File size: 3,336 Bytes
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 | # Copyright 2026 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 unittest
from unittest.mock import MagicMock, patch
import torch
from verl.utils.profiler.config import ProfilerConfig, TorchProfilerToolConfig
from verl.utils.profiler.torch_profile import Profiler, get_torch_profiler
class TestTorchProfile(unittest.TestCase):
def setUp(self):
# Reset Profiler class state
Profiler._define_count = 0
@patch("torch.profiler.profile")
def test_get_torch_profiler(self, mock_profile):
# Test wrapper function
get_torch_profiler(contents=["cpu", "cuda", "stack"], save_path="/tmp/test", rank=0)
mock_profile.assert_called_once()
_, kwargs = mock_profile.call_args
# Verify activities
activities = kwargs["activities"]
self.assertIn(torch.profiler.ProfilerActivity.CPU, activities)
self.assertIn(torch.profiler.ProfilerActivity.CUDA, activities)
# Verify options
self.assertTrue(kwargs["with_stack"])
self.assertFalse(kwargs["record_shapes"])
self.assertFalse(kwargs["profile_memory"])
@patch("verl.utils.profiler.torch_profile.get_torch_profiler")
def test_profiler_lifecycle(self, mock_get_profiler):
# Mock the underlying torch profiler object
mock_prof_instance = MagicMock()
mock_get_profiler.return_value = mock_prof_instance
# Initialize
tool_config = TorchProfilerToolConfig(contents=["cpu"], discrete=False)
config = ProfilerConfig(save_path="/tmp/test", enable=True, tool_config=tool_config)
profiler = Profiler(rank=0, config=config, tool_config=tool_config)
# Test Start
profiler.start()
mock_get_profiler.assert_called_once()
mock_prof_instance.start.assert_called_once()
# Test Step
profiler.step()
mock_prof_instance.step.assert_called_once()
# Test Stop
profiler.stop()
mock_prof_instance.stop.assert_called_once()
@patch("verl.utils.profiler.torch_profile.get_torch_profiler")
def test_discrete_mode(self, mock_get_profiler):
# Mock for discrete mode
mock_prof_instance = MagicMock()
mock_get_profiler.return_value = mock_prof_instance
tool_config = TorchProfilerToolConfig(contents=["cpu"], discrete=True)
config = ProfilerConfig(save_path="/tmp/test", enable=True, tool_config=tool_config)
profiler = Profiler(rank=0, config=config, tool_config=tool_config)
# In discrete mode, start/stop shouldn't trigger global profiler immediately
profiler.start()
mock_get_profiler.assert_not_called()
profiler.stop()
mock_prof_instance.stop.assert_not_called()
if __name__ == "__main__":
unittest.main()
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