feat: add llama3 eval (#2515)

python/sglang/llama3_eval.py

+# Adapt from https://github.com/fw-ai/llm_eval_meta
+
+import argparse
+import asyncio
+import os
+import pickle
+import re
+import shutil
+from collections import defaultdict
+from dataclasses import dataclass
+
+import httpx
+import numpy as np
+import openai
+import transformers
+from datasets import load_dataset
+from openai import AsyncOpenAI
+from tqdm import tqdm
+
+# Mapping providers to their clients and models
+provider_to_models = {
+    "b10": {
+        "8b": "meta-llama/Llama-3.1-8B-Instruct",
+        "70b": "meta-llama/Llama-3.1-70B-Instruct",
+        "405b": "meta-llama/Llama-3.1-405B-Instruct",
+    },
+    "oai": {
+        "8b": "meta-llama/Llama-3.1-8B-Instruct",
+        "70b": "meta-llama/Llama-3.1-70B-Instruct",
+        "405b": "meta-llama/Llama-3.1-405B-Instruct",
+    },
+    "sgl": {
+        "8b": "meta-llama/Llama-3.1-8B-Instruct",
+        "70b": "meta-llama/Llama-3.1-70B-Instruct",
+        "405b": "meta-llama/Llama-3.1-405B-Instruct",
+    },
+}
+
+
+async def fetch_responses(
+    client, prompt, semaphore, index, provider, model_size, output_dir, max_tokens
+):
+    output_file = os.path.join(output_dir, f"response_{index}.pkl")
+    if os.path.exists(output_file):
+        print(f"File {output_file} already exists, skipping.")
+        return
+
+    async with semaphore:
+        response = await client.completions.create(
+            model=provider_to_models[provider][model_size],
+            prompt=prompt,
+            temperature=0.0,
+            max_tokens=max_tokens,
+        )
+        if isinstance(response, openai.BadRequestError):
+            with open(output_file, "wb") as f:
+                pickle.dump("bad_response", f)
+        assert isinstance(response, openai.types.completion.Completion)
+        # Save response to a file
+        with open(output_file, "wb") as f:
+            pickle.dump(response, f)
+
+
+TASK_TO_MAX_TOKENS = {
+    "evals__mmlu__details": 1,
+    "evals__mmlu__0_shot__cot__details": 1024,
+    # Official meta uses 1024, but a small % (.05) of questions are answered correctly after relaxing
+    "evals__mmlu_pro__details": 2048,
+    "evals__gsm8k__details": 1024,
+}
+
+TASK_TO_EVAL_SET = {
+    "mmlu": "evals__mmlu__details",
+    "mmlu_0_shot": "evals__mmlu__0_shot__cot__details",
+    "mmlu_pro": "evals__mmlu_pro__details",
+    "gsm8k": "evals__gsm8k__details",
+}
+
+
+class CustomAsyncHTTPXClient(httpx.AsyncClient):
+    async def send(self, request: httpx.Request, *args, **kwargs) -> httpx.Response:
+        request.url = httpx.URL(
+            f"https://model-{os.getenv('MODEL_ID')}.api.baseten.co/development/predict"
+        )
+        return await super().send(request, *args, **kwargs)
+
+
+def get_client(provider):
+    if provider not in "b10":
+        if os.getenv("OPENAI_API_KEY") == None:
+            os.environ["OPENAI_API_KEY"] = "EMPTY"
+    return {
+        "oai": AsyncOpenAI(base_url="http://127.0.0.1:8000/v1/"),
+        "b10": AsyncOpenAI(
+            api_key=f"Api-Key {os.getenv('OPENAI_API_KEY')}",
+            base_url=f"https://model-{os.getenv('MODEL_ID')}.api.baseten.co/development/predict",
+            http_client=CustomAsyncHTTPXClient(),
+        ),
+        "sgl": AsyncOpenAI(base_url="http://127.0.0.1:30000/v1/"),
+    }[provider]
+
+
+# Define the benchmark function
+async def benchmark(args):
+    ds = load_dataset(
+        "meta-llama/Llama-3.1-405B-Instruct-evals",
+        f"Llama-3.1-405B-Instruct-{TASK_TO_EVAL_SET[args.task]}",
+    )
+    semaphore = asyncio.Semaphore(args.concurrency)  # Limit to 16 concurrent tasks
+
+    if args.num_examples is None:
+        args.num_examples = len(ds["latest"]["input_final_prompts"])
+    prompts = ds["latest"]["input_final_prompts"][: args.num_examples]
+
+    # Create the output directory if it does not exist
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    tasks = []
+    # Create the tasks with tqdm progress bar
+    max_tokens = TASK_TO_MAX_TOKENS[TASK_TO_EVAL_SET[args.task]]
+    client = get_client(args.provider)
+    for idx, prompt in enumerate(tqdm(prompts, desc="Creating tasks")):
+        tasks.append(
+            asyncio.create_task(
+                fetch_responses(
+                    client,
+                    f"<|begin_of_text|>{prompt[0]}",
+                    semaphore,
+                    idx,
+                    args.provider,
+                    args.model_size,
+                    args.output_dir,
+                    max_tokens=max_tokens,
+                )
+            )
+        )
+
+    # Run the tasks with tqdm progress bar
+    for future in tqdm(
+        asyncio.as_completed(tasks), total=len(tasks), desc="Processing tasks"
+    ):
+        await future
+
+
+def get_mmlu_answer(response):
+    if response is not None:
+        return response.choices[0].text.lstrip().rstrip().upper().replace(".", "")
+    return None
+
+
+def get_mmlu_cot_answer(response):
+    pattern = r"The best answer is (.+)\.?"
+    match = re.search(pattern, response.choices[0].text)
+    if match:
+        return match.group(1).replace(".", "").replace("*", "")
+
+    pattern = r"the best answer is (.+)\.?"
+    match = re.search(pattern, response.choices[0].text)
+    if match:
+        return match.group(1).replace(".", "")
+
+    pattern = r"The correct answer is (.+)\.?"
+    match = re.search(pattern, response.choices[0].text)
+    if match:
+        return match.group(1).replace(".", "")
+
+    pattern = r"the correct answer is (.+)\.?"
+    match = re.search(pattern, response.choices[0].text)
+    if match:
+        return match.group(1).replace(".", "")
+
+
+def get_answer_gsm8k(response):
+    pattern = r"The final answer is (.+)\.?"
+    match = re.search(pattern, response.choices[0].text)
+    if match:
+        s = match.group(1)
+        for ok_symbol in ["%", "$"]:
+            s = s.replace(ok_symbol, "")
+        return s
+
+
+TASK_TO_ANSWER_EXTRACTOR = {
+    "evals__mmlu__details": get_mmlu_answer,
+    "evals__mmlu__0_shot__cot__details": get_mmlu_cot_answer,
+    "evals__gsm8k__details": get_answer_gsm8k,
+    "evals__mmlu_pro__details": get_mmlu_cot_answer,
+}
+
+
+def get_dataset_from_task(task, response_path, model_size):
+    ds_405b = load_dataset(
+        f"meta-llama/Llama-3.1-405B-Instruct-evals",
+        f"Llama-3.1-405B-Instruct-{task}",
+    )
+    ds_405b_hash_order = [x[0] for x in ds_405b["latest"]["input_final_prompts_hash"]]
+
+    if "70b" in model_size or "8b" in model_size:
+        if "70" in model_size:
+            ref_model_ds = load_dataset(
+                f"meta-llama/Llama-3.1-70B-Instruct-evals",
+                f"Llama-3.1-70B-Instruct-{task}",
+            )
+        else:
+            ref_model_ds = load_dataset(
+                f"meta-llama/Llama-3.1-8B-Instruct-evals",
+                f"Llama-3.1-8B-Instruct-{task}",
+            )
+
+        hash_to_row = {}
+        for row in ref_model_ds["latest"]:
+            hash_to_row[row["input_final_prompts_hash"][0]] = row
+        reordered_rows = []
+        for prompt_hash in ds_405b_hash_order:
+            reordered_rows.append(hash_to_row[prompt_hash])
+        ref_model_ds["latest"] = reordered_rows
+        return ref_model_ds
+
+    return ds_405b
+
+
+def analyze(task, response_path, model_size):
+    ds = get_dataset_from_task(task, response_path, model_size)
+
+    responses = []
+    total = len(ds["latest"])
+
+    for i in range(0, total):
+        response = pickle.load(
+            open(os.path.join(response_path, f"response_{i}.pkl"), "rb")
+        )
+        responses.append(response)
+
+    @dataclass
+    class Stats:
+        correct: int = 0
+        total: int = 0
+        meta_correct: int = 0
+
+        average: float = None
+
+    subtask_name_to_stats = defaultdict(lambda: Stats())
+
+    for response, ds_row in zip(responses, ds["latest"]):
+        model_answer = TASK_TO_ANSWER_EXTRACTOR[task](response)
+
+        subtask = ds_row["subtask_name"]
+
+        is_eval_correct = model_answer in ds_row["input_correct_responses"]
+        if is_eval_correct:
+            subtask_name_to_stats[subtask].correct += 1
+
+        if ds_row["is_correct"]:
+            subtask_name_to_stats[subtask].meta_correct += 1
+
+        subtask_name_to_stats[subtask].total += 1
+
+    micro_stats = Stats()
+    for subtask, stats in subtask_name_to_stats.items():
+        stats.average = stats.correct / stats.total
+        stats.meta_average = stats.meta_correct / stats.total
+
+        micro_stats.correct += stats.correct
+        micro_stats.total += stats.total
+        micro_stats.meta_correct += stats.meta_correct
+
+    micro_stats.average = micro_stats.correct / micro_stats.total
+    micro_stats.meta_average = micro_stats.meta_correct / micro_stats.total
+
+    print("Macro average", np.mean([x.average for x in subtask_name_to_stats.values()]))
+    print(
+        "Meta Macro average",
+        np.mean([x.meta_average for x in subtask_name_to_stats.values()]),
+    )
+    print("Micro average", micro_stats.average)
+    print("Meta Micro average", micro_stats.meta_average)
+
+
+# Entry point for the script
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Script to run model with specified parameters."
+    )
+    parser.add_argument(
+        "--model-size",
+        type=str,
+        default="8b",
+        help="Size of the model (e.g., 8b or 70b)",
+    )
+    parser.add_argument(
+        "--provider",
+        type=str,
+        default="sgl",
+        help="Provider name (e.g., sgl, oai, b10)",
+    )
+    parser.add_argument(
+        "--task",
+        type=str,
+        required=True,
+        help="Task (e.g., mmlu, mmlu_0_shot, mmlu_pro, gsm8k)",
+    )
+    parser.add_argument(
+        "--num-examples", type=int, default=None, help="Number of examples to process"
+    )
+    parser.add_argument("--concurrency", type=int, default=16)
+    parser.add_argument(
+        "--output-dir",
+        type=str,
+        default="tmp-output-dir",
+        help="Directory to save responses",
+    )
+
+    args = parser.parse_args()
+    asyncio.run(benchmark(args))
+    analyze(TASK_TO_EVAL_SET[args.task], args.output_dir, args.model_size)
+    shutil.rmtree("tmp-output-dir", ignore_errors=True)