fix benchmark (#743)

Co-authored-by: hnyls2002 <hnyls2002@gmail.com>
Co-authored-by: Ying Sheng <sqy1415@gmail.com>

benchmark/blog_v0_2/405b_sglang.sh

-# create ~/llama-3.1-405b-fp8-dummy and create config.json and tokenizer:
-# config.json from ./config.md 
+# Create dummy weights:
+# 1. Create a folder `~/llama-3.1-405b-fp8-dummy` and create `config.json` and tokenizer under this folder.
+# 2. Get `config.json`` from ./config.md
+# 3. Download the tokenizer
 #   wget https://huggingface.co/neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8/resolve/main/tokenizer.json
 #   wget https://huggingface.co/neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8/resolve/main/tokenizer_config.json
 
 # Launch sglang
-# python -m sglang.launch_server --model ~/llama-3.1-405b-fp8-dummy/ --load-format dummy --tp 8 --quant fp8 --disable-radix --mem-frac 0.88
+# python -m sglang.launch_server --model ~/llama-3.1-405b-fp8-dummy/ --load-format dummy --tp 8 --quant fp8 --disable-radix --mem-frac 0.87
 
 # offline
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 2500 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 > sglang/log11
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 2500 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 > sglang/log12
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 2500 --random-input 1024 --random-output 512 --random-range-ratio 0.5 > sglang/log13
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 2500 --random-input 4096 --random-output 512 --random-range-ratio 0.5 > sglang/log14
-python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompt 2500 > sglang/log21
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 3000 --random-input 1024 --random-output 1024 > sglang_log11
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 4000 --random-input 1024 --random-output 512 > sglang_log12
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 800 --random-input 4096 --random-output 2048 > sglang_log13
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 1500 --random-input 4096 --random-output 1024 > sglang_log14
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 6000 --random-input 256 --random-output 512 > sglang_log15
+python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompt 2000 > sglang_log21
 
 # online
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 300 --request-rate 1 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > sglang/log31
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 600 --request-rate 2 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > sglang/log32
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 1200 --request-rate 4 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > sglang/log33
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 2400 --request-rate 8 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > sglang/log34
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > sglang/log35
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 300 --request-rate 1 --random-input 1024 --random-output 1024 > sglang_log31
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 600 --request-rate 2 --random-input 1024 --random-output 1024 > sglang_log32
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 1200 --request-rate 4 --random-input 1024 --random-output 1024 > sglang_log33
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 2400 --request-rate 8 --random-input 1024 --random-output 1024 > sglang_log34
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 1024 --random-output 1024 > sglang_log35
\ No newline at end of file

benchmark/blog_v0_2/405b_trt.sh

@@ -2,15 +2,16 @@
 # https://github.com/sgl-project/tensorrt-demo
 
 # offline
-python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 2500 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log11
-python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 2500 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log12
-python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 2500 --random-input 1024 --random-output 512 --random-range-ratio 0.5 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log13
-python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 2500 --random-input 4096 --random-output 512 --random-range-ratio 0.5 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log14
-python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name sharegpt --num-prompt 2500 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log21
+python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 3000 --random-input 1024 --random-output 1024 --model /root/Meta-Llama-3-8B-Instruct > trtllm_log11
+python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 4000 --random-input 1024 --random-output 512 --model /root/Meta-Llama-3-8B-Instruct > trtllm_log12
+python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 800 --random-input 4096 --random-output 2048 --model /root/Meta-Llama-3-8B-Instruct > trtllm_log13
+python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 1500 --random-input 4096 --random-output 1024 --model /root/Meta-Llama-3-8B-Instruct > trtllm_log14
+python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 6000 --random-input 256 --random-output 512 --model /root/Meta-Llama-3-8B-Instruct > trtllm_log15
+python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name sharegpt --num-prompt 2000 --model /root/Meta-Llama-3-8B-Instruct > trtllm_log21
 
 # online
-python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 300 --request-rate 1 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log31
-python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 600 --request-rate 2 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log32
-python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 1200 --request-rate 4 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log33
-python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 2400 --request-rate 8 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log34
-python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --model meta-llama/Meta-Llama-3-8B-Instruct > trtllm/log35
+python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 300 --request-rate 1 --random-input 1024 --random-output 1024 --model /root/Meta-Llama-3-8B-Instruct > trtllm_log31
+python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 600 --request-rate 2 --random-input 1024 --random-output 1024 --model /root/Meta-Llama-3-8B-Instruct > trtllm_log32
+python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 1200 --request-rate 4 --random-input 1024 --random-output 1024 --model /root/Meta-Llama-3-8B-Instruct > trtllm_log33
+python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 2400 --request-rate 8 --random-input 1024 --random-output 1024 --model /root/Meta-Llama-3-8B-Instruct > trtllm_log34
+python3 ../../python/sglang/bench_serving.py --backend trt --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 1024 --random-output 1024 --model /root/Meta-Llama-3-8B-Instruct > trtllm_log35

benchmark/blog_v0_2/405b_vllm.sh

-# create ~/llama-3.1-405b-fp8-dummy and create config.json and tokenizer:
-# config.json from ./config.md
-# remove the new llama3 rope_scaling entry to run with vLLM 0.5.2
+# Create dummy weights:
+# 1. Create a folder `~/llama-3.1-405b-fp8-dummy` and create `config.json` and tokenizer under this folder.
+# 2. Get `config.json`` from ./config.md
+# 3. Download the tokenizer
 #   wget https://huggingface.co/neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8/resolve/main/tokenizer.json
 #   wget https://huggingface.co/neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8/resolve/main/tokenizer_config.json
 
@@ -8,15 +9,16 @@
 # python3 -m vllm.entrypoints.openai.api_server --model ~/llama-3.1-405b-fp8-dummy/ --load-format dummy --disable-log-requests --tensor-parallel-size 8 --max-model-len 10000
 
 # offline
-python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 2500 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 > vllm/log11
-python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 2500 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 > vllm/log12
-python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 2500 --random-input 1024 --random-output 512 --random-range-ratio 0.5 > vllm/log13
-python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 2500 --random-input 4096 --random-output 512 --random-range-ratio 0.5 > vllm/log14
-python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name sharegpt --num-prompt 2500 > vllm/log21
+python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 3000 --random-input 1024 --random-output 1024 > vllm_log11
+python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 4000 --random-input 1024 --random-output 512 > vllm_log12
+python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 800 --random-input 4096 --random-output 2048 > vllm_log13
+python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 1500 --random-input 4096 --random-output 1024 > vllm_log14
+python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 6000 --random-input 256 --random-output 512 > vllm_log15
+python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name sharegpt --num-prompt 2000 > vllm_log21
 
 # online
-python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 300 --request-rate 1 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > vllm/log31
-python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 600 --request-rate 2 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > vllm/log32
-python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 1200 --request-rate 4 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > vllm/log33
-python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 2400 --request-rate 8 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > vllm/log34
-python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 4096 --random-output 1024 --random-range-ratio 0.125 > vllm/log35
+python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 300 --request-rate 1 --random-input 1024 --random-output 1024 > vllm_log31
+python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 600 --request-rate 2 --random-input 1024 --random-output 1024 > vllm_log32
+python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 1200 --request-rate 4 --random-input 1024 --random-output 1024 > vllm_log33
+python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 2400 --request-rate 8 --random-input 1024 --random-output 1024 > vllm_log34
+python3 ../../python/sglang/bench_serving.py --backend vllm --dataset-name random --num-prompt 3200 --request-rate 16 --random-input 1024 --random-output 1024 > vllm_log35
\ No newline at end of file

benchmark/blog_v0_2/README.md

@@ -48,45 +48,24 @@ Please ensure you have the appropriate hardware before running the benchmarks.
 #### Offline benchmark
 
 ```bash
-# Random dataset, Input [512, 1024], Output [512, 1024], num prompts 3k
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 --output-file sglang_offline_benchmark.jsonl
-		
-# Random dataset, Input [2048, 4096], Output [512, 1024], num prompts 3k      							
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 --output-file sglang_offline_benchmark.jsonl
-
-# Random dataset, Input [512, 1024], Output [256, 512], num prompts 3k							
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 512 --random-range-ratio 0.5 --output-file sglang_offline_benchmark.jsonl
-
-# Random dataset, Input [2048, 4096], Output [256, 512], num prompts 3k
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 512 --random-range-ratio 0.5 --output-file sglang_offline_benchmark.jsonl
-
-# ShareGPT dataset, num prompts 3k
-python3 -m sglang.bench_serving --backend sglang --num-prompts 3000 --output-file sglang_offline_benchmark.jsonl
-
-# get output token throughput
-cat sglang_offline_benchmark.jsonl | cut -d':' -f12 | cut -d',' -f1
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 4000 --random-input 1024 --random-output 1024 --output-file offline.jsonl
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 5000 --random-input 1024 --random-output 512 --output-file offline.jsonl
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 1000 --random-input 4096 --random-output 2048 --output-file offline.jsonl
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 2000 --random-input 4096 --random-output 1024 --output-file offline.jsonl
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --num-prompts 6000 --random-input 256 --random-output 512 --output-file offline.jsonl
+python3 -m sglang.bench_serving --backend sglang --dataset-name sharegpt --num-prompts 3000 --output-file offline.jsonl
+cat offline.jsonl | cut -d':' -f12 | cut -d',' -f1
 ```
 
 #### Online benchmark
 
 ```bash
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 1, num prompts 300
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 300 --request-rate 1 --output-file sglang_online_benchmark.jsonl
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 2, num prompts 600
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 600 --request-rate 2 --output-file sglang_online_benchmark.jsonl
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 4, num prompts 1200
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 1200 --request-rate 4 --output-file sglang_online_benchmark.jsonl
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 8, num prompts 2400
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 2400 --request-rate 8 --output-file sglang_online_benchmark.jsonl
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 16, num prompts 3200
-python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 3200 --request-rate 16 --output-file sglang_online_benchmark.jsonl
-
-# get median e2e latency
-cat sglang_online_benchmark.jsonl | cut -d':' -f9 | cut -d',' -f1
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 300 --request-rate 1 --output-file online.jsonl
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 600 --request-rate 2 --output-file online.jsonl
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 1200 --request-rate 4 --output-file online.jsonl
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 2400 --request-rate 8 --output-file online.jsonl
+python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 3200 --request-rate 16 --output-file online.jsonl
+cat online.jsonl | cut -d':' -f9 | cut -d',' -f1
 ```
 
 ## Other
@@ -98,6 +77,7 @@ Preparation for TensorRT LLM can refer to https://github.com/sgl-project/tensorr
 ```bash
 # vLLM
 pip install vllm==0.5.2
+pip install jsonschema==4.21.1
 
 # Meta-Llama-3-8B-Instruct
 python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-8B-Instruct --disable-log-requests
@@ -116,131 +96,68 @@ wget https://raw.githubusercontent.com/sgl-project/sglang/main/python/sglang/ben
 ```bash
 # vLLM Offline
 
-# Random dataset, Input [512, 1024], Output [512, 1024], num prompts 3k
-python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 --output-file vllm_offline_benchmark.jsonl
-
-# Random dataset, Input [2048, 4096], Output [512, 1024], num prompts 3k
-python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 --output-file vllm_offline_benchmark.jsonl
-
-# Random dataset, Input [512, 1024], Output [256, 512], num prompts 3k
-python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 512 --random-range-ratio 0.5 --output-file vllm_offline_benchmark.jsonl
-
-# Random dataset, Input [2048, 4096], Output [256, 512], num prompts 3k
-python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 512 --random-range-ratio 0.5 --output-file vllm_offline_benchmark.jsonl
-
-# ShareGPT dataset, num prompts 3k
-python3 bench_serving.py --backend vllm --num-prompts 3000 --output-file vllm_offline_benchmark.jsonl
-
-# get output token throughput
-cat vllm_offline_benchmark.jsonl | cut -d':' -f12 | cut -d',' -f1
+python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 4000 --random-input 1024 --random-output 1024 --output-file offline_vllm.jsonl
+python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 5000 --random-input 1024 --random-output 512 --output-file offline_vllm.jsonl
+python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 1000 --random-input 4096 --random-output 2048 --output-file offline_vllm.jsonl
+python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 2000 --random-input 4096 --random-output 1024 --output-file offline_vllm.jsonl
+python3 bench_serving.py --backend vllm --dataset-name random --num-prompts 6000 --random-input 256 --random-output 512 --output-file offline_vllm.jsonl
+python3 bench_serving.py --backend vllm --dataset-name sharegpt --num-prompts 3000 --output-file offline_vllm.jsonl
+cat offline_vllm.jsonl | cut -d':' -f12 | cut -d',' -f1
 ```
 
 ```bash
 # vLLM Online
 
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 1, num prompts 300
-python3 bench_serving.py --backend vllm --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 300 --request-rate 1 --output-file vllm_online_benchmark.jsonl
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 2, num prompts 600
-python3 bench_serving.py --backend vllm --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 600 --request-rate 2 --output-file vllm_online_benchmark.jsonl
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 4, num prompts 1200
-python3 bench_serving.py --backend vllm --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 1200 --request-rate 4 --output-file vllm_online_benchmark.jsonl
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 8, num prompts 2400
-python3 bench_serving.py --backend vllm --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 2400 --request-rate 8 --output-file vllm_online_benchmark.jsonl
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 16, num prompts 3200
-python3 bench_serving.py --backend vllm --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 3200 --request-rate 16 --output-file vllm_online_benchmark.jsonl
-
-# get median e2e latency
-cat vllm_online_benchmark.jsonl | cut -d':' -f9 | cut -d',' -f1
+python3 bench_serving.py --backend vllm --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 300 --request-rate 1 --output-file online_vllm.jsonl
+python3 bench_serving.py --backend vllm --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 600 --request-rate 2 --output-file online_vllm.jsonl
+python3 bench_serving.py --backend vllm --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 1200 --request-rate 4 --output-file online_vllm.jsonl
+python3 bench_serving.py --backend vllm --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 2400 --request-rate 8 --output-file online_vllm.jsonl
+python3 bench_serving.py --backend vllm --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 3200 --request-rate 16 --output-file online_vllm.jsonl
+cat online_vllm.jsonl | cut -d':' -f9 | cut -d',' -f1
 ```
 
 ```bash
 # TensorRT LLM Offline 8B
 
-# Random dataset, Input [512, 1024], Output [512, 1024], num prompts 3k
-python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 --output-file trt_offline_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
-
-# Random dataset, Input [2048, 4096], Output [512, 1024], num prompts 3k
-python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 --output-file trt_offline_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
-
-# Random dataset, Input [512, 1024], Output [256, 512], num prompts 3k
-python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 512 --random-range-ratio 0.5 --output-file trt_offline_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
-
-# Random dataset, Input [2048, 4096], Output [256, 512], num prompts 3k
-python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 512 --random-range-ratio 0.5 --output-file trt_offline_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
-
-# ShareGPT dataset, num prompts 3k
-python3 bench_serving.py --backend trt --num-prompts 3000 --output-file trt_offline_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
-
-# get output token throughput
-cat trt_offline_benchmark_8b.jsonl | cut -d':' -f12 | cut -d',' -f1
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-8B-Instruct --dataset-name random --num-prompts 4000 --random-input 1024 --random-output 1024 --output-file offline_trt_8b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-8B-Instruct --dataset-name random --num-prompts 5000 --random-input 1024 --random-output 512 --output-file offline_trt_8b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-8B-Instruct --dataset-name random --num-prompts 1000 --random-input 4096 --random-output 2048 --output-file offline_trt_8b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-8B-Instruct --dataset-name random --num-prompts 2000 --random-input 4096 --random-output 1024 --output-file offline_trt_8b.jsonl
+python3 bench_serving.py --backend trt --dataset-name random --num-prompts 6000 --random-input 256 --random-output 512 --output-file offline_trt_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-8B-Instruct --dataset-name sharegpt --num-prompts 3000 --output-file offline_trt_8b.jsonl
+cat offline_trt_8b.jsonl | cut -d':' -f12 | cut -d',' -f1
 ```
 
 ```bash
 # TensorRT LLM Online 8B
 
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 1, num prompts 300
-python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 300 --request-rate 1 --output-file trt_online_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 2, num prompts 600
-python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 600 --request-rate 2 --output-file trt_online_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 4, num prompts 1200
-python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 1200 --request-rate 4 --output-file trt_online_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 8, num prompts 2400
-python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 2400 --request-rate 8 --output-file trt_online_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 16, num prompts 3200
-python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 3200 --request-rate 16 --output-file trt_online_benchmark_8b.jsonl --model meta-llama/Meta-Llama-3-8B-Instruct
-
-# get median e2e latency
-cat trt_online_benchmark_8b.jsonl | cut -d':' -f9 | cut -d',' -f1
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-8B-Instruct --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 300 --request-rate 1 --output-file online_trt_8b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-8B-Instruct --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 600 --request-rate 2 --output-file online_trt_8b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-8B-Instruct --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 1200 --request-rate 4 --output-file online_trt_8b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-8B-Instruct --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 2400 --request-rate 8 --output-file online_trt_8b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-8B-Instruct --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 3200 --request-rate 16 --output-file online_trt_8b.jsonl
+cat online_trt_8b.jsonl | cut -d':' -f9 | cut -d',' -f1
 ```
 
 ```bash
 # TensorRT LLM Offline 70B
 
-# Random dataset, Input [512, 1024], Output [512, 1024], num prompts 3k
-python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 1024 --random-range-ratio 0.5 --output-file trt_offline_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
-
-# Random dataset, Input [2048, 4096], Output [512, 1024], num prompts 3k
-python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 1024 --random-range-ratio 0.5 --output-file trt_offline_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
-
-# Random dataset, Input [512, 1024], Output [256, 512], num prompts 3k
-python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 1024 --random-output 512 --random-range-ratio 0.5 --output-file trt_offline_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
-
-# Random dataset, Input [2048, 4096], Output [256, 512], num prompts 3k
-python3 bench_serving.py --backend trt --dataset-name random --num-prompts 3000 --random-input 4096 --random-output 512 --random-range-ratio 0.5 --output-file trt_offline_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
-
-# ShareGPT dataset, num prompts 3k
-python3 bench_serving.py --backend trt --num-prompts 3000 --output-file trt_offline_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
-
-# get output token throughput
-cat trt_offline_benchmark_70b.jsonl | cut -d':' -f12 | cut -d',' -f1
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-70B-Instruct --dataset-name random --num-prompts 4000 --random-input 1024 --random-output 1024 --output-file offline_trt_70b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-70B-Instruct --dataset-name random --num-prompts 5000 --random-input 1024 --random-output 512 --output-file offline_trt_70b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-70B-Instruct --dataset-name random --num-prompts 1000 --random-input 4096 --random-output 2048 --output-file offline_trt_70b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-70B-Instruct --dataset-name random --num-prompts 2000 --random-input 4096 --random-output 1024 --output-file offline_trt_70b.jsonl
+python3 bench_serving.py --backend trt --dataset-name random --num-prompts 6000 --random-input 256 --random-output 512 --output-file offline_trt_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-70B-Instruct --dataset-name sharegpt --num-prompts 3000 --output-file offline_trt_70b.jsonl
+cat offline_trt_70b.jsonl | cut -d':' -f12 | cut -d',' -f1
 ```
 
 ```bash
 # TensorRT LLM Online 70B
 
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 1, num prompts 300
-python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 300 --request-rate 1 --output-file trt_online_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 2, num prompts 600
-python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 600 --request-rate 2 --output-file trt_online_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 4, num prompts 1200
-python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 1200 --request-rate 4 --output-file trt_online_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 8, num prompts 2400
-python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 2400 --request-rate 8 --output-file trt_online_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
-
-# Random dataset, Input [512, 4096], Output [128, 1024], request rate 16, num prompts 3200
-python3 bench_serving.py --backend trt --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 3200 --request-rate 16 --output-file trt_online_benchmark_70b.jsonl --model meta-llama/Meta-Llama-3-70B-Instruct
-
-# get median e2e latency
-cat trt_online_benchmark_70b.jsonl | cut -d':' -f9 | cut -d',' -f1
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-70B-Instruct --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 300 --request-rate 1 --output-file online_trt_70b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-70B-Instruct --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 600 --request-rate 2 --output-file online_trt_70b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-70B-Instruct --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 1200 --request-rate 4 --output-file online_trt_70b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-70B-Instruct --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 2400 --request-rate 8 --output-file online_trt_70b.jsonl
+python3 bench_serving.py --backend trt --model meta-llama/Meta-Llama-3-70B-Instruct --dataset-name random --random-input 1024 --random-output 1024 --num-prompts 3200 --request-rate 16 --output-file online_trt_70b.jsonl
+cat online_trt_70b.jsonl | cut -d':' -f9 | cut -d',' -f1
 ```

python/sglang/bench_serving.py

@@ -369,7 +369,7 @@ def sample_random_requests(
 ) -> List[Tuple[str, int, int]]:
 
     input_lens = np.random.randint(
-        int(input_len * range_ratio),
+        max(int(input_len * range_ratio), 1),
         input_len + 1,
         size=num_prompts,
     )
@@ -415,7 +415,7 @@ def sample_random_requests(
             prompt_token_ids = tokenizer(prompt).input_ids
             prompt_len = len(prompt_token_ids)
 
-            if prompt_len <= input_lens[i]:
+            if prompt_len > input_lens[i]:
                 input_ids = prompt_token_ids[: input_lens[i]]
             else:
                 ratio = (input_lens[i] + prompt_len - 1) // prompt_len
@@ -935,7 +935,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--random-range-ratio",
         type=float,
-        default=1.0,
+        default=0.0,
         help="Range of sampled ratio of input/output length, "
         "used only for random dataset.",
     )

python/sglang/global_config.py

@@ -17,7 +17,7 @@ class GlobalConfig:
 
         # Runtime constants: New generation token ratio estimation
         self.init_new_token_ratio = 0.7
-        self.base_min_new_token_ratio = 0.2
+        self.base_min_new_token_ratio = 0.1
         self.new_token_ratio_decay = 0.001
         self.new_token_ratio_recovery = 0.05