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Running
on
Zero
| 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 | |
| def dummy_feature(self): | |
| return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) | |
| def dtype(self): | |
| return self.vision_encoder.dtype | |
| def device(self): | |
| return self.vision_encoder.device | |
| def config(self): | |
| if self.is_loaded: | |
| return self.vision_encoder.config | |
| else: | |
| return self.cfg_only | |
| def hidden_size(self): | |
| return self.config.hidden_size | |
| def num_patches(self): | |
| return (self.config.image_size // self.config.patch_size) ** 2 | |
| def num_patches_per_side(self): | |
| return self.config.image_size // self.config.patch_size | |
| 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 | |
| def dummy_feature(self): | |
| return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) | |
| def dtype(self): | |
| return self.vision_encoder.dtype | |
| def device(self): | |
| return self.vision_encoder.device | |
| def config(self): | |
| if self.is_loaded: | |
| return self.vision_encoder.config | |
| else: | |
| return self.cfg_only | |
| def hidden_size(self): | |
| return self.config.hidden_size | |
| def num_patches(self): | |
| return (self.config.image_size // self.config.patch_size) ** 2 | |
| def num_patches_per_side(self): | |
| return self.config.image_size // self.config.patch_size | |
| 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 | |
| def dummy_feature(self): | |
| return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) | |
| def dtype(self): | |
| return self.vision_encoder.dtype | |
| def device(self): | |
| return self.vision_encoder.device | |
| def config(self): | |
| if self.is_loaded: | |
| return self.vision_encoder.config | |
| else: | |
| return self.cfg_only | |
| def hidden_size(self): | |
| return self.config.hidden_size | |
| def num_patches(self): | |
| return -1 | |
| def num_patches_per_side(self): | |
| return -1 | |
| 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 | |
| def dummy_feature(self): | |
| return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) | |
| def dtype(self): | |
| return self.vision_encoder.dtype | |
| def device(self): | |
| return self.vision_encoder.device | |
| def config(self): | |
| if self.is_loaded: | |
| return self.vision_encoder.config | |
| else: | |
| return self.cfg_only | |
| def hidden_size(self): | |
| return self.config.hidden_size | |
| def num_patches(self): | |
| return -1 | |
| def num_patches_per_side(self): | |
| return -1 | |
| 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 | |