wehub-resource-sync
572 lines
20 KiB
Python
572 lines
20 KiB
Python
import copy
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import functools
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import os
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import unittest
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import numpy as np
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import torch
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import tree
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import ray
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from ray.rllib.models.repeated_values import RepeatedValues
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from ray.rllib.policy.sample_batch import (
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SampleBatch,
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attempt_count_timesteps,
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concat_samples,
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)
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from ray.rllib.utils.compression import is_compressed
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from ray.rllib.utils.framework import try_import_torch
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from ray.rllib.utils.test_utils import check
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from ray.rllib.utils.torch_utils import convert_to_torch_tensor
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class TestSampleBatch(unittest.TestCase):
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@classmethod
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def setUpClass(cls) -> None:
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ray.init(num_gpus=1)
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@classmethod
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def tearDownClass(cls) -> None:
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ray.shutdown()
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def test_len_and_size_bytes(self):
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s1 = SampleBatch(
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{
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"a": np.array([1, 2, 3]),
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"b": {"c": np.array([4, 5, 6])},
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SampleBatch.SEQ_LENS: [1, 2],
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}
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)
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check(len(s1), 3)
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check(
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s1.size_bytes(),
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s1["a"].nbytes + s1["b"]["c"].nbytes + s1[SampleBatch.SEQ_LENS].nbytes,
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)
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def test_dict_properties_of_sample_batches(self):
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base_dict = {
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"a": np.array([1, 2, 3]),
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"b": np.array([[0.1, 0.2], [0.3, 0.4]]),
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"c": True,
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}
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batch = SampleBatch(base_dict)
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keys_ = list(base_dict.keys())
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values_ = list(base_dict.values())
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items_ = list(base_dict.items())
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assert list(batch.keys()) == keys_
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assert list(batch.values()) == values_
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assert list(batch.items()) == items_
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# Add an item and check, whether it's in the "added" list.
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batch["d"] = np.array(1)
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assert batch.added_keys == {"d"}, batch.added_keys
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# Access two keys and check, whether they are in the
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# "accessed" list.
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print(batch["a"], batch["b"])
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assert batch.accessed_keys == {"a", "b"}, batch.accessed_keys
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# Delete a key and check, whether it's in the "deleted" list.
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del batch["c"]
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assert batch.deleted_keys == {"c"}, batch.deleted_keys
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def test_right_zero_padding(self):
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"""Tests, whether right-zero-padding work properly."""
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s1 = SampleBatch(
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{
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"a": np.array([1, 2, 3]),
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"b": {"c": np.array([4, 5, 6])},
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SampleBatch.SEQ_LENS: [1, 2],
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}
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)
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s1.right_zero_pad(max_seq_len=5)
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check(
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s1,
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{
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"a": [1, 0, 0, 0, 0, 2, 3, 0, 0, 0],
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"b": {"c": [4, 0, 0, 0, 0, 5, 6, 0, 0, 0]},
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SampleBatch.SEQ_LENS: [1, 2],
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},
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)
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def test_concat(self):
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"""Tests, SampleBatches.concat() and concat_samples()."""
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s1 = SampleBatch(
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{
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"a": np.array([1, 2, 3]),
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"b": {"c": np.array([4, 5, 6])},
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}
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)
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s2 = SampleBatch(
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{
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"a": np.array([2, 3, 4]),
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"b": {"c": np.array([5, 6, 7])},
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}
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)
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concatd = concat_samples([s1, s2])
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check(concatd["a"], [1, 2, 3, 2, 3, 4])
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check(concatd["b"]["c"], [4, 5, 6, 5, 6, 7])
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check(next(concatd.rows()), {"a": 1, "b": {"c": 4}})
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concatd_2 = s1.concat(s2)
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check(concatd, concatd_2)
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def test_concat_max_seq_len(self):
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"""Tests, SampleBatches.concat_samples() max_seq_len."""
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s1 = SampleBatch(
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{
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"a": np.array([1, 2, 3]),
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"b": {"c": np.array([4, 5, 6])},
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SampleBatch.SEQ_LENS: [1, 2],
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}
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)
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s2 = SampleBatch(
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{
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"a": np.array([2, 3, 4]),
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"b": {"c": np.array([5, 6, 7])},
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SampleBatch.SEQ_LENS: [3],
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}
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)
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s3 = SampleBatch(
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{
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"a": np.array([2, 3, 4]),
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"b": {"c": np.array([5, 6, 7])},
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}
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)
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concatd = concat_samples([s1, s2])
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check(concatd.max_seq_len, s2.max_seq_len)
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with self.assertRaises(ValueError):
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concat_samples([s1, s2, s3])
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def test_rows(self):
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s1 = SampleBatch(
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{
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"a": np.array([[1, 1], [2, 2], [3, 3]]),
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"b": {"c": np.array([[4, 4], [5, 5], [6, 6]])},
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SampleBatch.SEQ_LENS: np.array([1, 2]),
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}
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)
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check(
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next(s1.rows()),
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{"a": [1, 1], "b": {"c": [4, 4]}, SampleBatch.SEQ_LENS: 1},
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)
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def test_compression(self):
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"""Tests, whether compression and decompression work properly."""
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s1 = SampleBatch(
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{
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"a": np.array([1, 2, 3, 2, 3, 4]),
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"b": {"c": np.array([4, 5, 6, 5, 6, 7])},
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}
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)
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# Test, whether compressing happens in-place.
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s1.compress(columns={"a", "b"}, bulk=True)
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self.assertTrue(is_compressed(s1["a"]))
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self.assertTrue(is_compressed(s1["b"]["c"]))
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self.assertTrue(isinstance(s1["b"], dict))
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# Test, whether de-compressing happens in-place.
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s1.decompress_if_needed(columns={"a", "b"})
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check(s1["a"], [1, 2, 3, 2, 3, 4])
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check(s1["b"]["c"], [4, 5, 6, 5, 6, 7])
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it = s1.rows()
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next(it)
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check(next(it), {"a": 2, "b": {"c": 5}})
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def test_slicing(self):
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"""Tests, whether slicing can be done on SampleBatches."""
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s1 = SampleBatch(
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{
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"a": np.array([1, 2, 3, 2, 3, 4]),
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"b": {"c": np.array([4, 5, 6, 5, 6, 7])},
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}
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)
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check(
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s1[:3],
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{
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"a": [1, 2, 3],
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"b": {"c": [4, 5, 6]},
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},
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)
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check(
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s1[0:3],
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{
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"a": [1, 2, 3],
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"b": {"c": [4, 5, 6]},
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},
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)
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check(
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s1[1:4],
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{
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"a": [2, 3, 2],
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"b": {"c": [5, 6, 5]},
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},
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)
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check(
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s1[1:],
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{
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"a": [2, 3, 2, 3, 4],
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"b": {"c": [5, 6, 5, 6, 7]},
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},
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)
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check(
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s1[3:4],
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{
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"a": [2],
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"b": {"c": [5]},
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},
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)
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# When we change the slice, the original SampleBatch should also
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# change (shared underlying data).
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s1[:3]["a"][0] = 100
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s1[1:2]["a"][0] = 200
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check(s1["a"][0], 100)
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check(s1["a"][1], 200)
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# Seq-len batches should be auto-sliced along sequences,
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# no matter what.
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s2 = SampleBatch(
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{
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"a": np.array([1, 2, 3, 2, 3, 4]),
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"b": {"c": np.array([4, 5, 6, 5, 6, 7])},
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SampleBatch.SEQ_LENS: [2, 3, 1],
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"state_in_0": [1.0, 3.0, 4.0],
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}
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)
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# We would expect a=[1, 2, 3] now, but due to the sequence
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# boundary, we stop earlier.
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check(
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s2[:3],
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{
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"a": [1, 2],
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"b": {"c": [4, 5]},
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SampleBatch.SEQ_LENS: [2],
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"state_in_0": [1.0],
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},
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)
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# Split exactly at a seq-len boundary.
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check(
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s2[:5],
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{
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"a": [1, 2, 3, 2, 3],
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"b": {"c": [4, 5, 6, 5, 6]},
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SampleBatch.SEQ_LENS: [2, 3],
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"state_in_0": [1.0, 3.0],
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},
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)
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# Split above seq-len boundary.
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check(
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s2[:50],
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{
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"a": [1, 2, 3, 2, 3, 4],
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"b": {"c": [4, 5, 6, 5, 6, 7]},
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SampleBatch.SEQ_LENS: [2, 3, 1],
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"state_in_0": [1.0, 3.0, 4.0],
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},
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)
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check(
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s2[:],
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{
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"a": [1, 2, 3, 2, 3, 4],
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"b": {"c": [4, 5, 6, 5, 6, 7]},
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SampleBatch.SEQ_LENS: [2, 3, 1],
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"state_in_0": [1.0, 3.0, 4.0],
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},
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)
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def test_split_by_episode(self):
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s = SampleBatch(
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{
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"a": np.array([0, 1, 2, 3, 4, 5]),
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"eps_id": np.array([0, 0, 0, 0, 1, 1]),
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"terminateds": np.array([0, 0, 0, 1, 0, 1]),
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}
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)
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true_split = [np.array([0, 1, 2, 3]), np.array([4, 5])]
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# Check that splitting by EPS_ID works correctly
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eps_split = [b["a"] for b in s.split_by_episode()]
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check(true_split, eps_split)
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# Check that splitting by EPS_ID works correctly when explicitly specified
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eps_split = [b["a"] for b in s.split_by_episode(key="eps_id")]
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check(true_split, eps_split)
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# Check that splitting by DONES works correctly when explicitly specified
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eps_split = [b["a"] for b in s.split_by_episode(key="dones")]
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check(true_split, eps_split)
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# Check that splitting by DONES works correctly
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del s["eps_id"]
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terminateds_split = [b["a"] for b in s.split_by_episode()]
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check(true_split, terminateds_split)
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# Check that splitting without the EPS_ID or DONES key raise an error
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del s["terminateds"]
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with self.assertRaises(KeyError):
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s.split_by_episode()
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# Check that splitting with DONES always False returns the whole batch
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s["terminateds"] = np.array([0, 0, 0, 0, 0, 0])
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batch_split = [b["a"] for b in s.split_by_episode()]
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check(s["a"], batch_split[0])
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def test_copy(self):
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s = SampleBatch(
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{
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"a": np.array([1, 2, 3, 2, 3, 4]),
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"b": {"c": np.array([4, 5, 6, 5, 6, 7])},
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SampleBatch.SEQ_LENS: [2, 3, 1],
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"state_in_0": [1.0, 3.0, 4.0],
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}
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)
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s_copy = s.copy(shallow=False)
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s_copy["a"][0] = 100
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s_copy["b"]["c"][0] = 200
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s_copy[SampleBatch.SEQ_LENS][0] = 3
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s_copy[SampleBatch.SEQ_LENS][1] = 2
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s_copy["state_in_0"][0] = 400.0
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self.assertNotEqual(s["a"][0], s_copy["a"][0])
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self.assertNotEqual(s["b"]["c"][0], s_copy["b"]["c"][0])
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self.assertNotEqual(s[SampleBatch.SEQ_LENS][0], s_copy[SampleBatch.SEQ_LENS][0])
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self.assertNotEqual(s[SampleBatch.SEQ_LENS][1], s_copy[SampleBatch.SEQ_LENS][1])
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self.assertNotEqual(s["state_in_0"][0], s_copy["state_in_0"][0])
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s_copy = s.copy(shallow=True)
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s_copy["a"][0] = 100
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s_copy["b"]["c"][0] = 200
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s_copy[SampleBatch.SEQ_LENS][0] = 3
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s_copy[SampleBatch.SEQ_LENS][1] = 2
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s_copy["state_in_0"][0] = 400.0
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self.assertEqual(s["a"][0], s_copy["a"][0])
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self.assertEqual(s["b"]["c"][0], s_copy["b"]["c"][0])
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self.assertEqual(s[SampleBatch.SEQ_LENS][0], s_copy[SampleBatch.SEQ_LENS][0])
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self.assertEqual(s[SampleBatch.SEQ_LENS][1], s_copy[SampleBatch.SEQ_LENS][1])
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self.assertEqual(s["state_in_0"][0], s_copy["state_in_0"][0])
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def test_shuffle_with_interceptor(self):
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"""Tests, whether `shuffle()` clears the `intercepted_values` cache."""
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s = SampleBatch(
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{
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"a": np.array([1, 2, 3, 2, 3, 4, 3, 4, 5, 4, 5, 6, 5, 6, 7]),
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}
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)
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# Set a summy get-interceptor (returning all values, but plus 1).
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s.set_get_interceptor(lambda v: v + 1)
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# Make sure, interceptor works.
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check(s["a"], [2, 3, 4, 3, 4, 5, 4, 5, 6, 5, 6, 7, 6, 7, 8])
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s.shuffle()
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# Make sure, intercepted values are NOT the original ones (before the shuffle),
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# but have also been shuffled.
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check(s["a"], [2, 3, 4, 3, 4, 5, 4, 5, 6, 5, 6, 7, 6, 7, 8], false=True)
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def test_to_device(self):
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"""Tests whether to_device works properly under different circumstances"""
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torch, _ = try_import_torch()
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# sample batch includes
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# a numpy array (a)
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# a nested stucture of dict, tuple and lists (b) of numpys and None
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# info dict
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# a nested structure that ends up with tensors and ints(c)
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# a tensor with float64 values (d)
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# a float64 tensor with possibly wrong device (depends on if cuda available)
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# repeated value object with np.array leaves (f)
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cuda_available = int(os.environ.get("RLLIB_NUM_GPUS", "0")) > 0
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cuda_if_possible = torch.device("cuda:0" if cuda_available else "cpu")
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s = SampleBatch(
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{
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"a": np.array([1, 2]),
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"b": {"c": (np.array([4, 5]), np.array([5, 6]))},
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"c": {"d": torch.Tensor([1, 2]), "g": (torch.Tensor([3, 4]), 1)},
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"d": torch.Tensor([1.0, 2.0]).double(),
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"e": torch.Tensor([1.0, 2.0]).double().to(cuda_if_possible),
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"f": RepeatedValues(np.array([[1, 2, 0, 0]]), lengths=[2], max_len=4),
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SampleBatch.SEQ_LENS: np.array([2, 3, 1]),
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"state_in_0": np.array([1.0, 3.0, 4.0]),
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# INFO can have arbitrary elements, others need to conform in size
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SampleBatch.INFOS: np.array([{"a": 1}, {"b": [1, 2]}, {"c": None}]),
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}
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)
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# inplace operation for sample_batch
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s.to_device(cuda_if_possible, framework="torch")
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def _check_recursive_device_and_type(input_struct, target_device):
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def get_mismatched_types(v):
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if isinstance(v, torch.Tensor):
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if v.device.type != target_device.type:
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return (v.device, v.dtype)
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if v.is_floating_point() and v.dtype != torch.float32:
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return (v.device, v.dtype)
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tree_checks = {}
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for k, v in input_struct.items():
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tree_checks[k] = tree.map_structure(get_mismatched_types, v)
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self.assertTrue(
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all(v is None for v in tree.flatten((tree_checks))),
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f"the device type check dict: {tree_checks}",
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)
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# check if all tensors have the correct device and dtype
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_check_recursive_device_and_type(s, cuda_if_possible)
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# check repeated value
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check(s["f"].lengths, [2])
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check(s["f"].max_len, 4)
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check(s["f"].values, torch.from_numpy(np.asarray([[1, 2, 0, 0]])))
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# check infos
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check(s[SampleBatch.INFOS], np.array([{"a": 1}, {"b": [1, 2]}, {"c": None}]))
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# check c/g/1
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self.assertEqual(s["c"]["g"][1], torch.from_numpy(np.asarray(1)))
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with self.assertRaises(NotImplementedError):
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# should raise an error if framework is not torch
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s.to_device(cuda_if_possible, framework="tf")
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def test_count(self):
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# Tests if counts are what we would expect from different batches
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input_dicts_and_lengths = [
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(
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{
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SampleBatch.OBS: {
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"a": np.array([[1], [2], [3]]),
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"b": np.array([[0], [0], [1]]),
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"c": np.array([[4], [5], [6]]),
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}
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},
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3,
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),
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(
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{
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SampleBatch.OBS: {
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"a": np.array([[1, 2, 3]]),
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"b": np.array([[0, 0, 1]]),
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"c": np.array([[4, 5, 6]]),
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}
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},
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1,
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),
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(
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{
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SampleBatch.INFOS: {
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"a": np.array([[1], [2], [3]]),
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"b": np.array([[0], [0], [1]]),
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"c": np.array([[4], [5], [6]]),
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}
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},
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0, # This should have a length of zero, since we can ignore INFO
|
|
),
|
|
(
|
|
{
|
|
"state_in_0": {
|
|
"a": [[[1], [2], [3]], [[1], [2], [3]], [[1], [2], [3]]],
|
|
"b": [[[1], [2], [3]], [[1], [2], [3]], [[1], [2], [3]]],
|
|
"c": [[[1], [2], [3]], [[1], [2], [3]], [[1], [2], [3]]],
|
|
},
|
|
"state_out_0": {
|
|
"a": [[[1], [2], [3]], [[1], [2], [3]], [[1], [2], [3]]],
|
|
"b": [[[1], [2], [3]], [[1], [2], [3]], [[1], [2], [3]]],
|
|
"c": [[[1], [2], [3]], [[1], [2], [3]], [[1], [2], [3]]],
|
|
},
|
|
SampleBatch.OBS: {
|
|
"a": np.array([1, 2, 3]),
|
|
"b": np.array([0, 0, 1]),
|
|
"c": np.array([4, 5, 6]),
|
|
},
|
|
},
|
|
3, # This should have a length of three - we count from OBS
|
|
),
|
|
(
|
|
{
|
|
"state_in_0": {
|
|
"a": [[[1], [2], [3]], [[1], [2], [3]], [[1], [2], [3]]],
|
|
"b": [[[1], [2], [3]], [[1], [2], [3]], [[1], [2], [3]]],
|
|
"c": [[[1], [2], [3]], [[1], [2], [3]], [[1], [2], [3]]],
|
|
},
|
|
"state_out_0": {
|
|
"a": [[[1], [2], [3]], [[1], [2], [3]], [[1], [2], [3]]],
|
|
"b": [[[1], [2], [3]], [[1], [2], [3]], [[1], [2], [3]]],
|
|
"c": [[[1], [2], [3]], [[1], [2], [3]], [[1], [2], [3]]],
|
|
},
|
|
},
|
|
0, # This should have a length of zero, we don't attempt to count
|
|
),
|
|
(
|
|
{
|
|
SampleBatch.OBS: {
|
|
"a": np.array([[1], [2], [3]]),
|
|
"b": np.array([[0], [0], [1]]),
|
|
"c": np.array([[4], [5], [6]]),
|
|
},
|
|
SampleBatch.SEQ_LENS: np.array([[1], [2], [3]]),
|
|
},
|
|
6, # This should have a length of six, since we don't try to infer
|
|
# from inputs but count by sequence lengths
|
|
),
|
|
(
|
|
{
|
|
SampleBatch.NEXT_OBS: {
|
|
"a": {"b": np.array([[1], [2], [3]])},
|
|
"c": np.array([[4], [5], [6]]),
|
|
},
|
|
},
|
|
3, # Test if we properly support nesting
|
|
),
|
|
]
|
|
|
|
for input_dict, length in input_dicts_and_lengths:
|
|
self.assertEqual(attempt_count_timesteps(copy.deepcopy(input_dict)), length)
|
|
s = SampleBatch(input_dict)
|
|
self.assertEqual(s.count, length)
|
|
|
|
def test_interceptors(self):
|
|
# Tests whether interceptors work as intended
|
|
|
|
some_array = np.array([1, 2, 3])
|
|
batch = SampleBatch({SampleBatch.OBS: some_array})
|
|
|
|
device = torch.device("cpu")
|
|
|
|
self.assertTrue(batch[SampleBatch.OBS] is some_array)
|
|
|
|
batch.set_get_interceptor(
|
|
functools.partial(convert_to_torch_tensor, device=device)
|
|
)
|
|
|
|
self.assertTrue(
|
|
all(convert_to_torch_tensor(some_array) == batch[SampleBatch.OBS])
|
|
)
|
|
|
|
# This test requires a GPU, otherwise we can't test whether we are
|
|
# moving between devices
|
|
if not torch.cuda.is_available():
|
|
raise ValueError("This test can only fail if cuda is available.")
|
|
|
|
another_array = np.array([4, 5, 6])
|
|
another_batch = SampleBatch({SampleBatch.OBS: another_array})
|
|
|
|
another_device = torch.device("cuda")
|
|
|
|
self.assertTrue(another_batch[SampleBatch.OBS] is another_array)
|
|
another_batch.set_get_interceptor(
|
|
functools.partial(convert_to_torch_tensor, device=another_device)
|
|
)
|
|
check(another_batch[SampleBatch.OBS], another_array)
|
|
self.assertFalse(another_batch[SampleBatch.OBS] is another_array)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import sys
|
|
|
|
import pytest
|
|
|
|
sys.exit(pytest.main(["-v", __file__]))
|