{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Offline Engine API\n",
    "\n",
    "SGLang provides a direct inference engine without the need for an HTTP server, especially for use cases where additional HTTP server adds unnecessary complexity or overhead. Here are two general use cases:\n",
    "\n",
    "- Offline Batch Inference\n",
    "- Custom Server on Top of the Engine\n",
    "\n",
    "This document focuses on the offline batch inference, demonstrating four different inference modes:\n",
    "\n",
    "- Non-streaming synchronous generation\n",
    "- Streaming synchronous generation\n",
    "- Non-streaming asynchronous generation\n",
    "- Streaming asynchronous generation\n",
    "\n",
    "Additionally, you can easily build a custom server on top of the SGLang offline engine. A detailed example working in a python script can be found in [custom_server](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/custom_server.py).\n",
    "\n",
    "## SPECIAL WARNING!!!!\n",
    "\n",
    "**To launch the offline engine in your python scripts,** `__main__` **condition is necessary, since we use** `spawn` **mode to create subprocesses. Please refer to this simple example**:\n",
    "\n",
    "https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/launch_engine.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Usage\n",
    "\n",
    "The engine supports [vlm inference](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/offline_batch_inference_vlm.py) as well as [extracting hidden states](https://github.com/sgl-project/sglang/blob/main/examples/runtime/hidden_states). \n",
    "\n",
    "Please see [the examples](https://github.com/sgl-project/sglang/tree/main/examples/runtime/engine) for further use cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Offline Batch Inference\n",
    "\n",
    "SGLang offline engine supports batch inference with efficient scheduling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# launch the offline engine\n",
    "import asyncio\n",
    "import io\n",
    "import os\n",
    "\n",
    "from PIL import Image\n",
    "import requests\n",
    "import sglang as sgl\n",
    "\n",
    "from sglang.srt.conversation import chat_templates\n",
    "from sglang.test.test_utils import is_in_ci\n",
    "from sglang.utils import async_stream_and_merge, stream_and_merge\n",
    "\n",
    "if is_in_ci():\n",
    "    import patch\n",
    "\n",
    "\n",
    "llm = sgl.Engine(model_path=\"meta-llama/Meta-Llama-3.1-8B-Instruct\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-streaming Synchronous Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompts = [\n",
    "    \"Hello, my name is\",\n",
    "    \"The president of the United States is\",\n",
    "    \"The capital of France is\",\n",
    "    \"The future of AI is\",\n",
    "]\n",
    "\n",
    "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n",
    "\n",
    "outputs = llm.generate(prompts, sampling_params)\n",
    "for prompt, output in zip(prompts, outputs):\n",
    "    print(\"===============================\")\n",
    "    print(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Streaming Synchronous Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompts = [\n",
    "    \"Write a short, neutral self-introduction for a fictional character. Hello, my name is\",\n",
    "    \"Provide a concise factual statement about France’s capital city. The capital of France is\",\n",
    "    \"Explain possible future trends in artificial intelligence. The future of AI is\",\n",
    "]\n",
    "\n",
    "sampling_params = {\n",
    "    \"temperature\": 0.2,\n",
    "    \"top_p\": 0.9,\n",
    "}\n",
    "\n",
    "print(\"\\n=== Testing synchronous streaming generation with overlap removal ===\\n\")\n",
    "\n",
    "for prompt in prompts:\n",
    "    print(f\"Prompt: {prompt}\")\n",
    "    merged_output = stream_and_merge(llm, prompt, sampling_params)\n",
    "    print(\"Generated text:\", merged_output)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-streaming Asynchronous Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompts = [\n",
    "    \"Write a short, neutral self-introduction for a fictional character. Hello, my name is\",\n",
    "    \"Provide a concise factual statement about France’s capital city. The capital of France is\",\n",
    "    \"Explain possible future trends in artificial intelligence. The future of AI is\",\n",
    "]\n",
    "\n",
    "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n",
    "\n",
    "print(\"\\n=== Testing asynchronous batch generation ===\")\n",
    "\n",
    "\n",
    "async def main():\n",
    "    outputs = await llm.async_generate(prompts, sampling_params)\n",
    "\n",
    "    for prompt, output in zip(prompts, outputs):\n",
    "        print(f\"\\nPrompt: {prompt}\")\n",
    "        print(f\"Generated text: {output['text']}\")\n",
    "\n",
    "\n",
    "asyncio.run(main())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Streaming Asynchronous Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompts = [\n",
    "    \"Write a short, neutral self-introduction for a fictional character. Hello, my name is\",\n",
    "    \"Provide a concise factual statement about France’s capital city. The capital of France is\",\n",
    "    \"Explain possible future trends in artificial intelligence. The future of AI is\",\n",
    "]\n",
    "\n",
    "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n",
    "\n",
    "print(\"\\n=== Testing asynchronous streaming generation (no repeats) ===\")\n",
    "\n",
    "\n",
    "async def main():\n",
    "    for prompt in prompts:\n",
    "        print(f\"\\nPrompt: {prompt}\")\n",
    "        print(\"Generated text: \", end=\"\", flush=True)\n",
    "\n",
    "        # Replace direct calls to async_generate with our custom overlap-aware version\n",
    "        async for cleaned_chunk in async_stream_and_merge(llm, prompt, sampling_params):\n",
    "            print(cleaned_chunk, end=\"\", flush=True)\n",
    "\n",
    "        print()  # New line after each prompt\n",
    "\n",
    "\n",
    "asyncio.run(main())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm.shutdown()"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
