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[Deprecation] Deprecate seed_everything and scatter_mm_placeholders in v0.15 (#33362)

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Signed-off-by: yewentao256 <zhyanwentao@126.com>
This commit is contained in:
Wentao Ye
2026-01-30 21:54:16 -05:00
committed by GitHub
parent 64a40a7ab4
commit 010ec0c30e
3 changed files with 0 additions and 75 deletions
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8
vllm/model_executor/models/voxtral.py
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@@ -103,14 +103,6 @@ class VoxtralProcessorAdapter:
def begin_audio_token_id(self) -> int:
return self._audio_processor.special_ids.begin_audio
# @cached_property
# def begin_transcript_token_id(self) -> int:
# return self._audio_processor.special_ids.begin_transcript
# @cached_property
# def end_transcript_token_id(self) -> int:
# return self._audio_processor.special_ids.end_transcript
@cached_property
def sampling_rate(self) -> int:
return self._audio_processor.audio_config.sampling_rate

20
vllm/platforms/interface.py
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@@ -4,14 +4,11 @@ import contextlib
import enum
import os
import platform
import random
import sys
from datetime import timedelta
from typing import TYPE_CHECKING, Any, NamedTuple
import numpy as np
import torch
from typing_extensions import deprecated
from vllm.logger import init_logger
from vllm.v1.attention.backends.registry import AttentionBackendEnum
@@ -365,23 +362,6 @@ class Platform:
"""
return torch.inference_mode(mode=True)
@classmethod
@deprecated(
"`seed_everything` is deprecated. It will be removed in v0.15.0 or later. "
"Please use `vllm.utils.torch_utils.set_random_seed` instead."
)
def seed_everything(cls, seed: int | None = None) -> None:
"""
Set the seed of each random module.
`torch.manual_seed` will set seed on all devices.
Loosely based on: https://github.com/Lightning-AI/pytorch-lightning/blob/2.4.0/src/lightning/fabric/utilities/seed.py#L20
"""
if seed is not None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
@classmethod
def set_device(cls, device: torch.device) -> None:
"""

47
vllm/v1/worker/utils.py
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@@ -5,7 +5,6 @@ from collections import defaultdict
from dataclasses import dataclass, field
import torch
from typing_extensions import deprecated
from vllm.config import CacheConfig, VllmConfig
from vllm.logger import init_logger
@@ -201,52 +200,6 @@ def sanity_check_mm_encoder_outputs(
)
@deprecated("`scatter_mm_placeholders` is deprecated and will be removed in v0.15.0.")
def scatter_mm_placeholders(
embeds: torch.Tensor,
is_embed: torch.Tensor | None,
) -> torch.Tensor:
"""
Scatter the multimodal embeddings into a contiguous tensor that represents
the placeholder tokens.
[`vllm.multimodal.processing.PromptUpdateDetails.is_embed`][].
Args:
embeds: The multimodal embeddings.
Shape: `(num_embeds, embed_dim)`
is_embed: A boolean mask indicating which positions in the placeholder
tokens need to be filled with multimodal embeddings.
Shape: `(num_placeholders, num_embeds)`
"""
if is_embed is None:
return embeds
placeholders = embeds.new_full(
(is_embed.shape[0], embeds.shape[-1]),
fill_value=torch.nan,
)
placeholders[is_embed] = embeds
return placeholders
@deprecated("`gather_mm_placeholders` is deprecated and will be removed in v0.15.0.")
def gather_mm_placeholders(
placeholders: torch.Tensor,
is_embed: torch.Tensor | None,
) -> torch.Tensor:
"""
Reconstructs the embeddings from the placeholder tokens.
This is the operation of [`scatter_mm_placeholders`]
[vllm.v1.worker.utils.scatter_mm_placeholders].
"""
if is_embed is None:
return placeholders
return placeholders[is_embed]
def request_memory(init_snapshot: MemorySnapshot, cache_config: CacheConfig) -> int:
"""
Calculate the amount of memory required by vLLM, then validate