[Docs] clean up structured outputs docs (#2654)

.github/workflows/pr-test.yml

@@ -52,7 +52,7 @@ jobs:
     runs-on: 1-gpu-runner
     strategy:
       matrix:
-        range: [0-6, 6-15, 15-23, 23-30, 30-100]
+        range: [0-6, 6-16, 16-23, 23-30, 30-100]
     steps:
       - name: Checkout code
         uses: actions/checkout@v3

benchmark/deepseek_v3/README.md

 # DeepSeek V3 Support
 
-The SGLang and DeepSeek teams worked together to get DeepSeek V3 FP8 running on NVIDIA and AMD GPUs **from day one**. SGLang also has supported [MLA optimization](https://lmsys.org/blog/2024-09-04-sglang-v0-3/#deepseek-multi-head-latent-attention-mla-throughput-optimizations) and [DP attention](https://lmsys.org/blog/2024-12-04-sglang-v0-4/#data-parallelism-attention-for-deepseek-models), making SGLang one of the best open-source LLM engines for running DeepSeek models.
+The SGLang and DeepSeek teams collaborated to get DeepSeek V3 FP8 running on NVIDIA and AMD GPUs **from day one**. SGLang also supports [MLA optimization](https://lmsys.org/blog/2024-09-04-sglang-v0-3/#deepseek-multi-head-latent-attention-mla-throughput-optimizations) and [DP attention](https://lmsys.org/blog/2024-12-04-sglang-v0-4/#data-parallelism-attention-for-deepseek-models), making SGLang one of the best open-source LLM engines for running DeepSeek models. SGLang is the inference engine recommended by the official [DeepSeek team](https://github.com/deepseek-ai/DeepSeek-V3/tree/main?tab=readme-ov-file#62-inference-with-sglang-recommended).
 
 Special thanks to Meituan's Search & Recommend Platform Team and Baseten's Model Performance Team for implementing the model, and DataCrunch for providing GPU resources.
 
 ## Hardware Recommendation
 - 8 x NVIDIA H200 GPUs
 
-If you do not have GPUs with large enough memory, please try multi-node tensor parallelism ([help 1](https://github.com/sgl-project/sglang/blob/637de9e8ce91fd3e92755eb2a842860925954ab1/docs/backend/backend.md?plain=1#L88-L95) [help 2](https://github.com/sgl-project/sglang/blob/637de9e8ce91fd3e92755eb2a842860925954ab1/docs/backend/backend.md?plain=1#L152-L168)). Here is an example serving with [2 H20 node](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-2-h208)
+If you do not have GPUs with large enough memory, please try multi-node tensor parallelism. There is an example serving with [2 H20 nodes](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-2-h208) below.
 
 ## Installation & Launch
 
@@ -61,10 +61,10 @@ For example, there are two H20 nodes, each with 8 GPUs. The first node's IP is `
 
 ```bash
 # node 1
-GLOO_SOCKET_IFNAME=eth0 python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3 --tp 16 --nccl-init 10.0.0.1:5000 --nnodes 2 --node-rank 0 --trust-remote-code
+python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3 --tp 16 --nccl-init 10.0.0.1:5000 --nnodes 2 --node-rank 0 --trust-remote-code
 
 # node 2
-GLOO_SOCKET_IFNAME=eth0 python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3 --tp 16 --nccl-init 10.0.0.1:5000 --nnodes 2 --node-rank 1 --trust-remote-code
+python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3 --tp 16 --nccl-init 10.0.0.1:5000 --nnodes 2 --node-rank 1 --trust-remote-code
 ```
 
 If you have two H100 nodes, the usage is similar to the aforementioned H20.
@@ -72,9 +72,3 @@ If you have two H100 nodes, the usage is similar to the aforementioned H20.
 ## DeepSeek V3 Optimization Plan
 
 https://github.com/sgl-project/sglang/issues/2591
-
-## Appendix
-
-SGLang is the inference engine officially recommended by the DeepSeek team.
-
-https://github.com/deepseek-ai/DeepSeek-V3/tree/main?tab=readme-ov-file#62-inference-with-sglang-recommended

docs/backend/backend.md

@@ -159,10 +159,10 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instr
 
 # Run 405B (fp16) on two nodes
 ## on the first node, replace the `172.16.4.52:20000` with your own first node ip address and port
-GLOO_SOCKET_IFNAME=eth0 python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct --tp 16 --nccl-init-addr 172.16.4.52:20000 --nnodes 2 --node-rank 0 --disable-cuda-graph
+python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct --tp 16 --nccl-init-addr 172.16.4.52:20000 --nnodes 2 --node-rank 0
 
 ## on the first node, replace the `172.16.4.52:20000` with your own first node ip address and port
-GLOO_SOCKET_IFNAME=eth0 python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct --tp 16 --nccl-init-addr 172.16.4.52:20000 --nnodes 2 --node-rank 1 --disable-cuda-graph
+python3 -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instruct --tp 16 --nccl-init-addr 172.16.4.52:20000 --nnodes 2 --node-rank 1
 ```
 
 </details>

docs/backend/openai_api_completions.ipynb

@@ -221,17 +221,15 @@
    "metadata": {},
    "source": [
     "## Structured Outputs (JSON, Regex, EBNF)\n",
-    "You can specify a JSON schema, Regular Expression or [EBNF](https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form) to constrain the model output. The model output will be guaranteed to follow the given constraints. \n",
+    "You can specify a JSON schema, [regular expression](https://en.wikipedia.org/wiki/Regular_expression) or [EBNF](https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form) to constrain the model output. The model output will be guaranteed to follow the given constraints. Only one constraint parameter (`json_schema`, `regex`, or `ebnf`) can be specified for a request.\n",
     "\n",
     "SGLang supports two grammar backends:\n",
     "\n",
-    "- [Outlines](https://github.com/dottxt-ai/outlines) (default): Supports JSON schema and Regular Expression constraints.\n",
+    "- [Outlines](https://github.com/dottxt-ai/outlines) (default): Supports JSON schema and regular expression constraints.\n",
     "- [XGrammar](https://github.com/mlc-ai/xgrammar): Supports JSON schema and EBNF constraints.\n",
     "  - XGrammar currently uses the [GGML BNF format](https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md)\n",
     "\n",
-    "> 🔔 Only one constraint parameter (`json_schema`, `regex`, or `ebnf`) can be specified at a time.\n",
-    "\n",
-    "Initialise xgrammar backend using `--grammar-backend xgrammar` flag\n",
+    "Initialize the XGrammar backend using `--grammar-backend xgrammar` flag\n",
     "```bash\n",
     "python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \\\n",
     "--port 30000 --host 0.0.0.0 --grammar-backend [xgrammar|outlines] # xgrammar or outlines (default: outlines)\n",

docs/backend/constrained_decoding.ipynb → docs/backend/structured_outputs.ipynb

@@ -11,20 +11,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "With SGLang, You can define a JSON schema, EBNF or regular expression to constrain the model's output.\n",
+    "## Structured Outputs (JSON, Regex, EBNF)\n",
+    "You can specify a JSON schema, [regular expression](https://en.wikipedia.org/wiki/Regular_expression) or [EBNF](https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form) to constrain the model output. The model output will be guaranteed to follow the given constraints. Only one constraint parameter (`json_schema`, `regex`, or `ebnf`) can be specified for a request.\n",
     "\n",
-    "[JSON Schema](https://json-schema.org/): Formats output into structured JSON objects with validation rules.\n",
+    "SGLang supports two grammar backends:\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",
+    "- [Outlines](https://github.com/dottxt-ai/outlines) (default): Supports JSON schema and regular expression constraints.\n",
+    "- [XGrammar](https://github.com/mlc-ai/xgrammar): Supports JSON schema and EBNF constraints.\n",
+    "  - XGrammar currently uses the [GGML BNF format](https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md)\n",
     "\n",
-    "[Regular Expressions](https://en.wikipedia.org/wiki/Regular_expression): Matches text patterns for simple validation and formatting.\n",
+    "Initialize the XGrammar backend using `--grammar-backend xgrammar` flag\n",
+    "```bash\n",
+    "python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruct \\\n",
+    "--port 30000 --host 0.0.0.0 --grammar-backend [xgrammar|outlines] # xgrammar or outlines (default: outlines)\n",
+    "```\n",
     "\n",
-    "## Grammar Backend\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"
+    "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)."
    ]
   },
   {
@@ -208,15 +210,6 @@
    "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",

docs/references/sampling_params.md

@@ -39,10 +39,9 @@ The `sampling_params` follows this format
 ```python
 # The maximum number of output tokens
 max_new_tokens: int = 128,
-# Stop when hitting any of the strings in this list.
+# Stop when hitting any of the strings in this list
 stop: Optional[Union[str, List[str]]] = None,
-# Stop when hitting any of the token_ids in this list. Could be useful when mixed with
-# `min_new_tokens`.
+# Stop when hitting any of the token_ids in this list
 stop_token_ids: Optional[List[int]] = [],
 # Sampling temperature
 temperature: float = 1.0,
@@ -52,26 +51,26 @@ top_p: float = 1.0,
 top_k: int = -1,
 # Min-p sampling
 min_p: float = 0.0,
-# Whether to ignore EOS token.
+# Whether to ignore EOS token
 ignore_eos: bool = False,
-# Whether to skip the special tokens during detokenization.
+# Whether to skip the special tokens during detokenization
 skip_special_tokens: bool = True,
-# Whether to add spaces between special tokens during detokenization.
+# Whether to add spaces between special tokens during detokenization
 spaces_between_special_tokens: bool = True,
 # Do parallel sampling and return `n` outputs.
 n: int = 1,
 
 ## Structured Outputs
-# Only one of the below three can be set at a time:
+# Only one of the below three can be set for a request.
 
-# Constrains the output to follow a given regular expression.
-regex: Optional[str] = None,
-# Constrains the output to follow a given JSON schema.
+# Constrain the output to follow a given JSON schema.
 json_schema: Optional[str] = None,
-# Constrains the output to follow a given EBNF Grammar.
+# Constrain the output to follow a given regular expression.
+regex: Optional[str] = None,
+# Constrain the output to follow a given EBNF grammar.
 ebnf: Optional[str] = None,
 
-## Penalties. See [Performance Implications on Penalties] section below for more informations.
+## Penalties.
 
 # Float that penalizes new tokens based on their frequency in the generated text so far.
 # Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to
@@ -185,17 +184,15 @@ The `image_data` can be a file name, a URL, or a base64 encoded string. See also
 Streaming is supported in a similar manner as [above](#streaming).
 
 ### Structured Outputs (JSON, Regex, EBNF)
-You can specify a JSON schema, Regular Expression or [EBNF](https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form) to constrain the model output. The model output will be guaranteed to follow the given constraints.
+You can specify a JSON schema, regular expression or [EBNF](https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form) to constrain the model output. The model output will be guaranteed to follow the given constraints. Only one constraint parameter (`json_schema`, `regex`, or `ebnf`) can be specified for a request.
 
 SGLang supports two grammar backends:
 
-- [Outlines](https://github.com/dottxt-ai/outlines) (default): Supports JSON schema and Regular Expression constraints.
+- [Outlines](https://github.com/dottxt-ai/outlines) (default): Supports JSON schema and regular expression constraints.
 - [XGrammar](https://github.com/mlc-ai/xgrammar): Supports JSON schema and EBNF constraints.
   - XGrammar currently uses the [GGML BNF format](https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md)
 
-> 🔔 Only one constraint parameter (`json_schema`, `regex`, or `ebnf`) can be specified at a time.
-
-Initialise xgrammar backend using `--grammar-backend xgrammar` flag
+Initialize the XGrammar backend using `--grammar-backend xgrammar` flag
 ```bash
 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|outlines] # xgrammar or outlines (default: outlines)

python/sglang/srt/openai_api/protocol.py

@@ -171,15 +171,15 @@ class CompletionRequest(BaseModel):
     top_k: int = -1
     min_p: float = 0.0
     min_tokens: int = 0
-    regex: Optional[str] = None
     json_schema: Optional[str] = None
+    regex: Optional[str] = None
+    ebnf: Optional[str] = None
     repetition_penalty: float = 1.0
     stop_token_ids: Optional[List[int]] = None
     no_stop_trim: bool = False
     ignore_eos: bool = False
     skip_special_tokens: bool = True
     lora_path: Optional[Union[List[Optional[str]], Optional[str]]] = None
-    ebnf: Optional[str] = None
 
 
 class CompletionResponseChoice(BaseModel):
@@ -315,13 +315,13 @@ class ChatCompletionRequest(BaseModel):
     min_p: float = 0.0
     min_tokens: int = 0
     regex: Optional[str] = None
+    ebnf: Optional[str] = None
     repetition_penalty: float = 1.0
     stop_token_ids: Optional[List[int]] = None
     no_stop_trim: bool = False
     ignore_eos: bool = False
     skip_special_tokens: bool = True
     lora_path: Optional[Union[List[Optional[str]], Optional[str]]] = None
-    ebnf: Optional[str] = None
 
 
 class FunctionResponse(BaseModel):

python/sglang/srt/sampling/sampling_params.py

@@ -19,6 +19,14 @@ _SAMPLING_EPS = 1e-6
 
 
 class SamplingParams:
+    """
+    The sampling parameters.
+
+    See docs/references/sampling_params.md or
+    https://sgl-project.github.io/references/sampling_params.html
+    for the documentation.
+    """
+
     def __init__(
         self,
         max_new_tokens: int = 128,
@@ -33,9 +41,9 @@ class SamplingParams:
         repetition_penalty: float = 1.0,
         min_new_tokens: int = 0,
         spaces_between_special_tokens: bool = True,
-        regex: Optional[str] = None,
         n: int = 1,
         json_schema: Optional[str] = None,
+        regex: Optional[str] = None,
         ebnf: Optional[str] = None,
         no_stop_trim: bool = False,
         ignore_eos: bool = False,

python/sglang/srt/server.py

@@ -578,6 +578,8 @@ def _set_envs_and_config(server_args: ServerArgs):
     os.environ["NCCL_NVLS_ENABLE"] = "0"
     os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
     os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "4"
+    if "GLOO_SOCKET_IFNAME" not in os.environ:
+        os.environ["GLOO_SOCKET_IFNAME"] = "eth0"
 
     # Set prometheus env vars
     if server_args.enable_metrics:

python/sglang/srt/server_args.py

@@ -42,7 +42,6 @@ class ServerArgs:
     model_path: str
     tokenizer_path: Optional[str] = None
     tokenizer_mode: str = "auto"
-    skip_tokenizer_init: bool = False
     load_format: str = "auto"
     trust_remote_code: bool = True
     dtype: str = "auto"
@@ -54,6 +53,7 @@ class ServerArgs:
     chat_template: Optional[str] = None
     is_embedding: bool = False
     revision: Optional[str] = None
+    skip_tokenizer_init: bool = False
     return_token_ids: bool = False
 
     # Port for the HTTP server
@@ -276,17 +276,6 @@ class ServerArgs:
             "tokenizer if available, and 'slow' will "
             "always use the slow tokenizer.",
         )
-        parser.add_argument(
-            "--skip-tokenizer-init",
-            action="store_true",
-            help="If set, skip init tokenizer and pass input_ids in generate request",
-        )
-        parser.add_argument(
-            "--return-token-ids",
-            action="store_true",
-            default=ServerArgs.return_token_ids,
-            help="Whether to return token IDs in the output, this may introduce additional overhead.",
-        )
         parser.add_argument(
             "--load-format",
             type=str,
@@ -394,6 +383,17 @@ class ServerArgs:
             "name, a tag name, or a commit id. If unspecified, will use "
             "the default version.",
         )
+        parser.add_argument(
+            "--skip-tokenizer-init",
+            action="store_true",
+            help="If set, skip init tokenizer and pass input_ids in generate request",
+        )
+        parser.add_argument(
+            "--return-token-ids",
+            action="store_true",
+            default=ServerArgs.return_token_ids,
+            help="Whether to return token IDs in the output, this may introduce additional overhead.",
+        )
 
         # Memory and scheduling
         parser.add_argument(