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[Doc] Convert Sphinx directives ( {class}, {meth}, {attr}, ...) to MkDocs format for better documentation linking (#18663)

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Signed-off-by: Zerohertz <ohg3417@gmail.com>
This commit is contained in:
Hyogeun Oh (오효근)
2025-05-27 17:44:20 +09:00
committed by GitHub
parent 6881107948
commit a68e293cb9
37 changed files with 360 additions and 247 deletions
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56
vllm/inputs/preprocess.py
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@@ -67,11 +67,11 @@ class InputPreprocessor:
return self.tokenizer.get_lora_tokenizer(lora_request).eos_token_id
def get_decoder_start_token_id(self) -> Optional[int]:
'''
"""
Obtain the decoder start token id employed by an encoder/decoder
model. Returns None for non-encoder/decoder models or if the
model config is unavailable.
'''
"""
if not self.model_config.is_encoder_decoder:
logger.warning_once(
@@ -79,14 +79,14 @@ class InputPreprocessor:
"this is not an encoder/decoder model.")
return None
if (self.model_config is None or self.model_config.hf_config is None):
if self.model_config is None or self.model_config.hf_config is None:
logger.warning_once(
"Using None for decoder start token id because "
"model config is not available.")
return None
dec_start_token_id = getattr(self.model_config.hf_config,
'decoder_start_token_id', None)
"decoder_start_token_id", None)
if dec_start_token_id is None:
logger.warning_once(
"Falling back on <BOS> for decoder start token "
@@ -97,7 +97,7 @@ class InputPreprocessor:
return dec_start_token_id
def _get_default_enc_dec_decoder_prompt(self) -> list[int]:
'''
"""
Specifically for encoder/decoder models:
generate a default decoder prompt for when
the user specifies only the encoder prompt.
@@ -126,7 +126,7 @@ class InputPreprocessor:
Returns:
* prompt_token_ids
'''
"""
bos_token_id = self.get_bos_token_id()
assert bos_token_id is not None
@@ -224,7 +224,10 @@ class InputPreprocessor:
lora_request: Optional[LoRARequest],
tokenization_kwargs: Optional[dict[str, Any]] = None,
) -> list[int]:
"""Async version of {meth}`_tokenize_prompt`."""
"""
Async version of
[`_tokenize_prompt`][vllm.inputs.preprocess.InputPreprocessor._tokenize_prompt].
"""
tokenizer = self.get_tokenizer_group()
tokenization_kwargs = self._get_tokenization_kw(tokenization_kwargs)
@@ -287,7 +290,10 @@ class InputPreprocessor:
lora_request: Optional[LoRARequest],
return_mm_hashes: bool = False,
) -> MultiModalInputs:
"""Async version of {meth}`_process_multimodal`."""
"""
Async version of
[`_process_multimodal`][vllm.inputs.preprocess.InputPreprocessor._process_multimodal].
"""
tokenizer = await self._get_mm_tokenizer_async(lora_request)
mm_processor = self.mm_registry.create_processor(self.model_config,
@@ -472,7 +478,7 @@ class InputPreprocessor:
Returns:
* {class}`SingletonInputs` instance
* [`SingletonInputs`][vllm.inputs.data.SingletonInputs] instance
"""
parsed = parse_singleton_prompt(prompt)
@@ -508,7 +514,10 @@ class InputPreprocessor:
lora_request: Optional[LoRARequest] = None,
return_mm_hashes: bool = False,
) -> SingletonInputs:
"""Async version of {meth}`_prompt_to_llm_inputs`."""
"""
Async version of
[`_prompt_to_llm_inputs`][vllm.inputs.preprocess.InputPreprocessor._prompt_to_llm_inputs].
"""
parsed = parse_singleton_prompt(prompt)
if parsed["type"] == "embeds":
@@ -644,7 +653,9 @@ class InputPreprocessor:
) -> EncoderDecoderInputs:
"""
For encoder/decoder models only:
Process an input prompt into an {class}`EncoderDecoderInputs` instance.
Process an input prompt into an
[`EncoderDecoderInputs`][vllm.inputs.data.EncoderDecoderInputs]
instance.
There are two types of input prompts:
singleton prompts which carry only the
@@ -670,7 +681,8 @@ class InputPreprocessor:
Returns:
* {class}`EncoderDecoderInputs` instance
* [`EncoderDecoderInputs`][vllm.inputs.data.EncoderDecoderInputs]
instance
"""
encoder_inputs: SingletonInputs
decoder_inputs: Optional[SingletonInputs]
@@ -710,7 +722,10 @@ class InputPreprocessor:
prompt: PromptType,
tokenization_kwargs: Optional[dict[str, Any]] = None,
) -> EncoderDecoderInputs:
"""Async version of {meth}`_process_encoder_decoder_prompt`."""
"""
Async version of
[`_process_encoder_decoder_prompt`][vllm.inputs.preprocess.InputPreprocessor._process_encoder_decoder_prompt].
"""
encoder_inputs: SingletonInputs
decoder_inputs: Optional[SingletonInputs]
@@ -778,7 +793,8 @@ class InputPreprocessor:
) -> DecoderOnlyInputs:
"""
For decoder-only models:
Process an input prompt into an {class}`DecoderOnlyInputs` instance.
Process an input prompt into a
[`DecoderOnlyInputs`][vllm.inputs.data.DecoderOnlyInputs] instance.
Arguments:
@@ -789,7 +805,7 @@ class InputPreprocessor:
Returns:
* {class}`DecoderOnlyInputs` instance
* [`DecoderOnlyInputs`][vllm.inputs.data.DecoderOnlyInputs] instance
"""
prompt_comps = self._prompt_to_llm_inputs(
@@ -812,7 +828,10 @@ class InputPreprocessor:
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
return_mm_hashes: bool = False,
) -> DecoderOnlyInputs:
"""Async version of {meth}`_process_decoder_only_prompt`."""
"""
Async version of
[`_process_decoder_only_prompt`][vllm.inputs.preprocess.InputPreprocessor._process_decoder_only_prompt].
"""
prompt_comps = await self._prompt_to_llm_inputs_async(
prompt,
tokenization_kwargs=tokenization_kwargs,
@@ -863,7 +882,10 @@ class InputPreprocessor:
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
return_mm_hashes: bool = False,
) -> ProcessorInputs:
"""Async version of {meth}`preprocess`."""
"""
Async version of
[`preprocess`][vllm.inputs.preprocess.InputPreprocessor.preprocess].
"""
if self.model_config.is_encoder_decoder:
assert not return_mm_hashes, (
"Multimodal hashes for encoder-decoder models should not be ",