modeling_pvc_internvl.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
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_pvc_internvl import PVCInternVLConfig
from .conversation import get_conv_template
from .modeling_intern_vit import InternVisionModel, has_flash_attn
from .modeling_intern_vit_pvc import InternVisionTemporalModel, AdaLayerNorm, Timesteps, temporal_idx_abs_to_rel
from .modeling_internlm2 import InternLM2ForCausalLM

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 AdaLNMLP(nn.Module):
    def __init__(self, input_dim, output_dim, use_temporal_condition=False, 
                 use_rel_timestep=False, rel_timestep_scale=100):
        super().__init__()
        # condition proj
        self.condition_proj = nn.Sequential(
            nn.Linear(input_dim, input_dim),
            nn.SiLU(), # default use `SiLU`
            nn.Linear(input_dim, input_dim)
        )
        self.use_temporal_condition = use_temporal_condition
        self.use_rel_timestep = use_rel_timestep
        self.rel_timestep_scale = rel_timestep_scale
        # from Stable Diffusion v3
        if use_temporal_condition:
            self.time_embed = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
            self.time_proj = nn.Sequential(
                nn.Linear(256, input_dim),
                nn.SiLU(),
                nn.Linear(input_dim, input_dim)
            )
        
        # adaln
        self.adaln = AdaLayerNorm(input_dim, input_dim)
        # original mlp
        self.mlp = nn.Sequential(
            nn.Linear(input_dim, output_dim),
            nn.GELU(),
            nn.Linear(output_dim, output_dim)
        )
        self.gradient_checkpointing = False
        
    def forward(self, x, split_sizes, temporal_id=None):
        condition = self.condition_proj(x)
        # from Stable Diffusion v3
        if self.use_temporal_condition:
            t = temporal_id
            if self.use_rel_timestep:
                t = temporal_idx_abs_to_rel(temporal_id, split_sizes)
                t = t * self.rel_timestep_scale
            t_embed = self.time_embed(t)
            t_embed = self.time_proj(t_embed.to(x.dtype))
            condition = condition + t_embed.unsqueeze(1)
        x = self.adaln(x, condition)
        x = self.mlp(x)
        return x


def build_projector_module(config: PVCInternVLConfig):
    vit_hidden_size = config.vision_config.hidden_size
    llm_hidden_size = config.llm_config.hidden_size
    
    if config.mlp_add_ops is not None and 'adaln' in config.mlp_add_ops:
        mlp_input_dim = vit_hidden_size * int(1 / config.downsample_ratio) ** 2
        use_temporal_condition = ('temporal' in config.mlp_add_ops)
        use_rel_timestep = ('rel' in config.mlp_add_ops)
        mlp1 = AdaLNMLP(mlp_input_dim, llm_hidden_size,
                        use_temporal_condition=use_temporal_condition,
                        use_rel_timestep=use_rel_timestep)
    else:
        mlp1 = nn.Sequential(
            nn.LayerNorm(vit_hidden_size * int(1 / config.downsample_ratio) ** 2),
            nn.Linear(vit_hidden_size * int(1 / config.downsample_ratio) ** 2, llm_hidden_size),
            nn.GELU(),
            nn.Linear(llm_hidden_size, llm_hidden_size)
        )
    return mlp1


def forward_projector(projector, x, **kwargs):
    if isinstance(projector, nn.Sequential):
        return projector(x)
    else:
        return projector(x, **kwargs)


class PVCInternVLModel(PreTrainedModel):
    config_class = PVCInternVLConfig
    main_input_name = 'pixel_values'
    base_model_prefix = 'language_model'
    _supports_flash_attn_2 = True
    _no_split_modules = ['InternVisionModel', 'InternVisionTemporalModel', 'LlamaDecoderLayer', 'InternLM2DecoderLayer']

    def __init__(self, config: PVCInternVLConfig, vision_model=None, language_model=None, delay_init_new_param=False, 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.num_frame_token = self.num_image_token
        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'num_frame_token: {self.num_frame_token}')
        logger.info(f'ps_version: {self.ps_version}')

        if vision_model is not None:
            self.vision_model = vision_model
        else:
            if config.use_temporal:
                self.vision_model = InternVisionTemporalModel(config.vision_config, delay_init_new_param=delay_init_new_param)
            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)
            else:
                raise NotImplementedError(f'{config.llm_config.architectures[0]} is not implemented.')

        self.mlp1 = build_projector_module(config)

        self.img_context_token_id = None
        self.conv_template = get_conv_template(self.template)
        self.system_message = self.conv_template.system_message

    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,
            split_sizes: Optional[torch.LongTensor] = None,
            temporal_id: 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, split_sizes=split_sizes, temporal_id=temporal_id)
        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, split_sizes=None, temporal_id=None):
        kwargs = {}
        # add split_sizes for temporal module
        if self.config.use_temporal:
            if split_sizes is not None:
                if isinstance(split_sizes, torch.Tensor):
                    split_sizes = split_sizes.tolist()
            else:
                split_sizes = [pixel_values.shape[0]]
            assert sum(split_sizes) == pixel_values.shape[0]
            kwargs['split_sizes'] = split_sizes
            kwargs['temporal_id'] = temporal_id

        if self.select_layer == -1:
            vit_embeds = self.vision_model(
                pixel_values=pixel_values,
                output_hidden_states=False,
                return_dict=True,
                **kwargs
            ).last_hidden_state
        else:
            vit_embeds = self.vision_model(
                pixel_values=pixel_values,
                output_hidden_states=True,
                return_dict=True,
                **kwargs
            ).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 = forward_projector(self.mlp1, vit_embeds, split_sizes=split_sizes, temporal_id=temporal_id)
        return vit_embeds

    def batch_chat(self, tokenizer, pixel_values, questions, generation_config, split_sizes=None, data_flag=None, 
                   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):
        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,
            split_sizes=split_sizes,
            **generation_config
        )
        responses = tokenizer.batch_decode(generation_output, skip_special_tokens=True)
        responses = [response.split(template.sep)[0].strip() for response in responses]
        return responses

    def chat(self, tokenizer, pixel_values, question, generation_config, num_patches_list=None, 
             split_sizes=None, data_flag=None, history=None, return_history=False,
             IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>', IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False):
        # data flag: 0: pure text; 1: single image; 2: multi image; 3 video
        flag = data_flag[0].item() if data_flag is not None else 1 # default as single image

        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)
        # default as `tile id`: [0, 1, ..., n_tile]
        temporal_id = torch.arange(len(pixel_values), dtype=torch.long, device=pixel_values.device)
        if self.config.tile_repeat_way == 'cycle':
            new_temporal_id = []
            for tid, n_tile in enumerate(num_patches_list):
                new_temporal_id.append(torch.tensor([tid] * n_tile, dtype=torch.long, device=pixel_values.device))
            temporal_id = torch.cat(new_temporal_id)

        if (flag == 1 or flag == 2) and self.config.image_repeat_time > 1:
            if self.config.tile_repeat_way == 'cycle':
                cur_st = 0
                new_pixel_values, new_temporal_id = [], []
                for img_idx, n_tile in enumerate(num_patches_list):
                    image = pixel_values[cur_st:cur_st+n_tile]
                    new_pixel_values.append(torch.cat([image for _ in range(self.config.image_repeat_time)], dim=0))
                    new_temporal_id.append(torch.arange(img_idx * self.config.image_repeat_time, (img_idx + 1) * self.config.image_repeat_time, 
                                                        dtype=torch.long, device=temporal_id.device).repeat_interleave(n_tile, dim=0))
                    cur_st += n_tile
                new_pixel_values = torch.cat(new_pixel_values, dim=0)
                new_temporal_id = torch.cat(new_temporal_id, dim=0)
                assert cur_st == len(pixel_values)
                assert len(new_pixel_values) == len(new_temporal_id) == len(pixel_values) * self.config.image_repeat_time
                pixel_values, temporal_id = new_pixel_values, new_temporal_id
            else:
                pixel_values = pixel_values.repeat_interleave(self.config.image_repeat_time, dim=0)
                temporal_id = torch.arange(len(pixel_values), dtype=torch.long, device=pixel_values.device)
            split_sizes = [s * self.config.image_repeat_time for s in split_sizes] if split_sizes is not None else None
            num_patches_list = [n * self.config.image_repeat_time for n in num_patches_list] if num_patches_list is not None else None
        if flag == 3 and self.config.video_repeat_time > 1:
            pixel_values = pixel_values.repeat_interleave(self.config.video_repeat_time, dim=0)
            if self.config.tile_repeat_way == 'cycle':
                new_temporal_id = []
                for img_idx, n_tile in enumerate(num_patches_list):
                    new_temporal_id.append(torch.arange(img_idx * self.config.video_repeat_time, (img_idx + 1) * self.config.video_repeat_time, 
                                                        dtype=torch.long, device=temporal_id.device).repeat_interleave(n_tile, dim=0))
                temporal_id = torch.cat(new_temporal_id, dim=0)
            else:
                temporal_id = torch.arange(len(pixel_values), dtype=torch.long, device=pixel_values.device)
            split_sizes = [s * self.config.video_repeat_time for s in split_sizes] if split_sizes is not None else None
            num_patches_list = [n * self.config.video_repeat_time for n in num_patches_list] if num_patches_list is not None else None

        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}')

        for num_patches in num_patches_list:
            if flag == 0:
                num_image_token = 0
            elif (flag == 1 or flag == 2):
                num_image_token = self.num_image_token * num_patches
            else:
                num_image_token = self.num_frame_token * num_patches
            image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * num_image_token + 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,
            split_sizes=split_sizes,
            temporal_id=temporal_id,
            **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,
            return_dict: Optional[bool] = None,
            split_sizes: Optional[torch.LongTensor] = None,
            temporal_id: Optional[torch.LongTensor] = None,
            **generate_kwargs,
    ) -> torch.LongTensor:

        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:
                vit_embeds = self.extract_feature(pixel_values, split_sizes=split_sizes, temporal_id=temporal_id)
            input_embeds = self.language_model.get_input_embeddings()(input_ids)
            B, N, C = input_embeds.shape
            input_embeds = input_embeds.reshape(B * N, C)

            input_ids = input_ids.reshape(B * N)
            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,
            return_dict=return_dict,
            use_cache=True,
            **generate_kwargs,
        )

        return outputs