[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/ops/linear.py

@@ -277,18 +277,20 @@ class AscendRowParallelLinear(RowParallelLinear):
             weight_loader=(
                 self.weight_loader_v2 if self.quant_method.__class__.__name__
                 in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader))
+        bias_initialized_by_quant = ("bias" in self._parameters
+                                     and self._parameters["bias"] is not None)
         if not reduce_results and (bias and not skip_bias_add):
             raise ValueError("When not reduce the results, adding bias to the "
                              "results can lead to incorrect results")
 
-        if bias:
+        if bias and not bias_initialized_by_quant:
             self.bias = Parameter(
                 torch.empty(self.output_size, dtype=params_dtype))
             set_weight_attrs(self.bias, {
                 "output_dim": 0,
                 "weight_loader": self.weight_loader,
             })
-        else:
+        elif not bias and not bias_initialized_by_quant:
             self.register_parameter("bias", None)
 
         if self.custom_op is not None:
@@ -366,7 +368,9 @@ class AscendColumnParallelLinear(ColumnParallelLinear):
             weight_loader=(
                 self.weight_loader_v2 if self.quant_method.__class__.__name__
                 in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader))
-        if bias:
+        bias_initialized_by_quant = ("bias" in self._parameters
+                                     and self._parameters["bias"] is not None)
+        if bias and not bias_initialized_by_quant:
             self.bias = Parameter(
                 torch.empty(self.output_size_per_partition,
                             dtype=params_dtype))
@@ -374,7 +378,7 @@ class AscendColumnParallelLinear(ColumnParallelLinear):
                 "output_dim": 0,
                 "weight_loader": self.weight_loader,
             })
-        else:
+        elif not bias and not bias_initialized_by_quant:
             self.register_parameter("bias", None)
 
         if self.custom_op is not None:
@@ -445,14 +449,16 @@ class AscendReplicatedLinear(ReplicatedLinear):
                                          self.params_dtype,
                                          weight_loader=self.weight_loader)
 
-        if bias:
+        bias_initialized_by_quant = ("bias" in self._parameters
+                                     and self._parameters["bias"] is not None)
+        if bias and not bias_initialized_by_quant:
             self.bias = Parameter(
                 torch.empty(self.output_size, dtype=self.params_dtype))
             set_weight_attrs(self.bias, {
                 "output_dim": 0,
                 "weight_loader": self.weight_loader,
             })
-        else:
+        elif not bias and not bias_initialized_by_quant:
             self.register_parameter("bias", None)
 
         if self.custom_op is not None: