wehub-resource-sync
249 lines
9.2 KiB
Python
249 lines
9.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from typing import cast
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from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
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from vllm.entrypoints.openai.completion.protocol import CompletionRequest
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from vllm.entrypoints.openai.engine.protocol import ErrorResponse
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from vllm.entrypoints.openai.models.serving import (
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OpenAIModelRegistry,
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OpenAIServingModels,
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)
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from vllm.entrypoints.scale_out.token_in_token_out.mm_serde import encode_mm_kwargs_item
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from vllm.entrypoints.scale_out.token_in_token_out.protocol import (
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GenerateRequest,
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MultiModalFeatures,
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PlaceholderRangeInfo,
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)
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from vllm.entrypoints.serve.engine.serving import BaseServing
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from vllm.entrypoints.serve.utils.api_utils import get_max_tokens
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from vllm.entrypoints.serve.utils.request_logger import RequestLogger
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from vllm.inputs import (
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EngineInput,
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MultiModalHashes,
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MultiModalInput,
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MultiModalPlaceholders,
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)
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from vllm.logger import init_logger
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from vllm.renderers.inputs.preprocess import (
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extract_prompt_components,
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extract_prompt_len,
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)
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from vllm.renderers.online_renderer import OnlineRenderer
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from vllm.utils import random_uuid
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logger = init_logger(__name__)
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class ServingRender(BaseServing):
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def __init__(
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self,
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models: OpenAIServingModels | OpenAIModelRegistry,
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online_renderer: "OnlineRenderer",
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*,
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request_logger: RequestLogger | None = None,
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) -> None:
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super().__init__(
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models=models,
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model_config=online_renderer.model_config,
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request_logger=request_logger,
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)
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self.online_renderer = online_renderer
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self.default_sampling_params = (
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online_renderer.model_config.get_diff_sampling_param()
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)
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mc = online_renderer.model_config
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self.override_max_tokens = (
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self.default_sampling_params.get("max_tokens")
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if mc.generation_config not in ("auto", "vllm")
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else getattr(mc, "override_generation_config", {}).get("max_new_tokens")
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)
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async def render_chat_request(
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self,
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request: ChatCompletionRequest,
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) -> GenerateRequest | ErrorResponse:
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"""Validate the model and preprocess a chat completion request.
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This is the authoritative implementation used directly by the
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GPU-less render server and delegated to by OpenAIServingChat.
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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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logger.error("Error with model %s", error_check_ret)
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return error_check_ret
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if request.use_beam_search:
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return self.create_error_response(
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"Beam search is not supported by the render endpoint"
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)
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result = await self.online_renderer.render_chat(request, skip_mm_cache=True)
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if isinstance(result, ErrorResponse):
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return result
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_, engine_inputs = result
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if len(engine_inputs) != 1:
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return self.create_error_response(
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f"Expected exactly 1 engine prompt, got {len(engine_inputs)}"
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)
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engine_input = engine_inputs[0]
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prompt_components = extract_prompt_components(self.model_config, engine_input)
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token_ids = prompt_components.token_ids
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if not token_ids:
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return self.create_error_response("No token_ids rendered")
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token_ids = list(token_ids)
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input_length = extract_prompt_len(self.model_config, engine_input)
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max_tokens = get_max_tokens(
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self.model_config.max_model_len,
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request.max_completion_tokens
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if request.max_completion_tokens is not None
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else request.max_tokens,
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input_length,
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self.default_sampling_params,
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self.override_max_tokens,
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truncate_prompt_tokens=request.truncate_prompt_tokens,
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)
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params = request.to_sampling_params(max_tokens, self.default_sampling_params)
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assistant_tokens_mask: list[int] | None = engine_input.get( # type: ignore[assignment]
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"assistant_tokens_mask"
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)
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if assistant_tokens_mask is not None and len(assistant_tokens_mask) != len(
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token_ids
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):
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logger.warning(
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"assistant_tokens_mask length (%d) != token_ids length (%d); "
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"this can happen with multimodal inputs where "
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"placeholder expansion changes the token count. "
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"The mask may be positionally misaligned.",
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len(assistant_tokens_mask),
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len(token_ids),
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)
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if len(assistant_tokens_mask) < len(token_ids):
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assistant_tokens_mask.extend(
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[0] * (len(token_ids) - len(assistant_tokens_mask))
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)
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else:
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assistant_tokens_mask = assistant_tokens_mask[: len(token_ids)]
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request_id = f"chatcmpl-{random_uuid()}"
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return GenerateRequest(
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request_id=request_id,
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token_ids=token_ids,
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assistant_tokens_mask=assistant_tokens_mask,
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features=self._extract_mm_features(engine_input),
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sampling_params=params,
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model=request.model,
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stream=bool(request.stream),
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stream_options=(request.stream_options if request.stream else None),
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cache_salt=request.cache_salt,
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priority=request.priority,
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token_offsets=engine_input.get("prompt_token_offsets"),
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)
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async def render_completion_request(
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self,
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request: CompletionRequest,
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) -> list[GenerateRequest] | ErrorResponse:
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"""Validate the model and preprocess a completion request.
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This is the authoritative implementation used directly by the
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GPU-less render server and delegated to by OpenAIServingCompletion.
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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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result = await self.online_renderer.render_completion(
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request, skip_mm_cache=True
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)
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if isinstance(result, ErrorResponse):
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return result
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generate_requests: list[GenerateRequest] = []
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for engine_input in result:
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prompt_components = extract_prompt_components(
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self.model_config, engine_input
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)
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token_ids = prompt_components.token_ids
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if not token_ids:
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return self.create_error_response("No token_ids rendered")
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token_ids = list(token_ids)
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input_length = extract_prompt_len(self.model_config, engine_input)
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max_tokens = get_max_tokens(
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self.model_config.max_model_len,
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request.max_tokens,
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input_length,
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self.default_sampling_params,
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self.override_max_tokens,
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truncate_prompt_tokens=request.truncate_prompt_tokens,
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)
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params = request.to_sampling_params(
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max_tokens, self.default_sampling_params
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)
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request_id = f"cmpl-{random_uuid()}"
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generate_requests.append(
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GenerateRequest(
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request_id=request_id,
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token_ids=token_ids,
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features=self._extract_mm_features(engine_input),
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sampling_params=params,
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model=request.model,
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stream=bool(request.stream),
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stream_options=(request.stream_options if request.stream else None),
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cache_salt=request.cache_salt,
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priority=request.priority,
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token_offsets=engine_input.get("prompt_token_offsets"),
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)
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)
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return generate_requests
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@staticmethod
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def _extract_mm_features(
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engine_input: EngineInput,
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) -> MultiModalFeatures | None:
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"""Extract multimodal metadata from a rendered engine prompt.
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Returns ``None`` for text-only prompts.
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"""
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if engine_input.get("type") != "multimodal":
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return None
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# At this point engine_input is a MultiModalInput TypedDict.
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mm_engine_input = cast(MultiModalInput, engine_input)
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mm_hashes: MultiModalHashes = mm_engine_input["mm_hashes"]
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raw_placeholders: MultiModalPlaceholders = mm_engine_input["mm_placeholders"]
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mm_placeholders = {
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modality: [
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PlaceholderRangeInfo(offset=p.offset, length=p.length) for p in ranges
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]
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for modality, ranges in raw_placeholders.items()
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}
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# Serialize tensor data per modality.
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kwargs_data: dict[str, list[str | None]] | None = None
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if raw_mm_kwargs := mm_engine_input.get("mm_kwargs"):
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kwargs_data = {}
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for modality, items in raw_mm_kwargs.items():
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kwargs_data[modality] = [
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encode_mm_kwargs_item(item) if item is not None else None
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for item in items
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]
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return MultiModalFeatures(
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mm_hashes=mm_hashes,
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mm_placeholders=mm_placeholders,
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kwargs_data=kwargs_data,
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)
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