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Unverified Commit 769f27e7 authored by R3hankhan's avatar R3hankhan Committed by GitHub
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[OpenAI] Add parameter metadata to validation errors (#30134) 


Signed-off-by: default avatarRehan Khan <Rehan.Khan7@ibm.com>
parent 23daef54
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Showing with 232 additions and 72 deletions
vllm/entrypoints/openai/api_server.py
View file @ 769f27e7
......@@ -909,6 +909,16 @@ def build_app(args: Namespace) -> FastAPI:
@app.exception_handler(RequestValidationError)
async def validation_exception_handler(_: Request, exc: RequestValidationError):
from vllm.entrypoints.openai.protocol import VLLMValidationError
param = None
for error in exc.errors():
if "ctx" in error and "error" in error["ctx"]:
ctx_error = error["ctx"]["error"]
if isinstance(ctx_error, VLLMValidationError):
param = ctx_error.parameter
break
exc_str = str(exc)
errors_str = str(exc.errors())
......@@ -922,6 +932,7 @@ def build_app(args: Namespace) -> FastAPI:
message=message,
type=HTTPStatus.BAD_REQUEST.phrase,
code=HTTPStatus.BAD_REQUEST,
param=param,
)
)
return JSONResponse(err.model_dump(), status_code=HTTPStatus.BAD_REQUEST)
......
vllm/entrypoints/openai/protocol.py
View file @ 769f27e7
......@@ -131,6 +131,36 @@ class ErrorResponse(OpenAIBaseModel):
error: ErrorInfo
class VLLMValidationError(ValueError):
"""vLLM-specific validation error for request validation failures.
Args:
message: The error message describing the validation failure.
parameter: Optional parameter name that failed validation.
value: Optional value that was rejected during validation.
"""
def __init__(
self,
message: str,
*,
parameter: str | None = None,
value: Any = None,
) -> None:
super().__init__(message)
self.parameter = parameter
self.value = value
def __str__(self):
base = super().__str__()
extras = []
if self.parameter is not None:
extras.append(f"parameter={self.parameter}")
if self.value is not None:
extras.append(f"value={self.value}")
return f"{base} ({', '.join(extras)})" if extras else base
class ModelPermission(OpenAIBaseModel):
id: str = Field(default_factory=lambda: f"modelperm-{random_uuid()}")
object: str = "model_permission"
......@@ -466,7 +496,9 @@ class ResponsesRequest(OpenAIBaseModel):
@model_validator(mode="before")
def validate_prompt(cls, data):
if data.get("prompt") is not None:
raise ValueError("prompt template is not supported")
raise VLLMValidationError(
"prompt template is not supported", parameter="prompt"
)
return data
@model_validator(mode="before")
......@@ -850,7 +882,10 @@ class ChatCompletionRequest(OpenAIBaseModel):
@classmethod
def validate_stream_options(cls, data):
if data.get("stream_options") and not data.get("stream"):
raise ValueError("Stream options can only be defined when `stream=True`.")
raise VLLMValidationError(
"Stream options can only be defined when `stream=True`.",
parameter="stream_options",
)
return data
......@@ -859,19 +894,29 @@ class ChatCompletionRequest(OpenAIBaseModel):
def check_logprobs(cls, data):
if (prompt_logprobs := data.get("prompt_logprobs")) is not None:
if data.get("stream") and (prompt_logprobs > 0 or prompt_logprobs == -1):
raise ValueError(
"`prompt_logprobs` are not available when `stream=True`."
raise VLLMValidationError(
"`prompt_logprobs` are not available when `stream=True`.",
parameter="prompt_logprobs",
)
if prompt_logprobs < 0 and prompt_logprobs != -1:
raise ValueError("`prompt_logprobs` must be a positive value or -1.")
raise VLLMValidationError(
"`prompt_logprobs` must be a positive value or -1.",
parameter="prompt_logprobs",
value=prompt_logprobs,
)
if (top_logprobs := data.get("top_logprobs")) is not None:
if top_logprobs < 0 and top_logprobs != -1:
raise ValueError("`top_logprobs` must be a positive value or -1.")
raise VLLMValidationError(
"`top_logprobs` must be a positive value or -1.",
parameter="top_logprobs",
value=top_logprobs,
)
if (top_logprobs == -1 or top_logprobs > 0) and not data.get("logprobs"):
raise ValueError(
"when using `top_logprobs`, `logprobs` must be set to true."
raise VLLMValidationError(
"when using `top_logprobs`, `logprobs` must be set to true.",
parameter="top_logprobs",
)
return data
......@@ -1285,9 +1330,10 @@ class CompletionRequest(OpenAIBaseModel):
for k in ("json", "regex", "choice")
)
if count > 1:
raise ValueError(
raise VLLMValidationError(
"You can only use one kind of constraints for structured "
"outputs ('json', 'regex' or 'choice')."
"outputs ('json', 'regex' or 'choice').",
parameter="structured_outputs",
)
return data
......@@ -1296,14 +1342,23 @@ class CompletionRequest(OpenAIBaseModel):
def check_logprobs(cls, data):
if (prompt_logprobs := data.get("prompt_logprobs")) is not None:
if data.get("stream") and (prompt_logprobs > 0 or prompt_logprobs == -1):
raise ValueError(
"`prompt_logprobs` are not available when `stream=True`."
raise VLLMValidationError(
"`prompt_logprobs` are not available when `stream=True`.",
parameter="prompt_logprobs",
)
if prompt_logprobs < 0 and prompt_logprobs != -1:
raise ValueError("`prompt_logprobs` must be a positive value or -1.")
raise VLLMValidationError(
"`prompt_logprobs` must be a positive value or -1.",
parameter="prompt_logprobs",
value=prompt_logprobs,
)
if (logprobs := data.get("logprobs")) is not None and logprobs < 0:
raise ValueError("`logprobs` must be a positive value.")
raise VLLMValidationError(
"`logprobs` must be a positive value.",
parameter="logprobs",
value=logprobs,
)
return data
......@@ -1311,7 +1366,10 @@ class CompletionRequest(OpenAIBaseModel):
@classmethod
def validate_stream_options(cls, data):
if data.get("stream_options") and not data.get("stream"):
raise ValueError("Stream options can only be defined when `stream=True`.")
raise VLLMValidationError(
"Stream options can only be defined when `stream=True`.",
parameter="stream_options",
)
return data
......@@ -2138,7 +2196,15 @@ class TranscriptionRequest(OpenAIBaseModel):
stream_opts = ["stream_include_usage", "stream_continuous_usage_stats"]
stream = data.get("stream", False)
if any(bool(data.get(so, False)) for so in stream_opts) and not stream:
raise ValueError("Stream options can only be defined when `stream=True`.")
# Find which specific stream option was set
invalid_param = next(
(so for so in stream_opts if data.get(so, False)),
"stream_include_usage",
)
raise VLLMValidationError(
"Stream options can only be defined when `stream=True`.",
parameter=invalid_param,
)
return data
......@@ -2351,7 +2417,15 @@ class TranslationRequest(OpenAIBaseModel):
stream_opts = ["stream_include_usage", "stream_continuous_usage_stats"]
stream = data.get("stream", False)
if any(bool(data.get(so, False)) for so in stream_opts) and not stream:
raise ValueError("Stream options can only be defined when `stream=True`.")
# Find which specific stream option was set
invalid_param = next(
(so for so in stream_opts if data.get(so, False)),
"stream_include_usage",
)
raise VLLMValidationError(
"Stream options can only be defined when `stream=True`.",
parameter=invalid_param,
)
return data
......
vllm/entrypoints/openai/serving_chat.py
View file @ 769f27e7
......@@ -417,8 +417,7 @@ class OpenAIServingChat(OpenAIServing):
generators.append(generator)
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
return self.create_error_response(e)
assert len(generators) == 1
(result_generator,) = generators
......@@ -448,8 +447,7 @@ class OpenAIServingChat(OpenAIServing):
except GenerationError as e:
return self._convert_generation_error_to_response(e)
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
return self.create_error_response(e)
def get_chat_request_role(self, request: ChatCompletionRequest) -> str:
if request.add_generation_prompt:
......@@ -682,7 +680,7 @@ class OpenAIServingChat(OpenAIServing):
tool_parsers = [None] * num_choices
except Exception as e:
logger.exception("Error in tool parser creation.")
data = self.create_streaming_error_response(str(e))
data = self.create_streaming_error_response(e)
yield f"data: {data}\n\n"
yield "data: [DONE]\n\n"
return
......@@ -1328,9 +1326,8 @@ class OpenAIServingChat(OpenAIServing):
except GenerationError as e:
yield f"data: {self._convert_generation_error_to_streaming_response(e)}\n\n"
except Exception as e:
# TODO: Use a vllm-specific Validation Error
logger.exception("Error in chat completion stream generator.")
data = self.create_streaming_error_response(str(e))
data = self.create_streaming_error_response(e)
yield f"data: {data}\n\n"
# Send the final done message after all response.n are finished
yield "data: [DONE]\n\n"
......@@ -1354,8 +1351,7 @@ class OpenAIServingChat(OpenAIServing):
except asyncio.CancelledError:
return self.create_error_response("Client disconnected")
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
return self.create_error_response(e)
assert final_res is not None
......
vllm/entrypoints/openai/serving_completion.py
View file @ 769f27e7
......@@ -23,6 +23,7 @@ from vllm.entrypoints.openai.protocol import (
PromptTokenUsageInfo,
RequestResponseMetadata,
UsageInfo,
VLLMValidationError,
)
from vllm.entrypoints.openai.serving_engine import (
GenerationError,
......@@ -247,8 +248,7 @@ class OpenAIServingCompletion(OpenAIServing):
generators.append(generator)
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
return self.create_error_response(e)
result_generator = merge_async_iterators(*generators)
......@@ -308,8 +308,7 @@ class OpenAIServingCompletion(OpenAIServing):
except GenerationError as e:
return self._convert_generation_error_to_response(e)
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
return self.create_error_response(e)
# When user requests streaming but we don't stream, we still need to
# return a streaming response with a single event.
......@@ -510,9 +509,8 @@ class OpenAIServingCompletion(OpenAIServing):
except GenerationError as e:
yield f"data: {self._convert_generation_error_to_streaming_response(e)}\n\n"
except Exception as e:
# TODO: Use a vllm-specific Validation Error
logger.exception("Error in completion stream generator.")
data = self.create_streaming_error_response(str(e))
data = self.create_streaming_error_response(e)
yield f"data: {data}\n\n"
yield "data: [DONE]\n\n"
......@@ -660,8 +658,11 @@ class OpenAIServingCompletion(OpenAIServing):
token = f"token_id:{token_id}"
else:
if tokenizer is None:
raise ValueError(
"Unable to get tokenizer because `skip_tokenizer_init=True`"
raise VLLMValidationError(
"Unable to get tokenizer because "
"`skip_tokenizer_init=True`",
parameter="skip_tokenizer_init",
value=True,
)
token = tokenizer.decode(token_id)
......@@ -720,6 +721,15 @@ class OpenAIServingCompletion(OpenAIServing):
request: CompletionRequest,
max_input_length: int | None = None,
) -> RenderConfig:
# Validate max_tokens before using it
if request.max_tokens is not None and request.max_tokens > self.max_model_len:
raise VLLMValidationError(
f"'max_tokens' ({request.max_tokens}) cannot be greater than "
f"the model's maximum context length ({self.max_model_len}).",
parameter="max_tokens",
value=request.max_tokens,
)
max_input_tokens_len = self.max_model_len - (request.max_tokens or 0)
return RenderConfig(
max_length=max_input_tokens_len,
......
vllm/entrypoints/openai/serving_engine.py
View file @ 769f27e7
......@@ -57,6 +57,7 @@ from vllm.entrypoints.openai.protocol import (
TranscriptionRequest,
TranscriptionResponse,
TranslationRequest,
VLLMValidationError,
)
from vllm.entrypoints.openai.serving_models import OpenAIServingModels
from vllm.entrypoints.pooling.classify.protocol import (
......@@ -322,8 +323,10 @@ class OpenAIServing:
input_processor = self.input_processor
tokenizer = input_processor.tokenizer
if tokenizer is None:
raise ValueError(
"You cannot use beam search when `skip_tokenizer_init=True`"
raise VLLMValidationError(
"You cannot use beam search when `skip_tokenizer_init=True`",
parameter="skip_tokenizer_init",
value=True,
)
eos_token_id: int = tokenizer.eos_token_id # type: ignore
......@@ -706,8 +709,7 @@ class OpenAIServing:
return None
except Exception as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
return self.create_error_response(e)
async def _collect_batch(
self,
......@@ -738,14 +740,43 @@ class OpenAIServing:
return None
except Exception as e:
return self.create_error_response(str(e))
return self.create_error_response(e)
def create_error_response(
self,
message: str,
message: str | Exception,
err_type: str = "BadRequestError",
status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
param: str | None = None,
) -> ErrorResponse:
exc: Exception | None = None
if isinstance(message, Exception):
exc = message
from vllm.entrypoints.openai.protocol import VLLMValidationError
if isinstance(exc, VLLMValidationError):
err_type = "BadRequestError"
status_code = HTTPStatus.BAD_REQUEST
param = exc.parameter
elif isinstance(exc, (ValueError, TypeError, RuntimeError)):
# Common validation errors from user input
err_type = "BadRequestError"
status_code = HTTPStatus.BAD_REQUEST
param = None
elif exc.__class__.__name__ == "TemplateError":
# jinja2.TemplateError (avoid importing jinja2)
err_type = "BadRequestError"
status_code = HTTPStatus.BAD_REQUEST
param = None
else:
err_type = "InternalServerError"
status_code = HTTPStatus.INTERNAL_SERVER_ERROR
param = None
message = str(exc)
if self.log_error_stack:
exc_type, _, _ = sys.exc_info()
if exc_type is not None:
......@@ -753,18 +784,27 @@ class OpenAIServing:
else:
traceback.print_stack()
return ErrorResponse(
error=ErrorInfo(message=message, type=err_type, code=status_code.value)
error=ErrorInfo(
message=message,
type=err_type,
code=status_code.value,
param=param,
)
)
def create_streaming_error_response(
self,
message: str,
message: str | Exception,
err_type: str = "BadRequestError",
status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
param: str | None = None,
) -> str:
json_str = json.dumps(
self.create_error_response(
message=message, err_type=err_type, status_code=status_code
message=message,
err_type=err_type,
status_code=status_code,
param=param,
).model_dump()
)
return json_str
......@@ -825,6 +865,7 @@ class OpenAIServing:
message=f"The model `{request.model}` does not exist.",
err_type="NotFoundError",
status_code=HTTPStatus.NOT_FOUND,
param="model",
)
def _get_active_default_mm_loras(self, request: AnyRequest) -> LoRARequest | None:
......@@ -991,11 +1032,13 @@ class OpenAIServing:
ClassificationChatRequest: "classification",
}
operation = operations.get(type(request), "embedding generation")
raise ValueError(
raise VLLMValidationError(
f"This model's maximum context length is "
f"{self.max_model_len} tokens. However, you requested "
f"{token_num} tokens in the input for {operation}. "
f"Please reduce the length of the input."
f"Please reduce the length of the input.",
parameter="input_tokens",
value=token_num,
)
return TokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
......@@ -1017,20 +1060,24 @@ class OpenAIServing:
# Note: input length can be up to model context length - 1 for
# completion-like requests.
if token_num >= self.max_model_len:
raise ValueError(
raise VLLMValidationError(
f"This model's maximum context length is "
f"{self.max_model_len} tokens. However, your request has "
f"{token_num} input tokens. Please reduce the length of "
"the input messages."
"the input messages.",
parameter="input_tokens",
value=token_num,
)
if max_tokens is not None and token_num + max_tokens > self.max_model_len:
raise ValueError(
raise VLLMValidationError(
"'max_tokens' or 'max_completion_tokens' is too large: "
f"{max_tokens}. This model's maximum context length is "
f"{self.max_model_len} tokens and your request has "
f"{token_num} input tokens ({max_tokens} > {self.max_model_len}"
f" - {token_num})."
f" - {token_num}).",
parameter="max_tokens",
value=max_tokens,
)
return TokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
......
vllm/entrypoints/openai/serving_responses.py
View file @ 769f27e7
......@@ -94,6 +94,7 @@ from vllm.entrypoints.openai.protocol import (
ResponsesResponse,
ResponseUsage,
StreamingResponsesResponse,
VLLMValidationError,
)
from vllm.entrypoints.openai.serving_engine import (
GenerationError,
......@@ -271,6 +272,7 @@ class OpenAIServingResponses(OpenAIServing):
err_type="invalid_request_error",
message=error_message,
status_code=HTTPStatus.BAD_REQUEST,
param="input",
)
return None
......@@ -282,6 +284,7 @@ class OpenAIServingResponses(OpenAIServing):
err_type="invalid_request_error",
message="logprobs are not supported with gpt-oss models",
status_code=HTTPStatus.BAD_REQUEST,
param="logprobs",
)
if request.store and not self.enable_store and request.background:
return self.create_error_response(
......@@ -294,6 +297,7 @@ class OpenAIServingResponses(OpenAIServing):
"the vLLM server."
),
status_code=HTTPStatus.BAD_REQUEST,
param="background",
)
if request.previous_input_messages and request.previous_response_id:
return self.create_error_response(
......@@ -301,6 +305,7 @@ class OpenAIServingResponses(OpenAIServing):
message="Only one of `previous_input_messages` and "
"`previous_response_id` can be set.",
status_code=HTTPStatus.BAD_REQUEST,
param="previous_response_id",
)
return None
......@@ -457,8 +462,7 @@ class OpenAIServingResponses(OpenAIServing):
)
generators.append(generator)
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
return self.create_error_response(e)
assert len(generators) == 1
(result_generator,) = generators
......@@ -546,7 +550,7 @@ class OpenAIServingResponses(OpenAIServing):
except GenerationError as e:
return self._convert_generation_error_to_response(e)
except Exception as e:
return self.create_error_response(str(e))
return self.create_error_response(e)
async def _make_request(
self,
......@@ -630,8 +634,7 @@ class OpenAIServingResponses(OpenAIServing):
except asyncio.CancelledError:
return self.create_error_response("Client disconnected")
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
return self.create_error_response(e)
# NOTE: Implementation of stauts is still WIP, but for now
# we guarantee that if the status is not "completed", it is accurate.
......@@ -1074,7 +1077,7 @@ class OpenAIServingResponses(OpenAIServing):
response = self._convert_generation_error_to_response(e)
except Exception as e:
logger.exception("Background request failed for %s", request.request_id)
response = self.create_error_response(str(e))
response = self.create_error_response(e)
finally:
new_event_signal.set()
......@@ -1099,7 +1102,7 @@ class OpenAIServingResponses(OpenAIServing):
response = self._convert_generation_error_to_response(e)
except Exception as e:
logger.exception("Background request failed for %s", request.request_id)
response = self.create_error_response(str(e))
response = self.create_error_response(e)
if isinstance(response, ErrorResponse):
# If the request has failed, update the status to "failed".
......@@ -1116,7 +1119,11 @@ class OpenAIServingResponses(OpenAIServing):
starting_after: int | None = None,
) -> AsyncGenerator[StreamingResponsesResponse, None]:
if response_id not in self.event_store:
raise ValueError(f"Unknown response_id: {response_id}")
raise VLLMValidationError(
f"Unknown response_id: {response_id}",
parameter="response_id",
value=response_id,
)
event_deque, new_event_signal = self.event_store[response_id]
start_index = 0 if starting_after is None else starting_after + 1
......@@ -1172,6 +1179,7 @@ class OpenAIServingResponses(OpenAIServing):
return self.create_error_response(
err_type="invalid_request_error",
message="Cannot cancel a synchronous response.",
param="response_id",
)
# Update the status to "cancelled".
......@@ -1191,6 +1199,7 @@ class OpenAIServingResponses(OpenAIServing):
err_type="invalid_request_error",
message=f"Response with id '{response_id}' not found.",
status_code=HTTPStatus.NOT_FOUND,
param="response_id",
)
def _make_store_not_supported_error(self) -> ErrorResponse:
......@@ -1203,6 +1212,7 @@ class OpenAIServingResponses(OpenAIServing):
"starting the vLLM server."
),
status_code=HTTPStatus.BAD_REQUEST,
param="store",
)
async def _process_simple_streaming_events(
......
vllm/entrypoints/openai/speech_to_text.py
View file @ 769f27e7
......@@ -30,6 +30,7 @@ from vllm.entrypoints.openai.protocol import (
TranslationSegment,
TranslationStreamResponse,
UsageInfo,
VLLMValidationError,
)
from vllm.entrypoints.openai.serving_engine import OpenAIServing, SpeechToTextRequest
from vllm.entrypoints.openai.serving_models import OpenAIServingModels
......@@ -259,7 +260,11 @@ class OpenAISpeechToText(OpenAIServing):
)
if len(audio_data) / 1024**2 > self.max_audio_filesize_mb:
raise ValueError("Maximum file size exceeded.")
raise VLLMValidationError(
"Maximum file size exceeded",
parameter="audio_filesize_mb",
value=len(audio_data) / 1024**2,
)
with io.BytesIO(audio_data) as bytes_:
# NOTE resample to model SR here for efficiency. This is also a
......@@ -287,12 +292,18 @@ class OpenAISpeechToText(OpenAIServing):
)
if request.response_format == "verbose_json":
if not isinstance(prompt, dict):
raise ValueError(f"Expected prompt to be a dict,got {type(prompt)}")
raise VLLMValidationError(
"Expected prompt to be a dict",
parameter="prompt",
value=type(prompt).__name__,
)
prompt_dict = cast(dict, prompt)
decoder_prompt = prompt.get("decoder_prompt")
if not isinstance(decoder_prompt, str):
raise ValueError(
f"Expected decoder_prompt to bestr, got {type(decoder_prompt)}"
raise VLLMValidationError(
"Expected decoder_prompt to be str",
parameter="decoder_prompt",
value=type(decoder_prompt).__name__,
)
prompt_dict["decoder_prompt"] = decoder_prompt.replace(
"<|notimestamps|>", "<|0.00|>"
......@@ -412,7 +423,7 @@ class OpenAISpeechToText(OpenAIServing):
except ValueError as e:
logger.exception("Error in preprocessing prompt inputs")
return self.create_error_response(str(e))
return self.create_error_response(e)
list_result_generator: list[AsyncGenerator[RequestOutput, None]] | None = None
try:
......@@ -448,8 +459,7 @@ class OpenAISpeechToText(OpenAIServing):
for i, prompt in enumerate(prompts)
]
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
return self.create_error_response(e)
if request.stream:
return stream_generator_method(
......@@ -523,8 +533,7 @@ class OpenAISpeechToText(OpenAIServing):
except asyncio.CancelledError:
return self.create_error_response("Client disconnected")
except ValueError as e:
# TODO: Use a vllm-specific Validation Error
return self.create_error_response(str(e))
return self.create_error_response(e)
async def _speech_to_text_stream_generator(
self,
......@@ -634,9 +643,8 @@ class OpenAISpeechToText(OpenAIServing):
)
except Exception as e:
# TODO: Use a vllm-specific Validation Error
logger.exception("Error in %s stream generator.", self.task_type)
data = self.create_streaming_error_response(str(e))
data = self.create_streaming_error_response(e)
yield f"data: {data}\n\n"
# Send the final done message after all response.n are finished
yield "data: [DONE]\n\n"
......
vllm/entrypoints/renderer.py
View file @ 769f27e7
......@@ -12,6 +12,7 @@ import torch
from pydantic import Field
from vllm.config import ModelConfig
from vllm.entrypoints.openai.protocol import VLLMValidationError
from vllm.inputs.data import EmbedsPrompt, TextPrompt, TokensPrompt
from vllm.inputs.parse import get_prompt_components, parse_raw_prompts
from vllm.tokenizers import TokenizerLike
......@@ -162,8 +163,9 @@ class BaseRenderer(ABC):
) -> list[EmbedsPrompt]:
"""Load and validate base64-encoded embeddings into prompt objects."""
if not self.model_config.enable_prompt_embeds:
raise ValueError(
"You must set `--enable-prompt-embeds` to input `prompt_embeds`."
raise VLLMValidationError(
"You must set `--enable-prompt-embeds` to input `prompt_embeds`.",
parameter="prompt_embeds",
)
def _load_and_validate_embed(embed: bytes) -> EmbedsPrompt:
......@@ -396,10 +398,12 @@ class CompletionRenderer(BaseRenderer):
) -> TokensPrompt:
"""Create validated TokensPrompt."""
if max_length is not None and len(token_ids) > max_length:
raise ValueError(
raise VLLMValidationError(
f"This model's maximum context length is {max_length} tokens. "
f"However, your request has {len(token_ids)} input tokens. "
"Please reduce the length of the input messages."
"Please reduce the length of the input messages.",
parameter="input_tokens",
value=len(token_ids),
)
tokens_prompt = TokensPrompt(prompt_token_ids=token_ids)
......
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