233 lines
8.5 KiB
Python
233 lines
8.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
import asyncio
|
|
import base64
|
|
import time
|
|
from collections.abc import AsyncGenerator
|
|
from typing import Final, Literal, Optional, Union, cast
|
|
|
|
import jinja2
|
|
import numpy as np
|
|
from fastapi import Request
|
|
from typing_extensions import assert_never
|
|
|
|
from vllm.config import ModelConfig
|
|
from vllm.engine.protocol import EngineClient
|
|
from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption
|
|
from vllm.entrypoints.logger import RequestLogger
|
|
from vllm.entrypoints.openai.protocol import (ErrorResponse,
|
|
PoolingChatRequest,
|
|
PoolingRequest, PoolingResponse,
|
|
PoolingResponseData, UsageInfo)
|
|
from vllm.entrypoints.openai.serving_engine import OpenAIServing
|
|
from vllm.entrypoints.openai.serving_models import OpenAIServingModels
|
|
from vllm.entrypoints.utils import _validate_truncation_size
|
|
from vllm.logger import init_logger
|
|
from vllm.outputs import PoolingOutput, PoolingRequestOutput
|
|
from vllm.utils import merge_async_iterators
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
def _get_data(
|
|
output: PoolingOutput,
|
|
encoding_format: Literal["float", "base64"],
|
|
) -> Union[list[float], str]:
|
|
if encoding_format == "float":
|
|
return output.data.tolist()
|
|
elif encoding_format == "base64":
|
|
# Force to use float32 for base64 encoding
|
|
# to match the OpenAI python client behavior
|
|
pooling_bytes = np.array(output.data, dtype="float32").tobytes()
|
|
return base64.b64encode(pooling_bytes).decode("utf-8")
|
|
|
|
assert_never(encoding_format)
|
|
|
|
|
|
class OpenAIServingPooling(OpenAIServing):
|
|
|
|
def __init__(
|
|
self,
|
|
engine_client: EngineClient,
|
|
model_config: ModelConfig,
|
|
models: OpenAIServingModels,
|
|
*,
|
|
request_logger: Optional[RequestLogger],
|
|
chat_template: Optional[str],
|
|
chat_template_content_format: ChatTemplateContentFormatOption,
|
|
) -> None:
|
|
super().__init__(engine_client=engine_client,
|
|
model_config=model_config,
|
|
models=models,
|
|
request_logger=request_logger)
|
|
|
|
self.chat_template = chat_template
|
|
self.chat_template_content_format: Final = chat_template_content_format
|
|
|
|
async def create_pooling(
|
|
self,
|
|
request: PoolingRequest,
|
|
raw_request: Optional[Request] = None,
|
|
) -> Union[PoolingResponse, ErrorResponse]:
|
|
"""
|
|
See https://platform.openai.com/docs/api-reference/embeddings/create
|
|
for the API specification. This API mimics the OpenAI Embedding API.
|
|
"""
|
|
error_check_ret = await self._check_model(request)
|
|
if error_check_ret is not None:
|
|
return error_check_ret
|
|
|
|
encoding_format = request.encoding_format
|
|
if request.dimensions is not None:
|
|
return self.create_error_response(
|
|
"dimensions is currently not supported")
|
|
|
|
model_name = self._get_model_name(request.model)
|
|
request_id = f"pool-{self._base_request_id(raw_request)}"
|
|
created_time = int(time.time())
|
|
|
|
truncate_prompt_tokens = request.truncate_prompt_tokens
|
|
|
|
try:
|
|
truncate_prompt_tokens = _validate_truncation_size(
|
|
self.max_model_len, truncate_prompt_tokens)
|
|
(
|
|
lora_request,
|
|
prompt_adapter_request,
|
|
) = self._maybe_get_adapters(request)
|
|
|
|
tokenizer = await self.engine_client.get_tokenizer(lora_request)
|
|
|
|
if prompt_adapter_request is not None:
|
|
raise NotImplementedError("Prompt adapter is not supported "
|
|
"for pooling models")
|
|
|
|
if isinstance(request, PoolingChatRequest):
|
|
(
|
|
_,
|
|
request_prompts,
|
|
engine_prompts,
|
|
) = await self._preprocess_chat(
|
|
request,
|
|
tokenizer,
|
|
request.messages,
|
|
chat_template=request.chat_template or self.chat_template,
|
|
chat_template_content_format=self.
|
|
chat_template_content_format,
|
|
# In pooling requests, we are not generating tokens,
|
|
# so there is no need to append extra tokens to the input
|
|
add_generation_prompt=False,
|
|
continue_final_message=False,
|
|
truncate_prompt_tokens=truncate_prompt_tokens,
|
|
add_special_tokens=request.add_special_tokens,
|
|
)
|
|
else:
|
|
(request_prompts,
|
|
engine_prompts) = await self._preprocess_completion(
|
|
request,
|
|
tokenizer,
|
|
request.input,
|
|
truncate_prompt_tokens=truncate_prompt_tokens,
|
|
add_special_tokens=request.add_special_tokens,
|
|
)
|
|
except (ValueError, TypeError, jinja2.TemplateError) as e:
|
|
logger.exception("Error in preprocessing prompt inputs")
|
|
return self.create_error_response(str(e))
|
|
|
|
# Schedule the request and get the result generator.
|
|
generators: list[AsyncGenerator[PoolingRequestOutput, None]] = []
|
|
try:
|
|
pooling_params = request.to_pooling_params()
|
|
|
|
for i, engine_prompt in enumerate(engine_prompts):
|
|
request_id_item = f"{request_id}-{i}"
|
|
|
|
self._log_inputs(request_id_item,
|
|
request_prompts[i],
|
|
params=pooling_params,
|
|
lora_request=lora_request,
|
|
prompt_adapter_request=prompt_adapter_request)
|
|
|
|
trace_headers = (None if raw_request is None else await
|
|
self._get_trace_headers(raw_request.headers))
|
|
|
|
generator = self.engine_client.encode(
|
|
engine_prompt,
|
|
pooling_params,
|
|
request_id_item,
|
|
lora_request=lora_request,
|
|
trace_headers=trace_headers,
|
|
priority=request.priority,
|
|
)
|
|
|
|
generators.append(generator)
|
|
except ValueError as e:
|
|
# TODO: Use a vllm-specific Validation Error
|
|
return self.create_error_response(str(e))
|
|
|
|
result_generator = merge_async_iterators(*generators)
|
|
|
|
num_prompts = len(engine_prompts)
|
|
|
|
# Non-streaming response
|
|
final_res_batch: list[Optional[PoolingRequestOutput]]
|
|
final_res_batch = [None] * num_prompts
|
|
try:
|
|
async for i, res in result_generator:
|
|
final_res_batch[i] = res
|
|
|
|
assert all(final_res is not None for final_res in final_res_batch)
|
|
|
|
final_res_batch_checked = cast(list[PoolingRequestOutput],
|
|
final_res_batch)
|
|
|
|
response = self.request_output_to_pooling_response(
|
|
final_res_batch_checked,
|
|
request_id,
|
|
created_time,
|
|
model_name,
|
|
encoding_format,
|
|
)
|
|
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 response
|
|
|
|
def request_output_to_pooling_response(
|
|
self,
|
|
final_res_batch: list[PoolingRequestOutput],
|
|
request_id: str,
|
|
created_time: int,
|
|
model_name: str,
|
|
encoding_format: Literal["float", "base64"],
|
|
) -> PoolingResponse:
|
|
items: list[PoolingResponseData] = []
|
|
num_prompt_tokens = 0
|
|
|
|
for idx, final_res in enumerate(final_res_batch):
|
|
item = PoolingResponseData(
|
|
index=idx,
|
|
data=_get_data(final_res.outputs, encoding_format),
|
|
)
|
|
prompt_token_ids = final_res.prompt_token_ids
|
|
|
|
items.append(item)
|
|
num_prompt_tokens += len(prompt_token_ids)
|
|
|
|
usage = UsageInfo(
|
|
prompt_tokens=num_prompt_tokens,
|
|
total_tokens=num_prompt_tokens,
|
|
)
|
|
|
|
return PoolingResponse(
|
|
id=request_id,
|
|
created=created_time,
|
|
model=model_name,
|
|
data=items,
|
|
usage=usage,
|
|
)
|