[Hackathon 5th No.73] ToT

pipelines/examples/agents/tree-of-thought-llm/.gitignore

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+/*__pycache__/
+dist/
+src/tree_of_thoughts_llm.egg-info/
+.env
+*.pyc
+*.DS_Store
+spark/test.py

pipelines/examples/agents/tree-of-thought-llm/MANIFEST.in

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+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

pipelines/examples/agents/tree-of-thought-llm/README.md

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+# Tree of Thoughts (ToT)
+
+![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.
+
+
+
+
+
+## Setup
+1. 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)). 
+
+2. Install `tot` package in two ways:
+- Option 1: Install from PyPI
+```bash
+pip install tree-of-thoughts-llm
+```
+- Option 2: Install from source
+```bash
+git clone https://github.com/PaddlePaddle/PaddleNLP.git
+cd pipelines/examples/agents
+pip install -r requirements.txt
+pip install -e .  # install `tot` package
+```
+3. Intall meta/llama2 according to facebook tutorial. And then modify the model path in the llm_config.yaml
+
+## 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 llama-7b-chat):
+
+
+run in pipelines/examples/agents/tree-of-thought-llm
+
+```
+python demo.py
+```
+the detail code is the following
+
+```python
+import argparse
+from tot.methods.bfs import solve
+from tot.tasks.game24 import Game24Task
+
+args = argparse.Namespace(backend='llama-2-7b-chat', temperature=0.6, 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)
+
+
+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
+```
+
+## 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``.
+
+The very simple ``run.py`` implements the ToT + BFS algorithm, as well as the naive IO/CoT sampling. Some key arguments:
+
+- ``--naive_run``: if True, run naive IO/CoT sampling instead of ToT + BFS.
+-  ``--prompt_sample`` (choices=[``standard``, ``cot``]): sampling prompt
+- ``--method_generate`` (choices=[``sample``, ``propose``]): thought generator, whether to sample independent thoughts (used in Creative Writing) or propose sequential thoughts (used in Game of 24)
+- ``--method_evaluate`` (choices=[``value``, ``vote``]): state evaluator, whether to use the value states independently (used in Game of 24) or vote on states together (used in Creative Writing)
+- ``--n_generate_sample``: number of times to prompt for thought generation
+- ``--n_evaluate_sample``: number of times to prompt for state evaluation
+- ``--n_select_sample``: number of states to keep from each step (i.e. ``b`` in the paper's ToT + BFS algorithm)
+
+
+
+## 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. 
+
+If there are any questions, please contact ErnestinaQiu by ernestinaqiu@gmail.com

pipelines/examples/agents/tree-of-thought-llm/demo.py

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+import argparse
+
+from src.tot.methods.bfs import solve
+from src.tot.tasks.game24 import Game24Task
+
+args = argparse.Namespace(
+    backend="llama-2-7b-chat",
+    temperature=0.6,
+    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,
+)
+
+task = Game24Task()
+ys, infos = solve(args, task, 900)
+print(ys[0])
+print(infos)

pipelines/examples/agents/tree-of-thought-llm/llama2/__init__.py

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+"""
+author: Ernestina
+des: 1) set configure 2) initiate llama2
+"""
+import os
+import time
+from typing import List, Optional
+
+import yaml
+from llama2.llama.llama import Dialog, Llama
+
+os.environ["WORLD_SIZE"] = "1"
+os.environ["RANK"] = "0"
+os.environ["MASTER_ADDR"] = "localhost"
+os.environ["MASTER_PORT"] = "8020"
+
+llm_config_path = os.path.join(os.getcwd(), "llm_config.yml")
+with open(llm_config_path, "r") as f:
+    log_config = yaml.full_load(f.read())
+
+
+class ChatCompletion:
+    global log_config
+    global max_seq_len
+    global max_batch_size
+
+    def __init__(self, model="llama-2-7b-chat") -> None:
+        ckpt_dir = log_config[model]["ckpt_dir"]
+        tokenizer_path = log_config[model]["tokenizer_path"]
+        # ckpt_dir = f"/mnt/e/study/dl/llama2/{model}/"
+        # tokenizer_path = "/mnt/e/study/dl/llama2/tokenizer.model"
+        max_seq_len = 1000
+        max_batch_size = 6
+        self.generator = Llama.build(
+            ckpt_dir=ckpt_dir,
+            tokenizer_path=tokenizer_path,
+            max_seq_len=max_seq_len,
+            max_batch_size=max_batch_size,
+        )
+
+    # @staticmethod
+    def create(
+        self,
+        messages: List[Dialog],
+        temperature: float = 0.6,
+        top_p: float = 0.9,
+        max_gen_len: Optional[int] = None,
+    ):
+        """
+        Entry point of the program for generating text using a pretrained model.
+
+        Args:
+            messages (list): There are two roles including "system" and "user".
+            --Example  [[{"role": "user", "content": "what is the recipe of mayonnaise?"}, {"role": "system", "content": "Always answer with Haiku"}]]
+            ckpt_dir (str): The directory containing checkpoint files for the pretrained model.
+            tokenizer_path (str): The path to the tokenizer model used for text encoding/decoding.
+            temperature (float, optional): The temperature value for controlling randomness in generation.
+                Defaults to 0.6.
+            top_p (float, optional): The top-p sampling parameter for controlling diversity in generation.
+                Defaults to 0.9.
+            max_seq_len (int, optional): The maximum sequence length for input prompts. Defaults to 512.
+            max_batch_size (int, optional): The maximum batch size for generating sequences. Defaults to 8.
+            max_gen_len (int, optional): The maximum length of generated sequences. If None, it will be
+                set to the model's max sequence length. Defaults to None.
+        """
+        results = self.generator.chat_completion(
+            messages,  # type: ignore
+            max_gen_len=max_gen_len,
+            temperature=temperature,
+            top_p=top_p,
+        )
+
+        completion = {
+            "choices": [],
+            "created": time.time(),
+            "id": "llama2_{}".format(int(time.time())),
+            "model": "llama-2-7b-chat",
+            "object": "chat.completion",
+            "usage": {"completion_tokens": 0, "prompt_tokens": 0, "total_tokens": 0},
+        }
+
+        assert len(messages) == len(results)
+        for i in range(len(results)):
+            dialog = messages[i]
+            print(f"dialog: \n {dialog}")
+            result = results[i]
+            if i == len(results) - 1:
+                finish_reason = "stop"
+            else:
+                finish_reason = "length"
+            tmp = {
+                "finish_reason": finish_reason,
+                "index": i,
+                "message": {"content": "", "role": ""},
+            }
+            tmp["message"]["role"] = result["generation"]["role"]
+            tmp["message"]["content"] = result["generation"]["content"].replace(
+                "\n", ""
+            )
+
+            completion["choices"].append(tmp)
+            print(f"\n result: \n {result}")
+
+        return completion

pipelines/examples/agents/tree-of-thought-llm/llama2/llama/.gitignore

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+# Byte-compiled / optimized / DLL files
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+*.py[cod]
+*$py.class
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