# 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. import os import sys from abc import ABC, abstractmethod from typing import List, Optional, Tuple, Union import torch import torch.distributed as dist import torch.nn as nn import torch.nn.functional as F from torch.nn import CrossEntropyLoss dir_path = os.path.dirname(os.path.realpath(__file__)) sys.path.insert(0, dir_path) from transformers import (AutoConfig, AutoModelForCausalLM, LlamaForCausalLM, LlamaModel) from transformers.modeling_outputs import CausalLMOutputWithPast from .configuration_mplug_owl2 import (MPLUGOwl2Config, MplugOwlVisionConfig, MplugOwlVisualAbstractorConfig) from .modeling_llama2 import replace_llama_modality_adaptive from .utils import extend_list, find_prefix from .visual_encoder import MplugOwlVisionModel, MplugOwlVisualAbstractorModel IGNORE_INDEX = -100 IMAGE_TOKEN_INDEX = -200 DEFAULT_IMAGE_TOKEN = "<|image|>" from icecream import ic 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.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) # Initialize weights and apply final processing self.post_init() def get_model(self): return self.model def forward(self, input_type=None, **kwargs): if input_type is None: return self.forward_single(**kwargs) elif input_type == "single": kwargs_desp = self.get_subitem(kwargs, task_type="description") kwargs_score = self.get_subitem(kwargs, task_type="score") loss_desp = 0 if len(kwargs_desp["task_types"]) > 0: del kwargs_desp["task_types"] output_desp = self.forward_single(**kwargs_desp) loss_desp = output_desp.loss loss_score = 0 if len(kwargs_score["task_types"]) > 0: del kwargs_score["task_types"] output_score = self.forward_single( use_softkl_loss=self.config.softkl_loss, **kwargs_score, ) loss_score = output_score.loss if dist.get_rank() == 0: loss_desp_item = loss_desp if type(loss_desp) == int else loss_desp.item() loss_score_item = loss_score if type(loss_score) == int else loss_score.item() print( f"[loss (w/o weight) | " f"description loss: {round(loss_desp_item, 6)}, " f"score loss: {round(loss_score_item, 6)}]" ) loss = self.config.weight_desp * loss_desp + self.config.weight_next_token * loss_score return CausalLMOutputWithPast(loss=loss) elif input_type == "pair": return self.forward_pair(**kwargs) else: raise ValueError def softkl_loss(self, logits, labels, level_probs): batch_size = logits.shape[0] level_prefix = torch.tensor(self.config.level_prefix).to(labels.device) idx_prefix_label = find_prefix(labels, level_prefix) # B idx_level_label = idx_prefix_label + level_prefix.shape[0] level_ids_label = labels[torch.arange(batch_size), idx_level_label] for level_id in level_ids_label: assert level_id in self.config.level_ids # After padding in prepare_inputs_labels_for_multimodal(), the length of labels will be the same as logits assert logits.shape[1] == labels.shape[1] idx_level_logit = idx_level_label - 1 logits_level_ids = logits[ torch.arange(batch_size), idx_level_logit ].contiguous() # [B, V] preds = torch.softmax(logits_level_ids, dim=1) # [B, V] target = torch.zeros_like(preds) # [B, V] target[:, self.config.level_ids] = level_probs target = target.detach() pred_log = torch.log(preds) loss_kl = F.kl_div(pred_log, target, reduction="batchmean") return loss_kl, idx_level_label, idx_level_logit def forward_single( 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, use_softkl_loss: Optional[bool] = None, level_probs: Optional[torch.Tensor] = 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_kl = None if use_softkl_loss and labels is not None: loss_kl, idx_level_label, idx_level_logit = self.softkl_loss(logits, labels, level_probs) def del_elements(source, idx): """source: [B, N] / [B, N, V], idx: [B, ] with the value range [0, N-1]""" mask = torch.ones([*source.shape[:2]], dtype=torch.bool) for idx_1, idx_del in enumerate(idx): mask[idx_1, idx_del] = False if len(source.shape) == 2: source_del = source[mask].view(source.size(0), source.size(1)-1) else: assert len(source.shape) == 3 source_del = source[mask].view(source.size(0), source.size(1)-1, source.size(2)) return source_del labels_del = del_elements(labels, idx_level_label) logits_del = del_elements(logits, idx_level_logit) loss = None if labels is not None: # Shift so that tokens < n predict n if loss_kl is None: shift_logits = logits[..., :-1, :].contiguous() shift_labels = labels[..., 1:].contiguous() else: shift_logits = logits_del[..., :-1, :].contiguous() shift_labels = labels_del[..., 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 if loss is not None and loss_kl is not None: loss = loss + self.config.weight_softkl * loss_kl return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) def get_score(self, item): outputs = self.forward_single( input_ids=item["input_ids"], attention_mask=item["attention_mask"], labels=item["labels"], images=item["images"], return_dict=True, use_softkl_loss=self.config.softkl_loss, level_probs=item["level_probs"], ) batch_size = outputs.logits.shape[0] level_prefix = torch.tensor(self.config.level_prefix).to(item["labels"].device) idx_prefix_label = find_prefix(item["labels"], level_prefix) # B idx_level_label = idx_prefix_label + level_prefix.shape[0] level_ids_label = item["labels"][torch.arange(batch_size), idx_level_label] for level_id in level_ids_label: assert level_id in self.config.level_ids num_vision_tokens = outputs.logits.shape[1] - item["labels"].shape[1] idx_level_logit = idx_level_label + num_vision_tokens - 1 logits_level_ids = outputs.logits[ torch.arange(batch_size), idx_level_logit ].contiguous() # [B, V] probs_org = torch.softmax(logits_level_ids, dim=1) # [B, V] loss_in_level = 1 - probs_org[:, self.config.level_ids].contiguous().sum(dim=1) # [B, 5] -> [B, ] bound = torch.tensor(1e-2).to(loss_in_level) loss_in_level = torch.max(bound, loss_in_level.mean()) # level prob > 0.99 if self.config.closeset_rating_loss: logits_levels = logits_level_ids[:, self.config.level_ids].contiguous() probs = torch.softmax(logits_levels, dim=1) else: probs = probs_org[:, self.config.level_ids].contiguous() weights = torch.tensor([5, 4, 3, 2, 1]).to(probs) scores = torch.matmul(probs, weights) variances = (weights.repeat(batch_size, 1) - scores.unsqueeze(1)) ** 2 stds = torch.sqrt(torch.sum(probs * variances, dim=1)) return scores, stds, outputs.loss, loss_in_level def get_subitem(self, item, task_type): for key in list(item.keys()): if item[key] is None: del item[key] subitem = {} for key in item: subitem[key] = [] for idx in range(len(item["task_types"])): if item["task_types"][idx] == task_type: for key in item: subitem[key].append(item[key][idx]) batch_size = torch.tensor(len(subitem["task_types"])).cuda() world_size = dist.get_world_size() batch_size_allrank = [torch.tensor(0).cuda() for _ in range(world_size)] dist.barrier() dist.all_gather(batch_size_allrank, batch_size) batch_size_max = torch.stack(batch_size_allrank, dim=0).max().item() batch_size_min = torch.stack(batch_size_allrank, dim=0).min().item() for key in item: subitem[key] = extend_list(subitem[key], batch_size_max, batch_size_min) if torch.is_tensor(item[key]) and len(subitem[key]): subitem[key] = torch.stack(subitem[key], dim=0) return subitem def forward_pair(self, item_A, item_B, **kwargs): item_A_desp = self.get_subitem(item_A, task_type="description") item_B_desp = self.get_subitem(item_B, task_type="description") assert item_A_desp["task_types"] == item_B_desp["task_types"] item_A_score = self.get_subitem(item_A, task_type="score") item_B_score = self.get_subitem(item_B, task_type="score") assert item_A_score["task_types"] == item_B_score["task_types"] # calculate loss_desp for description tasks loss_desp = 0 if len(item_A_desp["task_types"]) > 0: outputs = self.forward_single( input_ids=item_A_desp["input_ids"], attention_mask=item_A_desp["attention_mask"], labels=item_A_desp["labels"], images=item_A_desp["images"], return_dict=True, use_softkl_loss=False, ) loss_desp = outputs.loss # calculate loss_score for score tasks loss_score = 0 if len(item_A_score["task_types"]) > 0: gt_scores_A = item_A_score["gt_scores"] pred_scores_A, pred_stds_A, loss_next_token_A, loss_in_level_A = self.get_score(item_A_score) gt_scores_B = item_B_score["gt_scores"] pred_scores_B, pred_stds_B, loss_next_token_B, loss_in_level_B = self.get_score(item_B_score) if not self.config.continuous_rating_loss: loss_rank = self.binary_rating_loss(pred_scores_A, gt_scores_A, pred_scores_B, gt_scores_B) else: gt_stds_A = item_A_score["stds"] gt_stds_B = item_B_score["stds"] assert (gt_stds_A >= 0).all() and (gt_stds_B >= 0).all() loss_rank = self.rating_loss( pred_scores_A, pred_stds_A, gt_scores_A, gt_stds_A, pred_scores_B, pred_stds_B, gt_scores_B, gt_stds_B, ) loss_next_token = loss_next_token_A + loss_next_token_B loss_in_level = loss_in_level_A + loss_in_level_B if dist.get_rank() == 0: print( f"[score loss (w/o weight) | " f"ranking loss: {round(loss_rank.item(), 6)}, " f"next token loss: {round(loss_next_token.item(), 6)}, " f"in level loss: {round(loss_in_level.item(), 6)}]" ) loss_rank = self.config.weight_rank * loss_rank if self.config.weight_next_token: assert self.config.weight_next_token > 0 loss_next_token = self.config.weight_next_token * loss_next_token else: loss_next_token = 0 if self.config.weight_in_level: assert self.config.weight_in_level > 0 loss_in_level = self.config.weight_in_level * loss_in_level else: loss_in_level = 0 loss_score = loss_rank + loss_next_token + loss_in_level if dist.get_rank() == 0: loss_desp_item = loss_desp if type(loss_desp) == int else loss_desp.item() loss_score_item = loss_score if type(loss_score) == int else loss_score.item() print( f"[loss (w/o weight) | " f"description loss: {round(loss_desp_item, 6)}, " f"score loss: {round(loss_score_item, 6)}]" ) loss = self.config.weight_desp * loss_desp + loss_score return CausalLMOutputWithPast(loss=loss) def rating_loss( self, pred_scores_A, pred_stds_A, gt_scores_A, gt_stds_A, pred_scores_B, pred_stds_B, gt_scores_B, gt_stds_B, ): # eps=1e-8 is important. eps=0 is unable to step, and lr keeps unchanged. eps = 1e-8 if self.config.use_fix_std: pred = 0.5 * (1 + torch.erf((pred_scores_A - pred_scores_B) / 2)) # 2 -> sqrt(2 * (1**2 + 1**2)) else: pred_var = pred_stds_A * pred_stds_A + pred_stds_B * pred_stds_B + eps if self.config.detach_pred_std: pred_var = pred_var.detach() pred = 0.5 * (1 + torch.erf((pred_scores_A - pred_scores_B) / torch.sqrt(2 * pred_var))) gt_var = gt_stds_A * gt_stds_A + gt_stds_B * gt_stds_B + eps gt = 0.5 * (1 + torch.erf((gt_scores_A - gt_scores_B) / torch.sqrt(2 * gt_var))).to(pred.device) gt = gt.detach() loss = (1 - (pred * gt + eps).sqrt() - ((1 - pred) * (1 - gt) + eps).sqrt()).mean() return loss def binary_rating_loss(self, pred_scores_A, gt_scores_A, pred_scores_B, gt_scores_B): pred = 0.5 * (1 + torch.erf((pred_scores_A - pred_scores_B) / 2)) # 2 -> sqrt(2 * (1**2 + 1**2)) gt = (gt_scores_A > gt_scores_B).to(pred.dtype).to(pred.device) gt = gt.detach() if self.config.binary_rating_loss == "bce": loss = F.binary_cross_entropy(pred, gt) elif self.config.binary_rating_loss == "fidelity": loss_1 = 1 - pred[gt == 1].sqrt() loss_2 = 1 - (1 - pred[gt == 0]).sqrt() loss = (loss_1.sum() + loss_2.sum()) / pred_scores_A.shape[0] else: raise NotImplementedError(f"Wrong type of binary_rating_loss: {self.config.binary_rating_loss}") return loss def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, images=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": images, } ) return model_inputs AutoConfig.register("mplug_owl2", MPLUGOwl2Config) AutoModelForCausalLM.register(MPLUGOwl2Config, MPLUGOwl2LlamaForCausalLM) replace_llama_modality_adaptive() if __name__ == "__main__": config = MPLUGOwl2Config.from_pretrained("zhiyuanyou/DeQA-Score-Mix3") 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)