[Main2Main][Deps][Misc] Upgrade vLLM to v0.15.0 (#6470)

### What this PR does / why we need it?
This PR upgrades the vLLM dependency from `v0.14.1` to `v0.15.0`. This
involves:
- Updating the `VLLM_TAG` in all `Dockerfile`.
- Updating the vLLM version in `docs/source/conf.py`.
- Removing conditional code paths specific to `v0.14.1` across the
codebase, which simplifies maintenance.
- Fix `TypeError: MMEncoderAttention.__init__() got an unexpected
keyword argument 'multimodal_config'` due to
https://github.com/vllm-project/vllm/pull/31972.
- Fix `_shared_experts: 'NoneType' object is not callable` due to
https://github.com/vllm-project/vllm/pull/32082 by
https://github.com/vllm-project/vllm-ascend/pull/6335.
- Fix `ReshapeAndCacheOperation setup failed!` due to
https://github.com/vllm-project/vllm/pull/25954 by overriding attention
metadata slots.

This upgrade is necessary to keep the project aligned with the latest
features, bug fixes, and API changes in the vLLM project.

### Does this PR introduce _any_ user-facing change?
No, this is an internal dependency update and does not introduce any
user-facing changes.

### How was this patch tested?
CI is expected to pass with these changes, ensuring that all existing
tests are successful with the new vLLM version.

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8


co-authored-by: shen-shanshan <467638484@qq.com>

---------

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

tests/ut/eplb/core/test_eplb_utils.py

@@ -9,7 +9,6 @@ from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig, FusedMoE
 
 from vllm_ascend.ascend_config import init_ascend_config
 from vllm_ascend.eplb.core.eplb_utils import init_eplb_config
-from vllm_ascend.utils import vllm_version_is
 # isort: on
 
 
@@ -21,24 +20,20 @@ class TestAscendConfig(unittest.TestCase):
             "refresh": True,
             "eplb_config": {"dynamic_eplb": True, "num_redundant_experts": 2},
         }
-        if vllm_version_is('0.14.1'):
-            moe_parallel_config = FusedMoEParallelConfig(2, 0, 1, 2, 1, 1, 1, 1, True, "hccl")
-            moe_config = FusedMoEConfig(8, 8, 8192, 5, moe_parallel_config, torch.float16)
-        else:
-            from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
-            moe_parallel_config = FusedMoEParallelConfig(2, 0, 1, 2, 1, 1, 1, 1, True, "hccl", enable_eplb=True)
-            moe_config = FusedMoEConfig(
-                num_experts=8,
-                experts_per_token=8,
-                hidden_dim=8192,
-                intermediate_size_per_partition=5,
-                num_local_experts=8,
-                activation="silu",
-                device="npu",
-                routing_method=RoutingMethodType.Simulated,
-                moe_parallel_config=moe_parallel_config,
-                in_dtype=torch.float16,
-            )
+        from vllm.model_executor.layers.fused_moe.config import RoutingMethodType
+        moe_parallel_config = FusedMoEParallelConfig(2, 0, 1, 2, 1, 1, 1, 1, True, "hccl", enable_eplb=True)
+        moe_config = FusedMoEConfig(
+            num_experts=8,
+            experts_per_token=8,
+            hidden_dim=8192,
+            intermediate_size_per_partition=5,
+            num_local_experts=8,
+            activation="silu",
+            device="npu",
+            routing_method=RoutingMethodType.Simulated,
+            moe_parallel_config=moe_parallel_config,
+            in_dtype=torch.float16,
+        )
         moe_config.supports_eplb = True
         self.vllm_config = vllm_config
         self.moe_config = moe_config