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Co-authored-by: Lianmin Zheng <lianminzheng@gmail.com>
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Ying Sheng
2024-07-19 09:54:01 -07:00
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README.md
View File

@@ -6,23 +6,29 @@
| [**Blog**](https://lmsys.org/blog/2024-01-17-sglang/) | [**Paper**](https://arxiv.org/abs/2312.07104) |
SGLang is a structured generation language designed for large language models (LLMs).
It makes your interaction with LLMs faster and more controllable by co-designing the frontend language and the runtime system.
SGLang is a fast serving framework for large language models and vision language models.
It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language.
The core features include:
- **Fast Backend Runtime**: Efficient serving with RadixAttention for prefix caching, continuous batching, token attention (paged attention), tensor parallelism, flashinfer kernels, jump-forward constrained decoding, and quantization (AWQ/FP8/GPTQ/Marlin).
- **Flexible Frontend Language**: Enables easy programming of LLM applications with chained generation calls, advanced prompting, control flow, multiple modalities, parallelism, and external interactions.
- **High-Performance Backend Runtime**: Features RadixAttention for accelerating complex LLM programs by reusing the KV cache across multiple calls. It can also serve as a standalone inference engine with all common techniques implemented (e.g., continuous batching and tensor parallelism).
## News
- [2024/04] 🔥 SGLang is used by the official **LLaVA-NeXT (video)** release ([blog](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/)).
- [2024/02] 🔥 SGLang enables **3x faster JSON decoding** with compressed finite state machine ([blog](https://lmsys.org/blog/2024-02-05-compressed-fsm/)).
- [2024/01] 🔥 SGLang powers the serving of the official **LLaVA v1.6** release demo ([usage](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#demo)).
- [2024/01] SGLang provides up to **5x faster inference** with RadixAttention ([blog](https://lmsys.org/blog/2024-01-17-sglang/)).
<details>
<summary>More</summary>
- [2024/01] SGLang powers the serving of the official **LLaVA v1.6** release demo ([usage](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#demo)).
</details>
## Contents
- [Install](#install)
- [Quick Start](#quick-start)
- [Frontend: Structured Generation Language (SGLang)](#frontend-structured-generation-language-sglang)
- [Backend: SGLang Runtime (SRT)](#backend-sglang-runtime-srt)
- [Frontend: Structured Generation Language (SGLang)](#frontend-structured-generation-language-sglang)
- [Benchmark And Performance](#benchmark-and-performance)
- [Roadmap](#roadmap)
- [Citation And Acknowledgment](#citation-and-acknowledgment)
@@ -70,13 +76,118 @@ pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/
- If you cannot install FlashInfer, check out its [installation](https://docs.flashinfer.ai/installation.html#) page. If you still cannot install it, you can use the slower Triton kernels by adding `--disable-flashinfer` when launching the server.
- If you only need to use the OpenAI backend, you can avoid installing other dependencies by using `pip install "sglang[openai]"`.
## Quick Start
## Backend: SGLang Runtime (SRT)
The SGLang Runtime (SRT) is an efficient serving engine.
### Launching a Server
Launch a server
```
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000
```
Send a request
```
curl http://localhost:30000/generate \
-H "Content-Type: application/json" \
-d '{
"text": "Once upon a time,",
"sampling_params": {
"max_new_tokens": 16,
"temperature": 0
}
}'
```
Learn more about the argument format [here](docs/sampling_params.md).
### OpenAI Compatible API
In addition, the server supports OpenAI-compatible APIs.
```python
import openai
client = openai.Client(
base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
# Text completion
response = client.completions.create(
model="default",
prompt="The capital of France is",
temperature=0,
max_tokens=32,
)
print(response)
# Chat completion
response = client.chat.completions.create(
model="default",
messages=[
{"role": "system", "content": "You are a helpful AI assistant"},
{"role": "user", "content": "List 3 countries and their capitals."},
],
temperature=0,
max_tokens=64,
)
print(response)
```
It supports streaming and most features of the Chat/Completions/Models endpoints specified by the [OpenAI API Reference](https://platform.openai.com/docs/api-reference/).
### Additional Server Arguments
- Add `--tp 2` to enable tensor parallelism. If it indicates `peer access is not supported between these two devices`, add `--enable-p2p-check` option.
```
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000 --tp 2
```
- Add `--dp 2` to enable data parallelism. It can also be used together with tp. Data parallelism is better for throughput if there is enough memory.
```
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000 --dp 2 --tp 2
```
- If you see out-of-memory errors during serving, please try to reduce the memory usage of the KV cache pool by setting a smaller value of `--mem-fraction-static`. The default value is `0.9`
```
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000 --mem-fraction-static 0.7
```
- See [hyperparameter_tuning.md](docs/hyperparameter_tuning.md) on tuning hyperparameters for better performance.
- Add `--nnodes 2` to run tensor parallelism on multiple nodes. If you have two nodes with two GPUs on each node and want to run TP=4, let `sgl-dev-1` be the hostname of the first node and `50000` be an available port.
```
# Node 0
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --tp 4 --nccl-init sgl-dev-1:50000 --nnodes 2 --node-rank 0
# Node 1
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --tp 4 --nccl-init sgl-dev-1:50000 --nnodes 2 --node-rank 1
```
- If the model does not have a template in the Hugging Face tokenizer, you can specify a [custom chat template](docs/custom_chat_template.md).
### Supported Models
- Llama / Llama 2 / Llama 3
- Mistral / Mixtral
- Gemma / Gemma 2
- Qwen / Qwen 2 / Qwen 2 MoE
- LLaVA 1.5 / 1.6
- `python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.5-7b --tokenizer-path llava-hf/llava-1.5-7b-hf --chat-template vicuna_v1.1 --port 30000`
- `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`
- `python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.6-34b --tokenizer-path liuhaotian/llava-v1.6-34b-tokenizer --port 3000`
- LLaVA-NeXT-Video
- see [srt_example_llava_v.sh](examples/usage/llava_video/srt_example_llava_v.sh)
- Yi-VL
- see [srt_example_yi_vl.py](examples/quick_start/srt_example_yi_vl.py).
- StableLM
- Command-R
- DBRX
- Grok
- ChatGLM
- InternLM 2
Instructions for supporting a new model are [here](https://github.com/sgl-project/sglang/blob/main/docs/model_support.md).
## Frontend: Structured Generation Language (SGLang)
The frontend language can be used with local models or API models.
### Quick Start
The example below shows how to use sglang to answer a mulit-turn question.
### Using Local Models
#### Using Local Models
First, launch a server with
```
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --port 30000
```
Then, connect to the server and answer a multi-turn question.
@@ -105,7 +216,7 @@ for m in state.messages():
print(state["answer_1"])
```
### Using OpenAI Models
#### Using OpenAI Models
Set the OpenAI API Key
```
export OPENAI_API_KEY=sk-******
@@ -136,13 +247,12 @@ for m in state.messages():
print(state["answer_1"])
```
### More Examples
#### More Examples
Anthropic and VertexAI (Gemini) models are also supported.
You can find more examples at [examples/quick_start](examples/quick_start).
## Frontend: Structured Generation Language (SGLang)
### Language Feature
To begin with, import sglang.
```python
import sglang as sgl
@@ -155,7 +265,7 @@ The system will manage the state, chat template, parallelism and batching for yo
The complete code for the examples below can be found at [readme_examples.py](examples/usage/readme_examples.py)
### Control Flow
#### Control Flow
You can use any Python code within the function body, including control flow, nested function calls, and external libraries.
```python
@@ -170,7 +280,7 @@ def tool_use(s, question):
s += "The key word to search is" + sgl.gen("word")
```
### Parallelism
#### Parallelism
Use `fork` to launch parallel prompts.
Because `sgl.gen` is non-blocking, the for loop below issues two generation calls in parallel.
@@ -192,7 +302,7 @@ def tip_suggestion(s):
s += "In summary" + sgl.gen("summary")
```
### Multi Modality
#### Multi Modality
Use `sgl.image` to pass an image as input.
```python
@@ -204,7 +314,7 @@ def image_qa(s, image_file, question):
See also [srt_example_llava.py](examples/quick_start/srt_example_llava.py).
### Constrained Decoding
#### Constrained Decoding
Use `regex` to specify a regular expression as a decoding constraint.
This is only supported for local models.
@@ -219,7 +329,7 @@ def regular_expression_gen(s):
)
```
### JSON Decoding
#### JSON Decoding
Use `regex` to specify a JSON schema with a regular expression.
```python
@@ -248,8 +358,7 @@ def character_gen(s, name):
See also [json_decode.py](examples/usage/json_decode.py) for an additional example on specifying formats with Pydantic models.
### Batching
#### Batching
Use `run_batch` to run a batch of requests with continuous batching.
```python
@@ -268,7 +377,7 @@ states = text_qa.run_batch(
)
```
### Streaming
#### Streaming
Add `stream=True` to enable streaming.
```python
@@ -287,139 +396,10 @@ for out in state.text_iter():
print(out, end="", flush=True)
```
### Tips and Implementation Details
#### Tips and Implementation Details
- The `choices` argument in `sgl.gen` is implemented by computing the [token-length normalized log probabilities](https://blog.eleuther.ai/multiple-choice-normalization/) of all choices and selecting the one with the highest probability.
- The `regex` argument in `sgl.gen` is implemented through autoregressive decoding with logit bias masking, according to the constraints set by the regex. It is compatible with `temperature=0` and `temperature != 0`.
## Backend: SGLang Runtime (SRT)
The SGLang Runtime (SRT) is designed to work best with the SGLang frontend.
However, it can also be used as a standalone API server.
In this case, the [RadixAttention](https://arxiv.org/abs/2312.07104) can still greatly accelerate many use cases with automatic KV cache reuse.
### Usage
Launch a server
```
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
```
Send a request
```
curl http://localhost:30000/generate \
-H "Content-Type: application/json" \
-d '{
"text": "Once upon a time,",
"sampling_params": {
"max_new_tokens": 16,
"temperature": 0
}
}'
```
Learn more about the argument format [here](docs/sampling_params.md).
### OpenAI Compatible API
In addition, the server supports an experimental OpenAI-compatible API.
```python
import openai
client = openai.Client(
base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")
# Text completion
response = client.completions.create(
model="default",
prompt="The capital of France is",
temperature=0,
max_tokens=32,
)
print(response)
# Chat completion
response = client.chat.completions.create(
model="default",
messages=[
{"role": "system", "content": "You are a helpful AI assistant"},
{"role": "user", "content": "List 3 countries and their capitals."},
],
temperature=0,
max_tokens=64,
)
print(response)
```
By default, the server uses the chat template specified in the model tokenizer from Hugging Face. It should just work for most official models such as Llama-2/Llama-3.
If needed, you can also override the chat template when launching the server:
```
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template llama-2
```
If the chat template you are looking for is missing, you are welcome to contribute it.
Meanwhile, you can also temporarily register your chat template as follows:
```json
{
"name": "my_model",
"system": "<|im_start|>system",
"user": "<|im_start|>user",
"assistant": "<|im_start|>assistant",
"sep_style": "CHATML",
"sep": "<|im_end|>",
"stop_str": ["<|im_end|>", "<|im_start|>"]
}
```
```
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template ./my_model_template.json
```
### Additional Arguments
- Add `--tp 2` to enable tensor parallelism. If it indicates `peer access is not supported between these two devices`, add `--enable-p2p-check` option.
```
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --tp 2
```
- Add `--dp 2` to enable data parallelism. It can also be used together with tp. Data parallelism is better for throughput if there is enough memory.
```
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --dp 2 --tp 2
```
- If you see out-of-memory errors during serving, please try to reduce the memory usage of the KV cache pool by setting a smaller value of `--mem-fraction-static`. The default value is `0.9`
```
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --mem-fraction-static 0.7
```
- See [hyperparameter_tuning.md](docs/hyperparameter_tuning.md) on tuning hyperparameters for better performance.
- Add `--nnodes 2` to run tensor parallelism on multiple nodes. If you have two nodes with two GPUs on each node and want to run TP=4, let `sgl-dev-1` be the hostname of the first node and `50000` be an available port.
```
# Node 0
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --tp 4 --nccl-init sgl-dev-1:50000 --nnodes 2 --node-rank 0
# Node 1
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --tp 4 --nccl-init sgl-dev-1:50000 --nnodes 2 --node-rank 1
```
### Supported Models
- Llama
- Mistral
- Mixtral
- Qwen / Qwen 2 / Qwen 2 MoE
- Gemma / Gemma 2
- `python -m sglang.launch_server --model-path google/gemma-7b-it --port 30000 --attention-reduce-in-fp32`
- LLaVA
- `python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.5-7b --tokenizer-path llava-hf/llava-1.5-7b-hf --chat-template vicuna_v1.1 --port 30000`
- `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`
- `python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.6-34b --tokenizer-path liuhaotian/llava-v1.6-34b-tokenizer --port 3000`
- LLaVA-NeXT-Video
- see [srt_example_llava_v.sh](examples/usage/llava_video/srt_example_llava_v.sh)
- Yi-VL
- see [srt_example_yi_vl.py](examples/quick_start/srt_example_yi_vl.py).
- StableLM
- Command-R
- DBRX
- Grok
- ChatGLM
- AWQ/GPTQ/Marlin quantization
Instructions for supporting a new model are [here](https://github.com/sgl-project/sglang/blob/main/docs/model_support.md).
## Benchmark And Performance
- Llama-7B on NVIDIA A10G, FP16, Tensor Parallelism=1
![llama_7b](assets/llama_7b.jpg)

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@@ -1,4 +1,4 @@
## Benchmark Results
# Benchmark Results
We tested our system on the following common LLM workloads and reported the achieved throughput:
- **[MMLU](https://arxiv.org/abs/2009.03300)**: A 5-shot, multi-choice, multi-task benchmark.

28
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@@ -0,0 +1,28 @@
# Custom Chat Template in SGLang Runtime
By default, the server uses the chat template specified in the model tokenizer from Hugging Face. It should just work for most official models such as Llama-2/Llama-3.
If needed, you can also override the chat template when launching the server:
```
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template llama-2
```
If the chat template you are looking for is missing, you are welcome to contribute it.
Meanwhile, you can also temporarily register your chat template as follows:
```json
{
"name": "my_model",
"system": "<|im_start|>system",
"user": "<|im_start|>user",
"assistant": "<|im_start|>assistant",
"sep_style": "CHATML",
"sep": "<|im_end|>",
"stop_str": ["<|im_end|>", "<|im_start|>"]
}
```
```
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000 --chat-template ./my_model_template.json
```

2
docs/model_support.md
View File

@@ -1,4 +1,4 @@
## How to Support a New Model
# How to Support a New Model
To support a new model in SGLang, you only need to add a single file under [SGLang Models Directory](https://github.com/sgl-project/sglang/tree/main/python/sglang/srt/models). You can learn from existing model implementations and create new files for the new models. Most models are based on the transformer architecture, making them very similar.

6
docs/sampling_params.md
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@@ -1,4 +1,4 @@
## Sampling Parameters of SGLang Runtime
# Sampling Parameters in SGLang Runtime
This doc describes the sampling parameters of the SGLang Runtime.
The `/generate` endpoint accepts the following arguments in the JSON format.
@@ -6,11 +6,11 @@ The `/generate` endpoint accepts the following arguments in the JSON format.
```python
@dataclass
class GenerateReqInput:
# The input prompt
# The input prompt. It can be a single prompt or a batch of prompts.
text: Union[List[str], str]
# 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
# The image input. It can be a file name.
image_data: Optional[Union[List[str], str]] = None
# The sampling_params
sampling_params: Union[List[Dict], Dict] = None

2
docs/test_process.md
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@@ -1,4 +1,4 @@
## SRT Unit Tests
# SRT Unit Tests
### Latency Alignment
Make sure your changes do not slow down the following benchmarks

3
python/sglang/README.md
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@@ -1,8 +1,7 @@
# Code Structure
- `backend`: Various backends for the language interpreter.
- `lang`: The frontend language.
- `srt`: The serving engine for running local models. (SRT = SGLang Runtime).
- `srt`: The backend engine for running local models. (SRT = SGLang Runtime).
- `test`: Test utilities.
- `api.py`: Public API.
- `bench_latency.py`: Benchmark utilities.

14
python/sglang/__init__.py
View File

@@ -22,16 +22,16 @@ from sglang.api import (
video,
)
# SGL Backends
from sglang.backend.anthropic import Anthropic
from sglang.backend.litellm import LiteLLM
from sglang.backend.openai import OpenAI
from sglang.backend.runtime_endpoint import RuntimeEndpoint
from sglang.backend.vertexai import VertexAI
# Global Configurations
from sglang.global_config import global_config
# SGL Backends
from sglang.lang.backend.anthropic import Anthropic
from sglang.lang.backend.litellm import LiteLLM
from sglang.lang.backend.openai import OpenAI
from sglang.lang.backend.runtime_endpoint import RuntimeEndpoint
from sglang.lang.backend.vertexai import VertexAI
# public APIs management
__all__ = [
"global_config",

2
python/sglang/api.py
View File

@@ -4,8 +4,8 @@ import os
import re
from typing import Callable, List, Optional, Union
from sglang.backend.base_backend import BaseBackend
from sglang.global_config import global_config
from sglang.lang.backend.base_backend import BaseBackend
from sglang.lang.ir import (
SglExpr,
SglExprList,

0
python/sglang/bench.py → python/sglang/bench_serving.py
View File

0
python/sglang/backend/__init__.py → python/sglang/lang/backend/__init__.py
View File

2
python/sglang/backend/anthropic.py → python/sglang/lang/backend/anthropic.py
View File

@@ -2,7 +2,7 @@ from typing import List, Optional, Union
import numpy as np
from sglang.backend.base_backend import BaseBackend
from sglang.lang.backend.base_backend import BaseBackend
from sglang.lang.chat_template import get_chat_template
from sglang.lang.interpreter import StreamExecutor
from sglang.lang.ir import SglSamplingParams

0
python/sglang/backend/base_backend.py → python/sglang/lang/backend/base_backend.py
View File

2
python/sglang/backend/litellm.py → python/sglang/lang/backend/litellm.py
View File

@@ -1,6 +1,6 @@
from typing import Mapping, Optional
from sglang.backend.base_backend import BaseBackend
from sglang.lang.backend.base_backend import BaseBackend
from sglang.lang.chat_template import get_chat_template_by_model_path
from sglang.lang.interpreter import StreamExecutor
from sglang.lang.ir import SglSamplingParams

2
python/sglang/backend/openai.py → python/sglang/lang/backend/openai.py
View File

@@ -6,7 +6,7 @@ from typing import Callable, List, Optional, Union
import numpy as np
from sglang.backend.base_backend import BaseBackend
from sglang.lang.backend.base_backend import BaseBackend
from sglang.lang.chat_template import ChatTemplate, get_chat_template_by_model_path
from sglang.lang.interpreter import StreamExecutor
from sglang.lang.ir import SglSamplingParams

2
python/sglang/backend/runtime_endpoint.py → python/sglang/lang/backend/runtime_endpoint.py
View File

@@ -3,8 +3,8 @@ from typing import List, Optional
import numpy as np
from sglang.backend.base_backend import BaseBackend
from sglang.global_config import global_config
from sglang.lang.backend.base_backend import BaseBackend
from sglang.lang.chat_template import get_chat_template_by_model_path
from sglang.lang.interpreter import StreamExecutor
from sglang.lang.ir import SglSamplingParams

2
python/sglang/backend/vertexai.py → python/sglang/lang/backend/vertexai.py
View File

@@ -2,7 +2,7 @@ import os
import warnings
from typing import Optional
from sglang.backend.base_backend import BaseBackend
from sglang.lang.backend.base_backend import BaseBackend
from sglang.lang.chat_template import get_chat_template
from sglang.lang.interpreter import StreamExecutor
from sglang.lang.ir import SglSamplingParams

2
python/sglang/lang/tracer.py
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@@ -3,8 +3,8 @@
import uuid
from typing import Any, Callable, Dict, List, Optional, Union
from sglang.backend.base_backend import BaseBackend
from sglang.global_config import global_config
from sglang.lang.backend.base_backend import BaseBackend
from sglang.lang.interpreter import ProgramState, ProgramStateGroup
from sglang.lang.ir import (
SglArgument,

2
python/sglang/srt/server.py
View File

@@ -26,7 +26,7 @@ import uvloop
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse, Response, StreamingResponse
from sglang.backend.runtime_endpoint import RuntimeEndpoint
from sglang.lang.backend.runtime_endpoint import RuntimeEndpoint
from sglang.srt.constrained import disable_cache
from sglang.srt.hf_transformers_utils import get_tokenizer
from sglang.srt.managers.controller.manager_multi import (

19
python/sglang/srt/server_args.py
View File

@@ -166,6 +166,15 @@ class ServerArgs:
"--quantization",
type=str,
default=ServerArgs.quantization,
choices=[
"awq",
"fp8",
"gptq",
"marlin",
"gptq_marlin",
"squeezellm",
"bitsandbytes",
],
help="The quantization method.",
)
parser.add_argument(
@@ -243,13 +252,13 @@ class ServerArgs:
parser.add_argument(
"--show-time-cost",
action="store_true",
help="Show time cost of custom marks",
help="Show time cost of custom marks.",
)
parser.add_argument(
"--api-key",
type=str,
default=ServerArgs.api_key,
help="Set API key of the server",
help="Set API key of the server.",
)
# Data parallelism
@@ -285,17 +294,17 @@ class ServerArgs:
parser.add_argument(
"--disable-flashinfer",
action="store_true",
help="Disable flashinfer inference kernels",
help="Disable flashinfer inference kernels.",
)
parser.add_argument(
"--disable-radix-cache",
action="store_true",
help="Disable RadixAttention",
help="Disable RadixAttention for prefix caching.",
)
parser.add_argument(
"--disable-regex-jump-forward",
action="store_true",
help="Disable regex jump-forward",
help="Disable regex jump-forward.",
)
parser.add_argument(
"--disable-cuda-graph",

2
python/sglang/test/test_programs.py
View File

@@ -306,7 +306,7 @@ def test_image_qa():
assert (
"taxi" in state.messages()[-1]["content"]
or "car" in state.messages()[-1]["content"]
)
), f"{state.messages()[-1]['content']}"
def test_stream():

4
python/sglang/test/test_utils.py
View File

@@ -6,9 +6,9 @@ from functools import partial
import numpy as np
import requests
from sglang.backend.openai import OpenAI
from sglang.backend.runtime_endpoint import RuntimeEndpoint
from sglang.global_config import global_config
from sglang.lang.backend.openai import OpenAI
from sglang.lang.backend.runtime_endpoint import RuntimeEndpoint
from sglang.utils import get_exception_traceback

2
test/lang/test_bind_cache.py
View File

@@ -1,7 +1,7 @@
import unittest
import sglang as sgl
from sglang.backend.runtime_endpoint import RuntimeEndpoint
from sglang.lang.backend.runtime_endpoint import RuntimeEndpoint
class TestBind(unittest.TestCase):

2
test/lang/test_tracing.py
View File

@@ -1,7 +1,7 @@
import unittest
import sglang as sgl
from sglang.backend.base_backend import BaseBackend
from sglang.lang.backend.base_backend import BaseBackend
from sglang.lang.chat_template import get_chat_template

1
test/srt/test_httpserver_llava.py
View File

@@ -10,7 +10,6 @@ The image features a man standing on the back of a yellow taxi cab, holding
import argparse
import asyncio
import json
import time
import aiohttp
import requests