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Add examples in sampling parameters (#4039)

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7
docs/backend/native_api.ipynb
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@@ -57,7 +57,7 @@
"metadata": {},
"source": [
"## Generate (text generation model)\n",
"Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](https://docs.sglang.ai/backend/sampling_params.html)."
"Generate completions. This is similar to the `/v1/completions` in OpenAI API. Detailed parameters can be found in the [sampling parameters](./sampling_params.md)."
]
},
{
@@ -73,6 +73,11 @@
"print_highlight(response.json())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},

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docs/backend/sampling_params.md
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@@ -1,72 +1,248 @@
# Sampling Parameters
This doc describes the sampling parameters of the SGLang Runtime.
It is the low-level endpoint of the runtime.
If you want a high-level endpoint that can automatically handle chat templates, consider using the [OpenAI Compatible API](https://docs.sglang.ai/backend/openai_api_completions.html).
This doc describes the sampling parameters of the SGLang Runtime. It is the low-level endpoint of the runtime.
If you want a high-level endpoint that can automatically handle chat templates, consider using the [OpenAI Compatible API](./openai_api_completions.ipynb).
## `/generate` Endpoint
The `/generate` endpoint accepts the following parameters in JSON format. For in detail usage see the [native api doc](https://docs.sglang.ai/backend/native_api.html).
The `/generate` endpoint accepts the following parameters in JSON format. For in detail usage see the [native api doc](./native_api.ipynb).
* `prompt`: The input prompt. Can be a single prompt or a batch of prompts. `Optional[Union[List[str], str]] = None`
* `input_ids`: Alternative to `text`. Specify the input as token IDs instead of text. `Optional[Union[List[List[int]], List[int]]] = None`
* `sampling_params`: The sampling parameters as described in the sections below. `Optional[Union[List[Dict], Dict]] = None`
* `return_logprob`: Whether to return log probabilities for tokens. `Optional[Union[List[bool], bool]] = None`
* `logprob_start_len`: If returning log probabilities, specifies the start position in the prompt. Default is "-1" which returns logprobs only for output tokens. `Optional[Union[List[int], int]] = None`
* `top_logprobs_num`: If returning log probabilities, specifies the number of top logprobs to return at each position. `Optional[Union[List[int], int]] = None`
* `stream`: Whether to stream the output. `bool = False`
* `lora_path`: Path to LoRA weights. `Optional[Union[List[Optional[str]], Optional[str]]] = None`
* `custom_logit_processor`: Custom logit processor for advanced sampling control. For usage see below. `Optional[Union[List[Optional[str]], str]] = None`
* `return_hidden_states`: Whether to return hidden states of the model. Note that each time it changes, the cuda graph will be recaptured, which might lead to a performance hit. See the [examples](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/hidden_states.py) for more information. `bool = False`
* `prompt: Optional[Union[List[str], str]] = None` The input prompt. Can be a single prompt or a batch of prompts.
* `input_ids: Optional[Union[List[List[int]], List[int]]] = None` Alternative to `text`. Specify the input as token IDs instead of text.
* `sampling_params: Optional[Union[List[Dict], Dict]] = None` The sampling parameters as described in the sections below.
* `return_logprob: Optional[Union[List[bool], bool]] = None` Whether to return log probabilities for tokens.
* `logprob_start_len: Optional[Union[List[int], int]] = None` If returning log probabilities, specifies the start position in the prompt. Default is "-1" which returns logprobs only for output tokens.
* `top_logprobs_num: Optional[Union[List[int], int]] = None` If returning log probabilities, specifies the number of top logprobs to return at each position.
* `stream: bool = False` Whether to stream the output.
* `lora_path: Optional[Union[List[Optional[str]], Optional[str]]] = None` Path to LoRA weights.
* `custom_logit_processor: Optional[Union[List[Optional[str]], str]] = None` Custom logit processor for advanced sampling control. For usage see below.
* `return_hidden_states: bool = False` Whether to return hidden states of the model. Note that each time it changes, the cuda graph will be recaptured, which might lead to a performance hit. See the [examples](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/hidden_states.py) for more information.
## Sampling params
### Core Parameters
* `max_new_tokens`: The maximum output length measured in tokens. `int = 128`
* `stop`: One or multiple [stop words](https://platform.openai.com/docs/api-reference/chat/create#chat-create-stop). Generation will stop if one of these words is sampled. `Optional[Union[str, List[str]]] = None`
* `stop_token_ids`: Provide stop words in form of token ids. Generation will stop if one of these token ids is sampled. `Optional[List[int]] = []`
* `temperature`: [Temperature](https://platform.openai.com/docs/api-reference/chat/create#chat-create-temperature) when sampling the next token. `temperature = 0` corresponds to greedy sampling, higher temperature leads to more diversity. `float = 1.0`
* `top_p`: [Top-p](https://platform.openai.com/docs/api-reference/chat/create#chat-create-top_p) selects tokens from the smallest sorted set whose cumulative probability exceeds `top_p`. When `top_p = 1`, this reduces to unrestricted sampling from all tokens. `top_p: float = 1.0`
* `top_k`: [Top-k](https://developer.nvidia.com/blog/how-to-get-better-outputs-from-your-large-language-model/#predictability_vs_creativity) randomly selects from the `k` highest-probability tokens. `int = -1`
* `min_p`: [Min-p](https://github.com/huggingface/transformers/issues/27670) samples from tokens with probability larger than `min_p * highest_token_probability`. `float = 0.0`
* `max_new_tokens: int = 128` The maximum output length measured in tokens.
* `stop: Optional[Union[str, List[str]]] = None` One or multiple [stop words](https://platform.openai.com/docs/api-reference/chat/create#chat-create-stop). Generation will stop if one of these words is sampled.
* `stop_token_ids: Optional[List[int]] = []` Provide stop words in form of token ids. Generation will stop if one of these token ids is sampled.
* `temperature: float = 1.0` [Temperature](https://platform.openai.com/docs/api-reference/chat/create#chat-create-temperature) when sampling the next token. `temperature = 0` corresponds to greedy sampling, higher temperature leads to more diversity.
* `top_p: float = 1.0` [Top-p](https://platform.openai.com/docs/api-reference/chat/create#chat-create-top_p) selects tokens from the smallest sorted set whose cumulative probability exceeds `top_p`. When `top_p = 1`, this reduces to unrestricted sampling from all tokens.
* `top_k: int = -1` [Top-k](https://developer.nvidia.com/blog/how-to-get-better-outputs-from-your-large-language-model/#predictability_vs_creativity) randomly selects from the `k` highest-probability tokens.
* `min_p: float = 0.0` [Min-p](https://github.com/huggingface/transformers/issues/27670) samples from tokens with probability larger than `min_p * highest_token_probability`.
### Penalizers
To use penalizers you will need to `--disable-overlap`. Please note that this might degrade performance.
* `frequency_penalty`: Penalizes tokens based on their frequency in generation so far. Must be between `-2` and `2` where negative numbers encourage repeatment of tokens and positive number encourages sampling of new tokens. The scaling of penalization grows linearly with each appearance of a token. `float = 0.0`
* `presence_penalty`: Penalizes tokens if they appeared in the generation so far. Must be between `-2` and `2` where negative numbers encourage repeatment of tokens and positive number encourages sampling of new tokens. The scaling of the penalization is constant if a token occured. `float = 0.0`
* `repetition_penalty`: Penalizes tokens if they appeared in prompt or generation so far. Must be between `0` and `2` where numbers smaller than `1` encourage repeatment of tokens and numbers larger than `2` encourages sampling of new tokens. The penalization scales multiplicatively. `float = 0.0`
* `min_new_tokens`: Forces the model to generate at least `min_new_tokens` until a stop word or EOS token is sampled. Note that this might lead to unintended behavior for example if the distribution is highly skewed towards these tokens. `int = 0`
* `frequency_penalty: float = 0.0`: Penalizes tokens based on their frequency in generation so far. Must be between `-2` and `2` where negative numbers encourage repeatment of tokens and positive number encourages sampling of new tokens. The scaling of penalization grows linearly with each appearance of a token.
* `presence_penalty: float = 0.0`: Penalizes tokens if they appeared in the generation so far. Must be between `-2` and `2` where negative numbers encourage repeatment of tokens and positive number encourages sampling of new tokens. The scaling of the penalization is constant if a token occured.
* `repetition_penalty: float = 0.0`: Penalizes tokens if they appeared in prompt or generation so far. Must be between `0` and `2` where numbers smaller than `1` encourage repeatment of tokens and numbers larger than `2` encourages sampling of new tokens. The penalization scales multiplicatively.
* `min_new_tokens: int = 0`: Forces the model to generate at least `min_new_tokens` until a stop word or EOS token is sampled. Note that this might lead to unintended behavior for example if the distribution is highly skewed towards these tokens.
### Constrained decoding
Please refer to our dedicated guide on [constrained decoding](https://docs.sglang.ai/backend/structured_outputs.html#Native-API-and-SGLang-Runtime-(SRT)) for the following parameters.
Please refer to our dedicated guide on [constrained decoding](./structured_outputs.ipynb) for the following parameters.
* `json_schema`: `Optional[str] = None`
* `regex`: `Optional[str] = None`
* `ebnf`: `Optional[str] = None`
* `json_schema: Optional[str] = None`: JSON schema for structured outputs.
* `regex: Optional[str] = None`: Regex for structured outputs.
* `ebnf: Optional[str] = None`: EBNF for structured outputs.
### Other options
* `n`: Specifies the number of output sequences to generate per request. (Generating multiple outputs in one request (n > 1) is discouraged; repeat the same prompts for several times offer better control and efficiency.) `int = 1`
* `spaces_between_special_tokens`: Whether or not to add spaces between special tokens during detokenization. `bool = True`
* `no_stop_trim`: Don't trim stop words or EOS token from the generated text. `bool = False`
* `ignore_eos`: Don't stop generation when EOS token is sampled. `bool = False`
* `skip_special_tokens`: Remove special tokens during decoding. `bool = True`
* `custom_params`: Used when employing `CustomLogitProcessor`. For usage see below. `Optional[List[Optional[Dict[str, Any]]]] = None`
* `n: int = 1`: Specifies the number of output sequences to generate per request. (Generating multiple outputs in one request (n > 1) is discouraged; repeat the same prompts for several times offer better control and efficiency.)
* `spaces_between_special_tokens: bool = True`: Whether or not to add spaces between special tokens during detokenization.
* `no_stop_trim: bool = False`: Don't trim stop words or EOS token from the generated text.
* `ignore_eos: bool = False`: Don't stop generation when EOS token is sampled.
* `skip_special_tokens: bool = True`: Remove special tokens during decoding.
* `custom_params: Optional[List[Optional[Dict[str, Any]]]] = None`: Used when employing `CustomLogitProcessor`. For usage see below.
## Examples
### Normal
Launch a server:
```
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000
```
Send a request:
```python
import requests
response = requests.post(
"http://localhost:30000/generate",
json={
"text": "The capital of France is",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 32,
},
},
)
print(response.json())
```
Detailed example in [send request](./send_request.ipynb).
### Streaming
Send a request and stream the output:
```python
import requests, json
response = requests.post(
"http://localhost:30000/generate",
json={
"text": "The capital of France is",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 32,
},
"stream": True,
},
stream=True,
)
prev = 0
for chunk in response.iter_lines(decode_unicode=False):
chunk = chunk.decode("utf-8")
if chunk and chunk.startswith("data:"):
if chunk == "data: [DONE]":
break
data = json.loads(chunk[5:].strip("\n"))
output = data["text"].strip()
print(output[prev:], end="", flush=True)
prev = len(output)
print("")
```
Detailed example in [openai compatible api](https://docs.sglang.ai/backend/openai_api_completions.html#id2).
### Multi modal
Launch a server:
```
python3 -m sglang.launch_server --model-path lmms-lab/llava-onevision-qwen2-7b-ov --chat-template chatml-llava
```
Download an image:
```
curl -o example_image.png -L https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true
```
Send a request:
```python
import requests
response = requests.post(
"http://localhost:30000/generate",
json={
"text": "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
"<|im_start|>user\n<image>\nDescribe this image in a very short sentence.<|im_end|>\n"
"<|im_start|>assistant\n",
"image_data": "example_image.png",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 32,
},
},
)
print(response.json())
```
The `image_data` can be a file name, a URL, or a base64 encoded string. See also `python/sglang/srt/utils.py:load_image`.
Streaming is supported in a similar manner as [above](#streaming).
Detailed example in [openai api vision](./openai_api_vision.ipynb).
### 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. 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.
- [XGrammar](https://github.com/mlc-ai/xgrammar): Supports JSON schema, regular expression, and EBNF constraints.
- XGrammar currently uses the [GGML BNF format](https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md)
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
import json
import requests
json_schema = json.dumps({
"type": "object",
"properties": {
"name": {"type": "string", "pattern": "^[\\w]+$"},
"population": {"type": "integer"},
},
"required": ["name", "population"],
})
# JSON (works with both Outlines and XGrammar)
response = requests.post(
"http://localhost:30000/generate",
json={
"text": "Here is the information of the capital of France in the JSON format.\n",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 64,
"json_schema": json_schema,
},
},
)
print(response.json())
# Regular expression (Outlines backend only)
response = requests.post(
"http://localhost:30000/generate",
json={
"text": "Paris is the capital of",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 64,
"regex": "(France|England)",
},
},
)
print(response.json())
# EBNF (XGrammar backend only)
response = requests.post(
"http://localhost:30000/generate",
json={
"text": "Write a greeting.",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 64,
"ebnf": 'root ::= "Hello" | "Hi" | "Hey"',
},
},
)
print(response.json())
```
Detailed example in [structured outputs](./structured_outputs.ipynb).
### Custom Logit Processor
Launch a server with `--enable-custom-logit-processor` flag on.
```
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000 --enable-custom-logit-processor
```
Define a custom logit processor that will always sample a specific token id.
```python
from sglang.srt.sampling.custom_logit_processor import CustomLogitProcessor
@@ -89,7 +265,6 @@ class DeterministicLogitProcessor(CustomLogitProcessor):
```
Send a request
```python
import requests

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@@ -6,7 +6,7 @@
```
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --tp 2
```
- To enable multi-GPU data parallelism, add `--dp 2`. Data parallelism is better for throughput if there is enough memory. It can also be used together with tensor parallelism. The following command uses 4 GPUs in total. We recommend [SGLang Router](https://docs.sglang.ai/router/router.html) for data parallelism.
- To enable multi-GPU data parallelism, add `--dp 2`. Data parallelism is better for throughput if there is enough memory. It can also be used together with tensor parallelism. The following command uses 4 GPUs in total. We recommend [SGLang Router](../router/router.md) for data parallelism.
```
python -m sglang_router.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --dp 2 --tp 2
```
@@ -49,8 +49,8 @@ Please consult the documentation below to learn more about the parameters you ma
* `kv_cache_dtype`: Dtype of the kv cache, defaults to the `dtype`.
* `context_length`: The number of tokens our model can process *including the input*. Note that extending the default might lead to strange behavior.
* `device`: The device we put the model, defaults to `cuda`.
* `chat_template`: The chat template to use. Deviating from the default might lead to unexpected responses. For multi-modal chat templates, refer to [here](https://docs.sglang.ai/backend/openai_api_vision.html#Chat-Template).
* `is_embedding`: Set to true to perform [embedding](https://docs.sglang.ai/backend/openai_api_embeddings.html) / [encode](https://docs.sglang.ai/backend/native_api.html#Encode-(embedding-model)) and [reward](https://docs.sglang.ai/backend/native_api.html#Classify-(reward-model)) tasks.
* `chat_template`: The chat template to use. Deviating from the default might lead to unexpected responses. For multi-modal chat templates, refer to [here](https://docs.sglang.ai/backend/openai_api_vision.ipynb#Chat-Template).
* `is_embedding`: Set to true to perform [embedding](./openai_api_embeddings.ipynb) / [encode](https://docs.sglang.ai/backend/native_api#Encode-(embedding-model)) and [reward](https://docs.sglang.ai/backend/native_api#Classify-(reward-model)) tasks.
* `revision`: Adjust if a specific version of the model should be used.
* `skip_tokenizer_init`: Set to true to provide the tokens to the engine and get the output tokens directly, typically used in RLHF. Please see this [example for reference](https://github.com/sgl-project/sglang/blob/main/examples/runtime/engine/token_in_token_out_llm.py).
* `json_model_override_args`: Override model config with the provided JSON.
@@ -79,7 +79,7 @@ Please consult the documentation below to learn more about the parameters you ma
### Data parallelism
* `dp_size`: Will be deprecated. The number of data-parallel copies of the model. [SGLang router](https://docs.sglang.ai/router/router.html) is recommended instead of the current naive data parallel.
* `dp_size`: Will be deprecated. The number of data-parallel copies of the model. [SGLang router](../router/router.md) is recommended instead of the current naive data parallel.
* `load_balance_method`: Will be deprecated. Load balancing strategy for data parallel requests.
### Expert parallelism

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@@ -52,7 +52,7 @@ These adjustments should return the desired accuracy.
## Extending Evaluation Capabilities
1. **Contribute New Benchmarks**
* Follow our [contribution guidelines](https://docs.sglang.ai/references/contribution_guide.html) to add new test scripts
* Follow our [contribution guidelines](../references/contribution_guide.md) to add new test scripts
2. **Request Implementations**
* Feel free to open an issue describing your evaluation needs
3. **Use Alternative Tools**

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docs/references/amd.md
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@@ -39,7 +39,7 @@ Again, please go through the entire documentation to confirm your system is usin
## Installing SGLang
For general installation instructions, see the official [SGLang Installation Docs](https://docs.sglang.ai/start/install.html). Below are the AMD-specific steps summarized for convenience.
For general installation instructions, see the official [SGLang Installation Docs](../start/install.md). Below are the AMD-specific steps summarized for convenience.
### Install from Source

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docs/references/nvidia_jetson.md
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@@ -43,7 +43,7 @@ python -m sglang.launch_server \
--mem-fraction-static 0.8 \
--context-length 8192
```
The quantization and limited context length (`--dtype half --context-length 8192`) are due to the limited computational resources in [Nvidia jetson kit](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/). A detailed explanation can be found in [Server Arguments](https://docs.sglang.ai/backend/server_arguments.html).
The quantization and limited context length (`--dtype half --context-length 8192`) are due to the limited computational resources in [Nvidia jetson kit](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/). A detailed explanation can be found in [Server Arguments](../backend/server_arguments.md).
After launching the engine, refer to [Chat completions](https://docs.sglang.ai/backend/openai_api_completions.html#Usage) to test the usability.
* * * * *
@@ -66,7 +66,7 @@ This enables TorchAO's int4 weight-only quantization with a 128-group size. The
* * * * *
Structured output with XGrammar
-------------------------------
Please refer to [SGLang doc structured output](https://docs.sglang.ai/backend/structured_outputs.html).
Please refer to [SGLang doc structured output](../backend/structured_outputs.ipynb).
* * * * *
Thanks to the support from [shahizat](https://github.com/shahizat).