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https://git.datalinker.icu/vllm-project/vllm.git
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241 lines
8.9 KiB
Python
241 lines
8.9 KiB
Python
import asyncio
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import base64
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import time
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from typing import AsyncGenerator, Final, List, Literal, Optional, Union, cast
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import numpy as np
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from fastapi import Request
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from typing_extensions import assert_never
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from vllm.config import ModelConfig
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.chat_utils import ChatTemplateContentFormatOption
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from vllm.entrypoints.logger import RequestLogger
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from vllm.entrypoints.openai.protocol import (EmbeddingChatRequest,
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EmbeddingRequest,
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EmbeddingResponse,
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EmbeddingResponseData,
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ErrorResponse, UsageInfo)
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from vllm.entrypoints.openai.serving_engine import OpenAIServing
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from vllm.entrypoints.openai.serving_models import OpenAIServingModels
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from vllm.logger import init_logger
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from vllm.outputs import (EmbeddingOutput, EmbeddingRequestOutput,
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PoolingRequestOutput)
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from vllm.utils import merge_async_iterators
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logger = init_logger(__name__)
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def _get_embedding(
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output: EmbeddingOutput,
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encoding_format: Literal["float", "base64"],
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) -> Union[List[float], str]:
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if encoding_format == "float":
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return output.embedding
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elif encoding_format == "base64":
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# Force to use float32 for base64 encoding
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# to match the OpenAI python client behavior
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embedding_bytes = np.array(output.embedding, dtype="float32").tobytes()
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return base64.b64encode(embedding_bytes).decode("utf-8")
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assert_never(encoding_format)
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class OpenAIServingEmbedding(OpenAIServing):
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def __init__(
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self,
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engine_client: EngineClient,
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model_config: ModelConfig,
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models: OpenAIServingModels,
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*,
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request_logger: Optional[RequestLogger],
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chat_template: Optional[str],
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chat_template_content_format: ChatTemplateContentFormatOption,
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) -> None:
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super().__init__(engine_client=engine_client,
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model_config=model_config,
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models=models,
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request_logger=request_logger)
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self.chat_template = chat_template
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self.chat_template_content_format: Final = chat_template_content_format
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async def create_embedding(
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self,
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request: EmbeddingRequest,
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raw_request: Optional[Request] = None,
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) -> Union[EmbeddingResponse, ErrorResponse]:
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"""
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Embedding API similar to OpenAI's API.
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See https://platform.openai.com/docs/api-reference/embeddings/create
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for the API specification. This API mimics the OpenAI Embedding API.
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"""
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error_check_ret = await self._check_model(request)
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if error_check_ret is not None:
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return error_check_ret
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encoding_format = request.encoding_format
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if request.dimensions is not None:
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return self.create_error_response(
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"dimensions is currently not supported")
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model_name = request.model
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request_id = f"embd-{self._base_request_id(raw_request)}"
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created_time = int(time.time())
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truncate_prompt_tokens = None
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if request.truncate_prompt_tokens is not None:
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if request.truncate_prompt_tokens <= self.max_model_len:
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truncate_prompt_tokens = request.truncate_prompt_tokens
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else:
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return self.create_error_response(
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"truncate_prompt_tokens value is "
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"greater than max_model_len."
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" Please, select a smaller truncation size.")
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try:
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(
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lora_request,
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prompt_adapter_request,
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) = self._maybe_get_adapters(request)
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tokenizer = await self.engine_client.get_tokenizer(lora_request)
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if prompt_adapter_request is not None:
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raise NotImplementedError("Prompt adapter is not supported "
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"for embedding models")
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if isinstance(request, EmbeddingChatRequest):
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(
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_,
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request_prompts,
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engine_prompts,
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) = await self._preprocess_chat(
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request,
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tokenizer,
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request.messages,
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chat_template=request.chat_template or self.chat_template,
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chat_template_content_format=self.
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chat_template_content_format,
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# In embedding requests, we are not generating tokens,
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# so there is no need to append extra tokens to the input
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add_generation_prompt=False,
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continue_final_message=False,
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truncate_prompt_tokens=truncate_prompt_tokens,
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add_special_tokens=request.add_special_tokens,
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)
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else:
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(request_prompts,
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engine_prompts) = await self._preprocess_completion(
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request,
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tokenizer,
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request.input,
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truncate_prompt_tokens=truncate_prompt_tokens,
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add_special_tokens=request.add_special_tokens,
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)
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except ValueError as e:
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logger.exception("Error in preprocessing prompt inputs")
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return self.create_error_response(str(e))
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# Schedule the request and get the result generator.
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generators: List[AsyncGenerator[PoolingRequestOutput, None]] = []
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try:
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pooling_params = request.to_pooling_params()
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for i, engine_prompt in enumerate(engine_prompts):
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request_id_item = f"{request_id}-{i}"
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self._log_inputs(request_id_item,
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request_prompts[i],
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params=pooling_params,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request)
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trace_headers = (None if raw_request is None else await
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self._get_trace_headers(raw_request.headers))
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generator = self.engine_client.encode(
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engine_prompt,
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pooling_params,
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request_id_item,
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lora_request=lora_request,
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trace_headers=trace_headers,
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priority=request.priority,
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)
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generators.append(generator)
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except ValueError as e:
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# TODO: Use a vllm-specific Validation Error
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return self.create_error_response(str(e))
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result_generator = merge_async_iterators(*generators)
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num_prompts = len(engine_prompts)
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# Non-streaming response
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final_res_batch: List[Optional[PoolingRequestOutput]]
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final_res_batch = [None] * num_prompts
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try:
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async for i, res in result_generator:
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final_res_batch[i] = res
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assert all(final_res is not None for final_res in final_res_batch)
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final_res_batch_checked = cast(List[PoolingRequestOutput],
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final_res_batch)
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response = self.request_output_to_embedding_response(
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final_res_batch_checked,
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request_id,
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created_time,
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model_name,
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encoding_format,
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)
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except asyncio.CancelledError:
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return self.create_error_response("Client disconnected")
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except ValueError as e:
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# TODO: Use a vllm-specific Validation Error
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return self.create_error_response(str(e))
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return response
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def request_output_to_embedding_response(
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self,
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final_res_batch: List[PoolingRequestOutput],
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request_id: str,
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created_time: int,
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model_name: str,
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encoding_format: Literal["float", "base64"],
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) -> EmbeddingResponse:
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items: List[EmbeddingResponseData] = []
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num_prompt_tokens = 0
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for idx, final_res in enumerate(final_res_batch):
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embedding_res = EmbeddingRequestOutput.from_base(final_res)
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item = EmbeddingResponseData(
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index=idx,
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embedding=_get_embedding(embedding_res.outputs,
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encoding_format),
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)
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prompt_token_ids = final_res.prompt_token_ids
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items.append(item)
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num_prompt_tokens += len(prompt_token_ids)
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usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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total_tokens=num_prompt_tokens,
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)
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return EmbeddingResponse(
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id=request_id,
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created=created_time,
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model=model_name,
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data=items,
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usage=usage,
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)
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