[Feat.]: support 310p w8a8 (#6454)

### What this PR does / why we need it?
Introduced 310P W8A8 Quantization Support: New modules and methods have
been added to enable W8A8 static quantization specifically for the
Ascend 310P platform.
Platform-Specific Quantization Configuration Loading: The system now
dynamically loads the appropriate quantization configurations
(AscendCompressedTensorsConfig, AscendModelSlimConfig) based on whether
the current hardware is an Ascend 310P device.
Implemented AscendW8A8LinearMethod310P: A dedicated linear quantization
method for 310P is provided, handling the specifics of weight and
activation quantization, including input parameter broadcasting and
weight data manipulation.
Extended AscendModelSlimConfig for 310P: A specialized configuration
class for 310P integrates the new W8A8 linear method for both standard
linear layers and vocabulary parallel embeddings, ensuring proper
quantization application.

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

---------

Signed-off-by: Tflowers-0129 <2906339855@qq.com>
Signed-off-by: Shaoxu Cheng <2906339855@qq.com>

vllm_ascend/quantization/methods/w8a8_static.py

@@ -138,24 +138,13 @@ class AscendW8A8LinearMethod(AscendLinearScheme):
         if ascend_quant_method == COMPRESSED_TENSORS_METHOD:
             quant_bias = bias
 
-        if get_ascend_device_type() == AscendDeviceType._310P:
-            # On 300I Duo platform, we need transpose again if
-            # using nz. This transpose can be skipped in torchair.
-            output = torch_npu.npu_quant_matmul(
-                x,
-                layer.weight.data.transpose(1, 0),
-                layer.deq_scale,
-                bias=quant_bias,
-                output_dtype=layer.params_dtype,
-            )
-        else:
-            output = torch_npu.npu_quant_matmul(
-                x,
-                layer.weight,
-                layer.deq_scale,
-                bias=quant_bias,
-                output_dtype=layer.params_dtype,
-            )
+        output = torch_npu.npu_quant_matmul(
+            x,
+            layer.weight,
+            layer.deq_scale,
+            bias=quant_bias,
+            output_dtype=layer.params_dtype,
+        )
         return output
 
     def process_weights_after_loading(self, layer):
@@ -169,8 +158,8 @@ class AscendW8A8LinearMethod(AscendLinearScheme):
         layer.aclnn_input_offset = torch.nn.Parameter(
             layer.input_offset.data.repeat(expanding_factor),
             requires_grad=False).to(layer.aclnn_input_scale.dtype)
-        if get_ascend_device_type() != AscendDeviceType._310P:
-            layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
+
+        layer.weight.data = layer.weight.data.transpose(0, 1).contiguous()
         layer.weight.data = maybe_trans_nz(layer.weight.data)
         layer.weight_scale.data = torch.flatten(layer.weight_scale.data)
         layer.weight_offset.data = torch.flatten(layer.weight_offset.data)