[ROCm][Bugfix] Ensure that the moe_wna16_gemm kernel is not built on ROCm platforms. (#14629)

Signed-off-by: Sage Moore <sage@neuralmagic.com>

CMakeLists.txt

@@ -559,7 +559,6 @@ target_compile_definitions(_C PRIVATE CUTLASS_ENABLE_DIRECT_CUDA_DRIVER_CALL=1)
 set(VLLM_MOE_EXT_SRC
   "csrc/moe/torch_bindings.cpp"
   "csrc/moe/moe_align_sum_kernels.cu"
-  "csrc/moe/moe_wna16.cu"
   "csrc/moe/topk_softmax_kernels.cu")
 
 set_gencode_flags_for_srcs(
@@ -574,6 +573,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     SRCS "${VLLM_MOE_WNA16_SRC}"
     CUDA_ARCHS "${CUDA_ARCHS}")
 
+  list(APPEND VLLM_MOE_EXT_SRC "${VLLM_MOE_WNA16_SRC}")
   cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
   if (MARLIN_MOE_ARCHS)
     set(MARLIN_MOE_SRC

csrc/moe/moe_ops.h

@@ -18,7 +18,7 @@ void sgl_moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
                               torch::Tensor sorted_token_ids,
                               torch::Tensor experts_ids,
                               torch::Tensor num_tokens_post_pad);
-
+#ifndef USE_ROCM
 torch::Tensor moe_wna16_gemm(torch::Tensor input, torch::Tensor output,
                              torch::Tensor b_qweight, torch::Tensor b_scales,
                              std::optional<torch::Tensor> b_qzeros,
@@ -28,3 +28,4 @@ torch::Tensor moe_wna16_gemm(torch::Tensor input, torch::Tensor output,
                              torch::Tensor num_tokens_post_pad, int64_t top_k,
                              int64_t BLOCK_SIZE_M, int64_t BLOCK_SIZE_N,
                              int64_t BLOCK_SIZE_K, int64_t bit);
+#endif
\ No newline at end of file

csrc/moe/torch_bindings.cpp

@@ -31,6 +31,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "                         Tensor! num_tokens_post_pad) -> ()");
   m.impl("sgl_moe_align_block_size", torch::kCUDA, &sgl_moe_align_block_size);
 
+#ifndef USE_ROCM
   m.def(
       "moe_wna16_gemm(Tensor input, Tensor! output, Tensor b_qweight, "
       "Tensor b_scales, Tensor? b_qzeros, "
@@ -41,7 +42,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
 
   m.impl("moe_wna16_gemm", torch::kCUDA, &moe_wna16_gemm);
 
-#ifndef USE_ROCM
   m.def(
       "marlin_gemm_moe(Tensor! a, Tensor! b_q_weights, Tensor! sorted_ids, "
       "Tensor! topk_weights, Tensor! topk_ids, Tensor! b_scales, Tensor! "

vllm/_custom_ops.py

@@ -1146,6 +1146,10 @@ def moe_wna16_gemm(input: torch.Tensor, output: torch.Tensor,
                    num_tokens_post_pad: torch.Tensor, top_k: int,
                    BLOCK_SIZE_M: int, BLOCK_SIZE_N: int, BLOCK_SIZE_K: int,
                    bit: int) -> torch.Tensor:
+    if not current_platform.is_cuda():
+        raise NotImplementedError(
+            "The optimized moe_wna16_gemm kernel is only "
+            "available on CUDA platforms")
     torch.ops._moe_C.moe_wna16_gemm(input, output, b_qweight, b_scales,
                                     b_qzeros, topk_weights, sorted_token_ids,
                                     experts_ids, num_tokens_post_pad, top_k,