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d65b589 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | # Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# 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 os
from typing import Any, Optional
import numpy as np
import pytest
import torch
from verl.protocol import DataProto, pad_dataproto_to_divisor, unpad_dataproto
def _get_data_proto(
tensors: Optional[dict[str, list[Any]]] = None,
non_tensors: Optional[dict[str, list[Any]]] = None,
meta_info: Optional[dict[str, Any]] = None,
) -> DataProto:
if tensors is None and non_tensors is None:
tensors = {"obs": [1, 2, 3, 4, 5, 6]}
non_tensors = {"labels": ["a", "b", "c", "d", "e", "f"]}
if tensors is not None:
tensors = {k: torch.tensor(v) if not isinstance(v, torch.Tensor) else v for k, v in tensors.items()}
if non_tensors is not None:
non_tensors = {
k: np.array(v, dtype=object) if not isinstance(v, np.ndarray) else v for k, v in non_tensors.items()
}
meta_info = meta_info or {"info": "test_info"}
return DataProto.from_dict(tensors=tensors, non_tensors=non_tensors, meta_info=meta_info)
def _assert_equal(data1: DataProto, data2: Optional[DataProto] = None):
data2 = data2 or _get_data_proto()
if data1.batch is not None:
assert data1.batch.keys() == data2.batch.keys()
for key in data1.batch.keys():
assert torch.all(data1.batch[key] == data2.batch[key])
else:
assert data2.batch is None
if data1.non_tensor_batch is not None:
assert data1.non_tensor_batch.keys() == data2.non_tensor_batch.keys()
for key in data1.non_tensor_batch.keys():
assert np.all(data1.non_tensor_batch[key] == data2.non_tensor_batch[key])
else:
assert data2.non_tensor_batch is None
assert data1.meta_info == data2.meta_info
def test_tensor_dict_constructor():
obs = torch.randn(100, 10)
act = torch.randn(100, 10, 3)
data = DataProto.from_dict(tensors={"obs": obs, "act": act})
assert len(data) == 100
with pytest.raises(AssertionError):
data = DataProto.from_dict(tensors={"obs": obs, "act": act}, num_batch_dims=2)
with pytest.raises(AssertionError):
data = DataProto.from_dict(tensors={"obs": obs, "act": act}, num_batch_dims=3)
labels = np.array(["a", "b", "c"], dtype=object)
data = DataProto.from_dict(non_tensors={"labels": labels})
assert len(data) == 3
def test_getitem():
data = _get_data_proto()
assert data[0].batch["obs"] == torch.tensor(1)
assert data[0].non_tensor_batch["labels"] == "a"
_assert_equal(data[1:3], _get_data_proto({"obs": [2, 3]}, {"labels": ["b", "c"]}))
_assert_equal(data[[0, 2]], _get_data_proto({"obs": [1, 3]}, {"labels": ["a", "c"]}))
_assert_equal(data[torch.tensor([1])], _get_data_proto({"obs": [2]}, {"labels": ["b"]}))
def test_select_pop():
obs = torch.randn(100, 10)
act = torch.randn(100, 3)
dataset = _get_data_proto(tensors={"obs": obs, "act": act}, meta_info={"p": 1, "q": 2})
selected_dataset = dataset.select(batch_keys=["obs"], meta_info_keys=["p"])
assert selected_dataset.batch.keys() == {"obs"}
assert selected_dataset.meta_info.keys() == {"p"}
assert dataset.batch.keys() == {"obs", "act"}
assert dataset.meta_info.keys() == {"p", "q"}
popped_dataset = dataset.pop(batch_keys=["obs"], meta_info_keys=["p"])
assert popped_dataset.batch.keys() == {"obs"}
assert popped_dataset.meta_info.keys() == {"p"}
assert dataset.batch.keys() == {"act"}
assert dataset.meta_info.keys() == {"q"}
def test_chunk_concat_split():
data = _get_data_proto()
with pytest.raises(AssertionError):
data.chunk(5)
chunked_data = data.chunk(2)
assert len(chunked_data) == 2
expected_data = _get_data_proto({"obs": [1, 2, 3]}, {"labels": ["a", "b", "c"]})
_assert_equal(chunked_data[0], expected_data)
concat_data = DataProto.concat(chunked_data)
_assert_equal(concat_data, data)
splitted_data = data.split(2)
assert len(splitted_data) == 3
expected_data = _get_data_proto({"obs": [1, 2]}, {"labels": ["a", "b"]})
_assert_equal(splitted_data[0], expected_data)
def test_reorder():
data = _get_data_proto()
data.reorder(torch.tensor([3, 4, 2, 0, 1, 5]))
expected_data = _get_data_proto({"obs": [4, 5, 3, 1, 2, 6]}, {"labels": ["d", "e", "c", "a", "b", "f"]})
_assert_equal(data, expected_data)
@pytest.mark.parametrize("interleave", [True, False])
def test_repeat(interleave: bool):
data = _get_data_proto({"obs": [1, 2]}, {"labels": ["a", "b"]})
repeated_data = data.repeat(repeat_times=2, interleave=interleave)
expected_tensors = {"obs": [1, 1, 2, 2] if interleave else [1, 2, 1, 2]}
expected_non_tensors = {"labels": ["a", "a", "b", "b"] if interleave else ["a", "b", "a", "b"]}
_assert_equal(repeated_data, _get_data_proto(expected_tensors, expected_non_tensors))
@pytest.mark.parametrize("size_divisor", [2, 3])
def test_dataproto_pad_unpad(size_divisor: int):
data = _get_data_proto({"obs": [1, 2, 3]}, {"labels": ["a", "b", "c"]})
# test size_divisor=2
padded_data, pad_size = pad_dataproto_to_divisor(data, size_divisor=size_divisor)
unpadded_data = unpad_dataproto(padded_data, pad_size=pad_size)
if size_divisor == 2:
assert pad_size == 1
expected_tensors = {"obs": [1, 2, 3, 1]}
expected_non_tensors = {"labels": ["a", "b", "c", "a"]}
expected_data = _get_data_proto(expected_tensors, expected_non_tensors)
else:
assert pad_size == 0
expected_data = data
_assert_equal(padded_data, expected_data)
_assert_equal(unpadded_data, data)
def test_data_proto_save_load():
data = _get_data_proto()
data.save_to_disk("test_data.pt")
loaded_data = DataProto.load_from_disk("test_data.pt")
os.remove("test_data.pt")
_assert_equal(data, loaded_data)
def test_union_tensor_dict():
obs = torch.randn(100, 10)
data1 = _get_data_proto({"obs": obs, "act": torch.randn(100, 3)})
data2 = _get_data_proto({"obs": obs, "rew": torch.randn(100)})
data1.union(data2)
data1 = _get_data_proto({"obs": obs, "act": torch.randn(100, 3)})
data2 = _get_data_proto({"obs": obs + 1, "rew": torch.randn(100)})
with pytest.raises(ValueError):
data1.union(data2)
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