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Delete cumo/model/llava_arch.py

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- # Copyright 2023 Haotian Liu
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- #
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- # Licensed under the Apache License, Version 2.0 (the "License");
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- # you may not use this file except in compliance with the License.
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- # You may obtain a copy of the License at
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- #
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- # http://www.apache.org/licenses/LICENSE-2.0
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- #
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- # Unless required by applicable law or agreed to in writing, software
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- # distributed under the License is distributed on an "AS IS" BASIS,
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- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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- # See the License for the specific language governing permissions and
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- # limitations under the License.
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- # ------------------------------------------------------------------------
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- # Modified from LLaVA (https://github.com/haotian-liu/LLaVA)
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- # Copyright 2024 Jiachen Li
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- # ------------------------------------------------------------------------
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-
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- from abc import ABC, abstractmethod
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-
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- import torch
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- import torch.nn as nn
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-
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- from .multimodal_encoder.builder import build_vision_tower
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- from .multimodal_projector.builder import build_vision_projector
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-
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- from cumo.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
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-
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- from cumo.mm_utils import get_anyres_image_grid_shape
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-
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- class LlavaMetaModel:
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-
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- def __init__(self, config):
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- super(LlavaMetaModel, self).__init__(config)
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-
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- if hasattr(config, "mm_vision_tower"):
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- self.vision_tower = build_vision_tower(config, delay_load=True)
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- self.mm_projector = build_vision_projector(config)
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-
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- if 'unpad' in getattr(config, 'mm_patch_merge_type', ''):
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- self.image_newline = nn.Parameter(
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- torch.empty(config.hidden_size, dtype=self.dtype)
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- )
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-
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- def get_vision_tower(self):
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- vision_tower = getattr(self, 'vision_tower', None)
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- if type(vision_tower) is list:
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- vision_tower = vision_tower[0]
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- return vision_tower
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-
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- def initialize_vision_modules(self, model_args, fsdp=None):
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- vision_tower = model_args.vision_tower
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- mm_vision_select_layer = model_args.mm_vision_select_layer
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- mm_vision_select_feature = model_args.mm_vision_select_feature
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- pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
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- vision_tower_dir = model_args.vision_tower_dir
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- mm_patch_merge_type = model_args.mm_patch_merge_type
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-
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- self.config.mm_vision_tower = vision_tower
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- self.config.scales = model_args.scales
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-
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- vision_tower = build_vision_tower(model_args)
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-
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- if fsdp is not None and len(fsdp) > 0:
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- self.vision_tower = [vision_tower]
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- else:
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- self.vision_tower = vision_tower
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-
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- self.config.use_mm_proj = True
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- self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
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- self.config.mm_hidden_size = vision_tower.hidden_size
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- self.config.mm_vision_select_layer = mm_vision_select_layer
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- self.config.mm_vision_select_feature = mm_vision_select_feature
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- self.config.mm_patch_merge_type = mm_patch_merge_type
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- self.config.num_experts = model_args.num_experts
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- self.config.num_selected = model_args.num_selected
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- self.config.num_layers = model_args.num_layers
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- self.config.dropout = model_args.dropout
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- self.config.mlp_smoe = model_args.mlp_smoe
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- self.config.clip_smoe = model_args.clip_smoe
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-
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- self.mm_projector = build_vision_projector(self.config)
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-
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- if 'unpad' in mm_patch_merge_type:
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- embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype))
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- self.image_newline = nn.Parameter(
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- torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std
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- )
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-
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- if pretrain_mm_mlp_adapter is not None:
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- mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
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- def get_w(weights, keyword):
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- return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
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-
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- if self.config.mlp_smoe:
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- for i in range(model_args.num_experts):
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- self.mm_projector.experts[i].load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
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- else:
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- self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
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-
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- if vision_tower_dir is not None:
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- vision_tower_weights = torch.load(vision_tower_dir, map_location='cpu')
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- self.vision_tower.load_state_dict(vision_tower_weights, strict=False)
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- if self.config.clip_smoe:
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- current_staet_dict = self.vision_tower.state_dict()
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- for key, value in current_staet_dict.items():
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- if 'experts' in key:
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- key_splits = key.split('.')
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- new_key = [key_splits[0], key_splits[1], key_splits[2], key_splits[3], 'mlp', key_splits[6], key_splits[7]]
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- current_staet_dict[key] = vision_tower_weights['.'.join(new_key)]
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- self.vision_tower.load_state_dict(current_staet_dict, strict=True)
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-
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- def unpad_image(tensor, original_size):
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- """
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- Unpads a PyTorch tensor of a padded and resized image.
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-
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- Args:
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- tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format.
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- original_size (tuple): The original size of the image (height, width).
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-
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- Returns:
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- torch.Tensor: The unpadded image tensor.
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- """
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- original_width, original_height = original_size
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- current_height, current_width = tensor.shape[1:]
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-
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- original_aspect_ratio = original_width / original_height
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- current_aspect_ratio = current_width / current_height
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-
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- if original_aspect_ratio > current_aspect_ratio:
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- scale_factor = current_width / original_width
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- new_height = int(original_height * scale_factor)
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- padding = (current_height - new_height) // 2
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- unpadded_tensor = tensor[:, padding:current_height - padding, :]
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- else:
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- scale_factor = current_height / original_height
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- new_width = int(original_width * scale_factor)
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- padding = (current_width - new_width) // 2
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- unpadded_tensor = tensor[:, :, padding:current_width - padding]
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-
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- return unpadded_tensor
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-
143
-
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- class LlavaMetaForCausalLM(ABC):
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-
146
- @abstractmethod
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- def get_model(self):
148
- pass
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-
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- def get_vision_tower(self):
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- return self.get_model().get_vision_tower()
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-
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- def prepare_inputs_labels_for_multimodal(
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- self, input_ids, position_ids, attention_mask, past_key_values, labels,
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- images, image_sizes=None
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- ):
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- clip_balanced_loss = None
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- clip_router_z_loss = None
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- mlp_balanced_loss = None
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- mlp_router_z_loss = None
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- vision_tower = self.get_vision_tower()
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- if vision_tower is None or images is None or input_ids.shape[1] == 1:
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- return input_ids, position_ids, attention_mask, past_key_values, None, labels, clip_balanced_loss, clip_router_z_loss, mlp_balanced_loss, mlp_router_z_loss
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-
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- if type(images) is list or images.ndim == 5:
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- if type(images) is list:
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- images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images]
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- concat_images = torch.cat([image for image in images], dim=0)
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- image_features, clip_balanced_loss, clip_router_z_loss = self.get_model().get_vision_tower()(images)
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- image_features, mlp_balanced_loss, mlp_router_z_loss = self.get_model().mm_projector(image_features)
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- split_sizes = [image.shape[0] for image in images]
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- image_features = torch.split(image_features, split_sizes, dim=0)
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- mm_patch_merge_type = getattr(self.config, 'mm_patch_merge_type', 'flat')
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- image_aspect_ratio = getattr(self.config, 'image_aspect_ratio', 'square')
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- if mm_patch_merge_type == 'flat':
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- image_features = [x.flatten(0, 1) for x in image_features]
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- elif mm_patch_merge_type.startswith('spatial'):
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- new_image_features = []
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- for image_idx, image_feature in enumerate(image_features):
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- if image_feature.shape[0] > 1:
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- base_image_feature = image_feature[0]
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- image_feature = image_feature[1:]
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- height = width = self.get_vision_tower().num_patches_per_side
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- assert height * width == base_image_feature.shape[0]
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- if image_aspect_ratio == 'anyres':
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- num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[image_idx], self.config.image_grid_pinpoints, self.get_vision_tower().config.image_size)
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- image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
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- else:
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- raise NotImplementedError
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- if 'unpad' in mm_patch_merge_type:
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- image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
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- image_feature = image_feature.flatten(1, 2).flatten(2, 3)
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- image_feature = unpad_image(image_feature, image_sizes[image_idx])
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- image_feature = torch.cat((
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- image_feature,
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- self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)
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- ), dim=-1)
198
- image_feature = image_feature.flatten(1, 2).transpose(0, 1)
199
- else:
200
- image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
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- image_feature = image_feature.flatten(0, 3)
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- image_feature = torch.cat((base_image_feature, image_feature), dim=0)
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- else:
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- image_feature = image_feature[0]
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- if 'unpad' in mm_patch_merge_type:
206
- image_feature = torch.cat((
207
- image_feature,
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- self.model.image_newline[None].to(image_feature.device)
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- ), dim=0)
210
- new_image_features.append(image_feature)
211
- image_features = new_image_features
212
- else:
213
- raise ValueError(f"Unexpected mm_patch_merge_type: {self.config.mm_patch_merge_type}")
214
- else:
215
- image_features, clip_balanced_loss, clip_router_z_loss = self.get_model().get_vision_tower()(images)
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- if self.config.mlp_smoe:
217
- image_features, mlp_balanced_loss, mlp_router_z_loss = self.get_model().mm_projector(image_features)
218
- else:
219
- image_features = self.get_model().mm_projector(image_features)
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- if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
221
- raise NotImplementedError
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- # Let's just add dummy tensors if they do not exist,
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- # it is a headache to deal with None all the time.
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- # But it is not ideal, and if you have a better idea,
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- # please open an issue / submit a PR, thanks.
226
- _labels = labels
227
- _position_ids = position_ids
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- _attention_mask = attention_mask
229
- if attention_mask is None:
230
- attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
231
- else:
232
- attention_mask = attention_mask.bool()
233
- if position_ids is None:
234
- position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
235
- if labels is None:
236
- labels = torch.full_like(input_ids, IGNORE_INDEX)
237
-
238
- # remove the padding using attention_mask -- FIXME
239
- _input_ids = input_ids
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- input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
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- labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
242
-
243
- new_input_embeds = []
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- new_labels = []
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- cur_image_idx = 0
246
- for batch_idx, cur_input_ids in enumerate(input_ids):
247
- num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
248
- if num_images == 0:
249
- cur_image_features = image_features[cur_image_idx]
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- cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
251
- cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
252
- new_input_embeds.append(cur_input_embeds)
253
- new_labels.append(labels[batch_idx])
254
- cur_image_idx += 1
255
- continue
256
-
257
- image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
258
- cur_input_ids_noim = []
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- cur_labels = labels[batch_idx]
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- cur_labels_noim = []
261
- for i in range(len(image_token_indices) - 1):
262
- cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]])
263
- cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]])
264
- split_sizes = [x.shape[0] for x in cur_labels_noim]
265
- cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
266
- cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
267
- cur_new_input_embeds = []
268
- cur_new_labels = []
269
-
270
- for i in range(num_images + 1):
271
- cur_new_input_embeds.append(cur_input_embeds_no_im[i])
272
- cur_new_labels.append(cur_labels_noim[i])
273
- if i < num_images:
274
- cur_image_features = image_features[cur_image_idx]
275
- cur_image_idx += 1
276
- cur_new_input_embeds.append(cur_image_features)
277
- cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))
278
-
279
- cur_new_input_embeds = [x.to(self.device) for x in cur_new_input_embeds]
280
- cur_new_input_embeds = torch.cat(cur_new_input_embeds)
281
- cur_new_labels = torch.cat(cur_new_labels)
282
-
283
- new_input_embeds.append(cur_new_input_embeds)
284
- new_labels.append(cur_new_labels)
285
-
286
- # Truncate sequences to max length as image embeddings can make the sequence longer
287
- tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
288
- if tokenizer_model_max_length is not None:
289
- new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
290
- new_labels = [x[:tokenizer_model_max_length] for x in new_labels]
291
-
292
- # Combine them
293
- max_len = max(x.shape[0] for x in new_input_embeds)
294
- batch_size = len(new_input_embeds)
295
-
296
- new_input_embeds_padded = []
297
- new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
298
- attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
299
- position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
300
-
301
- for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
302
- cur_len = cur_new_embed.shape[0]
303
- if getattr(self.config, 'tokenizer_padding_side', 'right') == "left":
304
- new_input_embeds_padded.append(torch.cat((
305
- torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device),
306
- cur_new_embed
307
- ), dim=0))
308
- if cur_len > 0:
309
- new_labels_padded[i, -cur_len:] = cur_new_labels
310
- attention_mask[i, -cur_len:] = True
311
- position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
312
- else:
313
- new_input_embeds_padded.append(torch.cat((
314
- cur_new_embed,
315
- torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)
316
- ), dim=0))
317
- if cur_len > 0:
318
- new_labels_padded[i, :cur_len] = cur_new_labels
319
- attention_mask[i, :cur_len] = True
320
- position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
321
-
322
- new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
323
-
324
- if _labels is None:
325
- new_labels = None
326
- else:
327
- new_labels = new_labels_padded
328
-
329
- if _attention_mask is None:
330
- attention_mask = None
331
- else:
332
- attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
333
-
334
- if _position_ids is None:
335
- position_ids = None
336
-
337
- return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels, clip_balanced_loss, clip_router_z_loss, mlp_balanced_loss, mlp_router_z_loss
338
-
339
- def initialize_vision_tokenizer(self, model_args, tokenizer):
340
- if model_args.mm_use_im_patch_token:
341
- tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
342
- self.resize_token_embeddings(len(tokenizer))
343
-
344
- if model_args.mm_use_im_start_end:
345
- num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
346
- self.resize_token_embeddings(len(tokenizer))
347
-
348
- if num_new_tokens > 0:
349
- input_embeddings = self.get_input_embeddings().weight.data
350
- output_embeddings = self.get_output_embeddings().weight.data
351
-
352
- input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
353
- dim=0, keepdim=True)
354
- output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
355
- dim=0, keepdim=True)
356
-
357
- input_embeddings[-num_new_tokens:] = input_embeddings_avg
358
- output_embeddings[-num_new_tokens:] = output_embeddings_avg
359
-
360
- if model_args.tune_mm_mlp_adapter:
361
- for p in self.get_input_embeddings().parameters():
362
- p.requires_grad = True
363
- for p in self.get_output_embeddings().parameters():
364
- p.requires_grad = False
365
-
366
- if model_args.pretrain_mm_mlp_adapter:
367
- mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
368
- embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
369
- assert num_new_tokens == 2
370
- if input_embeddings.shape == embed_tokens_weight.shape:
371
- input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
372
- elif embed_tokens_weight.shape[0] == num_new_tokens:
373
- input_embeddings[-num_new_tokens:] = embed_tokens_weight
374
- else:
375
- raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
376
- elif model_args.mm_use_im_patch_token:
377
- if model_args.tune_mm_mlp_adapter:
378
- for p in self.get_input_embeddings().parameters():
379
- p.requires_grad = False
380
- for p in self.get_output_embeddings().parameters():
381
- p.requires_grad = False