Upgrade to new vllm commit (#3719)

### What this PR does / why we need it?
Upgrade to new vllm commit:
c9461e05a4

- Fix many imports, caused by
https://github.com/vllm-project/vllm/pull/26908
- Fix import ```sha256```, caused by
https://github.com/vllm-project/vllm/pull/27169
- Remove ```SchedulerConfig.send_delta_data```, caused by
https://github.com/vllm-project/vllm/pull/27142
- Fix ```FusedMoE``` because of dual stream execution, caused by
https://github.com/vllm-project/vllm/pull/26440

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
CI passed with new added/existing test.


- vLLM version: v0.11.0rc3
- vLLM main:
17c540a993

---------

Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: Icey <1790571317@qq.com>
Co-authored-by: MengqingCao <cmq0113@163.com>

.github/workflows/format_pr_body.yaml

@@ -36,7 +36,7 @@ jobs:
 
       - name: Get vLLM version
         run: |
-          VLLM_COMMIT=17c540a993af88204ad1b78345c8a865cf58ce44
+          VLLM_COMMIT=c9461e05a4ed3557cfbf4b15ded1e26761cc39ca
           echo "VLLM_COMMIT=https://github.com/vllm-project/vllm/commit/$VLLM_COMMIT" >> $GITHUB_ENV
 
       - name: Checkout repository

.github/workflows/vllm_ascend_test.yaml

@@ -42,7 +42,7 @@ jobs:
   lint:
     uses: ./.github/workflows/pre-commit.yml
     with:
-      vllm: 17c540a993af88204ad1b78345c8a865cf58ce44
+      vllm: c9461e05a4ed3557cfbf4b15ded1e26761cc39ca
 
   changes:
     runs-on: ubuntu-latest
@@ -83,7 +83,7 @@ jobs:
         VLLM_USE_MODELSCOPE: True
     strategy:
       matrix:
-        vllm_version: [17c540a993af88204ad1b78345c8a865cf58ce44, v0.11.0]
+        vllm_version: [c9461e05a4ed3557cfbf4b15ded1e26761cc39ca, v0.11.0]
     steps:
       - name: Install packages
         run: |
@@ -140,7 +140,7 @@ jobs:
     name: e2e-light
     strategy:
       matrix:
-        vllm_version: [17c540a993af88204ad1b78345c8a865cf58ce44, v0.11.0]
+        vllm_version: [c9461e05a4ed3557cfbf4b15ded1e26761cc39ca, v0.11.0]
     # Note (yikun): If CI resource are limited we can split job into two chain jobs
     needs: [lint, changes]
     # only trigger e2e test after lint passed and the change is e2e related with pull request.

.github/workflows/vllm_ascend_test_full.yaml

@@ -69,7 +69,7 @@ jobs:
     name: e2e-full
     strategy:
       matrix:
-        vllm_version: [17c540a993af88204ad1b78345c8a865cf58ce44, v0.11.0]
+        vllm_version: [c9461e05a4ed3557cfbf4b15ded1e26761cc39ca, v0.11.0]
     needs: [changes]
     if: ${{ needs.changes.outputs.e2e_tracker == 'true' }}
     uses: ./.github/workflows/_e2e_test.yaml

examples/offline_data_parallel.py

@@ -63,7 +63,11 @@ import torch
 from vllm import LLM, SamplingParams
 from vllm.distributed.parallel_state import (  # noqa E402
     destroy_distributed_environment, destroy_model_parallel)
-from vllm.utils import get_open_port
+from vllm_ascend.utils import vllm_version_is
+if vllm_version_is("0.11.0"):
+    from vllm.utils import get_open_port
+else:
+    from vllm.utils.network_utils import get_open_port
 
 os.environ["VLLM_USE_MODELSCOPE"] = "True"
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

examples/offline_external_launcher.py

@@ -66,8 +66,14 @@ import torch
 from vllm import LLM, SamplingParams
 from vllm.distributed.parallel_state import (  # noqa E402
     destroy_distributed_environment, destroy_model_parallel, get_tp_group) 
-from vllm.utils import get_open_port, GiB_bytes
 from safetensors.torch import load_file
+from vllm_ascend.utils import vllm_version_is
+if vllm_version_is("0.11.0"):
+    from vllm.utils import GiB_bytes, get_open_port
+    
+else:
+    from vllm.utils.mem_constants import GiB_bytes
+    from vllm.utils.network_utils import get_open_port
 
 os.environ["VLLM_USE_MODELSCOPE"] = "True"
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

examples/offline_inference_sleep_mode_npu.py

@@ -20,7 +20,11 @@ import os
 
 import torch
 from vllm import LLM, SamplingParams
-from vllm.utils import GiB_bytes
+from vllm_ascend.utils import vllm_version_is
+if vllm_version_is("0.11.0"):
+    from vllm.utils import GiB_bytes
+else:
+    from vllm.utils.mem_constants import GiB_bytes
 
 os.environ["VLLM_USE_MODELSCOPE"] = "True"
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

examples/offline_weight_load.py

@@ -66,8 +66,14 @@ import torch
 from vllm import LLM, SamplingParams
 from vllm.distributed.parallel_state import (  # noqa E402
     destroy_distributed_environment, destroy_model_parallel, get_tp_group)
-from vllm.utils import get_open_port, GiB_bytes
 from safetensors.torch import load_file
+from vllm_ascend.utils import vllm_version_is
+if vllm_version_is("0.11.0"):
+    from vllm.utils import GiB_bytes, get_open_port
+    
+else:
+    from vllm.utils.mem_constants import GiB_bytes
+    from vllm.utils.network_utils import get_open_port
 
 os.environ["VLLM_USE_MODELSCOPE"] = "True"
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"

tests/e2e/conftest.py

@@ -45,7 +45,6 @@ from vllm.inputs import TextPrompt
 from vllm.outputs import RequestOutput
 from vllm.platforms import current_platform
 from vllm.transformers_utils.utils import maybe_model_redirect
-from vllm.utils import get_open_port
 
 from tests.e2e.model_utils import (TokensTextLogprobs,
                                    TokensTextLogprobsPromptLogprobs)
@@ -55,6 +54,12 @@ from vllm_ascend.ascend_config import clear_ascend_config
 # we not explicitly patch here, some of them might be effectiveless
 # in pytest scenario
 from vllm_ascend.utils import adapt_patch  # noqa E402
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import get_open_port
+else:
+    from vllm.utils.network_utils import get_open_port
 
 adapt_patch(True)
 adapt_patch(False)

tests/e2e/multicard/test_single_request_aclgraph.py

@@ -19,9 +19,14 @@ from typing import Any
 
 import openai
 import pytest
-from vllm.utils import get_open_port
 
 from tests.e2e.conftest import RemoteOpenAIServer
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import get_open_port
+else:
+    from vllm.utils.network_utils import get_open_port
 
 MODELS = [
     "Qwen/Qwen3-30B-A3B",

tests/e2e/nightly/models/test_qwen3_32b.py

@@ -18,10 +18,15 @@ from typing import Any
 
 import openai
 import pytest
-from vllm.utils import get_open_port
 
 from tests.e2e.conftest import RemoteOpenAIServer
 from tools.aisbench import run_aisbench_cases
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import get_open_port
+else:
+    from vllm.utils.network_utils import get_open_port
 
 MODELS = [
     "Qwen/Qwen3-32B",

tests/e2e/singlecard/test_camem.py

@@ -21,11 +21,16 @@ import gc
 
 import torch
 from vllm import SamplingParams
-from vllm.utils import GiB_bytes
 
 from tests.e2e.conftest import VllmRunner
 from tests.e2e.utils import fork_new_process_for_each_test
 from vllm_ascend.device_allocator.camem import CaMemAllocator
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import GiB_bytes
+else:
+    from vllm.utils.mem_constants import GiB_bytes
 
 
 @fork_new_process_for_each_test

tests/ut/core/test_schedule_config.py

@@ -78,21 +78,6 @@ class TestAscendSchedulerConfig(TestBase):
             str(context.exception),
         )
 
-    def test_not_implemented_send_delta_data(self):
-        with self.assertRaises(NotImplementedError) as context:
-            AscendSchedulerConfig.initialize_from_config(
-                self.basic_scheduler_config,
-                AscendSchedulerConfig(
-                    send_delta_data=True,
-                    max_num_batched_tokens=2048,
-                    max_model_len=2048,
-                ),
-            )
-        self.assertIn(
-            "currently AscendScheduler doesn't support send_delta_data",
-            str(context.exception),
-        )
-
     def test_no_override(self):
         ascend_config = AscendSchedulerConfig.initialize_from_config(
             self.basic_scheduler_config, {})

tests/ut/core/test_scheduler.py

@@ -9,7 +9,6 @@ from vllm.config import (CacheConfig, KVTransferConfig, ModelConfig,
 from vllm.multimodal.inputs import (MultiModalFeatureSpec,
                                     MultiModalKwargsItem, PlaceholderRange)
 from vllm.sampling_params import SamplingParams
-from vllm.utils import sha256
 from vllm.v1.core.kv_cache_utils import (get_request_block_hasher,
                                          init_none_hash)
 from vllm.v1.core.sched.output import SchedulerOutput
@@ -24,6 +23,11 @@ from vllm_ascend.core.scheduler import AscendScheduler
 from vllm_ascend.core.scheduler_dynamic_batch import SchedulerDynamicBatch
 from vllm_ascend.utils import vllm_version_is
 
+if vllm_version_is("0.11.0"):
+    from vllm.utils import sha256
+else:
+    from vllm.utils.hashing import sha256
+
 EOS_TOKEN_ID = 50256
 MODEL = "Qwen3-0.6B"
 ENABLE_PREFIX_CACHING = None

tests/ut/kv_connector/test_mooncake_connector.py

@@ -12,7 +12,13 @@ from unittest.mock import MagicMock, patch
 
 import msgspec
 import zmq
-from vllm.utils import make_zmq_path
+
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import make_zmq_path
+else:
+    from vllm.utils.network_utils import make_zmq_path
 
 fake_engine = types.ModuleType("mooncake.engine")
 fake_engine.TransferEngine = MagicMock()  # type: ignore[attr-defined]

tests/ut/kv_connector/utils.py

@@ -10,7 +10,6 @@ import torch
 from vllm import SamplingParams
 from vllm.config import (CacheConfig, DeviceConfig, KVTransferConfig,
                          ModelConfig, SchedulerConfig, VllmConfig)
-from vllm.utils import sha256
 from vllm.v1.core.kv_cache_utils import (get_request_block_hasher,
                                          init_none_hash)
 from vllm.v1.core.sched.scheduler import Scheduler
@@ -22,6 +21,11 @@ from vllm.v1.structured_output import StructuredOutputManager
 
 from vllm_ascend.utils import vllm_version_is
 
+if vllm_version_is("0.11.0"):
+    from vllm.utils import sha256
+else:
+    from vllm.utils.hashing import sha256
+
 EOS_TOKEN_ID = 50256
 os.environ["VLLM_USE_V1"] = "1"
 

tests/ut/model_loader/netloader/test_netloader.py

@@ -22,6 +22,7 @@ import torch
 from torch import nn
 
 from vllm_ascend.model_loader.netloader.netloader import ModelNetLoaderElastic
+from vllm_ascend.utils import vllm_version_is
 
 
 class DummyDeviceConfig:
@@ -173,7 +174,11 @@ def test_load_model_elastic_success(mock_logger, monkeypatch, tmp_path):
         "vllm_ascend.model_loader.netloader.netloader.process_weights_after_loading",
         lambda *a, **k: None)
     # patch get_ip
+    if vllm_version_is("0.11.0"):
         monkeypatch.setattr("vllm.utils.get_ip", lambda: "127.0.0.1")
+    else:
+        monkeypatch.setattr("vllm.utils.network_utils.get_ip",
+                            lambda: "127.0.0.1")
     # patch find_free_port
     monkeypatch.setattr(
         "vllm_ascend.model_loader.netloader.netloader.find_free_port",

tests/ut/worker/test_input_batch.py

@@ -20,14 +20,19 @@ import numpy as np
 import pytest
 import torch
 from vllm.sampling_params import SamplingParams
-from vllm.utils import make_tensor_with_pad
 from vllm.v1.pool.metadata import PoolingMetadata
 from vllm.v1.sample.logits_processor import LogitsProcessors
 from vllm.v1.sample.metadata import SamplingMetadata
 
+from vllm_ascend.utils import vllm_version_is
 from vllm_ascend.worker.block_table import BlockTable, MultiGroupBlockTable
 from vllm_ascend.worker.npu_input_batch import CachedRequestState, InputBatch
 
+if vllm_version_is("0.11.0"):
+    from vllm.utils import make_tensor_with_pad
+else:
+    from vllm.utils.torch_utils import make_tensor_with_pad
+
 VOCAB_SIZE = 1024
 NUM_OUTPUT_TOKENS = 20
 MAX_PROMPT_SIZE = 100

tests/ut/worker/test_worker_v1.py

@@ -5,6 +5,7 @@ import torch
 from vllm.config import CacheConfig, ModelConfig, ParallelConfig, VllmConfig
 
 from tests.ut.base import TestBase
+from vllm_ascend.utils import vllm_version_is
 
 
 class TestNPUWorker(TestBase):
@@ -178,6 +179,7 @@ class TestNPUWorker(TestBase):
         # Create NPUWorker instance
         from vllm_ascend.worker.worker_v1 import NPUWorker
 
+        if vllm_version_is("0.11.0"):
             with patch("vllm.utils.STR_DTYPE_TO_TORCH_DTYPE",
                        {"float32": torch.float32}):
                 worker = NPUWorker(
@@ -187,6 +189,16 @@ class TestNPUWorker(TestBase):
                     distributed_init_method=self.distributed_init_method,
                     is_driver_worker=self.is_driver_worker,
                 )
+        else:
+            with patch("vllm.utils.torch_utils.STR_DTYPE_TO_TORCH_DTYPE",
+                       {"float32": torch.float32}):
+                worker = NPUWorker(
+                    vllm_config=self.vllm_config_mock,
+                    local_rank=self.local_rank,
+                    rank=self.rank,
+                    distributed_init_method=self.distributed_init_method,
+                    is_driver_worker=self.is_driver_worker,
+                )
 
         # Verify cache_dtype is set to custom value
         self.assertEqual(worker.cache_dtype, torch.float32)

vllm_ascend/core/schedule_config.py

@@ -99,9 +99,6 @@ class AscendSchedulerConfig(SchedulerConfig):
             raise NotImplementedError(
                 f"currently AscendScheduler only supports fcfs policy, got {self.policy}"
             )
-        if self.send_delta_data:
-            raise NotImplementedError(
-                "currently AscendScheduler doesn't support send_delta_data.")
         if getattr(self, "scheduler_delay_factor", 0) > 0:
             raise NotImplementedError(
                 "currently AscendScheduler doesn't support scheduler_delay_factor."

vllm_ascend/distributed/cpu_offload_manager/metadata.py

@@ -9,11 +9,18 @@ import torch
 import vllm.envs as envs
 import zmq
 from vllm.config import KVTransferConfig, VllmConfig
-from vllm.utils import get_dtype_size, logger, make_zmq_socket
+from vllm.utils import logger
 from vllm.v1.kv_cache_interface import AttentionSpec
 
 from vllm_ascend.distributed.cpu_offload_manager.cpu_kv_cache_manager import \
     CPUKVCacheManager
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import get_dtype_size, make_zmq_socket
+else:
+    from vllm.utils.network_utils import make_zmq_socket
+    from vllm.utils.torch_utils import get_dtype_size
 
 
 @dataclass

vllm_ascend/distributed/llmdatadist_c_mgr_connector.py

@@ -25,19 +25,25 @@ from vllm.distributed.kv_transfer.kv_connector.v1.base import (
 from vllm.distributed.parallel_state import (get_dcp_group, get_tp_group,
                                              get_world_group)
 from vllm.forward_context import ForwardContext
-from vllm.utils import get_ip, logger
+from vllm.utils import logger
 from vllm.v1.core.kv_cache_manager import KVCacheBlocks
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.request import Request, RequestStatus
 
 import vllm_ascend.envs as envs_ascend
 from vllm_ascend.utils import (AscendSocVersion, get_ascend_soc_version,
-                               prefill_context_parallel_enable)
+                               prefill_context_parallel_enable,
+                               vllm_version_is)
 
 if prefill_context_parallel_enable():
     from vllm.distributed.parallel_state import \
         get_prefill_context_model_parallel_rank
 
+if vllm_version_is("0.11.0"):
+    from vllm.utils import get_ip
+else:
+    from vllm.utils.network_utils import get_ip
+
 TORCH_DTYPE_TO_NPU_DTYPE = {
     torch.half: llm_datadist.DataType.DT_FLOAT16,
     torch.float16: llm_datadist.DataType.DT_FLOAT16,

vllm_ascend/distributed/mooncake/mooncake_engine.py

@@ -7,7 +7,7 @@ from typing import Generator, List, Optional, Union
 # Third Party
 import torch
 from vllm.config import VllmConfig
-from vllm.utils import get_kv_cache_torch_dtype, logger
+from vllm.utils import logger
 
 from vllm_ascend.distributed.mooncake.config_data import (
     ChunkedTokenDatabase, LasyerMultiBlockReqMeta, MooncakeConnectorMetadata,
@@ -16,6 +16,12 @@ from vllm_ascend.distributed.mooncake.kv_transfer import (
     KVCacheStoreLayerRecvingThread, KVCacheStoreLayerSendingThread,
     KVCacheStoreRecvingThread, KVCacheStoreSendingThread, KVTransferThread)
 from vllm_ascend.distributed.mooncake.mooncake_store import Mooncakestore
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import get_kv_cache_torch_dtype
+else:
+    from vllm.utils.torch_utils import get_kv_cache_torch_dtype
 
 
 class MooncakeEngine:

vllm_ascend/distributed/mooncake_connector.py

@@ -26,13 +26,19 @@ from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1, KVConnectorMetadata, KVConnectorRole)
 from vllm.distributed.parallel_state import (get_tensor_model_parallel_rank,
                                              get_tp_group)
-from vllm.utils import get_ip, logger, make_zmq_path, make_zmq_socket
+from vllm.utils import logger
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.request import RequestStatus
 
 import vllm_ascend.envs as envs_ascend
 from vllm_ascend.ascend_config import get_ascend_config, init_ascend_config
 from vllm_ascend.distributed.mooncake.transfer_engine import get_global_te
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import get_ip, make_zmq_path, make_zmq_socket
+else:
+    from vllm.utils.network_utils import get_ip, make_zmq_path, make_zmq_socket
 
 if TYPE_CHECKING:
     from vllm.attention.backends.abstract import AttentionMetadata

vllm_ascend/distributed/mooncake_layerwise_connector.py

@@ -26,7 +26,7 @@ from vllm.distributed.kv_transfer.kv_connector.v1.base import (
     KVConnectorBase_V1, KVConnectorMetadata, KVConnectorRole)
 from vllm.distributed.parallel_state import (get_tensor_model_parallel_rank,
                                              get_tp_group, get_world_group)
-from vllm.utils import get_ip, logger, make_zmq_path, make_zmq_socket
+from vllm.utils import logger
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.request import RequestStatus
 
@@ -34,6 +34,12 @@ import vllm_ascend.envs as envs_ascend
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.distributed.utils import (align_memory,
                                            kv_alltoall_and_rearrange)
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import get_ip, make_zmq_path, make_zmq_socket
+else:
+    from vllm.utils.network_utils import get_ip, make_zmq_path, make_zmq_socket
 
 if TYPE_CHECKING:
     from vllm.attention.backends.abstract import AttentionMetadata

vllm_ascend/model_loader/netloader/netloader.py

@@ -28,12 +28,19 @@ from vllm.model_executor.model_loader import register_model_loader
 from vllm.model_executor.model_loader.base_loader import BaseModelLoader
 from vllm.model_executor.model_loader.default_loader import DefaultModelLoader
 from vllm.model_executor.model_loader.utils import (
-    initialize_model, process_weights_after_loading, set_default_torch_dtype)
+    initialize_model, process_weights_after_loading)
+
+from vllm_ascend.utils import vllm_version_is
 
 from .interaction.elastic import ElasticServer
 from .load import elastic_load
 from .utils import find_free_port, is_valid_path_prefix
 
+if vllm_version_is("0.11.0"):
+    from vllm.model_executor.model_loader.utils import set_default_torch_dtype
+else:
+    from vllm.utils.torch_utils import set_default_torch_dtype
+
 
 @register_model_loader("netloader")
 class ModelNetLoaderElastic(BaseModelLoader):
@@ -200,7 +207,10 @@ class ModelNetLoaderElastic(BaseModelLoader):
         if model is not None and (
             (self.listen_port and self.listen_port in range(1024, 65535)) or
             (self.listen_port is None)):
+            if vllm_version_is("0.11.0"):
                 from vllm.utils import get_ip
+            else:
+                from vllm.utils.network_utils import get_ip
             driver_ip = get_ip()
 
             if driver_ip == '0.0.0.0':

vllm_ascend/models/layers/mla.py

@@ -29,7 +29,6 @@ from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.forward_context import ForwardContext, get_forward_context
 from vllm.model_executor.layers.mla import MLAModules
 from vllm.model_executor.layers.quantization import QuantizationConfig
-from vllm.utils import direct_register_custom_op
 
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.utils import vllm_version_is
@@ -38,9 +37,11 @@ if vllm_version_is("0.11.0"):
     from vllm.attention import Attention
     from vllm.model_executor.layers.mla import \
         MultiHeadLatentAttention as MultiHeadLatentAttentionWrapper
+    from vllm.utils import direct_register_custom_op
 else:
     from vllm.attention.layer import MLAAttention
     from vllm.model_executor.layers.mla import MultiHeadLatentAttentionWrapper
+    from vllm.utils.torch_utils import direct_register_custom_op
 
 if vllm_version_is("0.11.0"):
     from vllm.attention import Attention

vllm_ascend/models/layers/sfa.py

@@ -31,7 +31,6 @@ from vllm.forward_context import ForwardContext, get_forward_context
 from vllm.model_executor.layers.linear import ReplicatedLinear
 from vllm.model_executor.layers.mla import MLAModules
 from vllm.model_executor.layers.quantization import QuantizationConfig
-from vllm.utils import direct_register_custom_op
 
 from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.utils import vllm_version_is
@@ -40,9 +39,11 @@ if vllm_version_is("0.11.0"):
     from vllm.attention import Attention
     from vllm.model_executor.layers.mla import \
         MultiHeadLatentAttention as MultiHeadLatentAttentionWrapper
+    from vllm.utils import direct_register_custom_op
 else:
     from vllm.attention.layer import MLAAttention
     from vllm.model_executor.layers.mla import MultiHeadLatentAttentionWrapper
+    from vllm.utils.torch_utils import direct_register_custom_op
 
 
 @dataclass

vllm_ascend/ops/fused_moe/fused_moe.py

@@ -435,10 +435,12 @@ class AscendSharedFusedMoE(SharedFusedMoE, AscendFusedMoE):
     def __init__(
         self,
         shared_experts: torch.nn.Module,
+        gate: Optional[torch.nn.Module] = None,
         use_overlapped: bool = True,
         **kwargs,
     ):
         AscendFusedMoE.__init__(self, **kwargs)
+
         self._shared_experts = shared_experts
         self.use_overlapped = use_overlapped
         self.shared_expert_stream = None
@@ -449,6 +451,16 @@ class AscendSharedFusedMoE(SharedFusedMoE, AscendFusedMoE):
                 "Sequence parallelism is enabled, shared experts are replicated for best performance."
             )
 
+        self._gate = gate
+
+    @property
+    def gate(self) -> Optional[torch.nn.Module]:
+        return self._gate if self.use_overlapped else None
+
+    @property
+    def is_internal_router(self) -> bool:
+        return False
+
     def forward(
         self,
         hidden_states: torch.Tensor,

vllm_ascend/ops/register_custom_ops.py

@@ -7,12 +7,17 @@ from vllm.distributed import (get_dp_group, get_ep_group,
                               tensor_model_parallel_all_reduce,
                               tensor_model_parallel_reduce_scatter)
 from vllm.forward_context import get_forward_context
-from vllm.utils import direct_register_custom_op
 
 import vllm_ascend.envs as envs_ascend
 from vllm_ascend.ascend_forward_context import MoECommType
 from vllm_ascend.ops.weight_prefetch import maybe_npu_prefetch
-from vllm_ascend.utils import npu_stream_switch, prefetch_stream
+from vllm_ascend.utils import (npu_stream_switch, prefetch_stream,
+                               vllm_version_is)
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import direct_register_custom_op
+else:
+    from vllm.utils.torch_utils import direct_register_custom_op
 
 
 def _maybe_all_gather_and_maybe_unpad_impl(

vllm_ascend/patch/platform/patch_mamba_config.py

@@ -3,9 +3,16 @@ import vllm.model_executor.models.config
 from vllm.logger import init_logger
 from vllm.model_executor.models import ModelRegistry
 from vllm.model_executor.models.config import MambaModelConfig
-from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, cdiv
+from vllm.utils import cdiv
 from vllm.v1.kv_cache_interface import FullAttentionSpec, MambaSpec
 
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE
+else:
+    from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+
 
 @classmethod
 def verify_and_update_config(cls, vllm_config) -> None:

vllm_ascend/patch/platform/patch_multiproc_executor.py

@@ -8,13 +8,21 @@ import vllm.v1.executor.multiproc_executor
 from vllm import envs
 from vllm.config import VllmConfig
 from vllm.distributed.device_communicators.shm_broadcast import MessageQueue
-from vllm.utils import (get_distributed_init_method, get_loopback_ip,
+from vllm.utils import get_mp_context
-                        get_mp_context, get_open_port)
 from vllm.v1.executor.abstract import FailureCallback
 from vllm.v1.executor.multiproc_executor import (
     MultiprocExecutor, UnreadyWorkerProcHandle, WorkerProc,
     set_multiprocessing_worker_envs)
 
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import (get_distributed_init_method, get_loopback_ip,
+                            get_open_port)
+else:
+    from vllm.utils.network_utils import (get_distributed_init_method,
+                                          get_loopback_ip, get_open_port)
+
 
 class AscendMultiprocExecutor(MultiprocExecutor):
     supports_pp: bool = True

vllm_ascend/patch/worker/patch_weight_loader.py

@@ -3,7 +3,13 @@ from torch.nn.parameter import Parameter
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import UnquantizedLinearMethod
 from vllm.model_executor.utils import set_weight_attrs
-from vllm.utils import GiB_bytes
+
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("0.11.0"):
+    from vllm.utils import GiB_bytes
+else:
+    from vllm.utils.mem_constants import GiB_bytes
 
 logger = init_logger(__name__)
 

vllm_ascend/platform.py

@@ -24,6 +24,9 @@ import vllm.envs as envs_vllm
 from vllm.logger import logger
 from vllm.platforms import Platform, PlatformEnum
 
+# todo: please remove it when solve cuda hard code in vllm
+os.environ["VLLM_DISABLE_SHARED_EXPERTS_STREAM"] = "True"
+
 from vllm_ascend.ascend_config import (check_ascend_config, get_ascend_config,
                                        init_ascend_config)
 from vllm_ascend.torchair.utils import (check_torchair_cache_exist,
@@ -142,7 +145,6 @@ class NPUPlatform(Platform):
             if not model_config.is_multimodal_model and \
                 structured_outputs_config.backend == "auto" and \
                 not getattr(scheduler_config, "scheduler_delay_factor", 0) > 0 and \
-                not scheduler_config.send_delta_data and \
                 scheduler_config.policy == "fcfs":
                 ascend_scheduler_config.enabled = True
                 chunked_prefill_enabled_in_ascend_scheduler = getattr(

vllm_ascend/spec_decode/mtp_proposer.py

@@ -9,8 +9,8 @@ from vllm.config import (CUDAGraphMode, VllmConfig,
 from vllm.forward_context import BatchDescriptor, get_forward_context
 from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
 from vllm.model_executor.model_loader import get_model_loader
-from vllm.model_executor.model_loader.utils import (
+from vllm.model_executor.model_loader.utils import \
-    process_weights_after_loading, set_default_torch_dtype)
+    process_weights_after_loading
 from vllm.model_executor.models.deepseek_mtp import DeepSeekMTP
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.sample.metadata import SamplingMetadata
@@ -24,7 +24,13 @@ from vllm_ascend.torchair.models.torchair_deepseek_mtp import \
     TorchairDeepSeekMTP
 from vllm_ascend.torchair.utils import (TORCHAIR_CACHE_DIR,
                                         TorchairCommonAttentionMetadata)
-from vllm_ascend.utils import ProfileExecuteDuration, lmhead_tp_enable
+from vllm_ascend.utils import (ProfileExecuteDuration, lmhead_tp_enable,
+                               vllm_version_is)
+
+if vllm_version_is("0.11.0"):
+    from vllm.model_executor.model_loader.utils import set_default_torch_dtype
+else:
+    from vllm.utils.torch_utils import set_default_torch_dtype
 
 PADDING_SLOT_ID = -1
 

vllm_ascend/worker/model_runner_v1.py

@@ -72,8 +72,7 @@ from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingType
 from vllm.sequence import IntermediateTensors
 from vllm.tasks import GenerationTask, PoolingTask, SupportedTask
-from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler, cdiv,
+from vllm.utils import cdiv, is_pin_memory_available
-                        get_dtype_size, is_pin_memory_available)
 from vllm.utils.jsontree import json_map_leaves
 from vllm.v1.attention.backends.gdn_attn import GDNAttentionMetadataBuilder
 from vllm.v1.attention.backends.utils import (
@@ -145,6 +144,13 @@ if prefill_context_parallel_enable():
         get_prefill_context_model_parallel_rank,
         get_prefill_context_model_parallel_world_size)
 
+if vllm_version_is("0.11.0"):
+    from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
+                            get_dtype_size)
+else:
+    from vllm.utils.mem_utils import DeviceMemoryProfiler
+    from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE, get_dtype_size
+
 # yapf: enable
 
 if vllm_version_is("0.11.0"):

vllm_ascend/worker/npu_input_batch.py

@@ -44,7 +44,7 @@ from vllm_ascend.worker.block_table import MultiGroupBlockTable
 if vllm_version_is("0.11.0"):
     from vllm.utils import swap_dict_values
 else:
-    from vllm.utils.collections import swap_dict_values
+    from vllm.utils.collection_utils import swap_dict_values
 
 
 @dataclass

vllm_ascend/worker/worker_v1.py

@@ -35,7 +35,6 @@ from vllm.logger import logger
 from vllm.lora.request import LoRARequest
 from vllm.sequence import IntermediateTensors
 from vllm.tasks import SupportedTask
-from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, GiB_bytes
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.kv_cache_interface import KVCacheConfig, KVCacheSpec
 from vllm.v1.outputs import (EMPTY_MODEL_RUNNER_OUTPUT, AsyncModelRunnerOutput,
@@ -51,7 +50,7 @@ from vllm_ascend.platform import NPUPlatform
 from vllm_ascend.utils import (init_ascend_soc_version,
                                prefill_context_parallel_enable,
                                register_ascend_customop, sleep_mode_enabled,
-                               try_register_lib)
+                               try_register_lib, vllm_version_is)
 from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
 
 torch._dynamo.trace_rules.clear_lru_cache()  # noqa: E402
@@ -66,6 +65,12 @@ torch_non_c_binding_in_graph_functions_npu[
 torch._dynamo.trace_rules.torch_name_rule_map.append(
     torch_non_c_binding_in_graph_functions_npu)  # noqa: E402
 
+if vllm_version_is("0.11.0"):
+    from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, GiB_bytes
+else:
+    from vllm.utils.mem_constants import GiB_bytes
+    from vllm.utils.torch_utils import STR_DTYPE_TO_TORCH_DTYPE
+
 
 class NPUWorker(WorkerBase):