# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from transformers import AutoProcessor, AutoModel, AutoConfig, GenerationConfig import torch import os import time from pathlib import Path from typing import List, Dict, Any, Optional, Union import logging import sys os.environ["HF_HUB_OFFLINE"] = "1" # Use local cache for models # Set up logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) def add_to_sys_path_direct(model_path): """Add model path directly to sys.path""" if model_path not in sys.path: sys.path.insert(0, model_path) # Insert at beginning for priority print(f"✓ Added to sys.path: {model_path}") else: print(f"Already in sys.path: {model_path}") class NVOmniVideoInference: """A class to handle NVOmni video model inference with improved error handling and flexibility.""" def __init__(self, model_path: str, torch_dtype="torch.float16", device_map="auto"): """ Initialize the NVOmni model for video inference. Args: model_path (str): Path to the model directory torch_dtype: PyTorch data type for model weights device_map (str): Device mapping strategy for model loading """ self.model_path = model_path self.torch_dtype = torch_dtype self.device_map = device_map self.model = None self.processor = None self.config = None self.device = None self.load_model() def validate_paths(self, model_path: str, video_path: str = None) -> bool: """Validate that required paths exist.""" if not Path(model_path).exists(): logger.error(f"Model path does not exist: {model_path}") return False if video_path and not Path(video_path).exists(): logger.error(f"Video path does not exist: {video_path}") return False return True def load_model(self) -> bool: """Load the model, processor, and config with error handling.""" if not self.validate_paths(self.model_path): return False if True: logger.info("Loading model configuration...") self.config = AutoConfig.from_pretrained(self.model_path, trust_remote_code=True) logger.info("Loading model...") start_time = time.time() self.model = AutoModel.from_pretrained( self.model_path, trust_remote_code=True, torch_dtype=self.torch_dtype, device_map=self.device_map, low_cpu_mem_usage=True # More memory efficient loading )#.to(eval(self.torch_dtype)) load_time = time.time() - start_time logger.info(f"Model loaded in {load_time:.2f} seconds") logger.info("Loading processor...") self.processor = AutoProcessor.from_pretrained(self.model_path, trust_remote_code=True) # Set device for single-device setups if hasattr(self.model, 'device'): self.device = self.model.device else: self.device = next(self.model.parameters()).device if self.model.parameters() else torch.device('cpu') logger.info(f"Model successfully loaded on device: {self.device}") self._print_model_info() return True def _print_model_info(self): """Print useful information about the loaded model.""" logger.info("=" * 50) logger.info("MODEL INFORMATION") logger.info("=" * 50) if self.config: logger.info(f"Model type: {getattr(self.config, 'model_type', 'Unknown')}") logger.info(f"Hidden size: {getattr(self.config, 'hidden_size', 'Unknown')}") if self.model and torch.cuda.is_available(): logger.info(f"GPU memory allocated: {torch.cuda.memory_allocated() / 1024**3:.2f} GB") logger.info(f"GPU memory reserved: {torch.cuda.memory_reserved() / 1024**3:.2f} GB") def create_conversation(self, video_path: str, text_prompt: str) -> List[Dict[str, Any]]: """ Create a conversation format for the model. Args: video_path (str): Path to the video file text_prompt (str): Text prompt for the model Returns: List[Dict]: Conversation in the expected format """ return [{ "role": "user", "content": [ {"type": "video", "video": video_path}, {"type": "text", "text": text_prompt} ] }] @torch.inference_mode() def generate_response( self, video_path: str, text_prompt: str, max_new_tokens: int = 256, temperature: float = None, top_p: float = None, do_sample: bool = None, num_video_frames: int = -1, load_audio_in_video: bool = True, audio_length: Union[int, str] = "max_3600", ) -> Optional[str]: """ Generate a response from the model given a video and text prompt. Args: video_path (str): Path to the video file text_prompt (str): Text prompt for the model max_new_tokens (int): Maximum number of new tokens to generate temperature (float): Sampling temperature top_p (float): Top-p sampling parameter do_sample (bool): Whether to use sampling custom_generation_config (GenerationConfig): Custom generation configuration Returns: Optional[str]: Generated response or None if failed """ if not self.model or not self.processor: logger.error("Model or processor not loaded. Please initialize the model first.") return None if not self.validate_paths(self.model_path, video_path): return None # try: if True: logger.info(f"Processing video: {video_path}") logger.info(f"Text prompt: {text_prompt}") # Create conversation conversation = self.create_conversation(video_path, text_prompt) # Apply chat template text = self.processor.apply_chat_template( conversation, tokenize=False, add_generation_prompt=True ) logger.info(f"Chat template applied") # set model params self.model.config.load_audio_in_video = load_audio_in_video self.processor.config.load_audio_in_video = load_audio_in_video if num_video_frames > 0: self.model.config.num_video_frames = num_video_frames self.processor.config.num_video_frames = num_video_frames if audio_length != -1: self.model.config.audio_chunk_length = audio_length self.processor.config.audio_chunk_length = audio_length logger.info(f"Model config - load_audio_in_video: {self.model.config.load_audio_in_video}, num_video_frames: {self.model.config.num_video_frames}, audio_chunk_length: {self.model.config.audio_chunk_length}") # Process inputs start_time = time.time() inputs = self.processor([text]) # Move inputs to the correct device if needed if hasattr(inputs, 'input_ids') and inputs.input_ids is not None: inputs.input_ids = inputs.input_ids.to(self.device) processing_time = time.time() - start_time logger.info(f"Input processing completed in {processing_time:.2f} seconds") logger.info("Generating response...") start_time = time.time() generation_kwargs = {"max_new_tokens": max_new_tokens, "max_length": 99999999} if top_p is not None: generation_kwargs["top_p"] = top_p if do_sample is not None: generation_kwargs["do_sample"] = do_sample if temperature is not None: generation_kwargs["temperature"] = temperature generation_config = self.model.default_generation_config generation_config.update(**generation_kwargs) logger.info(f"Generation config: {generation_config.to_dict()}") with torch.no_grad(): output_ids = self.model.generate( input_ids=inputs.input_ids, media=getattr(inputs, 'media', None), media_config=getattr(inputs, 'media_config', None), generation_config=generation_config, ) generation_time = time.time() - start_time logger.info(f"Generation completed in {generation_time:.2f} seconds") # Decode response response = self.processor.tokenizer.batch_decode( output_ids, skip_special_tokens=True )[0] return response def batch_generate( self, video_text_pairs: List[tuple], **generation_kwargs ) -> List[Optional[str]]: """ Generate responses for multiple video-text pairs. Args: video_text_pairs (List[tuple]): List of (video_path, text_prompt) tuples **generation_kwargs: Arguments passed to generate_response Returns: List[Optional[str]]: List of generated responses """ responses = [] for i, (video_path, text_prompt) in enumerate(video_text_pairs): logger.info(f"Processing batch item {i+1}/{len(video_text_pairs)}") response = self.generate_response(video_path, text_prompt, **generation_kwargs) responses.append(response) # Clear cache between generations to manage memory if torch.cuda.is_available(): torch.cuda.empty_cache() return responses def main(): """Main function demonstrating usage of the NVOmni model.""" # Configuration MODEL_PATH = "./" VIDEO_PATH = "xxx.mp4" TEXT_PROMPT = "Assess the video, followed by a detailed description of it's video and audio contents." num_video_frames=128 audio_length="max_3600" load_audio_in_video=True add_to_sys_path_direct(MODEL_PATH) # Initialize the inference class logger.info("Initializing NVOmni Video Inference...") inferencer = NVOmniVideoInference(MODEL_PATH, torch_dtype="torch.float16") if inferencer.model is None: logger.error("Failed to initialize model. Exiting.") return # Generate response logger.info("Starting inference...") response = inferencer.generate_response( video_path=VIDEO_PATH, text_prompt=TEXT_PROMPT, num_video_frames=num_video_frames, load_audio_in_video=load_audio_in_video, audio_length=audio_length, max_new_tokens=1024, ) if response: print("\n" + "="*60) print("GENERATED RESPONSE") print("="*60) print(response) print("="*60) else: logger.error("Failed to generate response") # Example of batch processing if False: logger.info("\nExample: Batch processing") batch_pairs = [ (VIDEO_PATH, "What is happening in this video?"), (VIDEO_PATH, "Describe the audio content of this video."), ] batch_responses = inferencer.batch_generate(batch_pairs, max_new_tokens=128) for i, (pair, response) in enumerate(zip(batch_pairs, batch_responses)): print(f"\n--- Batch Response {i+1} ---") print(f"Prompt: {pair[1]}") print(f"Response: {response}") if __name__ == "__main__": main()