fix(MoE): robust CUDA kernel loading + no-GPU precompile

1. precompile_moe_topk.py: skip GPU verification during Docker build
   (torch.cuda.is_available() check — .so compilation doesn't need GPU)

2. _custom_ops.py topk_softmax init: 3-tier loading
   - import precompiled module (torch cache)
   - scan known .so paths (torch_extensions cache dirs)
   - JIT compile from .cu source
   - PyTorch fallback with WARNING (not silent — must know if CUDA failed)

3. patch_ops.sh: report .so location after precompile for debugging

qwen3_6_scripts/patch_ops.sh

@@ -267,7 +267,10 @@ if [ -f "$MOE_TOPK_CU" ]; then
     echo "[patch_ops] Precompiling moe_topk_softmax_v3.cu ..."
     python3 /workspace/ex_engine/precompile_moe_topk.py 2>&1 || \
         echo "[patch_ops] WARNING: MoE topk precompile failed — will JIT at runtime"
-    # Also deploy .cu source to vllm for JIT fallback
+    # Find and report the compiled .so location
+    echo "[patch_ops] Searching for compiled .so ..."
+    find /root/.cache/torch_extensions /tmp/torch_extensions -name "*.so" -path "*moe_topk*" 2>/dev/null | head -3
+    # Also deploy .cu source to vllm dir for runtime JIT fallback
     cp "$MOE_TOPK_CU" "$VLLM/model_executor/models/" 2>/dev/null || true
     if [ -n "$VLLM2" ]; then
         cp "$MOE_TOPK_CU" "$VLLM2/model_executor/models/" 2>/dev/null || true