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from transformers import AutoProcessor, AutoModelForImageTextToText, TextIteratorStreamer
from transformers.image_utils import load_image
from threading import Thread
import logging
import torch
import time
import pynvml
class LFM2:
def __init__(self, model_id):
self.model_id = model_id
self.model = AutoModelForImageTextToText.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
self.processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
self.handle = None
if torch.cuda.is_available():
try:
pynvml.nvmlInit()
device_id = next(self.model.parameters()).device.index
self.handle = pynvml.nvmlDeviceGetHandleByIndex(device_id)
except Exception as e:
logging.error(f"Failed to initialize NVML: {e}")
def __del__(self):
if self.handle:
try:
pynvml.nvmlShutdown()
except:
pass
def generate(self, video, prompt):
start_time = time.time()
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": prompt},
],
},
]
# Assume video is a list of image paths
images = [load_image(image_path) for image_path in video]
for image in images:
conversation[0]["content"].append({"type": "image", "image": image})
# Generate Answer
inputs = self.processor.apply_chat_template(
conversation,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
tokenize=True,
).to(self.model.device)
logging.info(f"Prompt token length: {len(inputs.input_ids[0])}")
streamer = TextIteratorStreamer(self.processor, skip_prompt=True, skip_special_tokens=True)
generation_kwargs = dict(
**inputs,
streamer=streamer,
max_new_tokens=512
)
thread = Thread(target=self.model.generate, kwargs=generation_kwargs)
thread.start()
full_response = ""
print("Response: ", end="")
first_token_time = None
for new_text in streamer:
if first_token_time is None:
first_token_time = time.time()
full_response += new_text
print(new_text, end="", flush=True)
print()
thread.join()
end_time = time.time()
if first_token_time is not None:
generation_time = end_time - first_token_time
else:
generation_time = 0
num_generated_tokens = len(self.processor.tokenizer(full_response).input_ids)
tokens_per_second = num_generated_tokens / generation_time if generation_time > 0 else 0
peak_memory_mb = 0
if self.handle:
mem_info = pynvml.nvmlDeviceGetMemoryInfo(self.handle)
peak_memory_mb = mem_info.used / (1024 * 1024)
return {
"response": full_response,
"tokens_per_second": tokens_per_second,
"peak_gpu_memory_mb": peak_memory_mb,
"num_generated_tokens": num_generated_tokens,
}
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