[BugFix] Fix ascend config check

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

Author: wangxiyuan

docs/source/user_guide/additional_config.md

@@ -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,
 }
 ```

tests/singlecard/test_ascend_config.py

@@ -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):
@@ -39,28 +42,35 @@ 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.
             "enabled": False,
             "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,
             "enable_chunked_prefill": True,
         },
         "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

vllm_ascend/ascend_config.py

@@ -37,7 +37,7 @@ def __init__(self, vllm_config):
             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 @@ def __init__(self, ascend_scheduler_config: dict):
 
 
 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(
+                "Torchair graph mode is only supported for V1 Engine.")
+        if ascend_config.ascend_scheduler_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")
+                "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")

vllm_ascend/platform.py

@@ -133,7 +133,7 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
 
             # 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

vllm_ascend/worker/model_runner_v1.py

@@ -323,7 +323,7 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
 
         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: