def _play_game_against_neural_mcts(
devices: List[torch.device],
models: List[torch.jit.ScriptModule],
context: tube.Context,
actor_channel: tube.DataChannel,
) -> None:
nb_devices = len(devices)
context.start()
dcm = DataChannelManager([actor_channel])
while not context.terminated():
batch = dcm.get_input(max_timeout_s=1)
if len(batch) == 0:
continue
assert len(batch) == 1
# split in as many part as there are devices
batches_s = torch.chunk(batch[actor_channel.name]["s"],
nb_devices,
dim=0)
futures = []
reply_eval = {"v": None, "pi": None}
# multithread
with ThreadPoolExecutor(max_workers=nb_devices) as executor:
for device, model, batch_s in zip(devices, models, batches_s):
futures.append(
executor.submit(_forward_pass_on_device, device, model,
batch_s))
results = [future.result() for future in futures]
reply_eval["v"] = torch.cat([result["v"] for result in results],
dim=0)
reply_eval["pi"] = torch.cat([result["pi"] for result in results],
dim=0)
dcm.set_reply(actor_channel.name, reply_eval)
dcm.terminate()
def _play_game_against_mcts(context: tube.Context) -> None:
context.start()
while not context.terminated():
time.sleep(1)
