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0185029 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 | # Copyright 2026 The OraRL Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import pytest
import torch
from orarl.algorithm import select_sign_balanced_rollouts
class FakeDataProto:
def __init__(self, batch, non_tensor_batch, meta_info=None):
self.batch = batch
self.non_tensor_batch = non_tensor_batch
self.meta_info = {} if meta_info is None else meta_info
def __len__(self):
return next(iter(self.batch.values())).shape[0]
def __getitem__(self, rows):
return FakeDataProto(
{key: value[rows] for key, value in self.batch.items()},
{key: np.asarray(value)[rows] for key, value in self.non_tensor_batch.items()},
self.meta_info,
)
def _data(scores, groups, oracle):
scalar = torch.tensor(scores, dtype=torch.float32)
advantages = scalar.unsqueeze(-1).repeat(1, 2)
response_mask = torch.ones_like(advantages)
return FakeDataProto(
batch={
"advantages": advantages,
"response_mask": response_mask,
"attention_mask": torch.ones(len(scores), 3),
},
non_tensor_batch={
"uid": np.asarray(groups, dtype=object),
"is_oracle_row": np.asarray(oracle, dtype=bool),
"row_id": np.arange(len(scores)),
},
meta_info={"original": True},
)
def test_strict_selection_meets_budget_with_all_fallback_paths():
scores = [
0.8,
0.4,
-0.9,
-0.3,
0.0,
0.5,
-0.9,
-0.8,
-0.7,
0.0,
0.0,
0.5,
0.7,
0.0,
0.0,
0.0,
0.0,
0.5,
]
groups = ["a"] * 6 + ["b"] * 6 + ["c"] * 6
oracle = [False] * 5 + [True]
data = _data(scores, groups, oracle * 3)
selected, metrics = select_sign_balanced_rollouts(
data,
keep_per_group=4,
positive_quota=1,
negative_quota=2,
world_size=4,
)
assert selected.non_tensor_batch["row_id"].tolist() == [
0,
2,
3,
5,
6,
7,
8,
11,
12,
13,
14,
17,
]
assert len(selected) == 12
assert selected.non_tensor_batch["is_oracle_row"].sum() == 3
assert metrics["orarl/kept_rows"] == 12.0
assert metrics["orarl/oracle_rows_forced"] == 3.0
assert metrics["orarl/positive_policy_rows_kept"] == 2.0
assert metrics["orarl/negative_policy_rows_kept"] == 5.0
assert metrics["orarl/zero_policy_rows_kept"] == 2.0
assert metrics["orarl/cross_sign_fallback_rows"] == 1.0
assert metrics["orarl/zero_fallback_rows"] == 2.0
assert selected.meta_info["global_token_num"] == [3.0] * 12
assert data.meta_info == {"original": True}
assert all(key.startswith("orarl/") for key in metrics)
def test_selection_uses_sequence_mean_instead_of_response_length():
data = _data(
[0.3, 0.2, -0.4, -0.1, 0.0, 0.5],
["group"] * 6,
[False, False, False, False, False, True],
)
data.batch["response_mask"][0, 1] = 0.0
selected, _ = select_sign_balanced_rollouts(
data,
positive_quota=1,
negative_quota=1,
world_size=1,
n_rollouts=6,
prune_ratio=0.5,
)
assert selected.non_tensor_batch["row_id"].tolist() == [0, 2, 5]
def test_selection_checks_world_size_before_slicing():
data = _data(
[0.3, 0.2, -0.4, -0.1, 0.0, 0.5],
["group"] * 6,
[False, False, False, False, False, True],
)
with pytest.raises(RuntimeError, match="not divisible"):
select_sign_balanced_rollouts(
data,
keep_per_group=4,
positive_quota=1,
negative_quota=2,
world_size=3,
)
def test_selection_requires_exactly_one_oracle_per_group():
data = _data(
[0.3, 0.2, -0.4, -0.1],
["group"] * 4,
[False, False, False, False],
)
with pytest.raises(ValueError, match="exactly one oracle"):
select_sign_balanced_rollouts(
data,
keep_per_group=3,
positive_quota=1,
negative_quota=1,
)
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