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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "id": "view-in-github" | ||
| }, | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/camenduru/LLaVA-colab/blob/main/LLaVA_13b_4bit_colab.ipynb)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "id": "VjYy0F2gZIPR" | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "%cd /content\n", | ||
| "!git clone -b 5GB https://github.com/camenduru/LLaVA\n", | ||
| "%cd /content/LLaVA\n", | ||
| "\n", | ||
| "!pip install -e ." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from transformers import AutoTokenizer, BitsAndBytesConfig\n", | ||
| "from llava.model import LlavaLlamaForCausalLM\n", | ||
| "import torch\n", | ||
| "model_path = \"4bit/llava-v1.5-13b-3GB\"\n", | ||
| "kwargs = {\"device_map\": \"auto\"}\n", | ||
| "kwargs['load_in_4bit'] = True\n", | ||
| "kwargs['quantization_config'] = BitsAndBytesConfig(\n", | ||
| " load_in_4bit=True,\n", | ||
| " bnb_4bit_compute_dtype=torch.float16,\n", | ||
| " bnb_4bit_use_double_quant=True,\n", | ||
| " bnb_4bit_quant_type='nf4'\n", | ||
| ")\n", | ||
| "model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)\n", | ||
| "tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)\n", | ||
| "\n", | ||
| "vision_tower = model.get_vision_tower()\n", | ||
| "if not vision_tower.is_loaded:\n", | ||
| " vision_tower.load_model()\n", | ||
| "vision_tower.to(device='cuda')\n", | ||
| "image_processor = vision_tower.image_processor" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import requests\n", | ||
| "from PIL import Image\n", | ||
| "from io import BytesIO\n", | ||
| "\n", | ||
| "def load_image(image_file):\n", | ||
| " if image_file.startswith('http') or image_file.startswith('https'):\n", | ||
| " response = requests.get(image_file)\n", | ||
| " image = Image.open(BytesIO(response.content)).convert('RGB')\n", | ||
| " else:\n", | ||
| " image = Image.open(image_file).convert('RGB')\n", | ||
| " return image\n", | ||
| "\n", | ||
| "from llava.conversation import conv_templates, SeparatorStyle\n", | ||
| "from llava.utils import disable_torch_init\n", | ||
| "\n", | ||
| "disable_torch_init()\n", | ||
| "conv_mode = \"llava_v0\"\n", | ||
| "conv = conv_templates[conv_mode].copy()\n", | ||
| "roles = conv.roles\n", | ||
| "\n", | ||
| "image = load_image(\"https://llava-vl.github.io/static/images/view.jpg\")\n", | ||
| "image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda()\n", | ||
| "\n", | ||
| "from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN\n", | ||
| "from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria\n", | ||
| "from transformers import TextStreamer\n", | ||
| "import torch\n", | ||
| "\n", | ||
| "while True:\n", | ||
| " try:\n", | ||
| " inp = input(f\"{roles[0]}: \")\n", | ||
| " except EOFError:\n", | ||
| " inp = \"\"\n", | ||
| " if not inp:\n", | ||
| " print(\"exit...\")\n", | ||
| " break\n", | ||
| "\n", | ||
| " print(f\"{roles[1]}: \", end=\"\")\n", | ||
| "\n", | ||
| " if image is not None:\n", | ||
| " # first message\n", | ||
| " if model.config.mm_use_im_start_end:\n", | ||
| " inp = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\\n' + inp\n", | ||
| " else:\n", | ||
| " inp = DEFAULT_IMAGE_TOKEN + '\\n' + inp\n", | ||
| " conv.append_message(conv.roles[0], inp)\n", | ||
| " image = None\n", | ||
| " else:\n", | ||
| " # later messages\n", | ||
| " conv.append_message(conv.roles[0], inp)\n", | ||
| " conv.append_message(conv.roles[1], None)\n", | ||
| " prompt = conv.get_prompt()\n", | ||
| "\n", | ||
| " input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()\n", | ||
| " stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2\n", | ||
| " keywords = [stop_str]\n", | ||
| " stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)\n", | ||
| " streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n", | ||
| "\n", | ||
| " with torch.inference_mode():\n", | ||
| " output_ids = model.generate(\n", | ||
| " input_ids,\n", | ||
| " images=image_tensor,\n", | ||
| " do_sample=True,\n", | ||
| " temperature=0.2,\n", | ||
| " max_new_tokens=1024,\n", | ||
| " streamer=streamer,\n", | ||
| " use_cache=True,\n", | ||
| " stopping_criteria=[stopping_criteria])\n", | ||
| "\n", | ||
| " outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()\n", | ||
| " conv.messages[-1][-1] = outputs\n", | ||
| "\n", | ||
| " print(\"\\n\", {\"prompt\": prompt, \"outputs\": outputs}, \"\\n\")" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "accelerator": "GPU", | ||
| "colab": { | ||
| "gpuType": "T4", | ||
| "provenance": [] | ||
| }, | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "name": "python" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0 | ||
| } |
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