[Core]Refactor gptq_marlin ops (#4466)

vllm/_custom_ops.py

@@ -167,6 +167,22 @@ def aqlm_dequant(codes: torch.Tensor, codebooks: torch.Tensor,
     return vllm_ops.aqlm_dequant(codes, codebooks, codebook_partition_sizes)
 
 
+# gptq_marlin
+def gptq_marlin_repack(b_q_weight: torch.Tensor, perm: torch.Tensor,
+                       size_k: int, size_n: int) -> torch.Tensor:
+    return vllm_ops.gptq_marlin_repack(b_q_weight, perm, size_k, size_n)
+
+
+def gptq_marlin_gemm(a: torch.Tensor, b_q_weight: torch.Tensor,
+                     b_scales: torch.Tensor, g_idx: torch.Tensor,
+                     perm: torch.Tensor, workspace: torch.Tensor, size_m: int,
+                     size_n: int, size_k: int,
+                     is_k_full: bool) -> torch.Tensor:
+    return vllm_ops.gptq_marlin_gemm(a, b_q_weight, b_scales, g_idx, perm,
+                                     workspace, size_m, size_n, size_k,
+                                     is_k_full)
+
+
 # fp8
 def scaled_fp8_quant(
     input: torch.Tensor,

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -6,7 +6,7 @@ import numpy
 import torch
 from torch.nn.parameter import Parameter
 
-from vllm._C import ops
+from vllm import _custom_ops as ops
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                set_weight_attrs)
 from vllm.model_executor.layers.quantization.base_config import (