[MM][Model][Perf] Remove Qwen2.5-VL modeling files and add patch for VisionAttention (#4349)

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

- [x] Patch `Qwen2_5_VisionAttention` with
`AscendQwen2_5_VisionAttention`.
- [x] Replace `AscendQwen2_5_VisionTransformer` with
`Qwen2_5_VisionTransformer` in vllm.
- [x] Move padding logic (q/k/v and cos/sin) before FA to `forward()` of
`Qwen2_5_VisionAttention`.
- [x] Covert `cu_seqlens` in `Qwen2_5_VisionAttention` from cumulative
form to intervals and move it to cpu (compatible for npu FA).
- [x] Remove Qwen2.5-VL modeling files.
- [x] Remove Qwen2.5-VL (without padding) modeling files.
- [x] Remove related UT.
- [x] Make `set_forward_context` pluggable when getting MM embedding.
Find more details at https://github.com/vllm-project/vllm/pull/29388.
- [x] Simplify padding logic for FA.
- [x] Add patch for https://github.com/vllm-project/vllm/pull/28798.

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

No.

### How was this patch tested?

- [x] Functional test (eager mode)
- [x] Functional test (graph mode)
- [x] Benchmark


- vLLM version: v0.11.2

---------

Signed-off-by: shen-shanshan <467638484@qq.com>

vllm_ascend/patch/worker/patch_rope.py

@@ -0,0 +1,33 @@
+#
+# 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.
+#
+
+import torch
+import torch.nn as nn
+from vllm.model_executor.layers.rotary_embedding.base import \
+    RotaryEmbeddingBase
+
+
+class AscendRotaryEmbeddingBase(nn.Module):
+
+    def get_cos_sin(self, seqlen: int) -> tuple[torch.Tensor, torch.Tensor]:
+        cos_sin = self.cos_sin_cache[:seqlen]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        return cos, sin
+
+
+# NOTE: These will be removed after vllm-ascend is aligned with vllm latest main.
+RotaryEmbeddingBase.get_cos_sin = AscendRotaryEmbeddingBase.get_cos_sin