261 lines
9.3 KiB
Python
261 lines
9.3 KiB
Python
import json
|
|
import os
|
|
|
|
import jax, jax.numpy as jnp
|
|
import datetime, time
|
|
import numpy as np
|
|
|
|
from algorithm import BaseAlgorithm
|
|
from problem import BaseProblem
|
|
from problem.rl_env import RLEnv
|
|
from problem.func_fit import FuncFit
|
|
from utils import State, StatefulBaseClass
|
|
|
|
|
|
class Pipeline(StatefulBaseClass):
|
|
def __init__(
|
|
self,
|
|
algorithm: BaseAlgorithm,
|
|
problem: BaseProblem,
|
|
seed: int = 42,
|
|
fitness_target: float = 1,
|
|
generation_limit: int = 1000,
|
|
pre_update: bool = False,
|
|
update_batch_size: int = 10000,
|
|
save_dir=None,
|
|
is_save: bool = False,
|
|
):
|
|
assert problem.jitable, "Currently, problem must be jitable"
|
|
|
|
self.algorithm = algorithm
|
|
self.problem = problem
|
|
self.seed = seed
|
|
self.fitness_target = fitness_target
|
|
self.generation_limit = generation_limit
|
|
self.pop_size = self.algorithm.pop_size
|
|
|
|
# print(self.problem.input_shape, self.problem.output_shape)
|
|
|
|
# TODO: make each algorithm's input_num and output_num
|
|
assert (
|
|
algorithm.num_inputs == self.problem.input_shape[-1]
|
|
), f"algorithm input shape is {algorithm.num_inputs} but problem input shape is {self.problem.input_shape}"
|
|
|
|
self.best_genome = None
|
|
self.best_fitness = float("-inf")
|
|
self.generation_timestamp = None
|
|
self.pre_update = pre_update
|
|
self.update_batch_size = update_batch_size
|
|
if pre_update:
|
|
if isinstance(problem, RLEnv):
|
|
assert problem.record_episode, "record_episode must be True"
|
|
self.fetch_data = lambda episode: episode["obs"]
|
|
elif isinstance(problem, FuncFit):
|
|
assert problem.return_data, "return_data must be True"
|
|
self.fetch_data = lambda data: data
|
|
else:
|
|
raise NotImplementedError
|
|
else:
|
|
if isinstance(problem, RLEnv):
|
|
assert not problem.record_episode, "record_episode must be False"
|
|
elif isinstance(problem, FuncFit):
|
|
assert not problem.return_data, "return_data must be False"
|
|
self.is_save = is_save
|
|
|
|
if is_save:
|
|
if save_dir is None:
|
|
now = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
|
|
self.save_dir = f"./{self.__class__.__name__} {now}"
|
|
else:
|
|
self.save_dir = save_dir
|
|
print(f"save to {self.save_dir}")
|
|
if not os.path.exists(self.save_dir):
|
|
os.makedirs(self.save_dir)
|
|
|
|
def setup(self, state=State()):
|
|
print("initializing")
|
|
state = state.register(randkey=jax.random.PRNGKey(self.seed))
|
|
|
|
if self.pre_update:
|
|
# initial with mean = 0 and std = 1
|
|
state = state.register(
|
|
data=jax.random.normal(
|
|
state.randkey, (self.update_batch_size, self.algorithm.num_inputs)
|
|
)
|
|
)
|
|
|
|
state = self.algorithm.setup(state)
|
|
state = self.problem.setup(state)
|
|
|
|
if self.is_save:
|
|
# self.save(state=state, path=os.path.join(self.save_dir, "pipeline.pkl"))
|
|
with open(os.path.join(self.save_dir, "config.txt"), "w") as f:
|
|
f.write(json.dumps(self.show_config(), indent=4))
|
|
# create log file
|
|
with open(os.path.join(self.save_dir, "log.txt"), "w") as f:
|
|
f.write("Generation,Max,Min,Mean,Std,Cost Time\n")
|
|
|
|
print("initializing finished")
|
|
return state
|
|
|
|
def step(self, state):
|
|
|
|
randkey_, randkey = jax.random.split(state.randkey)
|
|
keys = jax.random.split(randkey_, self.pop_size)
|
|
|
|
pop = self.algorithm.ask(state)
|
|
|
|
pop_transformed = jax.vmap(self.algorithm.transform, in_axes=(None, 0))(
|
|
state, pop
|
|
)
|
|
|
|
if self.pre_update:
|
|
# update the population
|
|
_, pop_transformed = jax.vmap(
|
|
self.algorithm.update_by_batch, in_axes=(None, None, 0)
|
|
)(state, state.data, pop_transformed)
|
|
|
|
# raw_data: (Pop, Batch, num_inputs)
|
|
fitnesses, raw_data = jax.vmap(
|
|
self.problem.evaluate, in_axes=(None, 0, None, 0)
|
|
)(state, keys, self.algorithm.forward, pop_transformed)
|
|
|
|
# update population
|
|
pop_nodes, pop_conns = jax.vmap(self.algorithm.restore, in_axes=(None, 0))(
|
|
state, pop_transformed
|
|
)
|
|
state = state.update(pop_nodes=pop_nodes, pop_conns=pop_conns)
|
|
|
|
# update data for next generation
|
|
data = self.fetch_data(raw_data)
|
|
assert (
|
|
data.ndim == 3
|
|
and data.shape[0] == self.pop_size
|
|
and data.shape[2] == self.algorithm.num_inputs
|
|
)
|
|
# reshape to (Pop * Batch, num_inputs)
|
|
data = data.reshape(
|
|
data.shape[0] * data.shape[1], self.algorithm.num_inputs
|
|
)
|
|
# shuffle
|
|
data = jax.random.permutation(randkey_, data, axis=0)
|
|
# cutoff or expand
|
|
if data.shape[0] >= self.update_batch_size:
|
|
data = data[: self.update_batch_size] # cutoff
|
|
else:
|
|
data = (
|
|
jnp.full(state.data.shape, jnp.nan).at[: data.shape[0]].set(data)
|
|
) # expand
|
|
state = state.update(data=data)
|
|
|
|
else:
|
|
fitnesses = jax.vmap(self.problem.evaluate, in_axes=(None, 0, None, 0))(
|
|
state, keys, self.algorithm.forward, pop_transformed
|
|
)
|
|
|
|
# replace nan with -inf
|
|
fitnesses = jnp.where(jnp.isnan(fitnesses), -jnp.inf, fitnesses)
|
|
|
|
previous_pop = self.algorithm.ask(state)
|
|
state = self.algorithm.tell(state, fitnesses)
|
|
|
|
return state.update(randkey=randkey), previous_pop, fitnesses
|
|
|
|
def auto_run(self, state):
|
|
print("start compile")
|
|
tic = time.time()
|
|
compiled_step = jax.jit(self.step).lower(state).compile()
|
|
print(
|
|
f"compile finished, cost time: {time.time() - tic:.6f}s",
|
|
)
|
|
|
|
for _ in range(self.generation_limit):
|
|
|
|
self.generation_timestamp = time.time()
|
|
|
|
state, previous_pop, fitnesses = compiled_step(state)
|
|
|
|
fitnesses = jax.device_get(fitnesses)
|
|
|
|
self.analysis(state, previous_pop, fitnesses)
|
|
|
|
if max(fitnesses) >= self.fitness_target:
|
|
print("Fitness limit reached!")
|
|
return state, self.best_genome
|
|
|
|
print("Generation limit reached!")
|
|
return state, self.best_genome
|
|
|
|
def analysis(self, state, pop, fitnesses):
|
|
|
|
valid_fitnesses = fitnesses[~np.isinf(fitnesses)]
|
|
|
|
max_f, min_f, mean_f, std_f = (
|
|
max(valid_fitnesses),
|
|
min(valid_fitnesses),
|
|
np.mean(valid_fitnesses),
|
|
np.std(valid_fitnesses),
|
|
)
|
|
|
|
new_timestamp = time.time()
|
|
|
|
cost_time = new_timestamp - self.generation_timestamp
|
|
|
|
max_idx = np.argmax(fitnesses)
|
|
if fitnesses[max_idx] > self.best_fitness:
|
|
self.best_fitness = fitnesses[max_idx]
|
|
self.best_genome = pop[0][max_idx], pop[1][max_idx]
|
|
|
|
if self.is_save:
|
|
best_genome = jax.device_get(self.best_genome)
|
|
with open(os.path.join(self.save_dir, f"{int(self.algorithm.generation(state))}.npz"), "wb") as f:
|
|
np.savez(
|
|
f,
|
|
nodes=best_genome[0],
|
|
conns=best_genome[1],
|
|
fitness=self.best_fitness,
|
|
)
|
|
|
|
# save best if save path is not None
|
|
|
|
member_count = jax.device_get(self.algorithm.member_count(state))
|
|
species_sizes = [int(i) for i in member_count if i > 0]
|
|
|
|
pop = jax.device_get(pop)
|
|
pop_nodes, pop_conns = pop # (P, N, NL), (P, C, CL)
|
|
nodes_cnt = (~np.isnan(pop_nodes[:, :, 0])).sum(axis=1) # (P,)
|
|
conns_cnt = (~np.isnan(pop_conns[:, :, 0])).sum(axis=1) # (P,)
|
|
|
|
max_node_cnt, min_node_cnt, mean_node_cnt = (
|
|
max(nodes_cnt),
|
|
min(nodes_cnt),
|
|
np.mean(nodes_cnt),
|
|
)
|
|
|
|
max_conn_cnt, min_conn_cnt, mean_conn_cnt = (
|
|
max(conns_cnt),
|
|
min(conns_cnt),
|
|
np.mean(conns_cnt),
|
|
)
|
|
|
|
print(
|
|
f"Generation: {self.algorithm.generation(state)}, Cost time: {cost_time * 1000:.2f}ms\n",
|
|
f"\tnode counts: max: {max_node_cnt}, min: {min_node_cnt}, mean: {mean_node_cnt:.2f}\n",
|
|
f"\tconn counts: max: {max_conn_cnt}, min: {min_conn_cnt}, mean: {mean_conn_cnt:.2f}\n",
|
|
f"\tspecies: {len(species_sizes)}, {species_sizes}\n",
|
|
f"\tfitness: valid cnt: {len(valid_fitnesses)}, max: {max_f:.4f}, min: {min_f:.4f}, mean: {mean_f:.4f}, std: {std_f:.4f}\n",
|
|
)
|
|
|
|
# append log
|
|
if self.is_save:
|
|
with open(os.path.join(self.save_dir, "log.txt"), "a") as f:
|
|
f.write(
|
|
f"{self.algorithm.generation(state)},{max_f},{min_f},{mean_f},{std_f},{cost_time}\n"
|
|
)
|
|
|
|
def show(self, state, best, *args, **kwargs):
|
|
transformed = self.algorithm.transform(state, best)
|
|
self.problem.show(
|
|
state, state.randkey, self.algorithm.forward, transformed, *args, **kwargs
|
|
)
|