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import hydra
import pyrootutils
import os
import torch
from omegaconf import OmegaConf
from flask import Flask, request
import json
from typing import Optional
import transformers
from dataclasses import dataclass, field
import io
import base64
from PIL import Image
import gc
pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
BOI_TOKEN = '<img>'
EOI_TOKEN = '</img>'
IMG_TOKEN = '<img_{:05d}>'
IMG_FLAG = '<image>'
NUM_IMG_TOKNES = 32
NUM_IMG_CODES = 8192
app = Flask(__name__)
def decode_image(encoded_image: str) -> Image:
decoded_bytes = base64.b64decode(encoded_image.encode('utf-8'))
buffer = io.BytesIO(decoded_bytes)
image = Image.open(buffer)
return image
def encode_image(image: Image.Image, format: str = 'PNG') -> str:
with io.BytesIO() as buffer:
image.save(buffer, format=format)
encoded_image = base64.b64encode(buffer.getvalue()).decode('utf-8')
return encoded_image
@dataclass
class Arguments:
image_transform: Optional[str] = field(default=None, metadata={"help": "config path of image transform"})
tokenizer: Optional[str] = field(default=None, metadata={"help": "config path of tokenizer used to initialize tokenizer"})
model: Optional[str] = field(default=None, metadata={"help": "config path of llm"})
port: Optional[str] = field(default=80, metadata={"help": "network port"})
llm_device: Optional[str] = field(default='cuda:0', metadata={"help": "llm device"})
tokenizer_device: Optional[str] = field(default='cuda:0', metadata={"help": "tokenizer device"})
offload_encoder: Optional[bool] = field(default=False, metadata={"help": "offload image tokenizer"})
offload_decoder: Optional[bool] = field(default=False, metadata={"help": "offload image tokenizer"})
parser = transformers.HfArgumentParser(Arguments)
args, = parser.parse_args_into_dataclasses()
class LLMService:
def __init__(self, args) -> None:
image_transform_cfg = OmegaConf.load(args.image_transform)
tokenizer_cfg = OmegaConf.load(args.tokenizer)
model_cfg = OmegaConf.load(args.model)
self.image_id_shift = 32000
self.image_transform = hydra.utils.instantiate(image_transform_cfg)
model = hydra.utils.instantiate(model_cfg, device_map=args.llm_device).eval()
self.model = model
print(model.get_memory_footprint())
self.tokenizer = hydra.utils.instantiate(tokenizer_cfg, device=args.tokenizer_device, load_diffusion=True)
if args.offload_encoder:
self.tokenizer.image_tokenizer.model.visual_encoder.to('cpu')
if args.offload_decoder:
self.tokenizer.image_tokenizer.diffusion_model.to('cpu')
# model = hydra.utils.instantiate(model_cfg, torch_dtype=torch.float16)
# self.model = model.eval().to(args.llm_device)
self.llm_device = args.llm_device
self.tokenizer_device = args.tokenizer_device
self.offload_encoder = args.offload_encoder
self.offload_decoder = args.offload_decoder
self.boi_token_id = self.tokenizer(BOI_TOKEN, add_special_tokens=False).input_ids[0]
self.eoi_token_id = self.tokenizer(EOI_TOKEN, add_special_tokens=False).input_ids[0]
print('Init Done...')
service = LLMService(args)
@app.route('/generate', methods=['GET', 'POST'])
def generate():
request_info = request.get_json()
text_list = request_info['text'].split(IMG_FLAG)
image_list = request_info['images']
temperature = request_info.get('temperature', 0.7)
num_beams = request_info.get('num_beams', 1)
max_new_tokens = request_info.get('max_new_tokens', 256)
top_p = request_info.get('top_p', 0.5)
force_boi = request_info.get('force_boi', False)
assert len(text_list) == len(image_list) + 1
if len(image_list) > 0:
images_tensor_list = []
images_tensor_indices = []
images_ids_list = []
images_ids_indices = []
for idx, image_item in enumerate(image_list):
if isinstance(image_item, str):
image = decode_image(image_item)
image_tensor = service.image_transform(image)
images_tensor_list.append(image_tensor)
images_tensor_indices.append(idx)
else:
images_ids_list.append(image_item)
images_ids_indices.append(idx)
if len(images_tensor_list) > 0:
images_tensor = torch.stack(images_tensor_list, dim=0).to(service.tokenizer_device)
if service.offload_encoder:
service.tokenizer.image_tokenizer.model.visual_encoder.to(service.tokenizer_device)
images_ids_1 = service.tokenizer.encode_image(image_torch=images_tensor).cpu()
if args.offload_encoder:
service.tokenizer.image_tokenizer.model.visual_encoder.to('cpu')
torch.cuda.empty_cache()
gc.collect()
num_image_ids = images_ids_1.shape[-1]
else:
num_image_ids = len(images_ids_list[-1])
images_ids_2 = torch.tensor(images_ids_list, dtype=torch.long)
images_ids = torch.zeros((len(image_list), num_image_ids), dtype=torch.long)
if len(images_tensor_indices) > 0:
images_ids[images_tensor_indices, :] = images_ids_1
if len(images_ids_indices) > 0:
images_ids[images_ids_indices, :] = images_ids_2
input_text = ''
for i in range(images_ids.shape[0]):
single_image_ids = images_ids[i].view(-1).tolist()
image_tokens = BOI_TOKEN + ''.join([IMG_TOKEN.format(int(item)) for item in single_image_ids]) + EOI_TOKEN
input_text += text_list[i] + image_tokens
input_text = service.tokenizer.bos_token + input_text + text_list[-1]
images_ids_list = images_ids.tolist()
else:
input_text = service.tokenizer.bos_token + ''.join(text_list)
images_ids_list = []
if force_boi:
input_text += BOI_TOKEN
print(input_text)
input_ids = service.tokenizer(input_text, add_special_tokens=False, return_tensors='pt').input_ids
input_ids = input_ids.to(service.llm_device)
generation_config = {
'temperature': temperature,
'num_beams': num_beams,
'max_new_tokens': max_new_tokens,
'top_p': top_p,
'do_sample': True
}
generate_ids = service.model.generate(input_ids=input_ids, **generation_config)
if force_boi:
generate_ids = generate_ids[0][input_ids.shape[1] - 1:]
else:
generate_ids = generate_ids[0][input_ids.shape[1]:]
print('generated_ids: ', generate_ids)
boi_indices = torch.where(generate_ids == service.boi_token_id)[0].tolist()
eoi_indices = torch.where(generate_ids == service.eoi_token_id)[0].tolist()
# assert len(boi_indices) == len(eoi_indices)
generated_image_base64_list = []
text_mask = torch.ones_like(generate_ids, dtype=torch.bool)
error_msg = []
if len(boi_indices) != len(eoi_indices):
error_msg.append(
f'Num of BOI (begain of image) tokens: {len(boi_indices)} is not equal to EOI(end of image tokens): {len(eoi_indices)}, some image Some images will fail to decode.'
)
num_images = min(len(boi_indices), len(eoi_indices))
for idx in range(num_images):
boi_index, eoi_index = boi_indices[idx], eoi_indices[idx]
# for boi_index, eoi_index in zip(boi_indices, eoi_indices):
image_ids = generate_ids[boi_index + 1:eoi_index].unsqueeze(0).to(service.tokenizer_device)
image_ids = image_ids - service.image_id_shift
if image_ids.shape[-1] != NUM_IMG_TOKNES:
error_msg.append(f'Len(image_ids) {image_ids.shape[-1]} is not equal to {NUM_IMG_TOKNES}')
image_base64 = ''
elif (image_ids < 0).any() or (image_ids >= NUM_IMG_CODES).any():
error_msg.append(f'Some image_id out of range: [0, {NUM_IMG_CODES})')
image_base64 = ''
else:
if service.offload_decoder:
service.tokenizer.image_tokenizer.diffusion_model.to(service.tokenizer_device)
image = service.tokenizer.decode_image(image_ids)[0]
if service.offload_decoder:
service.tokenizer.image_tokenizer.diffusion_model.to('cpu')
torch.cuda.empty_cache()
gc.collect()
image_base64 = encode_image(image)
generated_image_base64_list.append(image_base64)
text_mask[boi_index + 1:eoi_index] = False
images_ids_list.append(image_ids.view(-1).tolist())
generate_ids = generate_ids[text_mask]
# print('generate_ids: ', generate_ids)
# generate_text = service.tokenizer.decode(generate_ids, skip_special_tokens=True)
generate_text = service.tokenizer.decode(generate_ids, skip_special_tokens=False)
# print('generate_text before: ', generate_text)
generate_text = generate_text.replace(BOI_TOKEN + ' ' + EOI_TOKEN + ' ', IMG_FLAG)
generate_text = generate_text.replace(service.tokenizer.eos_token, '')
print('generate_text: ', generate_text)
return {'text': generate_text, 'images': generated_image_base64_list, 'images_ids': images_ids_list, 'error_msg': error_msg}
if __name__ == '__main__':
app.run(host='0.0.0.0', port=args.port)
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