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[Feature] Add load generation config from model (#11164)

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Signed-off-by: liuyanyi <wolfsonliu@163.com>
Signed-off-by: Yanyi Liu <wolfsonliu@163.com>
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>
This commit is contained in:
Yanyi Liu
2024-12-19 18:50:38 +08:00
committed by GitHub
parent 98356735ac
commit 5aef49806d
10 changed files with 307 additions and 74 deletions
Download Patch File Download Diff File Expand all files Collapse all files

9
vllm/entrypoints/llm.py
View File

@@ -258,6 +258,13 @@ class LLM:
else:
tokenizer_group.tokenizer = get_cached_tokenizer(tokenizer)
def get_default_sampling_params(self) -> SamplingParams:
diff_sampling_param = (
self.llm_engine.model_config.get_diff_sampling_param())
if diff_sampling_param:
return SamplingParams.from_optional(**diff_sampling_param)
return SamplingParams()
@overload
def generate(
self,
@@ -441,7 +448,7 @@ class LLM:
if sampling_params is None:
# Use default sampling params.
sampling_params = SamplingParams()
sampling_params = self.get_default_sampling_params()
self._validate_and_add_requests(
prompts=parsed_prompts,

139
vllm/entrypoints/openai/protocol.py
View File

@@ -211,8 +211,8 @@ class ChatCompletionRequest(OpenAIBaseModel):
stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
stream: Optional[bool] = False
stream_options: Optional[StreamOptions] = None
temperature: Optional[float] = 1.0
top_p: Optional[float] = 1.0
temperature: Optional[float] = None
top_p: Optional[float] = None
tools: Optional[List[ChatCompletionToolsParam]] = None
tool_choice: Optional[Union[Literal["none"], Literal["auto"],
ChatCompletionNamedToolChoiceParam]] = "none"
@@ -224,9 +224,9 @@ class ChatCompletionRequest(OpenAIBaseModel):
# doc: begin-chat-completion-sampling-params
best_of: Optional[int] = None
use_beam_search: bool = False
top_k: int = -1
min_p: float = 0.0
repetition_penalty: float = 1.0
top_k: Optional[int] = None
min_p: Optional[float] = None
repetition_penalty: Optional[float] = None
length_penalty: float = 1.0
stop_token_ids: Optional[List[int]] = Field(default_factory=list)
include_stop_str_in_output: bool = False
@@ -348,15 +348,32 @@ class ChatCompletionRequest(OpenAIBaseModel):
# doc: end-chat-completion-extra-params
def to_beam_search_params(self,
default_max_tokens: int) -> BeamSearchParams:
# Default sampling parameters for chat completion requests
_DEFAULT_SAMPLING_PARAMS: dict = {
"repetition_penalty": 1.0,
"temperature": 1.0,
"top_p": 1.0,
"top_k": -1,
"min_p": 0.0,
}
def to_beam_search_params(
self,
default_max_tokens: int,
default_sampling_params: Optional[dict] = None
) -> BeamSearchParams:
# TODO(#9845): remove max_tokens when field is removed from OpenAI API
max_tokens = self.max_completion_tokens or self.max_tokens
if max_tokens is None:
max_tokens = default_max_tokens
if default_sampling_params is None:
default_sampling_params = {}
n = self.n if self.n is not None else 1
temperature = self.temperature if self.temperature is not None else 0.0
if (temperature := self.temperature) is None:
temperature = default_sampling_params.get(
"temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])
return BeamSearchParams(
beam_width=n,
@@ -367,13 +384,36 @@ class ChatCompletionRequest(OpenAIBaseModel):
include_stop_str_in_output=self.include_stop_str_in_output)
def to_sampling_params(
self, default_max_tokens: int,
logits_processor_pattern: Optional[str]) -> SamplingParams:
self,
default_max_tokens: int,
logits_processor_pattern: Optional[str],
default_sampling_params: Optional[dict] = None) -> SamplingParams:
# TODO(#9845): remove max_tokens when field is removed from OpenAI API
max_tokens = self.max_completion_tokens or self.max_tokens
if max_tokens is None:
max_tokens = default_max_tokens
if default_sampling_params is None:
default_sampling_params = {}
# Default parameters
if (repetition_penalty := self.repetition_penalty) is None:
repetition_penalty = default_sampling_params.get(
"repetition_penalty",
self._DEFAULT_SAMPLING_PARAMS["repetition_penalty"],
)
if (temperature := self.temperature) is None:
temperature = default_sampling_params.get(
"temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])
if (top_p := self.top_p) is None:
top_p = default_sampling_params.get(
"top_p", self._DEFAULT_SAMPLING_PARAMS["top_p"])
if (top_k := self.top_k) is None:
top_k = default_sampling_params.get(
"top_k", self._DEFAULT_SAMPLING_PARAMS["top_k"])
if (min_p := self.min_p) is None:
min_p = default_sampling_params.get(
"min_p", self._DEFAULT_SAMPLING_PARAMS["min_p"])
prompt_logprobs = self.prompt_logprobs
if prompt_logprobs is None and self.echo:
prompt_logprobs = self.top_logprobs
@@ -403,11 +443,11 @@ class ChatCompletionRequest(OpenAIBaseModel):
best_of=self.best_of,
presence_penalty=self.presence_penalty,
frequency_penalty=self.frequency_penalty,
repetition_penalty=self.repetition_penalty,
temperature=self.temperature,
top_p=self.top_p,
top_k=self.top_k,
min_p=self.min_p,
repetition_penalty=repetition_penalty,
temperature=temperature,
top_p=top_p,
top_k=top_k,
min_p=min_p,
seed=self.seed,
stop=self.stop,
stop_token_ids=self.stop_token_ids,
@@ -584,15 +624,15 @@ class CompletionRequest(OpenAIBaseModel):
stream: Optional[bool] = False
stream_options: Optional[StreamOptions] = None
suffix: Optional[str] = None
temperature: Optional[float] = 1.0
top_p: Optional[float] = 1.0
temperature: Optional[float] = None
top_p: Optional[float] = None
user: Optional[str] = None
# doc: begin-completion-sampling-params
use_beam_search: bool = False
top_k: int = -1
min_p: float = 0.0
repetition_penalty: float = 1.0
top_k: Optional[int] = None
min_p: Optional[float] = None
repetition_penalty: Optional[float] = None
length_penalty: float = 1.0
stop_token_ids: Optional[List[int]] = Field(default_factory=list)
include_stop_str_in_output: bool = False
@@ -669,14 +709,30 @@ class CompletionRequest(OpenAIBaseModel):
# doc: end-completion-extra-params
def to_beam_search_params(self,
default_max_tokens: int) -> BeamSearchParams:
# Default sampling parameters for completion requests
_DEFAULT_SAMPLING_PARAMS: dict = {
"repetition_penalty": 1.0,
"temperature": 1.0,
"top_p": 1.0,
"top_k": -1,
"min_p": 0.0,
}
def to_beam_search_params(
self,
default_max_tokens: int,
default_sampling_params: Optional[dict] = None
) -> BeamSearchParams:
max_tokens = self.max_tokens
if max_tokens is None:
max_tokens = default_max_tokens
if default_sampling_params is None:
default_sampling_params = {}
n = self.n if self.n is not None else 1
temperature = self.temperature if self.temperature is not None else 0.0
if (temperature := self.temperature) is None:
temperature = default_sampling_params.get("temperature", 1.0)
return BeamSearchParams(
beam_width=n,
@@ -687,12 +743,35 @@ class CompletionRequest(OpenAIBaseModel):
include_stop_str_in_output=self.include_stop_str_in_output)
def to_sampling_params(
self, default_max_tokens: int,
logits_processor_pattern: Optional[str]) -> SamplingParams:
self,
default_max_tokens: int,
logits_processor_pattern: Optional[str],
default_sampling_params: Optional[dict] = None) -> SamplingParams:
max_tokens = self.max_tokens
if max_tokens is None:
max_tokens = default_max_tokens
if default_sampling_params is None:
default_sampling_params = {}
# Default parameters
if (repetition_penalty := self.repetition_penalty) is None:
repetition_penalty = default_sampling_params.get(
"repetition_penalty",
self._DEFAULT_SAMPLING_PARAMS["repetition_penalty"],
)
if (temperature := self.temperature) is None:
temperature = default_sampling_params.get(
"temperature", self._DEFAULT_SAMPLING_PARAMS["temperature"])
if (top_p := self.top_p) is None:
top_p = default_sampling_params.get(
"top_p", self._DEFAULT_SAMPLING_PARAMS["top_p"])
if (top_k := self.top_k) is None:
top_k = default_sampling_params.get(
"top_k", self._DEFAULT_SAMPLING_PARAMS["top_k"])
if (min_p := self.min_p) is None:
min_p = default_sampling_params.get(
"min_p", self._DEFAULT_SAMPLING_PARAMS["min_p"])
prompt_logprobs = self.prompt_logprobs
if prompt_logprobs is None and self.echo:
prompt_logprobs = self.logprobs
@@ -718,11 +797,11 @@ class CompletionRequest(OpenAIBaseModel):
best_of=self.best_of,
presence_penalty=self.presence_penalty,
frequency_penalty=self.frequency_penalty,
repetition_penalty=self.repetition_penalty,
temperature=self.temperature,
top_p=self.top_p,
top_k=self.top_k,
min_p=self.min_p,
repetition_penalty=repetition_penalty,
temperature=temperature,
top_p=top_p,
top_k=top_k,
min_p=min_p,
seed=self.seed,
stop=self.stop,
stop_token_ids=self.stop_token_ids,

12
vllm/entrypoints/openai/serving_chat.py
View File

@@ -91,6 +91,10 @@ class OpenAIServingChat(OpenAIServing):
"been registered") from e
self.enable_prompt_tokens_details = enable_prompt_tokens_details
diff_sampling_param = self.model_config.get_diff_sampling_param()
if diff_sampling_param:
logger.info("Overwriting default chat sampling param with: %s",
diff_sampling_param)
async def create_chat_completion(
self,
@@ -191,13 +195,17 @@ class OpenAIServingChat(OpenAIServing):
sampling_params: Union[SamplingParams, BeamSearchParams]
default_max_tokens = self.max_model_len - len(
engine_prompt["prompt_token_ids"])
# Build default sampling params
default_sampling_params = (
self.model_config.get_diff_sampling_param())
if request.use_beam_search:
sampling_params = request.to_beam_search_params(
default_max_tokens)
default_max_tokens, default_sampling_params)
else:
sampling_params = request.to_sampling_params(
default_max_tokens,
self.model_config.logits_processor_pattern)
self.model_config.logits_processor_pattern,
default_sampling_params)
self._log_inputs(request_id,
request_prompts[i],

13
vllm/entrypoints/openai/serving_completion.py
View File

@@ -55,6 +55,11 @@ class OpenAIServingCompletion(OpenAIServing):
prompt_adapters=prompt_adapters,
request_logger=request_logger,
return_tokens_as_token_ids=return_tokens_as_token_ids)
diff_sampling_param = self.model_config.get_diff_sampling_param()
if diff_sampling_param:
logger.info(
"Overwriting default completion sampling param with: %s",
diff_sampling_param)
async def create_completion(
self,
@@ -118,13 +123,17 @@ class OpenAIServingCompletion(OpenAIServing):
sampling_params: Union[SamplingParams, BeamSearchParams]
default_max_tokens = self.max_model_len - len(
engine_prompt["prompt_token_ids"])
# Build default sampling params
default_sampling_params = (
self.model_config.get_diff_sampling_param())
if request.use_beam_search:
sampling_params = request.to_beam_search_params(
default_max_tokens)
default_max_tokens, default_sampling_params)
else:
sampling_params = request.to_sampling_params(
default_max_tokens,
self.model_config.logits_processor_pattern)
self.model_config.logits_processor_pattern,
default_sampling_params)
request_id_item = f"{request_id}-{i}"