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NGUYEN, Xuan Phi
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357985d
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Parent(s):
4f071ee
update
Browse files
multipurpose_chatbot/engines/image_processing_llava_next.py
ADDED
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1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
3 |
+
#
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4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
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5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
"""Image processor class for LLaVa-NeXT."""
|
16 |
+
|
17 |
+
import math
|
18 |
+
from typing import Dict, List, Optional, Union
|
19 |
+
|
20 |
+
import numpy as np
|
21 |
+
|
22 |
+
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict, select_best_resolution
|
23 |
+
from transformers.image_transforms import (
|
24 |
+
convert_to_rgb,
|
25 |
+
get_resize_output_image_size,
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26 |
+
pad,
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27 |
+
resize,
|
28 |
+
to_channel_dimension_format,
|
29 |
+
)
|
30 |
+
from transformers.image_utils import (
|
31 |
+
OPENAI_CLIP_MEAN,
|
32 |
+
OPENAI_CLIP_STD,
|
33 |
+
ChannelDimension,
|
34 |
+
ImageInput,
|
35 |
+
PILImageResampling,
|
36 |
+
get_image_size,
|
37 |
+
infer_channel_dimension_format,
|
38 |
+
is_scaled_image,
|
39 |
+
make_list_of_images,
|
40 |
+
to_numpy_array,
|
41 |
+
valid_images,
|
42 |
+
validate_preprocess_arguments,
|
43 |
+
)
|
44 |
+
from transformers.utils import TensorType, is_vision_available, logging
|
45 |
+
|
46 |
+
|
47 |
+
logger = logging.get_logger(__name__)
|
48 |
+
|
49 |
+
|
50 |
+
if is_vision_available():
|
51 |
+
from PIL import Image
|
52 |
+
|
53 |
+
|
54 |
+
def divide_to_patches(image: np.array, patch_size: int, input_data_format) -> List[np.array]:
|
55 |
+
"""
|
56 |
+
Divides an image into patches of a specified size.
|
57 |
+
|
58 |
+
Args:
|
59 |
+
image (`np.array`):
|
60 |
+
The input image.
|
61 |
+
patch_size (`int`):
|
62 |
+
The size of each patch.
|
63 |
+
input_data_format (`ChannelDimension` or `str`):
|
64 |
+
The channel dimension format of the input image.
|
65 |
+
|
66 |
+
Returns:
|
67 |
+
list: A list of np.array representing the patches.
|
68 |
+
"""
|
69 |
+
patches = []
|
70 |
+
height, width = get_image_size(image, channel_dim=input_data_format)
|
71 |
+
for i in range(0, height, patch_size):
|
72 |
+
for j in range(0, width, patch_size):
|
73 |
+
if input_data_format == ChannelDimension.LAST:
|
74 |
+
patch = image[i : i + patch_size, j : j + patch_size]
|
75 |
+
else:
|
76 |
+
patch = image[:, i : i + patch_size, j : j + patch_size]
|
77 |
+
patches.append(patch)
|
78 |
+
|
79 |
+
return patches
|
80 |
+
|
81 |
+
|
82 |
+
def expand_to_square(image: np.array, background_color, input_data_format) -> np.array:
|
83 |
+
"""
|
84 |
+
Expands an image to a square by adding a background color.
|
85 |
+
"""
|
86 |
+
|
87 |
+
height, width = get_image_size(image, channel_dim=input_data_format)
|
88 |
+
if width == height:
|
89 |
+
return image
|
90 |
+
elif width > height:
|
91 |
+
result = np.ones((width, width, image.shape[2]), dtype=image.dtype) * background_color
|
92 |
+
result[(width - height) // 2 : (width - height) // 2 + height, :] = image
|
93 |
+
return result
|
94 |
+
else:
|
95 |
+
result = np.ones((height, height, image.shape[2]), dtype=image.dtype) * background_color
|
96 |
+
result[:, (height - width) // 2 : (height - width) // 2 + width] = image
|
97 |
+
return result
|
98 |
+
|
99 |
+
|
100 |
+
def _get_patch_output_size(image, target_resolution, input_data_format):
|
101 |
+
original_height, original_width = get_image_size(image, channel_dim=input_data_format)
|
102 |
+
target_height, target_width = target_resolution
|
103 |
+
|
104 |
+
scale_w = target_width / original_width
|
105 |
+
scale_h = target_height / original_height
|
106 |
+
|
107 |
+
if scale_w < scale_h:
|
108 |
+
new_width = target_width
|
109 |
+
new_height = min(math.ceil(original_height * scale_w), target_height)
|
110 |
+
else:
|
111 |
+
new_height = target_height
|
112 |
+
new_width = min(math.ceil(original_width * scale_h), target_width)
|
113 |
+
|
114 |
+
return new_height, new_width
|
115 |
+
|
116 |
+
|
117 |
+
class LlavaNextImageProcessor(BaseImageProcessor):
|
118 |
+
r"""
|
119 |
+
Constructs a LLaVa-NeXT image processor. Based on [`CLIPImageProcessor`] with incorporation of additional techniques
|
120 |
+
for processing high resolution images as explained in the [LLaVa paper](https://arxiv.org/abs/2310.03744).
|
121 |
+
|
122 |
+
Args:
|
123 |
+
do_resize (`bool`, *optional*, defaults to `True`):
|
124 |
+
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
|
125 |
+
`do_resize` in the `preprocess` method.
|
126 |
+
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
|
127 |
+
Size of the image after resizing. The shortest edge of the image is resized to size["shortest_edge"], with
|
128 |
+
the longest edge resized to keep the input aspect ratio. Can be overridden by `size` in the `preprocess`
|
129 |
+
method.
|
130 |
+
image_grid_pinpoints (`List` *optional*, defaults to `[[672, 336], [336, 672], [672, 672], [336, 1008], [1008, 336]]`):
|
131 |
+
A list of possible resolutions to use for processing high resolution images. The best resolution is selected
|
132 |
+
based on the original size of the image. Can be overridden by `image_grid_pinpoints` in the `preprocess`
|
133 |
+
method.
|
134 |
+
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
|
135 |
+
Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
|
136 |
+
do_center_crop (`bool`, *optional*, defaults to `True`):
|
137 |
+
Whether to center crop the image to the specified `crop_size`. Can be overridden by `do_center_crop` in the
|
138 |
+
`preprocess` method.
|
139 |
+
crop_size (`Dict[str, int]` *optional*, defaults to 224):
|
140 |
+
Size of the output image after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
|
141 |
+
method.
|
142 |
+
do_rescale (`bool`, *optional*, defaults to `True`):
|
143 |
+
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by `do_rescale` in
|
144 |
+
the `preprocess` method.
|
145 |
+
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
|
146 |
+
Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
|
147 |
+
method.
|
148 |
+
do_normalize (`bool`, *optional*, defaults to `True`):
|
149 |
+
Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method.
|
150 |
+
image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
|
151 |
+
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
|
152 |
+
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
|
153 |
+
image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
|
154 |
+
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
|
155 |
+
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
|
156 |
+
Can be overridden by the `image_std` parameter in the `preprocess` method.
|
157 |
+
do_convert_rgb (`bool`, *optional*, defaults to `True`):
|
158 |
+
Whether to convert the image to RGB.
|
159 |
+
"""
|
160 |
+
|
161 |
+
model_input_names = ["pixel_values"]
|
162 |
+
|
163 |
+
def __init__(
|
164 |
+
self,
|
165 |
+
do_resize: bool = True,
|
166 |
+
size: Dict[str, int] = None,
|
167 |
+
image_grid_pinpoints: List = None,
|
168 |
+
resample: PILImageResampling = PILImageResampling.BICUBIC,
|
169 |
+
do_center_crop: bool = True,
|
170 |
+
crop_size: Dict[str, int] = None,
|
171 |
+
do_rescale: bool = True,
|
172 |
+
rescale_factor: Union[int, float] = 1 / 255,
|
173 |
+
do_normalize: bool = True,
|
174 |
+
image_mean: Optional[Union[float, List[float]]] = None,
|
175 |
+
image_std: Optional[Union[float, List[float]]] = None,
|
176 |
+
do_convert_rgb: bool = True,
|
177 |
+
**kwargs,
|
178 |
+
) -> None:
|
179 |
+
super().__init__(**kwargs)
|
180 |
+
size = size if size is not None else {"shortest_edge": 224}
|
181 |
+
size = get_size_dict(size, default_to_square=False)
|
182 |
+
image_grid_pinpoints = (
|
183 |
+
image_grid_pinpoints
|
184 |
+
if image_grid_pinpoints is not None
|
185 |
+
else [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]
|
186 |
+
)
|
187 |
+
crop_size = crop_size if crop_size is not None else {"height": 224, "width": 224}
|
188 |
+
crop_size = get_size_dict(crop_size, default_to_square=True, param_name="crop_size")
|
189 |
+
|
190 |
+
self.do_resize = do_resize
|
191 |
+
self.size = size
|
192 |
+
self.image_grid_pinpoints = image_grid_pinpoints
|
193 |
+
self.resample = resample
|
194 |
+
self.do_center_crop = do_center_crop
|
195 |
+
self.crop_size = crop_size
|
196 |
+
self.do_rescale = do_rescale
|
197 |
+
self.rescale_factor = rescale_factor
|
198 |
+
self.do_normalize = do_normalize
|
199 |
+
self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
|
200 |
+
self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
|
201 |
+
self.do_convert_rgb = do_convert_rgb
|
202 |
+
|
203 |
+
# Copied from transformers.models.clip.image_processing_clip.CLIPImageProcessor.resize with CLIP->LLaVa
|
204 |
+
def resize(
|
205 |
+
self,
|
206 |
+
image: np.ndarray,
|
207 |
+
size: Dict[str, int],
|
208 |
+
resample: PILImageResampling = PILImageResampling.BICUBIC,
|
209 |
+
data_format: Optional[Union[str, ChannelDimension]] = None,
|
210 |
+
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
211 |
+
**kwargs,
|
212 |
+
) -> np.ndarray:
|
213 |
+
"""
|
214 |
+
Resize an image. The shortest edge of the image is resized to size["shortest_edge"], with the longest edge
|
215 |
+
resized to keep the input aspect ratio.
|
216 |
+
|
217 |
+
Args:
|
218 |
+
image (`np.ndarray`):
|
219 |
+
Image to resize.
|
220 |
+
size (`Dict[str, int]`):
|
221 |
+
Size of the output image.
|
222 |
+
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
|
223 |
+
Resampling filter to use when resiizing the image.
|
224 |
+
data_format (`str` or `ChannelDimension`, *optional*):
|
225 |
+
The channel dimension format of the image. If not provided, it will be the same as the input image.
|
226 |
+
input_data_format (`ChannelDimension` or `str`, *optional*):
|
227 |
+
The channel dimension format of the input image. If not provided, it will be inferred.
|
228 |
+
"""
|
229 |
+
default_to_square = True
|
230 |
+
if "shortest_edge" in size:
|
231 |
+
size = size["shortest_edge"]
|
232 |
+
default_to_square = False
|
233 |
+
elif "height" in size and "width" in size:
|
234 |
+
size = (size["height"], size["width"])
|
235 |
+
else:
|
236 |
+
raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.")
|
237 |
+
|
238 |
+
output_size = get_resize_output_image_size(
|
239 |
+
image,
|
240 |
+
size=size,
|
241 |
+
default_to_square=default_to_square,
|
242 |
+
input_data_format=input_data_format,
|
243 |
+
)
|
244 |
+
|
245 |
+
return resize(
|
246 |
+
image,
|
247 |
+
size=output_size,
|
248 |
+
resample=resample,
|
249 |
+
data_format=data_format,
|
250 |
+
input_data_format=input_data_format,
|
251 |
+
**kwargs,
|
252 |
+
)
|
253 |
+
|
254 |
+
def _preprocess(
|
255 |
+
self,
|
256 |
+
images: ImageInput,
|
257 |
+
do_resize: bool = None,
|
258 |
+
size: Dict[str, int] = None,
|
259 |
+
resample: PILImageResampling = None,
|
260 |
+
do_center_crop: bool = None,
|
261 |
+
crop_size: int = None,
|
262 |
+
do_rescale: bool = None,
|
263 |
+
rescale_factor: float = None,
|
264 |
+
do_normalize: bool = None,
|
265 |
+
image_mean: Optional[Union[float, List[float]]] = None,
|
266 |
+
image_std: Optional[Union[float, List[float]]] = None,
|
267 |
+
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
|
268 |
+
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
269 |
+
) -> Image.Image:
|
270 |
+
"""
|
271 |
+
Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
|
272 |
+
|
273 |
+
Args:
|
274 |
+
images (`ImageInput`):
|
275 |
+
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
|
276 |
+
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
|
277 |
+
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
|
278 |
+
Whether to resize the image.
|
279 |
+
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
|
280 |
+
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
|
281 |
+
the longest edge resized to keep the input aspect ratio.
|
282 |
+
resample (`int`, *optional*, defaults to `self.resample`):
|
283 |
+
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
|
284 |
+
has an effect if `do_resize` is set to `True`.
|
285 |
+
do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
|
286 |
+
Whether to center crop the image.
|
287 |
+
crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
|
288 |
+
Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
|
289 |
+
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
|
290 |
+
Whether to rescale the image.
|
291 |
+
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
|
292 |
+
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
|
293 |
+
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
|
294 |
+
Whether to normalize the image.
|
295 |
+
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
|
296 |
+
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
|
297 |
+
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
|
298 |
+
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
|
299 |
+
`True`.
|
300 |
+
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
|
301 |
+
The channel dimension format for the output image. Can be one of:
|
302 |
+
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
303 |
+
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
304 |
+
- Unset: Use the channel dimension format of the input image.
|
305 |
+
input_data_format (`ChannelDimension` or `str`, *optional*):
|
306 |
+
The channel dimension format for the input image. If unset, the channel dimension format is inferred
|
307 |
+
from the input image. Can be one of:
|
308 |
+
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
309 |
+
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
310 |
+
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
|
311 |
+
"""
|
312 |
+
images = make_list_of_images(images)
|
313 |
+
|
314 |
+
if do_resize:
|
315 |
+
images = [
|
316 |
+
self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
|
317 |
+
for image in images
|
318 |
+
]
|
319 |
+
|
320 |
+
if do_center_crop:
|
321 |
+
images = [
|
322 |
+
self.center_crop(image=image, size=crop_size, input_data_format=input_data_format) for image in images
|
323 |
+
]
|
324 |
+
|
325 |
+
if do_rescale:
|
326 |
+
images = [
|
327 |
+
self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
|
328 |
+
for image in images
|
329 |
+
]
|
330 |
+
|
331 |
+
if do_normalize:
|
332 |
+
images = [
|
333 |
+
self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
|
334 |
+
for image in images
|
335 |
+
]
|
336 |
+
|
337 |
+
images = [
|
338 |
+
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
|
339 |
+
]
|
340 |
+
|
341 |
+
return images
|
342 |
+
|
343 |
+
def _resize_for_patching(
|
344 |
+
self, image: np.array, target_resolution: tuple, resample, input_data_format: ChannelDimension
|
345 |
+
) -> np.array:
|
346 |
+
"""
|
347 |
+
Resizes an image to a target resolution while maintaining aspect ratio.
|
348 |
+
|
349 |
+
Args:
|
350 |
+
image (np.array):
|
351 |
+
The input image.
|
352 |
+
target_resolution (tuple):
|
353 |
+
The target resolution (height, width) of the image.
|
354 |
+
resample (`PILImageResampling`):
|
355 |
+
Resampling filter to use if resizing the image.
|
356 |
+
input_data_format (`ChannelDimension` or `str`):
|
357 |
+
The channel dimension format of the input image.
|
358 |
+
|
359 |
+
Returns:
|
360 |
+
np.array: The resized and padded image.
|
361 |
+
"""
|
362 |
+
new_height, new_width = _get_patch_output_size(image, target_resolution, input_data_format)
|
363 |
+
|
364 |
+
# Resize the image
|
365 |
+
resized_image = resize(image, (new_height, new_width), resample=resample, input_data_format=input_data_format)
|
366 |
+
|
367 |
+
return resized_image
|
368 |
+
|
369 |
+
def _pad_for_patching(
|
370 |
+
self, image: np.array, target_resolution: tuple, input_data_format: ChannelDimension
|
371 |
+
) -> np.array:
|
372 |
+
"""
|
373 |
+
Pad an image to a target resolution while maintaining aspect ratio.
|
374 |
+
"""
|
375 |
+
target_height, target_width = target_resolution
|
376 |
+
new_height, new_width = _get_patch_output_size(image, target_resolution, input_data_format)
|
377 |
+
|
378 |
+
paste_x = (target_width - new_width) // 2
|
379 |
+
paste_y = (target_height - new_height) // 2
|
380 |
+
|
381 |
+
padded_image = pad(image, padding=((paste_y, paste_y), (paste_x, paste_x)))
|
382 |
+
|
383 |
+
return padded_image
|
384 |
+
|
385 |
+
def get_image_patches(
|
386 |
+
self,
|
387 |
+
image: np.array,
|
388 |
+
grid_pinpoints,
|
389 |
+
size: tuple,
|
390 |
+
patch_size: int,
|
391 |
+
resample: PILImageResampling,
|
392 |
+
data_format: ChannelDimension,
|
393 |
+
input_data_format: ChannelDimension,
|
394 |
+
) -> List[np.array]:
|
395 |
+
"""
|
396 |
+
Process an image with variable resolutions by dividing it into patches.
|
397 |
+
|
398 |
+
Args:
|
399 |
+
image (np.array):
|
400 |
+
The input image to be processed.
|
401 |
+
grid_pinpoints (List):
|
402 |
+
A string representation of a list of possible resolutions.
|
403 |
+
size (`tuple`):
|
404 |
+
Size to resize the original image to.
|
405 |
+
patch_size (`int`):
|
406 |
+
Size of the patches to divide the image into.
|
407 |
+
resample (`PILImageResampling`):
|
408 |
+
Resampling filter to use if resizing the image.
|
409 |
+
data_format (`ChannelDimension` or `str`):
|
410 |
+
The channel dimension format for the output image.
|
411 |
+
input_data_format (`ChannelDimension` or `str`):
|
412 |
+
The channel dimension format of the input image.
|
413 |
+
|
414 |
+
Returns:
|
415 |
+
List[np.array]: A list of NumPy arrays containing the processed image patches.
|
416 |
+
"""
|
417 |
+
if not isinstance(grid_pinpoints, list):
|
418 |
+
raise ValueError("grid_pinpoints must be a list of possible resolutions.")
|
419 |
+
|
420 |
+
possible_resolutions = grid_pinpoints
|
421 |
+
|
422 |
+
image_size = get_image_size(image, channel_dim=input_data_format)
|
423 |
+
best_resolution = select_best_resolution(image_size, possible_resolutions)
|
424 |
+
resized_image = self._resize_for_patching(
|
425 |
+
image, best_resolution, resample=resample, input_data_format=input_data_format
|
426 |
+
)
|
427 |
+
padded_image = self._pad_for_patching(resized_image, best_resolution, input_data_format=input_data_format)
|
428 |
+
|
429 |
+
patches = divide_to_patches(padded_image, patch_size=patch_size, input_data_format=input_data_format)
|
430 |
+
|
431 |
+
# make sure that all patches are in the input data format
|
432 |
+
patches = [
|
433 |
+
to_channel_dimension_format(patch, channel_dim=data_format, input_channel_dim=input_data_format)
|
434 |
+
for patch in patches
|
435 |
+
]
|
436 |
+
|
437 |
+
resized_original_image = resize(
|
438 |
+
image,
|
439 |
+
size=size,
|
440 |
+
resample=resample,
|
441 |
+
data_format=data_format,
|
442 |
+
input_data_format=input_data_format,
|
443 |
+
)
|
444 |
+
|
445 |
+
image_patches = [resized_original_image] + patches
|
446 |
+
|
447 |
+
return image_patches
|
448 |
+
|
449 |
+
def preprocess(
|
450 |
+
self,
|
451 |
+
images: ImageInput,
|
452 |
+
do_resize: bool = None,
|
453 |
+
size: Dict[str, int] = None,
|
454 |
+
image_grid_pinpoints: List = None,
|
455 |
+
resample: PILImageResampling = None,
|
456 |
+
do_center_crop: bool = None,
|
457 |
+
crop_size: int = None,
|
458 |
+
do_rescale: bool = None,
|
459 |
+
rescale_factor: float = None,
|
460 |
+
do_normalize: bool = None,
|
461 |
+
image_mean: Optional[Union[float, List[float]]] = None,
|
462 |
+
image_std: Optional[Union[float, List[float]]] = None,
|
463 |
+
do_convert_rgb: bool = None,
|
464 |
+
return_tensors: Optional[Union[str, TensorType]] = None,
|
465 |
+
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
|
466 |
+
input_data_format: Optional[Union[str, ChannelDimension]] = None,
|
467 |
+
concat_images: bool = True,
|
468 |
+
):
|
469 |
+
"""
|
470 |
+
Args:
|
471 |
+
images (`ImageInput`):
|
472 |
+
Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
|
473 |
+
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
|
474 |
+
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
|
475 |
+
Whether to resize the image.
|
476 |
+
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
|
477 |
+
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
|
478 |
+
the longest edge resized to keep the input aspect ratio.
|
479 |
+
image_grid_pinpoints (`List` *optional*, defaults to `self.image_grid_pinpoints`):
|
480 |
+
A list of possible resolutions to use for processing high resolution images. The best resolution is
|
481 |
+
selected based on the original size of the image.
|
482 |
+
resample (`int`, *optional*, defaults to `self.resample`):
|
483 |
+
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
|
484 |
+
has an effect if `do_resize` is set to `True`.
|
485 |
+
do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
|
486 |
+
Whether to center crop the image.
|
487 |
+
crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
|
488 |
+
Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
|
489 |
+
do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
|
490 |
+
Whether to rescale the image.
|
491 |
+
rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
|
492 |
+
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
|
493 |
+
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
|
494 |
+
Whether to normalize the image.
|
495 |
+
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
|
496 |
+
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
|
497 |
+
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
|
498 |
+
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
|
499 |
+
`True`.
|
500 |
+
do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
|
501 |
+
Whether to convert the image to RGB.
|
502 |
+
return_tensors (`str` or `TensorType`, *optional*):
|
503 |
+
The type of tensors to return. Can be one of:
|
504 |
+
- Unset: Return a list of `np.ndarray`.
|
505 |
+
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
|
506 |
+
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
|
507 |
+
- `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
|
508 |
+
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
|
509 |
+
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
|
510 |
+
The channel dimension format for the output image. Can be one of:
|
511 |
+
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
512 |
+
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
513 |
+
- Unset: Use the channel dimension format of the input image.
|
514 |
+
input_data_format (`ChannelDimension` or `str`, *optional*):
|
515 |
+
The channel dimension format for the input image. If unset, the channel dimension format is inferred
|
516 |
+
from the input image. Can be one of:
|
517 |
+
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
|
518 |
+
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
|
519 |
+
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
|
520 |
+
"""
|
521 |
+
do_resize = do_resize if do_resize is not None else self.do_resize
|
522 |
+
size = size if size is not None else self.size
|
523 |
+
size = get_size_dict(size, param_name="size", default_to_square=False)
|
524 |
+
image_grid_pinpoints = image_grid_pinpoints if image_grid_pinpoints is not None else self.image_grid_pinpoints
|
525 |
+
resample = resample if resample is not None else self.resample
|
526 |
+
do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
|
527 |
+
crop_size = crop_size if crop_size is not None else self.crop_size
|
528 |
+
crop_size = get_size_dict(crop_size, param_name="crop_size", default_to_square=True)
|
529 |
+
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
|
530 |
+
rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
|
531 |
+
do_normalize = do_normalize if do_normalize is not None else self.do_normalize
|
532 |
+
image_mean = image_mean if image_mean is not None else self.image_mean
|
533 |
+
image_std = image_std if image_std is not None else self.image_std
|
534 |
+
do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
|
535 |
+
|
536 |
+
images = make_list_of_images(images)
|
537 |
+
|
538 |
+
if not valid_images(images):
|
539 |
+
raise ValueError(
|
540 |
+
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
|
541 |
+
"torch.Tensor, tf.Tensor or jax.ndarray."
|
542 |
+
)
|
543 |
+
|
544 |
+
validate_preprocess_arguments(
|
545 |
+
do_rescale=do_rescale,
|
546 |
+
rescale_factor=rescale_factor,
|
547 |
+
do_normalize=do_normalize,
|
548 |
+
image_mean=image_mean,
|
549 |
+
image_std=image_std,
|
550 |
+
do_center_crop=do_center_crop,
|
551 |
+
crop_size=crop_size,
|
552 |
+
do_resize=do_resize,
|
553 |
+
size=size,
|
554 |
+
resample=resample,
|
555 |
+
)
|
556 |
+
|
557 |
+
if do_convert_rgb:
|
558 |
+
images = [convert_to_rgb(image) for image in images]
|
559 |
+
|
560 |
+
# All transformations expect numpy arrays.
|
561 |
+
images = [to_numpy_array(image) for image in images]
|
562 |
+
|
563 |
+
if is_scaled_image(images[0]) and do_rescale:
|
564 |
+
logger.warning_once(
|
565 |
+
"It looks like you are trying to rescale already rescaled images. If the input"
|
566 |
+
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
|
567 |
+
)
|
568 |
+
|
569 |
+
if input_data_format is None:
|
570 |
+
# We assume that all images have the same channel dimension format.
|
571 |
+
input_data_format = infer_channel_dimension_format(images[0])
|
572 |
+
|
573 |
+
new_images = []
|
574 |
+
image_sizes = [get_image_size(image, channel_dim=input_data_format) for image in images]
|
575 |
+
for image in images:
|
576 |
+
# convert image into a list of patches
|
577 |
+
# we intentially use the same data format as the input data format
|
578 |
+
image_patches = self.get_image_patches(
|
579 |
+
image,
|
580 |
+
image_grid_pinpoints,
|
581 |
+
size=(size["shortest_edge"], size["shortest_edge"]),
|
582 |
+
patch_size=crop_size["height"],
|
583 |
+
resample=resample,
|
584 |
+
data_format=input_data_format,
|
585 |
+
input_data_format=input_data_format,
|
586 |
+
)
|
587 |
+
|
588 |
+
# preprocess patches
|
589 |
+
pixel_values = self._preprocess(
|
590 |
+
image_patches,
|
591 |
+
do_resize=do_resize,
|
592 |
+
size=size,
|
593 |
+
resample=resample,
|
594 |
+
do_center_crop=do_center_crop,
|
595 |
+
crop_size=crop_size,
|
596 |
+
do_rescale=do_rescale,
|
597 |
+
rescale_factor=rescale_factor,
|
598 |
+
do_normalize=do_normalize,
|
599 |
+
image_mean=image_mean,
|
600 |
+
image_std=image_std,
|
601 |
+
data_format=data_format,
|
602 |
+
input_data_format=input_data_format,
|
603 |
+
)
|
604 |
+
pixel_values = np.array(pixel_values)
|
605 |
+
new_images.append(pixel_values)
|
606 |
+
|
607 |
+
if concat_images:
|
608 |
+
# image_num_patches = [len(x) for x in new_images]
|
609 |
+
pixel_values = np.concatenate(new_images, axis=0)
|
610 |
+
data = {
|
611 |
+
"pixel_values": pixel_values,
|
612 |
+
"image_sizes": image_sizes,
|
613 |
+
# "image_num_patches": image_num_patches,
|
614 |
+
}
|
615 |
+
else:
|
616 |
+
|
617 |
+
data = {"pixel_values": new_images, "image_sizes": image_sizes}
|
618 |
+
|
619 |
+
return BatchFeature(data=data, tensor_type=return_tensors)
|
multipurpose_chatbot/engines/modeling_sealava16.py
ADDED
@@ -0,0 +1,1022 @@
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|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
|
3 |
+
#
|
4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
""" PyTorch Llava-NeXT model."""
|
16 |
+
|
17 |
+
from dataclasses import dataclass
|
18 |
+
from typing import List, Optional, Tuple, Union
|
19 |
+
|
20 |
+
import torch
|
21 |
+
import torch.utils.checkpoint
|
22 |
+
from torch import nn
|
23 |
+
import numpy as np
|
24 |
+
|
25 |
+
from transformers import PreTrainedModel
|
26 |
+
from transformers.activations import ACT2FN
|
27 |
+
from transformers.cache_utils import Cache
|
28 |
+
from transformers.image_processing_utils import select_best_resolution
|
29 |
+
from transformers.modeling_outputs import ModelOutput
|
30 |
+
from transformers.configuration_utils import PretrainedConfig
|
31 |
+
from transformers.utils import (
|
32 |
+
add_start_docstrings,
|
33 |
+
add_start_docstrings_to_model_forward,
|
34 |
+
logging,
|
35 |
+
replace_return_docstrings,
|
36 |
+
)
|
37 |
+
from transformers.models.auto import AutoModel, AutoModelForCausalLM
|
38 |
+
# from .configuration_llava_next import LlavaNextConfig
|
39 |
+
from transformers.models.auto import CONFIG_MAPPING
|
40 |
+
|
41 |
+
logger = logging.get_logger(__name__)
|
42 |
+
|
43 |
+
|
44 |
+
class LlavaNextConfig(PretrainedConfig):
|
45 |
+
r"""
|
46 |
+
This is the configuration class to store the configuration of a [`LlavaNextForConditionalGeneration`]. It is used to instantiate an
|
47 |
+
Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
|
48 |
+
with the defaults will yield a similar configuration to that of the [llava-hf/llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf)
|
49 |
+
model.
|
50 |
+
|
51 |
+
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
52 |
+
documentation from [`PretrainedConfig`] for more information.
|
53 |
+
|
54 |
+
Args:
|
55 |
+
vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `CLIPVisionConfig`):
|
56 |
+
The config object or dictionary of the vision backbone.
|
57 |
+
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
|
58 |
+
The config object or dictionary of the text backbone.
|
59 |
+
ignore_index (`int`, *optional*, defaults to -100):
|
60 |
+
The ignore index for the loss function.
|
61 |
+
image_token_index (`int`, *optional*, defaults to 32000):
|
62 |
+
The image token index to encode the image prompt.
|
63 |
+
projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
|
64 |
+
The activation function used by the multimodal projector.
|
65 |
+
vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
|
66 |
+
The feature selection strategy used to select the vision feature from the vision backbone.
|
67 |
+
Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
|
68 |
+
If `"full"`, the full vision features are used.
|
69 |
+
vision_feature_layer (`int`, *optional*, defaults to -2):
|
70 |
+
The index of the layer to select the vision feature.
|
71 |
+
image_grid_pinpoints (`List`, *optional*, defaults to `[[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]`):
|
72 |
+
A list of possible resolutions to use for processing high resolution images. Each item in the list should be a tuple or list
|
73 |
+
of the form `(height, width)`.
|
74 |
+
|
75 |
+
Example:
|
76 |
+
|
77 |
+
```python
|
78 |
+
>>> from transformers import LlavaNextForConditionalGeneration, LlavaNextConfig, CLIPVisionConfig, LlamaConfig
|
79 |
+
|
80 |
+
>>> # Initializing a CLIP-vision config
|
81 |
+
>>> vision_config = CLIPVisionConfig()
|
82 |
+
|
83 |
+
>>> # Initializing a Llama config
|
84 |
+
>>> text_config = LlamaConfig()
|
85 |
+
|
86 |
+
>>> # Initializing a Llava-Next llava-hf/llava-v1.6-mistral-7b-hf style configuration
|
87 |
+
>>> configuration = LlavaNextConfig(vision_config, text_config)
|
88 |
+
|
89 |
+
>>> # Initializing a model from the llava-hf/llava-v1.6-mistral-7b-hf style configuration
|
90 |
+
>>> model = LlavaNextForConditionalGeneration(configuration)
|
91 |
+
|
92 |
+
>>> # Accessing the model configuration
|
93 |
+
>>> configuration = model.config
|
94 |
+
```"""
|
95 |
+
|
96 |
+
model_type = "llava_next"
|
97 |
+
is_composition = False
|
98 |
+
|
99 |
+
def __init__(
|
100 |
+
self,
|
101 |
+
vision_config=None,
|
102 |
+
text_config=None,
|
103 |
+
ignore_index=-100,
|
104 |
+
image_token_index=32000,
|
105 |
+
projector_hidden_act="gelu",
|
106 |
+
vision_feature_select_strategy="default",
|
107 |
+
vision_feature_layer=-2,
|
108 |
+
image_grid_pinpoints=None,
|
109 |
+
**kwargs,
|
110 |
+
):
|
111 |
+
self.ignore_index = ignore_index
|
112 |
+
self.image_token_index = image_token_index
|
113 |
+
self.projector_hidden_act = projector_hidden_act
|
114 |
+
|
115 |
+
if vision_feature_select_strategy not in ["default", "full"]:
|
116 |
+
raise ValueError(
|
117 |
+
"vision_feature_select_strategy should be one of 'default', 'full'."
|
118 |
+
f"Got: {vision_feature_select_strategy}"
|
119 |
+
)
|
120 |
+
|
121 |
+
self.vision_feature_select_strategy = vision_feature_select_strategy
|
122 |
+
self.vision_feature_layer = vision_feature_layer
|
123 |
+
image_grid_pinpoints = (
|
124 |
+
image_grid_pinpoints
|
125 |
+
if image_grid_pinpoints is not None
|
126 |
+
else [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]
|
127 |
+
)
|
128 |
+
self.image_grid_pinpoints = image_grid_pinpoints
|
129 |
+
|
130 |
+
if isinstance(vision_config, dict):
|
131 |
+
vision_config["model_type"] = (
|
132 |
+
vision_config["model_type"] if "model_type" in vision_config else "clip_vision_model"
|
133 |
+
)
|
134 |
+
vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
|
135 |
+
elif vision_config is None:
|
136 |
+
vision_config = CONFIG_MAPPING["clip_vision_model"](
|
137 |
+
intermediate_size=4096,
|
138 |
+
hidden_size=1024,
|
139 |
+
patch_size=14,
|
140 |
+
image_size=336,
|
141 |
+
num_hidden_layers=24,
|
142 |
+
num_attention_heads=16,
|
143 |
+
vocab_size=32000,
|
144 |
+
projection_dim=768,
|
145 |
+
)
|
146 |
+
|
147 |
+
self.vision_config = vision_config
|
148 |
+
|
149 |
+
if isinstance(text_config, dict):
|
150 |
+
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
|
151 |
+
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
|
152 |
+
elif text_config is None:
|
153 |
+
text_config = CONFIG_MAPPING["llama"]()
|
154 |
+
|
155 |
+
self.text_config = text_config
|
156 |
+
|
157 |
+
super().__init__(**kwargs)
|
158 |
+
|
159 |
+
|
160 |
+
|
161 |
+
|
162 |
+
|
163 |
+
|
164 |
+
_CONFIG_FOR_DOC = "LlavaNextConfig"
|
165 |
+
|
166 |
+
LLAVA_NEXT_PRETRAINED_MODEL_ARCHIVE_LIST = [
|
167 |
+
"llava-hf/llava-v1.6-mistral-7b-hf",
|
168 |
+
# See all LLaVA-NeXT models at https://huggingface.co/models?filter=llava_next
|
169 |
+
]
|
170 |
+
|
171 |
+
|
172 |
+
def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
|
173 |
+
"""
|
174 |
+
Calculate the shape of the image patch grid after the preprocessing for images of any resolution.
|
175 |
+
|
176 |
+
Args:
|
177 |
+
image_size (`tuple`):
|
178 |
+
The size of the input image in the format (width, height).
|
179 |
+
grid_pinpoints (`List`):
|
180 |
+
A list containing possible resolutions. Each item in the list should be a tuple or list
|
181 |
+
of the form `(height, width)`.
|
182 |
+
patch_size (`int`):
|
183 |
+
The size of each image patch.
|
184 |
+
|
185 |
+
Returns:
|
186 |
+
tuple: The shape of the image patch grid in the format (width, height).
|
187 |
+
"""
|
188 |
+
if not isinstance(grid_pinpoints, list):
|
189 |
+
raise ValueError("grid_pinpoints should be a list of tuples or lists")
|
190 |
+
|
191 |
+
# ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
|
192 |
+
if not isinstance(image_size, (list, tuple)):
|
193 |
+
assert isinstance(image_size, (torch.Tensor, np.ndarray)), f'image_size invalid type: {type(image_size)} | {image_size}'
|
194 |
+
image_size = image_size.tolist()
|
195 |
+
|
196 |
+
height, width = select_best_resolution(image_size, grid_pinpoints)
|
197 |
+
return height // patch_size, width // patch_size
|
198 |
+
|
199 |
+
|
200 |
+
def image_size_to_num_patches(image_size, grid_pinpoints, patch_size: int):
|
201 |
+
"""
|
202 |
+
Calculate the shape of the image patch grid after the preprocessing for images of any resolution.
|
203 |
+
|
204 |
+
Args:
|
205 |
+
image_size (`tuple`):
|
206 |
+
The size of the input image in the format (height, width). ?
|
207 |
+
grid_pinpoints (`List`):
|
208 |
+
A list containing possible resolutions. Each item in the list should be a tuple or list
|
209 |
+
of the form `(height, width)`.
|
210 |
+
patch_size (`int`):
|
211 |
+
The size of each image patch.
|
212 |
+
|
213 |
+
Returns:
|
214 |
+
tuple: The shape of the image patch grid in the format (height, width). ?
|
215 |
+
"""
|
216 |
+
if not isinstance(grid_pinpoints, list):
|
217 |
+
raise ValueError("grid_pinpoints should be a list of tuples or lists")
|
218 |
+
|
219 |
+
# ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
|
220 |
+
if not isinstance(image_size, (list, tuple)):
|
221 |
+
assert isinstance(image_size, (torch.Tensor, np.ndarray)), f'image_size invalid type: {type(image_size)} | {image_size}'
|
222 |
+
image_size = image_size.tolist()
|
223 |
+
|
224 |
+
best_resolution = select_best_resolution(image_size, grid_pinpoints)
|
225 |
+
height, width = best_resolution
|
226 |
+
num_patches = 0
|
227 |
+
for i in range(0, height, patch_size):
|
228 |
+
for j in range(0, width, patch_size):
|
229 |
+
num_patches += 1
|
230 |
+
# add the base patch
|
231 |
+
num_patches += 1
|
232 |
+
return num_patches
|
233 |
+
|
234 |
+
|
235 |
+
def unpad_image(tensor, original_size):
|
236 |
+
"""
|
237 |
+
Unpads a PyTorch tensor of a padded and resized image.
|
238 |
+
|
239 |
+
Args:
|
240 |
+
tensor (`torch.Tensor`):
|
241 |
+
The image tensor, assumed to be of shape (num_channels, height, width).
|
242 |
+
original_size (`tuple`):
|
243 |
+
The original size of the image (height, width).
|
244 |
+
|
245 |
+
Returns:
|
246 |
+
`torch.Tensor`: The unpadded image tensor.
|
247 |
+
"""
|
248 |
+
original_height, original_width = original_size
|
249 |
+
current_height, current_width = tensor.shape[1:]
|
250 |
+
|
251 |
+
original_aspect_ratio = original_width / original_height
|
252 |
+
current_aspect_ratio = current_width / current_height
|
253 |
+
|
254 |
+
if original_aspect_ratio > current_aspect_ratio:
|
255 |
+
scale_factor = current_width / original_width
|
256 |
+
new_height = int(original_height * scale_factor)
|
257 |
+
padding = (current_height - new_height) // 2
|
258 |
+
unpadded_tensor = tensor[:, padding : current_height - padding, :]
|
259 |
+
else:
|
260 |
+
scale_factor = current_height / original_height
|
261 |
+
new_width = int(original_width * scale_factor)
|
262 |
+
padding = (current_width - new_width) // 2
|
263 |
+
unpadded_tensor = tensor[:, :, padding : current_width - padding]
|
264 |
+
|
265 |
+
return unpadded_tensor
|
266 |
+
|
267 |
+
|
268 |
+
@dataclass
|
269 |
+
# Copied from transformers.models.idefics.modeling_idefics.IdeficsCausalLMOutputWithPast with Idefics->LlavaNext
|
270 |
+
class LlavaNextCausalLMOutputWithPast(ModelOutput):
|
271 |
+
"""
|
272 |
+
Base class for LlavaNext causal language model (or autoregressive) outputs.
|
273 |
+
|
274 |
+
Args:
|
275 |
+
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
|
276 |
+
Language modeling loss (for next-token prediction).
|
277 |
+
logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
|
278 |
+
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
279 |
+
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
280 |
+
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
281 |
+
`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
|
282 |
+
|
283 |
+
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
|
284 |
+
`past_key_values` input) to speed up sequential decoding.
|
285 |
+
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
286 |
+
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
|
287 |
+
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
|
288 |
+
|
289 |
+
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
|
290 |
+
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
291 |
+
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
|
292 |
+
sequence_length)`.
|
293 |
+
|
294 |
+
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
295 |
+
heads.
|
296 |
+
image_hidden_states (`tuple(torch.FloatTensor)`, *optional*):
|
297 |
+
Tuple of `torch.FloatTensor` (one for the output of the image embeddings, `(batch_size, num_images,
|
298 |
+
sequence_length, hidden_size)`.
|
299 |
+
|
300 |
+
image_hidden_states of the model produced by the vision encoder, and optionally by the perceiver
|
301 |
+
"""
|
302 |
+
|
303 |
+
loss: Optional[torch.FloatTensor] = None
|
304 |
+
logits: torch.FloatTensor = None
|
305 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None
|
306 |
+
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
307 |
+
attentions: Optional[Tuple[torch.FloatTensor]] = None
|
308 |
+
image_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
309 |
+
|
310 |
+
|
311 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaMultiModalProjector with Llava->LlavaNext
|
312 |
+
class LlavaNextMultiModalProjector(nn.Module):
|
313 |
+
def __init__(self, config: LlavaNextConfig):
|
314 |
+
super().__init__()
|
315 |
+
|
316 |
+
self.linear_1 = nn.Linear(config.vision_config.hidden_size, config.text_config.hidden_size, bias=True)
|
317 |
+
self.act = ACT2FN[config.projector_hidden_act]
|
318 |
+
self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size, bias=True)
|
319 |
+
|
320 |
+
def forward(self, image_features):
|
321 |
+
hidden_states = self.linear_1(image_features)
|
322 |
+
hidden_states = self.act(hidden_states)
|
323 |
+
hidden_states = self.linear_2(hidden_states)
|
324 |
+
return hidden_states
|
325 |
+
|
326 |
+
|
327 |
+
LLAVA_NEXT_START_DOCSTRING = r"""
|
328 |
+
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
329 |
+
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
330 |
+
etc.)
|
331 |
+
|
332 |
+
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
333 |
+
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
334 |
+
and behavior.
|
335 |
+
|
336 |
+
Parameters:
|
337 |
+
config ([`LlavaNextConfig`] or [`LlavaNextVisionConfig`]):
|
338 |
+
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
339 |
+
load the weights associated with the model, only the configuration. Check out the
|
340 |
+
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
341 |
+
"""
|
342 |
+
|
343 |
+
|
344 |
+
@add_start_docstrings(
|
345 |
+
"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
|
346 |
+
LLAVA_NEXT_START_DOCSTRING,
|
347 |
+
)
|
348 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaPreTrainedModel with Llava->LlavaNext,llava->llava_next
|
349 |
+
class LlavaNextPreTrainedModel(PreTrainedModel):
|
350 |
+
config_class = LlavaNextConfig
|
351 |
+
base_model_prefix = "model"
|
352 |
+
supports_gradient_checkpointing = True
|
353 |
+
_no_split_modules = ["LlavaNextVisionAttention"]
|
354 |
+
_skip_keys_device_placement = "past_key_values"
|
355 |
+
_supports_flash_attn_2 = True
|
356 |
+
|
357 |
+
def _init_weights(self, module):
|
358 |
+
# important: this ported version of LlavaNext isn't meant for training from scratch - only
|
359 |
+
# inference and fine-tuning - so the proper init weights code has been removed - the original codebase
|
360 |
+
# https://github.com/haotian-liu/LLaVA/tree/main/llava_next should serve for that purpose
|
361 |
+
std = (
|
362 |
+
self.config.initializer_range
|
363 |
+
if hasattr(self.config, "initializer_range")
|
364 |
+
else self.config.text_config.initializer_range
|
365 |
+
)
|
366 |
+
|
367 |
+
if hasattr(module, "class_embedding"):
|
368 |
+
module.class_embedding.data.normal_(mean=0.0, std=std)
|
369 |
+
|
370 |
+
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
371 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
372 |
+
if module.bias is not None:
|
373 |
+
module.bias.data.zero_()
|
374 |
+
elif isinstance(module, nn.Embedding):
|
375 |
+
module.weight.data.normal_(mean=0.0, std=std)
|
376 |
+
if module.padding_idx is not None:
|
377 |
+
module.weight.data[module.padding_idx].zero_()
|
378 |
+
|
379 |
+
@property
|
380 |
+
def _supports_sdpa(self):
|
381 |
+
"""
|
382 |
+
Retrieve language_model's attribute to check whether the model supports
|
383 |
+
SDPA or not.
|
384 |
+
"""
|
385 |
+
return self.language_model._supports_sdpa
|
386 |
+
|
387 |
+
|
388 |
+
LLAVA_NEXT_INPUTS_DOCSTRING = r"""
|
389 |
+
Args:
|
390 |
+
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
391 |
+
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
392 |
+
it.
|
393 |
+
|
394 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
395 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
396 |
+
|
397 |
+
[What are input IDs?](../glossary#input-ids)
|
398 |
+
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
|
399 |
+
The tensors corresponding to the input images. Pixel values can be obtained using
|
400 |
+
[`AutoImageProcessor`]. See [`LlavaNextImageProcessor.__call__`] for details. [`LlavaProcessor`] uses
|
401 |
+
[`LlavaNextImageProcessor`] for processing images.
|
402 |
+
image_sizes (`torch.LongTensor` of shape `(batch_size, 2)`, *optional*):
|
403 |
+
The sizes of the images in the batch, being (height, width) for each image.
|
404 |
+
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
405 |
+
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
406 |
+
|
407 |
+
- 1 for tokens that are **not masked**,
|
408 |
+
- 0 for tokens that are **masked**.
|
409 |
+
|
410 |
+
[What are attention masks?](../glossary#attention-mask)
|
411 |
+
|
412 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
413 |
+
[`PreTrainedTokenizer.__call__`] for details.
|
414 |
+
|
415 |
+
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
416 |
+
`past_key_values`).
|
417 |
+
|
418 |
+
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
419 |
+
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
420 |
+
information on the default strategy.
|
421 |
+
|
422 |
+
- 1 indicates the head is **not masked**,
|
423 |
+
- 0 indicates the head is **masked**.
|
424 |
+
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
425 |
+
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
426 |
+
config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
|
427 |
+
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
428 |
+
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
429 |
+
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
430 |
+
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
431 |
+
|
432 |
+
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
433 |
+
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
434 |
+
|
435 |
+
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
436 |
+
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
437 |
+
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
438 |
+
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
439 |
+
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
440 |
+
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
441 |
+
model's internal embedding lookup matrix.
|
442 |
+
vision_feature_layer (`int`, *optional*, defaults to -2):
|
443 |
+
The index of the layer to select the vision feature.
|
444 |
+
vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
|
445 |
+
The feature selection strategy used to select the vision feature from the vision backbone.
|
446 |
+
Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
|
447 |
+
If `"full"`, the full vision features are used.
|
448 |
+
use_cache (`bool`, *optional*):
|
449 |
+
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
450 |
+
`past_key_values`).
|
451 |
+
output_attentions (`bool`, *optional*):
|
452 |
+
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
453 |
+
tensors for more detail.
|
454 |
+
output_hidden_states (`bool`, *optional*):
|
455 |
+
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
456 |
+
more detail.
|
457 |
+
return_dict (`bool`, *optional*):
|
458 |
+
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
459 |
+
"""
|
460 |
+
|
461 |
+
|
462 |
+
@add_start_docstrings(
|
463 |
+
"""The LLAVA-NeXT model which consists of a vision backbone and a language model.""",
|
464 |
+
LLAVA_NEXT_START_DOCSTRING,
|
465 |
+
)
|
466 |
+
class LlavaNextForConditionalGeneration(LlavaNextPreTrainedModel):
|
467 |
+
def __init__(self, config: LlavaNextConfig):
|
468 |
+
super().__init__(config)
|
469 |
+
self.vision_tower = AutoModel.from_config(config.vision_config)
|
470 |
+
|
471 |
+
self.multi_modal_projector = LlavaNextMultiModalProjector(config)
|
472 |
+
|
473 |
+
self.image_newline = nn.Parameter(torch.empty(config.text_config.hidden_size, dtype=self.dtype))
|
474 |
+
|
475 |
+
self.vocab_size = config.text_config.vocab_size
|
476 |
+
self.language_model = AutoModelForCausalLM.from_config(
|
477 |
+
config.text_config, attn_implementation=config._attn_implementation
|
478 |
+
)
|
479 |
+
self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
|
480 |
+
self.post_init()
|
481 |
+
|
482 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_input_embeddings
|
483 |
+
def get_input_embeddings(self):
|
484 |
+
return self.language_model.get_input_embeddings()
|
485 |
+
|
486 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_input_embeddings
|
487 |
+
def set_input_embeddings(self, value):
|
488 |
+
self.language_model.set_input_embeddings(value)
|
489 |
+
|
490 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_output_embeddings
|
491 |
+
def get_output_embeddings(self):
|
492 |
+
return self.language_model.get_output_embeddings()
|
493 |
+
|
494 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_output_embeddings
|
495 |
+
def set_output_embeddings(self, new_embeddings):
|
496 |
+
self.language_model.set_output_embeddings(new_embeddings)
|
497 |
+
|
498 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.set_decoder
|
499 |
+
def set_decoder(self, decoder):
|
500 |
+
self.language_model.set_decoder(decoder)
|
501 |
+
|
502 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.get_decoder
|
503 |
+
def get_decoder(self):
|
504 |
+
return self.language_model.get_decoder()
|
505 |
+
|
506 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.tie_weights
|
507 |
+
def tie_weights(self):
|
508 |
+
return self.language_model.tie_weights()
|
509 |
+
|
510 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration.resize_token_embeddings
|
511 |
+
def resize_token_embeddings(self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None) -> nn.Embedding:
|
512 |
+
model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of)
|
513 |
+
# update vocab size
|
514 |
+
self.config.text_config.vocab_size = model_embeds.num_embeddings
|
515 |
+
self.vocab_size = model_embeds.num_embeddings
|
516 |
+
return model_embeds
|
517 |
+
|
518 |
+
def _merge_input_ids_with_image_features(
|
519 |
+
self, image_features, feature_lens, inputs_embeds, input_ids, attention_mask, position_ids=None,
|
520 |
+
labels=None, image_token_index=None,
|
521 |
+
ignore_index=-100,
|
522 |
+
padding_side: Optional[str] = "left",
|
523 |
+
):
|
524 |
+
"""
|
525 |
+
Args:
|
526 |
+
input_ids: [batch_size, tlen]
|
527 |
+
input_embeds: [batch_size, tlen, dt]
|
528 |
+
image_features: [all_feat_lens, di]
|
529 |
+
feature_lens: [num_images],
|
530 |
+
num_images=number of image in the batch
|
531 |
+
each value is the length of embedding featres of each image
|
532 |
+
Note: sum(feature_lens) == all_feat_lens
|
533 |
+
labels: None or [batch_size, tlen] --> must extend labels to input_ids,
|
534 |
+
padding_side: `left` or `right`,
|
535 |
+
must specify for generation because we cannot tell that from input_ids
|
536 |
+
see below
|
537 |
+
Returns:
|
538 |
+
final_embedding, final_attention_mask, position_ids, final_labels
|
539 |
+
|
540 |
+
Explanation:
|
541 |
+
each image has variable length embeddings, with length specified by feature_lens
|
542 |
+
image_features is concatenation of all visual embed vectors
|
543 |
+
task: fill each <image> with the correct number of visual embeddings
|
544 |
+
Example:
|
545 |
+
X (5 patches), Y (3 patches), Z (8)
|
546 |
+
X, Y is on the same sequence (in-context learning)
|
547 |
+
if right padding
|
548 |
+
input_ids: [
|
549 |
+
a b c d e f X g h i j k Y l m
|
550 |
+
o p q r Z s t u v _ _ _ _ _ _
|
551 |
+
]
|
552 |
+
input_ids should be: [
|
553 |
+
a b c d e f X X X X X g h i j k Y Y Y l m
|
554 |
+
o p q r Z Z Z Z Z Z Z Z s t u v _ _ _ _ _
|
555 |
+
]
|
556 |
+
labels should be: [
|
557 |
+
a b c d e f _ _ _ _ _ g h i j k _ _ _ l m
|
558 |
+
o p q r _ _ _ _ _ _ _ _ s t u v _ _ _ _ _
|
559 |
+
]
|
560 |
+
elif left padding
|
561 |
+
input_ids: [
|
562 |
+
a b c d e f X g h i j k Y l m
|
563 |
+
_ _ _ _ _ _ o p q r Z s t u v
|
564 |
+
]
|
565 |
+
input_ids should be: [
|
566 |
+
a b c d e f X X X X X g h i j k Y Y Y l m
|
567 |
+
_ _ _ _ _ o p q r Z Z Z Z Z Z Z Z s t u v
|
568 |
+
]
|
569 |
+
labels should be: [
|
570 |
+
a b c d e f _ _ _ _ _ g h i j k _ _ _ l m
|
571 |
+
_ _ _ _ _ o p q r _ _ _ _ _ _ _ _ s t u v
|
572 |
+
]
|
573 |
+
Edge cases:
|
574 |
+
* If tokens are same but image token sizes are different, then cannot infer left or right padding
|
575 |
+
```python
|
576 |
+
cat_img = Image.open(requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw)
|
577 |
+
chart_img = Image.open(requests.get("https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true", stream=True).raw)
|
578 |
+
prompts = [
|
579 |
+
"[INST] <image>\nWhat is shown in this image? [/INST]",
|
580 |
+
"[INST] <image>\nWhat is shown in this image? [/INST]",
|
581 |
+
]
|
582 |
+
inputs = processor(prompts, [chart_img, cat_img], return_tensors='pt', padding=True).to("cuda")
|
583 |
+
chart_img has 2634 tokens, while cat_img has 2340 tokens
|
584 |
+
```
|
585 |
+
|
586 |
+
input_ids: [
|
587 |
+
a b c d X g h
|
588 |
+
i j Y k l m n
|
589 |
+
]
|
590 |
+
where X is 3 tokens while Y is 5, this mean after merge
|
591 |
+
if left-padding (batched generation)
|
592 |
+
input_ids should be: [
|
593 |
+
_ _ a b c d X X X g h
|
594 |
+
i j Y Y Y Y Y k l m n
|
595 |
+
]
|
596 |
+
elif (right padding) (training)
|
597 |
+
input_ids should be: [
|
598 |
+
a b c d X X X g h _ _
|
599 |
+
i j Y Y Y Y Y k l m n
|
600 |
+
]
|
601 |
+
|
602 |
+
|
603 |
+
"""
|
604 |
+
image_token_index = image_token_index if image_token_index is not None else self.config.image_token_index
|
605 |
+
ignore_index = ignore_index if ignore_index is not None else self.config.ignore_index
|
606 |
+
|
607 |
+
with torch.no_grad():
|
608 |
+
# ! in llava 1.6, number of patches is variable
|
609 |
+
num_images = feature_lens.size(0)
|
610 |
+
num_image_features, embed_dim = image_features.shape
|
611 |
+
assert feature_lens.sum() == num_image_features, f'{feature_lens=} / {feature_lens.sum()} != {image_features.shape=}'
|
612 |
+
batch_size, sequence_length = input_ids.shape
|
613 |
+
_left_padding = torch.any(attention_mask[:, 0] == 0)
|
614 |
+
_right_padding = torch.any(attention_mask[:, -1] == 0)
|
615 |
+
|
616 |
+
if _left_padding and not _right_padding:
|
617 |
+
left_padding = True
|
618 |
+
elif not _left_padding and _right_padding:
|
619 |
+
left_padding = False
|
620 |
+
elif not _left_padding and not _right_padding:
|
621 |
+
# both side is 1, so cannot tell
|
622 |
+
left_padding = padding_side == "left"
|
623 |
+
else:
|
624 |
+
# invalid attention_mask
|
625 |
+
raise ValueError(f'both side of attention_mask has zero, invalid. {attention_mask}')
|
626 |
+
|
627 |
+
# Whether to turn off right padding
|
628 |
+
# 1. Create a mask to know where special image tokens are
|
629 |
+
special_image_token_mask = input_ids == image_token_index
|
630 |
+
# special_image_token_mask: [bsz, seqlen]
|
631 |
+
num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
|
632 |
+
# num_special_image_tokens: [bsz]
|
633 |
+
# Reserve for padding of num_images
|
634 |
+
total_num_special_image_tokens = torch.sum(special_image_token_mask)
|
635 |
+
assert total_num_special_image_tokens == num_images, (
|
636 |
+
f'{total_num_special_image_tokens=} != {num_images=} | {image_features.shape} {input_ids}'
|
637 |
+
)
|
638 |
+
# Compute the maximum embed dimension
|
639 |
+
# max_image_feature_lens is max_feature_lens per batch
|
640 |
+
feature_lens_batch = feature_lens.split(num_special_image_tokens.tolist(), dim=0)
|
641 |
+
feature_lens_batch_sum = torch.tensor([x.sum() for x in feature_lens_batch], device=feature_lens.device)
|
642 |
+
embed_sequence_lengths = (attention_mask == 1).long().sum(-1) - num_special_image_tokens + feature_lens_batch_sum
|
643 |
+
max_embed_dim = embed_sequence_lengths.max()
|
644 |
+
|
645 |
+
batch_indices, non_image_indices = torch.where((input_ids != image_token_index) & (attention_mask == 1))
|
646 |
+
# 2. Compute the positions where text should be written
|
647 |
+
# Calculate new positions for text tokens in merged image-text sequence.
|
648 |
+
# `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens.
|
649 |
+
# `torch.cumsum` computes how each image token shifts subsequent text token positions.
|
650 |
+
# - 1 to adjust for zero-based indexing, as `cumsum` inherently increases indices by one.
|
651 |
+
# ! instead of special_image_token_mask * (num_image_patches - 1)
|
652 |
+
# special_image_token_mask * (num_feature_len - 1)
|
653 |
+
special_image_len_mask = special_image_token_mask.clone().long()
|
654 |
+
special_image_len_mask[special_image_len_mask == 1] = feature_lens - 1
|
655 |
+
new_token_positions = torch.cumsum((special_image_len_mask + 1), -1) - 1
|
656 |
+
if left_padding:
|
657 |
+
# shift right token positions so that they are ending at the same number
|
658 |
+
new_token_positions += (new_token_positions[:, -1].max() - new_token_positions[:, -1:])
|
659 |
+
|
660 |
+
text_to_overwrite = new_token_positions[batch_indices, non_image_indices]
|
661 |
+
|
662 |
+
# 3. Create the full embedding, already padded to the maximum position
|
663 |
+
final_embedding = torch.zeros(
|
664 |
+
batch_size, max_embed_dim, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
|
665 |
+
)
|
666 |
+
final_attention_mask = torch.zeros(
|
667 |
+
batch_size, max_embed_dim, dtype=attention_mask.dtype, device=inputs_embeds.device
|
668 |
+
)
|
669 |
+
final_labels = None
|
670 |
+
if labels is not None:
|
671 |
+
final_labels = torch.full_like(final_attention_mask, ignore_index).to(torch.long)
|
672 |
+
# In case the Vision model or the Language model has been offloaded to CPU, we need to manually
|
673 |
+
# set the corresponding tensors into their correct target device.
|
674 |
+
target_device = inputs_embeds.device
|
675 |
+
batch_indices, non_image_indices, text_to_overwrite = (
|
676 |
+
batch_indices.to(target_device),
|
677 |
+
non_image_indices.to(target_device),
|
678 |
+
text_to_overwrite.to(target_device),
|
679 |
+
)
|
680 |
+
attention_mask = attention_mask.to(target_device)
|
681 |
+
|
682 |
+
# 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
|
683 |
+
# we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
|
684 |
+
final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
|
685 |
+
final_attention_mask[batch_indices, text_to_overwrite] = attention_mask[batch_indices, non_image_indices]
|
686 |
+
if labels is not None:
|
687 |
+
final_labels[batch_indices, text_to_overwrite] = labels[batch_indices, non_image_indices]
|
688 |
+
|
689 |
+
# 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling
|
690 |
+
with torch.no_grad():
|
691 |
+
image_to_overwrite = torch.all(final_embedding == 0, dim=-1)
|
692 |
+
if left_padding:
|
693 |
+
# exclude padding on the left
|
694 |
+
val = (max_embed_dim - torch.arange(max_embed_dim).unsqueeze(0).to(target_device).expand(batch_size, max_embed_dim)) <= embed_sequence_lengths[:, None].to(target_device)
|
695 |
+
image_to_overwrite &= val
|
696 |
+
else:
|
697 |
+
# exclude padding on the right
|
698 |
+
val = torch.arange(max_embed_dim).unsqueeze(0).to(target_device).expand(batch_size, max_embed_dim) < embed_sequence_lengths[:, None].to(target_device)
|
699 |
+
image_to_overwrite &= val
|
700 |
+
|
701 |
+
if image_to_overwrite.sum() != num_image_features:
|
702 |
+
raise ValueError(
|
703 |
+
f"{image_to_overwrite.sum()=} != {num_image_features=} The input provided to the model are wrong. "
|
704 |
+
f"The number of image tokens is {torch.sum(special_image_token_mask)} while"
|
705 |
+
f" the number of image given to the model is {num_images}. "
|
706 |
+
f"This prevents correct indexing and breaks batch generation."
|
707 |
+
)
|
708 |
+
final_embedding[image_to_overwrite] = image_features.to(target_device)
|
709 |
+
final_attention_mask |= image_to_overwrite
|
710 |
+
position_ids = (final_attention_mask.cumsum(-1) - 1).masked_fill_((final_attention_mask == 0), 1)
|
711 |
+
|
712 |
+
if not left_padding:
|
713 |
+
# Making sure its the same
|
714 |
+
seq_lens = final_attention_mask.sum(-1)
|
715 |
+
for i, (mask, seq_len) in enumerate(zip(final_attention_mask, seq_lens)):
|
716 |
+
# seq_len = mask.sum(-1)
|
717 |
+
assert torch.all(mask[:seq_len] == 1), f'final 1 mask[{i}]: {seq_len=} {final_attention_mask.size()=} {final_attention_mask.tolist()=} \n{text_to_overwrite.tolist()=}'
|
718 |
+
assert torch.all(mask[seq_len:] == 0), f'final 0 mask[{i}]: {seq_len=} {final_attention_mask.size()=} {final_attention_mask.tolist()=}'
|
719 |
+
|
720 |
+
return final_embedding, final_attention_mask, position_ids, final_labels
|
721 |
+
|
722 |
+
def pack_image_features(self, image_features, image_sizes, image_newline=None):
|
723 |
+
"""
|
724 |
+
List of image features
|
725 |
+
image_features: list (size num_images) [patches, feat, dim]
|
726 |
+
Returns:
|
727 |
+
image_features: [all_feat_len, embed_dim]
|
728 |
+
feature_lens: [num_images] # number of feature_lens
|
729 |
+
"""
|
730 |
+
new_image_features = []
|
731 |
+
feature_lens = []
|
732 |
+
for image_idx, image_feature in enumerate(image_features):
|
733 |
+
if image_feature.shape[0] > 1:
|
734 |
+
base_image_feature = image_feature[0]
|
735 |
+
image_feature = image_feature[1:]
|
736 |
+
height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
|
737 |
+
if height * width != base_image_feature.shape[0]:
|
738 |
+
raise ValueError("The number of patches is not consistent with the image size.")
|
739 |
+
num_patch_width, num_patch_height = get_anyres_image_grid_shape(
|
740 |
+
image_sizes[image_idx],
|
741 |
+
self.config.image_grid_pinpoints,
|
742 |
+
self.config.vision_config.image_size,
|
743 |
+
)
|
744 |
+
image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
|
745 |
+
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
|
746 |
+
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
|
747 |
+
image_feature = unpad_image(image_feature, image_sizes[image_idx])
|
748 |
+
if image_newline is not None:
|
749 |
+
image_feature = torch.cat(
|
750 |
+
(
|
751 |
+
image_feature,
|
752 |
+
image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature),
|
753 |
+
),
|
754 |
+
dim=-1,
|
755 |
+
)
|
756 |
+
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
|
757 |
+
image_feature = torch.cat((base_image_feature, image_feature), dim=0)
|
758 |
+
else:
|
759 |
+
image_feature = image_feature[0]
|
760 |
+
if image_newline is not None:
|
761 |
+
image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0)
|
762 |
+
new_image_features.append(image_feature)
|
763 |
+
feature_lens.append(image_feature.size(0))
|
764 |
+
image_features = torch.cat(new_image_features, dim=0)
|
765 |
+
feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features.device)
|
766 |
+
return image_features, feature_lens
|
767 |
+
|
768 |
+
@add_start_docstrings_to_model_forward(LLAVA_NEXT_INPUTS_DOCSTRING)
|
769 |
+
@replace_return_docstrings(output_type=LlavaNextCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
770 |
+
def forward(
|
771 |
+
self,
|
772 |
+
input_ids: torch.LongTensor = None,
|
773 |
+
pixel_values: torch.FloatTensor = None,
|
774 |
+
image_sizes: Optional[torch.LongTensor] = None,
|
775 |
+
attention_mask: Optional[torch.Tensor] = None,
|
776 |
+
position_ids: Optional[torch.LongTensor] = None,
|
777 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
778 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
779 |
+
vision_feature_layer: Optional[int] = None,
|
780 |
+
vision_feature_select_strategy: Optional[str] = None,
|
781 |
+
labels: Optional[torch.LongTensor] = None,
|
782 |
+
use_cache: Optional[bool] = None,
|
783 |
+
output_attentions: Optional[bool] = None,
|
784 |
+
output_hidden_states: Optional[bool] = None,
|
785 |
+
return_dict: Optional[bool] = None,
|
786 |
+
padding_side: Optional[str] = "left",
|
787 |
+
) -> Union[Tuple, LlavaNextCausalLMOutputWithPast]:
|
788 |
+
r"""
|
789 |
+
Args:
|
790 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
791 |
+
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
792 |
+
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
793 |
+
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
794 |
+
|
795 |
+
Returns:
|
796 |
+
|
797 |
+
Example:
|
798 |
+
|
799 |
+
```python
|
800 |
+
>>> from PIL import Image
|
801 |
+
>>> import requests
|
802 |
+
>>> from transformers import AutoProcessor, LlavaNextForConditionalGeneration
|
803 |
+
|
804 |
+
>>> model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
|
805 |
+
>>> processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
|
806 |
+
|
807 |
+
>>> prompt = "[INST] <image>\nWhat is shown in this image? [/INST]"
|
808 |
+
>>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
|
809 |
+
>>> image = Image.open(requests.get(url, stream=True).raw)
|
810 |
+
|
811 |
+
>>> inputs = processor(text=prompt, images=image, return_tensors="pt")
|
812 |
+
|
813 |
+
>>> # Generate
|
814 |
+
>>> generate_ids = model.generate(**inputs, max_length=30)
|
815 |
+
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
816 |
+
"[INST] \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot (...)"
|
817 |
+
```"""
|
818 |
+
|
819 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
820 |
+
output_hidden_states = (
|
821 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
822 |
+
)
|
823 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
824 |
+
vision_feature_layer = (
|
825 |
+
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
|
826 |
+
)
|
827 |
+
vision_feature_select_strategy = (
|
828 |
+
vision_feature_select_strategy
|
829 |
+
if vision_feature_select_strategy is not None
|
830 |
+
else self.config.vision_feature_select_strategy
|
831 |
+
)
|
832 |
+
|
833 |
+
if inputs_embeds is None:
|
834 |
+
# 1. Extract the input embeddings
|
835 |
+
# In case image_token_index is not in the embeddings (extra token but embedding don't have it)
|
836 |
+
for_inputs_embeds_ids = input_ids.clone()
|
837 |
+
for_inputs_embeds_ids[(input_ids == self.config.image_token_index)] = 0
|
838 |
+
inputs_embeds = self.get_input_embeddings()(for_inputs_embeds_ids)
|
839 |
+
|
840 |
+
# 2. Merge text and images
|
841 |
+
if pixel_values is not None and input_ids.shape[1] != 1 and pixel_values.size(0) > 0:
|
842 |
+
# ! infer image_num_patches from image_sizes
|
843 |
+
image_num_patches = [
|
844 |
+
image_size_to_num_patches(
|
845 |
+
image_size=imsize,
|
846 |
+
grid_pinpoints=self.config.image_grid_pinpoints,
|
847 |
+
patch_size=self.config.vision_config.image_size
|
848 |
+
)
|
849 |
+
for imsize in image_sizes
|
850 |
+
]
|
851 |
+
image_features = self.vision_tower(pixel_values, output_hidden_states=True)
|
852 |
+
selected_image_feature = image_features.hidden_states[vision_feature_layer]
|
853 |
+
|
854 |
+
if vision_feature_select_strategy == "default":
|
855 |
+
selected_image_feature = selected_image_feature[:, 1:]
|
856 |
+
elif vision_feature_select_strategy == "full":
|
857 |
+
selected_image_feature = selected_image_feature
|
858 |
+
|
859 |
+
image_features = self.multi_modal_projector(selected_image_feature)
|
860 |
+
|
861 |
+
image_features = torch.split(image_features, image_num_patches, dim=0)
|
862 |
+
|
863 |
+
# NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
|
864 |
+
height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
|
865 |
+
|
866 |
+
image_features, feature_lens = self.pack_image_features(
|
867 |
+
image_features, image_sizes,
|
868 |
+
image_newline=self.image_newline,
|
869 |
+
)
|
870 |
+
|
871 |
+
inputs_embeds, attention_mask, position_ids, labels = self._merge_input_ids_with_image_features(
|
872 |
+
image_features, feature_lens, inputs_embeds, input_ids, attention_mask, position_ids,
|
873 |
+
labels=labels,
|
874 |
+
padding_side=padding_side,
|
875 |
+
)
|
876 |
+
|
877 |
+
# pixel_values is not None but is empty ---> text only cases
|
878 |
+
elif pixel_values is not None and input_ids.shape[1] != 1 and pixel_values.size(0) == 0:
|
879 |
+
# there is no images
|
880 |
+
pass
|
881 |
+
|
882 |
+
# In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
|
883 |
+
# generation with cache
|
884 |
+
elif past_key_values is not None and pixel_values is not None and input_ids.shape[1] == 1:
|
885 |
+
# Retrieve the first layer to inspect the logits and mask out the hidden states
|
886 |
+
# that are set to 0
|
887 |
+
first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
|
888 |
+
|
889 |
+
# Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
|
890 |
+
batch_index, non_attended_tokens = torch.where(first_layer_past_key_value.float().sum(-2) == 0)
|
891 |
+
|
892 |
+
# Get the target length
|
893 |
+
target_seqlen = first_layer_past_key_value.shape[-1] + 1
|
894 |
+
|
895 |
+
extended_attention_mask = torch.ones(
|
896 |
+
(attention_mask.shape[0], target_seqlen - attention_mask.shape[1]),
|
897 |
+
dtype=attention_mask.dtype,
|
898 |
+
device=attention_mask.device,
|
899 |
+
)
|
900 |
+
|
901 |
+
# Filter out only the tokens that can be un-attended, this can happen
|
902 |
+
# if one uses Llava + Fused modules where the cache on the
|
903 |
+
# first iteration is already big enough, or if one passes custom cache
|
904 |
+
valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
|
905 |
+
new_batch_index = batch_index[valid_indices]
|
906 |
+
new_non_attended_tokens = non_attended_tokens[valid_indices]
|
907 |
+
|
908 |
+
# Zero-out the places where we don't need to attend
|
909 |
+
extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
|
910 |
+
|
911 |
+
# !(nxphi47) must ensure left-padding
|
912 |
+
# attention_mask is the new in-coming mask, while extended_attention_mask is the previous one
|
913 |
+
assert padding_side == "left", f"{padding_side=} is invalid for batched generation mode"
|
914 |
+
attention_mask = torch.cat((extended_attention_mask, attention_mask), dim=1)
|
915 |
+
position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
|
916 |
+
|
917 |
+
|
918 |
+
outputs = self.language_model(
|
919 |
+
attention_mask=attention_mask,
|
920 |
+
position_ids=position_ids,
|
921 |
+
past_key_values=past_key_values,
|
922 |
+
inputs_embeds=inputs_embeds,
|
923 |
+
use_cache=use_cache,
|
924 |
+
output_attentions=output_attentions,
|
925 |
+
output_hidden_states=output_hidden_states,
|
926 |
+
return_dict=return_dict,
|
927 |
+
)
|
928 |
+
|
929 |
+
logits = outputs[0]
|
930 |
+
|
931 |
+
loss = None
|
932 |
+
if labels is not None:
|
933 |
+
# Shift so that tokens < n predict n
|
934 |
+
if attention_mask is not None:
|
935 |
+
shift_attention_mask = attention_mask[..., 1:]
|
936 |
+
shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
|
937 |
+
shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
|
938 |
+
else:
|
939 |
+
shift_logits = logits[..., :-1, :].contiguous()
|
940 |
+
shift_labels = labels[..., 1:].contiguous()
|
941 |
+
# Flatten the tokens
|
942 |
+
loss_fct = nn.CrossEntropyLoss()
|
943 |
+
loss = loss_fct(
|
944 |
+
shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
|
945 |
+
)
|
946 |
+
|
947 |
+
if not return_dict:
|
948 |
+
output = (logits,) + outputs[1:]
|
949 |
+
return (loss,) + output if loss is not None else output
|
950 |
+
|
951 |
+
return LlavaNextCausalLMOutputWithPast(
|
952 |
+
loss=loss,
|
953 |
+
logits=logits,
|
954 |
+
past_key_values=outputs.past_key_values,
|
955 |
+
hidden_states=outputs.hidden_states,
|
956 |
+
attentions=outputs.attentions,
|
957 |
+
)
|
958 |
+
|
959 |
+
def prepare_inputs_for_generation(
|
960 |
+
self,
|
961 |
+
input_ids,
|
962 |
+
past_key_values=None,
|
963 |
+
inputs_embeds=None,
|
964 |
+
pixel_values=None,
|
965 |
+
image_sizes=None,
|
966 |
+
attention_mask=None,
|
967 |
+
**kwargs,
|
968 |
+
):
|
969 |
+
if past_key_values is not None:
|
970 |
+
if isinstance(past_key_values, Cache):
|
971 |
+
cache_length = past_key_values.get_seq_length()
|
972 |
+
past_length = past_key_values.seen_tokens
|
973 |
+
else:
|
974 |
+
cache_length = past_length = past_key_values[0][0].shape[2]
|
975 |
+
|
976 |
+
# Keep only the unprocessed tokens:
|
977 |
+
# 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
|
978 |
+
# some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
|
979 |
+
# input)
|
980 |
+
if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
|
981 |
+
input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
|
982 |
+
# 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
|
983 |
+
# input_ids based on the past_length.
|
984 |
+
elif past_length < input_ids.shape[1]:
|
985 |
+
input_ids = input_ids[:, past_length:]
|
986 |
+
# 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
|
987 |
+
elif self.config.image_token_index in input_ids:
|
988 |
+
input_ids = input_ids[:, input_ids.shape[1] - 1 :]
|
989 |
+
# If the cache has seen more tokens than it can hold, then the cache has a size limit. Let's discard the
|
990 |
+
# older attention values, as their corresponding values are not part of the input.
|
991 |
+
if cache_length < past_length and attention_mask is not None:
|
992 |
+
attention_mask = attention_mask[:, -(cache_length + input_ids.shape[1]) :]
|
993 |
+
|
994 |
+
position_ids = kwargs.get("position_ids", None)
|
995 |
+
if attention_mask is not None and position_ids is None:
|
996 |
+
# create position_ids on the fly for batch generation
|
997 |
+
position_ids = attention_mask.long().cumsum(-1) - 1
|
998 |
+
position_ids.masked_fill_(attention_mask == 0, 1)
|
999 |
+
if past_key_values:
|
1000 |
+
position_ids = position_ids[:, -input_ids.shape[1] :]
|
1001 |
+
|
1002 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
1003 |
+
if inputs_embeds is not None and past_key_values is None:
|
1004 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
1005 |
+
else:
|
1006 |
+
model_inputs = {"input_ids": input_ids}
|
1007 |
+
|
1008 |
+
model_inputs.update(
|
1009 |
+
{
|
1010 |
+
"position_ids": position_ids,
|
1011 |
+
"past_key_values": past_key_values,
|
1012 |
+
"use_cache": kwargs.get("use_cache"),
|
1013 |
+
"attention_mask": attention_mask,
|
1014 |
+
"pixel_values": pixel_values,
|
1015 |
+
"image_sizes": image_sizes,
|
1016 |
+
}
|
1017 |
+
)
|
1018 |
+
return model_inputs
|
1019 |
+
|
1020 |
+
# Copied from transformers.models.llava.modeling_llava.LlavaForConditionalGeneration._reorder_cache
|
1021 |
+
def _reorder_cache(self, *args, **kwargs):
|
1022 |
+
return self.language_model._reorder_cache(*args, **kwargs)
|
multipurpose_chatbot/engines/processing_llava_next.py
ADDED
@@ -0,0 +1,135 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2024 The HuggingFace Inc. team.
|
3 |
+
#
|
4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
"""
|
16 |
+
Processor class for LLaVa-NeXT.
|
17 |
+
"""
|
18 |
+
|
19 |
+
|
20 |
+
from typing import List, Optional, Union
|
21 |
+
|
22 |
+
from transformers.feature_extraction_utils import BatchFeature
|
23 |
+
from transformers.image_utils import ImageInput
|
24 |
+
from transformers.processing_utils import ProcessorMixin
|
25 |
+
from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
|
26 |
+
from transformers.utils import TensorType
|
27 |
+
|
28 |
+
|
29 |
+
class LlavaNextProcessor(ProcessorMixin):
|
30 |
+
r"""
|
31 |
+
Constructs a LLaVa-NeXT processor which wraps a LLaVa-NeXT image processor and a LLaMa tokenizer into a single processor.
|
32 |
+
|
33 |
+
[`LlavaNextProcessor`] offers all the functionalities of [`LlavaNextImageProcessor`] and [`LlamaTokenizerFast`]. See the
|
34 |
+
[`~LlavaNextProcessor.__call__`] and [`~LlavaNextProcessor.decode`] for more information.
|
35 |
+
|
36 |
+
Args:
|
37 |
+
image_processor ([`LlavaNextImageProcessor`], *optional*):
|
38 |
+
The image processor is a required input.
|
39 |
+
tokenizer ([`LlamaTokenizerFast`], *optional*):
|
40 |
+
The tokenizer is a required input.
|
41 |
+
"""
|
42 |
+
|
43 |
+
attributes = ["image_processor", "tokenizer"]
|
44 |
+
image_processor_class = "LlavaNextImageProcessor"
|
45 |
+
tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
|
46 |
+
|
47 |
+
def __init__(self, image_processor=None, tokenizer=None):
|
48 |
+
super().__init__(image_processor, tokenizer)
|
49 |
+
|
50 |
+
def __call__(
|
51 |
+
self,
|
52 |
+
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
|
53 |
+
images: ImageInput = None,
|
54 |
+
padding: Union[bool, str, PaddingStrategy] = False,
|
55 |
+
truncation: Union[bool, str, TruncationStrategy] = None,
|
56 |
+
max_length=None,
|
57 |
+
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
|
58 |
+
) -> BatchFeature:
|
59 |
+
"""
|
60 |
+
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
|
61 |
+
and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
|
62 |
+
the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
|
63 |
+
LlavaNextImageProcessor's [`~LlavaNextImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
|
64 |
+
of the above two methods for more information.
|
65 |
+
|
66 |
+
Args:
|
67 |
+
text (`str`, `List[str]`, `List[List[str]]`):
|
68 |
+
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
|
69 |
+
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
|
70 |
+
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
|
71 |
+
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
|
72 |
+
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
|
73 |
+
tensor. Both channels-first and channels-last formats are supported.
|
74 |
+
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
|
75 |
+
Select a strategy to pad the returned sequences (according to the model's padding side and padding
|
76 |
+
index) among:
|
77 |
+
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
|
78 |
+
sequence if provided).
|
79 |
+
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
|
80 |
+
acceptable input length for the model if that argument is not provided.
|
81 |
+
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
|
82 |
+
lengths).
|
83 |
+
max_length (`int`, *optional*):
|
84 |
+
Maximum length of the returned list and optionally padding length (see above).
|
85 |
+
truncation (`bool`, *optional*):
|
86 |
+
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
|
87 |
+
return_tensors (`str` or [`~utils.TensorType`], *optional*):
|
88 |
+
If set, will return tensors of a particular framework. Acceptable values are:
|
89 |
+
|
90 |
+
- `'tf'`: Return TensorFlow `tf.constant` objects.
|
91 |
+
- `'pt'`: Return PyTorch `torch.Tensor` objects.
|
92 |
+
- `'np'`: Return NumPy `np.ndarray` objects.
|
93 |
+
- `'jax'`: Return JAX `jnp.ndarray` objects.
|
94 |
+
|
95 |
+
Returns:
|
96 |
+
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
|
97 |
+
|
98 |
+
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
|
99 |
+
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
|
100 |
+
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
|
101 |
+
`None`).
|
102 |
+
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
|
103 |
+
"""
|
104 |
+
if images is not None:
|
105 |
+
image_inputs = self.image_processor(images, return_tensors=return_tensors)
|
106 |
+
else:
|
107 |
+
image_inputs = {}
|
108 |
+
text_inputs = self.tokenizer(
|
109 |
+
text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
|
110 |
+
)
|
111 |
+
|
112 |
+
return BatchFeature(data={**text_inputs, **image_inputs})
|
113 |
+
|
114 |
+
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
|
115 |
+
def batch_decode(self, *args, **kwargs):
|
116 |
+
"""
|
117 |
+
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
|
118 |
+
refer to the docstring of this method for more information.
|
119 |
+
"""
|
120 |
+
return self.tokenizer.batch_decode(*args, **kwargs)
|
121 |
+
|
122 |
+
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.decode with CLIP->Llama
|
123 |
+
def decode(self, *args, **kwargs):
|
124 |
+
"""
|
125 |
+
This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
|
126 |
+
the docstring of this method for more information.
|
127 |
+
"""
|
128 |
+
return self.tokenizer.decode(*args, **kwargs)
|
129 |
+
|
130 |
+
@property
|
131 |
+
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names
|
132 |
+
def model_input_names(self):
|
133 |
+
tokenizer_input_names = self.tokenizer.model_input_names
|
134 |
+
image_processor_input_names = self.image_processor.model_input_names
|
135 |
+
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
|
multipurpose_chatbot/engines/sealava16_transformers_engine.py
CHANGED
@@ -162,8 +162,11 @@ class SeaLlava16Engine(TransformersEngine):
|
|
162 |
sys.path.append(CODE_PATH)
|
163 |
|
164 |
|
165 |
-
from transformers.models.llava_next.modeling_llava_next import LlavaNextForConditionalGeneration
|
166 |
-
from transformers.models.llava_next.processing_llava_next import LlavaNextProcessor
|
|
|
|
|
|
|
167 |
model_path = MODEL_PATH
|
168 |
print(f'Loading model from {model_path}')
|
169 |
|
@@ -171,8 +174,12 @@ class SeaLlava16Engine(TransformersEngine):
|
|
171 |
if os.path.exists(f"{model_path}/pytorch_model_fsdp.bin") and not os.path.exists(f"{model_path}/pytorch_model.bin"):
|
172 |
os.symlink("pytorch_model_fsdp.bin", f"{model_path}/pytorch_model.bin")
|
173 |
|
174 |
-
self._processor = LlavaNextProcessor.from_pretrained(model_path)
|
|
|
|
|
|
|
175 |
self._model = LlavaNextForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map="cuda").eval()
|
|
|
176 |
|
177 |
self._model.sample_old = self._model.sample
|
178 |
self._model._sample = types.MethodType(NewGenerationMixin.sample_stream, self._model)
|
|
|
162 |
sys.path.append(CODE_PATH)
|
163 |
|
164 |
|
165 |
+
# from transformers.models.llava_next.modeling_llava_next import LlavaNextForConditionalGeneration
|
166 |
+
# from transformers.models.llava_next.processing_llava_next import LlavaNextProcessor
|
167 |
+
from .modeling_sealava16 import LlavaNextForConditionalGeneration
|
168 |
+
from .image_processing_llava_next import LlavaNextImageProcessor
|
169 |
+
from .processing_llava_next import LlavaNextProcessor
|
170 |
model_path = MODEL_PATH
|
171 |
print(f'Loading model from {model_path}')
|
172 |
|
|
|
174 |
if os.path.exists(f"{model_path}/pytorch_model_fsdp.bin") and not os.path.exists(f"{model_path}/pytorch_model.bin"):
|
175 |
os.symlink("pytorch_model_fsdp.bin", f"{model_path}/pytorch_model.bin")
|
176 |
|
177 |
+
# self._processor = LlavaNextProcessor.from_pretrained(model_path)
|
178 |
+
self._tokenizer = AutoTokenizer.from_pretrained(model_path)
|
179 |
+
self._image_processor = LlavaNextImageProcessor(model_path)
|
180 |
+
self._processor = LlavaNextProcessor(image_processor=self._image_processor, tokenizer=self._tokenizer)
|
181 |
self._model = LlavaNextForConditionalGeneration.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map="cuda").eval()
|
182 |
+
print(f'Loading llava1.6 from custom code')
|
183 |
|
184 |
self._model.sample_old = self._model.sample
|
185 |
self._model._sample = types.MethodType(NewGenerationMixin.sample_stream, self._model)
|