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[Frontend] Separate pooling APIs in offline inference (#11129)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2024-12-13 18:40:07 +08:00
committed by GitHub
parent f93bf2b189
commit eeec9e3390
21 changed files with 669 additions and 304 deletions
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143
vllm/entrypoints/llm.py
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@@ -26,7 +26,9 @@ from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.model_executor.guided_decoding.guided_fields import (
GuidedDecodingRequest, LLMGuidedOptions)
from vllm.outputs import PoolingRequestOutput, RequestOutput
from vllm.outputs import (ClassificationRequestOutput, EmbeddingRequestOutput,
PoolingRequestOutput, RequestOutput,
ScoringRequestOutput)
from vllm.pooling_params import PoolingParams
from vllm.prompt_adapter.request import PromptAdapterRequest
from vllm.sampling_params import (BeamSearchParams, GuidedDecodingParams,
@@ -120,7 +122,7 @@ class LLM:
serving, use the :class:`~vllm.AsyncLLMEngine` class instead.
"""
DEPRECATE_LEGACY: ClassVar[bool] = False
DEPRECATE_LEGACY: ClassVar[bool] = True
"""A flag to toggle whether to deprecate the legacy generate/encode API."""
DEPRECATE_INIT_POSARGS: ClassVar[bool] = True
@@ -257,11 +259,14 @@ class LLM:
self,
prompts: Union[PromptType, Sequence[PromptType]],
/,
*,
sampling_params: Optional[Union[SamplingParams,
Sequence[SamplingParams]]] = None,
*,
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
guided_options_request: Optional[Union[LLMGuidedOptions,
GuidedDecodingRequest]] = None,
) -> List[RequestOutput]:
...
@@ -275,6 +280,9 @@ class LLM:
prompt_token_ids: Optional[List[int]] = None,
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
guided_options_request: Optional[Union[LLMGuidedOptions,
GuidedDecodingRequest]] = None,
) -> List[RequestOutput]:
...
@@ -288,6 +296,9 @@ class LLM:
prompt_token_ids: Optional[List[List[int]]] = None,
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
guided_options_request: Optional[Union[LLMGuidedOptions,
GuidedDecodingRequest]] = None,
) -> List[RequestOutput]:
...
@@ -302,6 +313,9 @@ class LLM:
prompt_token_ids: List[int],
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
guided_options_request: Optional[Union[LLMGuidedOptions,
GuidedDecodingRequest]] = None,
) -> List[RequestOutput]:
...
@@ -316,6 +330,9 @@ class LLM:
prompt_token_ids: List[List[int]],
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
guided_options_request: Optional[Union[LLMGuidedOptions,
GuidedDecodingRequest]] = None,
) -> List[RequestOutput]:
...
@@ -328,6 +345,9 @@ class LLM:
prompt_token_ids: Union[List[int], List[List[int]]],
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
guided_options_request: Optional[Union[LLMGuidedOptions,
GuidedDecodingRequest]] = None,
) -> List[RequestOutput]:
...
@@ -678,11 +698,12 @@ class LLM:
self,
prompts: Union[PromptType, Sequence[PromptType]],
/,
*,
pooling_params: Optional[Union[PoolingParams,
Sequence[PoolingParams]]] = None,
*,
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> List[PoolingRequestOutput]:
...
@@ -696,6 +717,7 @@ class LLM:
prompt_token_ids: Optional[List[int]] = None,
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> List[PoolingRequestOutput]:
...
@@ -709,6 +731,7 @@ class LLM:
prompt_token_ids: Optional[List[List[int]]] = None,
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> List[PoolingRequestOutput]:
...
@@ -723,6 +746,7 @@ class LLM:
prompt_token_ids: List[int],
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> List[PoolingRequestOutput]:
...
@@ -737,6 +761,7 @@ class LLM:
prompt_token_ids: List[List[int]],
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> List[PoolingRequestOutput]:
...
@@ -749,6 +774,7 @@ class LLM:
prompt_token_ids: Union[List[int], List[List[int]]],
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> List[PoolingRequestOutput]:
...
@@ -768,7 +794,8 @@ class LLM:
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> List[PoolingRequestOutput]:
"""Generates the completions for the input prompts.
"""Apply pooling to the hidden states corresponding to the input
prompts.
This class automatically batches the given prompts, considering
the memory constraint. For the best performance, put all of your prompts
@@ -787,7 +814,7 @@ class LLM:
Returns:
A list of ``PoolingRequestOutput`` objects containing the
generated embeddings in the same order as the input prompts.
pooled hidden states in the same order as the input prompts.
Note:
Using ``prompts`` and ``prompt_token_ids`` as keyword parameters is
@@ -833,28 +860,110 @@ class LLM:
return self.engine_class.validate_outputs(outputs,
PoolingRequestOutput)
def embed(
self,
prompts: Union[PromptType, Sequence[PromptType]],
/,
*,
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> List[EmbeddingRequestOutput]:
"""
Generate an embedding vector for each prompt.
This class automatically batches the given prompts, considering
the memory constraint. For the best performance, put all of your prompts
into a single list and pass it to this method.
Args:
prompts: The prompts to the LLM. You may pass a sequence of prompts
for batch inference. See :class:`~vllm.inputs.PromptType`
for more details about the format of each prompts.
use_tqdm: Whether to use tqdm to display the progress bar.
lora_request: LoRA request to use for generation, if any.
prompt_adapter_request: Prompt Adapter request to use for
generation, if any.
Returns:
A list of ``EmbeddingRequestOutput`` objects containing the
embedding vectors in the same order as the input prompts.
"""
if self.llm_engine.model_config.task != "embed":
raise ValueError(
"Embedding API is only enabled for `--task embed`")
items = self.encode(prompts,
use_tqdm=use_tqdm,
lora_request=lora_request,
prompt_adapter_request=prompt_adapter_request)
return [EmbeddingRequestOutput.from_base(item) for item in items]
def classify(
self,
prompts: Union[PromptType, Sequence[PromptType]],
/,
*,
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> List[ClassificationRequestOutput]:
"""
Generate class logits for each prompt.
This class automatically batches the given prompts, considering
the memory constraint. For the best performance, put all of your prompts
into a single list and pass it to this method.
Args:
prompts: The prompts to the LLM. You may pass a sequence of prompts
for batch inference. See :class:`~vllm.inputs.PromptType`
for more details about the format of each prompts.
use_tqdm: Whether to use tqdm to display the progress bar.
lora_request: LoRA request to use for generation, if any.
prompt_adapter_request: Prompt Adapter request to use for
generation, if any.
Returns:
A list of ``ClassificationRequestOutput`` objects containing the
embedding vectors in the same order as the input prompts.
"""
if self.llm_engine.model_config.task != "classify":
raise ValueError(
"Classification API is only enabled for `--task classify`")
items = self.encode(prompts,
use_tqdm=use_tqdm,
lora_request=lora_request,
prompt_adapter_request=prompt_adapter_request)
return [ClassificationRequestOutput.from_base(item) for item in items]
def score(
self,
text_1: Union[SingletonPrompt, Sequence[SingletonPrompt]],
text_2: Union[SingletonPrompt, Sequence[SingletonPrompt]],
/,
*,
truncate_prompt_tokens: Optional[int] = None,
use_tqdm: bool = True,
lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
prompt_adapter_request: Optional[PromptAdapterRequest] = None,
) -> List[PoolingRequestOutput]:
"""Generates similarity scores for all pairs <text,text_pair>.
) -> List[ScoringRequestOutput]:
"""Generate similarity scores for all pairs ``<text,text_pair>``.
The inputs can be 1 -> 1, 1 -> N or N -> N. In the 1 - N case
the text_1 sentence will be replicated N times to pair with the text_2
sentences. The input pairs are used to build a list of prompts for the
The inputs can be ``1 -> 1``, ``1 -> N`` or ``N -> N``.
In the ``1 - N`` case the ``text_1`` sentence will be replicated ``N``
times to pair with the ``text_2`` sentences.
The input pairs are used to build a list of prompts for the
cross encoder model. This class automatically batches the prompts,
considering the memory constraint. For the best performance, put all
of your texts into a single list and pass it to this method.
Args:
text_1: can be a single prompt or a list of prompts, in which
case it has to have the same length as the text_2 list
case it has to have the same length as the ``text_2`` list
text_2: The texts to pair with the query to form the input
to the LLM. See :class:`~vllm.inputs.PromptType` for
more details about the format of each prompts.
@@ -864,7 +973,7 @@ class LLM:
generation, if any.
Returns:
A list of ``PoolingRequestOutput`` objects containing the
A list of ``ScoringRequestOutput`` objects containing the
generated scores in the same order as the input prompts.
"""
runner_type = self.llm_engine.model_config.runner_type
@@ -884,6 +993,8 @@ class LLM:
if not self.llm_engine.model_config.is_cross_encoder:
raise ValueError("Your model does not support cross encoding")
if self.llm_engine.model_config.task != "score":
raise ValueError("Score API is only enabled for `--task score`")
tokenizer = self.llm_engine.get_tokenizer()
@@ -954,8 +1065,10 @@ class LLM:
)
outputs = self._run_engine(use_tqdm=use_tqdm)
return self.engine_class.validate_outputs(outputs,
PoolingRequestOutput)
items = self.engine_class.validate_outputs(outputs,
PoolingRequestOutput)
return [ScoringRequestOutput.from_base(item) for item in items]
def start_profile(self) -> None:
self.llm_engine.start_profile()