import logging import os from importlib import import_module from typing import List, Callable, Union, Optional, Dict import PIL.Image import torch from torch import Tensor from torch.nn import init from torch.nn.functional import softmax, gumbel_softmax, pad from transformers import PreTrainedModel, AutoModel, AutoTokenizer, AutoModelForCausalLM, AutoImageProcessor from transformers import SiglipImageProcessor, SiglipVisionModel from transformers.cache_utils import HybridCache from transformers.generation.utils import GenerateOutput from .configuration_ovis import BaseVisualTokenizerConfig, SiglipVisualTokenizerConfig from .configuration_ovis import OvisConfig, ConversationFormatter from .configuration_ovis import IGNORE_ID, IMAGE_ATOM_ID, IMAGE_INDICATOR_IDS, IMAGE_TOKEN_ID # ---------------------------------------------------------------------- # Visual Tokenizer # ---------------------------------------------------------------------- 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) self.image_processor = AutoImageProcessor.from_pretrained(kwargs['image_processor_name_or_path']) self.backbone = AutoModel.from_config(self.config.backbone_config) head_dim = self.config.vocab_size - len(IMAGE_INDICATOR_IDS) # reserved tokens for IMAGE_INDICATORS self.head = torch.nn.Sequential( torch.nn.Linear( self.backbone.config.hidden_size * self.config.hidden_stride * self.config.hidden_stride, head_dim, bias=False ), torch.nn.LayerNorm(head_dim) ) 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_image_processor(self): return self.image_processor def mock_input(self): height, width = self.get_image_size() return torch.zeros(1, 3, height, width), self.construct_image_placeholders((1, 1)) def get_head(self): return self.head def get_image_size(self): raise NotImplementedError @staticmethod def construct_image_placeholders(grid): image_placeholders = [IMAGE_INDICATOR_IDS[0], IMAGE_ATOM_ID, IMAGE_INDICATOR_IDS[1]] if grid[0] * grid[1] > 1: for r in range(grid[0]): for c in range(grid[1]): image_placeholders.append(IMAGE_ATOM_ID) if c < grid[1] - 1: image_placeholders.append(IMAGE_INDICATOR_IDS[2]) if r < grid[0] - 1: image_placeholders.append(IMAGE_INDICATOR_IDS[3]) image_placeholders.append(IMAGE_INDICATOR_IDS[4]) return image_placeholders def preprocess_image(self, image: PIL.Image.Image, max_partition=9, covering_threshold=0.9, convert_to_rgb=True): def _preprocess(img: PIL.Image.Image, side): # first resize and preprocess w, h = img.size if w == h: new_width = new_height = side elif w > h: new_width = side new_height = int(h / w * new_width) else: new_height = side new_width = int(w / h * new_height) new_size = dict(height=new_height, width=new_width) pixel_values = self.image_processor.preprocess(img, 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 _partition(img, grid): w, h = img.size row_height = h // grid[0] col_width = w // grid[1] partition = [] for row in range(grid[0]): for col in range(grid[1]): left = col * col_width upper = row * row_height right = w if col == grid[1] - 1 else (col + 1) * col_width lower = h if row == grid[0] - 1 else (row + 1) * row_height partition.append((left, upper, right, lower)) return partition def _covering_area(left, upper, right, lower, side): w = right - left h = lower - upper w, h = max(w, h), min(w, h) if w > side: h = h / w * side w = side return w * h def _get_best_grid(img, side): img_area = img.size[0] * img.size[1] candidate_grids = [] for i in range(1, max_partition + 1): for j in range(1, max_partition + 1): if i * j <= max_partition: candidate_grids.append((i, j)) all_grids = [] good_grids = [] for grid in candidate_grids: partition = _partition(img, grid) covering_ratio = sum([_covering_area(*p, side) for p in partition]) / img_area assert covering_ratio <= 1.0 all_grids.append((grid, covering_ratio)) if covering_ratio > covering_threshold: good_grids.append((grid, covering_ratio)) if len(good_grids) > 0: # pick the good partition with minimum #sub_images and break the tie using covering_ratio return sorted(good_grids, key=lambda x: (x[0][0] * x[0][1], -x[1]))[0][0] else: # pick the partition with maximum covering_ratio and break the tie using #sub_images return sorted(all_grids, key=lambda x: (-x[1], x[0][0] * x[0][1]))[0][0] if convert_to_rgb and image.mode != 'RGB': image = image.convert('RGB') sides = self.get_image_size() if sides[0] != sides[1]: raise ValueError('get_image_size() returns non-square size') side = sides[0] grid = _get_best_grid(image, side) partition = _partition(image, grid) crops = [image.crop(p) for p in partition] if len(crops) > 1: crops.insert(0, image) pixel_values = torch.cat([_preprocess(crop, side) for crop in crops], dim=0) image_placeholders = self.construct_image_placeholders(grid) return pixel_values, image_placeholders 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 def encode(self, pixel_values): output = self.backbone(pixel_values, output_hidden_states=True, return_dict=True) features = output.hidden_states[-1] if self.config.drop_cls_token: features = features[:, 1:, :] # merge number of `hidden_stride * hidden_stride` hidden states together to reduce token sequence length # e.g., for hidden_stride=3, this leads to a token length reduction: 729 -> 81 for siglip if self.config.hidden_stride > 1: n, l, d = features.shape # this `d` maybe different from the above `d sqrt_l = int(l ** 0.5) assert sqrt_l ** 2 == l, "The token sequence length should be a perfect square." features = features.reshape(n, sqrt_l, sqrt_l, d) pl = (self.config.hidden_stride - (sqrt_l % self.config.hidden_stride)) % self.config.hidden_stride features = pad(features, (0, 0, 0, pl, 0, pl), "constant", 0) sqrt_l += pl features = features.reshape(n, sqrt_l // self.config.hidden_stride, self.config.hidden_stride, sqrt_l // self.config.hidden_stride, self.config.hidden_stride, d) features = features.permute(0, 1, 3, 2, 4, 5) # [n, sqrt_l/hs, sqrt_l/hs, hs, hs, d] features = features.flatten(3) # [n, sqrt_l/hs, sqrt_l/hs, hs*hs*d] features = features.reshape( n, -1, self.config.hidden_stride * self.config.hidden_stride * d) return features def forward(self, pixel_values) -> torch.Tensor: # [BatchSize, ImageShape] -> [BatchSize, #Token, VocabSize] features = self.encode(pixel_values) logits = self.head(features) tokens = self.tokenize(logits) # tokens' shape is [BatchSize, #Token, VocabSize-5], so padding with [BatchSize, #Token, 5], after # which, tokens' shape should become [BatchSize, #Token, VocabSize] batch_size, token_len, _ = tokens.shape padding_tensor = torch.zeros(size=(batch_size, token_len, len(IMAGE_INDICATOR_IDS)), dtype=tokens.dtype, device=tokens.device, layout=tokens.layout, requires_grad=False) tokens = torch.cat((tokens, padding_tensor), dim=2) return tokens class SiglipVisualTokenizer(BaseVisualTokenizer): config_class = SiglipVisualTokenizerConfig supports_gradient_checkpointing = True _no_split_modules = ["SiglipVisionTransformer"] _image_processor_class = SiglipImageProcessor _image_processor_kwargs = {} _backbone_class = SiglipVisionModel _backbone_name_or_path = "google/siglip-so400m-patch14-384" def get_image_size(self): height = self.image_processor.size["height"] width = self.image_processor.size["width"] return height, width AutoModel.register(SiglipVisualTokenizerConfig, SiglipVisualTokenizer) # ---------------------------------------------------------------------- # Ovis # ---------------------------------------------------------------------- class VisualEmbedding(torch.nn.Embedding): def forward(self, visual_tokens: Tensor) -> Tensor: if visual_tokens.dtype in [torch.int8, torch.int16, torch.int32, torch.int64, torch.long]: return super().forward(visual_tokens) return torch.matmul(visual_tokens, self.weight) def reset_parameters(self, mean=0., std=1.) -> None: init.normal_(self.weight, mean=mean, std=std) self._fill_padding_idx_with_zero() class OvisPreTrainedModel(PreTrainedModel): config_class = OvisConfig base_model_prefix = "ovis" class Ovis(OvisPreTrainedModel): def __init__(self, config: OvisConfig, *inputs, **kwargs): super().__init__(config, *inputs, **kwargs) attn_kwargs = dict() if self.config.llm_attn_implementation: attn_kwargs['attn_implementation'] = self.config.llm_attn_implementation self.llm = AutoModelForCausalLM.from_config(self.config.llm_config, **attn_kwargs) assert self.config.hidden_size == self.llm.config.hidden_size, "hidden size mismatch" self.text_tokenizer = AutoTokenizer.from_pretrained(self.config.name_or_path) self.visual_tokenizer = AutoModel.from_config(self.config.visual_tokenizer_config, image_processor_name_or_path=self.config.name_or_path) self.vte = VisualEmbedding( self.config.visual_tokenizer_config.vocab_size, self.config.hidden_size, device=self.visual_tokenizer.device, dtype=self.visual_tokenizer.dtype ) def _merge_modules(modules_list: tuple): merged_modules = [] for modules in modules_list: merged_modules.extend(modules if modules else []) return merged_modules self._no_split_modules = _merge_modules((self.llm._no_split_modules, self.visual_tokenizer._no_split_modules)) self._skip_keys_device_placement = self.llm._skip_keys_device_placement self._keep_in_fp32_modules = _merge_modules( (self.llm._keep_in_fp32_modules, self.visual_tokenizer._keep_in_fp32_modules)) self.is_parallelizable = all((self.llm.is_parallelizable, self.visual_tokenizer.is_parallelizable)) self.supports_gradient_checkpointing = all( (self.llm.supports_gradient_checkpointing, self.visual_tokenizer.supports_gradient_checkpointing)) self._supports_flash_attn_2 = all( (self.llm._supports_flash_attn_2, self.visual_tokenizer._supports_flash_attn_2)) self._supports_sdpa = all((self.llm._supports_sdpa, self.visual_tokenizer._supports_sdpa)) def get_text_tokenizer(self): return self.text_tokenizer def get_visual_tokenizer(self): return self.visual_tokenizer def tie_weights(self): if not self.config.disable_tie_weight: self.get_llm().tie_weights() def get_llm(self): return self.llm def get_vte(self): return self.vte def get_wte(self): return self.llm.get_input_embeddings() def get_conversation_formatter(self) -> ConversationFormatter: if getattr(self, 'conversation_formatter', None) is None: self.conversation_formatter = getattr(import_module(".configuration_ovis", __package__), self.config.conversation_formatter_class)(self.text_tokenizer) return self.conversation_formatter def forward( self, input_ids: torch.Tensor, attention_mask: torch.Tensor, labels: Optional[torch.Tensor], pixel_values: List[Optional[torch.Tensor]], **kwargs ): assert self.training, "`forward` can only be used in training. For inference, use `generate`." _, inputs_embeds, labels, attention_mask = self.merge_multimodal( text_input_ids=input_ids, text_attention_masks=attention_mask, text_labels=labels, pixel_values=pixel_values ) return self.llm(inputs_embeds=inputs_embeds, labels=labels, attention_mask=attention_mask, **kwargs) def merge_multimodal( self, text_input_ids: torch.Tensor, text_attention_masks: torch.Tensor, text_labels: Optional[torch.Tensor], pixel_values: List[Optional[torch.Tensor]], left_padding: bool = False ): input_device = text_input_ids.device visual_vocab_szie = self.get_visual_tokenizer().config.vocab_size visual_indicator_embeds = self.get_vte()( torch.tensor( list(range(visual_vocab_szie - 5, visual_vocab_szie)), dtype=torch.long, device=self.get_visual_tokenizer().device ) ).to(device=input_device) if self.training: # When training, to be compatible with deepspeed zero, each sample has to include pixel_value tensor. # For text-only sample, one can simply use a full zero tensor as pixel_value, which will be ignored # (see below in this function); so, the gradient will not be affected. num_images = [x.shape[0] for x in pixel_values] visual_tokens = self.visual_tokenizer(torch.cat([x for x in pixel_values], dim=0)) visual_embeds = torch.split(self.get_vte()(visual_tokens).to(dtype=self.dtype, device=input_device), split_size_or_sections=num_images, dim=0) visual_input_ids = torch.split(torch.argmax(visual_tokens, dim=-1).to(device=input_device), split_size_or_sections=num_images, dim=0) visual_labels = [torch.full(x.shape, IGNORE_ID, dtype=torch.long, device=input_device) for x in visual_input_ids] else: # When inference, sample can include only text with `None` pixel_value num_images = [x.shape[0] if x is not None else 0 for x in pixel_values] if sum(num_images) > 0: visual_tokens = self.visual_tokenizer(torch.cat([x for x in pixel_values if x is not None], dim=0)) visual_embeds = torch.split(self.get_vte()(visual_tokens).to(dtype=self.dtype, device=input_device), split_size_or_sections=num_images, dim=0) visual_input_ids = torch.split(torch.argmax(visual_tokens, dim=-1).to(device=input_device), split_size_or_sections=num_images, dim=0) visual_labels = [torch.full(x.shape, IGNORE_ID, dtype=torch.long, device=input_device) for x in visual_input_ids] else: # just placeholders visual_embeds = [None] * len(num_images) visual_input_ids = [None] * len(num_images) visual_labels = [None] * len(num_images) if text_labels is None: text_labels = torch.full(text_input_ids.shape, IGNORE_ID, dtype=torch.long, device=input_device) input_embeds = [] attention_masks = [] labels = [] for text_input_id, text_label, text_attention_mask, visual_embed, visual_input_id, visual_label in zip( text_input_ids, text_labels, text_attention_masks, visual_embeds, visual_input_ids, visual_labels ): placeholder_token_mask = torch.lt(text_input_id, 0) text_embed = self.get_wte()(torch.masked_fill(text_input_id, placeholder_token_mask, 0)) for i, indicator_id in enumerate(IMAGE_INDICATOR_IDS): text_embed[text_input_id == indicator_id] = visual_indicator_embeds[i] image_atom_positions = torch.where(torch.eq(text_input_id, IMAGE_ATOM_ID))[0].tolist() if len(image_atom_positions) > 0: input_embed_parts = [] attention_mask_parts = [] label_parts = [] prev_image_atom_position = -1 for index, image_atom_position in enumerate(image_atom_positions): input_embed_parts.append( text_embed[prev_image_atom_position + 1:image_atom_position, :]) label_parts.append( text_label[prev_image_atom_position + 1:image_atom_position]) attention_mask_parts.append( text_attention_mask[prev_image_atom_position + 1:image_atom_position]) input_embed_parts.append(visual_embed[index]) attention_mask_parts.append( torch.ones_like(visual_label[index], dtype=torch.bool)) label_parts.append(visual_label[index]) prev_image_atom_position = image_atom_position if prev_image_atom_position + 1 < text_input_id.shape[0]: input_embed_parts.append( text_embed[prev_image_atom_position + 1:, :]) attention_mask_parts.append( text_attention_mask[prev_image_atom_position + 1:]) label_parts.append( text_label[prev_image_atom_position + 1:]) input_embed = torch.cat(input_embed_parts, dim=0) attention_mask = torch.cat(attention_mask_parts, dim=0) label = torch.cat(label_parts, dim=0) else: input_embed = text_embed attention_mask = text_attention_mask label = text_label if self.training: # Make visual_embed & visual_indicator_embeds involved in the backward graph, # to be compatible with deepspeed zero and ddp. input_embed += torch.sum(visual_embed * 0.0) + torch.sum(visual_indicator_embeds * 0.0) input_embeds.append(input_embed) attention_masks.append(attention_mask) labels.append(label) if self.training: # padding to self.config.multimodal_max_length for increased training speed padding_size = max(0, self.config.multimodal_max_length - len(input_embeds[0])) input_embeds[0] = torch.nn.ConstantPad2d((0, 0, 0, padding_size), 0.0)(input_embeds[0]) attention_masks[0] = torch.nn.ConstantPad1d((0, padding_size), False)(attention_masks[0]) labels[0] = torch.nn.ConstantPad1d((0, padding_size), IGNORE_ID)(labels[0]) batch_input_embeds = self.pad_truncate_sequence(input_embeds, batch_first=True, padding_value=0.0, left_padding=left_padding) batch_attention_mask = self.pad_truncate_sequence(attention_masks, batch_first=True, padding_value=False, left_padding=left_padding) batch_labels = self.pad_truncate_sequence(labels, batch_first=True, padding_value=IGNORE_ID, left_padding=left_padding) return visual_input_ids, batch_input_embeds, batch_labels, batch_attention_mask def pad_truncate_sequence(self, sequences: List[torch.Tensor], batch_first: bool = True, padding_value: float = 0.0, left_padding: bool = False) -> torch.Tensor: if left_padding == False: pad_sequence = torch.nn.utils.rnn.pad_sequence(sequences, batch_first=batch_first, padding_value=padding_value) return pad_sequence[:,:self.config.multimodal_max_length] else: pad_sequence = torch.nn.utils.rnn.pad_sequence([i.flip(dims=[0]) for i in sequences],batch_first=True, padding_value=padding_value).flip(dims=[1]) return pad_sequence[:,-self.config.multimodal_max_length:] def preprocess_inputs( self, text_or_conversations: Union[List[Dict], str], images: Optional[List[PIL.Image.Image]], max_partition=9, generation_preface='', return_labels=False, propagate_exception=True ): # convert text to conversations if isinstance(text_or_conversations, str): conversations = [{ "from": "human", "value": text_or_conversations }] elif isinstance(text_or_conversations, list): conversations = text_or_conversations else: raise ValueError(f'Invalid type of `text_or_conversations`, expected `List[Dict]` or `str`,' f' but got {type(text_or_conversations)}') # format conversations prompt, raw_input_ids, raw_labels = self.get_conversation_formatter().format( conversations, generation_preface=generation_preface) # place image placeholders input_ids = [] labels = [] pixel_values = [] invalidate_label = False image_token_indices = [i for i, v in enumerate(raw_input_ids) if v == IMAGE_TOKEN_ID] last_image_token_index = -1 for i in range(len(image_token_indices)): head = 0 if i == 0 else image_token_indices[i - 1] + 1 tail = image_token_indices[i] last_image_token_index = tail input_ids.extend(raw_input_ids[head:tail]) labels.extend(raw_labels[head:tail]) try: image = images[i] raw_pixel_values, image_placeholders = self.visual_tokenizer.preprocess_image( image, max_partition=max_partition) except Exception as e: if propagate_exception: raise e logging.exception(e) invalidate_label = True raw_pixel_values, image_placeholders = self.visual_tokenizer.mock_input() input_ids.extend(image_placeholders) labels.extend([IGNORE_ID] * len(image_placeholders)) pixel_values.append(raw_pixel_values) input_ids.extend(raw_input_ids[last_image_token_index + 1:]) labels.extend(raw_labels[last_image_token_index + 1:]) # return tensors input_ids = torch.tensor(input_ids, dtype=torch.long) labels = torch.tensor([IGNORE_ID] * len(labels) if invalidate_label else labels, dtype=torch.long) pixel_values = torch.cat(pixel_values, dim=0) if len(pixel_values) > 0 else None if return_labels: return prompt, input_ids, pixel_values, labels else: return prompt, input_ids, pixel_values def save_pretrained( self, save_directory: Union[str, os.PathLike], is_main_process: bool = True, state_dict: Optional[dict] = None, save_function: Callable = torch.save, push_to_hub: bool = False, max_shard_size: Union[int, str] = "5GB", safe_serialization: bool = True, variant: Optional[str] = None, token: Optional[Union[str, bool]] = None, save_peft_format: bool = True, **kwargs ): super().save_pretrained(save_directory, is_main_process=is_main_process, state_dict=state_dict, save_function=save_function, safe_serialization=safe_serialization) self.get_text_tokenizer().save_pretrained(save_directory) self.get_visual_tokenizer().get_image_processor().save_pretrained(save_directory) def _get_hybrid_cache_for_llm(self, max_batch_size: int, max_cache_len: int): cache_cls = HybridCache llm = self.get_llm() need_new_cache = ( not hasattr(llm, "_cache") or (not isinstance(llm._cache, cache_cls)) or llm._cache.max_batch_size != max_batch_size or llm._cache.max_cache_len < max_cache_len ) if need_new_cache: if hasattr(llm.config, "_pre_quantization_dtype"): cache_dtype = llm.config._pre_quantization_dtype else: cache_dtype = llm.dtype llm._cache = cache_cls( config=llm.config, max_batch_size=max_batch_size, max_cache_len=max_cache_len, device=llm.device, dtype=cache_dtype, ) else: llm._cache.reset() return llm._cache # TODO: support batch generation def generate( self, inputs: Optional[torch.Tensor] = None, **kwargs ) -> Union[GenerateOutput, torch.LongTensor]: _, inputs_embeds, labels, attention_mask = self.merge_multimodal( text_input_ids=inputs, text_attention_masks=kwargs.pop('attention_mask'), text_labels=None, pixel_values=kwargs.pop('pixel_values'), left_padding=True ) if getattr(self.generation_config, 'cache_implementation') == 'hybrid': # mainly for Gemma2 kwargs['past_key_values'] = self._get_hybrid_cache_for_llm( getattr(kwargs, "num_beams", inputs_embeds.shape[0]), kwargs['max_new_tokens'] + inputs_embeds.shape[-2]) self.get_llm()._supports_cache_class = True kwargs['cache_implementation'] = None return self.llm.generate(inputs=None, inputs_embeds=inputs_embeds, attention_mask=attention_mask, **kwargs)