release initial code

Co-authored-by: Ying Sheng <sqy1415@gmail.com>
Co-authored-by: Liangsheng Yin <hnyls2002@gmail.com>
Co-authored-by: Zhiqiang Xie <xiezhq@stanford.edu>
Co-authored-by: parasol-aser <3848358+parasol-aser@users.noreply.github.com>
Co-authored-by: LiviaSun <33578456+ChuyueSun@users.noreply.github.com>
Co-authored-by: Cody Yu <hao.yu.cody@gmail.com>

benchmark/mmlu/bench_sglang.py

@@ -0,0 +1,143 @@
+import argparse
+import json
+import os
+import time
+
+import numpy as np
+import pandas as pd
+import tiktoken
+from tqdm import tqdm
+from sglang.test.test_utils import add_common_sglang_args_and_parse, select_sglang_backend
+
+
+choices = ["A", "B", "C", "D"]
+
+tokenizer = tiktoken.encoding_for_model("gpt-3.5-turbo")
+
+
+def format_subject(subject):
+    l = subject.split("_")
+    s = ""
+    for entry in l:
+        s += " " + entry
+    return s
+
+def format_example(df, idx, include_answer=True):
+    prompt = df.iloc[idx, 0]
+    k = df.shape[1] - 2
+    for j in range(k):
+        prompt += "\n{}. {}".format(choices[j], df.iloc[idx, j+1])
+    prompt += "\nAnswer:"
+    if include_answer:
+        prompt += " {}\n\n".format(df.iloc[idx, k + 1])
+    return prompt
+
+def gen_prompt(train_df, subject, k=-1):
+    prompt = "The following are multiple choice questions (with answers) about{}.\n\n".format(format_subject(subject))
+    if k == -1:
+        k = train_df.shape[0]
+    for i in range(k):
+        prompt += format_example(train_df, i)
+    return prompt
+
+def evaluate(args, subject, dev_df, test_df):
+    prompts = []
+    labels = []
+
+    k = args.ntrain
+    few_shot_examples = gen_prompt(dev_df, subject, k)
+    while len(tokenizer.encode(few_shot_examples)) > 1536:
+        k -= 1
+        few_shot_examples = gen_prompt(dev_df, subject, k)
+
+    for i in range(test_df.shape[0]):
+        prompt_end = format_example(test_df, i, include_answer=False)
+        prompts.append(prompt_end)
+
+        label = test_df.iloc[i, test_df.shape[1]-1]
+        labels.append(label)
+
+    arguments = [{"question": p} for p in prompts]
+
+    #####################################
+    ######### SGL Program Begin #########
+    #####################################
+
+    import sglang as sgl
+    
+    @sgl.function
+    def few_shot_mmlu(s, examples, question):
+        s += examples + question + sgl.gen("answer")
+
+    #####################################
+    ########## SGL Program End ##########
+    #####################################
+
+    # Select backend
+    backend = select_sglang_backend(args)
+
+    tic = time.time()
+    states = few_shot_mmlu.bind(examples=few_shot_examples).run_batch(
+        arguments, temperature=0, max_new_tokens=1,
+        backend=backend, num_threads=args.parallel)
+    preds = [s["answer"].strip()[0] if len(s["answer"].strip()) > 0 else ""
+             for s in states]
+    latency = time.time() - tic
+
+    cors = [pred == label for pred, label in zip(preds, labels)]
+    acc = np.mean(cors)
+    cors = np.array(cors)
+
+    print("Average accuracy {:.3f}, latency {:.2f}, #q: {} - {}".format(
+        acc, latency, len(prompts), subject))
+
+    return cors, acc, latency
+
+
+def main(args):
+    subjects = sorted([f.split("_test.csv")[0] for f in os.listdir(os.path.join(args.data_dir, "test")) if "_test.csv" in f])
+
+    all_cors = []
+    all_latencies = []
+    num_requests = 0
+
+    for subject in tqdm(subjects[:args.nsub]):
+        dev_df = pd.read_csv(os.path.join(args.data_dir, "dev", subject + "_dev.csv"), header=None)[:args.ntrain]
+        test_df = pd.read_csv(os.path.join(args.data_dir, "test", subject + "_test.csv"), header=None)
+
+        cors, acc, latency = evaluate(args, subject, dev_df, test_df)
+        all_cors.append(cors)
+        all_latencies.append(latency)
+        num_requests += len(test_df)
+
+    total_latency = np.sum(all_latencies)
+    print("Total latency: {:.3f}".format(total_latency))
+
+    weighted_acc = np.mean(np.concatenate(all_cors))
+    print("Average accuracy: {:.3f}".format(weighted_acc))
+
+    # Write results
+    with open(args.result_file, "a") as fout:
+        value = {
+            "task": "mmlu",
+            "backend": args.backend,
+            "num_gpus": 1,
+            "latency": round(total_latency, 3),
+            "accuracy": round(weighted_acc, 3),
+            "num_requests": num_requests,
+            "other": {
+                "nsub": args.nsub,
+                "parallel": args.parallel,
+            }
+        }
+        fout.write(json.dumps(value) + "\n")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--ntrain", "-k", type=int, default=5)
+    parser.add_argument("--data_dir", "-d", type=str, default="data")
+    parser.add_argument("--save_dir", "-s", type=str, default="results")
+    parser.add_argument("--nsub", type=int, default=60)
+    args = add_common_sglang_args_and_parse(parser)
+    main(args)