[Main2Main] Upgrade vLLM to 0226 (#6813)

### What this PR does / why we need it?

Breaking:
1. https://github.com/vllm-project/vllm/pull/33452
2. https://github.com/vllm-project/vllm/pull/33451
3. https://github.com/vllm-project/vllm/pull/32567
4. https://github.com/vllm-project/vllm/pull/32344

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main:
83b47f67b1

---------

Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: gcanlin <canlinguosdu@gmail.com>
Co-authored-by: MrZ20 <2609716663@qq.com>

.github/workflows/_e2e_nightly_multi_node.yaml

@@ -32,7 +32,7 @@ on:
         description: how many pods will be pulled up via lws.yaml, indicates number of nodes we need
       vllm_version:
         required: false
-        default: "v0.15.0"
+        default: "v0.16.0"
         type: string
         description: vllm version to use
       vllm_ascend_remote_url:

.github/workflows/bot_pr_create.yaml

@@ -37,7 +37,7 @@ jobs:
     steps:
       - name: Get vLLM version
         run: |
-          VLLM_COMMIT=83b47f67b1dfad505606070ae4d9f83e50ad4ebd
+          VLLM_COMMIT=15d76f74e2fdb12a95ea00f0ca283acf6219a2b7
           echo "VLLM_COMMIT=https://github.com/vllm-project/vllm/commit/$VLLM_COMMIT" >> "$GITHUB_ENV"
 
       - name: Checkout repository

.github/workflows/dockerfiles/Dockerfile.lint

@@ -27,7 +27,7 @@ RUN apt-get update -y && \
 
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
 # For lint purpose, actually we need make a main2main matching.
-ARG VLLM_COMMIT=83b47f67b1dfad505606070ae4d9f83e50ad4ebd
+ARG VLLM_COMMIT=15d76f74e2fdb12a95ea00f0ca283acf6219a2b7
 RUN git clone $VLLM_REPO /vllm-workspace/vllm && \
     cd /vllm-workspace/vllm && \
     git checkout $VLLM_COMMIT

.github/workflows/pr_test_full.yaml

@@ -75,7 +75,7 @@ jobs:
     name: e2e-full
     strategy:
       matrix:
-        vllm_version: [83b47f67b1dfad505606070ae4d9f83e50ad4ebd, v0.15.0]
+        vllm_version: [15d76f74e2fdb12a95ea00f0ca283acf6219a2b7, v0.16.0]
     needs: [changes]
     if: ${{ needs.changes.outputs.e2e_tracker == 'true' || needs.changes.outputs.e2e_tracker == true }}
     uses: ./.github/workflows/_e2e_test.yaml

.github/workflows/pr_test_light.yaml

@@ -41,7 +41,7 @@ jobs:
   lint:
     uses: ./.github/workflows/_pre_commit.yml
     with:
-      vllm: 83b47f67b1dfad505606070ae4d9f83e50ad4ebd
+      vllm: 15d76f74e2fdb12a95ea00f0ca283acf6219a2b7
   changes:
     runs-on: linux-aarch64-a2b3-0
     outputs:
@@ -87,7 +87,7 @@ jobs:
     if: ${{ needs.lint.result == 'success' && (needs.changes.outputs.e2e_tracker == 'true' || needs.changes.outputs.ut_tracker == 'true') }}
     strategy:
       matrix:
-        vllm_version: [83b47f67b1dfad505606070ae4d9f83e50ad4ebd, v0.15.0]
+        vllm_version: [15d76f74e2fdb12a95ea00f0ca283acf6219a2b7, v0.16.0]
     uses: ./.github/workflows/_unit_test.yaml
     with:
       vllm: ${{ matrix.vllm_version }}
@@ -99,7 +99,7 @@ jobs:
     name: e2e-light
     strategy:
       matrix:
-        vllm_version: [83b47f67b1dfad505606070ae4d9f83e50ad4ebd, v0.15.0]
+        vllm_version: [15d76f74e2fdb12a95ea00f0ca283acf6219a2b7, v0.16.0]
     # 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/schedule_codecov_refresh.yaml

@@ -33,7 +33,7 @@ jobs:
     name: refresh codecov
     strategy:
       matrix:
-        vllm_version: [83b47f67b1dfad505606070ae4d9f83e50ad4ebd]
+        vllm_version: [15d76f74e2fdb12a95ea00f0ca283acf6219a2b7]
     uses: ./.github/workflows/_unit_test.yaml
     with:
       vllm: ${{ matrix.vllm_version }}

.github/workflows/schedule_nightly_test_a2.yaml

@@ -133,7 +133,7 @@ jobs:
               - Qwen3-Omni-30B-A3B-Instruct
     uses: ./.github/workflows/_e2e_nightly_single_node_models.yaml
     with:
-      vllm: v0.15.0
+      vllm: v0.16.0
       runner: ${{ matrix.test_config.os }}
       model_list: ${{ toJson(matrix.test_config.model_list) }}
       image: 'swr.cn-southwest-2.myhuaweicloud.com/base_image/ascend-ci/cann:8.5.0-910b-ubuntu22.04-py3.11'

.github/workflows/schedule_test_benchmarks.yaml

@@ -51,7 +51,7 @@ jobs:
     strategy:
       matrix:
         include:
-          - vllm_branch: v0.15.0
+          - vllm_branch: v0.16.0
             vllm_ascend_branch: main
       max-parallel: 1
     container:

Dockerfile

@@ -50,7 +50,7 @@ RUN pip config set global.index-url ${PIP_INDEX_URL}
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.15.0
+ARG VLLM_TAG=v0.16.0
 RUN git clone --depth 1 $VLLM_REPO --branch $VLLM_TAG /vllm-workspace/vllm
 # In x86, triton will be installed by vllm. But in Ascend, triton doesn't work correctly. we need to uninstall it.
 RUN VLLM_TARGET_DEVICE="empty" python3 -m pip install -v -e /vllm-workspace/vllm/[audio] --extra-index https://download.pytorch.org/whl/cpu/ && \

Dockerfile.310p

@@ -40,7 +40,7 @@ RUN pip config set global.index-url ${PIP_INDEX_URL}
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.15.0
+ARG VLLM_TAG=v0.16.0
 RUN git clone --depth 1 $VLLM_REPO --branch $VLLM_TAG /vllm-workspace/vllm
 # In x86, triton will be installed by vllm. But in Ascend, triton doesn't work correctly. we need to uninstall it.
 RUN VLLM_TARGET_DEVICE="empty" python3 -m pip install -v -e /vllm-workspace/vllm/[audio] --extra-index https://download.pytorch.org/whl/cpu/ && \

Dockerfile.310p.openEuler

@@ -36,7 +36,7 @@ COPY . /vllm-workspace/vllm-ascend/
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.15.0
+ARG VLLM_TAG=v0.16.0
 RUN git clone --depth 1 $VLLM_REPO --branch $VLLM_TAG /vllm-workspace/vllm
 # In x86, triton will be installed by vllm. But in Ascend, triton doesn't work correctly. we need to uninstall it.
 RUN VLLM_TARGET_DEVICE="empty" python3 -m pip install -e /vllm-workspace/vllm/[audio] --extra-index https://download.pytorch.org/whl/cpu/ && \

Dockerfile.a3

@@ -49,7 +49,7 @@ RUN apt-get update -y && \
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.15.0
+ARG VLLM_TAG=v0.16.0
 RUN git clone --depth 1 $VLLM_REPO --branch $VLLM_TAG /vllm-workspace/vllm
 # In x86, triton will be installed by vllm. But in Ascend, triton doesn't work correctly. we need to uninstall it.
 RUN VLLM_TARGET_DEVICE="empty" python3 -m pip install -v -e /vllm-workspace/vllm/[audio] --extra-index https://download.pytorch.org/whl/cpu/ && \

Dockerfile.a3.openEuler

@@ -50,7 +50,7 @@ RUN yum update -y && \
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.15.0
+ARG VLLM_TAG=v0.16.0
 RUN git clone --depth 1 $VLLM_REPO --branch $VLLM_TAG /vllm-workspace/vllm
 # In x86, triton will be installed by vllm. But in Ascend, triton doesn't work correctly. we need to uninstall it.
 RUN VLLM_TARGET_DEVICE="empty" python3 -m pip install -e /vllm-workspace/vllm/[audio] --extra-index https://download.pytorch.org/whl/cpu/ && \

Dockerfile.openEuler

@@ -50,7 +50,7 @@ RUN yum update -y && \
 
 # Install vLLM
 ARG VLLM_REPO=https://github.com/vllm-project/vllm.git
-ARG VLLM_TAG=v0.15.0
+ARG VLLM_TAG=v0.16.0
 RUN git clone --depth 1 $VLLM_REPO --branch $VLLM_TAG /vllm-workspace/vllm
 # In x86, triton will be installed by vllm. But in Ascend, triton doesn't work correctly. we need to uninstall it.
 RUN VLLM_TARGET_DEVICE="empty" python3 -m pip install -e /vllm-workspace/vllm/[audio] --extra-index https://download.pytorch.org/whl/cpu/ && \

docs/source/community/versioning_policy.md

@@ -57,7 +57,7 @@ For main branch of vLLM Ascend, we usually make it compatible with the latest vL
 
 | vLLM Ascend | vLLM         | Python           | Stable CANN | PyTorch/torch_npu  |
 |-------------|--------------|------------------|-------------|--------------------|
-|     main    | 83b47f67b1dfad505606070ae4d9f83e50ad4ebd, v0.15.0 tag | >= 3.10, < 3.12   | 8.5.0 | 2.9.0 / 2.9.0 |
+|     main    | 4572a06afe96d0a6d5d3efacf130c71505dd2bc9, v0.16.0 tag | >= 3.10, < 3.12   | 8.5.0 | 2.9.0 / 2.9.0 |
 
 ## Release cadence
 

docs/source/conf.py

@@ -77,7 +77,7 @@ myst_substitutions = {
     # CANN image tag
     "cann_image_tag": "8.5.0-910b-ubuntu22.04-py3.11",
     # vllm version in ci
-    "ci_vllm_version": "v0.15.0",
+    "ci_vllm_version": "v0.16.0",
 }
 
 # For cross-file header anchors

tests/e2e/singlecard/compile/backend.py

@@ -20,15 +20,10 @@ from typing import Any
 
 import torch.fx as fx
 from torch._inductor.decomposition import select_decomp_table
+from vllm.compilation.passes.fx_utils import OpOverload
 from vllm.config import get_current_vllm_config
 
 from vllm_ascend.compilation.compiler_interface import compile_fx
-from vllm_ascend.utils import vllm_version_is
-
-if vllm_version_is("0.15.0"):
-    from vllm.compilation.fx_utils import OpOverload  # type: ignore
-else:
-    from vllm.compilation.passes.fx_utils import OpOverload
 
 
 class TestBackend:

tests/e2e/singlecard/compile/test_norm_quant_fusion.py

@@ -19,6 +19,7 @@ import pytest
 import torch
 import torch.nn as nn
 import vllm.config
+from vllm.compilation.passes.fx_utils import OpOverload
 from vllm.config import ModelConfig, VllmConfig
 from vllm.distributed import ensure_model_parallel_initialized, init_distributed_environment
 from vllm.utils.system_utils import update_environment_variables
@@ -27,13 +28,7 @@ import vllm_ascend.ops.register_custom_ops  # noqa
 from tests.e2e.singlecard.compile.backend import TestBackend
 from vllm_ascend.ascend_forward_context import set_ascend_forward_context
 from vllm_ascend.compilation.passes.norm_quant_fusion_pass import AddRMSNormQuantFusionPass
-from vllm_ascend.utils import enable_custom_op, vllm_version_is
+from vllm_ascend.utils import enable_custom_op
-
-if vllm_version_is("0.15.0"):
-    from vllm.compilation.fx_utils import OpOverload  # type: ignore
-else:
-    from vllm.compilation.passes.fx_utils import OpOverload
-
 
 # Cache backend to avoid duplicate pattern registration
 _backend_cache = None

tests/ut/eplb/core/test_eplb_utils.py

@@ -22,9 +22,9 @@ class TestAscendConfig(unittest.TestCase):
             "eplb_config": {"dynamic_eplb": True, "num_redundant_experts": 2},
         }
         from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
-        if vllm_version_is("0.15.0"):
+        if vllm_version_is("0.16.0"):
             moe_parallel_config = FusedMoEParallelConfig(
-                2, 0, 1, 2, 1, 1, 1, 1, True, "hccl", enable_eplb=True)
+                2, 0, 1, 2, 1, 1, 1, 1, True, "hccl", is_sequence_parallel=True, enable_eplb=True)
             moe_config = FusedMoEConfig(
                 num_experts=8,
                 experts_per_token=8,

tests/ut/quantization/test_modelslim_config.py

@@ -15,10 +15,7 @@ from vllm_ascend.quantization.modelslim_config import (
 )
 from vllm_ascend.utils import ASCEND_QUANTIZATION_METHOD, vllm_version_is
 
-if vllm_version_is("v0.15.0"):
+from vllm.model_executor.layers.attention import Attention
-    from vllm.attention.layer import Attention  # type: ignore
-else:
-    from vllm.model_executor.layers.attention import Attention
 
 
 class TestAscendModelSlimConfig(TestBase):

vllm_ascend/ascend_forward_context.py

@@ -19,7 +19,6 @@ from vllm_ascend.utils import (
     is_drafter_moe_model,
     is_moe_model,
     speculative_enable_dispatch_gmm_combine_decode,
-    vllm_version_is,
 )
 
 
@@ -152,10 +151,6 @@ def set_ascend_forward_context(
                 mc2_mask[:num_actual_tokens] = True
                 mc2_mask[num_actual_tokens:] = False
                 forward_context.mc2_mask = mc2_mask
-
-        if is_draft_model and vllm_version_is("0.15.0"):
-            forward_context.remaining_moe_layers = None
-
         try:
             yield
         finally:

vllm_ascend/compilation/graph_fusion_pass_manager.py

@@ -17,17 +17,10 @@
 #
 
 from torch import fx as fx
+from vllm.compilation.passes.inductor_pass import get_pass_context
+from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
 from vllm.config import VllmConfig
 
-from vllm_ascend.utils import vllm_version_is
-
-if vllm_version_is("0.15.0"):
-    from vllm.compilation.inductor_pass import get_pass_context  # type: ignore
-    from vllm.compilation.vllm_inductor_pass import VllmInductorPass  # type: ignore
-else:
-    from vllm.compilation.passes.inductor_pass import get_pass_context
-    from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
-
 
 class GraphFusionPassManager:
     """

vllm_ascend/compilation/passes/allreduce_rmsnorm_fusion_pass.py

@@ -16,6 +16,8 @@
 #
 import torch
 from torch._inductor.pattern_matcher import Match, PatternMatcherPass, PatternPrettyPrinter
+from vllm.compilation.passes.inductor_pass import get_pass_context
+from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
 from vllm.config import VllmConfig
 from vllm.config.compilation import Range
 from vllm.distributed import get_tensor_model_parallel_world_size, tensor_model_parallel_all_reduce
@@ -24,14 +26,6 @@ from vllm.logger import logger
 
 from vllm_ascend.compilation.passes.base_pattern import BasePattern
 from vllm_ascend.compilation.passes.utils.npugraph_ex_utils_check import extra_stream_scope_check
-from vllm_ascend.utils import vllm_version_is
-
-if vllm_version_is("0.15.0"):
-    from vllm.compilation.inductor_pass import get_pass_context  # type: ignore
-    from vllm.compilation.vllm_inductor_pass import VllmInductorPass  # type: ignore
-else:
-    from vllm.compilation.passes.inductor_pass import get_pass_context
-    from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
 
 # computation-communication tiling block is 512
 ALLREDUCE_NORM_FUSE_THRESHOLD = 512

vllm_ascend/compilation/passes/norm_quant_fusion_pass.py

@@ -17,17 +17,13 @@
 #
 import torch
 from torch._inductor.pattern_matcher import PatternMatcherPass
+from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
 from vllm.config import VllmConfig
 from vllm.config.compilation import Range
 from vllm.logger import logger
 
 from vllm_ascend.compilation.passes.base_pattern import BasePattern
-from vllm_ascend.utils import enable_custom_op, vllm_version_is
+from vllm_ascend.utils import enable_custom_op
-
-if vllm_version_is("0.15.0"):
-    from vllm.compilation.vllm_inductor_pass import VllmInductorPass  # type: ignore
-else:
-    from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
 
 
 class AddRMSNormQuantPattern(BasePattern):

vllm_ascend/compilation/passes/qknorm_rope_fusion_pass.py

@@ -17,19 +17,13 @@
 #
 import torch
 from torch._inductor.pattern_matcher import PatternMatcherPass, PatternPrettyPrinter
+from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
 from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.config.compilation import Range
 from vllm.logger import logger
+from vllm.model_executor.layers.attention import Attention
 
 from vllm_ascend.compilation.passes.base_pattern import BasePattern
-from vllm_ascend.utils import vllm_version_is
-
-if vllm_version_is("v0.15.0"):
-    from vllm.attention.layer import Attention  # type: ignore
-    from vllm.compilation.vllm_inductor_pass import VllmInductorPass  # type: ignore
-else:
-    from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
-    from vllm.model_executor.layers.attention import Attention
 
 
 class QKNormRopeFusionPattern(BasePattern):

vllm_ascend/distributed/kv_transfer/kv_pool/cpu_offload/cpu_offload_connector.py

@@ -26,7 +26,6 @@ from vllm_ascend.distributed.kv_transfer.kv_pool.cpu_offload.metadata import (
     MetadataServerProc,
     MLAConfig,
 )
-from vllm_ascend.utils import vllm_version_is
 
 if TYPE_CHECKING:
     from vllm.forward_context import ForwardContext
@@ -35,10 +34,7 @@ if TYPE_CHECKING:
     from vllm.v1.kv_cache_interface import KVCacheConfig
     from vllm.v1.request import Request
 
-if vllm_version_is("v0.15.0"):
+from vllm.model_executor.layers.attention import Attention, MLAAttention
-    from vllm.attention.layer import Attention, MLAAttention  # type: ignore
-else:
-    from vllm.model_executor.layers.attention import Attention, MLAAttention
 
 
 @dataclass

vllm_ascend/kv_offload/cpu_npu.py

@@ -6,8 +6,6 @@ from vllm.v1.attention.backend import AttentionBackend  # type: ignore
 from vllm.v1.kv_offload.mediums import CPULoadStoreSpec, GPULoadStoreSpec
 from vllm.v1.kv_offload.worker.worker import OffloadingHandler, TransferResult, TransferSpec
 
-from vllm_ascend.utils import vllm_version_is
-
 logger = init_logger(__name__)
 
 
@@ -155,14 +153,6 @@ class CpuNpuOffloadingHandler(OffloadingHandler):
 
     def get_finished(self) -> list[TransferResult]:
         results: list[TransferResult] = []
-        if vllm_version_is("v0.15.0"):
-            for job_id, event in self.transfer_events.items():
-                if event.query():
-                    results.append((job_id, True))
-                    self.events_pool.append(event)
-            for job_id, _ in results:
-                del self.transfer_events[job_id]
-        else:
         finished_job_ids = []
         for job_id, event in self.transfer_events.items():
             if event.query():

vllm_ascend/ops/fused_moe/fused_moe.py

@@ -31,7 +31,7 @@ from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
 
 from vllm_ascend.utils import vllm_version_is
 
-if not vllm_version_is("0.15.0"):
+if not vllm_version_is("0.16.0"):
     from vllm.model_executor.layers.fused_moe.fused_moe_method_base import FusedMoEMethodBase  # type: ignore
     from vllm.model_executor.layers.fused_moe.router.fused_moe_router import FusedMoERouter  # type: ignore
     from vllm.model_executor.layers.fused_moe.runner.default_moe_runner import DefaultMoERunner  # type: ignore
@@ -169,7 +169,7 @@ class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
         return final_hidden_states
 
 
-if not vllm_version_is("0.15.0"):
+if not vllm_version_is("0.16.0"):
     # Please remove this inheritance after extending vllm, todo(wxs)
     class AscendMoERunner(DefaultMoERunner):
         """
@@ -323,10 +323,10 @@ class AscendFusedMoE(FusedMoE):
 
         setup_moe_comm_method(self.moe_config)
         self.quant_type = self._get_quant_type()
-        if not vllm_version_is("0.15.0"):
+        if not vllm_version_is("0.16.0"):
             self.runner = self._init_runner()
 
-    if not vllm_version_is("0.15.0"):
+    if not vllm_version_is("0.16.0"):
 
         def _init_runner(self):
             # Storing the runner in the FusedMoE is an intermediate state, eventually
@@ -372,7 +372,7 @@ class AscendFusedMoE(FusedMoE):
         """
         return torch.ops.vllm.maybe_all_reduce_tensor_model_parallel(final_hidden_states)
 
-    if not vllm_version_is("0.15.0"):
+    if not vllm_version_is("0.16.0"):
 
         def forward(
             self,
@@ -519,7 +519,6 @@ class AscendSharedFusedMoE(SharedFusedMoE, AscendFusedMoE):
     ):
         AscendFusedMoE.__init__(self, **kwargs)
 
-        if not vllm_version_is("0.15.0"):
         self._routed_input_transform = routed_input_transform
         self._shared_experts = shared_experts
         self.use_overlapped = use_overlapped
@@ -533,7 +532,7 @@ class AscendSharedFusedMoE(SharedFusedMoE, AscendFusedMoE):
             logger.info_once("Sequence parallelism is enabled, shared experts are replicated for best performance.")
 
         self._gate = gate
-        if not vllm_version_is("0.15.0"):
+        if not vllm_version_is("0.16.0"):
             # Recreate the runner with the correct shared_experts parameter
             # The parent class created the runner before self._shared_experts was set
             self.runner = self._init_runner()

vllm_ascend/ops/mla.py

@@ -25,18 +25,13 @@ from torch import nn
 from vllm.config import CacheConfig, get_current_vllm_config
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.forward_context import ForwardContext, get_forward_context
+from vllm.model_executor.layers.attention import MLAAttention
 from vllm.model_executor.layers.mla import MLAModules, MultiHeadLatentAttentionWrapper
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.utils.torch_utils import direct_register_custom_op
 from vllm.v1.attention.backend import AttentionMetadata  # type: ignore
 
 from vllm_ascend.ascend_config import get_ascend_config
-from vllm_ascend.utils import vllm_version_is
-
-if vllm_version_is("v0.15.0"):
-    from vllm.attention.layer import MLAAttention  # type: ignore
-else:
-    from vllm.model_executor.layers.attention import MLAAttention
 
 
 class IndexerWrapper(nn.Module):
@@ -126,7 +121,6 @@ class AscendMultiHeadLatentAttention(MultiHeadLatentAttentionWrapper):
             o_proj=mla_modules.o_proj,
         )
 
-        if not vllm_version_is("v0.15.0"):
         original_process_weights = self.mla_attn.process_weights_after_loading
 
         def wrapped_process_weights(act_dtype: torch.dtype):

vllm_ascend/patch/platform/__init__.py

@@ -19,11 +19,6 @@ import os
 import vllm_ascend.patch.platform.patch_distributed  # noqa
 import vllm_ascend.patch.platform.patch_mamba_config  # noqa
 import vllm_ascend.patch.platform.patch_sched_yield  # noqa
-from vllm_ascend import envs
-from vllm_ascend.utils import vllm_version_is
 
 if os.getenv("DYNAMIC_EPLB", "false").lower() in ("true", "1") or os.getenv("EXPERT_MAP_RECORD", "false") == "true":
     import vllm_ascend.patch.platform.patch_multiproc_executor  # noqa
-
-if envs.VLLM_ASCEND_BALANCE_SCHEDULING and vllm_version_is("0.15.0"):
-    import vllm_ascend.patch.platform.patch_balance_schedule  # noqa

vllm_ascend/patch/worker/patch_qwen3_next_mtp.py

@@ -1,14 +1,8 @@
 import torch
 import vllm.v1.worker.utils as utils
+from vllm.model_executor.layers.attention import Attention
 from vllm.v1.worker.utils import defaultdict, extract_layer_index
 
-from vllm_ascend.utils import vllm_version_is
-
-if vllm_version_is("v0.15.0"):
-    from vllm.attention.layer import Attention  # type: ignore
-else:
-    from vllm.model_executor.layers.attention import Attention
-
 
 # Without this patch, it will raise an exception when initialize kv_cache.
 # TODO To remove the patch, we need check why the original bind_kv_cache raises an NotImplementedError.

vllm_ascend/patch/worker/patch_v2_eagle.py

@@ -21,7 +21,14 @@ import vllm
 from vllm.v1.worker.gpu.attn_utils import build_slot_mappings_by_layer
 from vllm.v1.worker.gpu.input_batch import InputBatch
 from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample
-from vllm.v1.worker.gpu.spec_decode.eagle import prepare_eagle_decode, prepare_eagle_inputs
+
+from vllm_ascend.utils import vllm_version_is
+
+if vllm_version_is("v0.16.0"):
+    from vllm.v1.worker.gpu.spec_decode.eagle import prepare_eagle_decode, prepare_eagle_inputs
+else:
+    from vllm.v1.worker.gpu.spec_decode.eagle.speculator import prepare_eagle_decode, prepare_eagle_inputs
+
 
 from vllm_ascend.worker.v2.attn_utils import build_attn_metadata
 
@@ -168,4 +175,7 @@ def propose(
     return self.draft_tokens[:num_reqs]
 
 
-vllm.v1.worker.gpu.spec_decode.eagle.EagleSpeculator.propose = propose
+if vllm_version_is("v0.16.0"):
+    vllm.v1.worker.gpu.spec_decode.eagle.EagleSpeculator.propose = propose
+else:
+    vllm.v1.worker.gpu.spec_decode.eagle.speculator.EagleSpeculator.propose = propose

vllm_ascend/platform.py

@@ -468,7 +468,7 @@ class NPUPlatform(Platform):
         _CUSTOM_OP_REGISTERED = True
 
     @classmethod
-    def get_attn_backend_cls(cls, selected_backend, attn_selector_config):
+    def get_attn_backend_cls(cls, selected_backend, attn_selector_config, num_heads: int | None = None):
         key = (attn_selector_config.use_mla, attn_selector_config.use_sparse)
 
         backend_map = {

vllm_ascend/quantization/method_adapters.py

@@ -117,6 +117,18 @@ class AscendLinearMethod(LinearMethodBase):
         if hasattr(self.quant_method, "process_weights_after_loading"):
             self.quant_method.process_weights_after_loading(layer)
 
+    def get_computed_params(self) -> set[str]:
+        """Return parameter name patterns that are computed, not loaded.
+
+        These parameters are computed during process_weights_after_loading
+        rather than loaded from checkpoint:
+        - weight_offset: Zero for symmetric quantization
+        - quant_bias: Computed from weight statistics
+        - deq_scale: Computed as input_scale * weight_scale
+        - weight_scale: May be computed or have default values for some models
+        """
+        return {"weight_offset", "quant_bias", "deq_scale", "weight_scale"}
+
     def apply(
         self,
         layer: torch.nn.Module,

vllm_ascend/quantization/modelslim_config.py

@@ -401,11 +401,6 @@ class AscendModelSlimConfig(QuantizationConfig):
             self.packed_modules_mapping = packed_modules_model_mapping[model_type]
         prefix = self.quant_prefix_mapper(model_type, prefix)
 
-        from vllm_ascend.utils import vllm_version_is
-
-        if vllm_version_is("v0.15.0"):
-            from vllm.attention.layer import Attention  # type: ignore
-        else:
         from vllm.model_executor.layers.attention import Attention
 
         if model_type != "kimi_k2":

vllm_ascend/spec_decode/eagle_proposer.py

@@ -41,7 +41,7 @@ from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
 from vllm_ascend.compilation.acl_graph import ACLGraphWrapper, update_full_graph_params
 from vllm_ascend.ops.triton.spec_decode.utils import prepare_inputs_padded_kernel
 from vllm_ascend.ops.triton.triton_utils import get_vectorcore_num
-from vllm_ascend.utils import enable_sp, lmhead_tp_enable, shared_expert_dp_enabled, vllm_version_is
+from vllm_ascend.utils import enable_sp, lmhead_tp_enable, shared_expert_dp_enabled
 
 # Currently we will fix block size to a small one since `num_reqs` can't be too large
 _PREPARE_INPUTS_BLOCK_SIZE = 4
@@ -357,7 +357,6 @@ class EagleProposer(VllmEagleProposer):
             is_draft_model=True,
             draft_attn_metadatas=multi_steps_attn_metadata,
         ):
-            if not vllm_version_is("v0.15.0"):
             # Reset MOE layer index before first model call
             forward_context = get_forward_context()
             if forward_context is not None:
@@ -522,7 +521,6 @@ class EagleProposer(VllmEagleProposer):
             is_draft_model=True,
             draft_attn_metadatas=multi_steps_attn_metadata,
         ):
-            if not vllm_version_is("v0.15.0"):
             # Reset MOE layer index for forward pass
             forward_context = get_forward_context()
             if forward_context is not None:
@@ -617,7 +615,6 @@ class EagleProposer(VllmEagleProposer):
         forward_context.num_accept_tokens = batch_size
 
         for draft_step in range(self.num_speculative_tokens - 1):
-            if not vllm_version_is("v0.15.0"):
             # Reset MOE layer index for each draft step iteration
             forward_context = get_forward_context()
             if forward_context is not None:

vllm_ascend/spec_decode/mtp_proposer.py

@@ -16,7 +16,7 @@ from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
 from vllm_ascend.compilation.acl_graph import ACLGraphWrapper
 from vllm_ascend.ops.rotary_embedding import get_cos_and_sin_mla, update_cos_sin
 from vllm_ascend.spec_decode.eagle_proposer import EagleProposer
-from vllm_ascend.utils import lmhead_tp_enable, vllm_version_is
+from vllm_ascend.utils import lmhead_tp_enable
 
 
 class MtpProposer(EagleProposer):
@@ -130,7 +130,6 @@ class MtpProposer(EagleProposer):
                 is_draft_model=True,
                 in_profile_run=is_profile,
             ):
-                if not vllm_version_is("v0.15.0"):
                 # Reset MOE layer index for each MTP step iteration
                 forward_context = get_forward_context()
                 if forward_context is not None:
@@ -341,7 +340,6 @@ class MtpProposer(EagleProposer):
                 num_actual_tokens=num_tokens,
                 is_draft_model=True,
             ):
-                if not vllm_version_is("v0.15.0"):
                 # Reset MOE layer index for each MTP step to match all_moe_layers registration
                 forward_context = get_forward_context()
                 if forward_context is not None:

vllm_ascend/utils.py

@@ -525,11 +525,6 @@ def update_aclgraph_sizes(vllm_config: VllmConfig) -> None:
             "increase the number of supported shapes, set HCCL_OP_EXPANSION_MODE=AIV."
         )
 
-    from vllm_ascend.utils import vllm_version_is
-
-    if vllm_version_is("0.15.0"):
-        arch_name = vllm_config.model_config.architectures[0]
-    else:
     arch_name = vllm_config.model_config.architecture
 
     # If original sizes exceed maximum, sample a representative subset

vllm_ascend/worker/model_runner_v1.py

@@ -138,12 +138,8 @@ if TYPE_CHECKING:
 else:
     xgr = LazyLoader("xgr", globals(), "xgrammar")
 
-from vllm_ascend.utils import vllm_version_is
 
-if vllm_version_is("v0.15.0"):
+from vllm.model_executor.layers.attention import Attention, MLAAttention
-    from vllm.attention.layer import Attention, MLAAttention  # type: ignore
-else:
-    from vllm.model_executor.layers.attention import Attention, MLAAttention
 
 # if true, allow tensor initialization and casting with internal format (e.g., NZ)
 torch.npu.config.allow_internal_format = True

vllm_ascend/worker/worker.py

@@ -531,6 +531,9 @@ class NPUWorker(WorkerBase):
     def pin_lora(self, lora_id: int) -> bool:
         return self.model_runner.pin_lora(lora_id)
 
+    def reset_encoder_cache(self) -> None:
+        self.model_runner.reset_encoder_cache()
+
     def execute_dummy_batch(self) -> None:
         self.model_runner._dummy_run(num_tokens=self.model_runner.decode_token_per_req, uniform_decode=True)