[3/N][Feat][Graph] Support all-to-all and quantized models with ACL Graph (#2614)

### What this PR does / why we need it?
* **Unify execution paths:** Consolidates the quantized and
non-quantized execution paths into a single `fused_experts` function,
removing duplicated logic and making the control flow clearer and easier
to maintain.
* **W8A8 dynamic quantization:** Adds support for W8A8 dynamic
quantization inside the unified MoE kernel. Communication routines are
updated to correctly handle dynamic quantization scales for activations.
* **Weight pre-processing:** Prae-transpose the `w13` and `w2` weight
matrices (as implemented in PR #2025) so that quantized and
non-quantized models follow the same code path for the MoE gating,
up-projection, and down-projection operations.
* **All-to-all communication:** Adds an `all-to-all` collective
communication pattern. For large token counts on modern hardware,
`all-to-all` is more efficient than the previous `all-gather` strategy.
However, `all-to-all` is not really captured and replayed due to
multiple D2H operations which will trigger synchronization, and thus
raise error when capture graphs. We only use `all-to-all` when fallback
to `compiled_graph_for_general_shape`.
* **Dynamic communication selection:** The model runner now selects the
optimal MoE communication method (`mc2`, `allgather`, or `alltoall`) at
runtime based on token count and the Ascend SoC version.
* **Limitation:** `all-gather` is not yet supported for quantized
models, which means there is still something left to do on A2.

### Does this PR introduce _any_ user-facing change?
None.

### How was this patch tested?
No further test cases needed.

- vLLM version: v0.10.1.1
- vLLM main:
d660c98c1b

---------

Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>

vllm_ascend/ops/fused_moe.py

@@ -230,7 +230,6 @@ def fused_experts_moge(
         0, sorted_topk_ids).unsqueeze(-1)
     group_list = num_tokens_per_expert.cumsum(dim=0).to(torch.int64)
 
-    w1 = w1.transpose(1, 2)
     gate_up_out = torch_npu.npu_grouped_matmul(
         x=[sorted_hidden_states],
         weight=[w1],
@@ -247,7 +246,6 @@ def fused_experts_moge(
         gate_up_out = torch_npu.npu_swiglu(gate_up_out)
     gate_up_out *= topk_scales
 
-    w2 = w2.transpose(1, 2)
     down_out_list = torch_npu.npu_grouped_matmul(
         x=[gate_up_out],
         weight=[w2],