MOVZXandHinata
5aa2ac64f8
Production runs one static model behind the OpenAI-compatible API. Every adapter path and non-OpenAI HTTP surface is unreachable there. - Delete the LoRA stack: vllm/lora, LoRARequest on the engine wire, the scheduler, worker, cudagraph and MoE plumbing, LoRA metrics, the lora CLI group, and the LoRA tests. - Drop the plugin loader in vllm/plugins. Platform resolution is now CUDA-only and the general/stat-logger plugin hooks are gone. - Delete the speech-to-text and realtime entrypoints, the scale-out render routes, and vllm/config/speech_to_text.py. GenerationTask is generate-only. - Cut DecodeBenchConnector and the disagg-prefill doc and image. The Multi, Offloading and Example connectors stay; production uses the CPU KV offload tier. - Delete the ROCm-only fused KDA chunk kernel (2346 lines) with its CMake arch gate, declarations and op registrations. The CUDA build never defined the guard and no Python caller existed. - Delete the orphan Mistral and Pixtral chat fixtures and examples/. - Fix _check_model: removing the LoRA early return made every request fail with "The model ... does not exist. Valid aliases: ...". Verified on an RTX 3090: import sweep reports 1242 modules with 7 known optional-dependency failures; a live server answers chat, completions, responses, tokenize and detokenize with zero log errors and 404 for the removed surfaces; collection yields 8558 tests with the 3 pre-existing errors; ruff adds no new finding; the incremental _C build succeeds. Co-authored-by: Hinata <hinata@idihore.id> Signed-off-by: MOVZX <movzx@yahoo.com>
42 lines
1.2 KiB
Python
42 lines
1.2 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 Literal, get_args
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from vllm.exceptions import VLLMValidationError
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GenerationTask = Literal["generate"]
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GENERATION_TASKS: tuple[GenerationTask, ...] = get_args(GenerationTask)
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PoolingTask = Literal[
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"embed",
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"classify",
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"token_embed",
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"token_classify",
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"plugin",
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"embed&token_classify",
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]
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POOLING_TASKS: tuple[PoolingTask, ...] = get_args(PoolingTask)
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_REMOVED_POOLING_TASK_MESSAGES = {
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"score": "`score` task was removed; use `classify` instead.",
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"encode": (
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"`encode` task was removed; use `token_embed` or `token_classify` instead."
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),
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}
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def check_removed_pooling_task(task: object) -> None:
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if isinstance(task, str) and (message := _REMOVED_POOLING_TASK_MESSAGES.get(task)):
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raise VLLMValidationError(message, parameter="task")
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ScoreType = Literal["bi-encoder", "cross-encoder", "late-interaction"]
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SCORE_TYPE_MAP: dict[PoolingTask, ScoreType] = {
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"embed": "bi-encoder",
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"classify": "cross-encoder",
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"token_embed": "late-interaction",
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}
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SupportedTask = Literal[GenerationTask, PoolingTask]
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FALLBACK_SUPPORTED_TASKS: tuple[SupportedTask, ...] = ("generate",)
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