[Kernel] Zero point support in fused MarlinMoE kernel + AWQ Fused MoE (#8973)

Co-authored-by: Dipika <dipikasikka1@gmail.com>
Co-authored-by: Dipika Sikka <ds3822@columbia.edu>

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

@@ -208,6 +208,7 @@ def marlin_moe_permute_scales(
         device=s.device,
         dtype=s.dtype,
     )
+
     for e in range(num_experts):
         output[e] = marlin_permute_scales(s[e], size_k, size_n, group_size)
     return output
@@ -258,6 +259,20 @@ def awq_to_marlin_zero_points(q_zp_packed: torch.Tensor, size_k: int,
     return marlin_zp
 
 
+def moe_awq_to_marlin_zero_points(q_zp_packed: torch.Tensor, size_k: int,
+                                  size_n: int, num_bits: int):
+    num_experts = q_zp_packed.shape[0]
+    output = torch.empty(
+        (num_experts, q_zp_packed.shape[1], q_zp_packed.shape[2]),
+        device=q_zp_packed.device,
+        dtype=q_zp_packed.dtype,
+    )
+    for e in range(num_experts):
+        output[e] = awq_to_marlin_zero_points(q_zp_packed[e], size_k, size_n,
+                                              num_bits)
+    return output
+
+
 def apply_gptq_marlin_linear(
         input: torch.Tensor,
         weight: torch.Tensor,