Sync from v0.13
This commit is contained in:
@@ -1,67 +1,90 @@
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import time
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from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, List,
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Optional, Tuple)
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import asyncio
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import time
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from collections.abc import AsyncGenerator, AsyncIterator
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from collections.abc import Sequence as GenericSequence
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from typing import cast
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import jinja2
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from fastapi import Request
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from vllm.engine.async_llm_engine import AsyncLLMEngine
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from vllm.entrypoints.openai.protocol import (CompletionRequest,
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CompletionResponse,
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CompletionResponseChoice,
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CompletionResponseStreamChoice,
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CompletionStreamResponse,
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LogProbs, UsageInfo)
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from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
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OpenAIServing)
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.logger import RequestLogger
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from vllm.entrypoints.openai.protocol import (
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CompletionLogProbs,
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CompletionRequest,
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CompletionResponse,
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CompletionResponseChoice,
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CompletionResponseStreamChoice,
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CompletionStreamResponse,
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ErrorResponse,
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PromptTokenUsageInfo,
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RequestResponseMetadata,
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UsageInfo,
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)
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from vllm.entrypoints.openai.serving_engine import (
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GenerationError,
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OpenAIServing,
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clamp_prompt_logprobs,
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)
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from vllm.entrypoints.openai.serving_models import OpenAIServingModels
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from vllm.entrypoints.renderer import RenderConfig
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from vllm.entrypoints.utils import get_max_tokens, should_include_usage
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from vllm.inputs.data import EmbedsPrompt, TokensPrompt, is_embeds_prompt
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from vllm.logger import init_logger
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from vllm.model_executor.guided_decoding import (
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get_guided_decoding_logits_processor)
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from vllm.logprobs import Logprob
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from vllm.outputs import RequestOutput
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from vllm.utils import merge_async_iterators, random_uuid
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from vllm.sampling_params import BeamSearchParams, SamplingParams
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from vllm.tokenizers import TokenizerLike
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from vllm.utils.async_utils import merge_async_iterators
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from vllm.utils.collection_utils import as_list
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from vllm.v1.sample.logits_processor import validate_logits_processors_parameters
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logger = init_logger(__name__)
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TypeTokenIDs = List[int]
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TypeTopLogProbs = List[Optional[Dict[int, float]]]
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TypeCreateLogProbsFn = Callable[
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[TypeTokenIDs, TypeTopLogProbs, Optional[int], int], LogProbs]
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def parse_prompt_format(prompt) -> Tuple[bool, list]:
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# get the prompt, openai supports the following
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# "a string, array of strings, array of tokens, or array of token arrays."
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prompt_is_tokens = False
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prompts = [prompt] # case 1: a string
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if isinstance(prompt, list):
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if len(prompt) == 0:
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raise ValueError("please provide at least one prompt")
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elif isinstance(prompt[0], str):
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prompt_is_tokens = False
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prompts = prompt # case 2: array of strings
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elif isinstance(prompt[0], int):
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prompt_is_tokens = True
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prompts = [prompt] # case 3: array of tokens
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elif isinstance(prompt[0], list) and isinstance(prompt[0][0], int):
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prompt_is_tokens = True
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prompts = prompt # case 4: array of token arrays
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else:
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raise ValueError("prompt must be a string, array of strings, "
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"array of tokens, or array of token arrays")
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return prompt_is_tokens, prompts
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class OpenAIServingCompletion(OpenAIServing):
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def __init__(
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self,
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engine_client: EngineClient,
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models: OpenAIServingModels,
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*,
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request_logger: RequestLogger | None,
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return_tokens_as_token_ids: bool = False,
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enable_prompt_tokens_details: bool = False,
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enable_force_include_usage: bool = False,
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log_error_stack: bool = False,
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):
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super().__init__(
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engine_client=engine_client,
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models=models,
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request_logger=request_logger,
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return_tokens_as_token_ids=return_tokens_as_token_ids,
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log_error_stack=log_error_stack,
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)
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def __init__(self,
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engine: AsyncLLMEngine,
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served_model_names: List[str],
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lora_modules: Optional[List[LoRAModulePath]] = None):
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super().__init__(engine=engine,
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served_model_names=served_model_names,
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lora_modules=lora_modules)
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# set up logits processors
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self.logits_processors = self.model_config.logits_processors
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async def create_completion(self, request: CompletionRequest,
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raw_request: Request):
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self.enable_prompt_tokens_details = enable_prompt_tokens_details
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self.default_sampling_params = self.model_config.get_diff_sampling_param()
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self.enable_force_include_usage = enable_force_include_usage
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if self.default_sampling_params:
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source = self.model_config.generation_config
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source = "model" if source == "auto" else source
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logger.info(
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"Using default completion sampling params from %s: %s",
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source,
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self.default_sampling_params,
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)
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async def create_completion(
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self,
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request: CompletionRequest,
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raw_request: Request | None = None,
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) -> AsyncGenerator[str, None] | CompletionResponse | ErrorResponse:
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"""Completion API similar to OpenAI's API.
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See https://platform.openai.com/docs/api-reference/completions/create
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@@ -75,90 +98,214 @@ class OpenAIServingCompletion(OpenAIServing):
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if error_check_ret is not None:
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return error_check_ret
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# If the engine is dead, raise the engine's DEAD_ERROR.
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# This is required for the streaming case, where we return a
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# success status before we actually start generating text :).
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if self.engine_client.errored:
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raise self.engine_client.dead_error
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# Return error for unsupported features.
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if request.suffix is not None:
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return self.create_error_response(
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"suffix is not currently supported")
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return self.create_error_response("suffix is not currently supported")
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model_name = self.served_model_names[0]
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request_id = f"cmpl-{random_uuid()}"
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if request.echo and request.prompt_embeds is not None:
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return self.create_error_response("Echo is unsupported with prompt embeds.")
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if request.prompt_logprobs is not None and request.prompt_embeds is not None:
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return self.create_error_response(
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"prompt_logprobs is not compatible with prompt embeds."
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)
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request_id = f"cmpl-{self._base_request_id(raw_request, request.request_id)}"
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created_time = int(time.time())
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# Schedule the request and get the result generator.
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generators: List[AsyncIterator[RequestOutput]] = []
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request_metadata = RequestResponseMetadata(request_id=request_id)
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if raw_request:
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raw_request.state.request_metadata = request_metadata
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try:
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sampling_params = request.to_sampling_params()
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lora_request = self._maybe_get_lora(request)
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decoding_config = await self.engine.get_decoding_config()
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guided_decoding_backend = request.guided_decoding_backend \
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or decoding_config.guided_decoding_backend
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guided_decode_logit_processor = (
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await get_guided_decoding_logits_processor(
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guided_decoding_backend, request, await
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self.engine.get_tokenizer()))
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if guided_decode_logit_processor is not None:
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if sampling_params.logits_processors is None:
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sampling_params.logits_processors = []
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sampling_params.logits_processors.append(
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guided_decode_logit_processor)
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prompt_is_tokens, prompts = parse_prompt_format(request.prompt)
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lora_request = self._maybe_get_adapters(request)
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for i, prompt in enumerate(prompts):
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if prompt_is_tokens:
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prompt_formats = self._validate_prompt_and_tokenize(
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request,
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prompt_ids=prompt,
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truncate_prompt_tokens=sampling_params.
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truncate_prompt_tokens)
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if self.model_config.skip_tokenizer_init:
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tokenizer = None
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else:
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tokenizer = await self.engine_client.get_tokenizer()
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renderer = self._get_renderer(tokenizer)
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engine_prompts = await renderer.render_prompt_and_embeds(
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prompt_or_prompts=request.prompt,
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prompt_embeds=request.prompt_embeds,
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config=self._build_render_config(request),
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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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except TypeError 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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except RuntimeError 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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except jinja2.TemplateError 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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# Extract data_parallel_rank from header (router can inject it)
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data_parallel_rank = self._get_data_parallel_rank(raw_request)
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# Schedule the request and get the result generator.
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generators: list[AsyncGenerator[RequestOutput, None]] = []
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try:
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for i, engine_prompt in enumerate(engine_prompts):
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prompt_text, prompt_token_ids, prompt_embeds = (
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self._get_prompt_components(engine_prompt)
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)
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input_length = None
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if prompt_token_ids is not None:
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input_length = len(prompt_token_ids)
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elif prompt_embeds is not None:
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input_length = len(prompt_embeds)
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else:
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prompt_formats = self._validate_prompt_and_tokenize(
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request,
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prompt=prompt,
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truncate_prompt_tokens=sampling_params.
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truncate_prompt_tokens)
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prompt_ids, prompt_text = prompt_formats
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raise NotImplementedError
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generators.append(
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self.engine.generate(prompt_text,
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sampling_params,
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f"{request_id}-{i}",
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prompt_token_ids=prompt_ids,
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lora_request=lora_request))
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if self.default_sampling_params is None:
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self.default_sampling_params = {}
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max_tokens = get_max_tokens(
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max_model_len=self.max_model_len,
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request=request,
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input_length=input_length,
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default_sampling_params=self.default_sampling_params,
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)
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sampling_params: SamplingParams | BeamSearchParams
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if request.use_beam_search:
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sampling_params = request.to_beam_search_params(
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max_tokens, self.default_sampling_params
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)
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else:
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sampling_params = request.to_sampling_params(
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max_tokens,
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self.model_config.logits_processor_pattern,
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self.default_sampling_params,
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)
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validate_logits_processors_parameters(
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self.logits_processors,
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sampling_params,
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)
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request_id_item = f"{request_id}-{i}"
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self._log_inputs(
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request_id_item,
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engine_prompt,
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params=sampling_params,
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lora_request=lora_request,
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)
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trace_headers = (
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None
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if raw_request is None
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else await self._get_trace_headers(raw_request.headers)
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)
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# Mypy inconsistently requires this second cast in different
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# environments. It shouldn't be necessary (redundant from above)
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# but pre-commit in CI fails without it.
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engine_prompt = cast(EmbedsPrompt | TokensPrompt, engine_prompt)
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if isinstance(sampling_params, BeamSearchParams):
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generator = self.beam_search(
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prompt=engine_prompt,
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request_id=request_id,
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params=sampling_params,
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lora_request=lora_request,
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trace_headers=trace_headers,
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)
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else:
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engine_request, tokenization_kwargs = await self._process_inputs(
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request_id_item,
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engine_prompt,
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sampling_params,
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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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generator = self.engine_client.generate(
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engine_request,
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sampling_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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prompt_text=prompt_text,
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tokenization_kwargs=tokenization_kwargs,
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data_parallel_rank=data_parallel_rank,
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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: AsyncIterator[Tuple[
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int, RequestOutput]] = merge_async_iterators(*generators)
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result_generator = merge_async_iterators(*generators)
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# Similar to the OpenAI API, when n != best_of, we do not stream the
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# results. In addition, we do not stream the results when use
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# beam search.
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stream = (request.stream
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and (request.best_of is None or request.n == request.best_of)
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and not request.use_beam_search)
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model_name = self.models.model_name(lora_request)
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num_prompts = len(engine_prompts)
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# We do not stream the results when using beam search.
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stream = request.stream and not request.use_beam_search
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# Streaming response
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if stream:
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return self.completion_stream_generator(request,
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raw_request,
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result_generator,
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request_id,
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created_time,
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model_name,
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num_prompts=len(prompts))
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return self.completion_stream_generator(
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request,
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engine_prompts,
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result_generator,
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request_id,
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created_time,
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model_name,
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num_prompts=num_prompts,
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tokenizer=tokenizer,
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request_metadata=request_metadata,
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)
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# Non-streaming response
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final_res_batch: List[Optional[RequestOutput]] = [None] * len(prompts)
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final_res_batch: list[RequestOutput | None] = [None] * num_prompts
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try:
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async for i, res in result_generator:
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if await raw_request.is_disconnected():
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# Abort the request if the client disconnects.
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await self.engine.abort(f"{request_id}-{i}")
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return self.create_error_response("Client disconnected")
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final_res_batch[i] = res
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for i, final_res in enumerate(final_res_batch):
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assert final_res is not None
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# The output should contain the input text
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# We did not pass it into vLLM engine to avoid being redundant
|
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# with the inputs token IDs
|
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if final_res.prompt is None:
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engine_prompt = engine_prompts[i]
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final_res.prompt = (
|
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None
|
||||
if is_embeds_prompt(engine_prompt)
|
||||
else engine_prompt.get("prompt")
|
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)
|
||||
|
||||
final_res_batch_checked = cast(list[RequestOutput], final_res_batch)
|
||||
|
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response = self.request_output_to_completion_response(
|
||||
final_res_batch, request, request_id, created_time, model_name)
|
||||
final_res_batch_checked,
|
||||
request,
|
||||
request_id,
|
||||
created_time,
|
||||
model_name,
|
||||
tokenizer,
|
||||
request_metadata,
|
||||
)
|
||||
except asyncio.CancelledError:
|
||||
return self.create_error_response("Client disconnected")
|
||||
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))
|
||||
@@ -179,80 +326,126 @@ class OpenAIServingCompletion(OpenAIServing):
|
||||
async def completion_stream_generator(
|
||||
self,
|
||||
request: CompletionRequest,
|
||||
raw_request: Request,
|
||||
result_generator: AsyncIterator[Tuple[int, RequestOutput]],
|
||||
engine_prompts: list[TokensPrompt | EmbedsPrompt],
|
||||
result_generator: AsyncIterator[tuple[int, RequestOutput]],
|
||||
request_id: str,
|
||||
created_time: int,
|
||||
model_name: str,
|
||||
num_prompts: int,
|
||||
tokenizer: TokenizerLike | None,
|
||||
request_metadata: RequestResponseMetadata,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
assert request.n is not None
|
||||
previous_texts = [""] * request.n * num_prompts
|
||||
previous_num_tokens = [0] * request.n * num_prompts
|
||||
has_echoed = [False] * request.n * num_prompts
|
||||
num_choices = 1 if request.n is None else request.n
|
||||
previous_text_lens = [0] * num_choices * num_prompts
|
||||
previous_num_tokens = [0] * num_choices * num_prompts
|
||||
has_echoed = [False] * num_choices * num_prompts
|
||||
num_prompt_tokens = [0] * num_prompts
|
||||
num_cached_tokens = None
|
||||
first_iteration = True
|
||||
|
||||
stream_options = request.stream_options
|
||||
include_usage, include_continuous_usage = should_include_usage(
|
||||
stream_options, self.enable_force_include_usage
|
||||
)
|
||||
|
||||
try:
|
||||
async for prompt_idx, res in result_generator:
|
||||
prompt_token_ids = res.prompt_token_ids
|
||||
prompt_logprobs = res.prompt_logprobs
|
||||
|
||||
# Abort the request if the client disconnects.
|
||||
if await raw_request.is_disconnected():
|
||||
await self.engine.abort(f"{request_id}-{prompt_idx}")
|
||||
raise StopAsyncIteration()
|
||||
if first_iteration:
|
||||
num_cached_tokens = res.num_cached_tokens
|
||||
first_iteration = False
|
||||
|
||||
prompt_text = res.prompt
|
||||
if prompt_text is None:
|
||||
engine_prompt = engine_prompts[prompt_idx]
|
||||
prompt_text = (
|
||||
None
|
||||
if is_embeds_prompt(engine_prompt)
|
||||
else engine_prompt.get("prompt")
|
||||
)
|
||||
|
||||
# Prompt details are excluded from later streamed outputs
|
||||
if prompt_token_ids is not None:
|
||||
num_prompt_tokens[prompt_idx] = len(prompt_token_ids)
|
||||
|
||||
delta_token_ids: GenericSequence[int]
|
||||
out_logprobs: GenericSequence[dict[int, Logprob] | None] | None
|
||||
|
||||
for output in res.outputs:
|
||||
i = output.index + prompt_idx * request.n
|
||||
# TODO(simon): optimize the performance by avoiding full
|
||||
# text O(n^2) sending.
|
||||
i = output.index + prompt_idx * num_choices
|
||||
|
||||
# Useful when request.return_token_ids is True
|
||||
# Returning prompt token IDs shares the same logic
|
||||
# with the echo implementation.
|
||||
prompt_token_ids_to_return: list[int] | None = None
|
||||
|
||||
assert request.max_tokens is not None
|
||||
if request.echo and request.max_tokens == 0:
|
||||
# only return the prompt
|
||||
delta_text = res.prompt
|
||||
delta_token_ids = res.prompt_token_ids
|
||||
top_logprobs = res.prompt_logprobs
|
||||
has_echoed[i] = True
|
||||
elif (request.echo and request.max_tokens > 0
|
||||
and not has_echoed[i]):
|
||||
# echo the prompt and first token
|
||||
delta_text = res.prompt + output.text
|
||||
delta_token_ids = (res.prompt_token_ids +
|
||||
output.token_ids)
|
||||
top_logprobs = res.prompt_logprobs + (output.logprobs
|
||||
or [])
|
||||
if request.echo and not has_echoed[i]:
|
||||
assert prompt_token_ids is not None
|
||||
if request.return_token_ids:
|
||||
prompt_text = ""
|
||||
assert prompt_text is not None
|
||||
if request.max_tokens == 0:
|
||||
# only return the prompt
|
||||
delta_text = prompt_text
|
||||
delta_token_ids = prompt_token_ids
|
||||
out_logprobs = prompt_logprobs
|
||||
else:
|
||||
# echo the prompt and first token
|
||||
delta_text = prompt_text + output.text
|
||||
delta_token_ids = [
|
||||
*prompt_token_ids,
|
||||
*output.token_ids,
|
||||
]
|
||||
out_logprobs = [
|
||||
*(prompt_logprobs or []),
|
||||
*(output.logprobs or []),
|
||||
]
|
||||
prompt_token_ids_to_return = prompt_token_ids
|
||||
has_echoed[i] = True
|
||||
else:
|
||||
# return just the delta
|
||||
delta_text = output.text[len(previous_texts[i]):]
|
||||
delta_token_ids = output.token_ids[
|
||||
previous_num_tokens[i]:]
|
||||
top_logprobs = output.logprobs[previous_num_tokens[
|
||||
i]:] if output.logprobs else None
|
||||
delta_text = output.text
|
||||
delta_token_ids = output.token_ids
|
||||
out_logprobs = output.logprobs
|
||||
|
||||
# has_echoed[i] is reused here to indicate whether
|
||||
# we have already returned the prompt token IDs.
|
||||
if not has_echoed[i] and request.return_token_ids:
|
||||
prompt_token_ids_to_return = prompt_token_ids
|
||||
has_echoed[i] = True
|
||||
|
||||
if (
|
||||
not delta_text
|
||||
and not delta_token_ids
|
||||
and not previous_num_tokens[i]
|
||||
):
|
||||
# Chunked prefill case, don't return empty chunks
|
||||
continue
|
||||
|
||||
if request.logprobs is not None:
|
||||
logprobs = self._create_logprobs(
|
||||
assert out_logprobs is not None, "Did not output logprobs"
|
||||
logprobs = self._create_completion_logprobs(
|
||||
token_ids=delta_token_ids,
|
||||
top_logprobs=top_logprobs,
|
||||
top_logprobs=out_logprobs,
|
||||
num_output_top_logprobs=request.logprobs,
|
||||
initial_text_offset=len(previous_texts[i]),
|
||||
tokenizer=tokenizer,
|
||||
initial_text_offset=previous_text_lens[i],
|
||||
return_as_token_id=request.return_tokens_as_token_ids,
|
||||
)
|
||||
else:
|
||||
logprobs = None
|
||||
|
||||
previous_texts[i] = output.text
|
||||
previous_num_tokens[i] = len(output.token_ids)
|
||||
previous_text_lens[i] += len(output.text)
|
||||
previous_num_tokens[i] += len(output.token_ids)
|
||||
finish_reason = output.finish_reason
|
||||
stop_reason = output.stop_reason
|
||||
if output.finish_reason is not None: # return final usage
|
||||
prompt_tokens = len(res.prompt_token_ids)
|
||||
completion_tokens = len(output.token_ids)
|
||||
final_usage = UsageInfo(
|
||||
prompt_tokens=prompt_tokens,
|
||||
completion_tokens=completion_tokens,
|
||||
total_tokens=prompt_tokens + completion_tokens,
|
||||
)
|
||||
else:
|
||||
final_usage = None
|
||||
response_json = CompletionStreamResponse(
|
||||
|
||||
self._raise_if_error(finish_reason, request_id)
|
||||
|
||||
chunk = CompletionStreamResponse(
|
||||
id=request_id,
|
||||
created=created_time,
|
||||
model=model_name,
|
||||
@@ -263,58 +456,129 @@ class OpenAIServingCompletion(OpenAIServing):
|
||||
logprobs=logprobs,
|
||||
finish_reason=finish_reason,
|
||||
stop_reason=stop_reason,
|
||||
prompt_token_ids=prompt_token_ids_to_return,
|
||||
token_ids=(
|
||||
as_list(output.token_ids)
|
||||
if request.return_token_ids
|
||||
else None
|
||||
),
|
||||
)
|
||||
],
|
||||
usage=final_usage,
|
||||
).model_dump_json(exclude_unset=True)
|
||||
)
|
||||
if include_continuous_usage:
|
||||
prompt_tokens = num_prompt_tokens[prompt_idx]
|
||||
completion_tokens = previous_num_tokens[i]
|
||||
chunk.usage = UsageInfo(
|
||||
prompt_tokens=prompt_tokens,
|
||||
completion_tokens=completion_tokens,
|
||||
total_tokens=prompt_tokens + completion_tokens,
|
||||
)
|
||||
|
||||
response_json = chunk.model_dump_json(exclude_unset=False)
|
||||
yield f"data: {response_json}\n\n"
|
||||
except ValueError as e:
|
||||
|
||||
total_prompt_tokens = sum(num_prompt_tokens)
|
||||
total_completion_tokens = sum(previous_num_tokens)
|
||||
final_usage_info = UsageInfo(
|
||||
prompt_tokens=total_prompt_tokens,
|
||||
completion_tokens=total_completion_tokens,
|
||||
total_tokens=total_prompt_tokens + total_completion_tokens,
|
||||
)
|
||||
|
||||
if self.enable_prompt_tokens_details and num_cached_tokens:
|
||||
final_usage_info.prompt_tokens_details = PromptTokenUsageInfo(
|
||||
cached_tokens=num_cached_tokens
|
||||
)
|
||||
|
||||
if include_usage:
|
||||
final_usage_chunk = CompletionStreamResponse(
|
||||
id=request_id,
|
||||
created=created_time,
|
||||
model=model_name,
|
||||
choices=[],
|
||||
usage=final_usage_info,
|
||||
)
|
||||
final_usage_data = final_usage_chunk.model_dump_json(
|
||||
exclude_unset=False, exclude_none=True
|
||||
)
|
||||
yield f"data: {final_usage_data}\n\n"
|
||||
|
||||
# report to FastAPI middleware aggregate usage across all choices
|
||||
request_metadata.final_usage_info = final_usage_info
|
||||
|
||||
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))
|
||||
yield f"data: {data}\n\n"
|
||||
yield "data: [DONE]\n\n"
|
||||
|
||||
def request_output_to_completion_response(
|
||||
self,
|
||||
final_res_batch: List[RequestOutput],
|
||||
final_res_batch: list[RequestOutput],
|
||||
request: CompletionRequest,
|
||||
request_id: str,
|
||||
created_time: int,
|
||||
model_name: str,
|
||||
tokenizer: TokenizerLike | None,
|
||||
request_metadata: RequestResponseMetadata,
|
||||
) -> CompletionResponse:
|
||||
choices: List[CompletionResponseChoice] = []
|
||||
choices: list[CompletionResponseChoice] = []
|
||||
num_prompt_tokens = 0
|
||||
num_generated_tokens = 0
|
||||
kv_transfer_params = None
|
||||
last_final_res = None
|
||||
for final_res in final_res_batch:
|
||||
assert final_res is not None
|
||||
last_final_res = final_res
|
||||
prompt_token_ids = final_res.prompt_token_ids
|
||||
prompt_logprobs = final_res.prompt_logprobs
|
||||
assert prompt_token_ids is not None
|
||||
prompt_logprobs = clamp_prompt_logprobs(final_res.prompt_logprobs)
|
||||
prompt_text = final_res.prompt
|
||||
|
||||
token_ids: GenericSequence[int]
|
||||
out_logprobs: GenericSequence[dict[int, Logprob] | None] | None
|
||||
|
||||
for output in final_res.outputs:
|
||||
self._raise_if_error(output.finish_reason, request_id)
|
||||
|
||||
assert request.max_tokens is not None
|
||||
if request.echo and request.max_tokens == 0:
|
||||
token_ids = prompt_token_ids
|
||||
top_logprobs = prompt_logprobs
|
||||
output_text = prompt_text
|
||||
elif request.echo and request.max_tokens > 0:
|
||||
token_ids = prompt_token_ids + output.token_ids
|
||||
top_logprobs = (prompt_logprobs + output.logprobs
|
||||
if request.logprobs else None)
|
||||
output_text = prompt_text + output.text
|
||||
if request.echo:
|
||||
if request.return_token_ids:
|
||||
prompt_text = ""
|
||||
assert prompt_text is not None
|
||||
if request.max_tokens == 0:
|
||||
token_ids = prompt_token_ids
|
||||
out_logprobs = prompt_logprobs
|
||||
output_text = prompt_text
|
||||
else:
|
||||
token_ids = [*prompt_token_ids, *output.token_ids]
|
||||
|
||||
if request.logprobs is None:
|
||||
out_logprobs = None
|
||||
else:
|
||||
assert prompt_logprobs is not None
|
||||
assert output.logprobs is not None
|
||||
out_logprobs = [
|
||||
*prompt_logprobs,
|
||||
*output.logprobs,
|
||||
]
|
||||
|
||||
output_text = prompt_text + output.text
|
||||
else:
|
||||
token_ids = output.token_ids
|
||||
top_logprobs = output.logprobs
|
||||
out_logprobs = output.logprobs
|
||||
output_text = output.text
|
||||
|
||||
if request.logprobs is not None:
|
||||
assert top_logprobs is not None, (
|
||||
"top_logprobs must be provided when logprobs "
|
||||
"is requested")
|
||||
logprobs = self._create_logprobs(
|
||||
assert out_logprobs is not None, "Did not output logprobs"
|
||||
logprobs = self._create_completion_logprobs(
|
||||
token_ids=token_ids,
|
||||
top_logprobs=top_logprobs,
|
||||
top_logprobs=out_logprobs,
|
||||
tokenizer=tokenizer,
|
||||
num_output_top_logprobs=request.logprobs,
|
||||
return_as_token_id=request.return_tokens_as_token_ids,
|
||||
)
|
||||
else:
|
||||
logprobs = None
|
||||
@@ -325,12 +589,19 @@ class OpenAIServingCompletion(OpenAIServing):
|
||||
logprobs=logprobs,
|
||||
finish_reason=output.finish_reason,
|
||||
stop_reason=output.stop_reason,
|
||||
prompt_logprobs=final_res.prompt_logprobs,
|
||||
prompt_token_ids=(
|
||||
prompt_token_ids if request.return_token_ids else None
|
||||
),
|
||||
token_ids=(
|
||||
as_list(output.token_ids) if request.return_token_ids else None
|
||||
),
|
||||
)
|
||||
choices.append(choice_data)
|
||||
|
||||
num_generated_tokens += len(output.token_ids)
|
||||
|
||||
num_prompt_tokens += len(prompt_token_ids)
|
||||
num_generated_tokens += sum(
|
||||
len(output.token_ids) for output in final_res.outputs)
|
||||
|
||||
usage = UsageInfo(
|
||||
prompt_tokens=num_prompt_tokens,
|
||||
@@ -338,10 +609,121 @@ class OpenAIServingCompletion(OpenAIServing):
|
||||
total_tokens=num_prompt_tokens + num_generated_tokens,
|
||||
)
|
||||
|
||||
if (
|
||||
self.enable_prompt_tokens_details
|
||||
and last_final_res
|
||||
and last_final_res.num_cached_tokens
|
||||
):
|
||||
usage.prompt_tokens_details = PromptTokenUsageInfo(
|
||||
cached_tokens=last_final_res.num_cached_tokens
|
||||
)
|
||||
|
||||
request_metadata.final_usage_info = usage
|
||||
if final_res_batch:
|
||||
kv_transfer_params = final_res_batch[0].kv_transfer_params
|
||||
return CompletionResponse(
|
||||
id=request_id,
|
||||
created=created_time,
|
||||
model=model_name,
|
||||
choices=choices,
|
||||
usage=usage,
|
||||
kv_transfer_params=kv_transfer_params,
|
||||
)
|
||||
|
||||
def _create_completion_logprobs(
|
||||
self,
|
||||
token_ids: GenericSequence[int],
|
||||
top_logprobs: GenericSequence[dict[int, Logprob] | None],
|
||||
num_output_top_logprobs: int,
|
||||
tokenizer: TokenizerLike | None,
|
||||
initial_text_offset: int = 0,
|
||||
return_as_token_id: bool | None = None,
|
||||
) -> CompletionLogProbs:
|
||||
"""Create logprobs for OpenAI Completion API."""
|
||||
out_text_offset: list[int] = []
|
||||
out_token_logprobs: list[float | None] = []
|
||||
out_tokens: list[str] = []
|
||||
out_top_logprobs: list[dict[str, float] | None] = []
|
||||
|
||||
last_token_len = 0
|
||||
|
||||
should_return_as_token_id = (
|
||||
return_as_token_id
|
||||
if return_as_token_id is not None
|
||||
else self.return_tokens_as_token_ids
|
||||
)
|
||||
for i, token_id in enumerate(token_ids):
|
||||
step_top_logprobs = top_logprobs[i]
|
||||
if step_top_logprobs is None:
|
||||
if should_return_as_token_id:
|
||||
token = f"token_id:{token_id}"
|
||||
else:
|
||||
if tokenizer is None:
|
||||
raise ValueError(
|
||||
"Unable to get tokenizer because `skip_tokenizer_init=True`"
|
||||
)
|
||||
|
||||
token = tokenizer.decode(token_id)
|
||||
|
||||
out_tokens.append(token)
|
||||
out_token_logprobs.append(None)
|
||||
out_top_logprobs.append(None)
|
||||
else:
|
||||
step_token = step_top_logprobs[token_id]
|
||||
|
||||
token = self._get_decoded_token(
|
||||
step_token,
|
||||
token_id,
|
||||
tokenizer,
|
||||
return_as_token_id=should_return_as_token_id,
|
||||
)
|
||||
token_logprob = max(step_token.logprob, -9999.0)
|
||||
|
||||
out_tokens.append(token)
|
||||
out_token_logprobs.append(token_logprob)
|
||||
|
||||
# makes sure to add the top num_output_top_logprobs + 1
|
||||
# logprobs, as defined in the openai API
|
||||
# (cf. https://github.com/openai/openai-openapi/blob/
|
||||
# 893ba52242dbd5387a97b96444ee1c742cfce9bd/openapi.yaml#L7153)
|
||||
out_top_logprobs.append(
|
||||
{
|
||||
# Convert float("-inf") to the
|
||||
# JSON-serializable float that OpenAI uses
|
||||
self._get_decoded_token(
|
||||
top_lp[1],
|
||||
top_lp[0],
|
||||
tokenizer,
|
||||
return_as_token_id=should_return_as_token_id,
|
||||
): max(top_lp[1].logprob, -9999.0)
|
||||
for i, top_lp in enumerate(step_top_logprobs.items())
|
||||
if num_output_top_logprobs >= i
|
||||
}
|
||||
)
|
||||
|
||||
if len(out_text_offset) == 0:
|
||||
out_text_offset.append(initial_text_offset)
|
||||
else:
|
||||
out_text_offset.append(out_text_offset[-1] + last_token_len)
|
||||
last_token_len = len(token)
|
||||
|
||||
return CompletionLogProbs(
|
||||
text_offset=out_text_offset,
|
||||
token_logprobs=out_token_logprobs,
|
||||
tokens=out_tokens,
|
||||
top_logprobs=out_top_logprobs,
|
||||
)
|
||||
|
||||
def _build_render_config(
|
||||
self,
|
||||
request: CompletionRequest,
|
||||
max_input_length: int | None = None,
|
||||
) -> RenderConfig:
|
||||
max_input_tokens_len = self.max_model_len - (request.max_tokens or 0)
|
||||
return RenderConfig(
|
||||
max_length=max_input_tokens_len,
|
||||
truncate_prompt_tokens=request.truncate_prompt_tokens,
|
||||
add_special_tokens=request.add_special_tokens,
|
||||
cache_salt=request.cache_salt,
|
||||
needs_detokenization=bool(request.echo and not request.return_token_ids),
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user