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Upload llava_arch.py
Browse files- llava_arch.py +381 -0
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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31 |
+
class LlavaMetaModel:
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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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38 |
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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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43 |
+
)
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44 |
+
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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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53 |
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mm_vision_select_layer = model_args.mm_vision_select_layer
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54 |
+
mm_vision_select_feature = model_args.mm_vision_select_feature
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55 |
+
pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
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56 |
+
vision_tower_dir = model_args.vision_tower_dir
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57 |
+
mm_patch_merge_type = model_args.mm_patch_merge_type
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58 |
+
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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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63 |
+
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64 |
+
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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68 |
+
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+
self.config.use_mm_proj = True
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70 |
+
self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
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71 |
+
self.config.mm_hidden_size = vision_tower.hidden_size
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72 |
+
self.config.mm_vision_select_layer = mm_vision_select_layer
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73 |
+
self.config.mm_vision_select_feature = mm_vision_select_feature
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74 |
+
self.config.mm_patch_merge_type = mm_patch_merge_type
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75 |
+
self.config.num_experts = model_args.num_experts
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76 |
+
self.config.num_selected = model_args.num_selected
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77 |
+
self.config.num_layers = model_args.num_layers
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78 |
+
self.config.dropout = model_args.dropout
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79 |
+
self.config.mlp_smoe = model_args.mlp_smoe
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80 |
+
self.config.clip_smoe = model_args.clip_smoe
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81 |
+
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82 |
+
self.mm_projector = build_vision_projector(self.config)
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83 |
+
|
84 |
+
if 'unpad' in mm_patch_merge_type:
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85 |
+
embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype))
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86 |
+
self.image_newline = nn.Parameter(
|
87 |
+
torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std
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88 |
+
)
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89 |
+
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90 |
+
if pretrain_mm_mlp_adapter is not None:
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91 |
+
mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
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92 |
+
def get_w(weights, keyword):
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93 |
+
return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword in k}
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94 |
+
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95 |
+
if self.config.mlp_smoe:
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96 |
+
for i in range(model_args.num_experts):
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97 |
+
self.mm_projector.experts[i].load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
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98 |
+
else:
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99 |
+
self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
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100 |
+
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101 |
+
if vision_tower_dir is not None:
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102 |
+
vision_tower_weights = torch.load(vision_tower_dir, map_location='cpu')
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103 |
+
self.vision_tower.load_state_dict(vision_tower_weights, strict=False)
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104 |
+
if self.config.clip_smoe:
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105 |
+
current_staet_dict = self.vision_tower.state_dict()
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106 |
+
for key, value in current_staet_dict.items():
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107 |
+
if 'experts' in key:
|
108 |
+
key_splits = key.split('.')
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109 |
+
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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110 |
+
current_staet_dict[key] = vision_tower_weights['.'.join(new_key)]
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111 |
+
self.vision_tower.load_state_dict(current_staet_dict, strict=True)
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112 |
+
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113 |
+
def unpad_image(tensor, original_size):
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114 |
+
"""
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115 |
+
Unpads a PyTorch tensor of a padded and resized image.
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116 |
+
|
117 |
+
Args:
|
118 |
+
tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format.
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119 |
+
original_size (tuple): The original size of the image (height, width).
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120 |
+
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121 |
+
Returns:
|
122 |
+
torch.Tensor: The unpadded image tensor.
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123 |
+
"""
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124 |
+
original_width, original_height = original_size
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125 |
+
current_height, current_width = tensor.shape[1:]
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126 |
+
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127 |
+
original_aspect_ratio = original_width / original_height
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128 |
+
current_aspect_ratio = current_width / current_height
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129 |
+
|
130 |
+
if original_aspect_ratio > current_aspect_ratio:
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131 |
+
scale_factor = current_width / original_width
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132 |
+
new_height = int(original_height * scale_factor)
|
133 |
+
padding = (current_height - new_height) // 2
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134 |
+
unpadded_tensor = tensor[:, padding:current_height - padding, :]
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135 |
+
else:
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136 |
+
scale_factor = current_height / original_height
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137 |
+
new_width = int(original_width * scale_factor)
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138 |
+
padding = (current_width - new_width) // 2
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139 |
+
unpadded_tensor = tensor[:, :, padding:current_width - padding]
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140 |
+
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141 |
+
return unpadded_tensor
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142 |
+
|
143 |
+
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144 |
+
class LlavaMetaForCausalLM(ABC):
|
145 |
+
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146 |
+
@abstractmethod
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147 |
+
def get_model(self):
|
148 |
+
pass
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149 |
+
|
150 |
+
def get_vision_tower(self):
|
151 |
+
return self.get_model().get_vision_tower()
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152 |
+
|
153 |
+
def prepare_inputs_labels_for_multimodal(
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154 |
+
self, input_ids, position_ids, attention_mask, past_key_values, labels,
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155 |
+
images, image_sizes=None
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156 |
+
):
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157 |
+
clip_balanced_loss = None
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158 |
+
clip_router_z_loss = None
|
159 |
+
mlp_balanced_loss = None
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160 |
+
mlp_router_z_loss = None
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161 |
+
vision_tower = self.get_vision_tower()
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162 |
+
if vision_tower is None or images is None or input_ids.shape[1] == 1:
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163 |
+
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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164 |
+
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165 |
+
if type(images) is list or images.ndim == 5:
|
166 |
+
if type(images) is list:
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167 |
+
images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images]
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168 |
+
concat_images = torch.cat([image for image in images], dim=0)
|
169 |
+
image_features, clip_balanced_loss, clip_router_z_loss = self.get_model().get_vision_tower()(images)
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170 |
+
image_features, mlp_balanced_loss, mlp_router_z_loss = self.get_model().mm_projector(image_features)
|
171 |
+
split_sizes = [image.shape[0] for image in images]
|
172 |
+
image_features = torch.split(image_features, split_sizes, dim=0)
|
173 |
+
mm_patch_merge_type = getattr(self.config, 'mm_patch_merge_type', 'flat')
|
174 |
+
image_aspect_ratio = getattr(self.config, 'image_aspect_ratio', 'square')
|
175 |
+
if mm_patch_merge_type == 'flat':
|
176 |
+
image_features = [x.flatten(0, 1) for x in image_features]
|
177 |
+
elif mm_patch_merge_type.startswith('spatial'):
|
178 |
+
new_image_features = []
|
179 |
+
for image_idx, image_feature in enumerate(image_features):
|
180 |
+
if image_feature.shape[0] > 1:
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181 |
+
base_image_feature = image_feature[0]
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182 |
+
image_feature = image_feature[1:]
|
183 |
+
height = width = self.get_vision_tower().num_patches_per_side
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184 |
+
assert height * width == base_image_feature.shape[0]
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185 |
+
if image_aspect_ratio == 'anyres':
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186 |
+
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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187 |
+
image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
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188 |
+
else:
|
189 |
+
raise NotImplementedError
|
190 |
+
if 'unpad' in mm_patch_merge_type:
|
191 |
+
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
|
192 |
+
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
|
193 |
+
image_feature = unpad_image(image_feature, image_sizes[image_idx])
|
194 |
+
image_feature = torch.cat((
|
195 |
+
image_feature,
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196 |
+
self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)
|
197 |
+
), 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()
|
201 |
+
image_feature = image_feature.flatten(0, 3)
|
202 |
+
image_feature = torch.cat((base_image_feature, image_feature), dim=0)
|
203 |
+
else:
|
204 |
+
image_feature = image_feature[0]
|
205 |
+
if 'unpad' in mm_patch_merge_type:
|
206 |
+
image_feature = torch.cat((
|
207 |
+
image_feature,
|
208 |
+
self.model.image_newline[None].to(image_feature.device)
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209 |
+
), 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)
|
216 |
+
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)
|
220 |
+
if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
|
221 |
+
raise NotImplementedError
|
222 |
+
# Let's just add dummy tensors if they do not exist,
|
223 |
+
# it is a headache to deal with None all the time.
|
224 |
+
# But it is not ideal, and if you have a better idea,
|
225 |
+
# please open an issue / submit a PR, thanks.
|
226 |
+
_labels = labels
|
227 |
+
_position_ids = position_ids
|
228 |
+
_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
|
240 |
+
input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
|
241 |
+
labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
|
242 |
+
|
243 |
+
new_input_embeds = []
|
244 |
+
new_labels = []
|
245 |
+
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]
|
250 |
+
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 = []
|
259 |
+
cur_labels = labels[batch_idx]
|
260 |
+
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
|