adapt to sglang v0.5.2rc1 on dcu

benchmark/mmlu/bench_other.py

@@ -0,0 +1,173 @@
+import argparse
+import asyncio
+import json
+import os
+import time
+from concurrent.futures import ThreadPoolExecutor
+
+import numpy as np
+import pandas as pd
+import tiktoken
+from tqdm import tqdm
+
+from sglang.test.test_utils import add_common_other_args_and_parse, get_call_generate
+
+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, call_generate):
+    prompts = []
+    labels = []
+
+    # Construct prompts
+    k = args.ntrain
+    train_prompt = gen_prompt(dev_df, subject, k)
+    while len(tokenizer.encode(train_prompt)) > 1536:
+        k -= 1
+        train_prompt = gen_prompt(dev_df, subject, k)
+
+    for i in range(test_df.shape[0]):
+        prompt_end = format_example(test_df, i, include_answer=False)
+        prompt = train_prompt + prompt_end
+        prompts.append(prompt)
+
+        label = test_df.iloc[i, test_df.shape[1] - 1]
+        labels.append(label)
+
+    preds = [None] * len(prompts)
+    max_tokens = 1
+
+    # Run requests
+    if args.backend != "lmql":
+        # Use thread pool
+        def get_one_answer(i):
+            pred = call_generate(prompts[i], temperature=0, max_tokens=max_tokens)
+            preds[i] = pred.strip()[0]
+
+        tic = time.perf_counter()
+        if args.parallel == 1:
+            for i in range(len(prompts)):
+                get_one_answer(i)
+        else:
+            with ThreadPoolExecutor(args.parallel) as executor:
+                executor.map(get_one_answer, list(range(len(prompts))))
+    else:
+        # Use asyncio
+        async def batched_call(batch_size):
+            for i in range(0, len(prompts), batch_size):
+                tasks = []
+                for p in prompts[i : i + batch_size]:
+                    tasks.append(call_generate(p, temperature=0, max_tokens=max_tokens))
+                rets = await asyncio.gather(*tasks)
+                for j in range(len(rets)):
+                    preds[i + j] = rets[j].strip()[0]
+
+        tic = time.perf_counter()
+        asyncio.run(batched_call(batch_size=args.parallel))
+    latency = time.perf_counter() - tic
+
+    # Compute accuracy
+    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
+
+    # Select backend
+    call_generate = get_call_generate(args)
+
+    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, call_generate)
+        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", type=int, default=5)
+    parser.add_argument("--data_dir", type=str, default="data")
+    parser.add_argument("--nsub", type=int, default=60)
+    args = add_common_other_args_and_parse(parser)
+    main(args)