BugFix: Resolve shape mismatch in eplb update and calculation issues in quant_apply_mlp (#4777)

## Description
This PR addresses two key issues in the MoE module when redundant
experts are enabled, and fixes a calculation precision bug in the
forward inference of quantized MLP:

### 1. Shape Mismatch in EPLB Expert Map Update
- **Root Cause**: 
When redundant experts are turned on, a shape inconsistency occurs
during the expert map update in `Vllm_apaptor`:
- The shape of `self.expert_map_per_layer[layer_id]` is
`[num_physical_experts,]` (aligned with physical expert count).
- The shape of `updated_expert_map` is `[num_logical_experts,]` (aligned
with logical expert count).
- Indices in `self.expert_map_per_layer[layer_id]` that exceed the
logical expert count cannot be properly mapped, leading to tensor shape
mismatch errors.
- The same shape mismatch exists in the `log2phy` map update (between
`self.log2phy_map_per_layer[layer_id]` and `updated_log2phy_map`).

- **Fix**:
- Fix the shape initialization of `expert_map_per_layer` and
`log2phy_map_per_layer` to be consistently set to
`[num_physical_experts,]` across the module lifecycle.
- Align the shape of `updated_expert_map` and `updated_log2phy_map` with
the pre-initialized physical-expert-sized tensors during update
operations, ensuring shape consistency for index mapping.

### 2. Calculation Precision Issue in Quantized MoE MLP Forward
Inference
- **Root Cause**:
In the forward pass of `moe_mlp`, the
`torch_npu.npu_dequant_swiglu_quant` operator only accepts group lists
in **Count format** as input. However, the group list provided by
`quant_apply_mlp` was in **Cumsum format**, which caused operator input
format mismatch and degraded calculation precision.

- **Fix**:
- Convert the cumsum-formatted group list from `quant_apply_mlp` to
Count format before passing it to `torch_npu.npu_dequant_swiglu_quant`.
- Ensure the input format of the dequantization operator meets its
requirements, restoring the expected calculation precision for quantized
MoE MLP layers.

## Impact
- Resolves shape mismatch errors in EPLB expert/log2phy map updates when
redundant experts are enabled, ensuring stable expert routing.
- Fixes quantized MoE MLP forward precision issues on NPU, aligning
operator input formats with NPU kernel requirements.
- No breaking changes to existing interfaces; the fixes are
backward-compatible for scenarios without redundant experts enabled.

---------

Signed-off-by: Che Ruan <cr623@ic.ac.uk>
Signed-off-by: Mercykid-bash <ruanche0218@gmail.com>
Co-authored-by: Che Ruan <cr623@ic.ac.uk>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

vllm_ascend/ops/expert_load_balancer.py

@@ -48,7 +48,7 @@ class ExpertLoadBalancer(object):
 
     def generate_expert_placement_map(self):
         expert_placement_map = torch.full(
-            (self.layers_num, self.ranks_num, self.global_expert_num),
+            (self.layers_num, self.ranks_num, self.num_experts),
             -1,
             dtype=torch.int32,
         )
@@ -71,7 +71,7 @@ class ExpertLoadBalancer(object):
                 result_dict[key] = []
             result_dict[key].append(idx)
 
-        log2phy_map = torch.full((self.ranks_num, self.global_expert_num),
+        log2phy_map = torch.full((self.ranks_num, self.num_experts),
                                  -1,
                                  dtype=torch.int32)
         for rank in range(self.ranks_num):