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[rllib] Add end-to-end tests for RNN sequencing (#4258)
This commit is contained in:
Eric Liang
@@ -6,9 +6,9 @@ SCRIPT=$1
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shift
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if [ -x $DIRECTORY/../$SCRIPT ]; then
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$DIRECTORY/../$SCRIPT "$@" >$TMPFILE 2>&1
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time $DIRECTORY/../$SCRIPT "$@" >$TMPFILE 2>&1
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else
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python $DIRECTORY/../$SCRIPT "$@" >$TMPFILE 2>&1
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time python $DIRECTORY/../$SCRIPT "$@" >$TMPFILE 2>&1
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fi
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CODE=$?
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@@ -2,9 +2,20 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import gym
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import numpy as np
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import pickle
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import unittest
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import tensorflow as tf
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import tensorflow.contrib.rnn as rnn
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from ray.rllib.models.lstm import chop_into_sequences
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import ray
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from ray.rllib.agents.ppo import PPOAgent
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from ray.rllib.models import ModelCatalog
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from ray.rllib.models.lstm import add_time_dimension, chop_into_sequences
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from ray.rllib.models.misc import linear, normc_initializer
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from ray.rllib.models.model import Model
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from ray.tune.registry import register_env
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class LSTMUtilsTest(unittest.TestCase):
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@@ -48,5 +59,209 @@ class LSTMUtilsTest(unittest.TestCase):
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self.assertEqual(seq_lens.tolist(), [1, 2])
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class RNNSpyModel(Model):
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capture_index = 0
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def _build_layers_v2(self, input_dict, num_outputs, options):
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def spy(sequences, state_in, state_out, seq_lens):
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if len(sequences) == 1:
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return 0 # don't capture inference inputs
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# TF runs this function in an isolated context, so we have to use
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# redis to communicate back to our suite
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ray.experimental.internal_kv._internal_kv_put(
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"rnn_spy_in_{}".format(RNNSpyModel.capture_index),
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pickle.dumps({
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"sequences": sequences,
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"state_in": state_in,
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"state_out": state_out,
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"seq_lens": seq_lens
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}),
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overwrite=True)
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RNNSpyModel.capture_index += 1
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return 0
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features = input_dict["obs"]
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cell_size = 3
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last_layer = add_time_dimension(features, self.seq_lens)
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# Setup the LSTM cell
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lstm = rnn.BasicLSTMCell(cell_size, state_is_tuple=True)
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self.state_init = [
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np.zeros(lstm.state_size.c, np.float32),
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np.zeros(lstm.state_size.h, np.float32)
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]
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# Setup LSTM inputs
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if self.state_in:
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c_in, h_in = self.state_in
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else:
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c_in = tf.placeholder(
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tf.float32, [None, lstm.state_size.c], name="c")
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h_in = tf.placeholder(
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tf.float32, [None, lstm.state_size.h], name="h")
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self.state_in = [c_in, h_in]
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# Setup LSTM outputs
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state_in = rnn.LSTMStateTuple(c_in, h_in)
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lstm_out, lstm_state = tf.nn.dynamic_rnn(
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lstm,
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last_layer,
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initial_state=state_in,
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sequence_length=self.seq_lens,
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time_major=False,
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dtype=tf.float32)
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self.state_out = list(lstm_state)
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spy_fn = tf.py_func(
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spy, [
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last_layer,
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self.state_in,
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self.state_out,
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self.seq_lens,
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],
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tf.int64,
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stateful=True)
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# Compute outputs
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with tf.control_dependencies([spy_fn]):
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last_layer = tf.reshape(lstm_out, [-1, cell_size])
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logits = linear(last_layer, num_outputs, "action",
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normc_initializer(0.01))
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return logits, last_layer
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class DebugCounterEnv(gym.Env):
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def __init__(self):
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self.action_space = gym.spaces.Discrete(2)
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self.observation_space = gym.spaces.Box(0, 100, (1, ))
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self.i = 0
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def reset(self):
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self.i = 0
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return [self.i]
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def step(self, action):
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self.i += 1
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return [self.i], self.i % 3, self.i >= 15, {}
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class RNNSequencing(unittest.TestCase):
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def testSimpleOptimizerSequencing(self):
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ModelCatalog.register_custom_model("rnn", RNNSpyModel)
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register_env("counter", lambda _: DebugCounterEnv())
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ppo = PPOAgent(
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env="counter",
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config={
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"num_workers": 0,
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"sample_batch_size": 10,
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"train_batch_size": 10,
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"sgd_minibatch_size": 10,
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"vf_share_layers": True,
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"simple_optimizer": True,
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"num_sgd_iter": 1,
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"model": {
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"custom_model": "rnn",
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"max_seq_len": 4,
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},
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})
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ppo.train()
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ppo.train()
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batch0 = pickle.loads(
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ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_0"))
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self.assertEqual(
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batch0["sequences"].tolist(),
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[[[0], [1], [2], [3]], [[4], [5], [6], [7]], [[8], [9], [0], [0]]])
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self.assertEqual(batch0["seq_lens"].tolist(), [4, 4, 2])
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self.assertEqual(batch0["state_in"][0][0].tolist(), [0, 0, 0])
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self.assertEqual(batch0["state_in"][1][0].tolist(), [0, 0, 0])
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self.assertGreater(abs(np.sum(batch0["state_in"][0][1])), 0)
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self.assertGreater(abs(np.sum(batch0["state_in"][1][1])), 0)
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self.assertTrue(
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np.allclose(batch0["state_in"][0].tolist()[1:],
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batch0["state_out"][0].tolist()[:-1]))
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self.assertTrue(
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np.allclose(batch0["state_in"][1].tolist()[1:],
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batch0["state_out"][1].tolist()[:-1]))
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batch1 = pickle.loads(
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ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_1"))
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self.assertEqual(batch1["sequences"].tolist(), [
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[[10], [11], [12], [13]],
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[[14], [0], [0], [0]],
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[[0], [1], [2], [3]],
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[[4], [0], [0], [0]],
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])
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self.assertEqual(batch1["seq_lens"].tolist(), [4, 1, 4, 1])
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self.assertEqual(batch1["state_in"][0][2].tolist(), [0, 0, 0])
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self.assertEqual(batch1["state_in"][1][2].tolist(), [0, 0, 0])
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self.assertGreater(abs(np.sum(batch1["state_in"][0][0])), 0)
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self.assertGreater(abs(np.sum(batch1["state_in"][1][0])), 0)
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self.assertGreater(abs(np.sum(batch1["state_in"][0][1])), 0)
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self.assertGreater(abs(np.sum(batch1["state_in"][1][1])), 0)
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self.assertGreater(abs(np.sum(batch1["state_in"][0][3])), 0)
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self.assertGreater(abs(np.sum(batch1["state_in"][1][3])), 0)
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def testMinibatchSequencing(self):
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ModelCatalog.register_custom_model("rnn", RNNSpyModel)
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register_env("counter", lambda _: DebugCounterEnv())
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ppo = PPOAgent(
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env="counter",
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config={
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"num_workers": 0,
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"sample_batch_size": 20,
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"train_batch_size": 20,
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"sgd_minibatch_size": 10,
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"vf_share_layers": True,
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"simple_optimizer": False,
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"num_sgd_iter": 1,
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"model": {
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"custom_model": "rnn",
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"max_seq_len": 4,
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},
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})
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ppo.train()
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ppo.train()
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# first epoch: 20 observations get split into 2 minibatches of 8
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# four observations are discarded
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batch0 = pickle.loads(
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ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_0"))
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batch1 = pickle.loads(
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ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_1"))
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if batch0["sequences"][0][0][0] > batch1["sequences"][0][0][0]:
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batch0, batch1 = batch1, batch0 # sort minibatches
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self.assertEqual(batch0["seq_lens"].tolist(), [4, 4])
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self.assertEqual(batch1["seq_lens"].tolist(), [4, 3])
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self.assertEqual(batch0["sequences"].tolist(), [
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[[0], [1], [2], [3]],
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[[4], [5], [6], [7]],
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])
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self.assertEqual(batch1["sequences"].tolist(), [
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[[8], [9], [10], [11]],
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[[12], [13], [14], [0]],
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])
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# second epoch: 20 observations get split into 2 minibatches of 8
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# four observations are discarded
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batch2 = pickle.loads(
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ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_2"))
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batch3 = pickle.loads(
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ray.experimental.internal_kv._internal_kv_get("rnn_spy_in_3"))
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if batch2["sequences"][0][0][0] > batch3["sequences"][0][0][0]:
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batch2, batch3 = batch3, batch2
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self.assertEqual(batch2["seq_lens"].tolist(), [4, 4])
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self.assertEqual(batch3["seq_lens"].tolist(), [2, 4])
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self.assertEqual(batch2["sequences"].tolist(), [
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[[5], [6], [7], [8]],
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[[9], [10], [11], [12]],
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])
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self.assertEqual(batch3["sequences"].tolist(), [
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[[13], [14], [0], [0]],
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[[0], [1], [2], [3]],
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])
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if __name__ == "__main__":
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ray.init(num_cpus=4)
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unittest.main(verbosity=2)
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