Refactor speed benchmark scripts

speed_benchmark/install.sh

@@ -0,0 +1 @@
+python3 -m pip install pettingzoo open_spiel tianshou pygame cloudpickle chess pgx

workspace/compariosn/vector_env.py → speed_benchmark/run_open_spiel_forloop.py

@@ -1,6 +1,15 @@
+import argparse
+import json
+import collections
+import time
+import numpy as np
+import pyspiel
+from open_spiel.python.rl_environment import Environment, ChanceEventSampler
 
-# Copied from https://github.com/deepmind/open_spiel/blob/master/open_spiel/python/vector_env.py
 
+# SyncVectorEnv is copied from 
+# https://github.com/deepmind/open_spiel/blob/master/open_spiel/python/vector_env.py
+# 
 # Copyright 2022 DeepMind Technologies Limited
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,9 +23,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""A vectorized RL Environment."""
-
-
 class SyncVectorEnv(object):
   """A vectorized RL Environment.
   This environment is synchronized - games do not execute in parallel. Speedups
@@ -75,4 +81,47 @@ def reset(self, envs_to_reset=None):
         if envs_to_reset[i] else self.envs[i].get_time_step()
         for i in range(len(self.envs))
     ]
-    return time_steps
+    return time_steps
+
+
+def make_single_env(env_name: str, seed: int):
+    def gen_env():
+        game = pyspiel.load_game(env_name)
+        return Environment(game,  chance_event_sampler=ChanceEventSampler(seed=seed))
+    return gen_env()
+
+
+def make_env(env_name: str, n_envs: int, seed: int) -> SyncVectorEnv:
+    return SyncVectorEnv([make_single_env(env_name, seed + i) for i in range(n_envs)])
+
+
+def random_play(env: SyncVectorEnv, n_steps_lim: int, batch_size: int):
+    # random play for  open spiel
+    StepOutput = collections.namedtuple("step_output", ["action"])
+    time_step = env.reset()
+    assert len(env.envs) == len(time_step)  # ensure parallerization
+    rng = np.random.default_rng()
+    step_num = 0
+    while step_num < n_steps_lim:
+        # See https://github.com/deepmind/open_spiel/blob/master/open_spiel/python/examples/rl_example.py
+        actions = [rng.choice(ts.observations["legal_actions"][ts.observations["current_player"]]) for ts in time_step]
+        step_outputs = [StepOutput(action=action) for action in actions]
+        time_step, reward, done, unreset_time_steps = env.step(step_outputs, reset_if_done=True)
+        step_num += batch_size
+    return step_num
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("env_name")  # go, chess backgammon tic_tac_toe
+    parser.add_argument("batch_size", type=int)
+    parser.add_argument("n_steps_lim", default=2 ** 10 * 10, type=int)
+    parser.add_argument("--seed", default=0, type=int)
+    args = parser.parse_args()
+    assert args.n_steps_lim % args.batch_size == 0
+    env = make_env(args.env_name, args.batch_size, args.seed)
+    time_sta = time.time()
+    steps_num = random_play(env, args.n_steps_lim, args.batch_size)
+    time_end = time.time()
+    sec = time_end-time_sta
+    json.dumps({"game": args.env_name, "venv": "for-loop", "library": "open_spiel", "total_steps": steps_num, "total_sec": sec, "steps/sec": steps_num/sec, "batch_size": args.batch_size})

workspace/compariosn/tianshou_env/pettingzoo_env.py → speed_benchmark/run_open_spiel_subproc.py

@@ -1,37 +1,26 @@
-"""
-Copied from TienShou repository:
-
-https://github.com/thu-ml/tianshou/blob/master/tianshou/env/pettingzoo_env.py
-
-Distributed under MIT LICENSE:
-
-https://github.com/thu-ml/tianshou/blob/master/LICENSE
-
-Modified to use OpenSpiel in SubprocVecEnv (see #384 for changes)
-"""
-
-
-
+import json
+from tianshou.env import SubprocVectorEnv
+import numpy as np
+import time
+import argparse
 import warnings
 from abc import ABC
 from typing import Any, Dict, List, Tuple
 
-import pettingzoo
-from gymnasium import spaces
-from packaging import version
+import pyspiel
 from pettingzoo.utils.env import AECEnv
-from pettingzoo.utils.wrappers import BaseWrapper
 from open_spiel.python.rl_environment import Environment, ChanceEventSampler
 
 
-if version.parse(pettingzoo.__version__) < version.parse("1.21.0"):
-    warnings.warn(
-        f"You are using PettingZoo {pettingzoo.__version__}. "
-        f"Future tianshou versions may not support PettingZoo<1.21.0. "
-        f"Consider upgrading your PettingZoo version.", DeprecationWarning
-    )
-
 
+# OpenSpielEnv is modified from TianShou repository (see #384 for changes):
+# This wrapper enables to use TianShou's SubprocVectorEnv for OpenSpiel
+# 
+# https://github.com/thu-ml/tianshou/blob/master/tianshou/env/pettingzoo_env.py
+# 
+# Distributed under MIT LICENSE:
+# 
+# https://github.com/thu-ml/tianshou/blob/master/LICENSE
 class OpenSpielEnv(AECEnv, ABC):
     """The interface for petting zoo environments.
 
@@ -100,3 +89,41 @@ def seed(self, seed: Any = None) -> None:
 
     def render(self) -> Any:
         return self.env.render()
+
+
+def make_single_env(env_name: str, seed: int):
+    def gen_env():
+        game = pyspiel.load_game(env_name)
+        return Environment(game,  chance_event_sampler=ChanceEventSampler(seed=seed))
+    return gen_env()
+
+def make_env(env_name: str, n_envs: int,  seed: int):
+    return SubprocVectorEnv([lambda: OpenSpielEnv(make_single_env(env_name, seed)) for _ in range(n_envs)])
+
+
+def random_play(env: SubprocVectorEnv, n_steps_lim: int, batch_size: int):
+    step_num = 0
+    rng = np.random.default_rng()
+    observation, info = env.reset()
+    while step_num < n_steps_lim:
+        legal_action_mask = [observation[i]["mask"] for i in range(len(observation))]
+        action = [rng.choice(legal_action_mask[i]) for i in range(len(legal_action_mask))]  # chose action randomly
+        observation, reward, terminated, _, info = env.step(action)
+        step_num += batch_size
+    return step_num
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("env_name")
+    parser.add_argument("batch_size", type=int)
+    parser.add_argument("n_steps_lim", default=2 ** 10 * 10, type=int)
+    parser.add_argument("--seed", default=100, type=bool)
+    args = parser.parse_args()
+    assert args.n_steps_lim % args.batch_size == 0
+    env = make_env(args.env_name, args.batch_size, args.seed)
+    time_sta = time.time()
+    steps_num = random_play(env, args.n_steps_lim, args.batch_size)
+    time_end = time.time()
+    sec = time_end - time_sta
+    print(json.dumps({"game": args.env_name, "venv": "subproc", "library": "open_spiel", "total_steps": steps_num, "total_sec": sec, "steps/sec": steps_num/sec, "batch_size": args.batch_size}))

speed_benchmark/run_petting_zoo.py

@@ -0,0 +1,77 @@
+import argparse
+import time
+import json
+import numpy as np
+import collections
+from tianshou.env.pettingzoo_env import PettingZooEnv
+
+
+class AutoResetPettingZooEnv(PettingZooEnv):
+    def __init__(self, env):
+        super().__init__(env)
+
+    def step(self, action):
+        obs, reward, term, trunc, info = super().step(action)
+        if term:
+            obs = super().reset()
+        return obs, reward, term, trunc, info
+
+
+def make_env(env_name, n_envs, vec_env):
+
+    def get_go_env():
+        from pettingzoo.classic.go import go
+        return AutoResetPettingZooEnv(go.env())
+
+    def get_tictactoe_env():
+        from pettingzoo.classic.tictactoe import tictactoe
+        return AutoResetPettingZooEnv(tictactoe.env())
+
+    def get_chess_env():
+        from pettingzoo.classic.chess import chess
+        return AutoResetPettingZooEnv(chess.env())
+
+    if vec_env == "for-loop": 
+        from tianshou.env import DummyVectorEnv as VecEnv
+    elif vec_env == "subproc":
+        from tianshou.env import SubprocVectorEnv as VecEnv
+
+    if env_name == "go":
+        env_fn = get_go_env
+    elif env_name == "tic_tac_toe":
+        env_fn = get_tictactoe_env
+    elif env_name == "chess":
+        env_fn = get_chess_env
+
+    return VecEnv([env_fn for _ in range(n_envs)])
+
+
+def random_play(env, n_steps_lim: int, batch_size: int) -> int:
+    # petting zooのgo環境でrandom gaentを終局まで動かす.
+    step_num = 0
+    rng = np.random.default_rng()
+    observation = env.reset()
+    assert len(env._env_fns) == len(observation)  # ensure parallerization
+    while step_num < n_steps_lim:
+        legal_action_mask = [observation[i]["mask"] for i in range(batch_size)]
+        action = [rng.choice(np.where(legal_action_mask[i])[0]) for i in range(batch_size)]  # chose action randomly
+        observation, reward, terminated, _, _ = env.step(action)
+        step_num += batch_size
+    return step_num
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("env_name")  # go, chess, tic_tac_toe
+    parser.add_argument("venv")  # for-loop, subproc
+    parser.add_argument("batch_size", type=int)
+    parser.add_argument("n_steps_lim", default=2 ** 10 * 10, type=int)
+    parser.add_argument("--seed", default=0, type=int)
+    args = parser.parse_args()
+    assert args.n_steps_lim % args.batch_size == 0
+    env = make_env(args.env_name, args.batch_size, args.venv)
+    time_sta = time.time()
+    steps_num = random_play(env, args.n_steps_lim, args.batch_size)
+    time_end = time.time()
+    sec = time_end - time_sta
+    print(json.dumps({"game": args.env_name, "venv": args.venv, "library": "open_spiel", "total_steps": steps_num, "total_sec": sec, "steps/sec": steps_num/sec, "batch_size": args.batch_size}))

workspace/speed_benchmark.py → speed_benchmark/run_pgx.py

@@ -1,4 +1,5 @@
 import time
+import json
 import jax
 import pgx
 from pgx.utils import act_randomly
@@ -37,28 +38,22 @@ def benchmark(env_id: pgx.EnvId, batch_size, num_steps=(2 ** 12) * 1000):
         state = step(state, action)
     te = time.time()
 
-    return f"{num_steps / (te - ts):.05f}"
+    return num_steps, te - ts
 
 
-N = (2 ** 12) * 100
-print(f"Total # of steps: {N}")
-bs_list = [2 ** i for i in range(5, 13)]
-print("| env_id |" + "|".join([str(bs) for bs in bs_list]) + "|")
-print("|:---:|" + "|".join([":---:" for bs in bs_list]) + "|")
-for env_id in get_args(pgx.EnvId):
-    s = f"|{env_id}|"
-    for bs in tqdm(bs_list, leave=False):
-        s += benchmark(env_id, bs, N)
-        s += "|"
-    print(s)
+games = {
+    "tic_tac_toe": "tic_tac_toe/v0",
+    "backgammon": "backgammon/v0",
+    "shogi": "shogi/v0",
+    "go": "go-19x19/v0",
+}
 
 
-"""
-Total # of steps: 409600
-| env_id |32|64|128|256|512|1024|2048|4096|
-|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
-|tic_tac_toe/v0|25193.54921|51203.80125|99197.43688|206175.78196|413948.81221|723250.23824|1664977.36893|3265886.46947|
-|go-19x19/v0|15146.45769|29891.62027|58064.94882|108400.19704|173638.40814|286740.95368|379331.88909|449555.32632|
-|shogi/v0|21047.48879|42130.05279|82988.81210|175415.01266|259940.79393|290410.20642|299800.69880|308552.26434|
-|minatar/asterix/v0|13066.68075|25836.00751|52134.46018|102929.03752|205880.59846|384825.85566|806843.53100|1553951.76960|
-"""
+N = 2 ** 10 * 10
+bs_list = [2 ** i for i in range(1, 11)]
+d = {}
+for game, env_id in games.items():
+    for bs in bs_list:
+        num_steps, sec = benchmark(env_id, bs, N)
+        print(json.dumps({"game": game, "library": "pgx",
+              "total_steps": num_steps, "total_sec": sec, "steps/sec": num_steps / sec, "batch_size": bs}))