{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Speculative Decoding\n",
    "\n",
    "SGLang now provides an EAGLE-based speculative decoding option. The implementation aims to maximize speed and efficiency and is considered to be among the fastest in open-source LLM engines.\n",
    "\n",
    "**Note:** Currently, Speculative Decoding in SGLang does not support radix cache.\n",
    "\n",
    "### Performance Highlights\n",
    "\n",
    "- Official EAGLE code ([SafeAILab/EAGLE](https://github.com/SafeAILab/EAGLE)): ~200 tokens/s\n",
    "- Standard SGLang Decoding: ~156 tokens/s\n",
    "- EAGLE Decoding in SGLang: ~297 tokens/s\n",
    "- EAGLE Decoding in SGLang (w/ `torch.compile`): ~316 tokens/s\n",
    "\n",
    "All benchmarks below were run on a single H100."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EAGLE Decoding\n",
    "\n",
    "To enable EAGLE-based speculative decoding, specify the draft model (`--speculative-draft-model-path`) and the relevant EAGLE parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sglang.test.test_utils import is_in_ci\n",
    "\n",
    "if is_in_ci():\n",
    "    from patch import launch_server_cmd\n",
    "else:\n",
    "    from sglang.utils import launch_server_cmd\n",
    "\n",
    "from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
    "\n",
    "server_process, port = launch_server_cmd(\n",
    "    \"\"\"\n",
    "python3 -m sglang.launch_server --model meta-llama/Llama-2-7b-chat-hf  --speculative-algorithm EAGLE \\\n",
    "    --speculative-draft-model-path lmsys/sglang-EAGLE-llama2-chat-7B --speculative-num-steps 5 \\\n",
    "    --speculative-eagle-topk 8 --speculative-num-draft-tokens 64\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "wait_for_server(f\"http://localhost:{port}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openai\n",
    "\n",
    "client = openai.Client(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n",
    "    ],\n",
    "    temperature=0,\n",
    "    max_tokens=64,\n",
    ")\n",
    "\n",
    "print_highlight(f\"Response: {response}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(server_process)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EAGLE Decoding with `torch.compile`\n",
    "\n",
    "You can also enable `torch.compile` for further optimizations and optionally set `--cuda-graph-max-bs`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "server_process, port = launch_server_cmd(\n",
    "    \"\"\"\n",
    "python3 -m sglang.launch_server --model meta-llama/Llama-2-7b-chat-hf  --speculative-algorithm EAGLE \\\n",
    "    --speculative-draft-model-path lmsys/sglang-EAGLE-llama2-chat-7B --speculative-num-steps 5 \\\n",
    "        --speculative-eagle-topk 8 --speculative-num-draft-tokens 64 --mem-fraction 0.6 \\\n",
    "            --enable-torch-compile --cuda-graph-max-bs 2\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "wait_for_server(f\"http://localhost:{port}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openai\n",
    "\n",
    "client = openai.Client(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n",
    "    ],\n",
    "    temperature=0,\n",
    "    max_tokens=64,\n",
    ")\n",
    "\n",
    "print_highlight(f\"Response: {response}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(server_process)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EAGLE Decoding via Frequency-Ranked Speculative Sampling\n",
    "\n",
    "By employing a truncated high-frequency token vocabulary in the draft model, Eagle speculative decoding reduces `lm_head` computational overhead while accelerating the pipeline without quality degradation. For more details, checkout [the paper](https://arxiv.org/pdf/arXiv:2502.14856).\n",
    "\n",
    "In our implementation, set `--speculative-token-map` to enable the optimization. You can get the high-frequency token in FR-Spec from [this model](https://huggingface.co/thunlp/LLaMA3-Instruct-8B-FR-Spec). Or you can obtain high-frequency token by directly downloading these token from [this repo](https://github.com/thunlp/FR-Spec/tree/main?tab=readme-ov-file#prepare-fr-spec-vocabulary-subset).\n",
    "\n",
    "Thanks for the contribution from [Weilin Zhao](https://github.com/https://github.com/Achazwl) and [Zhousx](https://github.com/Zhou-sx). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sglang.test.test_utils import is_in_ci\n",
    "\n",
    "if is_in_ci():\n",
    "    from patch import launch_server_cmd\n",
    "else:\n",
    "    from sglang.utils import launch_server_cmd\n",
    "\n",
    "from sglang.utils import wait_for_server, print_highlight, terminate_process\n",
    "\n",
    "server_process, port = launch_server_cmd(\n",
    "    \"\"\"\n",
    "python3 -m sglang.launch_server --model meta-llama/Meta-Llama-3-8B-Instruct --speculative-algorithm EAGLE \\\n",
    "    --speculative-draft-model-path lmsys/sglang-EAGLE-LLaMA3-Instruct-8B --speculative-num-steps 5 \\\n",
    "    --speculative-eagle-topk 8 --speculative-num-draft-tokens 64 --speculative-token-map thunlp/LLaMA3-Instruct-8B-FR-Spec/freq_32768.pt \\\n",
    "    --mem-fraction 0.7 --cuda-graph-max-bs 2 --dtype float16 \n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "wait_for_server(f\"http://localhost:{port}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import openai\n",
    "\n",
    "client = openai.Client(base_url=f\"http://127.0.0.1:{port}/v1\", api_key=\"None\")\n",
    "\n",
    "response = client.chat.completions.create(\n",
    "    model=\"meta-llama/Meta-Llama-3-8B-Instruct\",\n",
    "    messages=[\n",
    "        {\"role\": \"user\", \"content\": \"List 3 countries and their capitals.\"},\n",
    "    ],\n",
    "    temperature=0,\n",
    "    max_tokens=64,\n",
    ")\n",
    "\n",
    "print_highlight(f\"Response: {response}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "terminate_process(server_process)"
   ]
  }
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