Add EAGLE mtbench benchmark script (#4676)

Co-authored-by: chromecast56 <jamesll@mit.edu>

benchmark/mtbench/README.md

@@ -15,6 +15,17 @@ python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port
 python3 bench_sglang.py --num-questions 80
 ```
 
+### Benchmark sglang EAGLE
+```
+python3 -m sglang.launch_server --model meta-llama/Meta-Llama-3-8B-Instruct --speculative-algo EAGLE \
+    --speculative-draft lmsys/sglang-EAGLE-LLaMA3-Instruct-8B --speculative-num-steps 5 \
+    --speculative-eagle-topk 8 --speculative-num-draft-tokens 64 --dtype float16 --port 30000
+```
+
+```
+python3 bench_sglang_eagle.py --num-questions 80 --parallel 1
+```
+
 
 ### Benchmark vllm
 ```

benchmark/mtbench/bench_sglang_eagle.py

+"""
+Adapted from https://github.com/chromecast56/sglang/blob/6f145d2eadb93a116134f703358ce76f15381045/benchmark/mtbench/bench_sglang.py
+
+Benchmark SGLang EAGLE/EAGLE3 Speculative Decoding
+
+Usage:
+python3 benchmark/mtbench/bench_sglang_eagle.py --num-questions 80 --parallel 1
+"""
+
+import argparse
+import json
+import os
+import time
+import uuid
+
+import sglang as sgl
+from sglang.test.test_utils import (
+    add_common_sglang_args_and_parse,
+    select_sglang_backend,
+)
+
+
+def load_questions(filename):
+    questions = []
+    with open(filename, "r") as fin:
+        for line in fin:
+            obj = json.loads(line)
+            questions.append(obj)
+    return questions
+
+
+def write_answers(filename, model_id, questions, answers):
+    with open(os.path.expanduser(filename), "w") as fout:
+        for i in range(len(answers)):
+            ans_json = {
+                "question_id": questions[i]["question_id"],
+                "answer_id": uuid.uuid4().hex,
+                "model_id": model_id,
+                "choices": {
+                    "index": 0,
+                    "turns": [answers[i][0], answers[i][1]],
+                },
+                "tstamp": time.time(),
+            }
+            fout.write(json.dumps(ans_json) + "\n")
+
+
+@sgl.function
+def answer_mt_bench(s, question_1, question_2):
+    s += sgl.system(
+        "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."
+    )
+    s += sgl.user(question_1)
+    s += sgl.assistant(sgl.gen("answer_1"))
+    s += sgl.user(question_2)
+    s += sgl.assistant(sgl.gen("answer_2"))
+
+
+def main(args):
+    # Construct prompts
+    questions = load_questions(args.question_file)[: args.num_questions]
+    arguments = [
+        {"question_1": q["turns"][0], "question_2": q["turns"][1]} for q in questions
+    ]
+
+    # Select backend
+    backend = select_sglang_backend(args)
+    sgl.set_default_backend(backend)
+
+    # Run requests
+    tic = time.time()
+    rets = answer_mt_bench.run_batch(
+        arguments,
+        temperature=0,
+        max_new_tokens=2048,
+        num_threads=args.parallel,
+        progress_bar=True,
+    )
+    answers = [[s["answer_1"], s["answer_2"]] for s in rets]
+
+    latency = time.time() - tic
+    num_output_tokens = sum(
+        s.get_meta_info("answer_1")["completion_tokens"]
+        + s.get_meta_info("answer_2")["completion_tokens"]
+        for s in rets
+    )
+
+    # NOTE: acceptance length is just completion_tokens / spec_verify_ct
+    # {'id': '3bb9c5ead109488d8ed5ee9cbecaec29', 'finish_reason': {'type': 'length', 'length': 256}, 'prompt_tokens': 37, 'spec_verify_ct': 101, 'completion_tokens': 256, 'cached_tokens': 0}
+
+    output_throughput = num_output_tokens / latency
+
+    has_verify = "spec_verify_ct" in rets[0].get_meta_info("answer_1")
+    if has_verify:
+        num_verify_tokens = sum(
+            s.get_meta_info("answer_1")["spec_verify_ct"]
+            + s.get_meta_info("answer_2")["spec_verify_ct"]
+            for s in rets
+        )
+
+        accept_length = num_output_tokens / num_verify_tokens
+    else:
+        accept_length = 1.0
+
+    print(
+        f"#questions: {len(questions)}, Throughput: {output_throughput:.2f} token/s, Acceptance length: {accept_length:.2f}"
+    )
+
+    # Write results
+    model_id = backend.model_info["model_path"]
+    answer_file = args.answer_file or f"tmp_output_{args.backend}.txt"
+    write_answers(answer_file, model_id, questions, answers)
+
+    with open(args.result_file, "a") as fout:
+        value = {
+            "task": "mtbench",
+            "backend": args.backend,
+            "num_gpus": 1,
+            "latency": round(latency, 3),
+            "throughput": round(output_throughput, 3),
+            "accept_length": round(accept_length, 3),
+            "num_requests": args.num_questions,
+            "other": {
+                "num_questions": args.num_questions,
+                "parallel": args.parallel,
+            },
+        }
+        fout.write(json.dumps(value) + "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--question-file", type=str, default="question.jsonl")
+    parser.add_argument("--answer-file", type=str, default=None)
+    parser.add_argument("--num-questions", type=int, default=80)
+    args = add_common_sglang_args_and_parse(parser)
+    main(args)