Update readme (#568)

benchmark/latency_throughput/README.md

-### Download data
+
+# Benchmark Latency and Throughput
+
+## SGLang
+
+### Launch server
 ```
-wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
 ```
-Install [FlashInfer](https://github.com/flashinfer-ai/flashinfer) if you want it to be enabled.
 
+### Benchmark one batch
 
-### SGLang
 ```
-# use native attention
-python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --tp 1 --port 30000
-# use flashinfer attention: --enable-flashinfer
-# disable RadixAttention: --disable-radix-cache
+python3 bench_one.py
+python3 bench_one.py --batch-size 64
 ```
 
+### Benchmark online serving with many requests
+
 ```
-# run ShareGPT
-python3 bench_throughput.py --backend srt --tokenizer meta-llama/Llama-2-7b-chat-hf --dataset ShareGPT_V3_unfiltered_cleaned_split.json --num-prompts 10 --request-rate 10 --port 30000
+python3 bench_serving.py --backend srt --port 30000 --tokenizer meta-llama/Llama-2-7b-chat-hf --num-prompt 1000 --request-rate 100 --input-len 1024 --output-len 256
 ```
 
+### Benchmark online serving on the ShareGPT dataset
+
+#### Download data
 ```
-# run synthetic
-python3 bench_throughput.py --backend srt --tokenizer meta-llama/Llama-2-7b-chat-hf --num-prompt 1000 --request-rate 100 --input-len 1024 --output-len 256 --port 30000
+wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
 ```
 
+#### Run ShareGPT
+```
+python3 bench_throughput.py --backend srt --port 30000 --tokenizer meta-llama/Llama-2-7b-chat-hf --dataset ShareGPT_V3_unfiltered_cleaned_split.json --num-prompts 10 --request-rate 10
+```
+
+## Other baselines
 
 ### vLLM
 ```
@@ -30,13 +41,13 @@ python3 -m vllm.entrypoints.api_server --model meta-llama/Llama-2-7b-chat-hf --t
 ```
 
 ```
-# run ShareGPT
-python3 bench_throughput.py --backend vllm --tokenizer meta-llama/Llama-2-7b-chat-hf --dataset ShareGPT_V3_unfiltered_cleaned_split.json --num-prompts 10 --request-rate 10 --port 21000
+# run synthetic
+python3 bench_throughput.py --backend vllm --port 30000 --tokenizer meta-llama/Llama-2-7b-chat-hf --num-prompt 1000 --request-rate 100 --input-len 1024 --output-len 256
 ```
 
 ```
-# run synthetic
-python3 bench_throughput.py --backend vllm --tokenizer meta-llama/Llama-2-7b-chat-hf --num-prompt 1000 --request-rate 100 --input-len 1024 --output-len 256 --port 30000
+# run ShareGPT
+python3 bench_throughput.py --backend vllm --port 21000 --tokenizer meta-llama/Llama-2-7b-chat-hf --dataset ShareGPT_V3_unfiltered_cleaned_split.json --num-prompts 10 --request-rate 10
 ```
 
 
@@ -46,5 +57,5 @@ python -m lightllm.server.api_server --model_dir ~/model_weights/Llama-2-7b-chat
 ```
 
 ```
-python3 bench_throughput.py --backend lightllm --tokenizer meta-llama/Llama-2-7b-chat-hf --dataset ShareGPT_V3_unfiltered_cleaned_split.json --num-prompts 10 --request-rate 10 --port 22000
-```
+python3 bench_throughput.py --backend lightllm --port 22000 --tokenizer meta-llama/Llama-2-7b-chat-hf --dataset ShareGPT_V3_unfiltered_cleaned_split.json --num-prompts 10 --request-rate 10
+```
\ No newline at end of file

benchmark/latency_throughput/test_latency.py → benchmark/latency_throughput/bench_one.py

@@ -92,4 +92,4 @@ if __name__ == "__main__":
     print(ret)
 
     speed = args.batch_size * max_new_tokens / latency
-    print(f"latency: {latency:.2f} s, speed: {speed:.2f} token/s")
+    print(f"latency: {latency:.2f} s, speed: {speed:.2f} token/s")
\ No newline at end of file

benchmark/latency_throughput/bench_throughput.py → benchmark/latency_throughput/bench_serving.py

@@ -296,23 +296,27 @@ def main(args: argparse.Namespace):
     )
     benchmark_end_time = time.perf_counter()
     benchmark_time = benchmark_end_time - benchmark_start_time
-    print(f"Total time: {benchmark_time:.2f} s")
-    print(f"Throughput: {args.num_prompts / benchmark_time:.2f} requests/s")
 
-    # Compute the latency statistics.
+    # Compute the statistics.
     avg_latency = np.mean([latency for _, _, latency in REQUEST_LATENCY])
-    print(f"Average latency: {avg_latency:.2f} s")
     avg_per_token_latency = np.mean(
         [
             latency / (prompt_len + output_len)
             for prompt_len, output_len, latency in REQUEST_LATENCY
         ]
     )
-    print(f"Average latency per token: {avg_per_token_latency:.2f} s")
     avg_per_output_token_latency = np.mean(
         [latency / output_len for _, output_len, latency in REQUEST_LATENCY]
     )
-    print("Average latency per output token: " f"{avg_per_output_token_latency:.2f} s")
+    decoding_throughput = np.sum([
+        output_len for _, output_len, _ in REQUEST_LATENCY]) / benchmark_time
+
+    print(f"Total time: {benchmark_time:.2f} s")
+    print(f"Request throughput: {args.num_prompts / benchmark_time:.2f} requests/s")
+    print(f"Decoding throughput: {decoding_throughput:.2f} token/s")
+    print(f"Average latency: {avg_latency:.2f} s")
+    print(f"Average latency per token: {avg_per_token_latency:.2f} s")
+    print(f"Average latency per output token: {avg_per_output_token_latency:.2f} s")
 
 
 if __name__ == "__main__":