[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/_310p/quantization/methods/w8a8_dynamic.py

@@ -21,9 +21,9 @@ from typing import Any
 import torch
 from vllm.config import get_current_vllm_config
 from vllm.distributed import get_ep_group
-from vllm.forward_context import get_forward_context
 
 from vllm_ascend._310p.fused_moe.experts_selector import select_experts
+from vllm_ascend.ascend_forward_context import _EXTRA_CTX
 from vllm_ascend.ops.fused_moe.experts_selector import zero_experts_compute
 from vllm_ascend.quantization.methods.base import AscendMoEScheme, QuantType
 
@@ -125,7 +125,7 @@ class AscendW8A8DynamicFusedMoEMethod310(AscendMoEScheme):
 
         topk_weights = topk_weights.to(self.in_dtype)
 
-        moe_comm_method = get_forward_context().moe_comm_method
+        moe_comm_method = _EXTRA_CTX.moe_comm_method
 
         final_hidden_states = moe_comm_method.fused_experts(
             hidden_states=x,