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from dataclasses import dataclass, field
import logging
from flask import Flask, request, jsonify
import transformers
import torch
from datasets import load_from_disk
from multi_token.model_utils import MultiTaskType
from multi_token.training import (
ModelArguments,
)
from multi_token.inference import load_trained_lora_model
from multi_token.data_tools import encode_chat
import evaluate
import random
PRETRAIN_PHRASES = [
"What is happening in the given music <sound>?",
"Describe the sound. <sound>",
"Describe the music. <sound>",
"<sound> Provide a description of the music.",
"<sound> Provide a description of the sound.",
"Can you interpret <sound>?",
"Please explain what's happening in <sound>",
"What does <sound> represent?",
"Could you describe <sound> for me?",
"What's the content of <sound>?",
"Can you depict <sound>?",
"What is <sound>?",
"In the music clip, <sound>, what is happening?",
"Provide a description of the music. <sound>",
"Provide a description of the sound. <sound>",
"Provide a caption for the sound. <sound>",
"Provide a caption for the music. <sound>",
]
@dataclass
class ServeArguments(ModelArguments):
port: int = field(default=8080)
host: str = field(default="0.0.0.0")
load_bits: int = field(default=16)
max_new_tokens: int = field(default=128)
temperature: float = field(default=0.01)
def generate(input_json):
encoded_dict = encode_chat(input_json, tokenizer, model.modalities)
with torch.inference_mode():
output_ids = model.generate(
input_ids=encoded_dict["input_ids"].unsqueeze(0).to(model.device),
max_new_tokens=serve_args.max_new_tokens,
use_cache=True,
do_sample=True,
temperature=serve_args.temperature,
modality_inputs={
m.name: [encoded_dict[m.name]] for m in model.modalities
},
)
outputs = tokenizer.decode(
output_ids[0, encoded_dict["input_ids"].shape[0] :],
skip_special_tokens=True,
).strip()
return {"output": outputs}
if __name__ == "__main__":
logging.getLogger().setLevel(logging.INFO)
parser = transformers.HfArgumentParser((ServeArguments,))
serve_args, _ = parser.parse_args_into_dataclasses(return_remaining_strings=True)
dataset_path = "/data/musicbench_multitoken_official_split/val"
ds = load_from_disk(dataset_path)
model, tokenizer = load_trained_lora_model(
model_name_or_path=serve_args.model_name_or_path,
model_lora_path=serve_args.model_lora_path,
load_bits=serve_args.load_bits,
use_multi_task=MultiTaskType(serve_args.use_multi_task),
tasks_config=serve_args.tasks_config
)
predictions = []
references = []
content_phrase = random.choice(PRETRAIN_PHRASES)
for data_point_id in range(100):
data_point = ds[data_point_id]
# print("datapoint", data_point)
input_json={"messages": [{"role": "user", "content": content_phrase}], "sounds": data_point["sounds"]}
output_json = generate(input_json)
print("Prediction ",output_json["output"])
print("Reference ", data_point["messages"][1]["content"])
print()
print()
predictions.append(output_json["output"])
references.append(data_point["messages"][1]["content"])
sacrebleu = evaluate.load("sacrebleu")
sacrebleu_results=sacrebleu.compute(predictions=predictions, references=references)
print(sacrebleu_results["score"])
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