149 lines
4.7 KiB
Python
149 lines
4.7 KiB
Python
import unittest
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from array import array
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import torch
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from sglang.srt.utils.common import (
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flatten_arrays_to_int64_tensor,
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get_device_sm_nvidia_smi,
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get_nvidia_driver_version_str,
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)
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from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci
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from sglang.test.test_utils import CustomTestCase
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register_cuda_ci(est_time=5, stage="base-b", runner_config="1-gpu-small")
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register_amd_ci(est_time=5, stage="stage-b", runner_config="1-gpu-small-amd")
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@unittest.skipUnless(torch.cuda.is_available(), "requires CUDA")
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class TestFlattenArraysToInt64Tensor(CustomTestCase):
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"""`flatten_arrays_to_int64_tensor` is invoked by `prepare_for_extend`
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to build the per-batch input_ids tensor (pinned, async H2D) from a
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list of array.array('q') per-req get_fill_ids() slices. Tests the
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full matrix of (device, pin) the production code paths through.
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"""
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DEVICES = ("cpu", "cuda")
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PIN_OPTIONS = (False, True)
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def _check(self, parts: list, expected: list[int]) -> None:
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for device in self.DEVICES:
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for pin in self.PIN_OPTIONS:
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with self.subTest(device=device, pin=pin):
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out = flatten_arrays_to_int64_tensor(parts, device, pin)
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if device == "cuda":
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torch.cuda.synchronize()
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self.assertEqual(out.dtype, torch.int64)
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self.assertEqual(out.device.type, device)
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self.assertEqual(out.shape, (len(expected),))
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self.assertEqual(out.cpu().tolist(), expected)
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def test_single_part(self):
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parts = [array("q", [1, 2, 3, 4, 5])]
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self._check(parts, [1, 2, 3, 4, 5])
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def test_multiple_parts(self):
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parts = [
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array("q", [10, 20, 30]),
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array("q", [100, 200]),
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array("q", [1000]),
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]
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self._check(parts, [10, 20, 30, 100, 200, 1000])
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class TestNvidiaDriverVersionStr(CustomTestCase):
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"""`get_nvidia_driver_version_str` is typed as `str | None`: it returns
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`None` when nvidia-smi is missing, fails, or emits an empty string. These
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tests exercise both the success and the None-return paths by monkey-
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patching `subprocess.run`, so they don't require a GPU. The function is
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`@lru_cache`d, so the cache is cleared around each test to make the patch
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observable.
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"""
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def setUp(self):
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get_nvidia_driver_version_str.cache_clear()
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def tearDown(self):
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get_nvidia_driver_version_str.cache_clear()
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def test_returns_version_string(self):
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import subprocess
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class _R:
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stdout = "595.58.03\n"
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original = subprocess.run
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subprocess.run = lambda *a, **k: _R()
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try:
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self.assertEqual(get_nvidia_driver_version_str(), "595.58.03")
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finally:
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subprocess.run = original
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def test_returns_none_on_empty_output(self):
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import subprocess
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class _R:
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stdout = "\n"
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original = subprocess.run
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subprocess.run = lambda *a, **k: _R()
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try:
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self.assertIsNone(get_nvidia_driver_version_str())
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finally:
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subprocess.run = original
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def test_returns_none_on_called_process_error(self):
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import subprocess
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original = subprocess.run
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def boom(*a, **k):
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raise subprocess.CalledProcessError(1, "nvidia-smi")
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subprocess.run = boom
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try:
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self.assertIsNone(get_nvidia_driver_version_str())
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finally:
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subprocess.run = original
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def test_returns_none_on_file_not_found(self):
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import subprocess
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original = subprocess.run
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def boom(*a, **k):
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raise FileNotFoundError("nvidia-smi")
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subprocess.run = boom
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try:
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self.assertIsNone(get_nvidia_driver_version_str())
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finally:
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subprocess.run = original
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class TestGetDeviceSmNvidiaSmi(CustomTestCase):
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"""`get_device_sm_nvidia_smi` parses nvidia-smi output into a (major,
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minor) tuple and falls back to (0, 0) -- logging via `logger.error` --
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when nvidia-smi fails. The success path needs a GPU; the fallback path is
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covered here by forcing a failure and asserting the (0, 0) return. The
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fallback path needs no GPU, so this test runs on CPU.
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"""
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def test_fallback_on_failure_returns_zero_zero(self):
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import subprocess
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original = subprocess.run
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def boom(*a, **k):
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raise subprocess.CalledProcessError(1, "nvidia-smi")
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subprocess.run = boom
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try:
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self.assertEqual(get_device_sm_nvidia_smi(), (0, 0))
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finally:
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subprocess.run = original
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if __name__ == "__main__":
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unittest.main()
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