# Copyright 2023 Haotian Liu & Qinghao Ye (Modified from LLaVA) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from abc import ABC, abstractmethod from typing import List, Optional, Tuple, Union import torch import torch.nn as nn from torch.nn import CrossEntropyLoss import copy import os import sys dir_path = os.path.dirname(os.path.realpath(__file__)) sys.path.insert(0, dir_path) from transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig, LlamaModel, LlamaForCausalLM from transformers.modeling_outputs import CausalLMOutputWithPast from .configuration_mplug_owl2 import MPLUGOwl2Config, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig from .visual_encoder import MplugOwlVisionModel, MplugOwlVisualAbstractorModel from .modeling_llama2 import replace_llama_modality_adaptive IGNORE_INDEX = -100 IMAGE_TOKEN_INDEX = -200 DEFAULT_IMAGE_TOKEN = "<|image|>" from icecream import ic def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None): prompt_chunks = [tokenizer(chunk).input_ids if len(chunk) > 0 else [] for chunk in prompt.split(DEFAULT_IMAGE_TOKEN)] def insert_separator(X, sep): return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1] input_ids = [] offset = 0 if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id: offset = 1 input_ids.append(prompt_chunks[0][0]) for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)): input_ids.extend(x[offset:]) if return_tensors is not None: if return_tensors == 'pt': return torch.tensor(input_ids, dtype=torch.long) raise ValueError(f'Unsupported tensor type: {return_tensors}') return input_ids def expand2square(pil_img, background_color): width, height = pil_img.size if width == height: return pil_img elif width > height: result = Image.new(pil_img.mode, (width, width), background_color) result.paste(pil_img, (0, (width - height) // 2)) return result else: result = Image.new(pil_img.mode, (height, height), background_color) result.paste(pil_img, ((height - width) // 2, 0)) return result class MPLUGOwl2MetaModel: def __init__(self, config): super(MPLUGOwl2MetaModel, self).__init__(config) self.vision_model = MplugOwlVisionModel( MplugOwlVisionConfig(**config.visual_config["visual_model"]) ) self.visual_abstractor = MplugOwlVisualAbstractorModel( MplugOwlVisualAbstractorConfig(**config.visual_config["visual_abstractor"]), config.hidden_size ) def get_vision_tower(self): vision_model = getattr(self, 'vision_model', None) if type(vision_model) is list: vision_model = vision_model[0] return vision_model def get_visual_abstractor(self): visual_abstractor = getattr(self, 'visual_abstractor', None) if type(visual_abstractor) is list: visual_abstractor = visual_abstractor[0] return visual_abstractor class MPLUGOwl2MetaForCausalLM(ABC): @abstractmethod def get_model(self): pass def encode_images(self, images): image_features = self.get_model().vision_model(images).last_hidden_state image_features = self.get_model().visual_abstractor(encoder_hidden_states=image_features).last_hidden_state return image_features def prepare_inputs_labels_for_multimodal( self, input_ids, attention_mask, past_key_values, labels, images ): if images is None or input_ids.shape[1] == 1: if past_key_values is not None and images is not None and input_ids.shape[1] == 1: attention_mask = torch.ones((attention_mask.shape[0], past_key_values[-1][-1].shape[-2] + 1), dtype=attention_mask.dtype, device=attention_mask.device) multiway_indices = torch.zeros_like(input_ids).long().to(self.device) return input_ids, multiway_indices, attention_mask, past_key_values, None, labels if type(images) is list or images.ndim == 5: concat_images = torch.cat([image for image in images], dim=0) image_features = self.encode_images(concat_images) split_sizes = [image.shape[0] for image in images] image_features = torch.split(image_features, split_sizes, dim=0) image_features = [x.flatten(0, 1) for x in image_features] else: image_features = self.encode_images(images) new_input_embeds = [] new_modality_indicators = [] new_labels = [] if labels is not None else None cur_image_idx = 0 for batch_idx, cur_input_ids in enumerate(input_ids): if (cur_input_ids == IMAGE_TOKEN_INDEX).sum() == 0: # multimodal LLM, but the current sample is not multimodal # FIXME: this is a hacky fix, for deepspeed zero3 to work half_len = cur_input_ids.shape[0] // 2 cur_image_features = image_features[cur_image_idx] cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids[:half_len]) cur_input_embeds_2 = self.get_model().embed_tokens(cur_input_ids[half_len:]) cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0], cur_input_embeds_2], dim=0) new_input_embeds.append(cur_input_embeds) cur_modality_indicators = torch.zeros(len(cur_input_embeds)).long().to(self.device) new_modality_indicators.append(cur_modality_indicators) if labels is not None: new_labels.append(labels[batch_idx]) cur_image_idx += 1 continue image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] cur_new_input_embeds = [] cur_modality_indicators = [] if labels is not None: cur_labels = labels[batch_idx] cur_new_labels = [] assert cur_labels.shape == cur_input_ids.shape while image_token_indices.numel() > 0: cur_image_features = image_features[cur_image_idx] image_token_start = image_token_indices[0] cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids[:image_token_start])) cur_new_input_embeds.append(cur_image_features) # Add modality indicator assert image_token_start == len(cur_input_ids[:image_token_start]) cur_modality_indicators.append(torch.zeros(len(cur_input_ids[:image_token_start])).long()) cur_modality_indicators.append(torch.ones(len(cur_image_features)).long()) if labels is not None: cur_new_labels.append(cur_labels[:image_token_start]) cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=labels.device, dtype=labels.dtype)) cur_labels = cur_labels[image_token_start+1:] cur_image_idx += 1 cur_input_ids = cur_input_ids[image_token_start+1:] image_token_indices = torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0] if cur_input_ids.numel() > 0: cur_new_input_embeds.append(self.get_model().embed_tokens(cur_input_ids)) cur_modality_indicators.append(torch.zeros(len(cur_input_ids)).long()) if labels is not None: cur_new_labels.append(cur_labels) cur_new_input_embeds = [x.to(device=self.device) for x in cur_new_input_embeds] cur_new_input_embeds = torch.cat(cur_new_input_embeds, dim=0) new_input_embeds.append(cur_new_input_embeds) # Modality cur_modality_indicators = [x.to(device=self.device) for x in cur_modality_indicators] cur_modality_indicators = torch.cat(cur_modality_indicators, dim=0) new_modality_indicators.append(cur_modality_indicators) if labels is not None: cur_new_labels = torch.cat(cur_new_labels, dim=0) new_labels.append(cur_new_labels) if any(x.shape != new_input_embeds[0].shape for x in new_input_embeds): max_len = max(x.shape[0] for x in new_input_embeds) # Embedding new_input_embeds_align = [] for cur_new_embed in new_input_embeds: cur_new_embed = torch.cat((cur_new_embed, torch.zeros((max_len - cur_new_embed.shape[0], cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)), dim=0) new_input_embeds_align.append(cur_new_embed) new_input_embeds = torch.stack(new_input_embeds_align, dim=0) # Modality new_modality_indicators_align = [] for cur_modality_indicator in new_modality_indicators: cur_new_embed = torch.cat((cur_modality_indicator, torch.zeros(max_len - cur_modality_indicator.shape[0], dtype=cur_modality_indicator.dtype, device=cur_modality_indicator.device)), dim=0) new_modality_indicators_align.append(cur_new_embed) new_modality_indicators = torch.stack(new_modality_indicators_align, dim=0) # Label if labels is not None: new_labels_align = [] _new_labels = new_labels for cur_new_label in new_labels: cur_new_label = torch.cat((cur_new_label, torch.full((max_len - cur_new_label.shape[0],), IGNORE_INDEX, dtype=cur_new_label.dtype, device=cur_new_label.device)), dim=0) new_labels_align.append(cur_new_label) new_labels = torch.stack(new_labels_align, dim=0) # Attention Mask if attention_mask is not None: new_attention_mask = [] for cur_attention_mask, cur_new_labels, cur_new_labels_align in zip(attention_mask, _new_labels, new_labels): new_attn_mask_pad_left = torch.full((cur_new_labels.shape[0] - labels.shape[1],), True, dtype=attention_mask.dtype, device=attention_mask.device) new_attn_mask_pad_right = torch.full((cur_new_labels_align.shape[0] - cur_new_labels.shape[0],), False, dtype=attention_mask.dtype, device=attention_mask.device) cur_new_attention_mask = torch.cat((new_attn_mask_pad_left, cur_attention_mask, new_attn_mask_pad_right), dim=0) new_attention_mask.append(cur_new_attention_mask) attention_mask = torch.stack(new_attention_mask, dim=0) assert attention_mask.shape == new_labels.shape else: new_input_embeds = torch.stack(new_input_embeds, dim=0) new_modality_indicators = torch.stack(new_modality_indicators, dim=0) if labels is not None: new_labels = torch.stack(new_labels, dim=0) if attention_mask is not None: new_attn_mask_pad_left = torch.full((attention_mask.shape[0], new_input_embeds.shape[1] - input_ids.shape[1]), True, dtype=attention_mask.dtype, device=attention_mask.device) attention_mask = torch.cat((new_attn_mask_pad_left, attention_mask), dim=1) assert attention_mask.shape == new_input_embeds.shape[:2] return None, new_modality_indicators, attention_mask, past_key_values, new_input_embeds, new_labels class MPLUGOwl2LlamaModel(MPLUGOwl2MetaModel, LlamaModel): config_class = MPLUGOwl2Config def __init__(self, config: MPLUGOwl2Config): super(MPLUGOwl2LlamaModel, self).__init__(config) class MPLUGOwl2LlamaForCausalLM(LlamaForCausalLM, MPLUGOwl2MetaForCausalLM): config_class = MPLUGOwl2Config def __init__(self, config): super(LlamaForCausalLM, self).__init__(config) self.model = MPLUGOwl2LlamaModel(config) self.tokenizer = AutoTokenizer.from_pretrained("q-future/one-align") self.image_processor = CLIPImageProcessor.from_pretrained("q-future/one-align") self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) self.preferential_ids_ = [id_[1] for id_ in self.tokenizer(["excellent","good","fair","poor","bad"])["input_ids"]] self.weight_tensor = torch.Tensor([5.,4.,3.,2.,1.]).half().to(model.device) # Initialize weights and apply final processing self.post_init() def get_model(self): return self.model def score(self, images, task_: str = "quality", input_: str = "image", ): prompt = "USER: How would you rate the {} of this {}?\n<|image|>\nASSISTANT: The {} of the {} is".format(task_, input_, input_, task_) if input_ == "image": images = [expand2square(img, tuple(int(x*255) for x in self.image_processor.image_mean)) for img in images] input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device) with torch.inference_mode(): image_tensor = self.image_processor.preprocess(images, return_tensors="pt")["pixel_values"].half().to(self.device) output_logits = model(input_ids.repeat(image_tensor.shape[0], 1), images=image_tensor)["logits"][:,-1, self.preferential_ids_] return torch.softmax(output_logits, -1) @ self.weight_tensor else: video = [[expand2square(frame, tuple(int(x*255) for x in self.image_processor.image_mean)) for frame in vid] for vid in images] with torch.inference_mode(): video_tensors = [self.image_processor.preprocess(vid, return_tensors="pt")["pixel_values"].half().to(self.model.device) for vid in video] output_logits = self.model(self.input_ids.repeat(len(video_tensors), 1), images=video_tensors)["logits"][:,-1, self.preferential_ids_] return torch.softmax(output_logits, -1) @ self.weight_tensor def forward( self, input_ids: torch.LongTensor = None, # modality_indicators: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, images: Optional[torch.FloatTensor] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict input_ids, modality_indicators, attention_mask, past_key_values, inputs_embeds, labels = \ self.prepare_inputs_labels_for_multimodal(input_ids, attention_mask, past_key_values, labels, images) # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) outputs = self.model( input_ids=input_ids, modality_indicators=modality_indicators, attention_mask=attention_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict ) hidden_states = outputs[0] logits = self.lm_head(hidden_states) loss = None if labels is not None: # Shift so that tokens < n predict n shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() # Flatten the tokens loss_fct = CrossEntropyLoss() shift_logits = shift_logits.view(-1, self.config.vocab_size) shift_labels = shift_labels.view(-1) # Enable model/pipeline parallelism shift_labels = shift_labels.to(shift_logits.device) loss = loss_fct(shift_logits, shift_labels) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs ): if past_key_values: input_ids = input_ids[:, -1:] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids} model_inputs.update( { "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask, "images": kwargs.get("images", None), } ) return model_inputs AutoConfig.register("mplug_owl2", MPLUGOwl2Config) AutoModelForCausalLM.register(MPLUGOwl2Config, MPLUGOwl2LlamaForCausalLM) replace_llama_modality_adaptive() if __name__ == "__main__": config = MPLUGOwl2Config.from_pretrained('q-future/one-align') from icecream import ic # config = MPLUGOwl2Config() model = AutoModelForCausalLM(config) images = torch.randn(2, 3, 448, 448) input_ids = torch.cat([ torch.ones(8).long(), torch.tensor([-1]*1).long(), torch.ones(8).long(), torch.tensor([-1]*1).long(), torch.ones(8).long() ], dim=0).unsqueeze(0) labels = input_ids.clone() labels[labels < 0] = -100 # image_feature = model.encode_images(images) # ic(image_feature.shape) output = model(images=images, input_ids=input_ids, labels=labels) ic(output.loss) ic(output.logits.shape) model.save_pretrained('/cpfs01/shared/public/test/tmp_owl')