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# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
"""Script to benchmark non-RL environment."""
"""Launch Isaac Sim Simulator first."""
import argparse
import contextlib
import os
import sys
import time
from isaaclab.app import AppLauncher
from isaaclab_tasks.utils import setup_preset_cli
# add argparse arguments
parser = argparse.ArgumentParser(description="Train an RL agent with RL-Games.")
parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.")
parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).")
parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).")
parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
parser.add_argument("--task", type=str, default=None, help="Name of the task.")
parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
parser.add_argument(
"--distributed", action="store_true", default=False, help="Run training with multiple GPUs or nodes."
)
parser.add_argument("--num_frames", type=int, default=100, help="Number of environment frames to run benchmark for.")
parser.add_argument(
"--benchmark_backend",
type=str,
default="omniperf",
choices=[
"json",
"osmo",
"omniperf",
"summary",
"LocalLogMetrics",
"JSONFileMetrics",
"OsmoKPIFile",
"OmniPerfKPIFile",
],
help="Benchmarking backend options, defaults omniperf",
)
parser.add_argument("--output_path", type=str, default=".", help="Path to output benchmark results.")
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
args_cli, hydra_args = setup_preset_cli(parser)
sys.argv = [sys.argv[0]] + hydra_args
if args_cli.video:
args_cli.enable_cameras = True
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "../.."))
from isaaclab.test.benchmark import BaseIsaacLabBenchmark, BenchmarkMonitor
from isaaclab.utils.timer import Timer
from scripts.benchmarks.utils import (
get_backend_type,
get_preset_string,
log_app_start_time,
log_python_imports_time,
log_runtime_step_times,
log_scene_creation_time,
log_simulation_start_time,
log_task_start_time,
log_total_start_time,
)
imports_time_begin = time.perf_counter_ns()
import os
from datetime import datetime
import gymnasium as gym
import numpy as np
import torch
from isaaclab.envs import DirectMARLEnvCfg, DirectRLEnvCfg, ManagerBasedRLEnvCfg
from isaaclab.utils.dict import print_dict
import isaaclab_tasks # noqa: F401
# PLACEHOLDER: Extension template (do not remove this comment)
with contextlib.suppress(ImportError):
import isaaclab_tasks_experimental # noqa: F401
from isaaclab_tasks.utils import launch_simulation, resolve_task_config
imports_time_end = time.perf_counter_ns()
# Create the benchmark
backend_type = get_backend_type(args_cli.benchmark_backend)
benchmark = BaseIsaacLabBenchmark(
benchmark_name="benchmark_non_rl",
backend_type=backend_type,
output_path=args_cli.output_path,
use_recorders=True,
frametime_recorders=backend_type in ("summary", "omniperf"),
output_prefix=f"benchmark_non_rl_{args_cli.task}",
workflow_metadata={
"metadata": [
{"name": "task", "data": args_cli.task},
{"name": "seed", "data": args_cli.seed},
{"name": "num_envs", "data": args_cli.num_envs},
{"name": "num_frames", "data": args_cli.num_frames},
{"name": "presets", "data": get_preset_string(hydra_args)},
]
},
)
def main(
env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg,
app_start_time_begin: int,
app_start_time_end: int,
):
"""Benchmark without RL in the loop."""
# override configurations with non-hydra CLI arguments
env_cfg.scene.num_envs = args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
# For distributed training, launch_simulation() already resolved the
# correct per-rank device; only apply a CLI --device override for
# non-distributed runs (the default "cuda:0" would clobber the
# per-rank device otherwise).
if not args_cli.distributed:
env_cfg.sim.device = args_cli.device if args_cli.device is not None else env_cfg.sim.device
env_cfg.seed = args_cli.seed
# check for invalid combination of CPU device with distributed training
if args_cli.distributed and args_cli.device is not None and "cpu" in args_cli.device:
raise ValueError(
"Distributed training is not supported when using CPU device. "
"Please use GPU device (e.g., --device cuda) for distributed training."
)
# process distributed
# env_cfg.sim.device is already resolved by launch_simulation().
world_size = 1
world_rank = 0
if args_cli.distributed:
world_size = int(os.getenv("WORLD_SIZE", 1))
world_rank = int(os.getenv("RANK", "0"))
task_startup_time_begin = time.perf_counter_ns()
# create isaac environment
env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
# wrap for video recording
if args_cli.video:
log_root_path = os.path.abspath(f"benchmark/{args_cli.task}")
log_dir = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
video_kwargs = {
"video_folder": os.path.join(log_root_path, log_dir, "videos"),
"step_trigger": lambda step: step % args_cli.video_interval == 0,
"video_length": args_cli.video_length,
"disable_logger": True,
}
print("[INFO] Recording videos during training.")
print_dict(video_kwargs, nesting=4)
env = gym.wrappers.RecordVideo(env, **video_kwargs)
task_startup_time_end = time.perf_counter_ns()
env.reset()
# counter for number of frames to run for
num_frames = 0
# log frame times
step_times = []
# Run with continuous benchmark monitoring
with BenchmarkMonitor(benchmark, interval=1.0):
while num_frames < args_cli.num_frames:
# get upper and lower bounds of action space, sample actions randomly on this interval
action_high = 1
action_low = -1
actions = (action_high - action_low) * torch.rand(
env.unwrapped.num_envs, env.unwrapped.single_action_space.shape[0], device=env.unwrapped.device
) - action_high
# env stepping
env_step_time_begin = time.perf_counter_ns()
_ = env.step(actions)
end_step_time_end = time.perf_counter_ns()
step_times.append(end_step_time_end - env_step_time_begin)
num_frames += 1
if world_rank == 0:
# Final update after loop completes
benchmark.update_manual_recorders()
# compute stats
step_times = np.array(step_times) / 1e6 # ns to ms
fps = 1.0 / (step_times / 1000)
effective_fps = fps * env.unwrapped.num_envs * world_size
# prepare step timing dict
environment_step_times = {
"Environment step times": step_times.tolist(),
"Environment step FPS": fps.tolist(),
"Environment step effective FPS": effective_fps.tolist(),
}
log_app_start_time(benchmark, (app_start_time_end - app_start_time_begin) / 1e6)
log_python_imports_time(benchmark, (imports_time_end - imports_time_begin) / 1e6)
log_task_start_time(benchmark, (task_startup_time_end - task_startup_time_begin) / 1e6)
log_scene_creation_time(benchmark, Timer.get_timer_info("scene_creation") * 1000)
log_simulation_start_time(benchmark, Timer.get_timer_info("simulation_start") * 1000)
log_total_start_time(benchmark, (task_startup_time_end - app_start_time_begin) / 1e6)
log_runtime_step_times(benchmark, environment_step_times, compute_stats=True)
benchmark._finalize_impl()
# close the simulator
env.close()
if __name__ == "__main__":
env_cfg, _agent_cfg = resolve_task_config(args_cli.task, None)
app_start_time_begin = time.perf_counter_ns()
with launch_simulation(env_cfg, args_cli):
app_start_time_end = time.perf_counter_ns()
main(env_cfg, app_start_time_begin, app_start_time_end)
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