EngineX/xc-llm-ascend
3
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

[BugFix] Fix ascend config check (#1092)

Browse Source 
Fix the ascend config check logic:
1. refactor check_ascend_config to make it clear:
    1. torchair graph should not work with enforce_eager=True
    2. aclgraph should not work with torchair graph
3. add refresh config for rlhf case
4. fix a typo in model runner
5. change expert_tensor_parallel_size default to 0 to keep the same as
before

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-06-06 18:54:37 +08:00
committed by GitHub
parent 973f993a13
commit dab19d5dca
5 changed files with 136 additions and 42 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
docs/source/user_guide/additional_config.md
View File

@@ -28,7 +28,8 @@ The following table lists the additional configuration options available in vLLM
| ---- | ---- | ------- | ----------- |
| `torchair_graph_config` | dict | `{}` | The config options for torchair graph mode |
| `ascend_scheduler_config` | dict | `{}` | The config options for ascend scheduler |
| `expert_tensor_parallel_size` | str | `1` | Expert tensor parallel size the model to use. |
| `expert_tensor_parallel_size` | str | `0` | Expert tensor parallel size the model to use. |
| `refresh` | bool | `false` | Whether to refresh global ascend config content. This value is usually used by rlhf case. |
The details of each config option are as follows:
@@ -40,6 +41,7 @@ The details of each config option are as follows:
| `use_cached_graph` | bool | `False` | Whether to use cached graph |
| `graph_batch_sizes` | list[int] | `[]` | The batch size for torchair graph cache |
| `graph_batch_sizes_init` | bool | `False` | Init graph batch size dynamically if `graph_batch_sizes` is empty |
| `enable_multistream_shared_expert`| bool | `False` | Whether to enable multistream shared expert |
**ascend_scheduler_config**
@@ -59,12 +61,14 @@ A full example of additional configuration is as follows:
"enabled": true,
"use_cached_graph": true,
"graph_batch_sizes": [1, 2, 4, 8],
"graph_batch_sizes_init": true
"graph_batch_sizes_init": false,
"enable_multistream_shared_expert": false
},
"ascend_scheduler_config": {
"enabled": true,
"chunked_prefill_enabled": true,
},
"expert_tensor_parallel_size": 1
"expert_tensor_parallel_size": 1,
"refresh": false,
}
```

78
tests/singlecard/test_ascend_config.py
View File

@@ -13,10 +13,13 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
from tests.conftest import VllmRunner
from vllm_ascend.ascend_config import clear_ascend_config, get_ascend_config
from vllm_ascend.ascend_config import (clear_ascend_config, get_ascend_config,
init_ascend_config)
def _clean_up_ascend_config(func):
@@ -39,12 +42,15 @@ def test_run_without_ascend_config():
assert ascend_config.torchair_graph_config.graph_batch_sizes == []
assert not ascend_config.torchair_graph_config.graph_batch_sizes_init
assert not ascend_config.ascend_scheduler_config.enabled
assert ascend_config.expert_tensor_parallel_size == 1
assert ascend_config.expert_tensor_parallel_size == 0
@_clean_up_ascend_config
def test_run_with_ascend_config():
input_additional_config = {
if os.getenv("VLLM_USE_V1") == "0":
pytest.skip("graph only works on v1")
input_additional_config_1 = {
"torchair_graph_config": {
# torchair graph only works with deepseek. The e2e test should be added
# in multicard test with deepseek models.
@@ -52,6 +58,7 @@ def test_run_with_ascend_config():
"use_cached_graph": True,
"graph_batch_sizes": [1, 2, 4, 8],
"graph_batch_sizes_init": False,
"enable_multistream_shared_expert": True,
},
"ascend_scheduler_config": {
"enabled": True,
@@ -59,8 +66,11 @@ def test_run_with_ascend_config():
},
"expert_tensor_parallel_size": 1
}
# check passed with eager mode
with VllmRunner("facebook/opt-125m",
additional_config=input_additional_config):
enforce_eager=True,
additional_config=input_additional_config_1):
ascend_config = get_ascend_config()
assert not ascend_config.torchair_graph_config.enabled
@@ -69,6 +79,7 @@ def test_run_with_ascend_config():
1, 2, 4, 8
]
assert not ascend_config.torchair_graph_config.graph_batch_sizes_init
assert ascend_config.torchair_graph_config.enable_multistream_shared_expert
assert ascend_config.ascend_scheduler_config.enabled
assert ascend_config.ascend_scheduler_config.enable_chunked_prefill
assert ascend_config.expert_tensor_parallel_size == 1
@@ -83,6 +94,8 @@ def test_ascend_config_init_error():
@_clean_up_ascend_config
def test_ascend_config_load_error():
if os.getenv("VLLM_USE_V1") == "0":
pytest.skip("graph only works on v1")
# graph_batch_sizes should be list.
with pytest.raises(TypeError):
input_additional_config_fake_1 = {
@@ -117,3 +130,60 @@ def test_ascend_config_load_error():
enforce_eager=False,
additional_config=input_additional_config_fake_2):
pass
# torchair graph should not be enabled with eager mode
with pytest.raises(RuntimeError):
input_additional_config_fake_3 = {
"torchair_graph_config": {
"enabled": True,
},
}
with VllmRunner("facebook/opt-125m",
enforce_eager=True,
additional_config=input_additional_config_fake_3):
pass
@_clean_up_ascend_config
def test_check_ascend_config_v0():
if os.getenv("VLLM_USE_V1") == "1":
pytest.skip("graph only works on v1, this is the test for v0")
with pytest.raises(NotImplementedError):
input_additional_config_fake_1 = {
"torchair_graph_config": {
"enabled": True,
},
}
with VllmRunner("facebook/opt-125m",
additional_config=input_additional_config_fake_1):
pass
@_clean_up_ascend_config
def test_ascend_config_refresh():
from vllm.config import get_current_vllm_config
vllm_config = get_current_vllm_config()
# set additional_config with none
init_ascend_config(vllm_config)
input_additional_config = {
"torchair_graph_config": {
"enabled": False,
"use_cached_graph": True,
"graph_batch_sizes": [1, 2, 4, 8],
"graph_batch_sizes_init": False,
},
"refresh": True,
}
# refresh ascend config
with VllmRunner("facebook/opt-125m",
additional_config=input_additional_config):
ascend_config = get_ascend_config()
assert not ascend_config.torchair_graph_config.enabled
assert ascend_config.torchair_graph_config.use_cached_graph
assert ascend_config.torchair_graph_config.graph_batch_sizes == [
1, 2, 4, 8
]
assert not ascend_config.torchair_graph_config.graph_batch_sizes_init

86
vllm_ascend/ascend_config.py
View File

@@ -37,7 +37,7 @@ class AscendConfig:
ascend_scheduler_config)
self.expert_tensor_parallel_size = int(
additional_config.get("expert_tensor_parallel_size", 1))
additional_config.get("expert_tensor_parallel_size", 0))
class TorchairGraphConfig:
@@ -82,8 +82,11 @@ _ASCEND_CONFIG: Optional[AscendConfig] = None
def init_ascend_config(vllm_config):
additional_config = vllm_config.additional_config if vllm_config.additional_config is not None else {}
refresh = additional_config.get("refresh",
False) if additional_config else False
global _ASCEND_CONFIG
if _ASCEND_CONFIG is not None:
if _ASCEND_CONFIG is not None and not refresh:
return _ASCEND_CONFIG
_ASCEND_CONFIG = AscendConfig(vllm_config)
return _ASCEND_CONFIG
@@ -106,35 +109,52 @@ def get_ascend_config():
def check_ascend_config(vllm_config, enforce_eager):
ascend_config = get_ascend_config()
# Both for V0 and V1 Engine, torchair_graph cannot be enabled with eager mode.
if ascend_config.torchair_graph_config.enabled and enforce_eager:
raise RuntimeError(
"Can't enable graph mode and eager mode at the same time. Please set `enforce_eager=False` if you attempt to enable NPU graph mode."
)
# torchair_graph only work with deepseek model and mla enabled.
if ascend_config.torchair_graph_config.enabled:
if envs.VLLM_MLA_DISABLE:
logger.warning(
"Torchair graph mode is still experimental and not supported for V1 without mla currently, "
"it has been disabled automatically.")
ascend_config.ascend_scheduler_config.enabled = False
if vllm_config.model_config:
model_type = vllm_config.model_config.hf_config.model_type
if "deepseek" not in model_type:
raise NotImplementedError(
"Torchair graph mode only works with deepseek model.")
# for V1 Engine, aclgraph doesn't work with deepseek model and only qwen model is well tested.
if envs.VLLM_USE_V1 and vllm_config.model_config is not None and not enforce_eager:
model_type = vllm_config.model_config.hf_config.model_type
if "deepseek" in model_type:
# for v0 engine
if not envs.VLLM_USE_V1:
if ascend_config.torchair_graph_config.enabled:
raise NotImplementedError(
"ACL Graph does not support deepseek. Please "
"try torchair graph mode to serve deepseek models on vllm-ascend."
" Or set `enforce_eager=True` to use eager mode.")
if "qwen" not in model_type:
logger.warning(
"ACL Graph is currently experimental. Please "
"raise an issue on https://github.com/vllm-project/vllm-ascend/issues"
" if you encourage any Error")
"Torchair graph mode is only supported for V1 Engine.")
if ascend_config.ascend_scheduler_config.enabled:
raise NotImplementedError(
"Ascend scheduler is only supported for V1 Engine.")
# for v1 engine
else:
# for eager mode
if enforce_eager:
# torchair_graph cannot be enabled with eager mode.
if ascend_config.torchair_graph_config.enabled:
raise RuntimeError(
"Can't enable graph mode and eager mode at the same time. Please set `enforce_eager=False` if you attempt to enable NPU graph mode."
)
# for graph mode
else:
# torchair_graph case
if ascend_config.torchair_graph_config.enabled:
# torchair_graph is not supported for V1 without mla currently.
if envs.VLLM_MLA_DISABLE:
logger.warning(
"Torchair graph mode is still experimental and not supported for V1 without mla currently, "
"it has been disabled automatically.")
ascend_config.torchair_graph_config.enabled = False
# torchair_graph is supported for deepseek model only currently.
if vllm_config.model_config:
model_type = vllm_config.model_config.hf_config.model_type
if "deepseek" not in model_type:
raise NotImplementedError(
"Torchair graph mode only works with deepseek model."
)
# aclgraph case
else:
# aclgraph doesn't work with deepseek model and only qwen model is well tested.
if vllm_config.model_config:
model_type = vllm_config.model_config.hf_config.model_type
if "deepseek" in model_type:
raise NotImplementedError(
"ACL Graph does not support deepseek. Please "
"try torchair graph mode to serve deepseek models on vllm-ascend."
" Or set `enforce_eager=True` to use eager mode.")
if "qwen" not in model_type:
logger.warning(
"ACL Graph is currently experimental. Please "
"raise an issue on https://github.com/vllm-project/vllm-ascend/issues"
" if you encourage any Error")

2
vllm_ascend/platform.py
View File

@@ -133,7 +133,7 @@ class NPUPlatform(Platform):
# NOTE: When enable_expert_parallel is True, we follow vLLM convention:
# ep_size = world_size, which means expert_tensor_parallel_size must be 1
if ascend_config.expert_tensor_parallel_size > 1 and not parallel_config.enable_expert_parallel:
if ascend_config.expert_tensor_parallel_size > 0 and not parallel_config.enable_expert_parallel:
parallel_config.expert_tensor_parallel_size = ascend_config.expert_tensor_parallel_size
# Calculate expert parallel size based on world size

2
vllm_ascend/worker/model_runner_v1.py
View File

@@ -323,7 +323,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
ascend_config = get_ascend_config()
self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled and self.vllm_config.model_config.use_mla
self.torchair_graph_use_cached_npu_graph = ascend_config.torchair_graph_config.use_cached_graph
self.use_cached_npu_graph = ascend_config.torchair_graph_config.use_cached_graph
self.torchair_graph_batch_sizes = ascend_config.torchair_graph_config.graph_batch_sizes
if ascend_config.torchair_graph_config.graph_batch_sizes_init: