Why is it so slow to build a odeVLLM from source using Docker?

[fighterhit]

The compilation of C++ files is very slow. How can I use multi-core builds? I see that it seems to only use one core.

 => [build 6/8] RUN --mount=type=cache,target=/root/.cache/ccache     --mount=type=cache,target=/root/.cache/uv     --mount=type=bind,source=.git,target=.git      if [ "$USE_SCCACHE" != "1" ]; then   6609.0s
 => => # [53/508] Building CUDA object CMakeFiles/_C.dir/csrc/quantization/w8a8/cutlass/c3x/scaled_mm_blockwise_sm90_fp8.cu.o                                                                                  
 => => # [54/508] Building CUDA object CMakeFiles/_C.dir/csrc/quantization/w8a8/cutlass/scaled_mm_c2x.cu.o                                                                                                     
 => => # [55/508] Building CUDA object CMakeFiles/_C.dir/csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu.o                                                                                                         
 => => # [56/508] Building CUDA object CMakeFiles/_C.dir/csrc/quantization/w8a8/cutlass/moe/grouped_mm_c3x_sm90.cu.o                                                                                           
 => => # [57/508] Building CUDA object CMakeFiles/_C.dir/csrc/quantization/w8a8/cutlass/moe/moe_data.cu.o                                                                                                      
 => => # [58/508] Building CUDA object CMakeFiles/_C.dir/csrc/quantization/machete/generated/machete_mm_dispatch.cu.o

[RunLLM]

To enable multi-core builds and speed up C++/CUDA compilation in vLLM, set the environment variable MAX_JOBS to the number of parallel jobs you want (typically your CPU core count or higher). For CUDA builds, also set NVCC_THREADS (usually 2). For example: export MAX_JOBS=16; export NVCC_THREADS=2 before building, or use Docker build args --build-arg max_jobs=16 --build-arg nvcc_threads=2. This allows the build system to use multiple CPU cores for parallel compilation, significantly reducing build time. Be aware that high values can increase memory usage and may cause OOM on machines with limited RAM. The effective number of jobs is MAX_JOBS / NVCC_THREADS for CUDA compilation, so adjust accordingly for best performance (source, source, source).

If you are using CMake directly, you can also adjust parallelism via the jobs field in your CMakeUserPresets.json or by passing -j to the build command. For repeated builds, using ccache or sccache can further speed up rebuilds by caching compilation results (source, source). Would you like a step-by-step example or Dockerfile snippet?

Sources:

• https://github.com/vllm-project/vllm/issues/13281
• https://docs.vllm.ai/en/latest/deployment/docker/
• https://github.com/vllm-project/vllm/issues/20079
• https://docs.vllm.ai/en/latest/contributing/incremental_build/#__span-4-23
• https://docs.vllm.ai/en/latest/getting_started/installation/gpu.html#set-up-using-python-only-build-without-compilation

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[yangshanjun]

@RunLLM -–no-enable-chunked-prefill 加上这个配置之后，vllm serve启动时发现显存不足，enable的时候，就可以正常启动，请问可能时什么原因

[RunLLM]

原因是关闭chunked prefill（即加上--no-enable-chunked-prefill）后，vLLM会尝试为整个输入分配连续的KV cache空间，导致对显存的需求大幅增加，容易出现显存不足。而开启chunked prefill时，vLLM会将大输入拆分成小块分批处理，显存压力显著降低，因此可以正常启动。详见官方优化文档和相关issue讨论。

需要更详细的原理分析或参数调优建议吗？

Sources:

• https://docs.vllm.ai/en/latest/performance/optimization.html
• https://github.com/vllm-project/vllm/issues/11230

Hint: Mention @RunLLM in the post for followups.

[yangshanjun]

@RunLLM 什么是 bonus tokens

[RunLLM]

bonus token（奖励token）是在speculative decoding（推测解码）中，如果所有草稿（draft）token都被目标模型接受，则会额外采样一个token并追加到序列末尾，这个token只从目标模型分布中采样。这样可以提升每步生成的token数，提高推理效率。详见官方API文档和实现说明。

需要更详细的推理流程或代码示例吗？

Sources:

• https://docs.vllm.ai/en/latest/api/vllm/v1/sample/rejection_sampler/
• https://github.com/vllm-project/vllm/blob/main/vllm/v1/sample/rejection_sampler.py

Hint: Mention @RunLLM in the post for followups.

[yangshanjun]

@RunLLM

sampler_output = self.sampler(

                logits=bonus_logits,

                sampling_metadata=sampling_metadata,

            )

            bonus_token_ids = sampler_output.sampled_token_ids

代码里，bonus_token_ids 为什么等于 sampler_output.sampled_token_ids

[RunLLM]

因为在推测解码流程中，bonus_token_ids 就是通过对 bonus_logits 进行采样得到的结果。即 sampler_output.sampled_token_ids 实际上就是用目标模型的 bonus_logits 按采样参数采样出来的“奖励token”，所以两者相等。详见官方API代码说明和实现源码。

需要详细流程或更底层的采样逻辑解释吗？

Sources:

• https://docs.vllm.ai/en/latest/api/vllm/v1/sample/rejection_sampler/#__code_19
• https://github.com/vllm-project/vllm-omni/blob/main/vllm_omni/worker/npu/npu_ar_model_runner.py

Hint: Mention @RunLLM in the post for followups.

[yangshanjun]

@RunLLM

–served-model-name dpsk abo-vllm-inference-burlingwang-3 deepseek_r1_vllm_pd_amd_xshwu 这几个是vllm什么参数

[RunLLM]

这些是vLLM的--served-model-name参数的取值，用于指定API可接受的模型名称列表。你可以用空格分隔多个名称，服务端会响应任意一个名称的请求，返回结果中的model字段为第一个名称。详见官方参数说明和多名称用法PR说明。

需要详细用法示例或配置建议吗？

Sources:

• https://docs.vllm.ai/en/latest/configuration/engine_args/#-served-model-name
• https://github.com/vllm-project/vllm/pull/2894

Hint: Mention @RunLLM in the post for followups.

[yangshanjun]

@RunLLM

–override-generation-config ‘{“stream”:true, “top_p”:0.95, “repetition_penalty”:1.2}’ 这个对输出影响很大吗，影响到模型精度了

[RunLLM]

是的，这些参数会显著影响输出内容和模型表现。top_p控制采样多样性，repetition_penalty抑制重复，stream仅影响返回方式不影响内容。top_p和repetition_penalty设置不当可能导致生成文本质量下降或风格变化，进而影响“精度”或一致性。详见官方参数说明和采样参数文档。

需要具体参数调优建议或实验对比说明吗？

Sources:

• https://docs.vllm.ai/en/latest/api/vllm/#vllm.SamplingParams

Hint: Mention @RunLLM in the post for followups.

[yangshanjun]

@RunLLM 这个模型本身是不是有推荐的参数？

[RunLLM]

是的，大多数模型在 Hugging Face 仓库中会提供推荐的采样参数（如top_p、temperature等），通常保存在generation_config.json文件中。vLLM默认会自动读取并应用这些推荐参数，除非你手动覆盖。详见官方说明和采样参数机制。

需要了解如何查看或修改这些推荐参数吗？

Sources:

• https://docs.vllm.ai/en/latest/getting_started/quickstart/#__span-6-7

Hint: Mention @RunLLM in the post for followups.

[yangshanjun]

@RunLLM

cat generation_config.json
{
“_from_model_config”: true,
“bos_token_id”: 0,
“eos_token_id”: 1,
“do_sample”: true,
“temperature”: 0.6,
“top_p”: 0.95,
“transformers_version”: “4.46.3”
} 这个是模型本身的参数，差异就在于 repetition_penalty

[RunLLM]

是的，你展示的generation_config.json是模型自带的推荐采样参数，里面没有repetition_penalty字段，说明模型默认不会对重复惩罚做特殊设置。如果你在推理时手动加了repetition_penalty，会改变生成风格和内容，与模型原生推荐参数存在差异。详见官方参数机制说明。

需要进一步分析repetition_penalty的具体影响吗？

Sources:

• 官方参数机制说明

Hint: Mention @RunLLM in the post for followups.

[yangshanjun]

@RunLLM vllm FA算子的triton实现在哪里

[RunLLM]

我的理解是，vLLM中FA（FlashAttention）算子的Triton实现主要位于vllm/attention/backends/triton_flash_attn.py等文件，并依赖Triton自定义kernel。你可以在triton_flash_attn.py源码中看到相关实现细节。此外，部分Triton kernel代码也可能在benchmarks/kernels/目录下用于性能测试。官方文档也提到Triton算子可作为高性能自定义op接入，但Triton kernel源码本身通常嵌入在上述Python文件或随Triton库分发，并非全部在vLLM仓库单独列出。插件系统文档也有相关说明。

需要具体的kernel代码片段或调用流程解析吗？

Sources:

• 插件系统文档

Hint: Mention @RunLLM in the post for followups.

[yangshanjun]

@RunLLM vllm 有 mla算子的triton 实现吗

[RunLLM]

有，vLLM 已经集成了 MLA（Multi-head Latent Attention，多头潜变量注意力）的 Triton 实现，主要体现在 TritonMLAImpl 和 AiterTritonMLAImpl 这两个类中。TritonMLAImpl 是 vLLM 自己的 Triton MLA 实现，AiterTritonMLAImpl 则是基于 AITER 的 Triton MLA 实现，二者都调用了 Triton kernel 实现 MLA 算子。相关代码分别位于 triton_mla.py 和 aiter_triton_mla.py，官方文档也有说明：TritonMLAImpl、AiterTritonMLAImpl。

需要详细的调用流程或 Triton kernel 代码解析吗？

Sources:

• https://github.com/vllm-project/vllm/blob/main/vllm/v1/attention/backends/mla/triton_mla.py
• https://github.com/vllm-project/vllm/blob/main/vllm/v1/attention/backends/mla/aiter_triton_mla.py
• https://docs.vllm.ai/en/latest/api/vllm/v1/attention/backends/mla/triton_mla/
• https://docs.vllm.ai/en/latest/api/vllm/v1/attention/backends/mla/aiter_triton_mla/

Hint: Mention @RunLLM in the post for followups.