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config.json

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+{
+  "_name_or_path": "./ckpt/vicuna-7b-v1.5",
+  "architectures": [
+    "MGMLlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "freeze_mm_mlp_adapter": false,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "image_aspect_ratio": "pad",
+  "image_global": false,
+  "image_grid": 1,
+  "image_grid_pinpoints": null,
+  "image_size_aux": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 4096,
+  "mm_hidden_size": 3072,
+  "mm_hidden_size_aux": 2880,
+  "mm_hidden_size_uni": 1024,
+  "mm_projector_lr": null,
+  "mm_projector_type": "mlp2x_gelu",
+  "mm_use_im_patch_token": false,
+  "mm_use_im_start_end": false,
+  "mm_vision_select_feature": "patch",
+  "mm_vision_select_layer": -2,
+  "mm_vision_tower": "./ckpt/clip-vit-large-patch14-336",
+  "mm_vision_tower_aux": "./cache_dir/openclip-convnext-large-d-320-laion2B-s29B-b131K-ft-soup",
+  "model_type": "mgm",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "optimize_vision_tower": false,
+  "optimize_vision_tower_aux": false,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "tokenizer_model_max_length": 2048,
+  "tokenizer_padding_side": "right",
+  "torch_dtype": "float16",
+  "transformers_version": "4.36.2",
+  "tune_mm_mlp_adapter": false,
+  "use_cache": true,
+  "use_mm_proj": true,
+  "vocab_size": 32000
+}

mgm_arch.py

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+#    Copyright 2023 Haotian Liu
+#
+#    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.
+# ------------------------------------------------------------------------
+# Modified from LLaVA (https://github.com/haotian-liu/LLaVA)
+# Copyright 2024 Yanwei Li
+# ------------------------------------------------------------------------
+
+from abc import ABC, abstractmethod
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import json
+import os
+import transformers
+import safetensors
+from transformers.deepspeed import is_deepspeed_zero3_enabled
+import deepspeed
+
+from .multimodal_encoder.builder import build_vision_tower, build_vision_tower_aux
+from .multimodal_projector.builder import build_vision_projector
+
+from mgm.constants import (IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, 
+                             DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN)
+
+IS_NEW_TRANSFORMERS = transformers.__version__ >= "4.34.0"
+
+class MGMMetaModel:
+
+    def __init__(self, config):
+        super(MGMMetaModel, self).__init__(config)
+
+        if hasattr(config, "mm_vision_tower"):
+            self.vision_tower = build_vision_tower(config, delay_load=True)
+            self.mm_projector = build_vision_projector(config)
+
+        if hasattr(config, "mm_vision_tower_aux"):
+            self.vision_tower_aux = build_vision_tower_aux(config, delay_load=True)
+
+    def get_vision_tower(self):
+        vision_tower = getattr(self, 'vision_tower', None)
+        if type(vision_tower) is list:
+            vision_tower = vision_tower[0]
+        return vision_tower
+
+    def get_vision_tower_aux(self):
+        vision_tower_aux = getattr(self, 'vision_tower_aux', None)
+        if type(vision_tower_aux) is list:
+            vision_tower_aux = vision_tower_aux[0]
+        return vision_tower_aux
+
+    def initialize_vision_modules(self, model_args, fsdp=None):
+        vision_tower = model_args.vision_tower
+        vision_tower_aux = model_args.vision_tower_aux
+        mm_vision_select_layer = model_args.mm_vision_select_layer
+        mm_vision_select_feature = model_args.mm_vision_select_feature
+        pretrain_mm_mlp_adapter = model_args.pretrain_mm_mlp_adapter
+
+        self.config.mm_vision_tower = vision_tower
+        self.config.mm_vision_tower_aux = vision_tower_aux
+
+        if self.get_vision_tower() is None:
+            vision_tower = build_vision_tower(model_args)
+
+            if fsdp is not None and len(fsdp) > 0:
+                self.vision_tower = [vision_tower]
+            else:
+                self.vision_tower = vision_tower
+        else:
+            if fsdp is not None and len(fsdp) > 0:
+                vision_tower = self.vision_tower[0]
+            else:
+                vision_tower = self.vision_tower
+            vision_tower.load_model()
+
+        if vision_tower_aux is not None:
+            if self.get_vision_tower_aux() is None:
+                vision_tower_aux = build_vision_tower_aux(model_args)
+
+                if fsdp is not None and len(fsdp) > 0:
+                    self.vision_tower_aux = [vision_tower_aux]
+                else:
+                    self.vision_tower_aux = vision_tower_aux
+            else:
+                if fsdp is not None and len(fsdp) > 0:
+                    vision_tower_aux = self.vision_tower_aux[0]
+                else:
+                    vision_tower_aux = self.vision_tower_aux
+                vision_tower_aux.load_model()
+            self.config.mm_hidden_size_aux = vision_tower_aux.hidden_size
+
+        self.config.use_mm_proj = True
+        self.config.mm_projector_type = getattr(model_args, 'mm_projector_type', 'linear')
+        # self.config.mm_hidden_size = vision_tower.hidden_size
+        self.config.mm_hidden_size = 3072
+        self.config.mm_hidden_size_uni = vision_tower.hidden_size
+        self.config.mm_vision_select_layer = mm_vision_select_layer
+        self.config.mm_vision_select_feature = mm_vision_select_feature
+
+        if getattr(self, 'mm_projector', None) is None:
+            self.mm_projector = build_vision_projector(self.config)
+        else:
+            # In case it is frozen by LoRA
+            for p in self.mm_projector.parameters():
+                p.requires_grad = True
+
+        if pretrain_mm_mlp_adapter is not None:
+            mm_projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
+            def get_w(weights, keyword):
+                return {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword + '.' in k}
+
+            if 'model' in mm_projector_weights.keys():
+                mm_projector_weights = mm_projector_weights['model']
+                if is_deepspeed_zero3_enabled():
+                    if len(mm_projector_weights) > 0:
+                        with deepspeed.zero.GatheredParameters(mm_projector_weights, modifier_rank=0):
+                            if torch.distributed.get_rank() == 0:
+                                self.mm_projector.load_state_dict(mm_projector_weights)
+                else:
+                    status = self.mm_projector.load_state_dict(mm_projector_weights, strict=False)
+                    print('missing_keys:', status.missing_keys)
+            else:
+                if is_deepspeed_zero3_enabled():
+                    named_parameters = get_w(mm_projector_weights, 'mm_projector')
+                    if len(named_parameters) > 0:
+                        with deepspeed.zero.GatheredParameters(named_parameters, modifier_rank=0):
+                            if torch.distributed.get_rank() == 0:
+                                self.mm_projector.load_state_dict(named_parameters)
+                else:
+                    status = self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'), strict=False)
+                    print('missing_keys:', status.missing_keys)
+            self.mm_projector = self.mm_projector.to(device='cuda')
+
+    def initialize_uni_modules(self, model_args, for_eval=False):  
+        pretrain_mm_mlp_adapter = getattr(model_args, "pretrain_mm_mlp_adapter", None)
+        self.config.image_size_aux = getattr(model_args, 'image_size_aux', 320)
+        self.config.optimize_vision_tower = getattr(model_args, 'optimize_vision_tower', False)
+        self.config.optimize_vision_tower_aux = getattr(model_args, 'optimize_vision_tower_aux', False)
+
+        self.vlm_uni_query_projector  = nn.Sequential(nn.LayerNorm(self.config.mm_hidden_size_uni), 
+                                                      nn.Linear(self.config.mm_hidden_size_uni, self.config.mm_hidden_size_uni))
+        self.vlm_uni_aux_projector  = nn.Sequential(nn.LayerNorm(self.config.mm_hidden_size_aux),
+                                                    nn.Linear(self.config.mm_hidden_size_aux, self.config.mm_hidden_size_uni))
+        self.vlm_uni_val_projector  = nn.Sequential(nn.LayerNorm(self.config.mm_hidden_size_aux),
+                                                    nn.Linear(self.config.mm_hidden_size_aux, self.config.mm_hidden_size_uni))
+        
+        if pretrain_mm_mlp_adapter is not None:
+            projector_weights = torch.load(pretrain_mm_mlp_adapter, map_location='cpu')
+        else:
+            trainable_module = ['vlm_uni', 'vision_fpn', 'vision_stages']
+            if hasattr(model_args, 'model_name_or_path'):
+                model_save_path = model_args.model_name_or_path
+            else:
+                model_save_path = model_args.model_path
+            model_idx_path = getattr(model_args, 'model_path', model_save_path)
+            if IS_NEW_TRANSFORMERS:
+                try:
+                    weight_file = json.load(open(os.path.join(model_idx_path, 'model.safetensors.index.json'), 'r'))['weight_map']
+                except:
+                    weight_file = json.load(open(os.path.join(model_idx_path, 'pytorch_model.bin.index.json'), 'r'))['weight_map']
+            else:
+                weight_file = json.load(open(os.path.join(model_idx_path, 'pytorch_model.bin.index.json'), 'r'))['weight_map']
+            model_path = set([weight_file[_key] for _key in weight_file if any([_module in _key for _module in trainable_module])])
+            projector_weights = {}
+            for _model in model_path:
+                if not IS_NEW_TRANSFORMERS:
+                    projector_weights.update(torch.load(os.path.join(model_idx_path, _model), map_location='cpu'))
+                else:
+                    with safetensors.safe_open(os.path.join(model_idx_path, _model), framework="pt", device='cpu') as f:
+                        for _key in f.keys():
+                            projector_weights.update({_key: f.get_tensor(_key)})
+            if len(projector_weights) == 0:
+                return
+
+        def get_w(weights, keyword, main_module, sub_module):
+            if getattr(main_module, sub_module, None) is None:
+                return
+            
+            pretrain_weight = {k.split(keyword + '.')[1]: v for k, v in weights.items() if keyword + '.' in k}
+            if len(pretrain_weight) == 0:
+                return
+            if is_deepspeed_zero3_enabled():
+                named_parameters = [v for k, v in getattr(main_module, sub_module).named_parameters()]
+                if len(named_parameters) > 0:
+                    # because zero3 puts placeholders in model params, this context
+                    # manager gathers (unpartitions) the params of the current layer, then loads from
+                    # the state dict and then re-partitions them again
+                    with deepspeed.zero.GatheredParameters(named_parameters, modifier_rank=0):
+                        if torch.distributed.get_rank() == 0:
+                            getattr(main_module, sub_module).load_state_dict(pretrain_weight)
+                    with deepspeed.zero.GatheredParameters(self.mm_projector[0].weight, modifier_rank=None):
+                        weight_type = self.mm_projector[0].weight.dtype
+                        device_type = self.mm_projector[0].weight.device
+            else:
+                weight_type = self.mm_projector[0].weight.dtype
+                device_type = self.mm_projector[0].weight.device
+                getattr(main_module, sub_module).load_state_dict(pretrain_weight)
+            if weight_type == torch.uint8 or weight_type == torch.int8 or weight_type == torch.int16:
+                weight_type = torch.float16
+            getattr(main_module, sub_module).to(device=device_type, dtype=weight_type)
+            print(f"Loading {sub_module} weights...")
+        
+        # load pretrained weights
+        get_w(projector_weights, 'vision_tower.vision_tower', self.vision_tower, 'vision_tower')
+
+        # load pretrained weights
+        if self.config.optimize_vision_tower_aux:
+            # not optimize vision stem, just used to check
+            get_w(projector_weights, 'vision_tower_aux.vision_stem', self.vision_tower_aux, 'vision_stem')
+            get_w(projector_weights, 'vision_tower_aux.vision_stages', self.vision_tower_aux, 'vision_stages')
+        get_w(projector_weights, 'vlm_uni_query_projector', self, 'vlm_uni_query_projector')
+        get_w(projector_weights, 'vlm_uni_aux_projector', self, 'vlm_uni_aux_projector')
+        get_w(projector_weights, 'vlm_uni_val_projector', self, 'vlm_uni_val_projector')
+    
+class MGMMetaForCausalLM(ABC):
+
+    @abstractmethod
+    def get_model(self):
+        pass
+
+    def get_vision_tower(self):
+        return self.get_model().get_vision_tower()
+
+    def get_vision_tower_aux(self):
+        return self.get_model().get_vision_tower_aux()
+
+    def encode_images(self, images, images_aux=None, is_video=False):
+        image_grid = getattr(self.config, 'image_grid', 1)
+        image_global = getattr(self.config, 'image_global', False)
+        if image_grid > 1:
+            batch_size = images.shape[0]
+            if image_global:
+                global_images = images[:, -1:].flatten(0,1).contiguous()
+                grid_images = images[:, :-1].flatten(0,1).contiguous()
+                images = torch.cat([grid_images, global_images], dim=0)
+            else:
+                images = images.flatten(0,1).contiguous()
+        
+        image_features, image_forward_outs = self.get_model().get_vision_tower()(images)
+        
+        if image_global:    # false
+            image_feat_global = image_features[-len(global_images):]
+            image_features = image_features[:len(grid_images)]
+        
+        if images_aux is not None:
+            image_aux_features_raw = self.get_model().get_vision_tower_aux()(images_aux).to(
+                dtype=image_features.dtype, device=image_features.device)
+            
+            if image_global:
+                image_aux_features_global = F.interpolate(image_aux_features_raw.float(), 
+                                                            scale_factor=1/image_grid, 
+                                                            mode='bilinear', 
+                                                            align_corners=False).to(dtype=image_aux_features_raw.dtype)
+                image_feat_global, image_aux_feat_global = self.unified_resampler(image_feat_global, image_aux_features_global)
+
+            if image_grid > 1:
+                image_aux_features_raw = image_aux_features_raw.reshape(*image_aux_features_raw.shape[:2],
+                                                                        image_grid,
+                                                                        image_aux_features_raw.shape[-2]//image_grid,
+                                                                        image_grid,
+                                                                        image_aux_features_raw.shape[-1]//image_grid)
+                image_aux_features_raw = image_aux_features_raw.permute(0, 2, 4, 1, 3, 5).flatten(1,2).flatten(0,1).contiguous()
+            image_features, image_aux_features = self.unified_resampler(image_features, image_aux_features_raw)
+            
+            if image_grid > 1:
+                image_features = image_features.reshape(batch_size, image_grid**2, *image_features.shape[1:])
+                image_features = image_features.flatten(1,2).contiguous()
+                image_aux_features = image_aux_features.reshape(batch_size, image_grid**2, *image_aux_features.shape[1:])
+                image_aux_features = image_aux_features.flatten(1,2).contiguous()
+            
+            # add global features, [global, local]
+            if image_global:
+                image_features = torch.cat([image_feat_global, image_features], dim=1)
+                image_aux_features = torch.cat([image_aux_feat_global, image_aux_features], dim=1)
+            
+            # token generation
+            image_features = image_features + image_aux_features
+
+        # dense connector
+        image_features_1 = []
+        image_features_2 = []
+        for i in range(0, 12):
+            image_features_1.append(image_forward_outs.hidden_states[i][:, 1:].to(image_features.dtype))
+        image_features_1 = torch.stack(image_features_1, dim=0)
+        image_features_1 = torch.sum(image_features_1, dim=0) / 12
+        for i in range(12, 24):
+            image_features_2.append(image_forward_outs.hidden_states[i][:, 1:].to(image_features.dtype))
+        image_features_2 = torch.stack(image_features_2, dim=0)
+        image_features_2 = torch.sum(image_features_2, dim=0) / 12
+
+        image_features = torch.cat([image_features, image_features_1, image_features_2], dim=-1)
+        ## dense connector end
+        
+        # process image features after token generation
+        image_features = self.get_model().mm_projector(image_features)
+        
+        return image_features
+
+    def unified_resampler(self, images, images_aux):
+        # patchwise with square images
+        patch_num = int(images.shape[1]**0.5)
+        patch_size = images_aux.shape[-1]//patch_num
+        # within patch attention
+        images_aux = images_aux.permute(0,2,3,1)
+        images_aux = images_aux.reshape(len(images_aux), patch_num, patch_size, patch_num, patch_size, images_aux.shape[-1])
+        images_aux = images_aux.permute(0,1,3,2,4,5)
+        images_aux = images_aux.reshape(len(images_aux), patch_num**2, patch_size**2, images_aux.shape[-1]).contiguous()
+
+        # token attention
+        embed_query = self.get_model().vlm_uni_query_projector(images)
+        embed_aux = self.get_model().vlm_uni_aux_projector(images_aux)
+        embed_value = self.get_model().vlm_uni_val_projector(images_aux) 
+        embed_att = embed_query[:,:,None] @ (embed_aux.transpose(-1,-2) / (embed_aux.shape[-1]**0.5))
+        embed_att = embed_att.nan_to_num()
+        embed_feat = (embed_att.softmax(-1) @ embed_value).mean(2)
+        
+        return images, embed_feat
+
+    def prepare_inputs_labels_for_multimodal(
+        self, input_ids, position_ids, attention_mask, past_key_values, labels, images=None, images_aux=None,
+    ):        
+        vision_tower = self.get_vision_tower()
+        if vision_tower is None or images is None or input_ids.shape[1] == 1:
+            if past_key_values is not None and vision_tower is not None and images is not None and input_ids.shape[1] == 1:                
+                target_shape = past_key_values[-1][-1].shape[-2] + 1
+                attention_mask = torch.cat((attention_mask, torch.ones(
+                    (attention_mask.shape[0], target_shape - attention_mask.shape[1]),
+                    dtype=attention_mask.dtype,
+                    device=attention_mask.device
+                )), dim=1)
+                position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
+            return input_ids, position_ids, attention_mask, past_key_values, None, labels
+
+        if isinstance(images, list):
+            images = torch.stack(images, dim=0)
+        if isinstance(images_aux, list):
+            images_aux = torch.stack(images_aux, dim=0)
+
+        image_features = self.encode_images(images, images_aux)
+
+        # TODO: image start / end is not implemented here to support pretraining.
+        if getattr(self.config, 'tune_mm_mlp_adapter', False) and getattr(self.config, 'mm_use_im_start_end', False):
+            raise NotImplementedError
+
+        # Let's just add dummy tensors if they do not exist,
+        # it is a headache to deal with None all the time.
+        # But it is not ideal, and if you have a better idea,
+        # please open an issue / submit a PR, thanks.
+        _labels = labels
+        _position_ids = position_ids
+        _attention_mask = attention_mask
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
+        else:
+            attention_mask = attention_mask.bool()
+        if position_ids is None:
+            position_ids = torch.arange(0, input_ids.shape[1], dtype=torch.long, device=input_ids.device)
+        if labels is None:
+            labels = torch.full_like(input_ids, IGNORE_INDEX)
+
+        # remove the padding using attention_mask -- TODO: double check
+        input_ids = [cur_input_ids[cur_attention_mask] for cur_input_ids, cur_attention_mask in zip(input_ids, attention_mask)]
+        labels = [cur_labels[cur_attention_mask] for cur_labels, cur_attention_mask in zip(labels, attention_mask)]
+
+        new_input_embeds = []
+        new_labels = []
+        cur_image_idx = 0
+        for batch_idx, cur_input_ids in enumerate(input_ids):
+            num_images = (cur_input_ids == IMAGE_TOKEN_INDEX).sum()
+            if num_images == 0:
+                cur_image_features = image_features[cur_image_idx]
+                cur_input_embeds_1 = self.get_model().embed_tokens(cur_input_ids)
+                cur_input_embeds = torch.cat([cur_input_embeds_1, cur_image_features[0:0]], dim=0)
+                new_input_embeds.append(cur_input_embeds)
+                new_labels.append(labels[batch_idx])
+                cur_image_idx += 1
+                continue
+
+            image_token_indices = [-1] + torch.where(cur_input_ids == IMAGE_TOKEN_INDEX)[0].tolist() + [cur_input_ids.shape[0]]
+            cur_input_ids_noim = []
+            cur_labels = labels[batch_idx]
+            cur_labels_noim = []
+            for i in range(len(image_token_indices) - 1):
+                cur_input_ids_noim.append(cur_input_ids[image_token_indices[i]+1:image_token_indices[i+1]])
+                cur_labels_noim.append(cur_labels[image_token_indices[i]+1:image_token_indices[i+1]])
+            split_sizes = [x.shape[0] for x in cur_labels_noim]
+            cur_input_embeds = self.get_model().embed_tokens(torch.cat(cur_input_ids_noim))
+            cur_input_embeds_no_im = torch.split(cur_input_embeds, split_sizes, dim=0)
+            cur_new_input_embeds = []
+            cur_new_labels = []
+            
+            max_pos_id = 0
+            for i in range(num_images + 1):
+                cur_new_input_embeds.append(cur_input_embeds_no_im[i])
+                cur_new_labels.append(cur_labels_noim[i])
+                max_pos_id += cur_input_embeds_no_im[i].shape[0]
+                if i < num_images:
+                    cur_image_features = image_features[cur_image_idx]
+                    cur_image_idx += 1
+                    cur_new_input_embeds.append(cur_image_features)
+                    cur_new_labels.append(torch.full((cur_image_features.shape[0],), IGNORE_INDEX, device=cur_labels.device, dtype=cur_labels.dtype))
+                    max_pos_id += cur_image_features.shape[0]
+            
+            cur_new_input_embeds = [x.to(device=cur_input_embeds.device) for x in cur_new_input_embeds]
+            cur_new_input_embeds = torch.cat(cur_new_input_embeds)
+            cur_new_labels = torch.cat(cur_new_labels)
+
+            new_input_embeds.append(cur_new_input_embeds)
+            new_labels.append(cur_new_labels)
+
+        # Truncate sequences to max length as image embeddings can make the sequence longer
+        tokenizer_model_max_length = getattr(self.config, 'tokenizer_model_max_length', None)
+        if tokenizer_model_max_length is not None:
+            new_input_embeds = [x[:tokenizer_model_max_length] for x in new_input_embeds]
+            new_labels = [x[:tokenizer_model_max_length] for x in new_labels]
+
+        # Combine them
+        max_len = max(x.shape[0] for x in new_input_embeds)
+        batch_size = len(new_input_embeds)
+
+        new_input_embeds_padded = []
+        new_labels_padded = torch.full((batch_size, max_len), IGNORE_INDEX, dtype=new_labels[0].dtype, device=new_labels[0].device)
+        attention_mask = torch.zeros((batch_size, max_len), dtype=attention_mask.dtype, device=attention_mask.device)
+        position_ids = torch.zeros((batch_size, max_len), dtype=position_ids.dtype, device=position_ids.device)
+
+        for i, (cur_new_embed, cur_new_labels) in enumerate(zip(new_input_embeds, new_labels)):
+            cur_len = cur_new_embed.shape[0]
+            if getattr(self.config, 'tokenizer_padding_side', 'right') == "left":
+                new_input_embeds_padded.append(torch.cat((
+                    torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device),
+                    cur_new_embed
+                ), dim=0))
+                if cur_len > 0:
+                    new_labels_padded[i, -cur_len:] = cur_new_labels
+                    attention_mask[i, -cur_len:] = True
+                    position_ids[i, -cur_len:] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)  
+            else:
+                new_input_embeds_padded.append(torch.cat((
+                    cur_new_embed,
+                    torch.zeros((max_len - cur_len, cur_new_embed.shape[1]), dtype=cur_new_embed.dtype, device=cur_new_embed.device)
+                ), dim=0))
+                if cur_len > 0:
+                    new_labels_padded[i, :cur_len] = cur_new_labels
+                    attention_mask[i, :cur_len] = True
+                    position_ids[i, :cur_len] = torch.arange(0, cur_len, dtype=position_ids.dtype, device=position_ids.device)
+                    
+        new_input_embeds = torch.stack(new_input_embeds_padded, dim=0)
+
+        if _labels is None:
+            new_labels = None
+        else:
+            new_labels = new_labels_padded
+
+        if _attention_mask is None:
+            attention_mask = None
+        else:
+            attention_mask = attention_mask.to(dtype=_attention_mask.dtype)
+
+        if _position_ids is None:
+            position_ids = None
+
+        return None, position_ids, attention_mask, past_key_values, new_input_embeds, new_labels
+
+    def initialize_vision_tokenizer(self, model_args, tokenizer):
+        if model_args.mm_use_im_patch_token:
+            tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
+            self.resize_token_embeddings(len(tokenizer))
+
+        if model_args.mm_use_im_start_end:
+            num_new_tokens = tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
+            self.resize_token_embeddings(len(tokenizer))
+
+            if num_new_tokens > 0:
+                input_embeddings = self.get_input_embeddings().weight.data
+                output_embeddings = self.get_output_embeddings().weight.data
+
+                input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
+                    dim=0, keepdim=True)
+                output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
+                    dim=0, keepdim=True)
+
+                input_embeddings[-num_new_tokens:] = input_embeddings_avg
+                output_embeddings[-num_new_tokens:] = output_embeddings_avg
+
+            if model_args.tune_mm_mlp_adapter:
+                for p in self.get_input_embeddings().parameters():
+                    p.requires_grad = True
+                for p in self.get_output_embeddings().parameters():
+                    p.requires_grad = False
+
+            if model_args.pretrain_mm_mlp_adapter:
+                mm_projector_weights = torch.load(model_args.pretrain_mm_mlp_adapter, map_location='cpu')
+                embed_tokens_weight = mm_projector_weights['model.embed_tokens.weight']
+                assert num_new_tokens == 2
+                if input_embeddings.shape == embed_tokens_weight.shape:
+                    input_embeddings[-num_new_tokens:] = embed_tokens_weight[-num_new_tokens:]
+                elif embed_tokens_weight.shape[0] == num_new_tokens:
+                    input_embeddings[-num_new_tokens:] = embed_tokens_weight
+                else:
+                    raise ValueError(f"Unexpected embed_tokens_weight shape. Pretrained: {embed_tokens_weight.shape}. Current: {input_embeddings.shape}. Numer of new tokens: {num_new_tokens}.")
+        elif model_args.mm_use_im_patch_token:
+            if model_args.tune_mm_mlp_adapter:
+                for p in self.get_input_embeddings().parameters():
+                    p.requires_grad = False
+                for p in self.get_output_embeddings().parameters():
+                    p.requires_grad = False

model-00001-of-00003.safetensors

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+size 4938985248

model-00002-of-00003.safetensors

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+size 4947390768

model-00003-of-00003.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c43ac375c790ae5bab23ba5b4093f22d326f57e86aae3b1608f89e95b8f9f5a3
+size 4662729856

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<unk>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 2048,
+  "pad_token": "<unk>",
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}