feat: pybind wrapper for CUTLASS batched GEMM → MoE decode path

Based on verified result (issue #68):
  CUTLASS Cu10 TensorOp batched: 2.462ms (8 experts, 1 launch)
  vs 8× torch.matmul: 4.6ms (8 launches)
  vs Python F.linear loop: 10.36ms

New files:
  ex_engine/xllm_kernels/cuda/bindings/corex_batched_gemm_bind.cpp
    pybind11 wrapper: batched_gemm_fp16() + moe_decode_fused()
  ex_engine/xllm_kernels/cuda/corex_batched_gemm_kernel.cu
    CUTLASS GemmBatched<half> kernel (from cat_files/batched_gemm.cu)
  qwen3_6_scripts/build_corex_batched_gemm.sh
    Build script for BI-V100 (ivcore10)

Modified:
  qwen3_6_scripts/qwen3_5.py
    import corex_batched_gemm + _USE_COREX_BATCHED_GEMM flag
    Tier 1.5 in MoE decode: after corex_direct_routed, before corex_gather

Build on device: bash qwen3_6_scripts/build_corex_batched_gemm.sh
Output: prebuilt/corex-3.2.3-ivcore10/corex_batched_gemm.so

qwen3_6_scripts/build_corex_batched_gemm.sh

@@ -0,0 +1,90 @@
+#!/bin/bash
+# Build corex_batched_gemm.so — CUTLASS batched GEMM pybind for MoE decode
+#
+# Run on BI-V100:
+#   bash build_corex_batched_gemm.sh
+#
+# Output: corex_batched_gemm.so (deploy to vllm package dir)
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
+PROJ_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
+EX_ENGINE="$PROJ_ROOT/ex_engine"
+
+# Source files
+BIND_CPP="$EX_ENGINE/xllm_kernels/cuda/bindings/corex_batched_gemm_bind.cpp"
+KERNEL_CU="$EX_ENGINE/xllm_kernels/cuda/corex_batched_gemm_kernel.cu"
+
+# CUTLASS headers from cat_files (Iluvatar CoreX fork)
+CUTLASS_INCLUDE="/usr/local/corex/include"
+if [ ! -d "$CUTLASS_INCLUDE/cutlass" ]; then
+    # Fallback: check corex-samples
+    CUTLASS_INCLUDE="/usr/local/corex/samples/cutlass/include"
+fi
+
+# PyTorch/libtorch paths
+TORCH_DIR=$(python3 -c "import torch; print(torch.utils.cmake_prefix_path)" 2>/dev/null || echo "")
+TORCH_INCLUDE=$(python3 -c "import torch; print(torch.utils.cpp_extension.include_paths()[0])" 2>/dev/null || echo "/usr/local/corex/lib/python3/dist-packages/torch/include")
+TORCH_LIB=$(python3 -c "import torch; print(torch.utils.cpp_extension.library_paths()[0])" 2>/dev/null || echo "/usr/local/corex/lib/python3/dist-packages/torch/lib")
+PYTHON_INCLUDE=$(python3 -c "from sysconfig import get_path; print(get_path('include'))")
+
+echo "[build] CUTLASS_INCLUDE=$CUTLASS_INCLUDE"
+echo "[build] TORCH_INCLUDE=$TORCH_INCLUDE"
+echo "[build] TORCH_LIB=$TORCH_LIB"
+
+BUILD_DIR="/tmp/build_corex_batched_gemm"
+mkdir -p "$BUILD_DIR"
+OUT_SO="$SCRIPT_DIR/prebuilt/corex-3.2.3-ivcore10/corex_batched_gemm.so"
+
+# Step 1: Compile CUTLASS kernel .cu → .o
+echo "[build] compiling kernel..."
+nvcc -c "$KERNEL_CU" \
+    -o "$BUILD_DIR/kernel.o" \
+    -I "$CUTLASS_INCLUDE" \
+    -I "$TORCH_INCLUDE" \
+    -I "$TORCH_INCLUDE/torch/csrc/api/include" \
+    --gpu-architecture=ivcore10 \
+    -std=c++17 -O2 \
+    --expt-relaxed-constexpr \
+    -Xcompiler -fPIC
+
+# Step 2: Compile pybind .cpp → .o
+echo "[build] compiling pybind wrapper..."
+g++ -c "$BIND_CPP" \
+    -o "$BUILD_DIR/bind.o" \
+    -I "$TORCH_INCLUDE" \
+    -I "$TORCH_INCLUDE/torch/csrc/api/include" \
+    -I "$PYTHON_INCLUDE" \
+    -I "$CUTLASS_INCLUDE" \
+    -std=c++17 -O2 -fPIC \
+    -D_GLIBCXX_USE_CXX11_ABI=0 \
+    -DTORCH_EXTENSION_NAME=corex_batched_gemm
+
+# Step 3: Link → .so
+echo "[build] linking..."
+g++ -shared \
+    "$BUILD_DIR/kernel.o" \
+    "$BUILD_DIR/bind.o" \
+    -o "$OUT_SO" \
+    -L "$TORCH_LIB" \
+    -ltorch -ltorch_cpu -ltorch_cuda -lc10 -lc10_cuda \
+    -L /usr/local/corex/lib64 -lcudart \
+    -Wl,-rpath,"$TORCH_LIB" \
+    -Wl,-rpath,/usr/local/corex/lib64
+
+echo "[build] ✓ built $OUT_SO"
+echo "[build] size: $(du -h "$OUT_SO" | cut -f1)"
+
+# Quick import test
+python3 -c "
+import torch
+torch.ops.load_library('$OUT_SO')
+import importlib.util
+spec = importlib.util.spec_from_file_location('corex_batched_gemm', '$OUT_SO')
+mod = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(mod)
+print('[build] ✓ import OK, functions:', [x for x in dir(mod) if not x.startswith('_')])
+" 2>&1 || echo "[build] import test skipped (no GPU)"
+
+echo "[build] done"

qwen3_6_scripts/qwen3_5.py

@@ -138,6 +138,11 @@ try:
 except ImportError:
     _corex_moe_direct_routed = None
 
+try:
+    from vllm import corex_batched_gemm as _corex_batched_gemm
+except ImportError:
+    _corex_batched_gemm = None
+
 try:
     from vllm import corex_moe_topk_softmax as _corex_moe_topk_softmax
 except ImportError:
@@ -204,6 +209,9 @@ _USE_COREX_MOE_WEIGHT_GATHER = (
 _USE_COREX_MOE_DIRECT_ROUTED = (
     _corex_moe_direct_routed is not None
     and env_bool("BI100_MOE_COREX_DIRECT_ROUTED", False))
+_USE_COREX_BATCHED_GEMM = (
+    _corex_batched_gemm is not None
+    and env_bool("BI100_MOE_BATCHED_GEMM", True))
 _USE_COREX_MOE_TOPK_SOFTMAX = (
     _corex_moe_topk_softmax is not None
     and env_bool("BI100_MOE_COREX_TOPK_SOFTMAX", True))
@@ -1729,6 +1737,15 @@ class Qwen3_5MoeSparseBlock(nn.Module):
                 return _corex_moe_direct_routed.w2_reduce(
                     act, w2, eids, ws)
 
+            # Tier 1.5: CUTLASS batched GEMM (verified 2.462ms, issue #68)
+            # 1 launch for 8 experts vs 8 launches for F.linear loop
+            if (_USE_COREX_BATCHED_GEMM
+                    and hidden_states.dtype == torch.float16
+                    and w13.dtype == torch.float16
+                    and w2.dtype == torch.float16):
+                return _corex_batched_gemm.moe_decode_fused(
+                    hidden_states, w13[eids], w2[eids], ws)
+
             use_corex_gather = (
                 _USE_COREX_MOE_WEIGHT_GATHER
                 and hidden_states.dtype == torch.float16