f10acddb78
Ascend scheduler was added for non chunk prefill case before, since that the npu ops didn't work well with chunked prefill. Now the ops with chunked prefill work better, it's time to remove the ascend scheduler to use vLLM default scheduler. - vLLM version: v0.11.2 --------- Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
357 lines
14 KiB
Python
357 lines
14 KiB
Python
#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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import os
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from transformers import PretrainedConfig
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from vllm.config import ModelConfig, ParallelConfig, VllmConfig
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from tests.ut.base import TestBase
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from vllm_ascend.ascend_config import (_check_torchair_supported,
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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(TestBase):
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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.assertIsNone(ascend_config.expert_map_path)
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self.assertFalse(ascend_config.multistream_overlap_shared_expert)
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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.assertEqual(torchair_graph_config.mode, '')
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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.assertTrue(torchair_graph_config.enable_view_optimize)
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self.assertTrue(torchair_graph_config.enable_frozen_parameter)
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self.assertFalse(torchair_graph_config.enable_kv_nz)
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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_view_optimize": True,
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"enable_frozen_parameter": True,
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"enable_kv_nz": True
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},
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"multistream_overlap_shared_expert": True,
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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_map_path, "test_expert_map_path")
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self.assertTrue(ascend_config.multistream_overlap_shared_expert)
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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_view_optimize)
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self.assertTrue(torchair_graph_config.enable_frozen_parameter)
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self.assertTrue(torchair_graph_config.enable_kv_nz)
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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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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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# 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__), "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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def test_check_torchair_supported(self):
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test_cases = [('deepseek_v3', True), ('PanguProMoE', True),
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('qwen', True), ('llama', False)]
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for model_type, expected_output in test_cases:
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self.assertEqual(_check_torchair_supported(model_type),
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expected_output)
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@_clean_up_ascend_config
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def test_ascend_config_load_error(self):
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test_vllm_config = VllmConfig()
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# graph_batch_sizes should be list.
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with self.assertRaises(TypeError):
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test_vllm_config.additional_config = {
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"torchair_graph_config": {
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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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init_ascend_config(test_vllm_config)
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# use_cached_graph should not be enabled without torchair graph 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": False,
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"use_cached_graph": 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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# use_cached_kv_cache_bytes should not be enabled without torchair graph 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": False,
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"use_cached_kv_cache_bytes": 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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# graph_batch_sizes should not be set without torchair graph 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": False,
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"graph_batch_sizes": [1, 2, 4],
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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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# use_cached_kv_cache_bytes is valid only when torchair graph mode and use_cached_graph are enabled
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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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"use_cached_graph": False,
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"use_cached_kv_cache_bytes": 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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# graph_batch_sizes_init should not be enabled without torchair graph 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": False,
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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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init_ascend_config(test_vllm_config)
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# enable_multistream_mla should not be enabled without torchair graph 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": False,
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"enable_multistream_mla": 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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# mode should not be configured without torchair graph 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": False,
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"mode": 'max-autotune',
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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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# enable_kv_nz should not be enabled without torchair graph 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": False,
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"enable_kv_nz": 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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with self.assertRaises(AssertionError):
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test_vllm_config.additional_config = {
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"lmhead_tensor_parallel_size": 2,
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"refresh": True
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}
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test_vllm_config.parallel_config = ParallelConfig(
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data_parallel_size=4, tensor_parallel_size=2)
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init_ascend_config(test_vllm_config)
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with self.assertRaises(AssertionError):
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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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"oproj_tensor_parallel_size": 2,
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"refresh": True
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}
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test_vllm_config.parallel_config = ParallelConfig(
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data_parallel_size=4, tensor_parallel_size=2)
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init_ascend_config(test_vllm_config)
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with self.assertRaises(AssertionError):
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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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"oproj_tensor_parallel_size": 2,
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"refresh": True
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}
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test_vllm_config.parallel_config = ParallelConfig(
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data_parallel_size=4, tensor_parallel_size=1)
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model_path = os.path.join(os.path.dirname(__file__), "fake_weight")
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test_vllm_config.model_config = ModelConfig(model=model_path,
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enforce_eager=True)
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init_ascend_config(test_vllm_config)
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