Add benchmark scripts (#476)

benchmark/latency_throughput/README.md

@@ -2,26 +2,43 @@
 ```
 wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
 ```
+Install [FlashInfer](https://github.com/flashinfer-ai/flashinfer) if you want it to be enabled.
+
 
 ### SGLang
 ```
-python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
+# 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
 ```
 
 ```
+# 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
 ```
 
+```
+# run synthetic
+python3 synthetic_benchmark.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
+```
+
 
 ### vLLM
 ```
-python3 -m vllm.entrypoints.api_server --model meta-llama/Llama-2-7b-chat-hf --disable-log-requests --swap-space 16 --port 21000
+python3 -m vllm.entrypoints.api_server --model meta-llama/Llama-2-7b-chat-hf --tensor-parallel 1 --disable-log-requests --swap-space 16 --port 21000
 ```
 
 ```
+# 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 synthetic_benchmark.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
+```
+
 
 ### LightLLM
 ```

benchmark/latency_throughput/synthetic_benchmark.py

@@ -0,0 +1,291 @@
+"""Benchmark online serving throughput.
+
+On the server side, run one of the following commands:
+    (SRT backend)
+    python -m sglang.launch_server \
+        --model <your_model> --tp <num_gpus> \
+        --port 30000 --enable-flashinfer --disable-radix-cache
+
+    (vLLM backend)
+    python -m vllm.entrypoints.api_server \
+        --model <your_model> --tensor <num_gpus> --swap-space 16 \
+        --disable-log-requests --port 30000
+
+    (TGI backend)
+    ./launch_hf_server.sh <your_model>
+
+On the client side, run:
+    python benchmarks/benchmark_serving.py \
+        --backend <backend> \
+        --tokenizer <your_model> \
+        --num-prompt <num_prompts> \
+        --request-rate <request_rate>
+        --input-len <input_len> \
+        --output-len <output_len> \
+        --port 30000
+"""
+
+import argparse
+import asyncio
+import json
+import random
+import time
+from typing import AsyncGenerator, List, Tuple
+
+import aiohttp
+import numpy as np
+from tqdm.asyncio import tqdm_asyncio
+from transformers import PreTrainedTokenizerBase
+
+from sglang.srt.hf_transformers_utils import get_tokenizer
+
+
+def sample_requests(
+    num_requests: int,
+    tokenizer: PreTrainedTokenizerBase,
+    input_len: int,
+    output_len: int,
+) -> List[Tuple[str, int, int]]:
+    prompt = "Hello " * input_len
+    prompt_token_ids = list(tokenizer(prompt).input_ids)
+    requests = []
+    for i in range(num_requests):
+        requests.append((prompt, len(prompt_token_ids), output_len))
+    
+    return requests
+
+
+async def get_request(
+    input_requests: List[Tuple[str, int, int]],
+    request_rate: float,
+) -> AsyncGenerator[Tuple[str, int, int], None]:
+    input_requests = iter(input_requests)
+    for request in input_requests:
+        yield request
+
+        if request_rate == float("inf"):
+            # If the request rate is infinity, then we don't need to wait.
+            continue
+        # Sample the request interval from the exponential distribution.
+        interval = np.random.exponential(1.0 / request_rate)
+        # The next request will be sent after the interval.
+        await asyncio.sleep(interval)
+
+
+async def send_request(
+    backend: str,
+    api_url: str,
+    prompt: str,
+    prompt_len: int,
+    output_len: int,
+    best_of: int,
+    use_beam_search: bool,
+) -> None:
+    headers = {"User-Agent": "Benchmark Client"}
+    if backend == "vllm":
+        pload = {
+            "prompt": prompt,
+            "n": 1,
+            "best_of": best_of,
+            "use_beam_search": use_beam_search,
+            "temperature": 0.0 if use_beam_search else 1.0,
+            "top_p": 1.0,
+            "max_tokens": output_len,
+            "ignore_eos": True,
+            "stream": False,
+        }
+    elif backend == "tgi":
+        assert not use_beam_search
+        params = {
+            "best_of": best_of,
+            "max_new_tokens": output_len,
+            "do_sample": True,
+        }
+        pload = {
+            "inputs": prompt,
+            "parameters": params,
+        }
+    elif backend == "srt":
+        assert not use_beam_search
+        params = {
+            "ignore_eos": True,
+            "max_new_tokens": output_len,
+        }
+        pload = {
+            "text": prompt,
+            "sampling_params": params,
+        }
+    elif backend == "lightllm":
+        assert not use_beam_search
+        params = {
+            "ignore_eos": True,
+            "max_new_tokens": output_len,
+        }
+        pload = {
+            "inputs": prompt,
+            "parameters": params,
+        }
+    else:
+        raise ValueError(f"Unknown backend: {backend}")
+
+    request_start_time = time.perf_counter()
+    first_token_latency = None
+    timeout = aiohttp.ClientTimeout(total=3 * 3600)
+    async with aiohttp.ClientSession(timeout=timeout) as session:
+        while True:
+            async with session.post(api_url, headers=headers, json=pload) as response:
+                chunks = []
+                async for chunk, _ in response.content.iter_chunks():
+                    if first_token_latency is None:
+                        first_token_latency = time.perf_counter() - request_start_time
+                    chunks.append(chunk)
+            output = b"".join(chunks).decode("utf-8")
+            output = json.loads(output)
+
+            # Re-send the request if it failed.
+            if "error" not in output:
+                break
+
+    request_latency = time.perf_counter() - request_start_time
+    return (prompt_len, output_len, request_latency, first_token_latency)
+
+
+async def benchmark(
+    backend: str,
+    api_url: str,
+    input_requests: List[Tuple[str, int, int]],
+    best_of: int,
+    use_beam_search: bool,
+    request_rate: float,
+) -> None:
+    tasks: List[asyncio.Task] = []
+    async for request in get_request(input_requests, request_rate):
+        prompt, prompt_len, output_len = request
+        task = asyncio.create_task(
+            send_request(
+                backend,
+                api_url,
+                prompt,
+                prompt_len,
+                output_len,
+                best_of,
+                use_beam_search,
+            )
+        )
+        tasks.append(task)
+    request_latency = await tqdm_asyncio.gather(*tasks)
+    return request_latency
+
+
+def main(args: argparse.Namespace):
+    print(args)
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+
+    api_url = f"http://{args.host}:{args.port}/generate"
+    tokenizer = get_tokenizer(args.tokenizer, trust_remote_code=args.trust_remote_code)
+    input_requests = sample_requests(args.num_prompts, tokenizer, args.input_len, args.output_len)
+
+    benchmark_start_time = time.perf_counter()
+    # (prompt len, output len, latency, first_token_latency)
+    request_latency = asyncio.run(
+        benchmark(
+            args.backend,
+            api_url,
+            input_requests,
+            args.best_of,
+            args.use_beam_search,
+            args.request_rate,
+        )
+    )
+    benchmark_end_time = time.perf_counter()
+    benchmark_time = benchmark_end_time - benchmark_start_time
+    print(f"Total time: {benchmark_time:.2f} s")
+
+    # Compute the perf statistics.
+    throughput = np.sum([output_len for _, output_len, _, _ in request_latency]) / benchmark_time
+    print(f"Throughput: {throughput} token/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 - first_token_latency) / output_len for _, output_len, latency, first_token_latency in request_latency]
+    )
+    print(f"Average TPOT: {avg_per_output_token_latency * 1000:.0f} ms")
+    avg_first_token_latency = np.mean(
+        [first_token_latency for _, _, _, first_token_latency in request_latency]
+    )
+    print(f"Average TTFT: {avg_first_token_latency:.2f} s")
+
+    stats = {"num_prompts": args.num_prompts, "input_len": args.input_len, "output_len": args.output_len,
+            "total_time (s)": benchmark_time, "throughput (token/s)": throughput, "avg_per_token_latency (s)": avg_per_token_latency,
+            "TPOT (ms)": avg_per_output_token_latency, "TTFT (s)": avg_first_token_latency}
+
+    with open(args.output_file, "a") as f:
+        f.write(json.dumps(stats) + "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Benchmark the online serving throughput."
+    )
+    parser.add_argument(
+        "--backend",
+        type=str,
+        default="vllm",
+        choices=["vllm", "tgi", "srt", "lightllm"],
+    )
+    parser.add_argument("--host", type=str, default="localhost")
+    parser.add_argument("--port", type=int, default=8000)
+    parser.add_argument(
+        "--tokenizer", type=str, required=True, help="Name or path of the tokenizer."
+    )
+    parser.add_argument(
+        "--best-of",
+        type=int,
+        default=1,
+        help="Generates `best_of` sequences per prompt and " "returns the best one.",
+    )
+    parser.add_argument("--use-beam-search", action="store_true")
+    parser.add_argument(
+        "--num-prompts", type=int, default=1000, help="Number of prompts to process."
+    )
+    parser.add_argument(
+        "--request-rate",
+        type=float,
+        default=float("inf"),
+        help="Number of requests per second. If this is inf, "
+        "then all the requests are sent at time 0. "
+        "Otherwise, we use Poisson process to synthesize "
+        "the request arrival times.",
+    )
+    parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument(
+        "--trust-remote-code",
+        action="store_true",
+        help="trust remote code from huggingface",
+    )
+    parser.add_argument(
+        "--input-len",
+        type=int,
+        default=512,
+        help="Number of input tokens"
+    )
+    parser.add_argument(
+        "--output-len",
+        type=int,
+        default=128,
+        help="Number of output tokens"
+    )
+    parser.add_argument(
+        "--output-file",
+        type=str,
+        default="perf_stats.jsonl",
+        help="output file path for performance statistics"
+    )
+    args = parser.parse_args()
+    main(args)