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[doc] Fold long code blocks to improve readability (#19926)

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Signed-off-by: reidliu41 <reid201711@gmail.com>
Co-authored-by: reidliu41 <reid201711@gmail.com>
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Reid
2025-06-23 13:24:23 +08:00
committed by GitHub
parent 493c275352
commit f17aec0d63
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docs/features/spec_decode.md
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@@ -18,29 +18,31 @@ Speculative decoding is a technique which improves inter-token latency in memory
The following code configures vLLM in an offline mode to use speculative decoding with a draft model, speculating 5 tokens at a time.
```python
from vllm import LLM, SamplingParams
??? Code
prompts = [
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
```python
from vllm import LLM, SamplingParams
llm = LLM(
model="facebook/opt-6.7b",
tensor_parallel_size=1,
speculative_config={
"model": "facebook/opt-125m",
"num_speculative_tokens": 5,
},
)
outputs = llm.generate(prompts, sampling_params)
prompts = [
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
llm = LLM(
model="facebook/opt-6.7b",
tensor_parallel_size=1,
speculative_config={
"model": "facebook/opt-125m",
"num_speculative_tokens": 5,
},
)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
To perform the same with an online mode launch the server:
@@ -60,69 +62,73 @@ python -m vllm.entrypoints.openai.api_server \
Then use a client:
```python
from openai import OpenAI
??? Code
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
```python
from openai import OpenAI
client = OpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key=openai_api_key,
base_url=openai_api_base,
)
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
models = client.models.list()
model = models.data[0].id
client = OpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key=openai_api_key,
base_url=openai_api_base,
)
# Completion API
stream = False
completion = client.completions.create(
model=model,
prompt="The future of AI is",
echo=False,
n=1,
stream=stream,
)
models = client.models.list()
model = models.data[0].id
print("Completion results:")
if stream:
for c in completion:
print(c)
else:
print(completion)
```
# Completion API
stream = False
completion = client.completions.create(
model=model,
prompt="The future of AI is",
echo=False,
n=1,
stream=stream,
)
print("Completion results:")
if stream:
for c in completion:
print(c)
else:
print(completion)
```
## Speculating by matching n-grams in the prompt
The following code configures vLLM to use speculative decoding where proposals are generated by
matching n-grams in the prompt. For more information read [this thread.](https://x.com/joao_gante/status/1747322413006643259)
```python
from vllm import LLM, SamplingParams
??? Code
prompts = [
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
```python
from vllm import LLM, SamplingParams
llm = LLM(
model="facebook/opt-6.7b",
tensor_parallel_size=1,
speculative_config={
"method": "ngram",
"num_speculative_tokens": 5,
"prompt_lookup_max": 4,
},
)
outputs = llm.generate(prompts, sampling_params)
prompts = [
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
llm = LLM(
model="facebook/opt-6.7b",
tensor_parallel_size=1,
speculative_config={
"method": "ngram",
"num_speculative_tokens": 5,
"prompt_lookup_max": 4,
},
)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
## Speculating using MLP speculators
@@ -131,29 +137,31 @@ draft models that conditioning draft predictions on both context vectors and sam
For more information see [this blog](https://pytorch.org/blog/hitchhikers-guide-speculative-decoding/) or
[this technical report](https://arxiv.org/abs/2404.19124).
```python
from vllm import LLM, SamplingParams
??? Code
prompts = [
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
```python
from vllm import LLM, SamplingParams
llm = LLM(
model="meta-llama/Meta-Llama-3.1-70B-Instruct",
tensor_parallel_size=4,
speculative_config={
"model": "ibm-ai-platform/llama3-70b-accelerator",
"draft_tensor_parallel_size": 1,
},
)
outputs = llm.generate(prompts, sampling_params)
prompts = [
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
llm = LLM(
model="meta-llama/Meta-Llama-3.1-70B-Instruct",
tensor_parallel_size=4,
speculative_config={
"model": "ibm-ai-platform/llama3-70b-accelerator",
"draft_tensor_parallel_size": 1,
},
)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
Note that these speculative models currently need to be run without tensor parallelism, although
it is possible to run the main model using tensor parallelism (see example above). Since the
@@ -177,31 +185,33 @@ A variety of speculative models of this type are available on HF hub:
The following code configures vLLM to use speculative decoding where proposals are generated by
an [EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency)](https://arxiv.org/pdf/2401.15077) based draft model. A more detailed example for offline mode, including how to extract request level acceptance rate, can be found [here](gh-file:examples/offline_inference/eagle.py).
```python
from vllm import LLM, SamplingParams
??? Code
prompts = [
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
```python
from vllm import LLM, SamplingParams
llm = LLM(
model="meta-llama/Meta-Llama-3-8B-Instruct",
tensor_parallel_size=4,
speculative_config={
"model": "yuhuili/EAGLE-LLaMA3-Instruct-8B",
"draft_tensor_parallel_size": 1,
},
)
prompts = [
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
outputs = llm.generate(prompts, sampling_params)
llm = LLM(
model="meta-llama/Meta-Llama-3-8B-Instruct",
tensor_parallel_size=4,
speculative_config={
"model": "yuhuili/EAGLE-LLaMA3-Instruct-8B",
"draft_tensor_parallel_size": 1,
},
)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
outputs = llm.generate(prompts, sampling_params)
```
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
A few important things to consider when using the EAGLE based draft models: