update

unimernet/datasets/builders/__init__.py

+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+from unimernet.datasets.builders.base_dataset_builder import load_dataset_config
+from unimernet.common.registry import registry
+from unimernet.datasets.builders.formula import FormulaRecTrainBuilder, FormulaRecEvalBuilder, \
+    MultiScaleFormulaRecTrainBuilder
+
+__all__ = [
+    "FormulaRecTrainBuilder",
+    "FormulaRecEvalBuilder",
+    "MultiScaleFormulaRecTrainBuilder",
+]
+
+
+def load_dataset(name, cfg_path=None, vis_path=None, data_type=None):
+    """
+    Example
+
+    >>> dataset = load_dataset("coco_caption", cfg=None)
+    >>> splits = dataset.keys()
+    >>> print([len(dataset[split]) for split in splits])
+
+    """
+    if cfg_path is None:
+        cfg = None
+    else:
+        cfg = load_dataset_config(cfg_path)
+
+    try:
+        builder = registry.get_builder_class(name)(cfg)
+    except TypeError:
+        print(
+            f"Dataset {name} not found. Available datasets:\n"
+            + ", ".join([str(k) for k in dataset_zoo.get_names()])
+        )
+        exit(1)
+
+    if vis_path is not None:
+        if data_type is None:
+            # use default data type in the config
+            data_type = builder.config.data_type
+
+        assert (
+                data_type in builder.config.build_info
+        ), f"Invalid data_type {data_type} for {name}."
+
+        builder.config.build_info.get(data_type).storage = vis_path
+
+    dataset = builder.build_datasets()
+    return dataset
+
+
+class DatasetZoo:
+    def __init__(self) -> None:
+        self.dataset_zoo = {
+            k: list(v.DATASET_CONFIG_DICT.keys())
+            for k, v in sorted(registry.mapping["builder_name_mapping"].items())
+        }
+
+    def get_names(self):
+        return list(self.dataset_zoo.keys())
+
+
+dataset_zoo = DatasetZoo()

unimernet/datasets/builders/base_dataset_builder.py

+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import logging
+import os
+import shutil
+import warnings
+
+import unimernet.common.utils as utils
+import torch.distributed as dist
+from unimernet.common.dist_utils import is_dist_avail_and_initialized, is_main_process
+from unimernet.common.registry import registry
+from unimernet.processors.base_processor import BaseProcessor
+from omegaconf import OmegaConf
+from torchvision.datasets.utils import download_url
+
+
+class BaseDatasetBuilder:
+    train_dataset_cls, eval_dataset_cls = None, None
+
+    def __init__(self, cfg=None):
+        super().__init__()
+
+        if cfg is None:
+            # help to create datasets from default config.
+            self.config = load_dataset_config(self.default_config_path())
+        elif isinstance(cfg, str):
+            self.config = load_dataset_config(cfg)
+        else:
+            # when called from task.build_dataset()
+            self.config = cfg
+
+        self.data_type = self.config.data_type
+
+        self.vis_processors = {"train": BaseProcessor(), "eval": BaseProcessor()}
+        self.text_processors = {"train": BaseProcessor(), "eval": BaseProcessor()}
+
+    def build_datasets(self):
+        # download, split, etc...
+        # only called on 1 GPU/TPU in distributed
+
+        if is_main_process():
+            self._download_data()
+
+        if is_dist_avail_and_initialized():
+            dist.barrier()
+
+        # at this point, all the annotations and image/videos should be all downloaded to the specified locations.
+        logging.info("Building datasets...")
+        datasets = self.build()  # dataset['train'/'val'/'test']
+
+        return datasets
+
+    def build_processors(self):
+        vis_proc_cfg = self.config.get("vis_processor")
+        txt_proc_cfg = self.config.get("text_processor")
+
+        if vis_proc_cfg is not None:
+            vis_train_cfg = vis_proc_cfg.get("train")
+            vis_eval_cfg = vis_proc_cfg.get("eval")
+
+            self.vis_processors["train"] = self._build_proc_from_cfg(vis_train_cfg)
+            self.vis_processors["eval"] = self._build_proc_from_cfg(vis_eval_cfg)
+
+        if txt_proc_cfg is not None:
+            txt_train_cfg = txt_proc_cfg.get("train")
+            txt_eval_cfg = txt_proc_cfg.get("eval")
+
+            self.text_processors["train"] = self._build_proc_from_cfg(txt_train_cfg)
+            self.text_processors["eval"] = self._build_proc_from_cfg(txt_eval_cfg)
+
+    @staticmethod
+    def _build_proc_from_cfg(cfg):
+        return (
+            registry.get_processor_class(cfg.name).from_config(cfg)
+            if cfg is not None
+            else None
+        )
+
+    @classmethod
+    def default_config_path(cls, type="default"):
+        return utils.get_abs_path(cls.DATASET_CONFIG_DICT[type])
+
+    def _download_data(self):
+        self._download_ann()
+        self._download_vis()
+
+    def _download_ann(self):
+        """
+        Download annotation files if necessary.
+        All the vision-language datasets should have annotations of unified format.
+
+        storage_path can be:
+          (1) relative/absolute: will be prefixed with env.cache_root to make full path if relative.
+          (2) basename/dirname: will be suffixed with base name of URL if dirname is provided.
+
+        Local annotation paths should be relative.
+        """
+        anns = self.config.build_info.annotations
+
+        splits = anns.keys()
+
+        cache_root = registry.get_path("cache_root")
+
+        for split in splits:
+            info = anns[split]
+
+            urls, storage_paths = info.get("url", None), info.storage
+
+            if isinstance(urls, str):
+                urls = [urls]
+            if isinstance(storage_paths, str):
+                storage_paths = [storage_paths]
+
+            assert len(urls) == len(storage_paths)
+
+            for url_or_filename, storage_path in zip(urls, storage_paths):
+                # if storage_path is relative, make it full by prefixing with cache_root.
+                if not os.path.isabs(storage_path):
+                    storage_path = os.path.join(cache_root, storage_path)
+
+                dirname = os.path.dirname(storage_path)
+                if not os.path.exists(dirname):
+                    os.makedirs(dirname)
+
+                if os.path.isfile(url_or_filename):
+                    src, dst = url_or_filename, storage_path
+                    if not os.path.exists(dst):
+                        shutil.copyfile(src=src, dst=dst)
+                    else:
+                        logging.info("Using existing file {}.".format(dst))
+                else:
+                    if os.path.isdir(storage_path):
+                        # if only dirname is provided, suffix with basename of URL.
+                        raise ValueError(
+                            "Expecting storage_path to be a file path, got directory {}".format(
+                                storage_path
+                            )
+                        )
+                    else:
+                        filename = os.path.basename(storage_path)
+
+                    download_url(url=url_or_filename, root=dirname, filename=filename)
+
+    def _download_vis(self):
+
+        storage_path = self.config.build_info.get(self.data_type).storage
+        storage_path = utils.get_cache_path(storage_path)
+
+        if not os.path.exists(storage_path):
+            warnings.warn(
+                f"""
+                The specified path {storage_path} for visual inputs does not exist.
+                Please provide a correct path to the visual inputs or
+                refer to datasets/download_scripts/README.md for downloading instructions.
+                """
+            )
+
+    def build(self):
+        """
+        Create by split datasets inheriting torch.utils.data.Datasets.
+
+        # build() can be dataset-specific. Overwrite to customize.
+        """
+        self.build_processors()
+
+        build_info = self.config.build_info
+
+        ann_info = build_info.annotations
+        vis_info = build_info.get(self.data_type)
+
+        datasets = dict()
+        for split in ann_info.keys():
+            if split not in ["train", "val", "test"]:
+                continue
+
+            is_train = split == "train"
+
+            # processors
+            vis_processor = (
+                self.vis_processors["train"]
+                if is_train
+                else self.vis_processors["eval"]
+            )
+            text_processor = (
+                self.text_processors["train"]
+                if is_train
+                else self.text_processors["eval"]
+            )
+
+            # annotation path
+            ann_paths = ann_info.get(split).storage
+            if isinstance(ann_paths, str):
+                ann_paths = [ann_paths]
+
+            abs_ann_paths = []
+            for ann_path in ann_paths:
+                if not os.path.isabs(ann_path):
+                    ann_path = utils.get_cache_path(ann_path)
+                abs_ann_paths.append(ann_path)
+            ann_paths = abs_ann_paths
+
+            # visual data storage path
+            vis_path = vis_info.storage
+
+            if not os.path.isabs(vis_path):
+                # vis_path = os.path.join(utils.get_cache_path(), vis_path)
+                vis_path = utils.get_cache_path(vis_path)
+
+            if not os.path.exists(vis_path):
+                warnings.warn("storage path {} does not exist.".format(vis_path))
+
+            # create datasets
+            dataset_cls = self.train_dataset_cls if is_train else self.eval_dataset_cls
+            datasets[split] = dataset_cls(
+                vis_processor=vis_processor,
+                text_processor=text_processor,
+                ann_paths=ann_paths,
+                vis_root=vis_path,
+            )
+
+        return datasets
+
+
+def load_dataset_config(cfg_path):
+    cfg = OmegaConf.load(cfg_path).datasets
+    cfg = cfg[list(cfg.keys())[0]]
+
+    return cfg

unimernet/datasets/builders/formula.py

+import logging
+from unimernet.common.registry import registry
+from unimernet.datasets.builders.base_dataset_builder import BaseDatasetBuilder
+from unimernet.datasets.datasets.formula import Im2LatexDataset
+from unimernet.datasets.datasets.formula_multi_scale import MultiScaleIm2LatexDataset
+
+
+@registry.register_builder("formula_rec_train")
+class FormulaRecTrainBuilder(BaseDatasetBuilder):
+    train_dataset_cls = Im2LatexDataset
+    DATASET_CONFIG_DICT = {
+        "default": "configs/datasets/formula/formula_train.yaml"
+    }
+    LOG_INFO = "Formula Recgnition Train"
+
+    def build_datasets(self):
+        logging.info(f"Building {self.LOG_INFO} datasets ...")
+        self.build_processors()
+
+        build_info = self.config.build_info
+        anno_path = build_info.annotation,
+        vis_root = build_info.images
+        anno_path = [anno_path] if isinstance(anno_path, str) else anno_path
+        vis_root = [vis_root] if isinstance(vis_root, str) else vis_root
+        datasets = dict()
+
+        # create datasets
+        dataset_cls = self.train_dataset_cls
+        datasets['train'] = dataset_cls(
+            vis_processor=self.vis_processors["train"],
+            text_processor=self.text_processors["train"],
+            vis_root=vis_root,
+            anno_path=anno_path,
+        )
+        print(datasets['train'][0])
+
+        return datasets
+
+
+@registry.register_builder("multi_scale_formula_rec_train")
+class MultiScaleFormulaRecTrainBuilder(BaseDatasetBuilder):
+    train_dataset_cls = MultiScaleIm2LatexDataset
+    DATASET_CONFIG_DICT = {
+        "default": "configs/datasets/formula/multi_scale_formula_train.yaml"
+    }
+    LOG_INFO = "Multi Scale Formula Recgnition Train"
+
+    def build_datasets(self):
+        logging.info(f"Building {self.LOG_INFO} datasets ...")
+        self.build_processors()
+
+        build_info = self.config.build_info
+        anno_path = build_info.annotation,
+        vis_root = build_info.images
+
+        anno_path = [anno_path] if isinstance(anno_path, str) else anno_path
+        vis_root = [vis_root] if isinstance(vis_root, str) else vis_root
+
+        datasets = dict()
+
+        # create datasets
+        dataset_cls = self.train_dataset_cls
+        datasets['train'] = dataset_cls(
+            vis_processor=self.vis_processors["train"],
+            text_processor=self.text_processors["train"],
+            vis_root=vis_root,
+            anno_path=anno_path,
+        )
+        print(datasets['train'][0])
+
+        return datasets
+
+
+@registry.register_builder("formula_rec_eval")
+class FormulaRecEvalBuilder(BaseDatasetBuilder):
+    eval_dataset_cls = Im2LatexDataset
+    DATASET_CONFIG_DICT = {
+        "default": "configs/datasets/formula/formula_eval.yaml"
+    }
+    LOG_INFO = "Formula Recgnition Eval"
+
+    def build_datasets(self):
+        logging.info(f"Building {self.LOG_INFO} datasets ...")
+        self.build_processors()
+
+        build_info = self.config.build_info
+        anno_path = build_info.annotation,
+        vis_root = build_info.images
+
+        anno_path = [anno_path] if isinstance(anno_path, str) else anno_path
+        vis_root = [vis_root] if isinstance(vis_root, str) else vis_root
+
+        datasets = dict()
+
+        # create datasets
+        dataset_cls = self.eval_dataset_cls
+        datasets['eval'] = dataset_cls(
+            vis_processor=self.vis_processors["eval"],
+            text_processor=self.text_processors["eval"],
+            vis_root=vis_root,
+            anno_path=anno_path,
+        )
+        print(datasets['eval'][0])
+
+        return datasets

unimernet/datasets/data_utils.py

+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import gzip
+import logging
+import os
+import random as rnd
+import tarfile
+import zipfile
+
+import webdataset as wds
+import numpy as np
+import torch
+from torch.utils.data.dataset import IterableDataset, ChainDataset
+from unimernet.common.registry import registry
+from unimernet.datasets.datasets.base_dataset import ConcatDataset
+from tqdm import tqdm
+
+MAX_INT = registry.get("MAX_INT")
+
+
+def apply_to_sample(f, sample):
+    if len(sample) == 0:
+        return {}
+
+    def _apply(x):
+        if torch.is_tensor(x):
+            return f(x)
+        elif isinstance(x, dict):
+            return {key: _apply(value) for key, value in x.items()}
+        elif isinstance(x, list):
+            return [_apply(x) for x in x]
+        else:
+            return x
+
+    return _apply(sample)
+
+
+def move_to_cuda(sample):
+    def _move_to_cuda(tensor):
+        return tensor.cuda()
+
+    return apply_to_sample(_move_to_cuda, sample)
+
+
+def prepare_sample(samples, cuda_enabled=True):
+    if cuda_enabled:
+        samples = move_to_cuda(samples)
+
+    # TODO fp16 support
+
+    return samples
+
+
+def reorg_datasets_by_split(datasets):
+    """
+    Organizes datasets by split.
+
+    Args:
+        datasets: dict of torch.utils.data.Dataset objects by name.
+
+    Returns:
+        Dict of datasets by split {split_name: List[Datasets]}.
+    """
+    # if len(datasets) == 1:
+    #     return datasets[list(datasets.keys())[0]]
+    # else:
+    reorg_datasets = dict()
+
+    # reorganize by split
+    for _, dataset in datasets.items():
+        for split_name, dataset_split in dataset.items():
+            if split_name not in reorg_datasets:
+                reorg_datasets[split_name] = [dataset_split]
+            else:
+                reorg_datasets[split_name].append(dataset_split)
+
+    return reorg_datasets
+
+
+def concat_datasets(datasets):
+    """
+    Concatenates multiple datasets into a single dataset.
+
+    It supports may-style datasets and DataPipeline from WebDataset. Currently, does not support
+    generic IterableDataset because it requires creating separate samplers.
+
+    Now only supports conctenating training datasets and assuming validation and testing
+    have only a single dataset. This is because metrics should not be computed on the concatenated
+    datasets.
+
+    Args:
+        datasets: dict of torch.utils.data.Dataset objects by split.
+
+    Returns:
+        Dict of concatenated datasets by split, "train" is the concatenation of multiple datasets,
+        "val" and "test" remain the same.
+
+        If the input training datasets contain both map-style and DataPipeline datasets, returns
+        a tuple, where the first element is a concatenated map-style dataset and the second
+        element is a chained DataPipeline dataset.
+
+    """
+    # concatenate datasets in the same split
+    for split_name in datasets:
+        if split_name != "train":
+            assert (
+                len(datasets[split_name]) == 1
+            ), "Do not support multiple {} datasets.".format(split_name)
+            datasets[split_name] = datasets[split_name][0]
+        else:
+            iterable_datasets, map_datasets = [], []
+            for dataset in datasets[split_name]:
+                if isinstance(dataset, wds.DataPipeline):
+                    logging.info(
+                        "Dataset {} is IterableDataset, can't be concatenated.".format(
+                            dataset
+                        )
+                    )
+                    iterable_datasets.append(dataset)
+                elif isinstance(dataset, IterableDataset):
+                    raise NotImplementedError(
+                        "Do not support concatenation of generic IterableDataset."
+                    )
+                else:
+                    map_datasets.append(dataset)
+
+            # if len(iterable_datasets) > 0:
+            # concatenate map-style datasets and iterable-style datasets separately
+            chained_datasets = (
+                ChainDataset(iterable_datasets) if len(iterable_datasets) > 0 else None
+            )
+            concat_datasets = (
+                ConcatDataset(map_datasets) if len(map_datasets) > 0 else None
+            )
+
+            train_datasets = concat_datasets, chained_datasets
+            train_datasets = tuple([x for x in train_datasets if x is not None])
+            train_datasets = (
+                train_datasets[0] if len(train_datasets) == 1 else train_datasets
+            )
+
+            datasets[split_name] = train_datasets
+
+    return datasets
+
+
+def extract_archive(from_path, to_path=None, overwrite=False):
+    """Extract archive.
+
+    Args:
+        from_path: the path of the archive.
+        to_path: the root path of the extracted files (directory of from_path)
+        overwrite: overwrite existing files (False)
+
+    Returns:
+        List of paths to extracted files even if not overwritten.
+
+    Examples:
+        >>> url = 'http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz'
+        >>> from_path = './validation.tar.gz'
+        >>> to_path = './'
+        >>> torchtext.utils.download_from_url(url, from_path)
+        >>> torchtext.utils.extract_archive(from_path, to_path)
+        >>> ['.data/val.de', '.data/val.en']
+        >>> torchtext.utils.download_from_url(url, from_path)
+        >>> torchtext.utils.extract_archive(from_path, to_path)
+        >>> ['.data/val.de', '.data/val.en']
+
+    """
+
+    if to_path is None:
+        to_path = os.path.dirname(from_path)
+
+    if from_path.endswith((".tar.gz", ".tgz")):
+        logging.info("Opening tar file {} to {}.".format(from_path, to_path))
+        with tarfile.open(from_path, "r") as tar:
+            files = []
+            for file_ in tqdm(tar):
+                file_path = os.path.join(to_path, file_.name)
+                if file_.isfile():
+                    files.append(file_path)
+                    if os.path.exists(file_path):
+                        logging.info("{} already extracted.".format(file_path))
+                        if not overwrite:
+                            continue
+                tar.extract(file_, to_path)
+            logging.info("Finished extracting tar file {}.".format(from_path))
+            return files
+
+    elif from_path.endswith(".zip"):
+        assert zipfile.is_zipfile(from_path), from_path
+        logging.info("Opening zip file {} to {}.".format(from_path, to_path))
+        with zipfile.ZipFile(from_path, "r") as zfile:
+            files = []
+            for file_ in tqdm(zfile.namelist()):
+                file_path = os.path.join(to_path, file_)
+                files.append(file_path)
+                if os.path.exists(file_path):
+                    logging.info("{} already extracted.".format(file_path))
+                    if not overwrite:
+                        continue
+                zfile.extract(file_, to_path)
+        files = [f for f in files if os.path.isfile(f)]
+        logging.info("Finished extracting zip file {}.".format(from_path))
+        return files
+
+    elif from_path.endswith(".gz"):
+        logging.info("Opening gz file {} to {}.".format(from_path, to_path))
+        default_block_size = 65536
+        filename = from_path[:-3]
+        files = [filename]
+        with gzip.open(from_path, "rb") as gzfile, open(filename, "wb") as d_file:
+            while True:
+                block = gzfile.read(default_block_size)
+                if not block:
+                    break
+                else:
+                    d_file.write(block)
+            d_file.write(block)
+        logging.info("Finished extracting gz file {}.".format(from_path))
+        return files
+
+    else:
+        raise NotImplementedError(
+            "We currently only support tar.gz, .tgz, .gz and zip achives."
+        )
+
+
+def save_frames_grid(img_array, out_path):
+    import torch
+    from PIL import Image
+    from torchvision.utils import make_grid
+
+    if len(img_array.shape) == 3:
+        img_array = img_array.unsqueeze(0)
+    elif len(img_array.shape) == 5:
+        b, t, c, h, w = img_array.shape
+        img_array = img_array.view(-1, c, h, w)
+    elif len(img_array.shape) == 4:
+        pass
+    else:
+        raise NotImplementedError(
+            "Supports only (b,t,c,h,w)-shaped inputs. First two dimensions can be ignored."
+        )
+
+    assert img_array.shape[1] == 3, "Exepcting input shape of (H, W, 3), i.e. RGB-only."
+
+    grid = make_grid(img_array)
+    ndarr = grid.permute(1, 2, 0).to("cpu", torch.uint8).numpy()
+
+    img = Image.fromarray(ndarr)
+
+    img.save(out_path)

unimernet/datasets/datasets/base_dataset.py

+import json
+from PIL import Image, ImageFile
+import os.path as osp
+
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+from io import BytesIO
+from typing import Iterable
+from torch.utils.data import Dataset, ConcatDataset
+import torch
+
+
+class BaseDataset(Dataset):
+
+    def __init__(self, vis_processor, text_processor, vis_root, anno_path):
+
+        self.vis_root = vis_root
+        # if isinstance(anno_path, tuple) or isinstance(anno_path, list):
+        #     anno_path = anno_path[0]
+        self.anno_path = anno_path
+
+        self.vis_processor = vis_processor
+        self.text_processor = text_processor
+
+        self.samples = self.init_samples()
+        self.reader = self.init_reader()
+
+        print('total {} {} samples'.format(self.__len__(), self.__class__.__name__))
+
+        for idx in range(10):
+            self.__getitem__(idx)
+
+    def __len__(self):
+        return len(self.samples)
+
+    def __getitem__(self, index):
+        raise NotImplementedError
+
+    def init_samples(self):
+        # read annotation from ceph
+        if self.anno_path.startswith('cluster'):
+            from petrel_client.client import Client
+            client = Client("~/petreloss.conf")
+            samples = json.loads(client.get(self.anno_path))
+        else:
+            samples = json.load(open(self.anno_path, 'r'))
+        return samples
+
+    def init_reader(self):
+        if self.vis_root.startswith('cluster'):
+            from petrel_client.client import Client
+            client = Client("~/petreloss.conf")
+            reader = {'type': 'PetrelReader', 'body': client.get}
+        else:
+            reader = {'type': 'LocalReader', 'body': Image.open}
+        return reader
+
+    def _read_image(self, sample, image_key="image"):
+        img_file = sample[image_key]
+        image_path = osp.join(self.vis_root, img_file)
+        image = self.reader['body'](image_path)
+        if isinstance(image, bytes):
+            bytes_stream = BytesIO(image)
+            image = Image.open(bytes_stream)
+        image = image.convert("RGB")
+        return image
+
+    def collater(self, samples):
+        image_list, question_list, answer_list = [], [], []
+
+        for sample in samples:
+            image_list.append(sample["image"])
+            question_list.append(sample["text_input"])
+            answer_list.append(sample["text_output"])
+
+        return {
+            "image": torch.stack(image_list, dim=0),
+            "text_input": question_list,
+            "text_output": answer_list,
+            "data_type": "vqa",
+        }
+
+
+class ConcatDataset(ConcatDataset):
+    def __init__(self, datasets: Iterable[Dataset]) -> None:
+        super().__init__(datasets)
+
+    def collater(self, samples):
+        # TODO For now only supports datasets with same underlying collater implementations
+
+        all_keys = set()
+        for s in samples:
+            all_keys.update(s)
+
+        shared_keys = all_keys
+        for s in samples:
+            shared_keys = shared_keys & set(s.keys())
+
+        samples_shared_keys = []
+        for s in samples:
+            samples_shared_keys.append({k: s[k] for k in s.keys() if k in shared_keys})
+
+        return self.datasets[0].collater(samples_shared_keys)

unimernet/datasets/datasets/dataloader_utils.py

+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import time
+import random
+import torch
+from unimernet.datasets.data_utils import move_to_cuda
+from torch.utils.data import DataLoader
+
+
+class MultiIterLoader:
+    """
+    A simple wrapper for iterating over multiple iterators.
+
+    Args:
+        loaders (List[Loader]): List of Iterator loaders.
+        ratios (List[float]): List of ratios to sample from each loader. If None, all loaders are sampled uniformly.
+    """
+
+    def __init__(self, loaders, ratios=None):
+        # assert all loaders has __next__ method
+        for loader in loaders:
+            assert hasattr(
+                loader, "__next__"
+            ), "Loader {} has no __next__ method.".format(loader)
+
+        if ratios is None:
+            ratios = [1.0] * len(loaders)
+        else:
+            assert len(ratios) == len(loaders)
+            ratios = [float(ratio) / sum(ratios) for ratio in ratios]
+
+        self.loaders = loaders
+        self.ratios = ratios
+
+    def __next__(self):
+        # random sample from each loader by ratio
+        loader_idx = random.choices(range(len(self.loaders)), self.ratios, k=1)[0]
+        return next(self.loaders[loader_idx])
+
+    def __len__(self):
+        return sum([len(_) for _ in self.loaders if hasattr(_, "__len__")])
+
+
+class ConcatLoader:
+    """
+    A simple wrapper for iterating over multiple iterators.
+
+    Args:
+        loaders (List[Loader]): List of Iterator loaders.
+        ratios (List[float]): List of ratios to sample from each loader. If None, all loaders are sampled uniformly.
+    """
+
+    def __init__(self, loaders):
+        # assert all loaders has __next__ method
+        for loader in loaders:
+            assert hasattr(
+                loader, "__len__"
+            ), "Loader {} has no __len__ method.".format(loader)
+
+        self._epoch = 0
+        self._loader_lens = [len(_) for _ in loaders]
+        self._rest_lens = self._loader_lens.copy()
+
+        self.loaders = loaders
+
+    def __next__(self):
+        # random sample from each loader by ratio
+        loader_idx = random.choices(range(len(self.loaders)), self._rest_lens, k=1)[0]
+        self._rest_lens[loader_idx] -= 1
+        if sum(self._rest_lens) == 0:
+            self._epoch += 1
+            self._rest_lens = self._loader_lens.copy()
+        return next(self.loaders[loader_idx])
+
+    def __len__(self):
+        return sum([len(_) for _ in self.loaders if hasattr(_, "__len__")])
+
+
+class PrefetchLoader(object):
+    """
+    Modified from https://github.com/ChenRocks/UNITER.
+
+    overlap compute and cuda data transfer
+    (copied and then modified from nvidia apex)
+    """
+
+    def __init__(self, loader):
+        self.loader = loader
+        self.stream = torch.cuda.Stream()
+
+    def __iter__(self):
+        loader_it = iter(self.loader)
+        self.preload(loader_it)
+        batch = self.next(loader_it)
+        while batch is not None:
+            is_tuple = isinstance(batch, tuple)
+            if is_tuple:
+                task, batch = batch
+
+            if is_tuple:
+                yield task, batch
+            else:
+                yield batch
+            batch = self.next(loader_it)
+
+    def __len__(self):
+        return len(self.loader)
+
+    def preload(self, it):
+        try:
+            self.batch = next(it)
+        except StopIteration:
+            self.batch = None
+            return
+        # if record_stream() doesn't work, another option is to make sure
+        # device inputs are created on the main stream.
+        # self.next_input_gpu = torch.empty_like(self.next_input,
+        #                                        device='cuda')
+        # self.next_target_gpu = torch.empty_like(self.next_target,
+        #                                         device='cuda')
+        # Need to make sure the memory allocated for next_* is not still in use
+        # by the main stream at the time we start copying to next_*:
+        # self.stream.wait_stream(torch.cuda.current_stream())
+        with torch.cuda.stream(self.stream):
+            self.batch = move_to_cuda(self.batch)
+            # more code for the alternative if record_stream() doesn't work:
+            # copy_ will record the use of the pinned source tensor in this
+            # side stream.
+            # self.next_input_gpu.copy_(self.next_input, non_blocking=True)
+            # self.next_target_gpu.copy_(self.next_target, non_blocking=True)
+            # self.next_input = self.next_input_gpu
+            # self.next_target = self.next_target_gpu
+
+    def next(self, it):
+        torch.cuda.current_stream().wait_stream(self.stream)
+        batch = self.batch
+        if batch is not None:
+            record_cuda_stream(batch)
+        self.preload(it)
+        return batch
+
+    def __getattr__(self, name):
+        method = self.loader.__getattribute__(name)
+        return method
+
+
+def record_cuda_stream(batch):
+    if isinstance(batch, torch.Tensor):
+        batch.record_stream(torch.cuda.current_stream())
+    elif isinstance(batch, list) or isinstance(batch, tuple):
+        for t in batch:
+            record_cuda_stream(t)
+    elif isinstance(batch, dict):
+        for t in batch.values():
+            record_cuda_stream(t)
+    else:
+        pass
+
+
+class IterLoader:
+    """
+    A wrapper to convert DataLoader as an infinite iterator.
+
+    Modified from:
+        https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/iter_based_runner.py
+    """
+
+    def __init__(self, dataloader: DataLoader, use_distributed: bool = False):
+        self._dataloader = dataloader
+        self.iter_loader = iter(self._dataloader)
+        self._use_distributed = use_distributed
+        self._epoch = 0
+
+    @property
+    def epoch(self) -> int:
+        return self._epoch
+
+    def __next__(self):
+        try:
+            data = next(self.iter_loader)
+        except StopIteration:
+            self._epoch += 1
+            if hasattr(self._dataloader.sampler, "set_epoch") and self._use_distributed:
+                self._dataloader.sampler.set_epoch(self._epoch)
+            time.sleep(2)  # Prevent possible deadlock during epoch transition
+            self.iter_loader = iter(self._dataloader)
+            data = next(self.iter_loader)
+
+        return data
+
+    def __iter__(self):
+        return self
+
+    def __len__(self):
+        return len(self._dataloader)

unimernet/datasets/datasets/formula.py

+import torch
+from .base_dataset import BaseDataset
+import os.path as osp
+import glob
+from io import BytesIO
+from PIL import Image
+
+
+class Im2LatexDataset(BaseDataset):
+
+    def init_samples(self):
+        samples = []
+        for vis_root, anno_path in zip(self.vis_root, self.anno_path):
+            images = [path.replace('\\', '/') for path in glob.glob(osp.join(vis_root, '*.png'))]
+            indices = [int(osp.basename(img).split('.')[0]) for img in images]
+
+            eqs = open(anno_path, 'r').read().split('\n')
+            eqs = [eqs[_] for _ in indices]
+
+            for i, e in zip(images, eqs):
+                samples.append({"image": i, "equation": e, "vis_root": vis_root})
+        return samples
+
+    def __getitem__(self, index):
+        ann = self.samples[index]
+        try:
+            image = self.vis_processor(self._read_image(ann))
+        except Exception:
+            return self[(index + 1) % len(self)]
+        if image is None:
+            return self[(index + 1) % len(self)]
+        equation = ann["equation"]
+        return {"image": image, "text_input": equation, "id": index}
+
+    def _read_image(self, sample, image_key="image"):
+        img_file = sample[image_key]
+        vis_root = sample["vis_root"]
+        image_path = osp.join(vis_root, img_file)
+        image = self.reader['body'](image_path)
+        if isinstance(image, bytes):
+            bytes_stream = BytesIO(image)
+            image = Image.open(bytes_stream)
+        image = image.convert("RGB")
+        return image
+
+    def init_reader(self):
+        if not isinstance(self.vis_root, str):
+            vis_root = self.vis_root[0]
+        else:
+            vis_root = self.vis_root
+        if vis_root.startswith('cluster'):
+            from petrel_client.client import Client
+            client = Client("~/petreloss.conf")
+            reader = {'type': 'PetrelReader', 'body': client.get}
+        else:
+            reader = {'type': 'LocalReader', 'body': Image.open}
+        return reader
+
+    def collater(self, samples):
+        image_list, question_list, id_list = [], [], []
+
+        for sample in samples:
+            image_list.append(sample["image"])
+            question_list.append(sample["text_input"])
+            id_list.append(sample["id"])
+
+        return {
+            "image": torch.stack(image_list, dim=0),
+            "text_input": question_list,
+            "id": id_list
+        }

unimernet/datasets/datasets/formula_multi_scale.py

+import torch
+from .formula import Im2LatexDataset
+
+
+class MultiScaleIm2LatexDataset(Im2LatexDataset):
+
+    def __getitem__(self, index):
+        ann = self.samples[index]
+        try:
+            pil_image = self._read_image(ann)
+            image = self.vis_processor(pil_image)
+        except Exception:
+            return self[(index + 1) % len(self)]
+        if image is None:
+            return self[(index + 1) % len(self)]
+        equation = ann["equation"]
+        return {"image": image, "text_input": equation, "id": index, "raw_image": pil_image}
+
+    def collater(self, samples):
+        self.vis_processor.reset_scale()
+        image_list, question_list, id_list = [], [], []
+
+        for sample in samples:
+            image_list.append(self.vis_processor(sample["raw_image"]))
+            question_list.append(sample["text_input"])
+            id_list.append(sample["id"])
+
+        return {
+            "image": torch.stack(image_list, dim=0),
+            "text_input": question_list,
+            "id": id_list
+        }

unimernet/models/__init__.py

+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import logging
+import torch
+from omegaconf import OmegaConf
+from unimernet.common.registry import registry
+
+from unimernet.models.base_model import BaseModel
+
+from unimernet.processors.base_processor import BaseProcessor
+from unimernet.models.unimernet.unimernet import UniMERModel
+
+__all__ = [
+    "load_model",
+    "BaseModel",
+    "UniMERModel",
+]
+
+
+def load_model(name, model_type, is_eval=False, device="cpu", checkpoint=None):
+    """
+    Load supported models.
+
+    To list all available models and types in registry:
+    >>> from unimernet.models import model_zoo
+    >>> print(model_zoo)
+
+    Args:
+        name (str): name of the model.
+        model_type (str): type of the model.
+        is_eval (bool): whether the model is in eval mode. Default: False.
+        device (str): device to use. Default: "cpu".
+        checkpoint (str): path or to checkpoint. Default: None.
+            Note that expecting the checkpoint to have the same keys in state_dict as the model.
+
+    Returns:
+        model (torch.nn.Module): model.
+    """
+
+    model = registry.get_model_class(name).from_pretrained(model_type=model_type)
+
+    if checkpoint is not None:
+        model.load_checkpoint(checkpoint)
+
+    if is_eval:
+        model.eval()
+
+    if device == "cpu":
+        model = model.float()
+
+    return model.to(device)
+
+
+def load_preprocess(config):
+    """
+    Load preprocessor configs and construct preprocessors.
+
+    If no preprocessor is specified, return BaseProcessor, which does not do any preprocessing.
+
+    Args:
+        config (dict): preprocessor configs.
+
+    Returns:
+        vis_processors (dict): preprocessors for visual inputs.
+        txt_processors (dict): preprocessors for text inputs.
+
+        Key is "train" or "eval" for processors used in training and evaluation respectively.
+    """
+
+    def _build_proc_from_cfg(cfg):
+        return (
+            registry.get_processor_class(cfg.name).from_config(cfg)
+            if cfg is not None
+            else BaseProcessor()
+        )
+
+    vis_processors = dict()
+    txt_processors = dict()
+
+    vis_proc_cfg = config.get("vis_processor")
+    txt_proc_cfg = config.get("text_processor")
+
+    if vis_proc_cfg is not None:
+        vis_train_cfg = vis_proc_cfg.get("train")
+        vis_eval_cfg = vis_proc_cfg.get("eval")
+    else:
+        vis_train_cfg = None
+        vis_eval_cfg = None
+
+    vis_processors["train"] = _build_proc_from_cfg(vis_train_cfg)
+    vis_processors["eval"] = _build_proc_from_cfg(vis_eval_cfg)
+
+    if txt_proc_cfg is not None:
+        txt_train_cfg = txt_proc_cfg.get("train")
+        txt_eval_cfg = txt_proc_cfg.get("eval")
+    else:
+        txt_train_cfg = None
+        txt_eval_cfg = None
+
+    txt_processors["train"] = _build_proc_from_cfg(txt_train_cfg)
+    txt_processors["eval"] = _build_proc_from_cfg(txt_eval_cfg)
+
+    return vis_processors, txt_processors
+
+
+def load_model_and_preprocess(name, model_type, is_eval=False, device="cpu"):
+    """
+    Load model and its related preprocessors.
+
+    List all available models and types in registry:
+    >>> from unimernet.models import model_zoo
+    >>> print(model_zoo)
+
+    Args:
+        name (str): name of the model.
+        model_type (str): type of the model.
+        is_eval (bool): whether the model is in eval mode. Default: False.
+        device (str): device to use. Default: "cpu".
+
+    Returns:
+        model (torch.nn.Module): model.
+        vis_processors (dict): preprocessors for visual inputs.
+        txt_processors (dict): preprocessors for text inputs.
+    """
+    model_cls = registry.get_model_class(name)
+
+    # load model
+    model = model_cls.from_pretrained(model_type=model_type)
+
+    if is_eval:
+        model.eval()
+
+    # load preprocess
+    cfg = OmegaConf.load(model_cls.default_config_path(model_type))
+    if cfg is not None:
+        preprocess_cfg = cfg.preprocess
+
+        vis_processors, txt_processors = load_preprocess(preprocess_cfg)
+    else:
+        vis_processors, txt_processors = None, None
+        logging.info(
+            f"""No default preprocess for model {name} ({model_type}).
+                This can happen if the model is not finetuned on downstream datasets,
+                or it is not intended for direct use without finetuning.
+            """
+        )
+
+    if device == "cpu" or device == torch.device("cpu"):
+        model = model.float()
+
+    return model.to(device), vis_processors, txt_processors
+
+
+class ModelZoo:
+    """
+    A utility class to create string representation of available model architectures and types.
+
+    >>> from unimernet.models import model_zoo
+    >>> # list all available models
+    >>> print(model_zoo)
+    >>> # show total number of models
+    >>> print(len(model_zoo))
+    """
+
+    def __init__(self) -> None:
+        self.model_zoo = {
+            k: list(v.PRETRAINED_MODEL_CONFIG_DICT.keys())
+            for k, v in registry.mapping["model_name_mapping"].items()
+        }
+
+    def __str__(self) -> str:
+        return (
+                "=" * 50
+                + "\n"
+                + f"{'Architectures':<30} {'Types'}\n"
+                + "=" * 50
+                + "\n"
+                + "\n".join(
+            [
+                f"{name:<30} {', '.join(types)}"
+                for name, types in self.model_zoo.items()
+            ]
+        )
+        )
+
+    def __iter__(self):
+        return iter(self.model_zoo.items())
+
+    def __len__(self):
+        return sum([len(v) for v in self.model_zoo.values()])
+
+
+model_zoo = ModelZoo()

unimernet/models/base_model.py

+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import logging
+import os
+
+import numpy as np
+import torch
+import torch.nn as nn
+from unimernet.common.dist_utils import download_cached_file, is_dist_avail_and_initialized
+from unimernet.common.utils import get_abs_path, is_url
+from omegaconf import OmegaConf
+
+
+class BaseModel(nn.Module):
+    """Base class for models."""
+
+    def __init__(self):
+        super().__init__()
+
+    @property
+    def device(self):
+        return list(self.parameters())[0].device
+
+    def load_checkpoint(self, url_or_filename):
+        """
+        Load from a finetuned checkpoint.
+
+        This should expect no mismatch in the model keys and the checkpoint keys.
+        """
+
+        if is_url(url_or_filename):
+            cached_file = download_cached_file(
+                url_or_filename, check_hash=False, progress=True
+            )
+            checkpoint = torch.load(cached_file, map_location="cpu")
+        elif os.path.isfile(url_or_filename):
+            checkpoint = torch.load(url_or_filename, map_location="cpu")
+        else:
+            raise RuntimeError("checkpoint url or path is invalid")
+
+        if "model" in checkpoint.keys():
+            state_dict = checkpoint["model"]
+        else:
+            state_dict = checkpoint
+
+        msg = self.load_state_dict(state_dict, strict=False)
+
+        # logging.info("Missing keys {}".format(msg.missing_keys))
+        logging.info(f"Missing keys exist when loading '{url_or_filename}'.")
+        logging.info("load checkpoint from %s" % url_or_filename)
+
+        return msg
+
+    @classmethod
+    def from_pretrained(cls, model_type):
+        """
+        Build a pretrained model from default configuration file, specified by model_type.
+
+        Args:
+            - model_type (str): model type, specifying architecture and checkpoints.
+
+        Returns:
+            - model (nn.Module): pretrained or finetuned model, depending on the configuration.
+        """
+        model_cfg = OmegaConf.load(cls.default_config_path(model_type)).model
+        model = cls.from_config(model_cfg)
+
+        return model
+
+    @classmethod
+    def default_config_path(cls, model_type):
+        assert (
+                model_type in cls.PRETRAINED_MODEL_CONFIG_DICT
+        ), "Unknown model type {}".format(model_type)
+        return get_abs_path(cls.PRETRAINED_MODEL_CONFIG_DICT[model_type])
+
+    def load_checkpoint_from_config(self, cfg, **kwargs):
+        """
+        Load checkpoint as specified in the config file.
+
+        If load_finetuned is True, load the finetuned model; otherwise, load the pretrained model.
+        When loading the pretrained model, each task-specific architecture may define their
+        own load_from_pretrained() method.
+        """
+        load_pretrained = cfg.get("load_pretrained", True)
+        load_finetuned = cfg.get("load_finetuned", False)
+
+        if load_pretrained:
+            # load pre-trained weights
+            pretrain_path = cfg.get("pretrained", None)
+            assert pretrain_path, "Found load_finetuned is False, but pretrain_path is None."
+            self.load_from_pretrained(url_or_filename=pretrain_path, **kwargs)
+            logging.info(f"Loaded pretrained model '{pretrain_path}'.")
+
+        if load_finetuned:
+            finetune_path = cfg.get("finetuned", None)
+            assert finetune_path is not None, "Found load_finetuned is True, but finetune_path is None."
+            self.load_checkpoint(url_or_filename=finetune_path)
+            logging.info(f"Loaded finetuned model '{finetune_path}'.")
+
+    def before_evaluation(self, **kwargs):
+        pass
+
+    def show_n_params(self, return_str=True):
+        tot = 0
+        for p in self.parameters():
+            w = 1
+            for x in p.shape:
+                w *= x
+            tot += w
+        if return_str:
+            if tot >= 1e6:
+                return "{:.1f}M".format(tot / 1e6)
+            else:
+                return "{:.1f}K".format(tot / 1e3)
+        else:
+            return tot
+
+
+class BaseEncoder(nn.Module):
+    """
+    Base class for primitive encoders, such as ViT, TimeSformer, etc.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def forward_features(self, samples, **kwargs):
+        raise NotImplementedError
+
+    @property
+    def device(self):
+        return list(self.parameters())[0].device
+
+
+class SharedQueueMixin:
+    @torch.no_grad()
+    def _dequeue_and_enqueue(self, image_feat, text_feat, idxs=None):
+        # gather keys before updating queue
+        image_feats = concat_all_gather(image_feat)
+        text_feats = concat_all_gather(text_feat)
+
+        batch_size = image_feats.shape[0]
+
+        ptr = int(self.queue_ptr)
+        assert self.queue_size % batch_size == 0  # for simplicity
+
+        # replace the keys at ptr (dequeue and enqueue)
+        self.image_queue[:, ptr: ptr + batch_size] = image_feats.T
+        self.text_queue[:, ptr: ptr + batch_size] = text_feats.T
+
+        if idxs is not None:
+            idxs = concat_all_gather(idxs)
+            self.idx_queue[:, ptr: ptr + batch_size] = idxs.T
+
+        ptr = (ptr + batch_size) % self.queue_size  # move pointer
+        self.queue_ptr[0] = ptr
+
+
+class MomentumDistilationMixin:
+    @torch.no_grad()
+    def copy_params(self):
+        for model_pair in self.model_pairs:
+            for param, param_m in zip(
+                    model_pair[0].parameters(), model_pair[1].parameters()
+            ):
+                param_m.data.copy_(param.data)  # initialize
+                param_m.requires_grad = False  # not update by gradient
+
+    @torch.no_grad()
+    def _momentum_update(self):
+        for model_pair in self.model_pairs:
+            for param, param_m in zip(
+                    model_pair[0].parameters(), model_pair[1].parameters()
+            ):
+                param_m.data = param_m.data * self.momentum + param.data * (
+                        1.0 - self.momentum
+                )
+
+
+class GatherLayer(torch.autograd.Function):
+    """
+    Gather tensors from all workers with support for backward propagation:
+    This implementation does not cut the gradients as torch.distributed.all_gather does.
+    """
+
+    @staticmethod
+    def forward(ctx, x):
+        output = [
+            torch.zeros_like(x) for _ in range(torch.distributed.get_world_size())
+        ]
+        torch.distributed.all_gather(output, x)
+        return tuple(output)
+
+    @staticmethod
+    def backward(ctx, *grads):
+        all_gradients = torch.stack(grads)
+        torch.distributed.all_reduce(all_gradients)
+        return all_gradients[torch.distributed.get_rank()]
+
+
+def all_gather_with_grad(tensors):
+    """
+    Performs all_gather operation on the provided tensors.
+    Graph remains connected for backward grad computation.
+    """
+    # Queue the gathered tensors
+    world_size = torch.distributed.get_world_size()
+    # There is no need for reduction in the single-proc case
+    if world_size == 1:
+        return tensors
+
+    # tensor_all = GatherLayer.apply(tensors)
+    tensor_all = GatherLayer.apply(tensors)
+
+    return torch.cat(tensor_all, dim=0)
+
+
+@torch.no_grad()
+def concat_all_gather(tensor):
+    """
+    Performs all_gather operation on the provided tensors.
+    *** Warning ***: torch.distributed.all_gather has no gradient.
+    """
+    # if use distributed training
+    if not is_dist_avail_and_initialized():
+        return tensor
+
+    tensors_gather = [
+        torch.ones_like(tensor) for _ in range(torch.distributed.get_world_size())
+    ]
+    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
+
+    output = torch.cat(tensors_gather, dim=0)
+    return output
+
+
+def tile(x, dim, n_tile):
+    init_dim = x.size(dim)
+    repeat_idx = [1] * x.dim()
+    repeat_idx[dim] = n_tile
+    x = x.repeat(*(repeat_idx))
+    order_index = torch.LongTensor(
+        np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])
+    )
+    return torch.index_select(x, dim, order_index.to(x.device))

unimernet/models/blip2_models/Qformer.py

+"""
+ * Copyright (c) 2023, salesforce.com, inc.
+ * All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+ * By Junnan Li
+ * Based on huggingface code base
+ * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert
+"""
+
+import math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Tuple, Dict, Any
+
+import torch
+from torch import Tensor, device, dtype, nn
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+import torch.nn.functional as F
+
+from transformers.activations import ACT2FN
+from transformers.file_utils import (
+    ModelOutput,
+)
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    CausalLMOutputWithCrossAttentions,
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    NextSentencePredictorOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import (
+    PreTrainedModel,
+    apply_chunking_to_forward,
+    find_pruneable_heads_and_indices,
+    prune_linear_layer,
+)
+from transformers.utils import logging
+from transformers.models.bert.configuration_bert import BertConfig
+
+logger = logging.get_logger(__name__)
+
+
+class BertEmbeddings(nn.Module):
+    """Construct the embeddings from word and position embeddings."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(
+            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
+        )
+        self.position_embeddings = nn.Embedding(
+            config.max_position_embeddings, config.hidden_size
+        )
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+        self.register_buffer(
+            "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))
+        )
+        self.position_embedding_type = getattr(
+            config, "position_embedding_type", "absolute"
+        )
+
+        self.config = config
+
+    def forward(
+        self,
+        input_ids=None,
+        position_ids=None,
+        query_embeds=None,
+        past_key_values_length=0,
+    ):
+        if input_ids is not None:
+            seq_length = input_ids.size()[1]
+        else:
+            seq_length = 0
+
+        if position_ids is None:
+            position_ids = self.position_ids[
+                :, past_key_values_length : seq_length + past_key_values_length
+            ].clone()
+
+        if input_ids is not None:
+            embeddings = self.word_embeddings(input_ids)
+            if self.position_embedding_type == "absolute":
+                position_embeddings = self.position_embeddings(position_ids)
+                embeddings = embeddings + position_embeddings
+
+            if query_embeds is not None:
+                embeddings = torch.cat((query_embeds, embeddings), dim=1)
+        else:
+            embeddings = query_embeds
+
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class BertSelfAttention(nn.Module):
+    def __init__(self, config, is_cross_attention):
+        super().__init__()
+        self.config = config
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
+            config, "embedding_size"
+        ):
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size)
+        if is_cross_attention:
+            self.key = nn.Linear(config.encoder_width, self.all_head_size)
+            self.value = nn.Linear(config.encoder_width, self.all_head_size)
+        else:
+            self.key = nn.Linear(config.hidden_size, self.all_head_size)
+            self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.position_embedding_type = getattr(
+            config, "position_embedding_type", "absolute"
+        )
+        if (
+            self.position_embedding_type == "relative_key"
+            or self.position_embedding_type == "relative_key_query"
+        ):
+            self.max_position_embeddings = config.max_position_embeddings
+            self.distance_embedding = nn.Embedding(
+                2 * config.max_position_embeddings - 1, self.attention_head_size
+            )
+        self.save_attention = False
+
+    def save_attn_gradients(self, attn_gradients):
+        self.attn_gradients = attn_gradients
+
+    def get_attn_gradients(self):
+        return self.attn_gradients
+
+    def save_attention_map(self, attention_map):
+        self.attention_map = attention_map
+
+    def get_attention_map(self):
+        return self.attention_map
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (
+            self.num_attention_heads,
+            self.attention_head_size,
+        )
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+
+        # If this is instantiated as a cross-attention module, the keys
+        # and values come from an encoder; the attention mask needs to be
+        # such that the encoder's padding tokens are not attended to.
+        is_cross_attention = encoder_hidden_states is not None
+
+        if is_cross_attention:
+            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
+            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
+            attention_mask = encoder_attention_mask
+        elif past_key_value is not None:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+        else:
+            key_layer = self.transpose_for_scores(self.key(hidden_states))
+            value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+        mixed_query_layer = self.query(hidden_states)
+
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+
+        past_key_value = (key_layer, value_layer)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+        if (
+            self.position_embedding_type == "relative_key"
+            or self.position_embedding_type == "relative_key_query"
+        ):
+            seq_length = hidden_states.size()[1]
+            position_ids_l = torch.arange(
+                seq_length, dtype=torch.long, device=hidden_states.device
+            ).view(-1, 1)
+            position_ids_r = torch.arange(
+                seq_length, dtype=torch.long, device=hidden_states.device
+            ).view(1, -1)
+            distance = position_ids_l - position_ids_r
+            positional_embedding = self.distance_embedding(
+                distance + self.max_position_embeddings - 1
+            )
+            positional_embedding = positional_embedding.to(
+                dtype=query_layer.dtype
+            )  # fp16 compatibility
+
+            if self.position_embedding_type == "relative_key":
+                relative_position_scores = torch.einsum(
+                    "bhld,lrd->bhlr", query_layer, positional_embedding
+                )
+                attention_scores = attention_scores + relative_position_scores
+            elif self.position_embedding_type == "relative_key_query":
+                relative_position_scores_query = torch.einsum(
+                    "bhld,lrd->bhlr", query_layer, positional_embedding
+                )
+                relative_position_scores_key = torch.einsum(
+                    "bhrd,lrd->bhlr", key_layer, positional_embedding
+                )
+                attention_scores = (
+                    attention_scores
+                    + relative_position_scores_query
+                    + relative_position_scores_key
+                )
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_mask is not None:
+            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+            attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.Softmax(dim=-1)(attention_scores)
+
+        if is_cross_attention and self.save_attention:
+            self.save_attention_map(attention_probs)
+            attention_probs.register_hook(self.save_attn_gradients)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs_dropped = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs_dropped = attention_probs_dropped * head_mask
+
+        context_layer = torch.matmul(attention_probs_dropped, value_layer)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        outputs = (
+            (context_layer, attention_probs) if output_attentions else (context_layer,)
+        )
+
+        outputs = outputs + (past_key_value,)
+        return outputs
+
+
+class BertSelfOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertAttention(nn.Module):
+    def __init__(self, config, is_cross_attention=False):
+        super().__init__()
+        self.self = BertSelfAttention(config, is_cross_attention)
+        self.output = BertSelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads,
+            self.self.num_attention_heads,
+            self.self.attention_head_size,
+            self.pruned_heads,
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = (
+            self.self.attention_head_size * self.self.num_attention_heads
+        )
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+    ):
+        self_outputs = self.self(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            past_key_value,
+            output_attentions,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+
+        outputs = (attention_output,) + self_outputs[
+            1:
+        ]  # add attentions if we output them
+        return outputs
+
+
+class BertIntermediate(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        return hidden_states
+
+
+class BertOutput(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states, input_tensor):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    def __init__(self, config, layer_num):
+        super().__init__()
+        self.config = config
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = BertAttention(config)
+        self.layer_num = layer_num
+        if (
+            self.config.add_cross_attention
+            and layer_num % self.config.cross_attention_freq == 0
+        ):
+            self.crossattention = BertAttention(
+                config, is_cross_attention=self.config.add_cross_attention
+            )
+            self.has_cross_attention = True
+        else:
+            self.has_cross_attention = False
+        self.intermediate = BertIntermediate(config)
+        self.output = BertOutput(config)
+
+        self.intermediate_query = BertIntermediate(config)
+        self.output_query = BertOutput(config)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+        query_length=0,
+    ):
+        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+        self_attn_past_key_value = (
+            past_key_value[:2] if past_key_value is not None else None
+        )
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            output_attentions=output_attentions,
+            past_key_value=self_attn_past_key_value,
+        )
+        attention_output = self_attention_outputs[0]
+        outputs = self_attention_outputs[1:-1]
+
+        present_key_value = self_attention_outputs[-1]
+
+        if query_length > 0:
+            query_attention_output = attention_output[:, :query_length, :]
+
+            if self.has_cross_attention:
+                assert (
+                    encoder_hidden_states is not None
+                ), "encoder_hidden_states must be given for cross-attention layers"
+                cross_attention_outputs = self.crossattention(
+                    query_attention_output,
+                    attention_mask,
+                    head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    output_attentions=output_attentions,
+                )
+                query_attention_output = cross_attention_outputs[0]
+                outputs = (
+                    outputs + cross_attention_outputs[1:-1]
+                )  # add cross attentions if we output attention weights
+
+            layer_output = apply_chunking_to_forward(
+                self.feed_forward_chunk_query,
+                self.chunk_size_feed_forward,
+                self.seq_len_dim,
+                query_attention_output,
+            )
+            if attention_output.shape[1] > query_length:
+                layer_output_text = apply_chunking_to_forward(
+                    self.feed_forward_chunk,
+                    self.chunk_size_feed_forward,
+                    self.seq_len_dim,
+                    attention_output[:, query_length:, :],
+                )
+                layer_output = torch.cat([layer_output, layer_output_text], dim=1)
+        else:
+            layer_output = apply_chunking_to_forward(
+                self.feed_forward_chunk,
+                self.chunk_size_feed_forward,
+                self.seq_len_dim,
+                attention_output,
+            )
+        outputs = (layer_output,) + outputs
+
+        outputs = outputs + (present_key_value,)
+
+        return outputs
+
+    def feed_forward_chunk(self, attention_output):
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+    def feed_forward_chunk_query(self, attention_output):
+        intermediate_output = self.intermediate_query(attention_output)
+        layer_output = self.output_query(intermediate_output, attention_output)
+        return layer_output
+
+
+class BertEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList(
+            [BertLayer(config, i) for i in range(config.num_hidden_layers)]
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=True,
+        query_length=0,
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = (
+            () if output_attentions and self.config.add_cross_attention else None
+        )
+
+        next_decoder_cache = () if use_cache else None
+
+        for i in range(self.config.num_hidden_layers):
+            layer_module = self.layer[i]
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            past_key_value = past_key_values[i] if past_key_values is not None else None
+
+            if getattr(self.config, "gradient_checkpointing", False) and self.training:
+
+                if use_cache:
+                    logger.warn(
+                        "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                    )
+                    use_cache = False
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(
+                            *inputs, past_key_value, output_attentions, query_length
+                        )
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                    query_length,
+                )
+
+            hidden_states = layer_outputs[0]
+            if use_cache:
+                next_decoder_cache += (layer_outputs[-1],)
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+                all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    next_decoder_cache,
+                    all_hidden_states,
+                    all_self_attentions,
+                    all_cross_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=next_decoder_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+class BertPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states):
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class BertPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        if isinstance(config.hidden_act, str):
+            self.transform_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.transform_act_fn = config.hidden_act
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.transform_act_fn(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states)
+        return hidden_states
+
+
+class BertLMPredictionHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.transform = BertPredictionHeadTransform(config)
+
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+        self.decoder.bias = self.bias
+
+    def forward(self, hidden_states):
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.predictions = BertLMPredictionHead(config)
+
+    def forward(self, sequence_output):
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+class BertPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = BertConfig
+    base_model_prefix = "bert"
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+
+class BertModel(BertPreTrainedModel):
+    """
+    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+    cross-attention is added between the self-attention layers, following the architecture described in `Attention is
+    all you need <https://arxiv.org/abs/1706.03762>`__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+    argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an
+    input to the forward pass.
+    """
+
+    def __init__(self, config, add_pooling_layer=False):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = BertEmbeddings(config)
+
+        self.encoder = BertEncoder(config)
+
+        self.pooler = BertPooler(config) if add_pooling_layer else None
+
+        self.init_weights()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    def get_extended_attention_mask(
+        self,
+        attention_mask: Tensor,
+        input_shape: Tuple[int],
+        device: device,
+        is_decoder: bool,
+        has_query: bool = False,
+    ) -> Tensor:
+        """
+        Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
+
+        Arguments:
+            attention_mask (:obj:`torch.Tensor`):
+                Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
+            input_shape (:obj:`Tuple[int]`):
+                The shape of the input to the model.
+            device: (:obj:`torch.device`):
+                The device of the input to the model.
+
+        Returns:
+            :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`.
+        """
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        if attention_mask.dim() == 3:
+            extended_attention_mask = attention_mask[:, None, :, :]
+        elif attention_mask.dim() == 2:
+            # Provided a padding mask of dimensions [batch_size, seq_length]
+            # - if the model is a decoder, apply a causal mask in addition to the padding mask
+            # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length]
+            if is_decoder:
+                batch_size, seq_length = input_shape
+
+                seq_ids = torch.arange(seq_length, device=device)
+                causal_mask = (
+                    seq_ids[None, None, :].repeat(batch_size, seq_length, 1)
+                    <= seq_ids[None, :, None]
+                )
+
+                # add a prefix ones mask to the causal mask
+                # causal and attention masks must have same type with pytorch version < 1.3
+                causal_mask = causal_mask.to(attention_mask.dtype)
+
+                if causal_mask.shape[1] < attention_mask.shape[1]:
+                    prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1]
+                    if has_query:  # UniLM style attention mask
+                        causal_mask = torch.cat(
+                            [
+                                torch.zeros(
+                                    (batch_size, prefix_seq_len, seq_length),
+                                    device=device,
+                                    dtype=causal_mask.dtype,
+                                ),
+                                causal_mask,
+                            ],
+                            axis=1,
+                        )
+                    causal_mask = torch.cat(
+                        [
+                            torch.ones(
+                                (batch_size, causal_mask.shape[1], prefix_seq_len),
+                                device=device,
+                                dtype=causal_mask.dtype,
+                            ),
+                            causal_mask,
+                        ],
+                        axis=-1,
+                    )
+                extended_attention_mask = (
+                    causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
+                )
+            else:
+                extended_attention_mask = attention_mask[:, None, None, :]
+        else:
+            raise ValueError(
+                "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format(
+                    input_shape, attention_mask.shape
+                )
+            )
+
+        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+        # masked positions, this operation will create a tensor which is 0.0 for
+        # positions we want to attend and -10000.0 for masked positions.
+        # Since we are adding it to the raw scores before the softmax, this is
+        # effectively the same as removing these entirely.
+        extended_attention_mask = extended_attention_mask.to(
+            dtype=self.dtype
+        )  # fp16 compatibility
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+        return extended_attention_mask
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        head_mask=None,
+        query_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        is_decoder=False,
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        """
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if input_ids is None:
+            assert (
+                query_embeds is not None
+            ), "You have to specify query_embeds when input_ids is None"
+
+        # past_key_values_length
+        past_key_values_length = (
+            past_key_values[0][0].shape[2] - self.config.query_length
+            if past_key_values is not None
+            else 0
+        )
+
+        query_length = query_embeds.shape[1] if query_embeds is not None else 0
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            query_embeds=query_embeds,
+            past_key_values_length=past_key_values_length,
+        )
+
+        input_shape = embedding_output.size()[:-1]
+        batch_size, seq_length = input_shape
+        device = embedding_output.device
+
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                ((batch_size, seq_length + past_key_values_length)), device=device
+            )
+
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        if is_decoder:
+            extended_attention_mask = self.get_extended_attention_mask(
+                attention_mask,
+                input_ids.shape,
+                device,
+                is_decoder,
+                has_query=(query_embeds is not None),
+            )
+        else:
+            extended_attention_mask = self.get_extended_attention_mask(
+                attention_mask, input_shape, device, is_decoder
+            )
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if encoder_hidden_states is not None:
+            if type(encoder_hidden_states) == list:
+                encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[
+                    0
+                ].size()
+            else:
+                (
+                    encoder_batch_size,
+                    encoder_sequence_length,
+                    _,
+                ) = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+
+            if type(encoder_attention_mask) == list:
+                encoder_extended_attention_mask = [
+                    self.invert_attention_mask(mask) for mask in encoder_attention_mask
+                ]
+            elif encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+                encoder_extended_attention_mask = self.invert_attention_mask(
+                    encoder_attention_mask
+                )
+            else:
+                encoder_extended_attention_mask = self.invert_attention_mask(
+                    encoder_attention_mask
+                )
+        else:
+            encoder_extended_attention_mask = None
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            query_length=query_length,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = (
+            self.pooler(sequence_output) if self.pooler is not None else None
+        )
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+
+class BertLMHeadModel(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = BertModel(config, add_pooling_layer=False)
+        self.cls = BertOnlyMLMHead(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        head_mask=None,
+        query_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        labels=None,
+        past_key_values=None,
+        use_cache=True,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        return_logits=False,
+        is_decoder=True,
+        reduction="mean",
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
+            ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are
+            ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]``
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        Returns:
+        Example::
+            >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig
+            >>> import torch
+            >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+            >>> config = BertConfig.from_pretrained("bert-base-cased")
+            >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config)
+            >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+            >>> outputs = model(**inputs)
+            >>> prediction_logits = outputs.logits
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+        if labels is not None:
+            use_cache = False
+        if past_key_values is not None:
+            query_embeds = None
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            query_embeds=query_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            is_decoder=is_decoder,
+        )
+
+        sequence_output = outputs[0]
+        if query_embeds is not None:
+            sequence_output = outputs[0][:, query_embeds.shape[1] :, :]
+
+        prediction_scores = self.cls(sequence_output)
+
+        if return_logits:
+            return prediction_scores[:, :-1, :].contiguous()
+
+        lm_loss = None
+        if labels is not None:
+            # we are doing next-token prediction; shift prediction scores and input ids by one
+            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
+            labels = labels[:, 1:].contiguous()
+            loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1)
+            lm_loss = loss_fct(
+                shifted_prediction_scores.view(-1, self.config.vocab_size),
+                labels.view(-1),
+            )
+            if reduction == "none":
+                lm_loss = lm_loss.view(prediction_scores.size(0), -1).sum(1)
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return ((lm_loss,) + output) if lm_loss is not None else output
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=lm_loss,
+            logits=prediction_scores,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            cross_attentions=outputs.cross_attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, query_embeds, past=None, attention_mask=None, **model_kwargs
+    ):
+        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+        if attention_mask is None:
+            attention_mask = input_ids.new_ones(input_ids.shape)
+        query_mask = input_ids.new_ones(query_embeds.shape[:-1])
+        attention_mask = torch.cat([query_mask, attention_mask], dim=-1)
+
+        # cut decoder_input_ids if past is used
+        if past is not None:
+            input_ids = input_ids[:, -1:]
+
+        return {
+            "input_ids": input_ids,
+            "query_embeds": query_embeds,
+            "attention_mask": attention_mask,
+            "past_key_values": past,
+            "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None),
+            "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None),
+            "is_decoder": True,
+        }
+
+    def _reorder_cache(self, past, beam_idx):
+        reordered_past = ()
+        for layer_past in past:
+            reordered_past += (
+                tuple(
+                    past_state.index_select(0, beam_idx) for past_state in layer_past
+                ),
+            )
+        return reordered_past
+
+
+class BertForMaskedLM(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.bert = BertModel(config, add_pooling_layer=False)
+        self.cls = BertOnlyMLMHead(config)
+
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        head_mask=None,
+        query_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        return_logits=False,
+        is_decoder=False,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
+            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
+            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
+        """
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            query_embeds=query_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            is_decoder=is_decoder,
+        )
+
+        if query_embeds is not None:
+            sequence_output = outputs[0][:, query_embeds.shape[1] :, :]
+        prediction_scores = self.cls(sequence_output)
+
+        if return_logits:
+            return prediction_scores
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()  # -100 index = padding token
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
+            )
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return (
+                ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+            )
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

unimernet/models/blip2_models/blip2.py

+"""
+ Copyright (c) 2023, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+
+import contextlib
+import logging
+import os
+import time
+import datetime
+
+import torch
+import torch.nn as nn
+import torch.distributed as dist
+import torch.nn.functional as F
+
+import unimernet.common.dist_utils as dist_utils
+from unimernet.common.dist_utils import download_cached_file
+from unimernet.common.utils import is_url
+from unimernet.common.logger import MetricLogger
+from unimernet.models.base_model import BaseModel
+from unimernet.models.blip2_models.Qformer import BertConfig, BertLMHeadModel
+from unimernet.models.eva_vit import create_eva_vit_g
+from unimernet.models.clip_vit import create_clip_vit_L
+from transformers import BertTokenizer
+from transformers.utils import logging as tf_logging
+
+tf_logging.set_verbosity_error()
+
+
+class Blip2Base(BaseModel):
+    @classmethod
+    def init_tokenizer(cls, truncation_side="right"):
+        tokenizer = BertTokenizer.from_pretrained("/mnt/lustre/hanxiao/work/bert-base-uncased", truncation_side=truncation_side)
+        tokenizer.add_special_tokens({"bos_token": "[DEC]"})
+        return tokenizer
+
+    def maybe_autocast(self, dtype=torch.float16):
+        # if on cpu, don't use autocast
+        # if on gpu, use autocast with dtype if provided, otherwise use torch.float16
+        enable_autocast = self.device != torch.device("cpu")
+
+        if enable_autocast:
+            return torch.cuda.amp.autocast(dtype=dtype)
+        else:
+            return contextlib.nullcontext()
+
+    @classmethod
+    def init_Qformer(cls, num_query_token, vision_width, cross_attention_freq=2):
+        encoder_config = BertConfig.from_pretrained("/mnt/lustre/hanxiao/work/bert-base-uncased")
+        encoder_config.encoder_width = vision_width
+        # insert cross-attention layer every other block
+        encoder_config.add_cross_attention = True
+        encoder_config.cross_attention_freq = cross_attention_freq
+        encoder_config.query_length = num_query_token
+        Qformer = BertLMHeadModel.from_pretrained(
+            "/mnt/lustre/hanxiao/work/bert-base-uncased", config=encoder_config
+        )
+        query_tokens = nn.Parameter(
+            torch.zeros(1, num_query_token, encoder_config.hidden_size)
+        )
+        query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range)
+        return Qformer, query_tokens
+
+    def init_vision_encoder(
+            self, model_name, img_size, drop_path_rate, use_grad_checkpoint, precision
+    ):
+        assert model_name in [
+            "eva_clip_g",
+            "eva2_clip_L",
+            "clip_L",
+        ], "vit model must be eva_clip_g, eva2_clip_L or clip_L"
+        if model_name == "eva_clip_g":
+            visual_encoder = create_eva_vit_g(
+                img_size, drop_path_rate, use_grad_checkpoint, precision
+            )
+        #         elif model_name == "eva2_clip_L":
+        #             visual_encoder = create_eva2_vit_L(
+        #                 img_size, drop_path_rate, use_grad_checkpoint, precision
+        #             )
+        elif model_name == "clip_L":
+            visual_encoder = create_clip_vit_L(img_size, use_grad_checkpoint, precision)
+        ln_vision = LayerNorm(visual_encoder.num_features)
+        self.vit_name = model_name
+        return visual_encoder, ln_vision
+
+    def load_from_pretrained(self, url_or_filename):
+        if is_url(url_or_filename):
+            cached_file = download_cached_file(
+                url_or_filename, check_hash=False, progress=True
+            )
+            checkpoint = torch.load(cached_file, map_location="cpu")
+        elif os.path.isfile(url_or_filename):
+            checkpoint = torch.load(url_or_filename, map_location="cpu")
+        else:
+            raise RuntimeError("checkpoint url or path is invalid")
+
+        state_dict = checkpoint["model"]
+
+        msg = self.load_state_dict(state_dict, strict=False)
+
+        # logging.info("Missing keys {}".format(msg.missing_keys))
+        logging.info("load checkpoint from %s" % url_or_filename)
+
+        return msg
+
+    def get_optimizer_params(self, weight_decay, lr_scale=1):
+        if self.vit_name == "eva_clip_g":
+            vit_num_layers = self.visual_encoder.get_num_layer()
+            lr_scales = list(lr_scale ** (vit_num_layers + 1 - i) for i in range(vit_num_layers + 2))
+
+            parameter_group_names = {}
+            parameter_group_vars = {}
+
+            for name, param in self.named_parameters():
+                if not param.requires_grad:
+                    continue  # frozen weights
+                if len(param.shape) == 1 or name.endswith(".bias"):
+                    group_name = "no_decay"
+                    this_weight_decay = 0.
+                else:
+                    group_name = "decay"
+                    this_weight_decay = weight_decay
+                if 'visual_encoder' in name:
+                    layer_id = self.visual_encoder.get_num_layer(name.replace('visual_encoder.', ''))
+                    group_name = "vit_layer_%d_%s" % (layer_id, group_name)
+                else:
+                    layer_id = None
+
+                if group_name not in parameter_group_names:
+                    if layer_id is not None:
+                        scale = lr_scales[layer_id]
+                    else:
+                        scale = 1
+                    parameter_group_names[group_name] = {
+                        "weight_decay": this_weight_decay,
+                        "params": [],
+                        "lr_scale": scale
+                    }
+                    parameter_group_vars[group_name] = {
+                        "weight_decay": this_weight_decay,
+                        "params": [],
+                        "lr_scale": scale
+                    }
+                parameter_group_vars[group_name]["params"].append(param)
+                parameter_group_names[group_name]["params"].append(name)
+            # import json
+            # print("Param groups = %s" % json.dumps(parameter_group_names, indent=2))
+            optim_params = list(parameter_group_vars.values())
+            return optim_params
+        else:
+            return super().get_optimizer_params(weight_decay, lr_scale)
+
+    def _lemmatize(self, answers):
+        def apply(answer):
+            doc = self.lemmatizer(answer)
+
+            words = []
+            for token in doc:
+                if token.pos_ in ["NOUN", "VERB"]:
+                    words.append(token.lemma_)
+                else:
+                    words.append(token.text)
+            answer = " ".join(words)
+
+            return answer
+
+        return [apply(answer) for answer in answers]
+
+    @property
+    def lemmatizer(self):
+        if self._lemmatizer is None:
+            try:
+                import spacy
+
+                self._lemmatizer = spacy.load("en_core_web_sm")
+            except ImportError:
+                logging.error(
+                    """
+                    Please install spacy and en_core_web_sm model to apply lemmatization.
+                    python -m spacy download en_core_web_sm
+                    OR
+                    import spacy.cli
+                    spacy.cli.download("en_core_web_sm")
+                    """
+                )
+                exit(1)
+
+        return self._lemmatizer
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+class LayerNorm(nn.LayerNorm):
+    """Subclass torch's LayerNorm to handle fp16."""
+
+    def forward(self, x: torch.Tensor):
+        orig_type = x.dtype
+        ret = super().forward(x.type(torch.float32))
+        return ret.type(orig_type)
+
+
+def compute_sim_matrix(model, data_loader, **kwargs):
+    k_test = kwargs.pop("k_test")
+
+    metric_logger = MetricLogger(delimiter="  ")
+    header = "Evaluation:"
+
+    logging.info("Computing features for evaluation...")
+    start_time = time.time()
+
+    texts = data_loader.dataset.text
+    num_text = len(texts)
+    text_bs = 256
+    text_ids = []
+    text_embeds = []
+    text_atts = []
+    for i in range(0, num_text, text_bs):
+        text = texts[i: min(num_text, i + text_bs)]
+        text_input = model.tokenizer(
+            text,
+            padding="max_length",
+            truncation=True,
+            max_length=35,
+            return_tensors="pt",
+        ).to(model.device)
+        text_feat = model.forward_text(text_input)
+        text_embed = F.normalize(model.text_proj(text_feat))
+        text_embeds.append(text_embed)
+        text_ids.append(text_input.input_ids)
+        text_atts.append(text_input.attention_mask)
+
+    text_embeds = torch.cat(text_embeds, dim=0)
+    text_ids = torch.cat(text_ids, dim=0)
+    text_atts = torch.cat(text_atts, dim=0)
+
+    vit_feats = []
+    image_embeds = []
+    for samples in data_loader:
+        image = samples["image"]
+
+        image = image.to(model.device)
+        image_feat, vit_feat = model.forward_image(image)
+        image_embed = model.vision_proj(image_feat)
+        image_embed = F.normalize(image_embed, dim=-1)
+
+        vit_feats.append(vit_feat.cpu())
+        image_embeds.append(image_embed)
+
+    vit_feats = torch.cat(vit_feats, dim=0)
+    image_embeds = torch.cat(image_embeds, dim=0)
+
+    sims_matrix = []
+    for image_embed in image_embeds:
+        sim_q2t = image_embed @ text_embeds.t()
+        sim_i2t, _ = sim_q2t.max(0)
+        sims_matrix.append(sim_i2t)
+    sims_matrix = torch.stack(sims_matrix, dim=0)
+
+    score_matrix_i2t = torch.full(
+        (len(data_loader.dataset.image), len(texts)), -100.0
+    ).to(model.device)
+
+    num_tasks = dist_utils.get_world_size()
+    rank = dist_utils.get_rank()
+    step = sims_matrix.size(0) // num_tasks + 1
+    start = rank * step
+    end = min(sims_matrix.size(0), start + step)
+
+    for i, sims in enumerate(
+            metric_logger.log_every(sims_matrix[start:end], 50, header)
+    ):
+        topk_sim, topk_idx = sims.topk(k=k_test, dim=0)
+        image_inputs = vit_feats[start + i].repeat(k_test, 1, 1).to(model.device)
+        score = model.compute_itm(
+            image_inputs=image_inputs,
+            text_ids=text_ids[topk_idx],
+            text_atts=text_atts[topk_idx],
+        ).float()
+        score_matrix_i2t[start + i, topk_idx] = score + topk_sim
+
+    sims_matrix = sims_matrix.t()
+    score_matrix_t2i = torch.full(
+        (len(texts), len(data_loader.dataset.image)), -100.0
+    ).to(model.device)
+
+    step = sims_matrix.size(0) // num_tasks + 1
+    start = rank * step
+    end = min(sims_matrix.size(0), start + step)
+
+    for i, sims in enumerate(
+            metric_logger.log_every(sims_matrix[start:end], 50, header)
+    ):
+        topk_sim, topk_idx = sims.topk(k=k_test, dim=0)
+        image_inputs = vit_feats[topk_idx.cpu()].to(model.device)
+        score = model.compute_itm(
+            image_inputs=image_inputs,
+            text_ids=text_ids[start + i].repeat(k_test, 1),
+            text_atts=text_atts[start + i].repeat(k_test, 1),
+        ).float()
+        score_matrix_t2i[start + i, topk_idx] = score + topk_sim
+
+    if dist_utils.is_dist_avail_and_initialized():
+        dist.barrier()
+        torch.distributed.all_reduce(
+            score_matrix_i2t, op=torch.distributed.ReduceOp.SUM
+        )
+        torch.distributed.all_reduce(
+            score_matrix_t2i, op=torch.distributed.ReduceOp.SUM
+        )
+
+    total_time = time.time() - start_time
+    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+    logging.info("Evaluation time {}".format(total_time_str))
+
+    return score_matrix_i2t.cpu().numpy(), score_matrix_t2i.cpu().numpy()

unimernet/models/blip2_models/blip2_vicuna_instruct.py

+"""
+Requires Transformer 4.28 and above, implementation may change according the Llama implementation
+"""
+import logging
+from packaging import version
+
+import torch
+import torch.nn as nn
+
+import transformers
+
+from unimernet.common.registry import registry
+from unimernet.models.blip2_models.blip2 import Blip2Base, disabled_train
+
+
+@registry.register_model("blip2_vicuna_instruct")
+class Blip2VicunaInstruct(Blip2Base):
+    """
+    BLIP2 Vicuna model.
+    Supported model types:
+        - vicuna7b
+        - vicuna13b
+    Usage:
+        >>> from unimernet.models import load_model
+        >>> model = load_model("blip2_vicuna_instruct", "vicuna7b")
+    """
+
+    PRETRAINED_MODEL_CONFIG_DICT = {
+        "vicuna7b": "configs/models/blip2_instruct_vicuna7b.yaml",
+        "vicuna13b": "configs/models/blip2_instruct_vicuna13b.yaml",
+        "minigpt4_vicuna7b": "configs/models/mini_gpt4_vicuna7b.yaml",
+        "minigpt4_vicuna13b": "configs/models/mini_gpt4_vicuna13b.yaml",
+    }
+
+    def __init__(
+            self,
+            vit_model="eva_clip_g",
+            img_size=224,
+            drop_path_rate=0,
+            use_grad_checkpoint=False,
+            vit_precision="fp16",
+            freeze_vit=True,
+            freeze_vit_ln=False,
+            num_query_token=32,
+            llm_model="",
+            prompt="",
+            max_txt_len=128,
+            max_output_txt_len=256,
+            apply_lemmatizer=False,
+            qformer_text_input=True,
+            truncate_q_former_output=True
+    ):
+        super().__init__()
+        transformers_version = version.parse(transformers.__version__)
+        assert transformers_version >= version.parse("4.28"), "BLIP-2 Vicuna requires transformers>=4.28"
+        from transformers import LlamaTokenizer
+        from unimernet.models.blip2_models.modeling_llama import LlamaForCausalLM
+
+        self.tokenizer = self.init_tokenizer(truncation_side="left")
+
+        self.visual_encoder, self.ln_vision = self.init_vision_encoder(
+            vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
+        )
+        if freeze_vit:
+            for name, param in self.visual_encoder.named_parameters():
+                param.requires_grad = False
+            self.visual_encoder = self.visual_encoder.eval()
+            self.visual_encoder.train = disabled_train
+            logging.info("freeze vision encoder")
+
+        if freeze_vit_ln:
+            for name, param in self.ln_vision.named_parameters():
+                param.requires_grad = False
+            self.ln_vision = self.ln_vision.eval()
+            self.ln_vision.train = disabled_train
+            logging.info("freeze vit layner norm")
+
+        self.Qformer, self.query_tokens = self.init_Qformer(
+            num_query_token, self.visual_encoder.num_features
+        )
+
+        if not qformer_text_input:
+            self.Qformer.bert.embeddings.word_embeddings = None
+            self.Qformer.bert.embeddings.position_embeddings = None
+            for layer in self.Qformer.bert.encoder.layer:
+                layer.output = None
+                layer.intermediate = None
+        else:
+            self.Qformer.resize_token_embeddings(len(self.tokenizer))
+        self.Qformer.cls = None
+
+        self.llm_tokenizer = LlamaTokenizer.from_pretrained(llm_model, use_fast=False, truncation_side="left")
+        self.llm_tokenizer_for_generate = LlamaTokenizer.from_pretrained(llm_model, use_fast=False,
+                                                                         truncation_side="left")
+        self.llm_model = LlamaForCausalLM.from_pretrained(
+            llm_model, torch_dtype=torch.float16
+        )
+        self.llm_tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+        self.llm_tokenizer.add_special_tokens({'bos_token': '</s>'})
+        self.llm_tokenizer.add_special_tokens({'eos_token': '</s>'})
+        self.llm_tokenizer.add_special_tokens({'unk_token': '</s>'})
+        # self.llm_tokenizer.pad_token = self.llm_tokenizer.unk_token
+
+        self.llm_tokenizer_for_generate.add_special_tokens({'pad_token': '[PAD]'})
+        self.llm_tokenizer_for_generate.add_special_tokens({'bos_token': '</s>'})
+        self.llm_tokenizer_for_generate.add_special_tokens({'eos_token': '</s>'})
+        self.llm_tokenizer_for_generate.add_special_tokens({'unk_token': '</s>'})
+        self.llm_model.resize_token_embeddings(len(self.llm_tokenizer))
+
+        # self.eos_token_id = self.llm_tokenizer(
+        #     self.llm_tokenizer.eos_token, add_special_tokens=False
+        # ).input_ids[0]
+
+        for name, param in self.llm_model.named_parameters():
+            param.requires_grad = False
+
+        self.llm_proj = nn.Linear(
+            self.Qformer.config.hidden_size, self.llm_model.config.hidden_size
+        )
+
+        self.max_txt_len = max_txt_len
+        self.max_output_txt_len = max_output_txt_len
+        self.prompt = prompt
+        prompt_tokens = self.llm_tokenizer(self.prompt, return_tensors="pt")
+        self.prompt_length = prompt_tokens.attention_mask.sum(1)
+
+        self._lemmatizer = None
+
+        self.qformer_text_input = qformer_text_input
+        self.truncate_q_former_output = truncate_q_former_output
+
+    def concat_text_input_output(self, input_ids, input_atts, output_ids, output_atts):
+        input_part_targets_len = []
+        llm_tokens = {"input_ids": [], "attention_mask": []}
+        for i in range(input_ids.size(0)):
+            this_input_ones = input_atts[i].sum()
+            input_part_targets_len.append(this_input_ones)
+            llm_tokens['input_ids'].append(
+                torch.cat([
+                    input_ids[i][:this_input_ones],
+                    output_ids[i][1:],
+                    input_ids[i][this_input_ones:]
+                ])
+            )
+            llm_tokens['attention_mask'].append(
+                torch.cat([
+                    input_atts[i][:this_input_ones],
+                    output_atts[i][1:],
+                    input_atts[i][this_input_ones:]
+                ])
+            )
+        llm_tokens['input_ids'] = torch.stack(llm_tokens['input_ids'])
+        llm_tokens['attention_mask'] = torch.stack(llm_tokens['attention_mask'])
+        return llm_tokens, input_part_targets_len
+
+    def forward(self, samples):
+        # print('-----------------')
+        # print(samples["text_input"])
+        # print(samples["text_output"])
+        # print('-----------------')
+
+        image = samples["image"]
+        with self.maybe_autocast():
+            image_embeds = self.ln_vision(self.visual_encoder(image))
+        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image.device)
+
+        bs = image.size(0)
+
+        query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
+        if self.qformer_text_input:
+            text_Qformer = self.tokenizer(
+                samples["text_input"],
+                padding='longest',
+                truncation=True,
+                max_length=self.max_txt_len,
+                return_tensors="pt",
+            ).to(image.device)
+            query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image.device)
+            Qformer_atts = torch.cat([query_atts, text_Qformer.attention_mask], dim=1)
+
+            query_output = self.Qformer.bert(
+                text_Qformer.input_ids,
+                attention_mask=Qformer_atts,
+                query_embeds=query_tokens,
+                encoder_hidden_states=image_embeds,
+                encoder_attention_mask=image_atts,
+                return_dict=True,
+            )
+        else:
+            query_output = self.Qformer.bert(
+                query_embeds=query_tokens,
+                encoder_hidden_states=image_embeds,
+                encoder_attention_mask=image_atts,
+                return_dict=True,
+            )
+
+        if self.truncate_q_former_output:
+            inputs_llm = self.llm_proj(query_output.last_hidden_state[:, :query_tokens.size(1), :])
+        else:
+            inputs_llm = self.llm_proj(query_output.last_hidden_state)
+        atts_llm = torch.ones(inputs_llm.size()[:-1], dtype=torch.long).to(image.device)
+
+        self.llm_tokenizer.padding_side = "right"
+        self.llm_tokenizer.truncation_side = 'left'
+        text_input_tokens = self.llm_tokenizer(
+            samples['text_input'],
+            return_tensors="pt",
+            padding="longest",
+            truncation=True,
+            max_length=self.max_txt_len,
+        ).to(image.device)
+
+        self.llm_tokenizer.truncation_side = 'right'
+        text_output_tokens = self.llm_tokenizer(
+            [t + self.llm_tokenizer.eos_token for t in samples['text_output']],
+            return_tensors="pt",
+            padding="longest",
+            truncation=True,
+            max_length=self.max_output_txt_len,
+        ).to(image.device)
+
+        llm_tokens, input_part_targets_len = self.concat_text_input_output(
+            text_input_tokens.input_ids,
+            text_input_tokens.attention_mask,
+            text_output_tokens.input_ids,
+            text_output_tokens.attention_mask,
+        )
+
+        # do not apply loss to the padding
+        targets = llm_tokens['input_ids'].masked_fill(
+            llm_tokens['input_ids'] == self.llm_tokenizer.pad_token_id, -100
+        )
+
+        # do not apply loss to the text input (i.e., instruction)
+        for i, l in enumerate(input_part_targets_len):
+            targets[i][:l] = -100
+
+        # do not apply loss to the query tokens
+        empty_targets = (
+            torch.ones(atts_llm.size(), dtype=torch.long).to(image.device).fill_(-100)
+        )
+        targets = torch.cat([empty_targets, targets], dim=1)
+
+        inputs_embeds = self.llm_model.get_input_embeddings()(llm_tokens['input_ids'])
+        inputs_embeds = torch.cat([inputs_llm, inputs_embeds], dim=1)
+        attention_mask = torch.cat([atts_llm, llm_tokens['attention_mask']], dim=1)
+
+        with self.maybe_autocast():
+            outputs = self.llm_model(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                return_dict=True,
+                labels=targets,
+                use_cache=False,
+            )
+
+        loss = outputs.loss
+
+        return {"loss": loss}
+
+    def get_vision_feats(self, image, prompt):
+        bs = image.size(0)
+
+        if isinstance(prompt, str):
+            prompt = [prompt] * bs
+        else:
+            assert len(prompt) == bs, "The number of prompts must be equal to the batch size."
+
+        query_tokens = self.query_tokens.expand(bs, -1, -1)
+
+        text_Qformer = self.tokenizer(
+            prompt,
+            padding='longest',
+            truncation=True,
+            max_length=self.max_txt_len,
+            return_tensors="pt",
+        ).to(image.device)
+        query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image.device)
+        Qformer_atts = torch.cat([query_atts, text_Qformer.attention_mask], dim=1)
+
+        with self.maybe_autocast():
+            image_embeds = self.ln_vision(self.visual_encoder(image))
+        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image.device)
+
+        query_output = self.Qformer.bert(
+            text_Qformer.input_ids,
+            attention_mask=Qformer_atts,
+            query_embeds=query_tokens,
+            encoder_hidden_states=image_embeds,
+            encoder_attention_mask=image_atts,
+            return_dict=True,
+        )
+        if self.truncate_q_former_output:
+            inputs_llm = self.llm_proj(query_output.last_hidden_state[:, :query_tokens.size(1), :])
+        else:
+            inputs_llm = self.llm_proj(query_output.last_hidden_state)
+        atts_llm = torch.ones(inputs_llm.size()[:-1], dtype=torch.long).to(image.device)
+        return inputs_llm, atts_llm
+
+    def shift_padding_to_left(self, inputs_embeds, attention_mask):
+        llm_tokens = {"input_embeds": [], "attention_mask": []}
+        for i in range(inputs_embeds.size(0)):
+            this_input_ones = attention_mask[i].sum()
+            llm_tokens['input_embeds'].append(
+                torch.cat([
+                    inputs_embeds[i][this_input_ones:],
+                    inputs_embeds[i][:this_input_ones],
+                ])
+            )
+            llm_tokens['attention_mask'].append(
+                torch.cat([
+                    attention_mask[i][this_input_ones:],
+                    attention_mask[i][:this_input_ones],
+                ])
+            )
+        llm_tokens['input_embeds'] = torch.stack(llm_tokens['input_embeds'])
+        llm_tokens['attention_mask'] = torch.stack(llm_tokens['attention_mask'])
+        return llm_tokens['input_embeds'], llm_tokens['attention_mask']
+
+    @torch.no_grad()
+    def generate(
+            self,
+            samples,
+            use_nucleus_sampling=False,
+            num_beams=5,
+            max_length=256,
+            min_length=1,
+            top_p=0.9,
+            repetition_penalty=1.5,
+            length_penalty=1,
+            num_captions=1,
+            temperature=1,
+    ):
+
+        if "prompt" in samples.keys():
+            prompt = samples["prompt"]
+        else:
+            prompt = self.prompt
+
+        image = samples["image"]
+
+        inputs_llm, atts_llm = self.get_vision_feats(image, prompt)
+
+        self.llm_tokenizer_for_generate.padding_side = "right"
+
+        self.llm_tokenizer_for_generate.pad_token = self.llm_tokenizer_for_generate.eos_token  # debug
+        ori_pad_token_id = self.llm_model.config.pad_token_id
+        self.llm_model.config.pad_token_id = self.llm_model.config.eos_token_id  # debug
+
+        if "prefix" in samples:
+            prompt = [f"{prompt_} {prefix_}".strip() for prompt_, prefix_ in zip(prompt, samples["prefix"])]
+
+        llm_tokens = self.llm_tokenizer_for_generate(
+            prompt,
+            padding="longest",
+            return_tensors="pt",
+        ).to(image.device)
+
+        inputs_embeds = self.llm_model.get_input_embeddings()(llm_tokens.input_ids)
+        inputs_embeds = torch.cat([inputs_llm, inputs_embeds], dim=1)
+        inputs_embeds = inputs_embeds.to(next(self.llm_model.parameters()).dtype)
+        attention_mask = torch.cat([atts_llm, llm_tokens.attention_mask], dim=1)
+        inputs_embeds, attention_mask = self.shift_padding_to_left(inputs_embeds, attention_mask)
+
+        with self.maybe_autocast():
+            outputs = self.llm_model.generate(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                do_sample=use_nucleus_sampling,
+                top_p=top_p,
+                temperature=temperature,
+                num_beams=num_beams,
+                max_length=max_length,
+                min_length=min_length,
+                repetition_penalty=repetition_penalty,
+                length_penalty=length_penalty,
+                num_return_sequences=num_captions,
+                use_cache=True
+            )
+
+        outputs[outputs == 0] = 2  # convert output id 0 to 2 (eos_token_id)
+        outputs[outputs == -1] = 1  # debug
+        output_text = self.llm_tokenizer_for_generate.batch_decode(outputs, skip_special_tokens=True)
+        output_text = [text.strip() for text in output_text]
+
+        self.llm_model.config.pad_token_id = ori_pad_token_id
+
+        return output_text
+
+    @torch.no_grad()
+    def generate_multi(
+            self,
+            samples,
+            use_nucleus_sampling=False,
+            num_beams=5,
+            max_length=256,
+            min_length=1,
+            top_p=0.9,
+            repetition_penalty=1.5,
+            length_penalty=1,
+            temperature=1,
+    ):
+
+        if "prompt" in samples.keys():
+            prompt = samples["prompt"]
+        else:
+            prompt = self.prompt
+
+        image = samples["image"]
+
+        inputs_llm, atts_llm = self.get_vision_feats(image, prompt)
+
+        self.llm_tokenizer_for_generate.padding_side = "right"
+
+        self.llm_tokenizer_for_generate.pad_token = self.llm_tokenizer_for_generate.eos_token  # debug
+        ori_pad_token_id = self.llm_model.config.pad_token_id
+        self.llm_model.config.pad_token_id = self.llm_model.config.eos_token_id  # debug
+
+        if "prefix" in samples:
+            prompt = [f"{prompt_} {prefix_}".strip() for prompt_, prefix_ in zip(prompt, samples["prefix"])]
+
+        llm_tokens = self.llm_tokenizer_for_generate(
+            prompt,
+            padding="longest",
+            return_tensors="pt",
+        ).to(image.device)
+
+        inputs_embeds = self.llm_model.get_input_embeddings()(llm_tokens.input_ids)
+        inputs_embeds = torch.cat([inputs_llm, inputs_embeds], dim=1)
+        inputs_embeds = inputs_embeds.to(next(self.llm_model.parameters()).dtype)
+        attention_mask = torch.cat([atts_llm, llm_tokens.attention_mask], dim=1)
+        inputs_embeds, attention_mask = self.shift_padding_to_left(inputs_embeds, attention_mask)
+
+        with self.maybe_autocast():
+            raw_output = self.llm_model.generate(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                do_sample=use_nucleus_sampling,
+                top_p=top_p,
+                temperature=temperature,
+                num_beams=num_beams,
+                max_length=max_length,
+                min_length=min_length,
+                repetition_penalty=repetition_penalty,
+                length_penalty=length_penalty,
+                num_return_sequences=num_beams,
+                output_scores=True,
+                return_dict_in_generate=True,
+                use_cache=True
+            )
+        outputs = raw_output.sequences
+        outputs[outputs == 0] = 2  # convert output id 0 to 2 (eos_token_id)
+        outputs[outputs == -1] = 1  # debug
+        output_text = self.llm_tokenizer_for_generate.batch_decode(outputs, skip_special_tokens=True)
+
+        output_text = [text.strip() for text in output_text]
+        scores = torch.exp(raw_output.sequences_scores).cpu().numpy() ** 3 * 100  # TODO
+
+        all_texts = []
+        all_scores = []
+        for i in range(0, len(output_text), num_beams):
+            this_text = output_text[i:i + num_beams]
+            all_texts.append(this_text)
+            this_score = scores[i: i + num_beams]
+            all_scores.append(this_score)
+
+        self.llm_model.config.pad_token_id = ori_pad_token_id
+
+        return all_texts, all_scores
+
+    def predict_by_rank(
+            self,
+            samples,
+            **kwargs
+    ):
+        image = samples["image"]
+        prompt = samples["prompt"]
+        candidates = samples["candidates"][0]
+        if isinstance(prompt, str):
+            prompt = [prompt]
+        assert image.size(0) == len(prompt) == 1, "When doing predict by rank, the batch size must be 1."
+
+        with self.maybe_autocast():
+            image_embeds = self.ln_vision(self.visual_encoder(image))
+        image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image.device)
+
+        batch_size = len(candidates)
+
+        query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
+        if self.qformer_text_input:
+            text_Qformer = self.tokenizer(
+                prompt,
+                padding='longest',
+                truncation=True,
+                max_length=self.max_txt_len,
+                return_tensors="pt",
+            ).to(image.device)
+            query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image.device)
+            Qformer_atts = torch.cat([query_atts, text_Qformer.attention_mask], dim=1)
+
+            query_output = self.Qformer.bert(
+                text_Qformer.input_ids,
+                attention_mask=Qformer_atts,
+                query_embeds=query_tokens,
+                encoder_hidden_states=image_embeds,
+                encoder_attention_mask=image_atts,
+                return_dict=True,
+            )
+        else:
+            query_output = self.Qformer.bert(
+                query_embeds=query_tokens,
+                encoder_hidden_states=image_embeds,
+                encoder_attention_mask=image_atts,
+                return_dict=True,
+            )
+
+        if self.truncate_q_former_output:
+            inputs_llm = self.llm_proj(query_output.last_hidden_state[:, :query_tokens.size(1), :])
+        else:
+            inputs_llm = self.llm_proj(query_output.last_hidden_state)
+        atts_llm = torch.ones(inputs_llm.size()[:-1], dtype=torch.long).to(image.device)
+
+        self.llm_tokenizer.padding_side = "right"
+        self.llm_tokenizer.truncation_side = 'left'
+        text_input_tokens = self.llm_tokenizer(
+            prompt,
+            return_tensors="pt",
+            padding="longest",
+            truncation=True,
+            max_length=self.max_txt_len,
+        ).to(image.device)
+
+        inputs_llm = inputs_llm.repeat(batch_size, 1, 1)
+        atts_llm = atts_llm.repeat(batch_size, 1)
+        text_input_ids = text_input_tokens.input_ids.repeat(batch_size, 1)
+        text_input_mask = text_input_tokens.attention_mask.repeat(batch_size, 1)
+
+        self.llm_tokenizer.truncation_side = 'right'
+        text_output_tokens = self.llm_tokenizer(
+            [t + self.llm_tokenizer.eos_token for t in candidates],
+            return_tensors="pt",
+            padding="longest",
+            truncation=True,
+            max_length=self.max_output_txt_len,
+        ).to(image.device)
+
+        llm_tokens, input_part_targets_len = self.concat_text_input_output(
+            text_input_ids,
+            text_input_mask,
+            text_output_tokens.input_ids,
+            text_output_tokens.attention_mask,
+        )
+
+        # do not apply loss to the padding
+        targets = llm_tokens['input_ids'].masked_fill(
+            llm_tokens['input_ids'] == self.llm_tokenizer.pad_token_id, -100
+        )
+
+        # do not apply loss to the text input (i.e., instruction)
+        for i, l in enumerate(input_part_targets_len):
+            targets[i][:l] = -100
+
+        # do not apply loss to the query tokens
+        empty_targets = (
+            torch.ones(atts_llm.size(), dtype=torch.long).to(image.device).fill_(-100)
+        )
+        targets = torch.cat([empty_targets, targets], dim=1)
+
+        inputs_embeds = self.llm_model.get_input_embeddings()(llm_tokens['input_ids'])
+        inputs_embeds = torch.cat([inputs_llm, inputs_embeds], dim=1)
+        attention_mask = torch.cat([atts_llm, llm_tokens['attention_mask']], dim=1)
+
+        with self.maybe_autocast():
+            outputs = self.llm_model(
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                return_dict=True,
+                labels=targets,
+                reduction="none",
+                use_cache=False
+            )
+
+        loss = outputs.loss.view(batch_size)
+        top1 = int(torch.argsort(loss, dim=-1)[0])
+
+        return [candidates[top1]]
+
+    def _lemmatize(self, answers):
+        def apply(answer):
+            doc = self.lemmatizer(answer)
+
+            words = []
+            for token in doc:
+                if token.pos_ in ["NOUN", "VERB"]:
+                    words.append(token.lemma_)
+                else:
+                    words.append(token.text)
+            answer = " ".join(words)
+
+            return answer
+
+        return [apply(answer) for answer in answers]
+
+    @property
+    def lemmatizer(self):
+        if self._lemmatizer is None:
+            try:
+                import spacy
+
+                self._lemmatizer = spacy.load("en_core_web_sm")
+            except ImportError:
+                logging.error(
+                    """
+                    Please install spacy and en_core_web_sm model to apply lemmatization.
+                    python -m spacy download en_core_web_sm
+                    OR
+                    import spacy.cli
+                    spacy.cli.download("en_core_web_sm")
+                    """
+                )
+                exit(1)
+
+        return self._lemmatizer
+
+    @classmethod
+    def from_config(cls, cfg):
+        vit_model = cfg.get("vit_model", "eva_clip_g")
+        img_size = cfg.get("image_size")
+        num_query_token = cfg.get("num_query_token")
+        llm_model = cfg.get("llm_model")
+
+        drop_path_rate = cfg.get("drop_path_rate", 0)
+        use_grad_checkpoint = cfg.get("use_grad_checkpoint", False)
+        vit_precision = cfg.get("vit_precision", "fp16")
+        freeze_vit = cfg.get("freeze_vit", True)
+        freeze_vit_ln = cfg.get("freeze_vit_ln", False)
+        prompt = cfg.get("prompt", "")
+        max_txt_len = cfg.get("max_txt_len", 128)
+        max_output_txt_len = cfg.get("max_output_txt_len", 256)
+
+        apply_lemmatizer = cfg.get("apply_lemmatizer", False)
+
+        qformer_text_input = cfg.get("qformer_text_input", True)
+        truncate_q_former_output = cfg.get("truncate_q_former_output", True)
+
+        model = cls(
+            vit_model=vit_model,
+            img_size=img_size,
+            drop_path_rate=drop_path_rate,
+            use_grad_checkpoint=use_grad_checkpoint,
+            vit_precision=vit_precision,
+            freeze_vit=freeze_vit,
+            freeze_vit_ln=freeze_vit_ln,
+            num_query_token=num_query_token,
+            llm_model=llm_model,
+            prompt=prompt,
+            max_txt_len=max_txt_len,
+            max_output_txt_len=max_output_txt_len,
+            apply_lemmatizer=apply_lemmatizer,
+            qformer_text_input=qformer_text_input,
+            truncate_q_former_output=truncate_q_former_output
+        )
+
+        model.load_checkpoint_from_config(cfg)
+
+        return model

unimernet/models/blip2_models/modeling_llama.py

+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch LLaMA model."""
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, \
+    SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, \
+    replace_return_docstrings
+from transformers.models.llama.configuration_llama import LlamaConfig
+from einops import rearrange
+
+FLASH_ATTN_FLAG = True
+try:
+    from flash_attn.flash_attn_interface import (  # pip3 install "flash-attn>=2.0"
+        flash_attn_varlen_qkvpacked_func,
+    )
+    from flash_attn.bert_padding import unpad_input, pad_input
+
+    cuda_major, cuda_minor = torch.cuda.get_device_capability()
+    if cuda_major < 8:
+        logging.warning(
+            "Flash attention is only supported on A100 or H100 GPU during training due to head dim > 64 backward."
+            "ref: https://github.com/HazyResearch/flash-attention/issues/190#issuecomment-1523359593"
+        )
+        FLASH_ATTN_FLAG = False
+except ImportError:
+    FLASH_ATTN_FLAG = False
+    logging.warning("You haven't installed flash attention")
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LlamaConfig"
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+        input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+class LlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        LlamaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+        return (self.weight * hidden_states).to(input_dtype)
+
+
+class LlamaRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+        self.register_buffer("inv_freq", inv_freq)
+
+        # Build here to make `torch.jit.trace` work.
+        self.max_seq_len_cached = max_position_embeddings
+        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
+        if seq_len > self.max_seq_len_cached:
+            self.max_seq_len_cached = seq_len
+            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
+            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+            # Different from paper, but it uses a different permutation in order to obtain the same calculation
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+            self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+            self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2:]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    gather_indices = position_ids[:, None, :, None]  # [bs, 1, seq_len, 1]
+    gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
+    cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class LlamaMLP(nn.Module):
+    def __init__(
+            self,
+            hidden_size: int,
+            intermediate_size: int,
+            hidden_act: str,
+    ):
+        super().__init__()
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class LlamaAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def flash_attn_forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            past_key_value: Optional[Tuple[torch.Tensor]] = None,
+            output_attentions: bool = False,
+            use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        """Input shape: Batch x Time x Channel
+
+        attention_mask: [bsz, q_len]
+        """
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = (
+            self.q_proj(hidden_states)
+                .view(bsz, q_len, self.num_heads, self.head_dim)
+                .transpose(1, 2)
+        )
+        key_states = (
+            self.k_proj(hidden_states)
+                .view(bsz, q_len, self.num_heads, self.head_dim)
+                .transpose(1, 2)
+        )
+        value_states = (
+            self.v_proj(hidden_states)
+                .view(bsz, q_len, self.num_heads, self.head_dim)
+                .transpose(1, 2)
+        )
+        # [bsz, q_len, nh, hd]
+        # [bsz, nh, q_len, hd]
+
+        kv_seq_len = key_states.shape[-2]
+        assert past_key_value is None, "past_key_value is not supported"
+
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin, position_ids
+        )
+        # [bsz, nh, t, hd]
+        assert not output_attentions, "output_attentions is not supported"
+        assert not use_cache, "use_cache is not supported"
+
+        # Flash attention codes from
+        # https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py
+
+        # transform the data into the format required by flash attention
+        qkv = torch.stack(
+            [query_states, key_states, value_states], dim=2
+        )  # [bsz, nh, 3, q_len, hd]
+        qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
+        # We have disabled _prepare_decoder_attention_mask in LlamaModel
+        # the attention_mask should be the same as the key_padding_mask
+        key_padding_mask = attention_mask
+
+        if key_padding_mask is None:
+            qkv = rearrange(qkv, "b s ... -> (b s) ...")
+            max_s = q_len
+            cu_q_lens = torch.arange(
+                0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device
+            )
+            output = flash_attn_varlen_qkvpacked_func(
+                qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
+            )
+            output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
+        else:
+            nheads = qkv.shape[-2]
+            x = rearrange(qkv, "b s three h d -> b s (three h d)")
+            x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
+            x_unpad = rearrange(
+                x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads
+            )
+            output_unpad = flash_attn_varlen_qkvpacked_func(
+                x_unpad, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
+            )
+            output = rearrange(
+                pad_input(
+                    rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, bsz, q_len
+                ),
+                "b s (h d) -> b s h d",
+                h=nheads,
+            )
+        return self.o_proj(rearrange(output, "b s h d -> b s (h d)")), None, None
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Tuple[torch.Tensor]] = None,
+            output_attentions: bool = False,
+            use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if FLASH_ATTN_FLAG and not use_cache:
+            return self.flash_attn_forward(hidden_states, attention_mask, position_ids, past_key_value,
+                                           output_attentions, use_cache)
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+        # [bsz, nh, t, hd]
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class LlamaDecoderLayer(nn.Module):
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = LlamaAttention(config=config)
+        self.mlp = LlamaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+        )
+        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_value: Optional[Tuple[torch.Tensor]] = None,
+            output_attentions: Optional[bool] = False,
+            use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+LLAMA_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`LlamaConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class LlamaPreTrainedModel(PreTrainedModel):
+    config_class = LlamaConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LlamaDecoderLayer"]
+    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, LlamaModel):
+            module.gradient_checkpointing = value
+
+
+LLAMA_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class LlamaModel(LlamaPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
+
+    Args:
+        config: LlamaConfig
+    """
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+            )
+        if not (FLASH_ATTN_FLAG and (use_cache is False)):
+            attention_mask = self._prepare_decoder_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class LlamaForCausalLM(LlamaPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = LlamaModel(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+            reduction: Optional[str] = "mean",
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, LlamaForCausalLM
+
+        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you consciours? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss(reduction=reduction)
+            shift_logits = shift_logits.view(-1, self.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 reduction == "none":
+                # loss = loss.view(logits.size(0), -1).sum(1)
+                loss = loss.view(logits.size(0), -1).mean(1)
+
+        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=hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+            self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        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[:, -1].unsqueeze(-1)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The LLaMa Model transformer with a sequence classification head on top (linear layer).
+
+    [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    LLAMA_START_DOCSTRING,
+)
+class LlamaForSequenceClassification(LlamaPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = LlamaModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

unimernet/models/blip2_models/modeling_llama_.py

+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch LLaMA model."""
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from transformers.models.llama.configuration_llama import LlamaConfig
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LlamaConfig"
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+class LlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        LlamaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+        return (self.weight * hidden_states).to(input_dtype)
+
+
+class LlamaRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+        self.register_buffer("inv_freq", inv_freq)
+
+        # Build here to make `torch.jit.trace` work.
+        self.max_seq_len_cached = max_position_embeddings
+        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
+        if seq_len > self.max_seq_len_cached:
+            self.max_seq_len_cached = seq_len
+            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
+            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+            # Different from paper, but it uses a different permutation in order to obtain the same calculation
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+            self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+            self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    gather_indices = position_ids[:, None, :, None]  # [bs, 1, seq_len, 1]
+    gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
+    cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class LlamaMLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+    ):
+        super().__init__()
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class LlamaAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+        # [bsz, nh, t, hd]
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class LlamaDecoderLayer(nn.Module):
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = LlamaAttention(config=config)
+        self.mlp = LlamaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+        )
+        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+LLAMA_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`LlamaConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class LlamaPreTrainedModel(PreTrainedModel):
+    config_class = LlamaConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LlamaDecoderLayer"]
+    _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, LlamaModel):
+            module.gradient_checkpointing = value
+
+
+LLAMA_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class LlamaModel(LlamaPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
+
+    Args:
+        config: LlamaConfig
+    """
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+        )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class LlamaForCausalLM(LlamaPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = LlamaModel(config)
+
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        reduction: Optional[str] = "mean",
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, LlamaForCausalLM
+
+        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you consciours? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss(reduction=reduction)
+            shift_logits = shift_logits.view(-1, self.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 reduction == "none":
+                # loss = loss.view(logits.size(0), -1).sum(1)
+                loss = loss.view(logits.size(0), -1).mean(1)
+
+        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=hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        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[:, -1].unsqueeze(-1)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The LLaMa Model transformer with a sequence classification head on top (linear layer).
+
+    [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    LLAMA_START_DOCSTRING,
+)
+class LlamaForSequenceClassification(LlamaPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = LlamaModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
\ No newline at end of file

unimernet/models/clip_vit.py

+from collections import OrderedDict
+from itertools import repeat
+import collections.abc
+import math
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
+
+from unimernet.models.eva_vit import convert_weights_to_fp16
+from unimernet.common.dist_utils import download_cached_file
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1):
+        super().__init__()
+
+        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
+        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu1 = nn.ReLU(inplace=True)
+
+        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.relu2 = nn.ReLU(inplace=True)
+
+        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
+
+        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu3 = nn.ReLU(inplace=True)
+
+        self.downsample = None
+        self.stride = stride
+
+        if stride > 1 or inplanes != planes * Bottleneck.expansion:
+            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
+            self.downsample = nn.Sequential(OrderedDict([
+                ("-1", nn.AvgPool2d(stride)),
+                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
+                ("1", nn.BatchNorm2d(planes * self.expansion))
+            ]))
+
+    def forward(self, x: torch.Tensor):
+        identity = x
+
+        out = self.relu1(self.bn1(self.conv1(x)))
+        out = self.relu2(self.bn2(self.conv2(out)))
+        out = self.avgpool(out)
+        out = self.bn3(self.conv3(out))
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu3(out)
+        return out
+
+
+class AttentionPool2d(nn.Module):
+    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
+        self.k_proj = nn.Linear(embed_dim, embed_dim)
+        self.q_proj = nn.Linear(embed_dim, embed_dim)
+        self.v_proj = nn.Linear(embed_dim, embed_dim)
+        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
+        self.num_heads = num_heads
+
+    def forward(self, x):
+        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1)  # NCHW -> (HW)NC
+        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
+        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
+        x, _ = F.multi_head_attention_forward(
+            query=x, key=x, value=x,
+            embed_dim_to_check=x.shape[-1],
+            num_heads=self.num_heads,
+            q_proj_weight=self.q_proj.weight,
+            k_proj_weight=self.k_proj.weight,
+            v_proj_weight=self.v_proj.weight,
+            in_proj_weight=None,
+            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
+            bias_k=None,
+            bias_v=None,
+            add_zero_attn=False,
+            dropout_p=0,
+            out_proj_weight=self.c_proj.weight,
+            out_proj_bias=self.c_proj.bias,
+            use_separate_proj_weight=True,
+            training=self.training,
+            need_weights=False
+        )
+
+        return x[0]
+    
+
+class LayerNorm(nn.LayerNorm):
+    """Subclass torch's LayerNorm to handle fp16."""
+
+    def forward(self, x: torch.Tensor):
+        orig_type = x.dtype
+        ret = super().forward(x.type(torch.float32))
+        return ret.type(orig_type)
+
+
+class QuickGELU(nn.Module):
+    def forward(self, x: torch.Tensor):
+        return x * torch.sigmoid(1.702 * x)
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None, use_grad_checkpointing=False):
+        super().__init__()
+
+        self.attn = nn.MultiheadAttention(d_model, n_head)
+        self.ln_1 = LayerNorm(d_model)
+        self.mlp = nn.Sequential(OrderedDict([
+            ("c_fc", nn.Linear(d_model, d_model * 4)),
+            ("gelu", QuickGELU()),
+            ("c_proj", nn.Linear(d_model * 4, d_model))
+        ]))
+        self.ln_2 = LayerNorm(d_model)
+        self.attn_mask = attn_mask
+
+        if use_grad_checkpointing:
+            self.attn = checkpoint_wrapper(self.attn)
+            self.mlp = checkpoint_wrapper(self.mlp)
+            
+    def attention(self, x: torch.Tensor):
+        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
+        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
+
+    def forward(self, x: torch.Tensor):
+        x = x + self.attention(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class Transformer(nn.Module):
+    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, use_grad_checkpointing=False):
+        super().__init__()
+        self.width = width
+        self.layers = layers
+        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask, use_grad_checkpointing and i>12) for i in range(layers)])
+
+    def forward(self, x: torch.Tensor):
+        return self.resblocks(x)
+
+
+class VisionTransformer(nn.Module):
+    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, use_grad_checkpointing: bool):
+        super().__init__()
+        self.input_resolution = input_resolution
+        self.num_features = width
+        self.num_heads = heads
+        self.num_patches = (input_resolution // patch_size) ** 2
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
+
+        scale = width ** -0.5
+        self.class_embedding = nn.Parameter(scale * torch.randn(width))
+        self.positional_embedding = nn.Parameter(scale * torch.randn(self.num_patches + 1, width))
+        self.ln_pre = LayerNorm(width)
+        
+        self.transformer = Transformer(width, layers, heads, use_grad_checkpointing=use_grad_checkpointing)
+           
+#         self.ln_final = LayerNorm(width)
+
+    def forward(self, x: torch.Tensor):
+
+        x = self.conv1(x)  # shape = [*, width, grid, grid]
+        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
+        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
+        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
+        x = x + self.positional_embedding.to(x.dtype)
+        x = self.ln_pre(x)
+
+        x = x.permute(1, 0, 2)  # NLD -> LND
+        x = self.transformer(x)
+        x = x.permute(1, 0, 2)  # LND -> NLD
+        
+#         x = self.ln_final(x)
+        return x
+    
+    
+            
+# From PyTorch internals
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable):
+            return x
+        return tuple(repeat(x, n))
+    return parse
+to_2tuple = _ntuple(2)        
+def interpolate_pos_embed(model, state_dict, interpolation: str = 'bicubic', seq_dim=1):
+    # Rescale the grid of position embeddings when loading from state_dict
+    old_pos_embed = state_dict.get('positional_embedding', None)
+    
+    grid_size = round((model.positional_embedding.shape[0] - 1) ** 0.5)
+    if old_pos_embed is None:
+        return
+    grid_size = to_2tuple(grid_size)
+    extra_tokens = 1  # FIXME detect different token configs (ie no class token, or more)
+    new_seq_len = grid_size[0] * grid_size[1] + extra_tokens
+    if new_seq_len == old_pos_embed.shape[0]:
+        return
+
+    if extra_tokens:
+        pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:]
+    else:
+        pos_emb_tok, pos_emb_img = None, old_pos_embed
+        
+    old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img))))
+
+    print('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size)
+    pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2)
+    pos_emb_img = F.interpolate(
+        pos_emb_img,
+        size=grid_size,
+        mode=interpolation,
+        align_corners=True,
+    )
+    pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0]
+    if pos_emb_tok is not None:
+        new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0)
+    else:
+        new_pos_embed = pos_emb_img
+    state_dict['positional_embedding'] = new_pos_embed
+    
+    
+def create_clip_vit_L(img_size=224,use_checkpoint=False,precision="fp16"):
+    model = VisionTransformer(
+            input_resolution=img_size,
+            patch_size=14,
+            width=1024,
+            layers=23,
+            heads=16,
+            use_grad_checkpointing=use_checkpoint,
+        )         
+    url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/clip_vit_L.pth"
+    cached_file = download_cached_file(
+        url, check_hash=False, progress=True
+    )
+    state_dict = torch.load(cached_file, map_location="cpu")    
+    interpolate_pos_embed(model,state_dict)
+    
+    incompatible_keys = model.load_state_dict(state_dict, strict=False)
+    # print(incompatible_keys)
+    
+    if precision == "fp16":
+        convert_weights_to_fp16(model)
+    return model

unimernet/models/eva_vit.py

+# Based on EVA, BEIT, timm and DeiT code bases
+# https://github.com/baaivision/EVA
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/facebookresearch/deit/
+# https://github.com/facebookresearch/dino
+# --------------------------------------------------------'
+import math
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import drop_path, to_2tuple, trunc_normal_
+from timm.models.registry import register_model
+
+from unimernet.common.dist_utils import download_cached_file
+
+
+def _cfg(url='', **kwargs):
+    return {
+        'url': url,
+        'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
+        'crop_pct': .9, 'interpolation': 'bicubic',
+        'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5),
+        **kwargs
+    }
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+    def extra_repr(self) -> str:
+        return 'p={}'.format(self.drop_prob)
+
+
+class Mlp(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        # x = self.drop(x)
+        # commit this for the orignal BERT implement 
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class Attention(nn.Module):
+    def __init__(
+            self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.,
+            proj_drop=0., window_size=None, attn_head_dim=None):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        if attn_head_dim is not None:
+            head_dim = attn_head_dim
+        all_head_dim = head_dim * self.num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
+            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+
+        if window_size:
+            self.window_size = window_size
+            self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
+            self.relative_position_bias_table = nn.Parameter(
+                torch.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+            # cls to token & token 2 cls & cls to cls
+
+            # get pair-wise relative position index for each token inside the window
+            coords_h = torch.arange(window_size[0])
+            coords_w = torch.arange(window_size[1])
+            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+            coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+            relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+            relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
+            relative_coords[:, :, 1] += window_size[1] - 1
+            relative_coords[:, :, 0] *= 2 * window_size[1] - 1
+            relative_position_index = \
+                torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
+            relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+            relative_position_index[0, 0:] = self.num_relative_distance - 3
+            relative_position_index[0:, 0] = self.num_relative_distance - 2
+            relative_position_index[0, 0] = self.num_relative_distance - 1
+
+            self.register_buffer("relative_position_index", relative_position_index)
+        else:
+            self.window_size = None
+            self.relative_position_bias_table = None
+            self.relative_position_index = None
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(all_head_dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, rel_pos_bias=None):
+        B, N, C = x.shape
+        qkv_bias = None
+        if self.q_bias is not None:
+            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
+        # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        if self.relative_position_bias_table is not None:
+            relative_position_bias = \
+                self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+                    self.window_size[0] * self.window_size[1] + 1,
+                    self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
+            relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+            attn = attn + relative_position_bias.unsqueeze(0)
+
+        if rel_pos_bias is not None:
+            attn = attn + rel_pos_bias
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class Block(nn.Module):
+
+    def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., init_values=None, act_layer=nn.GELU, norm_layer=nn.LayerNorm,
+                 window_size=None, attn_head_dim=None):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
+            attn_drop=attn_drop, proj_drop=drop, window_size=window_size, attn_head_dim=attn_head_dim)
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+        if init_values is not None and init_values > 0:
+            self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
+            self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
+        else:
+            self.gamma_1, self.gamma_2 = None, None
+
+    def forward(self, x, rel_pos_bias=None):
+        if self.gamma_1 is None:
+            x = x + self.drop_path(self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
+            x = x + self.drop_path(self.mlp(self.norm2(x)))
+        else:
+            x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias))
+            x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """ Image to Patch Embedding
+    """
+
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
+        self.patch_shape = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, x, **kwargs):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        assert H == self.img_size[0] and W == self.img_size[1], \
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)
+        return x
+
+
+class RelativePositionBias(nn.Module):
+
+    def __init__(self, window_size, num_heads):
+        super().__init__()
+        self.window_size = window_size
+        self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros(self.num_relative_distance, num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+        # cls to token & token 2 cls & cls to cls
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(window_size[0])
+        coords_w = torch.arange(window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * window_size[1] - 1
+        relative_position_index = \
+            torch.zeros(size=(window_size[0] * window_size[1] + 1,) * 2, dtype=relative_coords.dtype)
+        relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        relative_position_index[0, 0:] = self.num_relative_distance - 3
+        relative_position_index[0:, 0] = self.num_relative_distance - 2
+        relative_position_index[0, 0] = self.num_relative_distance - 1
+
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        # trunc_normal_(self.relative_position_bias_table, std=.02)
+
+    def forward(self):
+        relative_position_bias = \
+            self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
+                self.window_size[0] * self.window_size[1] + 1,
+                self.window_size[0] * self.window_size[1] + 1, -1)  # Wh*Ww,Wh*Ww,nH
+        return relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+
+
+class VisionTransformer(nn.Module):
+    """ Vision Transformer with support for patch or hybrid CNN input stage
+    """
+
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
+                 num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
+                 drop_path_rate=0., norm_layer=nn.LayerNorm, init_values=None,
+                 use_abs_pos_emb=True, use_rel_pos_bias=False, use_shared_rel_pos_bias=False,
+                 use_mean_pooling=True, init_scale=0.001, use_checkpoint=False):
+        super().__init__()
+        self.image_size = img_size
+        self.num_classes = num_classes
+        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        if use_abs_pos_emb:
+            self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
+        else:
+            self.pos_embed = None
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        if use_shared_rel_pos_bias:
+            self.rel_pos_bias = RelativePositionBias(window_size=self.patch_embed.patch_shape, num_heads=num_heads)
+        else:
+            self.rel_pos_bias = None
+        self.use_checkpoint = use_checkpoint
+
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.use_rel_pos_bias = use_rel_pos_bias
+        self.blocks = nn.ModuleList([
+            Block(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
+                init_values=init_values, window_size=self.patch_embed.patch_shape if use_rel_pos_bias else None)
+            for i in range(depth)])
+        #         self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
+        #         self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
+        #         self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+        if self.pos_embed is not None:
+            trunc_normal_(self.pos_embed, std=.02)
+        trunc_normal_(self.cls_token, std=.02)
+        # trunc_normal_(self.mask_token, std=.02)
+        #         if isinstance(self.head, nn.Linear):
+        #             trunc_normal_(self.head.weight, std=.02)
+        self.apply(self._init_weights)
+        self.fix_init_weight()
+
+    #         if isinstance(self.head, nn.Linear):
+    #             self.head.weight.data.mul_(init_scale)
+    #             self.head.bias.data.mul_(init_scale)
+
+    def fix_init_weight(self):
+        def rescale(param, layer_id):
+            param.div_(math.sqrt(2.0 * layer_id))
+
+        for layer_id, layer in enumerate(self.blocks):
+            rescale(layer.attn.proj.weight.data, layer_id + 1)
+            rescale(layer.mlp.fc2.weight.data, layer_id + 1)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def get_classifier(self):
+        return self.head
+
+    def reset_classifier(self, num_classes, global_pool=''):
+        self.num_classes = num_classes
+        self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+    def forward_features(self, x):
+        x = self.patch_embed(x)
+        batch_size, seq_len, _ = x.size()
+
+        cls_tokens = self.cls_token.expand(batch_size, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+        rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, rel_pos_bias)
+            else:
+                x = blk(x, rel_pos_bias)
+        return x
+
+    #         x = self.norm(x)
+
+    #         if self.fc_norm is not None:
+    #             t = x[:, 1:, :]
+    #             return self.fc_norm(t.mean(1))
+    #         else:
+    #             return x[:, 0]
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        #         x = self.head(x)
+        return x
+
+    def get_intermediate_layers(self, x):
+        x = self.patch_embed(x)
+        batch_size, seq_len, _ = x.size()
+
+        cls_tokens = self.cls_token.expand(batch_size, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+        features = []
+        rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
+        for blk in self.blocks:
+            x = blk(x, rel_pos_bias)
+            features.append(x)
+
+        return features
+
+
+def interpolate_pos_embed(model, checkpoint_model):
+    if 'pos_embed' in checkpoint_model:
+        pos_embed_checkpoint = checkpoint_model['pos_embed'].float()
+        embedding_size = pos_embed_checkpoint.shape[-1]
+        num_patches = model.patch_embed.num_patches
+        num_extra_tokens = model.pos_embed.shape[-2] - num_patches
+        # height (== width) for the checkpoint position embedding
+        orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
+        # height (== width) for the new position embedding
+        new_size = int(num_patches ** 0.5)
+        # class_token and dist_token are kept unchanged
+        if orig_size != new_size:
+            print("Position interpolate from %dx%d to %dx%d" % (orig_size, orig_size, new_size, new_size))
+            extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+            # only the position tokens are interpolated
+            pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+            pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
+            pos_tokens = torch.nn.functional.interpolate(
+                pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False)
+            pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+            new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+            checkpoint_model['pos_embed'] = new_pos_embed
+
+
+def convert_weights_to_fp16(model: nn.Module):
+    """Convert applicable model parameters to fp16"""
+
+    def _convert_weights_to_fp16(l):
+        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
+            l.weight.data = l.weight.data.half()
+            if l.bias is not None:
+                l.bias.data = l.bias.data.half()
+
+    #         if isinstance(l, (nn.MultiheadAttention, Attention)):
+    #             for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
+    #                 tensor = getattr(l, attr)
+    #                 if tensor is not None:
+    #                     tensor.data = tensor.data.half()
+
+    model.apply(_convert_weights_to_fp16)
+
+
+def create_eva_vit_g(img_size=224, drop_path_rate=0.4, use_checkpoint=False, precision="fp16"):
+    model = VisionTransformer(
+        img_size=img_size,
+        patch_size=14,
+        use_mean_pooling=False,
+        embed_dim=1408,
+        depth=39,
+        num_heads=1408 // 88,
+        mlp_ratio=4.3637,
+        qkv_bias=True,
+        drop_path_rate=drop_path_rate,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        use_checkpoint=use_checkpoint,
+    )
+    url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth"
+    cached_file = download_cached_file(
+        url, check_hash=False, progress=True
+    )
+    state_dict = torch.load(cached_file, map_location="cpu")
+    interpolate_pos_embed(model, state_dict)
+
+    incompatible_keys = model.load_state_dict(state_dict, strict=False)
+    #     print(incompatible_keys)
+
+    if precision == "fp16":
+        #         model.to("cuda")
+        convert_weights_to_fp16(model)
+    return model

unimernet/models/unimernet/configuration_unimernet_decoder.py

+# coding=utf-8
+# Copyright 2021, The Facebook AI Research Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MBART model configuration"""
+
+from collections import OrderedDict
+from typing import Any, Mapping, Optional
+
+from transformers import PreTrainedTokenizer
+from transformers.configuration_utils import PretrainedConfig
+from transformers.onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeq2SeqConfigWithPast
+from transformers.onnx.utils import compute_effective_axis_dimension
+from transformers.utils import TensorType, is_torch_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MBartConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MBartModel`]. It is used to instantiate an MBART
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the MBART
+    [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50265):
+            Vocabulary size of the MBART model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MBartModel`] or [`TFMBartModel`].
+        d_model (`int`, *optional*, defaults to 1024):
+            Dimensionality of the layers and the pooler layer.
+        encoder_layers (`int`, *optional*, defaults to 12):
+            Number of encoder layers.
+        decoder_layers (`int`, *optional*, defaults to 12):
+            Number of decoder layers.
+        encoder_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        decoder_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        decoder_ffn_dim (`int`, *optional*, defaults to 4096):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+        encoder_ffn_dim (`int`, *optional*, defaults to 4096):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
+        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        dropout (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        activation_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for activations inside the fully connected layer.
+        classifier_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for classifier.
+        max_position_embeddings (`int`, *optional*, defaults to 1024):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        init_std (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        encoder_layerdrop (`float`, *optional*, defaults to 0.0):
+            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
+            for more details.
+        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
+            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
+            for more details.
+        scale_embedding (`bool`, *optional*, defaults to `False`):
+            Scale embeddings by diving by sqrt(d_model).
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models)
+        forced_eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
+            `eos_token_id`.
+
+    Example:
+
+    ```python
+    >>> from transformers import MBartConfig, MBartModel
+
+    >>> # Initializing a MBART facebook/mbart-large-cc25 style configuration
+    >>> configuration = MBartConfig()
+
+    >>> # Initializing a model (with random weights) from the facebook/mbart-large-cc25 style configuration
+    >>> model = MBartModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "mbart"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
+
+    def __init__(
+        self,
+        vocab_size=50265,
+        max_position_embeddings=1024,
+        encoder_layers=12,
+        encoder_ffn_dim=4096,
+        encoder_attention_heads=16,
+        decoder_layers=12,
+        decoder_ffn_dim=4096,
+        decoder_attention_heads=16,
+        encoder_layerdrop=0.0,
+        decoder_layerdrop=0.0,
+        use_cache=True,
+        is_encoder_decoder=True,
+        activation_function="gelu",
+        d_model=1024,
+        dropout=0.1,
+        attention_dropout=0.0,
+        activation_dropout=0.0,
+        init_std=0.02,
+        classifier_dropout=0.0,
+        scale_embedding=False,
+        pad_token_id=1,
+        bos_token_id=0,
+        eos_token_id=2,
+        forced_eos_token_id=2,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.d_model = d_model
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.encoder_layers = encoder_layers
+        self.encoder_attention_heads = encoder_attention_heads
+        self.decoder_ffn_dim = decoder_ffn_dim
+        self.decoder_layers = decoder_layers
+        self.decoder_attention_heads = decoder_attention_heads
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.activation_dropout = activation_dropout
+        self.activation_function = activation_function
+        self.init_std = init_std
+        self.encoder_layerdrop = encoder_layerdrop
+        self.decoder_layerdrop = decoder_layerdrop
+        self.classifier_dropout = classifier_dropout
+        self.use_cache = use_cache
+        self.num_hidden_layers = encoder_layers
+        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            is_encoder_decoder=is_encoder_decoder,
+            forced_eos_token_id=forced_eos_token_id,
+            **kwargs,
+        )
+
+
+# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig with Bart->MBart
+class MBartOnnxConfig(OnnxSeq2SeqConfigWithPast):
+    @property
+    def inputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task in ["default", "seq2seq-lm"]:
+            common_inputs = OrderedDict(
+                [
+                    ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+                    ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+                ]
+            )
+
+            if self.use_past:
+                common_inputs["decoder_input_ids"] = {0: "batch"}
+                common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
+            else:
+                common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
+                common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}
+
+            if self.use_past:
+                self.fill_with_past_key_values_(common_inputs, direction="inputs")
+        elif self.task == "causal-lm":
+            # TODO: figure this case out.
+            common_inputs = OrderedDict(
+                [
+                    ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+                    ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+                ]
+            )
+            if self.use_past:
+                num_encoder_layers, _ = self.num_layers
+                for i in range(num_encoder_layers):
+                    common_inputs[f"past_key_values.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
+                    common_inputs[f"past_key_values.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
+        else:
+            common_inputs = OrderedDict(
+                [
+                    ("input_ids", {0: "batch", 1: "encoder_sequence"}),
+                    ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
+                    ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
+                    ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
+                ]
+            )
+
+        return common_inputs
+
+    @property
+    def outputs(self) -> Mapping[str, Mapping[int, str]]:
+        if self.task in ["default", "seq2seq-lm"]:
+            common_outputs = super().outputs
+        else:
+            common_outputs = super(OnnxConfigWithPast, self).outputs
+            if self.use_past:
+                num_encoder_layers, _ = self.num_layers
+                for i in range(num_encoder_layers):
+                    common_outputs[f"present.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
+                    common_outputs[f"present.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}
+        return common_outputs
+
+    def _generate_dummy_inputs_for_default_and_seq2seq_lm(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        batch_size: int = -1,
+        seq_length: int = -1,
+        is_pair: bool = False,
+        framework: Optional[TensorType] = None,
+    ) -> Mapping[str, Any]:
+        encoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+            tokenizer, batch_size, seq_length, is_pair, framework
+        )
+
+        # Generate decoder inputs
+        decoder_seq_length = seq_length if not self.use_past else 1
+        decoder_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+            tokenizer, batch_size, decoder_seq_length, is_pair, framework
+        )
+        decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
+        common_inputs = dict(**encoder_inputs, **decoder_inputs)
+
+        if self.use_past:
+            if not is_torch_available():
+                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
+            else:
+                import torch
+            batch, encoder_seq_length = common_inputs["input_ids"].shape
+            decoder_seq_length = common_inputs["decoder_input_ids"].shape[1]
+            num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
+            encoder_shape = (
+                batch,
+                num_encoder_attention_heads,
+                encoder_seq_length,
+                self._config.hidden_size // num_encoder_attention_heads,
+            )
+            decoder_past_length = decoder_seq_length + 3
+            decoder_shape = (
+                batch,
+                num_decoder_attention_heads,
+                decoder_past_length,
+                self._config.hidden_size // num_decoder_attention_heads,
+            )
+
+            common_inputs["decoder_attention_mask"] = torch.cat(
+                [common_inputs["decoder_attention_mask"], torch.ones(batch, decoder_past_length)], dim=1
+            )
+
+            common_inputs["past_key_values"] = []
+            # If the number of encoder and decoder layers are present in the model configuration, both are considered
+            num_encoder_layers, num_decoder_layers = self.num_layers
+            min_num_layers = min(num_encoder_layers, num_decoder_layers)
+            max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
+            remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
+
+            for _ in range(min_num_layers):
+                common_inputs["past_key_values"].append(
+                    (
+                        torch.zeros(decoder_shape),
+                        torch.zeros(decoder_shape),
+                        torch.zeros(encoder_shape),
+                        torch.zeros(encoder_shape),
+                    )
+                )
+            # TODO: test this.
+            shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape
+            for _ in range(min_num_layers, max_num_layers):
+                common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))
+        return common_inputs
+
+    def _generate_dummy_inputs_for_causal_lm(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        batch_size: int = -1,
+        seq_length: int = -1,
+        is_pair: bool = False,
+        framework: Optional[TensorType] = None,
+    ) -> Mapping[str, Any]:
+        common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+            tokenizer, batch_size, seq_length, is_pair, framework
+        )
+
+        if self.use_past:
+            if not is_torch_available():
+                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
+            else:
+                import torch
+            batch, seqlen = common_inputs["input_ids"].shape
+            # Not using the same length for past_key_values
+            past_key_values_length = seqlen + 2
+            num_encoder_layers, _ = self.num_layers
+            num_encoder_attention_heads, _ = self.num_attention_heads
+            past_shape = (
+                batch,
+                num_encoder_attention_heads,
+                past_key_values_length,
+                self._config.hidden_size // num_encoder_attention_heads,
+            )
+
+            mask_dtype = common_inputs["attention_mask"].dtype
+            common_inputs["attention_mask"] = torch.cat(
+                [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
+            )
+            common_inputs["past_key_values"] = [
+                (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(num_encoder_layers)
+            ]
+        return common_inputs
+
+    def _generate_dummy_inputs_for_sequence_classification_and_question_answering(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        batch_size: int = -1,
+        seq_length: int = -1,
+        is_pair: bool = False,
+        framework: Optional[TensorType] = None,
+    ) -> Mapping[str, Any]:
+        # Copied from OnnxConfig.generate_dummy_inputs
+        # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
+        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
+        batch_size = compute_effective_axis_dimension(
+            batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
+        )
+
+        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
+        token_to_add = tokenizer.num_special_tokens_to_add(is_pair)
+        seq_length = compute_effective_axis_dimension(
+            seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
+        )
+
+        # Generate dummy inputs according to compute batch and sequence
+        dummy_input = [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
+        common_inputs = dict(tokenizer(dummy_input, return_tensors=framework))
+        return common_inputs
+
+    def generate_dummy_inputs(
+        self,
+        tokenizer: PreTrainedTokenizer,
+        batch_size: int = -1,
+        seq_length: int = -1,
+        is_pair: bool = False,
+        framework: Optional[TensorType] = None,
+    ) -> Mapping[str, Any]:
+        if self.task in ["default", "seq2seq-lm"]:
+            common_inputs = self._generate_dummy_inputs_for_default_and_seq2seq_lm(
+                tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+            )
+
+        elif self.task == "causal-lm":
+            common_inputs = self._generate_dummy_inputs_for_causal_lm(
+                tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+            )
+        else:
+            common_inputs = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
+                tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
+            )
+
+        return common_inputs
+
+    def _flatten_past_key_values_(self, flattened_output, name, idx, t):
+        if self.task in ["default", "seq2seq-lm"]:
+            flattened_output = super()._flatten_past_key_values_(flattened_output, name, idx, t)
+        else:
+            flattened_output = super(OnnxSeq2SeqConfigWithPast, self)._flatten_past_key_values_(
+                flattened_output, name, idx, t
+            )