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# Standard library imports
import base64
import json
import os
import time
from copy import deepcopy
from io import BytesIO
import numpy as np
import requests as url_requests
# Related third-party imports
from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
from accelerate.state import AcceleratorState
from loguru import logger as eval_logger
from openai import OpenAI
from PIL import Image
from tqdm import tqdm
# Local application/library specific imports
from lmms_eval.api.instance import Instance
from lmms_eval.api.model import lmms
from lmms_eval.api.registry import register_model
# Conditional imports
try:
from decord import VideoReader, cpu
except ImportError:
eval_logger.warning("Decord is not installed. Video input will not be supported.")
# Constants and global configurations
API_TYPE = os.getenv("API_TYPE", "openai")
NUM_SECONDS_TO_SLEEP = 5
if API_TYPE == "openai":
API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json",
}
elif API_TYPE == "azure":
API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
headers = {
"api-key": API_KEY,
"Content-Type": "application/json",
}
else:
API_URL = "YOUR_API_URL"
API_KEY = "YOUR_API_KEY"
@register_model("batch_gpt4")
class BatchGPT4(lmms):
def __init__(
self,
model_version: str = "gpt-4o",
api_key: str = API_KEY,
api_url: str = API_URL,
modality: str = "image",
max_frames_num: int = 10,
timeout: int = 120,
**kwargs,
) -> None:
super().__init__()
# Manually set a image token for GPT4V so that we can search for it
# and split the text and image
# Here we just use the same token as llava for convenient
self.model_version = model_version
self.modality = modality
self.max_frames_num = max_frames_num
self.image_token = "<image>"
self.timeout = timeout
self.api_key = api_key
self.api_url = api_url
self.client = OpenAI(api_key=api_key)
accelerator = Accelerator()
assert accelerator.state.local_process_index == 0, "BatchGPT4 does not support distributed inference."
assert accelerator.state.num_processes == 1, "BatchGPT4 does not support distributed inference."
# Function to encode the image
def encode_image(self, image: Image):
output_buffer = BytesIO()
image.save(output_buffer, format="PNG")
byte_data = output_buffer.getvalue()
base64_str = base64.b64encode(byte_data).decode("utf-8")
return base64_str
# Function to encode the video
def encode_video(self, video_path, for_get_frames_num):
vr = VideoReader(video_path, ctx=cpu(0))
total_frame_num = len(vr)
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, for_get_frames_num, dtype=int)
frame_idx = uniform_sampled_frames.tolist()
frames = vr.get_batch(frame_idx).asnumpy()
base64_frames = []
for frame in frames:
img = Image.fromarray(frame)
output_buffer = BytesIO()
img.save(output_buffer, format="PNG")
byte_data = output_buffer.getvalue()
base64_str = base64.b64encode(byte_data).decode("utf-8")
base64_frames.append(base64_str)
return base64_frames
def flatten(self, input):
new_list = []
for i in input:
for j in i:
new_list.append(j)
return new_list
def generate_until(self, requests):
# Prepare the batch requests data
requests_data = {}
pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Batch Preparing")
for idx, (contexts, gen_kwargs, doc_to_visual, doc_id, task, split) in enumerate([reg.args for reg in requests]):
visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
visuals = self.flatten(visuals)
imgs = []
for visual in visuals:
if self.modality == "image":
img = self.encode_image(visual)
imgs.append(img)
elif self.modality == "video":
frames = self.encode_video(visual, self.max_frames_num)
imgs.extend(frames)
messages = []
if self.image_token not in contexts:
messages.append({"role": "user", "content": contexts})
for img in imgs:
messages.append({"role": "user", "content": f"data:image/jpeg;base64,{img}"})
else:
contexts_split = contexts.split(self.image_token)
for idx, context in enumerate(contexts_split):
if idx < len(imgs):
messages.append({"role": "user", "content": context})
messages.append({"role": "user", "content": f"data:image/jpeg;base64,{imgs[idx]}"})
if len(contexts_split) > len(imgs):
messages.append({"role": "user", "content": contexts_split[-1]})
requests_data[f"request-{idx}"] = {"model": self.model_version, "messages": messages, "max_tokens": gen_kwargs.get("max_new_tokens", 1024)}
pbar.update(1)
file_path = os.getenv("HF_HOME", "~/.cache/huggingface") + f"/batchinput_{len(requests_data)}.jsonl"
file_path = self.create_batch_input_file(requests_data, file_path)
file_id = self.upload_input_file(file_path)
batch_response = self.create_batch(file_id, metadata={"description": "Batch Processing for GPT-4"})
batch_status = self.check_batch_status(batch_response.id)
while True:
batch_status = self.check_batch_status(batch_response.id)
if batch_status.status == "completed":
eval_logger.info("Batch processing completed.")
batch_results = self.retrieve_batch_results(batch_status.output_file_id)
res = [result["response"]["choices"][0]["message"]["content"] for result in json.loads(batch_results)]
return res
elif batch_status.status == "failed":
eval_logger.info("Batch processing failed.")
res = ["Batch failed"] * len(requests)
return res
else:
eval_logger.info(f"Batch status: {batch_status.status}. Retrying in {NUM_SECONDS_TO_SLEEP} seconds.")
time.sleep(NUM_SECONDS_TO_SLEEP)
def loglikelihood(self, requests):
# TODO
assert False, "GPT4V not support"
def create_batch_input_file(self, requests_data, file_path="batchinput.jsonl"):
with open(file_path, "w") as file:
for request_id, data in requests_data.items():
json_record = json.dumps({"custom_id": request_id, "method": "POST", "url": "/v1/chat/completions", "body": data})
file.write(json_record + "\n")
return file_path
def upload_input_file(self, file_path):
with open(file_path, "rb") as file:
response = self.client.files.create(file=file, purpose="batch")
return response.id
def create_batch(self, file_id, metadata=None):
if metadata is None:
metadata = {}
response = self.client.batches.create(input_file_id=file_id, endpoint="/v1/chat/completions", completion_window="24h", metadata=metadata)
return response
def check_batch_status(self, batch_id):
return self.client.batches.retrieve(batch_id)
def retrieve_batch_results(self, file_id):
return self.client.files.content(file_id)
def cancel_batch(self, batch_id):
return self.client.batches.cancel(batch_id)
def list_batches(self, limit=10):
return self.client.batches.list(limit=limit)
def generate_until_multi_round(self, requests) -> List[str]:
raise NotImplementedError("TODO: Implement multi-round generation for BatchGPT4")
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