ref(upstream): FULL TREE — Deep-Spark xllm (1470) + ds_vllm csrc/models (703)

Replaces cherry-picked upstream_ref with complete source trees.

xllm/ — Iluvatar official C++ inference engine (15MB, 1470 files)
  Complete: kernels → layers → models → runtime → scheduler → api
  Excluded: .git, binary images, third_party submodule checkouts

ds_vllm/ — Iluvatar official vllm fork (8MB, 703 files)
  Included: csrc/ (ALL CUDA kernels), fused_moe/, qwen3_5 model, _custom_ops
  Excluded: tests, benchmarks, docs, examples (not needed for reference)

Critical call chains now fully traceable:
  MoE: moe_topk_softmax_kernels.cuh → ixformer.h → fused_moe.cpp → layer
  GDN: qwen3_gated_delta_net_base.cpp → qwen3_5_gated_delta_net.cpp
  Attention: ixformer.h → xllm_paged_attention → attention.cpp

upstream_ref/xllm/tools/README.md

@@ -0,0 +1,30 @@
+# NPU Timeline Generation Guide
+## Prerequisites
+- Python environment
+- Chrome browser (for visualization)
+## Implementation Steps
+### 1. Code Modification
+#### Register the subscriber
+Add the following at the beginning of your program:
+```cpp
+MsptiMetrics::register_subscriber();
+```
+#### Add tracing to ACLNN functions (work for msprof as well)
+Insert the following macro in your ACLNN functions where you want to measure performance:
+```cpp
+LLM_MSTX_RANGE();
+```
+#### Release the subscriber
+Add this at the end of your program:
+```cpp
+MsptiMetrics::release_subscriber();
+```
+### 2. Log Processing
+After running your program, process the generated log file using the timeline script:
+```bash
+python npu_timeline.py -i custom_log.log -o custom_output.json
+```
+### 3. Visualization
+Open Chrome browser
+Navigate to: chrome://tracing
+Load the generated JSON file: custom_output.json