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from PIL import Image
import torch
from transformers import AutoModel, AutoTokenizer, TextIteratorStreamer
from transformers.image_utils import load_image
from threading import Thread
import logging
import time
import pynvml
class MiniCPM:
def __init__(self, model_id):
self.model_id = model_id
self.model = AutoModel.from_pretrained(
model_id,
trust_remote_code=True,
attn_implementation='sdpa',
torch_dtype=torch.bfloat16
)
self.model = self.model.eval().cuda()
self.tokenizer = AutoTokenizer.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()
images = [Image.open(frame).convert('RGB') for frame in video]
content = images + [prompt]
msgs = [{'role': 'user', 'content': content}]
# MiniCPM's chat method handles streaming internally
res = self.model.chat(
image=None,
msgs=msgs,
tokenizer=self.tokenizer,
stream=True
)
full_response = ""
print("Response: ", end="")
first_token_time = None
for new_text in res:
if first_token_time is None:
first_token_time = time.time()
full_response += new_text
print(new_text, end="", flush=True)
print()
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.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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