{
 "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)."
   ]
  },
  {
   "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": {
    "execution": {
     "iopub.execute_input": "2024-11-07T18:46:04.789536Z",
     "iopub.status.busy": "2024-11-07T18:46:04.789418Z",
     "iopub.status.idle": "2024-11-07T18:46:27.038169Z",
     "shell.execute_reply": "2024-11-07T18:46:27.037540Z"
    }
   },
   "outputs": [],
   "source": [
    "# launch the offline engine\n",
    "\n",
    "import sglang as sgl\n",
    "from sglang.utils import print_highlight\n",
    "import asyncio\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": {
    "execution": {
     "iopub.execute_input": "2024-11-07T18:46:27.040005Z",
     "iopub.status.busy": "2024-11-07T18:46:27.039872Z",
     "iopub.status.idle": "2024-11-07T18:46:30.203840Z",
     "shell.execute_reply": "2024-11-07T18:46:30.203368Z"
    }
   },
   "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_highlight(\"===============================\")\n",
    "    print_highlight(f\"Prompt: {prompt}\\nGenerated text: {output['text']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Streaming Synchronous Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-07T18:46:30.205880Z",
     "iopub.status.busy": "2024-11-07T18:46:30.205719Z",
     "iopub.status.idle": "2024-11-07T18:46:39.256561Z",
     "shell.execute_reply": "2024-11-07T18:46:39.255880Z"
    }
   },
   "outputs": [],
   "source": [
    "prompts = [\n",
    "    \"Hello, my name is\",\n",
    "    \"The capital of France is\",\n",
    "    \"The future of AI is\",\n",
    "]\n",
    "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n",
    "\n",
    "print_highlight(\"\\n=== Testing synchronous streaming generation ===\")\n",
    "\n",
    "for prompt in prompts:\n",
    "    print_highlight(f\"\\nPrompt: {prompt}\")\n",
    "    print(\"Generated text: \", end=\"\", flush=True)\n",
    "\n",
    "    for chunk in llm.generate(prompt, sampling_params, stream=True):\n",
    "        print(chunk[\"text\"], end=\"\", flush=True)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-streaming Asynchronous Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-07T18:46:39.259464Z",
     "iopub.status.busy": "2024-11-07T18:46:39.259309Z",
     "iopub.status.idle": "2024-11-07T18:46:42.384955Z",
     "shell.execute_reply": "2024-11-07T18:46:42.384378Z"
    }
   },
   "outputs": [],
   "source": [
    "prompts = [\n",
    "    \"Hello, my name 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",
    "print_highlight(\"\\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_highlight(f\"\\nPrompt: {prompt}\")\n",
    "        print_highlight(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": {
    "execution": {
     "iopub.execute_input": "2024-11-07T18:46:42.387431Z",
     "iopub.status.busy": "2024-11-07T18:46:42.387279Z",
     "iopub.status.idle": "2024-11-07T18:46:51.448572Z",
     "shell.execute_reply": "2024-11-07T18:46:51.447781Z"
    }
   },
   "outputs": [],
   "source": [
    "prompts = [\n",
    "    \"Hello, my name is\",\n",
    "    \"The capital of France is\",\n",
    "    \"The future of AI is\",\n",
    "]\n",
    "sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95}\n",
    "\n",
    "print_highlight(\"\\n=== Testing asynchronous streaming generation ===\")\n",
    "\n",
    "\n",
    "async def main():\n",
    "    for prompt in prompts:\n",
    "        print_highlight(f\"\\nPrompt: {prompt}\")\n",
    "        print(\"Generated text: \", end=\"\", flush=True)\n",
    "\n",
    "        generator = await llm.async_generate(prompt, sampling_params, stream=True)\n",
    "        async for chunk in generator:\n",
    "            print(chunk[\"text\"], end=\"\", flush=True)\n",
    "        print()\n",
    "\n",
    "\n",
    "asyncio.run(main())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-07T18:46:51.451177Z",
     "iopub.status.busy": "2024-11-07T18:46:51.450952Z",
     "iopub.status.idle": "2024-11-07T18:46:51.497530Z",
     "shell.execute_reply": "2024-11-07T18:46:51.496850Z"
    }
   },
   "outputs": [],
   "source": [
    "llm.shutdown()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "AlphaMeemory",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
