90a75a90a9
### What this PR does / why we need it?
This PR ports #2312 #2506 #2531 to main branch.
Original implementation of torchair caching forces users to make
everything prepared, fix all the configuration and enable
`use_cached_npu_graph`, and it might cause some problems confusing to
understand and tackle for users. It is better to compile the graph twice
instead of reusing the old kvcaches and cached torchair graph. And the
extra duration time is acceptable. Additionally, this pr fixes a
recompilation problem of torchair graph mode caused by
`running_in_graph` variable in `AscendMLATorchairImpl`.
### Does this PR introduce _any_ user-facing change?
If users want to enabling torchair.cache_compile with high compilation
speed, it is recommended to enable both `use_cached_kv_cache_bytes` and
`use_cached_graph` in `torchair_graph_config`. Without
`use_cached_kv_cache_bytes`, we'll compile torchair computation graph
twice to avoid runtime error caused by configuration mismtaches (the
second compilation will be much faster). Additionally, we've made a
change to how the TORCHAIR_CACHE_HOME enviroment variable is utilized to
enhance safety and prevent accidental file deletion by adding a suffix
directory.
### How was this patch tested?
CI and e2e vllm serving pass.
- vLLM version: v0.10.1.1
- vLLM main:
70549c1245
---------
Signed-off-by: linfeng-yuan <1102311262@qq.com>
362 lines
14 KiB
Python
362 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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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.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', 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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# enable_multistream_moe 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_moe": 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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