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modeling_mplug_owl2.py

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+#    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, 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
+
+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_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')