# 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 from transformers import AutoConfig, AutoModelForCausalLM from .modeling_llama2_mam import LlamaConfig, LlamaModel, LlamaForCausalLM from transformers.modeling_outputs import CausalLMOutputWithPast from .configuration_mplug_docowl import (MPLUGDocOwlConfig, MplugOwlVisionConfig, MplugDocOwlHReducerConfig, MplugDocOwlHRDocCompressorConfig) from .visual_encoder import MplugOwlVisionModel, MplugDocOwlHReducerModel from .visual_compressor import MplugDocOwlHRDocCompressor from .processor import DocProcessor from .constants import IMAGE_TOKEN_INDEX, IGNORE_INDEX from icecream import ic from transformers import StoppingCriteria, TextStreamer class KeywordsStoppingCriteria(StoppingCriteria): def __init__(self, keywords, tokenizer, input_ids): self.keywords = keywords self.keyword_ids = [] self.max_keyword_len = 0 for keyword in keywords: cur_keyword_ids = tokenizer(keyword).input_ids if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id: cur_keyword_ids = cur_keyword_ids[1:] if len(cur_keyword_ids) > self.max_keyword_len: self.max_keyword_len = len(cur_keyword_ids) self.keyword_ids.append(torch.tensor(cur_keyword_ids)) self.tokenizer = tokenizer self.start_len = input_ids.shape[1] def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)" # TODO offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len) self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids] for keyword_id in self.keyword_ids: if (output_ids[0, -keyword_id.shape[0]:] == keyword_id).all(): return True outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0] for keyword in self.keywords: if keyword in outputs: return True return False class MPLUGDocOwlMetaModel: _no_split_modules = ["MplugOwlVisionModel", "MplugDocOwlHReducerModel", "MplugDocOwlHRDocCompressor"] def __init__(self, config): super(MPLUGDocOwlMetaModel, self).__init__(config) self.vision_model = MplugOwlVisionModel( MplugOwlVisionConfig(**config.visual_config["visual_model"]) ) v_img_row_tokens = int((config.visual_config["visual_model"]['image_size']/config.visual_config["visual_model"]['patch_size'])) v_img_col_tokens = v_img_row_tokens self.vision2text = MplugDocOwlHReducerModel( MplugDocOwlHReducerConfig(**config.visual_config["visual_hreducer"]), config.hidden_size ) horizontal_reduce = int(config.visual_config["visual_hreducer"]['conv_shape'].split('x')[1]) v2t_img_col_tokens = int(v_img_row_tokens / horizontal_reduce) self.hr_compressor = MplugDocOwlHRDocCompressor( MplugDocOwlHRDocCompressorConfig(**config.visual_config["visual_hrcompressor"]), config.hidden_size, v2t_img_col_tokens ) 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_vision2text(self): vision2text = getattr(self, 'vision2text', None) if type(vision2text) is list: vision2text = vision2text[0] return vision2text def get_hrcompressor(self): hrcompressor = getattr(self, 'hr_compressor', None) if type(hrcompressor) is list: hrcompressor = hrcompressor[0] return hrcompressor class MPLUGDocOwlMetaForCausalLM(ABC): @abstractmethod def get_model(self): pass def encode_images(self, images, patch_positions): image_features = self.get_model().vision_model(images).last_hidden_state image_features = self.get_model().vision2text(encoder_hidden_states=image_features) image_features = self.get_model().hr_compressor(hidden_states=image_features, patch_positions=patch_positions) return image_features def prepare_inputs_labels_for_multimodal( self, input_ids, attention_mask, past_key_values, labels, images, patch_positions ): # ic(images.shape, patch_positions.shape) 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, patch_positions) 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, patch_positions) # Sum(Crop Image Number) x L x d 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 MPLUGDocOwlLlamaModel(MPLUGDocOwlMetaModel, LlamaModel): config_class = MPLUGDocOwlConfig def __init__(self, config: MPLUGDocOwlConfig): super(MPLUGDocOwlLlamaModel, self).__init__(config) class MPLUGDocOwl2(LlamaForCausalLM, MPLUGDocOwlMetaForCausalLM): config_class = MPLUGDocOwlConfig def __init__(self, config): super(LlamaForCausalLM, self).__init__(config) self.model = MPLUGDocOwlLlamaModel(config) self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def init_processor(self, tokenizer, basic_image_size, crop_anchors): self.processor = DocProcessor(tokenizer=tokenizer, image_size=basic_image_size, anchors=crop_anchors) return self.processor def get_model(self): return self.model 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, patch_positions: Optional[torch.LongTensor] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, CausalLMOutputWithPast]: # print('modeling_mplug_docow2.py patch_positions:', patch_positions) 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, patch_positions) # ic(inputs_embeds.shape, labels.shape) # 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 ) # ic(outputs[0].shape) 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) # ic(loss.shape) 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), "patch_positions": kwargs.get("patch_positions", None), } ) return model_inputs def chat(self, messages, images, tokenizer): streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) image_tensor, patch_positions, input_ids = self.processor(images=images, messages=messages) image_tensor = image_tensor.to(self.model.device, dtype=torch.float16) patch_positions = patch_positions.to(self.model.device) input_ids = input_ids.unsqueeze(0).to(self.model.device) stopping_criteria = KeywordsStoppingCriteria([""], tokenizer, input_ids) with torch.inference_mode(): output_ids = self.generate( input_ids, images=image_tensor, patch_positions=patch_positions, do_sample=False, temperature=1.0, max_new_tokens=512, streamer=streamer, use_cache=True, stopping_criteria=[stopping_criteria]) outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip() return outputs.replace('', '') AutoConfig.register("mplug_docowl", MPLUGDocOwlConfig) AutoModelForCausalLM.register(MPLUGDocOwlConfig, MPLUGDocOwl2)