class DeepseekVLV2Processor(ProcessorMixin):
tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
attributes = ["tokenizer"]
def __init__(
self,
tokenizer: LlamaTokenizerFast,
candidate_resolutions: tuple[tuple[int, int]],
patch_size: int,
downsample_ratio: int,
image_mean: tuple[float, float, float] = (0.5, 0.5, 0.5),
image_std: tuple[float, float, float] = (0.5, 0.5, 0.5),
normalize: bool = True,
image_token: str = "<image>",
pad_token: str = "<｜▁pad▁｜>",
add_special_token: bool = False,
sft_format: str = "deepseek",
mask_prompt: bool = True,
ignore_id: int = -100,
**kwargs,
):
self.candidate_resolutions = candidate_resolutions
self.image_size = candidate_resolutions[0][0]
self.patch_size = patch_size
self.image_mean = image_mean
self.image_std = image_std
self.normalize = normalize
self.downsample_ratio = downsample_ratio
self.image_transform = ImageTransform(
mean=image_mean, std=image_std, normalize=normalize
)
self.tokenizer = tokenizer
self.tokenizer.padding_side = "left" # must set this，padding side with make a difference in batch inference
# add the pad_token as special token to use 'tokenizer.pad_token' and 'tokenizer.pad_token_id'
if tokenizer.pad_token is None:
self.tokenizer.add_special_tokens({"pad_token": pad_token})
# add image token
image_token_id = self.tokenizer.vocab.get(image_token)
if image_token_id is None:
special_tokens = [image_token]
special_tokens_dict = {"additional_special_tokens": special_tokens}
self.tokenizer.add_special_tokens(special_tokens_dict)
self.image_token_id = self.tokenizer.vocab.get(image_token)
# add five special tokens for grounding-related tasks
# <|ref|>, <|/ref|>, <|det|>, <|/det|>, <|grounding|>
special_tokens = ["<|ref|>", "<|/ref|>", "<|det|>", "<|/det|>", "<|grounding|>"]
special_tokens_dict = {"additional_special_tokens": special_tokens}
self.tokenizer.add_special_tokens(special_tokens_dict)
# add special tokens for SFT data
special_tokens = ["<|User|>", "<|Assistant|>"]
special_tokens_dict = {"additional_special_tokens": special_tokens}
self.tokenizer.add_special_tokens(special_tokens_dict)
self.image_token = image_token
self.pad_token = pad_token
self.add_special_token = add_special_token
self.sft_format = sft_format
self.mask_prompt = mask_prompt
self.ignore_id = ignore_id
super().__init__(
tokenizer,
**kwargs,
)
def select_best_resolution(self, image_size):
# used for cropping
original_width, original_height = image_size
best_fit = None
max_effective_resolution = 0
min_wasted_resolution = float("inf")
for width, height in self.candidate_resolutions:
scale = min(width / original_width, height / original_height)
downscaled_width, downscaled_height = (
int(original_width * scale),
int(original_height * scale),
)
effective_resolution = min(
downscaled_width * downscaled_height, original_width * original_height
)
wasted_resolution = (width * height) - effective_resolution
if effective_resolution > max_effective_resolution or (
effective_resolution == max_effective_resolution
and wasted_resolution < min_wasted_resolution
):
max_effective_resolution = effective_resolution
min_wasted_resolution = wasted_resolution
best_fit = (width, height)
return best_fit
@property
def bos_id(self):
return self.tokenizer.bos_token_id
@property
def eos_id(self):
return self.tokenizer.eos_token_id
@property
def pad_id(self):
return self.tokenizer.pad_token_id
def encode(self, text: str, bos: bool = True, eos: bool = False):
t = self.tokenizer.encode(text, add_special_tokens=False)
if bos:
t = [self.bos_id] + t
if eos:
t = t + [self.eos_id]
return t
def decode(self, t: list[int], **kwargs) -> str:
return self.tokenizer.decode(t, **kwargs)
def process_one(
self,
prompt: str,
images: list[Image.Image],
inference_mode: bool = True,
**kwargs: Any,
):
"""
Args:
prompt (str): the formatted prompt;
images (list[ImageType]): the list of images;
inference_mode (bool): if True, then remove the last eos token;
**kwargs: Additional keyword arguments.
Returns:
outputs (BaseProcessorOutput): the output of the processor,
- input_ids (torch.LongTensor): [N + image tokens]
- target_ids (torch.LongTensor): [N + image tokens]
- pixel_values (torch.FloatTensor): [n_patches, 3, H, W]
- image_id (int): the id of the image token
- num_image_tokens (list[int]): the number of image tokens
"""
assert prompt is not None and images is not None, (
"prompt and images must be used at the same time."
)
sft_format = prompt
(
tokenized_str,
images_list,
images_seq_mask,
images_spatial_crop,
num_image_tokens,
) = self.tokenize_with_images(
sft_format, images, bos=True, eos=True, cropping=len(images) <= 2
)
masked_tokenized_str = []
for token_index in tokenized_str:
if token_index != self.image_token_id:
masked_tokenized_str.append(token_index)
else:
masked_tokenized_str.append(self.ignore_id)
assert (
len(tokenized_str) == len(images_seq_mask) == len(masked_tokenized_str)
), (
f"tokenized_str's length {len(tokenized_str)}, input_ids' length {len(masked_tokenized_str)}, "
f"imags_seq_mask's length {len(images_seq_mask)}, are not equal"
)
input_ids = torch.LongTensor(tokenized_str)
target_ids = torch.LongTensor(masked_tokenized_str)
images_seq_mask = torch.tensor(images_seq_mask, dtype=torch.bool)
# set input_ids < 0 | input_ids == self.image_token_id as ignore_id
target_ids[(input_ids < 0) | (input_ids == self.image_token_id)] = (
self.ignore_id
)
input_ids[input_ids < 0] = self.pad_id
if inference_mode:
# Remove the ending eos token
assert input_ids[-1] == self.eos_id
input_ids = input_ids[:-1]
target_ids = target_ids[:-1]
images_seq_mask = images_seq_mask[:-1]
if len(images_list) == 0:
pixel_values = torch.zeros((1, 3, self.image_size, self.image_size))
images_spatial_crop = torch.zeros((1, 2), dtype=torch.long)
else:
pixel_values = torch.stack(images_list, dim=0)
images_spatial_crop = torch.tensor(images_spatial_crop, dtype=torch.long)
input_ids = input_ids.unsqueeze(0)
prepare = BatchFeature(
data=dict(
input_ids=input_ids,
pixel_values=pixel_values,
images_seq_mask=images_seq_mask,
images_spatial_crop=images_spatial_crop,
num_image_tokens=num_image_tokens,
),
tensor_type="pt",
)
return prepare
def __call__(
self,
*,
text: str,
images: list[Image.Image],
inference_mode: bool = True,
**kwargs: Any,
):
"""
Args:
text (str): the formatted prompt;
images (list[ImageType]): the list of images;
inference_mode (bool): if True, then remove the last eos token;
**kwargs:
Returns:
outputs (BaseProcessorOutput): the output of the processor,
- input_ids (torch.LongTensor): [N + image tokens]
- images (torch.FloatTensor): [n_images, 3, H, W]
- image_id (int): the id of the image token
- num_image_tokens (list[int]): the number of image tokens
"""
prepare = self.process_one(
prompt=text,
images=images,
inference_mode=inference_mode,
)
return prepare
def tokenize_with_images(
self,
conversation: str,
images: list[Image.Image],
bos: bool = True,
eos: bool = True,
cropping: bool = True,
):
"""Tokenize text with <image> tags."""
assert conversation.count(self.image_token) == len(images)
text_splits = conversation.split(self.image_token)
images_list, images_seq_mask, images_spatial_crop = [], [], []
num_image_tokens = []
tokenized_str = []
for text_sep, image in zip(text_splits, images):
"""encode text_sep"""
tokenized_sep = self.encode(text_sep, bos=False, eos=False)
tokenized_str += tokenized_sep
images_seq_mask += [False] * len(tokenized_sep)
"""select best resolution for anyres"""
if cropping:
best_width, best_height = self.select_best_resolution(image.size)
else:
best_width, best_height = self.image_size, self.image_size
"""process the global view"""
global_view = ImageOps.pad(
image,
(self.image_size, self.image_size),
color=tuple(int(x * 255) for x in self.image_transform.mean),
)
images_list.append(self.image_transform(global_view))
"""process the local views"""
local_view = ImageOps.pad(
image,
(best_width, best_height),
color=tuple(int(x * 255) for x in self.image_transform.mean),
)
for i in range(0, best_height, self.image_size):
for j in range(0, best_width, self.image_size):
images_list.append(
self.image_transform(
local_view.crop(
(j, i, j + self.image_size, i + self.image_size)
)
)
)
"""record height / width crop num"""
num_width_tiles, num_height_tiles = (
best_width // self.image_size,
best_height // self.image_size,
)
images_spatial_crop.append([num_width_tiles, num_height_tiles])
"""add image tokens"""
h = w = math.ceil(
(self.image_size // self.patch_size) / self.downsample_ratio
)
# global views tokens h * (w + 1), 1 is for line separator
tokenized_image = [self.image_token_id] * h * (w + 1)
# add a separator between global and local views
tokenized_image += [self.image_token_id]
# local views tokens, (num_height_tiles * h) * (num_width_tiles * w + 1)
tokenized_image += (
[self.image_token_id]
* (num_height_tiles * h)
* (num_width_tiles * w + 1)
)
tokenized_str += tokenized_image
images_seq_mask += [True] * len(tokenized_image)
num_image_tokens.append(len(tokenized_image))
"""process the last text split"""
tokenized_sep = self.encode(text_splits[-1], bos=False, eos=False)
tokenized_str += tokenized_sep
images_seq_mask += [False] * len(tokenized_sep)
"""add the bos and eos tokens"""
if bos:
tokenized_str = [self.bos_id] + tokenized_str
images_seq_mask = [False] + images_seq_mask
if eos:
tokenized_str = tokenized_str + [self.eos_id]
images_seq_mask = images_seq_mask + [False]
assert len(tokenized_str) == len(images_seq_mask), (
f"tokenize_with_images func: tokenized_str's length {len(tokenized_str)} is not equal to imags_seq_mask's length {len(images_seq_mask)}"
)
return (
tokenized_str,
images_list,
images_seq_mask,
images_spatial_crop,
num_image_tokens,
)