# Copyright (c) 2023-2024 DeepSeek. # # Permission is hereby granted, free of charge, to any person obtaining a copy of # this software and associated documentation files (the "Software"), to deal in # the Software without restriction, including without limitation the rights to # use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of # the Software, and to permit persons to whom the Software is furnished to do so, # subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS # FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR # COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER # IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN # CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # -*- coding: utf-8 -*- import argparse import os import sys from threading import Thread import torch from PIL import Image from transformers import TextIteratorStreamer from deepseek_vl.utils.io import load_pretrained_model def load_image(image_file): image = Image.open(image_file).convert("RGB") return image def get_help_message(image_token): help_msg = ( f"\t\t DeepSeek-VL-Chat is a chatbot that can answer questions based on the given image. Enjoy it! \n" f"Usage: \n" f" 1. type `exit` to quit. \n" f" 2. type `{image_token}` to indicate there is an image. You can enter multiple images, " f"e.g '{image_token} is a dot, {image_token} is a cat, and what is it in {image_token}?'. " f"When you type `{image_token}`, the chatbot will ask you to input image file path. \n" f" 4. type `help` to get the help messages. \n" f" 5. type `new` to start a new conversation. \n" f" Here is an example, you can type: 'Describe the image.'\n" ) return help_msg @torch.inference_mode() def response( args, conv, pil_images, tokenizer, vl_chat_processor, vl_gpt, generation_config ): prompt = conv.get_prompt() prepare_inputs = vl_chat_processor.__call__( prompt=prompt, images=pil_images, force_batchify=True ).to(vl_gpt.device) # run image encoder to get the image embeddings inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs) streamer = TextIteratorStreamer( tokenizer=tokenizer, skip_prompt=True, skip_special_tokens=True ) generation_config["inputs_embeds"] = inputs_embeds generation_config["attention_mask"] = prepare_inputs.attention_mask generation_config["streamer"] = streamer thread = Thread(target=vl_gpt.language_model.generate, kwargs=generation_config) thread.start() yield from streamer def get_user_input(hint: str): user_input = "" while user_input == "": try: user_input = input(f"{hint}") except KeyboardInterrupt: print() continue except EOFError: user_input = "exit" return user_input def chat(args, tokenizer, vl_chat_processor, vl_gpt, generation_config): image_token = vl_chat_processor.image_token help_msg = get_help_message(image_token) while True: print(help_msg) pil_images = [] conv = vl_chat_processor.new_chat_template() roles = conv.roles while True: # get user input user_input = get_user_input( f"{roles[0]} [{image_token} indicates an image]: " ) if user_input == "exit": print("Chat program exited.") sys.exit(0) elif user_input == "help": print(help_msg) elif user_input == "new": os.system("clear") pil_images = [] conv = vl_chat_processor.new_chat_template() torch.cuda.empty_cache() print("New conversation started.") else: conv.append_message(conv.roles[0], user_input) conv.append_message(conv.roles[1], None) # check if the user input is an image token num_images = user_input.count(image_token) cur_img_idx = 0 while cur_img_idx < num_images: try: image_file = input(f"({cur_img_idx + 1}/{num_images}) Input the image file path: ") image_file = image_file.strip() # trim whitespaces around path, enables drop-in from for example Dolphin except KeyboardInterrupt: print() continue except EOFError: image_file = None if image_file and os.path.exists(image_file): pil_image = load_image(image_file) pil_images.append(pil_image) cur_img_idx += 1 elif image_file == "exit": print("Chat program exited.") sys.exit(0) else: print( f"File error, `{image_file}` does not exist. Please input the correct file path." ) # get the answer by the model's prediction answer = "" answer_iter = response( args, conv, pil_images, tokenizer, vl_chat_processor, vl_gpt, generation_config, ) sys.stdout.write(f"{conv.roles[1]}: ") for char in answer_iter: answer += char sys.stdout.write(char) sys.stdout.flush() sys.stdout.write("\n") sys.stdout.flush() conv.update_last_message(answer) # conv.messages[-1][-1] = answer def main(args): # setup tokenizer, vl_chat_processor, vl_gpt = load_pretrained_model(args.model_path) generation_config = dict( pad_token_id=vl_chat_processor.tokenizer.eos_token_id, bos_token_id=vl_chat_processor.tokenizer.bos_token_id, eos_token_id=vl_chat_processor.tokenizer.eos_token_id, max_new_tokens=args.max_gen_len, use_cache=True, ) if args.temperature > 0: generation_config.update( { "do_sample": True, "top_p": args.top_p, "temperature": args.temperature, "repetition_penalty": args.repetition_penalty, } ) else: generation_config.update({"do_sample": False}) chat(args, tokenizer, vl_chat_processor, vl_gpt, generation_config) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--model_path", type=str, default="deepseek-ai/deepseek-vl-7b-chat", help="the huggingface model name or the local path of the downloaded huggingface model.", ) parser.add_argument("--temperature", type=float, default=0.2) parser.add_argument("--top_p", type=float, default=0.95) parser.add_argument("--repetition_penalty", type=float, default=1.1) parser.add_argument("--max_gen_len", type=int, default=512) args = parser.parse_args() main(args)