286 lines
10 KiB
Python
286 lines
10 KiB
Python
# 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
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from collections.abc import Sequence as GenericSequence
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from fastapi import Request
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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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ChatCompletionLogProb,
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ChatCompletionLogProbs,
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ChatCompletionLogProbsContent,
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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 OpenAIServing, clamp_prompt_logprobs
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from vllm.entrypoints.openai.serving_models import OpenAIServingModels
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from vllm.entrypoints.serve.disagg.protocol import (
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GenerateRequest,
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GenerateResponse,
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GenerateResponseChoice,
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)
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from vllm.inputs.data import TokensPrompt
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from vllm.logger import init_logger
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from vllm.logprobs import Logprob
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from vllm.outputs import RequestOutput
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from vllm.sampling_params import SamplingParams
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from vllm.utils.collection_utils import as_list
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logger = init_logger(__name__)
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class ServingTokens(OpenAIServing):
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"""Provides Tokens IN <> Tokens OUT functionality to vLLM API."""
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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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force_no_detokenize: bool = False,
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return_tokens_as_token_ids: bool = False,
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log_error_stack: bool = False,
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enable_prompt_tokens_details: bool = False,
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enable_log_outputs: 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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self.enable_prompt_tokens_details = enable_prompt_tokens_details
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self.enable_log_outputs = enable_log_outputs
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self.force_no_detokenize = force_no_detokenize
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if force_no_detokenize:
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logger.info(
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"Tokens-only mode is enabled, skipping detokenization "
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"step for incoming requests."
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)
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async def serve_tokens(
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self,
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request: GenerateRequest,
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raw_request: Request | None = None,
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) -> GenerateResponse | ErrorResponse:
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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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logger.error("Error with model %s", error_check_ret)
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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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lora_request = None
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lora_request = self._maybe_get_adapters(request, supports_default_mm_loras=True)
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model_name = self.models.model_name(lora_request)
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request_id = (
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f"generate-tokens-{self._base_request_id(raw_request, request.request_id)}"
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)
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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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# TODO(NickLucche): Change to EngineCoreRequest once Renderer work is
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# completed
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engine_prompt = TokensPrompt(prompt_token_ids=request.token_ids)
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if request.features is not None:
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engine_prompt["multi_modal_data"] = None
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if hasattr(request, "cache_salt") and request.cache_salt is not None:
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engine_prompt["cache_salt"] = request.cache_salt
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# Schedule the request and get the result generator.
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result_generator: AsyncGenerator[RequestOutput, None] | None = None
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try:
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sampling_params = request.sampling_params
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if self.force_no_detokenize:
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sampling_params.detokenize = False
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self._log_inputs(
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request_id,
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TokensPrompt(prompt_token_ids=request.token_ids),
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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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result_generator = self.engine_client.generate(
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engine_prompt,
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sampling_params,
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request_id,
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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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except ValueError as e:
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return self.create_error_response(str(e))
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# TODO(NickLucche): Implement streaming response
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try:
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assert result_generator is not None
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return await self.serve_tokens_full_generator(
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request, result_generator, request_id, model_name, request_metadata
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)
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except ValueError as e:
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return self.create_error_response(str(e))
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async def serve_tokens_full_generator(
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self,
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request: GenerateRequest,
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result_generator: AsyncGenerator[RequestOutput, None],
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request_id: str,
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model_name: str,
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request_metadata: RequestResponseMetadata,
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) -> ErrorResponse | GenerateResponse:
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created_time = int(time.time())
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final_res: RequestOutput | None = None
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sampling_params: SamplingParams = request.sampling_params
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try:
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async for res in result_generator:
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final_res = res
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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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return self.create_error_response(str(e))
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assert final_res is not None
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choices: list[GenerateResponseChoice] = []
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num_generated_tokens = 0
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for output in final_res.outputs:
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token_ids = output.token_ids
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out_logprobs = output.logprobs
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# This is top_logprobs in completions API
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if sampling_params.logprobs:
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assert out_logprobs is not None, "Did not output logprobs"
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logprobs = self._create_tokens_logprobs(
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token_ids=token_ids,
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top_logprobs=out_logprobs,
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num_output_top_logprobs=sampling_params.logprobs,
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)
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else:
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logprobs = None
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choice_data = GenerateResponseChoice(
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index=output.index,
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logprobs=logprobs,
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finish_reason=output.finish_reason if output.finish_reason else "stop",
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token_ids=as_list(output.token_ids),
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)
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choices.append(choice_data)
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num_generated_tokens += len(output.token_ids)
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assert final_res.prompt_token_ids is not None
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num_prompt_tokens = len(final_res.prompt_token_ids)
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if final_res.encoder_prompt_token_ids is not None:
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num_prompt_tokens += len(final_res.encoder_prompt_token_ids)
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usage = UsageInfo(
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prompt_tokens=num_prompt_tokens,
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completion_tokens=num_generated_tokens,
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total_tokens=num_prompt_tokens + num_generated_tokens,
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)
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if self.enable_prompt_tokens_details and final_res.num_cached_tokens:
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# This info is not available at the /coordinator level
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usage.prompt_tokens_details = PromptTokenUsageInfo(
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cached_tokens=final_res.num_cached_tokens
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)
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request_metadata.final_usage_info = usage
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response = GenerateResponse(
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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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choices=choices,
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usage=usage,
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prompt_logprobs=clamp_prompt_logprobs(final_res.prompt_logprobs),
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kv_transfer_params=final_res.kv_transfer_params,
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)
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# Log complete response if output logging is enabled
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if self.enable_log_outputs and self.request_logger:
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for choice in choices:
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# Get the corresponding output token IDs
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output_token_ids = None
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if choice.index < len(final_res.outputs):
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output_token_ids = final_res.outputs[choice.index].token_ids
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if output_token_ids:
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# Log token_ids only.
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self.request_logger.log_outputs(
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request_id=request_id,
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outputs="",
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output_token_ids=output_token_ids,
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finish_reason=choice.finish_reason,
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is_streaming=False,
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delta=False,
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)
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return response
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def _create_tokens_logprobs(
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self,
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token_ids: GenericSequence[int],
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top_logprobs: GenericSequence[dict[int, Logprob] | None],
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num_output_top_logprobs: int | None = None,
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) -> ChatCompletionLogProbs:
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"""Create OpenAI-style logprobs."""
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logprobs_content: list[ChatCompletionLogProbsContent] = []
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for i, token_id in enumerate(token_ids):
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token = f"token_id:{token_id}"
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step_top_logprobs = top_logprobs[i]
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if step_top_logprobs is None or step_top_logprobs.get(token_id) is None:
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logprobs_content.append(
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ChatCompletionLogProbsContent(
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token=token,
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)
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)
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else:
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step_token = step_top_logprobs[token_id]
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logprobs_content.append(
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ChatCompletionLogProbsContent(
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token=token,
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logprob=max(step_token.logprob, -9999.0),
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top_logprobs=[
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ChatCompletionLogProb(
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token=token,
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logprob=max(p[1].logprob, -9999.0),
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)
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for i, p in enumerate(step_top_logprobs.items())
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if num_output_top_logprobs and i < num_output_top_logprobs
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],
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)
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)
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return ChatCompletionLogProbs(content=logprobs_content)
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