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Unverified Commit 06175286 authored by Lianmin Zheng's avatar Lianmin Zheng Committed by GitHub
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Update quick start examples (#120)

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README.md
View file @ 06175286
......@@ -39,18 +39,20 @@ pip install -e "python[all]"
- For NVIDIA V100, please install the [nightly](https://triton-lang.org/main/getting-started/installation.html) version.
- If you only need to use the OpenAI backend, you can avoid installing other dependencies by using `pip install "sglang[openai]"`
## Quick Start
The example below shows how to use sglang to answer a mulit-turn question.
### Using OpenAI Models
Set the OpenAI API Key
### Using Local Models
First, launch a server with
```
export OPENAI_API_KEY=sk-******
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
```
Then, answer a multi-turn question.
Then, connect to the server and answer a multi-turn question.
```python
from sglang import function, system, user, assistant, gen, set_default_backend, OpenAI
from sglang import function, system, user, assistant, gen, set_default_backend, RuntimeEndpoint
@function
def multi_turn_question(s, question_1, question_2):
......@@ -60,7 +62,7 @@ def multi_turn_question(s, question_1, question_2):
s += user(question_2)
s += assistant(gen("answer_2", max_tokens=256))
set_default_backend(OpenAI("gpt-3.5-turbo"))
set_default_backend(RuntimeEndpoint("http://localhost:30000"))
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
......@@ -73,16 +75,15 @@ for m in state.messages():
print(state["answer_1"])
```
### Using Local Models
First, launch a server with
### Using OpenAI Models
Set the OpenAI API Key
```
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
export OPENAI_API_KEY=sk-******
```
Then, connect to the server and answer a multi-turn question.
Then, answer a multi-turn question.
```python
from sglang import function, system, user, assistant, gen, set_default_backend, RuntimeEndpoint
from sglang import function, system, user, assistant, gen, set_default_backend, OpenAI
@function
def multi_turn_question(s, question_1, question_2):
......@@ -92,7 +93,7 @@ def multi_turn_question(s, question_1, question_2):
s += user(question_2)
s += assistant(gen("answer_2", max_tokens=256))
set_default_backend(RuntimeEndpoint("http://localhost:30000"))
set_default_backend(OpenAI("gpt-3.5-turbo"))
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
......@@ -120,7 +121,7 @@ import sglang as sgl
`sglang` provides some simple primitives such as `gen`, `select`, `fork`, `image`.
You can implement your prompt flow in a function decorated by `sgl.function`.
You can then invoke the function with `run` or `run_batch`.
The system will manage the state, chat template, and parallelism for you.
The system will manage the state, chat template, parallelism and batching for you.
### Control Flow
You can use any Python code within the function body, including control flow, nested function calls, and external libraries.
......
examples/quick_start/anthropic_example_chat.py
View file @ 06175286
from sglang import function, system, user, assistant, gen, set_default_backend, Anthropic
"""
Usage:
export ANTHROPIC_API_KEY=sk-******
python3 anthropic_example_chat.py
"""
import sglang as sgl
@function
@sgl.function
def multi_turn_question(s, question_1, question_2):
s += user(question_1)
s += assistant(gen("answer_1", max_tokens=256))
s += user(question_2)
s += assistant(gen("answer_2", max_tokens=256))
s += sgl.user(question_1)
s += sgl.assistant(sgl.gen("answer_1", max_tokens=256))
s += sgl.user(question_2)
s += sgl.assistant(sgl.gen("answer_2", max_tokens=256))
set_default_backend(Anthropic("claude-2"))
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
)
def single():
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
)
for m in state.messages():
print(m["role"], ":", m["content"])
for m in state.messages():
print(m["role"], ":", m["content"])
print("answer_1", state["answer_1"])
def stream():
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
stream=True
)
for out in state.text_iter():
print(out, end="", flush=True)
print()
def batch():
states = multi_turn_question.run_batch([
{"question_1": "What is the capital of the United States?",
"question_2": "List two local attractions."},
{"question_1": "What is the capital of France?",
"question_2": "What is the population of this city?"},
])
for s in states:
print(s.messages())
if __name__ == "__main__":
sgl.set_default_backend(sgl.Anthropic("claude-2"))
# Run a single request
print("\n========== single ==========\n")
single()
# Stream output
print("\n========== stream ==========\n")
stream()
# Run a batch of requests
print("\n========== batch ==========\n")
batch()
examples/quick_start/anthropic_example_complete.py
View file @ 06175286
from sglang import function, gen, set_default_backend, Anthropic
"""
Usage:
export ANTHROPIC_API_KEY=sk-******
python3 anthropic_example_complete.py
"""
import sglang as sgl
@function
@sgl.function
def few_shot_qa(s, question):
s += (
"""
......@@ -13,14 +19,49 @@ def few_shot_qa(s, question):
\n\nAssistant: Rome
""")
s += "\n\nHuman: " + question + "\n"
s += "\n\nAssistant:" + gen("answer", stop="\n", temperature=0)
s += "\n\nAssistant:" + sgl.gen("answer", stop="\n", temperature=0)
def single():
state = few_shot_qa.run(question="What is the capital of the United States?")
answer = state["answer"].strip().lower()
assert "washington" in answer, f"answer: {state['answer']}"
print(state.text())
def stream():
state = few_shot_qa.run(
question="What is the capital of the United States?",
stream=True)
for out in state.text_iter("answer"):
print(out, end="", flush=True)
print()
def batch():
states = few_shot_qa.run_batch([
{"question": "What is the capital of the United States?"},
{"question": "What is the capital of China?"},
])
for s in states:
print(s["answer"])
set_default_backend(Anthropic("claude-2"))
if __name__ == "__main__":
sgl.set_default_backend(sgl.Anthropic("claude-2"))
state = few_shot_qa.run(question="What is the capital of the United States?")
answer = state["answer"].strip().lower()
# Run a single request
print("\n========== single ==========\n")
single()
assert "washington" in answer, f"answer: {state['answer']}"
# Stream output
print("\n========== stream ==========\n")
stream()
print(state.text())
# Run a batch of requests
print("\n========== batch ==========\n")
batch()
examples/quick_start/anthropic_example_stream.py deleted 100644 → 0
View file @ 4ea92f83
from sglang import function, system, user, assistant, gen, set_default_backend, Anthropic
@function
def multi_turn_question(s, question_1, question_2):
s += user(question_1)
s += assistant(gen("answer_1", max_tokens=256))
s += user(question_2)
s += assistant(gen("answer_2", max_tokens=256))
set_default_backend(Anthropic("claude-2"))
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
stream=True
)
for out in state.text_iter():
print(out, end="", flush=True)
examples/quick_start/gemini_example_chat.py 0 → 100644
View file @ 06175286
"""
Usage:
export GCP_PROJECT_ID=******
python3 gemini_example_chat.py
"""
import sglang as sgl
@sgl.function
def multi_turn_question(s, question_1, question_2):
s += sgl.user(question_1)
s += sgl.assistant(sgl.gen("answer_1", max_tokens=256))
s += sgl.user(question_2)
s += sgl.assistant(sgl.gen("answer_2", max_tokens=256))
def single():
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
)
for m in state.messages():
print(m["role"], ":", m["content"])
print("answer_1", state["answer_1"])
def stream():
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
stream=True
)
for out in state.text_iter():
print(out, end="", flush=True)
print()
def batch():
states = multi_turn_question.run_batch([
{"question_1": "What is the capital of the United States?",
"question_2": "List two local attractions."},
{"question_1": "What is the capital of France?",
"question_2": "What is the population of this city?"},
])
for s in states:
print(s.messages())
if __name__ == "__main__":
sgl.set_default_backend(sgl.VertexAI("gemini-pro"))
# Run a single request
print("\n========== single ==========\n")
single()
# Stream output
print("\n========== stream ==========\n")
stream()
# Run a batch of requests
print("\n========== batch ==========\n")
batch()
examples/quick_start/gemini_example_complete.py
View file @ 06175286
from sglang import function, gen, set_default_backend, VertexAI
"""
Usage:
export GCP_PROJECT_ID=******
python3 gemini_example_complete.py
"""
import sglang as sgl
@function
@sgl.function
def few_shot_qa(s, question):
s += (
"""The following are questions with answers.
......@@ -13,14 +19,49 @@ Q: What is the capital of Italy?
A: Rome
""")
s += "Q: " + question + "\n"
s += "A:" + gen("answer", stop="\n", temperature=0)
s += "A:" + sgl.gen("answer", stop="\n", temperature=0)
def single():
state = few_shot_qa.run(question="What is the capital of the United States?")
answer = state["answer"].strip().lower()
assert "washington" in answer, f"answer: {state['answer']}"
print(state.text())
def stream():
state = few_shot_qa.run(
question="What is the capital of the United States?",
stream=True)
for out in state.text_iter("answer"):
print(out, end="", flush=True)
print()
def batch():
states = few_shot_qa.run_batch([
{"question": "What is the capital of the United States?"},
{"question": "What is the capital of China?"},
])
for s in states:
print(s["answer"])
set_default_backend(VertexAI("gemini-pro"))
if __name__ == "__main__":
sgl.set_default_backend(sgl.VertexAI("gemini-pro"))
state = few_shot_qa.run(question="What is the capital of the United States?")
answer = state["answer"].strip().lower()
# Run a single request
print("\n========== single ==========\n")
single()
assert "washington" in answer, f"answer: {state['answer']}"
# Stream output
print("\n========== stream ==========\n")
stream()
print(state.text())
# Run a batch of requests
print("\n========== batch ==========\n")
batch()
examples/quick_start/gemini_example_multimodal_chat.py
View file @ 06175286
from sglang import function, user, assistant, gen, image, set_default_backend, VertexAI
"""
Usage:
export GCP_PROJECT_ID=******
python3 gemini_example_multimodal_chat.py
"""
import sglang as sgl
@function
@sgl.function
def image_qa(s, image_file1, image_file2, question):
s += user(image(image_file1) + image(image_file2) + question)
s += assistant(gen("answer_1", max_tokens=256))
s += sgl.user(sgl.image(image_file1) + sgl.image(image_file2) + question)
s += sgl.assistant(sgl.gen("answer", max_tokens=256))
set_default_backend(VertexAI("gemini-pro-vision"))
state = image_qa.run(
image_file1="./images/cat.jpeg",
image_file2="./images/dog.jpeg",
question="Describe difference of the 2 images in one sentence.",
stream=True
)
if __name__ == "__main__":
sgl.set_default_backend(sgl.VertexAI("gemini-pro-vision"))
for out in state.text_iter():
print(out, end="", flush=True)
state = image_qa.run(
image_file1="./images/cat.jpeg",
image_file2="./images/dog.jpeg",
question="Describe difference of the two images in one sentence.",
stream=True
)
for out in state.text_iter("answer"):
print(out, end="", flush=True)
print()
print(state["answer"])
examples/quick_start/gemini_example_stream.py deleted 100644 → 0
View file @ 4ea92f83
from sglang import function, user, assistant, gen, set_default_backend, VertexAI
@function
def multi_turn_question(s, question_1, question_2):
s += user(question_1)
s += assistant(gen("answer_1", max_tokens=256))
s += user(question_2)
s += assistant(gen("answer_2", max_tokens=256))
set_default_backend(VertexAI("gemini-pro"))
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
stream=True
)
for out in state.text_iter():
print(out, end="", flush=True)
examples/quick_start/openai_example_chat.py
View file @ 06175286
from sglang import function, system, user, assistant, gen, set_default_backend, OpenAI
"""
Usage:
export OPENAI_API_KEY=sk-******
python3 openai_example_chat.py
"""
import sglang as sgl
@function
@sgl.function
def multi_turn_question(s, question_1, question_2):
s += system("You are a helpful assistant.")
s += user(question_1)
s += assistant(gen("answer_1", max_tokens=256))
s += user(question_2)
s += assistant(gen("answer_2", max_tokens=256))
s += sgl.system("You are a helpful assistant.")
s += sgl.user(question_1)
s += sgl.assistant(sgl.gen("answer_1", max_tokens=256))
s += sgl.user(question_2)
s += sgl.assistant(sgl.gen("answer_2", max_tokens=256))
set_default_backend(OpenAI("gpt-3.5-turbo"))
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
)
def single():
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
)
for m in state.messages():
print(m["role"], ":", m["content"])
for m in state.messages():
print(m["role"], ":", m["content"])
print("answer_1", state["answer_1"])
def stream():
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
stream=True
)
for out in state.text_iter():
print(out, end="", flush=True)
print()
def batch():
states = multi_turn_question.run_batch([
{"question_1": "What is the capital of the United States?",
"question_2": "List two local attractions."},
{"question_1": "What is the capital of France?",
"question_2": "What is the population of this city?"},
])
for s in states:
print(s.messages())
if __name__ == "__main__":
sgl.set_default_backend(sgl.OpenAI("gpt-3.5-turbo"))
# Run a single request
print("\n========== single ==========\n")
single()
# Stream output
print("\n========== stream ==========\n")
stream()
# Run a batch of requests
print("\n========== batch ==========\n")
batch()
examples/quick_start/openai_example_complete.py
View file @ 06175286
from sglang import function, gen, set_default_backend, OpenAI
"""
Usage:
export OPENAI_API_KEY=sk-******
python3 openai_example_complete.py
"""
import sglang as sgl
@function
@sgl.function
def few_shot_qa(s, question):
s += (
"""The following are questions with answers.
......@@ -13,14 +19,49 @@ Q: What is the capital of Italy?
A: Rome
""")
s += "Q: " + question + "\n"
s += "A:" + gen("answer", stop="\n", temperature=0)
s += "A:" + sgl.gen("answer", stop="\n", temperature=0)
def single():
state = few_shot_qa.run(question="What is the capital of the United States?")
answer = state["answer"].strip().lower()
assert "washington" in answer, f"answer: {state['answer']}"
print(state.text())
def stream():
state = few_shot_qa.run(
question="What is the capital of the United States?",
stream=True)
for out in state.text_iter("answer"):
print(out, end="", flush=True)
print()
def batch():
states = few_shot_qa.run_batch([
{"question": "What is the capital of the United States?"},
{"question": "What is the capital of China?"},
])
for s in states:
print(s["answer"])
set_default_backend(OpenAI("gpt-3.5-turbo-instruct"))
if __name__ == "__main__":
sgl.set_default_backend(sgl.OpenAI("gpt-3.5-turbo-instruct"))
state = few_shot_qa.run(question="What is the capital of the United States?")
answer = state["answer"].strip().lower()
# Run a single request
print("\n========== single ==========\n")
single()
assert "washington" in answer, f"answer: {state['answer']}"
# Stream output
print("\n========== stream ==========\n")
stream()
print(state.text())
# Run a batch of requests
print("\n========== batch ==========\n")
batch()
examples/quick_start/openai_example_stream.py deleted 100644 → 0
View file @ 4ea92f83
from sglang import function, system, user, assistant, gen, set_default_backend, OpenAI
@function
def multi_turn_question(s, question_1, question_2):
s += system("You are a helpful assistant.")
s += user(question_1)
s += assistant(gen("answer_1", max_tokens=256))
s += user(question_2)
s += assistant(gen("answer_2", max_tokens=256))
set_default_backend(OpenAI("gpt-3.5-turbo"))
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
stream=True
)
for out in state.text_iter():
print(out, end="", flush=True)
examples/quick_start/srt_example_chat.py
View file @ 06175286
from sglang import function, system, user, assistant, gen, set_default_backend, Runtime
"""
Usage:
python3 srt_example_chat.py
"""
import sglang as sgl
@function
@sgl.function
def multi_turn_question(s, question_1, question_2):
s += system("You are a helpful assistant.")
s += user(question_1)
s += assistant(gen("answer_1", max_tokens=256))
s += user(question_2)
s += assistant(gen("answer_2", max_tokens=256))
s += sgl.user(question_1)
s += sgl.assistant(sgl.gen("answer_1", max_tokens=256))
s += sgl.user(question_2)
s += sgl.assistant(sgl.gen("answer_2", max_tokens=256))
runtime = Runtime(model_path="meta-llama/Llama-2-7b-chat-hf")
#runtime = Runtime(model_path="mistralai/Mixtral-8x7B-Instruct-v0.1")
set_default_backend(runtime)
def single():
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
)
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
)
for m in state.messages():
print(m["role"], ":", m["content"])
for m in state.messages():
print(m["role"], ":", m["content"])
print("answer_1", state["answer_1"])
runtime.shutdown()
def stream():
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
stream=True
)
for out in state.text_iter():
print(out, end="", flush=True)
print()
def batch():
states = multi_turn_question.run_batch([
{"question_1": "What is the capital of the United States?",
"question_2": "List two local attractions."},
{"question_1": "What is the capital of France?",
"question_2": "What is the population of this city?"},
])
for s in states:
print(s.messages())
if __name__ == "__main__":
runtime = sgl.Runtime(model_path="meta-llama/Llama-2-7b-chat-hf")
sgl.set_default_backend(runtime)
# Run a single request
print("\n========== single ==========\n")
single()
# Stream output
print("\n========== stream ==========\n")
stream()
# Run a batch of requests
print("\n========== batch ==========\n")
batch()
runtime.shutdown()
examples/quick_start/srt_example_complete.py
View file @ 06175286
from sglang import function, gen, set_default_backend, Runtime
"""
Usage:
python3 srt_example_complete.py
"""
import sglang as sgl
@function
@sgl.function
def few_shot_qa(s, question):
s += (
"""The following are questions with answers.
......@@ -13,16 +17,52 @@ Q: What is the capital of Italy?
A: Rome
""")
s += "Q: " + question + "\n"
s += "A:" + gen("answer", stop="\n", temperature=0)
s += "A:" + sgl.gen("answer", stop="\n", temperature=0)
runtime = Runtime(model_path="meta-llama/Llama-2-7b-chat-hf")
set_default_backend(runtime)
def single():
state = few_shot_qa.run(question="What is the capital of the United States?")
answer = state["answer"].strip().lower()
state = few_shot_qa.run(question="What is the capital of the United States?")
assert "washington" in answer, f"answer: {state['answer']}"
answer = state["answer"].strip().lower()
assert "washington" in answer, f"answer: {state['answer']}"
print(state.text())
print(state.text())
runtime.shutdown()
def stream():
state = few_shot_qa.run(
question="What is the capital of the United States?",
stream=True)
for out in state.text_iter("answer"):
print(out, end="", flush=True)
print()
def batch():
states = few_shot_qa.run_batch([
{"question": "What is the capital of the United States?"},
{"question": "What is the capital of China?"},
])
for s in states:
print(s["answer"])
if __name__ == "__main__":
runtime = sgl.Runtime(model_path="meta-llama/Llama-2-7b-chat-hf")
sgl.set_default_backend(runtime)
# Run a single request
print("\n========== single ==========\n")
single()
# Stream output
print("\n========== stream ==========\n")
stream()
# Run a batch of requests
print("\n========== batch ==========\n")
batch()
runtime.shutdown()
examples/quick_start/srt_example_llava.py
View file @ 06175286
......@@ -10,29 +10,53 @@ def image_qa(s, image_path, question):
s += sgl.assistant(sgl.gen("answer"))
runtime = sgl.Runtime(model_path="liuhaotian/llava-v1.5-7b",
tokenizer_path="llava-hf/llava-1.5-7b-hf")
sgl.set_default_backend(runtime)
# Single
state = image_qa.run(
image_path="images/cat.jpeg",
question="What is this?",
max_new_tokens=64)
print(state["answer"], "\n")
# Batch
states = image_qa.run_batch(
[
{"image_path": "images/cat.jpeg", "question":"What is this?"},
{"image_path": "images/dog.jpeg", "question":"What is this?"},
],
max_new_tokens=64,
)
for s in states:
print(s["answer"], "\n")
runtime.shutdown()
def single():
state = image_qa.run(
image_path="images/cat.jpeg",
question="What is this?",
max_new_tokens=64)
print(state["answer"], "\n")
def stream():
state = image_qa.run(
image_path="images/cat.jpeg",
question="What is this?",
max_new_tokens=64,
stream=True)
for out in state.text_iter("answer"):
print(out, end="", flush=True)
print()
def batch():
states = image_qa.run_batch(
[
{"image_path": "images/cat.jpeg", "question":"What is this?"},
{"image_path": "images/dog.jpeg", "question":"What is this?"},
],
max_new_tokens=64,
)
for s in states:
print(s["answer"], "\n")
if __name__ == "__main__":
runtime = sgl.Runtime(model_path="liuhaotian/llava-v1.5-7b",
tokenizer_path="llava-hf/llava-1.5-7b-hf")
sgl.set_default_backend(runtime)
# Run a single request
print("\n========== single ==========\n")
single()
# Stream output
print("\n========== stream ==========\n")
stream()
# Run a batch of requests
print("\n========== batch ==========\n")
batch()
runtime.shutdown()
examples/quick_start/srt_example_stream.py deleted 100644 → 0
View file @ 4ea92f83
from sglang import function, system, user, assistant, gen, set_default_backend, Runtime
@function
def multi_turn_question(s, question_1, question_2):
s += system("You are a helpful assistant.")
s += user(question_1)
s += assistant(gen("answer_1", max_tokens=256))
s += user(question_2)
s += assistant(gen("answer_2", max_tokens=256))
runtime = Runtime("meta-llama/Llama-2-7b-chat-hf")
set_default_backend(runtime)
state = multi_turn_question.run(
question_1="What is the capital of the United States?",
question_2="List two local attractions.",
temperature=0,
stream=True,
)
for out in state.text_iter():
print(out, end="", flush=True)
print()
runtime.shutdown()
python/sglang/lang/interpreter.py
View file @ 06175286
......@@ -651,7 +651,7 @@ class ProgramState:
def sync(self):
return self.stream_executor.sync()
def text_iter(self, var_name=None):
def text_iter(self, var_name: Optional[str] = None):
if self.stream_executor.stream:
prev = 0
if var_name is None:
......@@ -682,7 +682,9 @@ class ProgramState:
else:
yield self.get_var(name)
async def text_async_iter(self, var_name=None, return_meta_data=False):
async def text_async_iter(
self, var_name: Optional[str] = None, return_meta_data: bool = False
):
loop = asyncio.get_running_loop()
if self.stream_executor.stream:
......
python/sglang/lang/ir.py
View file @ 06175286
......@@ -74,7 +74,9 @@ class SglSamplingParams:
)
return {
"max_tokens_to_sample": self.max_new_tokens,
"stop_sequences": self.stop,
"stop_sequences": self.stop
if isinstance(self.stop, (list, tuple))
else [self.stop],
"temperature": self.temperature,
"top_p": self.top_p,
"top_k": self.top_k,
......
python/sglang/srt/models/qwen2.py
View file @ 06175286
......@@ -8,7 +8,6 @@ from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.managers.router.model_runner import InputMetadata
from torch import nn
from transformers import Qwen2Config
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (
......@@ -30,6 +29,8 @@ from vllm.model_executor.weight_utils import (
hf_model_weights_iterator,
)
Qwen2Config = None
class Qwen2MLP(nn.Module):
def __init__(
......
python/sglang/srt/server.py
View file @ 06175286
......@@ -445,18 +445,26 @@ class Runtime:
pipe_reader, pipe_writer = mp.Pipe(duplex=False)
proc = mp.Process(target=launch_server, args=(self.server_args, pipe_writer))
proc.start()
pipe_writer.close()
self.pid = proc.pid
init_state = pipe_reader.recv()
try:
init_state = pipe_reader.recv()
except EOFError:
init_state = ""
if init_state != "init ok":
self.shutdown()
raise RuntimeError("Launch failed")
raise RuntimeError("Launch failed. Please see the error messages above.")
self.endpoint = RuntimeEndpoint(self.url)
def shutdown(self):
if self.pid is not None:
parent = psutil.Process(self.pid)
try:
parent = psutil.Process(self.pid)
except psutil.NoSuchProcess:
return
children = parent.children(recursive=True)
for child in children:
child.kill()
......
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