[Refactor] Rename gptq_marlin to marlin to match MoE (#32952)

Signed-off-by: mgoin <mgoin64@gmail.com>

vllm/_custom_ops.py

@@ -591,8 +591,8 @@ if hasattr(torch.ops._C, "gptq_marlin_24_gemm"):
     ) -> torch.Tensor:
         return torch.empty((size_m, size_n), device=a.device, dtype=a.dtype)
 
-    @register_fake("_C::gptq_marlin_gemm")
-    def _gptq_marlin_gemm_fake(
+    @register_fake("_C::marlin_gemm")
+    def _marlin_gemm_fake(
         a: torch.Tensor,
         c: torch.Tensor | None,
         b_q_weight: torch.Tensor,
@@ -1312,7 +1312,7 @@ def marlin_int4_fp8_preprocess(
     return torch.ops._C.marlin_int4_fp8_preprocess(qweight, qzeros_or_none, inplace)
 
 
-def gptq_marlin_gemm(
+def marlin_gemm(
     a: torch.Tensor,
     c: torch.Tensor | None,
     b_q_weight: torch.Tensor,
@@ -1333,7 +1333,7 @@ def gptq_marlin_gemm(
     use_fp32_reduce: bool = False,
     is_zp_float: bool = False,
 ) -> torch.Tensor:
-    return torch.ops._C.gptq_marlin_gemm(
+    return torch.ops._C.marlin_gemm(
         a,
         c,
         b_q_weight,

vllm/model_executor/layers/quantization/utils/marlin_utils.py

@@ -563,7 +563,7 @@ def apply_gptq_marlin_linear(
 
         reshaped_x, a_scales = marlin_quant_input(reshaped_x, input_dtype)
 
-    output = ops.gptq_marlin_gemm(
+    output = ops.marlin_gemm(
         reshaped_x,
         None,
         weight,
@@ -628,7 +628,7 @@ def apply_awq_marlin_linear(
         )
         reshaped_x, a_scales = marlin_quant_input(reshaped_x, input_dtype)
 
-    output = ops.gptq_marlin_gemm(
+    output = ops.marlin_gemm(
         reshaped_x,
         None,
         weight,

vllm/model_executor/layers/quantization/utils/marlin_utils_fp4.py

@@ -121,7 +121,7 @@ def apply_fp4_marlin_linear(
 
         inputs, a_scales = marlin_quant_input(inputs, torch.float8_e4m3fn)
 
-    output = ops.gptq_marlin_gemm(
+    output = ops.marlin_gemm(
         a=inputs,
         c=None,
         b_q_weight=weight,

vllm/model_executor/layers/quantization/utils/marlin_utils_fp8.py

@@ -66,7 +66,7 @@ def apply_fp8_marlin_linear(
         # inputs, a_scales = marlin_quant_input(inputs, torch.float8_e4m3fn)
         raise RuntimeError("Marlin W8A8 is not supported.")
 
-    output = ops.gptq_marlin_gemm(
+    output = ops.marlin_gemm(
         a=inputs,
         c=None,
         b_q_weight=weight,