fix(EX): corex ivcore10 build flags + deploy pipeline + topk kernel cleanup

Real machine log (2d5232c dockerrizhi.txt) shows two AST call chain breaks:

1. EVERY layer EVERY token:
   _custom_ops.py:58 'ixformer.functions has no attribute vllm_moe_topk_softmax'
   -> FusedMoE falls to PyTorch loop (2304 calls/token)

2. EVERY GDN layer (4 layers):
   'NaN in prefill GatedDeltaNet layer N (frac=0.9998-1.0000)'
   -> _torch_chunk_gated_delta_rule produces all-NaN

Fixes:
- build.sh: --cuda-gpu-arch=ivcore10, -D__ILUVATAR__ flags from real log
- Dockerfile: add ex_engine build before patch_ops
- patch_ops.sh: deploy .so + python into vllm model dir
- ex_loader.py: search co-located .so paths
- patch_model.py: remove premature auto-apply
- factor_moe_topk_softmax.cu: remove dead parallel branch

ex_engine/build.sh

@@ -57,19 +57,32 @@ compile_factor() {
 
     if [[ "$COMPILER" == "corex" ]]; then
         # CoreX/Iluvatar: clang-based CUDA compilation
-        # SM70 = BI-V100 architecture
+        # From real machine GDN compile log (dockerrizhi.txt):
+        #   /usr/local/corex/bin/clang++ ... --cuda-gpu-arch=ivcore10
+        #   --cuda-path=/usr/local/corex -std=c++17
+        #   -D__ILUVATAR__ -D__ILUVATAR_WORKAROUND__
+        local OBJ="${BUILD_DIR}/$(basename ${cu_file} .cu).cuda.o"
         "${COREX_ROOT}/bin/clang++" \
-            -x cuda \
-            --cuda-gpu-arch=sm_70 \
-            -std=c++17 \
+            -D__ILUVATAR__ \
+            -D__ILUVATAR_WORKAROUND__ \
+            -D__ILUVATAR_DIAG__ \
+            -fPIC \
             -O2 \
-            -shared -fPIC \
+            --cuda-gpu-arch=ivcore10 \
+            --cuda-path="${COREX_ROOT}" \
+            -std=c++17 \
             -I"${INCLUDE_DIR}" \
-            -I"${COREX_ROOT}/include" \
+            -isystem "${COREX_ROOT}/include" \
+            -c "${cu_file}" \
+            -o "${OBJ}"
+
+        # Link .o → .so (match real machine: c++ ... -shared -L ... -lcudart)
+        c++ "${OBJ}" -shared \
             -L"${COREX_ROOT}/lib64" \
             -lcudart \
-            -o "${so_path}" \
-            "${cu_file}"
+            -o "${so_path}"
+
+        rm -f "${OBJ}"
     else
         # Standard nvcc
         nvcc \

ex_engine/csrc/factor_moe_topk_softmax.cu

@@ -98,28 +98,13 @@ __global__ void moe_topk_softmax_kernel(
     float global_sum = s_sum[0] + s_sum[1];
     float my_prob = my_exp / global_sum;  // softmax output
 
-    // Step 5: Top-K selection via shared memory partial sort
-    // Use shared memory to collect all (prob, id) pairs, then
-    // do a register-based bitonic top-K.
+    // Step 5: Top-K selection via shared memory
+    // 64 elements is tiny — thread-0 serial insertion sort is faster than
+    // launching a parallel radix/bitonic for k=8 from n=64.
     __shared__ float  s_probs[64];
-    __shared__ int    s_ids[64];
     s_probs[tid] = my_prob;
-    s_ids[tid]   = my_id;
     __syncthreads();
 
-    // Thread 0 does a simple insertion sort for top_k=8 from 64 elements
-    // This is faster than full bitonic for k << n.
-    // 64 elements × 8 comparisons = 512 ops (fits in registers)
-    if (tid < top_k) {
-        // Each of the first top_k threads finds one winner
-        // We use a parallel argmax approach: each thread looks for
-        // the (tid+1)-th largest element.
-        // Simple approach: tid=0 finds max, tid=1 finds 2nd max, etc.
-        // Use iterative suppression in shared memory.
-
-        // Actually, simpler: thread 0 does all work (64 experts is tiny)
-    }
-
     if (tid == 0) {
         float* out_w = topk_weights + token_idx * top_k;
         int32_t* out_id = topk_ids + token_idx * top_k;
@@ -134,12 +119,11 @@ __global__ void moe_topk_softmax_kernel(
             best_id[k] = -1;
         }
 
-        for (int e = 0; e < num_experts; e++) {
+        for (int e = 0; e < num_experts && e < BLOCK_SIZE; e++) {
             float p = s_probs[e];
-            // Insert into sorted top-K
             if (p > best_w[TOP_K - 1]) {
                 best_w[TOP_K - 1] = p;
-                best_id[TOP_K - 1] = e;
+                best_id[TOP_K - 1] = e;  // expert index = thread index
                 // Bubble up
                 #pragma unroll
                 for (int k = TOP_K - 1; k > 0; k--) {

ex_engine/python/ex_loader.py

@@ -156,13 +156,27 @@ class EXEngine:
             return False
 
     def load_all(self) -> int:
-        """Load all available factor .so files from build_dir."""
+        """Load all available factor .so files from build_dir or co-located."""
         loaded = 0
+        # Search paths: build_dir first, then directory containing this module
+        search_dirs = [self.build_dir]
+        module_dir = os.path.dirname(os.path.abspath(__file__))
+        if module_dir not in search_dirs:
+            search_dirs.append(module_dir)
+        # Also check parent's build dir
+        parent_build = os.path.join(os.path.dirname(module_dir), "build")
+        if parent_build not in search_dirs:
+            search_dirs.append(parent_build)
+
         for fid in range(EX_FACTOR_COUNT):
-            so_path = os.path.join(self.build_dir, f"ex_factor_{fid}.so")
-            if self.load_factor(fid, so_path):
-                loaded += 1
-        logger.info("EX Engine: loaded %d/%d factors", loaded, EX_FACTOR_COUNT)
+            for d in search_dirs:
+                so_path = os.path.join(d, f"ex_factor_{fid}.so")
+                if os.path.exists(so_path):
+                    if self.load_factor(fid, so_path):
+                        loaded += 1
+                    break
+        logger.info("EX Engine: loaded %d/%d factors from %s", loaded, EX_FACTOR_COUNT,
+                    search_dirs)
         return loaded
 
     def has_factor(self, factor_id: int) -> bool:

ex_engine/python/patch_model.py

@@ -191,11 +191,7 @@ def _patch_gdn_prefill(engine):
 
 
 # ---------------------------------------------------------------------------
-# Auto-apply on import if build dir exists
+# Call apply_patches() explicitly AFTER vllm model modules are loaded.
+# Integration point: qwen3_5.py calls this at the end of model __init__,
+# or patch_ops.sh adds it to the startup sequence.
 # ---------------------------------------------------------------------------
-_AUTO_BUILD_DIR = os.environ.get("EX_ENGINE_BUILD_DIR", "/workspace/ex_engine/build")
-if os.path.isdir(_AUTO_BUILD_DIR):
-    try:
-        apply_patches(_AUTO_BUILD_DIR)
-    except Exception as e:
-        logger.warning("EX Engine auto-apply failed: %s", e)