File size: 3,086 Bytes
fec1b45 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | import json
from vllm import LLM, SamplingParams
from tqdm import tqdm
import os
import argparse
def parse_arguments():
parser = argparse.ArgumentParser(description='Cell Type Annotation Evaluation')
parser.add_argument('--model_name', default = "Qwen2.5-72B-Instruct", help='Name of the model to use')
parser.add_argument('--output_path', default= "./results/response.json", help='Path to save full output results')
parser.add_argument('--dataset_path', default= "./data/cta_scrna_full.json", help='Path to test dataset JSON file')
return parser.parse_args()
args = parse_arguments()
model_name = args.model_name
metric_path = args.metric_path
output_path = args.output_path
dataset_path = args.dataset_path
llm = LLM(
model=model_name,
tensor_parallel_size=4,
dtype="half",
gpu_memory_utilization=0.95,
enforce_eager=True,
trust_remote_code=True
)
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
model_name,
trust_remote_code=True
)
sampling_params = SamplingParams(
temperature=0.0,
max_tokens=4096,
repetition_penalty=1.1,
stop_token_ids=[tokenizer.eos_token_id]
)
def messages_to_prompt(messages):
system_prompt = ""
chat_history = []
for msg in messages:
if msg["role"] == "system":
system_prompt = msg["content"]
else:
chat_history.append({"role": msg["role"], "content": msg["content"]})
if tokenizer.chat_template:
prompt = tokenizer.apply_chat_template(
chat_history,
tokenize=False,
add_generation_prompt=True
)
else:
prompt = "\n".join([f"{m['role']}: {m['content']}" for m in messages])
prompt += "\nassistant: "
if system_prompt:
prompt = f"System: {system_prompt}\n\n{prompt}"
return prompt
with open(dataset_path, 'r') as f:
dataset = json.load(f)
print("Preparing prompts...")
prompts = [messages_to_prompt(item["messages"][:-1]) for item in dataset]
batch_size = 1
results = []
all_outputs = []
for i in tqdm(range(0, len(prompts), batch_size), desc="Processing"):
batch_prompts = prompts[i:i+batch_size]
try:
outputs = llm.generate(
batch_prompts,
sampling_params,
use_tqdm=False
)
all_outputs.extend(outputs)
for out in outputs:
results.append(out.outputs[0].text)
except Exception as e:
print(f"batch process {i}-{i+batch_size} fail: {str(e)}")
results.extend([""] * len(batch_prompts))
all_outputs.extend([None] * len(batch_prompts))
output_data = []
for item, pred in zip(dataset, results):
output_item = {
"messages": item["messages"],
"model_response": pred,
"ground_truth": item["messages"][-1]["content"]
}
output_data.append(output_item)
with open(output_path, 'w') as f:
json.dump(output_data, f, indent=2)
print("Done")
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