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Docs: Add constrained decoding tutorial (#2614)

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Co-authored-by: Chayenne <zhaochen20@outlook.com>
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Shi Shuai
2024-12-28 07:54:28 +00:00
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Constrained Decoding Tutorial\n",
"\n",
"This tutorial shows how to format model outputs using constrained decoding in SGLang."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Constrained Decoding\n",
"\n",
"With SGLang, You can define a JSON schema, EBNF or regular expression to constrain the model's output.\n",
"\n",
"[JSON Schema](https://json-schema.org/): Formats output into structured JSON objects with validation rules.\n",
"\n",
"[EBNF (Extended Backus-Naur Form)](https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form): Defines complex syntax rules, especially for recursive patterns like nested structures.\n",
"\n",
"[Regular Expressions](https://en.wikipedia.org/wiki/Regular_expression): Matches text patterns for simple validation and formatting.\n",
"\n",
"### Constrained Decoding Backends\n",
"\n",
"SGLang has two backends: [Outlines](https://github.com/dottxt-ai/outlines) (default) and [XGrammar](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar). We suggest using XGrammar whenever possible for its better performance. For more details, see [XGrammar technical overview](https://blog.mlc.ai/2024/11/22/achieving-efficient-flexible-portable-structured-generation-with-xgrammar).\n",
"\n",
"* Xgrammar Backend: JSON and EBNF\n",
"* Outlines Backend: JSON and regular expressions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## OpenAI Compatible API"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To use Xgrammar, simply add `--grammar-backend xgrammar` when launching the server. If no backend is specified, Outlines will be used as the default."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sglang.utils import (\n",
" execute_shell_command,\n",
" wait_for_server,\n",
" terminate_process,\n",
" print_highlight,\n",
")\n",
"import openai\n",
"\n",
"server_process = execute_shell_command(\n",
" \"python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --port 30000 --host 0.0.0.0 --grammar-backend xgrammar\"\n",
")\n",
"\n",
"wait_for_server(\"http://localhost:30000\")\n",
"client = openai.Client(base_url=\"http://127.0.0.1:30000/v1\", api_key=\"None\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### JSON"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"json_schema = json.dumps(\n",
" {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"name\": {\"type\": \"string\", \"pattern\": \"^[\\\\w]+$\"},\n",
" \"population\": {\"type\": \"integer\"},\n",
" },\n",
" \"required\": [\"name\", \"population\"],\n",
" }\n",
")\n",
"\n",
"response = client.chat.completions.create(\n",
" model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"Give me the information of the capital of France in the JSON format.\",\n",
" },\n",
" ],\n",
" temperature=0,\n",
" max_tokens=128,\n",
" response_format={\n",
" \"type\": \"json_schema\",\n",
" \"json_schema\": {\"name\": \"foo\", \"schema\": json.loads(json_schema)},\n",
" },\n",
")\n",
"\n",
"print_highlight(response.choices[0].message.content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### EBNF"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ebnf_grammar = \"\"\"\n",
"root ::= city | description\n",
"city ::= \"London\" | \"Paris\" | \"Berlin\" | \"Rome\"\n",
"description ::= city \" is \" status\n",
"status ::= \"the capital of \" country\n",
"country ::= \"England\" | \"France\" | \"Germany\" | \"Italy\"\n",
"\"\"\"\n",
"\n",
"response = client.chat.completions.create(\n",
" model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": \"You are a helpful geography bot.\"},\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": \"Give me the information of the capital of France.\",\n",
" },\n",
" ],\n",
" temperature=0,\n",
" max_tokens=32,\n",
" extra_body={\"ebnf\": ebnf_grammar},\n",
")\n",
"\n",
"print_highlight(response.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"terminate_process(server_process)\n",
"server_process = execute_shell_command(\n",
" \"python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct --port 30000 --host 0.0.0.0\"\n",
")\n",
"\n",
"wait_for_server(\"http://localhost:30000\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Regular expression"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = client.chat.completions.create(\n",
" model=\"meta-llama/Meta-Llama-3.1-8B-Instruct\",\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": \"What is the capital of France?\"},\n",
" ],\n",
" temperature=0,\n",
" max_tokens=128,\n",
" extra_body={\"regex\": \"(Paris|London)\"},\n",
")\n",
"\n",
"print_highlight(response.choices[0].message.content)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"terminate_process(server_process)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Native API and SGLang Runtime (SRT)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sglang.utils import (\n",
" execute_shell_command,\n",
" wait_for_server,\n",
" terminate_process,\n",
" print_highlight,\n",
")\n",
"\n",
"import requests\n",
"\n",
"server_process = execute_shell_command(\n",
" \"\"\"\n",
"python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-1B-Instruct --port=30010 --grammar-backend xgrammar\n",
"\"\"\"\n",
")\n",
"\n",
"wait_for_server(\"http://localhost:30010\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### JSON"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import requests\n",
"\n",
"json_schema = json.dumps(\n",
" {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"name\": {\"type\": \"string\", \"pattern\": \"^[\\\\w]+$\"},\n",
" \"population\": {\"type\": \"integer\"},\n",
" },\n",
" \"required\": [\"name\", \"population\"],\n",
" }\n",
")\n",
"\n",
"# JSON\n",
"response = requests.post(\n",
" \"http://localhost:30010/generate\",\n",
" json={\n",
" \"text\": \"Here is the information of the capital of France in the JSON format.\\n\",\n",
" \"sampling_params\": {\n",
" \"temperature\": 0,\n",
" \"max_new_tokens\": 64,\n",
" \"json_schema\": json_schema,\n",
" },\n",
" },\n",
")\n",
"\n",
"print_highlight(response.json())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### EBNF"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"\n",
"response = requests.post(\n",
" \"http://localhost:30010/generate\",\n",
" json={\n",
" \"text\": \"Give me the information of the capital of France.\",\n",
" \"sampling_params\": {\n",
" \"max_new_tokens\": 128,\n",
" \"temperature\": 0,\n",
" \"n\": 3,\n",
" \"ebnf\": (\n",
" \"root ::= city | description\\n\"\n",
" 'city ::= \"London\" | \"Paris\" | \"Berlin\" | \"Rome\"\\n'\n",
" 'description ::= city \" is \" status\\n'\n",
" 'status ::= \"the capital of \" country\\n'\n",
" 'country ::= \"England\" | \"France\" | \"Germany\" | \"Italy\"'\n",
" ),\n",
" },\n",
" \"stream\": False,\n",
" \"return_logprob\": False,\n",
" },\n",
")\n",
"\n",
"print_highlight(response.json())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"terminate_process(server_process)\n",
"server_process = execute_shell_command(\n",
" \"\"\"\n",
"python3 -m sglang.launch_server --model-path meta-llama/Llama-3.2-1B-Instruct --port=30010\n",
"\"\"\"\n",
")\n",
"\n",
"wait_for_server(\"http://localhost:30010\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Regular expression"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"response = requests.post(\n",
" \"http://localhost:30010/generate\",\n",
" json={\n",
" \"text\": \"Paris is the capital of\",\n",
" \"sampling_params\": {\n",
" \"temperature\": 0,\n",
" \"max_new_tokens\": 64,\n",
" \"regex\": \"(France|England)\",\n",
" },\n",
" },\n",
")\n",
"print_highlight(response.json())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"terminate_process(server_process)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Offline Engine API"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sglang as sgl\n",
"\n",
"llm_xgrammar = sgl.Engine(\n",
" model_path=\"meta-llama/Meta-Llama-3.1-8B-Instruct\", grammar_backend=\"xgrammar\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### JSON"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"prompts = [\n",
" \"Give me the information of the capital of China in the JSON format.\",\n",
" \"Give me the information of the capital of France in the JSON format.\",\n",
" \"Give me the information of the capital of Ireland in the JSON format.\",\n",
"]\n",
"\n",
"json_schema = json.dumps(\n",
" {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"name\": {\"type\": \"string\", \"pattern\": \"^[\\\\w]+$\"},\n",
" \"population\": {\"type\": \"integer\"},\n",
" },\n",
" \"required\": [\"name\", \"population\"],\n",
" }\n",
")\n",
"\n",
"sampling_params = {\"temperature\": 0.1, \"top_p\": 0.95, \"json_schema\": json_schema}\n",
"\n",
"outputs = llm_xgrammar.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": [
"### EBNF\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"prompts = [\n",
" \"Give me the information of the capital of France.\",\n",
" \"Give me the information of the capital of Germany.\",\n",
" \"Give me the information of the capital of Italy.\",\n",
"]\n",
"\n",
"sampling_params = {\n",
" \"temperature\": 0.8,\n",
" \"top_p\": 0.95,\n",
" \"ebnf\": (\n",
" \"root ::= city | description\\n\"\n",
" 'city ::= \"London\" | \"Paris\" | \"Berlin\" | \"Rome\"\\n'\n",
" 'description ::= city \" is \" status\\n'\n",
" 'status ::= \"the capital of \" country\\n'\n",
" 'country ::= \"England\" | \"France\" | \"Germany\" | \"Italy\"'\n",
" ),\n",
"}\n",
"\n",
"outputs = llm_xgrammar.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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_xgrammar.shutdown()\n",
"llm_outlines = sgl.Engine(model_path=\"meta-llama/Meta-Llama-3.1-8B-Instruct\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Regular expression"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"prompts = [\n",
" \"Please provide information about London as a major global city:\",\n",
" \"Please provide information about Paris as a major global city:\",\n",
"]\n",
"\n",
"sampling_params = {\"temperature\": 0.8, \"top_p\": 0.95, \"regex\": \"(France|England)\"}\n",
"\n",
"outputs = llm_outlines.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": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_outlines.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
}

1
docs/index.rst
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@@ -29,6 +29,7 @@ The core features include:
backend/native_api.ipynb
backend/offline_engine_api.ipynb
backend/backend.md
backend/constrained_decoding.ipynb
.. toctree::