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1faccd4 | 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 | # Copyright 2025 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 shutil
import tempfile
from pathlib import Path
from unittest.mock import MagicMock
import pytest
import torch
from verl.utils.rollout_skip import DataProto, RolloutSkip
len_prompt = 50
len_response = 100
def temp_dir():
# Create a temporary directory
temp_dir = Path(tempfile.mkdtemp())
yield temp_dir
# Cleanup
shutil.rmtree(temp_dir)
def build_generate_fn(gen_bs, n):
len_tokenizer = 1024
def iterate():
while True:
prompt = torch.randint(len_tokenizer, size=(gen_bs, len_prompt)).repeat_interleave(n, dim=0)
generate = torch.randint(len_tokenizer, size=(gen_bs * n, len_response))
data = DataProto.from_dict(tensors={"prompt": prompt, "response": generate})
yield data
mock_infer_engine = iterate()
def fn(batch, **kwargs):
# Simulate the inference engine returning the next batch
return next(mock_infer_engine)
return fn
@pytest.fixture(params=[(32, 4), (64, 4), (64, 8)])
def mock_rollout_wg(request):
gen_bs, n = request.param
rollout_wg = MagicMock()
config = MagicMock()
config.actor_rollout_ref.rollout = {
"n": n,
"skip_dump_dir": next(temp_dir()),
}
config.data = {"gen_batch_size": gen_bs}
rollout_wg.generate_sequences = build_generate_fn(gen_bs, n)
yield config, rollout_wg
# Cleanup
shutil.rmtree(next(temp_dir()))
class TestRolloutSkip:
def test_initialization(self, capsys):
"""Test that RolloutSkip initializes correctly"""
config = MagicMock()
config.actor_rollout_ref.rollout = {
"n": 16,
"skip_dump_dir": "tmp/rollout_dump",
}
config.data = {"gen_batch_size": 128}
mock_rollout_wg = MagicMock()
skip = RolloutSkip(config, mock_rollout_wg)
assert skip.n == 16
assert skip.gbs == 128
assert str(skip.dumped_dir) == "tmp/rollout_dump"
assert skip._rollout_wg == mock_rollout_wg
skip.wrap_generate_sequences()
captured = capsys.readouterr()
assert "Successfully patched" in captured.out
def test_generate_without_wrap(self, mock_rollout_wg):
"""Test that generate_sequences works without wrapping"""
config, rollout_wg = mock_rollout_wg
_ = RolloutSkip(config, rollout_wg)
_result = rollout_wg.generate_sequences(MagicMock())
for _ in range(10):
result = rollout_wg.generate_sequences(MagicMock())
assert isinstance(result, DataProto)
# * make sure the data is different
assert torch.abs(_result.batch["prompt"] - result.batch["prompt"]).sum() > 0
assert torch.abs(_result.batch["response"] - result.batch["response"]).sum() > 0
_result = result
def test_dump(self, mock_rollout_wg, capsys):
config, rollout_wg = mock_rollout_wg
skip = RolloutSkip(config, rollout_wg)
skip.wrap_generate_sequences()
result = rollout_wg.generate_sequences(MagicMock())
# * check if dump is OK
assert skip.curr_path_dump.exists()
captured = capsys.readouterr()
assert "Successfully dump data in" in captured.out
# * get file size, estimate file size
file_size = skip.curr_path_dump.stat().st_size
est_file_size = (len_prompt + len_response) * skip.gbs * skip.n * result.batch["prompt"].dtype.itemsize
assert file_size >= est_file_size, "Dumped file size is smaller than expected"
def test_generate_with_wrap(self, mock_rollout_wg, capsys):
"""Test that generate_sequences works without wrapping"""
config, rollout_wg = mock_rollout_wg
skip = RolloutSkip(config, rollout_wg)
skip.wrap_generate_sequences()
_result = rollout_wg.generate_sequences(MagicMock())
for _ in range(10):
result = rollout_wg.generate_sequences(MagicMock())
assert isinstance(result, DataProto)
# * make sure the data is different
assert torch.abs(_result.batch["prompt"] - result.batch["prompt"]).sum() == 0
assert torch.abs(_result.batch["response"] - result.batch["response"]).sum() == 0
captured = capsys.readouterr()
assert "Successfully load pre-generated data from" in captured.out
_result = result
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