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[V1][VLM] V1 support for selected single-image models. (#11632)

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Signed-off-by: Roger Wang <ywang@roblox.com>
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: Isotr0py <2037008807@qq.com>
Co-authored-by: DarkLight1337 <tlleungac@connect.ust.hk>
Co-authored-by: Isotr0py <2037008807@qq.com>
This commit is contained in:
Roger Wang
2024-12-31 13:17:22 -08:00
committed by GitHub
parent 8c3230d8c1
commit e7c7c5e822
19 changed files with 575 additions and 621 deletions
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12
vllm/model_executor/models/pixtral.py
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@@ -1,8 +1,8 @@
import math
from dataclasses import dataclass, fields
from functools import cached_property
from typing import Iterable, List, Mapping, Optional, Set, Tuple, Union
import numpy
import torch
import torch.nn as nn
import torch.nn.functional as F
@@ -306,7 +306,7 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
images: Optional[Union[List[List[torch.Tensor]], List[torch.Tensor],
torch.Tensor]] = None,
image_tokens: Optional[torch.Tensor] = None,
) -> Optional[List[torch.Tensor]]:
) -> Tuple[Optional[List[torch.Tensor]], Optional[torch.Tensor]]:
if images is None:
return None, None
@@ -604,11 +604,11 @@ class VisionTransformer(nn.Module):
return self.args.image_size // self.args.patch_size
@property
def device(self) -> torch.device:
def device(self) -> torch.types.Device:
return next(self.parameters()).device
@property
def dtype(self) -> torch.device:
def dtype(self) -> torch.dtype:
return next(self.parameters()).dtype
@property
@@ -741,8 +741,8 @@ def get_pixtral_hf_image_feature_size(hf_config: PixtralVisionConfig,
ratio = max(image_width / max_width, image_height / max_height)
if ratio > 1:
image_width = int(numpy.ceil(image_width / ratio))
image_height = int(numpy.ceil(image_height / ratio))
image_width = int(math.ceil(image_width / ratio))
image_height = int(math.ceil(image_height / ratio))
num_height_tokens, num_width_tokens = _get_pixtral_hf_num_image_tokens(
(image_height, image_width),