Delete 8-bit zero point code

CMakeLists.txt

@@ -332,8 +332,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
       "csrc/moe/marlin_kernels/marlin_moe_kernel_ku8b128.cu"
       "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4.h"
       "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4.cu"
-      "csrc/moe/marlin_kernels/marlin_moe_kernel_ku8.h"
-      "csrc/moe/marlin_kernels/marlin_moe_kernel_ku8.cu"
       "csrc/moe/marlin_moe_ops.cu")
 endif()
 

csrc/moe/marlin_moe_ops.cu

@@ -30,7 +30,6 @@
 #include "marlin_kernels/marlin_moe_kernel_ku4b8.h"
 #include "marlin_kernels/marlin_moe_kernel_ku8b128.h"
 #include "marlin_kernels/marlin_moe_kernel_ku4.h"
-#include "marlin_kernels/marlin_moe_kernel_ku8.h"
 
 template <typename T>
 inline std::string str(T x) {
@@ -461,7 +460,6 @@ void marlin_mm_moe(const void* A, const void* B, void* C,
       CALL_MOE_KERNEL_FUNCTION(call_marlin_moe_kernel_ku4b8)
       CALL_MOE_KERNEL_FUNCTION(call_marlin_moe_kernel_ku8b128)
       CALL_MOE_KERNEL_FUNCTION(call_marlin_moe_kernel_ku4)
-      CALL_MOE_KERNEL_FUNCTION(call_marlin_moe_kernel_ku8)
       else {
         TORCH_CHECK(false, "Unsupported shapes: MNK = [" + str(prob_m) + ", " +
                                str(prob_n) + ", " + str(prob_k) + "]" +
@@ -488,9 +486,9 @@ torch::Tensor marlin_gemm_moe(
     int64_t moe_block_size, bool replicate_input, bool apply_weights) {
   bool has_zp = b_zeros.size(1) != 0;
   if (has_zp) {
-    TORCH_CHECK(*b_q_type == vllm::kU4 || *b_q_type == vllm::kU8,
-                "b_q_type must be u4 or u8 when has_zp = True. Got = ",
-                b_q_type->str());
+    TORCH_CHECK(
+        *b_q_type == vllm::kU4,
+        "b_q_type must be u4 when has_zp = True. Got = ", b_q_type->str());
   } else {
     TORCH_CHECK(
         *b_q_type == vllm::kU4B8 || *b_q_type == vllm::kU8B128,

tests/kernels/test_awq_marlin.py

@@ -21,19 +21,18 @@
 @pytest.mark.parametrize("e", [8, 64])
 @pytest.mark.parametrize("topk", [2, 6])
 @pytest.mark.parametrize("group_size", [-1, 32, 64, 128])
-@pytest.mark.parametrize("num_bits", [4, 8])
 def test_fused_marlin_moe_awq(
     m: int,
     n: int,
     k: int,
     e: int,
     topk: int,
     group_size: int,
-    num_bits: int,
 ):
     torch.manual_seed(7)
 
-    quant_type = (scalar_types.uint4 if num_bits == 4 else scalar_types.uint8)
+    num_bits = 4
+    quant_type = scalar_types.uint4
     dtype = torch.float16
     a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
     w1 = torch.randn((e, 2 * n, k), device="cuda", dtype=dtype) / 10
@@ -111,19 +110,18 @@ def test_fused_marlin_moe_awq(
 @pytest.mark.parametrize("e", [8, 64])
 @pytest.mark.parametrize("topk", [2, 6])
 @pytest.mark.parametrize("group_size", [-1, 32, 64, 128])
-@pytest.mark.parametrize("num_bits", [4, 8])
 def test_single_marlin_moe_multiply_awq(
     m: int,
     n: int,
     k: int,
     e: int,
     topk: int,
     group_size: int,
-    num_bits: int,
 ):
     torch.manual_seed(7)
 
-    quant_type = (scalar_types.uint4 if num_bits == 4 else scalar_types.uint8)
+    num_bits = 4
+    quant_type = scalar_types.uint4
     dtype = torch.float16
     a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
     w = torch.randn((e, n, k), device="cuda", dtype=dtype) / 10

vllm/model_executor/layers/fused_moe/fused_marlin_moe.py

@@ -12,7 +12,8 @@
 
 def get_scalar_type(num_bits: int, has_zp: bool):
     if has_zp:
-        return scalar_types.uint4 if num_bits == 4 else scalar_types.uint8
+        assert num_bits == 4
+        return scalar_types.uint4
     else:
         return scalar_types.uint4b8 if num_bits == 4 else scalar_types.uint8b128