diff --git a/.gitignore b/.gitignore index c0f8b63f7..91991f7c0 100644 --- a/.gitignore +++ b/.gitignore @@ -137,7 +137,7 @@ data/* deprecated/* *.csv - +*.jsonl #backup backup/* diff --git a/docs/pages/benchmark.md b/docs/pages/benchmark.md new file mode 100644 index 000000000..368432e18 --- /dev/null +++ b/docs/pages/benchmark.md @@ -0,0 +1,11 @@ +# Benchmark your model as a social agent in Sotopia + +``` +sotopia_benchmark --model= +``` +or + +``` +python sotopia/benchmark/cli.py --model= +``` +Currently this script would run over 100 simulations on the Sotopia Hard tasks. And the partner model is fixed to be `meta-llama/Llama-3-70b-chat-hf` diff --git a/notebooks/redis_stats.ipynb b/notebooks/redis_stats.ipynb index 5ca616e25..e4c710eb4 100644 --- a/notebooks/redis_stats.ipynb +++ b/notebooks/redis_stats.ipynb @@ -183,7 +183,8 @@ "metadata": {}, "source": [ "## EnvAgentComboStorage\n", - "\n", + "Identify the combination of environment and agent that is used in the episodes.\n", + "Once we have the combination, we can use it to start the simulation.\n", "Combo is a combination of Environment and two agents." ] }, @@ -204,9 +205,99 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Episode Log\n", + "## EnvironmentList\n", + "Store a list of special environments (e.g., sotopia hard) that can be used to start certain simulations. Agent index is used to identify the special agent in the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sotopia.database.persistent_profile import EnvironmentList\n", + "\n", + "all_list = EnvironmentList.all_pks()\n", + "all_list = list(all_list)\n", + "print(len(all_list))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sotopia.samplers import ConstraintBasedSampler\n", + "from sotopia.messages import AgentAction, Observation\n", + "from sotopia.agents import LLMAgent\n", + "import json\n", + "# In this example, we will demonstrate using the EnvironmentList class to sample a list of EnvAgentComboStorage and serialize it to a json file that can be used for sharing with others for benchmarking purposes.\n", + "\n", + "\n", + "def _sample_env_agent_combo_and_push_to_db(env_id: str) -> list[EnvAgentComboStorage]:\n", + " combo_list = []\n", + " sampler = ConstraintBasedSampler[Observation, AgentAction](env_candidates=[env_id])\n", + " env_agent_combo_list = list(\n", + " sampler.sample(agent_classes=[LLMAgent] * 2, replacement=False, size=10)\n", + " )\n", + " for env, agent in env_agent_combo_list:\n", + " combo = EnvAgentComboStorage(\n", + " env_id=env.profile.pk,\n", + " agent_ids=[agent[0].profile.pk, agent[1].profile.pk],\n", + " )\n", + " combo_list.append(combo)\n", + " return combo_list\n", + "\n", + "\n", + "# First we will extrat the hard environments from the EnvironmentList\n", + "hard_envs = EnvironmentList.get(\"01HAK34YPB1H1RWXQDASDKHSNS\").environments\n", + "print(len(hard_envs))\n", + "hard_envs_set = set(hard_envs)\n", + "\n", + "# Next we will sample 10 EnvAgentComboStorage from each hard environment\n", + "final_list_for_benchmark_agents = []\n", + "for env in hard_envs_set:\n", + " combo_list = EnvAgentComboStorage.find(EnvAgentComboStorage.env_id == env).all()\n", + " print(len(combo_list))\n", + " final_list_for_benchmark_agents.extend(combo_list)\n", + "\n", + "# Finally we will serialize the list to a json file\n", + "with open(\"../data/benchmark_agents.json\", \"w\") as f:\n", + " json.dump(\n", + " [combo.dict() for combo in final_list_for_benchmark_agents],\n", + " f,\n", + " indent=4,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "EnvironmentList.get(\"01HAK34YPB1H1RWXQDASDKHSNS\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Episode Log" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# find episode log by tag\n", + "Episodes = EpisodeLog.find(EpisodeLog.tag == \"aug20_gpt4_llama-2-70b-chat_zqi2\").all()\n", + "len(Episodes) ## Episode Log\n", "\n", - "Episodelog stores the social conversation between two agents in an environment." + "## Episodelog stores the social conversation between two agents in an environment." ] }, { @@ -289,7 +380,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/poetry.lock b/poetry.lock index 3fb7d9cc2..57edce3e8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -148,13 +148,13 @@ vertex = ["google-auth (>=2,<3)"] [[package]] name = "anyio" -version = "4.3.0" +version = "4.4.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"}, - {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"}, + {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, + {file = 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(>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [extras] anthropic = ["anthropic"] chat = ["fastapi"] cohere = ["cohere"] -examples = ["datasets", "scipy", "torch", "transformers"] +examples = ["datasets", "pandas", "scipy", "torch", "transformers"] google-generativeai = ["google-generativeai"] groq = ["groq"] [metadata] lock-version = "2.0" -python-versions = ">=3.9, <=3.12" -content-hash = "8ce0edb923a09683941fc9f9869e3322b21de89259e2615d28ba1f62dfbb9277" +python-versions = ">=3.10, <3.13" +content-hash = "096a882b6e14ad14a42bbda97a856641da4c8d5c078618cb8474b9f1d91dc0ef" diff --git a/pyproject.toml b/pyproject.toml index 1fcda8612..16618ff00 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,28 +2,26 @@ name = "sotopia" version = "0.0.9" description = "A platform for simulating and evaluating social interaction." -authors = ["Hao Zhu "] +authors = ["Hao Zhu , Xuhui Zhou "] license = "MIT License" readme = "README.md" packages = [{include = "sotopia"}] [tool.poetry.dependencies] python = ">=3.10, <3.13" -pandas = "^2.1.1" lxml = "^4.9.3" openai = "^1.11.0" langchain = "0.1.5" rich = "^13.6.0" PettingZoo = "1.24.0" redis-om = "^0.2.1" -pandas-stubs = "*" types-tqdm = "*" gin-config = "^0.5.0" absl-py = "^2.0.0" together = "^0.2.4" pydantic = "1.10.12" beartype = "^0.14.0" -langchain-openai = "^0.0.5" +langchain-openai = ">=0.0.5,<0.0.7" litellm = "~1.23.12" # dependency versions for extras @@ -31,13 +29,14 @@ fastapi = { version = "^0.109.2", optional = true } tabulate = { version = "^0.9.0", optional = true } anthropic = { version = "^0.26.0", optional = true } xmltodict = { version = "^0.13.0", optional = true } -groq = { version = "^0.4.2", optional = true } +groq = { version = ">=0.4.2,<0.10.0", optional = true } cohere = { version = "^5.1.8", optional = true } -google-generativeai = { version = "^0.5.4", optional = true } +google-generativeai = { version = ">=0.5.4,<0.7.0", optional = true } transformers = { version = "^4.41.0", optional = true } datasets = { version = "^2.19.0", optional = true } scipy = { version = "^1.13.1", optional = true } torch = { version = "^2.3.0", optional = true } +pandas = { version = "^2.1.1", optional = true } [tool.poetry.group.dev.dependencies] pre-commit = "*" @@ -55,7 +54,7 @@ anthropic = ["anthropic"] groq = ["groq"] cohere = ["cohere"] google-generativeai = ["google-generativeai"] -examples = ["transformers", "datasets", "scipy", "torch"] +examples = ["transformers", "datasets", "scipy", "torch", "pandas"] chat = ["fastapi"] [build-system] @@ -77,3 +76,6 @@ ignore_missing_imports = true [tool.pytest.ini_options] testpaths = ["tests"] python_files = "test_*.py" + +[tool.poetry.scripts] +sotopia_benchmark = "sotopia.benchmark:app" diff --git a/sotopia-chat/chat_server.py b/sotopia-chat/chat_server.py index a352ee067..0b3388b5a 100644 --- a/sotopia-chat/chat_server.py +++ b/sotopia-chat/chat_server.py @@ -76,7 +76,6 @@ async def _start_server_with_two_session_ids_and_agent_env_combo( await arun_one_episode( env, agents, - {"env": "gpt-4", "agent1": "redis", "agent2": "redis"}, tag="human_human_v0.0.3_dryrun", push_to_db=True, ) @@ -126,11 +125,6 @@ async def _start_server_with_one_session_id_and_agent_env_combo( await arun_one_episode( env, agents, - { - "env": "gpt-4", - "agent1": "redis" if left_or_right == "left" else "gpt-4", - "agent2": "redis" if left_or_right == "right" else "gpt-4", - }, tag="human_human_v0.0.3_dryrun", push_to_db=True, ) diff --git a/sotopia/agents/base_agent.py b/sotopia/agents/base_agent.py index 8033ca34b..1454104e5 100644 --- a/sotopia/agents/base_agent.py +++ b/sotopia/agents/base_agent.py @@ -34,6 +34,7 @@ def __init__( self.agent_name = agent_name self._goal: str | None = None + self.model_name: str = "" @property def goal(self) -> str: diff --git a/sotopia/agents/llm_agent.py b/sotopia/agents/llm_agent.py index 88dba7873..045336ee6 100644 --- a/sotopia/agents/llm_agent.py +++ b/sotopia/agents/llm_agent.py @@ -5,6 +5,7 @@ from sotopia.agents import BaseAgent from sotopia.database import AgentProfile from sotopia.generation_utils.generate import ( + LLM_Name, agenerate_action, agenerate_script, generate_action, @@ -171,6 +172,7 @@ def __init__( uuid_str=uuid_str, agent_profile=agent_profile, ) + self.model_name: LLM_Name = "human" @property def goal(self) -> str: diff --git a/sotopia/agents/redis_agent.py b/sotopia/agents/redis_agent.py index a072f805f..7288fe282 100644 --- a/sotopia/agents/redis_agent.py +++ b/sotopia/agents/redis_agent.py @@ -9,6 +9,7 @@ from sotopia.agents import BaseAgent from sotopia.database import AgentProfile, MessageTransaction +from sotopia.generation_utils.generate import LLM_Name from sotopia.messages import AgentAction, Observation @@ -30,6 +31,7 @@ def __init__( # super().__init__(agent_name=agent_name, uuid_str=uuid_str) self.session_id = session_id or str(uuid4()) self.sender_id = str(uuid4()) + self.model_name: LLM_Name = "redis" print(f"session id: {self.session_id}") print("step 1: connect to the server") assert ( diff --git a/sotopia/benchmark/__init__.py b/sotopia/benchmark/__init__.py new file mode 100644 index 000000000..72727355b --- /dev/null +++ b/sotopia/benchmark/__init__.py @@ -0,0 +1,3 @@ +from .cli import app + +__all__ = ["app"] diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py new file mode 100644 index 000000000..9f519ac5b --- /dev/null +++ b/sotopia/benchmark/cli.py @@ -0,0 +1,303 @@ +from datetime import datetime +import requests +import rich +from sotopia.database.persistent_profile import EnvironmentList +import asyncio +import logging +from typing import cast + +from logging import FileHandler +from rich.logging import RichHandler + +from tqdm import tqdm + +from sotopia.agents import LLMAgent +from sotopia.database import ( + AgentProfile, + EnvAgentComboStorage, + EnvironmentProfile, + EpisodeLog, +) +from sotopia.database.serialization import get_rewards_from_episode +from sotopia.envs.evaluators import ( + ReachGoalLLMEvaluator, + RuleBasedTerminatedEvaluator, +) +from sotopia.envs.parallel import ParallelSotopiaEnv +from sotopia.generation_utils.generate import LLM_Name +from sotopia.messages import AgentAction, Observation +from sotopia.samplers import ( + BaseSampler, + EnvAgentCombo, +) +from sotopia.server import run_async_server + +import typer +from pathlib import Path + +app = typer.Typer() + + +def check_existing_episodes( + env_id: str, + agent_ids: list[str], + models: dict[str, LLM_Name], + index: str, + tag: str | None = None, +) -> bool: + if tag: + existing_episode = EpisodeLog.find( + (EpisodeLog.environment == env_id) & (EpisodeLog.tag == tag) + ).all() + else: + existing_episode = EpisodeLog.find(EpisodeLog.environment == env_id).all() + if existing_episode: + for episode in existing_episode: + assert isinstance(episode, EpisodeLog), "episode should be an EpisodeLog" + models_list = ( + [models["env"], models["test_model"], models["partner_model"]] + if index == "0" + else [models["env"], models["partner_model"], models["test_model"]] + ) + if episode.agents == agent_ids and episode.models == models_list: + return True + return False + else: + return False + + +def initilize_benchmark_combo(data: list[dict[str, str]]) -> list[EnvAgentComboStorage]: + list_of_env_agent_combo_storage = [] + for combo in data: + env_agent_combo_storage = EnvAgentComboStorage( + env_id=combo["env_id"], agent_ids=combo["agent_ids"] + ) + list_of_env_agent_combo_storage.append(env_agent_combo_storage) + return list_of_env_agent_combo_storage + + +def get_avg_reward(episodes: list[EpisodeLog], model_name: str) -> dict[str, float]: + rewards_list = [] + avg_reward_dict = {} + for episode in episodes: + assert episode.models is not None, "episode.models should not be None" + if episode.models[1] == model_name: + reward = get_rewards_from_episode(episode)[0][1] + else: + reward = get_rewards_from_episode(episode)[1][1] + rewards_list.append(reward) + for dimension in rewards_list[0].keys(): + rewards = [reward[dimension] for reward in rewards_list] + avg_reward = sum(rewards) / len(rewards) + avg_reward_dict[dimension] = avg_reward + return avg_reward_dict + + +def _list_all_env_agent_combo_not_in_db( + model_names: dict[str, LLM_Name], + env_agent_combo_storage_list: list[EnvAgentComboStorage], + tag: str | None = None, +) -> list[EnvAgentCombo[Observation, AgentAction]]: + """We iterate over each environment and return the **first** env-agent combo that is not in the database.""" + hard_envs = EnvironmentList.get("01HAK34YPB1H1RWXQDASDKHSNS").environments + agent_index = EnvironmentList.get("01HAK34YPB1H1RWXQDASDKHSNS").agent_index + assert isinstance(agent_index, list), "agent_index should be a list" + + env_agent_combo_storage_index_list = [] + for env_id, index in zip(hard_envs, agent_index): + for env_agent_combo_storage in env_agent_combo_storage_list: + if env_agent_combo_storage.env_id == env_id: + env_agent_combo_storage_index_list.append( + (env_agent_combo_storage, index) + ) + + list_of_env_agent_combo_storage = [] + for env_agent_combo_storage, index in env_agent_combo_storage_index_list: + agent_ids = env_agent_combo_storage.agent_ids + env_id = env_agent_combo_storage.env_id + if check_existing_episodes( + env_id=env_id, agent_ids=agent_ids, models=model_names, index=index, tag=tag + ): + logging.info( + f"Episode for {env_id} with agents {agent_ids} using {list(model_names.values())} already exists" + ) + continue + env_profile = EnvironmentProfile.get(env_id) + env = ParallelSotopiaEnv( + env_profile=env_profile, + model_name=model_names["env"], + action_order="round-robin", + evaluators=[ + RuleBasedTerminatedEvaluator(max_turn_number=20, max_stale_turn=2), + ], + terminal_evaluators=[ + ReachGoalLLMEvaluator(model_names["env"]), + ], + ) + agent_profiles = [AgentProfile.get(id) for id in agent_ids] + # make sure the second agent (i.e., the agent being benchmarked) is always the indexed agent + agents = [ + LLMAgent(agent_profile=agent_profile, model_name=agent_model) + for agent_profile, agent_model in zip( + agent_profiles, + [model_names["test_model"], model_names["partner_model"]] + if index == "0" + else [model_names["partner_model"], model_names["test_model"]], + ) + ] + list_of_env_agent_combo_storage.append((env, agents)) + return list_of_env_agent_combo_storage # type: ignore + + +def run_async_benchmark_in_batch( + *, + batch_size: int = 1, + model_names: dict[str, LLM_Name] = { + "env": "gpt-4", + "test_model": "gpt-3.5-turbo", + "partner_model": "gpt-3.5-turbo", + }, + tag: str | None = None, + push_to_db: bool = False, + verbose: bool = False, +) -> None: + url = "https://huggingface.co/datasets/cmu-lti/sotopia/resolve/main/benchmark_agents.json?raw=true" + response = requests.get(url) + # Check if the request was successful + if response.status_code == 200: + data = response.json() + print("Data fetched successfully") + else: + print(f"Failed to fetch data. Status code: {response.status_code}") + raise ValueError("Failed to fetch data") + benchmark_combo = initilize_benchmark_combo(data) + env_agent_combo_list = _list_all_env_agent_combo_not_in_db( + model_names=model_names, tag=tag, env_agent_combo_storage_list=benchmark_combo + ) + env_agent_combo_batch: list[EnvAgentCombo[Observation, AgentAction]] = [] + number_of_fix_turns = 0 + loop = asyncio.get_event_loop() + while True: + for env_agent_combo in tqdm( + env_agent_combo_list, + desc="Running all envs in batch", + ): + env_agent_combo_batch.append(env_agent_combo) + if len(env_agent_combo_batch) == batch_size: + logging.info( + f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" + ) + loop.run_until_complete( + run_async_server( + sampler=BaseSampler[Observation, AgentAction](), + env_agent_combo_list=env_agent_combo_batch, + push_to_db=push_to_db, + tag=tag, + ) + ) + env_agent_combo_batch = [] + else: + if env_agent_combo_batch: + logging.info( + f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" + ) + loop.run_until_complete( + run_async_server( + model_dict=model_names, + sampler=BaseSampler[Observation, AgentAction](), + env_agent_combo_list=env_agent_combo_batch, + push_to_db=push_to_db, + tag=tag, + ) + ) + # remove episodes that has bad rewards + simulated_episodes = EpisodeLog.find(EpisodeLog.tag == tag).all() + valid_episodes = [ + not isinstance(relevant_episode.rewards[0], float) # type: ignore + for relevant_episode in simulated_episodes + ] + for valid, episode in zip(valid_episodes, simulated_episodes): + if not valid: + pk = episode.pk + assert isinstance(pk, str) + EpisodeLog.delete(pk) + + env_agent_combo_list = _list_all_env_agent_combo_not_in_db( + model_names=model_names, + tag=tag, + env_agent_combo_storage_list=benchmark_combo, + ) + env_agent_combo_batch = [] + number_of_fix_turns += 1 + if len(env_agent_combo_list) == 0 or number_of_fix_turns >= 5: + rewards_dict = get_avg_reward( + simulated_episodes, # type: ignore + model_names["test_model"], + ) + rewards_dict["model_name"] = model_names["test_model"] # type: ignore + rewards_dict["episode_count"] = len(simulated_episodes) + rich.print(rewards_dict) + return + + +def _set_up_logs( + *, + log_file_level: int = logging.DEBUG, + log_rich_level: int = logging.INFO, + log_format: str = "%(asctime)s - %(levelname)s - %(name)s - %(message)s", + log_file: str = datetime.now().strftime("./logs/%H_%M_%d_%m_%Y.log"), + print_logs: bool = False, +) -> None: + # date and message only + logging_path = Path(log_file) + logging_path.parent.mkdir(parents=True, exist_ok=True) + + logging.basicConfig( + level=log_file_level, + format=log_format, + datefmt="[%X]", + handlers=[ + FileHandler(logging_path), + RichHandler(level=log_rich_level) if print_logs else RichHandler(level=100), + ], + ) + + +@app.command() +def cli( + model: str = typer.Option(..., help="The language model you want to benchmark."), + partner_model: str = typer.Option( + "together_ai/meta-llama/Llama-3-70b-chat-hf", + help="The partner model you want to use.", + ), + evaluator_model: str = typer.Option( + "gpt-4o", help="The evaluator model you want to use." + ), + batch_size: int = typer.Option(10, help="The batch size you want to use."), + task: str = typer.Option("hard", help="The task id you want to benchmark."), + print_logs: bool = typer.Option(False, help="Print logs."), +) -> None: + """A simple command-line interface example.""" + _set_up_logs(print_logs=print_logs) + typer.echo( + f"Running benchmark for {model} chatting with {partner_model} on task {task} with {evaluator_model} as the evaluator." + ) + model = cast(LLM_Name, model) + partner_model = cast(LLM_Name, partner_model) + evaluator_model = cast(LLM_Name, evaluator_model) + tag = f"benchmark_{model}_{partner_model}_{evaluator_model}_{task}_trial0" + run_async_benchmark_in_batch( + batch_size=batch_size, + model_names={ + "env": evaluator_model, + "test_model": model, + "partner_model": partner_model, + }, + tag=tag, + verbose=False, + push_to_db=True, + ) + + +if __name__ == "__main__": + app() diff --git a/sotopia/database/serialization.py b/sotopia/database/serialization.py index af60c31fe..cd10ef384 100644 --- a/sotopia/database/serialization.py +++ b/sotopia/database/serialization.py @@ -1,6 +1,7 @@ +import csv import json +from typing import Any -import pandas as pd from pydantic import BaseModel, Field from .env_agent_combo_storage import EnvAgentComboStorage @@ -175,6 +176,25 @@ def get_social_interactions_from_episode( return "\n\n".join(overall_social_interaction) +def _serialize_data_to_csv(data: dict[str, list[Any]], csv_file_path: str) -> None: + """ + Serialize data to a csv file without pandas + """ + max_length = max(len(lst) for lst in data.values()) + + # Create and write to the csv file + with open(csv_file_path, "w", newline="") as file: + writer = csv.writer(file) + # Write the header + writer.writerow(data.keys()) + + # Write the data rows + for i in range(max_length): + # Create a row for each index in the longest list + row = [data[key][i] if i < len(data[key]) else "" for key in data.keys()] + writer.writerow(row) + + def episodes_to_csv( episodes: list[EpisodeLog], csv_file_path: str = "episodes.csv" ) -> None: @@ -184,7 +204,7 @@ def episodes_to_csv( episodes (list[EpisodeLog]): List of episodes. filepath (str, optional): The file path. Defaults to "episodes.csv". """ - data = { + data: dict[str, list[Any]] = { "episode_id": [episode.pk for episode in episodes], "environment_id": [episode.environment for episode in episodes], "agent_ids": [episode.agents for episode in episodes], @@ -207,8 +227,9 @@ def episodes_to_csv( "reasoning": [episode.reasoning for episode in episodes], "rewards": [get_rewards_from_episode(episode) for episode in episodes], } - df = pd.DataFrame(data) - df.to_csv(csv_file_path, index=False) + + # Serialize data to a csv file without pandas + _serialize_data_to_csv(data, csv_file_path) def episodes_to_jsonl( @@ -253,15 +274,16 @@ def agentprofiles_to_csv( agent_profiles (list[AgentProfile]): List of agent profiles. filepath (str, optional): The file path. Defaults to "agent_profiles.csv". """ - data = { + data: dict[str, list[Any]] = { "agent_id": [profile.pk for profile in agent_profiles], "first_name": [profile.first_name for profile in agent_profiles], "last_name": [profile.last_name for profile in agent_profiles], "age": [profile.age for profile in agent_profiles], "occupation": [profile.occupation for profile in agent_profiles], } - df = pd.DataFrame(data) - df.to_csv(csv_file_path, index=False) + + # Serialize data to a csv file without pandas + _serialize_data_to_csv(data, csv_file_path) def agentprofiles_to_jsonl( @@ -308,7 +330,7 @@ def environmentprofiles_to_csv( environment_profiles (list[EnvironmentProfile]): List of environment profiles. filepath (str, optional): The file path. Defaults to "environment_profiles.csv". """ - data = { + data: dict[str, list[Any]] = { "env_id": [profile.pk for profile in environment_profiles], "codename": [profile.codename for profile in environment_profiles], "source": [profile.source for profile in environment_profiles], @@ -323,8 +345,9 @@ def environmentprofiles_to_csv( profile.agent_constraint for profile in environment_profiles ], } - df = pd.DataFrame(data) - df.to_csv(csv_file_path, index=False) + + # Serialize data to a csv file without pandas + _serialize_data_to_csv(data, csv_file_path) def environmentprofiles_to_jsonl( @@ -366,7 +389,7 @@ def relationshipprofiles_to_csv( relationship_profiles (list[RelationshipProfile]): List of relationship profiles. filepath (str, optional): The file path. Defaults to "relationship_profiles.csv". """ - data = { + data: dict[str, list[Any]] = { "relationship_id": [profile.pk for profile in relationship_profiles], "agent1_id": [profile.agent_1_id for profile in relationship_profiles], "agent2_id": [profile.agent_2_id for profile in relationship_profiles], @@ -375,8 +398,9 @@ def relationshipprofiles_to_csv( profile.background_story for profile in relationship_profiles ], } - df = pd.DataFrame(data) - df.to_csv(csv_file_path, index=False) + + # Serialize data to a csv file without pandas + _serialize_data_to_csv(data, csv_file_path) def envagnetcombostorage_to_csv( @@ -389,13 +413,14 @@ def envagnetcombostorage_to_csv( env_agent_combo_storages (list[EnvAgentComboStorage]): List of environment-agent combo storages. filepath (str, optional): The file path. Defaults to "env_agent_combo_storages.csv". """ - data = { + data: dict[str, list[Any]] = { "combo_id": [storage.pk for storage in env_agent_combo_storages], "env_id": [storage.env_id for storage in env_agent_combo_storages], "agent_ids": [storage.agent_ids for storage in env_agent_combo_storages], } - df = pd.DataFrame(data) - df.to_csv(csv_file_path, index=False) + + # Serialize data to a csv file without pandas + _serialize_data_to_csv(data, csv_file_path) def relationshipprofiles_to_jsonl( diff --git a/sotopia/envs/evaluators.py b/sotopia/envs/evaluators.py index b61679385..d3a9b5ab3 100644 --- a/sotopia/envs/evaluators.py +++ b/sotopia/envs/evaluators.py @@ -246,7 +246,7 @@ async def __acall__( turn_number: int, messages: list[tuple[str, Message]] | None, history: str = "", - temperature: float = 0.7, + temperature: float = 0.0, ) -> list[tuple[str, tuple[tuple[str, int | float | bool], str]]]: # filter did nothing if not history and messages: diff --git a/sotopia/envs/parallel.py b/sotopia/envs/parallel.py index 199be857d..4821d8c70 100644 --- a/sotopia/envs/parallel.py +++ b/sotopia/envs/parallel.py @@ -266,7 +266,6 @@ def reset( background_for_a = agent_backgrounds[0] background_for_b = agent_backgrounds[1] - print("Is the agent omniscient?", omniscient) if not omniscient: background_for_a.p2_goal = "Unknown" background_for_b.p1_goal = "Unknown" diff --git a/sotopia/generation_utils/generate.py b/sotopia/generation_utils/generate.py index 7e8ec5e0c..c256a86c2 100644 --- a/sotopia/generation_utils/generate.py +++ b/sotopia/generation_utils/generate.py @@ -1,11 +1,12 @@ import logging +import os import re from typing import Any, TypeVar import gin from beartype import beartype from beartype.typing import Type -from langchain.chains import LLMChain +from langchain.chains.llm import LLMChain from langchain.output_parsers import PydanticOutputParser from langchain.prompts import ( ChatPromptTemplate, @@ -14,6 +15,7 @@ ) from langchain.schema import BaseOutputParser, OutputParserException from langchain_community.chat_models import ChatLiteLLM +from langchain_openai import ChatOpenAI from pydantic import BaseModel, Field from rich import print from typing_extensions import Literal @@ -32,20 +34,18 @@ logging_handler = LoggingCallbackHandler("langchain") LLM_Name = Literal[ - "togethercomputer/llama-2-7b-chat", - "togethercomputer/llama-2-70b-chat", - "togethercomputer/mpt-30b-chat", + "together_ai/meta-llama/Llama-2-7b-chat-hf", + "together_ai/meta-llama/Llama-2-70b-chat-hf", + "together_ai/mistralai/Mixtral-8x22B-Instruct-v0.1", + "together_ai/meta-llama/Llama-3-8b-chat-hf", + "together_ai/meta-llama/Llama-3-70b-chat-hf", "gpt-3.5-turbo", "gpt-3.5-turbo-finetuned", "gpt-3.5-turbo-ft-MF", - "text-davinci-003", "gpt-4", "gpt-4-turbo", "human", "redis", - "mistralai/Mixtral-8x7B-Instruct-v0.1", - "together_ai/togethercomputer/llama-2-7b-chat", - "together_ai/togethercomputer/falcon-7b-instruct", "groq/llama3-70b-8192", ] @@ -326,17 +326,54 @@ def obtain_chain( Using langchain to sample profiles for participants """ model_name = _return_fixed_model_version(model_name) - chat = PatchedChatLiteLLM( - model=model_name, - temperature=temperature, - max_retries=max_retries, - ) - human_message_prompt = HumanMessagePromptTemplate( - prompt=PromptTemplate(template=template, input_variables=input_variables) - ) - chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt]) - chain = LLMChain(llm=chat, prompt=chat_prompt_template) - return chain + if "together_ai" in model_name: + model_name = "/".join(model_name.split("/")[1:]) + human_message_prompt = HumanMessagePromptTemplate( + prompt=PromptTemplate( + template=template, + input_variables=input_variables, + ) + ) + chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt]) + chat_openai = ChatOpenAI( + model_name=model_name, + temperature=temperature, + max_retries=max_retries, + openai_api_base="https://api.together.xyz/v1", + openai_api_key=os.environ.get("TOGETHER_API_KEY"), + ) + chain = LLMChain(llm=chat_openai, prompt=chat_prompt_template) + return chain + elif "groq" in model_name: + model_name = "/".join(model_name.split("/")[1:]) + human_message_prompt = HumanMessagePromptTemplate( + prompt=PromptTemplate( + template=template, + input_variables=input_variables, + ) + ) + chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt]) + chat_openai = ChatOpenAI( + model_name=model_name, + temperature=temperature, + max_retries=max_retries, + openai_api_base="https://api.groq.com/openai/v1", + openai_api_key=os.environ.get("GROQ_API_KEY"), + ) + chain = LLMChain(llm=chat_openai, prompt=chat_prompt_template) + return chain + else: + chat = ChatOpenAI( + model=model_name, + temperature=temperature, + max_retries=max_retries, + ) + human_message_prompt = HumanMessagePromptTemplate( + prompt=PromptTemplate(template=template, input_variables=input_variables) + ) + chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt]) + chain = LLMChain(llm=chat, prompt=chat_prompt_template) + return chain @beartype diff --git a/sotopia/server.py b/sotopia/server.py index 9a3347697..01421f520 100644 --- a/sotopia/server.py +++ b/sotopia/server.py @@ -6,7 +6,6 @@ import gin import rich from beartype import beartype -from tqdm.asyncio import tqdm_asyncio from sotopia.agents import ( Agents, @@ -106,7 +105,6 @@ def run_sync_server( async def arun_one_episode( env: ParallelSotopiaEnv, agent_list: Sequence[BaseAgent[Observation, AgentAction]], - model_dict: dict[str, LLM_Name], omniscient: bool = False, script_like: bool = False, json_in_script: bool = False, @@ -115,23 +113,6 @@ async def arun_one_episode( ) -> list[tuple[str, str, Message]]: agents = Agents({agent.agent_name: agent for agent in agent_list}) environment_messages = env.reset(agents=agents, omniscient=omniscient) - agents_model_names = [model_dict["agent1"], model_dict["agent2"]] - for agent_name, agent_model in zip(env.agents, agents_model_names): - if agent_model == "human": - agents[agent_name] = HumanAgent(agent_name) - elif agent_model == "redis": - agents[agent_name] = RedisAgent(agent_name) - elif script_like and not json_in_script: - agents[agent_name] = ScriptWritingAgent( - agent_name, - model_name=agent_model, - background=env.background, - agent_names=env.agents, - ) - else: - agents[agent_name] = LLMAgent( - agent_name, model_name=agent_model, script_like=script_like - ) agents.reset() messages: list[list[tuple[str, str, Message]]] = [] @@ -202,7 +183,7 @@ async def arun_one_episode( environment=env.profile.pk, agents=[agent.profile.pk for agent in agent_list], tag=tag, - models=[model_dict["env"], model_dict["agent1"], model_dict["agent2"]], + models=[env.model_name, agent_list[0].model_name, agent_list[1].model_name], messages=[ [(m[0], m[1], m[2].to_natural_language()) for m in messages_in_turn] for messages_in_turn in messages @@ -230,9 +211,9 @@ async def arun_one_episode( @gin.configurable @beartype async def run_async_server( - model_dict: dict[str, LLM_Name], sampler: BaseSampler[Observation, AgentAction] = BaseSampler(), - action_order: Literal["simultaneous", "round-robin", "random"] = "round-robin", + action_order: Literal["simutaneous", "round-robin", "random"] = "round-robin", + model_dict: dict[str, LLM_Name] = {}, env_agent_combo_list: list[EnvAgentCombo[Observation, AgentAction]] = [], omniscient: bool = False, script_like: bool = False, @@ -254,30 +235,20 @@ async def run_async_server( """ assert not (push_to_db and tag is None), "please provide a tag when push to db" + assert ( + model_dict or env_agent_combo_list + ), "please provide model_dict or env_agent_combo_list" # Create Environment and agents # This step will be moved to outside this function - env_params = { - "model_name": model_dict["env"], - "action_order": action_order, - "evaluators": [ - RuleBasedTerminatedEvaluator(max_turn_number=20, max_stale_turn=2), - ], - "terminal_evaluators": [ - ReachGoalLLMEvaluator(model_dict["env"]), - ], - } - agents_model_dict = { - "agent1": model_dict["agent1"], - "agent2": model_dict["agent2"], - } - def get_agent_class( model_name: str, ) -> Type[BaseAgent[Observation, AgentAction]]: if model_name == "human": return HumanAgent + elif model_name == "redis": + return RedisAgent elif script_like and not json_in_script: return ScriptWritingAgent else: @@ -289,6 +260,20 @@ def get_agent_class( ), "No sampler should be used when `env_agent_combo_list` is not empty" env_agent_combo_iter = iter(env_agent_combo_list) else: + env_params = { + "model_name": model_dict["env"], + "action_order": action_order, + "evaluators": [ + RuleBasedTerminatedEvaluator(max_turn_number=20, max_stale_turn=2), + ], + "terminal_evaluators": [ + ReachGoalLLMEvaluator(model_dict["env"]), + ], + } + agents_model_dict = { + "agent1": model_dict["agent1"], + "agent2": model_dict["agent2"], + } env_agent_combo_iter = sampler.sample( agent_classes=[ get_agent_class(model_name) for model_name in agents_model_dict.values() @@ -304,7 +289,6 @@ def get_agent_class( arun_one_episode( env=env_agent_combo[0], agent_list=env_agent_combo[1], - model_dict=model_dict, omniscient=omniscient, script_like=script_like, json_in_script=json_in_script, @@ -315,7 +299,7 @@ def get_agent_class( ] batch_results = ( - await tqdm_asyncio.gather(*episode_futures, desc="Running one batch") + await asyncio.gather(*episode_futures) if using_async else [await i for i in episode_futures] ) @@ -432,6 +416,7 @@ async def aevaluate_one_episode( turn_number=-1, history=history, messages=None, + temperature=0.0, ) for sing_evaluator in [evaluator] ] diff --git a/sotopia_conf/server.py b/sotopia_conf/server.py index 2f502a0ca..d8d9fe263 100644 --- a/sotopia_conf/server.py +++ b/sotopia_conf/server.py @@ -22,7 +22,7 @@ def main(_: Any) -> None: FLAGS.gin_bindings, ) - asyncio.run(run_async_server()) # type: ignore[call-arg] + asyncio.run(run_async_server()) if __name__ == "__main__": diff --git a/tests/database/test_serialization.py b/tests/database/test_serialization.py index a5655e95a..897c932b4 100644 --- a/tests/database/test_serialization.py +++ b/tests/database/test_serialization.py @@ -21,6 +21,8 @@ jsonl_to_agentprofiles, ) +import csv + def test_episode_log_serialization() -> None: episode_log = EpisodeLog( @@ -175,11 +177,24 @@ def test_episode_log_serialization() -> None: rewards_prompt='Prompt after formatting:\n\n Given the string that can not be parsed by json parser, reformat it to a string that can be parsed by json parser.\n Original string: \n\n Turn #9: You decide to speak.\n\n "I understand your concerns, Noah, but I believe that supporting children in conflict zones is a cause worth investing in. While we may not be able to solve the problem on our own, every little bit helps. I plan to donate, and I hope you will consider doing the same."\n\n You wait for Noah Davis\'s response.\n\n Turn #10: Noah Davis said: "I understand your perspective, Finnegan. However, I still have my own financial commitments to consider. Is there a way we can support\n\n Format instructions: The output should be formatted as a JSON instance that conforms to the JSON schema below.\n\nAs an example, for the schema {"properties": {"foo": {"title": "Foo", "description": "a list of strings", "type": "array", "items": {"type": "string"}}}, "required": ["foo"]}}\nthe object {"foo": ["bar", "baz"]} is a well-formatted instance of the schema. The object {"properties": {"foo": ["bar", "baz"]}} is not well-formatted.\n\nHere is the output schema:\n```\n{"description": "An interface for messages.\\nThere is only one required method: to_natural_language", "properties": {"action_type": {"title": "Action Type", "description": "whether to speak at this turn or choose to not do anything", "enum": ["none", "speak", "non-verbal communication", "action", "leave"], "type": "string"}, "argument": {"title": "Argument", "description": "the utterance if choose to speak, the expression or gesture if choose non-verbal communication, or the physical action if choose action", "type": "string"}}, "required": ["action_type", "argument"]}\n```\n\n Please only generate the JSON:\n \x1b[0m', ) - episodes_to_jsonl([episode_log], "./test_episode_log.jsonl") - rebuild_episode_log = jsonl_to_episodes("./test_episode_log.jsonl")[0] + episodes_to_jsonl([episode_log], "/tmp/test_episode_log.jsonl") + rebuild_episode_log = jsonl_to_episodes("/tmp/test_episode_log.jsonl")[0] assert episode_log.dict() == rebuild_episode_log.dict() - episodes_to_csv([episode_log], "./test_episode_log.csv") + episodes_to_csv([episode_log], "/tmp/test_episode_log.csv") + with open("/tmp/test_episode_log.csv") as f: + reader = csv.DictReader(f) + for row in reader: + print(row.keys()) + assert row["episode_id"] == episode_log.pk + assert row["environment_id"] == episode_log.environment + assert row["agent_ids"] == str(episode_log.agents) + assert row["experiment_tag"] == episode_log.tag + assert row["experiment_model_name_pairs"] == str(episode_log.models) + assert row["raw_messages"] == str(episode_log.messages) + assert row["reasoning"] == episode_log.reasoning + assert row["raw_rewards"] == str(episode_log.rewards) + assert row["raw_rewards_prompt"] == episode_log.rewards_prompt def test_relationship_profile_serialization() -> None: @@ -192,17 +207,26 @@ def test_relationship_profile_serialization() -> None: ) relationshipprofiles_to_jsonl( - [relationship_profile], "./test_relationship_profile.jsonl" + [relationship_profile], "/tmp/test_relationship_profile.jsonl" ) rebuild_relationship_profile = jsonl_to_relationshipprofiles( - "./test_relationship_profile.jsonl" + "/tmp/test_relationship_profile.jsonl" )[0] assert relationship_profile.dict() == rebuild_relationship_profile.dict() relationshipprofiles_to_csv( - [relationship_profile], "./test_relationship_profile.csv" + [relationship_profile], "/tmp/test_relationship_profile.csv" ) + with open("/tmp/test_relationship_profile.csv") as f: + reader = csv.DictReader(f) + for row in reader: + assert row["relationship_id"] == relationship_profile.pk + assert row["agent1_id"] == relationship_profile.agent_1_id + assert row["agent2_id"] == relationship_profile.agent_2_id + assert row["relationship"] == str(relationship_profile.relationship) + assert row["background_story"] == relationship_profile.background_story + def test_environment_profile_serialization() -> None: environment_profile = EnvironmentProfile( @@ -221,14 +245,34 @@ def test_environment_profile_serialization() -> None: ) environmentprofiles_to_jsonl( - [environment_profile], "./test_environment_profile.jsonl" + [environment_profile], "/tmp/test_environment_profile.jsonl" ) rebuild_environment_profile = jsonl_to_environmentprofiles( - "./test_environment_profile.jsonl" + "/tmp/test_environment_profile.jsonl" )[0] assert environment_profile.dict() == rebuild_environment_profile.dict() - environmentprofiles_to_csv([environment_profile], "./test_environment_profile.csv") + environmentprofiles_to_csv( + [environment_profile], "/tmp/test_environment_profile.csv" + ) + + with open("/tmp/test_environment_profile.csv") as f: + reader = csv.DictReader(f) + for row in reader: + assert row["env_id"] == environment_profile.pk + assert row["codename"] == environment_profile.codename + assert row["source"] == environment_profile.source + assert row["scenario"] == environment_profile.scenario + assert row["agent_goals"] == str(environment_profile.agent_goals) + assert row["relationship"] == str(environment_profile.relationship) + assert row["age_constraint"] == environment_profile.age_constraint or "" + assert ( + row["occupation_constraint"] + == environment_profile.occupation_constraint + ) + assert row["agent_constraint"] == str( + environment_profile.agent_constraint or "" + ) def test_envagentcombostorage_serialization() -> None: @@ -239,17 +283,24 @@ def test_envagentcombostorage_serialization() -> None: ) envagnetcombostorage_to_jsonl( - [envagentcombo_storage], "./test_envagentcombo_storage.jsonl" + [envagentcombo_storage], "/tmp/test_envagentcombo_storage.jsonl" ) rebuild_envagentcombo_storage = jsonl_to_envagnetcombostorage( - "./test_envagentcombo_storage.jsonl" + "/tmp/test_envagentcombo_storage.jsonl" )[0] assert envagentcombo_storage.dict() == rebuild_envagentcombo_storage.dict() envagnetcombostorage_to_csv( - [envagentcombo_storage], "./test_envagentcombo_storage.csv" + [envagentcombo_storage], "/tmp/test_envagentcombo_storage.csv" ) + with open("/tmp/test_envagentcombo_storage.csv") as f: + reader = csv.DictReader(f) + for row in reader: + assert row["combo_id"] == envagentcombo_storage.pk + assert row["env_id"] == envagentcombo_storage.env_id + assert row["agent_ids"] == str(envagentcombo_storage.agent_ids) + def test_agentprofile_serialization() -> None: agent_profile = AgentProfile( @@ -271,8 +322,17 @@ def test_agentprofile_serialization() -> None: mbti="ENTP", ) - agentprofiles_to_jsonl([agent_profile], "./test_agent_profile.jsonl") - rebuild_agent_profile = jsonl_to_agentprofiles("./test_agent_profile.jsonl")[0] + agentprofiles_to_jsonl([agent_profile], "/tmp/test_agent_profile.jsonl") + rebuild_agent_profile = jsonl_to_agentprofiles("/tmp/test_agent_profile.jsonl")[0] assert agent_profile.dict() == rebuild_agent_profile.dict() - agentprofiles_to_csv([agent_profile], "./test_agent_profile.csv") + agentprofiles_to_csv([agent_profile], "/tmp/test_agent_profile.csv") + + with open("/tmp/test_agent_profile.csv") as f: + reader = csv.DictReader(f) + for row in reader: + assert row["agent_id"] == agent_profile.pk + assert row["first_name"] == agent_profile.first_name + assert row["last_name"] == agent_profile.last_name + assert row["age"] == str(agent_profile.age) + assert row["occupation"] == agent_profile.occupation