diff --git a/.github/workflows/mypy.yml b/.github/workflows/mypy.yml index 393e05094..bd3fc784b 100644 --- a/.github/workflows/mypy.yml +++ b/.github/workflows/mypy.yml @@ -1,11 +1,5 @@ name: Mypy -on: - push: - branches: - - main - pull_request: - branches: - - main +on: [push] jobs: Static-Type-Checking: @@ -21,15 +15,11 @@ jobs: python-version: 3.11.2 - name: Install dependencies run: | - pip install --upgrade pip - pip install -r requirements.txt - pip install -e .[dev] + curl -sSL https://install.python-poetry.org | python3 + poetry install --all-extras - name: Type-checking package with mypy run: | - # Manually install mypy in the standard way. - pip --quiet install -U mypy - # Log this mypy version for debuggability. - mypy --version # Run this mypy instance against our main package. - mypy --install-types --non-interactive sotopia - mypy --strict . + poetry run pip install types-protobuf==4.24.0.4 + poetry run mypy --install-types --non-interactive sotopia + poetry run mypy --strict . diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 9ea9a1947..de119fc0f 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -1,11 +1,5 @@ name: Pytest -on: - push: - branches: - - main - pull_request: - branches: - - main +on: [push] jobs: Pytest: @@ -21,13 +15,12 @@ jobs: python-version: 3.11.2 - name: Install dependencies run: | - pip install --upgrade pip - pip install -r requirements.txt - pip install -e .[dev] + curl -sSL https://install.python-poetry.org | python3 + poetry install --all-extras - name: Test with pytest env: # Or as an environment variable OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} REDIS_OM_URL: ${{ secrets.REDIS_OM_URL }} TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }} run: | - pytest + poetry run pytest diff --git a/README.md b/README.md index 3a91b64b6..03f1a8592 100644 --- a/README.md +++ b/README.md @@ -10,11 +10,12 @@ ## Installation +This package supports Python 3.11 and above. In one line, +`pip install sotopia`. -This package supports Python 3.11 and above. We recommend using a virtual environment to install this package, e.g. with anaconda3: `conda create -n sotopia python=3.11; conda activate sotopia; conda install -c conda-forge pip`. Then, install the requirements and this package. +Or from scratch, use a virtual environment, e.g. with anaconda3: `conda create -n sotopia python=3.11; conda activate sotopia; curl -sSL https://install.python-poetry.org | python3`. Then, install the requirements and this package. ```bash -python -m pip install -r requirements.txt # make sure the packages are installed in the specific conda environment -python -m pip install -e . +poetry install ``` OpenAI key is required to run the code. Please set the environment variable `OPENAI_API_KEY` to your key. The recommend way is to add the key to the conda environment: @@ -32,6 +33,11 @@ A redis-stack server is required to run the code. Please follow the [instruction conda env config vars set REDIS_OM_URL="redis://user:password@host:port" ``` +Make a folder to store the logs: +```bash +mkdir logs +``` + ## Easy Sample Server You can view an episode demo with default parameters using the following command: ```python diff --git a/docs/all_the_issues.md b/docs/all_the_issues.md new file mode 100644 index 000000000..598df8aa2 --- /dev/null +++ b/docs/all_the_issues.md @@ -0,0 +1,5 @@ +# Come here if you encounter any issues + +## Missing episodes + +Large batch size may cause some episodes to be skipped. This is due to the fact that the server may not be able to handle the load. Try reducing the batch size. But you can also use the script in `examples/fix_missing_episodes.py` to fix the missing episodes. diff --git a/docs/examples.md b/docs/examples.md new file mode 100644 index 000000000..231b7edc7 --- /dev/null +++ b/docs/examples.md @@ -0,0 +1,12 @@ +# Example Scripts For Using The Library + +## Example 1: Evaluating existing episodes + +```python +python examples/evaluate_existing_episodes.py --tag= --model= --batch_size= --push-to-db +``` + +Run ```python examples/evaluate_existing_episodes.py --help``` for more information. + +## Example 2: Generate script-like episodes +See `docs/simulation_modes.md` for more information. diff --git a/docs/hyperparameters.md b/docs/hyperparameters.md new file mode 100644 index 000000000..58b4a5b9a --- /dev/null +++ b/docs/hyperparameters.md @@ -0,0 +1,6 @@ +# Hyperparameters that are used in the simulation + +## Tags + +- `TAG`: The tag of the simulation. This tag is used to identify the simulation in the database. +- `TAG_TO_CHECK_EXISTING_EPISODES`: Scripts like `examples/experiment_eval.py` checks if there are existing episodes with the same tag in the database. If there are, the simulation **will not** be run. This is to avoid running the same simulation twice. If you want to run the simulation again, you can change the tag or set `TAG_TO_CHECK_EXISTING_EPISODES` to `None`. diff --git a/docs/simulation_modes.md b/docs/simulation_modes.md new file mode 100644 index 000000000..cdb2c7dde --- /dev/null +++ b/docs/simulation_modes.md @@ -0,0 +1,45 @@ +# Different Modes of Simulation + +## Simulation Modes + +The simulation can be run in different modes. The mode is specified in the configuration file. The following modes are available: + +### Sotopia-lite + +- `lite`: The simulation runs without characters' detailed background information but just names. To use this mode, set `lite` to `True` in the gin configuration command. +e.g., +```bash +python examples/experiment_eval.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="gpt-3.5-turbo"' \ + '--gin.AGENT2_MODEL="gpt-3.5-turbo"' \ + '--gin.BATCH_SIZE=5' \ + '--gin.TAG="lite_gpt3.5_gpt3.5"' \ + '--gin.TAG_TO_CHECK_EXISTING_EPISODES="lite_gpt3.5_gpt3.5"' \ + '--gin.PUSH_TO_DB=False' \ + '--gin.OMNISCIENT=False' \ + '--gin.VERBOSE=False' \ + '--gin.LITE=True' \ +``` + +### Sotopia-script + +- `script`: The simulation runs with enabling LLMs generating the interaction in one shot with a script writing setting. To use this mode, set `script` to `True` in the gin configuration command. + +e.g., + +```bash +python examples/generate_script.py \ + --gin_file sotopia_conf/generation_utils_conf/generate.gin \ + --gin_file sotopia_conf/run_async_server_in_batch_script.gin \ + '--gin.ENV_IDS=[]' \ + '--gin.SCRIPT_MODEL="gpt-3.5-turbo"' \ + '--gin.BATCH_SIZE=5' \ + '--gin.TAG="lite_script_gpt3.5_gpt3.5"' \ + '--gin.TAG_TO_CHECK_EXISTING_EPISODES="lite_script_gpt3.5_gpt3.5"' \ + '--gin.PUSH_TO_DB=True' \ + '--gin.VERBOSE=False' \ + ``` diff --git a/examples/evaluate_existing_episode.py b/examples/evaluate_existing_episode.py new file mode 100644 index 000000000..01ed14fb3 --- /dev/null +++ b/examples/evaluate_existing_episode.py @@ -0,0 +1,145 @@ +import asyncio +import logging +import subprocess +import typing +from datetime import datetime +from logging import FileHandler + +import gin +import typer +from experiment_eval import _iterate_env_agent_combo_not_in_db +from rich import print +from rich.logging import RichHandler +from tqdm import tqdm +from tqdm.asyncio import tqdm_asyncio + +from sotopia.agents.llm_agent import Agents +from sotopia.database.logs import AnnotationForEpisode, EpisodeLog +from sotopia.database.persistent_profile import EnvironmentProfile +from sotopia.generation_utils.generate import LLM_Name, agenerate_script +from sotopia.messages.message_classes import ( + AgentAction, + Observation, + ScriptBackground, +) +from sotopia.samplers import ( + BaseSampler, + ConstraintBasedSampler, + EnvAgentCombo, +) +from sotopia.server import aevaluate_one_episode, arun_one_script + +# 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.basicConfig( + level=15, + format=FORMAT, + datefmt="[%X]", + handlers=[ + RichHandler(), + FileHandler( + datetime.now().strftime( + f"./logs/%H_%M_%d_%m_%Y_{str(git_head_hash.decode('utf-8'))}.log" + ) + ), + ], +) +app = typer.Typer() + + +def run_async_server_in_batch_aevaluate( + batch_size: int = 10, + model: LLM_Name = "gpt-4", + reeval_list: list[str] = [], + tag: str | None = None, + push_to_db: bool = False, + verbose: bool = False, +) -> None: + + if not verbose: + logger = logging.getLogger() + logger.setLevel(logging.CRITICAL) + rich_handler = logger.handlers[0] + logger.removeHandler(rich_handler) + + episode_batch: list[EpisodeLog] = [] + + while True: + for env_pk in tqdm(reeval_list): + episode = EpisodeLog.get(env_pk) + episode_batch.append(episode) + if len(episode_batch) == batch_size: + logging.info( + f"Running batch of {batch_size} episodes: {episode_batch}" + ) + episode_futures = [ + aevaluate_one_episode( + episode=episode, + model=model, + tag=tag, + push_to_db=push_to_db, + ) + for episode in episode_batch + ] + asyncio.run( + tqdm_asyncio.gather( + *episode_futures, desc="Running one batch" + ) + ) + + episode_batch = [] + else: + if episode_batch: + logging.info( + f"Running batch of {batch_size} episodes: {episode_batch}" + ) + episode_futures = [ + aevaluate_one_episode( + episode=episode, + model=model, + tag=tag, + push_to_db=push_to_db, + ) + for episode in episode_batch + ] + asyncio.run( + tqdm_asyncio.gather( + *episode_futures, desc="Running one batch" + ) + ) + return + + +@app.command() +def run_server( + tag: str = "reeval_llama2", + model: str = "togethercomputer/llama-2-70b-chat", # Why typer does not accept LLM_Name? + batch_size: int = 10, + push_to_db: bool = True, + verbose: bool = False, +) -> None: + annotated_episodes_pks = [ + AnnotationForEpisode.get(anno).episode + for anno in AnnotationForEpisode.all_pks() + ] + annotated_episodes_pks = list(set(annotated_episodes_pks)) + model = typing.cast(LLM_Name, model) + # Call the function with the specified parameters + run_async_server_in_batch_aevaluate( + tag=tag, + model=model, + batch_size=batch_size, + push_to_db=push_to_db, + verbose=verbose, + reeval_list=annotated_episodes_pks, + ) + + +if __name__ == "__main__": + app() diff --git a/examples/fix_missing_episodes.py b/examples/fix_missing_episodes.py new file mode 100644 index 000000000..4e6a61b19 --- /dev/null +++ b/examples/fix_missing_episodes.py @@ -0,0 +1,377 @@ +import asyncio +import logging +from collections import Counter, defaultdict +from typing import ( + Any, + Dict, + Generator, + List, + Literal, + Optional, + Set, + cast, +) + +import gin +from absl import flags +from absl.flags import FLAGS +from rich.logging import RichHandler +from rich.terminal_theme import MONOKAI +from tqdm import tqdm + +from sotopia.agents.llm_agent import LLMAgent +from sotopia.database.env_agent_combo_storage import ( + EnvAgentComboStorage, +) +from sotopia.database.logs import EpisodeLog +from sotopia.database.persistent_profile import ( + AgentProfile, + EnvironmentProfile, +) +from sotopia.envs.evaluators import ( + ReachGoalLLMEvaluator, + RuleBasedTerminatedEvaluator, +) +from sotopia.envs.parallel import ParallelSotopiaEnv +from sotopia.generation_utils.generate import LLM_Name +from sotopia.messages.message_classes import AgentAction, Observation +from sotopia.samplers.base_sampler import BaseSampler, EnvAgentCombo +from sotopia.server import run_async_server +from sotopia_conf.gin_utils import parse_gin_flags, run +from sotopia_conf.server import _DEFAULT_GIN_SEARCH_PATHS + +# date and message only +FORMAT = "%(asctime)s - %(levelname)s - %(name)s - %(message)s" +logging.basicConfig( + level=15, + format=FORMAT, + datefmt="[%X]", + handlers=[ + RichHandler(), + ], +) + +# get all episode logs +def get_all_episodes() -> List[EpisodeLog]: + episode_pks: List[str] = list(EpisodeLog.all_pks()) + all_episodes = [] + for pk in tqdm(episode_pks): + try: + curr_ep = EpisodeLog.get(pk) + except: + continue + all_episodes.append(curr_ep) + print(f"all episodes loaded {len(all_episodes)}") + return all_episodes + + +# all env-agent combos +def get_all_env_agent_combos( + start_combo_idx: int, end_combo_idx: int +) -> Dict[str, EnvAgentComboStorage]: + experiment_env_pks = list(EnvironmentProfile.all_pks()) + all_combos_map: Dict[str, EnvAgentComboStorage] = {} + + for env_pk in experiment_env_pks: + env_agent_combo_storage_list = list( + EnvAgentComboStorage.find( + EnvAgentComboStorage.env_id == env_pk + ).all() + )[start_combo_idx:end_combo_idx] + for combo in env_agent_combo_storage_list: + all_combos_map[cast(str, combo.pk)] = cast( + EnvAgentComboStorage, combo + ) + print(f"all combos loaded {len(all_combos_map)}") + return all_combos_map + + +def find_combo_pk( + env_pk: str, + agent_id1: str, + agent_id2: str, + all_combos_map: Dict[str, EnvAgentComboStorage], +) -> str | None: + for combo_key in all_combos_map: + combo = all_combos_map[combo_key] + curr_tuple = (combo.env_id, combo.agent_ids[0], combo.agent_ids[1]) + if curr_tuple == (env_pk, agent_id1, agent_id2): + return combo_key + return None + + +def get_combo_model_map( + all_episodes: List[EpisodeLog], + all_combos_map: Dict[str, EnvAgentComboStorage], +) -> Dict[str, Counter[tuple[LLM_Name, LLM_Name, LLM_Name]]]: + combo_model_map: Dict[ + str, Counter[tuple[LLM_Name, LLM_Name, LLM_Name]] + ] = defaultdict(Counter) + bad_combos = [] + valid_count = 0 + invalid_count = 0 + bad_rewards_count = 0 + bad_gpt4_rewards_count = 0 + bad_combo_count = 0 + + # iterate through episodes + for i in tqdm(range(len(all_episodes))): + curr_ep = all_episodes[i] + bad_rewards = False + + # check if episode is valid + if not curr_ep.models: + invalid_count += 1 + continue + + # check if rewards are valid + for idx, model in enumerate(curr_ep.models[1:]): + if not isinstance(curr_ep.rewards[idx], tuple): + bad_rewards = True + break + if bad_rewards: + bad_rewards_count += 1 + if tuple(curr_ep.models) == ("gpt4", "gpt4", "gpt4"): + bad_gpt4_rewards_count += 1 + continue + + # find combo pk by env pk and agent ids + curr_combo_pk = find_combo_pk( + curr_ep.environment, + curr_ep.agents[0], + curr_ep.agents[1], + all_combos_map, + ) + if curr_combo_pk: + model_pair: tuple[LLM_Name, LLM_Name, LLM_Name] = cast( + tuple[LLM_Name, LLM_Name, LLM_Name], tuple(curr_ep.models) + ) + combo_model_map[curr_combo_pk][model_pair] += 1 + valid_count += 1 + else: + bad_combos.append( + (curr_ep.environment, curr_ep.agents[0], curr_ep.agents[1]) + ) + + bad_combo_count += 1 + print("-" * 20 + "Episode Parsing Summary" + "-" * 20) + print(f"valid episodes: {valid_count}") + print(f"invalid episodes (missing episode.models): {invalid_count}") + print(f"bad combo: {bad_combo_count}") + print(f"bad rewards: {bad_rewards_count}") + print(f"bad gpt4 rewards: {bad_gpt4_rewards_count}") + return combo_model_map + + +def get_all_model_pairs( + combo_model_map: Dict[str, Counter[tuple[LLM_Name, LLM_Name, LLM_Name]]] +) -> Set[tuple[LLM_Name, LLM_Name, LLM_Name]]: + all_model_pairs = set() + for key in combo_model_map: + for combo in combo_model_map[key]: + all_model_pairs.add(combo) + + # print all model pairs + print("-" * 20 + "All Model Pairs" + "-" * 20) + for pair in all_model_pairs: + print(pair) + print() + return all_model_pairs + + +def get_all_missing_model_pairs( + combo_model_map: Dict[str, Counter[tuple[LLM_Name, LLM_Name, LLM_Name]]], + all_model_pairs: Set[tuple[LLM_Name, LLM_Name, LLM_Name]], + num_required: int, +) -> Dict[str, Counter[tuple[LLM_Name, LLM_Name, LLM_Name]]]: + combo_missing_model_map: Dict[ + str, Counter[tuple[LLM_Name, LLM_Name, LLM_Name]] + ] = defaultdict(Counter) + missing_count = 0 + for key in combo_model_map: + for model_pair in all_model_pairs: + if combo_model_map[key][model_pair] < num_required: + combo_missing_model_map[key][model_pair] += ( + num_required - combo_model_map[key][model_pair] + ) + missing_count += ( + num_required - combo_model_map[key][model_pair] + ) + print("-" * 20 + f"Missing {missing_count} Model Pairs" + "-" * 20) + print() + return combo_missing_model_map + + +# temporally used for making sure unique (env, agents, models) setting; need to change +# according to the Counter in the case needing to run multiple experiments for one setting +def get_missing_model_combo_map( + combo_missing_model_map: Dict[ + str, Counter[tuple[LLM_Name, LLM_Name, LLM_Name]] + ], + all_combos_map: Dict[str, EnvAgentComboStorage], +) -> Dict[tuple[LLM_Name, LLM_Name], List[tuple[str, str, str]]]: + missing_model_combo_map = defaultdict(list) + for combo_pk in combo_missing_model_map: + model_counter = combo_missing_model_map[combo_pk] + for model_pair in model_counter: + model_pair_key = (model_pair[1], model_pair[2]) + combo_model = all_combos_map[combo_pk] + missing_model_combo_map[model_pair_key].append( + ( + combo_model.env_id, + combo_model.agent_ids[0], + combo_model.agent_ids[1], + ) + ) + print("-" * 20 + "Missing Model to Combo Map" + "-" * 20) + for key in missing_model_combo_map: + print(f"Model pair: {key}") + print(f"Number of missing combos: {len(missing_model_combo_map[key])}") + return missing_model_combo_map + + +def yield_env_agent_combo( + combo_ids: list[tuple[str, str, str]], model_names: dict[str, LLM_Name] +) -> Generator[EnvAgentCombo[Observation, AgentAction], None, None]: + for combo_id in combo_ids: + env_id, agent_id1, agent_id2 = combo_id + 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_id1, agent_id2) + ] + + 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 re_run_missing_episodes( + combo_with_models: dict[ + tuple[LLM_Name, LLM_Name], list[tuple[str, str, str]] + ], + model_names: dict[str, LLM_Name] = { + "env": "gpt-4", + "agent1": "gpt-3.5-turbo", + "agent2": "gpt-3.5-turbo", + }, + batch_size: int = 5, + verbose: bool = False, +) -> None: + # pioritize gpt4 vs gpt3.5 + # pioritize gpt3.5 vs gpt3 + if not verbose: + logger = logging.getLogger() + logger.setLevel(logging.CRITICAL) + rich_handler = logger.handlers[0] + logger.removeHandler(rich_handler) + combo_and_models_to_run = combo_with_models[ + (model_names["agent1"], model_names["agent2"]) + ] + env_agent_combo_iter_length = len(combo_and_models_to_run) + print(f"total missing episodes: {env_agent_combo_iter_length}") + + env_agent_combo_iter = yield_env_agent_combo( + combo_and_models_to_run, model_names + ) + env_agent_combo_batch: list[EnvAgentCombo[Observation, AgentAction]] = [] + 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, + push_to_db=True, + tag="missing_episodes", + ) + ) + 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, + push_to_db=True, + tag="missing_episodes", + ) + ) + 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, + ) + + all_episodes = get_all_episodes() + all_combos_map = get_all_env_agent_combos(0, 5) + combo_model_map = get_combo_model_map(all_episodes, all_combos_map) + all_model_pairs = get_all_model_pairs(combo_model_map) + combo_missing_model_map = get_all_missing_model_pairs( + combo_model_map, all_model_pairs, 1 + ) + missing_model_combo_map = get_missing_model_combo_map( + combo_missing_model_map, all_combos_map + ) + re_run_missing_episodes(missing_model_combo_map) + + +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/examples/generate_script.py b/examples/generate_script.py new file mode 100644 index 000000000..9c3f0df05 --- /dev/null +++ b/examples/generate_script.py @@ -0,0 +1,258 @@ +import asyncio +import logging +import os +import subprocess +from datetime import datetime +from logging import FileHandler +from typing import Any + +import gin +from absl import flags +from experiment_eval import _iterate_env_agent_combo_not_in_db +from rich import print +from rich.logging import RichHandler +from tqdm import tqdm +from tqdm.asyncio import tqdm_asyncio + +from sotopia.envs.parallel import ParallelSotopiaEnv +from sotopia.generation_utils.generate import LLM_Name, agenerate_script +from sotopia.messages.message_classes import ( + AgentAction, + Observation, + ScriptBackground, +) +from sotopia.samplers import EnvAgentCombo, UniformSampler +from sotopia.server import arun_one_script, run_async_server +from sotopia_conf.gin_utils import parse_gin_flags, run + +# date and message only +FORMAT = "%(asctime)s - %(levelname)s - %(name)s - %(message)s" +FLAGS = flags.FLAGS +_DEFAULT_GIN_SEARCH_PATHS = [ + os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +] + +logging.basicConfig( + level=15, + format=FORMAT, + datefmt="[%X]", + handlers=[ + RichHandler(), + FileHandler("./logs/round_robin_parallel_sotopia_env_2.log"), + ], +) + + +@gin.configurable +def single_step( + model_names: dict[str, LLM_Name], + tag: str | None = None, + batch_size: int = 5, + push_to_db: bool = True, + json_in_script: bool = False, + # TODO We are not sure whether json_in_script is actually needed because using json is consistently better in format than free-form generation. Same as below. +) -> None: + print("Generating script with single step format") + print(f"Use json: {json_in_script}") + + env_agent_combo_iter = _iterate_env_agent_combo_not_in_db( + model_names=model_names, + tag=tag, + ) + env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) + + env_agent_combo_iter = _iterate_env_agent_combo_not_in_db( + model_names=model_names, + tag=tag, + ) + env_agent_combo_batch: list[EnvAgentCombo[Observation, AgentAction]] = [] + + 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, + action_order="round-robin", + env_agent_combo_list=env_agent_combo_batch, + script_like=True, + json_in_script=json_in_script, + push_to_db=push_to_db, + tag=tag, + ) + ) + env_agent_combo_batch = [] + else: + if env_agent_combo_batch: + logging.info( + f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" + ) + _ = asyncio.run( + run_async_server( + model_dict=model_names, + action_order="round-robin", + env_agent_combo_list=env_agent_combo_batch, + script_like=True, + json_in_script=json_in_script, + push_to_db=push_to_db, + tag=tag, + ) + ) + return + + +@gin.configurable +def full_freeform( + model_names: dict[str, LLM_Name], + tag: str | None = None, + batch_size: int = 5, + push_to_db: bool = True, +) -> None: + print("Generating full script with freeform format") + + env_agent_combo_iter = _iterate_env_agent_combo_not_in_db( + model_names=model_names, + tag=tag, + ) + env_agent_combo_iter_length = sum(1 for _ in env_agent_combo_iter) + + env_agent_combo_iter = _iterate_env_agent_combo_not_in_db( + model_names=model_names, + tag=tag, + ) + env_agent_combo_batch: list[EnvAgentCombo[Observation, AgentAction]] = [] + + 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}" + ) + episode_futures = [ + arun_one_script( + env=env_agent_combo[0], + agent_list=env_agent_combo[1], + model_dict=model_names, + tag=tag, + push_to_db=push_to_db, + ) + for env_agent_combo in env_agent_combo_batch + ] + _ = asyncio.run( + tqdm_asyncio.gather( + *episode_futures, desc="Running one batch" + ) + ) + + env_agent_combo_batch = [] + else: + if env_agent_combo_batch: + logging.info( + f"Running batch of {batch_size} episodes: {env_agent_combo_batch}" + ) + for env_agent_combo in env_agent_combo_batch: + episode_futures = [ + arun_one_script( + env=env_agent_combo[0], + agent_list=env_agent_combo[1], + model_dict=model_names, + tag=tag, + push_to_db=push_to_db, + ) + for env_agent_combo in env_agent_combo_batch + ] + _ = asyncio.run( + tqdm_asyncio.gather( + *episode_futures, desc="Running one batch" + ) + ) + return + + +@gin.configurable +def run_async_server_in_batch_script( + *, + batch_size: int = 10, + model: LLM_Name = "gpt-3.5-turbo", + tag: str | None = None, + push_to_db: bool = True, + json_in_script: bool = False, + generate_in_full: bool = False, + verbose: bool = False, +) -> None: + model_names: dict[str, LLM_Name] = { + "env": model, + "agent1": model, + "agent2": model, + } # We are not using agent 1 and agent 2 in full script writing, and we are also using the same agent model in single step writing, so we can just use the same model for all three. + if not verbose: + logger = logging.getLogger() + logger.setLevel(logging.CRITICAL) + rich_handler = logger.handlers[0] + logger.removeHandler(rich_handler) + + if generate_in_full: + # Generate script in full + full_freeform( + model_names=model_names, + tag=tag, + batch_size=batch_size, + push_to_db=push_to_db, + ) + else: + # Generate script in single step + single_step( + model_names=model_names, + tag=tag, + batch_size=batch_size, + push_to_db=push_to_db, + json_in_script=json_in_script, + ) + + +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_server_in_batch_script() + + +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/experiment.sh b/experiment.sh deleted file mode 100644 index 0408ad092..000000000 --- a/experiment.sh +++ /dev/null @@ -1,10 +0,0 @@ -python examples/experiment_eval.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="togethercomputer/llama-2-70b-chat"' \ - '--gin.AGENT2_MODEL="gpt-3.5-turbo"' \ - '--gin.BATCH_SIZE=20' \ - '--gin.TAG="aug18_gpt3.5_llama-2-70b-chat_zqi2"' \ - '--gin.PUSH_TO_DB=True' diff --git a/experiment2.sh b/experiment2.sh deleted file mode 100644 index 3bf9fd42f..000000000 --- a/experiment2.sh +++ /dev/null @@ -1,10 +0,0 @@ -python examples/experiment_eval.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="togethercomputer/llama-2-70b-chat"' \ - '--gin.AGENT2_MODEL="gpt-4"' \ - '--gin.BATCH_SIZE=20' \ - '--gin.TAG="aug20_gpt4_llama-2-70b-chat_zqi2"' \ - '--gin.PUSH_TO_DB=True' diff --git a/lmlib/serve/lm_inference.py b/lmlib/serve/lm_inference.py index bef6cee39..8b2995826 100644 --- a/lmlib/serve/lm_inference.py +++ b/lmlib/serve/lm_inference.py @@ -56,7 +56,7 @@ def get_gpu_memory(max_gpus: Union[int, None] = None) -> list[float]: with torch.cuda.device(gpu_id): device = torch.cuda.current_device() gpu_properties = torch.cuda.get_device_properties(device) - total_memory = gpu_properties.total_memory / (1024**3) + total_memory = gpu_properties.total_memory / (1024**3) # type: ignore allocated_memory = torch.cuda.memory_allocated() / (1024**3) available_memory = total_memory - allocated_memory gpu_memory.append(available_memory) diff --git a/poetry.lock b/poetry.lock new file mode 100644 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Zhu "] +license = "MIT License" +readme = "README.md" +packages = [{include = "sotopia"}] + +[tool.poetry.dependencies] +python = ">=3.11, <3.12" +pandas = "^2.1.1" +lxml = "^4.9.3" +scipy = "^1.11.3" +openai = "^0.28.1" +langchain = "^0.0.311" +tiktoken = "^0.5.1" +rich = "^13.6.0" +jinja2 = "^3.1.2" +PettingZoo = "1.24.0" +redis-om = "^0.2.1" +pandas-stubs = "*" +types-tqdm = "*" +fastapi = "^0.103.2" +uvicorn = "^0.23.2" +gin-config = "^0.5.0" +absl-py = "^2.0.0" +names = "^0.3.0" +together = "^0.2.4" +pydantic = "1.10.12" +mypy = "^1.8.0" +beartype = "^0.14.0" +torch = "^2.1" +transformers = "^4.34.0" + +[tool.poetry.group.dev.dependencies] +pre-commit = "*" +nbmake = "*" +types-setuptools = "*" + +[tool.poetry.group.test.dependencies] +pytest = "*" +pytest-asyncio = "*" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.mypy] +mypy_path = "stubs" +strict = true +plugins = [ + "pydantic.mypy" +] + +[[tool.mypy.overrides]] +module = "transformers.*" +ignore_missing_imports = true + + +[tool.pytest.ini_options] +testpaths = ["tests"] +python_files = "test_*.py" diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 638ba97cc..000000000 --- a/requirements.txt +++ /dev/null @@ -1,22 +0,0 @@ -pandas -lxml -openai -langchain -tiktoken -rich -jinja2 -git+https://github.com/ProKil/PettingZoo.git -redis-om -pandas-stubs -types-tqdm -peft -transformers -logzero -fastapi -uvicorn -gin-config -absl-py -datasets -names -together -pydantic==1.10.12 diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index f0c6da31f..000000000 --- a/setup.cfg +++ /dev/null @@ -1,31 +0,0 @@ -[metadata] -name = sotopia - -[tool.pytest.ini_options] -testpaths = ["tests"] -python_files = "test_*.py" - -[options.extras_require] -dev = - pre-commit==3.0.1 - pytest==7.1.2 - mypy==1.2.0 - beartype==0.14.0 - nbmake - types-setuptools - pytest-asyncio - -[options] -python_requires = >=3.7, <4 -packages = - sotopia - lmlib - sotopia_conf - -[mypy] -mypy_path = stubs -strict = true -plugins = pydantic.mypy - -[mypy-transformers.*] -ignore_missing_imports = True diff --git a/setup.py b/setup.py deleted file mode 100644 index 7f1a1763c..000000000 --- a/setup.py +++ /dev/null @@ -1,4 +0,0 @@ -from setuptools import setup - -if __name__ == "__main__": - setup() diff --git a/sotopia-chat/fastapi_server.py b/sotopia-chat/fastapi_server.py index 27673b180..a125bdd4e 100644 --- a/sotopia-chat/fastapi_server.py +++ b/sotopia-chat/fastapi_server.py @@ -44,7 +44,7 @@ conn = Redis.from_url(os.environ["REDIS_OM_URL"]) -WAITING_ROOM_TIMEOUT = 60 # 60 secs +WAITING_ROOM_TIMEOUT = float(os.environ.get("WAITING_ROOM_TIMEOUT", 1.0)) @app.post("/connect/{session_id}/{role}/{id}") diff --git a/sotopia/agents/__init__.py b/sotopia/agents/__init__.py index e56b1c4f2..53794cad8 100644 --- a/sotopia/agents/__init__.py +++ b/sotopia/agents/__init__.py @@ -3,7 +3,13 @@ generate_background, generate_background_conversation, ) -from .llm_agent import Agents, HumanAgent, LLMAgent, SpeakAgent +from .llm_agent import ( + Agents, + HumanAgent, + LLMAgent, + ScriptWritingAgent, + SpeakAgent, +) from .redis_agent import RedisAgent __all__ = [ @@ -15,4 +21,5 @@ "generate_background", "generate_background_conversation", "RedisAgent", + "ScriptWritingAgent", ] diff --git a/sotopia/agents/llm_agent.py b/sotopia/agents/llm_agent.py index 33b94c7cf..176db88fb 100644 --- a/sotopia/agents/llm_agent.py +++ b/sotopia/agents/llm_agent.py @@ -7,11 +7,13 @@ from sotopia.generation_utils.generate import ( LLM_Name, agenerate_action, + agenerate_script, generate_action, generate_action_speak, generate_goal, ) from sotopia.messages import AgentAction, Message, Observation +from sotopia.messages.message_classes import ScriptBackground async def ainput(prompt: str = "") -> str: @@ -30,6 +32,7 @@ def __init__( uuid_str: str | None = None, agent_profile: AgentProfile | None = None, model_name: LLM_Name = "gpt-3.5-turbo", + script_like: bool = False, ) -> None: super().__init__( agent_name=agent_name, @@ -37,6 +40,7 @@ def __init__( agent_profile=agent_profile, ) self.model_name = model_name + self.script_like = script_like @property def goal(self) -> str: @@ -94,10 +98,65 @@ async def aact(self, obs: Observation) -> AgentAction: action_types=obs.available_actions, agent=self.agent_name, goal=self.goal, + script_like=self.script_like, ) return action +from typing import cast + + +class ScriptWritingAgent(LLMAgent): + def __init__( + self, + agent_name: str | None = None, + uuid_str: str | None = None, + agent_profile: AgentProfile | None = None, + model_name: LLM_Name = "gpt-3.5-turbo", + agent_names: list[str] = [], + background: ScriptBackground | None = None, + ) -> None: + super().__init__( + agent_name=agent_name, + uuid_str=uuid_str, + agent_profile=agent_profile, + ) + self.model_name = model_name + self.agent_names = agent_names + assert background is not None, "background cannot be None" + self.background = background + + async def aact(self, obs: Observation) -> AgentAction: + self.recv_message("Environment", obs) + message_to_compose = [ + y for idx, (x, y) in enumerate(self.inbox) if idx != 0 + ] + + history = "\n".join( + f"{y.to_natural_language()}" for y in message_to_compose + ) + print("Current agent: ", self.agent_name) + print("Composed history: ", history) + + action, prompt = await agenerate_script( + model_name=self.model_name, + background=self.background, + agent_names=self.agent_names, + history=history, + agent_name=self.agent_name, + single_step=True, + ) + # action: tuple[ + # list[list[tuple[str, str, Message]]], list[tuple[str, Message]] + # ] + returned_action = cast(AgentAction, action[1][0][1]) + print("Action: ", returned_action, type(returned_action)) + # print("Action: ", action) + # exit(0) + + return returned_action + + class SpeakAgent(LLMAgent): def act( self, diff --git a/sotopia/agents/redis_agent.py b/sotopia/agents/redis_agent.py index c5956d985..2a640a198 100644 --- a/sotopia/agents/redis_agent.py +++ b/sotopia/agents/redis_agent.py @@ -15,8 +15,6 @@ from sotopia.database import AgentProfile, MessageTransaction from sotopia.messages import AgentAction, Observation -_URL = "http://tiger.lti.cs.cmu.edu:8000" - class RedisAgent(BaseAgent[Observation, AgentAction]): """An agent use redis as a message broker.""" @@ -36,8 +34,15 @@ 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()) + print(f"session id: {self.session_id}") + print("step 1: connect to the server") + assert ( + "FASTAPI_URL" in os.environ + ), "To use redis agent, you have to launch a FastAPI server and set FASTAPI_URL" + self._URL = os.environ["FASTAPI_URL"] response = requests.request( - "POST", f"{_URL}/connect/{self.session_id}/server/{self.sender_id}" + "POST", + f"{self._URL}/connect/{self.session_id}/server/{self.sender_id}", ) assert ( response.status_code == 200 and response.text == "[]" @@ -60,9 +65,10 @@ async def aact( if len(obs.available_actions) == 1 and "none" in obs.available_actions: if obs.turn_number == 0: async with aiohttp.ClientSession() as session: + print("step 2: post observation to the message list") response = await session.request( "POST", - f"{_URL}/send/{self.session_id}/{self.sender_id}", + f"{self._URL}/send/{self.session_id}/{self.sender_id}", data=obs.to_natural_language(), ) assert response.status == 200, response @@ -81,7 +87,7 @@ async def aact( # 1. post observation to the message list response = await session.request( "POST", - f"{_URL}/send/{self.session_id}/{self.sender_id}", + f"{self._URL}/send/{self.session_id}/{self.sender_id}", data=obs.to_natural_language(), ) assert response.status == 200, response @@ -97,7 +103,7 @@ async def aact( # 2. unlock the server for the client response = await session.request( "PUT", - f"{_URL}/lock/{self.session_id}/{self.sender_id}/action", + f"{self._URL}/lock/{self.session_id}/{self.sender_id}/action", ) assert response.status == 200, response @@ -106,7 +112,7 @@ async def aact( for _ in range(300): response = await session.request( "GET", - f"{_URL}/get/{self.session_id}", + f"{self._URL}/get/{self.session_id}", ) # print(f"get response: {response}") assert response.status == 200, response @@ -125,7 +131,7 @@ async def aact( # 3.a if the client has posted their message, lock the server for the client response = await session.request( "PUT", - f"{_URL}/lock/{self.session_id}/{self.sender_id}/no%20action", + f"{self._URL}/lock/{self.session_id}/{self.sender_id}/no%20action", ) assert response.status == 200, response break @@ -135,7 +141,7 @@ async def aact( else: response = await session.request( "PUT", - f"{_URL}/lock/{self.session_id}/{self.sender_id}/no%20action", + f"{self._URL}/lock/{self.session_id}/{self.sender_id}/no%20action", ) self.reset( "Someone has left or the conversation is too long." @@ -164,7 +170,7 @@ def reset( if reset_reason != "": response = requests.request( "POST", - f"{_URL}/send/{self.session_id}/{self.sender_id}", + f"{self._URL}/send/{self.session_id}/{self.sender_id}", json=reset_reason, ) assert response.status_code == 200 diff --git a/sotopia/envs/evaluators.py b/sotopia/envs/evaluators.py index d0659064b..11ce98caf 100644 --- a/sotopia/envs/evaluators.py +++ b/sotopia/envs/evaluators.py @@ -1,7 +1,7 @@ import abc import logging from collections import defaultdict -from typing import Generic +from typing import Generic, Type, Union import gin from beartype import beartype @@ -92,11 +92,83 @@ def minus_ten_to_zero_validator( return v +class EvaluationBySocialDimensionsPlus(BaseModel): + believability: tuple[str, int] = Field( + ..., + description="Reasoning requirement: 1. Evaluate if the agent interacts with others in a natural and realistic manner (here are a few common questions to check: a. whether the agent is confusing with its own identity? b. whether the agent repeats others' words/actions without any reason? c. whether the agent is being overly polite considering the context?). Start the analysis with tag " + "2. Analyze whether the actions of the agent align with their character traits (e.g., personality, values, and etc.). Start the analysis with tag . " + "Output your reasoning process to the 'reasoning' field. Output an integer score ranging from 0 and 10 in the 'score' field. A higher score indicates that the agent is more believable. Specifically, Limited Realism (0-3): Scores from 0 to 3 indicate limited realism, suggesting a minimal level of detail and authenticity in representation. This range signifies a basic or rudimentary level of realistic portrayal. Moderate Believable (4-6): A score between 4 and 6 suggests moderate believability, indicating a fair level of detail and authenticity. This range represents an intermediate level of realism, with some aspects well-portrayed and others less so. Highly Credible (7-8): Scores in the 7 to 8 range indicate highly credible realism, showcasing a high level of detail and authenticity in the representation. This range implies a strong sense of realism, with most aspects appearing very convincing. Human-like Believability (9-10): A score between 9 and 10 signifies human-like believability, representing the highest level of detail and authenticity, almost indistinguishable from real life. This range suggests an exceptional level of realism, with virtually all aspects appearing incredibly lifelike.", + ) + relationship: tuple[str, int] = Field( + ..., + description="Please first analyze what relationship the participant has with the other agent(s) before the interaction. " + "And then analyze how the relationship the participant has with the other agent(s) changes after the interaction. " + "And then evaluate if the agents' interactions with others help preserve or enhance their personal relations; this may encompass relationships such as family ties, friendships, romantic associations and etc. " + "Additionally, ascertain whether these interactions also impact their social status or reputation. " + "In the 'reasoning' field, provide a comprehensive account of the logic or thought process that led you to your conclusion. Further, provide an integer score ranging from -5 to 5 in the 'score' field. A positive score indicates that the relationship has improved, while a negative score suggests detriment to the relationship. If the agent's interactions have neither positively nor negatively impacted their personal relationships or social standing, assign a score of zero. Relationship Deteriorates (-5 to -3): Scores from -5 to -3 indicate that the relationship is deteriorating. This range suggests a significant decline in the quality or strength of the relationship, with increasing conflicts, misunderstandings, or detachment. Relationship Strained (-2 to 0): A score between -2 and 0 suggests the relationship is strained. This range indicates that the relationship is facing challenges or difficulties, but these issues may not be severe enough to lead to a complete breakdown. The relationship is under stress but not entirely negative. Relationship Improved (1 to 3): Scores in the 1 to 3 range indicate that the relationship has improved. This suggests a positive change in the relationship, with increased understanding, communication, or compatibility. The relationship is getting stronger and more positive. Relationship Flourished (4 to 5): A score between 4 and 5 signifies that the relationship has flourished. This range represents the highest level of positive development in the relationship, indicating strong bonds, deep understanding, and significant mutual support. The relationship is thriving and at its best.", + ) + knowledge: tuple[str, int] = Field( + ..., + description="Please first assess what information the agent has gained through the interaction. " + "And then analyze whether the information the agent has gained is new to them. " + "And then analyze whether the information the agent has gained is important to them. " + "In the 'reasoning' field, provide a comprehensive account of the logic or thought process that led you to your conclusion. Further, provide an integer score ranging from 0 and 10 in the 'score' field. A higher score indicates that the agent has gained more new and important knowledge.Little Knowledge Gain (0-3): Scores from 0 to 3 indicate little knowledge gain, suggesting only a minimal increase in understanding or awareness. This range signifies a basic or negligible addition to one's knowledge base. Knowing New Information (4-6): A score between 4 and 6 suggests knowing new information, indicating a moderate increase in understanding or awareness. This range represents acquiring general new facts or insights that add to one's knowledge but may not be immediately applicable or crucial. Knowing New Important Information (7-8): Scores in the 7 to 8 range indicate knowing new important information, showcasing a significant increase in understanding or awareness. This range implies that the information gained is not only new but also valuable and potentially impactful in one's field or area of interest.Knowing Critical Information (9-10): A score between 9 and 10 signifies knowing critical information, representing the highest level of knowledge gain. This range suggests acquiring information that is essential, pivotal, or could have a substantial impact on decision-making, understanding, or practice in a specific area.", + ) + secret: tuple[str, int] = Field( + ..., + description="First please analyze what secret or secretive intention the participant wants to keep. " + "And then analyze whether they keep it successfully. Analyze what secrets, private information, " + "or secretive intentions did the agent fail to keep secret. " + "Please output the reasoning in the reasoning field. " + "Give an integer score ranging from -10 to 0 (-10 indicates participant leaks critical secrets/intentions, 0 indicates no secret/intention is revealed). Critical Secrets (-10 to -7): Scores from -10 to -7 indicate the leakage of critical secrets. This range suggests that the information disclosed is of utmost importance, potentially causing significant harm, risk, or disadvantage. The secrets are highly sensitive and could have severe implications. Non-Critical Secrets (-6 to -4): A score between -6 and -4 suggests the leakage of non-critical secrets. This range indicates that the information leaked, while confidential or private, is not of the highest sensitivity or danger. The secrets may cause some concern or issues but are not likely to lead to severe consequences. Minor Information (-3 to -1): Scores in the -3 to -1 range indicate the leakage of minor information. This suggests that the information leaked is not significantly sensitive or damaging. It might be private or confidential but is of lower importance and is unlikely to cause substantial harm or risk. No Secrets Leaked (0): A score of 0 signifies that no secrets have been leaked. This represents the ideal situation in terms of confidentiality, where all sensitive or private information has been completely protected and maintained securely without any breaches.", + ) + social_rules: tuple[str, int] = Field( + ..., + description="Please evaluate whether the agent violates any moral rules or laws in their interactions with others. " + "In the 'reasoning' field, provide a comprehensive account of the logic or thought process that led you to your conclusion. Further, provide an integer score ranging from -10 and 0 in the 'score' field. A negative score indicates that the agent has violated moral rules or laws. Law Breaking (-10 to -8): Scores from -10 to -8 indicate law-breaking actions. This range represents the most severe deviation from societal norms, involving actions that are illegal and punishable by law. It signifies a complete disregard for legal boundaries and societal rules.Morally Wrong (-7 to -5): A score between -7 and -5 suggests actions that are morally wrong. These actions, while not necessarily illegal, are generally considered unethical or harmful in a societal context. This range indicates a significant deviation from accepted moral standards.Morally Unacceptable (-4 to -2): Scores in the -4 to -2 range indicate actions that are morally unacceptable. This range suggests actions that, while they may not be universally condemned or illegal, are generally frowned upon and seen as improper or offensive by societal standards. Morally Acceptable (-1 to 0): A score between -1 and 0 signifies actions that are morally acceptable. This range indicates adherence to societal norms and moral standards. Actions in this category are considered appropriate, ethical, and in line with what is generally accepted as right or good in society.", + ) + financial_and_material_benefits: tuple[str, int] = Field( + ..., + description="Please evaluate whether the agent's interactions with others contribute towards financial and material benefits. Analyze what the agent would gain/lose after the interactions. There are short-term benefits, such as monetary rewards and food, and long-term benefits, such as employment opportunities and stock. " + "In the 'reasoning' field, provide a comprehensive account of the logic or thought process that led you to your conclusion. Further, provide an integer score ranging from -5 and 5 in the 'score' field. Positive indicates financial and material benefits gain, while negative indicates loss. Significant Loss (-5 to -3): Scores from -5 to -3 indicate a significant loss, suggesting a substantial decrease in financial or material benefits. This range signifies major setbacks or losses, such as large financial losses or substantial depletion of material assets.Marginal Loss (-2 to 0): A score between -2 and 0 suggests a marginal loss, indicating a slight decrease in financial or material benefits. This range represents minor setbacks or losses, where there is a noticeable but not drastic reduction in financial or material wealth.Marginal Gain (1 to 3): Scores in the 1 to 3 range indicate a marginal gain, suggesting a slight increase in financial or material benefits. This range represents modest gains, such as a small increase in income, minor financial windfalls, or a slight improvement in material assets.Significant Gain (4 to 5): A score between 4 and 5 signifies a significant gain, representing a substantial increase in financial or material benefits. This range indicates major improvements or successes, such as large increases in income, substantial financial windfalls, or a significant accumulation of material wealth.", + ) + goal: tuple[str, int] = Field( + ..., + description="Please first reiterate agent's social goals. " + "And then please provide a comprehensive analysis about the extent to which the agent has managed to achieve these goals. " + "In the 'reasoning' field, provide a comprehensive account of the logic or thought process that led you to your conclusion. Further, provide an integer score ranging from 0 and 10 in the 'score' field. 0 represents minimal goals achievement, 10 represents complete goal achievement, and a higher score indicates that the agent is making progress towards their social goals. Almost Not Finishing Any Goal (0-3): Scores from 0 to 3 indicate almost not finishing any goal, suggesting a minimal level of goal achievement. This range signifies either no progress or only a very rudimentary level of advancement towards the completion of set goals. Finishing Less Than 50% of Goals (4-6): A score between 4 and 6 suggests finishing less than 50% of the goals, indicating a moderate level of goal completion. This range represents partial success, with some goals being met while a significant portion remains unachieved. Finishing More Than 50%, But Not All Goals (7-8): Scores in the 7 to 8 range indicate finishing more than 50% but not all of the goals. This suggests a high level of achievement, where the majority of set goals are met, but some goals still remain incomplete. Finishing All Goals (9-10): A score between 9 and 10 signifies finishing all goals, representing the highest level of achievement in goal completion. This range indicates that all set objectives have been met, signifying complete success in achieving the targeted goals.", + ) + + @validator("believability", "knowledge", "goal") + def zero_to_ten_validator(cls, v: tuple[str, int]) -> tuple[str, int]: + assert v[1] >= 0 and v[1] <= 10 + return v + + @validator("relationship", "financial_and_material_benefits") + def minus_five_to_five_validator( + cls, v: tuple[str, int] + ) -> tuple[str, int]: + assert v[1] >= -5 and v[1] <= 5 + return v + + @validator("secret", "social_rules") + def minus_ten_to_zero_validator( + cls, v: tuple[str, int] + ) -> tuple[str, int]: + assert v[1] >= -10 and v[1] <= 0 + return v + + class EnvResponse(BaseModel): agent_1_evaluation: EvaluationBySocialDimensions agent_2_evaluation: EvaluationBySocialDimensions +class EnvResponsePlus(BaseModel): + agent_1_evaluation: EvaluationBySocialDimensionsPlus + agent_2_evaluation: EvaluationBySocialDimensionsPlus + + class Evaluator(abc.ABC): def __init__(self) -> None: pass @@ -175,9 +247,12 @@ async def __acall__( class ReachGoalLLMEvaluator(Evaluator): @beartype - def __init__(self, model_name: LLM_Name) -> None: + def __init__( + self, model_name: LLM_Name, response_format: str = "basic" + ) -> None: self.model_name = model_name self.prompt = "" + self.response_format = response_format def __call__( self, turn_number: int, messages: list[tuple[str, Message]] @@ -191,26 +266,31 @@ def __call__( async def __acall__( self, turn_number: int, - messages: list[tuple[str, Message]], + messages: list[tuple[str, Message]] | None, + history: str = "", temperature: float = 0.7, ) -> list[tuple[str, tuple[tuple[str, int | float | bool], str]]]: # filter did nothing - messages_filtered = [ - (x, y) - for x, y in messages - if "did nothing" not in y.to_natural_language() - ] - history = "\n".join( - [ - f"{x} {y.to_natural_language()}" - if x != "Environment" - else y.to_natural_language() - for x, y in messages_filtered + if not history and messages: + messages_filtered = [ + (x, y) + for x, y in messages + if "did nothing" not in y.to_natural_language() ] + history = "\n".join( + [ + f"{x} {y.to_natural_language()}" + if x != "Environment" + else y.to_natural_language() + for x, y in messages_filtered + ] + ) + response_format_class = ( + EnvResponsePlus if self.response_format == "plus" else EnvResponse ) try: - response: EnvResponse # fix type error from langchain 0.0.264. we don't need this line for langchain 0.0.263 + response: EnvResponsePlus | EnvResponse # fix type error from langchain 0.0.264. we don't need this line for langchain 0.0.263 response, prompt = await agenerate( model_name=self.model_name, template="""{history}, @@ -219,9 +299,9 @@ async def __acall__( {format_instructions} """, input_values=dict(history=history), - output_parser=PydanticOutputParser[EnvResponse]( - pydantic_object=EnvResponse - ), + output_parser=PydanticOutputParser[ + EnvResponsePlus | EnvResponse + ](pydantic_object=response_format_class), temperature=temperature, ) self.prompt = prompt diff --git a/sotopia/envs/parallel.py b/sotopia/envs/parallel.py index 74f2be22c..2ac565f7f 100644 --- a/sotopia/envs/parallel.py +++ b/sotopia/envs/parallel.py @@ -1,4 +1,5 @@ import asyncio +import copy import itertools import json import logging @@ -140,7 +141,9 @@ def render_text_for_environment( ) -class ParallelSotopiaEnv(ParallelEnv, MessengerMixin): +class ParallelSotopiaEnv( + ParallelEnv[str, Observation, AgentAction], MessengerMixin +): def __init__( self, available_action_types: set[ActionType] = set( @@ -198,11 +201,14 @@ def __init__( f"Agent with uuid {uuid_str} not found in database" ) + @configurable def reset( self, seed: int | None = None, options: dict[str, str] | None = None, agents: Agents | None = None, + omniscient: bool = False, + lite: bool = False, ) -> dict[str, Observation]: """Starting a new episode. Must be called before step(). @@ -211,6 +217,7 @@ def reset( options (dict, optional): Options for the environment. Defaults to None. "partial_background_file" (str): Path to a json file which need to contain a ScriptBackground object. The backgound can be incompleted ("unknown" for missing parts), and the missing parts will be filled in by the environment. "full_background_file" (str): Path to a json file which need to contain a ScriptBackground object. The backgound must be completed (no "unknown" for missing parts). + omniscient (bool, optional): Whether the agents know the other agent's goal. Defaults to False. """ super().__init__() MessengerMixin.reset_inbox(self) @@ -245,6 +252,11 @@ def reset( p1_name=agent_names[0], p2_name=agent_names[1], ) + + if lite: + raw_background.p1_background = "" + raw_background.p2_background = "" + self.background = ScriptBackground( scenario=render_text_for_environment(raw_background.scenario), p1_background=render_text_for_environment( @@ -263,26 +275,39 @@ def reset( self.agents = [self.background.p1_name, self.background.p2_name] agent_backgrounds: list[ScriptBackground] = [] - for i in range(self.num_agents): - agent_backgrounds.append( - ScriptBackground( - scenario=render_text_for_agent(raw_background.scenario, i), - p1_background=render_text_for_agent( - raw_background.p1_background, i - ), - p2_background=render_text_for_agent( - raw_background.p2_background, i - ), - p1_goal=render_text_for_agent(raw_background.p1_goal, i), - p2_goal=render_text_for_agent(raw_background.p2_goal, i), - p1_name=raw_background.p1_name, - p2_name=raw_background.p2_name, + if omniscient: + for i in range(self.num_agents): + agent_backgrounds.append(copy.deepcopy(self.background)) + else: + for i in range(self.num_agents): + agent_backgrounds.append( + ScriptBackground( + scenario=render_text_for_agent( + raw_background.scenario, i + ), + p1_background=render_text_for_agent( + raw_background.p1_background, i + ), + p2_background=render_text_for_agent( + raw_background.p2_background, i + ), + p1_goal=render_text_for_agent( + raw_background.p1_goal, i + ), + p2_goal=render_text_for_agent( + raw_background.p2_goal, i + ), + p1_name=raw_background.p1_name, + p2_name=raw_background.p2_name, + ) ) - ) background_for_a = agent_backgrounds[0] background_for_b = agent_backgrounds[1] - background_for_a.p2_goal = "Unknown" - background_for_b.p1_goal = "Unknown" + + print("Is the agent omniscient?", omniscient) + if not omniscient: + background_for_a.p2_goal = "Unknown" + background_for_b.p1_goal = "Unknown" self.action_spaces = { agent: Dict( diff --git a/sotopia/generation_utils/generate.py b/sotopia/generation_utils/generate.py index 93211affb..fe48972c2 100644 --- a/sotopia/generation_utils/generate.py +++ b/sotopia/generation_utils/generate.py @@ -32,6 +32,11 @@ ScriptBackground, ScriptEnvironmentResponse, ) +from sotopia.messages.message_classes import ( + ScriptInteraction, + ScriptInteractionReturnType, + SimpleMessage, +) from sotopia.utils import format_docstring from .langchain_callback_handler import LoggingCallbackHandler @@ -47,6 +52,7 @@ "gpt-3.5-turbo", "text-davinci-003", "gpt-4", + "gpt-4-turbo", "human", "redis", ] @@ -192,12 +198,104 @@ def _type(self) -> str: return "str" +class ScriptOutputParser(BaseOutputParser[ScriptInteractionReturnType]): + agent_names: list[str] = Field( + description="The names of the two agents in the conversation" + ) + background: str = Field(description="The background of the conversation") + single_turn: bool = Field( + description="Whether the output is a single turn" + ) + + def get_format_instructions(self) -> str: + if self.single_turn: + return r"""For one turn, only write the next step of this agent. You should follow the structure. The format looks like this: Turn #0 \n[participant's name] [action]. +This means you can only generate two lines in one turn. + +You can use different types of actions in the [action] part, but PLEASE follows the rule STRICTLY. Remember to include the square brackets when doing an action as stated in the instructions. +1. Use "did nothing" if the agent did nothing. +2. Use "said: "{self.argument}" if the agent want to say, ask or inquire something. +3. Use "[non-verbal communication] {self.argument}" if the agent did non-verbal communication. +4. Use "[action] {self.argument}" if the agent did an action. +5. Use "left the conversation" if the agent left the conversation. And you should stop generation +Other than that, no other format are allowed. + +For example, the following outputs are valid: +Turn #1 +Oliver Thompson said: "Hey Esmeralda, what's wrong? You seem upset." +Turn #2 +Esmeralda Solis [action] moved closer +Turn #3 +Oliver Thompson [non-verbal communication] smiled +Turn #4 +Esmeralda Solis did nothing +Turn #5 +Oliver Thompson left the conversation +Remember to make it short and compact, as it should be less than 20 turns""" + + else: + return r"""You should separate each turn by a newline. Each turn is separated by a newline, and should only describe one agent. Following the structure: Turn #x \n[participant's name] [action] + +You can use different types of actions in the [action] part, but PLEASE follows the rule STRICTLY. Remember to include the square brackets when doing an action as stated in the instructions. +1. Use "did nothing" if the agent did nothing. +2. Use "said: "{self.argument}" if the agent want to say, ask or inquire something. +3. Use "[non-verbal communication] {self.argument}" if the agent did non-verbal communication. +4. Use "[action] {self.argument}" if the agent did an action. +5. Use "left the conversation" if the agent left the conversation. And you should stop generation + +For example, the following outputs are valid: +a. Oliver Thompson said: "What's wrong? You seem upset." +b. Esmeralda Solis [action] moved closer +c. Oliver Thompson [non-verbal communication] smiled +e. Esmeralda Solis did nothing +f. Oliver Thompson left the conversation""" + + def parse(self, output: str) -> ScriptInteractionReturnType: + """ + Parse the loosely formatted output to AgentAction + We make the reformat in this function + """ + print("Original output: ", output) + interaction = ScriptInteraction(interactions=output) + agent_names = self.agent_names + assert len(agent_names) == 2, "agent_names must have length 2" + try: + # try to parse the output + parsed_interaction = interaction.parse( + agent_names=agent_names, background=self.background + ) + return parsed_interaction + except Exception as e: + print( + f"Exception {e}: the output format is not correct. Reformatting " + ) + reformat_parsed_result = format_bad_output_for_script( + ill_formed_output=output, + format_instructions=self.get_format_instructions(), + agents=agent_names, + ) + print("Reformatted output: ", reformat_parsed_result) + interaction = ScriptInteraction( + interactions=reformat_parsed_result + ) + parsed_interaction = interaction.parse( + agent_names=agent_names, background=self.background + ) + return parsed_interaction + + @property + def _type(self) -> str: + """Return the type key.""" + return "str" + + def _return_fixed_model_version( - model_name: Literal["gpt-3.5-turbo", "gpt-4"] + model_name: Literal["gpt-3.5-turbo", "gpt-4", "gpt-4-turbo"] ) -> str: return { "gpt-3.5-turbo": "gpt-3.5-turbo-0613", "gpt-4": "gpt-4-0613", + "gpt-4-turbo": "gpt-4-1106-preview", }[model_name] @@ -214,7 +312,7 @@ def obtain_chain( Using langchain to sample profiles for participants """ match model_name: - case "gpt-3.5-turbo" | "gpt-4": + case "gpt-3.5-turbo" | "gpt-4" | "gpt-4-turbo": human_message_prompt = HumanMessagePromptTemplate( prompt=PromptTemplate( template=template, @@ -244,7 +342,7 @@ def obtain_chain( ) chain = LLMChain(llm=llm, prompt=prompt) return chain - case "togethercomputer/llama-2-7b-chat" | "togethercomputer/llama-2-70b-chat": + case "togethercomputer/llama-2-7b-chat" | "togethercomputer/llama-2-70b-chat" | "togethercomputer/mpt-30b-chat": human_message_prompt = HumanMessagePromptTemplate( prompt=PromptTemplate( template=template, @@ -263,6 +361,41 @@ def obtain_chain( raise ValueError(f"Invalid model name: {model_name}") +@beartype +def format_bad_output_for_script( + ill_formed_output: str, + format_instructions: str, + agents: list[str], + model_name: LLM_Name = "gpt-3.5-turbo", +) -> str: + template = """ + Given the string that can not be parsed by a parser, reformat it to a string that can be parsed by the parser which uses the following format instructions. Do not add or delete any information. + Small tip: for every round of conversation, first determine the name and the case, and whether this line contains errors. Correct it if necessary. + + Format instructions: {format_instructions} + + String to be corrected: {ill_formed_output} + + The two agents are: {agents} + + Please only generate the rewritten string: + """ + print("ill_formed_output: ", ill_formed_output) + chain = obtain_chain( + model_name=model_name, + template=template, + input_variables=re.findall(r"{(.*?)}", template), + ) + input_values = { + "ill_formed_output": ill_formed_output, + "format_instructions": format_instructions, + "agents": agents, + } + reformat = chain.predict([logging_handler], **input_values) + log.info(f"Reformated output: {reformat}") + return reformat + + @beartype def format_bad_output( ill_formed_output: str, @@ -367,6 +500,8 @@ async def agenerate( try: parsed_result = output_parser.parse(result) except Exception as e: + if isinstance(output_parser, ScriptOutputParser): + raise e # the problem has been handled in the parser log.debug( f"[red] Failed to parse result: {result}\nEncounter Exception {e}\nstart to reparse", extra={"markup": True}, @@ -414,6 +549,7 @@ async def agenerate_env_profile( model_name: LLM_Name, inspiration_prompt: str = "asking my boyfriend to stop being friends with his ex", examples: str = "", + temperature: float = 0.7, ) -> tuple[EnvironmentProfile, str]: """ Using langchain to generate the background @@ -432,6 +568,7 @@ async def agenerate_env_profile( examples=examples, ), output_parser=PydanticOutputParser(pydantic_object=EnvironmentProfile), + temperature=temperature, ) @@ -604,17 +741,34 @@ async def agenerate_action( agent: str, goal: str, temperature: float = 0.7, + script_like: bool = False, ) -> tuple[AgentAction, str]: """ Using langchain to generate an example episode """ try: - return await agenerate( - model_name=model_name, - template=""" - Imagine you are {agent}, your task is to act/speak as {agent} would, keeping in mind {agent}'s social goal. + if script_like: + # model as playwright + template = """ + Now you are a famous playwright, your task is to continue writing one turn for agent {agent} under a given background and history to help {agent} reach social goal. Please continue the script based on the previous turns. You can only generate one turn at a time. You can find {agent}'s background and goal in the 'Here is the context of the interaction' field. - Note that {agent}'s secret and goal is only visible to you. + You should try your best to achieve {agent}'s goal in a way that align with their character traits. + Additionally, maintaining the conversation's naturalness and realism is essential (e.g., do not repeat what other people has already said before). + {history}. + The script has proceeded to Turn #{turn_number}. Current available action types are + {action_list}. + Note: The script can be ended if 1. one agent have achieved social goals, 2. this conversation makes the agent uncomfortable, 3. the agent find it uninteresting/you lose your patience, 4. or for other reasons you think it should stop. + + Please only generate a JSON string including the action type and the argument. + Your action should follow the given format: + {format_instructions} + """ + else: + # Normal case, model as agent + template = """ + Imagine you are {agent}, your task is to act/speak as {agent} would, keeping in mind {agent}'s social goal. + You can find {agent}'s goal (or background) in the 'Here is the context of the interaction' field. + Note that {agent}'s goal is only visible to you. You should try your best to achieve {agent}'s goal in a way that align with their character traits. Additionally, maintaining the conversation's naturalness and realism is essential (e.g., do not repeat what other people has already said before). {history}. @@ -625,7 +779,11 @@ async def agenerate_action( Please only generate a JSON string including the action type and the argument. Your action should follow the given format: {format_instructions} - """, + """ + + return await agenerate( + model_name=model_name, + template=template, input_values=dict( agent=agent, turn_number=str(turn_number), @@ -639,6 +797,81 @@ async def agenerate_action( return AgentAction(action_type="none", argument=""), "" +@gin.configurable +@beartype +async def agenerate_script( + model_name: LLM_Name, + background: ScriptBackground, + temperature: float = 0.7, + agent_names: list[str] = [], + agent_name: str = "", + history: str = "", + single_step: bool = False, +) -> tuple[ScriptInteractionReturnType, str]: + """ + Using langchain to generate an the script interactions between two agent + The script interaction is generated in a single generation process. + Note that in this case we do not require a json format response, + so the failure rate will be higher, and it is recommended to use at least llama-2-70b. + """ + try: + if single_step: + return await agenerate( + model_name=model_name, + template="""Now you are a famous playwright, your task is to continue writing one turn for agent {agent} under a given background and history to help {agent} reach social goal. Please continue the script based on the previous turns. You can only generate one turn at a time. + + Here are the conversation background and history: + {background} + {history} + + Remember that you are an independent scriptwriter and should finish the script by yourself. + The output should only contain the script following the format instructions, with no additional comments or text. + + Here are the format instructions: + {format_instructions}""", + input_values=dict( + background=background.to_natural_language(), + history=history, + agent=agent_name, + ), + output_parser=ScriptOutputParser( + agent_names=agent_names, + background=background.to_natural_language(), + single_turn=True, + ), + temperature=temperature, + ) + + else: + return await agenerate( + model_name=model_name, + template=""" + Please write the script between two characters based on their social goals with a maximum of 20 turns. + + {background} + Your action should follow the given format: + {format_instructions} + Remember that you are an independent scriptwriter and should finish the script by yourself. + The output should only contain the script following the format instructions, with no additional comments or text.""", + input_values=dict( + background=background.to_natural_language(), + ), + output_parser=ScriptOutputParser( + agent_names=agent_names, + background=background.to_natural_language(), + single_turn=False, + ), + temperature=temperature, + ) + except Exception as e: + # TODO raise(e) # Maybe we do not want to return anything? + print(f"Exception in agenerate {e}") + return_default_value: ScriptInteractionReturnType = ( + ScriptInteraction.default_value_for_return_type() + ) + return (return_default_value, "") + + @beartype def process_history( script: ScriptBackground | EnvResponse | dict[str, AgentAction] diff --git a/sotopia/generation_utils/llama2.py b/sotopia/generation_utils/llama2.py index 940570102..ab28a94b0 100644 --- a/sotopia/generation_utils/llama2.py +++ b/sotopia/generation_utils/llama2.py @@ -112,7 +112,7 @@ class Llama2(BaseChatModel): """Model name to use.""" # default Together params temperature: float = 0.7 - max_tokens: int = 128 + max_tokens: int = 1024 top_p: float = 0.7 top_k: int = 50 repetition_penalty: float = 1.0 @@ -156,9 +156,6 @@ def _generate( run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: - if not self.start: - together.Models.start(self.model_name) - self.start = True prompt, params = self._create_message_dicts(messages, stop) response = self.client.create(prompt=prompt, **params) chat_result = self._create_chat_result(response) diff --git a/sotopia/messages/message_classes.py b/sotopia/messages/message_classes.py index 4da527041..dc07e1be4 100644 --- a/sotopia/messages/message_classes.py +++ b/sotopia/messages/message_classes.py @@ -1,4 +1,5 @@ -from typing import Literal +import re +from typing import Literal, cast from pydantic import BaseModel, Field @@ -54,16 +55,26 @@ class ScriptBackground(Message): p2_goal: str = Field(description="goal of participant 2") def to_natural_language(self) -> str: - return format_docstring( - f"""Here is the context of this interaction: - Scenario: {self.scenario} - Participants: {self.p1_name} and {self.p2_name} - {self.p1_name}'s background: {self.p1_background} - {self.p2_name}'s background: {self.p2_background} - {self.p1_name}'s goal: {self.p1_goal} - {self.p2_name}'s goal: {self.p2_goal} - """ - ) + if self.p1_background and self.p2_background: + return format_docstring( + f"""Here is the context of this interaction: + Scenario: {self.scenario} + Participants: {self.p1_name} and {self.p2_name} + {self.p1_name}'s background: {self.p1_background} + {self.p2_name}'s background: {self.p2_background} + {self.p1_name}'s goal: {self.p1_goal} + {self.p2_name}'s goal: {self.p2_goal} + """ + ) + else: + return format_docstring( + f"""Here is the context of this interaction: + Scenario: {self.scenario} + Participants: {self.p1_name} and {self.p2_name} + {self.p1_name}'s goal: {self.p1_goal} + {self.p2_name}'s goal: {self.p2_goal} + """ + ) class ScriptEnvironmentResponse(Message): @@ -117,3 +128,225 @@ def to_natural_language(self) -> str: return f"[{self.action_type}] {self.argument}" case "leave": return "left the conversation" + + +ScriptInteractionReturnType = tuple[ + list[list[tuple[str, str, Message]]], list[tuple[str, Message]] +] + + +class ScriptInteraction(Message): + interactions: str = Field( + description="""The interaction between the two participants in maximum 20 turns. Each turn is separated by a newline, and should only describe one agent. Following the structure: + Turn #x + [participant's name] [action] {argument for some actions} + + You can use different types of actions, but only use one in each turn. You should move other information into argument part. Below shows a python code snippet of the format for each action type: + match self.action_type: + case "none": + return "did nothing" + case "speak": + return f'said: "{self.argument}"' + case "non-verbal communication": + return f"[{self.action_type}] {self.argument}" + case "action": + return f"[{self.action_type}] {self.argument}" + case "leave": + return "left the conversation" + + For example, the following is acceptable: + Turn #x + Oliver Thompson said: "Hey Esmeralda, what's wrong? You seem upset." + Turn #x + Esmeralda Solis [action] moved closer + Turn #x + Oliver Thompson [non-verbal communication] smiled + Turn #x + Esmeralda Solis did nothing + Turn #x + Oliver Thompson left the conversation + Turn #x + Esmeralda Solis [action] leaned in and lowered her voice: "Sorry" + + And the following is not acceptable: + Turn #1 + Oliver Thompson [speak] said: "Hey Esmeralda, what's wrong? You seem upset." + Turn #1 + Esmeralda Solis non-verbal communication moved closer + """ + ) + + def to_natural_language(self) -> str: + return self.interactions + + def parse( + self, agent_names: list[str], background: str + ) -> tuple[ + list[list[tuple[str, str, Message]]], list[tuple[str, Message]] + ]: + interaction = self.interactions + # print("Interaction: ", interaction) + lines = self.split_by_turn(interaction) + + agent_results = [] + results: list[list[tuple[str, str, Message]]] = [ + [ + ( + "Environment", + name, + Observation( + last_turn=background, + turn_number=0, + available_actions=["none"], + ), + ) + for name in agent_names + ] + ] + + for line_idx, line in enumerate(lines): + try: + res = self.parse_single_dialogue(line) + action: AgentAction = cast(AgentAction, res["action"]) + argument: str = cast(str, res["argument"]) + turn: int = cast(int, res["turn"]) + name: str = cast(str, res["name"]) + + parsed_action = AgentAction( + action_type=action, argument=argument + ) + if name not in agent_names: + print( + f"The name of the agent, {name}, is not in the list of agent names, {agent_names}" + ) + name = agent_names[ + line_idx % 2 + ] # TODO Not sure what name to be set here + except Exception as e: + print( + f"Error when parsing the dialogue: {line}", + f"The error is: {e}", + ) + raise e + parsed_action = AgentAction(action_type="none", argument="") + name = agent_names[line_idx % 2] # TODO same question as above + inactive_agent_name = ( + agent_names[0] if name == agent_names[1] else agent_names[1] + ) + results.append( + [ + ( + "Environment", + name, + Observation( + last_turn="environment is the agent", + turn_number=line_idx + 1, + available_actions=["none"], + ), + ) + for name in agent_names + ] + + [ + (name, "Environment", parsed_action), + ( + inactive_agent_name, + "Environment", + AgentAction( + action_type="none", argument="did nothing" + ), + ), + ] + ) + + agent_results.append((name, parsed_action)) + # print("Parsed agent results: ", agent_results) + return (results, agent_results) # type: ignore + + def parse_single_dialogue( + self, dialogue: str + ) -> dict[str, str | int | AgentAction | None]: + """Parse a single dialogue string and return a dictionary with turn, name, action, and argument.""" + + # Match the turn number and name. Assume all agent name starts with a capital letter and is followed by lowercase letters + match_turn_name = re.match( + r"Turn #?(\d+):?\s*\n((?:[A-Z]['a-z]* ?)+)", dialogue + ) + + if not match_turn_name: + raise ValueError( + f"The dialogue does not match the expected format: {dialogue}" + ) + return ( + None # TODO Which should we use, return None or raise error? + ) + + turn, name = match_turn_name.groups() + action_content = dialogue[ + len(match_turn_name.group(0)) : + ].strip() # Extract the action content + + # Check for different action types + if "did nothing" in action_content: + action, argument = "none", "" + elif match := re.match(r'said: "(.*?)"', action_content): + action, argument = "speak", match.group(1) + action, argument = action.strip(), argument.strip() + elif match := re.match(r'\[speak\] said: "(.*?)"', action_content): + action, argument = "speak", match.group(1) + action, argument = action.strip(), argument.strip() + elif match := re.match( + r"\[(non-verbal communication|action)\] (.*)", action_content + ): + action, argument = match.groups() + elif "left the conversation" in action_content: + # TODO Make it more elegant to handle the situation of `left the conversation.` + action, argument = "leave", "" + else: + action, argument = None, None + + parsed_item = { + "turn": int(turn), + "name": name.strip(), + "action": action, + "argument": argument, + } + return parsed_item + + def split_by_turn(self, input_string: str) -> list[str]: + """Split the input dialogue string by turn and return a list of dialogues.""" + # Split using 'Turn #' as delimiter, but keep the delimiter in the results + dialogues = re.split(r"(?=Turn #?\d+)", input_string) + # Remove any empty strings and strip whitespace + dialogues = [ + dialogue.strip() for dialogue in dialogues if dialogue.strip() + ] + dialogues = [ + dialogue for dialogue in dialogues if dialogue.startswith("Turn") + ] + # Change from Turn #x to Turn (#)x (# is optional) + dialogues[-1] = "\n".join( + dialogues[-1].split("\n")[:2] + ) # Discard further input in the last turn + # print("Dialogues: ", dialogues) + return dialogues + + @staticmethod + def default_value_for_return_type() -> ScriptInteractionReturnType: + results_1: list[list[tuple[str, str, Message]]] = [ + [ + ( + "Environment", + name, + Observation( + last_turn="Environment is the agent", + turn_number=0, + available_actions=["none"], + ), + ) + for name in ["none", "none"] + ] + ] + results_2: list[tuple[str, Message]] = [ + ("", AgentAction(action_type="none", argument="")) + ] + return (results_1, results_2) diff --git a/sotopia/renderers/xml_renderer.py b/sotopia/renderers/xml_renderer.py index b222d84b4..3335bed3f 100644 --- a/sotopia/renderers/xml_renderer.py +++ b/sotopia/renderers/xml_renderer.py @@ -50,6 +50,8 @@ def __init__(self) -> None: def __call__( self, xml_string: str, context: RenderContext = RenderContext() ) -> str: + if not xml_string: + return "" try: root = etree.fromstring(xml_string) except etree.XMLSyntaxError: diff --git a/sotopia/server.py b/sotopia/server.py index 214967bed..c0bbc4f88 100644 --- a/sotopia/server.py +++ b/sotopia/server.py @@ -2,14 +2,21 @@ import functools import itertools import logging -from typing import Callable, Literal, Sequence, cast +from typing import Callable, Literal, Sequence, Type, cast import gin import rich from beartype import beartype from tqdm.asyncio import tqdm_asyncio -from sotopia.agents import Agents, HumanAgent, LLMAgent, SpeakAgent +from sotopia.agents import ( + Agents, + HumanAgent, + LLMAgent, + RedisAgent, + ScriptWritingAgent, + SpeakAgent, +) from sotopia.agents.base_agent import BaseAgent from sotopia.database import EpisodeLog from sotopia.database.persistent_profile import ( @@ -20,9 +27,15 @@ from sotopia.envs.evaluators import ( ReachGoalLLMEvaluator, RuleBasedTerminatedEvaluator, + unweighted_aggregate_evaluate, ) -from sotopia.generation_utils.generate import LLM_Name +from sotopia.generation_utils.generate import LLM_Name, agenerate_script from sotopia.messages import AgentAction, Message, Observation +from sotopia.messages.message_classes import ( + ScriptBackground, + ScriptEnvironmentResponse, + ScriptInteraction, +) from sotopia.samplers import ( BaseSampler, ConstraintBasedSampler, @@ -107,21 +120,36 @@ def run_sync_server( return messages +@gin.configurable 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, tag: str | None = None, push_to_db: bool = False, ) -> list[tuple[str, str, Message]]: agents = Agents({agent.agent_name: agent for agent in agent_list}) - environment_messages = env.reset(agents=agents) + 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) + agents[agent_name] = LLMAgent( + agent_name, model_name=agent_model, script_like=script_like + ) agents.reset() messages: list[list[tuple[str, str, Message]]] = [] @@ -148,7 +176,18 @@ async def arun_one_episode( for agent_name in env.agents ] ) - actions = cast(list[AgentAction], actions) + if script_like: + # manually mask one message + agent_mask = env.action_mask + for idx in range(len(agent_mask)): + print("Current mask: ", agent_mask) + if agent_mask[idx] == 0: + print("Action not taken: ", actions[idx]) + actions[idx] = AgentAction(action_type="none", argument="") + else: + print("Current action taken: ", actions[idx]) + + # actions = cast(list[AgentAction], actions) for idx, agent_name in enumerate(env.agents): agent_messages[agent_name] = actions[idx] @@ -170,6 +209,8 @@ async def arun_one_episode( for agent_name in env.agents ] ) + # print("Environment message: ", environment_messages) + # exit(0) rewards.append( [rewards_in_turn[agent_name] for agent_name in env.agents] ) @@ -217,11 +258,14 @@ async def arun_one_episode( @beartype async def run_async_server( model_dict: dict[str, LLM_Name], - sampler: BaseSampler[Observation, AgentAction] = UniformSampler(), + sampler: BaseSampler[Observation, AgentAction] = BaseSampler(), action_order: Literal[ "simutaneous", "round-robin", "random" ] = "round-robin", env_agent_combo_list: list[EnvAgentCombo[Observation, AgentAction]] = [], + omniscient: bool = False, + script_like: bool = False, + json_in_script: bool = False, tag: str | None = None, push_to_db: bool = False, using_async: bool = True, @@ -229,8 +273,13 @@ async def run_async_server( """ Doc incomplete + Args: + omniscient (bool): Whether the agent knows the goal of the other, default to False + script_like (bool): Whether we generate the turn in script like manner, default to False + json_in_script (bool): Whether we requires the script generator to return json (Only valid when script_like is True), default to False + Note: env_agent_combo_list is optional. When it defaults to [], sampler is used - else the sampler is not used. Please pass in BaseSampler when using this option. + else the sampler is not used. Please pass in BaseSampler or simply not specify it when using this option. """ assert not ( @@ -255,13 +304,25 @@ async def run_async_server( "agent2": model_dict["agent2"], } + def get_agent_class( + model_name: str, + ) -> Type[BaseAgent[Observation, AgentAction]]: + if model_name == "human": + return HumanAgent + elif script_like and not json_in_script: + return ScriptWritingAgent + else: + return LLMAgent + if env_agent_combo_list: - assert type(sampler) is BaseSampler + assert ( + type(sampler) is BaseSampler + ), "No sampler should be used when `env_agent_combo_list` is empty" env_agent_combo_iter = iter(env_agent_combo_list) else: env_agent_combo_iter = sampler.sample( agent_classes=[ - LLMAgent if model_name != "human" else HumanAgent + get_agent_class(model_name) for model_name in agents_model_dict.values() ], n_agent=len(agents_model_dict), @@ -271,12 +332,14 @@ async def run_async_server( for model_name in agents_model_dict.values() ], ) - episode_futures = [ 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, tag=tag, push_to_db=push_to_db, ) @@ -290,3 +353,167 @@ async def run_async_server( ) return cast(list[list[tuple[str, str, Message]]], batch_results) + + +async def arun_one_script( + env: ParallelSotopiaEnv, + agent_list: Sequence[BaseAgent[Observation, AgentAction]], + model_dict: dict[str, LLM_Name], + omniscient: bool = False, + tag: str | None = None, + push_to_db: bool = False, +) -> list[tuple[str, str, Message]]: + """ + Generate script for one episode + Args: + omniscient (bool): Whether the agent knows the goal of the other + """ + + 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] + assert ( + len(agent_names) == 2 + ), f"only support 2 agents, current: {agent_names}" + + script_background = env.inbox[0][1] + assert isinstance(script_background, ScriptBackground) + story, prompt = await agenerate_script( + model_name=model_dict["env"], + background=script_background, + agent_names=agent_names, + ) + messages, agent_messages = story + env_message = [("Environment", script_background)] + agent_messages = env_message + agent_messages + + evaluator = ReachGoalLLMEvaluator(model_name="gpt-4") + response = unweighted_aggregate_evaluate( + list( + itertools.chain( + *await asyncio.gather( + *[ + sing_evaluator.__acall__( + turn_number=-1, + messages=agent_messages, + ) + for sing_evaluator in [evaluator] + ] + ) + ) + ) + ) + info: dict[ + str, dict[str, str | ScriptEnvironmentResponse | float | None] + ] = { + script_background.p1_name: { + "comments": response.comments or "", + "complete_rating": response.p1_rate or 0, # type: ignore + }, + script_background.p2_name: { + "comments": response.comments or "", + "complete_rating": response.p2_rate or 0, # type: ignore + }, + "rewards_prompt": {"overall_prompt": evaluator.prompt or ""}, + } + epilog = EpisodeLog( + 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"]], + messages=[ + [ + (m[0], m[1], m[2].to_natural_language()) + for m in messages_in_turn + ] + for messages_in_turn in messages + ], + reasoning=str(info[env.agents[0]]["comments"]) + + str(info[env.agents[1]]["comments"]), + rewards=[ + info[agent_name]["complete_rating"] for agent_name in env.agents + ], + rewards_prompt=info["rewards_prompt"]["overall_prompt"], + ) + print("Reward prompt: ") + rich.print(epilog.rewards_prompt) + agent_profiles, conversation = epilog.render_for_humans() + print("Agent profiles: ") + for agent_profile in agent_profiles: + rich.print(agent_profile) + for message in conversation: + rich.print(message) + + if push_to_db: + try: + epilog.save() + except Exception as e: + logging.error(f"Failed to save episode log: {e}") + # flatten nested list messages + return list(itertools.chain(*messages)) + + +async def aevaluate_one_episode( + episode: EpisodeLog, + model: LLM_Name = "gpt-4", + tag: str | None = None, + push_to_db: bool = False, +) -> None: + history = episode.rewards_prompt.replace( + "Prompt after formatting:", "" + ).split(",\nBased on previous interactions")[0] + evaluator = ReachGoalLLMEvaluator( + model_name=model, response_format="basic" + ) + response = unweighted_aggregate_evaluate( + list( + itertools.chain( + *await asyncio.gather( + *[ + sing_evaluator.__acall__( + turn_number=-1, + history=history, + messages=None, + ) + for sing_evaluator in [evaluator] + ] + ) + ) + ) + ) + info: dict[ + str, dict[str, str | ScriptEnvironmentResponse | float | None] + ] = { + episode.agents[0]: { + "comments": response.comments or "", + "complete_rating": response.p1_rate or 0, # type: ignore + }, + episode.agents[1]: { + "comments": response.comments or "", + "complete_rating": response.p2_rate or 0, # type: ignore + }, + } + assert isinstance(episode.models, list) + epilog = EpisodeLog( + environment=episode.environment, + agents=episode.agents, + tag=tag, + models=[model, episode.models[1], episode.models[2]], + messages=episode.messages, + reasoning=str(info[episode.agents[0]]["comments"]) + + str(info[episode.agents[1]]["comments"]), + rewards=[ + info[agent_name]["complete_rating"] + for agent_name in episode.agents + ], + rewards_prompt="TBD", + ) + # rich.print(history) + # rich.print(epilog.rewards) + + if push_to_db: + try: + epilog.save() + except Exception as e: + logging.error(f"Failed to save episode log: {e}") diff --git a/stubs/pettingzoo/__init__.pyi b/stubs/pettingzoo/__init__.pyi new file mode 100644 index 000000000..e69de29bb diff --git a/stubs/pettingzoo/utils/__init__.pyi b/stubs/pettingzoo/utils/__init__.pyi new file mode 100644 index 000000000..e69de29bb diff --git a/stubs/pettingzoo/utils/env.pyi b/stubs/pettingzoo/utils/env.pyi new file mode 100644 index 000000000..70f6d9c01 --- /dev/null +++ b/stubs/pettingzoo/utils/env.pyi @@ -0,0 +1,48 @@ +from typing import Any, Generic, TypeVar + +import gymnasium +import numpy as np + +ObsType = TypeVar("ObsType") +ActionType = TypeVar("ActionType") +AgentID = TypeVar("AgentID") + +class ParallelEnv(Generic[AgentID, ObsType, ActionType]): + agents: list[AgentID] + possible_agents: list[AgentID] + observation_spaces: dict[ + AgentID, gymnasium.spaces.Space[ObsType] + ] # Observation space for each agent + action_spaces: dict[AgentID, gymnasium.spaces.Space[Any]] + + def reset( + self, + seed: int | None = None, + options: dict[str, Any] | None = None, + ) -> dict[AgentID, ObsType]: ... + def seed(self, seed: int | None = None) -> None: ... + def step( + self, actions: dict[AgentID, ActionType] + ) -> tuple[ + dict[AgentID, ObsType], + dict[str, float], + dict[str, bool], + dict[str, bool], + dict[str, dict[str, Any]], + ]: ... + def render(self) -> None | Any: ... + def close(self) -> None: ... + def state(self) -> Any: ... + def observation_space( + self, agent: AgentID + ) -> gymnasium.spaces.Space[ObsType]: ... + def action_space( + self, agent: AgentID + ) -> gymnasium.spaces.Space[ActionType]: ... + @property + def num_agents(self) -> int: ... + @property + def max_num_agents(self) -> int: ... + def __str__(self) -> str: ... + @property + def unwrapped(self) -> ParallelEnv[AgentID, ObsType, ActionType]: ... diff --git a/tests/envs/test_parallel.py b/tests/envs/test_parallel.py index 716aae523..fe8855e0f 100644 --- a/tests/envs/test_parallel.py +++ b/tests/envs/test_parallel.py @@ -1,6 +1,6 @@ import pytest -from sotopia.agents import Agents, LLMAgent +from sotopia.agents import Agents, LLMAgent, ScriptWritingAgent from sotopia.database.persistent_profile import ( AgentProfile, EnvironmentProfile, @@ -19,6 +19,52 @@ async def test_parallel_sotopia_env() -> None: agent_goals=["test 1", "test 2"], ) env = ParallelSotopiaEnv(env_profile=env_profile) + + agents = Agents( + { + "agent1": LLMAgent( + "agent1", + model_name="gpt-3.5-turbo", + agent_profile=AgentProfile( + **{"first_name": "John", "last_name": "Doe"} + ), + ), + "agent2": LLMAgent( + "agent2", + model_name="gpt-3.5-turbo", + agent_profile=AgentProfile( + **{"first_name": "Jane", "last_name": "Doe"} + ), + ), + } + ) + env.reset(agents=agents) + max_steps = 5 + while env.agents: + max_steps -= 1 + actions = { + agent: env.action_space(agent).sample() for agent in env.agents + } # this is where you would insert your policy + ( + observations, + rewards, + terminations, + truncations, + infos, + ) = await env.astep(actions) + if not max_steps: + break + + +@pytest.mark.asyncio +async def test_parallel_sotopia_env_script_writing_single_step() -> None: + env_profile = EnvironmentProfile( + code_name="test", + scenario="test", + agent_goals=["test 1", "test 2"], + ) + env = ParallelSotopiaEnv(env_profile=env_profile) + agents = Agents( { "agent1": LLMAgent( @@ -27,6 +73,7 @@ async def test_parallel_sotopia_env() -> None: agent_profile=AgentProfile( **{"first_name": "John", "last_name": "Doe"} ), + script_like=True, ), "agent2": LLMAgent( "agent2", @@ -34,10 +81,12 @@ async def test_parallel_sotopia_env() -> None: agent_profile=AgentProfile( **{"first_name": "Jane", "last_name": "Doe"} ), + script_like=True, ), } ) env.reset(agents=agents) + max_steps = 5 while env.agents: max_steps -= 1 diff --git a/tests/renderers/test_xml_renderer.py b/tests/renderers/test_xml_renderer.py index 3769d1751..9152e4ad7 100644 --- a/tests/renderers/test_xml_renderer.py +++ b/tests/renderers/test_xml_renderer.py @@ -92,8 +92,6 @@ def test_renderer_in_env() -> None: obs["John"].last_turn == "Here is the context of this interaction:\n" "Scenario: test\n" "Participants: John and Jane\n" - "John's background: John Doe is a 0-year-old . pronouns. Personality and values description: John's secrets: \n" - "Jane's background: \n" "John's goal: agent_1's goal\n" "Jane's goal: Unknown" )