[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/quantization/methods/w8a8_mxfp8.py

@@ -22,9 +22,9 @@ import torch
 import torch_npu
 from vllm.config import CompilationMode, get_current_vllm_config
 from vllm.distributed import get_ep_group
-from vllm.forward_context import get_forward_context
 
 from vllm_ascend.ascend_config import get_ascend_config
+from vllm_ascend.ascend_forward_context import _EXTRA_CTX
 from vllm_ascend.device.mxfp_compat import (
     FLOAT8_E8M0FNU_DTYPE,
     ensure_mxfp8_linear_available,
@@ -187,7 +187,7 @@ class AscendW8A8MXFP8DynamicFusedMoEMethod(AscendMoEScheme):
 
         topk_weights = topk_weights.to(x.dtype)
 
-        moe_comm_method = get_forward_context().moe_comm_method
+        moe_comm_method = _EXTRA_CTX.moe_comm_method
         return moe_comm_method.fused_experts(
             hidden_states=x,
             w1=layer.w13_weight,