206 lines
6.5 KiB
Python
206 lines
6.5 KiB
Python
import argparse
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import asyncio
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from concurrent.futures import ThreadPoolExecutor
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import json
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from functools import partial
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import os
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import time
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import numpy as np
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import pandas as pd
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import tiktoken
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from tqdm import tqdm
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from sglang.test.test_utils import add_common_other_args_and_parse, call_generate_lightllm, call_generate_vllm, call_generate_srt_raw
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choices = ["A", "B", "C", "D"]
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tokenizer = tiktoken.encoding_for_model("gpt-3.5-turbo")
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def format_subject(subject):
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l = subject.split("_")
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s = ""
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for entry in l:
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s += " " + entry
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return s
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def format_example(df, idx, include_answer=True):
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prompt = df.iloc[idx, 0]
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k = df.shape[1] - 2
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for j in range(k):
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prompt += "\n{}. {}".format(choices[j], df.iloc[idx, j+1])
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prompt += "\nAnswer:"
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if include_answer:
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prompt += " {}\n\n".format(df.iloc[idx, k + 1])
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return prompt
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def gen_prompt(train_df, subject, k=-1):
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prompt = "The following are multiple choice questions (with answers) about{}.\n\n".format(format_subject(subject))
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if k == -1:
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k = train_df.shape[0]
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for i in range(k):
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prompt += format_example(train_df, i)
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return prompt
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model_initialized = None
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def evaluate(args, subject, dev_df, test_df):
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prompts = []
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labels = []
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# Construct prompts
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k = args.ntrain
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train_prompt = gen_prompt(dev_df, subject, k)
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while len(tokenizer.encode(train_prompt)) > 1536:
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k -= 1
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train_prompt = gen_prompt(dev_df, subject, k)
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for i in range(test_df.shape[0]):
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prompt_end = format_example(test_df, i, include_answer=False)
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prompt = train_prompt + prompt_end
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prompts.append(prompt)
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label = test_df.iloc[i, test_df.shape[1]-1]
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labels.append(label)
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preds = [None] * len(prompts)
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max_tokens = 1
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# Select backend
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global model_initialized
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if args.backend == "lightllm":
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url = f"{args.host}:{args.port}/generate"
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call_generate = partial(call_generate_lightllm, url=url, stop=None)
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elif args.backend == "vllm":
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url = f"{args.host}:{args.port}/generate"
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call_generate = partial(call_generate_vllm, url=url, stop=None)
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elif args.backend == "srt-raw":
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url = f"{args.host}:{args.port}/generate"
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call_generate = partial(call_generate_srt_raw, url=url, stop=None)
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elif args.backend == "guidance":
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from guidance import models, gen
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if model_initialized is None:
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model = models.LlamaCpp("/home/ubuntu/model_weights/Llama-2-7b-chat.gguf", n_gpu_layers=-1, n_ctx=4096)
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model_initialized = model
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else:
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model = model_initialized
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def call_generate(prompt, temperature, max_tokens):
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out = model + prompt + gen(name="answer",
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max_tokens=max_tokens, temperature=0)
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return out["answer"]
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# warmup
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call_generate("Hello,", temperature=1.0, max_tokens=8)
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elif args.backend == "lmql":
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import lmql
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model = lmql.model("meta-llama/Llama-2-7b-chat-hf",
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endpoint=f"{args.host}:{args.port}")
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@lmql.query(model=model)
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async def program(question):
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'''lmql
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"""{question}[ANSWER]""" where len(TOKENS(ANSWER)) < 2
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return ANSWER
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'''
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async def call_generate(prompt, temperature, max_tokens):
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return await program(question=prompt, temperature=temperature)
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else:
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raise ValueError(f"Invalid backend: {args.backend}")
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# Run requests
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if args.backend != "lmql":
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# Use thread pool
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def get_one_answer(i):
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pred = call_generate(prompts[i], temperature=0,
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max_tokens=max_tokens)
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preds[i] = pred.strip()[0]
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tic = time.time()
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if args.parallel == 1:
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for i in range(len(prompts)):
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get_one_answer(i)
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else:
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with ThreadPoolExecutor(args.parallel) as executor:
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executor.map(get_one_answer, list(range(len(prompts))))
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else:
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# Use asyncio
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async def batched_call(batch_size):
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for i in range(0, len(prompts), batch_size):
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tasks = []
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for p in prompts[i:i+batch_size]:
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tasks.append(call_generate(p,
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temperature=0, max_tokens=max_tokens))
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rets = await asyncio.gather(*tasks)
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for j in range(len(rets)):
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preds[i+j] = rets[j].strip()[0]
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tic = time.time()
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asyncio.run(batched_call(batch_size=args.parallel))
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latency = time.time() - tic
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# Compute accuracy
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cors = [pred == label for pred, label in zip(preds, labels)]
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acc = np.mean(cors)
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cors = np.array(cors)
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print("Average accuracy {:.3f}, latency {:.2f}, #q: {} - {}".format(
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acc, latency, len(prompts), subject))
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return cors, acc, latency
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def main(args):
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subjects = sorted([f.split("_test.csv")[0] for f in os.listdir(os.path.join(args.data_dir, "test")) if "_test.csv" in f])
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all_cors = []
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all_latencies = []
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num_requests = 0
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for subject in tqdm(subjects[:args.nsub]):
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dev_df = pd.read_csv(os.path.join(args.data_dir, "dev", subject + "_dev.csv"), header=None)[:args.ntrain]
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test_df = pd.read_csv(os.path.join(args.data_dir, "test", subject + "_test.csv"), header=None)
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cors, acc, latency = evaluate(args, subject, dev_df, test_df)
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all_cors.append(cors)
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all_latencies.append(latency)
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num_requests += len(test_df)
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total_latency = np.sum(all_latencies)
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print("Total latency: {:.3f}".format(total_latency))
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weighted_acc = np.mean(np.concatenate(all_cors))
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print("Average accuracy: {:.3f}".format(weighted_acc))
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# Write results
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with open(args.result_file, "a") as fout:
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value = {
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"task": "mmlu",
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"backend": args.backend,
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"num_gpus": 1,
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"latency": round(total_latency, 3),
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"accuracy": round(weighted_acc, 3),
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"num_requests": num_requests,
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"other": {
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"nsub": args.nsub,
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"parallel": args.parallel,
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}
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}
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fout.write(json.dumps(value) + "\n")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--ntrain", type=int, default=5)
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parser.add_argument("--data_dir", type=str, default="data")
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parser.add_argument("--nsub", type=int, default=60)
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args = add_common_other_args_and_parse(parser)
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main(args)
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