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[Refactor] Call renderer for online IO processor request (#34490)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
This commit is contained in:
Cyrus Leung
2026-02-13 14:48:45 +08:00
committed by GitHub
parent eea3024f43
commit ec090c2429
4 changed files with 39 additions and 18 deletions
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16
vllm/entrypoints/llm.py
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@@ -500,7 +500,7 @@ class LLM:
engine_prompts: Sequence[DictPrompt | TokPrompt] = [
engine_prompt
for prompt, param in zip(seq_prompts, seq_params)
for engine_prompt in self._preprocess_completion(
for engine_prompt in self._preprocess_cmpl(
[prompt],
tokenization_kwargs=merge_kwargs(
tokenization_kwargs,
@@ -509,7 +509,7 @@ class LLM:
)
]
else:
engine_prompts = self._preprocess_completion(
engine_prompts = self._preprocess_cmpl(
seq_prompts,
tokenization_kwargs=tokenization_kwargs,
)
@@ -889,7 +889,7 @@ class LLM:
add_special_tokens=not model_config.is_encoder_decoder,
).with_kwargs(tokenization_kwargs)
def _preprocess_completion(
def _preprocess_cmpl(
self,
prompts: Sequence[PromptType],
tokenization_kwargs: dict[str, Any] | None = None,
@@ -901,7 +901,7 @@ class LLM:
Refer to [LLM.generate][] for a complete description of the arguments.
Returns:
A list of `TokensPrompts` objects containing the tokenized prompt
A list of `TokPrompt` objects containing the tokenized prompt
after chat template interpolation, and the raw multi-modal inputs.
"""
renderer = self.renderer
@@ -943,7 +943,7 @@ class LLM:
Refer to [LLM.chat][] for a complete description of the arguments.
Returns:
A list of `TokensPrompts` objects containing the tokenized prompt
A list of `TokPrompt` objects containing the tokenized prompt
after chat template interpolation, and the raw multi-modal inputs.
"""
renderer = self.renderer
@@ -1823,11 +1823,11 @@ class LLM:
if any(param.truncate_prompt_tokens is not None for param in seq_params):
# TODO: Remove this after deprecating `param.truncate_prompt_tokens`
# Then, move the code from the `else` block to the top and let
# `self._preprocess_completion` handle prompt normalization
# `self._preprocess_cmpl` handle prompt normalization
engine_prompts: Sequence[DictPrompt | TokPrompt] = [
engine_prompt
for prompt, param in zip(seq_prompts, seq_params)
for engine_prompt in self._preprocess_completion(
for engine_prompt in self._preprocess_cmpl(
[prompt],
tokenization_kwargs=merge_kwargs(
tokenization_kwargs,
@@ -1836,7 +1836,7 @@ class LLM:
)
]
else:
engine_prompts = self._preprocess_completion(
engine_prompts = self._preprocess_cmpl(
seq_prompts,
tokenization_kwargs=tokenization_kwargs,
)