[Build] Add build info (#1386)
Add static build_info py file to show soc and sleep mode info. It helps to make the code clean and the error info will be more friendly for users This PR also added the unit test for vllm_ascend/utils.py This PR also added the base test class for all ut in tests/ut/base.py Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
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wangxiyuan
12
tests/ut/base.py
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12
tests/ut/base.py
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import unittest
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from vllm_ascend.utils import adapt_patch
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class TestBase(unittest.TestCase):
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def setUp(self):
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# adapt patch by default.
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adapt_patch(True)
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adapt_patch()
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super().setUp()
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12
tests/ut/patch/worker/patch_common/test_patch_distributed.py
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12
tests/ut/patch/worker/patch_common/test_patch_distributed.py
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from tests.ut.base import TestBase
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class TestPatchDistributed(TestBase):
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def test_GroupCoordinator_patched(self):
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from vllm.distributed.parallel_state import GroupCoordinator
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from vllm_ascend.patch.worker.patch_common.patch_distributed import \
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GroupCoordinatorPatch
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self.assertIs(GroupCoordinator, GroupCoordinatorPatch)
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251
tests/ut/test_utils.py
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251
tests/ut/test_utils.py
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import math
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import os
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import unittest
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from threading import Lock
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from unittest import mock
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import torch
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from vllm.config import (CompilationConfig, ModelConfig, ParallelConfig,
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VllmConfig)
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from vllm_ascend import utils
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class TestUtils(unittest.TestCase):
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def test_is_310p(self):
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utils._IS_310P = None
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with mock.patch("vllm_ascend._build_info.__soc_version__",
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"Ascend310P3"):
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self.assertTrue(utils.is_310p())
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utils._IS_310P = None
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with mock.patch("vllm_ascend._build_info.__soc_version__",
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"Ascend910P1"):
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self.assertFalse(utils.is_310p())
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def test_sleep_mode_enabled(self):
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utils._SLEEP_MODE_ENABLED = None
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with mock.patch("vllm_ascend._build_info.__sleep_mode_enabled__",
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True):
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self.assertTrue(utils.sleep_mode_enabled())
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utils._SLEEP_MODE_ENABLED = None
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with mock.patch("vllm_ascend._build_info.__sleep_mode_enabled__",
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False):
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self.assertFalse(utils.sleep_mode_enabled())
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def test_nd_to_nz_2d(self):
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# can be divided by 16
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input_tensor = torch.randn(32, 64)
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output = utils.nd_to_nz_2d(input_tensor)
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self.assertEqual(output.shape[0], 1)
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self.assertEqual(output.shape[1], 64 // 16)
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self.assertEqual(output.shape[2], 32)
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self.assertEqual(output.shape[3], 16)
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# cannot be divided by 16
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input_tensor = torch.randn(30, 62)
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output = utils.nd_to_nz_2d(input_tensor)
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self.assertEqual(output.shape[0], 1)
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self.assertEqual(output.shape[1], math.ceil(62 / 16))
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self.assertEqual(output.shape[2], 32)
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self.assertEqual(output.shape[3], 16)
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# pad to 16
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input_tensor = torch.randn(8, 12)
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output = utils.nd_to_nz_2d(input_tensor)
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self.assertEqual(output.shape[0], 1)
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self.assertEqual(output.shape[1], 1) # 12->16, 16//16=1
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self.assertEqual(output.shape[2], 16) # 8->16
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self.assertEqual(output.shape[3], 16)
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# check if the output is contiguous
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input_tensor = torch.randn(32, 64)
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output = utils.nd_to_nz_2d(input_tensor)
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self.assertTrue(output.is_contiguous())
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# check if the output values are preserved
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input_tensor = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]])
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output = utils.nd_to_nz_2d(input_tensor)
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expected = torch.tensor(
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[[[[1, 2, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[5, 6, 7, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]]])
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self.assertTrue(torch.allclose(output, expected))
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def test_aligned_16(self):
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# align to 16
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input_tensor = torch.randn(15, 64)
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output_tensor = utils.aligned_16(input_tensor)
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self.assertEqual(output_tensor.shape[0], 16)
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# align to 16
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input_tensor = torch.randn(16, 64)
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output_tensor = utils.aligned_16(input_tensor)
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self.assertEqual(output_tensor.shape[0], 16)
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self.assertTrue(torch.equal(input_tensor, output_tensor))
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# align to 32
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input_tensor = torch.randn(17, 64)
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output_tensor = utils.aligned_16(input_tensor)
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self.assertEqual(output_tensor.shape[0], 32)
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@mock.patch('importlib.util.find_spec')
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@mock.patch('importlib.import_module')
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def test_try_register_lib(self, mock_import_module, mock_find_spec):
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# import OK
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mock_find_spec.return_value = mock.MagicMock()
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mock_import_module.return_value = mock.MagicMock()
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lib_name = "existing_lib"
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lib_info = "Library found and imported successfully"
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utils.try_register_lib(lib_name, lib_info)
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mock_find_spec.assert_called_once_with(lib_name)
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mock_import_module.assert_called_once_with(lib_name)
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# Can't find lib
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mock_find_spec.return_value = None
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lib_name = "non_existing_lib"
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utils.try_register_lib(lib_name)
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self.assertEqual(2, mock_find_spec.call_count)
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self.assertEqual(1, mock_import_module.call_count)
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# import error
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mock_find_spec.return_value = mock.MagicMock()
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mock_import_module.side_effect = ImportError("import error")
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lib_name = "error_lib"
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utils.try_register_lib(lib_name)
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self.assertEqual(3, mock_find_spec.call_count)
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self.assertEqual(2, mock_import_module.call_count)
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def test_enable_custom_op(self):
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result = utils.enable_custom_op()
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self.assertTrue(result)
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utils._CUSTOM_OP_ENABLED = None
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with mock.patch('builtins.__import__') as mock_import_module:
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mock_import_module.side_effect = ImportError("import error")
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self.assertFalse(utils.enable_custom_op())
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def test_find_hccl_library(self):
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with mock.patch.dict(os.environ,
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{"HCCL_SO_PATH": "/path/to/hccl/libhccl.so"}):
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self.assertEqual(utils.find_hccl_library(),
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"/path/to/hccl/libhccl.so")
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with mock.patch("torch.version.cann", None):
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self.assertRaises(ValueError, utils.find_hccl_library)
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with mock.patch("torch.version.cann", "Ascend910"):
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self.assertEqual(utils.find_hccl_library(), "libhccl.so")
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def test_current_stream(self):
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with mock.patch("torch.npu.current_stream") as mock_current_stream:
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self.assertEqual(utils.current_stream(), mock_current_stream())
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def test_vllm_version_is(self):
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with mock.patch.dict(os.environ, {"VLLM_VERSION": "1.0.0"}):
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with mock.patch("vllm.__version__", "1.0.0"):
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self.assertTrue(utils.vllm_version_is("1.0.0"))
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self.assertFalse(utils.vllm_version_is("2.0.0"))
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with mock.patch("vllm.__version__", "2.0.0"):
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self.assertTrue(utils.vllm_version_is("1.0.0"))
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self.assertFalse(utils.vllm_version_is("2.0.0"))
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with mock.patch("vllm.__version__", "1.0.0"):
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self.assertTrue(utils.vllm_version_is("1.0.0"))
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self.assertFalse(utils.vllm_version_is("2.0.0"))
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with mock.patch("vllm.__version__", "2.0.0"):
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self.assertTrue(utils.vllm_version_is("2.0.0"))
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self.assertFalse(utils.vllm_version_is("1.0.0"))
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def test_update_aclgraph_sizes(self):
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# max_num_batch_sizes < len(original_sizes)
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test_compilation_config = CompilationConfig(
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cudagraph_capture_sizes=[i for i in range(150)])
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model_path = os.path.join(os.path.dirname(__file__), "fake_weight")
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test_model_config = ModelConfig(model=model_path, enforce_eager=True)
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test_parallel_config = ParallelConfig()
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test_vllm_config = VllmConfig(
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model_config=test_model_config,
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compilation_config=test_compilation_config,
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parallel_config=test_parallel_config,
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)
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utils.update_aclgraph_sizes(test_vllm_config)
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self.assertEqual(
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147,
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len(test_vllm_config.compilation_config.cudagraph_capture_sizes))
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# max_num_batch_sizes >= len(original_sizes)
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test_compilation_config = CompilationConfig(
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cudagraph_capture_sizes=[1, 2, 3])
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test_vllm_config = VllmConfig(
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model_config=test_model_config,
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compilation_config=test_compilation_config,
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parallel_config=test_parallel_config,
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)
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utils.update_aclgraph_sizes(test_vllm_config)
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self.assertEqual(
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3,
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len(test_vllm_config.compilation_config.cudagraph_capture_sizes))
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class TestProfileExecuteDuration(unittest.TestCase):
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def setUp(self):
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utils.ProfileExecuteDuration._instance = None
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utils.ProfileExecuteDuration._observations = []
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utils.ProfileExecuteDuration._lock = Lock()
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def test_singleton_creation(self):
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instance1 = utils.ProfileExecuteDuration()
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self.assertIsNotNone(instance1)
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self.assertIs(instance1, utils.ProfileExecuteDuration._instance)
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instance2 = utils.ProfileExecuteDuration()
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self.assertIs(instance1, instance2)
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def test_thread_safety(self):
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from threading import Thread
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instances = []
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def create_instance():
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instances.append(utils.ProfileExecuteDuration())
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threads = [Thread(target=create_instance) for _ in range(10)]
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for t in threads:
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t.start()
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for t in threads:
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t.join()
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first_instance = instances[0]
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for instance in instances[1:]:
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self.assertIs(first_instance, instance)
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def test_atexit_registration(self):
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with mock.patch('atexit.register') as mock_register:
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instance = utils.ProfileExecuteDuration()
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mock_register.assert_called_once_with(instance.destroy)
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def test_lock_usage(self):
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original_lock = utils.ProfileExecuteDuration._lock
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with mock.patch.object(utils.ProfileExecuteDuration,
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'_lock',
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wraps=original_lock) as mock_lock:
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utils.ProfileExecuteDuration()
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mock_lock.__enter__.assert_called()
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mock_lock.__exit__.assert_called()
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def test_observations_initialization(self):
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instance = utils.ProfileExecuteDuration()
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self.assertEqual(instance._observations, [])
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