[Quantization] [Performance] Enable Marlin GEMM kernels for the calibration-free RTN-based quantization (#26051)

Signed-off-by: Alex Kogan <alex.kogan@oracle.com>
Signed-off-by: Alex Kogan <82225080+sakogan@users.noreply.github.com>

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

@@ -528,3 +528,48 @@ def apply_awq_marlin_linear(
     )
 
     return output.reshape(out_shape)
+
+
+def apply_rtn_marlin_linear(
+    input: torch.Tensor,
+    weight: torch.Tensor,
+    weight_scale: torch.Tensor,
+    workspace: torch.Tensor,
+    quant_type: ScalarType,
+    output_size_per_partition: int,
+    input_size_per_partition: int,
+    bias: torch.Tensor | None = None,
+    use_fp32_reduce: bool = USE_FP32_REDUCE_DEFAULT,
+) -> torch.Tensor:
+    reshaped_x = input.reshape(-1, input.shape[-1])
+    out_shape = input.shape[:-1] + (output_size_per_partition,)
+
+    use_atomic_add = should_use_atomic_add_reduce(
+        m=reshaped_x.size(0),
+        n=output_size_per_partition,
+        k=reshaped_x.size(1),
+        device=input.device,
+        dtype=input.dtype,
+    )
+
+    output = ops.gptq_marlin_gemm(
+        reshaped_x,
+        None,
+        weight,
+        bias,
+        weight_scale,
+        None,
+        None,
+        None,
+        None,
+        workspace,
+        quant_type,
+        size_m=reshaped_x.shape[0],
+        size_n=output_size_per_partition,
+        size_k=input_size_per_partition,
+        use_atomic_add=use_atomic_add,
+        use_fp32_reduce=use_fp32_reduce,
+        is_zp_float=False,
+    )
+
+    return output.reshape(out_shape)