[Feat] Support native Kimi-K2-Thinking native W4A16 quantized experts weights (#4516)

### What this PR does / why we need it?

Adds W4A16 quantization method for the Kimi-K2-Thinking model and
updates relevant modules to support the new quantization method.

- Implements complete W4A16 quantization method including weight
packing/unpacking, per-group quantization parameter generation,
post-processing logic and MoE method application.
- Adds parameters `use_int4_w4a16`, `w1_offset` and `w2_offset`, adjusts
`with_quant` conditional logic to support W4A16 matrix multiplication.
- Adds `packed_modules_model_mapping` for Kimi-K2-Thinking model and
processing logic for `weight_packed` field.

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>
Signed-off-by: Ruri <33858552+zhoux77899@users.noreply.github.com>
Signed-off-by: Ruri <zhouxiang100@huawei.com>

vllm_ascend/quantization/quant_config.py

@@ -65,6 +65,9 @@ class AscendQuantConfig(QuantizationConfig):
             if "shared_head" in k:
                 new_k = k.replace(".shared_head.", ".")
                 extra_quant_dict[new_k] = self.quant_description[k]
+            if "weight_packed" in k:
+                new_k = k.replace("weight_packed", "weight")
+                extra_quant_dict[new_k] = self.quant_description[k]
         self.quant_description.update(extra_quant_dict)
 
     def __repr__(self) -> str:
@@ -200,7 +203,8 @@ packed_modules_model_mapping = {
     "kimi_k2": {
         "gate_up_proj": ["gate_proj", "up_proj"],
         "experts":
-        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]
+        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
+        "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"]
     },
     "deepseek_v32": {
         "gate_up_proj": ["gate_proj", "up_proj"],
@@ -439,7 +443,9 @@ class AscendFusedMoEMethod(FusedMoEMethodBase):
             {"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value})
         per_group_param = [
             "weight_scale_second", "weight_offset_second", "scale_bias"
-        ]
+        ] + ["weight_scale", "weight_offset"] if hasattr(
+            self.quant_method,
+            "group_size") and self.quant_method.group_size > 0 else []
         dynamic_quant_param = self.quant_method.get_dynamic_quant_param(
             num_experts, intermediate_size_per_partition, hidden_size,
             params_dtype)