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run_popgym.py
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run_popgym.py
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import jax
import jax.numpy as jnp
import time
from envs import make
from envs.wrappers import AliasPrevActionV2
from algorithms.ppo_gru import make_train as make_train_gru
from algorithms.ppo_s5 import make_train as make_train_s5
import argparse
def run(num_runs, env_name, arch="gru", file_tag=""):
print("*"*10)
print(f"Running {num_runs} runs of {env_name} with arch {arch}")
env, env_params = make(env_name)
config = {
"LR": 5e-5,
"NUM_ENVS": 64,
"NUM_STEPS": 1024,
"TOTAL_TIMESTEPS": 15e6,
"UPDATE_EPOCHS": 30,
"NUM_MINIBATCHES": 8,
"GAMMA": 0.99,
"GAE_LAMBDA": 1.0,
"CLIP_EPS": 0.2,
"ENT_COEF": 0.0,
"VF_COEF": 1.0,
"MAX_GRAD_NORM": 0.5,
"ENV": AliasPrevActionV2(env),
"ENV_PARAMS": env_params,
"ANNEAL_LR": False,
"DEBUG": True,
"S5_D_MODEL": 256,
"S5_SSM_SIZE": 256,
"S5_N_LAYERS": 4,
"S5_BLOCKS": 1,
"S5_ACTIVATION": "full_glu",
"S5_DO_NORM": False,
"S5_PRENORM": False,
"S5_DO_GTRXL_NORM": False,
}
rng = jax.random.PRNGKey(42)
train_vjit_rnn = jax.jit(jax.vmap(make_train_gru(config)))
train_vjit_s5 = jax.jit(jax.vmap(make_train_s5(config)))
rngs = jax.random.split(rng, num_runs)
info_dict = {}
if arch == "s5":
t0 = time.time()
compiled_s5 = train_vjit_s5.lower(rngs).compile()
compile_s5_time = time.time() - t0
print(f"s5 compile time: {compile_s5_time}")
t0 = time.time()
out_s5 = jax.block_until_ready(compiled_s5(rngs))
run_s5_time = time.time() - t0
print(f"s5 time: {run_s5_time}")
info_dict["s5"] = {
"compile_s5_time": compile_s5_time,
"run_s5_time": run_s5_time,
"out": out_s5[1],
}
elif arch == "gru":
t0 = time.time()
compiled_rnn = train_vjit_rnn.lower(rngs).compile()
compile_rnn_time = time.time() - t0
print(f"gru compile time: {compile_rnn_time}")
t0 = time.time()
out_rnn = jax.block_until_ready(compiled_rnn(rngs))
run_rnn_time = time.time() - t0
print(f"gru time: {run_rnn_time}")
info_dict["gru"] = {
"compile_rnn_time": compile_rnn_time,
"run_rnn_time": run_rnn_time,
"out": out_rnn[1],
}
else:
raise NotImplementedError
jnp.save(f"results/{num_runs}_{env_name}_{arch}_{file_tag}.npy", info_dict)
parser = argparse.ArgumentParser()
parser.add_argument("--num-runs", type=int, required=True)
parser.add_argument("--env", type=str, default="")
parser.add_argument("--arch", type=str, default="s5")
args = parser.parse_args()
if __name__ == "__main__":
run(args.num_runs, args.env, args.arch)