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import argparse
import time
import logging
import requests
import os
from PIL import Image
from io import BytesIO
from PIL import Image
import torch
from transformers import AutoTokenizer
from modeling_tinyllava_elm import TinyLlavaForConditionalGeneration
from configuration import *
from conversion import *
from utils import *
def load_image(image_file):
if image_file.startswith("http") or image_file.startswith("https"):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert("RGB")
else:
image = Image.open(image_file).convert("RGB")
return image
def generate(
prompt: str,
model: str,
tokenizer = None,
image: str = None,
device: str = None,
max_new_tokens: int = 1024,
num_beams = 1,
top_p=None,
temperature=0.2
):
if not device:
if torch.cuda.is_available() and torch.cuda.device_count():
device = "cuda:0"
logging.warning(
'inference device is not set, using cuda:0, %s',
torch.cuda.get_device_name(0)
)
else:
device = 'cpu'
logging.warning(
(
'No CUDA device detected, using cpu, '
'expect slower speeds.'
)
)
if 'cuda' in device and not torch.cuda.is_available():
raise ValueError('CUDA device requested but no CUDA device detected.')
if isinstance(model, str):
checkpoint_path = model
# print(f'loading model from {checkpoint_path}...')
model = TinyLlavaForConditionalGeneration.from_pretrained(
checkpoint_path,
torch_dtype=torch.float16,
)
# print('model load over')
config = model.config
if tokenizer is None:
tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False, model_max_length = config.tokenizer_model_max_length,
padding_side = config.tokenizer_padding_side)
image_processor = model.vision_tower._image_processor
context_len = getattr(config, 'max_sequence_length', 2048)
model.to(device).eval()
if image is not None:
prompt = DEFAULT_IMAGE_TOKEN + '\n' + prompt
conv = conv_phi_v0.copy()
conv.append_message(conv.roles[0], prompt)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
if image is not None:
# print('loading image...')
image = load_image(image)
# print('load image over')
image_tensor = process_images(image, image_processor, config).to(model.device, dtype=torch.float16)
input_ids = (
tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt")
.unsqueeze(0)
.cuda()
)
# Generate
stime = time.time()
# stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
# keywords = [stop_str]
# stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
# print('start inference...')
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
do_sample=True if temperature > 0 else False,
temperature=temperature,
top_p=top_p,
num_beams=num_beams,
pad_token_id=tokenizer.pad_token_id,
max_new_tokens=max_new_tokens,
use_cache=True,
# stopping_criteria=[stopping_criteria],
)
# print('inference over')
generation_time = time.time() - stime
outputs = tokenizer.batch_decode(
output_ids, skip_special_tokens=True
)[0]
# outputs = outputs.strip()
# if outputs.endswith(stop_str):
# outputs = outputs[: -len(stop_str)]
outputs = outputs.strip()
return outputs, generation_time
def tinyllava_elm_generate_parser():
"""Argument Parser"""
class KwargsParser(argparse.Action):
"""Parser action class to parse kwargs of form key=value"""
def __call__(self, parser, namespace, values, option_string=None):
setattr(namespace, self.dest, dict())
for val in values:
if '=' not in val:
raise ValueError(
(
'Argument parsing error, kwargs are expected in'
' the form of key=value.'
)
)
kwarg_k, kwarg_v = val.split('=')
try:
converted_v = int(kwarg_v)
except ValueError:
try:
converted_v = float(kwarg_v)
except ValueError:
converted_v = kwarg_v
getattr(namespace, self.dest)[kwarg_k] = converted_v
parser = argparse.ArgumentParser('TinyLLaVA-OpenELM Generate Module')
parser.add_argument(
'--model',
dest='model',
help='Path to the hf converted model.',
required=True,
type=str,
)
parser.add_argument(
'--prompt',
dest='prompt',
help='Prompt for LLM call.',
default='',
type=str,
)
parser.add_argument(
'--device',
dest='device',
help='Device used for inference.',
type=str,
)
parser.add_argument("--image", type=str, default=None)
parser.add_argument("--temperature", type=float, default=0)
parser.add_argument("--top_p", type=float, default=None)
parser.add_argument("--num_beams", type=int, default=1)
parser.add_argument("--max_new_tokens", type=int, default=512)
return parser.parse_args()
if __name__ == '__main__':
args = tinyllava_elm_generate_parser()
prompt = args.prompt
model = TinyLlavaForConditionalGeneration.from_pretrained(args.model)
output_text, genertaion_time = generate(
prompt=prompt,
image=args.image,
model=args.model,
device=args.device,
max_new_tokens = args.max_new_tokens,
num_beams = args.num_beams,
top_p=args.top_p,
temperature=args.temperature
)
print_txt = (
f'\r\n{"=" * os.get_terminal_size().columns}\r\n'
'\033[1m Prompt + Generated Output\033[0m\r\n'
f'{"-" * os.get_terminal_size().columns}\r\n'
f'{output_text}\r\n'
f'{"-" * os.get_terminal_size().columns}\r\n'
'\r\nGeneration took'
f'\033[1m\033[92m {round(genertaion_time, 2)} \033[0m'
'seconds.\r\n'
)
print(print_txt) |