Delete cumo/model/llava_arch.py

cumo/model/llava_arch.py

@@ -1,381 +0,0 @@
-#    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 Jiachen Li
-# ------------------------------------------------------------------------
-
-from abc import ABC, abstractmethod
-
-import torch
-import torch.nn as nn
-
-from .multimodal_encoder.builder import build_vision_tower
-from .multimodal_projector.builder import build_vision_projector
-
-from cumo.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
-
-from cumo.mm_utils import get_anyres_image_grid_shape
-
-class LlavaMetaModel:
-
-    def __init__(self, config):
-        super(LlavaMetaModel, 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 'unpad' in getattr(config, 'mm_patch_merge_type', ''):
-                self.image_newline = nn.Parameter(
-                    torch.empty(config.hidden_size, dtype=self.dtype)
-                )
-
-    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 initialize_vision_modules(self, model_args, fsdp=None):
-        vision_tower = model_args.vision_tower
-        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
-        vision_tower_dir = model_args.vision_tower_dir
-        mm_patch_merge_type = model_args.mm_patch_merge_type
-
-        self.config.mm_vision_tower = vision_tower
-        self.config.scales = model_args.scales
-
-        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
-
-        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_vision_select_layer = mm_vision_select_layer
-        self.config.mm_vision_select_feature = mm_vision_select_feature
-        self.config.mm_patch_merge_type = mm_patch_merge_type
-        self.config.num_experts = model_args.num_experts
-        self.config.num_selected = model_args.num_selected
-        self.config.num_layers = model_args.num_layers
-        self.config.dropout = model_args.dropout
-        self.config.mlp_smoe = model_args.mlp_smoe
-        self.config.clip_smoe = model_args.clip_smoe
-
-        self.mm_projector = build_vision_projector(self.config)
-
-        if 'unpad' in mm_patch_merge_type:
-            embed_std = 1 / torch.sqrt(torch.tensor(self.config.hidden_size, dtype=self.dtype))
-            self.image_newline = nn.Parameter(
-                torch.randn(self.config.hidden_size, dtype=self.dtype) * embed_std
-            )
-
-        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 self.config.mlp_smoe:
-                for i in range(model_args.num_experts):
-                    self.mm_projector.experts[i].load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
-            else:
-                self.mm_projector.load_state_dict(get_w(mm_projector_weights, 'mm_projector'))
-
-        if vision_tower_dir is not None:
-            vision_tower_weights = torch.load(vision_tower_dir, map_location='cpu')
-            self.vision_tower.load_state_dict(vision_tower_weights, strict=False)
-            if self.config.clip_smoe:
-                current_staet_dict = self.vision_tower.state_dict()
-                for key, value in current_staet_dict.items():
-                    if 'experts' in key:
-                        key_splits = key.split('.')
-                        new_key = [key_splits[0], key_splits[1], key_splits[2], key_splits[3], 'mlp', key_splits[6], key_splits[7]]
-                        current_staet_dict[key] = vision_tower_weights['.'.join(new_key)]
-                self.vision_tower.load_state_dict(current_staet_dict, strict=True)
-
-def unpad_image(tensor, original_size):
-    """
-    Unpads a PyTorch tensor of a padded and resized image.
-
-    Args:
-    tensor (torch.Tensor): The image tensor, assumed to be in CxHxW format.
-    original_size (tuple): The original size of the image (height, width).
-
-    Returns:
-    torch.Tensor: The unpadded image tensor.
-    """
-    original_width, original_height = original_size
-    current_height, current_width = tensor.shape[1:]
-
-    original_aspect_ratio = original_width / original_height
-    current_aspect_ratio = current_width / current_height
-
-    if original_aspect_ratio > current_aspect_ratio:
-        scale_factor = current_width / original_width
-        new_height = int(original_height * scale_factor)
-        padding = (current_height - new_height) // 2
-        unpadded_tensor = tensor[:, padding:current_height - padding, :]
-    else:
-        scale_factor = current_height / original_height
-        new_width = int(original_width * scale_factor)
-        padding = (current_width - new_width) // 2
-        unpadded_tensor = tensor[:, :, padding:current_width - padding]
-
-    return unpadded_tensor
-
-
-class LlavaMetaForCausalLM(ABC):
-
-    @abstractmethod
-    def get_model(self):
-        pass
-
-    def get_vision_tower(self):
-        return self.get_model().get_vision_tower()
-
-    def prepare_inputs_labels_for_multimodal(
-        self, input_ids, position_ids, attention_mask, past_key_values, labels,
-        images, image_sizes=None
-    ):
-        clip_balanced_loss = None
-        clip_router_z_loss = None
-        mlp_balanced_loss = None
-        mlp_router_z_loss = None
-        vision_tower = self.get_vision_tower()
-        if vision_tower is None or images is None or input_ids.shape[1] == 1:
-            return input_ids, position_ids, attention_mask, past_key_values, None, labels, clip_balanced_loss, clip_router_z_loss, mlp_balanced_loss, mlp_router_z_loss 
-        
-        if type(images) is list or images.ndim == 5:
-            if type(images) is list:
-                images = [x.unsqueeze(0) if x.ndim == 3 else x for x in images]
-            concat_images = torch.cat([image for image in images], dim=0)
-            image_features, clip_balanced_loss, clip_router_z_loss = self.get_model().get_vision_tower()(images)
-            image_features, mlp_balanced_loss, mlp_router_z_loss = self.get_model().mm_projector(image_features)
-            split_sizes = [image.shape[0] for image in images]
-            image_features = torch.split(image_features, split_sizes, dim=0)
-            mm_patch_merge_type = getattr(self.config, 'mm_patch_merge_type', 'flat')
-            image_aspect_ratio = getattr(self.config, 'image_aspect_ratio', 'square')
-            if mm_patch_merge_type == 'flat':
-                image_features = [x.flatten(0, 1) for x in image_features]
-            elif mm_patch_merge_type.startswith('spatial'):
-                new_image_features = []
-                for image_idx, image_feature in enumerate(image_features):
-                    if image_feature.shape[0] > 1:
-                        base_image_feature = image_feature[0]
-                        image_feature = image_feature[1:]
-                        height = width = self.get_vision_tower().num_patches_per_side
-                        assert height * width == base_image_feature.shape[0]
-                        if image_aspect_ratio == 'anyres':
-                            num_patch_width, num_patch_height = get_anyres_image_grid_shape(image_sizes[image_idx], self.config.image_grid_pinpoints, self.get_vision_tower().config.image_size)
-                            image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
-                        else:
-                            raise NotImplementedError
-                        if 'unpad' in mm_patch_merge_type:
-                            image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
-                            image_feature = image_feature.flatten(1, 2).flatten(2, 3)
-                            image_feature = unpad_image(image_feature, image_sizes[image_idx])
-                            image_feature = torch.cat((
-                                image_feature,
-                                self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)
-                            ), dim=-1)
-                            image_feature = image_feature.flatten(1, 2).transpose(0, 1)
-                        else:
-                            image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
-                            image_feature = image_feature.flatten(0, 3)
-                        image_feature = torch.cat((base_image_feature, image_feature), dim=0)
-                    else:
-                        image_feature = image_feature[0]
-                        if 'unpad' in mm_patch_merge_type:
-                            image_feature = torch.cat((
-                                image_feature,
-                                self.model.image_newline[None].to(image_feature.device)
-                            ), dim=0)
-                    new_image_features.append(image_feature)
-                image_features = new_image_features
-            else:
-                raise ValueError(f"Unexpected mm_patch_merge_type: {self.config.mm_patch_merge_type}")
-        else:
-            image_features, clip_balanced_loss, clip_router_z_loss = self.get_model().get_vision_tower()(images)
-            if self.config.mlp_smoe:
-                image_features, mlp_balanced_loss, mlp_router_z_loss = self.get_model().mm_projector(image_features)
-            else:
-                image_features = self.get_model().mm_projector(image_features)
-        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 -- FIXME
-        _input_ids = input_ids
-        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 = []
-
-            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])
-                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))
-
-            cur_new_input_embeds = [x.to(self.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, clip_balanced_loss, clip_router_z_loss, mlp_balanced_loss, mlp_router_z_loss
-
-    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