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import os
import streamlit as st
import torch
from PIL import Image
from dotenv import load_dotenv
from qwen_vl_utils import process_vision_info
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
load_dotenv()
HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN")
def print_gpu_memory(label, memory_allocated, memory_reserved):
if torch.cuda.is_available():
print("-----------------------------------")
print(f"{label} GPU Memory Usage:")
print(f"Allocated: {memory_allocated / 1024 ** 2:.2f} MB")
print(f"Cached: {memory_reserved / 1024 ** 2:.2f} MB")
# Inference steps taken from https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct
@st.cache_resource
def get_model(model_path):
try:
with st.spinner(f"Loading model {model_path}"):
# Load the model here
model_import = Qwen2VLForConditionalGeneration.from_pretrained(
model_path, torch_dtype="auto", device_map="auto",
attn_implementation="flash_attention_2",
token=HUGGINGFACE_TOKEN,
)
size = {
"shortest_edge": 224,
"longest_edge": 1024,
}
processor_import = AutoProcessor.from_pretrained("itsumi-st/imgtikz_qwen2vl",
size=size,
min_pixels=256 * 256,
max_pixels=1024 * 1024,
token=HUGGINGFACE_TOKEN)
processor_import.tokenizer.padding_side = 'left'
return model_import, processor_import
except Exception as e:
st.error(f"Error loading model: {e}")
return None, None
def run_inference(input_file, model_path, args):
model, processor = get_model(model_path)
if model is None or processor is None:
return "Error loading model."
# GPU Memory after model loading:
after_model_dump = (torch.cuda.memory_allocated(), torch.cuda.memory_reserved())
image = Image.open(input_file)
conversation = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": "Please generate TikZ code to draw the diagram of the given image."}
],
}
]
text_prompt = processor.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
image_input, video_inputs = process_vision_info(conversation)
inputs = processor(
text=[text_prompt],
images=image_input,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
# GPU Memory after input processing
after_input_dump = (torch.cuda.memory_allocated(), torch.cuda.memory_reserved())
with torch.inference_mode():
output_ids = model.generate(
**inputs,
max_new_tokens=int(args["max_length"]),
do_sample=True,
top_p=float(args["top_p"]),
top_k=int(args["top_k"]),
temperature=float(args["temperature"]),
use_cache=True,
num_return_sequences=1,
pad_token_id=processor.tokenizer.pad_token_id,
)
generated_ids = [
output_ids[len(input_ids):]
for input_ids, output_ids in zip(inputs.input_ids, output_ids)
]
output_text = processor.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
# GPU Memory after generation
after_gen_dump = (torch.cuda.memory_allocated(), torch.cuda.memory_reserved())
print_gpu_memory("Before Model", after_model_dump[0], after_model_dump[1])
print_gpu_memory("After Input", after_input_dump[0], after_input_dump[1])
print_gpu_memory("After Generation", after_gen_dump[0], after_gen_dump[1])
# Clean up
del inputs, output_ids, generated_ids, image, image_input, video_inputs
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
return output_text
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