[feature] Support W8A8 PD-Mix Quantization (#4235)

In PD-separated deployment scenarios:

* MoE layers use dynamic quantization exclusively.
* For the Attention module, Prefill (P) nodes use **dynamic**
quantization, while Decode (D) nodes use **static** quantization.

In PD-mixed deployment scenarios:
* **All components fall back to dynamic quantization**, as it is
difficult to distinguish between Prefill and Decode tokens.
___

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

---------

Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
Signed-off-by: Slightwind <slightwindsec@gmail.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

vllm_ascend/quantization/w8a8_pdmix.py

@@ -0,0 +1,70 @@
+from typing import Any, Dict, cast
+
+import torch
+from vllm.config import get_current_vllm_config
+
+from .w8a8 import AscendW8A8LinearMethod
+from .w8a8_dynamic import (AscendW8A8DynamicFusedMoEMethod,
+                           AscendW8A8DynamicLinearMethod)
+
+
+class AscendW8A8PDMixLinearMethod(AscendW8A8DynamicLinearMethod):
+
+    def __init__(self):
+        self.kv_transfer_config = get_current_vllm_config().kv_transfer_config
+        super().__init__()
+
+    @staticmethod
+    def apply(layer, x, bias=None, tp_rank=0):
+        if layer.is_kv_consumer:
+            return AscendW8A8LinearMethod.apply(layer, x, bias, tp_rank)
+        else:
+            return AscendW8A8DynamicLinearMethod.apply(layer, x, bias, tp_rank)
+
+    @staticmethod
+    def get_pertensor_param(params_dtype: torch.dtype) -> Dict[str, Any]:
+        return AscendW8A8LinearMethod.get_pertensor_param(params_dtype)
+
+    @staticmethod
+    def get_perchannel_param(
+        output_size: int,
+        params_dtype: torch.dtype,
+    ) -> Dict[str, Any]:
+        return AscendW8A8LinearMethod.get_perchannel_param(
+            output_size, params_dtype)
+
+    def process_weights_after_loading(self, layer):
+        AscendW8A8LinearMethod.process_weights_after_loading(
+            cast(AscendW8A8LinearMethod, self), layer)
+        layer.weight_scale_fp32 = layer.weight_scale.data.to(torch.float32)
+        layer.is_kv_consumer = self.kv_transfer_config is not None and self.kv_transfer_config.is_kv_consumer
+
+
+class AscendW8A8PDMixFusedMoeMethod(AscendW8A8DynamicFusedMoEMethod):
+
+    def __init__(self):
+        super().__init__()
+
+    @staticmethod
+    def get_dynamic_quant_param(num_experts: int,
+                                intermediate_size_per_partition: int,
+                                hidden_sizes: int,
+                                params_dtype: torch.dtype) -> Dict[str, Any]:
+        param_dict = AscendW8A8DynamicFusedMoEMethod.get_dynamic_quant_param(
+            num_experts, intermediate_size_per_partition, hidden_sizes,
+            params_dtype)
+        param_dict["w2_deq_scale"] = torch.empty(num_experts,
+                                                 hidden_sizes,
+                                                 dtype=torch.float32)
+        param_dict["w13_deq_scale"] = torch.empty(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            dtype=torch.float32)
+        param_dict["w2_input_offset"] = torch.empty(num_experts,
+                                                    1,
+                                                    dtype=torch.int8)
+        param_dict["w13_input_offset"] = torch.empty(num_experts,
+                                                     1,
+                                                     dtype=torch.int8)
+
+        return param_dict