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/docs/en/features/chunked_scheduler.md

@@ -0,0 +1,19 @@
+# ChunkedPrefill Scheduler
+
+## Feature Introduction
+xLLM supports the chunked prefill scheduling strategy. Chunked prefill is a technique that optimizes large language model inference by splitting long prompts into smaller chunks for batch processing, rather than processing the entire prompt at once.
+This method can effectively reduce peak GPU memory usage, improve device utilization, and better schedule and mix processing with requests from the decode stage.
+
+## Usage
+The aforementioned strategy has been implemented in xLLM and is exposed through gflags parameters to control the feature's on/off state.
+
+- Enable chunked prefill and set the chunked size, if not set chunked size, its default value is equal to max_tokens_per_batch.
+```bash
+--enable_chunked_prefill=true
+--max_tokens_per_chunk_for_prefill=20480 # optional
+```
+
+
+
+## Performance Impact
+After enabling chunked prefill, on the Qwen3-8B model with a TPOT constraint of 50ms, the TTFT latency **decreased by 46%**.