import numpy as np import pickle import unittest import ray from ray.rllib.agents.ppo import PPOTrainer from ray.rllib.examples.env.debug_counter_env import DebugCounterEnv from ray.rllib.examples.models.rnn_spy_model import RNNSpyModel from ray.rllib.models import ModelCatalog from ray.rllib.policy.rnn_sequencing import chop_into_sequences from ray.rllib.policy.sample_batch import SampleBatch from ray.rllib.utils.test_utils import check from ray.tune.registry import register_env class TestLSTMUtils(unittest.TestCase): def test_basic(self): eps_ids = [1, 1, 1, 5, 5, 5, 5, 5] agent_ids = [1, 1, 1, 1, 1, 1, 1, 1] f = [[101, 102, 103, 201, 202, 203, 204, 205], [[101], [102], [103], [201], [202], [203], [204], [205]]] s = [[209, 208, 207, 109, 108, 107, 106, 105]] f_pad, s_init, seq_lens = chop_into_sequences( episode_ids=eps_ids, unroll_ids=np.ones_like(eps_ids), agent_indices=agent_ids, feature_columns=f, state_columns=s, max_seq_len=4) self.assertEqual([f.tolist() for f in f_pad], [ [101, 102, 103, 0, 201, 202, 203, 204, 205, 0, 0, 0], [[101], [102], [103], [0], [201], [202], [203], [204], [205], [0], [0], [0]], ]) self.assertEqual([s.tolist() for s in s_init], [[209, 109, 105]]) self.assertEqual(seq_lens.tolist(), [3, 4, 1]) def test_nested(self): eps_ids = [1, 1, 1, 5, 5, 5, 5, 5] agent_ids = [1, 1, 1, 1, 1, 1, 1, 1] f = [{ "a": np.array([1, 2, 3, 4, 13, 14, 15, 16]), "b": { "ba": np.array([5, 6, 7, 8, 9, 10, 11, 12]) } }] s = [[209, 208, 207, 109, 108, 107, 106, 105]] f_pad, s_init, seq_lens = chop_into_sequences( episode_ids=eps_ids, unroll_ids=np.ones_like(eps_ids), agent_indices=agent_ids, feature_columns=f, state_columns=s, max_seq_len=4, handle_nested_data=True, ) check(f_pad, [[[1, 2, 3, 0, 4, 13, 14, 15, 16, 0, 0, 0], [5, 6, 7, 0, 8, 9, 10, 11, 12, 0, 0, 0]]]) self.assertEqual([s.tolist() for s in s_init], [[209, 109, 105]]) self.assertEqual(seq_lens.tolist(), [3, 4, 1]) def test_multi_dim(self): eps_ids = [1, 1, 1] agent_ids = [1, 1, 1] obs = np.ones((84, 84, 4)) f = [[obs, obs * 2, obs * 3]] s = [[209, 208, 207]] f_pad, s_init, seq_lens = chop_into_sequences( episode_ids=eps_ids, unroll_ids=np.ones_like(eps_ids), agent_indices=agent_ids, feature_columns=f, state_columns=s, max_seq_len=4) self.assertEqual([f.tolist() for f in f_pad], [ np.array([obs, obs * 2, obs * 3]).tolist(), ]) self.assertEqual([s.tolist() for s in s_init], [[209]]) self.assertEqual(seq_lens.tolist(), [3]) def test_batch_id(self): eps_ids = [1, 1, 1, 5, 5, 5, 5, 5] batch_ids = [1, 1, 2, 2, 3, 3, 4, 4] agent_ids = [1, 1, 1, 1, 1, 1, 1, 1] f = [[101, 102, 103, 201, 202, 203, 204, 205], [[101], [102], [103], [201], [202], [203], [204], [205]]] s = [[209, 208, 207, 109, 108, 107, 106, 105]] _, _, seq_lens = chop_into_sequences( episode_ids=eps_ids, unroll_ids=batch_ids, agent_indices=agent_ids, feature_columns=f, state_columns=s, max_seq_len=4) self.assertEqual(seq_lens.tolist(), [2, 1, 1, 2, 2]) def test_multi_agent(self): eps_ids = [1, 1, 1, 5, 5, 5, 5, 5] agent_ids = [1, 1, 2, 1, 1, 2, 2, 3] f = [[101, 102, 103, 201, 202, 203, 204, 205], [[101], [102], [103], [201], [202], [203], [204], [205]]] s = [[209, 208, 207, 109, 108, 107, 106, 105]] f_pad, s_init, seq_lens = chop_into_sequences( episode_ids=eps_ids, unroll_ids=np.ones_like(eps_ids), agent_indices=agent_ids, feature_columns=f, state_columns=s, max_seq_len=4, dynamic_max=False) self.assertEqual(seq_lens.tolist(), [2, 1, 2, 2, 1]) self.assertEqual(len(f_pad[0]), 20) self.assertEqual(len(s_init[0]), 5) def test_dynamic_max_len(self): eps_ids = [5, 2, 2] agent_ids = [2, 2, 2] f = [[1, 1, 1]] s = [[1, 1, 1]] f_pad, s_init, seq_lens = chop_into_sequences( episode_ids=eps_ids, unroll_ids=np.ones_like(eps_ids), agent_indices=agent_ids, feature_columns=f, state_columns=s, max_seq_len=4) self.assertEqual([f.tolist() for f in f_pad], [[1, 0, 1, 1]]) self.assertEqual([s.tolist() for s in s_init], [[1, 1]]) self.assertEqual(seq_lens.tolist(), [1, 2]) class TestRNNSequencing(unittest.TestCase): def setUp(self) -> None: ray.init(num_cpus=4) def tearDown(self) -> None: ray.shutdown() def test_simple_optimizer_sequencing(self): ModelCatalog.register_custom_model("rnn", RNNSpyModel) register_env("counter", lambda _: DebugCounterEnv()) ppo = PPOTrainer( env="counter", config={ "num_workers": 0, "rollout_fragment_length": 10, "train_batch_size": 10, "sgd_minibatch_size": 10, "num_sgd_iter": 1, "simple_optimizer": True, "model": { "custom_model": "rnn", "max_seq_len": 4, "vf_share_layers": True, }, "framework": "tf", }) ppo.train() ppo.train() batch0 = pickle.loads( ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_0")) self.assertEqual( batch0["sequences"].tolist(), [[[0], [1], [2], [3]], [[4], [5], [6], [7]], [[8], [9], [0], [0]]]) self.assertEqual(batch0[SampleBatch.SEQ_LENS].tolist(), [4, 4, 2]) self.assertEqual(batch0["state_in"][0][0].tolist(), [0, 0, 0]) self.assertEqual(batch0["state_in"][1][0].tolist(), [0, 0, 0]) self.assertGreater(abs(np.sum(batch0["state_in"][0][1])), 0) self.assertGreater(abs(np.sum(batch0["state_in"][1][1])), 0) self.assertTrue( np.allclose(batch0["state_in"][0].tolist()[1:], batch0["state_out"][0].tolist()[:-1])) self.assertTrue( np.allclose(batch0["state_in"][1].tolist()[1:], batch0["state_out"][1].tolist()[:-1])) batch1 = pickle.loads( ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_1")) self.assertEqual(batch1["sequences"].tolist(), [ [[10], [11], [12], [13]], [[14], [0], [0], [0]], [[0], [1], [2], [3]], [[4], [0], [0], [0]], ]) self.assertEqual(batch1[SampleBatch.SEQ_LENS].tolist(), [4, 1, 4, 1]) self.assertEqual(batch1["state_in"][0][2].tolist(), [0, 0, 0]) self.assertEqual(batch1["state_in"][1][2].tolist(), [0, 0, 0]) self.assertGreater(abs(np.sum(batch1["state_in"][0][0])), 0) self.assertGreater(abs(np.sum(batch1["state_in"][1][0])), 0) self.assertGreater(abs(np.sum(batch1["state_in"][0][1])), 0) self.assertGreater(abs(np.sum(batch1["state_in"][1][1])), 0) self.assertGreater(abs(np.sum(batch1["state_in"][0][3])), 0) self.assertGreater(abs(np.sum(batch1["state_in"][1][3])), 0) def test_minibatch_sequencing(self): ModelCatalog.register_custom_model("rnn", RNNSpyModel) register_env("counter", lambda _: DebugCounterEnv()) ppo = PPOTrainer( env="counter", config={ "shuffle_sequences": False, # for deterministic testing "num_workers": 0, "rollout_fragment_length": 20, "train_batch_size": 20, "sgd_minibatch_size": 10, "num_sgd_iter": 1, "model": { "custom_model": "rnn", "max_seq_len": 4, "vf_share_layers": True, }, "framework": "tf", }) ppo.train() ppo.train() # first epoch: 20 observations get split into 2 minibatches of 8 # four observations are discarded batch0 = pickle.loads( ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_0")) batch1 = pickle.loads( ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_1")) if batch0["sequences"][0][0][0] > batch1["sequences"][0][0][0]: batch0, batch1 = batch1, batch0 # sort minibatches self.assertEqual(batch0[SampleBatch.SEQ_LENS].tolist(), [4, 4, 2]) self.assertEqual(batch1[SampleBatch.SEQ_LENS].tolist(), [2, 3, 4, 1]) check(batch0["sequences"], [ [[0], [1], [2], [3]], [[4], [5], [6], [7]], [[8], [9], [0], [0]], ]) check(batch1["sequences"], [ [[10], [11], [0], [0]], [[12], [13], [14], [0]], [[0], [1], [2], [3]], [[4], [0], [0], [0]], ]) # second epoch: 20 observations get split into 2 minibatches of 8 # four observations are discarded batch2 = pickle.loads( ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_2")) batch3 = pickle.loads( ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_3")) if batch2["sequences"][0][0][0] > batch3["sequences"][0][0][0]: batch2, batch3 = batch3, batch2 self.assertEqual(batch2[SampleBatch.SEQ_LENS].tolist(), [4, 4, 2]) self.assertEqual(batch3[SampleBatch.SEQ_LENS].tolist(), [4, 4, 2]) check(batch2["sequences"], [ [[0], [1], [2], [3]], [[4], [5], [6], [7]], [[8], [9], [0], [0]], ]) check(batch3["sequences"], [ [[5], [6], [7], [8]], [[9], [10], [11], [12]], [[13], [14], [0], [0]], ]) if __name__ == "__main__": import pytest import sys sys.exit(pytest.main(["-v", __file__]))