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/examples/generate_vlm.py

@@ -0,0 +1,75 @@
+# python generate_vlm.py --model /path/to/Qwen2.5-VL-7B-Instruct/ --disable_prefix_cache --disable_chunked_prefill --max_seqs_per_batch 4 --devices='npu:0' --enable_shm
+
+from xllm import ArgumentParser, SamplingParams
+from xllm import LLM
+# from xllm import VLM
+import base64
+import os
+
+def encode_image_from_file(file_path: str) -> str:
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"not found image: {file_path}")
+    with open(file_path, "rb") as image_file:
+        result = base64.b64encode(image_file.read()).decode("utf-8")
+    return result
+
+parser = ArgumentParser()
+args = parser.parse_args()
+# vlm = VLM(**vars(args))
+vlm = LLM(**vars(args))
+
+
+image_1 = "./images/3.jpg"
+image_2 = "./images/4.jpg"
+
+# image_base64_1 = encode_image_from_file(image_1)
+# image_base64_2 = encode_image_from_file(image_2)
+
+# image_1 = f"data:image/jpeg;base64,{image_base64_1}"
+# image_2 = f"data:image/jpeg;base64,{image_base64_2}"
+
+requests = [
+    {
+        "prompt": (
+            "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+            "<|im_start|>user\n"
+            "<|vision_start|><|image_pad|><|vision_end|>"
+            "请描述这张图片。<|im_end|>\n"
+            "<|im_start|>assistant\n"
+        ),
+        "multi_modal_data": {
+            "image": image_1,
+        },
+    },
+    {
+        "prompt": (
+            "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
+            "<|im_start|>user\n"
+            "<|vision_start|><|image_pad|><|vision_end|>"
+            "<|vision_start|><|image_pad|><|vision_end|>"
+            "请对比这两张图片的主要区别。<|im_end|>\n"
+            "<|im_start|>assistant\n"
+        ),
+        "multi_modal_data": {
+            "image": [image_1, image_2],
+        },
+    },
+]
+
+sampling_params = SamplingParams(
+    temperature=0.8,
+    top_p=0.95,
+    max_tokens=50,
+)
+
+outputs = vlm.generate(
+    requests,
+    sampling_params=sampling_params
+)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+vlm.finish()