[main2main] upgrade vllm main 0202 (#6560)

### What this PR does / why we need it?
1. Fix `TypeError: FusedMoEParallelConfig.__init__() missing 1 required
positional argument: 'is_sequence_parallel'` due to
https://github.com/vllm-project/vllm/pull/32567
2. Fix ` TypeError: '>' not supported between instances of 'MagicMock'
and 'int'` due to https://github.com/vllm-project/vllm/pull/33035
3. Fix `TypeError: Can't instantiate abstract class AscendMLAImpl with
abstract methods forward_mha, forward_mqa` and AttributeError: 'bool'
object has no attribute 'process_weights_after_loading' due to
https://github.com/vllm-project/vllm/pull/33284
4. Fix `'AscendSharedFusedMoE' object has no attribute
'_routed_input_transform'`due to
https://github.com/vllm-project/vllm/pull/32790
5. Fix `NPUModelRunner._dummy_run() got an unexpected keyword argument
'num_active_loras'` due to
https://github.com/vllm-project/vllm/pull/32005
6. Fix the problem caused by` 'tuple' object has no attribute 'job_id'`
due to https://github.com/vllm-project/vllm/pull/27492
7. Fix the problem that all_moe_layers is not equal to vllm.moe_forward,
vllm.moe_forward_shared due to
https://github.com/vllm-project/vllm/pull/33184
8. Add patch to fix the problem "got multiple values for keyword
argument 'add_special_tokens'" due to
https://github.com/vllm-project/vllm/pull/32863
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.15.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.15.0

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>

vllm_ascend/attention/mla_v1.py

@@ -1450,6 +1450,28 @@ class AscendMLAImpl(MLAAttentionImpl):
     def get_num_actual_tokens(self, attn_metadata: M):
         return attn_metadata.num_actual_tokens
 
+    def forward_mha(
+        self,
+        layer_name: str,
+        hidden_states: torch.Tensor,
+        kv_cache: tuple[torch.Tensor],
+        attn_metadata: M,
+        need_gather_q_kv: bool = False,
+        output: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        raise NotImplementedError("forward_mha is not supported for MLA attention. Use forward() instead.")
+
+    def forward_mqa(
+        self,
+        layer_name: str,
+        hidden_states: torch.Tensor,
+        kv_cache: tuple[torch.Tensor],
+        attn_metadata: M,
+        need_gather_q_kv: bool = False,
+        output: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        raise NotImplementedError("forward_mqa is not supported for MLA attention. Use forward() instead.")
+
     def forward(
         self,
         layer_name,

vllm_ascend/attention/sfa_v1.py

@@ -1062,3 +1062,24 @@ class AscendSFAImpl(MLAAttentionImpl):
             torch.distributed.all_to_all_single(attn_output, send, group=get_tp_group().device_group)
 
             return attn_output, True
+
+    def forward_mha(
+        self,
+        q: torch.Tensor,
+        kv_c_normed: torch.Tensor,
+        k_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: M,
+        k_scale: torch.Tensor,
+        output: torch.Tensor,
+    ) -> None:
+        raise NotImplementedError("forward_mha is not supported for SFA attention. Use forward() instead.")
+
+    def forward_mqa(
+        self,
+        q: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: M,
+        layer,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        raise NotImplementedError("forward_mqa is not supported for SFA attention. Use forward() instead.")

vllm_ascend/compilation/npugraph_ex_passes/graphex_qknorm_rope_fusion_pass.py

@@ -18,7 +18,6 @@
 
 import torch
 import torchair
-from vllm.attention.layer import Attention
 from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.config.compilation import Range
 from vllm.logger import logger
@@ -27,6 +26,12 @@ from vllm_ascend.compilation.npugraph_ex_passes.utils.npugraph_ex_utils_check im
     check_and_register_fusion_pass,
     extra_stream_scope_check,
 )
+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
 
 
 class GraphEXQKNormRopeFusionPattern:

vllm_ascend/compilation/passes/qknorm_rope_fusion_pass.py

@@ -18,12 +18,18 @@
 import torch
 import torch._inductor.pattern_matcher as pm
 from torch._inductor.pattern_matcher import PatternMatcherPass, PatternPrettyPrinter
-from vllm.attention.layer import Attention
 from vllm.compilation.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_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
+
 
 class QKNormRopeFusionPattern:
     def __init__(self, vllm_config, head_dim, num_heads, num_kv_heads, eps=1e-6):

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

@@ -10,7 +10,6 @@ from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Optional
 
 import torch
-from vllm.attention.layer import Attention, MLAAttention
 from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.distributed.ec_transfer import get_ec_transfer, has_ec_transfer
 from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorBase_V1, KVConnectorMetadata, KVConnectorRole
@@ -27,6 +26,7 @@ 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
@@ -35,6 +35,11 @@ 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.attention.layer import Attention, MLAAttention  # type: ignore
+else:
+    from vllm.model_executor.layers.attention import Attention, MLAAttention
+
 
 @dataclass
 class ReqMeta:

vllm_ascend/kv_offload/cpu_npu.py

@@ -6,6 +6,8 @@ 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__)
 
 
@@ -153,12 +155,30 @@ class CpuNpuOffloadingHandler(OffloadingHandler):
 
     def get_finished(self) -> list[TransferResult]:
         results: list[TransferResult] = []
-        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]
+        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():
+                    results.append(
+                        TransferResult(
+                            job_id=job_id,
+                            success=True,
+                            transfer_size=None,
+                            transfer_time=None,
+                            transfer_type=None,
+                        )
+                    )
+                    finished_job_ids.append(job_id)
+                    self.events_pool.append(event)
+            for job_id in finished_job_ids:
+                del self.transfer_events[job_id]
         return results
 
     def wait(self, job_ids: set[int]) -> None:

vllm_ascend/ops/fused_moe/fused_moe.py

@@ -46,7 +46,8 @@ from vllm_ascend.ops.fused_moe.prepare_finalize import QuantType
 from vllm_ascend.utils import (AscendDeviceType, enable_sp,
                                get_ascend_device_type, maybe_trans_nz,
                                npu_stream_switch, shared_expert_dp_enabled,
-                               shared_experts_calculation_stream)
+                               shared_experts_calculation_stream,
+                               vllm_version_is)
 
 @dataclass
 class FusedMoEResult:
@@ -407,10 +408,13 @@ class AscendSharedFusedMoE(SharedFusedMoE, AscendFusedMoE):
         shared_experts: torch.nn.Module,
         gate: Optional[torch.nn.Module] = None,
         use_overlapped: bool = True,
+        routed_input_transform: Optional[torch.nn.Module] = None,
         **kwargs,
     ):
         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
         self.shared_expert_stream = None

vllm_ascend/ops/mla.py

@@ -23,7 +23,6 @@ from typing import Optional
 
 import torch
 from torch import nn
-from vllm.attention.layer import MLAAttention
 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
@@ -34,6 +33,12 @@ 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):
@@ -125,6 +130,16 @@ class AscendMultiHeadLatentAttention(MultiHeadLatentAttentionWrapper):
             o_proj=mla_modules.o_proj,
         )
 
+        original_process_weights = self.mla_attn.process_weights_after_loading
+
+        def wrapped_process_weights(act_dtype: torch.dtype):
+            from vllm_ascend.attention.sfa_v1 import AscendSFAImpl
+            if not isinstance(self.mla_attn.impl, AscendSFAImpl):
+                original_process_weights(act_dtype)
+            self.mla_attn.impl.process_weights_after_loading(act_dtype)
+
+        self.mla_attn.process_weights_after_loading = wrapped_process_weights
+
         compilation_config = get_current_vllm_config().compilation_config
         if prefix in compilation_config.static_forward_context:
             raise ValueError(f"Duplicate layer name: {prefix}")

vllm_ascend/patch/worker/__init__.py

@@ -33,3 +33,4 @@ import vllm_ascend.patch.worker.patch_qwen3_next_mtp  # noqa
 import vllm_ascend.patch.worker.patch_rejection_sampler  # noqa
 import vllm_ascend.patch.worker.patch_qwen3_next  # noqa
 import vllm_ascend.patch.worker.patch_v2_egale  # noqa
+import vllm_ascend.patch.worker.patch_huanyuan_vl  # noqa

vllm_ascend/patch/worker/patch_huanyuan_vl.py

@@ -0,0 +1,27 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+#from collections.abc import Iterable
+
+from vllm.transformers_utils.processors.hunyuan_vl import HunYuanVLProcessor
+
+_original_call = HunYuanVLProcessor.__call__
+
+def _patched_call(self, images=None, text=None, videos=None, **kwargs):
+    """Remove add_special_tokens requirement."""
+    kwargs.pop("add_special_tokens", None)
+    return _original_call(self, images=images, text=text, videos=videos, **kwargs)
+
+HunYuanVLProcessor.__call__ = _patched_call

vllm_ascend/patch/worker/patch_qwen3_next_mtp.py

@@ -1,8 +1,12 @@
 import torch
 import vllm.v1.worker.utils as utils
-from vllm.attention.layer 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/quantization/modelslim_config.py

@@ -401,7 +401,13 @@ class AscendModelSlimConfig(QuantizationConfig):
             self.packed_modules_mapping = packed_modules_model_mapping[
                 model_type]
         prefix = self.quant_prefix_mapper(model_type, prefix)
-        from vllm.attention.layer import Attention
+
+        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 prefix.startswith("language_model"):
             prefix = prefix.split('.', 1)[-1]
         if isinstance(layer, LinearBase):

vllm_ascend/spec_decode/eagle_proposer.py

@@ -41,7 +41,7 @@ from vllm_ascend.ops.rotary_embedding import update_cos_sin
 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, shared_expert_dp_enabled, lmhead_tp_enable
+from vllm_ascend.utils import enable_sp, shared_expert_dp_enabled, lmhead_tp_enable, vllm_version_is
 
 # Currently we will fix block size to a small one since `num_reqs` can't be too large
 _PREPARE_INPUTS_BLOCK_SIZE = 4
@@ -400,6 +400,12 @@ 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:
+                    forward_context.moe_layer_index = 0
+
             self._runnable(
                 num_input_tokens=num_tokens,
                 batch_size=batch_size,
@@ -559,6 +565,12 @@ 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:
+                    forward_context.moe_layer_index = 0
+
             draft_token_ids = self._runnable(
                 num_input_tokens=num_input_tokens,
                 batch_size=batch_size,
@@ -660,6 +672,12 @@ 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:
+                    forward_context.moe_layer_index = 0
+            
             # Update the inputs.
             # cast to int32 is crucial when eagle model is compiled.
             # tensor.argmax() returns int64 by default.

vllm_ascend/spec_decode/mtp_proposer.py

@@ -18,7 +18,7 @@ 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
 from vllm_ascend.spec_decode.eagle_proposer import EagleProposer
-from vllm_ascend.utils import lmhead_tp_enable
+from vllm_ascend.utils import lmhead_tp_enable, vllm_version_is
 
 
 class MtpProposer(EagleProposer):
@@ -122,6 +122,11 @@ class MtpProposer(EagleProposer):
                     batch_descriptor=batch_descriptor,
                     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:
+                        forward_context.moe_layer_index = 0
                 previous_hidden_states, positions = self.maybe_pad_and_reduce(
                     previous_hidden_states, positions)
                 self.model(input_ids=input_ids,
@@ -330,6 +335,13 @@ class MtpProposer(EagleProposer):
                     batch_descriptor=batch_descriptor,
                     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:
+                        forward_context.moe_layer_index = 0
+
                 with record_function_or_nullcontext('mtp_forward'):
                     model_kwargs = {}
                     model_kwargs["attn_metadata"] = attn_metadata

vllm_ascend/worker/model_runner_v1.py

@@ -30,7 +30,6 @@ import numpy as np
 import torch
 import torch.distributed as dist
 import torch.nn as nn
-from vllm.attention.layer import Attention, MLAAttention
 from vllm.compilation.cuda_graph import CUDAGraphStat
 from vllm.config import CompilationMode, CUDAGraphMode, VllmConfig, get_layers_from_vllm_config
 from vllm.distributed import get_tensor_model_parallel_world_size, tensor_model_parallel_all_gather
@@ -137,6 +136,12 @@ 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.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
@@ -2026,6 +2031,7 @@ class NPUModelRunner(GPUModelRunner):
         remove_lora: bool = True,
         activate_lora: bool = False,
         is_graph_capturing: bool = False,
+        num_active_loras: int = 0,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         # only support eager mode and piecewise graph now
         assert cudagraph_runtime_mode is None or cudagraph_runtime_mode.valid_runtime_modes()