[Feat] [310p] Support w8a8sc quantization method (#7075)

### What this PR does / why we need it?
New Quantization Method: Introduced support for the W8A8SC static linear
quantization scheme specifically for 310P hardware, enabling more
efficient model compression.
Refactored the save_sharded_state_310.py to avoid multi-process issue.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
W8A8SC quant E2E test.

- vLLM version: v0.16.0
- vLLM main:
4034c3d32e

---------

Signed-off-by: pu-zhe <zpuaa@outlook.com>

vllm_ascend/_310p/quantization/methods/__init__.py

@@ -19,4 +19,5 @@ from . import (
     w8a8_dynamic,  # noqa: F401
     w8a8_static,  # noqa: F401
     w8a8s,  # noqa: F401
+    w8a8sc,  # noqa: F401
 )

vllm_ascend/_310p/quantization/methods/w8a8sc.py

@@ -0,0 +1,116 @@
+#
+# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+
+import math
+from typing import Any
+
+import torch
+import torch_npu
+from vllm.distributed import get_tensor_model_parallel_rank
+
+from vllm_ascend.ops.linear import AscendRowParallelLinear
+from vllm_ascend.quantization.methods.base import AscendLinearScheme
+
+from .registry import register_scheme
+
+
+@register_scheme("W8A8SC", "linear")
+class AscendW8A8SCLinearMethod310(AscendLinearScheme):
+    """310P-only W8A8SC static linear scheme.
+
+    Notes:
+      - This scheme is discovered via 310P local registry.
+    """
+
+    def get_weight(
+        self,
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype = torch.float16,
+    ) -> dict[str, Any]:
+        """
+        Get the weight tensors for the W8A8SC quantization scheme.
+
+        Args:
+            input_size: Size of the input dimension (k)
+            output_size: Size of the output dimension (n)
+            params_dtype: Data type for parameters, default is torch.float16
+
+        Returns:
+            A dictionary containing:
+            - "weight": The compressed weight tensor with shape [c], where c is greater than 0
+              and not larger than k * n
+            - "index": Compression index generated simultaneously with compressed weights,
+              with shape [x], where x = k_index * n_index * 8, k_index = ceil(k1 / tilingK),
+              n_index = ceil(n1 / tilingN), k1 = k / 32, n1 = n / 16
+            - "info": Compression information with length 5, containing compression block
+              information tilingN, tilingK, original shape of the pre-compression x2 matrix,
+              and identifier for the compression block traversal direction
+        """
+        self.input_size = input_size
+        index_len = math.ceil(input_size / 256) * math.ceil(output_size / 128) * 8
+        return {
+            "weight": torch.empty(input_size * output_size, dtype=torch.int8),
+            "index": torch.empty(index_len, dtype=torch.int8),
+            "info": torch.empty(5, dtype=torch.int64),
+        }
+
+    def get_pertensor_param(self, params_dtype: torch.dtype) -> dict[str, Any]:
+        return {
+            "input_scale": torch.empty(1, dtype=params_dtype),
+            "input_offset": torch.empty(1, dtype=torch.int8),
+        }
+
+    def get_perchannel_param(self, output_size: int, params_dtype: torch.dtype) -> dict[str, Any]:
+        return {
+            "quant_bias": torch.empty(output_size, dtype=torch.int32),
+            "deq_scale": torch.empty(output_size, dtype=torch.int64),
+        }
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+        tp_rank: int | None = 0,
+    ) -> torch.Tensor:
+        if x.dtype != torch.int8:
+            x = torch.ops.vllm.quantize(
+                x,
+                layer.aclnn_input_scale,
+                layer.aclnn_input_scale_reciprocal,
+                layer.aclnn_input_offset,
+            )
+
+        return torch_npu.npu_matmul_compress_dequant(
+            x,
+            layer.weight,
+            layer.index,
+            layer.quant_bias,
+            layer.deq_scale,
+        )
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        layer.aclnn_input_scale = layer.input_scale.data.repeat(self.input_size)
+        layer.aclnn_input_scale_reciprocal = 1.0 / layer.aclnn_input_scale.data
+        layer.aclnn_input_offset = layer.input_offset.data.repeat(self.input_size).to(layer.aclnn_input_scale.dtype)
+        layer.deq_scale.data = layer.deq_scale.data.unsqueeze(0).to(torch.uint64)
+        layer.quant_bias.data = layer.quant_bias.data.unsqueeze(0)
+        # Only apply bias on row_parallel_linear when tp_rank is 0.
+        # torch_npu.npu_matmul_compress_dequant's quant_bias cannot be None.
+        if isinstance(layer, AscendRowParallelLinear) and get_tensor_model_parallel_rank() != 0:
+            layer.quant_bias.data = torch.zeros_like(layer.quant_bias)