[EPLB][Ops] Integerate grouped_matmul_swiglu_quant_weight_nz_tensor_list operator into dynamic EPLB (#4216)

### What this PR does / why we need it?
Integerate grouped_matmul_swiglu_quant_weight_nz_tensor_list into
dynamic EPLB to support list-type parameters
This PR also modify the logic of loading model in dynamic-eplb scenario.
The operator is based on this pr:
https://github.com/vllm-project/vllm-ascend/pull/3804

### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?

```
vllm serve /home/weight/DeepSeek-V3.1_w8a8mix_mtp \
    --max_num_seqs 8 \
    --max-model-len 8192 \
    --max-num-batched-tokens 16384 \
    --tensor-parallel-size 8 \
    --data-parallel-size 2 \
    --enable-expert-parallel \
    --served-model-name ds_r1 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --no-enable-prefix-caching \
    --port 8999 \
    --quantization "ascend" \
    --gpu-memory-utilization 0.85 \
    --trust-remote-code \
    --compilation_config '{"cudagraph_capture_sizes":[1,2,4,8,16,32]}' \
    --additional-config='{"dynamic_eplb":true, "num_iterations_eplb_update":100, "num_wait_worker_iterations":100}'
 
```
input&output: 2k 2k
This PR:
<img width="1318" height="695" alt="fusion"
src="https://github.com/user-attachments/assets/f8657813-0c02-42f4-8396-d99e730f48cd"
/>

Baseline:
<img width="1323" height="690" alt="baseline"
src="https://github.com/user-attachments/assets/e1323a78-af26-4523-820c-e20e5642a38e"
/>


- vLLM version: v0.11.2
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2

---------

Signed-off-by: 白永斌 <baiyongbin3@h-partners.com>
Signed-off-by: 欧派果奶我还要 <845473182@qq.com>
Co-authored-by: 白永斌 <baiyongbin3@h-partners.com>

vllm_ascend/quantization/w4a8_dynamic.py

@@ -379,10 +379,10 @@ class AscendW4A8DynamicFusedMoEMethod:
         moe_comm_method = get_forward_context().moe_comm_method
         return moe_comm_method.fused_experts(
             hidden_states=x,
-            w1=layer.w13_weight,
-            w2=layer.w2_weight,
-            w1_scale=layer.w13_weight_scale,
-            w2_scale=layer.w2_weight_scale,
+            w1=[layer.w13_weight],
+            w2=[layer.w2_weight],
+            w1_scale=[layer.w13_weight_scale],
+            w2_scale=[layer.w2_weight_scale],
             w1_scale_bias=layer.w13_scale_bias,
             w2_scale_bias=layer.w2_scale_bias,
             topk_weights=topk_weights,

vllm_ascend/quantization/w8a8_dynamic.py

@@ -236,13 +236,24 @@ class AscendW8A8DynamicFusedMoEMethod:
         topk_weights = topk_weights.to(self.in_dtype)
 
         moe_comm_method = get_forward_context().moe_comm_method
+        if self.dynamic_eplb:
+            w1 = layer.w13_weight_list
+            w1_scale = layer.w13_weight_scale_fp32_list
+            w2 = layer.w2_weight_list
+            w2_scale = layer.w2_weight_scale_list
+        else:
+            w1 = [layer.w13_weight]
+            w1_scale = [layer.w13_weight_scale_fp32]
+            w2 = [layer.w2_weight]
+            w2_scale = [layer.w2_weight_scale]
+
         return moe_comm_method.fused_experts(
             hidden_states=x,
             pertoken_scale=pertoken_scale,
-            w1=layer.w13_weight,
-            w1_scale=layer.w13_weight_scale_fp32,
-            w2=layer.w2_weight,
-            w2_scale=layer.w2_weight_scale,
+            w1=w1,
+            w1_scale=w1_scale,
+            w2=w2,
+            w2_scale=w2_scale,
             topk_weights=topk_weights,
             topk_ids=topk_ids,
             use_int8_w8a8=True,
@@ -274,3 +285,25 @@ class AscendW8A8DynamicFusedMoEMethod:
             layer.w2_weight_scale.data.shape[0], -1)
         layer.w2_weight_offset.data = layer.w2_weight_offset.data.view(
             layer.w2_weight_offset.data.shape[0], -1)
+        if self.dynamic_eplb:
+            layer.w13_weight_list = [
+                weight.clone()
+                for weight in layer.w13_weight.data.unbind(dim=0)
+            ]
+            layer.w2_weight_list = [
+                weight.clone() for weight in layer.w2_weight.data.unbind(dim=0)
+            ]
+            layer.w13_weight_scale_fp32_list = [
+                weight.clone()
+                for weight in layer.w13_weight_scale.data.unbind(dim=0)
+            ]
+            layer.w2_weight_scale_list = [
+                weight.clone()
+                for weight in layer.w2_weight_scale.data.unbind(dim=0)
+            ]
+            del layer.w13_weight
+            del layer.w2_weight
+            del layer.w13_weight_scale
+            del layer.w13_weight_scale_fp32
+            del layer.w2_weight_scale
+            torch.npu.empty_cache()