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c934ed6567
Fixes #131020 As discussed in the issue thread, we can use ` KINETO_DAEMON_INIT_DELAY_S` to delay the initialization of `kineto` in case `kineto` is initialized before `libtorch_cuda.so`. It's not clear to set a proper value of environmental variable `KINETO_DAEMON_INIT_DELAY_S`, here's a trick to make the initialization of `kineto` after the initialization of module `torch`. I'm not sure whether this is an acceptable trick, please take a look at this pr, thanks. Pull Request resolved: https://github.com/pytorch/pytorch/pull/131448 Approved by: https://github.com/sraikund16, https://github.com/briancoutinho
52 lines
1.8 KiB
Python
52 lines
1.8 KiB
Python
# Owner(s): ["oncall: profiler"]
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import os
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import subprocess
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import sys
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from unittest.mock import patch
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import torch
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from torch.testing._internal.common_utils import run_tests, TestCase
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class SimpleKinetoInitializationTest(TestCase):
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@patch.dict(os.environ, {"KINETO_USE_DAEMON": "1"})
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def test_kineto_profiler_with_environment_variable(self):
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"""
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This test checks whether kineto works with torch in daemon mode, please refer to issue #112389 and #131020.
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Besides that, this test will also check that kineto will not be initialized when user loads the shared library
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directly.
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"""
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script = """
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import torch
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if torch.cuda.is_available() > 0:
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torch.cuda.init()
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"""
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try:
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subprocess.check_output(
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[sys.executable, "-W", "always", "-c", script],
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cwd=os.path.dirname(os.path.realpath(__file__)),
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)
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except subprocess.CalledProcessError as e:
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if e.returncode != 0:
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self.assertTrue(
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False,
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"Kineto is not working properly with the Dynolog environment variable",
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)
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# import the shared library directly - it triggers static init but doesn't call kineto_init
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env = os.environ.copy()
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env["KINETO_USE_DAEMON"] = "1"
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if "KINETO_DAEMON_INIT_DELAY_S" in env:
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env.pop("KINETO_DAEMON_INIT_DELAY_S")
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_, stderr = TestCase.run_process_no_exception(
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f"from ctypes import CDLL; CDLL('{torch._C.__file__}')"
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)
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self.assertNotRegex(
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stderr.decode("ascii"),
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"Registering daemon config loader",
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"kineto should not be initialized when the shared library is imported directly",
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)
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if __name__ == "__main__":
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run_tests()
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