From d0811b6fd439a572fe94d86f95a340a828959082 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 15 May 2024 20:13:27 -0400 Subject: [PATCH 01/40] add benchmark social agents --- examples/benchmark_social_agents.py | 235 ++++++++++++++++++++++++++++ notebooks/redis_stats.ipynb | 140 ++++++++++++++++- 2 files changed, 370 insertions(+), 5 deletions(-) create mode 100644 examples/benchmark_social_agents.py diff --git a/examples/benchmark_social_agents.py b/examples/benchmark_social_agents.py new file mode 100644 index 000000000..9bf8850a8 --- /dev/null +++ b/examples/benchmark_social_agents.py @@ -0,0 +1,235 @@ +from datasets import load_dataset +from sotopia.database.persistent_profile import EnvironmentList +from sotopia.database.env_agent_combo_storage import EnvAgentComboStorage +import asyncio +import logging +import os +import subprocess +from datetime import datetime +from logging import FileHandler +from typing import Any, Generator, cast + +import gin +from absl import flags +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, + ConstraintBasedSampler, + EnvAgentCombo, +) +from sotopia.server import run_async_server +from sotopia_conf.gin_utils import parse_gin_flags, run +from experiment_eval import check_existing_episodes + +_DEFAULT_GIN_SEARCH_PATHS = [ + os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +] +FLAGS = flags.FLAGS + +# date and message only +FORMAT = "%(asctime)s - %(levelname)s - %(name)s - %(message)s" + +process = subprocess.Popen( + ["git", "rev-parse", "HEAD"], shell=False, stdout=subprocess.PIPE +) +git_head_hash = process.communicate()[0].strip() + +def initilize_benchmark_combo(dataset)->list[EnvAgentComboStorage]: # type: ignore + list_of_env_agent_combo_storage = [] + for combo in dataset["train"]: + 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] + else: + reward = get_rewards_from_episode(episode)[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 + + + +@gin.configurable +def _iterate_env_agent_combo_not_in_db( + model_names: dict[str, LLM_Name], + env_agent_combo_storage_list: list[EnvAgentComboStorage], + tag: str | None = None, +) -> Generator[EnvAgentCombo[Observation, AgentAction], None, None]: + """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" + for index, env_id in zip(agent_index, hard_envs): + assert env_id is not None, "env_id should not be None" + first_env_agent_combo_storage_to_run: EnvAgentComboStorage | None = None + for env_agent_combo_storage in env_agent_combo_storage_list: + agent_ids = env_agent_combo_storage.agent_ids + if check_existing_episodes(env_id, agent_ids, model_names, tag): + logging.info( + f"Episode for {env_id} with agents {agent_ids} using {list(model_names.values())} already exists" + ) + continue + first_env_agent_combo_storage_to_run = env_agent_combo_storage + break + if first_env_agent_combo_storage_to_run: + 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 + if int(index) == 0: + model_names["agent1"], model_names["agent2"] = model_names["agent2"], model_names["agent1"] + agents = [ + LLMAgent(agent_profile=agent_profile, model_name=agent_model) + for agent_profile, agent_model in zip( + agent_profiles, + [model_names["agent1"], model_names["agent2"]], + ) + ] + yield env, agents + +@gin.configurable +def run_async_benchmark_in_batch( + *, + batch_size: int = 1, + model_names: dict[str, LLM_Name] = { + "env": "gpt-4", + "agent1": "gpt-3.5-turbo", + "agent2": "gpt-3.5-turbo", + }, + tag: str | None = None, + verbose: bool = False, +) -> None: + dataset = load_dataset("cmu-lti/sotopia", data_files="benchmark_agents.json") + benchmark_combo = initilize_benchmark_combo(dataset) + env_agent_combo_iter = _iterate_env_agent_combo_not_in_db(model_names=model_names, tag=tag ,env_agent_combo_storage_list=benchmark_combo) + env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) + env_agent_combo_batch: list[EnvAgentCombo[Observation, AgentAction]] = [] + number_of_fix_turns = 0 + while True: + for env_agent_combo in tqdm( + env_agent_combo_iter, + total=env_agent_combo_iter_length, + 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}" + ) + asyncio.run( + run_async_server( + model_dict=model_names, + sampler=BaseSampler[Observation, AgentAction](), + env_agent_combo_list=env_agent_combo_batch, + ) + ) + env_agent_combo_batch = [] + else: + if env_agent_combo_batch: + logging.info( + f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" + ) + asyncio.run( + run_async_server( + model_dict=model_names, + sampler=BaseSampler[Observation, AgentAction](), + env_agent_combo_list=env_agent_combo_batch, + ) + ) + # remove episodes that has bad rewards + simulated_episodes = EpisodeLog.find(EpisodeLog.tag == tag).all() # type: ignore + 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_iter = _iterate_env_agent_combo_not_in_db(model_names=model_names, tag=tag ,env_agent_combo_storage_list=benchmark_combo) + env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) + env_agent_combo_batch = [] + number_of_fix_turns += 1 + if env_agent_combo_iter_length == 0 or number_of_fix_turns >= 5: + rewards_dict = get_avg_reward(simulated_episodes, model_names["agent2"]) # type: ignore + print(rewards_dict) + return + +def main(_: Any) -> None: + parse_gin_flags( + # User-provided gin paths take precedence if relative paths conflict. + FLAGS.gin_search_paths + _DEFAULT_GIN_SEARCH_PATHS, + FLAGS.gin_file, + FLAGS.gin_bindings, + ) + run_async_benchmark_in_batch() + + + +if __name__ == "__main__": + flags.DEFINE_multi_string( + "gin_file", + default=None, + help="Path to gin configuration file. Multiple paths may be passed and " + "will be imported in the given order, with later configurations " + "overriding earlier ones.", + ) + + flags.DEFINE_multi_string( + "gin_bindings", default=[], help="Individual gin bindings." + ) + + flags.DEFINE_list( + "gin_search_paths", + default=["."], + help="Comma-separated list of gin config path prefixes to be prepended " + "to suffixes given via `--gin_file`. If a file appears in. Only the " + "first prefix that produces a valid path for each suffix will be " + "used.", + ) + + run(main) + diff --git a/notebooks/redis_stats.ipynb b/notebooks/redis_stats.ipynb index fdf3cf66d..f8f31be06 100644 --- a/notebooks/redis_stats.ipynb +++ b/notebooks/redis_stats.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -261,14 +261,45 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## EnvAgentComboStorage" + "## EnvAgentComboStorage\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." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "450\n" + ] + }, + { + "data": { + "text/html": [ + "
EnvAgentComboStorage(\n",
+       "    pk='01H7ZBJD10S1N84RJGJ6K2J5YK',\n",
+       "    env_id='01H7VFHP8AN5643B0NR0NP00VE',\n",
+       "    agent_ids=['01H5TNE5P7RVY0TYX8VTCXABR6', '01H5TNE5PKW8P500417PMSGSAC']\n",
+       ")\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;35mEnvAgentComboStorage\u001b[0m\u001b[1m(\u001b[0m\n", + " \u001b[33mpk\u001b[0m=\u001b[32m'01H7ZBJD10S1N84RJGJ6K2J5YK'\u001b[0m,\n", + " \u001b[33menv_id\u001b[0m=\u001b[32m'01H7VFHP8AN5643B0NR0NP00VE'\u001b[0m,\n", + " \u001b[33magent_ids\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m'01H5TNE5P7RVY0TYX8VTCXABR6'\u001b[0m, \u001b[32m'01H5TNE5PKW8P500417PMSGSAC'\u001b[0m\u001b[1m]\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# all env-agent combos\n", "all_combos = EnvAgentComboStorage().all_pks()\n", @@ -294,6 +325,105 @@ " cache.add(curr_tuple)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 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": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "from sotopia.database.persistent_profile import EnvironmentList\n", + "all_list = EnvironmentList.all_pks()\n", + "all_list = list(all_list)\n", + "print(len(all_list))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "from sotopia.samplers import ConstraintBasedSampler\n", + "from sotopia.messages import AgentAction, Observation\n", + "from sotopia.agents import LLMAgent\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", + "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", + "# First we will extrat the hard environments from the EnvironmentList\n", + "hard_envs = EnvironmentList.get(\"01HAK34YPB1H1RWXQDASDKHSNS\").environments\n", + "print(len(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:\n", + " combo_list = _sample_env_agent_combo_and_push_to_db(env)\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": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EnvironmentList(pk='01HAK34YPB1H1RWXQDASDKHSNS', name='hard_env_set', environments=['01H7VFHNV13MHN97GAH73E3KM8', '01H7VFHN5WVC5HKKVBHZBA553R', '01H7VFHNN7XTR99319DS8KZCQM', '01H7VFHN9W0WAFZCBT09PKJJNK', '01H7VFHPDZVVCDZR3AARA547CY', '01H7VFHPQQQY6H4DNC6NBQ8XTG', '01H7VFHPQQQY6H4DNC6NBQ8XTG', '01H7VFHN7WJK7VWVRZZTQ6DX9T', '01H7VFHN7A1ZX5KSMT2YN9RXC4', '01H7VFHPS5WJW2694R1MNC8JFY', '01H7VFHPS5WJW2694R1MNC8JFY', '01H7VFHNN7XTR99319DS8KZCQM', '01H7VFHQ11NAMZS4A2RDGDB01V', '01H7VFHQ11NAMZS4A2RDGDB01V', '01H7VFHPSWGDGEYRP63H2DJKV0', '01H7VFHPSWGDGEYRP63H2DJKV0', '01H7VFHNF4G18PC9JHGRC8A1R6', '01H7VFHNNYH3W0VRWVY178K2TK', '01H7VFHP8AN5643B0NR0NP00VE', '01H7VFHN7A1ZX5KSMT2YN9RXC4'], agent_index=['0', '0', '0', '1', '0', '0', '1', '0', '0', '1', '0', '1', '1', '0', '0', '1', '1', '0', '1', '1'])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "EnvironmentList.get(\"01HAK34YPB1H1RWXQDASDKHSNS\")" + ] + }, { "cell_type": "markdown", "metadata": {}, From 51a9132caa87bf43db26a551e951a2ca64203465 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 15 May 2024 21:13:43 -0400 Subject: [PATCH 02/40] add benchmark agents --- docs/examples.md | 19 ++++++++ examples/benchmark_social_agents.py | 76 +++++++++++++++-------------- sotopia/server.py | 1 + 3 files changed, 60 insertions(+), 36 deletions(-) diff --git a/docs/examples.md b/docs/examples.md index 231b7edc7..9fcaf7ce6 100644 --- a/docs/examples.md +++ b/docs/examples.md @@ -10,3 +10,22 @@ Run ```python examples/evaluate_existing_episodes.py --help``` for more informat ## Example 2: Generate script-like episodes See `docs/simulation_modes.md` for more information. + +## Example 3: Benchmarking the models as social agents + +```Bash +EVAL_MODEL="gpt-4o-2024-05-13" +python examples/benchmark_social_agents.py \ + --gin_file sotopia_conf/generation_utils_conf/generate.gin \ + --gin_file sotopia_conf/server_conf/server.gin \ + --gin_file sotopia_conf/run_async_server_in_batch.gin \ + '--gin.ENV_IDS=[]' \ + '--gin.AGENT1_MODEL="groq/llama3-70b-8192"' \ + "--gin.AGENT2_MODEL=${EVAL_MODEL}" \ + '--gin.BATCH_SIZE=10' \ + "--gin.TAG=benchmark_${EVAL_MODEL}" \ + "--gin.TAG_TO_CHECK_EXISTING_EPISODES=benchmark_${EVAL_MODEL}" \ + '--gin.PUSH_TO_DB=True' \ + '--gin.OMNISCIENT=False' \ + '--gin.VERBOSE=False' +``` diff --git a/examples/benchmark_social_agents.py b/examples/benchmark_social_agents.py index 9bf8850a8..7e8616bac 100644 --- a/examples/benchmark_social_agents.py +++ b/examples/benchmark_social_agents.py @@ -1,17 +1,15 @@ from datasets import load_dataset +import typer from sotopia.database.persistent_profile import EnvironmentList from sotopia.database.env_agent_combo_storage import EnvAgentComboStorage import asyncio import logging import os import subprocess -from datetime import datetime -from logging import FileHandler from typing import Any, Generator, cast import gin from absl import flags -from rich.logging import RichHandler from tqdm import tqdm from sotopia.agents import LLMAgent @@ -31,20 +29,31 @@ from sotopia.messages import AgentAction, Observation from sotopia.samplers import ( BaseSampler, - ConstraintBasedSampler, EnvAgentCombo, ) from sotopia.server import run_async_server from sotopia_conf.gin_utils import parse_gin_flags, run from experiment_eval import check_existing_episodes +app = typer.Typer() + _DEFAULT_GIN_SEARCH_PATHS = [ os.path.dirname(os.path.dirname(os.path.abspath(__file__))) ] -FLAGS = flags.FLAGS -# date and message only -FORMAT = "%(asctime)s - %(levelname)s - %(name)s - %(message)s" +DEFAULT_GIN_FILES = [ + "sotopia_conf/generation_utils_conf/generate.gin", + "sotopia_conf/server_conf/server.gin", + "sotopia_conf/run_async_server_in_batch.gin", +] + +DEFAULT_GIN_BINDINGS = [ + '--gin.ENV_IDS=[]', + '--gin.AGENT1_MODEL="groq/llama3-70b-8192"', + '--gin.PUSH_TO_DB=True', + '--gin.OMNISCIENT=False', + '--gin.VERBOSE=False', +] process = subprocess.Popen( ["git", "rev-parse", "HEAD"], shell=False, stdout=subprocess.PIPE @@ -178,7 +187,7 @@ def run_async_benchmark_in_batch( ) ) # remove episodes that has bad rewards - simulated_episodes = EpisodeLog.find(EpisodeLog.tag == tag).all() # type: ignore + 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 @@ -195,41 +204,36 @@ def run_async_benchmark_in_batch( number_of_fix_turns += 1 if env_agent_combo_iter_length == 0 or number_of_fix_turns >= 5: rewards_dict = get_avg_reward(simulated_episodes, model_names["agent2"]) # type: ignore + rewards_dict["model_name"] = model_names["agent2"] # type: ignore print(rewards_dict) return -def main(_: Any) -> None: +@app.command() +def main( + eval_model: str = "gpt-4o-2024-05-13", + batch_size: int = 10, + gin_file: list[str] = typer.Option( + DEFAULT_GIN_FILES, help="Path to gin configuration file. Multiple paths may be passed and will be imported in the given order, with later configurations overriding earlier ones." + ), + gin_search_paths: list[str] = typer.Option( + _DEFAULT_GIN_SEARCH_PATHS, help="Comma-separated list of gin config path prefixes to be prepended to suffixes given via `--gin_file`. Only the first prefix that produces a valid path for each suffix will be used." + ), +)-> None: + gin_bindings = DEFAULT_GIN_BINDINGS + [ + f'--gin.AGENT2_MODEL="{eval_model}"', + f'--gin.BATCH_SIZE={batch_size}', + f'--gin.TAG="benchmark_{eval_model}"', + f'--gin.TAG_TO_CHECK_EXISTING_EPISODES="benchmark_{eval_model}"' + ] + parse_gin_flags( - # User-provided gin paths take precedence if relative paths conflict. - FLAGS.gin_search_paths + _DEFAULT_GIN_SEARCH_PATHS, - FLAGS.gin_file, - FLAGS.gin_bindings, + gin_search_paths, + gin_file, + gin_bindings, ) run_async_benchmark_in_batch() - - if __name__ == "__main__": - flags.DEFINE_multi_string( - "gin_file", - default=None, - help="Path to gin configuration file. Multiple paths may be passed and " - "will be imported in the given order, with later configurations " - "overriding earlier ones.", - ) - - flags.DEFINE_multi_string( - "gin_bindings", default=[], help="Individual gin bindings." - ) - - flags.DEFINE_list( - "gin_search_paths", - default=["."], - help="Comma-separated list of gin config path prefixes to be prepended " - "to suffixes given via `--gin_file`. If a file appears in. Only the " - "first prefix that produces a valid path for each suffix will be " - "used.", - ) + app() - run(main) diff --git a/sotopia/server.py b/sotopia/server.py index 4f519ec71..4b9541b31 100644 --- a/sotopia/server.py +++ b/sotopia/server.py @@ -432,6 +432,7 @@ async def aevaluate_one_episode( turn_number=-1, history=history, messages=None, + temperature=0.0, ) for sing_evaluator in [evaluator] ] From b576be7b8f86ae33d262dcf3d61250767b68c264 Mon Sep 17 00:00:00 2001 From: Hao Date: Thu, 16 May 2024 11:54:30 -0400 Subject: [PATCH 03/40] Add sotopia_benchmark cli api --- examples/benchmark_social_agents.py | 71 ++++++++++++++++------------- pyproject.toml | 3 ++ sotopia/benchmark/__init__.py | 3 ++ sotopia/benchmark/cli.py | 18 ++++++++ 4 files changed, 64 insertions(+), 31 deletions(-) create mode 100644 sotopia/benchmark/__init__.py create mode 100644 sotopia/benchmark/cli.py diff --git a/examples/benchmark_social_agents.py b/examples/benchmark_social_agents.py index 7e8616bac..da0fba5e8 100644 --- a/examples/benchmark_social_agents.py +++ b/examples/benchmark_social_agents.py @@ -1,15 +1,13 @@ from datasets import load_dataset import typer from sotopia.database.persistent_profile import EnvironmentList -from sotopia.database.env_agent_combo_storage import EnvAgentComboStorage import asyncio import logging import os import subprocess -from typing import Any, Generator, cast +from typing import Generator import gin -from absl import flags from tqdm import tqdm from sotopia.agents import LLMAgent @@ -32,7 +30,7 @@ EnvAgentCombo, ) from sotopia.server import run_async_server -from sotopia_conf.gin_utils import parse_gin_flags, run +from sotopia_conf.gin_utils import parse_gin_flags from experiment_eval import check_existing_episodes app = typer.Typer() @@ -48,11 +46,11 @@ ] DEFAULT_GIN_BINDINGS = [ - '--gin.ENV_IDS=[]', + "--gin.ENV_IDS=[]", '--gin.AGENT1_MODEL="groq/llama3-70b-8192"', - '--gin.PUSH_TO_DB=True', - '--gin.OMNISCIENT=False', - '--gin.VERBOSE=False', + "--gin.PUSH_TO_DB=True", + "--gin.OMNISCIENT=False", + "--gin.VERBOSE=False", ] process = subprocess.Popen( @@ -60,19 +58,19 @@ ) git_head_hash = process.communicate()[0].strip() -def initilize_benchmark_combo(dataset)->list[EnvAgentComboStorage]: # type: ignore + +def initilize_benchmark_combo(dataset) -> list[EnvAgentComboStorage]: # type: ignore list_of_env_agent_combo_storage = [] for combo in dataset["train"]: env_agent_combo_storage = EnvAgentComboStorage( - env_id=combo["env_id"], - agent_ids=combo["agent_ids"] + 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 = [] + rewards_list = [] avg_reward_dict = {} for episode in episodes: assert episode.models is not None, "episode.models should not be None" @@ -88,7 +86,6 @@ def get_avg_reward(episodes: list[EpisodeLog], model_name: str) -> dict[str, flo return avg_reward_dict - @gin.configurable def _iterate_env_agent_combo_not_in_db( model_names: dict[str, LLM_Name], @@ -127,7 +124,10 @@ def _iterate_env_agent_combo_not_in_db( 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 if int(index) == 0: - model_names["agent1"], model_names["agent2"] = model_names["agent2"], model_names["agent1"] + model_names["agent1"], model_names["agent2"] = ( + model_names["agent2"], + model_names["agent1"], + ) agents = [ LLMAgent(agent_profile=agent_profile, model_name=agent_model) for agent_profile, agent_model in zip( @@ -137,9 +137,10 @@ def _iterate_env_agent_combo_not_in_db( ] yield env, agents + @gin.configurable def run_async_benchmark_in_batch( - *, + *, batch_size: int = 1, model_names: dict[str, LLM_Name] = { "env": "gpt-4", @@ -151,7 +152,9 @@ def run_async_benchmark_in_batch( ) -> None: dataset = load_dataset("cmu-lti/sotopia", data_files="benchmark_agents.json") benchmark_combo = initilize_benchmark_combo(dataset) - env_agent_combo_iter = _iterate_env_agent_combo_not_in_db(model_names=model_names, tag=tag ,env_agent_combo_storage_list=benchmark_combo) + env_agent_combo_iter = _iterate_env_agent_combo_not_in_db( + model_names=model_names, tag=tag, env_agent_combo_storage_list=benchmark_combo + ) env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) env_agent_combo_batch: list[EnvAgentCombo[Observation, AgentAction]] = [] number_of_fix_turns = 0 @@ -187,9 +190,9 @@ def run_async_benchmark_in_batch( ) ) # remove episodes that has bad rewards - simulated_episodes = EpisodeLog.find(EpisodeLog.tag == tag).all() + simulated_episodes = EpisodeLog.find(EpisodeLog.tag == tag).all() valid_episodes = [ - not isinstance(relevant_episode.rewards[0], float) # type: ignore + not isinstance(relevant_episode.rewards[0], float) # type: ignore for relevant_episode in simulated_episodes ] for valid, episode in zip(valid_episodes, simulated_episodes): @@ -197,35 +200,42 @@ def run_async_benchmark_in_batch( pk = episode.pk assert isinstance(pk, str) EpisodeLog.delete(pk) - - env_agent_combo_iter = _iterate_env_agent_combo_not_in_db(model_names=model_names, tag=tag ,env_agent_combo_storage_list=benchmark_combo) + + env_agent_combo_iter = _iterate_env_agent_combo_not_in_db( + model_names=model_names, + tag=tag, + env_agent_combo_storage_list=benchmark_combo, + ) env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) - env_agent_combo_batch = [] + env_agent_combo_batch = [] number_of_fix_turns += 1 if env_agent_combo_iter_length == 0 or number_of_fix_turns >= 5: - rewards_dict = get_avg_reward(simulated_episodes, model_names["agent2"]) # type: ignore - rewards_dict["model_name"] = model_names["agent2"] # type: ignore + rewards_dict = get_avg_reward(simulated_episodes, model_names["agent2"]) # type: ignore + rewards_dict["model_name"] = model_names["agent2"] # type: ignore print(rewards_dict) return + @app.command() def main( eval_model: str = "gpt-4o-2024-05-13", batch_size: int = 10, gin_file: list[str] = typer.Option( - DEFAULT_GIN_FILES, help="Path to gin configuration file. Multiple paths may be passed and will be imported in the given order, with later configurations overriding earlier ones." + DEFAULT_GIN_FILES, + help="Path to gin configuration file. Multiple paths may be passed and will be imported in the given order, with later configurations overriding earlier ones.", ), gin_search_paths: list[str] = typer.Option( - _DEFAULT_GIN_SEARCH_PATHS, help="Comma-separated list of gin config path prefixes to be prepended to suffixes given via `--gin_file`. Only the first prefix that produces a valid path for each suffix will be used." + _DEFAULT_GIN_SEARCH_PATHS, + help="Comma-separated list of gin config path prefixes to be prepended to suffixes given via `--gin_file`. Only the first prefix that produces a valid path for each suffix will be used.", ), -)-> None: +) -> None: gin_bindings = DEFAULT_GIN_BINDINGS + [ f'--gin.AGENT2_MODEL="{eval_model}"', - f'--gin.BATCH_SIZE={batch_size}', + f"--gin.BATCH_SIZE={batch_size}", f'--gin.TAG="benchmark_{eval_model}"', - f'--gin.TAG_TO_CHECK_EXISTING_EPISODES="benchmark_{eval_model}"' + f'--gin.TAG_TO_CHECK_EXISTING_EPISODES="benchmark_{eval_model}"', ] - + parse_gin_flags( gin_search_paths, gin_file, @@ -233,7 +243,6 @@ def main( ) run_async_benchmark_in_batch() + if __name__ == "__main__": app() - - diff --git a/pyproject.toml b/pyproject.toml index 2c95fd80b..b2b111011 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -65,3 +65,6 @@ 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/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..8c2079d8c --- /dev/null +++ b/sotopia/benchmark/cli.py @@ -0,0 +1,18 @@ +from typing import Annotated +import typer + +app = typer.Typer() + + +@app.command() +def cli( + model: Annotated[ + str, typer.Option(help="The language model you want to benchmark.") + ], + partner_model: Annotated[ + str, typer.Option(help="The partner model you want to use.") + ], + task: Annotated[str, typer.Option(help="The task id you want to benchmark.")], +) -> None: + """A simple command-line interface example.""" + typer.echo(f"Running benchmark for {model} and {partner_model} on task {task}.") From 7922e16e5efa47cbf3765bffe663bf4477dfffa8 Mon Sep 17 00:00:00 2001 From: Hao Date: Thu, 16 May 2024 12:01:32 -0400 Subject: [PATCH 04/40] fix pre-commit --- notebooks/redis_stats.ipynb | 80 +++++++------------------------------ 1 file changed, 14 insertions(+), 66 deletions(-) diff --git a/notebooks/redis_stats.ipynb b/notebooks/redis_stats.ipynb index f8f31be06..44d6404a8 100644 --- a/notebooks/redis_stats.ipynb +++ b/notebooks/redis_stats.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -268,38 +268,9 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "450\n" - ] - }, - { - "data": { - "text/html": [ - "
EnvAgentComboStorage(\n",
-       "    pk='01H7ZBJD10S1N84RJGJ6K2J5YK',\n",
-       "    env_id='01H7VFHP8AN5643B0NR0NP00VE',\n",
-       "    agent_ids=['01H5TNE5P7RVY0TYX8VTCXABR6', '01H5TNE5PKW8P500417PMSGSAC']\n",
-       ")\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1;35mEnvAgentComboStorage\u001b[0m\u001b[1m(\u001b[0m\n", - " \u001b[33mpk\u001b[0m=\u001b[32m'01H7ZBJD10S1N84RJGJ6K2J5YK'\u001b[0m,\n", - " \u001b[33menv_id\u001b[0m=\u001b[32m'01H7VFHP8AN5643B0NR0NP00VE'\u001b[0m,\n", - " \u001b[33magent_ids\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m'01H5TNE5P7RVY0TYX8VTCXABR6'\u001b[0m, \u001b[32m'01H5TNE5PKW8P500417PMSGSAC'\u001b[0m\u001b[1m]\u001b[0m\n", - "\u001b[1m)\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# all env-agent combos\n", "all_combos = EnvAgentComboStorage().all_pks()\n", @@ -335,19 +306,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - } - ], + "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))" @@ -355,23 +319,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n" - ] - } - ], + "outputs": [], "source": [ "from sotopia.samplers import ConstraintBasedSampler\n", "from sotopia.messages import AgentAction, Observation\n", "from sotopia.agents import LLMAgent\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", @@ -385,6 +342,8 @@ " )\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", @@ -406,20 +365,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "EnvironmentList(pk='01HAK34YPB1H1RWXQDASDKHSNS', name='hard_env_set', environments=['01H7VFHNV13MHN97GAH73E3KM8', '01H7VFHN5WVC5HKKVBHZBA553R', '01H7VFHNN7XTR99319DS8KZCQM', '01H7VFHN9W0WAFZCBT09PKJJNK', '01H7VFHPDZVVCDZR3AARA547CY', '01H7VFHPQQQY6H4DNC6NBQ8XTG', '01H7VFHPQQQY6H4DNC6NBQ8XTG', '01H7VFHN7WJK7VWVRZZTQ6DX9T', '01H7VFHN7A1ZX5KSMT2YN9RXC4', '01H7VFHPS5WJW2694R1MNC8JFY', '01H7VFHPS5WJW2694R1MNC8JFY', '01H7VFHNN7XTR99319DS8KZCQM', '01H7VFHQ11NAMZS4A2RDGDB01V', '01H7VFHQ11NAMZS4A2RDGDB01V', '01H7VFHPSWGDGEYRP63H2DJKV0', '01H7VFHPSWGDGEYRP63H2DJKV0', '01H7VFHNF4G18PC9JHGRC8A1R6', '01H7VFHNNYH3W0VRWVY178K2TK', '01H7VFHP8AN5643B0NR0NP00VE', '01H7VFHN7A1ZX5KSMT2YN9RXC4'], agent_index=['0', '0', '0', '1', '0', '0', '1', '0', '0', '1', '0', '1', '1', '0', '0', '1', '1', '0', '1', '1'])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "EnvironmentList.get(\"01HAK34YPB1H1RWXQDASDKHSNS\")" ] From 9c13c029ee23d2164e0360d0ec007ee28ebd222e Mon Sep 17 00:00:00 2001 From: Hao Date: Thu, 16 May 2024 12:14:27 -0400 Subject: [PATCH 05/40] add evaluator model argument --- sotopia/benchmark/cli.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 8c2079d8c..597b03ab0 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -12,7 +12,12 @@ def cli( partner_model: Annotated[ str, typer.Option(help="The partner model you want to use.") ], + evaluator_model: Annotated[ + str, typer.Option(help="The evaluator model you want to use.") + ], task: Annotated[str, typer.Option(help="The task id you want to benchmark.")], ) -> None: """A simple command-line interface example.""" - typer.echo(f"Running benchmark for {model} and {partner_model} on task {task}.") + typer.echo( + f"Running benchmark for {model} and {partner_model} on task {task} with {evaluator_model}." + ) From 27aa56505e8c1a358a7c11b0736f1f2c6c1eea20 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 22 May 2024 21:42:50 -0400 Subject: [PATCH 06/40] finish benchmarking --- examples/benchmark_social_agents.py | 248 ---------------------------- sotopia/benchmark/cli.py | 230 ++++++++++++++++++++++++-- sotopia/envs/evaluators.py | 2 +- 3 files changed, 219 insertions(+), 261 deletions(-) delete mode 100644 examples/benchmark_social_agents.py diff --git a/examples/benchmark_social_agents.py b/examples/benchmark_social_agents.py deleted file mode 100644 index da0fba5e8..000000000 --- a/examples/benchmark_social_agents.py +++ /dev/null @@ -1,248 +0,0 @@ -from datasets import load_dataset -import typer -from sotopia.database.persistent_profile import EnvironmentList -import asyncio -import logging -import os -import subprocess -from typing import Generator - -import gin -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 -from sotopia_conf.gin_utils import parse_gin_flags -from experiment_eval import check_existing_episodes - -app = typer.Typer() - -_DEFAULT_GIN_SEARCH_PATHS = [ - os.path.dirname(os.path.dirname(os.path.abspath(__file__))) -] - -DEFAULT_GIN_FILES = [ - "sotopia_conf/generation_utils_conf/generate.gin", - "sotopia_conf/server_conf/server.gin", - "sotopia_conf/run_async_server_in_batch.gin", -] - -DEFAULT_GIN_BINDINGS = [ - "--gin.ENV_IDS=[]", - '--gin.AGENT1_MODEL="groq/llama3-70b-8192"', - "--gin.PUSH_TO_DB=True", - "--gin.OMNISCIENT=False", - "--gin.VERBOSE=False", -] - -process = subprocess.Popen( - ["git", "rev-parse", "HEAD"], shell=False, stdout=subprocess.PIPE -) -git_head_hash = process.communicate()[0].strip() - - -def initilize_benchmark_combo(dataset) -> list[EnvAgentComboStorage]: # type: ignore - list_of_env_agent_combo_storage = [] - for combo in dataset["train"]: - 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] - else: - reward = get_rewards_from_episode(episode)[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 - - -@gin.configurable -def _iterate_env_agent_combo_not_in_db( - model_names: dict[str, LLM_Name], - env_agent_combo_storage_list: list[EnvAgentComboStorage], - tag: str | None = None, -) -> Generator[EnvAgentCombo[Observation, AgentAction], None, None]: - """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" - for index, env_id in zip(agent_index, hard_envs): - assert env_id is not None, "env_id should not be None" - first_env_agent_combo_storage_to_run: EnvAgentComboStorage | None = None - for env_agent_combo_storage in env_agent_combo_storage_list: - agent_ids = env_agent_combo_storage.agent_ids - if check_existing_episodes(env_id, agent_ids, model_names, tag): - logging.info( - f"Episode for {env_id} with agents {agent_ids} using {list(model_names.values())} already exists" - ) - continue - first_env_agent_combo_storage_to_run = env_agent_combo_storage - break - if first_env_agent_combo_storage_to_run: - 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 - if int(index) == 0: - model_names["agent1"], model_names["agent2"] = ( - model_names["agent2"], - model_names["agent1"], - ) - agents = [ - LLMAgent(agent_profile=agent_profile, model_name=agent_model) - for agent_profile, agent_model in zip( - agent_profiles, - [model_names["agent1"], model_names["agent2"]], - ) - ] - yield env, agents - - -@gin.configurable -def run_async_benchmark_in_batch( - *, - batch_size: int = 1, - model_names: dict[str, LLM_Name] = { - "env": "gpt-4", - "agent1": "gpt-3.5-turbo", - "agent2": "gpt-3.5-turbo", - }, - tag: str | None = None, - verbose: bool = False, -) -> None: - dataset = load_dataset("cmu-lti/sotopia", data_files="benchmark_agents.json") - benchmark_combo = initilize_benchmark_combo(dataset) - env_agent_combo_iter = _iterate_env_agent_combo_not_in_db( - model_names=model_names, tag=tag, env_agent_combo_storage_list=benchmark_combo - ) - env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) - env_agent_combo_batch: list[EnvAgentCombo[Observation, AgentAction]] = [] - number_of_fix_turns = 0 - while True: - for env_agent_combo in tqdm( - env_agent_combo_iter, - total=env_agent_combo_iter_length, - 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}" - ) - asyncio.run( - run_async_server( - model_dict=model_names, - sampler=BaseSampler[Observation, AgentAction](), - env_agent_combo_list=env_agent_combo_batch, - ) - ) - env_agent_combo_batch = [] - else: - if env_agent_combo_batch: - logging.info( - f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" - ) - asyncio.run( - run_async_server( - model_dict=model_names, - sampler=BaseSampler[Observation, AgentAction](), - env_agent_combo_list=env_agent_combo_batch, - ) - ) - # 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_iter = _iterate_env_agent_combo_not_in_db( - model_names=model_names, - tag=tag, - env_agent_combo_storage_list=benchmark_combo, - ) - env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) - env_agent_combo_batch = [] - number_of_fix_turns += 1 - if env_agent_combo_iter_length == 0 or number_of_fix_turns >= 5: - rewards_dict = get_avg_reward(simulated_episodes, model_names["agent2"]) # type: ignore - rewards_dict["model_name"] = model_names["agent2"] # type: ignore - print(rewards_dict) - return - - -@app.command() -def main( - eval_model: str = "gpt-4o-2024-05-13", - batch_size: int = 10, - gin_file: list[str] = typer.Option( - DEFAULT_GIN_FILES, - help="Path to gin configuration file. Multiple paths may be passed and will be imported in the given order, with later configurations overriding earlier ones.", - ), - gin_search_paths: list[str] = typer.Option( - _DEFAULT_GIN_SEARCH_PATHS, - help="Comma-separated list of gin config path prefixes to be prepended to suffixes given via `--gin_file`. Only the first prefix that produces a valid path for each suffix will be used.", - ), -) -> None: - gin_bindings = DEFAULT_GIN_BINDINGS + [ - f'--gin.AGENT2_MODEL="{eval_model}"', - f"--gin.BATCH_SIZE={batch_size}", - f'--gin.TAG="benchmark_{eval_model}"', - f'--gin.TAG_TO_CHECK_EXISTING_EPISODES="benchmark_{eval_model}"', - ] - - parse_gin_flags( - gin_search_paths, - gin_file, - gin_bindings, - ) - run_async_benchmark_in_batch() - - -if __name__ == "__main__": - app() diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 597b03ab0..2bb7991d0 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -1,23 +1,229 @@ -from typing import Annotated -import typer +from datasets import load_dataset +from sotopia.database.persistent_profile import EnvironmentList +import asyncio +import logging +from typing import Generator, Annotated + +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 app = typer.Typer() +def check_existing_episodes( + env_id: str, + agent_ids: list[str], + models: dict[str, LLM_Name], + 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" + if episode.agents == agent_ids and episode.models == list(models.values()): + return True + return False + else: + return False + + +def initilize_benchmark_combo(dataset) -> list[EnvAgentComboStorage]: # type: ignore + list_of_env_agent_combo_storage = [] + for combo in dataset["train"]: + 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 _iterate_env_agent_combo_not_in_db( + model_names: dict[str, LLM_Name], + env_agent_combo_storage_list: list[EnvAgentComboStorage], + tag: str | None = None, +) -> Generator[EnvAgentCombo[Observation, AgentAction], None, None]: + """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" + for index, env_id in zip(agent_index, hard_envs): + assert env_id is not None, "env_id should not be None" + first_env_agent_combo_storage_to_run: EnvAgentComboStorage | None = None + for env_agent_combo_storage in env_agent_combo_storage_list: + agent_ids = env_agent_combo_storage.agent_ids + if check_existing_episodes(env_id, agent_ids, model_names, tag): + logging.info( + f"Episode for {env_id} with agents {agent_ids} using {list(model_names.values())} already exists" + ) + continue + first_env_agent_combo_storage_to_run = env_agent_combo_storage + break + if first_env_agent_combo_storage_to_run: + 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 + if int(index) == 0: + model_names["agent1"], model_names["agent2"] = ( + model_names["agent2"], + model_names["agent1"], + ) + agents = [ + LLMAgent(agent_profile=agent_profile, model_name=agent_model) + for agent_profile, agent_model in zip( + agent_profiles, + [model_names["agent1"], model_names["agent2"]], + ) + ] + yield env, agents + + +def run_async_benchmark_in_batch( + *, + batch_size: int = 1, + model_names: dict[str, LLM_Name] = { + "env": "gpt-4", + "agent1": "gpt-3.5-turbo", + "agent2": "gpt-3.5-turbo", + }, + tag: str | None = None, + verbose: bool = False, +) -> None: + dataset = load_dataset("cmu-lti/sotopia", data_files="benchmark_agents.json") + benchmark_combo = initilize_benchmark_combo(dataset) + env_agent_combo_iter = _iterate_env_agent_combo_not_in_db( + model_names=model_names, tag=tag, env_agent_combo_storage_list=benchmark_combo + ) + env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) + env_agent_combo_batch: list[EnvAgentCombo[Observation, AgentAction]] = [] + number_of_fix_turns = 0 + while True: + for env_agent_combo in tqdm( + env_agent_combo_iter, + total=env_agent_combo_iter_length, + 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}" + ) + asyncio.run( + run_async_server( + model_dict=model_names, + sampler=BaseSampler[Observation, AgentAction](), + env_agent_combo_list=env_agent_combo_batch, + ) + ) + env_agent_combo_batch = [] + else: + if env_agent_combo_batch: + logging.info( + f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" + ) + asyncio.run( + run_async_server( + model_dict=model_names, + sampler=BaseSampler[Observation, AgentAction](), + env_agent_combo_list=env_agent_combo_batch, + ) + ) + # 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_iter = _iterate_env_agent_combo_not_in_db( + model_names=model_names, + tag=tag, + env_agent_combo_storage_list=benchmark_combo, + ) + env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) + env_agent_combo_batch = [] + number_of_fix_turns += 1 + if env_agent_combo_iter_length == 0 or number_of_fix_turns >= 5: + rewards_dict = get_avg_reward(simulated_episodes, model_names["agent2"]) # type: ignore + rewards_dict["model_name"] = model_names["agent2"] # type: ignore + print(rewards_dict) + return + + + + @app.command() def cli( - model: Annotated[ - str, typer.Option(help="The language model you want to benchmark.") - ], - partner_model: Annotated[ - str, typer.Option(help="The partner model you want to use.") - ], - evaluator_model: Annotated[ - str, typer.Option(help="The evaluator model you want to use.") - ], - task: Annotated[str, typer.Option(help="The task id you want to benchmark.")], + model: str = typer.Option(..., help="The language model you want to benchmark."), + partner_model: str = typer.Option("groq/llama3-70b-8192", 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.") ) -> None: """A simple command-line interface example.""" typer.echo( f"Running benchmark for {model} and {partner_model} on task {task} with {evaluator_model}." ) + tag = f"benchmark_{model}" + run_async_benchmark_in_batch(batch_size=batch_size, model_names={"agent1": model, "agent2": partner_model, "env": evaluator_model}, tag=tag, verbose=False) + +if __name__ == "__main__": + app() 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: From b625cfc67d89ecb5dbf5a6534b426fc013b913b7 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Thu, 23 May 2024 21:24:48 -0400 Subject: [PATCH 07/40] benchmark done --- sotopia/benchmark/cli.py | 98 ++++++++++++++++++++++------------------ sotopia/envs/parallel.py | 1 - 2 files changed, 53 insertions(+), 46 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 2bb7991d0..90e05232e 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -64,6 +64,7 @@ def initilize_benchmark_combo(dataset) -> list[EnvAgentComboStorage]: # type: i def get_avg_reward(episodes: list[EpisodeLog], model_name: str) -> dict[str, float]: + breakpoint() rewards_list = [] avg_reward_dict = {} for episode in episodes: @@ -80,7 +81,7 @@ def get_avg_reward(episodes: list[EpisodeLog], model_name: str) -> dict[str, flo return avg_reward_dict -def _iterate_env_agent_combo_not_in_db( +def _iterate_all_env_agent_combo_not_in_db( model_names: dict[str, LLM_Name], env_agent_combo_storage_list: list[EnvAgentComboStorage], tag: str | None = None, @@ -89,46 +90,48 @@ def _iterate_env_agent_combo_not_in_db( hard_envs = EnvironmentList.get("01HAK34YPB1H1RWXQDASDKHSNS").environments agent_index = EnvironmentList.get("01HAK34YPB1H1RWXQDASDKHSNS").agent_index assert isinstance(agent_index, list), "agent_index should be a list" - for index, env_id in zip(agent_index, hard_envs): - assert env_id is not None, "env_id should not be None" - first_env_agent_combo_storage_to_run: EnvAgentComboStorage | None = None - for env_agent_combo_storage in env_agent_combo_storage_list: - agent_ids = env_agent_combo_storage.agent_ids - if check_existing_episodes(env_id, agent_ids, model_names, tag): - logging.info( - f"Episode for {env_id} with agents {agent_ids} using {list(model_names.values())} already exists" - ) - continue - first_env_agent_combo_storage_to_run = env_agent_combo_storage - break - if first_env_agent_combo_storage_to_run: - 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"]), - ], + envs_index_mapping:dict[str, list[str]] = {env_id: [] for env_id in set(hard_envs)} + # Repeat 10 times to match the number of combos + for _ in range(10): + for index, env_id in zip(agent_index, hard_envs): + envs_index_mapping[env_id].append(index) + + for env_agent_combo_storage in env_agent_combo_storage_list: + agent_ids = env_agent_combo_storage.agent_ids + env_id = env_agent_combo_storage.env_id + if check_existing_episodes(env_id, agent_ids, model_names, tag): + logging.info( + f"Episode for {env_id} with agents {agent_ids} using {list(model_names.values())} already exists" ) - 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 - if int(index) == 0: - model_names["agent1"], model_names["agent2"] = ( - model_names["agent2"], - model_names["agent1"], - ) - agents = [ - LLMAgent(agent_profile=agent_profile, model_name=agent_model) - for agent_profile, agent_model in zip( - agent_profiles, - [model_names["agent1"], model_names["agent2"]], - ) - ] - yield env, agents + 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 + index = envs_index_mapping[env_id].pop(0) + if int(index) == 0: + model_names["agent1"], model_names["agent2"] = ( + model_names["agent2"], + model_names["agent1"], + ) + agents = [ + LLMAgent(agent_profile=agent_profile, model_name=agent_model) + for agent_profile, agent_model in zip( + agent_profiles, + [model_names["agent1"], model_names["agent2"]], + ) + ] + yield env, agents def run_async_benchmark_in_batch( @@ -144,10 +147,13 @@ def run_async_benchmark_in_batch( ) -> None: dataset = load_dataset("cmu-lti/sotopia", data_files="benchmark_agents.json") benchmark_combo = initilize_benchmark_combo(dataset) - env_agent_combo_iter = _iterate_env_agent_combo_not_in_db( + env_agent_combo_iter = _iterate_all_env_agent_combo_not_in_db( model_names=model_names, tag=tag, env_agent_combo_storage_list=benchmark_combo ) env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) + env_agent_combo_iter = _iterate_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 while True: @@ -166,6 +172,8 @@ def run_async_benchmark_in_batch( model_dict=model_names, sampler=BaseSampler[Observation, AgentAction](), env_agent_combo_list=env_agent_combo_batch, + push_to_db=True, + tag=tag, ) ) env_agent_combo_batch = [] @@ -179,6 +187,8 @@ def run_async_benchmark_in_batch( model_dict=model_names, sampler=BaseSampler[Observation, AgentAction](), env_agent_combo_list=env_agent_combo_batch, + push_to_db=True, + tag=tag, ) ) # remove episodes that has bad rewards @@ -193,7 +203,7 @@ def run_async_benchmark_in_batch( assert isinstance(pk, str) EpisodeLog.delete(pk) - env_agent_combo_iter = _iterate_env_agent_combo_not_in_db( + env_agent_combo_iter = _iterate_all_env_agent_combo_not_in_db( model_names=model_names, tag=tag, env_agent_combo_storage_list=benchmark_combo, @@ -208,8 +218,6 @@ def run_async_benchmark_in_batch( return - - @app.command() def cli( model: str = typer.Option(..., help="The language model you want to benchmark."), @@ -222,7 +230,7 @@ def cli( typer.echo( f"Running benchmark for {model} and {partner_model} on task {task} with {evaluator_model}." ) - tag = f"benchmark_{model}" + tag = f"benchmark_{model}_q" run_async_benchmark_in_batch(batch_size=batch_size, model_names={"agent1": model, "agent2": partner_model, "env": evaluator_model}, tag=tag, verbose=False) if __name__ == "__main__": diff --git a/sotopia/envs/parallel.py b/sotopia/envs/parallel.py index 32120f78d..0669baad5 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" From 7066a2ab794381506fcbeb8511d96f6a1ad0bf7b Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Sat, 25 May 2024 16:20:34 -0400 Subject: [PATCH 08/40] chore: Fix formatting issue in redis_stats.ipynb and cli.py --- notebooks/redis_stats.ipynb | 6 ++---- sotopia/benchmark/cli.py | 25 ++++++++++++++++++------- 2 files changed, 20 insertions(+), 11 deletions(-) diff --git a/notebooks/redis_stats.ipynb b/notebooks/redis_stats.ipynb index 0c14757e1..4083264fe 100644 --- a/notebooks/redis_stats.ipynb +++ b/notebooks/redis_stats.ipynb @@ -184,8 +184,7 @@ "source": [ "## EnvAgentComboStorage\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", + "Once we have the combination, we can use it to start the simulation.\n", "Combo is a combination of Environment and two agents." ] }, @@ -293,8 +292,7 @@ "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", + "len(Episodes) ## Episode Log\n", "\n", "## Episodelog stores the social conversation between two agents in an environment." ] diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 90e05232e..bf048ab47 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -2,7 +2,7 @@ from sotopia.database.persistent_profile import EnvironmentList import asyncio import logging -from typing import Generator, Annotated +from typing import Generator from tqdm import tqdm @@ -28,6 +28,7 @@ from sotopia.server import run_async_server import typer + app = typer.Typer() @@ -90,7 +91,7 @@ def _iterate_all_env_agent_combo_not_in_db( hard_envs = EnvironmentList.get("01HAK34YPB1H1RWXQDASDKHSNS").environments agent_index = EnvironmentList.get("01HAK34YPB1H1RWXQDASDKHSNS").agent_index assert isinstance(agent_index, list), "agent_index should be a list" - envs_index_mapping:dict[str, list[str]] = {env_id: [] for env_id in set(hard_envs)} + envs_index_mapping: dict[str, list[str]] = {env_id: [] for env_id in set(hard_envs)} # Repeat 10 times to match the number of combos for _ in range(10): for index, env_id in zip(agent_index, hard_envs): @@ -220,18 +221,28 @@ def run_async_benchmark_in_batch( @app.command() def cli( - model: str = typer.Option(..., help="The language model you want to benchmark."), - partner_model: str = typer.Option("groq/llama3-70b-8192", help="The partner model you want to use."), - evaluator_model: str = typer.Option("gpt-4o", help="The evaluator model you want to use."), + model: str = typer.Option(..., help="The language model you want to benchmark."), + partner_model: str = typer.Option( + "groq/llama3-70b-8192", 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.") + task: str = typer.Option("hard", help="The task id you want to benchmark."), ) -> None: """A simple command-line interface example.""" typer.echo( f"Running benchmark for {model} and {partner_model} on task {task} with {evaluator_model}." ) tag = f"benchmark_{model}_q" - run_async_benchmark_in_batch(batch_size=batch_size, model_names={"agent1": model, "agent2": partner_model, "env": evaluator_model}, tag=tag, verbose=False) + run_async_benchmark_in_batch( + batch_size=batch_size, + model_names={"agent1": model, "agent2": partner_model, "env": evaluator_model}, + tag=tag, + verbose=False, + ) + if __name__ == "__main__": app() From b710ea6a8a24a024f76d78d23b52e270f83e8aa9 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Sat, 25 May 2024 16:51:39 -0400 Subject: [PATCH 09/40] switch back to LLM_Name --- sotopia/benchmark/cli.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index bf048ab47..71ecca54b 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -221,11 +221,13 @@ def run_async_benchmark_in_batch( @app.command() def cli( - model: str = typer.Option(..., help="The language model you want to benchmark."), - partner_model: str = typer.Option( + model: LLM_Name = typer.Option( + ..., help="The language model you want to benchmark." + ), + partner_model: LLM_Name = typer.Option( "groq/llama3-70b-8192", help="The partner model you want to use." ), - evaluator_model: str = typer.Option( + evaluator_model: LLM_Name = 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."), From ad9e7416366713269fa7aefc62797819bb8ca1c2 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Sun, 26 May 2024 13:51:14 -0400 Subject: [PATCH 10/40] merge main --- pyproject.toml | 1 + sotopia/benchmark/cli.py | 2 +- sotopia/generation_utils/generate.py | 43 +++++++++++++++++++++------- 3 files changed, 34 insertions(+), 12 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index b2b111011..efdb933f3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,6 +34,7 @@ beartype = "^0.14.0" torch = ">=2.1 <2.3" transformers = "^4.34.0" langchain-openai = "^0.0.5" +langchain-together = "^0.1.2" litellm = "^1.23.12" [tool.poetry.group.dev.dependencies] diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 71ecca54b..c20a4d812 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -225,7 +225,7 @@ def cli( ..., help="The language model you want to benchmark." ), partner_model: LLM_Name = typer.Option( - "groq/llama3-70b-8192", help="The partner model you want to use." + "meta-llama/Llama-3-70b-chat-hf", help="The partner model you want to use." ), evaluator_model: LLM_Name = typer.Option( "gpt-4o", help="The evaluator model you want to use." diff --git a/sotopia/generation_utils/generate.py b/sotopia/generation_utils/generate.py index 7e8ec5e0c..f0be8ad2d 100644 --- a/sotopia/generation_utils/generate.py +++ b/sotopia/generation_utils/generate.py @@ -14,6 +14,7 @@ ) from langchain.schema import BaseOutputParser, OutputParserException from langchain_community.chat_models import ChatLiteLLM +from langchain_together import ChatTogether from pydantic import BaseModel, Field from rich import print from typing_extensions import Literal @@ -46,6 +47,8 @@ "mistralai/Mixtral-8x7B-Instruct-v0.1", "together_ai/togethercomputer/llama-2-7b-chat", "together_ai/togethercomputer/falcon-7b-instruct", + "meta-llama/Llama-3-8b-chat-hf", + "meta-llama/Llama-3-70b-chat-hf", "groq/llama3-70b-8192", ] @@ -326,17 +329,35 @@ 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 "togethercomputer" in model_name: + human_message_prompt = HumanMessagePromptTemplate( + prompt=PromptTemplate( + template=template, + input_variables=input_variables, + ) + ) + chat_prompt_template = ChatPromptTemplate.from_messages( + [human_message_prompt] + ) + chat_openai = ChatTogether( + model_name=model_name, + temperature=temperature, + max_retries=max_retries, + ) + chain = LLMChain(llm=chat_openai, prompt=chat_prompt_template) + return chain + else: + 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 @beartype From ee87f84337bcfecf939030315c991c1a68178245 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Sun, 26 May 2024 13:52:33 -0400 Subject: [PATCH 11/40] add together ai --- sotopia/generation_utils/generate.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/sotopia/generation_utils/generate.py b/sotopia/generation_utils/generate.py index f0be8ad2d..26ca359ce 100644 --- a/sotopia/generation_utils/generate.py +++ b/sotopia/generation_utils/generate.py @@ -336,9 +336,7 @@ def obtain_chain( input_variables=input_variables, ) ) - chat_prompt_template = ChatPromptTemplate.from_messages( - [human_message_prompt] - ) + chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt]) chat_openai = ChatTogether( model_name=model_name, temperature=temperature, From 41ad0e346b6b101055f6847a7e76bcecf939d217 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Sun, 26 May 2024 14:48:21 -0400 Subject: [PATCH 12/40] fix naming error --- sotopia/benchmark/cli.py | 15 +++++++++------ sotopia/generation_utils/generate.py | 17 +++++++---------- 2 files changed, 16 insertions(+), 16 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index c20a4d812..91ccd3828 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -2,7 +2,7 @@ from sotopia.database.persistent_profile import EnvironmentList import asyncio import logging -from typing import Generator +from typing import Generator, cast from tqdm import tqdm @@ -221,13 +221,13 @@ def run_async_benchmark_in_batch( @app.command() def cli( - model: LLM_Name = typer.Option( + model: str = typer.Option( ..., help="The language model you want to benchmark." ), - partner_model: LLM_Name = typer.Option( - "meta-llama/Llama-3-70b-chat-hf", help="The partner model you want to use." + partner_model: str = typer.Option( + "together_ai/meta-llama/Llama-2-70b-chat-hf", help="The partner model you want to use." ), - evaluator_model: LLM_Name = typer.Option( + 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."), @@ -235,8 +235,11 @@ def cli( ) -> None: """A simple command-line interface example.""" typer.echo( - f"Running benchmark for {model} and {partner_model} on task {task} with {evaluator_model}." + f"Running benchmark for {model} and {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}_q" run_async_benchmark_in_batch( batch_size=batch_size, diff --git a/sotopia/generation_utils/generate.py b/sotopia/generation_utils/generate.py index 26ca359ce..0647289f7 100644 --- a/sotopia/generation_utils/generate.py +++ b/sotopia/generation_utils/generate.py @@ -33,22 +33,18 @@ 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", - "meta-llama/Llama-3-8b-chat-hf", - "meta-llama/Llama-3-70b-chat-hf", "groq/llama3-70b-8192", ] @@ -329,7 +325,8 @@ def obtain_chain( Using langchain to sample profiles for participants """ model_name = _return_fixed_model_version(model_name) - if "togethercomputer" in model_name: + if "together_ai" in model_name: + model_name = "/".join(model_name.split("/")[1:]) human_message_prompt = HumanMessagePromptTemplate( prompt=PromptTemplate( template=template, From 5ae2b2682a5678c905e19a2681c82e8ac85b7f27 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Sun, 26 May 2024 16:54:55 -0400 Subject: [PATCH 13/40] roll back to llama2 --- sotopia/generation_utils/generate.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/sotopia/generation_utils/generate.py b/sotopia/generation_utils/generate.py index 0647289f7..e26267769 100644 --- a/sotopia/generation_utils/generate.py +++ b/sotopia/generation_utils/generate.py @@ -14,12 +14,12 @@ ) from langchain.schema import BaseOutputParser, OutputParserException from langchain_community.chat_models import ChatLiteLLM -from langchain_together import ChatTogether from pydantic import BaseModel, Field from rich import print from typing_extensions import Literal from sotopia.database import EnvironmentProfile, RelationshipProfile +from sotopia.generation_utils.llama2 import Llama2 from sotopia.messages import ActionType, AgentAction, ScriptBackground from sotopia.messages.message_classes import ( ScriptInteraction, @@ -328,18 +328,18 @@ def obtain_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, + prompt=PromptTemplate( + template=template, + input_variables=input_variables, ) ) - chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt]) - chat_openai = ChatTogether( - model_name=model_name, - temperature=temperature, - max_retries=max_retries, + chat_prompt_template = ChatPromptTemplate.from_messages( + [human_message_prompt] + ) + together_llm = Llama2( + model_name=model_name, temperature=temperature ) - chain = LLMChain(llm=chat_openai, prompt=chat_prompt_template) + chain = LLMChain(llm=together_llm, prompt=chat_prompt_template) return chain else: chat = PatchedChatLiteLLM( From af4e2ee4934aa1f7089f97c6a8bbbc743a8a12f6 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Sun, 26 May 2024 16:58:45 -0400 Subject: [PATCH 14/40] chore: Update langchain-together dependency to version 0.1.2 --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index efdb933f3..b2b111011 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,6 @@ beartype = "^0.14.0" torch = ">=2.1 <2.3" transformers = "^4.34.0" langchain-openai = 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"ray[tune] (>=2.7.0)", "sigopt"] @@ -4044,7 +4042,7 @@ natten = ["natten (>=0.14.6,<0.15.0)"] onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"] onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"] optuna = ["optuna"] -quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"] +quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"] ray = ["ray[tune] (>=2.7.0)"] retrieval = ["datasets (!=2.5.0)", "faiss-cpu"] sagemaker = ["sagemaker (>=2.31.0)"] @@ -4053,16 +4051,16 @@ serving = ["fastapi", "pydantic", "starlette", "uvicorn"] sigopt = ["sigopt"] sklearn = ["scikit-learn"] speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] -testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate 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(>=0.5.0)", "python-dotenv (>=0.13)", [[package]] name = "virtualenv" -version = "20.26.1" +version = "20.26.2" description = "Virtual Python Environment builder" optional = false python-versions = ">=3.7" files = [ - {file = "virtualenv-20.26.1-py3-none-any.whl", hash = "sha256:7aa9982a728ae5892558bff6a2839c00b9ed145523ece2274fad6f414690ae75"}, - {file = "virtualenv-20.26.1.tar.gz", hash = "sha256:604bfdceaeece392802e6ae48e69cec49168b9c5f4a44e483963f9242eb0e78b"}, + {file = "virtualenv-20.26.2-py3-none-any.whl", hash = "sha256:a624db5e94f01ad993d476b9ee5346fdf7b9de43ccaee0e0197012dc838a0e9b"}, + {file = "virtualenv-20.26.2.tar.gz", hash = "sha256:82bf0f4eebbb78d36ddaee0283d43fe5736b53880b8a8cdcd37390a07ac3741c"}, ] [package.dependencies] @@ -4512,18 +4510,18 @@ multidict = ">=4.0" [[package]] name = "zipp" -version = "3.18.1" +version = "3.19.0" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.8" files = [ - {file = 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"pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" diff --git a/pyproject.toml b/pyproject.toml index efdb933f3..b2b111011 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,6 @@ beartype = "^0.14.0" torch = ">=2.1 <2.3" transformers = "^4.34.0" langchain-openai = "^0.0.5" -langchain-together = "^0.1.2" litellm = "^1.23.12" [tool.poetry.group.dev.dependencies] diff --git a/sotopia/generation_utils/generate.py b/sotopia/generation_utils/generate.py index 0647289f7..1c61cfe25 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, @@ -14,7 +15,7 @@ ) from langchain.schema import BaseOutputParser, OutputParserException from langchain_community.chat_models import ChatLiteLLM -from langchain_together import ChatTogether +from langchain_openai import ChatOpenAI from pydantic import BaseModel, Field from rich import print from typing_extensions import Literal @@ -334,10 +335,12 @@ def obtain_chain( ) ) chat_prompt_template = ChatPromptTemplate.from_messages([human_message_prompt]) - chat_openai = ChatTogether( + 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 From 7087f4087c3398d715b39a15ca3af7ab2437ad24 Mon Sep 17 00:00:00 2001 From: Hao Date: Sun, 26 May 2024 17:58:51 -0400 Subject: [PATCH 16/40] add logging --- sotopia/benchmark/cli.py | 40 +++++++++++++++++++++++++++++++++++----- sotopia/server.py | 3 +-- 2 files changed, 36 insertions(+), 7 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 91ccd3828..1c5a3ffbd 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -1,9 +1,14 @@ +from datetime import datetime +import subprocess from datasets import load_dataset from sotopia.database.persistent_profile import EnvironmentList import asyncio import logging from typing import Generator, cast +from logging import FileHandler +from rich.logging import RichHandler + from tqdm import tqdm from sotopia.agents import LLMAgent @@ -28,9 +33,35 @@ from sotopia.server import run_async_server import typer +from pathlib import Path app = typer.Typer() +# date and message only +FORMAT = "%(asctime)s - %(levelname)s - %(name)s - %(message)s" + +process = subprocess.Popen( + ["git", "rev-parse", "HEAD"], shell=False, stdout=subprocess.PIPE +) +git_head_hash = process.communicate()[0].strip() + +logging_path = Path( + datetime.now().strftime( + "./logs/%H_%M_%d_%m_%Y_{str(git_head_hash.decode('utf-8'))}.log" + ) +) +logging_path.mkdir(parents=True, exist_ok=True) + +logging.basicConfig( + level=15, + format=FORMAT, + datefmt="[%X]", + handlers=[ + RichHandler(), + FileHandler(logging_path), + ], +) + def check_existing_episodes( env_id: str, @@ -221,11 +252,10 @@ def run_async_benchmark_in_batch( @app.command() def cli( - model: str = typer.Option( - ..., help="The language model you want to benchmark." - ), + model: str = typer.Option(..., help="The language model you want to benchmark."), partner_model: str = typer.Option( - "together_ai/meta-llama/Llama-2-70b-chat-hf", help="The partner model you want to use." + "together_ai/meta-llama/Llama-2-70b-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." @@ -237,7 +267,7 @@ def cli( typer.echo( f"Running benchmark for {model} and {partner_model} on task {task} with {evaluator_model} as the evaluator." ) - model = cast(LLM_Name, model) + model = cast(LLM_Name, model) partner_model = cast(LLM_Name, partner_model) evaluator_model = cast(LLM_Name, evaluator_model) tag = f"benchmark_{model}_q" diff --git a/sotopia/server.py b/sotopia/server.py index 4b9541b31..8dacfc6b6 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, @@ -315,7 +314,7 @@ 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] ) From 4be892d98313eb8bd6d60f3de8830ce5b711576e Mon Sep 17 00:00:00 2001 From: Hao Date: Sun, 26 May 2024 18:09:13 -0400 Subject: [PATCH 17/40] fix pre-commit --- notebooks/redis_stats.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/notebooks/redis_stats.ipynb b/notebooks/redis_stats.ipynb index 4083264fe..7445b043f 100644 --- a/notebooks/redis_stats.ipynb +++ b/notebooks/redis_stats.ipynb @@ -231,6 +231,7 @@ "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", @@ -377,7 +378,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.9" } }, "nbformat": 4, From e8a59d28188e10a72e694a0cda83ed721b41e235 Mon Sep 17 00:00:00 2001 From: Hao Date: Mon, 27 May 2024 12:39:47 -0400 Subject: [PATCH 18/40] add more logging options --- sotopia/benchmark/cli.py | 51 ++++++++++++++-------------- sotopia/generation_utils/generate.py | 18 ++++++++++ 2 files changed, 43 insertions(+), 26 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 1c5a3ffbd..1ded00d84 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -1,5 +1,4 @@ from datetime import datetime -import subprocess from datasets import load_dataset from sotopia.database.persistent_profile import EnvironmentList import asyncio @@ -37,31 +36,6 @@ app = typer.Typer() -# date and message only -FORMAT = "%(asctime)s - %(levelname)s - %(name)s - %(message)s" - -process = subprocess.Popen( - ["git", "rev-parse", "HEAD"], shell=False, stdout=subprocess.PIPE -) -git_head_hash = process.communicate()[0].strip() - -logging_path = Path( - datetime.now().strftime( - "./logs/%H_%M_%d_%m_%Y_{str(git_head_hash.decode('utf-8'))}.log" - ) -) -logging_path.mkdir(parents=True, exist_ok=True) - -logging.basicConfig( - level=15, - format=FORMAT, - datefmt="[%X]", - handlers=[ - RichHandler(), - FileHandler(logging_path), - ], -) - def check_existing_episodes( env_id: str, @@ -250,6 +224,29 @@ def run_async_benchmark_in_batch( 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."), @@ -262,8 +259,10 @@ def cli( ), 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} and {partner_model} on task {task} with {evaluator_model} as the evaluator." ) diff --git a/sotopia/generation_utils/generate.py b/sotopia/generation_utils/generate.py index 219ec7add..c256a86c2 100644 --- a/sotopia/generation_utils/generate.py +++ b/sotopia/generation_utils/generate.py @@ -344,6 +344,24 @@ def obtain_chain( ) 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, From 9e15790b7687166cd66cf4747a0edf42dc5afb74 Mon Sep 17 00:00:00 2001 From: Hao Date: Mon, 27 May 2024 13:11:08 -0400 Subject: [PATCH 19/40] probably fix the event loop closed error: following https://github.com/NVIDIA/NeMo-Guardrails/pull/336 --- sotopia/benchmark/cli.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 1ded00d84..ec598ec2e 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -162,6 +162,7 @@ def run_async_benchmark_in_batch( ) 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_iter, @@ -173,7 +174,7 @@ def run_async_benchmark_in_batch( logging.info( f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" ) - asyncio.run( + loop.run_until_complete( run_async_server( model_dict=model_names, sampler=BaseSampler[Observation, AgentAction](), @@ -188,7 +189,7 @@ def run_async_benchmark_in_batch( logging.info( f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" ) - asyncio.run( + loop.run_until_complete( run_async_server( model_dict=model_names, sampler=BaseSampler[Observation, AgentAction](), From 4906efe848c1baa7a7db048c4ed2884962c0edae Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Tue, 28 May 2024 14:30:38 -0700 Subject: [PATCH 20/40] modify cli; fix model position bug --- sotopia/benchmark/cli.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index ec598ec2e..fc768e2d1 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -54,6 +54,7 @@ def check_existing_episodes( assert isinstance(episode, EpisodeLog), "episode should be an EpisodeLog" if episode.agents == agent_ids and episode.models == list(models.values()): return True + breakpoint() return False else: return False @@ -70,7 +71,6 @@ def initilize_benchmark_combo(dataset) -> list[EnvAgentComboStorage]: # type: i def get_avg_reward(episodes: list[EpisodeLog], model_name: str) -> dict[str, float]: - breakpoint() rewards_list = [] avg_reward_dict = {} for episode in episodes: @@ -157,6 +157,7 @@ def run_async_benchmark_in_batch( model_names=model_names, tag=tag, env_agent_combo_storage_list=benchmark_combo ) env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) + breakpoint() env_agent_combo_iter = _iterate_all_env_agent_combo_not_in_db( model_names=model_names, tag=tag, env_agent_combo_storage_list=benchmark_combo ) @@ -220,7 +221,7 @@ def run_async_benchmark_in_batch( number_of_fix_turns += 1 if env_agent_combo_iter_length == 0 or number_of_fix_turns >= 5: rewards_dict = get_avg_reward(simulated_episodes, model_names["agent2"]) # type: ignore - rewards_dict["model_name"] = model_names["agent2"] # type: ignore + rewards_dict["model_name"] = model_names["agent1"] # type: ignore print(rewards_dict) return @@ -265,7 +266,7 @@ def cli( """A simple command-line interface example.""" _set_up_logs(print_logs=print_logs) typer.echo( - f"Running benchmark for {model} and {partner_model} on task {task} with {evaluator_model} as the evaluator." + 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) @@ -273,7 +274,7 @@ def cli( tag = f"benchmark_{model}_q" run_async_benchmark_in_batch( batch_size=batch_size, - model_names={"agent1": model, "agent2": partner_model, "env": evaluator_model}, + model_names={"env": evaluator_model, "agent1": model, "agent2": partner_model}, tag=tag, verbose=False, ) From 0026b3e55326d2160353015edc1ed74432dedd31 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 29 May 2024 08:43:48 -0700 Subject: [PATCH 21/40] chore: Update benchmark tag to "benchmark_{model}_final" --- sotopia/benchmark/cli.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index fc768e2d1..3c8e9c144 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -54,7 +54,6 @@ def check_existing_episodes( assert isinstance(episode, EpisodeLog), "episode should be an EpisodeLog" if episode.agents == agent_ids and episode.models == list(models.values()): return True - breakpoint() return False else: return False @@ -98,14 +97,19 @@ def _iterate_all_env_agent_combo_not_in_db( assert isinstance(agent_index, list), "agent_index should be a list" envs_index_mapping: dict[str, list[str]] = {env_id: [] for env_id in set(hard_envs)} # Repeat 10 times to match the number of combos + model_names_switched = { + "env": model_names["env"], + "agent1": model_names["agent2"], + "agent2": model_names["agent1"], + } for _ in range(10): for index, env_id in zip(agent_index, hard_envs): envs_index_mapping[env_id].append(index) - + for env_agent_combo_storage in env_agent_combo_storage_list: agent_ids = env_agent_combo_storage.agent_ids env_id = env_agent_combo_storage.env_id - if check_existing_episodes(env_id, agent_ids, model_names, tag): + if check_existing_episodes(env_id, agent_ids, model_names, tag) or check_existing_episodes(env_id=env_id, agent_ids=agent_ids, models=model_names_switched, tag=tag): logging.info( f"Episode for {env_id} with agents {agent_ids} using {list(model_names.values())} already exists" ) @@ -157,7 +161,6 @@ def run_async_benchmark_in_batch( model_names=model_names, tag=tag, env_agent_combo_storage_list=benchmark_combo ) env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) - breakpoint() env_agent_combo_iter = _iterate_all_env_agent_combo_not_in_db( model_names=model_names, tag=tag, env_agent_combo_storage_list=benchmark_combo ) @@ -210,6 +213,7 @@ def run_async_benchmark_in_batch( pk = episode.pk assert isinstance(pk, str) EpisodeLog.delete(pk) + breakpoint() env_agent_combo_iter = _iterate_all_env_agent_combo_not_in_db( model_names=model_names, @@ -217,6 +221,11 @@ def run_async_benchmark_in_batch( env_agent_combo_storage_list=benchmark_combo, ) env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) + env_agent_combo_iter = _iterate_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 env_agent_combo_iter_length == 0 or number_of_fix_turns >= 5: @@ -271,7 +280,7 @@ def cli( model = cast(LLM_Name, model) partner_model = cast(LLM_Name, partner_model) evaluator_model = cast(LLM_Name, evaluator_model) - tag = f"benchmark_{model}_q" + tag = f"benchmark_{model}_final" run_async_benchmark_in_batch( batch_size=batch_size, model_names={"env": evaluator_model, "agent1": model, "agent2": partner_model}, From 1d873be87e84816a7f1ba62727e6eedecdd9ad41 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 29 May 2024 08:45:25 -0700 Subject: [PATCH 22/40] Refactor _iterate_all_env_agent_combo_not_in_db function --- sotopia/benchmark/cli.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 3c8e9c144..f275982c6 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -105,11 +105,15 @@ def _iterate_all_env_agent_combo_not_in_db( for _ in range(10): for index, env_id in zip(agent_index, hard_envs): envs_index_mapping[env_id].append(index) - + for env_agent_combo_storage in env_agent_combo_storage_list: agent_ids = env_agent_combo_storage.agent_ids env_id = env_agent_combo_storage.env_id - if check_existing_episodes(env_id, agent_ids, model_names, tag) or check_existing_episodes(env_id=env_id, agent_ids=agent_ids, models=model_names_switched, tag=tag): + if check_existing_episodes( + env_id, agent_ids, model_names, tag + ) or check_existing_episodes( + env_id=env_id, agent_ids=agent_ids, models=model_names_switched, tag=tag + ): logging.info( f"Episode for {env_id} with agents {agent_ids} using {list(model_names.values())} already exists" ) From cb4907970c465252f0a097d31a63abd488f614be Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 29 May 2024 08:45:30 -0700 Subject: [PATCH 23/40] chore: Update python version to 3.11.2 --- notebooks/redis_stats.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/redis_stats.ipynb b/notebooks/redis_stats.ipynb index 7445b043f..dfb39e2a2 100644 --- a/notebooks/redis_stats.ipynb +++ b/notebooks/redis_stats.ipynb @@ -378,7 +378,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.2" } }, "nbformat": 4, From f983b445e1cfa492706dfd8607521d90f2ccf671 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Sat, 1 Jun 2024 12:20:37 -0700 Subject: [PATCH 24/40] change to dict comparison --- sotopia/benchmark/cli.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index f275982c6..e4ecdd3c5 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -52,7 +52,13 @@ def check_existing_episodes( if existing_episode: for episode in existing_episode: assert isinstance(episode, EpisodeLog), "episode should be an EpisodeLog" - if episode.agents == agent_ids and episode.models == list(models.values()): + assert isinstance(episode.models, list), "episode.models should be a list" + episode_models_dict = { + 'env': episode.models[0], + 'agent1': episode.models[1], + 'agent2': episode.models[2], + } + if episode.agents == agent_ids and episode_models_dict == models: return True return False else: @@ -90,7 +96,7 @@ def _iterate_all_env_agent_combo_not_in_db( model_names: dict[str, LLM_Name], env_agent_combo_storage_list: list[EnvAgentComboStorage], tag: str | None = None, -) -> Generator[EnvAgentCombo[Observation, AgentAction], 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 From dbc873ca00ff422d7a6ee5a8e0857cbc12b193fc Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Mon, 3 Jun 2024 20:06:18 -0700 Subject: [PATCH 25/40] ignore jsonl --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index c0f8b63f7..91991f7c0 100644 --- a/.gitignore +++ b/.gitignore @@ -137,7 +137,7 @@ data/* deprecated/* *.csv - +*.jsonl #backup backup/* From 6a79d7e61a2affa30585b9c3f73525929756c625 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 5 Jun 2024 14:42:39 -0700 Subject: [PATCH 26/40] =?UTF-8?q?=E2=9C=A8=20finish=20benchmarking=20scrip?= =?UTF-8?q?t?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- notebooks/redis_stats.ipynb | 79 +++++++++++++++++++++++++---- sotopia-chat/chat_server.py | 6 --- sotopia/agents/base_agent.py | 1 + sotopia/agents/llm_agent.py | 2 + sotopia/agents/redis_agent.py | 2 + sotopia/benchmark/cli.py | 94 ++++++++++++++--------------------- sotopia/server.py | 55 +++++++------------- 7 files changed, 130 insertions(+), 109 deletions(-) diff --git a/notebooks/redis_stats.ipynb b/notebooks/redis_stats.ipynb index dfb39e2a2..3c30c1c31 100644 --- a/notebooks/redis_stats.ipynb +++ b/notebooks/redis_stats.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -190,9 +190,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "450\n" + ] + }, + { + "data": { + "text/html": [ + "
EnvAgentComboStorage(\n",
+       "    pk='01H7ZGR3THBCVCVZK6X4TXHZ91',\n",
+       "    env_id='01H7VFHPS5WJW2694R1MNC8JFY',\n",
+       "    agent_ids=['01H5TNE5PJRM958QWP3BHWY9DY', '01H5TNE5Q1J7Z7Q12WA1W90MR9']\n",
+       ")\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;35mEnvAgentComboStorage\u001b[0m\u001b[1m(\u001b[0m\n", + " \u001b[33mpk\u001b[0m=\u001b[32m'01H7ZGR3THBCVCVZK6X4TXHZ91'\u001b[0m,\n", + " \u001b[33menv_id\u001b[0m=\u001b[32m'01H7VFHPS5WJW2694R1MNC8JFY'\u001b[0m,\n", + " \u001b[33magent_ids\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m'01H5TNE5PJRM958QWP3BHWY9DY'\u001b[0m, \u001b[32m'01H5TNE5Q1J7Z7Q12WA1W90MR9'\u001b[0m\u001b[1m]\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# all env-agent combos\n", "all_combos = EnvAgentComboStorage().all_pks()\n", @@ -211,9 +240,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], "source": [ "from sotopia.database.persistent_profile import EnvironmentList\n", "\n", @@ -224,9 +261,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n", + "5\n", + "5\n", + "5\n", + "5\n", + "5\n", + "5\n", + "5\n", + "5\n", + "5\n", + "5\n", + "5\n", + "5\n", + "5\n", + "5\n" + ] + } + ], "source": [ "from sotopia.samplers import ConstraintBasedSampler\n", "from sotopia.messages import AgentAction, Observation\n", @@ -253,11 +312,13 @@ "# 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:\n", - " combo_list = _sample_env_agent_combo_and_push_to_db(env)\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", 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/cli.py b/sotopia/benchmark/cli.py index e4ecdd3c5..a48cd7978 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -1,5 +1,6 @@ from datetime import datetime from datasets import load_dataset +import rich from sotopia.database.persistent_profile import EnvironmentList import asyncio import logging @@ -41,6 +42,7 @@ 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: @@ -52,13 +54,8 @@ def check_existing_episodes( if existing_episode: for episode in existing_episode: assert isinstance(episode, EpisodeLog), "episode should be an EpisodeLog" - assert isinstance(episode.models, list), "episode.models should be a list" - episode_models_dict = { - 'env': episode.models[0], - 'agent1': episode.models[1], - 'agent2': episode.models[2], - } - if episode.agents == agent_ids and episode_models_dict == models: + 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: @@ -91,8 +88,7 @@ def get_avg_reward(episodes: list[EpisodeLog], model_name: str) -> dict[str, flo avg_reward_dict[dimension] = avg_reward return avg_reward_dict - -def _iterate_all_env_agent_combo_not_in_db( +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, @@ -101,25 +97,22 @@ def _iterate_all_env_agent_combo_not_in_db( hard_envs = EnvironmentList.get("01HAK34YPB1H1RWXQDASDKHSNS").environments agent_index = EnvironmentList.get("01HAK34YPB1H1RWXQDASDKHSNS").agent_index assert isinstance(agent_index, list), "agent_index should be a list" - envs_index_mapping: dict[str, list[str]] = {env_id: [] for env_id in set(hard_envs)} - # Repeat 10 times to match the number of combos - model_names_switched = { - "env": model_names["env"], - "agent1": model_names["agent2"], - "agent2": model_names["agent1"], - } - for _ in range(10): - for index, env_id in zip(agent_index, hard_envs): - envs_index_mapping[env_id].append(index) - for env_agent_combo_storage in env_agent_combo_storage_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, agent_ids, model_names, tag - ) or check_existing_episodes( - env_id=env_id, agent_ids=agent_ids, models=model_names_switched, tag=tag - ): + 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" ) @@ -138,20 +131,15 @@ def _iterate_all_env_agent_combo_not_in_db( ) 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 - index = envs_index_mapping[env_id].pop(0) - if int(index) == 0: - model_names["agent1"], model_names["agent2"] = ( - model_names["agent2"], - model_names["agent1"], - ) agents = [ LLMAgent(agent_profile=agent_profile, model_name=agent_model) for agent_profile, agent_model in zip( agent_profiles, - [model_names["agent1"], model_names["agent2"]], + [model_names["test_model"], model_names["partner_model"]] if index == "0" else [model_names["partner_model"], model_names["test_model"]], ) ] - yield env, agents + list_of_env_agent_combo_storage.append((env, agents)) + return list_of_env_agent_combo_storage # type: ignore def run_async_benchmark_in_batch( @@ -159,19 +147,16 @@ def run_async_benchmark_in_batch( batch_size: int = 1, model_names: dict[str, LLM_Name] = { "env": "gpt-4", - "agent1": "gpt-3.5-turbo", - "agent2": "gpt-3.5-turbo", + "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: dataset = load_dataset("cmu-lti/sotopia", data_files="benchmark_agents.json") benchmark_combo = initilize_benchmark_combo(dataset) - env_agent_combo_iter = _iterate_all_env_agent_combo_not_in_db( - model_names=model_names, tag=tag, env_agent_combo_storage_list=benchmark_combo - ) - env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) - env_agent_combo_iter = _iterate_all_env_agent_combo_not_in_db( + 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]] = [] @@ -179,8 +164,7 @@ def run_async_benchmark_in_batch( loop = asyncio.get_event_loop() while True: for env_agent_combo in tqdm( - env_agent_combo_iter, - total=env_agent_combo_iter_length, + env_agent_combo_list, desc="Running all envs in batch", ): env_agent_combo_batch.append(env_agent_combo) @@ -190,10 +174,9 @@ def run_async_benchmark_in_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=True, + push_to_db=push_to_db, tag=tag, ) ) @@ -208,7 +191,7 @@ def run_async_benchmark_in_batch( model_dict=model_names, sampler=BaseSampler[Observation, AgentAction](), env_agent_combo_list=env_agent_combo_batch, - push_to_db=True, + push_to_db=push_to_db, tag=tag, ) ) @@ -223,25 +206,19 @@ def run_async_benchmark_in_batch( pk = episode.pk assert isinstance(pk, str) EpisodeLog.delete(pk) - breakpoint() - env_agent_combo_iter = _iterate_all_env_agent_combo_not_in_db( - model_names=model_names, - tag=tag, - env_agent_combo_storage_list=benchmark_combo, - ) - env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) - env_agent_combo_iter = _iterate_all_env_agent_combo_not_in_db( + 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 env_agent_combo_iter_length == 0 or number_of_fix_turns >= 5: - rewards_dict = get_avg_reward(simulated_episodes, model_names["agent2"]) # type: ignore - rewards_dict["model_name"] = model_names["agent1"] # type: ignore - print(rewards_dict) + if len(env_agent_combo_list) == 0 or number_of_fix_turns >= 5: + rewards_dict = get_avg_reward(simulated_episodes, model_names["test_model"]) # type: ignore + rewards_dict["model_name"] = model_names["test_model"] # type: ignore + rewards_dict["episode_count"] = len(simulated_episodes) + rich.print(rewards_dict) return @@ -290,12 +267,13 @@ def cli( model = cast(LLM_Name, model) partner_model = cast(LLM_Name, partner_model) evaluator_model = cast(LLM_Name, evaluator_model) - tag = f"benchmark_{model}_final" + tag = f"benchmark_{model}_{partner_model}_{evaluator_model}_{task}_trial0" run_async_benchmark_in_batch( batch_size=batch_size, - model_names={"env": evaluator_model, "agent1": model, "agent2": partner_model}, + model_names={"env": evaluator_model, "test_model": model, "partner_model": partner_model}, tag=tag, verbose=False, + push_to_db=True, ) diff --git a/sotopia/server.py b/sotopia/server.py index 8dacfc6b6..7ebdbb750 100644 --- a/sotopia/server.py +++ b/sotopia/server.py @@ -105,7 +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, @@ -114,23 +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]]] = [] @@ -201,7 +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 @@ -229,9 +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["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, @@ -253,30 +235,18 @@ 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: @@ -288,6 +258,20 @@ 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() @@ -303,7 +287,6 @@ 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, From 9a38b9315bc3826b74411d1bb20a4ebbfa10e2f6 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 5 Jun 2024 14:43:00 -0700 Subject: [PATCH 27/40] chore: Refactor server.py and redis_stats.ipynb --- notebooks/redis_stats.ipynb | 73 ++++--------------------------------- sotopia/benchmark/cli.py | 35 ++++++++++++------ sotopia/server.py | 10 +++-- 3 files changed, 37 insertions(+), 81 deletions(-) diff --git a/notebooks/redis_stats.ipynb b/notebooks/redis_stats.ipynb index 3c30c1c31..e4c710eb4 100644 --- a/notebooks/redis_stats.ipynb +++ b/notebooks/redis_stats.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -190,38 +190,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "450\n" - ] - }, - { - "data": { - "text/html": [ - "
EnvAgentComboStorage(\n",
-       "    pk='01H7ZGR3THBCVCVZK6X4TXHZ91',\n",
-       "    env_id='01H7VFHPS5WJW2694R1MNC8JFY',\n",
-       "    agent_ids=['01H5TNE5PJRM958QWP3BHWY9DY', '01H5TNE5Q1J7Z7Q12WA1W90MR9']\n",
-       ")\n",
-       "
\n" - ], - "text/plain": [ - "\u001b[1;35mEnvAgentComboStorage\u001b[0m\u001b[1m(\u001b[0m\n", - " \u001b[33mpk\u001b[0m=\u001b[32m'01H7ZGR3THBCVCVZK6X4TXHZ91'\u001b[0m,\n", - " \u001b[33menv_id\u001b[0m=\u001b[32m'01H7VFHPS5WJW2694R1MNC8JFY'\u001b[0m,\n", - " \u001b[33magent_ids\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m'01H5TNE5PJRM958QWP3BHWY9DY'\u001b[0m, \u001b[32m'01H5TNE5Q1J7Z7Q12WA1W90MR9'\u001b[0m\u001b[1m]\u001b[0m\n", - "\u001b[1m)\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# all env-agent combos\n", "all_combos = EnvAgentComboStorage().all_pks()\n", @@ -240,17 +211,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1\n" - ] - } - ], + "outputs": [], "source": [ "from sotopia.database.persistent_profile import EnvironmentList\n", "\n", @@ -261,31 +224,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20\n", - "5\n", - "5\n", - "5\n", - "5\n", - "5\n", - "5\n", - "5\n", - "5\n", - "5\n", - "5\n", - "5\n", - "5\n", - "5\n", - "5\n" - ] - } - ], + "outputs": [], "source": [ "from sotopia.samplers import ConstraintBasedSampler\n", "from sotopia.messages import AgentAction, Observation\n", diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index a48cd7978..f4fdc8f2b 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -4,7 +4,7 @@ from sotopia.database.persistent_profile import EnvironmentList import asyncio import logging -from typing import Generator, cast +from typing import cast from logging import FileHandler from rich.logging import RichHandler @@ -54,7 +54,11 @@ def check_existing_episodes( 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']] + 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 @@ -88,6 +92,7 @@ def get_avg_reward(episodes: list[EpisodeLog], model_name: str) -> dict[str, flo 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], @@ -101,18 +106,18 @@ def _list_all_env_agent_combo_not_in_db( 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: + if env_agent_combo_storage.env_id == env_id: env_agent_combo_storage_index_list.append( - (env_agent_combo_storage, index) - ) - + (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" ) @@ -135,11 +140,13 @@ def _list_all_env_agent_combo_not_in_db( 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"]], + [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 + return list_of_env_agent_combo_storage # type: ignore def run_async_benchmark_in_batch( @@ -215,7 +222,9 @@ def run_async_benchmark_in_batch( 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, model_names["test_model"]) # type: ignore + rewards_dict = get_avg_reward( + simulated_episodes, model_names["test_model"] + ) # type: ignore rewards_dict["model_name"] = model_names["test_model"] # type: ignore rewards_dict["episode_count"] = len(simulated_episodes) rich.print(rewards_dict) @@ -270,7 +279,11 @@ def cli( 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}, + model_names={ + "env": evaluator_model, + "test_model": model, + "partner_model": partner_model, + }, tag=tag, verbose=False, push_to_db=True, diff --git a/sotopia/server.py b/sotopia/server.py index 7ebdbb750..4479d80ca 100644 --- a/sotopia/server.py +++ b/sotopia/server.py @@ -235,7 +235,9 @@ 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" + 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 @@ -269,9 +271,9 @@ def get_agent_class( ], } agents_model_dict = { - "agent1": model_dict["agent1"], - "agent2": model_dict["agent2"], - } + "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() From 0996090263cb6059c489bf1ecbb311a3404d218f Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 5 Jun 2024 14:46:01 -0700 Subject: [PATCH 28/40] add type ignore --- sotopia/benchmark/cli.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index f4fdc8f2b..40ff42516 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -223,8 +223,8 @@ def run_async_benchmark_in_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, model_names["test_model"] - ) # type: ignore + simulated_episodes, model_names["test_model"] # type: ignore + ) rewards_dict["model_name"] = model_names["test_model"] # type: ignore rewards_dict["episode_count"] = len(simulated_episodes) rich.print(rewards_dict) From f1a626b4eba08df222d73be73313233888546cb4 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 5 Jun 2024 14:50:42 -0700 Subject: [PATCH 29/40] push for the eval --- sotopia/benchmark/cli.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 40ff42516..4543392be 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -223,8 +223,9 @@ def run_async_benchmark_in_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, model_names["test_model"] # type: ignore - ) + simulated_episodes, + model_names["test_model"], # type: ignore + ) rewards_dict["model_name"] = model_names["test_model"] # type: ignore rewards_dict["episode_count"] = len(simulated_episodes) rich.print(rewards_dict) From b56722454a3ed1d070834622c1ad6894d4109a69 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 5 Jun 2024 14:56:08 -0700 Subject: [PATCH 30/40] Refactor run_async_benchmark_in_batch function --- sotopia/benchmark/cli.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 4543392be..6dde1559d 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -223,8 +223,8 @@ def run_async_benchmark_in_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, - model_names["test_model"], # type: ignore + 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) From bfadacd7075a1a0e77fc495c035c38053b7edaa3 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 5 Jun 2024 15:00:29 -0700 Subject: [PATCH 31/40] Refactor run_async_benchmark_in_batch function --- sotopia/benchmark/cli.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 6dde1559d..e167b11a4 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -223,7 +223,7 @@ def run_async_benchmark_in_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 + simulated_episodes, # type: ignore model_names["test_model"], ) rewards_dict["model_name"] = model_names["test_model"] # type: ignore From 9a5dce92249ad4590f2332bad79517a3ff46bae2 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 5 Jun 2024 15:09:04 -0700 Subject: [PATCH 32/40] add doc --- docs/pages/benchmark.md | 6 ++++++ sotopia_conf/server.py | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) create mode 100644 docs/pages/benchmark.md diff --git a/docs/pages/benchmark.md b/docs/pages/benchmark.md new file mode 100644 index 000000000..6690a7e7b --- /dev/null +++ b/docs/pages/benchmark.md @@ -0,0 +1,6 @@ +# Benchmark your model as a social agent in Sotopia + +``` +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 `together_ai/meta-llama/Llama-2-70b-chat-hf` \ No newline at end of file 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__": From 3dbde75ceb81b8f061c89315f29151c5c35679d3 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 5 Jun 2024 15:10:09 -0700 Subject: [PATCH 33/40] precommit fix --- docs/pages/benchmark.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/pages/benchmark.md b/docs/pages/benchmark.md index 6690a7e7b..ef45a802a 100644 --- a/docs/pages/benchmark.md +++ b/docs/pages/benchmark.md @@ -3,4 +3,4 @@ ``` 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 `together_ai/meta-llama/Llama-2-70b-chat-hf` \ No newline at end of file +Currently this script would run over 100 simulations on the Sotopia Hard tasks. And the partner model is fixed to be `together_ai/meta-llama/Llama-2-70b-chat-hf` From 897389a63fdca481b574d3f3247e8c2d17247ac1 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Wed, 5 Jun 2024 15:19:20 -0700 Subject: [PATCH 34/40] pre-commit --- sotopia/server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sotopia/server.py b/sotopia/server.py index 4479d80ca..eddec31ef 100644 --- a/sotopia/server.py +++ b/sotopia/server.py @@ -321,7 +321,7 @@ async def arun_one_script( omniscient (bool): Whether the agent knows the goal of the other """ - agents = Agents({agent.agent_name: agent for agent in agent_list}) + agents = Agents({agent.agent_name: agent for agent in agent_list}) env.reset(agents=agents, omniscient=omniscient) agent_names = [agent.agent_name for agent in agent_list] From b7fd148394d01c09c653afab2bcfeba607099b79 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Sun, 9 Jun 2024 16:45:31 -0700 Subject: [PATCH 35/40] refactor --- sotopia/server.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sotopia/server.py b/sotopia/server.py index eddec31ef..4479d80ca 100644 --- a/sotopia/server.py +++ b/sotopia/server.py @@ -321,7 +321,7 @@ async def arun_one_script( omniscient (bool): Whether the agent knows the goal of the other """ - agents = Agents({agent.agent_name: agent for agent in agent_list}) + agents = Agents({agent.agent_name: agent for agent in agent_list}) env.reset(agents=agents, omniscient=omniscient) agent_names = [agent.agent_name for agent in agent_list] From 8352ea7ae328a60638be2f49bf5e05698d4065c1 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Thu, 13 Jun 2024 18:10:35 -0700 Subject: [PATCH 36/40] update w feedback --- docs/pages/benchmark.md | 7 ++++++- docs/pages/examples.mdx | 19 ------------------- sotopia/benchmark/cli.py | 2 +- 3 files changed, 7 insertions(+), 21 deletions(-) diff --git a/docs/pages/benchmark.md b/docs/pages/benchmark.md index ef45a802a..35af62ca9 100644 --- a/docs/pages/benchmark.md +++ b/docs/pages/benchmark.md @@ -1,6 +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 `together_ai/meta-llama/Llama-2-70b-chat-hf` +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/docs/pages/examples.mdx b/docs/pages/examples.mdx index 950158346..aac169c80 100644 --- a/docs/pages/examples.mdx +++ b/docs/pages/examples.mdx @@ -19,22 +19,3 @@ python examples/benchmark_evaluator.py --push-to-db --model= Date: Thu, 13 Jun 2024 18:14:48 -0700 Subject: [PATCH 37/40] pre commit --- docs/pages/benchmark.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/pages/benchmark.md b/docs/pages/benchmark.md index 35af62ca9..368432e18 100644 --- a/docs/pages/benchmark.md +++ b/docs/pages/benchmark.md @@ -3,7 +3,7 @@ ``` sotopia_benchmark --model= ``` -or +or ``` python sotopia/benchmark/cli.py --model= From 6bcb178412685cbea3bc7e822531fd607cc109cb Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Thu, 13 Jun 2024 22:23:36 -0700 Subject: [PATCH 38/40] chore: Update authors in pyproject.toml and fetch benchmark_agents.json from Hugging Face API --- poetry.lock | 673 ++++++++++++++++++++------------------- pyproject.toml | 2 +- sotopia/benchmark/cli.py | 17 +- 3 files changed, 352 insertions(+), 340 deletions(-) diff --git a/poetry.lock b/poetry.lock index 78a5cde34..9d2d55df7 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "absl-py" @@ -247,17 +247,17 @@ test-tox-coverage = ["coverage (>=5.5)"] [[package]] name = "boto3" -version = "1.34.113" +version = "1.34.126" description = "The AWS SDK for Python" optional = true python-versions = ">=3.8" files = [ - {file = "boto3-1.34.113-py3-none-any.whl", hash = "sha256:7e59f0a848be477a4c98a90e7a18a0e284adfb643f7879d2b303c5f493661b7a"}, - {file = "boto3-1.34.113.tar.gz", hash = "sha256:009cd143509f2ff4c37582c3f45d50f28c95eed68e8a5c36641206bdb597a9ea"}, + {file = "boto3-1.34.126-py3-none-any.whl", hash = "sha256:7f676daef674fe74f34ce4063228eccc6e60c811f574720e31f230296c4bf29a"}, + {file = "boto3-1.34.126.tar.gz", hash = "sha256:7e8418b47dd43954a9088d504541bed8a42b6d06e712d02befba134c1c4d7c6d"}, ] [package.dependencies] -botocore = ">=1.34.113,<1.35.0" +botocore = ">=1.34.126,<1.35.0" jmespath = ">=0.7.1,<2.0.0" s3transfer = ">=0.10.0,<0.11.0" @@ -266,13 +266,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] [[package]] name = "botocore" -version = "1.34.113" +version = "1.34.126" description = "Low-level, data-driven core of boto 3." optional = true python-versions = ">=3.8" files = [ - {file = "botocore-1.34.113-py3-none-any.whl", hash = "sha256:8ca87776450ef41dd25c327eb6e504294230a5756940d68bcfdedc4a7cdeca97"}, - {file = "botocore-1.34.113.tar.gz", hash = "sha256:449912ba3c4ded64f21d09d428146dd9c05337b2a112e15511bf2c4888faae79"}, + {file = "botocore-1.34.126-py3-none-any.whl", hash = "sha256:7eff883c638fe30e0b036789df32d851e093d12544615a3b90062b42ac85bdbc"}, + {file = "botocore-1.34.126.tar.gz", hash = "sha256:7a8ccb6a7c02456757a984a3a44331b6f51c94cb8b9b287cd045122fd177a4b0"}, ] [package.dependencies] @@ -281,7 +281,7 @@ python-dateutil = ">=2.1,<3.0.0" urllib3 = {version = ">=1.25.4,<2.2.0 || >2.2.0,<3", markers = "python_version >= \"3.10\""} [package.extras] -crt = ["awscrt (==0.20.9)"] +crt = ["awscrt (==0.20.11)"] [[package]] name = "cachetools" @@ -296,13 +296,13 @@ files = [ [[package]] name = "certifi" -version = 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a/pyproject.toml +++ b/pyproject.toml @@ -2,7 +2,7 @@ name = "sotopia" version = "0.0.8" 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"}] diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index ed931d447..6e1090df6 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -1,5 +1,5 @@ from datetime import datetime -from datasets import load_dataset +import requests import rich from sotopia.database.persistent_profile import EnvironmentList import asyncio @@ -66,9 +66,9 @@ def check_existing_episodes( return False -def initilize_benchmark_combo(dataset) -> list[EnvAgentComboStorage]: # type: ignore +def initilize_benchmark_combo(data: list[dict[str, str]]) -> list[EnvAgentComboStorage]: list_of_env_agent_combo_storage = [] - for combo in dataset["train"]: + for combo in data: env_agent_combo_storage = EnvAgentComboStorage( env_id=combo["env_id"], agent_ids=combo["agent_ids"] ) @@ -161,8 +161,15 @@ def run_async_benchmark_in_batch( push_to_db: bool = False, verbose: bool = False, ) -> None: - dataset = load_dataset("cmu-lti/sotopia", data_files="benchmark_agents.json") - benchmark_combo = initilize_benchmark_combo(dataset) + 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}") + 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 ) From f5c269c6b77577557f2976070b9a8e25ef5650ec Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Sat, 15 Jun 2024 13:16:20 -0700 Subject: [PATCH 39/40] hotfix --- sotopia/benchmark/cli.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index 6e1090df6..b71aa1c0a 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -1,5 +1,5 @@ from datetime import datetime -import requests +import requests # type: ignore import rich from sotopia.database.persistent_profile import EnvironmentList import asyncio @@ -169,6 +169,7 @@ def run_async_benchmark_in_batch( 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 @@ -266,7 +267,7 @@ def _set_up_logs( def cli( model: str = typer.Option(..., help="The language model you want to benchmark."), partner_model: str = typer.Option( - "meta-llama/Llama-3-70b-chat-hf", + "together_ai/meta-llama/Llama-3-70b-chat-hf", help="The partner model you want to use.", ), evaluator_model: str = typer.Option( From 7165109e7e05019ae779be7960f5d7ed4a87c517 Mon Sep 17 00:00:00 2001 From: XuhuiZhou Date: Sat, 15 Jun 2024 13:23:14 -0700 Subject: [PATCH 40/40] chore: Remove unnecessary type hint in benchmark/cli.py --- sotopia/benchmark/cli.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sotopia/benchmark/cli.py b/sotopia/benchmark/cli.py index b71aa1c0a..9f519ac5b 100644 --- a/sotopia/benchmark/cli.py +++ b/sotopia/benchmark/cli.py @@ -1,5 +1,5 @@ from datetime import datetime -import requests # type: ignore +import requests import rich from sotopia.database.persistent_profile import EnvironmentList import asyncio