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import os
import torch
import torch.nn as nn
from transformers import (CLIPImageProcessor, CLIPVisionConfig,
CLIPVisionModel, SiglipImageProcessor,
SiglipVisionConfig, SiglipVisionModel)
from .qwen2vl_encoder import (Qwen2VisionTransformerPretrainedModel,
Qwen2VLImageProcessor, Qwen2VLVisionConfig)
from .damovl_encoder import (DAMOVLImageProcessor, DAMOVLVisionModel)
class CLIPVisionEncoder(nn.Module):
def __init__(self, vision_encoder, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_encoder_name = vision_encoder
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
if not delay_load:
self.attn_implementation = getattr(args, 'mm_attn_implementation', 'flash_attention_2')
self.load_model()
else:
# uncertain whether flash-attention-2 is supported during inference phase.
self.attn_implementation = 'sdpa' # 'eager'
self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_encoder_name)
def load_model(self):
if self.is_loaded:
print('Vision tower is already loaded, `load model` call again, skipping.')
return
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_encoder_name)
self.vision_encoder = CLIPVisionModel.from_pretrained(self.vision_encoder_name,
attn_implementation=self.attn_implementation)
self.is_loaded = True
def feature_select(self, image_forward_outs):
image_features = image_forward_outs.hidden_states[self.select_layer]
if self.select_feature == 'patch':
image_features = image_features[:, 1:]
elif self.select_feature == 'cls_patch':
image_features = image_features
else:
raise ValueError(f'Unexpected select feature: {self.select_feature}')
return image_features
def forward(self, images, **kwargs):
images = torch.cat(images)
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.vision_encoder(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
image_feature = self.feature_select(image_forward_out).to(image.dtype)
image_features.append(image_feature)
else:
image_forward_outs = self.vision_encoder(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
@property
def dummy_feature(self):
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
@property
def dtype(self):
return self.vision_encoder.dtype
@property
def device(self):
return self.vision_encoder.device
@property
def config(self):
if self.is_loaded:
return self.vision_encoder.config
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2
@property
def num_patches_per_side(self):
return self.config.image_size // self.config.patch_size
@property
def image_size(self):
return self.config.image_size
class SiglipVisionEncoder(nn.Module):
def __init__(self, vision_encoder, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_encoder_name = vision_encoder
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
if not delay_load:
self.attn_implementation = getattr(args, 'mm_attn_implementation', 'flash_attention_2')
self.load_model()
else:
# uncertain whether flash-attention-2 is supported during inference phase.
self.attn_implementation = 'sdpa' # 'eager'
self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_encoder_name)
def load_model(self):
if self.is_loaded:
print('Vision tower is already loaded, `load model` call again, skipping.')
return
self.image_processor = SiglipImageProcessor.from_pretrained(self.vision_encoder_name)
self.vision_encoder = SiglipVisionModel.from_pretrained(self.vision_encoder_name,
attn_implementation=self.attn_implementation)
self.is_loaded = True
def feature_select(self, image_forward_outs):
image_features = image_forward_outs.hidden_states[self.select_layer]
if self.select_feature == 'patch':
image_features = image_features
else:
raise ValueError(f'Unexpected select feature: {self.select_feature}')
return image_features
def forward(self, images, **kwargs):
images = torch.cat(images)
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.vision_encoder(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
image_feature = self.feature_select(image_forward_out).to(image.dtype)
image_features.append(image_feature)
else:
image_forward_outs = self.vision_encoder(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
@property
def dummy_feature(self):
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
@property
def dtype(self):
return self.vision_encoder.dtype
@property
def device(self):
return self.vision_encoder.device
@property
def config(self):
if self.is_loaded:
return self.vision_encoder.config
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2
@property
def num_patches_per_side(self):
return self.config.image_size // self.config.patch_size
@property
def image_size(self):
return self.config.image_size
class Qwen2VLVisionEncoder(nn.Module):
def __init__(self, vision_encoder, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_encoder_name = vision_encoder
self.select_layer = args.mm_vision_select_layer
if not delay_load:
self.attn_implementation = getattr(args, 'mm_attn_implementation', 'flash_attention_2')
self.load_model(args)
else:
# uncertain whether flash-attention-2 is supported during inference phase.
self.attn_implementation = 'sdpa' # 'eager'
self.cfg_only = Qwen2VLVisionConfig.from_pretrained(self.vision_encoder_name)
def load_model(self, args):
if self.is_loaded:
print('Vision tower is already loaded, `load model` call again, skipping.')
return
# merge_size is set to 1 by default, because STAGE1, STAGE1.5, STAGE2 are trained with merge_size=1
# for stage 3, the merge_size is set to 2 by argments.
self.image_processor = Qwen2VLImageProcessor.from_pretrained(self.vision_encoder_name)
self.image_processor.merge_size = args.spatial_merge_size
# NOTE: The maximum number of vision tokens is 8192 by default.
mm_max_length = args.mm_max_length if hasattr(args, 'mm_max_length') else 9477 // (args.spatial_merge_size**2)
self.image_processor.max_pixels = mm_max_length * (args.spatial_merge_size**2 * self.image_processor.patch_size**2)
self.image_processor.size["max_pixels"] = self.image_processor.max_pixels
# merge_size is fixed to 1 for STAGE1, STAGE1.5, STAGE2, STAGE3 in encoder and can be modified in connector.
self.cfg_only = Qwen2VLVisionConfig.from_pretrained(self.vision_encoder_name)
self.cfg_only.spatial_merge_size = args.spatial_merge_size
self.vision_encoder = Qwen2VisionTransformerPretrainedModel.from_pretrained(
self.vision_encoder_name,
config=self.cfg_only,
torch_dtype=args.torch_dtype,
attn_implementation=self.attn_implementation)
self.is_loaded = True
def forward(self, images, grid_thws, strides, **kwargs):
images = [image for sub_images in images for image in sub_images]
grid_thws = [grid_thw for sub_grid_thws in grid_thws for grid_thw in sub_grid_thws]
strides = [stride for sub_strides in strides for stride in sub_strides]
images = torch.cat(images, dim=0)
grid_thws = torch.cat(grid_thws, dim=0)
image_features = self.vision_encoder(images, grid_thws, strides=strides)
return image_features
@property
def dummy_feature(self):
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
@property
def dtype(self):
return self.vision_encoder.dtype
@property
def device(self):
return self.vision_encoder.device
@property
def config(self):
if self.is_loaded:
return self.vision_encoder.config
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size
@property
def num_patches(self):
return -1
@property
def num_patches_per_side(self):
return -1
@property
def image_size(self):
return 14 * self.vision_encoder.config.spatial_merge_size
class DAMOVLVisionEncoder(nn.Module):
def __init__(self, vision_encoder, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_encoder_name = vision_encoder
self.args = args
if not delay_load:
self.attn_implementation = getattr(args, 'mm_attn_implementation', 'flash_attention_2')
self.load_model(self.args)
else:
# uncertain whether flash-attention-2 is supported during inference phase.
self.attn_implementation = 'sdpa' # 'eager'
self.cfg_only = DAMOVLVisionConfig.from_pretrained(self.vision_encoder_name)
def load_model(self, args):
if self.is_loaded:
print('Vision tower is already loaded, `load model` call again, skipping.')
return
# merge_size is set to 1 by default, because STAGE1, STAGE1.5, STAGE2 are trained with merge_size=1
# for stage 3, the merge_size is set to 2 by argments.
self.image_processor = DAMOVLImageProcessor.from_pretrained(self.vision_encoder_name)
self.image_processor.merge_size = args.spatial_merge_size
# NOTE: The maximum number of vision tokens is 8192 by default.
mm_max_length = args.mm_max_length if hasattr(args, 'mm_max_length') else 9477 // (args.spatial_merge_size**2)
self.image_processor.max_pixels = mm_max_length * (args.spatial_merge_size**2 * self.image_processor.patch_size**2)
self.image_processor.size["max_pixels"] = self.image_processor.max_pixels
# merge_size is fixed to 1 for STAGE1, STAGE1.5, STAGE2, STAGE3 in encoder and can be modified in connector.
self.cfg_only = Qwen2VLVisionConfig.from_pretrained(self.vision_encoder_name)
self.cfg_only.spatial_merge_size = args.spatial_merge_size
self.vision_encoder = DAMOVLVisionModel.from_pretrained(
self.vision_encoder_name,
spatial_merge_size=args.spatial_merge_size,
torch_dtype=args.torch_dtype,
attn_implementation=self.attn_implementation)
self.is_loaded = True
def forward(self, images, grid_thws, strides, **kwargs):
images = [image for sub_images in images for image in sub_images]
grid_thws = [grid_thw for sub_grid_thws in grid_thws for grid_thw in sub_grid_thws]
strides = [stride for sub_strides in strides for stride in sub_strides]
images = torch.cat(images, dim=0)
grid_thws = torch.cat(grid_thws, dim=0)
image_features = self.vision_encoder(images, grid_thws, strides)
return image_features
@property
def dummy_feature(self):
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
@property
def dtype(self):
return self.vision_encoder.dtype
@property
def device(self):
return self.vision_encoder.device
@property
def config(self):
if self.is_loaded:
return self.vision_encoder.config
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size
@property
def num_patches(self):
return -1
@property
def num_patches_per_side(self):
return -1
@property
def image_size(self):
return 14 * self.vision_encoder.config.spatial_merge_size
def build_vision_encoder(vision_encoder_cfg, **kwargs):
vision_encoder = getattr(vision_encoder_cfg, 'mm_vision_encoder', getattr(vision_encoder_cfg, 'vision_encoder', None))
vision_encoder = DAMOVLVisionEncoder(vision_encoder, args=vision_encoder_cfg, **kwargs)
return vision_encoder
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