[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/quantization/w8a8_dynamic.py

@@ -19,12 +19,16 @@ from typing import Any, Callable, Dict, Optional, Tuple, Union
 
 import torch
 import torch_npu
+from vllm.config import CompilationLevel, get_current_vllm_config
 from vllm.distributed import get_ep_group
 from vllm.forward_context import get_forward_context
 
 import vllm_ascend.envs as envs_ascend
+from vllm_ascend.ascend_config import get_ascend_config
 from vllm_ascend.ascend_forward_context import FusedMoEState
 from vllm_ascend.distributed.parallel_state import get_mc2_group
+from vllm_ascend.ops.common_fused_moe import \
+    fused_experts as unified_fused_experts
 from vllm_ascend.ops.fused_moe import unified_fused_experts_eager
 from vllm_ascend.ops.layers.experts_selector import select_experts
 from vllm_ascend.utils import ACL_FORMAT_FRACTAL_NZ, dispose_tensor
@@ -283,6 +287,13 @@ class AscendW8A8DynamicFusedMoEMethod:
 
         self.ep_group = get_ep_group()
 
+        vllm_config = get_current_vllm_config()
+        ascend_config = get_ascend_config()
+        self.use_aclgraph = (
+            vllm_config.compilation_config.level == CompilationLevel.PIECEWISE
+            and not vllm_config.model_config.enforce_eager
+            and not ascend_config.torchair_graph_config.enabled)
+
         try:
             device_group = get_mc2_group().device_group
             # TODO: Try local_rank = ep_group.rank_in_group
@@ -375,6 +386,19 @@ class AscendW8A8DynamicFusedMoEMethod:
             e_score_correction_bias=e_score_correction_bias,
             global_num_experts=global_num_experts)
 
+        if self.use_aclgraph:
+            return unified_fused_experts(
+                hidden_states=x,
+                w1=layer.w13_weight,
+                w2=layer.w2_weight,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                use_int8_w8a8=True,
+                w1_scale=layer.w13_weight_scale,
+                w2_scale=layer.w2_weight_scale,
+                expert_map=expert_map,
+            )
+
         fused_moe_state = get_forward_context().fused_moe_state
         shared_gate_up, shared_dequant_scale = None, None
         if shared_experts is not None and fused_moe_state == FusedMoEState.MC2: