init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

vllm_ascend/quantization/methods/w8a16.py

@@ -0,0 +1,78 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# 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.
+#
+
+from typing import Any
+
+import torch
+import torch_npu
+
+from vllm_ascend.utils import maybe_trans_nz
+
+from .base import AscendLinearScheme
+from .registry import register_scheme
+
+
+@register_scheme("W8A16", "linear")
+class AscendW8A16LinearMethod(AscendLinearScheme):
+    """Linear method for Ascend W8A16.
+
+    This scheme uses 8-bit quantized weights with 16-bit activations.
+    """
+
+    def __init__(self) -> None:
+        pass
+
+    def get_weight(
+        self,
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype = torch.bfloat16,
+    ) -> dict[str, Any]:
+        params_dict = {"weight": torch.empty(output_size, input_size, dtype=torch.int8)}
+        return params_dict
+
+    def get_perchannel_param(
+        self,
+        output_size: int,
+        params_dtype: torch.dtype,
+    ) -> dict[str, Any]:
+        params_dict = {}
+        params_dict["weight_scale"] = torch.empty(output_size, 1, dtype=params_dtype)
+        params_dict["weight_offset"] = torch.empty(output_size, 1, dtype=params_dtype)
+        return params_dict
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+        tp_rank: int | None = 0,
+    ) -> torch.Tensor:
+        output = torch_npu.npu_weight_quant_batchmatmul(
+            x=x,
+            weight=layer.weight,
+            antiquant_scale=layer.weight_scale,
+            antiquant_offset=layer.weight_offset,
+            bias=bias,
+        )
+        return output
+
+    def process_weights_after_loading(self, layer):
+        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)