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from datetime import datetime
from typing import Dict, Union, Optional
import deepspeed
import torch
import PIL.Image
from torch.nn.functional import softmax, gumbel_softmax
from torch import Tensor
from transformers import PretrainedConfig, PreTrainedModel, AutoImageProcessor, AutoConfig, AutoModel
from transformers import CLIPVisionModel, CLIPImageProcessor
from transformers.integrations import is_deepspeed_zero3_enabled
from .utils import BEGIN_LINE, END_LINE, rank0_print
MODEL_TYPE = "clip_visual_tokenizer"
class BaseVisualTokenizerConfig(PretrainedConfig):
def __init__(self,
vocab_size=16384,
tokenize_function="softmax",
tau=1.0,
depths=None,
use_indicators=False,
drop_cls_token=False,
backbone_config: Optional[Union[PretrainedConfig, dict]] = None,
hidden_stride: int = 1,
**kwargs):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.tokenize_function = tokenize_function
self.tau = tau
if isinstance(depths, str):
depths = [int(x) for x in depths.split('|')]
self.depths = depths
self.backbone_kwargs = {}
self.use_indicators = use_indicators
self.drop_cls_token = drop_cls_token
if backbone_config is not None:
assert isinstance(backbone_config, (PretrainedConfig, dict)), \
f"expect `backbone_config` to be instance of PretrainedConfig or dict, but got {type(backbone_config)} type"
if not isinstance(backbone_config, PretrainedConfig):
model_type = backbone_config['model_type']
backbone_config.pop('model_type')
backbone_config = AutoConfig.for_model(model_type, **backbone_config)
self.backbone_config = backbone_config
self.hidden_stride = hidden_stride
class BaseVisualTokenizer(PreTrainedModel):
base_model_prefix = "backbone"
main_input_name = None
_image_processor_class = None
_image_processor_kwargs = {}
_backbone_class = None
_backbone_name_or_path = None
def __init__(self, config: BaseVisualTokenizerConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if kwargs.get('train_from_scratch'):
self.image_processor = self._image_processor_class.from_pretrained(self._backbone_name_or_path,
**self._image_processor_kwargs)
self.backbone = self._backbone_class.from_pretrained(self._backbone_name_or_path,
**self.config.backbone_kwargs)
self.config.backbone_config = self.backbone.config
else:
self.image_processor = AutoImageProcessor.from_pretrained(kwargs['image_processor_name_or_path'])
self.backbone = AutoModel.from_config(self.config.backbone_config)
self.head = None
assert all((self.image_processor.do_resize,
not getattr(self.image_processor, 'do_center_crop', False),
self.image_processor.do_rescale,
self.image_processor.do_normalize
)), f"image_processor `{self.image_processor}` is not supported currently"
def get_backbone(self):
return self.backbone
def get_monitor_tensors(self):
raise NotImplementedError
def get_image_processor(self):
return self.image_processor
def get_head(self):
return self.head
def get_image_size(self):
raise NotImplementedError
def preprocess_image(self, image: PIL.Image.Image, convert_to_rgb=True):
if convert_to_rgb and image.mode != 'RGB':
image = image.convert('RGB')
# first resize and preprocess
sides = self.get_image_size()
if sides[0] != sides[1]:
raise ValueError('get_image_size() returns non-square size')
side = sides[0]
width, height = image.size
if width == height:
new_width = new_height = side
elif width > height:
new_width = side
new_height = int(height / width * new_width)
else:
new_height = side
new_width = int(width / height * new_height)
new_size = dict(height=new_height, width=new_width)
pixel_values = self.image_processor.preprocess(image, size=new_size, return_tensors='pt')['pixel_values']
# then pad to square
square_values = torch.zeros([1, 3, side, side], dtype=pixel_values.dtype, device=pixel_values.device)
new_height, new_width = pixel_values.shape[2:]
if new_height == new_width:
square_values[:, :, :, :] = pixel_values
elif new_height > new_width:
from_index = (side - new_width) // 2
square_values[:, :, :, from_index:from_index + new_width] = pixel_values
else:
from_index = (side - new_height) // 2
square_values[:, :, from_index:from_index + new_height, :] = pixel_values
return square_values
def get_layer_norm(self):
return self.layer_norm
def tokenize(self, logits):
def st_argmax(y_soft, dim): # straight-through softmax
index = y_soft.max(dim, keepdim=True)[1]
y_hard = torch.zeros_like(y_soft, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
ret = y_hard - y_soft.detach() + y_soft
return ret
if self.config.tokenize_function == 'softmax':
tokens = softmax(logits, dim=-1)
elif self.config.tokenize_function == 'gumbel_argmax':
tokens = gumbel_softmax(logits, tau=self.config.tau, hard=True)
elif self.config.tokenize_function == 'st_argmax':
tokens = st_argmax(logits, dim=-1)
else:
raise ValueError(
f'Invalid `max_type`, expected softmax or gumbel_argmax or st_argmax, but got {self.config.tokenize_function}')
return tokens
class ClipVisualTokenizerConfig(BaseVisualTokenizerConfig):
model_type = MODEL_TYPE
def __init__(self, **kwargs):
super().__init__(**kwargs)
if self.depths:
assert len(self.depths) == 1
self.backbone_kwargs['num_hidden_layers'] = self.depths[0]
class ClipVisualTokenizer(BaseVisualTokenizer):
config_class = ClipVisualTokenizerConfig
supports_gradient_checkpointing = True
_no_split_modules = ["CLIPEncoderLayer"]
_image_processor_class = CLIPImageProcessor
_image_processor_kwargs = dict(do_center_crop=False)
_backbone_class = CLIPVisionModel
_backbone_name_or_path = "openai/clip-vit-large-patch14-336"
def __init__(self, config: ClipVisualTokenizerConfig = None, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
head_dim = self.config.vocab_size
if self.config.use_indicators:
head_dim -= 2 # reserved for two image indicator tokens
self.head = torch.nn.Sequential(
torch.nn.Linear(self.backbone.config.hidden_size, head_dim, bias=False),
torch.nn.LayerNorm(head_dim)
)
def re_init_layers(self, re_init_layer_begin):
layer_dict = self.get_re_init_layer_dict(re_init_layer_begin)
for name, layer in layer_dict.items():
rank0_print(BEGIN_LINE)
rank0_print(f'[{datetime.now()}] Before layer re-initialization of {name}: ')
for k, v in layer.named_parameters():
with deepspeed.zero.GatheredParameters([v]):
rank0_print(f'{k}: {v}')
with deepspeed.zero.GatheredParameters(list(layer.parameters(recurse=True)), modifier_rank=0):
if not is_deepspeed_zero3_enabled() or deepspeed.comm.get_rank() == 0:
layer.apply(self.backbone._init_weights)
rank0_print(f'[{datetime.now()}] After layer re-initialization of {name}:')
for k, v in layer.named_parameters():
with deepspeed.zero.GatheredParameters([v]):
rank0_print(f'{k}: {v}')
rank0_print(END_LINE)
def get_re_init_layer_dict(self, re_init_layer_begin: int) -> Dict[str, torch.nn.Module]:
assert re_init_layer_begin >= 0, "negative index is prohibited"
layer_dict = dict()
for i in range(re_init_layer_begin, self.backbone.config.num_hidden_layers):
layer_dict[f'backbone.vision_model.encoder.layers.{i}'] = self.backbone.vision_model.encoder.layers[i]
return layer_dict
def get_monitor_tensors(self):
return dict(
backbone_bottom=self.backbone.vision_model.encoder.layers[0].self_attn.k_proj.weight,
backbone_top=self.backbone.vision_model.encoder.layers[-1].self_attn.out_proj.weight,
head=self.head[0].weight
)
def get_image_size(self):
height = self.image_processor.crop_size["height"]
width = self.image_processor.crop_size["width"]
return height, width
def forward(self, pixel_values) -> Tensor: # [BatchSize, ImageShape] -> [BatchSize, #Token, VocabSize]
output = self.backbone(
pixel_values, output_hidden_states=True, return_dict=True)
features = output.last_hidden_state
if self.config.drop_cls_token:
features = features[:, 1:, :]
logits = self.head(features)
tokens = self.tokenize(logits)
if self.config.use_indicators:
# tokens' shape is [BatchSize, #Token, VocabSize-2], so padding with [BatchSize, #Token, 2], after
# which, tokens' shape should become [BatchSize, #Token, VocabSize]
batch_size, token_len, _ = tokens.shape
padding_tensor = torch.zeros(size=(batch_size, token_len, 2),
dtype=tokens.dtype,
device=tokens.device,
layout=tokens.layout,
requires_grad=False)
tokens = torch.cat((tokens, padding_tensor), dim=2)
# adding indicator tokens, after which tokens' shape should become [BatchSize, 1+#Token+1, VocabSize]
begin_indicator = torch.zeros(size=(batch_size, 1),
dtype=torch.long,
device=tokens.device,
requires_grad=False) + self.config.vocab_size - 2
begin_indicator_token = torch.nn.functional.one_hot(begin_indicator,
num_classes=self.config.vocab_size).to(
dtype=tokens.dtype)
end_indicator = torch.zeros(size=(batch_size, 1),
dtype=torch.long,
device=tokens.device,
requires_grad=False) + self.config.vocab_size - 1
end_indicator_token = torch.nn.functional.one_hot(end_indicator,
num_classes=self.config.vocab_size).to(dtype=tokens.dtype)
tokens = torch.cat((begin_indicator_token, tokens, end_indicator_token), dim=1)
return tokens
AutoConfig.register(MODEL_TYPE, ClipVisualTokenizerConfig)
AutoModel.register(ClipVisualTokenizerConfig, ClipVisualTokenizer)
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