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"""Generate answers with local models.
Usage:
python3 gen_model_answer.py --model-path lmsys/fastchat-t5-3b-v1.0 --model-id fastchat-t5-3b-v1.0
"""
import argparse
import json
import os
import random
import time
import shortuuid
import torch
from tqdm import tqdm
from fastchat.llm_judge.common import load_questions, temperature_config
from fastchat.model import load_model, get_conversation_template
from fastchat.utils import str_to_torch_dtype
def run_eval(
model_path,
model_id,
question_file,
question_begin,
question_end,
answer_file,
max_new_token,
num_choices,
num_gpus_per_model,
num_gpus_total,
max_gpu_memory,
dtype,
revision,
):
questions = load_questions(question_file, question_begin, question_end)
# random shuffle the questions to balance the loading
random.shuffle(questions)
# Split the question file into `num_gpus` files
assert num_gpus_total % num_gpus_per_model == 0
use_ray = num_gpus_total // num_gpus_per_model > 1
if use_ray:
get_answers_func = ray.remote(num_gpus=num_gpus_per_model)(
get_model_answers
).remote
else:
get_answers_func = get_model_answers
chunk_size = len(questions) // (num_gpus_total // num_gpus_per_model)
ans_handles = []
for i in range(0, len(questions), chunk_size):
ans_handles.append(
get_answers_func(
model_path,
model_id,
questions[i : i + chunk_size],
answer_file,
max_new_token,
num_choices,
num_gpus_per_model,
max_gpu_memory,
dtype=dtype,
revision=revision,
)
)
if use_ray:
ray.get(ans_handles)
@torch.inference_mode()
def get_model_answers(
model_path,
model_id,
questions,
answer_file,
max_new_token,
num_choices,
num_gpus_per_model,
max_gpu_memory,
dtype,
revision,
):
model, tokenizer = load_model(
model_path,
revision=revision,
device="cuda",
num_gpus=num_gpus_per_model,
max_gpu_memory=max_gpu_memory,
dtype=dtype,
load_8bit=False,
cpu_offloading=False,
debug=False,
)
for question in tqdm(questions):
if question["category"] in temperature_config:
temperature = temperature_config[question["category"]]
else:
temperature = 0.7
choices = []
for i in range(num_choices):
torch.manual_seed(i)
conv = get_conversation_template(model_id)
turns = []
for j in range(len(question["turns"])):
qs = question["turns"][j]
conv.append_message(conv.roles[0], qs)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer([prompt]).input_ids
if temperature < 1e-4:
do_sample = False
else:
do_sample = True
# some models may error out when generating long outputs
try:
output_ids = model.generate(
torch.as_tensor(input_ids).cuda(),
do_sample=do_sample,
temperature=temperature,
max_new_tokens=max_new_token,
)
if model.config.is_encoder_decoder:
output_ids = output_ids[0]
else:
output_ids = output_ids[0][len(input_ids[0]) :]
# be consistent with the template's stop_token_ids
if conv.stop_token_ids:
stop_token_ids_index = [
i
for i, id in enumerate(output_ids)
if id in conv.stop_token_ids
]
if len(stop_token_ids_index) > 0:
output_ids = output_ids[: stop_token_ids_index[0]]
output = tokenizer.decode(
output_ids,
spaces_between_special_tokens=False,
)
if conv.stop_str and isinstance(conv.stop_str, list):
stop_str_indices = sorted(
[
output.find(stop_str)
for stop_str in conv.stop_str
if output.find(stop_str) > 0
]
)
if len(stop_str_indices) > 0:
output = output[: stop_str_indices[0]]
elif conv.stop_str and output.find(conv.stop_str) > 0:
output = output[: output.find(conv.stop_str)]
for special_token in tokenizer.special_tokens_map.values():
if isinstance(special_token, list):
for special_tok in special_token:
output = output.replace(special_tok, "")
else:
output = output.replace(special_token, "")
output = output.strip()
if conv.name == "xgen" and output.startswith("Assistant:"):
output = output.replace("Assistant:", "", 1).strip()
except RuntimeError as e:
print("ERROR question ID: ", question["question_id"])
output = "ERROR"
conv.update_last_message(output)
turns.append(output)
choices.append({"index": i, "turns": turns})
# Dump answers
os.makedirs(os.path.dirname(answer_file), exist_ok=True)
with open(os.path.expanduser(answer_file), "a") as fout:
ans_json = {
"question_id": question["question_id"],
"answer_id": shortuuid.uuid(),
"model_id": model_id,
"choices": choices,
"tstamp": time.time(),
}
fout.write(json.dumps(ans_json) + "\n")
def reorg_answer_file(answer_file):
"""Sort by question id and de-duplication"""
answers = {}
with open(answer_file, "r") as fin:
for l in fin:
qid = json.loads(l)["question_id"]
answers[qid] = l
qids = sorted(list(answers.keys()))
with open(answer_file, "w") as fout:
for qid in qids:
fout.write(answers[qid])
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--model-path",
type=str,
required=True,
help="The path to the weights. This can be a local folder or a Hugging Face repo ID.",
)
parser.add_argument(
"--model-id", type=str, required=True, help="A custom name for the model."
)
parser.add_argument(
"--bench-name",
type=str,
default="mt_bench",
help="The name of the benchmark question set.",
)
parser.add_argument(
"--question-begin",
type=int,
help="A debug option. The begin index of questions.",
)
parser.add_argument(
"--question-end", type=int, help="A debug option. The end index of questions."
)
parser.add_argument("--answer-file", type=str, help="The output answer file.")
parser.add_argument(
"--max-new-token",
type=int,
default=1024,
help="The maximum number of new generated tokens.",
)
parser.add_argument(
"--num-choices",
type=int,
default=1,
help="How many completion choices to generate.",
)
parser.add_argument(
"--num-gpus-per-model",
type=int,
default=1,
help="The number of GPUs per model.",
)
parser.add_argument(
"--num-gpus-total", type=int, default=1, help="The total number of GPUs."
)
parser.add_argument(
"--max-gpu-memory",
type=str,
help="Maxmum GPU memory used for model weights per GPU.",
)
parser.add_argument(
"--dtype",
type=str,
choices=["float32", "float16", "bfloat16"],
help="Override the default dtype. If not set, it will use float16 on GPU and float32 on CPU.",
default=None,
)
parser.add_argument(
"--revision",
type=str,
default="main",
help="The model revision to load.",
)
args = parser.parse_args()
if args.num_gpus_total // args.num_gpus_per_model > 1:
import ray
ray.init()
question_file = f"data/{args.bench_name}/question.jsonl"
if args.answer_file:
answer_file = args.answer_file
else:
answer_file = f"data/{args.bench_name}/model_answer/{args.model_id}.jsonl"
print(f"Output to {answer_file}")
run_eval(
model_path=args.model_path,
model_id=args.model_id,
question_file=question_file,
question_begin=args.question_begin,
question_end=args.question_end,
answer_file=answer_file,
max_new_token=args.max_new_token,
num_choices=args.num_choices,
num_gpus_per_model=args.num_gpus_per_model,
num_gpus_total=args.num_gpus_total,
max_gpu_memory=args.max_gpu_memory,
dtype=str_to_torch_dtype(args.dtype),
revision=args.revision,
)
reorg_answer_file(answer_file)
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