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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>
This commit is contained in:
Reid
2025-06-23 13:24:23 +08:00
committed by GitHub
parent 493c275352
commit f17aec0d63
50 changed files with 3455 additions and 3180 deletions
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92
docs/design/arch_overview.md
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@ -22,31 +22,33 @@ server.
Here is a sample of `LLM` class usage:
```python
from vllm import LLM, SamplingParams
??? Code
# Define a list of input prompts
prompts = [
"Hello, my name is",
"The capital of France is",
"The largest ocean is",
]
```python
from vllm import LLM, SamplingParams
# Define sampling parameters
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Define a list of input prompts
prompts = [
"Hello, my name is",
"The capital of France is",
"The largest ocean is",
]
# Initialize the LLM engine with the OPT-125M model
llm = LLM(model="facebook/opt-125m")
# Define sampling parameters
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Generate outputs for the input prompts
outputs = llm.generate(prompts, sampling_params)
# Initialize the LLM engine with the OPT-125M model
llm = LLM(model="facebook/opt-125m")
# Print the generated outputs
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
# Generate outputs for the input prompts
outputs = llm.generate(prompts, sampling_params)
# Print the generated outputs
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
More API details can be found in the [Offline Inference](#offline-inference-api) section of the API docs.
@ -178,32 +180,34 @@ vision-language model.
To avoid accidentally passing incorrect arguments, the constructor is now keyword-only. This ensures that the constructor will raise an error if old configurations are passed. vLLM developers have already made this change for all models within vLLM. For out-of-tree registered models, developers need to update their models, for example by adding shim code to adapt the old constructor signature to the new one:
```python
class MyOldModel(nn.Module):
def __init__(
self,
config,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
prefix: str = "",
) -> None:
...
??? Code
from vllm.config import VllmConfig
class MyNewModel(MyOldModel):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
super().__init__(config, cache_config, quant_config, lora_config, prefix)
```python
class MyOldModel(nn.Module):
def __init__(
self,
config,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
lora_config: Optional[LoRAConfig] = None,
prefix: str = "",
) -> None:
...
if __version__ >= "0.6.4":
MyModel = MyNewModel
else:
MyModel = MyOldModel
```
from vllm.config import VllmConfig
class MyNewModel(MyOldModel):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
super().__init__(config, cache_config, quant_config, lora_config, prefix)
if __version__ >= "0.6.4":
MyModel = MyNewModel
else:
MyModel = MyOldModel
```
This way, the model can work with both old and new versions of vLLM.

38
docs/design/kernel/paged_attention.md
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@ -448,27 +448,29 @@ elements of the entire head for all context tokens. However, overall,
all results for output have been calculated but are just stored in
different thread register memory.
```cpp
float* out_smem = reinterpret_cast<float*>(shared_mem);
for (int i = NUM_WARPS; i > 1; i /= 2) {
// Upper warps write to shared memory.
...
float* dst = &out_smem[(warp_idx - mid) * HEAD_SIZE];
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
...
dst[row_idx] = accs[i];
}
??? Code
// Lower warps update the output.
const float* src = &out_smem[warp_idx * HEAD_SIZE];
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
```cpp
float* out_smem = reinterpret_cast<float*>(shared_mem);
for (int i = NUM_WARPS; i > 1; i /= 2) {
// Upper warps write to shared memory.
...
accs[i] += src[row_idx];
}
float* dst = &out_smem[(warp_idx - mid) * HEAD_SIZE];
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
...
dst[row_idx] = accs[i];
}
// Write out the accs.
}
```
// Lower warps update the output.
const float* src = &out_smem[warp_idx * HEAD_SIZE];
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
...
accs[i] += src[row_idx];
}
// Write out the accs.
}
```
## Output

40
docs/design/plugin_system.md
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@ -13,28 +13,30 @@ Plugins are user-registered code that vLLM executes. Given vLLM's architecture (
vLLM's plugin system uses the standard Python `entry_points` mechanism. This mechanism allows developers to register functions in their Python packages for use by other packages. An example of a plugin:
```python
# inside `setup.py` file
from setuptools import setup
??? Code
setup(name='vllm_add_dummy_model',
version='0.1',
packages=['vllm_add_dummy_model'],
entry_points={
'vllm.general_plugins':
["register_dummy_model = vllm_add_dummy_model:register"]
})
```python
# inside `setup.py` file
from setuptools import setup
# inside `vllm_add_dummy_model.py` file
def register():
from vllm import ModelRegistry
setup(name='vllm_add_dummy_model',
version='0.1',
packages=['vllm_add_dummy_model'],
entry_points={
'vllm.general_plugins':
["register_dummy_model = vllm_add_dummy_model:register"]
})
if "MyLlava" not in ModelRegistry.get_supported_archs():
ModelRegistry.register_model(
"MyLlava",
"vllm_add_dummy_model.my_llava:MyLlava",
)
```
# inside `vllm_add_dummy_model.py` file
def register():
from vllm import ModelRegistry
if "MyLlava" not in ModelRegistry.get_supported_archs():
ModelRegistry.register_model(
"MyLlava",
"vllm_add_dummy_model.my_llava:MyLlava",
)
```
For more information on adding entry points to your package, please check the [official documentation](https://setuptools.pypa.io/en/latest/userguide/entry_point.html).