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/continuous_scheduler.md

@@ -0,0 +1,7 @@
+# Continuous Scheduler
+
+## Feature Introduction
+xLLM implements a scheduling strategy that supports continuous batching. Continuous batching is a dynamic batching strategy that does not wait for a batch to be filled. Instead, it starts processing as soon as requests are available, while continuously accepting new requests and adding them to the currently executing batch. This approach significantly reduces latency while maintaining high throughput.
+
+## Usage
+xLLM implements the continuous batching scheduling strategy. And, the default scheduler is chunked prefill, `enable_chunked_prefill=true` by default.