modeling_internvl_chat.py

# --------------------------------------------------------
# InternVL
# Copyright (c) 2024 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------

import warnings
from typing import List, Optional, Tuple, Union

import torch.utils.checkpoint
import transformers
from torch import nn
from torch.nn import CrossEntropyLoss
import torch.nn.functional as F
from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
                          LlamaTokenizer)
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import ModelOutput, logging

from .configuration_internvl_chat import InternVLChatConfig
from .conversation import get_conv_template
from .modeling_intern_vit import InternVisionModel, has_flash_attn
from .modeling_phi3 import Phi3ForCausalLM
from .modeling_radio import RADIOModel

# Import all required modules.
from .radio_adaptor_base import AdaptorBase, RadioOutput, AdaptorInput
from .radio_adaptor_generic import GenericAdaptor, AdaptorBase
from .radio_adaptor_mlp import create_mlp_from_state
from .radio_adaptor_registry import adaptor_registry
from .radio_cls_token import ClsToken
from .radio_enable_cpe_support import enable_cpe
from .radio_enable_spectral_reparam import configure_spectral_reparam_from_args
from .radio_eradio_model import eradio
from .radio_model import create_model_from_args
from .radio_model import RADIOModel as RADIOModelBase, Resolution
from .radio_input_conditioner import get_default_conditioner, InputConditioner
from .radio_open_clip_adaptor import OpenCLIP_RADIO
from .radio_vit_patch_generator import ViTPatchGenerator
from .radio_vitdet import apply_vitdet_arch, VitDetArgs

# Register extra models
from .radio_extra_timm_models import *

from .configuration_radio import RADIOConfig

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 InternVLChatModel(PreTrainedModel):
    config_class = InternVLChatConfig
    main_input_name = 'pixel_values'
    base_model_prefix = 'language_model'
    _supports_flash_attn_2 = True
    _no_split_modules = ['InternVisionModel', 'LlamaDecoderLayer', 'Phi3DecoderLayer']

    def __init__(self, config: InternVLChatConfig, vision_model=None, language_model=None, radio_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.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
        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] == 'Phi3ForCausalLM':
                self.language_model = Phi3ForCausalLM(config.llm_config)
            else:
                raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')
        if radio_model is not None:
            self.object_tokenizer = radio_model
        else:
            self.object_tokenizer = RADIOModel(config.radio_config)

        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)
        )

        # additional modules
        ot_hidden_size = self.object_tokenizer.model.num_features
        self.ot_mlp1 = nn.Sequential(
            nn.LayerNorm(ot_hidden_size,),
            nn.Linear(ot_hidden_size, config.llm_config.hidden_size,),
            nn.GELU(),
            nn.Linear(config.llm_config.hidden_size, config.llm_config.hidden_size)
        )
        
        self.ot_config = config.radio_config
        self.img_context_token_id = None
        self.conv_template = get_conv_template(self.template)
        self.system_message = self.conv_template.system_message
    
    def _add_special_tokens(self, tokenizer):
        special_tokens = ['<VPT_CONTEXT>', ]
        num_new_tokens = tokenizer.add_tokens(special_tokens, special_tokens=True)
        return tokenizer

    def 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,
    ) -> 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)
        input_embeds = self.language_model.get_input_embeddings()(input_ids).clone()

        vit_embeds = self.extract_feature(pixel_values)
        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)

        if torch.distributed.is_initialized() and torch.distributed.get_rank() == 0:
            print(f'dynamic ViT batch size: {vit_batch_size}, images per sample: {vit_batch_size / B}, dynamic token length: {N}')

        input_ids = input_ids.reshape(B * N)
        selected = (input_ids == self.img_context_token_id)
        try:
            input_embeds[selected] = input_embeds[selected] * 0.0 + vit_embeds.reshape(-1, C)
        except Exception as e:
            vit_embeds = vit_embeds.reshape(-1, C)
            print(f'warning: {e}, input_embeds[selected].shape={input_embeds[selected].shape}, '
                  f'vit_embeds.shape={vit_embeds.shape}')
            n_token = selected.sum()
            input_embeds[selected] = input_embeds[selected] * 0.0 + vit_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,
        )

    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

    def batch_chat(self, tokenizer, pixel_values, questions, generation_config, num_patches_list=None,
                   history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
                   IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None):
        raise NotImplementedError
        # if history is not None or return_history:
        #     print('Now multi-turn chat is not supported in batch_chat.')
        #     raise NotImplementedError

        # if image_counts is not None:
        #     num_patches_list = image_counts
        #     print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')

        # img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
        # self.img_context_token_id = img_context_token_id

        # if verbose and pixel_values is not None:
        #     image_bs = pixel_values.shape[0]
        #     print(f'dynamic ViT batch size: {image_bs}')

        # queries = []
        # for idx, num_patches in enumerate(num_patches_list):
        #     question = questions[idx]
        #     if pixel_values is not None and '<image>' not in question:
        #         question = '<image>\n' + question
        #     template = get_conv_template(self.template)
        #     template.system_message = self.system_message
        #     template.append_message(template.roles[0], question)
        #     template.append_message(template.roles[1], None)
        #     query = template.get_prompt()

        #     image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
        #     query = query.replace('<image>', image_tokens, 1)
        #     queries.append(query)

        # tokenizer.padding_side = 'left'
        # model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
        # input_ids = model_inputs['input_ids'].to(self.device)
        # attention_mask = model_inputs['attention_mask'].to(self.device)
        # eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())
        # generation_config['eos_token_id'] = eos_token_id
        # generation_output = self.generate(
        #     pixel_values=pixel_values,
        #     input_ids=input_ids,
        #     attention_mask=attention_mask,
        #     **generation_config
        # )
        # responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
        # responses = [response.split(template.sep.strip())[0].strip() for response in responses]
        # return responses

    def chat(self, tokenizer, pixel_values, question, generation_config, history=None, return_history=False,
             num_patches_list=None, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>',
             verbose=False, ot_pixel_values=None, ot_visual_prompts=None):
        
        tokenizer = self._add_special_tokens(tokenizer)
        self.vpt_content_token_idx = tokenizer('<VPT_CONTEXT>', add_special_tokens=False).input_ids[0]

        if history is None and pixel_values is not None and '<image>' not in question:
            question = '<image>\n' + question

        if num_patches_list is None:
            num_patches_list = [pixel_values.shape[0]] if pixel_values is not None else []
        assert pixel_values is None or len(pixel_values) == sum(num_patches_list)

        img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
        self.img_context_token_id = img_context_token_id

        template = get_conv_template(self.template)
        template.system_message = self.system_message
        eos_token_id = tokenizer.convert_tokens_to_ids(template.sep.strip())

        history = [] if history is None else history
        for (old_question, old_answer) in history:
            template.append_message(template.roles[0], old_question)
            template.append_message(template.roles[1], old_answer)
        template.append_message(template.roles[0], question)
        template.append_message(template.roles[1], None)
        query = template.get_prompt()

        if verbose and pixel_values is not None:
            image_bs = pixel_values.shape[0]
            print(f'dynamic ViT batch size: {image_bs}')
        
        # object tokenizer
        if ot_visual_prompts is not None and len(ot_visual_prompts) > 0:
            ot_pixel_values = ot_pixel_values.to(self.object_tokenizer.dtype)
            ot_h, ot_w = ot_pixel_values.shape[-2:]
            ot_num_tokens_h, ot_num_tokens_w = ot_h // self.ot_config.patch_size, ot_w // self.ot_config.patch_size
            summary, ot_embeds = self.object_tokenizer(ot_pixel_values)
            # for param in self.ot_mlp1.parameters():
            #     if param.dtype != ot_embeds.dtype:
            #         param.data = param.data.to(ot_embeds)
            with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
                ot_embeds = self.ot_mlp1(ot_embeds)
            
            ot_object_embeds_list = []
            for fidx, ot_visual_prompts_fi in enumerate(ot_visual_prompts):
                nvp, h, w = ot_visual_prompts_fi.shape
                ot_visual_prompts_fi = ot_visual_prompts_fi[:, None, :, :].to("cuda").to(self.object_tokenizer.dtype)
                ot_visual_prompts_fi = F.interpolate(ot_visual_prompts_fi.to(ot_embeds.dtype), (ot_num_tokens_h, ot_num_tokens_w), mode="bilinear")
                ot_visual_prompts_fi = (ot_visual_prompts_fi > 0.55).to(ot_embeds.dtype)
                ot_visual_prompts_fi = ot_visual_prompts_fi.reshape(nvp, -1)

                num_vp_tokens = torch.sum(ot_visual_prompts_fi, dim=-1, keepdim=False)
                ot_embeds_fi = ot_embeds[fidx]
                object_embeds = (ot_visual_prompts_fi[:, :, None] / (num_vp_tokens[:, None, None] + 1e-4) * ot_embeds_fi[None, :, :])
                object_embeds = torch.sum(object_embeds, dim=1)
                ot_object_embeds_list.append(object_embeds)
            ot_object_embeds = torch.cat(ot_object_embeds_list)
        else:
            ot_object_embeds = None

        for num_patches in num_patches_list:
            image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
            query = query.replace('<image>', image_tokens, 1)

        model_inputs = tokenizer(query, return_tensors='pt')
        input_ids = model_inputs['input_ids'].to(self.device)
        attention_mask = model_inputs['attention_mask'].to(self.device)
        generation_config['eos_token_id'] = eos_token_id
        generation_output = self.generate(
            pixel_values=pixel_values,
            input_ids=input_ids,
            attention_mask=attention_mask,
            ot_object_embeds=ot_object_embeds,
            **generation_config
        )
        response = tokenizer.batch_decode(generation_output, skip_special_tokens=True)[0]
        response = response.split(template.sep.strip())[0].strip()
        history.append((question, response))
        if return_history:
            return response, history
        else:
            query_to_print = query.replace(IMG_CONTEXT_TOKEN, '')
            query_to_print = query_to_print.replace(f'{IMG_START_TOKEN}{IMG_END_TOKEN}', '<image>')
            if verbose:
                print(query_to_print, response)
            return response

    @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,
            ot_object_embeds: Optional[torch.FloatTensor] = None,
            **generate_kwargs,
    ) -> torch.LongTensor:
        
        assert self.img_context_token_id is not None
        if pixel_values is not None:
            B, N = input_ids.shape
            temp_input_ids = input_ids.clone().flatten()
            temp_input_ids[temp_input_ids == self.vpt_content_token_idx] = self.img_context_token_id

            if visual_features is not None:
                vit_embeds = visual_features
            else:
                vit_embeds = self.extract_feature(pixel_values)
            input_embeds = self.language_model.get_input_embeddings()(temp_input_ids.reshape(B, N))
            B, N, C = input_embeds.shape
            input_embeds = input_embeds.reshape(B * N, C)

            input_ids = input_ids.reshape(B * N)

            if ot_object_embeds is not None:
                selected = (input_ids == self.vpt_content_token_idx)
                input_embeds[selected] = input_embeds[selected] * 0.0 + ot_object_embeds.to(input_embeds.dtype)

            selected = (input_ids == self.img_context_token_id)
            assert selected.sum() != 0
            input_embeds[selected] = vit_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,
            generation_config=generation_config,
            output_hidden_states=output_hidden_states,
            use_cache=True,
            **generate_kwargs,
        )

        return outputs