This doc describes the sampling parameters of the SGLang Runtime.
The /generate
endpoint accepts the following arguments in the JSON format.
@dataclass
class GenerateReqInput:
# The input prompt. It can be a single prompt or a batch of prompts.
text: Optional[Union[List[str], str]] = None
# The token ids for text; one can either specify text or input_ids.
input_ids: Optional[Union[List[List[int]], List[int]]] = None
# The image input. It can be a file name, a url, or base64 encoded string.
# See also python/sglang/srt/utils.py:load_image.
image_data: Optional[Union[List[str], str]] = None
# The sampling_params.
sampling_params: Union[List[Dict], Dict] = None
# The request id.
rid: Optional[Union[List[str], str]] = None
# Whether to return logprobs.
return_logprob: Optional[Union[List[bool], bool]] = None
# The start location of the prompt for return_logprob.
logprob_start_len: Optional[Union[List[int], int]] = None
# The number of top logprobs to return.
top_logprobs_num: Optional[Union[List[int], int]] = None
# Whether to detokenize tokens in logprobs.
return_text_in_logprobs: bool = False
# Whether to stream output.
stream: bool = False
The sampling_params
follows this format
class SamplingParams:
def __init__(
self,
max_new_tokens: int = 16,
stop: Optional[Union[str, List[str]]] = None,
temperature: float = 1.0,
top_p: float = 1.0,
top_k: int = -1,
frequency_penalty: float = 0.0,
presence_penalty: float = 0.0,
ignore_eos: bool = False,
skip_special_tokens: bool = True,
dtype: Optional[str] = None,
regex: Optional[str] = None,
) -> None:
max_new_tokens
,stop
,temperature
,top_p
,top_k
are common sampling parameters.ignore_eos
means ignoring the EOS token and continue decoding, which is helpful for benchmarking purposes.regex
constrains the output to follow a given regular expression.
Launch a server
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000
Send a request
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())
Send a request and stream the output
import requests, json
response = requests.post(
"http://localhost:30000/generate",
json={
"text": "The capital of France is",
"sampling_params": {
"temperature": 0,
"max_new_tokens": 256,
},
"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("")
Launch a server
python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.6-vicuna-7b --tokenizer-path llava-hf/llava-1.5-7b-hf --chat-template vicuna_v1.1 --port 30000
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
import requests
response = requests.post(
"http://localhost:30000/generate",
json={
"text": "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <image>\nDescribe this picture ASSISTANT:",
"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.