VTimeLLM/vtimellm/mm_utils.py

from PIL import Image
from io import BytesIO
import base64
import numpy as np
import torch
import decord
from transformers import StoppingCriteria
from vtimellm.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, IMAGE_SEGMENT_TOKEN_INDEX, DEFAULT_IMAGE_SEGMENT_TOKEN


def load_image_from_base64(image):
    return Image.open(BytesIO(base64.b64decode(image)))


def process_images(images, image_processor, model_cfg):
    return image_processor(images, return_tensors='pt')['pixel_values']


def tokenizer_image_token_bf(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):

    def insert_separator(X, sep):
        return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]

    prompt_chunks_t = prompt.split(DEFAULT_IMAGE_TOKEN)

    if (len(prompt_chunks_t) > 1 and DEFAULT_IMAGE_SEGMENT_TOKEN in prompt_chunks_t[1]):
        # incase <video segment> is present 
        prompt_chunks_seg_t = prompt_chunks_t[1].split(DEFAULT_IMAGE_SEGMENT_TOKEN)
        prompt_t = [prompt_chunks_t[0]] + prompt_chunks_seg_t

        prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt_t]

        input_ids = []
        offset = 0
        # if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
        #     offset = 1
        #     input_ids.append(prompt_chunks[0][0])

        input_ids = input_ids + prompt_chunks[0] + ([image_token_index] * (offset + 1))

        offset = 1
        # image segment token
        for x in insert_separator(prompt_chunks[1:], [IMAGE_SEGMENT_TOKEN_INDEX] * (offset + 1)): 
            input_ids.extend(x[offset:])

        if return_tensors is not None:
            if return_tensors == 'pt':
                return torch.tensor(input_ids, dtype=torch.long)
            raise ValueError(f'Unsupported tensor type: {return_tensors}')

        return input_ids
    
    elif (len(prompt_chunks_t) == 1 and DEFAULT_IMAGE_SEGMENT_TOKEN in prompt_chunks_t[0]):
        # Assumed no image token in such prompt
        prompt_chunks_seg_t = prompt_chunks_t[0].split(DEFAULT_IMAGE_SEGMENT_TOKEN)
        prompt_t = prompt_chunks_seg_t

        prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt_t]

        input_ids = []
        offset = 0

        # image segment token]
        offset = 1
        input_ids.append(prompt_chunks[0][0])
        for x in insert_separator(prompt_chunks, [IMAGE_SEGMENT_TOKEN_INDEX] * (offset + 1)): input_ids.extend(x[offset:])

        if return_tensors is not None:
            if return_tensors == 'pt':
                return torch.tensor(input_ids, dtype=torch.long)
            raise ValueError(f'Unsupported tensor type: {return_tensors}')

        return input_ids

    else:
        prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split(DEFAULT_IMAGE_TOKEN)]

        input_ids = []
        offset = 0
        if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
            offset = 1
            input_ids.append(prompt_chunks[0][0])
        elif tokenizer.name == "GLMTokenizer":
            offset = 2
            input_ids = prompt_chunks[0][:2]

        for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
            input_ids.extend(x[offset:])

        if return_tensors is not None:
            if return_tensors == 'pt':
                return torch.tensor(input_ids, dtype=torch.long)
            raise ValueError(f'Unsupported tensor type: {return_tensors}')
        
        return input_ids


def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
    prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split(DEFAULT_IMAGE_TOKEN)]

    def insert_separator(X, sep):
        return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]

    input_ids = []
    offset = 0
    if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
        offset = 1
        input_ids.append(prompt_chunks[0][0])
    elif tokenizer.name == "GLMTokenizer":
        offset = 2
        input_ids = prompt_chunks[0][:2]

    for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
        input_ids.extend(x[offset:])

    if return_tensors is not None:
        if return_tensors == 'pt':
            return torch.tensor(input_ids, dtype=torch.long)
        raise ValueError(f'Unsupported tensor type: {return_tensors}')
    return input_ids


def get_model_name_from_path(model_path):
    model_path = model_path.strip("/")
    model_paths = model_path.split("/")
    if model_paths[-1].startswith('checkpoint-'):
        return model_paths[-2] + "_" + model_paths[-1]
    else:
        return model_paths[-1]




class KeywordsStoppingCriteria(StoppingCriteria):
    def __init__(self, keywords, tokenizer, input_ids):
        self.keywords = keywords
        self.keyword_ids = []
        for keyword in keywords:
            cur_keyword_ids = tokenizer(keyword).input_ids
            if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
                cur_keyword_ids = cur_keyword_ids[1:]
            self.keyword_ids.append(torch.tensor(cur_keyword_ids))
        self.tokenizer = tokenizer
        self.start_len = input_ids.shape[1]

    def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)"  # TODO
        offset = min(output_ids.shape[1] - self.start_len, 3)
        self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
        for keyword_id in self.keyword_ids:
            if output_ids[0, -keyword_id.shape[0]:].equal(keyword_id):
                return True
        outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
        for keyword in self.keywords:
            if keyword in outputs:
                return True
        return False

def print_trainable_parameters(model):
    trainable_params = 0
    all_param = 0
    for _, param in model.named_parameters():
        all_param += param.numel()
        # print(_, param.requires_grad, param.numel())
        if param.requires_grad:
            trainable_params += param.numel()
    print(
        f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param:.2f}"
    )

class VideoExtractor():
    """Dataset for supervised fine-tuning."""

    def __init__(self, N=100):
        self.N = N

    def extract(self, data):
        video_path = data['video']
        id = data['id']
        
        try:
            video_reader = decord.VideoReader(video_path)
            total_frames = len(video_reader)
            start = 0
            end = total_frames - 1

            split = data.get('split', None)
            if split is not None:
                fps = video_reader.get_avg_fps()
                start = max(int(fps * split[0]), 0)
                end = min(int(fps * split[1]), total_frames - 1)
            sampled_indices = np.linspace(start, end, self.N, dtype=np.int32)
            sampled_frames = video_reader.get_batch(sampled_indices).asnumpy()
        except Exception as e:
            print(e)
            return None, torch.zeros(1)
        
        images = torch.from_numpy(sampled_frames.transpose((0, 3, 1, 2)))
        return id, images