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"cells": [
{
"cell_type": "markdown",
"id": "0",
"metadata": {},
"source": [
"# Query VLM with Offline Engine\n",
"\n",
"This tutorial demonstrates how to use SGLang's **offline Engine API** to query VLMs. We will demonstrate usage with Qwen2.5-VL and Llama 4. This section demonstrates three different calling approaches:\n",
"\n",
"1. **Basic Call**: Directly pass images and text.\n",
"2. **Processor Output**: Use HuggingFace processor for data preprocessing.\n",
"3. **Precomputed Embeddings**: Pre-calculate image features to improve inference efficiency."
]
},
{
"cell_type": "markdown",
"id": "1",
"metadata": {},
"source": [
"## Understanding the Three Input Formats\n",
"\n",
"SGLang supports three ways to pass visual data, each optimized for different scenarios:\n",
"\n",
"### 1. **Raw Images** - Simplest approach\n",
"- Pass PIL Images, file paths, URLs, or base64 strings directly\n",
"- SGLang handles all preprocessing automatically\n",
"- Best for: Quick prototyping, simple applications\n",
"\n",
"### 2. **Processor Output** - For custom preprocessing\n",
"- Pre-process images with HuggingFace processor\n",
"- Pass the complete processor output dict with `format: \"processor_output\"`\n",
"- Best for: Custom image transformations, integration with existing pipelines\n",
"- Requirement: Must use `input_ids` instead of text prompt\n",
"\n",
"### 3. **Precomputed Embeddings** - For maximum performance\n",
"- Pre-calculate visual embeddings using the vision encoder\n",
"- Pass embeddings with `format: \"precomputed_embedding\"`\n",
"- Best for: Repeated queries on same images, caching, high-throughput serving\n",
"- Performance gain: Avoids redundant vision encoder computation (30-50% speedup)\n",
"\n",
"**Key Rule**: Within a single request, use only one format for all images. Don't mix formats.\n",
"\n",
"The examples below demonstrate all three approaches with both Qwen2.5-VL and Llama 4 models."
]
},
{
"cell_type": "markdown",
"id": "2",
"metadata": {},
"source": [
"## Querying Qwen2.5-VL Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3",
"metadata": {},
"outputs": [],
"source": [
"import nest_asyncio\n",
"\n",
"nest_asyncio.apply()\n",
"\n",
"model_path = \"Qwen/Qwen2.5-VL-3B-Instruct\"\n",
"chat_template = \"qwen2-vl\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4",
"metadata": {},
"outputs": [],
"source": [
"from io import BytesIO\n",
"import requests\n",
"from PIL import Image\n",
"\n",
"from sglang.srt.parser.conversation import chat_templates\n",
"\n",
"image = Image.open(\n",
" BytesIO(\n",
" requests.get(\n",
" \"https://github.com/sgl-project/sglang/blob/main/examples/assets/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(\"Generated prompt text:\")\n",
"print(conv.get_prompt())\n",
"print(f\"\\nImage size: {image.size}\")\n",
"image"
]
},
{
"cell_type": "markdown",
"id": "5",
"metadata": {},
"source": [
"### Basic Offline Engine API Call"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6",
"metadata": {},
"outputs": [],
"source": [
"from sglang import Engine\n",
"\n",
"llm = Engine(model_path=model_path, chat_template=chat_template, log_level=\"warning\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7",
"metadata": {},
"outputs": [],
"source": [
"out = llm.generate(prompt=conv.get_prompt(), image_data=[image])\n",
"print(\"Model response:\")\n",
"print(out[\"text\"])"
]
},
{
"cell_type": "markdown",
"id": "8",
"metadata": {},
"source": [
"### Call with Processor Output\n",
"\n",
"Using a HuggingFace processor to preprocess text and images, and passing the `processor_output` directly into `Engine.generate`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9",
"metadata": {},
"outputs": [],
"source": [
"from transformers import AutoProcessor\n",
"\n",
"processor = AutoProcessor.from_pretrained(model_path, use_fast=True)\n",
"processor_output = processor(\n",
" images=[image], text=conv.get_prompt(), return_tensors=\"pt\"\n",
")\n",
"\n",
"out = llm.generate(\n",
" input_ids=processor_output[\"input_ids\"][0].detach().cpu().tolist(),\n",
" image_data=[dict(processor_output, format=\"processor_output\")],\n",
")\n",
"print(\"Response using processor output:\")\n",
"print(out[\"text\"])"
]
},
{
"cell_type": "markdown",
"id": "10",
"metadata": {},
"source": [
"### Call with Precomputed Embeddings\n",
"\n",
"You can pre-calculate image features to avoid repeated visual encoding processes."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "11",
"metadata": {},
"outputs": [],
"source": [
"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": "12",
"metadata": {},
"outputs": [],
"source": [
"processor_output = processor(\n",
" images=[image], text=conv.get_prompt(), return_tensors=\"pt\"\n",
")\n",
"\n",
"input_ids = processor_output[\"input_ids\"][0].detach().cpu().tolist()\n",
"\n",
"precomputed_embeddings = vision(\n",
" processor_output[\"pixel_values\"].cuda(), processor_output[\"image_grid_thw\"].cuda()\n",
")\n",
"\n",
"multi_modal_item = dict(\n",
" processor_output,\n",
" format=\"precomputed_embedding\",\n",
" feature=precomputed_embeddings,\n",
")\n",
"\n",
"out = llm.generate(input_ids=input_ids, image_data=[multi_modal_item])\n",
"print(\"Response using precomputed embeddings:\")\n",
"print(out[\"text\"])\n",
"\n",
"llm.shutdown()"
]
},
{
"cell_type": "markdown",
"id": "13",
"metadata": {},
"source": [
"## Querying Llama 4 Vision Model\n",
"\n",
"```python\n",
"model_path = \"meta-llama/Llama-4-Scout-17B-16E-Instruct\"\n",
"chat_template = \"llama-4\"\n",
"\n",
"from io import BytesIO\n",
"import requests\n",
"from PIL import Image\n",
"\n",
"from sglang.srt.parser.conversation import chat_templates\n",
"\n",
"# Download the same example image\n",
"image = Image.open(\n",
" BytesIO(\n",
" requests.get(\n",
" \"https://github.com/sgl-project/sglang/blob/main/examples/assets/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(\"Llama 4 generated prompt text:\")\n",
"print(conv.get_prompt())\n",
"print(f\"Image size: {image.size}\")\n",
"\n",
"image\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "14",
"metadata": {},
"source": [
"### Llama 4 Basic Call\n",
"\n",
"Llama 4 requires more computational resources, so it's configured with multi-GPU parallelism (tp_size=4) and larger context length.\n",
"\n",
"```python\n",
"llm = Engine(\n",
" model_path=model_path,\n",
" enable_multimodal=True,\n",
" attention_backend=\"fa3\",\n",
" tp_size=4,\n",
" context_length=65536,\n",
")\n",
"\n",
"out = llm.generate(prompt=conv.get_prompt(), image_data=[image])\n",
"print(\"Llama 4 response:\")\n",
"print(out[\"text\"])\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "15",
"metadata": {},
"source": [
"### Call with Processor Output\n",
"\n",
"Using HuggingFace processor to preprocess data can reduce computational overhead during inference.\n",
"\n",
"```python\n",
"from transformers import AutoProcessor\n",
"\n",
"processor = AutoProcessor.from_pretrained(model_path, use_fast=True)\n",
"processor_output = processor(\n",
" images=[image], text=conv.get_prompt(), return_tensors=\"pt\"\n",
")\n",
"\n",
"out = llm.generate(\n",
" input_ids=processor_output[\"input_ids\"][0].detach().cpu().tolist(),\n",
" image_data=[dict(processor_output, format=\"processor_output\")],\n",
")\n",
"print(\"Response using processor output:\")\n",
"print(out)\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "16",
"metadata": {},
"source": [
"### Call with Precomputed Embeddings\n",
"\n",
"```python\n",
"from transformers import AutoProcessor\n",
"from transformers import Llama4ForConditionalGeneration\n",
"\n",
"processor = AutoProcessor.from_pretrained(model_path, use_fast=True)\n",
"model = Llama4ForConditionalGeneration.from_pretrained(\n",
" model_path, torch_dtype=\"auto\"\n",
").eval()\n",
"\n",
"vision = model.vision_model.cuda()\n",
"multi_modal_projector = model.multi_modal_projector.cuda()\n",
"\n",
"print(f'Image pixel values shape: {processor_output[\"pixel_values\"].shape}')\n",
"input_ids = processor_output[\"input_ids\"][0].detach().cpu().tolist()\n",
"\n",
"# Process image through vision encoder\n",
"image_outputs = vision(\n",
" processor_output[\"pixel_values\"].to(\"cuda\"), \n",
" aspect_ratio_ids=processor_output[\"aspect_ratio_ids\"].to(\"cuda\"),\n",
" aspect_ratio_mask=processor_output[\"aspect_ratio_mask\"].to(\"cuda\"),\n",
" output_hidden_states=False\n",
")\n",
"image_features = image_outputs.last_hidden_state\n",
"\n",
"# Flatten image features and pass through multimodal projector\n",
"vision_flat = image_features.view(-1, image_features.size(-1))\n",
"precomputed_embeddings = multi_modal_projector(vision_flat)\n",
"\n",
"# Build precomputed embedding data item\n",
"mm_item = dict(\n",
" processor_output, \n",
" format=\"precomputed_embedding\", \n",
" feature=precomputed_embeddings\n",
")\n",
"\n",
"# Use precomputed embeddings for efficient inference\n",
"out = llm.generate(input_ids=input_ids, image_data=[mm_item])\n",
"print(\"Llama 4 precomputed embedding response:\")\n",
"print(out[\"text\"])\n",
"```"
]
}
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