8cfd257992
### What this PR does / why we need it?
Remove ETP/EP maintained in branch main. We drop this as there is no
relevant scenarios to use ETP now, and we may subsequently advocate
implementing expert tensor parallelism in vLLM to support scenarios
where the expert is needed to be sliced
This is a part of #1422 backport.
Fixes https://github.com/vllm-project/vllm-ascend/issues/1396
https://github.com/vllm-project/vllm-ascend/issues/1154
### Does this PR introduce _any_ user-facing change?
We'll not maintain etp/ep in vllm-ascend anymore, and use the tp/ep in
vllm instead.
### How was this patch tested?
CI passed with new added and existing test.
- vLLM version: v0.9.2
- vLLM main:
fe8a2c544a
Signed-off-by: MengqingCao <cmq0113@163.com>
239 lines
9.2 KiB
Python
239 lines
9.2 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, 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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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_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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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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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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# 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__), "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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def test_check_torchair_supported(self):
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test_cases = [('deepseek_v3', True), ('PanguProMoE', True),
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('qwen', False), ('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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