[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>

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, vllm_version_is
+from vllm_ascend.utils import lmhead_tp_enable
 
 
 class MtpProposer(EagleProposer):
@@ -245,12 +245,8 @@ class MtpProposer(EagleProposer):
         # Note(qcs): We may need to refactor these check logics.
         if self.use_cuda_graph and num_scheduled_tokens <= self.runner.cudagraph_batch_sizes[
                 -1]:
-            if vllm_version_is('0.14.1'):
-                num_input_tokens = self.vllm_config.pad_for_cudagraph(
-                    num_scheduled_tokens)
-            else:
-                num_input_tokens = self.runner.cudagraph_dispatcher._bs_to_padded_graph_size[
-                    num_scheduled_tokens]
+            num_input_tokens = self.runner.cudagraph_dispatcher._bs_to_padded_graph_size[
+                num_scheduled_tokens]
         else:
             # Eager mode, no padding needed
             num_input_tokens = num_tokens