Upload Sa2va-Instance-4B/modeling_sa2va_chat.py

Sa2va-Instance-4B/modeling_sa2va_chat.py

@@ -0,0 +1,923 @@
+# --------------------------------------------------------
+# InternVL
+# Copyright (c) 2024 OpenGVLab
+# Licensed under The MIT License [see LICENSE for details]
+# --------------------------------------------------------
+
+import warnings
+from typing import Any, List, Optional, Tuple, Union
+
+import torchvision.transforms as T
+from torchvision.transforms.functional import InterpolationMode
+
+import torch.utils.checkpoint
+import transformers
+import re
+
+from .modeling_internlm2 import InternLM2ForCausalLM
+from .modeling_phi3 import Phi3ForCausalLM
+from peft import LoraConfig, get_peft_model
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
+                          LlamaTokenizer, Qwen2ForCausalLM)
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import ModelOutput, logging
+from transformers import StoppingCriteriaList, StoppingCriteria
+
+from .configuration_sa2va_chat import Sa2VAChatConfig
+from .modeling_intern_vit import InternVisionModel, has_flash_attn
+
+from .sam2 import SAM2
+from .templates import PROMPT_TEMPLATE
+
+import numpy as np
+from torchvision.transforms.functional import resize, to_pil_image
+
+from types import MethodType
+import torch.nn.functional as F
+
+try:
+    from .flash_attention import FlashAttention
+    has_flash_attn = True
+except:
+    print('FlashAttention is not installed.')
+    has_flash_attn = False
+
+logger = logging.get_logger(__name__)
+
+def version_cmp(v1, v2, op='eq'):
+    import operator
+
+    from packaging import version
+    op_func = getattr(operator, op)
+    return op_func(version.parse(v1), version.parse(v2))
+
+class StopWordStoppingCriteria(StoppingCriteria):
+    """StopWord stopping criteria."""
+
+    def __init__(self, tokenizer, stop_word):
+        self.tokenizer = tokenizer
+        self.stop_word = stop_word
+        self.length = len(self.stop_word)
+
+    def __call__(self, input_ids, *args, **kwargs) -> bool:
+        cur_text = self.tokenizer.decode(input_ids[0])
+        cur_text = cur_text.replace('\r', '').replace('\n', '')
+        return cur_text[-self.length:] == self.stop_word
+
+def get_stop_criteria(
+    tokenizer,
+    stop_words=[],
+):
+    stop_criteria = StoppingCriteriaList()
+    for word in stop_words:
+        stop_criteria.append(StopWordStoppingCriteria(tokenizer, word))
+    return stop_criteria
+
+class DirectResize:
+    def __init__(self, target_length: int) -> None:
+        self.target_length = target_length
+
+    def apply_image(self, image: np.ndarray) -> np.ndarray:
+        """
+        Expects a numpy array with shape HxWxC in uint8 format.
+        """
+        img = to_pil_image(image, mode='RGB')
+        return np.array(img.resize((self.target_length, self.target_length)))
+
+class Sa2VAChatModel(PreTrainedModel):
+    config_class = Sa2VAChatConfig
+    main_input_name = 'pixel_values'
+    base_model_prefix = 'language_model'
+    _no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer', 'InternLM2DecoderLayer',
+                         'Phi3DecoderLayer', 'Qwen2DecoderLayer', 'SAM2']
+    _supports_flash_attn_2 = True
+    supports_gradient_checkpointing = True
+
+    def __init__(self, config: Sa2VAChatConfig, vision_model=None, language_model=None, use_flash_attn=True):
+        super().__init__(config)
+
+        assert version_cmp(transformers.__version__, '4.37.0', 'ge')
+        image_size = config.force_image_size or config.vision_config.image_size
+        patch_size = config.vision_config.patch_size
+        self.patch_size = patch_size
+        self.select_layer = config.select_layer
+        self.template = config.template
+        self.template = self.template.replace('-', '_')
+        self.num_image_token = int((image_size // patch_size) ** 2 * (config.downsample_ratio ** 2))
+        self.downsample_ratio = config.downsample_ratio
+        self.ps_version = config.ps_version
+        self.llm_arch_name = config.llm_config.architectures[0]
+
+        use_flash_attn = use_flash_attn if has_flash_attn else False
+        config.vision_config.use_flash_attn = True if use_flash_attn else False
+        config.llm_config._attn_implementation = 'flash_attention_2' if use_flash_attn else 'eager'
+
+        logger.info(f'num_image_token: {self.num_image_token}')
+        logger.info(f'ps_version: {self.ps_version}')
+        if vision_model is not None:
+            self.vision_model = vision_model
+        else:
+            self.vision_model = InternVisionModel(config.vision_config)
+        if language_model is not None:
+            self.language_model = language_model
+        else:
+            if config.llm_config.architectures[0] == 'LlamaForCausalLM':
+                self.language_model = LlamaForCausalLM(config.llm_config)
+            elif config.llm_config.architectures[0] == 'InternLM2ForCausalLM':
+                self.language_model = InternLM2ForCausalLM(config.llm_config)
+            elif config.llm_config.architectures[0] == 'Phi3ForCausalLM':
+                self.language_model = Phi3ForCausalLM(config.llm_config)
+            elif config.llm_config.architectures[0] == 'Qwen2ForCausalLM':
+                self.language_model = Qwen2ForCausalLM(config.llm_config)
+            else:
+                raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
+
+        vit_hidden_size = config.vision_config.hidden_size
+        llm_hidden_size = config.llm_config.hidden_size
+
+        self.mlp1 = nn.Sequential(
+            nn.LayerNorm(vit_hidden_size * int(1 / self.downsample_ratio) ** 2),
+            nn.Linear(vit_hidden_size * int(1 / self.downsample_ratio) ** 2, llm_hidden_size),
+            nn.GELU(),
+            nn.Linear(llm_hidden_size, llm_hidden_size)
+        )
+
+        self.img_context_token_id = None
+        self.conv_template = PROMPT_TEMPLATE[self.template]
+        self.template = self.conv_template
+        if hasattr(config, 'system_message'):
+            self.system_message = config.system_message
+        self.num_samples = 0
+
+        if config.use_backbone_lora:
+            self.wrap_backbone_lora(r=config.use_backbone_lora, lora_alpha=2 * config.use_backbone_lora)
+
+        if config.use_llm_lora:
+            self.wrap_llm_lora(r=config.use_llm_lora, lora_alpha=2 * config.use_llm_lora)
+
+        self.grounding_encoder = SAM2()
+        out_dim = self.grounding_encoder.hidden_dim
+        in_dim = llm_hidden_size
+        self.text_hidden_fcs = nn.Sequential(
+            nn.Linear(in_dim, in_dim), nn.ReLU(inplace=True),
+            nn.Linear(in_dim, out_dim), nn.Dropout(0.0)
+        )
+
+        self.init_prediction_config = False
+
+    def wrap_backbone_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
+        lora_config = LoraConfig(
+            r=r,
+            target_modules=['attn.qkv', 'attn.proj', 'mlp.fc1', 'mlp.fc2'],
+            lora_alpha=lora_alpha,
+            lora_dropout=lora_dropout,
+        )
+        self.vision_model = get_peft_model(self.vision_model, lora_config)
+        self.vision_model.print_trainable_parameters()
+
+    def wrap_llm_lora(self, r=128, lora_alpha=256, lora_dropout=0.05):
+        # Determine the target modules based on the architecture of the language model
+        if self.llm_arch_name == 'InternLM2ForCausalLM':
+            target_modules = ['attention.wqkv', 'attention.wo', 'feed_forward.w1', 'feed_forward.w2', 'feed_forward.w3']
+        elif self.llm_arch_name == 'Phi3ForCausalLM':
+            target_modules = ['mlp.down_proj', 'mlp.gate_up_proj', 'self_attn.o_proj', 'self_attn.qkv_proj']
+        elif self.llm_arch_name in ['Qwen2ForCausalLM', 'LlamaForCausalLM']:
+            target_modules = ['self_attn.q_proj', 'self_attn.k_proj', 'self_attn.v_proj', 'self_attn.o_proj',
+                              'mlp.gate_proj', 'mlp.down_proj', 'mlp.up_proj']
+        else:
+            raise NotImplemented
+        lora_config = LoraConfig(
+            r=r,
+            target_modules=target_modules,
+            lora_alpha=lora_alpha,
+            lora_dropout=lora_dropout,
+            task_type='CAUSAL_LM'
+        )
+        self.language_model = get_peft_model(self.language_model, lora_config)
+        self.language_model.enable_input_require_grads()
+        self.language_model.print_trainable_parameters()
+
+    def pixel_shuffle(self, x, scale_factor=0.5):
+        n, w, h, c = x.size()
+        # N, W, H, C --> N, W, H * scale, C // scale
+        x = x.view(n, w, int(h * scale_factor), int(c / scale_factor))
+        # N, W, H * scale, C // scale --> N, H * scale, W, C // scale
+        x = x.permute(0, 2, 1, 3).contiguous()
+        # N, H * scale, W, C // scale --> N, H * scale, W * scale, C // (scale ** 2)
+        x = x.view(n, int(h * scale_factor), int(w * scale_factor),
+                   int(c / (scale_factor * scale_factor)))
+        if self.ps_version == 'v1':
+            warnings.warn("In ps_version 'v1', the height and width have not been swapped back, "
+                          'which results in a transposed image.')
+        else:
+            x = x.permute(0, 2, 1, 3).contiguous()
+        return x
+
+    def extract_feature(self, pixel_values):
+        if self.select_layer == -1:
+            vit_embeds = self.vision_model(
+                pixel_values=pixel_values,
+                output_hidden_states=False,
+                return_dict=True).last_hidden_state
+        else:
+            vit_embeds = self.vision_model(
+                pixel_values=pixel_values,
+                output_hidden_states=True,
+                return_dict=True).hidden_states[self.select_layer]
+        vit_embeds = vit_embeds[:, 1:, :]
+
+        h = w = int(vit_embeds.shape[1] ** 0.5)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
+        vit_embeds = self.pixel_shuffle(vit_embeds, scale_factor=self.downsample_ratio)
+        vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], -1, vit_embeds.shape[-1])
+        vit_embeds = self.mlp1(vit_embeds)
+        return vit_embeds
+
+    @property
+    def lm_head(self):
+        return self.language_model.get_output_embeddings()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def get_output_embeddings(self):
+        return self.language_model.get_output_embeddings()
+
+    def forward(self, data, data_samples=None, mode='loss'):
+        pixel_values = data['pixel_values']
+
+        if type(pixel_values) is list or pixel_values.ndim == 5:
+            if type(pixel_values) is list:
+                pixel_values = [
+                    x.unsqueeze(0) if x.ndim == 3 else x for x in pixel_values
+                ]
+            # b*n, c, h, w
+            concat_images = torch.cat(
+                [image.to(self.vision_model.dtype) for image in pixel_values], dim=0)
+        else:
+            raise NotImplementedError()
+
+        input_ids = data['input_ids']
+        position_ids = data['position_ids']
+        attention_mask = data['attention_mask']
+        # sum is 0 are text
+        image_flags = torch.sum(concat_images, dim=(1, 2, 3)) != 0
+        image_flags = image_flags.long()
+
+        labels = data['labels']
+        use_cache = False
+
+        if 'vp_overall_mask' not in data.keys():
+            vp_overall_mask = None
+        else:
+            vp_overall_mask = data['vp_overall_mask']
+
+        if 'prompt_masks' in data.keys():
+            prompt_masks = data['prompt_masks']
+        else:
+            prompt_masks = None
+
+        outputs = self._llm_forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            image_flags=image_flags,
+            pixel_values=concat_images,
+            labels=labels,
+            use_cache=use_cache,
+            output_hidden_states=True,
+            vp_overall_mask=vp_overall_mask,
+            prompt_masks=prompt_masks,
+        )
+
+        return outputs
+
+    def _llm_forward(
+            self,
+            pixel_values: torch.FloatTensor,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            image_flags: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            vp_overall_mask=None,
+            prompt_masks=None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        return_dict = return_dict if return_dict is not None \
+            else self.config.use_return_dict
+
+        image_flags = image_flags.squeeze(-1)
+        # We only added the clone code here to avoid the error.
+        input_embeds = self.language_model.get_input_embeddings()(
+            input_ids).clone()
+
+        vit_embeds = self.extract_feature(pixel_values)
+        vit_embeds = vit_embeds.to(input_embeds.dtype)  # FIXME: why vit_embeds is float16?
+        fast_vit_embeds = None
+
+        vit_embeds = vit_embeds[image_flags == 1]
+        vit_batch_size = pixel_values.shape[0]
+
+        B, N, C = input_embeds.shape
+        input_embeds = input_embeds.reshape(B * N, C)
+
+        self._count += 1
+
+        if vp_overall_mask is not None and prompt_masks is not None:
+            vp_embeds = []
+            vp_overall_mask = vp_overall_mask.to(vit_embeds.device).bool()
+            prompt_masks = [item.to(vit_embeds.device).bool() for item in prompt_masks]
+
+            vp_overall_mask = vp_overall_mask[image_flags == 1]
+            overall_tile_vit_embeds = vit_embeds[vp_overall_mask]  # (n_img, hw, c)
+
+            i_vp_img = 0
+            for i_img in range(len(vit_embeds)):
+                vp_embeds.append(vit_embeds[i_img].reshape(-1, C))
+                if vp_overall_mask[i_img]:
+                    tile_vit_embeds = overall_tile_vit_embeds[i_vp_img].reshape(-1, C)  # (hw, C)
+                    objects_prompt_masks = prompt_masks[i_vp_img]
+                    n_obj = len(objects_prompt_masks)
+                    tile_vit_embeds = tile_vit_embeds.unsqueeze(0).repeat(n_obj, 1, 1)
+                    objects_prompt_masks = objects_prompt_masks.reshape(n_obj, -1)
+                    vp_embeds.append(tile_vit_embeds[objects_prompt_masks])
+                    i_vp_img += 1
+            vp_embeds = torch.cat(vp_embeds, dim=0)
+        else:
+            vp_embeds = None
+
+        input_ids = input_ids.reshape(B * N)
+        selected = (input_ids == self.img_context_token_id)
+
+        if vp_embeds is None:
+            try:
+                input_embeds[selected] = vit_embeds.reshape(-1, C)
+            except Exception as e:
+                vit_embeds = vit_embeds.reshape(-1, C)
+                print(f'warning: {e}, input_embeds[selected].shape='
+                      f'{input_embeds[selected].shape}, '
+                      f'vit_embeds.shape={vit_embeds.shape}')
+                n_token = selected.sum()
+                if n_token > len(vit_embeds):
+                    print(f"Wrong !!! {n_token} image tokens in text but only {len(vit_embeds)} vit embeds !!!")
+                    expand_ratio = n_token // len(vit_embeds) + 1
+                    vit_embeds = torch.cat([vit_embeds] * expand_ratio, dim=0)
+
+                input_embeds[selected] = vit_embeds[:n_token]
+        else:
+            try:
+                input_embeds[selected] = vp_embeds.reshape(-1, C)
+            except Exception as e:
+                vp_embeds = vp_embeds.reshape(-1, C)
+                print(f'warning: {e}, input_embeds[selected].shape='
+                      f'{input_embeds[selected].shape}, '
+                      f'vp_embeds.shape={vp_embeds.shape}')
+                n_token = selected.sum()
+                if n_token > len(vp_embeds):
+                    print(f"Wrong !!! {n_token} image tokens in text but only {len(vp_embeds)} vit embeds !!!")
+                    expand_ratio = n_token // len(vp_embeds) + 1
+                    vp_embeds = torch.cat([vp_embeds] * expand_ratio, dim=0)
+
+                input_embeds[selected] = vp_embeds[:n_token]
+
+        input_embeds = input_embeds.reshape(B, N, C)
+
+        outputs = self.language_model(
+            inputs_embeds=input_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        logits = outputs.logits
+
+        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.language_model.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model 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,
+        )
+
+    @torch.no_grad()
+    def generate(
+            self,
+            pixel_values: Optional[torch.FloatTensor] = None,
+            input_ids: Optional[torch.FloatTensor] = None,
+            attention_mask: Optional[torch.LongTensor] = None,
+            visual_features: Optional[torch.FloatTensor] = None,
+            generation_config: Optional[GenerationConfig] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            prompt_masks=None,
+            vp_overall_mask=None,
+            **generate_kwargs,
+    ) -> torch.LongTensor:
+        device = self.device
+        assert self.img_context_token_id is not None
+
+        if pixel_values is not None:
+            if visual_features is not None:
+                vit_embeds = visual_features
+            else:
+                if type(pixel_values) is list or pixel_values.ndim == 5:
+                    if type(pixel_values) is list:
+                        pixel_values = [
+                            x.unsqueeze(0) if x.ndim == 3 else x for x in pixel_values
+                        ]
+                    # b*n, c, h, w
+                    pixel_values = torch.cat(
+                        [image.to(self.vision_model.dtype) for image in pixel_values], dim=0)
+
+                vit_embeds = self.extract_feature(pixel_values.to(device))
+            image_flags = torch.sum(pixel_values, dim=(1, 2, 3)) != 0
+            image_flags = image_flags.long()
+            vit_embeds = vit_embeds[image_flags == 1]
+
+            input_embeds = self.language_model.get_input_embeddings()(input_ids.to(device))
+            B, N, C = input_embeds.shape
+            input_embeds = input_embeds.reshape(B * N, C)
+
+            if vp_overall_mask is not None and prompt_masks is not None:
+                vp_embeds = []
+                vp_overall_mask = vp_overall_mask.to(vit_embeds.device).bool()
+                prompt_masks = [item.to(vit_embeds.device).bool() for item in prompt_masks]
+
+                vp_overall_mask = vp_overall_mask[image_flags == 1]
+                overall_tile_vit_embeds = vit_embeds[vp_overall_mask]  # (n_img, hw, c)
+
+                i_vp_img = 0
+                for i_img in range(len(vit_embeds)):
+                    vp_embeds.append(vit_embeds[i_img].reshape(-1, C))
+                    if vp_overall_mask[i_img]:
+                        tile_vit_embeds = overall_tile_vit_embeds[i_vp_img].reshape(-1, C)  # (hw, C)
+                        objects_prompt_masks = prompt_masks[i_vp_img]
+                        n_obj = len(objects_prompt_masks)
+                        tile_vit_embeds = tile_vit_embeds.unsqueeze(0).repeat(n_obj, 1, 1)
+                        objects_prompt_masks = objects_prompt_masks.reshape(n_obj, -1)
+                        vp_embeds.append(tile_vit_embeds[objects_prompt_masks])
+                        i_vp_img += 1
+
+                vp_embeds = torch.cat(vp_embeds, dim=0)
+            else:
+                vp_embeds = None
+
+            input_ids = input_ids.reshape(B * N)
+            selected = (input_ids == self.img_context_token_id)
+            assert selected.sum() != 0
+            if vp_embeds is None:
+                input_embeds[selected] = vit_embeds.reshape(-1, C).to(input_embeds.device)
+            else:
+                if len(input_embeds[selected]) != len(vp_embeds.reshape(-1, C)):
+                    print("Shape mismatch, selected is {}, vp embeds is {} !!!" \
+                          .format(len(input_embeds[selected]), len(vp_embeds.reshape(-1, C))))
+                    min_tokens = min(len(input_embeds[selected]), len(vp_embeds.reshape(-1, C)))
+                    input_embeds[selected][:min_tokens] = vp_embeds.reshape(-1, C)[:min_tokens].to(input_embeds.device)
+                else:
+                    input_embeds[selected] = vp_embeds.reshape(-1, C).to(input_embeds.device)
+
+            input_embeds = input_embeds.reshape(B, N, C)
+        else:
+            input_embeds = self.language_model.get_input_embeddings()(input_ids)
+
+        outputs = self.language_model.generate(
+            inputs_embeds=input_embeds,
+            attention_mask=attention_mask.to(device),
+            generation_config=generation_config,
+            output_hidden_states=output_hidden_states,
+            # return_dict=return_dict,
+            use_cache=True,
+            **generate_kwargs,
+        )
+
+        return outputs
+
+    def preparing_for_generation(self, tokenizer, max_new_tokens=2048, torch_dtype=torch.bfloat16):
+        # set stop criteria and generation configs for model
+        if not hasattr(self, 'tokenizer'):
+            self.tokenizer = tokenizer
+        self.bot_name = 'BOT'
+        stop_words = []
+        stop_words += self.template.get('STOP_WORDS', [])
+        stop_criteria = get_stop_criteria(
+            tokenizer=self.tokenizer, stop_words=stop_words)
+        self.stop_criteria = stop_criteria
+
+        default_generation_kwargs = dict(
+            max_new_tokens=max_new_tokens,
+            do_sample=False,
+            eos_token_id=self.tokenizer.eos_token_id,
+            pad_token_id=(
+                self.tokenizer.pad_token_id
+                if self.tokenizer.pad_token_id is not None
+                else self.tokenizer.eos_token_id
+            ),
+        )
+
+        self.gen_config = GenerationConfig(**default_generation_kwargs)
+        self.init_prediction_config = True
+        self.torch_dtype = torch_dtype
+        # self.to(torch_dtype)
+        self.extra_image_processor = DirectResize(target_length=1024, )
+        # for multi image process
+        self.min_dynamic_patch = 1
+        self.max_dynamic_patch = 12
+        self.downsample_ratio = 0.5
+        self.image_size = 448
+        self.use_thumbnail = True
+        patch_size = 14
+        self.patch_size = patch_size
+
+        self.patch_token = int((self.image_size // patch_size) ** 2 * (self.downsample_ratio ** 2))
+        self.IMAGENET_MEAN = (0.485, 0.456, 0.406)
+        self.IMAGENET_STD = (0.229, 0.224, 0.225)
+        self.IMG_CONTEXT_TOKEN = '<IMG_CONTEXT>'
+        self.IMG_START_TOKEN = '<img>'
+        self.IMG_END_TOKEN = '</img>'
+
+        self.transformer = T.Compose([
+            T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+            T.Resize((self.image_size, self.image_size), interpolation=InterpolationMode.BICUBIC),
+            T.ToTensor(),
+            T.Normalize(mean=self.IMAGENET_MEAN, std=self.IMAGENET_STD)
+        ])
+        self.VP_START_TOKEN = '<vp>'
+        self.VP_END_TOKEN = '</vp>'
+
+        # change phi3 prepare for generation fuction
+        if self.config.llm_config.architectures[0] == 'Phi3ForCausalLM':
+            self.language_model.prepare_inputs_for_generation = MethodType(prepare_inputs_for_generation_phi3, self.language_model)
+
+        img_context_token_id = tokenizer.convert_tokens_to_ids('<IMG_CONTEXT>')
+        self.img_context_token_id = img_context_token_id
+        self.seg_token_idx = tokenizer.convert_tokens_to_ids('[SEG]')
+        return
+
+    def _parse_seg_ids_from_input(self, input_ids):
+        """
+        从input_ids中解析每个seg token对应的ID
+        例如：文本中的[SEG] <1> [SEG] <1> [SEG] <2>应该返回[1, 1, 2]
+        """
+
+        tokens_text = self.tokenizer.decode(input_ids, skip_special_tokens=False)
+        pattern = r'\[SEG\]\s*<(\d+)>'
+        matches = re.findall(pattern, tokens_text)
+        numbers = [int(match) for match in matches]
+        
+        return numbers
+            
+
+    def predict_forward(
+            self,
+            image=None,
+            video=None,
+            text=None,
+            past_text='',
+            mask_prompts=None,
+            tokenizer=None,
+    ):
+        if not self.init_prediction_config:
+            assert tokenizer
+            self.preparing_for_generation(tokenizer=tokenizer)
+
+        if image is None and video is None and '<image>' not in past_text:
+            text = text.replace('<image>', "")
+            input_text = ''
+            input_text += self.template['INSTRUCTION'].format(
+                input=text, round=1, bot_name=self.bot_name)
+            input_text = past_text + input_text
+            ids = self.tokenizer.encode(input_text)
+            ids = torch.tensor(ids).cuda().unsqueeze(0)
+
+            attention_mask = torch.ones_like(ids, dtype=torch.bool)
+
+            mm_inputs = {
+                'pixel_values': None,
+                'input_ids': ids,
+                'attention_mask': attention_mask,
+                'position_ids': None,
+                'past_key_values': None,
+                'labels': None,
+                'prompt_masks': None,
+                'vp_overall_mask': None,
+            }
+            ret_masks = []
+        else:
+            input_dict = {}
+            if video is not None:
+                pixel_values = []
+                extra_pixel_values = []
+                ori_image_size = video[0].size
+                for frame_idx, frame_image in enumerate(video):
+                    # assert ori_image_size == frame_image.size
+                    g_image = np.array(frame_image)  # for grounding
+                    g_image = self.extra_image_processor.apply_image(g_image)
+                    g_image = torch.from_numpy(g_image).permute(2, 0, 1).contiguous()
+                    extra_pixel_values.append(g_image)
+                    if frame_idx < 5:
+                        img = self.transformer(frame_image)
+                        pixel_values.append(img)
+
+                pixel_values = torch.stack(pixel_values, dim=0).to(self.torch_dtype)  # (n_f, 3, h, w)
+                g_pixel_values = torch.stack([
+                    self.grounding_encoder.preprocess_image(pixel) for pixel in extra_pixel_values
+                ]).to(self.torch_dtype)
+                num_image_tokens = self.patch_token
+                num_frames = len(pixel_values)
+
+                input_dict['vp_overall_mask'] = None
+            else:
+                ori_image_size = image.size
+
+                # prepare grounding images
+                g_image = np.array(image)  # for grounding
+                g_image = self.extra_image_processor.apply_image(g_image)
+                g_pixel_values = torch.from_numpy(g_image).permute(2, 0, 1).contiguous().to(self.torch_dtype)
+                extra_pixel_values = [g_pixel_values]
+                g_pixel_values = torch.stack([
+                    self.grounding_encoder.preprocess_image(pixel) for pixel in extra_pixel_values
+                ]).to(self.torch_dtype)
+
+                images = dynamic_preprocess(image, self.min_dynamic_patch,
+                                            self.max_dynamic_patch,
+                                            self.image_size, self.use_thumbnail)
+
+                if mask_prompts is not None:
+                    vp_overall_mask = torch.Tensor([False] * (len(images) - 1) + [True])
+                    input_dict['vp_overall_mask'] = vp_overall_mask
+                else:
+                    input_dict['vp_overall_mask'] = None
+
+                pixel_values = [self.transformer(image) for image in images]
+                pixel_values = torch.stack(pixel_values).to(self.torch_dtype)
+                num_image_tokens = pixel_values.shape[0] * self.patch_token
+                num_frames = 1
+            input_dict['g_pixel_values'] = g_pixel_values
+            input_dict['pixel_values'] = pixel_values
+
+            if mask_prompts is not None:
+                # reshape mask prompts to feature size
+                mask_prompts = [torch.Tensor(item).to(pixel_values.device) for item in mask_prompts]
+                mask_prompts = [F.interpolate(
+                    item.unsqueeze(0),
+                    size=(int(self.image_size // self.patch_size * self.downsample_ratio),
+                          int(self.image_size // self.patch_size * self.downsample_ratio)),
+                    mode='nearest').squeeze(0) for item in mask_prompts]
+                region_pixels = []
+                for mask_prompt in mask_prompts[0]:
+                    region_pixels.append(mask_prompt.bool().to(torch.int64).sum())
+
+                vp_token_str = '\nThere are {} part regions in the picture: '.format(len(mask_prompts[0]))
+                for i in range(len(mask_prompts[0])):
+                    vp_token_str = vp_token_str + \
+                                   f"region{i + 1}" + self.VP_START_TOKEN + \
+                                   self.IMG_CONTEXT_TOKEN * region_pixels[i] + \
+                                   self.VP_END_TOKEN
+                    if i == len(mask_prompts[0]) - 1:
+                        vp_token_str = vp_token_str + '.\n'
+                    else:
+                        vp_token_str = vp_token_str + ', '
+            else:
+                vp_token_str = ''
+
+            image_token_str = f'{self.IMG_START_TOKEN}' \
+                              f'{self.IMG_CONTEXT_TOKEN * num_image_tokens}' \
+                              f'{self.IMG_END_TOKEN}'
+            image_token_str = image_token_str + '\n'
+            image_token_str = image_token_str * num_frames
+            image_token_str = image_token_str.strip()
+
+            ret_masks = []
+
+            if '<image>' in text or mask_prompts is not None:
+                assert past_text is None or len(past_text) == 0
+            text = text.replace('<image>', image_token_str + vp_token_str)
+            input_text = ''
+            input_text += self.template['INSTRUCTION'].format(
+                input=text, round=1, bot_name=self.bot_name)
+            input_text = past_text + input_text
+            ids = self.tokenizer.encode(input_text)
+            ids = torch.tensor(ids).cuda().unsqueeze(0)
+
+            attention_mask = torch.ones_like(ids, dtype=torch.bool)
+
+            mm_inputs = {
+                'pixel_values': input_dict['pixel_values'],
+                'input_ids': ids,
+                'attention_mask': attention_mask,
+                'position_ids': None,
+                'past_key_values': None,
+                'labels': None,
+                'prompt_masks': mask_prompts,
+                'vp_overall_mask': input_dict['vp_overall_mask'],
+            }
+
+        generate_output = self.generate(
+            **mm_inputs,
+            generation_config=self.gen_config,
+            streamer=None,
+            bos_token_id=self.tokenizer.bos_token_id,
+            stopping_criteria=self.stop_criteria,
+            output_hidden_states=True,
+            return_dict_in_generate=True
+        )
+        predict = self.tokenizer.decode(
+            generate_output.sequences[0], skip_special_tokens=False).strip()
+        seg_ids = self._parse_seg_ids_from_input(generate_output.sequences[0])
+        # print(predict)
+
+        if image is None and video is None and '<image>' not in past_text:
+            return {'prediction': predict, 'prediction_masks': ret_masks, }
+
+        # if have seg result, find the seg hidden states
+        hidden_states = generate_output.hidden_states
+        last_hidden_states = [item[-1][0] for item in hidden_states]
+        last_hidden_states = torch.cat(last_hidden_states, dim=0)
+        seg_hidden_states = get_seg_hidden_states(
+            last_hidden_states, generate_output.sequences[0][:-1],
+            seg_id=self.seg_token_idx
+        )
+        all_seg_hidden_states = self.text_hidden_fcs(seg_hidden_states)
+        
+        ret_ious = []
+        ret_scores = []
+        # for seg_hidden_states in all_seg_hidden_states:
+        for seg_id in seg_ids:
+            _idxs = [i for i, seg in enumerate(seg_ids) if seg == seg_id]
+            seg_hidden_states = all_seg_hidden_states[_idxs].unsqueeze(0)
+            # print("=========")
+            # print(seg_id,_idxs)
+            # seg_hidden_states = seg_hidden_states.unsqueeze(0)
+            # print(seg_hidden_states.shape)
+            g_pixel_values = input_dict['g_pixel_values']
+            sam_states = self.grounding_encoder.get_sam2_embeddings(g_pixel_values)
+            
+            
+            pred_masks,ious,object_score_logits = self.grounding_encoder.language_embd_inference(sam_states, [seg_hidden_states] * num_frames)
+            
+            w, h = ori_image_size
+            masks = F.interpolate(pred_masks, size=(h, w), mode='bilinear', align_corners=False)
+            masks = masks[:, 0]
+            masks = masks.sigmoid() > 0.5
+            masks = masks.cpu().numpy()
+            ret_masks.append(masks)
+            ret_ious.append(ious.detach().cpu())
+            ret_scores.append(object_score_logits.detach().cpu())
+
+
+        return {'prediction': predict, 'prediction_masks': ret_masks, 'ious': ret_ious, 'scores': ret_scores}
+        # return {'prediction': predict, 'prediction_masks': ret_masks}
+
+def get_seg_hidden_states(hidden_states, output_ids, seg_id):
+    seg_mask = output_ids == seg_id
+    n_out = len(seg_mask)
+    if n_out == 0:
+        return hidden_states[0:0]
+    return hidden_states[-n_out:][seg_mask]
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height,
+                              image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image,
+                       min_num=1,
+                       max_num=6,
+                       image_size=448,
+                       use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = {(i, j)
+                     for n in range(min_num, max_num + 1)
+                     for i in range(1, n + 1) for j in range(1, n + 1)
+                     if i * j <= max_num and i * j >= min_num}
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(aspect_ratio,
+                                                    target_ratios, orig_width,
+                                                    orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = ((i % (target_width // image_size)) * image_size,
+               (i // (target_width // image_size)) * image_size,
+               ((i % (target_width // image_size)) + 1) * image_size,
+               ((i // (target_width // image_size)) + 1) * image_size)
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+
+from transformers.cache_utils import Cache, DynamicCache
+
+def prepare_inputs_for_generation_phi3(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+):
+    if past_key_values is not None:
+        if isinstance(past_key_values, Cache):
+            cache_length = past_key_values.get_seq_length()
+            past_length = past_key_values.seen_tokens
+            max_cache_length = past_key_values.get_max_length()
+        else:
+            cache_length = past_length = past_key_values[0][0].shape[2]
+            max_cache_length = None
+
+        # Keep only the unprocessed tokens:
+        # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+        # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+        # input)
+        if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+            input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
+        # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+        # input_ids based on the past_length.
+        elif past_length < input_ids.shape[1]:
+            input_ids = input_ids[:, past_length:]
+        # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+        # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+        if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+        ):
+            attention_mask = attention_mask[:, -max_cache_length:]
+
+    position_ids = kwargs.get('position_ids', None)
+    if attention_mask is not None and position_ids is None:
+        # create position_ids on the fly for batch generation
+        position_ids = attention_mask.long().cumsum(-1) - 1
+        position_ids.masked_fill_(attention_mask == 0, 1)
+        if past_key_values:
+            position_ids = position_ids[:, -input_ids.shape[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 or len(past_key_values)==0):
+        model_inputs = {'inputs_embeds': inputs_embeds}
+    else:
+        model_inputs = {'input_ids': input_ids}
+
+    model_inputs.update(
+        {
+            'position_ids': position_ids,
+            'past_key_values': past_key_values,
+            'use_cache': kwargs.get('use_cache'),
+            'attention_mask': attention_mask,
+        }
+    )
+    return model_inputs
+