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/accuracy_test.md

@@ -0,0 +1,59 @@
+# 1. LLM Accuracy Test
+## 1.1 Setup ais_bench
+```bash
+# Create a virtual environment for ais_bench using conda or uv
+conda create --name ais_bench python=3.10 -y
+conda activate ais_bench
+
+# Clone ais_bench and install dependencies
+git clone https://gitee.com/aisbench/benchmark.git
+cd benchmark/
+pip3 install -e ./ --use-pep517
+
+# Download the dataset and copy it to the ais_bench directory
+cp -r /path/to/dataset  /path/to/benchmark/ais_bench/datasets
+```
+
+## 1.2 Modify Configuration
+Modify the accuracy test configuration file according to your actual situation: `/path/to/benchmark/ais_bench/benchmark/configs/models/vllm_api/vllm_api_general_chat.py`. It is recommended to set the sampling parameters as follows:
+```python
+models = [
+    dict(
+        attr="service",
+        type=VLLMCustomAPIChat,
+        abbr='vllm-api-general-chat',
+        path="/path/to/model/Qwen3-8B", # Model path
+        model="Qwen3-8B", # Model name
+        request_rate = 0,
+        retry = 2,
+        host_ip = "127.0.0.1",
+        host_port = 19000, # xllm server port
+        max_out_len = 32768, # Limit maximum model length
+        batch_size=32,
+        trust_remote_code=False,
+        generation_kwargs = dict(
+            temperature = 0.6,
+            # top_k = -1,
+            top_p = 0.95,
+            # seed = None,
+            # repetition_penalty = 1,
+        ),
+        pred_postprocessor=dict(type=extract_non_reasoning_content)
+    )
+]
+```
+
+## 1.3 Launch ais_bench
+Before using ais_bench, you need to start the xllm server first. Use `ais_bench -h` to get parameter descriptions. The launch commands for gsm8k and ceval datasets are as follows:
+```bash
+# Using gsm8k dataset
+ais_bench --models vllm_api_general_chat --datasets gsm8k_gen_0_shot_cot_chat_prompt --dump-eval-details
+
+# Using ceval dataset
+ais_bench --models vllm_api_general_chat --datasets ceval_gen_0_shot_cot_chat_prompt --merge-ds --dump-eval-details
+```
+
+We will integrate ais_bench and datasets (ceval and gsm8k) into the development image in the future. The ais_bench documentation and datasets are as follows:
+* [ais_bench Documentation](https://ais-bench-benchmark.readthedocs.io/en/latest/index.html)
+* [Datasets](https://ais-bench-benchmark.readthedocs.io/en/latest/base_tutorials/all_params/datasets.html)
+