[Feature] support aclgraph for model runner v2 (#7110)

### What this PR does / why we need it?
This PR aims to support aclgraph for model runner v2, please see RFC
#5208. The PR contains these modifications:
- adapt to newest commit of vllm main branch.
- supply a unified interface of extra forward context for both model
runner v1 and model runner v2.
- implement graph mode for main model. 

### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?

- vLLM version: v0.16.0
- vLLM main:
4034c3d32e

---------

Signed-off-by: Ronald1995 <ronaldautomobile@163.com>

vllm_ascend/worker/v2/block_table.py

@@ -0,0 +1,58 @@
+# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/block_table.py
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# 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.
+# This file is a part of the vllm-ascend project.
+#
+
+import torch
+from vllm.v1.worker.gpu.block_table import BlockTables
+
+
+class AscendBlockTables(BlockTables):
+    """Block table for Ascend NPUs."""
+
+    def __init__(
+        self,
+        block_sizes: list[int],
+        max_num_reqs: int,
+        max_num_batched_tokens: int,
+        max_model_len: int,
+        device: torch.device,
+        cp_size: int = 1,
+        cp_rank: int = 0,
+        cp_interleave: int = 1,
+    ):
+        super().__init__(
+            block_sizes,
+            max_num_reqs,
+            max_num_batched_tokens,
+            max_model_len,
+            device,
+            cp_size,
+            cp_rank,
+            cp_interleave,
+        )
+        # because we will override these attribute, delete these attribute to
+        # make sure it's collected by python gc immediately.
+        del self.slot_mappings
+        # vllm-ascend' reshape_and_cache function requires slot_mappings to be int32.
+        # so we need to redefine slot_mappings to be int32.
+        self.slot_mappings: torch.Tensor = torch.zeros(
+            self.num_kv_cache_groups,
+            self.max_num_batched_tokens,
+            dtype=torch.int32,
+            device=self.device,
+        )