Skip to content

[Bugfix] Fix img_sizes Parsing in Phi3-Vision #5888

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Jun 27, 2024
Merged

[Bugfix] Fix img_sizes Parsing in Phi3-Vision #5888

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
8831fc6
fix
ywang96 Jun 27, 2024
7097a20
remove unsued functions
ywang96 Jun 27, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
26 changes: 6 additions & 20 deletions vllm/model_executor/models/phi3v.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -65,12 +65,6 @@ def __init__(self, wte=None) -> None:
self.type_feature: str
self.img_processor: CLIPVisionModel

def set_img_features(self, img_features: torch.FloatTensor) -> None:
self.img_features = img_features

def set_img_sizes(self, img_sizes: torch.LongTensor) -> None:
self.img_sizes = img_sizes

def get_img_features(self,
img_embeds: torch.FloatTensor) -> torch.FloatTensor:
LAYER_IDX = self.layer_idx
Expand Down Expand Up @@ -144,21 +138,16 @@ def __init__(self,
self.layer_idx = config.img_processor.get('layer_idx', -2)
self.type_feature = config.img_processor.get('type_feature', 'patch')

def forward(self,
input_ids: torch.LongTensor,
def forward(self, input_ids: torch.LongTensor,
pixel_values: torch.FloatTensor,
image_sizes=None) -> torch.FloatTensor:
image_sizes: torch.Tensor) -> torch.FloatTensor:
"""process and merge text embeddings with image embeddings."""

# (batch_size, max_num_crops, 3, height, width)
img_embeds = pixel_values
img_sizes = image_sizes

if self.img_features is not None:
img_embeds = self.img_features.clone()
self.img_features = None

if self.img_sizes is not None:
img_sizes = self.img_sizes
# (batch_size, 2)
img_sizes = image_sizes

input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
Expand Down Expand Up @@ -190,11 +179,8 @@ def forward(self,
output_imgs = []
output_len = []

if isinstance(img_sizes, torch.Tensor):
img_sizes.squeeze_(0)

for _bs in range(bs):
h, w = img_sizes
h, w = img_sizes[_bs]
h = h // 336
w = w // 336
B_ = h * w
Expand Down