[Feature] support compressed-tensors w4a16 quantization (#154)

- native int4 kimi model inference is supported

Signed-off-by: Li Wei <liwei.109@outlook.com>

vllm_kunlun/ops/quantization/kernels/kunlun_exllama_linear.py

@@ -0,0 +1,57 @@
+#
+# Copyright (c) 2026 Baidu, Inc. All Rights Reserved.
+# Author: Li Wei
+# Email: liwei157@baidu.com
+# This file is a part of the vllm-kunlun 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 Optional
+
+import torch
+import xspeedgate_ops
+from vllm.model_executor.layers.quantization.kernels.mixed_precision import (
+    ExllamaLinearKernel,
+    _POSSIBLE_KERNELS,
+)
+
+
+class KunlunExllamaLinearKernel(ExllamaLinearKernel):
+
+    def apply_weights(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        c = self.config
+
+        x_2d = x.reshape(-1, x.shape[-1])
+        out_shape = x.shape[:-1] + (c.partition_weight_shape[1],)
+
+        w_q, w_s, w_zp, w_g_idx = self._get_weight_params(layer)
+
+        assert w_zp is not None, "Zero points are required by Exllama"
+        assert w_g_idx is not None, "Group index is required by Exllama"
+        output = torch.ops.xspeedgate_ops.gptq_gemm(
+            x_2d, w_q, w_zp, w_s, w_g_idx, True, c.weight_type.size_bits
+        )
+
+        if bias is not None:
+            output.add_(bias)
+        return output.reshape(out_shape)
+
+
+# remove ExllamaLinearKernel and add KunlunExllamaLinearKernel
+_POSSIBLE_KERNELS.remove(ExllamaLinearKernel)
+_POSSIBLE_KERNELS.append(KunlunExllamaLinearKernel)