add_argument('--temperature', type=float, default=0.7)
 
     args.add_argument('--task', type=str, required=True, choices=['game24', 'text', 'crosswords'])
-    args.add_argument('--task_file_path', type=str, required=True)
     args.add_argument('--task_start_index', type=int, default=900)
     args.add_argument('--task_end_index', type=int, default=1000)
 
@@ -145,7 +54,7 @@ def parse_args():
 
     args.add_argument('--method_generate', type=str, choices=['sample', 'propose'])
     args.add_argument('--method_evaluate', type=str, choices=['value', 'vote'])
-    args.add_argument('--method_select', type=str, choices=['sample', 'greedy'])
+    args.add_argument('--method_select', type=str, choices=['sample', 'greedy'], default='greedy')
     args.add_argument('--n_generate_sample', type=int, default=1)  # only thing needed if naive_run
     args.add_argument('--n_evaluate_sample', type=int, default=1)
     args.add_argument('--n_select_sample', type=int, default=1)

requirements.txt

@@ -1,6 +1,5 @@
 python run.py \
     --task crosswords \
-    --task_file_path mini0505_0_100_5.json \
     --task_start_index 0 \
     --task_end_index 20 \
     --naive_run \

run.py

@@ -14,7 +14,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cd ../.."
+    "cd .."
    ]
   },
   {
@@ -24,9 +24,9 @@
    "outputs": [],
    "source": [
     "import json\n",
-    "from prompts.crosswords import propose_prompt, value_prompt\n",
-    "from models import gpt\n",
-    "from tasks.crosswords import MiniCrosswordsEnv\n",
+    "from tot.prompts.crosswords import propose_prompt, value_prompt\n",
+    "from tot.models import gpt\n",
+    "from tot.tasks.crosswords import MiniCrosswordsEnv\n",
     "\n",
     "env = MiniCrosswordsEnv()"
    ]
@@ -61,7 +61,7 @@
    "source": [
     "import re\n",
     "import copy\n",
-    "from models import gpt\n",
+    "from tot.models import gpt\n",
     "\n",
     "def parse_line(input_str):\n",
     "    # regular expression pattern to match the input string format\n",

scripts/crosswords/cot_sampling.sh

@@ -1,6 +1,5 @@
 python run.py \
     --task crosswords \
-    --task_file_path mini0505_0_100_5.json \
     --task_start_index 0 \
     --task_end_index 20 \
     --naive_run \