{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0",
   "metadata": {},
   "source": [
    "# Querying Qwen-VL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nest_asyncio\n",
    "\n",
    "nest_asyncio.apply()  # Run this first.\n",
    "\n",
    "model_path = \"Qwen/Qwen2.5-VL-3B-Instruct\"\n",
    "chat_template = \"qwen2-vl\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets create a prompt.\n",
    "\n",
    "from io import BytesIO\n",
    "import requests\n",
    "from PIL import Image\n",
    "\n",
    "from sglang.srt.openai_api.protocol import ChatCompletionRequest\n",
    "from sglang.srt.conversation import chat_templates\n",
    "\n",
    "image = Image.open(\n",
    "    BytesIO(\n",
    "        requests.get(\n",
    "            \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\"\n",
    "        ).content\n",
    "    )\n",
    ")\n",
    "\n",
    "conv = chat_templates[chat_template].copy()\n",
    "conv.append_message(conv.roles[0], f\"What's shown here: {conv.image_token}?\")\n",
    "conv.append_message(conv.roles[1], \"\")\n",
    "conv.image_data = [image]\n",
    "\n",
    "print(conv.get_prompt())\n",
    "image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3",
   "metadata": {},
   "source": [
    "## Query via the offline Engine API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sglang import Engine\n",
    "\n",
    "llm = Engine(\n",
    "    model_path=model_path, chat_template=chat_template, mem_fraction_static=0.8\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5",
   "metadata": {},
   "outputs": [],
   "source": [
    "out = llm.generate(prompt=conv.get_prompt(), image_data=[image])\n",
    "print(out[\"text\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6",
   "metadata": {},
   "source": [
    "## Query via the offline Engine API, but send precomputed embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute the image embeddings using Huggingface.\n",
    "\n",
    "from transformers import AutoProcessor\n",
    "from transformers import Qwen2_5_VLForConditionalGeneration\n",
    "\n",
    "processor = AutoProcessor.from_pretrained(model_path, use_fast=True)\n",
    "vision = (\n",
    "    Qwen2_5_VLForConditionalGeneration.from_pretrained(model_path).eval().visual.cuda()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8",
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_prompt = processor(\n",
    "    images=[image], text=conv.get_prompt(), return_tensors=\"pt\"\n",
    ")\n",
    "input_ids = processed_prompt[\"input_ids\"][0].detach().cpu().tolist()\n",
    "precomputed_features = vision(\n",
    "    processed_prompt[\"pixel_values\"].cuda(), processed_prompt[\"image_grid_thw\"].cuda()\n",
    ")\n",
    "\n",
    "mm_item = dict(\n",
    "    modality=\"IMAGE\",\n",
    "    image_grid_thws=processed_prompt[\"image_grid_thw\"],\n",
    "    precomputed_features=precomputed_features,\n",
    ")\n",
    "out = llm.generate(input_ids=input_ids, image_data=[mm_item])\n",
    "print(out[\"text\"])"
   ]
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
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