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

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This commit is contained in:
Lianmin Zheng
2024-12-29 23:57:16 -08:00
committed by GitHub
parent e6f523b5f2
commit 8c3b420eec
10 changed files with 62 additions and 70 deletions
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2
.github/workflows/pr-test.yml vendored
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@@ -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

14
benchmark/deepseek_v3/README.md
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@@ -1,13 +1,13 @@
# 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

4
docs/backend/backend.md
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@@ -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>

8
docs/backend/openai_api_completions.ipynb
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@@ -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",

31
docs/backend/constrained_decoding.ipynb → docs/backend/structured_outputs.ipynb
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@@ -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",

31
docs/references/sampling_params.md
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@@ -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)

6
python/sglang/srt/openai_api/protocol.py
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@@ -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):

10
python/sglang/srt/sampling/sampling_params.py
View File

@@ -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,

2
python/sglang/srt/server.py
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@@ -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:

24
python/sglang/srt/server_args.py
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@@ -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(