245 lines
9.7 KiB
Python
245 lines
9.7 KiB
Python
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import os
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import unittest
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from unittest import mock
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from transformers import PretrainedConfig
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from vllm.config import ModelConfig, VllmConfig
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from vllm_ascend.ascend_config import (check_ascend_config,
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clear_ascend_config, get_ascend_config,
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init_ascend_config)
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class TestAscendConfig(unittest.TestCase):
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@staticmethod
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def _clean_up_ascend_config(func):
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def wrapper(*args, **kwargs):
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clear_ascend_config()
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func(*args, **kwargs)
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clear_ascend_config()
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return wrapper
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@_clean_up_ascend_config
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def test_init_ascend_config_without_additional_config(self):
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test_vllm_config = VllmConfig()
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# No additional config given, check the default value here.
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ascend_config = init_ascend_config(test_vllm_config)
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self.assertEqual(ascend_config.expert_tensor_parallel_size, 0)
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self.assertIsNone(ascend_config.expert_map_path)
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torchair_graph_config = ascend_config.torchair_graph_config
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self.assertFalse(torchair_graph_config.enabled)
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self.assertFalse(torchair_graph_config.use_cached_graph)
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self.assertEqual(torchair_graph_config.graph_batch_sizes, [])
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self.assertFalse(torchair_graph_config.graph_batch_sizes_init)
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self.assertFalse(torchair_graph_config.enable_multistream_mla)
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self.assertFalse(torchair_graph_config.enable_multistream_moe)
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self.assertTrue(torchair_graph_config.enable_view_optimize)
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self.assertFalse(torchair_graph_config.enable_kv_nz)
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ascend_scheduler_config = ascend_config.ascend_scheduler_config
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self.assertFalse(ascend_scheduler_config.enabled)
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@_clean_up_ascend_config
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def test_init_ascend_config_with_additional_config(self):
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test_vllm_config = VllmConfig()
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test_vllm_config.additional_config = {
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"torchair_graph_config": {
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"enabled": True,
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"use_cached_graph": True,
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"graph_batch_sizes": [1, 2, 4],
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"graph_batch_sizes_init": False,
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"enable_multistream_mla": True,
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"enable_multistream_moe": True,
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"enable_view_optimize": True,
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"enable_kv_nz": True
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},
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"ascend_scheduler_config": {
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"enabled": True
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},
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"expert_tensor_parallel_size": 1,
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"expert_map_path": "test_expert_map_path",
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"refresh": True
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}
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ascend_config = init_ascend_config(test_vllm_config)
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self.assertEqual(ascend_config.expert_tensor_parallel_size, 1)
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self.assertEqual(ascend_config.expert_map_path, "test_expert_map_path")
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torchair_graph_config = ascend_config.torchair_graph_config
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self.assertTrue(torchair_graph_config.enabled)
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self.assertTrue(torchair_graph_config.use_cached_graph)
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self.assertEqual(torchair_graph_config.graph_batch_sizes, [1, 2, 4])
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self.assertFalse(torchair_graph_config.graph_batch_sizes_init)
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self.assertTrue(torchair_graph_config.enable_multistream_mla)
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self.assertTrue(torchair_graph_config.enable_multistream_moe)
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self.assertTrue(torchair_graph_config.enable_view_optimize)
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self.assertTrue(torchair_graph_config.enable_kv_nz)
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ascend_scheduler_config = ascend_config.ascend_scheduler_config
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self.assertTrue(ascend_scheduler_config.enabled)
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@_clean_up_ascend_config
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def test_init_ascend_config_with_refresh(self):
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test_vllm_config = VllmConfig()
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ascend_config = init_ascend_config(test_vllm_config)
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self.assertFalse(ascend_config.torchair_graph_config.enabled)
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test_vllm_config.additional_config = {
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"torchair_graph_config": {
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"enabled": True,
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},
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}
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ascend_config = init_ascend_config(test_vllm_config)
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self.assertFalse(ascend_config.torchair_graph_config.enabled)
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test_vllm_config.additional_config = {
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"torchair_graph_config": {
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"enabled": True,
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},
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"refresh": True,
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}
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ascend_config = init_ascend_config(test_vllm_config)
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self.assertTrue(ascend_config.torchair_graph_config.enabled)
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@_clean_up_ascend_config
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def test_init_ascend_config_with_wrong_input(self):
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test_vllm_config = VllmConfig()
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test_vllm_config.additional_config = {
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"torchair_graph_config": {
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"enabled": True,
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"graph_batch_sizes": "fake_size",
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},
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"refresh": True,
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}
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with self.assertRaises(TypeError):
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init_ascend_config(test_vllm_config)
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test_vllm_config.additional_config = {
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"torchair_graph_config": {
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"enabled": False,
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"graph_batch_sizes": [1, 2, 4, 8],
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"graph_batch_sizes_init": True,
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},
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"refresh": True,
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}
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with self.assertRaises(ValueError):
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init_ascend_config(test_vllm_config)
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@_clean_up_ascend_config
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def test_get_ascend_config(self):
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test_vllm_config = VllmConfig()
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ascend_config = init_ascend_config(test_vllm_config)
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self.assertEqual(get_ascend_config(), ascend_config)
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@_clean_up_ascend_config
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def test_get_ascend_config_without_init(self):
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with self.assertRaises(RuntimeError):
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get_ascend_config()
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@_clean_up_ascend_config
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def test_clear_ascend_config(self):
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test_vllm_config = VllmConfig()
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ascend_config = init_ascend_config(test_vllm_config)
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self.assertEqual(get_ascend_config(), ascend_config)
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clear_ascend_config()
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with self.assertRaises(RuntimeError):
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get_ascend_config()
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@_clean_up_ascend_config
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def test_check_ascend_config_pass(self):
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test_vllm_config = VllmConfig()
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init_ascend_config(test_vllm_config)
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check_ascend_config(test_vllm_config, False)
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# For V1 engine
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with mock.patch.dict(os.environ, {"VLLM_USE_V1": "1"}):
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test_vllm_config.additional_config = {
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"torchair_graph_config": {
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"enabled": True,
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},
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"refresh": True
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}
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init_ascend_config(test_vllm_config)
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check_ascend_config(test_vllm_config, False)
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test_vllm_config.additional_config = {
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"torchair_graph_config": {
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"enabled": False,
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},
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"refresh": True
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}
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init_ascend_config(test_vllm_config)
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check_ascend_config(test_vllm_config, False)
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@_clean_up_ascend_config
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def test_check_ascend_config_wrong_case(self):
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test_vllm_config = VllmConfig()
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# For V0 engine
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with mock.patch.dict(os.environ, {"VLLM_USE_V1": "0"}):
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with self.assertRaises(NotImplementedError):
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test_vllm_config.additional_config = {
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"torchair_graph_config": {
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"enabled": True,
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},
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"refresh": True
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}
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init_ascend_config(test_vllm_config)
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check_ascend_config(test_vllm_config, False)
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with self.assertRaises(NotImplementedError):
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test_vllm_config.additional_config = {
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"ascend_scheduler_config": {
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"enabled": True,
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},
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"refresh": True
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}
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init_ascend_config(test_vllm_config)
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check_ascend_config(test_vllm_config, True)
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# For V1 engine
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with mock.patch.dict(os.environ, {"VLLM_USE_V1": "1"}):
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# torchair + eager mode
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with self.assertRaises(RuntimeError):
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test_vllm_config.additional_config = {
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"torchair_graph_config": {
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"enabled": True,
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},
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"refresh": True
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}
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init_ascend_config(test_vllm_config)
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enforce_eager = True
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check_ascend_config(test_vllm_config, enforce_eager)
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# torchair + non deepseek model
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with self.assertRaises(NotImplementedError):
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test_vllm_config.additional_config = {
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"torchair_graph_config": {
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"enabled": True,
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},
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"refresh": True
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}
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model_path = os.path.join(os.path.dirname(__file__),
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"fake_weight")
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fake_model_config = ModelConfig(model=model_path)
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fake_model_config.hf_config = PretrainedConfig()
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fake_model_config.hf_config.model_type = "llama"
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test_vllm_config.model_config = fake_model_config
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init_ascend_config(test_vllm_config)
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check_ascend_config(test_vllm_config, False)
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# aclgraph + deepseek model
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with self.assertRaises(NotImplementedError):
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test_vllm_config.additional_config = {
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"torchair_graph_config": {
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"enabled": False,
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},
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"refresh": True
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}
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model_path = os.path.join(os.path.dirname(__file__),
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"fake_weight")
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fake_model_config = ModelConfig(model=model_path)
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fake_model_config.hf_config = PretrainedConfig()
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fake_model_config.hf_config.model_type = "deepseek"
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test_vllm_config.model_config = fake_model_config
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init_ascend_config(test_vllm_config)
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check_ascend_config(test_vllm_config, False)
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