BentoML 发布 llm-optimizer，LLM 推理和性能优化开源工具

BentoML近日发布了llm-optimizer，这是一个用于基准测试和优化 LLM 推理的开源工具。它支持多个推理框架，并兼容任何开源 LLM。

llm-optimizer 旨在将 LLM 性能优化的繁琐手动工作自动化。您可以在一个地方运行结构化实验、应用约束并可视化结果，只需几个命令即可。

使用示例

llm-optimizer estimate \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --input-len 1024 \
  --output-len 512 \
  --gpu A100 \
  --num-gpus 2

预期输出

=== Configuration ===
Model: meta-llama/Llama-3.1-8B-Instruct
GPU: 2x A100
Precision: fp16
Input/Output: 1024/512 tokens
Target: throughput

Fetching model configuration...
Model: 8029995008.0B parameters, 32 layers

=== Performance Analysis ===
Best Latency (concurrency=1):
  TTFT: 43.1 ms
  ITL: 2.6 ms
  E2E: 1.39 s

Best Throughput (concurrency=512):
  Output: 18873.3 tokens/s
  Input: 23767.8 tokens/s
  Requests: 14.24 req/s
  Bottleneck: Memory

=== Roofline Analysis ===
Hardware Ops/Byte Ratio: 142.5 ops/byte
Prefill Arithmetic Intensity: 52205.5 ops/byte
Decode Arithmetic Intensity: 50.9 ops/byte
Prefill Phase: Compute Bound
Decode Phase: Memory Bound

=== Concurrency Analysis ===
KV Cache Memory Limit: 688 concurrent requests
Prefill Compute Limit: 8 concurrent requests
Decode Capacity Limit: 13 concurrent requests
Theoretical Overall Limit: 8 concurrent requests
Empirical Optimal Concurrency: 16 concurrent requests

=== Tuning Commands ===

--- SGLANG ---
Simple (concurrency + TP/DP):
  llm-optimizer --framework sglang --model meta-llama/Llama-3.1-8B-Instruct --gpus 2 --host 127.0.0.1 --server-args "tp_size*dp_size=[(1, 2), (2, 1)]" --client-args "num_prompts=1000;dataset_name=sharegpt;random_input=1024;random_output=512;num_prompts=1000;max_concurrency=[256, 512, 768]" --output-dir tuning_results --output-json tuning_results/config_1_sglang.json
Advanced (additional parameters):
  llm-optimizer --framework sglang --model meta-llama/Llama-3.1-8B-Instruct --gpus 2 --host 127.0.0.1 --server-args "tp_size*dp_size=[(1, 2), (2, 1)];chunked_prefill_size=[1434, 2048, 2662];schedule_conservativeness=[0.3, 0.6, 1.0];schedule_policy=fcfs" --client-args "num_prompts=1000;dataset_name=sharegpt;random_input=1024;random_output=512;num_prompts=1000;max_concurrency=[256, 512, 768]" --output-dir tuning_results --output-json tuning_results/config_1_sglang.json

--- VLLM ---
Simple (concurrency + TP/DP):
  llm-optimizer --framework vllm --model meta-llama/Llama-3.1-8B-Instruct --gpus 2 --host 127.0.0.1 --server-args "tensor_parallel_size*data_parallel_size=[(1, 2), (2, 1)]" --client-args "num_prompts=1000;dataset_name=sharegpt;random_input=1024;random_output=512;num_prompts=1000;max_concurrency=[256, 512, 768]" --output-dir tuning_results --output-json tuning_results/config_1_vllm.json
Advanced (additional parameters):
  llm-optimizer --framework vllm --model meta-llama/Llama-3.1-8B-Instruct --gpus 2 --host 127.0.0.1 --server-args "tensor_parallel_size*data_parallel_size=[(1, 2), (2, 1)];max_num_batched_tokens=[1024, 1177, 1331]" --client-args "num_prompts=1000;dataset_name=sharegpt;random_input=1024;random_output=512;num_prompts=1000;max_concurrency=[256, 512, 768]" --output-dir tuning_results --output-json tuning_results/config_1_vllm.json

该工具解决了LLM部署中的一个常见挑战：在不依赖手动试错的情况下，为延迟、吞吐量和成本找到最佳配置。llm-optimizer为探索LLM性能景观提供了一种结构化的方式。它通过实现系统基准和跨可能配置的自动搜索，消除了重复的猜测。

开源地址：https://github.com/bentoml/llm-optimizer