fix: ex_engine.python subpackage + flash_qla_sm70 deploy + vllm v0.5.5 MoE kernels

真机验证发现的问题:
1. qwen3_5.py 做 'from ex_engine.python.ix_bridge' 但包结构是 ex_engine.ix_bridge
   → 创建 python/ 子目录 + symlinks
2. flash_qla_sm70 只部署到 /workspace 没有到 vllm models 目录
   → 显式 cp -r 到 VLLM/model_executor/models/
3. 从 vllm v0.5.5 搬 MoE CUDA kernels (torch::Tensor API):
   - topk_softmax_kernels.cu (506行, CUB BlockReduce)
   - moe_align_block_size_kernels.cu (134行)
   - moe_pybind.cpp (pybind11 入口)

真机验证结果:
  ✓ ix_bridge import OK, available=True
  ✓ topk_softmax (64 experts, top8) OK — CUDA kernel 命中
  ✓ ix_full_bridge silu_and_mul OK
  ✓ qwen3_5.py import OK
  ✓ ex_engine build 2/2 factors
  ✓ moe_topk_softmax_v3.so 编译成功
  ✓ flash_qla_sm70_gdn_strided.so 编译成功
  ✗ 单卡 32GB OOM (正常, 竞赛 4卡 tp=4)

ex_engine/csrc/moe_v055/moe_pybind.cpp

@@ -0,0 +1,42 @@
+/*
+ * moe_pybind.cpp — pybind11 entry for vllm MoE CUDA kernels
+ * 
+ * Compiled via torch.utils.cpp_extension.load() on BI-V100 (CoreX)
+ * Exposes:
+ *   - topk_softmax(topk_weights, topk_indices, token_expert_indices, gating_output)
+ *   - moe_align_block_size(topk_ids, num_experts, block_size, sorted_token_ids, experts_ids, num_tokens_post_pad)
+ *
+ * Source: vllm v0.5.5 csrc/moe/ (torch::Tensor API, pre-libtorch_stable)
+ */
+
+#include <torch/extension.h>
+
+// Forward declarations matching vllm v0.5.5 signatures
+void topk_softmax(torch::Tensor& topk_weights,
+                  torch::Tensor& topk_indices,
+                  torch::Tensor& token_expert_indices,
+                  torch::Tensor& gating_output);
+
+void moe_align_block_size(torch::Tensor topk_ids,
+                           int64_t num_experts,
+                           int64_t block_size,
+                           torch::Tensor sorted_token_ids,
+                           torch::Tensor experts_ids,
+                           torch::Tensor num_tokens_post_pad);
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+    m.def("topk_softmax", &topk_softmax,
+          "MoE topk softmax (vllm v0.5.5 CUDA kernel)",
+          py::arg("topk_weights"),
+          py::arg("topk_indices"),
+          py::arg("token_expert_indices"),
+          py::arg("gating_output"));
+    m.def("moe_align_block_size", &moe_align_block_size,
+          "MoE align block size (vllm v0.5.5 CUDA kernel)",
+          py::arg("topk_ids"),
+          py::arg("num_experts"),
+          py::arg("block_size"),
+          py::arg("sorted_token_ids"),
+          py::arg("experts_ids"),
+          py::arg("num_tokens_post_pad"));
+}