002f9879b2
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
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1. LLM Accuracy Test
1.1 Setup ais_bench
# 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:
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:
# 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: